Search Results (299)

This study investigates the impact of Time-of-Use (TOU) scheduling and battery energy storage systems (BESS) on voltage stability in a typical Malaysian medium-voltage distribution network with high photovoltaic (PV) system penetration. The analyzed network comprises 110 nodes connected via eight feeders to a pair of 132/11 kV, 15 MVA transformers, supplying a total load of 20.006 MVA. Each node is integrated with a 100 kW PV system, enabling up to 100% PV penetration scenarios. A hybrid mitigation strategy combining TOU-based load shifting and BESS was implemented to address voltage violations occurring, particularly during low-load night hours. Dynamic simulations using DIgSILENT PowerFactory were conducted under worst-case (no load and peak load) conditions. The novelty of this research is the use of real rural network data to validate a hybrid BESS–TOU strategy, supported by detailed sensitivity analysis across PV penetration levels. This provides practical voltage stabilization insights not shown in earlier studies. Results show that at 100% PV penetration, TOU or BESS alone are insufficient to fully mitigate voltage drops. However, a hybrid application of 0.4 MWh BESS with 20% TOU load shifting eliminates voltage violations across all nodes, raising the minimum voltage from 0.924 p.u. to 0.951 p.u. while reducing active power losses and grid dependency. A sensitivity analysis further reveals that a 60% PV penetration can be supported reliably using only 0.4 MWh of BESS and 10% TOU. Beyond this, hybrid mitigation becomes essential to maintain stability. The proposed solution demonstrates a scalable approach to enable large-scale PV integration in dense rural grids and addresses the specific operational characteristics of Malaysian networks, which differ from commonly studied IEEE test systems. This work fills a critical research gap by using real local data to propose and validate practical voltage mitigation strategies. Full article

The varying characteristics of grid-connected energy resources necessitate a clear and effective approach for managing and scheduling generation units. Without proper control, high levels of renewable integration can pose challenges to optimal dispatch, especially as more generation sources, like wind and solar PV, are introduced. As a result, conventional power sources require an advanced management system, for instance, a virtual power plant (VPP), capable of accurately monitoring power supply and demand. This study thoroughly explores the dispatch of battery energy storage systems (BESSs) and diesel generators (DGs) through a distributionally robust joint chance-constrained optimization (DR-JCCO) framework utilizing the conditional value at risk (CVaR) and heuristic-X (H-X) algorithm, structured as a bilevel optimization problem. Furthermore, Binomial expansion (BE) is employed to linearize the model, enabling the assessment of BESS dispatch through a mathematical program with equilibrium constraints (MPECs). The findings confirm the effectiveness of the DRO-CVaR and H-X methods in dispatching grid network resources and BE under the MPEC framework. Full article

With rising energy costs and the need for sustainable power solutions in urban South African settings, grid-tied renewable energy systems have become viable alternatives for reducing dependence on traditional grid supply. This study investigates the techno-economic feasibility of a grid-connected hybrid photovoltaic (PV) and battery storage system designed for a commercial facility located in Johannesburg, South Africa—an area characterized by a subtropical highland climate. We conducted the analysis using the HOMER Grid software and evaluated the performance of the proposed PV/battery system against the baseline grid-only configuration. Simulation results indicate that the optimal systems, comprising 337 kW of flat-plate PV and 901 kWh of lithium-ion battery storage, offers a significant reduction in electricity expenditure, lowering the annual utility cost from $39,229 to $897. The system demonstrates a simple payback period of less than two years and achieves a net present value (NPV) of approximately $449,491 over a 25-year project lifespan. In addition to delivering substantial cost savings, the proposed configuration also enhances energy resilience. Sensitivity analyses were conducted to assess the impact of variables such as inflation rate, discount rate, and load profile fluctuations on system performance and economic returns. The results affirm the suitability of hybrid grid-tied PV/battery systems for cost-effective, sustainable urban energy solutions in climates with high solar potential. Full article

As the global demand for clean and sustainable energy grows, photovoltaics (PVs) have become an important technology in this industry. Thin-film and flexible PV modules offer noticeable advantages for irregular surface mounts and mobile applications. This study investigates the use of four machine learning models to detect different flexible PV module geometries based on power output data. Three identical flexible PV modules were mounted in flat, concave, and convex configurations and connected to batteries via solar chargers. The experimental results showed that all geometries fully charged their batteries within 6–7 h on a sunny day with the flat, concave-, and convex-shaped modules achieving a peak power of 95 W. On a cloudy day, the concave and convex modules recorded peak outputs of 72 W and 65 W, respectively. Simulation results showed that the XGBoost model delivered the best classification performance, showing 93% precision with the flat-mounted module and 98% recall across all geometries. In comparison, the KAN model recorded the lowest precision (78%) with the curved geometries. A calibration analysis on the ML models showed that Random Forest and XGBoost were well calibrated for the flat-mounted module. However, they also showed overconfidence and underconfidence issues with the curved module geometries. Full article

