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The modern quest for sustainable cities increasingly relies on using distributed energy resources (DERs), which requires new planning practices. This paper proposes an optimisation strategy to solve the fast charging station (FCS) allocation of electric vehicles (EVs). A mixed-integer programming (MIP) model minimises investment and operation costs, considering the building of FCSs with photovoltaic (PV) systems over carports and battery energy storage systems (BESSs) as planning alternatives. The embedded set covering problem has special aspects that allow the development of a novel approach to evaluate candidate sites to accommodate FCSs. A preprocessing strategy is developed to fine-tune the entire solution space. A multiobjective approach is used to obtain an optimal compromise solution for the MIP model when it is required to serve the maximum number of EV owners at the lowest possible cost. The combined strategies reduce the computational burden, allowing full-scale studies of EV charging system planning. The results of studies using a real-world Brazilian case certify the benefits of the proposed strategy in the FCS allocation problem and in optimising the operation when considering renewable alternatives. [ABSTRACT FROM AUTHOR]

This paper presents a rule‐based energy management system (EMS) designed for a standalone DC microgrid incorporating solar photovoltaic (PV), fuel cell, battery energy storage system (BESS), and electric vehicle. The unpredictable nature of renewable energy sources and the instability of loads pose challenges for maintaining DC bus voltages and power‐sharing arrangements, impacting the microgrid's smooth operation. The proposed EMS aims to ensure power balance between generation and demand, mitigating vulnerabilities of the DC bus to voltage instability caused by fluctuations from both the load and source sides. This is achieved through an autonomous DC bus voltage stabilization strategy, involving the maintenance of a nominal state of energy (SoE) for the BESS and hydrogen fuel consumption for the fuel cell within predefined lower and upper limits. By regulating these two factors, the EMS algorithm facilitates optimal performance of the PV, battery, and fuel cell components. Consequently, the EMS provides decision‐making instructions to each individual energy source, ensuring efficient operation under various conditions. The effectiveness of the proposed EMS is evaluated through hardware‐based testing on a DC microgrid and simulations in the MATLAB Simulink environment across multiple operating scenarios. [ABSTRACT FROM AUTHOR]

Fuel cell electric vehicles (FCEVs) hold great promise for achieving sustainable and environmentally friendly transportation. However, their widespread adoption depends on the efficient development of the hydrogen supply chain (HSC) required for refueling these vehicles. Given the interdependence between the HSC and power distribution networks (PDNs), this paper presents an innovative model to jointly plan the HSC and reinforce PDNs. In this new model, the inherent uncertainties related to wind energy generation, load demand, and FCEV consumption are modeled. Furthermore, coordinating the planning of hydrogen storage and battery energy storage systems is incorporated. The results underscore the critical importance of simultaneous planning for the HSC and PDN reinforcement. Moreover, the findings reveal that hydrogen storage alone can provide sufficient energy arbitrage, leading to a 9.6% reduction in total costs compared to the scenario where storage is disregarded. [Display omitted] • A novel planning model for hydrogen supply chains and power distribution networks. • Both hydrogen storage and battery energy storage are included. • Uncertainties related to wind generation and loads are modeled. • Model results show that simultaneous planning cuts costs by 25.9%. • Hydrogen storage alone reduces costs by 9.6%. [ABSTRACT FROM AUTHOR]

This study investigates the IEEE 69-bus distribution network with three wind turbines (WTs) connected at the same buses of three battery energy storage systems (BESSs), with three 20- or 30-outlet electric vehicle charging stations (EVCSs) for charging electric vehicles (EVs). The honey badger algorithm (HBA) is adopted to minimize daily energy loss. The HBA determines the best size and position for three WT-BESS buses and three EVCS buses. The HBA calculates BESS size and operation mode to minimize daily energy loss. The demand of EVCSs varies throughout the day depending on the random choice of the number and state of charge of EVs entering the station. This results in the active and reactive energy losses and utility input energy decreasing by 63.5%, 60.6% and 59.6%, respectively, and the minimum voltage increasing from 0.9256 to 0.9839 pu. The network voltage profile and stability are also improved. [ABSTRACT FROM AUTHOR]

In the two‐stage inverter system, due to the space vector modulation (SVM), the midpoint potential of active neutral point clamped three‐level inverter system (ANPC‐TLIS) will have triple frequency fluctuation, under the influence of the closed‐loop control strategy, the fluctuation amplitude of the lithium battery energy storage system (LBESS) output current will increase. To reduce the low‐frequency output current fluctuation, a voltage loop strategy based on active disturbance rejection control (ADRC) with notch filter (NF)is proposed in this paper. In this scheme, the voltage outer‐loop control strategy not only maintains voltage stability, but also reduces the fluctuation amplitude of reference current. In addition, during the electromagnetic transient process, the LBESS output current fluctuation frequency is constantly changing because the power supply frequency required by linear motor is constantly changing. To solve this problem, the inverter output current frequency is feedforward to the LBESS controller, forming a variable frequency notch filter (VFNF). Finally, the effectiveness of the proposed strategy is verified by fixed‐frequency and variable frequency conditions experiments on RT‐LAB experimental platform. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Battery energy storage systems (BESS) are becoming increasingly important and their number of applications in energy systems is constantly increasing. Although batteries cannot solve the problem of electricity storage in the long term, BESS systems have proven to be suitable for short-term provision of flexibility within the day. Their real usability is shaped by the provision of balancing services to ensure a stable supply of electricity in distribution networks. BESS systems were able to respond to the dynamic development of the electricity market in recent years and achieve excellent results from the point of view of economic return. The aim of the contribution is to present the results achieved in selected cases of BESS use and to determine their economic return. The size of the battery (installed power and capacity) is designed to be able to work in individual modes and operating cases. A critical element for the design of the battery size is the technical conditions for the provision of balancing services. On the basis of market research, we determine the costs of acquiring and operating the battery, including the need for a software superstructure for controlling the battery itself and ensuring the necessary business functions. According to the defined criteria, we evaluate the technical operation of the battery located at the point of consumption (behind the meter) using the battery model in several operating cases. Subsequently, we analyse its sales based on the development of market prices and the conditions in which the battery is placed. [ABSTRACT FROM AUTHOR]

