Renewable Energy Supply Research Articles

Comprehensive Analysis of Impedance Source Converters and Quasi-Z-Source Converters for Renewable Power Generation

As global attention on renewable energy continues to grow, impedance source converters (ISCs), including Z-source converters (ZSCs) and quasi-Z-source converters (QZSCs), have become focal points in research on renewable energy generation systems. These converters are valued for their adaptability to a broad range of voltage fluctuations, as well as their ability to enhance system efficiency and stability. Building upon the foundations of ZSCs and QZSCs, this paper explores and summarizes their applications in several pivotal areas of renewable energy research. These include addressing challenges such as low and fluctuating input voltages in hybrid renewable energy supply systems, their application in batteries of electric vehicles, and the implementation of high-performance buck-boost DC-DC converters. Additionally, the paper analyzes the strengths and weaknesses associated with various applications. Ultimately, the paper concludes with recommendations and future outlooks for improving the stability and efficiency of ZSCs and QZSCs in practical applications.

From Ground to Grid: The Environmental Footprint of Minerals in Renewable Energy Supply Chains

From Ground to Grid: The Environmental Footprint of Minerals in Renewable Energy Supply Chains

Synergistic Enhancement of Plasma-Driven Ammonia Synthesis Using a AuCu3/Cu Composite Catalyst.

Green ammonia synthesis using fluctuating renewable energy supply in decentralized process is a goal that has been long sought after. Ammonia synthesis with non-thermal plasma under mild conditions is a promising technology, but it faces the critical challenge of low energy efficiency. Herein, we develop an easily-scalable AuCu3/Cu catalyst, which consists of a decimeter-scale metallic Cu antenna and nano-scale AuCu3 catalytic sites on metallic Cu surface, significantly enhancing the energy efficiency and ammonia yield in a radio-frequency (RF) plasma system. Compared to plasma alone, the single-pass ammonia yield over AuCu3/Cu increases by a factor of 20, approaching 10 %. Mechanistic studies indicate that Cu antenna can amplify the millimeter-scale local electric field, thereby facilitating the generation of active nitrogen species, including nitrogen radicals and vibration-excited nitrogen molecules. Due to the downshifted d-band center and unique Cu-Au interface structure, the AuCu3 nanoalloy modified on Cu antenna surface significantly reduces hydrogenation barriers of active NHX (x=0,1,2) species (the rate-determining step) and facilitates ammonia desorption at lower temperature. The synergistic effect of Cu antenna and surface AuCu3 nanoalloy comprehensively enhances ammonia synthesis through both the nitrogen radical-mediated Eley-Rideal pathway and the vibration-excited nitrogen molecule-mediated Langmuir-Hinshelwood pathway.

Synergistic Enhancement of Plasma‐Driven Ammonia Synthesis Using a AuCu3/Cu Composite Catalyst

Green ammonia synthesis using fluctuating renewable energy supply in decentralized process is a goal that has been long sought after. Ammonia synthesis with non‐thermal plasma under mild conditions is a promising technology, but it faces the critical challenge of low energy efficiency. Herein, we develop an easily‐scalable AuCu3/Cu catalyst, which consists of a decimeter‐scale metallic Cu antenna and nano‐scale AuCu3 catalytic sites on metallic Cu surface, significantly enhancing the energy efficiency and ammonia yield in a radio‐frequency (RF) plasma system. Compared to plasma alone, the single‐pass ammonia yield over AuCu3/Cu increases by a factor of 20, approaching 10%. Mechanistic studies indicate that Cu antenna can amplify the millimeter‐scale local electric field, thereby facilitating the generation of active nitrogen species, including nitrogen radicals and vibration‐excited nitrogen molecules. Due to the downshifted d‐band center and unique Cu‐Au interface structure, the AuCu3 nanoalloy modified on Cu antenna surface significantly reduces hydrogenation barriers of active NHX (x=0,1,2) species (the rate‐determining step) and facilitates ammonia desorption at lower temperature. The synergistic effect of Cu antenna and surface AuCu3 nanoalloy comprehensively enhances ammonia synthesis through both the nitrogen radical‐mediated Eley‐Rideal pathway and the vibration‐excited nitrogen molecule‐mediated Langmuir‐Hinshelwood pathway.

