Energy Development Research Articles

Gyrotactic Mixed Bioconvective Flow of Copper–Water Nanofluid Past a Rotating Cone with Non-uniform Heat Source and Activation Energy

Gyrotactic Mixed Bioconvective Flow of Copper–Water Nanofluid Past a Rotating Cone with Non-uniform Heat Source and Activation Energy

Building Blocks for an Energy Transition

The present need for an energy transition in the wake of a global climate catastrophe led to the “EU green deal” which requires a transition of the energy system in all countries. This study aimis survey aims to identify strategies within such an energy transition based on global trend analyses and reports on available technologies for implementation. Based on a literature analysis of over 300 pieces (of feasibility studies and technology implementation reports with a focus on Central Europe) and a statistical analysis of the levels of “energy intensity” (E/GDP) covering three decades and all countries worldwide, the diagnosis is made that steady improvements in several dedicated technological focus areas can be made and were made and could still be increased in the future, as required by climate targets. Across several distinct fields of energy economy, the concrete options for such improvements are portrayed: Energy infrastructure, Smart grids, Transmission grid management, Electricity storage, Heat storage, and Industrial waste heat; and linked with the diagnosed long-term trends emerging from the “Global Change Data Base” GCDB, allowing for a sectorial analysis of the so-called energy intensity, which was not yet delivered until now as time series in the literature. The main findings show which economic sectors and clusters of technologies can be most appropriate to achieve climate targets while safeguarding social aspects of sustainability.

Applying collective action frameworks to analyse local-level collaboration for electric vehicle-related policies

Many of the most pernicious contemporary urban problems require local governments to organise collectively across jurisdictions and reorganise or coordinate internally across bureaucratic silos. Climate change, for example, is a complex system phenomenon impacting a range of interconnected socio-environmental systems in a region, such as water, transportation and energy infrastructures which may not be directly under the control of a single department or government. This often requires managers and policymakers to coordinate policy responses across siloed units within governments and through network-based arrangements across governments. Theories of polycentricity and collective action have long drawn attention to the barriers and opportunities of collaboration and multilevel governance in fashioning adequate responses to complex problems. However, these approaches typically fail to explicate the relationships or interactions between internal and external collaboration risks and the institutional mechanisms for ameliorating them. This article empirically explores this relationship between functional collective action (collaboration across departmental units within a single government) and intergovernmental collective action (collaboration across governments). Situated in the context of climate adaptation and electric vehicle (EV) policy efforts in cities, the article highlights the need for greater scholarly attention more broadly to the development of institutional collective action theory.

Analyzing the Energy Usage of a Community and the Benefits of Energy Storage

Understanding the energy usage of a community is crucial for policymaking, energy planning, and achieving sustainable development. The advent of the smart grid has made it feasible to gather fine-grain energy usage data at large-scales, providing us with new opportunities to understand demand patterns at different spatial and temporal scales. In this paper, we conduct a large-scale empirical study of energy usage of 14,849 residential and commercial energy consumers from a small city in the United States. We conduct a wide ranging analysis of energy usage at multiple granularities—citywide, transformer-level, and individual home levels. In doing so, we demonstrate how city-wide smart meter datasets can answer a variety of questions on energy consumption, such as the impact of weather on energy usage. For example, we show that extreme weather events significantly increase energy usage, e.g., by 36% and 11.5% on hot summer and cold winter days, respectively. As another example, we show 19.2% of transformers in the grid get overloaded during peak load periods. Finally, we evaluate the impact of incorporating varying amounts of energy storage within the distribution grid and the impact such deployments will have on the peak demand patterns seen by the grid as well as the ability to reduce overloads seen by distribution transformers during peak periods.

Community Based Identification of Energy-Related Needs and Priorities of Poor Communities In Karangmojo Village, Yogyakarta

The current fossil energy crisis is the right momentum to develop renewable energy potential. The purpose of this study is to identify renewable energy utilization programs in Karangmojo Village, in order to meet the energy needs of poor communities. The study uses in-depth interviews with the community to obtain various energy problems faced, especially for poor communities and farmers. We find various renewable energy development programs, including: Biogas Energy Package (BGEP) for cooking purposes, Biogas Energy Package (BGEP) for local entrepreneur, Biodiesel Energy Package (BDEP) for household and irrigation system, and Biomass Energy Package (BMEP) from rice husk. This study is also equipped with a review of stakeholder analysis

Driving innovation in energy and telecommunications: next-generation energy storage and 5G technology for enhanced connectivity and energy solutions

