Renewable Energy Capacity Research Articles

Assessing renewable energy efficiency and policies: A combined analysis of LMDI, super-SBM, and fieldwork in Qinghai, China

The significance of renewable energy as a pivotal green industry in decarbonizing the global economy and addressing the climate change is profound. The rise of China's regional renewable energy serves as a testament to its transition from an industrial powerhouse to a leader of sustainable growth, offering valuable insights for a sustainable future worldwide. This study analyzes renewable energy efficiency, alongside the factors that influence it, in Qinghai Province over 2000–2021. It uses a combination of the Logarithmic Mean Divisia Index factor decomposition, Data Envelopment Analysis of Super-SBM models, and field research with a rounded approach. The study finds a robust increase in renewable energy capacity, particularly after 2016. Efficiency in renewable energy generally has risen, although periods of inefficiency were noted from 2014 to 2019. Despite fluctuations, the overall impact of factors such as renewable energy structure, electricity intensity, energy security, energy intensity, carbon productivity, and carbon emissions, all contribute positively to the development of renewable energy, notably in recent years. The paper concludes with a discussion of green industrial policies with wider implications for the renewable energy sector.

Understanding the nexus between fintech, natural resources, green investment, and environmental sustainability in China: A DYNARDL approach

China is one of the world's biggest polluters and emitters of Carbon diaoxide Emissions (CO2). The recent environmental issues suggest the issues they are facing in light of the increasing level of floods and storms. The impact of the environmental calamities has reached the level that it is causing serious damage to the food security of the country. Despite the advances made in the development of renewable energy capacity, the natural calamities continue to rise. This study identified the role of Fintech (FNT), Natural Resources (NTR), and Green Investment (GINV) on Environmental Sustainability (ENS). Using the DYNARDL approach, the findings of the study suggest that FNT and GINV can be a useful tool in achieving ENS. In contrast, the continued use of NTR can cause some serious damage towards ENS targets for China. The study recommends the adoption of carbon taxes for its industries to force the transition towards green energy and sustainable practices. The extra cost associated with carbon taxes would ensure that firms would now invest heavily in the renewable energy sector, which would be essential for the transition towards the renewable energy sector. Furthermore, using FNT to promote green growth is also an important recommendation. The use of crowdfunding and the development of a green finance market can be some of the ways China could achieve ENS in the long run.

Evaluating the Impact of Renewable Energy Policy Instruments on Capacity Expansion: Insights from the Visegrad Group Countries

Renewable energy technologies have reached a pivotal point, demonstrating cost competitiveness with traditional energy sources and a continuing trend of affordability. Nevertheless, the successful deployment of renewable energy capacity crucially depends on the efficacy of renewable energy policy tools, casting a veil of uncertainty over their actual influence on the expansion of renewable electricity generation. This research delves into the array of policy instruments designed to bolster renewable energy capacity. Employing an econometric analysis, this study meticulously examines these policy tools, with a particular emphasis on feed-in tariffs, quotas, tenders, and tax incentives, seeking to facilitate the adoption of renewable energy within the Visegrad group countries, comprising the Czech Republic, Hungary, Poland, and Slovakia. Drawing from a comprehensive panel dataset spanning the years from 1990 to 2020, our findings unveil a nuanced perspective on the effectiveness of these renewable energy policy tools. Notably, our results underscore the varying degrees of success among different policy instruments, all of which significantly contribute to the advancement of renewable energy sources. A key revelation arises from the limited impact of quotas, a frequently employed regulatory measure, in stimulating the expansion of renewable electricity generation capacity. In contrast, feed-in tariffs, tenders, and tax incentives emerge as potent drivers in achieving this pivotal objective. This research sheds light on the dynamic landscape of renewable energy policy instruments, offering valuable insights for policymakers and stakeholders engaged in advancing sustainable and resilient energy ecosystems in the Visegrad group countries and beyond.

