Search Results (823)

To address the challenge of coordinating economic cost control and low-carbon objectives in microgrid scheduling, while overcoming the performance limitations of the traditional Zebra Optimization Algorithm (ZOA) in complex problems, this paper proposes a Synergistic Zebra Optimization Algorithm (SZOA) and integrates it with innovative management concepts to enhance the microgrid scheduling process. The SZOA incorporates three core strategies: a multi-population cooperative search mechanism to strengthen global exploration, a vertical crossover–mutation strategy to meet high-dimensional scheduling requirements, and a leader-guided boundary control strategy to ensure variable feasibility. These strategies not only improve algorithmic performance but also provide technical support for innovative management in microgrid scheduling. Extensive experiments on the CEC2017 (d = 30) and CEC2022 (d = 10, 20) benchmark sets demonstrate that the SZOA achieves higher optimization accuracy and stability compared with those of nine state-of-the-art algorithms, including IAGWO and EWOA. Friedman tests further confirm its superiority, with the best average rankings of 1.20 for CEC2017 and 1.08/1.25 for CEC2022 (d = 10, 20). To validate practical applicability, the SZOA is applied to grid-connected microgrid scheduling, where the system model integrates renewable energy sources such as photovoltaic (PV) generation and wind turbines (WT); controllable sources including fuel cells (FC), microturbines (MT), and gas engines (GS); a battery (BT) storage unit; and the main grid. The optimization problem is formulated as a bi-objective model minimizing both economic costs—including fuel, operation, pollutant treatment, main-grid interactions, and imbalance penalties—and carbon emissions, subject to constraints on generation limits and storage state-of-charge safety ranges. Simulation results based on typical daily data from Guangdong, China, show that the optimized microgrid achieves a minimum operating cost of USD 5165.96, an average cost of USD 6853.07, and a standard deviation of only USD 448.53, consistently outperforming all comparison algorithms across economic indicators. Meanwhile, the SZOA dynamically coordinates power outputs: during the daytime, it maximizes PV utilization (with peak output near 35 kW) and WT contribution (30–40 kW), while reducing reliance on fossil-based units such as FC and MT; at night, BT discharges (−20 to −30 kW) to cover load deficits, thereby lowering fossil fuel consumption and pollutant emissions. Overall, the SZOA effectively realizes the synergy of “economic efficiency and low-carbon operation”, offering a reliable and practical technical solution for innovative management and sustainable operation of microgrid scheduling. Full article

This paper presents a performance evaluation of a 140 kW solar array installed on the rooftop of the Mountain Line Transit Authority (MLTA) building in Morgantown, West Virginia (WV), USA, covering the period from 2013 to 2024. The grid-connected photovoltaic (PV) system consists of 572 polycrystalline PV modules, each rated at 245 watts. The study examines key performance parameters, including annual electricity production, average daily and annual capacity utilization hours (CUH), current array efficiency, and performance degradation. Monthly ambient temperature and global tilted irradiance (GTI) data were obtained from the NASA POWER website. During the assessment, observations were made regarding the tilt angles of the panels and corrosion of metal parts. From 2013 to 2024, the total electricity production was 1588 MWh, with an average annual output of 132 MWh. Over this 12-year period, the CO₂ emissions reduction attributed to the solar array is estimated at 1,413,497 kg, or approximately 117,791 kg/year, compared to emissions from coal-fired power plants in WV. The average daily CUH was found to be 2.93 h, while the current PV array efficiency in April 2024 was 10.70%, with a maximum efficiency of 14.30% observed at 2:00 PM. Additionally, an analysis of annual average performance degradation indicated a 2.28% decline from 2013 to 2016, followed by a much lower degradation of 0.17% from 2017 to 2023, as electricity production data were unavailable for most summer months of 2024. Full article