Driving innovation in energy and telecommunications involves leveraging next-generation energy storage and 5G technology to enhance connectivity and energy solutions. This review explores the intersection of these two domains, highlighting the importance of advancements in energy storage and 5G technology for a sustainable and connected future. Energy storage is crucial for balancing the supply and demand of electricity in modern power systems. Traditional energy storage methods, such as batteries and pumped hydro, have limitations in terms of scalability, efficiency, and cost-effectiveness. Next-generation energy storage technologies, including advanced batteries, hydrogen storage, and thermal storage, offer promising solutions to overcome these limitations. These technologies enable efficient energy storage at scale, facilitating the integration of renewable energy sources like solar and wind into the grid. By storing excess energy generated during periods of low demand, next-generation energy storage systems ensure a reliable and stable power supply, reducing the reliance on fossil fuels and lowering greenhouse gas emissions. In parallel, the evolution of telecommunications technology, particularly the advent of 5G networks, is revolutionizing connectivity and communication. 5G technology offers significantly higher data transfer speeds, lower latency, and increased network capacity compared to its predecessors. These capabilities are essential for supporting emerging technologies such as the Internet of Things (IoT), autonomous vehicles, and smart grids. With 5G-enabled IoT devices, utilities can monitor energy consumption in real-time, optimize grid operations, and detect and respond to faults more efficiently. Moreover, 5G connectivity enhances the efficiency and reliability of energy storage systems by enabling seamless communication between distributed energy resources and grid operators. The convergence of next-generation energy storage and 5G technology presents numerous opportunities for driving innovation in both energy and telecommunications sectors. One of the key areas of innovation is the development of smart energy storage systems equipped with 5G connectivity. These systems can autonomously adjust their operation based on grid conditions, weather forecasts, and energy demand patterns, optimizing energy storage and distribution in real-time. Furthermore, advanced energy management algorithms leveraging artificial intelligence (AI) and machine learning (ML) algorithms can optimize energy usage and storage, further improving the efficiency and reliability of the grid. Another area of innovation lies in the integration of renewable energy resources with 5G-enabled microgrids. Microgrids are localized energy systems that can operate independently or in conjunction with the main grid. By combining renewable energy sources with energy storage and 5G-enabled communication, microgrids can provide reliable, clean, and resilient power to remote or urban areas. These microgrids can also facilitate peer-to-peer energy trading, allowing consumers to buy and sell excess energy within their communities, fostering energy independence and sustainability. Furthermore, advancements in battery technology, such as solid-state batteries and flow batteries, are enhancing the performance and reliability of energy storage systems. Solid-state batteries offer higher energy density, faster charging rates, and improved safety compared to conventional lithium-ion batteries. Flow batteries, on the other hand, provide scalability and long-duration storage capabilities, making them suitable for grid-scale applications. Integrating these advanced battery technologies with 5G-enabled monitoring and control systems enhances the overall resilience and flexibility of the energy infrastructure. In addition to technological advancements, driving innovation in energy and telecommunications requires collaboration among various stakeholders, including policymakers, regulators, industry players, and research institutions. Policies and regulations should incentivize the deployment of next-generation energy storage and 5G infrastructure, promote interoperability standards, and ensure data privacy and security. Public-private partnerships can facilitate the investment and deployment of innovative solutions, while research and development initiatives can spur further technological advancements. Driving innovation in energy and telecommunications through next-generation energy storage and 5G technology is essential for building a sustainable, connected, and resilient future. By leveraging advanced energy storage systems, smart grids, and 5G-enabled communication networks, we can optimize energy usage, reduce carbon emissions, and enhance the reliability and efficiency of our energy infrastructure. Collaboration and investment across various sectors are key to unlocking the full potential of these transformative technologies and achieving a brighter, more sustainable future for generations to come. Keywords: Innovation, Energy, Telecommunications, Next-Generation, 5G technology, Enhanced connectivity.