The need for electrical energy is dramatically increasing, pushing researchers and industrial communities towards the development and improvement of microgrids (MGs). It also encourages the use of renewable energies to benefit from available sources. Thereby, the implementation of a photovoltaic (PV) system with a hybrid energy storage system (HESS) can create a standalone MG. This paper presents an MG that uses photovoltaic energy as a principal source. An HESS is required, combining batteries and supercapacitors. This MG responds “insure” both alternating current (AC) and direct current (DC) loads. The batteries and supercapacitors have separate parallel connections to the DC bus through bidirectional converters. The DC loads are directly connected to the DC bus where the AC loads use a DC-AC inverter. A control strategy is implemented to manage the fluctuation of solar irradiation and the load variation. This strategy was implemented with a new logic control based on Boolean analysis. The logic analysis was implemented for analyzing binary data by using Boolean functions (‘0’ or ‘1’). The methodology presented in this paper reduces the stress and the faults of analyzing a flowchart and does not require a large concentration. It is used in this paper in order to simplify the control of the EMS. It permits the flowchart to be translated to a real application. This analysis is based on logic functions: “Or” corresponds to the addition and “And” corresponds to the multiplication. The simulation tests were executed at T_au  =  6 s of the low-pass filter and conducted in 60 s. The DC bus voltage was 400 V. It demonstrates that the proposed management strategy can respond to the AC and DC loads. Full article

The growing integration of photovoltaic (PV) energy systems and electric vehicles (EVs) introduces new challenges in managing energy flow within smart grid environments. The intermittent nature of solar energy and the variable charging demands of EVs complicate reliable and efficient power management. Existing strategies for grid-connected PV–battery systems often fail to effectively handle bidirectional power flow between EVs and the grid, particularly in scenarios requiring seamless transitions between vehicle-to-grid (V2G) and grid-to-vehicle (G2V) operations. This paper presents a novel neural network-based model predictive control (NN-MPC) approach for optimizing energy management in a grid-connected PV–battery–EV system. The proposed method combines neural networks for forecasting PV generation, EV load demand, and grid conditions with a model predictive control framework that optimizes real-time power flow under various constraints. This integration enables intelligent, adaptive, and dynamic decision making across multiple objectives, including maximizing renewable energy usage, minimizing grid dependency, reducing transient responses, and extending battery life. Unlike conventional methods that treat V2G and G2V separately, the NN-MPC framework supports seamless mode switching based on real-time system status and user requirements. Simulation results demonstrate a 12.9% improvement in V2G power delivery, an 8% increase in renewable energy utilization, and a 50% reduction in total harmonic distortion (THD) compared to PI control. The results highlight the practical effectiveness and robustness of NN-MPC, making it an effective solution for future smart grids that require bidirectional energy management between distributed energy resources and electric vehicles. Full article

This paper presents the optimal sizing of a hybrid renewable energy system (HRES) for an isolated residential building using modified smell agent optimization (mSAO). The paper introduces a time-dependent approach that adapts the selection of the original SAO control parameters as the algorithm progresses through the optimization hyperspace. This modification addresses issues of poor convergence and suboptimal search in the original algorithm. Both the modified and standard algorithms were employed to design an HRES system comprising photovoltaic panels, wind turbines, fuel cells, batteries, and hydrogen storage, all connected via a DC-bus microgrid. The components were integrated with the microgrid using DC-DC power converters and supplied a designated load through a DC-AC inverter. Multiple operational scenarios and multi-objective criteria, including techno-economic metrics such as levelized cost of energy (LCOE) and loss of power supply probability (LPSP), were evaluated. Comparative analysis demonstrated that mSAO outperforms the standard SAO and the honey badger algorithm (HBA) used for the purpose of comparison only. Our simulation results highlighted that the PV–wind turbine–battery system achieved the best economic performance. In this case, the mSAO reduced the LPSP by approximately 38.89% and 87.50% over SAO and the HBA, respectively. Similarly, the mSAO also recorded LCOE performance superiority of 4.05% and 28.44% over SAO and the HBA, respectively. These results underscore the superiority of the mSAO in solving optimization problems. Full article