Energy system optimization is needed for optimal sustainable net-zero electricity (NZE) mix even at regional/local scales because of the energy storage needs for addressing the intermittency of renewable energy supply. This study presents a novel regional/local energy planning model for optimum sustainable NZE mix under spatiotemporal climate/meteorological and electrical load demand constraints. A generic robust non-linear constrained mathematical programming (NLP) algorithm has been developed for energy system optimization; it minimizes the levelized cost and greenhouse gas emissions while maximizing reliability against stored energy discharge analysis (RADA). Reliability, defined as the ratio of excess stored renewable power discharge to unmet load demand, is a measure of the extent of unmet load demand met by the excess stored renewable power. Coupled with the NLP, the RADA and energy storage evaluations are used to determine the seasonal energy storage (SES) conditions and realistic renewable proportions for NZE. The significance of the proposed framework lies in determining the maximum hours of viable electrical energy storage beyond which the reliability enhancement is infinitesimal. The significant observations of this work include 96 h of maximum viable electrical energy storage beyond which the reliability enhancement is infinitesimal. While this observation is robust based on previous reports for the case of the United States, a realistic NZE mix for Southern United Kingdom is obtained as follows. Direct wind and solar sources can meet 63%, 62%, and 55% of the electricity demands in the southwest, Greater London, and southeast regions of the United Kingdom, respectively; further, battery energy storage systems can increase the renewable proportions by 21%, 22%, and 13% in these three regions. The unmet demands can be met by renewable electricity through SES. Compressed air energy storage (CAES) and pumped hydro storage offer viable SES. Following these, natural gas with carbon capture and storage (CCS), bioenergy, and hydrogen SES are the choices based on increasing cost per lifecycle climate impact potential to meet the electricity demands. [ABSTRACT FROM AUTHOR]

Background: Virtual power plants (VPPs) represent a pivotal evolution in power system management, offering dynamic solutions to the challenges of renewable energy integration, grid stability, and demand-side management. Originally conceived as a concept to aggregate small-scale distributed energy resources, VPPs have evolved into sophisticated enablers of diverse energy assets, including solar panels, wind turbines, battery storage systems, and demand response units. This review article explores the evolution of VPPs and their pivotal roles as major stakeholders within contemporary power systems. The review opens with a definition of VPPs that clarifies both their fundamental traits and technological foundations. A historical examination of their development highlights major turning points and milestones that illustrate their transforming journey. Main text: The methodology used for this article entailed a thorough examination to identify relevant studies, articles, and scholarly works related to virtual power plants. Academic databases were used to gather relevant literature. The literature was organized into categories helping to structure and present information in a logical flow based on the outline created for the review article. The discussions in the article show that the various functions that VPPs perform in power systems are of major interest. VPPs promote the seamless integration of renewable energy sources and provide optimum grid management by aggregating distributed energy resources, which improves sustainability. One of the important components of this evaluation involves taking market and policy considerations. Examining worldwide market patterns and forecasts reveals that VPP usage is rising, and that regulatory frameworks and incentives have a bigger impact on how well they integrate. Conclusion: Overcoming obstacles is a necessary step towards realizing full VPP potential. For VPPs to be widely adopted, it is still essential to address technological and operational challenges as they arise. Diverse stakeholders must work together to overcome market obstacles and promote the expansion of the VPP market. This analysis highlights the potential for VPPs to propel the evolution of contemporary power systems toward a more sustainable and effective future by highlighting areas for future research and development. [ABSTRACT FROM AUTHOR]

The integration of new battery technologies has become a focal point for distribution utilities, driven by decreasing costs and the need for fast responsiveness. Transportable battery energy storage systems (TBESSs) offer additional flexibility, allowing connection at multiple substations or grid feed points. However, concerns remain regarding their impact on distribution systems (DSs), particularly on protection devices (PDs). This study addresses these concerns by investigating the influence of TBESSs on the protection systems of a real-world distribution network. Given the lack of studies in the current literature on this topic, this research aims to fill this gap by examining the potential effects of TBESS integration on PDs, such as reclosers and fuses, within a DS. Utilizing a model based on real data from a Brazilian utility, we conducted simulations to analyze the effects of TBESSs in both charging and discharging modes on the protection systems of three feeders. The methodology involved assessing variations in the operation times and coordination of PDs to determine if TBESS integration would necessitate adjustments to existing protection configurations. The results demonstrated that TBESS integration resulted in only minor variations in PD operating times, typically within hundredths of a second, indicating a negligible impact on protection performance. Consequently, no significant modifications to the protection system are required to accommodate TBESSs. These findings suggest that TBESSs can be seamlessly integrated into existing distribution networks, maintaining system reliability and operational integrity. This study provides valuable insights and a robust procedure for utilities to analyze the integration of TBESSs, supporting the effective deployment of modern energy storage solutions in DSs. [ABSTRACT FROM AUTHOR]

Renewable energy communities foster the users' engagement in the energy transition, paving the way to the integration of distributed renewable energy sources. So far, the scientific literature has focused on residential users in energy communities, thus overlooking the opportunities for industrial and commercial members. This paper seeks to bridge this gap by extending the analysis to the role of non-residential users. The proposed methodology develops an effective clustering approach targeted to actual non-residential consumption profiles. It is based on the k-means algorithm and statistical characterization based on relevant probability density function curves. The employed clusterization procedure allows for effectively reducing a sample of 49 real industrial load profiles up to 11 typical consumption curves, whilst capturing all the relevant characteristics of the initial population. Furthermore, a peer-to-peer sharing strategy is developed accounting for distributed and shared storage. Three scenarios are considered to validate the model with different shares of non-residential users, and the results are then evaluated by means of shared energy, self-consumption, and self-sufficiency indices. Moreover, the results show that the integration of a large non-residential prosumer in a REC may increase the self-sufficiency of residential members by 8.2%, self-consumption by 4.4%, and the overall shared energy by 37.3%. Therefore, the residential community consistently benefits from the presence of non-residential users, with larger users inducing more pronounced effects. [ABSTRACT FROM AUTHOR]

With the rapid development of sensors and other devices, precise control for the generation of new energy, especially in the context of highly stochastic wind power generation, has been strongly supported. However, large-scale wind farm grid connection can cause the power system to enter a low inertia state, leading to frequency instability. Battery energy storage systems (BESSs) have the advantages of a fast response speed and high flexibility, and can be applied to wind farm systems to improve the frequency fluctuation problem in the process of grid connection. To address the frequency fluctuation problem caused by the parameter error of the fuzzy membership function in the fuzzy control of a doubly fed induction generator (DFIG) and a BESS, this paper proposes an improved Artificial Bee Colony (ABC) algorithm based on multi-source sensor data for optimizing the fuzzy controller to improve the frequency control ability of BESSs and DFIGs. A Gaussian wandering mechanism was introduced to improve the ABC algorithm and enhance the convergence speed of the algorithm, and the improved ABC algorithm was optimized for the selection of fuzzy control affiliation function parameters to improve the frequency response performance. The effectiveness of the proposed control strategy was verified on the MATLAB/Simulink simulation platform. After optimization using the proposed control strategy, the oscillation amplitude was reduced by 0.15 Hz, the precision was increased by 40%, and the steady-state frequency deviation was reduced by 26%. The results show that the method proposed in this paper provides a great improvement in the frequency stability of coordinated systems of wind farms and BESSs. [ABSTRACT FROM AUTHOR]