Study of the differences in collection scope of raw materials of biomass CHP plants caused by regional factors

Combined heat and power (CHP) plants fueled by biomass can collect and utilize waste straw resources in a productive way. This paper considers the impact of regional factors on biomass energy potential and the energy needs of the population, so as to study the differences in construction of biomass CHP plants and the collection scope of raw materials, and proposes evaluating suitability for biomass energy development based on scope of resource collection. Taking five counties in China as its study areas, this paper assesses biomass energy potential. A topology system of biomass CHP plants has been reasonably established in different counties through ArcGIS, the required installed capacity has been calculated according to the number of persons served by such plants. Finally, the collection length and corresponding value range of raw materials of CHP plants along roads has been obtained based on biomass energy potential and energy demand. The result shows that the differences in area, straw yield and biomass fuelization rate depending on regions have a great impact on biomass energy potential, while the residue-to-product ratio of straw and biomass calorific value have less of an impact. When the biomass energy per capita of a region reaches 9.75GJ/person, it is suitable for biomass energy development. The installed capacity in the biomass CHP plant system of each study area is mostly within the scope of 3–59 MW, and the collection length of corresponding biomass resources of such plants along roads is mostly within the scope of 5.09–25.23 km.

Marginal land in China suitable for bioenergy crops under diverse socioeconomic and climate scenarios from 2020–2100

ABSTRACT Maximizing the development of renewable energy plays a critical role in mitigating the climate crisis. Marginal land provides space for the development of biomass energy; however, it remains unclear how the amount and spatial distribution of marginal land that is suitable for energy crop development will change in the future. Here, we project energy marginal land changes in China following the shared socioeconomic pathway (SSP) and/or representative concentration path (RCP). We provide datasets of marginal land, agriculturally suitable land, and potentially suitable for energy crops under historical scenarios and six future scenarios (i.e. SSP1–1.9, SSP1–2.6, SSP4–3.4, SSP2–4.5, SSP4–6.0, and SSP3–7.0) for the period 2020–2100, with a spatial resolution of 5 km. Under the six scenarios, from 2020–2100, the area of suitable marginal land ranged from 1.90–16.28 (Jatropha curcas L.) to 37.37–73.97 (Panicum virgatum L.) (×104 km2), depending on the choice of energy crops and climate scenario. Based on the growing suitability of eight important bioenergy crops—Ricinus communis L., Saccharum officinarum L., Pistacia chinensis Bunge, Panicum virgatum L., Jatropha curcas L., Miscanthus giganteus J., Manihot esculenta Crantz, and Sorghum bicolor Moench—our dataset can be used to identify suitable locations for specific energy crops. This new synthetic dataset could support the development of multiscenario-based solutions related to carbon neutrality, ecosystem services, and energy transition.

Coordinated Control of Proton Exchange Membrane Electrolyzers and Alkaline Electrolyzers for a Wind-to-Hydrogen Islanded Microgrid

In recent years, the development of hydrogen energy has been widely discussed, particularly in combination with renewable energy sources, enabling the production of “green” hydrogen. With the significant increase in wind power generation, a promising solution for obtaining green hydrogen is the development of wind-to-hydrogen (W2H) systems. However, the high proportion of wind power and electrolyzers in a large-scale W2H system will bring about the problem of renewable energy consumption and frequency stability reduction. This paper analyzes the operational characteristics and economic feasibility of mainstream electrolyzers, leading to the proposal of a coordinated hydrogen production scheme involving both a proton exchange membrane (PEM) electrolyzer and an alkaline (ALK) electrolyzer. Subsequently, a coordinated control based on Model Predictive Control (MPC) is proposed for system frequency regulation in a large-scale W2H islanded microgrid. Finally, simulation results demonstrate that the system under PEM/ALK electrolyzers coordinated control not only flexibly accommodates fluctuating wind power but also maintains frequency stability in the face of large disturbances. Compared with the traditional system with all ALK electrolyzers, the frequency deviation of this system is reduced by 25%, the regulation time is shortened by 80%, and the demand for an energy storage system (ESS) is reduced. The result validates the effectiveness of MPC and the benefits of the PEM/ALK electrolyzers coordinated hydrogen production scheme.

Composition of Lignocellulose Hydrolysate in Different Biorefinery Strategies: Nutrients and Inhibitors

The hydrolysis and biotransformation of lignocellulose, i.e., biorefinery, can provide human beings with biofuels, bio-based chemicals, and materials, and is an important technology to solve the fossil energy crisis and promote global sustainable development. Biorefinery involves steps such as pretreatment, saccharification, and fermentation, and researchers have developed a variety of biorefinery strategies to optimize the process and reduce process costs in recent years. Lignocellulosic hydrolysates are platforms that connect the saccharification process and downstream fermentation. The hydrolysate composition is closely related to biomass raw materials, the pretreatment process, and the choice of biorefining strategies, and provides not only nutrients but also possible inhibitors for downstream fermentation. In this review, we summarized the effects of each stage of lignocellulosic biorefinery on nutrients and possible inhibitors, analyzed the huge differences in nutrient retention and inhibitor generation among various biorefinery strategies, and emphasized that all steps in lignocellulose biorefinery need to be considered comprehensively to achieve maximum nutrient retention and optimal control of inhibitors at low cost, to provide a reference for the development of biomass energy and chemicals.