A nationwide multi-location multi-resource stochastic programming based energy planning framework

With the escalating global energy demands, nations face greater urgency in diversifying their electricity market portfolio with various energy sources. The efficient utilization of these fuel resources necessitates the integrated optimization of the entire energy portfolio. However, the inherent uncertainty of variable renewable energy generators renders traditional deterministic planning models ineffective. Conversely, expansive stochastic models that capture all the complexities of the power generation problem are often computationally intractable. This paper introduces a scalable energy planning framework tailored for policymakers, which is adaptable to diverse geopolitical contexts, including individual countries, regions, or coalitions formed through energy trade agreements. A two-stage stochastic programming approach, including a scenario-based Benders decomposition method, is employed to achieve computational efficiency. Scenarios for representing parameter uncertainty are developed using a k-means clustering algorithm. An illustrative case study is presented to show the practical application of the proposed framework and its ability to inform policymaking by identifying actionable cost-reduction strategies. The findings underscore the importance of promoting resource coordination and demonstrate the impact of increasing interchange and renewable energy capacity on overall gains. Notably, the study shows the superiority of stochastic optimization over deterministic models in energy planning.

The Role of Renewable Energies for Sustainable Energy Governance and Environmental Policies for the Mitigation of Climate Change in Nigeria

In response to the escalating global challenges posed by climate change, this research investigates the role of renewable energies in shaping sustainable energy governance and environmental policies for climate change mitigation in Nigeria. The study underscores the pivotal relationship between renewable energies and governance structures, emphasizing the need for innovative policy approaches. In Nigeria, a country grappling with a surge in energy demand, particularly in densely populated urban areas, this research delves into the current state of renewable energy adoption, technological aspects, and the policy and regulatory frameworks governing its integration. The study scrutinizes the technological aspects of solar, wind, hydropower, and biomass energy in Nigeria, exploring advancements in solar photovoltaic, hydropower, bioenergy, and geothermal technologies. Additionally, it dissects the nation's policy landscape, assessing key initiatives such as the National Energy Policy, the National Policy and Guidelines on Renewable Electricity, and Feed-in Tariffs. Furthermore, the research examines the alignment of environmental policies with renewable energy goals, highlighting the importance of targets, incentives, and regulatory frameworks. It also scrutinizes incentives for sustainable practices in Nigeria, including investment tax credits, the Renewable Energy Development Fund, and concessional import duty rates for renewable equipment. The study assesses the impact of renewable energy initiatives in Nigeria, gauging investment trends, ongoing projects, and the effectiveness of policy frameworks. It also delves into the economic, social, and environmental co-benefits of renewable energy projects, emphasizing their role in job creation, investment attraction, and reduction in energy costs. Finally, the research evaluates the effectiveness of renewable energy initiatives in reducing the nation's carbon footprint and fostering environmental sustainability. It highlights progress in achieving climate change mitigation goals through increased renewable energy capacity, policy frameworks, and off-grid electrification initiatives.

From Local Energy Communities towards National Energy System: A Grid-Aware Techno-Economic Analysis

Energy communities are key actors in the energy transition since they optimally interconnect renewable energy capacities with the consumers. Despite versatile objectives, they usually aim at improving the self-consumption of renewable electricity within low-voltage grids to maximize revenues. In addition, energy communities are an excellent opportunity to supply renewable electricity to regional and national energy systems. However, effective price signals have to be designed to coordinate the needs of the energy infrastructure with the interests of these local stakeholders. The aim of this paper is to demonstrate the integration of energy communities at the national level with a bottom–up approach. District energy systems with a building scale resolution are modeled in a mixed-integer linear programming problem. The Dantzig–Wolfe decomposition is applied to reduce the computational time. The methodology lies within the framework of a renewable energy hub, characterized by a high share of photovoltaic capacities. Both investments into equipment and its operation are considered. The model is applied on a set of five typical districts and weather locations representative of the Swiss building stock. The extrapolation to the national scale reveals a heterogeneous photovoltaic potential throughout the country. Present electricity tariffs promote a maximal investment into photovoltaic panels in every region, reaching an installed capacity of 67.2 GW and generating 80 TWh per year. Placed in perspective with the optimal PV capacity forecast at 15.4 GW peak at the national level, coordinated investment between local and national actors is needed to prevent dispensable expenses. An uncoordinated design is expected to increase the total costs for residential energy systems from 12% to 83% and curtails 48% of local renewable electricity.

Carbon Dioxide, Nitrous Oxide, and Methane: What Types of Greenhouse Gases Are Most Affected by Green Investments and Renewable Energy Development?