The wake effect reduces the wind speed at downstream wind turbines (WTs), making it necessary for the central controller to collect wind power generation data from each WT. However, wind farms (WFs) face a more complex problem in maintaining the voltage stability at the WT terminal while following the transmission system operator (TSO) instructions due to the information collection as well as the possible data loss during transmission. Therefore, this study proposes a coordinated control method for WTs and multiple power sources based on model predictive control under wake disturbance conditions, aiming to reduce the average voltage deviation in WT terminals and go close to the rated voltage and ensure effective compliance with TSO commands in large-scale WFs. Accordingly, the Jensen wake model was utilized to accurately calculate the available active and reactive power limits for each WT. Energy storage systems and static Var generators were modeled to coordinate and maintain the voltage in all WT terminals within the feasible range, providing peak shaving and valley filling support to reduce wind energy waste and shortfall, thereby enhancing the economic and operational reliability of WF. Further, the effectiveness of the proposed method was validated in MATLAB/Simulink. [ABSTRACT FROM AUTHOR]

The widespread integration of wind turbines poses voltage stability challenges to power systems. To enhance the ability of wind power systems to actively support grid voltage, grid-forming control techniques are increasingly being employed. However, current research primarily focuses on voltage stability challenges at the point of common coupling in wind power systems, lacking thorough investigation into system voltage response characterization. This paper establishes the voltage response model of a grid-forming wind power system. Based on this model, mathematical derivation and theoretical analysis are conducted, and the effect factors of the voltage at the point of common coupling are investigated. Furthermore, a voltage stabilization method is explored by adjusting the above effect factors. Finally, based on the MATLAB/Simulink platform, the simulation verification of each effect factor is carried out. The results indicate that voltage response characterization obtained by the theoretical analysis and simulation is similar and that the proposed method is valid. [ABSTRACT FROM AUTHOR]

Hybrid Renewable Energy Systems (HRES) have recently been proposed as a way to improve dependability and reduce losses in grid-connected load systems. This research study suggests a novel hybrid optimization technique that regulates UPQC in order to address the Power Quality (PQ) problems in the HRES system. The load system serves as the primary link between the battery energy storage systems (BESS), wind turbine (WT), and solar photovoltaic (PV) components of the HRES system. The major objective of the study is to reduce PQ issues and make up for the load requirement inside the HRES system. The addition of an Optimized Fractional Order Proportional Integral Derivative (O-FOPID) controller improves the efficiency of the UPQC. The Crow-Tunicate Swarm Optimization Algorithm (CT-SOA), an enhanced variant of the traditional Tunicate Swarm Optimization (TSA) and Crow Search Optimization (CSO), is used to optimize the control parameters of the FOPID controller. Utilizing the MATLAB/Simulink platform, the proposed method is put into practice, and the system's performance is assessed for sag, swell, and Total Harmonic Distortion (THD). The THD values for the PI, FOPID, and CSA techniques, respectively, are 5.9038%, 4.9592%, and 3.7027%, under the sag condition. This validates the superiority of the proposed approach over existing approaches. [ABSTRACT FROM AUTHOR]

With advancements in communication systems and measurement technologies, smart grids have become more observable and controllable, evolving into cyberphysical-power systems (CPPS). The impact of network security and secondary equipment on power system stability has become more evident. To support the existing grid toward a smart grid scenario, smart metering plays a vital role at the customer end side. Cyber-Physical systems are vulnerable to cyber-attacks and various techniques have been evolved to detect a cyber attack in the smart grid. Weighted trust-based models are suggested as one of the most effective security mechanisms. A hardware-in-loop CPPS co-simulation platform is established to facilitate the theoretical study of CPPS and the formulation of grid operation strategies. This paper examines current co-simulation platform schemes and highlights the necessity for a real-time hard-ware-in-the-loop platform to accurately simulate cyber-attack processes. This consideration takes into account the fundamental differences in modeling between power and communication systems. The architecture of the co-simulation platform based on RT-LAB and OPNET is described, including detailed modeling of the power system, communication system, and security and stability control devices. Additionally, an analysis of the latency of the co-simulation is provided. The paper focuses on modeling and implementing methods for addressing DDOS attacks and man-in-the-middle at-tacks in the communication network. The results from simulating a 7-bus system show the effectiveness and rationality of the co-simulation platform that has been designed. [ABSTRACT FROM AUTHOR]

In recent years, the domain of renewable energy communities has experienced dynamic growth, spurred by European Union (EU) legislation that became law for all 27 Member States in June 2021. This legislative framework intensified research efforts aimed at discovering new methods for sustainable energy sources through the development of individual and collective energy communities. Each EU country has implemented distinct frameworks for renewable energy communities, leading to varied legislative approaches. This has spurred exponential investment, facilitating the deployment of photovoltaic and battery energy storage systems, offering significant economic and environmental benefits to community members. Against this backdrop, the purpose of this analysis is to investigate academic publications related to renewable energy communities. Using a dataset extracted from the ISI Web of Science database, this study employs a bibliometric approach to identify the main authors, affiliations, and journals and analyze collaboration networks, as well as discern key topics and the countries involved. The analysis reveals an annual growth rate of 42.82%. Through thematic maps, WordClouds, three-field plots, and a review of the top 10 globally cited documents, this study provides a comprehensive perspective on the evolving domain of renewable energy communities. [ABSTRACT FROM AUTHOR]

Summary Mitigating inter‐area low‐frequency oscillations is a significant concern in multi‐machine power systems due to their adverse effects on system stability. These oscillations are intricately linked with power oscillations. So appropriate power modulation through the Battery Energy Storage System (BESS) can be an effective strategy for preserving system stability. In this paper, the maximal of all minimal residue indices under variations in power system operating conditions is utilized as the index for identifying the location for installation of the BESS, the damping control loop, and the feedback signal. A fixed‐structure H∞$$ {H}_{\infty } $$ scheme‐based wide‐area damping controller (WADC) is proposed for the BESS, providing sufficient damping of inter‐area oscillation modes. A modified IEEE 39‐bus system is simulated using a real‐time digital simulator as a test system in this work. The simulation results confirmed that the proposed WADC could effectively damp various inter‐area oscillation modes. Furthermore, it offers robust damping performance over contingencies associated with the system's various operating scenarios as well as the uncertainty associated with fixed and variable communication delays in the feedback signal of WADC and the integration of solar photovoltaic systems. Moreover, a comparative analysis of the proposed WADC is carried out with a BESS‐based wide‐area power system stabilizer, which is found to be more effective in mitigating system inter‐area oscillations. [ABSTRACT FROM AUTHOR]

