renewable energy sources case study

Case Studies in Energy Transitions: A Special Collection

Dustin Mulvaney, San Jose State University, and Maria Petrova, Georgetown University, USA

INTRODUCTION:  The emphasis of this special collection of articles is case studies research and teaching activities about energy transitions—long term structural changes to energy systems, technologies, and patterns of use.

This curation of Case Studies in the Environment articles brings together papers that cover the core concepts, keywords, debates, best practices, techniques, tools, skills, and observations needed to improve our understandings of energy transitions. This special issue collection invites papers that engage with ideas and themes about energy transitions or that are incorporated into pedagogical activities. Examples topics in energy transitions include questions of temporal and spatial relevance on the magnitude of energy transitions, land use change, just transitions, life cycle assessment, finance/business, economics, behavioral concepts, socio-cultural change, policy tools and techniques, environmental justice issues, technological dependency, public participation, and carbon lock-in.

Green Energy from Garbage? A Case Study of Municipal Solid Waste’s Contested Inclusion in Maryland’s Renewable Portfolio Standard

Ingrid Behrsin, University of California, Davis, USA

Abstract:  Renewable portfolio standards (RPSs) are powerful state-level climate policy tools that set minimum renewable energy targets. They have been adopted by 29 states, in the United States (U.S.) as well as Washington, D.C., and have fueled much of the growth in the U.S. renewable energy sector. However, because these policy tools are state-driven, the technologies and fuel types included in each state’s RPS vary. In this article, I discuss the inclusion of municipal solid waste in Maryland’s RPS, and a social movement for environmental justice that has emerged around this decision. Given the prominence of RPSs in both fueling renewable energy adoption in the U.S., as well as in encouraging particular technologies, it is increasingly important to interrogate the types of technologies and fuel sources that climate policies like RPSs incentivize, and how they are received by the communities for which they are proposed. Thus, this article’s objective is to inspire critical thought about the classification schemes that govern renewable energy production.  Read more...

Renewable Energy on Tribal Lands: A Feasibility Study for a Biomass-to-Energy Plant on the Cocopah Reservation in Arizona

Lauren K. D’Souza, Renewable Resources Group LLC, Los Angeles, California, USA

William L. Ascher, Claremont McKenna College, Claremont, California, USA

Tanja Srebotnjak, Harvey Mudd College, Claremont, California, USA

Abstract:  Native American reservations are among the most economically disadvantaged regions in the United States; lacking access to economic and educational opportunities that are exacerbated by “energy insecurity” due to insufficient connectivity to the electric grid and power outages. Local renewable energy sources such as wind, solar, and biomass offer energy alternatives but their implementation encounters barriers such as lack of financing, infrastructure, and expertise, as well as divergent attitudes among tribal leaders. Biomass, in particular, could be a source of stable base-load power that is abundant and scalable in many rural communities. This case study examines the feasibility of a biomass energy plant on the Cocopah reservation in southwestern Arizona. It considers feedstock availability, cost and energy content, technology options, nameplate capacity, discount and interest rates, construction, operation and maintenance (O&M) costs, and alternative investment options.  Read more...

Barriers to the Uptake of Off-Grid Solar Lighting Products in Bihar

Sandeep Pai and Savannah Carr-Wilson, Central European University, Budapest, Hungary

Abstract:  The federal government of India and the state government of Bihar, India’s least electrified state, have always focused on grid expansion to bring power to those living without grid access. However, grid expansion has been slow. In Bihar, 83% of people still live without electricity, relying on dangerous kerosene lamps to light their homes. In the 1980s, an alternative—a market for solar home systems and solar lanterns—started to develop in Bihar. Yet, this market has failed to thrive, despite three decades of intervention by the government and activity by private companies. Today, fewer than 4.2% of unelectrified Bihar households use a solar lighting product. Based on interviews with key stakeholders, this case study found that the biggest obstacle to market growth is the government kerosene subsidy, which halves the price of kerosene, and makes people less interested in solar lighting products.  Read more...

Sidrap: A Study of the Factors That Led to the Development of Indonesia’s First Large-Scale Wind Farm

Martha Maulidia, University of Queensland, Brisbane, Australia

Paul Dargusch, University of Queensland, Brisbane, Australia

Peta Ashworth, University of Queensland, Brisbane, QLD, Australia

Agung Wicaksono, Institut Teknologi Bandung, Jakarta, Indonesia

Abstract:  The first utility-scale (75 MW) wind farm facility in Indonesia (the “Sidrap” project) was launched in South Sulawesi in early 2018. In this case study, we assess how several factors contributed to the successful development of the Sidrap project including strong signals of support from the Indonesian Government; long-term local presence of private sector partners; familiarity of private sector partners with the risks and nuances of investing in Indonesia; and an innovative private-public sector partnership model. In the last 2 years, Indonesia’s electricity sector has changed much in terms of pricing policy and private sector involvement. Much effort has been directed toward the Indonesian Government meeting its renewable energy deployment target of 23% of the total energy mix by 2025. The question remains, however, on whether Indonesia will be able to develop additional renewable energy projects to Sidrap in the future, given the continuing changes and uncertainty in Indonesian’s renewable energy policy and politics.  Read more...

Using Concepts from the Study of Social Movements to Understand Community Response to Liquefied Natural Gas Development in Clatsop County, Oregon

Trang Tran, University of Alaska, Anchorage, USA

Casey L. Taylor, University of Delaware, Newark, USA

Hilary S. Boudet, Oregon State University, Corvallis, USA

Keith Baker, SUNY College at Brockport, USA

Holly L. Peterson, Oregon State University, Corvallis, USA

Abstract:  Shifts in natural gas supply and demand since the early 2000s have triggered proposals for import and export terminals in coastal locations around the United States. Demand for such facilities is likely to grow with increasing rates of natural gas exports. Clatsop County, Oregon, is one such location that experienced over 10 years of debate surrounding the development of these facilities. The first liquefied natural gas (LNG) facility was proposed in this area in 2004; the final was withdrawn in 2016. While residents expressed both support and opposition early on, opposition dominated by the end. Drawing on insights from the literature on social movements, we conduct a case study of community response to LNG proposals in Clatsop County. We show how opponents were able to successfully frame the potential risks of LNG in a manner that had strong community salience, allowing them to appropriate resources and create political opportunities to advance their cause and influence local and state decisions.  Read more...

Closing Diablo Canyon Nuclear Power Plant, 2009–2018: Decision-Making on Energy Investments Relevant to Climate Change

John H. Perkins, The Evergreen State College (Emeritus), Washington, DC, USA

Abstract:  Modern economies cannot function without electricity, and production of electric power affects citizens in many ways, including climate change. Production of electricity requires investments that easily reach billions of dollars, and streams of investment capital must be perpetual to procure fuel, build and maintain plants, and transmit electricity to customers. This case study addresses whether a California decision relevant to investments about generating electricity adequately considered competing concerns. In 2009, Pacific Gas and Electric (PG&E, a private, investor-owned utility) applied to renew the operating licenses of its two nuclear reactors at the Diablo Canyon Nuclear Power Plant (the “plant”). By 2016, PG&E had decided not to seek license renewal and asked the California Public Utilities Commission (CPUC) to approve a price increase for its electricity to pay for specified expenses in closing the plant, which generated 24% of PG&E’s electricity.  Read more...

Pedagogy for the Ethical Dimensions of Energy Transitions from Ethiopia to Appalachia

Jen Fuller, Arizona State University, Tempe, AZ, USA

Sharlissa Moore, Michigan State University, East Lansing, MI, USA

Abstract:  Education on energy ethics is a crucial part of engaging students in learning about energy systems and energy transitions that needs further development. This article describes the use of case studies and active learning tools to achieve learning outcomes related to the ethical and social dimensions of energy. It discusses a daylong workshop held for undergraduate and graduate students at Michigan State University in February 2017 and evaluates pre- and postlearning outcomes. Two case studies are described that highlight ethical trade-offs in energy transitions. An international case study on Ethiopia and the Grand Renaissance Dam illustrates the benefits and drawbacks of cross-border electricity trade related to energy access, economic growth, and the energy-water nexus. A domestic case study on coal miners and coal towns in Appalachia examines the layered influences of place attachment and the challenges of economic diversification post-peak coal extraction.  Read more...

Socially Not Acceptable: Lessons from a Wind Farm Project in St-Valentin, Quebec

Louis Simard, University of Ottawa, Canada

Abstract:  Social acceptability appears as a new public norm that major projects must meet in order to be authorized and realized. This article proposes to analyze the case of a wind farm project in the municipality of St-Valentin, Quebec, Canada near the border with Vermont, which was cancelled by the government due to lack of social acceptance, in order to illustrate the importance of this norm today. The project involved the construction of 25 turbines to generate 52 MW of power. Launched in 2006, the project was already significantly under way by 2008; however, in 2011, the government permanently shelved it. Through a combination of document analysis and 11 interviews, we identified the main reasons for the lack of social acceptability: lack of upstream consultation from the developer and wrong scale planned for the consultation process, controversies surrounding the public decision-making process, profound contradictions between the community’s values and interests and the nature of the project, and perceptions of the impacts on the landscape and conflicting uses.  Read more...

Using a Community Vote for Wind Energy Development Decision-Making in King Island, Tasmania

R.M. Colvin, Australian National University, Canberra, Australia

G. Bradd Witt, The University of Queensland, Brisbane, Australia

Justine Lacey, Commonwealth Scientific and Industry Research Organisation (CSIRO), Brisbane, Australia

Abstract:  In 2012, a large scale wind energy project was proposed for development in King Island, Tasmania, Australia. The project proponents adopted what they described as a ‘best practice’ approach to community engagement; an approach expected to achieve positive outcomes for developer and community by maximising community involvement in decision-making, limiting social conflict, and enhancing the potential of achieving the social licence to operate. Despite this, the community experience during the time of the proposal was one of conflict and distress, and the proposal was eventually cancelled due to exogenous economic factors. This case study explores a key element of the engagement process—holding a community vote—that caused significant problems for people and process. The vote appeared to be a democratic means to facilitate community empowerment in the decision-making process. However, in this study, we show that the vote resulted in an increase in conflict and polarisation, challenged the legitimacy of the consultative process and credibility of the proponents, and ultimately led to legal actions taken by opponents against the proponent. Factors including voter eligibility, the benchmark for success of the vote, campaigning, and responses to the outcome of the vote are examined to demonstrate the complexity of decision-making for renewable energy and land use change more generally.  Read more...

Shooting for Perfection: Hawaii’s Goal of 100% Renewable Energy Use

Barry D. Solomon, Michigan Technological University, USA

Adam M. Wellstead, Michigan Technological University, USA

Abstract:  In the United States, 29 states, Washington, D.C. and three territories have adopted a mandatory Renewable Portfolio Standard (RPS) for their electric power systems, while eight states and one territory have set renewable energy goals. Many foreign nations have adopted an RPS as well. Thus far, almost all RPSs across the United States have met their interim goals with targets and timetables that vary widely. Hawaii’s RPS is the most ambitious, with a 100% target set for 2045 (though Vermont set a 75% target for 2032). This paper provides a case study of the Hawai’i RPS. The paper focuses on geographical issues and perspectives that may tease out the course of the states’ electricity future: sensitivity to climate change, population distribution, interisland rivalries, as well as the need for greater energy storage and complementary policies. An important complexity is the challenge of meeting electricity demand on six separate Hawaiian Islands (because of the lack of an interisland transmission cable), although all of them have substantial renewable energy resources.  Read more...

Evaluating Community Engagement and Benefit-Sharing Practices in Australian Wind Farm Development

Nina Lansbury Hall, The University of Queensland, Australia

Jarra Hicks, University of New South Wales, Australia

Taryn Lane, Embark, Victoria, Australia

Emily Wood, Independent Communications Contractor, Victoria, Australia

Abstract:  The wind industry is positioned to contribute significantly to a clean energy future, yet the level of community opposition has at times led to unviable projects. Social acceptance is crucial and can be improved in part through better practice community engagement and benefit-sharing. This case study provides a “snapshot” of current community engagement and benefit-sharing practices for Australian wind farms, with a particular emphasis on practices found to be enhancing positive social outcomes in communities. Five methods were used to gather views on effective engagement and benefit-sharing: a literature review, interviews and a survey of the wind industry, a Delphi panel, and a review of community engagement plans. The overarching finding was that each community engagement and benefit-sharing initiative should be tailored to a community’s context, needs and expectations as informed by community involvement. This requires moving away from a “one size fits all” approach. This case study is relevant to wind developers, energy regulators, local communities and renewable energy-focused non-government organizations. It is applicable beyond Australia to all contexts where wind farm development has encountered conflicted societal acceptance responses.  Read more...

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• Published: 06 July 2023

Fundamental theory on multiple energy resources and related case studies

• A. J. Jin 1  

Scientific Reports volume  13 , Article number:  10965 ( 2023 ) Cite this article

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• Energy science and technology
• Engineering
• Renewable energy

Herein, I methodically optimize a distributed energy resource in terms of the production, management, utilization, and/or transaction of renewable energies during the deployment process. I deliver a theoretical mathematical model that allows users to visualize three critical output functions of their energy preference, including output power, energy economy, and carbon footprint. The model delivers three eigenstates derived by a power utility matrix (PUM) model. PUM transforms three-input parameters (3i) into three-output functions (3o) through 3i3o-transformation. It is ubiquitous, and its systematic characterization is discussed. Moreover, I discover a mathematical conversion relationship translating energy generation to carbon emissions. Various case-studies demonstrate the optimal energy resource utilization. Furthermore, an energy blockchain approach is employed for microgrid design, development, and carbon reduction. Finally, the authors demonstrate the energy–matter conversion relationship that improves carbon emissions for energy production, reducing the beta factor of carbon emissions to 0.22 kg/kilowatt hour for carbon peak and to zero for carbon neutrality.