The Future of China‐Gulf Solar and Wind Supply Chains

ABSTRACTThis paper examines the medium‐term trajectory of China‐Gulf renewable energy supply chains amid growing global trade restrictions on China. The research explores how the Arab Gulf states, key collaborators in China's renewable energy sector, navigate these challenges, focusing on the dominance of China in global supply chains for solar and wind components. Using a demand‐pull, supply‐push framework, qualitative analysis assesses two potential scenarios: ‘Blue Sky’, where China‐Gulf supply chains grow stronger through ambitious renewable energy targets and expanded foreign direct investment, and ‘Sand Storm’, where geopolitical tensions and export restrictions could weaken these ties. The findings indicate that while supply chain engagement with China has risks, on balance China‐Gulf supply solar and wind technology supply chains are currently robust and projected to remain so in the future. The study concludes with policy recommendations for strengthening China's engagement with the broader Global South, highlighting opportunities for countries as they scale up domestic renewable energy deployment.

Prospects for Small Hydropower in Ukraine in the Context of Feasibility and Environmental Impact: Summarising after the World Small Hydropower Development Report 2022

The use of renewable energy and decentralization of energy generation in Ukraine has become a crucial and topical problem because of the war and devastating russian enemies’ attacks on the Ukrainian critical energy infrastructure. Localized renewable energy generation stimulates humanitarian engineering that can support community peace-sustainability. However, before decision-making, appropriate resources and opportunities should be analyzed. An optimal way for a local energy generation portfolio development has to include a comprehensive feasibility analysis of projects accounting for key enablers and barriers and a full-fledged holistic environmental impact analysis of proposed technologies. At the same time, unworthy speculations touching people suffering have to be excluded, especially on the side of investors interested in the profits of the feed-in tariff. This article analyzes prospects for small hydropower in Ukraine in the context of its feasibility and environmental impact. The principal aim of the article is to answer the two questions. The first one is whether small-scale hydropower in Ukraine can principally pretend to play an essential role in localized renewable energy supply. The second one is whether the environmental impact of small hydropower in the country would be admissible in social and ecological contexts. The article is based on the World Small Hydropower Development Report 2022 materials, primarily regarding Ukraine.

Low-carbon optimal scheduling for distribution networks under supply and demand uncertainty

This paper presents a low-carbon optimal scheduling model for distribution networks with wind and photovoltaic (PV), accounting for supply and demand uncertainties. The model optimizes thermal generation costs, wind and PV maintenance costs, and carbon emissions using a chance-constrained approach with fuzzy variables. The clear equivalent class method simplifies these constraints for easier problem-solving. Validation on the IEEE-30 node system shows the model reduces costs by 32.9% and carbon emissions by 19.2% compared to traditional scheduling, effectively lowering both costs and the carbon footprint. This real-world optimization approach tackles uncertainty in renewable energy supply and improves system efficiency and sustainability.

Joint Short-term Power Forecasting of Hydro-Wind-Photovoltaic Considering Spatiotemporal Delay of Weather Processes

Regional integrated energy systems (RIES) represented by hydro, wind, and solar energy are crucial avenues for future clean energy development, fully leveraging their complementarity can facilitate the orderly supply of renewable energy, significantly enhance the level of new energy consumption, and make outstanding contributions to achieving carbon neutrality goals. However, run-of-river hydropower (RHP), wind power (WP), and photovoltaic (PV) are affected by the chaotic characteristics of the weather system, with significant random fluctuations, and their output characteristics have significant heterogeneity, which brings a big challenge for the complementary coordinated scheduling. The high-accuracy power prediction of RHP, WP, and PV in the region is one of the key means for solving the above problems. To meet this challenge, by analyzing the spatio-temporal correlation properties between weather processes and station output, a novel joint prediction framework for short-term power forecasting (STPF) of regional hydro-wind-PV clusters is proposed. Firstly, to solve the problem of significant heterogeneity of water, wind, and solar, a Differentiated Spatio-Temporal Mixture Network (DSTMN) is proposed, which differentially encodes the weather and power information at multiple locations, enabling the extraction of common features while preserving their specificities, which addresses the difficulty of representing the inherent correlation patterns between wide-area meteorological information and heterogeneous energy sources. Secondly, to further explore the spatio-temporal correlation between meteorological information and power information, to improve the forecasting performance of the model, a spatio-temporal-lagged correlation (STLC) that can quantify their spatio-temporal delays is proposed. By analyzing the correlation characteristics between regional grid numerical weather prediction (NWP) information and station output at different spatial and temporal scales, the optimal spatial location and delay time of NWP inputs under the current scenario are determined for each station. Based on this, the best NWP scene probabilistic transfer model is established based on the Rank Bayesian Ensemble (RBE) method. By fitting the conditional probability distribution of the current best NWP spatiotemporal location and the 2nd day’s best NWP spatiotemporal location, providing a reliable input for the STPF model. Finally, a case study with 164 hydro-wind-solar stations in China demonstrates the effectiveness of the proposed method. Specifically, we achieved an accuracy improvement of 0.46% — 11.03% on the 2nd day of forecasting compared to benchmark methods.