Background: PV/diesel microgrids are getting more popular in rural areas of sub-Saharan Africa, where the national grid is often unavailable. Most of the time, for economic purposes, these hybrid PV/diesel power plants in rural areas do not include any storage system. This is the case in the Bilgo village in Burkina Faso, where a PV/diesel microgrid without any battery storage system has been set up. This power plant is composed of three diesel generators operating in parallel (two of 16 kW and one of 24 kW), coupled with a photovoltaic field of 30 kWp. It was observed that for such power plants, the grid management is not always efficient due to constantly fluctuating solar output and loads. This inconsistency in energy output raises the question if integrating battery energy storage systems could improve the grid's performance. While many studies in the literature focus on hybrid energy systems, only a few of them have tackled the optimization of existing and operational systems. Methods: This study investigated three scenarios based on the existing microgrid's characteristics: conventional standalone diesel generators, PV/diesel without battery storage and PV/diesel with a battery storage system which are the main technologies used for off-grid rural electrification in Burkina Faso. The levelized cost of electricity (LCOE) was used to assess the economic performance of each scenario, and the calculations were made using the HOMER software. Results: It was found that the best among the scenarios considered is the PV/diesel/battery configuration which has the lowest LCOE of US$ 0.524/kWh. The battery storage system for the optimal configuration has a capacity of 182 kWh with about 8 h of autonomy. Conclusions: It can be inferred from this study that a storage unit is necessary for an optimal management of a PV/diesel microgrid. Indeed, the storage unit significantly reduces the operating and maintenance costs associated with running diesel generators, as well as the excess electricity. The storage system also allows for a greater reduction in CO2 emissions compared to systems without storage. [ABSTRACT FROM AUTHOR]

The high penetration of distributed photovoltaics (PV) in distribution networks (DNs) results in voltage violations, imbalances, and flickers, leading to significant disruptions in DN stability. To address this issue, this paper proposes a multi-timescale voltage regulation approach that involves the coordinated control of a step voltage regulator (SVR), switched capacitor (SC), battery energy storage system (BESS), and electric vehicle (EV) across different timescales. During the day-ahead stage, the proposed method utilizes artificial hummingbird algorithm optimization-based least squares support vector machine (AHA-LSSVM) forecasting to predict the PV output, enabling the formulation of a day-ahead schedule for SVR and SC adjustments to maintain the voltage and voltage unbalance factor (VUF) within the limits. In the intra-day stage, a novel floating voltage threshold band (FVTB) control strategy is introduced to refine the day-ahead schedule, enhancing the voltage quality while reducing the erratic operation of SVR and SC under dead band control. For real-time operation, the African vulture optimization algorithm (AVOA) is employed to optimize the BESS output for precise voltage regulation. Additionally, a novel smoothing fluctuation threshold band (SFTB) control strategy and an initiate charging and discharging strategy (ICD) for the BESS are proposed to effectively smooth voltage fluctuations and expand the BESS capacity. To enhance user-side participation and optimize the BESS capacity curtailment, some BESSs are replaced by EVs for voltage regulation. Finally, a simulation conducted on a modified IEEE 33 system validates the efficacy of the proposed voltage regulation strategy. [ABSTRACT FROM AUTHOR]

The current study proposes a strategy for sensing fault detection in the secondary control of an isolated Microgrid based on a high-order Sliding Mode Robust Observers design. The proposed strategy's main objective is to support future diagnostic and fault tolerance systems in handling these extreme situations. The proposal is based on a generation system and a waste management system. Four test scenarios were generated in a typical Microgrid to validate the designed strategy, including two Battery Energy Storage Systems in parallel, linear, and non-linear loads. The scenarios included normal grid operation and three types of sensing faults (abrupt, incipient, and random) directly affecting the secondary control of a hierarchical control strategy. The results showed that the proposed strategy could provide a real-time decision for detection and reduce the occurrence of false alarms in this process. The effectiveness of the fault detection strategy was verified and tested by digital simulation in Matlab/Simulink R2023b. [ABSTRACT FROM AUTHOR]

The increasing use of renewable energies places new challenges on the balance of the electricity system between demand and supply, due to the intermittent nature of renewable energy resources. However, through frequency regulation (FR) services, owners of battery storage systems can become an essential part of the future smart grids. We propose a thorough first study on the use of batteries associated with base stations (BSs) of a cellular network, to participate in ancillary services with respect to FR services, via an auction system. Trade-offs must be made among the number of participating BSs, the degradation of their batteries and the revenues generated by FR participation. We propose a large-scale mathematical programming model to identify the best participation periods from the perspective of a cellular network operator. The objective is to maximize profit while considering the aging of the batteries following their usage to stabilize the electrical grid. Experiments are conducted with data sets from different real data sources. They not only demonstrate the effectiveness of the optimization model in terms of the selection of BSs participating in ancillary services and providing extra revenues to cellular network operators, but also show the feasibility of ancillary services being provided to cellular network operators. [ABSTRACT FROM AUTHOR]

This paper addresses the growing challenges and developments in frequency control within power systems influenced by the increasing penetration of renewable energy sources. It evaluates the advancements and limitations of renewable-based control technologies and explores the critical role of diverse energy storage technologies in providing fast frequency response ancillary services. Through a comprehensive analysis of the global literature, this paper categorises energy storage solutions according to their efficacy in meeting fast frequency response demands and potential for revenue generation. It reveals significant gaps in the current research, which predominantly focuses on battery energy storage systems and microgrid applications, with insufficient attention to grid-scale storage solutions and innovative energy storage technologies. This analysis identifies a lack of detailed technical simulations and hybrid storage models for frequency control, as well as a minimal exploration of the environmental benefits, particularly in terms of carbon dioxide emission reductions, associated with deploying new energy storage technologies in ancillary service markets. The paper concludes by emphasising the urgent need for further research incorporating detailed techno-economic evaluations and the carbon dioxide reduction potential of modular, scalable energy storage technologies, which should be facilitated by advanced network simulation models and comprehensive market analysis to drive future advancements in the field. [ABSTRACT FROM AUTHOR]

This paper deals with the optimal scheduling of the resources of a renewable energy community, whose coordination is aimed at providing flexibility services to the electrical distribution network. The available resources are renewable generation units, battery energy storage systems, dispatchable loads, and power-to-hydrogen systems. The main purposes behind the proposed strategy are enhancement of self-consumption and hydrogen production from local resources and the maximization of the economic benefits derived from both the selling of hydrogen and the subsidies given to the community for the shared energy. The proposed approach is formulated as an economic problem accounting for the perspectives of both community members and the distribution system operator. In more detail, a mixed-integer constrained non-linear optimization problem is formulated. Technical constraints related to the resources and the power flows in the electrical grid are considered. Numerical applications allow for verifying the effectiveness of the procedure. The results show that it is possible to increase self-consumption and the production of green hydrogen while providing flexibility services through the exploitation of community resources in terms of active and reactive power support. More specifically, the application of the proposed strategy to different case studies showed that daily revenues of up to EUR 1000 for each MW of renewable energy generation installed can be obtained. This value includes the benefit obtained thanks to the provision of flexibility services, which contribute about 58% of the total. [ABSTRACT FROM AUTHOR]