Advances in Two-Dimensional Ion-Selective Membranes: Bridging Nanoscale Insights to Industrial-Scale Salinity Gradient Energy Harvesting.

Salinity gradient energy, often referred to as the Gibbs free energy difference between saltwater and freshwater, is recognized as "blue energy" due to its inherent cleanliness, renewability, and continuous availability. Reverse electrodialysis (RED), relying on ion-selective membranes, stands as one of the most prevalent and promising methods for harnessing salinity gradient energy to generate electricity. Nevertheless, conventional RED membranes face challenges such as insufficient ion selectivity and transport rates and the difficulty of achieving the minimum commercial energy density threshold of 5 W/m2. In contrast, two-dimensional nanostructured materials, featuring nanoscale channels and abundant functional groups, offer a breakthrough by facilitating rapid ion transport and heightened selectivity. This comprehensive review delves into the mechanisms of osmotic power generation within a single nanopore and nanochannel, exploring optimal nanopore dimensions and nanochannel lengths. We subsequently examine the current landscape of power generation using two-dimensional nanostructured materials in laboratory-scale settings across various test areas. Furthermore, we address the notable decline in power density observed as test areas expand and propose essential criteria for the industrialization of two-dimensional ion-selective membranes. The review concludes with a forward-looking perspective, outlining future research directions, including scalable membrane fabrication, enhanced environmental adaptability, and integration into multiple industries. This review aims to bridge the gap between previous laboratory-scale investigations of two-dimensional ion-selective membranes in salinity gradient energy conversion and their potential large-scale industrial applications.

Research on technical cooperation path of renewable energy between China and South Africa

South Africa possesses extremely rich renewable energy resources, including solar energy and wind energy, in spite of this, South Africa traditionally relies on coal as its primary energy source. Recently, South Africa confronted with severe electricity shortage. With pressure of power shortage and the transition to low-carbon energy, South Africa has been actively promoting the development of renewable energy, aiming to diversify its energy structure. China, as the world’s leading country in the development and utilization of renewable energy, has long been committed to helping South Africa achieve renewable energy development. This article reviews the cooperation progress between China and South Africa in renewable energy, and provides an in-depth analysis of the challenges currently faced by both sides. To address such challenges, it is suggested to strengthen the design of top-level cooperation mechanisms, create diverse collaboration platforms, prioritize the exchange and collaboration of scientific talents. This paper aims to provide a typical reference for deepening renewable energy cooperation between China and South Africa.

Research Progress in High-Temperature-Resistant Electromagnetic Wire

Electromagnetic wire is the carrier of energy and signal transmission. With the rapid development in aerospace, atomic energy, and other industrial fields, there is an increasing demand for the high-temperature-resistance of electromagnetic wires. In using traditional electromagnetic wires, it is difficult to meet the current temperature-resistance requirements. Therefore, the development of high-temperature-resistant electromagnetic wire has extremely important application value. In this paper, high-temperature-resistant electromagnetic wires are divided into organic insulated high-temperature-resistant electromagnetic wires, organic–inorganic insulated composite high-temperature-resistant electromagnetic wires, and inorganic insulated high-temperature-resistant electromagnetic wires. The method of improving the temperature-resistance level of organic insulated high-temperature-resistant electromagnetic wire is introduced. The selection principle of organic–inorganic and inorganic insulation high-temperature-resistant electromagnetic-wire conductor materials is analyzed. The current research status of organic–inorganic and inorganic insulated high-temperature-resistant electromagnetic wires is reviewed. The technical routes for preparing inorganic insulated high-temperature-resistant electromagnetic wire are compared. Finally, the challenges faced by the current high-temperature-resistant electromagnetic wire are pointed out, and the future development direction of organic–inorganic-composite insulation and inorganic insulation high-temperature-resistant electromagnetic wire is proposed.