With growing urban populations and rapid technological advancement, major cities worldwide are facing pressing challenges from surging energy demands. Interestingly, substantial unused space within residential buildings offers potential for installing renewable energy systems coupled with energy storage. This study innovatively proposes a grid-connected photovoltaic (PV) system integrated with pumped hydro storage (PHS) and battery storage for residential applications. A novel optimization algorithm is employed to achieve techno-economic optimization of the hybrid system. The results indicate a remarkably short payback period of about 5 years, significantly outperforming previous studies. Additionally, a threshold is introduced to activate pumping and water storage during off-peak nighttime electricity hours, strategically directing surplus power to either the pump or battery according to system operation principles. This nighttime water storage strategy not only promises considerable cost savings for residents, but also helps to mitigate grid stress under time-of-use pricing schemes. Overall, this study demonstrates that, through optimized system sizing, costs can be substantially reduced. Importantly, with the nighttime storage strategy, the payback period can be shortened even further, underscoring the novelty and practical relevance of this research. Full article

Insulation degradation in photovoltaic (PV) cables can cause electrical faults and fire hazards, thereby compromising system reliability and safety. Early detection of precursor signals is crucial for preventive maintenance. However, conventional diagnostic techniques are limited to static assessments and fail to capture early-stage electrical anomalies in real-time. This study investigates the time-series behavior of voltage, current, and temperature in PV cables under thermal stress conditions. Experiments were conducted using TFR-CV cables installed in a vertically stacked and tight-contact configuration. A gas torch was applied for localized heating to induce insulation degradation. A grid-connected testbed with six series-connected PV modules was constructed. Each module was instrumented with PV-M sensors, temperature sensors, and an infrared camera. Data were acquired at 1 Hz intervals. Results showed that cable surface temperature exceeded 280 °C during degradation. The output voltage exhibited transient surges of up to +13.3% and drops of −68%, while the output current decreased by over 20%, particularly in the PV-M3 module. These anomalies, such as thermal imbalance, voltage spikes/dips, and current drops, were closely associated with critical degradation points and are interpreted as precursor signals. This work confirms the feasibility of identifying fire-related precursors through real-time monitoring of PV cable electrical characteristics. The observed correlation between electrical responses and thermal expansion behaviors suggests a strong link to the stages of insulation degradation. Future work will focus on quantifying the relationship between degradation and electrical behavior under controlled environmental conditions. Full article

This paper presents a DC microgrid architecture with autonomous decentralized control that exhibits high resilience against increasingly common threats, such as natural disasters and cyber-physical attacks, as well as its operational characteristics under normal circumstances. The proposed system achieves autonomous decentralized cooperative control by combining a battery-integrated DC baseline, in which multiple distributed small-scale batteries are directly connected to the grid baseline, with a weakly coupled grid architecture in which each power device is loosely coupled via the grid baseline. Unlike conventional approaches that assign grid formation, inertial support, and power balancing functions to DC/DC converters, the proposed approach delegates these fundamental grid roles to the distributed batteries. This configuration simplifies the control logic of the DC/DC converters, limiting their role to power exchange only. To evaluate system performance, a four-family DC microgrid model incorporating a typical Japanese home environment, including an EV charger, was constructed in MATLAB/Simulink R2025a and subjected to one-year simulations. The results showed that with approximately 5 kW of PV panels and a 20 kWh battery capacity per household, a stable power supply could be maintained throughout the year, with more than 50% of the total power consumption covered by solar energy. Furthermore, the predicted battery life was over 20 years, confirming the practicality and economic viability of the proposed residential microgrid design. Full article