Solar generation systems with battery energy storage have become a research hotspot in recent years. This paper proposes a grid-forming control for such a system. The inverter control consists of the inner dq-axis current control, the dq-axis voltage control, the phase-locked loop (PLL) based frequency control, and the DC voltage control. The proposed control embeds the PLL into the grid-forming inverter control, offering the advantages of better synchronization and fault current-limiting capability. With the proposed control, the battery energy storage is able to provide inertial and primary frequency support during the grid frequency disturbance. Simulation models are established in PSCAD/EMTDC, and the results during the active power variation and AC voltage variation, the grid frequency disturbance, grid fault, and mode switch validate the effectiveness of the proposed control. [ABSTRACT FROM AUTHOR]

This paper presents a capacity planning framework for a microgrid based on renewable energy sources and supported by a hybrid battery energy storage system which is composed of three different battery types, including lithium-ion (Li-ion), lead acid (LA), and second-life Li-ion batteries for supplying electric vehicle (EV) charging stations. The objective of this framework is to determine the optimal size for the wind generation systems, PV generation systems, and hybrid battery energy storage systems (HBESS) with the least cost. The framework is formulated as a mixed integer linear programming (MILP) problem, which incorporates constraints for battery ageing and the amount of unmet load for each year. The system uncertainties are managed by conducting the studies for various scenarios, generated and reduced by generative adversarial networks (GAN) and the k-means clustering algorithm for wind speed, global horizontal irradiation, and EV charging load. The studies are conducted for three levels of unmet load, and the outputs are compared for these reliability levels. The results indicate that the cost of hybrid energy storage is lower than individual battery technologies (21% compared to Li-ion, 4.6% compared to LA, and 6% compared to second-life Li-ion batteries). Additionally, by using HBESS, the capacity fade of LA batteries is decreased (for the unmet load levels of 0, 1%, 5%, 4.2%, 6.1%, and 9.7%, respectively), and the replacement of the system is deferred proportional to the degradation reduction. [ABSTRACT FROM AUTHOR]

Renewable energy‐powered DC microgrids have emerged as a sustainable alternative for standalone power systems in remote locations, which were traditionally reliant on diesel generators (DIG) only. To ensure power quality and reliability, energy storage systems (ESS) and demand‐side management (DSM) techniques are employed, addressing the intermittent nature of renewable energy sources (RES). This manuscript presents a novel multi‐objective optimisation framework to determine the equipment sizing, depth of discharge (DoD) of ESS, and share of controllable loads contributing to DSM in a standalone DC microgrid incorporated with RES as a primary energy source and a backup DIG. The proposed optimisation strategy utilises genetic algorithm with the objectives of minimizing lifecycle cost and carbon footprint. A novel battery energy storage system (BESS) management criterion is introduced, which accounts for battery degradation in the lifecycle cost calculation. The minimum allowable DoD of the BESS is considered a decision variable in the optimisation problem to assess the impact of higher DoD on lifecycle cost improvement. MATLAB simulation results demonstrate that the proposed optimisation model significantly reduces the levelized cost of electricity and per unit carbon footprint compared to previous models. Additionally, it identifies an optimal range of DoD for the BESS to enhance the lifecycle cost of a standalone DC microgrid. [ABSTRACT FROM AUTHOR]

Accurate prediction of the remaining useful life (RUL) of energy storage batteries plays a significant role in ensuring the safe and reliable operation of battery energy storage systems. This paper proposes an RUL prediction framework for energy storage batteries based on INGO-BiLSTM-TPA. First, the battery's indirect health index is extracted by combining principal component analysis and the Pearson correlation coefficient in the battery charge/discharge cycle data. Second, for the problem that the Northern Goshawk Optimization (NGO) algorithm is prone to falling into local optimum, the Gaussian variation mechanism and nonlinear hunting radius are introduced to improve the NGO algorithm, and the Improved Northern Goshawk Optimization (INGO) algorithm is proposed. Finally, the temporal pattern attention (TPA) mechanism is introduced in the bi-directional long short-term memory (BiLSTM), which makes the model weighted to focus on the features of important time steps, and the INGO algorithm is applied to it to build the RUL prediction framework. Based on the CALCE battery dataset, the root-mean-square error (RMSE) of RUL prediction based on the proposed framework is controlled within 1.3%, which provides better prediction accuracy and generalization. [ABSTRACT FROM AUTHOR]

In recent years, the demand side micro-grid had a lot of challenges, most of them being the uninterrupted power supply. The effective energy management of residential structures concerning diverse and often conflicting objectives is one of the most challenging problems associated with hybrid renewable energy sources (HREs) generation, an energy storage system (ESS), and electric vehicles (EV). Therefore, an intelligent home energy management system (HEM) is essential with consumers who can change their load according to their comfort with demand response program schemes (DRP). The key contribution is as follows: (1) Home energy management (HEM) through optimal scheduling of home appliances to reduce energy consumption costs with DRP. (2) The net present cost of the HREs has been minimized for the smart HEM appliances. (3) The techno-economic feasibility and optimal energy management analysis of household loads have been analyzed. Grid and without a grid-connected mode of operation of HEM is analyzed. The HOMER, an energy management tool, is used to solve energy management based on RES and ESS in this study. The HEM has considered DRP and RES to reduce the net present cost (NPC) by 21.95% and 5.71%, with grid and without grid-connected, respectively. [ABSTRACT FROM AUTHOR]

In this paper, a novel power management strategy (PMS) is proposed for optimal real-time power distribution between battery and supercapacitor hybrid energy storage system in a DC microgrid. The DC-bus voltage regulation and battery life expansion are the main control objectives. Contrary to the previous works that tried to reduce the battery current magnitude and its fluctuation to increase the battery life, the battery thermal considerations, which are a key factor in battery life, are also considered as an objective function in the proposed PMS. Also, the quick charging capability of the battery is another objective in the optimization problem. To solve this multi-objective problem, the Karush–Kuhn–Tucker conditions have been used to make the PMS suitable for real-time operation. In addition, one of the important inputs of conventional PMS is the amount of grid excess/deficiency power. Due to the use of the adaptive Kalman filter estimator, another superior of the proposed PMS is using local data and no need for communication and knowledge of the load or generation power. Using simulation, the performance of the proposed method is compared with three traditional methods filter-based, droop control, and fuzzy logic in terms of controlling the battery current, battery temperature, and battery health, and its superiority has been shown. Finally, the validation of the proposed PMS is experimentally tested on a real setup. [ABSTRACT FROM AUTHOR]