Island communities that depend on mainland grid connections face substantial risks when natural disasters sever undersea or overhead cables, often resulting in long-lasting outages. This paper presents a comprehensive and novel two-part methodological framework for enhancing the resilience of these communities through networked microgrids that interconnect local renewable energy resources and battery storage. The framework integrates techno-economic capacity optimization using HOMER Pro with agent-based simulation in AnyLogic to determine cost-effective solar and storage capacities and to model dynamic real-time dispatch under varying conditions. Six island communities across three Indonesian provinces serve as illustrative case studies, tested under best-case and worst-case disruption scenarios that reflect seasonal extremes of solar availability. Simulation results reveal that isolated expansions of PV and battery storage can ensure critical residential loads, though certain islands with limited resources continue to experience shortfalls. By contrast, networked microgrids enable surplus power transfers between islands, significantly reducing unmet demand and alleviating the need for large-scale, individual storage. These findings demonstrate the significant potential of clustered microgrid designs to improve reliability, lower operational costs, and facilitate secure energy supply even during prolonged cable outages. The proposed framework offers a scalable roadmap for deploying resilient microgrid clusters in remote regions, with direct policy implications for system planners and local stakeholders seeking to leverage renewable energy in high-risk environments. Full article

The increasing integration of renewable energy into modern power systems has prompted the need for efficient hybrid energy solutions to ensure reliability, sustainability, and economic viability. However, optimizing the design of hybrid renewable energy systems, particularly those incorporating both hydrogen and battery storage, remains challenging due to system complexity and fluctuating energy trading conditions. This study addresses these gaps by proposing a novel framework that combines the Chimp Optimization Algorithm (ChOA) with a rule-based energy management strategy (REMS) to optimize component sizing and operational efficiency in a grid-connected microgrid. The proposed system integrates photovoltaic (PV) panels, wind turbines (WT), electrolyzers (ELZ), hydrogen storage, fuel cells (FC), and battery storage (BAT), while accounting for seasonal variations and dynamic energy trading. Each contribution in the Research Contributions section directly addresses critical limitations in previous studies, including the lack of advanced metaheuristic optimization, underutilization of hydrogen-battery synergy, and the absence of practical control strategies for energy management. Simulation results show that the proposed ChOA-based model achieves the most cost-effective and efficient configuration, with a PV capacity of 1360 kW, WT capacity of 462 kW, 164 kWh of BAT storage, 138 $H_2$ tanks, a 571 kW ELZ, and a 381 kW FC. This configuration yields the lowest cost of energy (COE) at $0.272/kWh and an annualized system cost (ASC) of $544,422. Comparatively, the Genetic Algorithm (GA), Salp Swarm Algorithm (SSA), and Grey Wolf Optimizer (GWO) produce slightly higher COE values of $0.274, $0.275, and $0.276 per kWh, respectively. These findings highlight the superior performance of ChOA in optimizing hybrid energy systems and offer a scalable, adaptable framework to support future renewable energy deployment and smart grid development. Full article

With the rapid development of renewable energy technologies, large numbers of photovoltaic (PV) and battery energy storage systems (BESS) have been connected to distribution networks. However, both PV and the BESS are inverter interfaced power sources, which may cause the traditional protection relays to mis-operate or mal-operate. Moreover, according to the latest grid connection specifications, PV and BESS are required to absorb negative sequence current during asymmetric faults of distribution networks, indicating that they both must adopt new control strategies during the fault ride through period. In response to the above challenges, this work first studies the fault ride through control strategies of PV and BESS when different phase-to-phase faults occur according to the latest grid connection requirements. Second, it analyzes the negative sequence impedance characteristics of PV and BESS under asymmetric faults and quantitatively calculates its variation range. Third, during symmetric faults, the differences in fault current provided by PV and BESS and those provided by the large power grid are compared. Then, this work proposes a fault direction detection principle for the distribution network with PV and BESS. For asymmetric phase-to-phase faults, this principle detects the fault direction by using the negative sequence power angle; for symmetric faults, it detects the fault direction by using the reactive current and active current. Finally, simulation tests are carried out to verify the operation performance of the proposed principle. Full article