High-Accuracy Photovoltaic Power Prediction under Varying Meteorological Conditions: Enhanced and Improved Beluga Whale Optimization Extreme Learning Machine

Accurate photovoltaic (PV) power prediction plays a crucial role in promoting energy structure transformation and reducing greenhouse gas emissions. This study aims to improve the accuracy of PV power generation prediction. Extreme learning machine (ELM) was used as the core model, and enhanced and improved beluga whale optimization (EIBWO) was proposed to optimize the internal parameters of ELM, thereby improving its prediction accuracy for PV power generation. Firstly, this study introduced the chaotic mapping strategy, sine dynamic adaptive factor, and disturbance strategy to beluga whale optimization, and EIBWO was proposed with high convergence accuracy and strong optimization ability. It was verified through standard testing functions that EIBWO performed better than comparative algorithms. Secondly, EIBWO was used to optimize the internal parameters of ELM and establish a PV power prediction model based on enhanced and improved beluga whale optimization algorithm–optimization extreme learning machine (EIBWO-ELM). Finally, the measured data of the PV output were used for verification, and the results show that the PV power prediction results of EIBWO-ELM were more accurate regardless of whether it was cloudy or sunny. The R2 of EIBWO-ELM exceeded 0.99, highlighting its efficient ability to adapt to PV power generation. The prediction accuracy of EIBWO-ELM is better than that of comparative models. Compared with existing models, EIBWO-ELM significantly improves the predictive reliability and economic benefits of PV power generation. This study not only provides a technological foundation for the optimization of intelligent energy systems but also contributes to the sustainable development of clean energy.

Optimal integration and planning of PV and wind renewable energy sources into distribution networks using the hybrid model of analytical techniques and metaheuristic algorithms: A deep learning-based approach

The optimal integration of Renewable Energy Sources (RESs) into distribution networks is crucial for sustainable energy generation. However, existing approaches utilized for optimal RES integration, including metaheuristic and analytical methods, exhibit limitations. Analytical methods are simple to implement, computationally efficient, and able to model power system mechanisms, yet they face scalability issues, making them inefficient for large-scale distribution systems. On the other hand, metaheuristic approaches, while flexible for complex problems, suffer from premature convergence problems, limiting their application in real distribution networks. To address these issues, this paper introduces a novel hybrid model that combines the strengths of analytical and metaheuristic techniques. The proposed model employs a deep learning-based method called bi-directional long short-term memory (B-LSTM) network is utilized to handle the uncertainties associated with the Photovoltaic (PV) and Wind Turbine (WT) generation and load demand. Subsequently, a distinctive analytical multi-objective index-based procedure is developed to minimize the real and reactive energy loss and voltage deviation by analyzing the branch current change caused by the RES penetration in distribution networks. The optimization problem is then solved using the binary particle swarm optimization (BPSO) technique. Importantly, the presented method considers the long-term nature of optimal RES integration and reflects the seasonal impacts of the load and RES output powers. The proposed approach is applied to the IEEE 33-bus test system. The simulation results show that the introduced hybrid model, by addressing the limitations of existing approaches, offers a promising solution for efficient and effective RES integration in distribution systems while requiring low computational effort, thereby advancing the transition towards sustainable energy infrastructure.

Examining nonlinear effects of socioecological drivers on urban solar energy development in China using machine learning and high-dimensional data

In the context of carbon neutrality target, renewable energy sources have been transforming from “supplementary energy” to “main energy”, which have promoted the green and low-carbon transition of global energy supply system. In-depth analyzing the spatial patterns and driving mechanisms of renewable energy expansion are of significance for optimizing the spatial layout of clean power, and avoiding the phenomenon of wind and solar power curtailment. In this paper, we proposed an ensemble learning model to examine the nonlinear effects of physical geography, resource endowment, and socio-economic factors on solar photovoltaic (PV) capacity at the prefecture-level city scale in China. Using the city-level multi-sources geospatial big data, we extensively collected a total of 175 related explanatory variables and cumulative installed capacity of solar PV power for 295 prefecture-level cities of China. The recursive feature elimination algorithm (SVM-REF) is firstly used to extract the optimal feature subset of urban PV capacity from multi-dimensional features variables. Furthermore, three advanced machine learning models (random forest, decision tree, extreme gradient boosting) are developed to identify the key influencing factors and nonlinear driving effect of urban solar PV power expansion in China. The results show that China's PV installation capacity is highly concentrated in Northern and Northwest parts of China, with the occupancy over 70% in 2019. Moreover, the XGBoost model has the best prediction accuracy (R2 = 0.97) among three methods. We also found that total amount of urban water resources, average solar radiation, and population density are the most important controlling factors for urban solar PV capacity expansion in China, with contribution of 35.6%, 17.7%, and 13.3%, respectively. We suggested that urban solar PV layout mode in China is recommended to gradually shift from resource orientation to the “resource-environment-demand” comprehensive orientation. The paper provides a replicable, scalable machine learning models for simulating solar PV power capacity at the prefecture-level city scale, and serves as a motivation for decision-making reference of the macro siting optimization and sustainable development of China's green power industry.