The article aims to analyze the impact of green investments and the development of renewable energy on greenhouse gas emissions based on 223 countries in 2011–2021. The information base is the International Renewable Energy Agency, Our World in Data, Climate Policy Initiative, and FTSE Russell. Correlation analysis was used to check the data multicollinearity, multivariate regression analysis with stepwise variable entry—to formalize functional relationships. All variables characterizing the dynamics of green investments and the development of alternative energy, the number of annual investments in off-grid renewable energy has the largest impact on the amount of CO2 and N2O. Thus, an annual investment increase of USD 1 million leads to a CO2 emission increase of 4.5 kt and an N2O emission increase of 0.272 kt. Simultaneously, the green economy’s market capitalization level has the largest impact on the amount of CH4. In this case, a capitalization increases of USD 1 trillion leads to a CH4 emission increase of 129.53 kt. The dynamics of renewable energy development have a statistically significant effect on only one of the three studied greenhouse gases—CO2 emissions. Here, 1 MW growth of an absolute increase in off-grid renewable energy capacity leads to a 1171.17 kt reduction of CO2 emissions. Checking input data for lags confirmed a time lag of one year between the level of green investments and the level of greenhouse gas emissions. That is, the impact of green investments on the level of greenhouse gas emissions is delayed by one year. The results of regression models taking into account lags confirmed that an increase in the level of green investments has a positive effect on reducing the level of greenhouse gas emissions (an increase in off-grid renewable energy annual investments of USD 1 million leads to a decrease in CO2 of 1.18 kt and N2O of 1.102 kt; the increase in green economy market capitalization of USD 1 trillion leads to a decrease in CH4 emissions of 0.64 kt).

Peak load estimation of renewable energy generation based on imitator dynamic algorithm

AbstractIn the field of renewable energy generation forecasting, it is crucial to accurately estimate the peak load. However, due to the complex nonlinear characteristics of the data, the traditional long short-term memory network performs poorly in processing these data. This study introduces the imitator dynamic algorithm, which is able to generate samples close to the real situation by learning the change pattern of the data. Extensive experimental tests show that with the number of iterations increasing to 200, the prediction accuracy of the model reaches 62.35%, which is significantly better than that of the long short-term memory network, although it is decreased compared with the initial iteration. The imitator dynamic algorithm accurately learns the unknown data distribution according to two metrics of probability density and cumulative distribution within 5% error, demonstrating good generalization ability and robustness. These research results are of great significance for predicting the actual generation capacity of renewable energy. It not only enables grid operators to accurately predict and schedule power generation, but also supports sustainable energy development by improving grid stability and promoting the use of renewable energy.

Quantifying and enhancing the energy resilience of zero-emission train station systems against power outages via electrified train-building integrations

Addressing power outages due to renewable energy penetration and conventional utility grid issues is vital to ensure the reliability and resilience of any energy system. Therefore, this project begins by modelling a renewable energy-supported swappable battery-powered railway system with stochastically driven power outages. Then, it comprehensively quantified energy resilience using novel technological, economic and environmental indicators for different annual power outage percentages and renewable energy capacities. Two implementations are systematically analysed: one that uses a unique control strategy for dynamically handling the power outage periods in addition to the existing control strategy, and another that finds the right renewable energy mix within and between the stations. The first one improves the autonomous net-zero energy system by enhancing the energy-based resilience factor, annual carbon emissions from the system, and net present value for the estimated lifetime of 20 years from 0.83 to 0.99, 264.7 to 12.7 ton and –7.3 × 106 to 51.4 × 106 HKD, respectively. The second investigation reveals that technological and environmental indicators favour solar energy of 70 %, and the economic indicator prefers more wind energy. Using 100 % wind energy on both stations of the metro railway system is economically consistently effective as it provides a net present value of up to 59.8 × 106 HKD owing to the 30 % lower capital cost of wind energy compared to solar energy.

Research on low-carbon development path of new energy industry under the background of smart grid