Turbulence and flapping pivot axis effects on torsional flutter harvester efficiency by closed-form formula

This “Short Communication” investigates the dynamics of a torsional-flutter energy harvester in atmospheric winds with stationary turbulence. This apparatus is an example of a flutter mill, which operates by exploiting aeroelastic instability as a competitive alternative and as a renewable energy supply for one or few housing units. The apparatus has a rigid blade-airfoil that rotates about a pivot to generate flapping motion. Contrary to recent studies by the second author, the effect of random stationary turbulence on flutter onset is examined by an analytical approach, employed by Scanlan (1997) for bridge flutter analysis. Turbulence effect is simulated by suitably modifying the span-wise coherence equation of the aeroelastic load. The incipient flutter threshold is found as a function of turbulence properties. Various configurations are studied, i.e., pivot position, aspect ratio, turbulence coherence decay parameter and structural damping. The objective is to perform a thorough sensitivity analysis as the necessary premise for the planned, future examination of post-critical instability and operational efficiency of the harvester by suitable modeling and wind tunnel tests.

Correlation analysis and feature extraction using impedance spectroscopy over aging of lithium ion batteries

To optimize the operation of batteries and to accelerate the transition to a renewable energy supply and sustainable transportation, it is crucial to determine the condition of Lithium-Ion Batteries at all time. A non-invasive tool for determining the State-of-Health of a battery cell is the electrochemical impedance spectroscopy, in which the impedance is calculated as a function of the excitation frequency. This paper presents a detailed correlation analysis between impedance and State-of-Health and, therefore, the dependency between capacity and impedance over the life cycle of a battery. After extensive testing series with 48 cells resulting in 25,344 impedance spectra, the highest correlations between the impedance and the State-of-Health could be obtained at a State-of-Charge of 10%. Further, features are extracted from the impedance based on their correlation to estimate the State-of-Health. To validate the results of the correlation analysis, a support vector regression and a multi-layer perceptron are trained and tested resulting in a mean absolute error of 0.86% and 0.84%. The estimation results confirm the correlation analysis and further substantiate the need for an appropriate feature extraction method.

Optimizing Renewable Strategies for Emission Reduction Through Robotic Process Automation in Smart Grid Management

The integration of renewable energy into Intelligent Distribution Networks (IDNs) is challenged by the inherent variability and fluctuations in energy supply, particularly with photovoltaic (PV) generation as the primary form of distributed generation (DG). However, managing the fluctuations and variability in renewable energy supply presents significant challenges. To address these complexities, it is vital to optimally coordinate flexible resources from source–network–storage–load (SNSL) in a manner that aligns with cross-sectoral emission reduction strategies while enhancing grid stability and efficiency. This paper addresses these challenges by proposing a strategy that optimizes the coordination of PV-based DG, storage, and load resources through Robotic Process Automation (RPA) to enhance grid stability and support emissions reduction. We use a two-layer dispatching framework: the lower-layer model, formulated as a quadratic programming problem, maximizes PV utilization for individual users, while the upper-layer model, based on a second-order cone relaxation approach, manages the overall IDN to minimize operational costs. The iterative solution leverages tie-line power flow as boundary information to ensure convergence across the network. Validated on an enhanced IEEE 33-bus system, the approach demonstrates a 62% increase in PV-based DG consumption and a 25% reduction in active power losses, highlighting its potential to improve grid efficiency and contribute to emission reduction goals.

Methodological Approach to an Integrated Assessment of Systems for Remote Renewable Energy Supply

The use of renewable energy sources to reduce greenhouse gas emissions has become a global trend. According to forecasts of various international energy research organizations, this trend will persist in the future. As resources located near consumption centers are developed, the long-distance renewable energy transportation from areas where renewable sources operate with greater efficiency becomes a more pressing issue. Thus, the objective to establish an energy system using remote renewable sources arises. The study aims to elaborate a methodological approach for an integrated assessment of systems for energy supply from remote renewable energy sources. A distinctive feature of the proposed approach is a separate analysis of the main technological processes of production, conversion, storage, and use of energy carriers, with specific attention given to the generation of electricity from renewable energy sources. The Technique for order of preference by similarity to ideal solution (TOPSIS) is used for multi-criteria analysis of various solutions. This study aims to compare different energy systems for a project designed to export renewable energy from Russia (Sakhalin Island) to Japan (Yamagata Prefecture).