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Introduction

As a part of the industrial power goal to address emissions and climate change issues, the global scientific community has reached a consensus on the need to curb carbon emissions 1 , 2 , 3 . Scientists have dedicated great efforts for decades to both energy-efficient and carbon-free methods to address power industry needs. The field of distributed energy resources (DERs) has been very interesting and has gained considerable attention for its potential in helping reduce emissions 4 , 5 , 6 .

The Paris Agreement set goals to address climate change issues 1 , 2 , defined steps for governments and multiple technology sectors to achieve, and proposed ways to mitigate the currently large carbon emissions. Carbon greenhouse gas (GHG) emissions from energy production can lead to climate anomalies, and thus, there is an urgent need to reduce carbon emissions. Because GHGs increase solar irradiance absorption, which leads to rapid glacier melting and the disruption of fragile ecosystems 7 , a climate emergency has been declared 2 .

The desire to meet the carbon neutrality goal has resulted in unprecedented international collaboration between citizens, academics, industry leaders, and government officials. Ideally, all sources of electricity will be stable, economical, and environmentally friendly 5 . Over the past few decades, renewable energy (RE) technology that can definitively meet the world’s energy demands has been developed, such as solar photovoltaic (PV) energy, wind energy, ocean energy 8 , 9 , 10 , 11 , 12 , 13 , hydrogen fuel cells, and energy storage (ES) technologies 14 , 15 , 16 , 17 , 18 . For instance, governments have been able to achieve major reductions in carbon emissions across all major business sectors. California has introduced a series of energy-themed goals, policies, and programs. Carbon cap and trade initiatives have begun to gain traction as a system to lower economic barriers to carbon reduction measures 19 .

Recently, with rapid advances in commercialization, renewable energy technologies have been widely applied. Major commercial RE sources, including solar and wind power, are unstable or intermittent by nature. The best utilization of RE usually involves integrating various types of complementary power generation (PG), ES, and commercial grid power (GP). Researchers have innovated or advanced technology that enables customers to leverage the great value of RE sources. The Carbon Border Adjustment Mechanism (CBAM) is designed and abided by the European Union (EU) to put a fair price on the carbon emitted during the production of carbon intensive goods that are trading in the EU. It is critical for advanced renewable technologies to remain competitive when commercial rules for carbon pricing, such as CBAM, are met.

Carbon pricing scenarios include a wide range of low-cost and cost-saving options associated with high energy efficiency, schedule optimization, alternative energies, ES, and fuel switching, i.e., transitioning from less environmentally friendly energy sources to more RE sources. The recent exponential growth in energy consumption demand has resulted in an urgent need to identify RE sources that can meet this demand and be operationalized at large scales.

Many valuable commercial and technological advances in energy decarbonization have been achieved 20 , 21 . Despite the unforeseeable challenges of daily changes in solar power and wind power instability, many countries have developed advanced technologies that enable them to use RE sources 22 , 23 , 24 .

For those who cannot participate in public utilities with mainly RE grids, RE can include locally produced systems that form microgrids and integrate various types of complementary PG and storage processes 25 , 26 .

The energy generated in a microgrid can be monitored and distributed using meticulously derived and advanced algorithms 27 that match the supply of energy producers with the demands of consumers, ensuring that power stability and quality are maintained.

In addition to producing and consuming energy, producers and consumers can trade surplus energy with other users and/or profit from energy-related transactions. Blockchain technology encourages data sharing and collaboration through the Internet of Things, which enables buyers and sellers to conduct such energy transactions in an easy and transparent manner 28 , 29 .

Regarding RE sources, it is necessary to study how these systems perform and evolve both over time and space. For example, a detailed investigation of alternative energy sources in Sweden revealed that solar irradiance and wind speed are negatively correlated with one another on both hourly and annual scales 30 . To optimize the stability of the energy output on a seasonal basis, it was recommended that this system should operate with 70% solar power and 30% wind power. The complementary outputs of solar PV and wind power should be verified in the partition on a case-by-case basis when designed for tandem use. The fluctuations in these two energy sources are much smaller than when each energy source is used separately. As microgrids become more popular and efficient than ever before, the need for large datasets related to microgrid consumption, transactions, and management will correspondingly increase.

Transactions between end-users and prosumers 31 , 32 have been investigated by several research groups. For example, some researchers have explored data and machine learning 6 , while other researchers have published investigative results 33 regarding energy blockchain (EBC) applications involving homes and buildings and for distributed peer-to-peer (P2P) energy trading. In this study, I proposed a smart algorithm for a distributed energy resource (DER) that employs the inputs of utilization data to help achieve the stated optimal goals. Prior researchers have achieved significant progress in this area 30 , 35 , 36 ,–37 ; for example, Zhang et al. provided valuable technology components for RE systems. Here, I investigate a general approach to obtain a novel and valuable fundamental framework that will lay the groundwork for determining the process exergy. A novel framework that advances our knowledge in this field and the present cause for RE applications is imperative.

Power utility matrix (PUM) methods 34 provide a typical case of PUM design with a smart energy approach. The PUM is a key component for best use realizing a smart energy system.

In studies on extensive RE systems, there are several factors for system characterization. It is crucial to identify the best DER and/or energy management system for three kinds of critical outputs for any given microgrid: power output, smart energy use/transactions, and carbon footprint.

RE is becoming  a mainstream energy that contributes to the 20 TW of annual power required to entirely meet the world's current energy demand. Researchers have made significant progress in increasing our knowledge about RE and distributed energy resources 38 , 39 , 40 . This knowledge is applicable to the exploitation of the full use of RE capacity; for example, it is valuable to be able to fully benefit from the current installed solar-wind power capacity that can deliver 15%of total electricity in China. Based on the above knowledge, I proposed a novel mathematical framework to solve the challenge of multiple RE supplies. The multiple energy resource (MER) system can facilitate the smart energy of a smart energy system. An important application can be derived from the aforementioned mathematical framework. The energy–matter conversion relationship (EMCR) is inspired by the mass and energy conversion relationship and applied as an important concept 41 . In this article, the EMCR connects GHG production to energy production.

The focus in this article is to formulate a simple and ubiquitous framework of smart microgrids and energy blockchain. To achieve a stable power supply, the employment of the PUM model can reduce the power strain on the grid during peak demand periods. Additionally, users can employ commercially available control strategies such as load shedding, demand response, and energy storage to maintain a robust microgrid 42 .

Moreover, it is valuable to develop communication and networking technologies for smart microgrids and to leverage machine learning in smart microgrids 42 . Energy blockchain utilizes blockchain technology to manage energy transactions among producers, consumers, and microgrids. The decentralized system of EBC ensures secure and transparent transactions, making it an ideal solution for managing energy transactions between renewable energy producers and consumers, which is a very exciting research area 43 .

This article is Structured as follows. In Section I, I introduce the background and the urgent need for green and economical power resources. Then, REs and the PUM are introduced. In Section II, the mathematical model of the PUM and several applications are discussed. Furthermore, in Section III, the system characteristic matrix (K,ij) and systematic ways to solve PUM models with the PUM (K,ij) characteristics are discussed. In Section IV, the EBC approach is explored and studied. Finally, in Section VI, future work and the directions and valuable assets for solutions to meet carbon neutrality goals at a global scale are discussed.

Characteristics of common renewable energy sources

It is important to establish a foundational framework to provide a background for the general mathematical description below. The energy optimization scheduling of a microgrid is a multiobjective optimization problem with multiple constraints. The maximal energy utilization efficiency, which is known as the exergy of the DER system, can be achieved and requires the proper scheduling of the DERs, ES, and power. The objective of the optimization is to achieve the maximum overall benefit through reasonable coordination, where all parties representing sources, storage, grids, and loads communicate to coordinate utilization scheduling. The microgrid can operate in either the grid-connected mode or the independent mode, both of which require proper scheduling of the DERs, ES, and load. For instance, a microgrid can operate more reliably under the independent operation mode. However, the scheduling process for DERs is complex. A mathematical model may describe a ubiquitous law, using the PUM model, as described below.

Figure  1 illustrates a DER, microgrid, and simplified PUM model to address the power, cost, and carbon emissions of supplying energy. The left-hand side of Fig.  1 represents three inputs, namely PG, ES, and GP, which are large power resources. The right-hand side includes the power output, the user’s power economy, and carbon emission data. The focus of optimized energy scheduling is to achieve both economic and environmental goals while delivering the required power output.

Detailed schematic of the power input elements and triple outputs. The middle column illustrates a working model and its solutions that render a smart power utility matrix (PUM) system.

A microgrid usually offers much better predictability and reliability in the long term when operated in the grid-connected mode than in the independent mode. The PUM model for a microgrid matrix is investigated to produce stable output power to meet demand and to meet certain economic and environmental goals in terms of capacity. The PUM model can be expressed as follows:

where \(\tau \, = \,\frac{\Delta t}{T}\) , ∆t is the scheduling time, and T is 1 h. For example, if the scheduling time is 10 min, \(\uptau =1/6\) .

Pt,CP denotes the power output. Ct,ECO denotes the financial value of the power consumption to its user. et,CD represents the carbon emissions derived from both the DER and the power utility.

Following these formulae, one may derive the value dependency relationship 44 . In the microgrid economic and carbon emission calculation model, the first row of the matrix indicates that the power demand of the load in a microgrid must be met in total by the current level of energy production, the available stored energy, and the energy provided by an external power grid. In other words, consumers are ensured that they can use electricity without service disruptions.

The typical value of each of the PUM coefficients in the first row typically ranges from 0.95 to 1. Therefore, each K,1j is simply assigned a value of 1, as follows:

To maintain a specific power quality, the power balance constraint must be satisfied. Therefore, the first row of the PUM can be rewritten as follows:

where \({P}_{CP}^{t}\) stands for the electricity power demand of consumers, \({P}_{PG}^{t}\) for the power generated by a generator, \({\mathrm{P}}_{\mathrm{ES}}^{\mathrm{t}}\) for the power exchanged by the ES systems, and \({\mathrm{P}}_{\mathrm{GP}}^{\mathrm{t}}\) for the power exchanged by the microgrids and power grids.

The net electricity cost is the sum of income and expenses. Income is generated when users purchase electricity from the microgrid and/or from the power grid. By managing peaks, valleys, and storage, it should be possible to maintain a balance between supply and demand without costly power spikes or deficits. Based on current technological development, I demonstrate scientific principles that may be universally applicable in the field. Each principle is an applicable tool for experts in the field to aid in smart power system design. The net electricity cost of operating a microgrid is shown in the second row of Eq. ( 1 ).

\({K}_{C\_PG}\) is the cost coefficient of generating 1 kWh of electricity from a generator, which may include the equipment cost, depreciation, and operation maintenance.

The cost of operating an ES system is expressed as follows:

where \({K}_{C\_ES}\) stands for the cost coefficient of charging and discharging 1 kWh of electricity from the ES system, which may include the equipment cost, depreciation, and operation maintenance; \(K_{Bat - opm}\) for the operating and maintenance cost coefficient of charging and discharging 1 kWh of electricity from the ES system; \(K_{Bat - ll} \,\) for the equipment depreciation cost coefficient of charging and discharging 1 kWh of electricity from the ES system; \(K_{Bat - el}\) for the energy loss cost coefficient of charging and discharging 1 kWh of electricity from the ES system.

The income and the cost of purchasing energy from an external power grid are expressed as follows:

where \({f}_{s}\) is the price at which energy is sold to the power grid, and \({c}_{b}\) is the price at which electricity is purchased by the microgrid from the power grid.

The cumulative net electricity cost of operating a microgrid over a period \(t_{n}\) is given as follows:

Values of typical K-characteristics in the transformation matrix

The commercial cost structure of current technology with solar RE (i.e., lithium battery) grid power is presented as follows 44 . At the current commercial stage, some K-values are provided as follows:

K2,1 is the cost of PV power per kWh, which mainly includes the depreciation of the purchase and installation cost of the PV modules, inverters, transformers, brackets, and power distribution equipment, as well as daily maintenance costs. The fuel cost of the PV modules is zero; the ratio of equipment depreciation and maintenance costs (including the operation cost) is generally approximately 7:3. The actual equipment lifetime is approximately 20 years. The largest variable affecting the depreciation cost is the local optical resources. The richer the optical resources are, the lower the investment equivalent to the unit of installation is, and the lower the depreciation is. The value of K2,1 is derived from the average level in China, and for regions with extremely rich optical resources, such as India and Pakistan, the Middle East, North Africa, and Central America, K2,1 may be halved with the same equipment.

K2,2 is the cost of energy storage, which mainly includes equipment depreciation and power loss. Maintenance costs are relatively low, mainly composed of personnel salaries, but solar/wind power distribution network energy storage power stations are generally maintained by solar/wind power station personnel part time. Under the condition of determining the technical conditions, the cost of energy storage is most affected by the utilization frequency of the energy storage system: a higher recycling frequency can reduce the apportioned depreciation cost of the latter two factors of equipment within a certain range, which is mainly due to the calendar and cycle lives of energy storage batteries. When any one of the calendar years or cycles reaches the design value, the energy storage equipment needs to be replaced; therefore, when the calendar life is reached before the cycle life, the increase in the frequency of use can proportionally reduce the depreciation and allocation, and vice versa, and the impact of reaching lifetime lmit is not significant. In addition, a higher recycling frequency can also reduce the power loss, which is mainly due to the internal thermal management, monitoring, and other supporting power consumption ratios of the energy storage system decreasing with increasing frequency of use. This K2,2 value is estimated based on the average charge and discharge frequency once a day.