ObjectivesThe transition to a low-carbon and sustainable energy system is essential to combating climate change and ensuring the long-term viability of the global energy industry. Smart grid technologies are emerging as a transformative force in this transition. This research explores the multifaceted impact of smart grid technologies on the new energy industry and evaluates the influence of policies and regulations in shaping its development. MethodsThe study employs a mixed-method approach, combining quantitative analysis of key performance indicators (KPIs) and a qualitative examination of stakeholder perspectives. The quantitative analysis focuses on the impact of smart grids on renewable energy penetration, carbon emissions, and energy efficiency. The qualitative analysis draws on interviews with experts from industry, academia, and government to gain insights into the role of policies and regulations in driving smart grid development. ResultsThe quantitative analysis reveals a significant increase in renewable energy penetration, a reduction in carbon emissions, and enhanced energy efficiency in regions with high smart grid adoption. These findings indicate the substantial impact of smart grid technologies in facilitating the transition to a clean and sustainable energy system. The qualitative analysis highlights the role of policies and regulations in driving the growth of renewable energy capacity, reducing carbon emissions, and creating jobs in the new energy industry. Supportive regulatory frameworks have been instrumental in stimulating investment in smart grid technologies and renewable energy projects. A regression analysis was conducted to shed light on the impact of sustainability indicators, economic assessments, and renewable energy capacity on carbon emissions. The analysis shows that these variables significantly influence carbon emissions, providing insights for policymakers and industry stakeholders. ConclusionThe findings of this study underscore the importance of smart grid technologies and supportive regulatory frameworks in facilitating sustainable energy development. Smart grid technologies play a critical role in enabling the integration of renewable energy into the grid, improving energy efficiency, and reducing carbon emissions. Governments and industry stakeholders should collaborate to accelerate the deployment of smart grid technologies and create a supportive policy environment for the new energy industry.Research and development should be funded for innovative smart grid technology.

Wind energy potential assessment: a case study in Central India

Access to sustainable energy is important for economic development and environmental protection. The shift towards renewable energy is essential for mitigating climate change and ensuring long-term energy sustainability. Though developing nations possess significant renewable energy capacity, transitioning to renewable sources remains a challenge. India's expanding wind energy sector has the potential to lower carbon dioxide emissions and produce energy in a sustainable manner. This research uses the Windographer software to conduct a comprehensive analysis of the wind energy potential at a distinct site in Nagpur, India. The assessment includes monthly average wind speeds, wind rose diagrams that show the prevailing wind directions, and wind power density (WPD) calculations. These factors together establish the site's classification. In this study, Weibull parameters are derived using four distinct methods to estimate the probability distribution function. The average WPD is found to be 13.63 and 71.08 W/m2 at heights of 10 and 100 m above ground level, respectively. Through wind turbine output analysis, numerous turbine models are assessed for suitability at the specified location. The Windtec DD 3000-125 model, interestingly, has a maximum capacity factor of 9.27%. The results highlight comparatively low wind potential in the studied region. Nevertheless, a wind turbine at this location can produce about 2 412 379 kW of electricity annually using the aforementioned wind turbine model.

Research on the calculation method of the reasonable utilization rate of renewable energy considering generation–grid–load–storage coordinated planning

The continuous advancement of the dual carbon goal will lead to fundamental changes in China’s energy structure, with renewable energy emerging as the dominant force in the power supply system. As the proportion of installed capacity for renewable energy continues to increase, the absorption capacity and reasonable utilization rate of renewable energy will become a concern for all sectors of society. At present, the degree of utilization of renewable energy has emerged as a significant factor limiting the development of renewable energy sources. Due to variations in the power structure, load characteristics, and flexible adjustment resources across different regions, blindly pursuing full absorption or higher utilization of renewable energy as a goal can result in increased reserve costs for the system and limited economic benefits, and hinder the development of renewable energy. Therefore, the present study develops a generation–grid–load–storage collaborative planning model aimed at achieving economic optimization by setting different renewable energy utilization rates and obtains the installed capacity of renewable energy and storage under different conditions in the planned year of Gansu Province. The economic analysis indicates that the optimal utilization rate of renewable energy in Gansu Province is projected to decrease from 100% during the period of 2024–2028 to approximately 90% from 2040 to 2060. It serves as a valuable tool for informing the planning and economic development of renewable energy and storage initiatives in Gansu Province, facilitating the achievement of its dual-carbon objectives.

Two-stage robust programming model for active distribution network with a high proportion of renewable energy

To reduce operating costs and obtain the optimal location and capacity of renewable energy access, a two-stage robust planning model is studied for active distribution networks with a high proportion of renewable energy access. To build a function to minimize annual comprehensive costs, the constraints of power flow, branch power, and node voltage of the active distribution network are studied. Considering the characteristics and properties of power flow safety constraints, the mixed integer second-order cone transformation is used to construct a mixed integer convex programming model, which is convenient for efficiently obtaining the global optimal solution. To avoid the influence of uncertainty, a two-stage robust model is constructed. The explicit maximum value method is used to obtain the solution results that satisfy the planning model of all uncertain problems. Experiments show that the model can obtain the optimal access position and capacity of renewable energy. After application, it can reduce the annual comprehensive cost of the distribution network and ensure the stable and safe operation of the active distribution network.