In the growing world, the utilization of electrical energy is increasing rapidly. Excessive use of fossil fuels will drain them and also invite hazardous pollution. Integrating renewable energy resources as distributed generators (DGs) can fulfill the rapidly increasing electrical energy demand and promote green energy generation to a large extent. The intermittent nature of renewable energy and higher penetration of DG may adversely affect the operation of the distribution network (DN). As a result, power disparity, reverse power flow to the grid, and voltage instability may exist. One key solution is to optimally integrate the renewable energy-based DG and battery energy storage system (BESS) in the coordination of demand response (DR). This paper proposes a multilevel particle swarm optimization technique to synchronize the distributed energy resources (DER) and DR in the DN. The proposed approach is implemented on the IEEE 33 bus system energized by coal power plant (CPP). The first level of optimization finds the sizes and locations of DER (DG and BESS), and the next level determines the optimal power dispatch in the coordination of DR. The outcomes of this framework exhibit effectiveness in the optimal utilization of renewable energy resources and the enhancement of power quality parameters in DN so that CO2 emissions are reduced by 32.71% from CPP. [ABSTRACT FROM AUTHOR]

Electricity network across the globe has witnessed an increase in penetration of distributed energy resources (DER) to the grid. A maiden attempt to integrate 50% loaded combined cycle gas turbine (CCGT) plant and doubly fed induction generator (DFIG) plant with DER is carried out for investigations to study frequency excursions in the hybrid system along with temperature excursions in the CCGT plant. A novel attempt is done to introduce modified beluga whale optimizer for tuning the controller gains and system parameters of the hybrid isolated system. This article emphasizes on the system behaviour of an isolated power system consisting of a CCGT plant and a DFIG plant integrated with distributed energy resources comprising of diesel engine generator, wind turbine generator and battery energy storage system. Among the classical secondary controllers, comparison of system responses is carried out to select the best among proportional, proportional–integral, and proportional–integral–derivative controllers. A modified beluga whale optimization algorithm is utilized to tune the controllers. Various investigations conducted with different turbo generators confirm the control strategy to the frequency of excursions. The hybrid CCGT—DFIG—DER system is also evaluated under random load demand, change in generation rate constraints and DFIG power output. [ABSTRACT FROM AUTHOR]

The electricity sector is witnessing a rise in renewable energy sources and the widespread adoption of electric vehicles, posing new challenges for distribution system. Additionally, the surge in carbon emissions resulting from industrialization and population growth continues to worsen global warming and climate change. In response, integrating electric vehicles (EVs) and battery energy storage systems (BESS) has emerged as a critical strategy, presenting both challenges and opportunities in effective energy management. BESSs offer potential solutions to mitigate these impacts. Furthermore, this review thoroughly explores issues related to lithium‐ion batteries, particularly in the context of EVs and energy management systems (EMS), identifies challenges, and provides recommendations for future research directions. The article concludes by outlining the current extent of investigation in the field of BESS and EV systems to provide researchers with a clear understanding. The escalation of carbon emissions stemming from industrialization and population expansion has worsened the effects of global warming and climate change. To address this challenge, the integration of Electric EVs and energy storage systems (ESS) has emerged as a pivotal strategy. This study examines optimization techniques, methodologies, and the evolving market landscape in distributed systems, with a focus on EVs and BESS. It also explores issues related to lithium‐ion batteries, particularly in the context of EVs and energy management systems. The article highlights the challenges and opportunities in the field of BESS and EV systems, emphasizing the need for ongoing research. BESSs offer potential solutions to mitigate these impacts. Moreover, it offers an extensive analysis of the existing BESS installations, outlining main areas of interest, pointing out difficulties, clarifying areas of unfinished study, and providing future directions. [ABSTRACT FROM AUTHOR]

Advances in non‐conventional energy technologies and increasing fossil fuel prices along with environmental concerns have made hybrid renewable energy systems important. In view of this scenario, solar panel mounted on a vertical axis wind turbine (called as solar‐wind tower) can be utilized to produce more electric energy than individual one. This solar‐wind tower will be located in the space available between two opposite roads of expressways/highways. Solar‐wind tower located in such a manner that the air velocity produced from driving vehicles on both sides of the road is adequate to cut the turbine blades which will produce unidirectional torque. A battery energy storage system (BESS) stores the power produced by the solar‐wind tower so that it can subsequently be used for local loads and electric vehicle charging stations (EVCS) and remaining energy can be supplied to the grid. In this work, a hybrid system composed of wind and solar is designed and modelled in Simulink (MATLAB) and tested on real data of wind speed and validated by Opal‐RT simulator. From the simulation result, it is estimated that total electrical power output of a single solar‐wind tower is around 15 to 20 kWh in a day under the assumed conditions. [ABSTRACT FROM AUTHOR]

In order to achieve the "carbon peaking and carbon neutrality" goals, we must vigorously develop renewable energy power generation. As the penetration of renewables progressively escalates, the corresponding demand for battery energy storage systems (BESS) within the power grid rises concomitantly. This paper presents an innovative optimization approach for configuring BESS, taking into account the incremental variations in renewable energy penetration levels and BESS charge‐discharge cycles. Employing incremental analytical techniques and pivotal metrics such as capacity elasticity, the proposed method determines the optimal penetration rate and corresponding BESS capacity outcomes for deploying energy storage systems. An example analysis of a rural power distribution benchmark is carried out by using the method in this paper, which proves the effectiveness of the method in this paper. This methodology was substantiated through its application to a case study of a rural power distribution benchmark, thereby validating its efficacy. Furthermore, it was compared with the particle swarm optimization, providing a comparative assessment of their relative performance. [ABSTRACT FROM AUTHOR]

In developing countries, electrical power distribution networks are often inadequate, particularly in small health centers. As a result, the electrical energy supplied by the grid is frequently interrupted. The productivity and quality of service delivered by these health centers to the people who live in these areas are severely affected by this issue. This issue can be resolved by incorporating battery storage systems along with renewable energy sources into the distribution system. The direct delivery of energy to customers is greatly aided by these renewable energy supplies. Partially, the grid supports such a system on a limited scale to guarantee the continuity of the energy supply. This study tried to resolve the problem due to these frequent power outages and its economic expenditures. To address the illustrated challenges, we tried to renovate the diesel generator with a solar and battery energy supply. The PVsyst software shows the average global solar radiation in the selected zone is5.84kmh/m2/day$$ 5.84\ \mathrm{kmh}/{\mathrm{m}}^2/\mathrm{day} $$. The annual energy demand for Gedeo health centers in 2023 is 3.32 MWH and the proposed PV‐battery hybrid system has a 10.95 MWH capacity. Moreover, when we utilize a diesel generator the Capital cost (CC), Net present cost (NPC), levelized cost of energy (LCOE), and payback period are 12 452.25$, 13 369.12$, 0.1$, and 10.7 years respectively. The economic assessment result of the proposed system is 4083$, 4727$, 0.059$, and 3.8 years consecutively. In southern Ethiopia, the annual emission from diesel generators alone, excluding the emission from the vehicles is close to 692 tons. Consequently, from the empirical economic assessment the installed solar energy is 90% more beneficial than the existing system. [ABSTRACT FROM AUTHOR]