K2,3 is the cost of the external purchase and sale of electricity. In general, the fs and Cb values are not equal during the trade, depending on whether the microgrid investor and the power grid company have signed a power purchase and sales agreement (generally, the absolute value of Cb will be equal to the absolute value of fs). The K2,3 value is the average of the market conditions of the previous two factors. In countries such as Germany and China, governments have implemented tariff policies over two decades that have provided substantial RE pricing incentives to increase RE sales on the grid.

As a result, the entire matrix is completed with estimates of a typical carbon emission case as follows. Energy production may vary. Thus, actual numbers depend, in part, on the procurement process within the industrial eco-chain.

The investigation leads to typical technical specifications at the current level.

The provided specifications based on technical data are extracted from commercially available products.

Furthermore, the typical values of all the PUM coefficients in the second row are as follows. The detailed model in the studies on K,2j shows time dependence. The third row of the matrix represents the carbon emissions generated by the operation of the microgrid 45 , 46 .

I selected typical values of CO2 emissions. For the current technological stage of development, some K-values in the specification are as follows:

The amount of carbon released (in commercial DERs) is dependent on the maturity of the related technology, which may show substantial dependence on the time, manufacturing approach, operating conditions, and location in the supply chain.

Power utility matrix for smart energy

Linear integrative model of smart energy.

In this study, a scientific description that may be universally applicable for engineers skilled in the smart grid field is provided. In accordance with Eq. ( 1 ), a more general mathematical equation is an integral equation. In another form of summarizing a typical overall PUM characterization, the PUM model (with the 3i3o model) and the PUM matrix are expressed as follows:

The energy, cost, and carbon emissions are obtained from Eq. ( 10 ). The PUM values are obtained from Eqs. ( 2 ), ( 8 ), and ( 9 ).

The general problem becomes an interesting mathematical formulation that can be solved using the following framework. The linear algebraic equation may be simplified by solving for the three eigenvalues and deriving the three eigenstates in the eigenspace. The linear algebraic problem leads to characteristic (eigenvalue) equations in the eigenspace that have a diagonalizable matrix and three orthogonal variables. The 3 × 3 square matrix is a diagonalizable matrix. I can derive such eigenstates from linear algebraic calculations of the matrix as the determinant, minors, and cofactors.

Simulation studies

Electricity from a microgrid can be transferred to users through a series of physical transactions; these transactions occur at both on-peak and off-peak times. There are many independent power producers and microgrids currently competing for customers in energy distribution networks.

Conducting power demand management (PDM) is imperative for a microgrid to ensure that it can meet the relevant energy demands during emergencies. Microgrid loads are generally classified as critical loads, controllable loads, or uncontrollable loads. Power systems must be able to meet the critical load requirement at any given moment.

Dual energy storage: a working mode

In the case of an emergency, a controllable load can be cut off or adjusted as needed. Under normal circumstances, the purpose of optimizing the load use and energy savings may be to manage the response to demand. For example, transferable loads such as heat loads can be used when electricity prices are low and the system is not experiencing peak demand. The load in a given household is directly related to users’ electricity preferences and comfort level. Users can carry out oversight of their power needs and consumption through the use of smart devices (e.g., smart switches and smart thermostats). ES is one of the key constituents of a regular microgrid; dual ES (DES) is both useful in grid operation and valuable for the battery lifetime. RE, such as solar PV, has variable availability that is approximately one quarter of the time; its actual time is dependent on the location of its operation. To supply power continuously, the remaining power must be provided through complementarity, such as ES and batteries. The working of the ES is discussed below.

The working caveats may be extended to a variety of applications. For example, I have designed an improvement in the utilization of ES.

ES is one of the critical components in a DER. Researchers have discovered that all ES batteries have optimal charge‒discharge cycle depths. Their battery lifetime depends on an important parameter: the depth of discharge of batteries (DoDb).

The smart microgrid architecture is shown in Fig.  2 below. Each ES device is programmed in the DES mode; it recurrently works in a full cycle of charge and discharge. Deep cycles are very beneficial for the ES lifetime by removing numerous insufficient charge–discharge cycles.

Microgrid architecture illustrating the dual energy storage (DES) mode.

Simulations of PUM model with distributed energy-resources

Figure  3 contains the test results for a simulation of a metro-transportation system. These test results demonstrate the typical energy in-and-out flow and are shown as follows: (a) scheduling of the distributed energy system with energy storage; (b) comparison at the point of common connection. In the DES system, DES can be used effectively to overcome the prediction error and to track the day-ahead trading plan of the microgrid system in real time.

Typical energy in-and-out flow: ( a ) scheduling of distributed energy system with energy storage and ( b ) comparison at the point of common connection.

A comparison of various methods is presented in Table 1 . The table shows a list of the economic costs of the methods based on simulations once per day, under peak rotation, and with various hardware configurations.

According to the analysis in the last section, the DES mode is utilized with benefits. The working schematic diagram is shown in Fig.  4 , along with its benefits. The energy supply appears the same for the outside viewer, providing ES, increasing the storage lifetime, and  including many operational nuances for potential benefits.

Schematic diagram of the charge‒discharge cycle showing the cooperative working mode for a DES system.

Microgrid system architecture and the transformation matrix

The mathematical model of the PUM can be readily applied to all microgrid systems. Equation ( 1 ) is rewritten as follows:

In the previous section, I deployed the PUM transformation matrix, which provides the output solutions based on the input DER variables. As a result, the derivation of the technically complex architecture of a DER becomes a simplified DER specification. The three-output functions (3o) in the simplified PUM design problem are as follows:

where 3i represents the three input parameters and PUM is a 3 × 3 square matrix.

According to Wolfram et al. 47 , the solution to Eq. ( 12 ) is readily obtained in orthogonal space and can be characterized by a set of three distinct eigenstates with three eigenvalues: λ, 1 , λ, 2 , and λ, 3 . Thus, the details of this derivation are omitted here.

In the relevant eigenspace, each output function is specifically related to each particular eigenstate with the corresponding input variable, which is called the orthogonal variable. Each of the critical outputs of the three eigenstates depends on its orthogonal variable. For example, the increment in the power output in an eigenspace is achieved by tuning its orthogonal eigenvariable, which will not affect the value of the carbon emissions within a specified range.

The carbon index of the PUM system is derived as follows:

The output function is built based on the critical values in the eigenstates within its critical variables in the eigenspace. The beta factor is the ratio of output energy and carbon emissions.

An artificial intelligence (AI) algorithm issues the command to set the output power values automatically. When the EBC is implemented, the power conversion will be connected to the RE and to the ES that has a point of common connection with an inverter/converter function.

A distributed energy approach is shown in the working diagram in Fig.  4 with a DES and in the 3i3o model illustrated in Fig.  1 . The model can be expanded in applications in the case of a DES.

Energy blockchain

In a decentralized energy system, energy supply contracts can be directly communicated between producers and consumers. Enabling an EBC can result in a considerable number of transactions between producers and consumers, which makes each transaction less expensive overall. Blockchains facilitate direct interactions and transactions between local energy producers and consumers by eliminating the need for a third-party monitoring platform.

 Software instructs the system connected to the output terminal, i.e., the client node. A ledger from the EBC may be implemented in 5 min upon request (or in a different agreed-upon set of time. There are several network blockchain options for energy-industry applications. Many researchers have discovered various scientific phenomena/data, and many studies have been reported 48 , 49 , 50 , 51 , 52 , 53 , 54 . Researchers have conducted investigations and have expanded the knowledge on this subject by referring to the results in the literature 55 , 56 , 57 , 58 , 59 .

The distributed energy resources can be traded with clients via the internet by choosing one of the blockchain options. The classic blockchain structure of EBC illustrates layers of provider-customer-clients and peer-to-peer gateway to the internet. For example, a smart grid can be applied for all digital electricity where the client nodes stand for all goods: AG1, AG2, AG3. Keyless blockchain-as-a-service interfaces (KBaaS) are presented in Fig.  5 . Its advantage is trust and it meets the security needs established for both supply and demand groups. Figure  5 provides microgrid-management data provenance based on a lightweight and keyless blockchain -as-a-service (KBaaS). The blockchain structure of EBC is illustrated as follows.

Flowchart of keyless blockchain-as-a-service interfaces.

The share of global electric generation is expected to reach a total of 20 TW soon. The shares of global RE generation should be increased: solar and wind power must reach 78% to achieve carbon neutrality 60 . To achieve the emission peak by 2025 and the carbon neutrality goals by 2050, the current policy calls for 47–78% RE in the primary energy supply. The world requires 20 TW to maintain quality of life, with approximately 78% accounted for by renewables. This corresponds to 6.2 and 9.4 TW of solar and wind power to achieve carbon neutrality.

I studied the beta factor, which is a metric defined as the rate of EMCR across multiple energy systems. It is 0.45 kg/kWh for traditional coal-fired power plants. However, it should be less than 0.22 kg/kWh to achieve the desired carbon emission peak; and it should be nearly zero to achieve carbon neutrality.

Researchers may be challenged to attain a pure categorical input (Cat). Each category may be composed of some combination of reated input entities. The current PUM in this article has advantages in deriving the eigenstates and eigenvalues because the PUM is a 3X3 square matrix. This PUM can deliver three eigenstates or eigenvalues. As a result, some simple metrics for carbon neutrality have emerged elegantly.

The current PUM utilized the essential classification of three input categories that may be expanded in every category as follows: Cat-1 has renewable energy resources; Cat-2 has energy storagies; and Cat-3 has microgrids. A remark is that the PUM model may encounter situations that exceed the basic PUM scheme in practice; each input parameter may be combination of multiple parameters, such as several renewable energy sources. Furthermore, customers may have more competing requests in an application while their knowledge of data and machine deep learning can be valuable for a good resolution. Each Cat may be properly bundled to meet the model requirements shown later.

To define the predicted RE power supplies as a single input in the PUM model, one would need to formulate Cat-1 as the single input. Moreover, the text at above has demonstrated an example of Cat-2 as illustrated in Fig.  2 ; the DES is managed in an algorithm that may be expanded in various applications. Furthermore, to formulate Cat-3 as the single input by supplier(s), I believe that the general categorical solution can be conveniently applied from the input power. In general, Cat-3 is provided by the power input, e.g., the grid provider. This input power may be composed of EBC and/or power supplier solutions of the microgrids. Our future work will collect data systematically across all the afore-mentioned categories so that the PUM model may be implemented for all types of smart microgrids.

In practice, one may handle complexity and employ a more complex system-solution 61 . The PUM model is an advantageous approach as an overall solution technique for microgrids. Several more complex systems are studied experimentally and methodically in a separate work 62 .

Conclusions

In conclusion, I have presented a theoretical study toward developing a predictive model that in turn realized a mathematical PUM specified by a 3i3o square matrix. Every element in the 3 × 3 PUM matrix is important in that it contributes to the specification of the total distributed energy system. Moreover, the PUM forms orthogonal variables based on linear algebraic operations of the input parameters. For example, a rule-of-algebra can be applied in the eigenspace to tune the output power in a specified range without affecting the other two functions that include cost and/or carbon emissions. Moreover, an important discovery is the proposal of a beta factor to illustrate the ratio of carbon dioxide emissions and the total output power. The beta factor was less than 0.22 kg/kWh when a carbon emissions peak was achieved, and it was nearly zero for the carbon neutral system.

I conclude the knowledge of the power utility matrix in detail is crucial to resolve critical outputs for the designer's tools, to enlist critical output parameters, and to identify the fundamental mathematical model. The related knowledge is very important to determine the direction and provide guidance to effectively achieve carbon neutrality with REs. The above framework provides important guidance for DER design, construction, and operation.

Data availability

The data that support the findings of this study are available from Ningbo University, China; but restrictions apply to the availability of these data, which were used under licence for the current study, and so are not publicly available. Data are however available from us upon reasonable request and with permission from Ningbo University, China. The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Abbreviations

Renewable energies

Distributed energy resource

Three-input-parameters

Three-input-functions

Greenhouse gas

Solar photovoltaic

Energy storage

Power generation

Carbon border adjustment mechanism

Internet of Things

Peer-to-peer

Power utility matrix

Energy matter conversion relationship

Multiple energy resources

Power demand management

Discharge of batteries

Energy management system

Artificial intelligence

Electric vehicle

Power management unit

Distributed energy prosumer

Distribution system operator

Tera Watt hour

Giga ton CO 2

NASA. Scientific Consensus: Earth’s Climate Is Warming. 13 December 2021. Available online: https://climate.nasa.gov/scientific-consensus/ (Accessed 10 August 2022).

UN Climate Press. COP26 reaches consensus on key actions to address climate change. https://unfccc.int/news/cop26-reaches-consensus-on-key-actions-to-address-climate-change (2021).

European Commission. Over 190 member states have signed onto the Paris agreement, climate action. https://ec.europa.eu/clima/policies/international/negotiations/paris_ (2015).

Zhang, L. & Ruan, X. Control schemes for reducing second harmonic current in two-stage single-phase converter: An overview from DC-bus port-impedance characteristics. IEEE Trans. Power Electron. 34 , 10341–10358 (2019).

Article   ADS   Google Scholar  

Zhang, L. et al. Design considerations for high-voltage insulated gate drive power supply for 10-kV SiC MOSFET applied in medium-voltage converter. IEEE Trans. Ind. Electron. 68 , 5712–5724 (2021).

Jamil, F., Iqbal, N., Ahmad, S. & Kim, D. Peer-to-peer energy trading mechanism based on blockchain and machine learning for sustainable electrical power supply in smart grid. IEEE Access 9 (39193), 39217 (2021).