Recent Trends in Renewable Energy Adoption and their Economic Implications

In this work we have conducted systematic review following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and explore how recent changes in using renewable energy have affected the economy. We have examined 50 articles that were published in peer-reviewed journals between 2010 and 2024. These articles were chosen after a careful screening based on relevance and methodological quality. The main drivers find were technological advancement in solar and wind energy along with policy interventions such as subsidies and carbon pricing. Our main results assert the significant growth in renewable energy capacity with solar energy expanding at rate of 15 percent and wind energy at 10 percent. This growth has been accompanied by a 120 percent increase in investment in solar energy and creation of millions of jobs globally. However, transition also presents challenges particularly, regarding economic impact on traditional fossil fuel industries and the need for equitable acess to renewable resources. While renewable energy adoption offers substantial economic benefits, achieving just transition will require targeted policy interventions and infrastructure investment.

Scenarios for combating global warming: China's critical role as a leader in the energy transition

<p>China is now the world leader with green capital actually creating renewable energy technologies. We address the possibility of whether China can emerge a new ecological civilization, as well as become the global leader to reach climate security. In particular, China has the potential to implement a solar energy infrastructure in the deserts of the Middle East as part of the Belt and Road Initiative, mainly in the form of concentrated solar power. The creation of this renewable energy capacity could supply electricity to much of the world, as well as power the direct air capture (DAC) of carbon dioxide with a permanent burial in the crust, in particular, using the ultramafic deposits in Oman. This realization would increase the chances of not breaching the International Panel on Climate Change (IPCC) warming target of 1.5 ℃, of course only if coupled with the termination of utilizing fossil fuels as early as possible and their replacement with renewable energy supplies globally. In this context, our pioneering model establishes that coupling a DAC-driven drawdown of atmospheric carbon dioxide with a rapid increase in the renewable energy capacity is more than capable of producing enough clean energy to eliminate energy insecurity globally (to all 8–10 billion humans) and to provide energy for the drawdown for the rest of the 21st century, thereby reducing atmospheric carbon dioxide to safe levels. This contingency will likely only be realized with a shift to demilitarization of the global economy. Likewise, this potential emergence of China as the global leader for climate security would likely inspire the push for a global Green New Deal to make this goal possible.</p>

Effect of Renewable Energy Consumption on Economic Growth in Kenya

Despite Kenya having a massive generating capacity of renewable energy, its consumption levels remain significantly low. The low supply levels have driven the costs of production upwards leading to high energy prices thereby slowing economic activity occasioned by inadequate investments in the energy sector. However, there are conflicting results on the existing link between economic growth and renewable energy consumption in the developing world. This study purposed to establish the effect of renewable energy consumption on economic growth in Kenya. This study was anchored on the Neo-Classical Solow-Swan growth model. This study adopted the correlational research design and used time series data for the period 1980-2017 to determine the nature of the existing linkages. Capital and labour were incorporated as control variables. The granger causality results established the existence of a bidirectional and feedback relationship existing between renewable energy consumption and economic growth in Kenya. Vector Error Correction Model results showed that a unit increase in total renewable energy consumption led to a gross domestic product rise by 13,340 million dollars in the second year. Therefore, both the granger causality and Vector Error Correction Model (VECM) tests advocate for investment in the consumption of renewable energy if desired levels of economic growth are to be attained. Therefore, investment in modern energy generation and supply technologies and demonopolisation of the generation and supply of renewable energies to encourage private investments are recommended as the necessary measures to increase the level of renewable energy consumption, which in turn accelerates the rate of economic growth. Interventions such as tax exemptions on renewable energy equipment may encourage more uptake and consumption of renewable energies. This study may assist the government in the formulation of sustainable energy policies. This study may assist the government in the formulation of sound and sustainable energy policies for industrial and economic growth.