In this paper, an optimisation framework is presented for planning a stand-alone microgrid for supplying EV charging (EVC) stations as a design and modelling approach for the FEVER (future electric vehicle energy networks supporting renewables) project. The main problem of the microgrid capacity sizing is making a compromise between the planning cost and providing the EV charging load with a renewable generation-based system. Hence, obtaining the optimal capacity for the microgrid components in order to acquire the desired level of reliability at minimum cost can be challenging. The proposed planning scheme specifies the size of the renewable generation and battery energy storage systems not only to maintain the generation–load balance but also to minimise the capital cost (CAPEX) and operational expenditures (OPEX). To study the impact of renewable generation and EV charging uncertainties, the information gap decision theory (IGDT) is used to include risk-averse (RA) and opportunity-seeking (OS) strategies in the planning optimisation framework. The simulations indicate that the planning scheme can acquire the global optimal solution for the capacity of each element and for a certain level of reliability or obtain the global optimal level of reliability in addition to the capacities to maximise the net present value (NPV) of the system. The total planning cost changes in the range of GBP 79,773 to GBP 131,428 when the expected energy not supplied (EENS) changes in the interval of 10 to 1%. The optimiser plans PV generation systems in the interval of 50 to 63 kW and battery energy storage system in the interval of 130 to 280 kWh and with trivial capacities of wind turbine generation. The results also show that by increasing the total cost according to an uncertainty budget, the uncertainties caused by EV charging load and PV generation can be managed according to a robustness radius. Furthermore, by adopting an opportunity-seeking strategy, the total planning cost can be decreased proportional to the variations in these uncertain parameters within an opportuneness radius. [ABSTRACT FROM AUTHOR]

The reliable operation of a power system requires a real-time balance between supply and demand. However, it is difficult to achieve this balance solely by relying on supply-side regulation. Therefore, it is necessary to cooperate with effective demand-side management, which is a key strategy within smart grid systems, encouraging end-users to actively engage and optimize their electricity usage. This paper proposes a novel bi-level optimization model for integrating solar, hydrogen, and battery storage systems with charging stations (SHS-EVCSs) to maximize social welfare. The first level employs a non-cooperative game theory model for each individual EVCS to minimize capital and operational costs. The second level uses a cooperative game framework with an internal management system to optimize energy transactions among multiple EVCSs while considering EV owners' economic interests. A Markov decision process models uncertainties in EV charging times, and Monte Carlo simulations predict charging demand. Real-time electricity pricing based on the dual theory enables demand-side management strategies like peak shaving and valley filling. Case studies demonstrate the model's effectiveness in reducing peak loads, balancing energy utilization, and enhancing overall system efficiency and sustainability through optimized renewable integration, energy storage, EV charging coordination, social welfare maximization, and cost minimization. The proposed approach offers a promising pathway toward sustainable energy infrastructure by harmonizing renewable sources, storage technologies, EV charging demands, and societal benefits. [ABSTRACT FROM AUTHOR]

Coordinated control technology attracts increasing attention to the photovoltaic–battery energy storage (PV-BES) systems for the grid-forming (GFM) operation. However, there is an absence of a unified perspective that reviews the coordinated GFM control for PV-BES systems based on different system configurations. This paper aims to fill the gap by providing a comprehensive review of coordinated GFM control strategies for PV-BES, considering various system configurations. Typical configurations of PV-BES systems are explored, followed by a detailed discussion of conventional GFM control methods used in the PV-BES systems. Furthermore, coordinated GFM controls are analyzed in PV-BES systems based on different configurations, providing the common DC bus configuration as a widely adopted configuration due to its more control degrees of freedom and ease of expansion. Moreover, the mode division and switching coordinated control based on the system power is the most widely used. Furthermore, challenges in the coordinated GFM controls for the PV-BES system in future applications are briefed and emphasized before the conclusion. [ABSTRACT FROM AUTHOR]

One of the main challenges of Lombok Island, Indonesia, is the significant disparity between peak load and base load, reaching 100 MW during peak hours, which is substantial considering the island's specific energy dynamics. Battery energy storage systems provide power during peak times, alleviating grid stress and reducing the necessity for grid upgrades. By 2030, one of the proposed capacity development scenarios on the island involves deploying large-scale lithium-ion batteries to better manage the integration of solar generation. This paper focuses on the life cycle assessment and life cycle costing of a lithium iron phosphate large-scale battery energy storage system in Lombok to evaluate the environmental and economic impacts of this battery development scenario. This analysis considers a cradle-to-grave model and defines 10 environmental and 4 economic midpoint indicators to assess the impact of battery energy storage system integration with Lombok's grid across manufacturing, operation, and recycling processes. From a life cycle assessment perspective, the operation subsystem contributes most significantly to global warming, while battery manufacturing is responsible for acidification, photochemical ozone formation, human toxicity, and impacts on marine and terrestrial ecosystems. Recycling processes notably affect freshwater due to their release of 4.69 × 10−4 kg of lithium. The life cycle costing results indicate that over 85% of total costs are associated with annualized capital costs at a 5% discount rate. The levelized cost of lithium iron phosphate batteries for Lombok is approximately 0.0066, demonstrating that lithium-ion batteries are an economically viable option for Lombok's 2030 capacity development scenario. A sensitivity analysis of input data and electricity price fluctuations confirms the reliability of our results within a 20% margin of error. Moreover, increasing electricity prices for battery energy storage systems in Lombok can reduce the payback period to 3.5 years. [ABSTRACT FROM AUTHOR]

As maritime technology advances, exploration of the oceans has progressively moved from surface exploration to underwater ventures. Unmanned underwater vehicles (UUVs), now prevalent for such exploration, effectively reduce human labor and lower operational costs. These vehicles rely on an internal Battery Storage System (BSS) that sustains device operation by extending operational duration and providing stable voltage. Typically arranged in series, BSSs face challenges due to differences in the chemical characteristics of individual batteries, which lead to discrepancies in battery voltages and cause imbalances during charge and discharge cycles. This results in varied utilization rates among the batteries and uneven aging of the battery pack, potentially decreasing operational efficiency and increasing failure rates, thus reducing reliability and safety. Considering the harsh environmental conditions and maintenance difficulties associated with underwater operations, this paper proposes a robust solution: a balancing system featuring a modular switch with electrical isolation. Through theoretical analysis and circuit simulation, this study constructs and tests a novel prototype of a capacitor-based equalizer circuit with electrical isolation, verifying its feasibility. [ABSTRACT FROM AUTHOR]