Google Scholar  

William, J., Wolf, R. C., Newsome, T. M., Barnard, P. & Moomaw, W. R. The climate emergency: 2020 in review, Scientific American. /article/the-climate-emergency-2020-in-review/ (2021).

Khan, N., Kalair, A., Abas, N. & Haider, A. Review of ocean tidal, wave and thermal energy technologies. Renew. Sustain. Energy Rev. 72 , 590–604 (2017).

Article   Google Scholar  

Jia, Y., Alva, G. & Fang, G. Development and applications of photovoltaic–thermal systems: A review. Renew. Sustain. Energy Rev. 102 , 249–265 (2019).

Zhou, X. et al. Strategies towards low-cost dual-ion batteries with high performance. Angew. Chem. Int. Ed. 59 , 3802–3832 (2020).

Article   CAS   Google Scholar  

Olabi, A. G. Renewable energy and energy storage systems. Energy 136 , 1–6 (2017).

Ming, J., Guo, J., Xia, C., Wang, W. & Alshareef, H. N. Zinc-ion batteries: Materials, mechanisms, and applications. Mater. Sci. Eng. 135 , 58–84 (2018).

Valera-Medina, A., Xiao, H., Owen-Jones, M., David, W. I. F. & Bowen, P. J. Ammonia for power. Prog. Energy Combust. Sci. 69 , 63–102 (2018).

Shao, Z. G. & Yi, B. L. Development status and prospect of hydrogen energy and fuel cell. Proc. Chin. Acad. Sci. 34 , 469–477 (2019).

Smith, K. et al. Life prediction model for grid-connected Li-ion battery energy storage system. in 2017 American Control Conference (ACC) 4062–4068.

Zhang, Y. et al. Identifying degradation patterns of lithium-ion batteries from impedance spectroscopy using machine learning. Nat. Commun. 11 , 1706 (2020).

Article   ADS   CAS   PubMed   PubMed Central   Google Scholar  

Naumann, M., Schimpe, M., Keil, P., Hesse, H. C. & Jossen, A. Analysis and modeling of calendar aging of a commercial LiFePO4/graphite cell. J. Energy Storage 17 , 153–169 (2018).

Guerra, O. J. Beyond short-duration energy storage. Nat. Energy 6 , 460–461 (2021).

Climate Policy Initiative. Cap and trade in practice: barriers and opportunities for industrial emissions reductions in California. http://climatepolicyinitiative.org/publication/cap-and-trade-in-practicebarriers-and-opportunities-for-industrialemissions-reductions-in-california . Retrieved 22 Nov 2022.

Supasa, T. et al. Sustainable energy and CO2 reduction policy in Thailand: An input–output approach from production- and consumption-based perspectives. Energy Sustain. Dev. 41 , 36–48 (2017).

Clark, W. W. & Kooke, G. The Green Revolution of Industry (Electric Power Press, 2015).

Jin, A. J. & Peng, W. Development partnership of renewable energies technology and smart grid in China. In Sustainable Cities and Communities Design Handbook 111–128 (Elsevier, 2018).

Chapter   Google Scholar  

Li, Z. et al. Review of an emerging solar energy system: The perovskite solar cells and energy storages. Adv. Mater. Lett. 11 , 1–8 (2019).

ADS   Google Scholar  

Zhao, Y. Q. et al. Wind turbine principle and wind power generation technology. Sci. Technol. Inf. 13 , 25–26 (2015).

Huang, W., Zhang, N., Yang, J., Wang, Y. & Kang, C. Optimal configuration planning of multi-energy systems considering distributed renewable energy. IEEE Trans. Smart Grid 10 , 1452–1464 (2017).

Guelpa, E. & Verda, V. Thermal energy storage in district heating and cooling systems: A review. Appl. Energy 252 , 113474 (2019).

PECODER tracks both input and output variables; it utilizes an advanced algorithm to follow through both output results and key metrics. Authors appreciate Prof. G Chen for insights given in July, 2022.

Morstyn, T. & McCulloch, M. D. Multiclass energy management for peer-to-peer energy trading driven by prosumer preferences. IEEE Trans. Power Syst. 34 , 4005–4014 (2019).

Andoni, M. et al. Blockchain technology in the energy sector: A systematic review of challenges and opportunities. Renew. Sustain. Energy Rev. 100 , 143–174 (2019).

Li, Z., Su, J. & Jin, A. J. Perspectives on published energy sources and smart energy supplies. Adv. Mater. Lett. 12 , 21031607 (2021).

Arantegui, R. L. & Jäger-Waldau, A. Photovoltaics and wind status in the European Union after the Paris agreement. Renew. Sustain. Energy Rev. 81 , 2460–2471 (2017).

Shivakumar, A., Dobbins, A., Fahl, U. & Singh, A. Drivers of renewable energy deployment in the EU: An analysis of past trends and projections. Energy Strategy Rev. 26 , 100402 (2019).

Couto, A. & Estanqueiro, A. Exploring wind and solar PV generation complementarity to meet electricity demand. Energies 13 , 4132 (2020).

Buttler, A., Dinkel, F., Franz, S. & Spliethoff, H. Variability of wind and solar power–an assessment of the current situation in the European Union based on the year 2014. Energy 106 , 147–161 (2016).

Heydari, A., Garcia, D. A., Keynia, F., Bisegna, F. & De Santoli, L. A novel composite neural network based method for wind and solar power forecasting in microgrids. Appl. Energy 251 , 113353 (2019).

Meng, X. L. et al. Real-time energy optimal dispatching method for microgrid based on energy storage Soc day-ahead plan. J. Agric. Eng. 32 , 155–161 (2016).

Fan, W. User-Side Microgrid Energy Management Method Based on Online Optimization, Graduate thesis (North China Electric Power University, 2017).

Ang, T.-Z., Salem, M., Kamarol, M., Das, H. & Shekhar; Nazari, M.A., Prabaharan, N.,. A comprehensive study of renewable energy sources: Classifications, challenges and suggestions. Energ. Strat. Rev. 43 (100939), 2022. https://doi.org/10.1016/j.esr.2022.100939.ISSN2211-467X.S2CID251889236.Retrieved14October (2022).

"Electricity – from other renewable sources - The World Factbook". www.cia.gov . Archived from the original on 27 October 2021. Retrieved 12 Jan. 2023. Link: cia.gov/the-world-factbook/about/archives/2021/field/ electricity-from-other-renewable- sources/country-comparison/.

"Renewable Energy". Center for Climate and Energy Solutions. 27 Oct. 2021. Archived from the original on 18 Nov. 2021. Retrieved 22 Nov. 2021.

Einstein, A. Über einen die Erzeugung und Verwandlung des Lichtes betreffenden heuristischen Gesichtspunkt", Translated as "On a heuristic point of view concerning the generation and transformation of light. AdP 17 , 132. https://doi.org/10.1002/andp.19053220607 (1905).

Article   CAS   MATH   Google Scholar  

Morstyn, T., Hredzak, B. & Agelidis, V. G. Control strategies for microgrids with distributed energy storage systems: An overview. IEEE Trans. Smart Grid. 9 (4), 3652–3666 (2018).

Jebamikyous, H., Li, M., Suhas, Y. & Kashef, R. Leveraging machine learning and blockchain in E-commerce and beyond: Benefits, models, and application. Discov. Artif. Intell. 3 , 3. https://doi.org/10.1007/s44163-022-00046-0 (2023).

Su, J., Li, Z. & Jin, A. J. Practical model for optimal carbon control with distributed energy resources. IEEE Access 9 , 161603–161612 (2021).

Ma, W., Fang, S., Liu, G. & Zhou, R. Modeling of district load forecasting for distributed energy system. Appl. Energy 204 , 181–205 (2017).

Bartolini, A., Mazzoni, S., Comodi, G. & Romagnoli, A. Impact of carbon pricing on distributed energy systems planning. Appl. Energy 301 , 117324 (2021).

Wang, J. et al. Incentivizing distributed energy resource aggregation in energy and capacity markets: an energy sharing scheme and mechanism design. Appl. Energy 252 , 113471 (2019).

Wolfram|Alpha is a great tool for solving systems of the linear algebra equations with a link as follows. https://www.Wolframalpha.com/examples/mathematics/algebra/ . The solution of a matrix equation is provided by Wolfram Research at above.

Long, M. X. Research on optimal dispatching of residents’ load in smart communities considering new energy grid-connected. In Energy Transfer (Hunan University, 2018).

Park, L., Lee, S. & Chang, H. A sustainable home energy prosumer-chain methodology with energy tags over the blockchain. Sustainability 10 , 658 (2018).

Sabounchi, M. & Wei, J. Towards resilient networked microgrids: blockchain-enabled peer-to-peer electricity trading mechanism in 2017 IEEE Conference on Energy Internet and Energy System Integration (EI2) 1–5 (IEEE, 2017).

Gao, F., Yang, K., Hui, D. & Li, D. Cycle-life energy analysis of LiFePO4 batteries for energy storage. Proc. Chin. Soc. Electr. Eng. 33 , 41–45 (2013).

Yang, X. F. et al. Overview on micro-grid technology. Proc. CSEE 34 , 57–70 (2014).

CAS   Google Scholar  

Pratt, A. Addressing Challenges for Single Microgrids and Networked Microgrids at Large Scales (National Renewable Energy Laboratory, 2021).

Tai, X., Sun, H. & Guo, Q. Blockchain-based power transaction and congestion management method in the Energy Internet. Power Syst. Technol. 40 , 3630–3638 (2016).

Jin, A. J., Li, C., Su, J. & Tan, J. Fundamental studies of smart distributed energy resources along with energy blockchain. Energies 15 , 8067 (2022).

Mylrea, M.; Gupta, S.; Gourisetti, N.; Bishop, R.; Johnson, M. “Keyless Signature Blockchain Infrastructure: Facilitating NERC CIP 439 Compliance and Responding to Evolving Cyber Threats and Vulnerabilities to Energy Infrastructure”, in 2018 IEEE/PES Transmission and Distribution Conference and Exposition (T&D) vols 2018- 1–9 (IEEE, 2018).

Zhang, H., Wang, J. & Ding, Y. Blockchain-based decentralized and secure keyless signature scheme for smart grid. Energy Oxf. 180 , 955–967 (2019).

Sebastian-Cardenas, D. “Digital data provenance for the power grid based on a Keyless Infrastructure Security Solution”, in 2021 Resilience Week (RWS) 1–10 (2021). https://doi.org/10.1109/RWS52686.2021.9611800 .

Su, J. “Research on Multi-time Scale Optimal Scheduling of Microgrid Based on Load Side Management and Dual Energy Storage Mode”; Master of Science Thesis, Ningbo University, China; June, 2022.

Shahgholian, G. “A brief review on microgrids: Operation, applications, modeling, and control”; International Transactions on Electrical Energy Systems; 31 Mar. 2021; https://doi.org/10.1002/2050-7038.12885 .

Liu, D., Jin*, A.J., Su, J., Li, Z. “Case Studies of Low-Carbon Solutions for Integrated Energy Resources”, (submitted).

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• ORIGINAL ARTICLE
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• Published: 06 March 2017

A case study of a procedure to optimize the renewable energy coverage in isolated systems: an astronomical center in the North of Chile

• H. Abos 2 ,
• M. Ave   ORCID: orcid.org/0000-0001-7386-4828 1 &
• H. Martínez-Ortiz 2  

Energy, Sustainability and Society volume  7 , Article number:  7 ( 2017 ) Cite this article

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Metrics details

Renewable energy resources show variabilities at different characteristic time scales. For a given resource and demand pro le, there is an absolute maximum achievable coverage (when limiting the fraction of energy lost during production and delivery to a reasonable value). To reach larger coverage factors, two plausible paths can be taken: a mix of resources with different time variabilities and/or an energy storage system. The case treated in this paper is the electricity supply of an Astronomical Center in the North of Chile. The economical feasibility of both possibilities is explored and compared to a grid connected alternative.

First, data from local weather stations was collected to have a realistic evaluation of the variability of the solar/wind resource at all time scales. Then, we developed a scalable design of a solar/wind plant and a pumped hydro energy storage system. The free parameters of the design are the maximum installed power for each resource and the capacity of the storage system. Finally, the electricity production is calculated to determine the coverage factor and losses for different values of these parameters.

We found that a coverage factor of 64% is economically feasible for systems without storage. The associated total losses are 24%. To reach larger coverage factors is not economically possible and a storage system must be introduced. If this is done, there is a quantum increase of the total cost of about 30%. However, losses are reduced to about 5% and the coverage factor reaches almost 90%. The cost increase is marginally economically feasible, but it has some other advantages: the consumer is independent of the volatility of electricity prices, and is more sustainable.

The time variability of renewable energy resources difficults reaching coverage levels larger than 60%. Energy storage systems are a requirement. Periods of zero net production seem unavoidable unless the renewable energy and storage system are largely overdimensioned. Back up systems based on fossil fuels seem to be unavoidable. Both the energy storage and back up system add an extra cost that has to be paid if such high coverage levels are a requirement.

The case treated in this paper is the electricity supply of an Astronomical Center in the North of Chile. The ESO is the European Organization for Astronomical research in the Southern hemisphere. It operates the VLT (very large telescope), located at Cerro Paranal in the Atacama desert, North of Chile. The E-ELT (European extremely large optical/infrared telescope) in Cerro Armazones (20 km away from Cerro Paranal) is in advanced design phase and will be the largest optical telescope in the world. Finally, the CTA collaboration (Cherenkov Telescope Array) has chosen the Armazones-Paranal site for construction of its Southern Observatory.