This review presents a synthesis of fuzzy logic-based (FL) controllers applied to photovoltaic (PV) systems over the last decade, with a specific focus on maximum power point tracking (MPPT) and power management. These subsystems are critical for improving the efficiency of PV energy conversion, as they directly address the nonlinear, time-varying, and uncertain behavior of solar generation under dynamic environmental conditions. FL-based control has proven to be a powerful and versatile tool for enhancing MPPT accuracy, inverter performance, and hybrid energy management strategies. The analysis concentrates on three main categories, namely, Mamdani, Takagi–Sugeno (T-S), and Type-2, highlighting their architectures, operational characteristics, and application domains. Mamdani controllers remain the most widely adopted due to their simplicity, interpretability, and effectiveness in scenarios with moderate response time requirements. T-S controllers excel in real-time high-frequency operations by eliminating the defuzzification stage and approximating system nonlinearities through local linear models, achieving rapid convergence to the maximum power point (MPP) and improved power quality in grid-connected PV systems. Type-2 fuzzy controllers represent the most advanced evolution, incorporating footprints of uncertainty (FOU) to handle high variability, sensor noise, and environmental disturbances, thereby strengthening MPPT accuracy under challenging conditions. This review also examines the integration of metaheuristic algorithms for automated tuning of membership functions and hybrid architectures that combine fuzzy control with artificial intelligence (AI) techniques. A bibliometric perspective reveals a growing research interest in T-S and Type-2 approaches. Quantitatively, Mamdani controllers account for 54.20% of publications, T-S controllers for 26.72%, and Type-2 fuzzy controllers for 19.08%, reflecting the balance between interpretability, computational performance, and robustness to uncertainty in PV-based MPPT and power management applications. Full article

To address the conflicts between high-penetration distributed photovoltaics (PV) integration causing voltage limit violations, reverse power flow issues, and the grid connection needs of industrial and commercial users, this paper proposes an optimal capacity planning method for distributed PV considering the user’s grid connection locations. This method effectively increases the acceptance capacity of the distribution transformer network for distributed PV while ensuring the safe and stable operation of the distribution network. First, the source–load uncertainty is considered, and the k-means clustering algorithm is used to select multiple typical daily probability scenarios. Then, the PV optimal connection node range is obtained through a PV site selection and sizing model. For the planning of nodes within the optimal range, an optimal capacity planning model focusing on the economic benefits of users is established. This model aims to optimize the improvement of wheeling cost and maximize the economic benefits of grid-connected users by determining the optimal PV access capacity for each node. Finally, for PV users outside this range, after determining the maximum allowable capacity for each node, the capacity margin and static voltage stability are comprehensively considered to evaluate the network access scheme. Simulation examples are used to verify the effectiveness of the proposed method, and the simulation results show that the proposed method can effectively increase the acceptance capacity of the distribution network for photovoltaic systems. By fully considering the wheeling cost collection strategy, the distributed PV acceptance capacity is increased by 20.14%, while both user benefits and the operational safety and economic performance of the distribution network are significantly improved, ultimately resulting in a 27.77% increase in total revenue. Full article

As hybrid microgrids become increasingly widespread in real-world applications, the need for intelligent energy management strategies that ensure reliability, economic efficiency, and robustness to uncertainties is growing. This study presents a real-time capable model predictive control (MPC)-based energy management for a medium-sized hybrid microgrid at the Karabuk University Demir Çelik campus. The system comprises 100 kW photovoltaic (PV) panels, a 500 Ah battery energy storage system (BESS), a 440 kW diesel generator, and a 75 MVA utility connection. The proposed MPC approach is evaluated under ten realistic operating scenarios, incorporating dynamic pricing and fault conditions. Simulation results show up to 43% reduction in operational costs and 35% decrease in grid dependency, while keeping unserved critical loads below 3%. Compared to conventional rule-based methods, the proposed strategy offers improved scalability, adaptability, and resilience, highlighting its practical potential for deployment in smart energy systems. Full article

This study evaluates the technical, economic, and environmental feasibility of grid-connected and off-grid hybrid energy systems designed to meet the energy demands of a greenhouse facility. Various system configurations were developed based on combinations of solar, wind, diesel, and battery storage technologies. The analysis considers a daily electricity consumption of 369.52 kWh and a peak load of 52.59 kW for the greenhouse complex. Among the grid-connected systems, the grid/PV configuration was identified as the most optimal, offering the lowest Net Present Cost (NPC) of USD 282,492, the lowest Levelized Cost of Energy (LCOE) at USD 0.0401/kWh, and a reasonable emissions reduction of 54.94%. For off-grid scenarios, the generator/PV/battery configuration was the most cost-effective option, with a total cost of USD 1.19 million and an LCOE of USD 0.342/kWh. Environmentally, this system showed a strong performance, achieving a 64.58% reduction in CO₂ emissions; in contrast, fully renewable systems such as PV/wind/battery and wind/battery configurations succeeded in reaching zero-emission targets but were economically unfeasible due to their very high investment costs and limited practical applicability. Sensitivity analyses revealed that economic factors such as inflation and energy prices have a critical effect on the payback time and the Internal Rate of Return (IRR). Full article