Molten Salt Corrosion Study of Alloys for Energy Application

A current challenge in concentrated solar power plants is corrosion with storing heat in molten salt to provide 24/7 renewable energy supply. This research aims to study optimisation of molten salt storage system through an understanding of the electrochemical interactions of the molten salt with the storage vessel materials. A unique facility was built to measure electrochemical performance in molten salt at high temperature with a capability of testing alloy samples. This will allow rapid performance evaluation of material and coatings in simulated high temperature service environment. Corrosion failure of these materials is associated with initiation of cracking because of electrochemical interaction between phases in the metallurgical microstructure. This can give rise to local attack, leading to crack formation and ultimately, plant failure. By using these electrochemical methods, we can better understand the corrosion mechanism.This ongoing project enables rapid and precise evaluation of energy materials degradation by measuring jointly and in situ the electrochemical performance of sample within a molten salt as a function of temperature. The study will also be compliment using microscope study using SEM /EDX to understand the microstructural changes and XRD for corrosion products evaluation. A combination of differed salt mix at different temperature ranges will be studied in this project.

Effect of Surface Fluorination on LaNiO3 Perovskite-Type Oxides for Oxygen Evolution Reactions

The technology of hydrogen production by alkaline water electrolysis using renewable energy has the advantages of abundant energy sources and minimal environmental pollution. However, the oxygen evolution reaction (OER) catalyst in water electrolysis faces several challenges. One problem is the unstable supply of renewable energy, which leads to frequent starting and stopping of the electrolyzer. This intermittency exposes the anode catalyst to drastic potential changes that generate reverse currents, resulting in anode catalyst degradation. Therefore, there is a critical need for research on more durable electrocatalysts. In this study, we attempted to improve the stability of perovskite-type oxides by using fluorination chemistry for surface treatment. We prepared fluorine-treated LaNiO3 by calcination with polyvinylidene fluoride (PVDF) and analyzed the electronic structures of the prepared samples by X-ray absorption spectroscopy.LaNiO3 and PVDF, a fluorine additive, were mixed using a mortar. The mixture was then pelletized and sintered in a sealed vessel at 350 °C for 12 hours. This experimental condition was based on the fluorination reaction of HfO2 [1]. Four different PVDF-to-LaNiO3 mixing ratios were synthesized: 0% (pure LaNiO3), 2.5%, 5%, and 10% by weight. To confirm whether the catalyst surfaces were fluorinated, XRD and XAFS measurements were performed to analyze the changes in the crystal structure and the valence changes by the introduction of fluorine. For electrochemical measurements, a measuring cell for electrolysis was used[2], which is reported to be analyzable only with powder catalysts.A comparison of the X-ray diffraction (XRD) patterns of pure LaNiO3 and LaNiO3 treated with PVDF showed that the addition of PVDF shifted the XRD peak to lower angles. This shift confirms a structural expansion in the LaNiO3 crystal lattice due to the PVDF treatment. For a more detailed investigation, XAFS measurements were performed. Analysis in the F K-edge XAFS identified a distinct fluorine peak in the PVDF-treated LaNiO3 sample, indicating successful introduction of fluorine into the structure. In addition, in the Ni K-edge X-ray absorption near edge structure, the spectrum of the PVDF-added sample shifted to a lower energy compared to pure LaNiO3, indicating a reduction in nickel valence, likely due to fluorine incorporation. However, electrochemical measurements indicated a decrease in the initial OER activity of LaNiO3 treated with PVDF.[1] Steven Flynn, Chi Zhang, Kent J. Griffith, Jiahong Shen, Christopher Wolverton, Vinayak P. Dravid, and Kenneth R. Poeppelmeier, Inorganic Chemistry, 60(7), 4463-4474 (2021)[2] K. Nagasawa, T. Ishida, H. Kashiwagi, Y. Sano, and S. Mitsushima, Int. J. Hydrogen Energy, 46, 36619 (2021)

Conductive ionic thermoelectric hydrogel with negative Seebeck coefficient, self-healing and highly sensitive to temperature for photothermoelectric conversion and non-contact sensing device