When the two new observatories enter in operation, the peak power demand of the Armazones-Paranal site is estimated to be ∼ 8.5 MW and the total annual energy consumption ∼ 70 GWh. Currently, the VLT is generating its own electricity using fossil fuel-based generators.

The two main characteristics of this consumption center are the strong requirements on the stability of the electricity supply, and the relatively large power demanded. Due to these two factors, the use of liquid fossil fuels is economically un-viable. The only two non-renewable solutions plausible are connection to the Chilean national grid or self production of electricity using generators run with natural gas from a nearby pipeline.

The main renewable energy resources available at the site are wind and solar. In this work, we consider a wind-solar PV plant with Pumped Hydro Energy Storage (PHES). We calculate the coverage factor for different values of total Power, Maximum Energy Storage and wind to solar fraction to find energy systems that maximize coverage but with costs below the non-renewable energy solutions. Embedded in this procedure is the fact that renewable energy time variability can be diminished by considering a mixture. An important ingredient of this procedure is the relative cost of each technology. Government estimates are taken when possible.

Additionally, a concentrated solar power (CSP) plant with thermal energy storage is analyzed. This technology is considered separately since the storage system cannot be used by the wind farm.

The design of the systems is not detailed but all sources of inefficiencies are taken into account. The wind and solar input data used is from local weather stations, which provides realistic time series that account for all possible sources of the variability of the resources. Overall, the estimates of electricity production and cost are as realistic as possible so they can be used as a guide if such energy systems are eventually implemented. The total cost of each system includes operation and maintenance over the 25 year lifetime of the astronomical center.

The paper is organized as follows: in “ Energy demand ” section, the energy demand is described; in “ Non-renewable energy systems ” section, the non-renewable energy systems and their cost are analyzed; in “ Renewable energy resources available in the site: solar and wind ” section, the solar and wind data used in our calculations is described; in “ Renewable energy systems ” section, the methodology to calculate the time series of electricity production for the Wind-Solar PV plant with PHES is presented, together with a modular design of each of the subsystems and their cost; in “ Results ” section, an algorithm to find the optimum system is presented and compared to the non-renewable energy alternatives. The CSP with thermal storage design and cost are presented in the Appendix .

Energy demand

The energy demand of the VLT is known [ 1 ]. The power demand changes from day to night but is rather constant along the year (less than 5% variability). The projected E-ELT (CTA) consumption is taken from ESO estimates [ 2 ]. All the sub-systems, including lodging, offices and workshops are included. A simplified model is adopted: a constant power with different day/night values. The start/end for day/night will be calculated using the sunrise and sunset, even though the start/end of astronomical observations is typically later/earlier.

Table 1 shows a summary of the site energy demand. Night consumption is smaller than day for the E-ELT and VLT due to the strict thermal control system.

All observatories work in slow tracking mode during the night. In between observational windows, telescopes are re-positioned to track new objects. The instantaneous power required for re-positioning is large compared to the average power: 700, 3200, and 2000 kW for the VLT, CTA, and E-ELT compared to 1000, 2750, and 4250 kW. However, the total energy for repositioning is small (<5%). The extra power for repositioning can be supplied by energy storage systems with extremely fast responses like flywheels, STATCOMs or a battery system.

Non-renewable energy systems

Connection to the chilean electrical network.

The grid connection alternative envisages the connection of the Armazones-Paranal site to the Paposo substation. It requires the construction of a ∼ 60 km 66 kV line, one 220–66 kV transformer (at Paposo) and one 66–23 kV substation (located halfway between Cerro Armazones and Paranal). The projected investment cost or CAPEX is 12.5 MUSD ( ∼ 11 M e ). The OPEX is calculated multiplying the total annual energy consumed (70 GWh) by a nominal price. Two cases are considered: no inter-annual increase and 1% inflation.

The electrical network in Chile consist of four independent networks, the two most important being Sistema Interconectado Central (SIC) and Sistema Interconectado del Norte Grande (SING). The electricity market is liberalized but there is a distinction between regulated ( P < 2000 kW) and special clients ( P > 2000 kW). Special clients can negotiate directly electricity prices with the producers and/or produce its own energy. Regulated clients are subject to prices fixed twice a year by the government based on the liberalized market prices. Figure 1 shows the time evolution of the mean market price in Chile for the SING/SIC in Chilean Pesos per kWh and e per MWh [ 3 ]. Prior the 2007 crisis, prices were around 40 e /MWh, and during the last 5 years have been stable around 80 e /MWh with 15% oscillations. This is the nominal price that will be considered in this work.

Time evolution of the mean market electricity price in Chile for the SING/SIC in Chilean Pesos per kWh and e per MWh

Multifuel generators

A 8.5 MW combined cycle gas turbine (CCGT) is considered in this case: it has high efficiencies ∼ 55% and fast time responses. Since there is already a 2.5 MW generator with these characteristics in the site, it will be only necessary to upgrade it with 6 MW more. We consider an investment cost of 1000 e /kW, i.e., a CAPEX of ∼ 6 M e . Natural Gas supplied by Gas Atacama, whose pipeline passes through the middle of the Armazones-Paranal site, can be used to run these generators. The expected connection cost is ∼ 2.5 M e : a gas sub-station, a low capacity (7000 m 3 per day) 5-km pipeline and a low capacity tank for regulation. In total, the CAPEX of the back-up system is 8.5 M e .

The OPEX is mainly due to the purchase of natural gas. The natural gas prices are high in Chile. The projections from the Chilean government are taken to correct the world market prices to the special case of Chile. The following equation is adopted to estimate the time-dependent price of a kWh generated by CCGT:

where C gas is given by (1+ f N years )·9, N years is the number of years since 2015 and f takes into account the interannual increase of prices. We consider two values: f =0.01 and f =0.1. This equation yields 0.07 e /kWh for 2015.

Due to the strong requirements on the stability of the supply, this system is also a requirement for all renewable energy systems considered.

Cost estimation

The total cost normalized to year 0 is estimated using:

where k is the interest rate, 3%. The lifetime of the observatories and the renewable energy system is taken as 25 years. Table 2 shows the results.

Renewable energy resources available in the site: solar and wind

The Armazones-Paranal site is located in the Atacama desert, 130 km south from Antofagasta and 1200 km north of Santiago de Chile. The Cerro Paranal and Cerro Armazones have a height of 2635 and 3000 m respectively, and they are 22 km apart. The Cerro Paranal is 15 km away from the coast.

The topography in the North of Chile is dominated by the Central Andes, characterized by four topographical segments from West to East: the coast mountain range, the central hollow, the pre Cordillera, and the Cordillera. The Armazones-Paranal site is located in the coast mountain range, 20–40 km wide and with mean heights of 1500–2000 m. The coast mountain range falls rapidly into the sea with active segments of sea abrasion where sea cliffs are present and inactive segments where there is an emerged platform.

The climate is typical of a desert region: day/night thermal differences of up to 10 o C , rainfall smaller than 30 mm and relative humidities in the 5–20% range. The average temperature is ∼ 15 with ∼ 5 o C seasonal variations.

The solar resource

The solar resource is characterized using the 2011 data from a weather station installed in the area [ 4 ]. The measurements available are global and diffuse irradiance in horizontal plane and one axis tracking mode (North-South orientation), temperature. Only 25 days have missing measurements. This data is directly used in the estimation of the electricity production of a solar based renewable energy system. This data contains all sources of time variability and in that sense is more suited for our purpose than satellite based models.

In some special cases, e.g., for missing data periods or to evaluate the inter-annual variability of a wind-solar plant, a simplified model of solar irradiance is used:

where I G is the global solar irradiance incident on a surface that subtends an angle Φ with the sun direction, f d is the fraction of diffuse irradiance and I D is the direct irradiance in the sun direction:

where θ s is the solar zenith angle, I 0 the irradiance when the sun is in the zenith and τ is an atmospheric extinction parameter. Adopting f d =0.05, I 0 =1200 W/m 2 1 and τ =0.1, a good description of the data is found. 2

Figure 2 shows the global irradiance incident on a horizontal and a one-axis tracking surface from data (dashed lines) compared to the model (solid lines) for the 23rd of June 2011. Figure 3 shows the same for the accumulated day irradiance. 34 days out of the 340 analyzed has a predicted irradiance 10% larger than measured (“Cloudy Days”) but only 5 are consecutive.

Global irradiance incident on a horizontal and a one-axis tracking surface from data ( dashed lines ) compared to the model ( solid lines ) for the 23rd of June 2011

Same as Fig. 2 but for the accumulated day irradiance

The temperature is also an important factor that determines the performance of solar plants. The weather station temperature time series is used in our calculations.

The wind resource

Wind and speed direction from the VLT meteo mast is used to characterized the wind resource [ 5 ]. Measurements at 10 and 30 m from the last 15 years exist. Table 3 shows the average wind speeds at 30 m for the last 10 years. Figure 4 shows the wind speed distribution for the year 2011 at 30 m.

Wind speed distribution at 30 m in Cerro Paranal for the year 2011

Renewable energy systems

In this section, the methodology to calculate the time series of electricity production for the wind-solar PV plant with PHES is presented. Then, a modular design of each of the subsystems is described. Finally, the procedure to calculate the cost given any value of installed power, wind to solar fraction and size of the storage system is described.

Electricity production time series: methodology

The following definitions will be adopted:

P P ( t ) MW : time series of power produced by solar/wind plant.

P D ( t ) MW : time series of power demand.

P A ( t ) MW : time series of power available to satisfy the demand (either from wind/solar plant or storage system).

E S and E MSC MWh: storage level and maximum storage capacity.

P to store and P \(_{max}^{to~store}\) : power to store and maximum instantaneous power that the storage system is able to store.

s 1 /s 2 : efficiency of the storage system to store/deliver electricity. It can depend on load.

t 1 /t 2 /t 3 : transport efficiencies (transformer and lines) between solar/wind plant-storage system (t 1 ), storage system-demand site (t 2 ) and solar/wind plant-demand site (t 3 ). t 1 /t 2 /t 3 depends on the location of each subsystem and transmission line type. For our case and using standard calculations they are: 97, 97.5, and 98%.

Time series are calculated in 10 min intervals. If P P > P D energy is stored with efficiency s 1 × t 1 , unless P to store > P \(_{max}^{to~store}\) or the storage system is full. If P P < P D energy is extracted from the storage system with efficiency s 2 × t 2 until depleted. The efficiency t 3 is also applied to the fraction of P P that directly satisfy the demand.

E \(_{loss}^{Stg}\) accounts for the energy lost because of P \(_{max}^{to~store}\) and E MSC . E \(_{loss}^{Eff}\) accounts for losses due to s 1 / s 2 . E \(_{loss}^{Transport}\) accounts for losses in transport. E \(_{loss}^{Avail}\) accounts for availability: it is included assuming that on the 15th day of each month all systems are stopped for maintenance (3.3%). It is only applied to the annual energy production.

E P , E D , and E A are the annual sum P P , P D , and P A . Other definitions:

f cover =E A /E D : energy coverage.

\(f_{loss}^{Stg}\) = E \(_{loss}^{Stg}\) /E P : energy loss due to storage size and storage maximum power.

\(f_{loss}^{All}\) =( E \(_{loss}^{Stg}\) + E \(_{loss}^{Eff}\) + E \(_{loss}^{Transport}\) + E \(_{loss}^{Avail.}\) )/ E P : total energy loss.

Solar PV plant

We present a modular design of a solar PV plant. The unit cells corresponds to ∼ 1 MWp. The components of the Solar PV plant selected are the following:

Solar panels: Jinko Solar JKM300M. This is a silicon poly-crystalline 300 Wp panel. These modules have the IEC61215 certification which is the standard in Europe.

Inverter/transformer: the Sunny Central SC1000MV. This is a central inverter optimal for large system where production is uniform across the array

Trackers: the ExoSun ExoTrackHZ, suitable for large plants deployed in flat areas. This is a one axis tracker (axis orientation North-South).

The number of panels to be placed in series is calculated using: N series = V op,inv / V mpp,panel , where V op,inv is 450–820 V and V mpp,panel is 35–40 V depending on irradiance. This gives between 11 and 23 panels per string . The open circuit voltage of a string ( N series x45 V) should not exceed the maximum operating voltage of the inverter (880 V). For that reason 18 panels per string are chosen. 30 string s will be connected to a tracker forming a block , fulfilling the tracker specifications. All strings within a block are connected in parallel to an inverter. The number of blocks to be connected in parallel to reach the nominal inverter power is given by \(\frac {P_{inverter}}{N_{blocks}\times 30 \times 18 \times P_{nom,panel}}\) . This yields six blocks per inverter, which also complies with the restriction that the short circuit current does not exceed the maximum allowable current of the inverter.

Each string is a 2 x 18 m rectangle. 30 of them are placed consecutively (with a spacing of 7 m) to form a block. The spacing is chosen to minimize shading losses. 3 x 3 blocks are placed side by side to minimize DC cabling forming a unit cell, a rectangle of ∼ 280 x 64 m.

The power produced by the solar PV plant in a given time period is given by:

where I G ( Φ ) is the global solar irradiance on a surface with an incidence angle Φ , I stc the irradiance in standard conditions 1000 W/m 2 , the factors f therm . and f shading take into account the thermal and shading losses that depend on irradiance, ambient temperature and sun position, the factor f cte are losses that have no dependencies on the time period considered. The angle Φ is calculated for each period so the solar vector lies within the plane perpendicular to the aperture. The only exception is when the required solar panel elevation is smaller than what trackers allow (40 o , since trackers can rotate ±50 o ). In that case, the incidence angle is calculated for a fixed elevation of 40 o .