This research focused on determining the technical and economic feasibility of the design of a solar-powered electric vehicle charging station (EVCS) in Cairo, Egypt. Using HOMER Grid, hybrid system configurations are assessed technically and economically to reduce costs and ensure reliability. These systems incorporate photovoltaic (PV) systems, lithium-ion battery energy storage systems (ESS), and diesel generators. A comprehensive analysis was conducted in Cairo, Egypt, focusing on small vehicle charging needs in both grid-connected and generator-supported scenarios. In this study, a 468 kW PV array integrated with 29 units of 1 kWh lithium-ion batteries and supported by time-of-use (TOU) tariffs, were used to optimize energy utilization. This study demonstrated the feasibility of the system in a case of eight chargers of 150 kW each and forty chargers of 48 kW. Conclusions suggest that the PV + ESS has the lowest pure power costs and reduced carbon emissions compared to traditional network-dependent solutions. The optimal configuration of USD 10.23 million over 25 years, with lifelong savings, results in annual savings of tool billing of around USD 409,326. This study concludes that a solar-powered EVC in Egypt is both technically and economically attractive, especially in the light of increasing energy costs. Full article

The Dynamic Voltage Restorer (DVR), which is connected in series between the power grid and the load, can rapidly compensate for voltage disturbances to maintain stable voltage at the load end. To enhance the energy supply capacity of the DVR and utilize its shared circuit topology with photovoltaic (PV) inverters—which enables the dual functions of voltage compensation and PV-storage power generation—this study integrates PV and energy storage as a coordinated energy unit into the DVR, forming a PV-storage-integrated DVR system. The core innovation of this system lies in extending the voltage disturbance detection capability of the DVR to include harmonics. By incorporating a Butterworth filtering module and voltage fluctuation tracking technology, high-precision disturbance identification is achieved, thereby supporting power balance control and functional coordination. Furthermore, a multi-mode-power coordinated regulation method is proposed, enabling dynamic switching between operating modes based on PV output. Simulation and experimental results demonstrate that the proposed system and strategy enable smooth mode transitions. This approach not only ensures reliable voltage compensation for sensitive loads but also enhances the grid-support capability of PV systems, offering an innovative technical solution for the integration of renewable energy and power quality management. Full article

As energy systems transition toward high shares of variable renewable generation, local energy communities (ECs) are increasingly relevant for enabling demand-side flexibility and self-sufficiency. This shift is particularly evident in the residential sector, where the deployment of photovoltaic (PV) systems is rapidly growing. While mixed-integer linear programming (MILP) remains the standard for operational optimization and demand response in such systems, its computational burden limits scalability and responsiveness under real-time or uncertain conditions. Reinforcement learning (RL), by contrast, offers a model-free, adaptive alternative. However, its application to real-world energy system operation remains limited. This study explores the application of a Deep Q-Network (DQN) to a real residential EC, which has received limited attention in prior work. The system comprises three single-family homes sharing a centralized heating system with a thermal energy storage (TES), a PV installation, and a grid connection. We compare the performance of MILP and RL controllers across economic and environmental metrics. Relative to a reference scenario without TES, MILP and RL reduce energy costs by 10.06% and 8.78%, respectively, and both approaches yield lower total energy consumption and CO₂-equivalent emissions. Notably, the trained RL agent achieves a near-optimal outcome while requiring only 22% of the MILP’s computation time. These results demonstrate that DQNs can offer a computationally efficient and practically viable alternative to MILP for real-time control in residential energy systems. Full article