In order to solve the problem of variable steady-state operation nodes and poor coordination control effect in photovoltaic energy storage plants, the coordination control strategy of photovoltaic energy storage plants based on ADP is studied. Establish the photovoltaic energy storage power station model including photovoltaic system model, super capacitor system model and battery system model; Set the maximum limit of active power change as the power constraint condition for coordinated control of photovoltaic energy storage station; The optimal control problem of multi voltage and reactive power resource coordination is fully considered, the optimal voltage control model is established by using ADP algorithm, and the optimal coordinated control strategy is obtained by online learning the collected dynamic operation information. The experimental results show that this strategy can improve the coordinated control effect of the photovoltaic energy storage station, ensure the photovoltaic energy storage station in a stable operation state, improve the service life of the energy storage device in the photovoltaic energy storage station, and stabilize the steady-state operating node voltage within the ideal fluctuation range. [ABSTRACT FROM AUTHOR]

With the increasing penetration level of electric vehicles (EVs) in distribution networks, the limited capacity of distribution networks has become a bottleneck for EV integration. Considering the difficulties of capacity expansion in distribution networks, especially in large cities, integrating EVs with photovoltaic (PV) generation systems and battery energy storage systems has become a possible technical solution for distribution networks. However, uncertainties in the PV generation systems and EV loads make planning and operating methods difficult for such systems. This paper presents an evaluation model that simulates the uncertainties of EV and PV power generation systems using a large number of stochastic scenarios generated by the Monte Carlo method to assess the revenue of various operators under multiple possible scenarios. Multiple operation constraints were considered in the proposed method, including voltage deviations, capacity limitation of the transformer, EV owner satisfaction, and other physical constraints. In order to accelerate the evaluation process of the EVs, the Distflow equations for distribution networks were applied in the proposed evaluation model. The results of case studies indicate that the maximum capacity of EVs with different scenarios can be calculated by the proposed model. [ABSTRACT FROM AUTHOR]

The integration of supercapacitors as hybrid energy storage systems in electric vehicles has attracted the attention of many researchers and has been considered as a promising solution. Bidirectional DC/DC converters (BDDCs) play a fundamental role in HESS, as they manage the power flow by controlling currents and regulating the DC bus voltage. However, they encounter the challenge of uncertainties and high fluctuation power loads, necessitating the fast dynamics, stability, and high robustness of the controller. This paper proposes a novel hybrid proportional–integral and backstepping cascade controller to regulate the DC‐bus voltage under uncertainties and load variations, and to control the current references of the on‐boarded sources. To confirm the asymptotic stability of the whole system, a nonlinear stability analysis is conducted using the Lyapunov theorem. A power management strategy is applied to distribute the power loads and generate reference currents for the BDDCs controller. Simulations results under various driving cycles using MATLAB/Simulink demonstrate the superiority of the proposed controller compared to conventional proportional–integral and backstepping controllers. A real‐time controller‐hardware‐in‐the‐loop test bench is developed to validate the effectiveness of the proposed strategy. [ABSTRACT FROM AUTHOR]

With a single-switched DC-DC converter, a hybrid approach is proposed for a fast-charging (FC) dc to dc converter with the controller for an electric vehicle charging station (EVCS) that combines solar photovoltaic (PV), fuel cells (FC), and a battery energy storage system (BESS). The proposed hybrid technique integrates a radial-basis-function-neural-network (RBFNN) and a student-psychology-optimization-algorithm (SPOA); commonly known as the RBFNN-SPOA technique. The PV-FC hybrid electric vehicle systems, like controllers, electric vehicle, and the vehicle’s power source, are displayed. The proposed converter architectures have less voltage waveform changes, a low voltage stress on semiconductor power sources, and a high-voltage ratio of conversion. The proposed method is to use FC successfully at battery charge states and varied radiation levels. The FC in the controller of SPOA may change the inputs and outputs depending on the battery state of charge (SoC) and PV irradiation. Similarly, the needed input-torque for the motor is forecasted by the recalling recurrent neural network controller using several manually produced torque signals. The performance of the proposed technique is evaluated in MATLAB and is compared to ant colony optimization (ACO), crow optimization algorithm (COA), and fuzzy logic controller (FLC) approaches. According to the results, the efficiency of the proposed technique is 99.17%. [ABSTRACT FROM AUTHOR]

To tackle the carbon emission problem, the exploitation of renewable energy resources (RESs) with zero emissions has gained much importance. In this context, hydrogen has become an important resource to involve itself in distributed energy resources (DERs) to generate electricity, energize vehicles, and heat our living areas. The utilization of hydrogen technologies is indispensable due to their procurement, environmental friendliness, and high efficiency for DERs. A challenging area in the field of hydrogen energy technologies is extracting efficient power and integration to DERs. This paper investigates how to model and operate a proton exchange membrane fuel cell (PEMFC) stack in a DER application to establish a living lab facility for educational and research purposes by covering Power-to-X (P2X). The PEMFC model of the studied system is designed in a MATLAB/Simulink environment and implemented in an experimental testbed. Thus, a fuel cell (FC) unit as a complementary source is preferred to be used near a photovoltaic (PV) array, a small-scale wind turbine (WT), and a lead acid battery energy storage system (BESS) by creating a P2X concept to form a living lab platform. Beyond the model, regarding the experimental part, a PV array, a WT, and a BESS are combined with a PEMFC module that is rated output of 1200 W. Expect for all systems, the FC part is emphasized in this study. Also, a dynamic model of the mentioned PEMFC is proposed and verified. • Importance of hydrogen energy is focused on its advantages, future role, potential, and challenges. • FC unit as a complementary source is evaluated by creating a P2X concept. • This platform is designed to meet the energy needs of research-education, diversify the supply, increase energy independence. • Energy procurement is supplied via green energy during a lesson, which is noteworthy and instructive. • Feasibility of combined PV/WT/BESS/PEMFC system is investigated. [ABSTRACT FROM AUTHOR]

Hybrid microgrids, integrating local energy resources, present a promising but challenging solution, especially in areas with limited or no access to the national grid. Reliable operation of off-grid energy systems necessitates sustainable energy sources, given the intermittent nature of renewables. While fossil fuel diesel generators mitigate risks, they increase carbon emissions. This study assesses the viability of integrating a very small modular renewable energy reactor into a microgrid for replacing conventional diesel generators, substantially curbing greenhouse gas emissions. A comprehensive analysis, including design and economic evaluation, was conducted for an off-grid community microgrid with an annual generation and load of 8.5 GWh and 7.8 GWh, respectively. The proposed microgrid configurations incorporate very small modular reactors, alongside solar, wind, and battery storage systems. MATLAB modeling and simulation across eight cases, accounting for seasonal variations, demonstrate the technical and economic feasibility of case 7. This configuration, integrating modular reactors, photovoltaics, wind turbines, and battery storage, satisfactorily meets load demands. Notably, it boasts a high internal rate of return up to ~31% and a shorter payback period of around 4 years compared to alternative scenarios. [ABSTRACT FROM AUTHOR]