The thermal losses are calculated using:

where g is the thermal losses coefficient (0.4% per o C ), T std is the temperature in standard conditions (25 o C ) and T panel is the panel temperature that can be calculated using:

where T c is the characteristic temperature of the panel, 45 o C in our case, and T ambient is the ambient temperature taken from the weather station.

The shading losses are estimated by geometric calculations for each time period considered. The constant losses are 7%, see Table 4 .

Panel degradation is 20% over 25 years. Only the production of the first year is calculated. To maintain it over 25 years, extra power will be deployed that will be accounted in the OPEX of the plant.

Using the meteo-mast data and a topographic map of the area, we followed the standard procedure to design a wind farm. The software WASP is used to generate a wind resource map (WRG), see Fig. 5 . Then, the OpenWind Software is used to design wind farms with two, five, and ten turbines. The location is 15 km to the west of Cerro Paranal in the Coastal Cliff, where the wind power density is the highest. The wind turbine chosen is the Alstom ECO 80 2.0 Class 2. It is a pitch regulated 2 MW wind turbine, with a hub height of 80 m, a cut-in wind of 4 m/s and a cut-off wind of 25 m/s.

Map of the wind power density to the west of Cerro Paranal. The wind power density is higher in the pink areas . We also show the wind direction rose at the location of the Cerro Paranal. The areas with high wind density on the left correspond to the Coastal Cliff, about 15 km away from the Cerro Paranal

The mentioned software does not provide a time series of the produced electricity. This is a problem for our study: an storage system cannot be dimensioned without them. To overcome this problem, we use the following assumption to characterized the time series:

where P Turbine is the turbine power as a function of air density and wind speed at hub height:

where v 30 ( t ) is the measured temporal series of the meteo mast at 30 m, f vertical is a factor to extrapolate measurements to different heights:

and f horizontal is a factor that takes into account the geographical variations of the wind speed. The value of f horizontal is adjusted so Eq. 8 gives the same duty factor as OpenWind.

The PV and Wind plant requires an electricity based storage system that fulfills the following criteria:

Power: ∼ 10 MW.

Discharge time at output power: more than 12 h.

Response time: ∼ 10–30 min.

Lifetime 25 years.

Efficiency: high, at least 75%.

Technologically mature.

The only technology that matches these criteria is the pumped hydro energy storage (PHES). The site is located in the Atacama desert where water is scarce. Due to the proximity to the coast, there is the possibility to use sea water as storage medium. However, due to the size of the facility and plausible technological and environmental problems, it is advised the use of desalinated water either self produced or bought.

The PHES plant consist in an upper and lower water reservoir connected by penstocks, and a system of turbines and pumps than convert gravitational energy into electricity or vice versa. The system is closed, so filling of the reservoirs has to be done only once. A separate turbine and pumping system is planned, so typical elapsed times to go from pumping to full load generation are of the order of minutes. Water evaporation 4 and filtration of water are important and will be taken into account in the design. P \(_{max}^{to~store}\) is fixed to 14 MW, so hydraulic losses does not severely affect the design.

The hydro power in W is given by:

where ρ is the water density in kg/m 3 , g is the gravity acceleration constant in m/s 2 , Q is the water flow rate through the penstocks in m 3 /s, and δ h n is the net height difference given by:

where δ h g is the gross height difference and δ h ( Q ) are the hydraulic losses in the whole system that depend on the flow rate. The electric power in generation mode is given by:

where η turb and η gen is the efficiency of the turbine (that depends on load) and the generator. The electric power in storage mode is given by:

where η pump and η mot is the efficiency of the pump system (that depends on load) and the motor.

The required value of P e turb / P e pump is 8.5/14 MW.

The design of the system proceeds in two phases:

Site selection.

Plant design.

The site selection implies indirectly choosing two important variables: δ h g and penstock length. The second variable is crucial when determining the hydraulic losses, and is an important contributor to the total cost of the system. As a general rule, larger values of δ h g and smaller penstock length yield smaller investment costs. However, other factors have been analyzed:

Existence of infrastructures like roads and transmission lines.

Existence of hydro resources or possibilities to obtain them.

Earthquake risks.

Detritus removal: short but intense rainfalls can generate detritus removal that can affect the integrity of the PHES.

Topographic maps have been used to choose four possible sites. All sites have similar availability of water/infrastructures and geological risks. Therefore, the site with larger height difference and the smaller penstock length was chosen. Figure 6 shows a detailed topographic map of the site. It is located in the Coastal Cliff, close to the Wind Farm location.

3D map of the selected PHES site together with the elevation contour

Our choice for the turbine system is the use of two Pelton turbines with one injector that can work in parallel to provide the maximum power. The Pelton turbines can work up to 10% of the nominal load, have efficiencies around 90% and are adequate for the site height differences and required nominal flows. The turbines will be coupled to two generators with nominal power 5 MW, AC output voltage of 6 kV and 98% efficiency.

Regarding the pumping system configuration, our choice is the use of multistage centrifugal pumps: 6 of 2 MW and 2 of 0.5 MW. To simplify the calculations an efficiency of 90% for all loads is considered. The motors that drive the pumps work at 6 kV with an efficiency of 98%.

Steel penstocks have rugosities of ∼ 0.6 mm. The hydraulic losses are calculated using standard formulas for different pipe diameters. For each case, the nominal flow rate in production and storage mode is calculated by solving iteratively Eq. 13 /Eq. 14 . The hydraulic losses drop below 5% in both modes at nominal conditions for a tube diameter of 0.85 m. Losses because of other hydraulic components like valves, bypasses, contractions/expansions, etc. are small (10% of Penstock losses) and taken into account. Table 5 gives the final nominal flow rate and hydraulic losses in both modes. Using these calculations the storage efficiencies s 1 and s 2 are calculated.

The penstock wall thickness required to withstand the hydrostatic pressure is given by:

where e s is extra thickness in meters to allow for corrosion, k f is the weld efficiency (0.9), D is the pipe diameter in meters, σ f is the allowable tensile stress in Pascals (1400 kgf/cm 2 , i.e., 1.373 10 5 Pa) and P is the hydrostatic pressure in Pascals. Since the hydrostatic pressure changes from the upper to the lower reservoir, the penstock is built in sections of 100 m with decreasing thickness (10–30 mm). The total weight of the penstock is ∼ 1000 tons.

The surge pressure for the water-hammer effect at the pumping nominal load is 450 m, which would require a substantial increase of the thickness walls that would yield to a doubling of the total penstock weight, i.e. its cost. For that reason, the installation of a surge tower or relief valves is necessary.

The free parameter of the design is the maximum storage capacity, E MSC MWh. For a given value of E MSC , the volume of the water reservoirs is calculated by multiplying the flow rate in generation mode by E MSC /8.5 h, adding a 20% safety margin. In the selected site, there is room for reservoirs with storage capacities up to 1000 MWh.

The reservoirs will be constructed following the scheme of an Earth/Rock filled dam. The depth of the reservoir will be 14 m, leaving 1.3 m between the maximum water level and the top of the dam. The digged material will be reused to build the trapezoidal perimetral dike (3:1), which fixes the dimensions of the reservoir. The surface in contact with the water and the air-water layer is covered by a geotextile cloth.

To build and maintain the upper reservoir it is necessary to construct a 12 km access road. In the case of the lower reservoir there is a nearby access road, so only a short and flat connection to it is necessary. It will be also necessary to build a housing for the electromechanical equipment.

The total cost after 25 years of the wind-solar PV plant with PHES storage is estimated using Eq. 2 . The CAPEX in that equation has the following components:

Solar PV and wind plant: total power installed times a unitary cost of 1,700 e /kW.

PHES: the cost of a PHES system with E MSC =110 MWh is estimated to be 26.4 M e . Table 6 shows a breakdown. The PHES cost for different values of E MSC is estimated using:

where C 1 =3.8 M e is the baseline cost of water and reservoir and C 2 =19.51 M e is the cost of the rest of the subsystems.

Back up system: 8.5 M e .

Electrical infrastructures: 3.5 M e , see Table 7 .

The OPEX has the following components:

Insurance and O&M: we assume 2% of CAPEX with 3% inter annual increase.

Gas purchases: (1− f cover )·70 GWh times the unitary price given by Eq. ??.

Solar PV: the required annual enhancement of the power installed to reach the same nominal production as the first year 5 .

The electricity production is simulated for the following parameters:

P total MW : 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, and 40.

f solar : 0, 0.25, 0.5, 0.75, and 1.

E MSC MWh: 0, 20, 40, 60, 80, 100, 120, 240, 480, 1000.

An example of the time series is shown in Fig. 7 for P total =20 MW, f solar =0.5 and E MSC =100 MWh. For each simulated case, the annual value of f cover and \(f_{loss}^{All}\) is calculated. The left (right) panel in Fig. 8 show an example: f cover ( \(f_{loss}^{All}\) ) as a function of the total installed power for E MSC =0 MWh and three cases of f solar , 0, 0.5, and 1. For this value of E MSC , the optimum system in terms of coverage is neither purely wind or solar, but a mixture. \(f_{loss}^{All}\) for small P total is due to availability and transport.

An example of the time series of electricity production for P total =20 MW, f solar =0.5 and E MSC =100 MWh

f cover and \(f_{loss}^{All}\) as a function of the total installed power for E MSC =0 MWh and three cases of f solar , 0, 0.5, and 1

On the basis of the costs shown in Table 2 , we select two target maximum costs: 100 and 130 M e . For each simulated case, the total cost over 25 years is calculated as in “ Cost estimation ” section. The case with a cost below the target and with maximum coverage is kept. The two cases selected for the two targets are shown in Table 8 . The coverage factors are as large as 64 and 88%. It should be mentioned that the losses for the high cost target are driven by the storage efficiency, transport losses, and availability.

Finally, the case of a concentrated solar power (CSP) plant with thermal energy storage is analyzed. This technology is considered separately since the storage system cannot be used simultaneously by the Wind farm 6 . The design and costs are presented in Appendix . The total cost is 124 M e and f cover 72.5%. This alternative is within the high cost target, but it has lower coverage factor than the case presented in this section.

1 I 0 is only 12% smaller than the irradiance outside the atmosphere (1370 W/m 2 ), which is an indicator of the quality of the site.

2 The electricity production using the model and the raw data for the reference year agrees within 5%.

3 α =0.08 from the ratio of the measurements at 10 and 30 m. A conservatively smaller value is taken: measurements at 10 m can be affected by the surrounding buildings

4 According to our estimations, it can be severe, reducing the water level by almost 3 m per year.

5 It is calculated assuming: PV system prices will decrease at a rate of 20% over 25 years; PV module degradation is 20% over 25 years.

6 Electricity from the Wind Farm would have to be converted into thermal energy. To convert back to electricity the efficiency is given by the steam turbine, ∼ 32%.

Concentrated solar power (CSP) plant with thermal energy storage

The CSP is a technology that needs to be considered when there is plenty available land, the cloudy fraction is small and the fraction of direct irradiance is high. The dessert characteristics of the site fulfill these three criteria. The technology considered in this work is the parabolic trough collectors (PTC), widely considered in a stage of maturity.

In a CSP plant, an oil is heated in the solar field from 293 o C to 393 o C and sent either to the thermal storage system or to a heat exchanger that produces water vapour at 380 o C and 104 bar. The vapour is then conducted to a steam turbine coupled to a generator. After the turbine, the vapour is taken to a condenser and fed again into the loop. Due to the scarcity of water in the site, aerocondensers are considered. The efficiency to convert thermal energy into electricity depends on the nominal power of the turbine and for a 10 MW steam turbine is ∼ 32%.

The solar field is an array of PTCs. The mirrors have a one axis tracking system (North-South) that ensures that at all moments the solar vector lies within the plane perpendicular to the aperture of the collector. Alignment is a strong requirement in PTCs, and also cleaning.

The PTCs have lengths between 100 and 150 m. The 8 module EuroTrough collector with PTR-70 Schott tubes is selected. N series of these modules are placed in series to form a group. N parallel groups are connected in parallel in central feeding configuration to minimize pipe lengths. The separation between rows of collectors is three times the width of the parabola to ensure that annual shadowing losses are below 1%.

The thermal power captured by the collector is given by:

where \(\eta _{opt \phi =0^{0}}~K(\Phi)\phantom {\dot {i}\!}\) is a parameterization of the optical and geometrical losses of the collector, A c is the aperture area, I D is the direct irradiance in W/m 2 at the period considered, F e is a factor that takes into account the dirt in the mirrors (0.95), and P losses are the thermal losses parameterized with its dependence on the temperature difference between the fluid and the ambient, as well as on the direct irradiance and incidence angle.

The collected power can also be written as:

where Q m is the fluid mass flow in kg/s, C p is the specific heat in J/K Kg and T in /T out is the start/final temperature of the fluid. The thermal fluid chosen is an oil called Therminol VP1. Its maximum working temperature is 398 o and solidification temperature is 12 o . This fluid has to be pressurized to 10.5 bar so it is not gas phase at the maximum working temperature. The specific heat and density depends on temperature and is taken from a parameterization provided by the manufacturer.

N series of collectors have to rise the fluid temperature from T in =293 o C to T out =393 o C. The necessary value of Q m is calculated iteratively by equating Eqs. 17 and 18 in 1 m intervals.

The fluid must circulate in a regime turbulent enough to avoid thermal gradients between the external/internal face of the tube that can cause fractures. The optimum value of N series is calculated by imposing a condition on the Reynolds number of the circulating fluid for the time of maximum direct irradiance. In our design, N series must be 4.

The hydraulic losses are calculated for each configuration of the system ( N series , N parallel ) and time period considered using the oil and tube characteristics and ambient conditions. Losses in the pipes that connect the collectors with the heat exchanger and the losses in the pump are also taken into account. 7 .