Wyoming Conservation Exchange: A Case Study in Grassroots Conservation Program Design

This article describes the conception and development of a market-based conservation program in Wyoming whose purpose was threefold: to compensate landowners for good stewardship of their land and water resources; facilitate meaningful conservation; and encourage environmentally responsible energy development. The program was developed with strong stakeholder support against a backdrop of significant regulatory change. The project, called the Wyoming Conservation Exchange (WCE), represents a project-based alternative to more traditional program-based Extension efforts. This article evaluates ways in which the WCE can act as a road map—but also a cautionary tale—for similar Extension efforts in the future.

Arguments for a Community-Based Approach to Geothermal Energy Development

This paper investigates the theoretical foundation for developing renewable geothermal resources locally. For this reason, we pay attention to the role of communities in geothermal development. We derive it from the integral characteristics of geothermal energy next to the shift in the energy transition policies to focus on managing green resources locally. This study presents arguments for a framework that approaches geothermal resources as an endogenous factor of community development. To analyse it, we create a model that explains the local economic characteristics of geothermal exploitation beyond its geological conditions. It aims to conceptualise a community-based geothermal development standard referring to the endogeneity principle. Geothermal energy is given attention since the characteristics of this resource determine its use locally. This induces the internalisation of labour and technology in the local economic system, a specific condition for local geothermal projects where a community remains a prime beneficiary. We argue that the role of communities in geothermal exploitation is pivotal in the process of green growth for further expansion of geothermal energy use.

A [formula omitted]-radioisotope battery based on betavoltaic and beta-photovoltaic dual effects

Betavoltaic batteries with strontium sources are expected to achieve higher output power because of their high energy density and low self-absorption rate with the development of MEMS. However, the high energy of beta particles emitted from 90Sr/90Y can prone to radiation damage of the semiconductor. In order to exploit the high energy of beta particles emitted from 90Sr/90Y and to avoid radiation damage to the semiconductor, this paper presents a 90Sr/90Y-radioisotope battery based on betavoltaic (BV) and beta-photovoltaic (BPV) dual effects. In the work, the energy deposition of beta particles in LYSO:Ce and GaAs was simulated by Monte Carlo code, and thickness of the scintillator was determined. And the doping concentrations and junction depth of semiconductor were optimized based on the theoretical calculations to obtain the best output performance of device. When the thickness of LYSO:Ce is 0.158 cm, the output power density Pm of the optimized dual-effect battery is 0.61 μW/cm2. And the conversion efficiency of the device is 0.92%. At this time, the doping concentrations are Na=1.58×1017cm−3 and Nd=3.16×1018cm−3, and the junction depth xj=0.05μm. All calculated parameter values are considered as theoretical limit values. In addition, the contribution of BV effect and BPV effect to the output performance of the dual-effect radioisotope battery was investigated. Different scintillator thicknesses lead to different percentages of the two mechanics. In addition, the BV effect and BPV effect output proportion is also affected by the average energy of the radiation source. In the case that the average electron energy on the semiconductor surface is 0.27 MeV, the higher the radioactive source energy, the thicker the scintillator is required, resulting in more BPV effect and less BV effect.

An attainable site suitability index for utility-scale solar facilities

Abstract Large, utility-scale solar energy facilities are increasingly being deployed throughout the United States, with nearly 10,000 projects over 1 megawatt completed by the end of 2023. One understudied aspect of the decision making process for these solar projects concerns site suitability. The current body of literature on this topic has identified core infrastructure, such as substations or transmission lines, as important in this process, yet other variables serve as potentially important drivers. This research aims to provide policymakers, planners, and regulators with a practically applicable method to better identify areas that are suitable candidates for such solar facilities. In particular, this paper develops a replicable method that produces a site suitability index that accounts for a suite of variables that are considered key, positive factors in where solar facilities ought to locate, including: 1) economic factors; 2) critical infrastructure and high energy users; and 3) workforce. Ultimately, our work is valuable to government and local officials such as policymakers and planners with limited resources and in-house capacity to understand the suitability for energy infrastructure. In fact, providing an attainable index for the site suitability of utility-scale solar facilities reduces the burden on policy and planning related stakeholders to understand the competitive advantage of their regions and to convey it to interested developers.