This paper addresses stability issues in modern power grids arising from extensive integration of power electronic converters, which introduce complex multi-time-scale interactions. A symbolic simplification method is proposed to accurately model grid-connected converter dynamics, significantly reducing computational complexity through transfer function approximations and yielding efficient reduced-order models. An impedance-based approach utilizing impedance ratio (IR) is developed for stability assessment under active-reactive (PQ) and active power-AC voltage (PV) control strategies. The impacts of Phase-Locked Loop (PLL) and proportional-integral (PI) controllers on system stability are analysed, with a particular focus on quantifying remote converter interactions and delineating stability boundaries across varying network strengths and configurations. Furthermore, time-scale separation effectively simplifies Multi-Voltage Source Converter (MVSC) systems by minimizing inner-loop dynamics. Validation is conducted through frequency response evaluations, IR characterizations, and eigenvalue analyses, demonstrating enhanced accuracy, particularly with the application of lead–lag compensators within the critical 50–250 Hz frequency band. Time-domain simulations further illustrate the adaptability of the proposed models and reduction methodology, providing an effective and computationally efficient tool for stability assessment in converter-dominated power networks. Full article

This paper presents a comprehensive economic optimization of a grid-connected hybrid renewable energy system (HRES) enhanced with an electromagnetic frequency regulator (EFR) to improve frequency stability and provide clean and continuous electricity to the Macau City Campus while reducing dependence on fossil sources. The system includes photovoltaic (PV) arrays, wind turbines, battery storage, EFR, and a backup diesel generator. A genetic algorithm (GA) is employed to optimally size these components with the objective of maximizing the net present value (NPV) over the system’s lifetime. The GA implementation was validated on standard benchmark functions to ensure correctness and was finely tuned for robust convergence. Comprehensive sensitivity analyses of key parameters (discount rate, component costs, resource availability, etc.) were performed to assess solution robustness. The optimized design ($\mathrm{PV}\approx 35\mathrm{kWp}$, $\mathrm{WT}=30\mathrm{kW}$, $\mathrm{ESS}\approx 200\mathrm{kWh}$, and $\mathrm{EFR}=30\mathrm{kW}$) achieves a highly positive net present value of BRL 1.86 M in 2015 values (BRL 3.11 M in 2025) and discounted payback in approximately 9 years. A comparative assessment with the 2015 baseline project revealed up to a 10.1% enhancement in the net present value, underscoring the economic advantages of the optimized design. These results confirm the system’s strong economic viability and environmental benefits, providing a valuable guideline for future grid-connected hybrid energy systems. Full article

This paper addresses the challenges encountered by grid-connected photovoltaic (PV) systems, including the stochastic behavior of the system, harmonic distortion, and variations in grid impedance. To this end, an in-depth technical and pedagogical analysis of three linear multivariable current control strategies is performed: proportional-integral (PI), proportional-resonant (PR), and deadbeat (DB). The study contributes to theoretical formulations, detailed system modeling, and controller tuning procedures, promoting a comprehensive understanding of their structures and performance. The strategies are investigated and compared in both the rotating ($dq$) and stationary ($\alpha \beta$) reference frames, offering a broad perspective on system behavior under various operating conditions. Additionally, an in-depth analysis of the PR controller is presented, highlighting its potential to regulate both positive- and negative-sequence components. This enables the development of more effective and robust tuning methodologies for steady-state and dynamic scenarios. The evaluation is conducted under three main conditions: steady-state operation, transient response to input power variations, and robustness analysis in the presence of grid parameter changes. The study examines the impact of each controller on the total harmonic distortion (THD) of the injected current, as well as on system stability margins and dynamic performance. Practical aspects that are often overlooked are also addressed, such as the modeling of the inverter and photovoltaic generator, the implementation of space vector pulse-width modulation (SVPWM), and the influence of the output LC filter capacitor. The control structures under analysis are validated through numerical simulations performed in MatLab^® software (R2021b) using dedicated computational routines, enabling the identification of strategies that enhance performance and ensure compliance of grid-connected photovoltaic systems. Full article