Ionic thermoelectric (i-TE) hydrogels have a high thermal energy utilization rate for low-grade heat, presenting a renewable energy supply option for sustainable global development. Conventional i-TE materials need to be conductive, stable and environmentally friendly, which remains a challenge today. This study presents a highly temperature-sensitive i-TE hydrogel with negative Seebeck coefficient, self-healing properties and conductive for photothermoelectric (PTE) conversion and non-contact sensing. The prepared PSFC-0.5 hydrogel exhibits a negative Seebeck coefficient and features a dual-crosslinked structure of polyacrylamide (PAM) and sodium alginate (SA), which provides the hydrogel with good mechanical strength (>2.7 kPa) and tensile properties (>1300 %). Photothermal (PT) conversion properties is effectively enhanced by combining carbon black/multi-walled carbon nanotubes (CB/MWCNTs) PT materials, and the addition of FeCl3 provides anions and cations for ionic diffusion. Under a 5 K temperature gradient, the optimized Seebeck coefficient was measured to be −2.01 mV·K−1 and the conductivity was approximately 1.70 mS·cm−1. Moreover, reversible hydrogen bonding interactions provides ionic hydrogels with good mechanical strength and self-healing capabilities. Due to the high sensitivity of PSFC-0.5 hydrogel to temperature, it can be effectively utilized in the field of non-contact sensing for the precise detection of temperature signals. This study presents an effective method for fabricating hydrogels that exhibit exceptional toughness and electric properties, demonstrating its significant potential for applications in PTE conversion and non-contact sensing technologies.

Developing an optimal energy system model for a sustainable urban transportation framework planning in the long term through different climatic zones

The modern lifestyle highlights the demand for an improved urban transportation infrastructure and a shift to renewable, efficient technologies. This evolution is essential for both developed and developing countries. The transportation system in a developing country such as Iran faces significant challenges, including low energy efficiency and high emissions. This situation makes the development of a sustainable transportation plan increasingly critical. Furthermore, there is considerable potential for renewable energy across various climatic zones in the country that should be utilized. This study presents a sustainable development model for the transportation system that incorporates zero-emission vehicles and renewable energy supply technologies in competition with traditional ones. The model is designed for long-term application and includes five case studies from various climatic zones in Iran, each featuring different renewable potentials and system scales. Determining the optimal structure of the system in each case study requires solving a linear, dynamic, and multi-criteria optimization problem. The objective function focuses on minimizing total costs, which include technological, energy, and social. All case studies indicate that developing a sustainable transportation system is optimal. However, larger case studies tend to produce more favorable optimization outcomes. According to the results, the scale of the case study is the most important factor in this research.

The Need for Solar Power Energy Utilization in the Northern Part of Nigeria for Sustainability and Economic Growth (A case study of Nigeria)

Northern Nigeria is one of the most recognizable areas, having vast amounts of solar radiation and rapidly growing populations. Thus, there is an opportunistic aspect of the use of solar power in this region. Since this region enjoys all-year sunshine, it is one of the best places for the integration of solar energy. With huge problems of energy she presently faces such as irregular supply of electricity, people's growth and thus demand, solar power presents an immediate usable, reliable, and sustainable solution. The energy demand in northern Nigeria is expected to sharply increase from about 48 billion kWh in 2024 to 64.5 billion kWh by 2034. This escalated energy requirement directly puts a demand for an efficient and sustainable source of energy, and as such, the promise of steady and renewable energy supply through solar power makes it very appealing. The average regional solar radiation exceeds 6 kWh/m2 per day, which points towards a very vast potential for solar energy generation. But to meet the energy requirements that will arise in the future from solar, it would call for a tremendous investment in infrastructure. This is because it is estimated that approximately 246,000 km2 of solar panels might be needed by 2034 in order to be able to cover the energy needs of the country. However, this massive area is required, but the benefits of solar energy do not stop there at solving shortages of energy. Solar power would increase economic activities due to job creation, stimulation of local industries, and the end reliance on fossil fuels. It has implications on environmental sustainability as it reduces greenhouse gas emissions and promotes cleaner practices of energy consumption. Northern Nigeria will unlock a stable and reliable source of power through investment in this infrastructure. This is important for economic development, thereby eventually improving the quality of life. Indeed, the integration of solar power addresses the immediate needs of satisfying energy challenges but builds a foundation for sustainable growth and environmental stewardship over the long term. Solar power in northern Nigeria is important for ensuring future demands for energy and significant economic development while emphasizing sustainability within the environment. A lot still has to be tapped into by tapping into the solar resources that are available. Key words: Solar Energy,Sustainability,Economic Growth,Energy Demand,Renewable Resources