This paper analyzes the power quality in a 400 kWp grid-connected solar photovoltaic system with storage (BESS), considering standards IEEE Std 519TM, IEEE Std 1159TM, and IEC 61000-4-30. For system analysis, a photovoltaic array model is developed. Neplan-Smarter Tools software is used for model validation, and experimental measurements are performed on the actual photovoltaic system, recording total harmonic distortion (THDi/THDv). A class B power quality monitor was used to measure three-phase electrical variables: current, voltage, power, power factor, and THD. The THD level was generated at an energy level below 20% of the rated power, resulting in high THDi. The recorded THDv remained below 2.5%, which means that its value is limited by the IEEE 519 standard. When the BESS was connected to the PCC grid, the voltage level remained regulated, and the electrical system appeared to be stable. This paper contributes a methodology and procedure for measurement and power quality assessment, allowing for THD identification and enabling designers to configure better designs and energy system protections when integrating solar photovoltaic energy into an electrical distribution network. Full article

An analysis of the possibilities of using alternative energy to solve the problem of electricity shortages in developing countries shows that solar energy can potentially play an essential role in the fuel and energy complex. The geographical location, on the one hand, and the global development of solar energy technologies, on the other, create an opportunity for a fairly complete and rapid solution to problems of insufficient energy supply. An autonomous solar installation is expensive; 50% of the cost is solar modules, 45% of the cost consists of other elements (battery, inverter, charge controller), and 5% is for other materials. This work proposes the most efficient PV system, based on the technical characteristics of the SB and AB. It has a direct connection between the SB and AB and provides almost full use of the solar panel’s installed power with a variable orientation to the Sun. The development of a small solar photovoltaic (PV) installation, operating both in parallel with the grid and in autonomous mode, can improve the power supply of household consumers more efficiently and faster than the development of a large energy system. It is suggested that two minimized criteria be used to create a model for forecasting FOU. This model can be used with a genetic algorithm to make a prediction that fits a specific case, such as a time series representation based on discrete fuzzy sets of the second type. The goal is to make decisions that are more valid and useful by creating a forecast model and algorithms for analyzing small PV indicators whose current values are shown by short time series and automating the processes needed for forecasting and analysis. Full article

The potential for electricity distribution in power systems has significantly increased over the years. This is mainly because of the discovery of alternative electricity generation sources, such as renewable energy, coupled with distributed generation (DG), making electricity more widely accessible. However, challenges remain in distributing electricity to remote area communities (RACs), especially because of difficult terrain and the complexity of installing power plants, leaving some areas without access to electricity. In this study, we used an improved particle swarm optimization (IPSO) technique to propose multi-objective energy management for remote area communities within a hybrid integrated Photovoltaic–(PV)–Hydropower plant (HPP)–Battery Energy Storage System (BESS). The multi-objective functions enhance power quality and voltage stability to meet grid code requirements. The proposed method was applied to the IEEE 15-bus system, which is consistent with systems commonly used in remote area communities, under the following scenarios: Case I—random installation of PV-HPP-BESS and PI parameter control of BESS; Case II—optimal location of PV-HP-BESS and PI parameter control of BESS using IPSO; Case III—sudden short circuit of the transmission line in Case II. Effectiveness was verified through hardware-in-the-loop (HIL) testing. The experimental results indicate that the proposed method significantly improves power quality and stability under disturbances, demonstrating superior performance. Full article

Shifting towards renewable energy sources is essential for achieving sustainability goals. This research aims to develop and practically validate an integrated photovoltaic (PV) system with battery storage and electric vehicle (EV) charging, combined with smart energy management, to optimize energy use and minimize fossil fuel reliance. Conducted in Constanta, Romania, the study presents a novel practical solution involving a real-world grid-connected PV system leveraging battery storage to effectively retain excess solar energy for future consumption, thereby enhancing self-sufficiency and grid independence. Utilizing PV*SOL for system simulation and Global Solar Atlas for solar irradiance data, the authors provide accurate assessments of energy yield, economic feasibility, and environmental benefits. Results demonstrate a high energy autonomy level, with solar energy covering 92.2% of total consumption, alongside a notable annual reduction of 6239 kg in CO₂ emissions. The study significantly contributes to the field by providing an empirically validated model that bridges theory and practice, offering a scalable blueprint for sustainable and economically viable renewable energy systems. Full article

Grid-connected battery energy storage systems are usually used 24/7, which could prevent the utilization of typical diagnosis and prognosis techniques that require controlled conditions. While some new approaches have been proposed at the laboratory level, the impact of real-world conditions could still be problematic. This work investigates both the impact of additional residential usage on the cells while charging and of inhomogeneities on the diagnosability of batteries charged from photovoltaic systems. Using Big-Data synthetic datasets covering more than ten thousand possible degradations, we will show that these impacts can be accommodated to retain good diagnosability under auspicious conditions to reach average RMSEs around 2.75%. Full article