Predicting country-specific financing capacity for renewable energy project

This study aims to scrutinize the various determinants that influence a nation’s ability to fund and support renewable energy ventures, encompassing factors such as economic stability, regulatory environment, energy demand, and access to capital markets. By drawing on a range of empirical data, financial indicators, and statistical models, this study seeks to determine which factor most potent when predicting financing capacity of a specific country towards renewable. A secondary research using published data by government publications and non-governmental databases is the research method for the present study. The data derived from these databases organized into tables to allow for regression analysis to be conducted to achieve the research objectives. The results from the regression analysis indicate that stock market and inflation rate are significant variables should be included in the predictive model of financing capacity for renewable energy.

Autarky penalty versus system cost effects for Europe of large-scale renewable energy imports from North Africa

We quantify in more detail than earlier studies the cost trade-offs of establishing import-export links between Europe and North Africa for two quintessential energy carriers needed for the energy transition: electricity and hydrogen. Using the TIAM-ECN model, we show under what assumptions Europe will make a net energy system cost gain or, inversely, pay a net price for exploiting such interlinkages. We find that allowing for trade of renewable energy between Europe and North Africa substantially reduces the EU's domestic renewable energy capacity investments needed to implement its Green Deal and Fit for 55 Programme. In addition to creating import-export scenarios for electricity and hydrogen across the Mediterranean and investigating the cost implications of a European-African energy partnership until 2050, we perform a detailed sensitivity analysis regarding possible technological developments and sectoral shifts, as well as vis-à-vis potential cost, sectoral and technical asymmetries between Europe and North Africa. We introduce a new concept in energy analysis, autarky penalty, which is the price paid by a region for restricting energy trade with its neighbors. If domestic hydrogen production from solar and wind energy through electrolysis is constrained, Europe's autarky penalty may increase to 30 billion $ annually by 2050.

RENEWABLE HORIZONS: INVESTIGATING FEASIBILITY IN CONSUMPTION TRENDS

The European Union's ambitious target to achieve zero emissions from passenger cars by 2035 has sparked significant strategic overhauls among European car manufacturers, pushing them towards a 100% electric vehicle fleet. Concurrently, a forthcoming heating law, slated to take effect from 2024 initially in Germany, aims to drastically curtail the installation of gas and oil heating systems in favour of heat exchangers. These legislative shifts are already straining the power grid, necessitating a substantial expansion of renewable energy capacity. Projections indicate an ongoing surge in energy demand, underscoring the imperative of a global transition away from fossil fuels. However, this endeavour poses formidable challenges for nations worldwide. This study delves into the current state of renewable energy and its projected evolution, stressing the imperative of significantly boosting renewable energy deployment to achieve sustainable electrification of passenger vehicles globally. It highlights the need to triple the share of renewable energy sources, quadruple efforts across all transportation sectors, and increase the overall transition away from fossil fuels fifteenfold. While conventional renewables such as solar, wind, hydro, and biofuels remain integral, emerging technologies like nuclear fusion offer promising avenues. Nonetheless, attaining complete independence from fossil fuels may prove elusive, considering non-energy applications of crude oil and other fossil resources. The study also assesses potential ecological ramifications and balances the environmental impacts of these energy transitions.

Optimal siting of shared energy storage projects from a sustainable development perspective: A two-stage framework

The rapidly increasing installed renewable energy capacity has drawn greater attention to energy storage technology in China. However, the commercial implementation of energy storage is constrained by several obstacles. One potential solution to overcome these constraints is the shared energy storage model. The optimal location layout plays a crucial role in addressing the strategic decision problem of sustainable development. Therefore, a two-stage multi-criteria decision-making model is proposed to identify the optimal locations of shared energy storage projects in this work. In the first stage, the power attraction model is established to determine the macroscopic layout of shared energy storage. In the second stage, a large-scale group decision making (LSGDM) framework is developed to select the optimal micro location. The empirical study conducted in China reveals that Shandong, Henan, and Hebei provinces exhibit greater power flow attraction, from which eight alternatives are selected as site preferences and evaluated by twenty experts. Based on the improved EDAS method, the evaluation score function values of the eight alternatives are 0.497, 0.5, 0.476, 0.499, 0.507, 0.477, 0.506, and 0.498, respectively. Specifically, A5 in Rao Yang County, Hengshui City and A7 in Qing Long Manchu Autonomous County, Qinhuangdao City are preferred locations. Furthermore, the dependability and stability of the model are illustrated through the sensitivity analysis and comparative analysis. This model not only assists in determining the layout of shared energy storage but also contributes to the theoretical study of geographic information system and LSGDM.