The required electrical pumping power is given by:

where η m ( ∼ 70%) and η e ( ∼ 99%) are the mechanical and electrical efficiency of the pump.

The electrical power produced by the plant is given by:

where η is the efficiency to convert thermal to electrical energy (32%). The storage efficiencies considered are s 1 = s 2 =96% (Round trip efficiency of 92%). Transport losses are only applicable to t 3 (2%). The availability is included as described before.

The electricity production described in “ Electricity production time series: methodology ” section is calculated in 10 min intervals during a period of 48 hours around the summer solstice. N parallel is increased until f cover =100%. The required value of N parallel is 33. E MSC is given by the maximum storage level during the design period (100 MWh).

The storage system must be able to store 100 MWh, i.e., 312 MWth. This capacity is increased by a safety margin of 8%, i.e., 337.5 MWhth. The temperature in the hot/cold tank corresponds to the temperature of the oil before/after the heat exchanger. Nitrate salt (60% by weight NaNO 3 and 40% KNO 3 ) is considered as storage medium. The mass required can be calculated using:

which yields 8530 tons of salt to store 337.5 MWth. The corresponding volume of the hot and cold tank is different due to temperature. The volume required for the cold/hot tank is 4471 and 4618 m 3 . Fast fluctuations of the solar resource are easily tracked by the thermal storage system by controlling the flow from the solar field that is diverted to the heat exchanger of the storage system.

The electricity production is then calculated for the whole year. The results are shown in Table 9 together with the main design parameters.

A flat area is necessary to ease installation of the solar field. A possible site has been found 10 km away from Cerro Paranal. The access road to the Cerro Paranal passes by the solar field, so no extra civil works are planned. For electrical infrastructures and their cost, see Table 10 .

The investment cost (CAPEX) of the CSP plant is estimated to be 58.5 M e . Table 11 shows the breakdown. The OPEX considered is 2% of the CAPEX with a 3% inter annual increase. The total cost after 25 years normalized to year 0 is 124 M e .

Abbreviations

British thermal unit

Capital expenditure

Combined cycle gas turbine

• Concentrated solar power

Cherenkov telescope array

European extremely large telescope

European southern observatory

Million US dollars

Open software to design Wind Farms

Operating expenditure

Pumped hydro energy storage

• Photovoltaic

Parabolic Trough Collectors

Sistema interconectado central

Sistema interconectado del Norte Grande

Static synchronous compensator

Very large telescope

Wind energy industry-standard software

Wind Resource Map (Power density)

Interest rate

Global solar irradiance in a surface W/m 2

Direct irradiance in the sun direction

Diffuse irradiance in a surface expressed as a fraction of the direct irradiance

Angle subtended by the normal of a surface with the sun direction

Solar zenith angle

Atmospheric extinction parameter

Time series of power produced by solar/wind plant in MW

Time series of power demand in MW

Time series of power available to satisfy the demand in MW

Annual sum P P ,P D and P A E S and E MSC : Storage Level and Maximum Storage Capacity in MWh

Power to store and Maximum Instantaneous Power that the storage system is able to store

Efficiency of the storage system to store/deliver electricity

Transport efficiencies (transformer and lines) between solar/wind plant-storage system (t 1 ), storage system-demand site (t 2 ) and solar/wind plant-demand site (t 3 )

Energy lost during storage operations due to P \(_{max}^{to~store}\) and E MSC

Energy lost during storage operations due to s 1 / s 2

Energy lost due to transport inefficiencies

Energy lost due to operation and maintenance (availability)

E A /E D energy coverage

E \(_{loss}^{Stg}\) /E P , energy loss due to storage size and storage maximum power

(E \(_{loss}^{Stg}\) +E \(_{loss}^{Eff}\) +E \(_{loss}^{Transport}\) +E \(_{loss}^{Avail.}\) )/E P , total energy loss

The irradiance in standard conditions 1000 W/m 2

Watt Peak, solar panel power for I stc

The solar panel temperature in standard conditions (25 o C )

Solar panel losses, thermal, shading and those that do not depend on solar irradiance

Solar panel thermal loss coefficient

Inverter input voltage range

Solar panel volage at maximum power

Wind speed at hub height

Wind speed height coefficient

Air density

Water density

Gravity acceleration constant

Water flow rate through the penstocks in m 3 /s

Net height difference between upper and lower reservoir in a PHES

Gross height difference

Q dependent hydraulic losses

Efficiencies of turbine, generator, pump and motor in a PHES

Hydro power

Electrical power

Penstock diameter

Length of penstock

Penstock weld efficiency

Allowable tensile stress in Pascals

Hydrostatic pressure in penstock

PTC thermal losses

PTC losses due to dirtying

Thermal power captured by a PTC

Optical and geometrical losses of the collector

Specific heat of PTC thermal fluid

Mass flow in kg/s of the thermal fluid

Temperature in/out of the thermal fluid

Towards a Green Observatory. https://www.eso.org/sci/libraries/SPIE2010/7737-73.pdf . Accessed Feb 2017.

The E-ELT construction proposal. http://www.eso.org/public/products/books . Accessed Feb 2017.

Comision Nacional de Energia de Chile. www.cne.cl. Accessed Feb 2017.

Ministerio de Energía de Chile. http://antiguo.minenergia.cl . Accessed May 2015.

European Southern Observatory. http://archive.eso.org . Accessed Feb 2017.

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Acknowledgements

This work would not be possible without the financial support of the CNPq, FAPESP (PROCESSO 2015/15897-1) and the resources of the Instituto de Física de São Carlos. We thank Vitor de Souza for the careful reading of the manuscript, Eduardo Zarza for his guidance with CSP technology, Marcos Blanco for providing the WASP simulations needed to estimate the Wind Resource, Marc Sarazin for his help with the Wind data and ESO water supply, and Natalia Serre for all the information she provided concerning CTA power supply. Finally, we also thank all the Escuela de Organizacion Industrial (EOI) staff for their support.

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All authors contributed to the development of the work. The corresponding author prepared the manuscript, but all authors read and approved the final manuscript.

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Abos, H., Ave, M. & Martínez-Ortiz, H. A case study of a procedure to optimize the renewable energy coverage in isolated systems: an astronomical center in the North of Chile. Energ Sustain Soc 7 , 7 (2017). https://doi.org/10.1186/s13705-017-0109-0

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Renewable Energy Data, Analysis, and Decisions Viewed through a Case Study in Bangladesh: Preprint

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T1 - Renewable Energy Data, Analysis, and Decisions Viewed through a Case Study in Bangladesh: Preprint

AU - Watson, Andrea

AU - Jacobson, Mark

AU - Cox, Sarah

N2 - Many developing countries around the world have signaled their intention to transition their energy sectors to rely on cleaner sources of electricity generation for a variety of reasons that may include complying with their Nationally Determined Contributions (in support of the Paris Climate Agreement), increasing energy security, or reducing air pollution. Renewable energy resources are increasingly a cost competitive option for new electricity generation; however, nations must consider renewable energy potential if they wish to increase these technologies in their electricity generation mix. Yet, goal setting, policymaking, grid modeling, and investment decisions that will enable renewable energy development all depend on the existence and quality of renewable energy resource data. This paper aims to summarize the relationship between renewable energy data, analysis, and decision making for developing countries seeking to transition their energy sector and to consider Bangladesh as a case study of a country that has worked with development organizations and the National Renewable Energy Laboratory to develop a national inventory of renewable energy resource data to enable critical decision making.

AB - Many developing countries around the world have signaled their intention to transition their energy sectors to rely on cleaner sources of electricity generation for a variety of reasons that may include complying with their Nationally Determined Contributions (in support of the Paris Climate Agreement), increasing energy security, or reducing air pollution. Renewable energy resources are increasingly a cost competitive option for new electricity generation; however, nations must consider renewable energy potential if they wish to increase these technologies in their electricity generation mix. Yet, goal setting, policymaking, grid modeling, and investment decisions that will enable renewable energy development all depend on the existence and quality of renewable energy resource data. This paper aims to summarize the relationship between renewable energy data, analysis, and decision making for developing countries seeking to transition their energy sector and to consider Bangladesh as a case study of a country that has worked with development organizations and the National Renewable Energy Laboratory to develop a national inventory of renewable energy resource data to enable critical decision making.

KW - analysis

KW - decision making

KW - developing countries

KW - renewable energy data

KW - Renewable Energy Data Explorer

KW - renewable energy resource data

T2 - 2019 Decision Analysis Conference

Y2 - 19 July 2019 through 21 July 2019

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Renewable Energy Resources: Case Studies

Balaji Devarajan 1 , V Bhuvaneswari 1 , A K Priya 1 , G Nambirajan 1 , J Joenas 1 , P Nishanth 1 , L Rajeshkumar 2 , G Kathiresan 3 and V Amarnath 3

Published under licence by IOP Publishing Ltd IOP Conference Series: Materials Science and Engineering , Volume 1145 , International Conference on Chemical, Mechanical and Environmental Sciences (ICCMES 2021), 25th-26th March 2021, Coimbatore, India Citation Balaji Devarajan et al 2021 IOP Conf. Ser.: Mater. Sci. Eng. 1145 012026 DOI 10.1088/1757-899X/1145/1/012026

This article is retracted by 2021 IOP Conf. Ser.: Mater. Sci. Eng. 1145 012145

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1 Department of Mechanical Engineering, KPR Institute of Engineering and Technology, Coimbatore, Tamilnadu 641407, India

2 Head R&D Shanthi gears, Coimbatore, Tamilnadu 641406, India

3 Sri Ramakrishna Engineering College, Coimbatore, Tamilnadu 641022, India

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The energy need is the only demand which wouldn't have seen negative trend since the origin of this universe. Its requirement keeps demanding the usage of energy, during this urge people around globe working with many energy production techniques. Amongst most of them act as a resource including fossil fuel, coal and others are polluting vicinity to larger extend. The other alternative is renewable energy resources (RERs) which quite natural gift to the mankind owing to its vicinity aiding resource. The energy harvesting by utilising these RERs also have limitation that, can't provide huge in quantity due to many reasons including seasonal, inadequate equipment, larger storage so on and so forth. The focus herein is that, by considering its limitations to which extend it can be utilised. It is obvious that production industries require enormous quantity of power, therein it may not be utilised as such. So, the house as well as small industries whose power requirement is minimum thereby this RERs can be effectively utilised. That is considered as a primary factor for consolidating of this survey in the form of various test cases.

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[1] Cabanac G, Labbé C and Magazinov A 2021 arXiv: 2107.06751v1

Retraction published: 23 February 2022

The Renewable Energy Transition in Africa

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A renewables-based energy transition promises to deliver vast socio-economic benefits to countries across Africa, improving energy access, creating jobs and boosting energy security. To realise these benefits, African countries have an opportunity to leapfrog fossil fuel technologies to a more sustainable, climate-friendly power strategy aligned with the Paris Agreement and low-carbon growth.

The report is also available in French ( français ) and German ( Deutsch )

The Renewable Energy Transition in Africa , jointly prepared by Germany's KfW Development Bank, Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ), and the International Renewable Energy Agency (IRENA) on behalf of the German Federal Ministry for Economic Cooperation and Development (BMZ), explores how African countries can achieve universal energy access within the 2030 Agenda timeframe and identifies four areas of action:

• Promote access to energy;
• De-risk and promoting private sector investments;
• Strengthen and modernise the grid;
• Support systemic innovation.

The study also explores the transformational potential of the electricity sector in five Africa countries: Ghana, Ivory Coast, Morocco, Rwanda and South Africa. Specifically developed by IRENA, country case studies show the real-life applicability of power sector transformation and demonstrates how countries can:

• Take advantage of the abundancy and competitiveness of renewables;
• Align ambitious renewable targets in energy and climate plans;
• Continue supporting the development of regional markets;
• Leverage renewables and distributed energy resources to achieve universal energy access;
• Develop tailored power sector transformation plans based on a systemic innovation approach;
• Build on policy frameworks for just and inclusive transitions.

Additional analyses

North africa: policies and finance for renewable energy, socio-economic footprint of the energy transition: south africa, towards a prosperous and sustainable africa, renewable energy market analysis: africa and its regions, renewables readiness assessment: botswana, related content.

Record Growth in Renewables, but Progress Needs to be Equitable

North Africa's Renewable Potential and Strategic Location Reinforce Its Role in Energy Transition

The 360 Gigawatts Reason to Boost Finance for Energy Storage Now

Renewables Receive Major Boost with Pledges to IRENA's ETAF Platform Exceeding USD 4 Billion

IRENA Roundtable Sheds Light on the Role of Energy Plans in Southeast Europe’s Green Financing

Energy Management of Microgrid With Renewable Energy Sources: A Case Study in Hurghada Egypt

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Requirements of sustainable renewable energy systems case study (Samawah city)

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Hayder Lafta Albdiery , Suhad K. A. Al-Mosawy; Requirements of sustainable renewable energy systems case study (Samawah city). AIP Conf. Proc. 14 February 2024; 3009 (1): 030052. https://doi.org/10.1063/5.0190804

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Renewable energy is a crucial and necessary means of attaining sustainable development. Nevertheless, relying on renewable energy sources is not always sustainable. These sources need advanced and expensive means and techniques to exploit them, in addition to a sophisticated technology that are sometimes monopolistic, and the raw materials needed for the manufacture and production of such equipment are much less prevalent than fossil fuel sources. Therefore, their monopoly is aggravated. Such as Nickel, Lithium, Cadmium, Zinc and other materials that are increasingly needed with the increase in the trend towards using renewable energies. Moreover, the increase in its supply will not meet the increase in demand for it, especially as it is subject to political fluctuations. Such as what happened during the war in Ukraine, where the price of lithium rose by 500% at the beginning of 2022. And the supply chains of photovoltaic equipment and wind turbines be governed by China by 80% and it witnessed great confusion because of what happened due to the closures to combat the Covid 19 epidemic. All of these requests for an extensive study of the requirements for the sustainability of operating and utilization of renewable energies, and this should be our top priority before moving forward on the path of transition to full dependence on renewable energies. Accordingly, we need to localize the industrial base associated with investing renewable energies as an introduction to reach sustainable investment and real solutions to energy issues. We have to determine accurately what is available to us from the elements of the renewable energy industry and what we can replace with local alternatives or provide from sustainable sources if it is not available locally. Only by doing this we will achieve a safe and sustainable transition from traditional energy sector to effective, efficient, sustainable renewable energies investment.