The New Power Couple: Artificial Intelligence and Renewable Energy

Achieving net-zero emissions is one of the most challenging goals of our time, requiring large-scale integration of renewable energy (RE) into national energy supply chains. This demands new competencies for firms to preserve their competitive advantages in rapidly evolving market environments, often called dynamic capabilities. A promising technology for integrating, scaling, and diffusing renewable energy within energy supply chains is artificial intelligence. However, the literature on AI-renewable energy supply chains is still in its early stages and often lacks broader theoretical development or managerial insights. In response, we introduce a theoretical framework to identify and develop AI-based dynamic capabilities in renewable energy supply chains through a case study approach. Supply chain predictability and optimization, key components of sensing and seizing capabilities, are crucial for developing effective renewable energy supply chains. Our study provides valuable insights also for practitioners aiming to establish AI-driven renewable energy supply chains.

Multi-criteria decision analysis using GIS in assessing suitability for a solar-powered biomass briquetting plant in the Gambella region, Ethiopia

Biomass briquetting presents a promising avenue for alternative cooking energy, enhancing livelihoods, and fostering sustainable development. This study underscores the abundant biomass resources in the Gambella region, primarily composed of savanna and woody savanna, which encompass 77 % of the land area, consisting mainly of forest and agricultural waste. Leveraging these resources can ensure a renewable and sustainable cooking energy supply, aligning with Sustainable Development Goals (SDGs) related to clean energy and environmental conservation. Consequently, this thesis endeavors to identify optimal sites for establishing solar-powered non-wooden biomass briquetting plants across the region. Various spatial and non-spatial datasets, including solar radiation, slope, land use/land cover (LULC), and proximity to roads, rivers, and towns, were utilized for area delineation and mapping. Parameters such as solar radiation, slope, and river streams were derived from 30 m resolution digital elevation models (DEMs). The Analytical Hierarchy Process (AHP) was employed to calculate criteria weights for overlay analysis, resulting in a suitable solar farm site map. The study reveals that land use/land cover exerts the most significant influence, with a weight of 46.58 %, followed by solar radiation strength (20.42 %), slope (15.52 %), proximity to roads (8.26 %), proximity to rivers (5.46 %), and proximity to towns (3.77 %). By integrating parameter suitability and weight assignment in ArcGIS spatial analysis, a suitability map was generated, highlighting highly suitable areas predominantly along the North-South axis through central Gambella. This area covers approximately 12,094.49 km², constituting 47 % of the study area. These findings have practical implications, as they can inform local policymakers and actors in strategically addressing the region's dire need for sustainable cooking energy. This contribution can support the development of localized renewable energy strategies and promote long-term energy security in line with local energy policies.

Hybrid regret-based p-robust and distributionally robust optimization models for electric vehicle charging station network design

The rise of electric vehicles (EVs) in recent years has underscored the need for well-established Electric Vehicle Charging Station (EVCS) infrastructure. The lack of a reliable network of EVCS is a significant obstacle to the widespread adoption of EVs. Also, one often overlooked aspect of electric vehicles is the importance of using renewable energy sources to recharge their batteries. Without sustainable energy, EVs could produce more greenhouse gas emissions than traditional vehicles. Therefore, it seems essential to design networks of EVCSs that depend on renewable sources to meet their energy needs and ensure convenient access for customers while reducing environmental impact. This study proposes a two-stage stochastic optimization model to determine the optimal locations and capacities for EVCSs and supporting sustainable energy plants. The model considers uncertain factors such as vehicle flows and renewable energy supply. The model combines a hybrid adjusted p-robust and min–max regret approach to address uncertainties to ensure robustness in the network design process. Two approaches including Sample Average Approximation (SAA) and Fuzzy C-means Clustering are employed to solve the model efficiently. Additionally, the Distributionally Robust Optimization (DRO) approach accounts for uncertainty in the probability distribution function of scenarios. The results of these solution approaches are validated through parameter realization, which involves simulating uncertain parameters and verifying the robustness of the solutions obtained from each approach. This validation process is done by comparing the obtained results with the optimal values based on two criteria: the sum of deviation from optimality and the maximum deviation from optimality. By adopting these solution methods, the proposed methodology contributes to developing efficient and sustainable EV infrastructure. This methodology gives investors and city officials robust decision support for establishing EVCS.