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Assessing the environmental impacts of renewable energy sources: A case study on air pollution and carbon emissions in China

Affiliations.

• 1 Edinburgh Business School, Heriot-Watt University, Edinburgh, UK. Electronic address: [email protected].
• 2 James Cook University, Singapore. Electronic address: [email protected].
• 3 Newcastle University Business School, Newcastle University, UK. Electronic address: [email protected].
• 4 Kent Business School, University of Kent, UK. Electronic address: [email protected].
• PMID: 37421726
• DOI: 10.1016/j.jenvman.2023.118525

This study investigates the impact of renewable and non-renewable energy sources on carbon emissions in the context of China's 14th Five-Year Plan (2021-2025). The plan emphasises a "Dual-control" strategy of simultaneously setting energy consumption limits and reducing energy intensity for GDP (gross domestic product) in order to meet the targets of the five-year plan. Using a comprehensive dataset of Chinese energy and macroeconomic information spanning from 1990 to 2022, we conduct a Granger causality analysis to explore the relationship between energy sources and the level of air pollution. Our findings reveal a unidirectional link, wherein renewable energy contributes to a reduction in air pollution, while non-renewable energy sources lead to an increase. Despite the government's investment in renewable energy, our results show that China's economy remains heavily reliant on traditional energy sources (e.g., fossil fuels). This research is the first systematic examination of the interplay between energy usage and carbon emissions in the Chinese context. Our findings provide valuable insights for policy and market strategies aimed at promoting carbon neutrality and driving technological advancements in both government and industries.

Keywords: COP27; Carbon footprint; Carbon neutrality; Climate change; Innovation diffusion; Non-renewable energies; Renewable energies.

Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.

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• Carbon* / analysis
• Economic Development
• Energy-Generating Resources
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• Renewable Energy
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University of Missouri District Energy Microgrid Case Study

Attachments: University of Missouri District Energy Microgrid Case Study.pdf (549.83 KB) Publication Date: Friday, March 29, 2024

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Walmart Accelerates Clean Energy Purchases and Investments With Nearly 1 GW of New Projects Across the U.S.

By Vishal Kapadia, Senior Vice President, Energy Transformation

March 26, 2024

In January, we announced Walmart’s intent to accelerate our energy transformation strategy, an important element of our continued growth as a people-led, tech-powered omnichannel retailer dedicated to helping people save money and live better. Today, I am pleased to announce that we have made commitments that will enable the construction of nearly 1 gigawatt (GW) of new clean energy projects across the country.

Our approach to new clean energy commitments is aimed at identifying high impact, high quality projects that drive positive outcomes. These projects are expected to expand access to clean energy, drive new tax revenue to communities, create local jobs and in the case of community solar initiatives, offer direct benefits to Walmart's customers, members and local communities by helping them save money on energy costs. These new investments will add to Walmart's existing portfolio , which consists of more than 600 onsite and offsite renewable energy projects already in operation or under development in over 10 countries.

Community Solar: Around $6 million in annual savings for LMI households

The two community solar and distributed generation portfolios, developed by Pivot Energy and Reactivate , include 70 megawatts (MWac) from 26 new community solar and distributed generation installations across six states. Once operational, they are anticipated to produce ~160,000 MWh of clean energy annually, producing enough electricity to support community solar subscriptions for ~13,000 residential households in the U.S. These will enable approximately $8 million in annual savings on energy bills for households and commercial off-takers while bringing new clean energy online. Notably, around $6 million of these savings are expected to benefit low-to-moderate income (LMI) communities.

Renewable Energy Purchase Agreements: Expanding grid capacity with new clean energy

In addition to community solar, the execution of long-term renewable energy purchase agreements is key to reducing emissions and delivering more clean energy to our power grids. Through these long-term agreements, Walmart is enabling new clean energy projects that help support local jobs, generate new tax revenue in rural communities, drive clean energy to local grids and deliver meaningful progress toward our renewable energy and emissions reduction goals.

Developed by NextEra Energy Resources , EDP Renewable North America and Invenergy , these solar projects represent commitments by Walmart in new regions, including our home state of Arkansas, Louisiana and Mississippi. The portfolio also includes additions to our renewable portfolio in Texas. Once in service, these projects will add 842 MW of capacity to the grid and will support progress toward our renewable energy goal – to be 100% powered by renewable energy by 2035 – and contribute toward our zero emissions targets.  These new projects are in addition to the 2 GW of renewable energy projects we’ve previously helped bring in service through long-term renewable energy purchase agreements.

Walmart has also executed several clean energy agreements directly with utilities, helping add new clean energy capacity within those service areas resulting in an additional 77 MW of capacity in Louisiana, Michigan and Texas.

Our commitment to enable 10 GW of new clean energy projects by 2030 – enough electricity to power the equivalent of over 2 million households – is designed to serve customers and communities and power our growth. An energy system that thrives on emissions-free energy, helps people save money and enables energy resilience for our domestic grid is good for everyone – our business, customers and members, communities and, of course, our planet. Walmart will continue to seek out investments that allow us to expand clean energy capacity and help customers and communities save money and live better, contributing to a more sustainable future.

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Renewable Natural Gas Production

This milk transport truck fuels up at a renewable natural gas station. Photo from ampCNG

Renewable natural gas (RNG) is a pipeline-quality gas that is fully interchangeable with conventional natural gas and thus can be used in natural gas vehicles. RNG is essentially biogas (the gaseous product of the decomposition of organic matter) that has been processed to purity standards. Like conventional natural gas, RNG can be used as a transportation fuel in the form of compressed natural gas (CNG) or liquefied natural gas (LNG). RNG qualifies as an advanced biofuel under the Renewable Fuel Standard .

Biomethane, which is another term for this purified pipeline-quality fuel, refers to biogas that has also been cleaned and conditioned to remove or reduce non-methane elements. Biogas is produced from various biomass sources through a biochemical process, such as anaerobic digestion , or through thermochemical means, such as gasification. With minor cleanup, biogas can be used to generate electricity and heat and is used as a replacement for traditional natural gas to generate combined electricity and heating for power plants—not in vehicle applications.

To fuel vehicles, biogas must be processed to a higher purity standard. This process is called conditioning or upgrading, and involves the removal of water, carbon dioxide, hydrogen sulfide, and other trace elements. The resulting RNG, or biomethane, has a higher content of methane than raw biogas, which makes it comparable to conventional natural gas and thus a suitable energy source in applications that require pipeline-quality gas, such as vehicle applications.

For a comprehensive list of projects that are upgrading gas for pipeline injection or use as vehicle fuel, see the Renewable Natural Gas Database developed and maintained by Argonne National Laboratory.

Biogas from Landfills

Landfills are designated locations for disposal of waste collected from residential, industrial, and commercial entities. Landfills are the third-largest source of human-related methane emissions in the United States, according to the U.S. Environmental Protection Agency (EPA). Biogas from landfills is also called landfill gas (LFG), as the digestion process takes place in the ground rather than in an anaerobic digester. As of August 2022, there were 538 operational LFG projects in the United States, according to the EPA. However, most of these projects use biogas to produce electricity rather than power natural gas vehicles.

Learn about these LFG alternative fuel transportation projects:

• Waste Management's Altamont Landfill near Livermore, California
• St. Landry Parish Landfill in Washington, Louisiana
• Joint Water Pollution Control Plants in Los Angeles County, California

Biogas from Livestock Operations

Biogas recovery systems at livestock operations can be used to produce RNG. Animal manure is collected and delivered to an anaerobic digester to stabilize and optimize methane production. The resulting biogas can be processed into RNG and used to fuel natural gas vehicles or produce electricity.

As of May 2022, there were about 330 anaerobic digester systems operating at commercial livestock farms in the United States. Most of these facilities use biogas for electricity generation. A few farms are using biogas to produce transportation fuel, including Calgren Dairy Fuels in California and Fair Oaks Farms in Indiana. EPA's AgSTAR database provides more information about the use of such systems in the United States.

Biogas from Wastewater Treatment

Biogas can be produced by digesting the solids removed in the wastewater treatment process. According to EPA estimates, this biogas potential is about 1 cubic foot of digester gas per 100 gallons of wastewater. Energy generated at U.S. wastewater treatment plants (WWTPs) could potentially meet 12% of the national electricity demand, according to a study released by the National Association of Clean Water Agencies, the Water Environment Research Foundation, and the Water Environment Federation. This could spur some production of RNG for vehicle use as well.

There are more than 16,000 WWTPs in the United States, but only about 1,200 have anaerobic digesters and of those, 860 have the equipment to use their biogas on site . The City of Longmont Wastewater Treatment Plant in Colorado is an example of a plant that uses biogas to produce RNG for use in vehicles.

Other Sources of Biogas

Other sources of biogas include organic waste from industrial, institutional, and commercial entities, such as food manufacturing and wholesalers, supermarkets, restaurants, hospitals, and educational facilities. Learn about the City of Perris, California, biodigester , that produces enough RNG to fuel their fleet of 900 vehicles.

Biogas can also be produced from lignocellulosic material (such as crop residues, woody biomass, and dedicated energy crops) via thermochemical conversions, co-digestion, and dry fermentation. These technologies are underway in Europe, with limited applications in the United States.

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Home » Blogs » RealVNC Connect: Remote Management for the Renewable Energy Sector

• 2 April 2024

RealVNC Connect: Remote Management for the Renewable Energy Sector

Perfect for the unique challenges of the renewable energy sector, RealVNC Connect is not just about technology; it’s your digital ally in your fight for sustainability. This blog post will discuss the role that RealVNC Connect plays in enhancing the management of renewable energy resources. The focus will be on maximizing operational uptime and device functionality.

Remote Management Redefined in Renewable Energy

In the wilderness or on top of a wind-swept hill, clean energy assets are the means to achieving a green future. However, managing these assets can be a logistical challenge. And this especially happens when they’re geographically dispersed, possibly in a harsh environment. Engaging with turbines and solar arrays in remote areas is complicated, requiring a solution that is efficient and very secure.

RealVNC Connect comes with a complex suite of features, able to engage with renewable energy assets from any location, securely. The fact that it’s multiplatform and its simple setup ensures that even remote installations, such as those in underdeveloped regions, can be done with ease. But how does this translate into benefits for the industry?

Secure Remote Access for Operational Continuity

Ensuring the round-the-clock performance of renewable assets is crucial. A single turbine downtime left unattended, can lead to a significant loss in the energy harvesting cycle. RealVNC Connect’s secure remote access allows you to keep an eye on the assets, enabling immediate troubleshooting in the event of an anomaly.

Proactive Issue Resolution Enhances Asset Integrity

RealVNC Connect helps energy professionals see real-time insights into the performance of their assets, as engineers can constantly keep an eye on them. This way, issues are resolved before they escalate, thereby maintaining the integrity of the infrastructure.

By enabling remote configuration, monitoring, and troubleshooting of renewable assets, RealVNC Connect software is helping make things more efficient.

Continuous Monitoring for Useful Performance Insights

With remote monitoring capabilities, renewable energy professionals can easily gain in-depth visibility into operational data. One engineer can constantly keep an eye on the screens of multiple devices and intervene as and when needed. This continuous stream of information enables performance analysis, and operational fine-tuning, for increases in efficiency.

Case Study – Centurion Solar: Renewable Success

Centurion Solar’s adoption of RealVNC Connect exemplifies the software’s profound impact. By utilizing RealVNC Connect, Centurion was able to overcome the challenges of remote troubleshooting and configuration that had complicated their customer support processes.

The solution not only resolved their existing operational hurdles but also opened new avenues for expansion – a testimony to the scalability and adaptability of the software within the renewable energy landscape. As they prepare to expand their operations into India and Australia, the remote access capabilities of RealVNC Connect remain their reliable partner for further success.

Takeaways for Renewable Energy Leaders

In the field of renewable energy, the stakes have never been higher. The transition from traditional to sustainable power sources is very dependent on the sector’s ability to innovate, adapt, and leverage technologies that allow companies to make faster progress. RealVNC Connect offers a robust platform for remote management that resonates with the industry’s quest for greener pastures. Furthermore, when packaged together with RPort into the RealONE solution , it allows you to effectively monitor, manage, and support your systems, by adding comprehensive remote management tools.

For leaders in the renewable energy sector, the message is clear – the future of remote management has already begun, and it’s happening with RealVNC Connect.

But don’t take our word for it – get a free trial now !

• Posted on 2 April 2024
• A journalist by formation and experience, and a content writer by trade. I’ve been writing content, both online and offline, for more than 15 years. My focus has always been technology, but I’ve also ventured into fields as diverse as music, football or news. I am RealVNC’s in-house Digital Content Editor, so a lot of what you’re reading on this blog is written by me. I also edit a lot of our content output. When I’m not writing, editing or reading, you’ll probably find me at a concert or watching a Chelsea FC game.

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