Search Results (1,005)

The large-scale utilization of wind and solar energy is crucial for achieving carbon neutrality targets. However, as extensive wind and solar power generation is integrated via power electronic devices, the inertia level of power systems continues to decline. This leads to a significant reduction in the system’s frequency regulation capability, posing a serious threat to frequency stability. Optimizing the system is an essential measure to ensure its safe and stable operation. Traditional optimization approaches, which separately optimize transmission and distribution systems, may fail to adequately account for the variability and uncertainty of renewable energy sources, as well as the impact of inertia changes on system stability. Therefore, this paper proposes a two-layer optimization method aimed at simultaneously optimizing the operation of transmission and distribution systems while satisfying minimum inertia constraints. The upper-layer model comprehensively optimizes the operational costs of wind, solar, and thermal power systems under the minimum inertia requirement constraint. It considers the operational costs of energy storage, virtual inertia costs, and renewable energy curtailment costs to determine the total thermal power generation, energy storage charge/discharge power, and the proportion of renewable energy grid connection. The lower-layer model optimizes the spatiotemporal distribution of energy storage units within the distribution network, aiming to minimize total network losses and further reduce system operational costs. Through simulation analysis and computational verification using typical daily scenarios, this model enhances the disturbance resilience of the transmission network layer while reducing power losses in the distribution network layer. Building upon this optimization strategy, the model employs multi-scenario stochastic optimization to simulate the variability of wind, solar, and load, addressing uncertainties and correlations within the system. Case studies demonstrate that the proposed model not only effectively increases the integration rate of new energy sources but also enables timely responses to real-time system demands and fluctuations. Full article

The increasing deployment of stand-alone photovoltaic (PV) power supply systems is driven by their capability to convert solar irradiance into electrical energy. A typical application of such systems is solar-powered water pumping. However, since solar irradiance varies throughout the day, the maximum power output of PV panels may be lower than the load demand. A viable solution to this issue is the integration of hybrid energy storage systems (HESSs) combining batteries and supercapacitors (SCs). In this work, HESS charging and discharging control strategies were developed based on adaptive droop control, which regulates the power distribution between the SC and the battery and limits DC grid voltage deviations. In the developed method, the SC droop coefficient is adaptively adjusted in a stepwise manner depending on the SC state of charge (SoC), while the battery droop coefficient remains constant. The performance of the proposed strategies was evaluated through simulations, showing SC-battery internal loss minimization by up to 50% compared with the scenario without droop control when the SC is discharged first, and only then is the battery engaged. Step response of the converter was investigated experimentally, showing less than a 2 ms response time, and no undesired influence from the proposed control method was detected. Full article

This study presents the development and validation of a high-efficiency optical interface designed for ultra-high-concentration photovoltaic (UHCPV) systems, with a focus on enabling clean and sustainable solar energy conversion. A Fresnel lens serves as the primary optical concentrator in a novel system architecture that integrates advanced optical design with system-level thermal management. The proposed modeling framework combines detailed 3D ray tracing with coupled thermal simulations to accurately predict key performance metrics, including optical concentration ratios, thermal loads, and component temperature distributions. Validation against theoretical and experimental benchmarks demonstrates high predictive accuracies within 1% for optical efficiency and 2.18% for thermal performance. The results identify critical thermal thresholds for long-term operational stability, such as limiting mirror temperatures to below 52 °C and photovoltaic cell temperatures to below 130 °C. The model achieves up to 89.08% optical efficiency, with concentration ratios ranging from 240 to 600 suns and corresponding focal spot temperatures between 37.2 °C and 61.7 °C. Experimental benchmarking confirmed reliable performance, with the measured results closely matching the simulations. These findings highlight the originality of the coupled optical–thermal approach and its applicability to concentrated photovoltaic design and deployment. This integrated design and analysis approach supports the development of scalable, clean photovoltaic technologies and provides actionable insights for real-world deployment of UHCPV systems with minimal environmental impact. Full article

With the rapid development of distributed energy and electric vehicles (EVs), the limited hosting capacity of distribution networks has severely impacted their economic dispatch and safe operation. To address these challenges, in this work, an optimal planning model considering the uncertainty of wind and solar power output is proposed, aiming to determine the location and capacity of electric vehicle charging stations (EVCSs). The model seeks to minimize the total costs, voltage fluctuations, and network losses, subject to constraints such as EV user satisfaction and grid company satisfaction. A multi-objective heat exchange optimization algorithm under Gaussian mutation (MOTEO-GM) is employed to validate the model on an extended IEEE-33 bus system and a real-world case in the University Town area of Chenggong District, Kunming City. Simulation results indicate that, in the test system, voltage fluctuations and system power losses are decreased by 43.05% and 37.47%, respectively, significantly enhancing the economic operation of the distribution grid. Full article

Microgrid systems play a vital role in managing distributed energy resources like solar, wind, batteries, and supercapacitors. However, maintaining stable AC/DC bus voltages and minimizing grid reliance under dynamic conditions is challenging. Traditional control methods such as Sliding Mode Controllers (SMCs) suffer from issues like chattering and slow convergence, reducing practical effectiveness. This paper proposes a hybrid AC/DC microgrid that operates in both grid-connected and islanded modes while ensuring voltage stability and efficient energy use. A Conditional-Based Super-Twisting Sliding Mode Controller (CBSTSMC) is employed to address the limitations of conventional SMCs. The CBSTSMC enhances system performance by reducing chattering, improving convergence speed, and offering better tracking and disturbance rejection. To further refine controller performance, an Improved Grey Wolf Optimization (IGWO) algorithm is used for gain tuning, resulting in enhanced system robustness and precision. An Energy Management System (EMS) is integrated to intelligently regulate power flow based on renewable generation and storage availability. The proposed system is tested in real time using a Texas Instruments Delfino C2000 microcontroller through a Controller-in-the-Loop (CIL) setup. The simulation and hardware results confirm the system’s ability to maintain stability and reliability under diverse operating scenarios, proving its suitability for future smart grid applications. Full article

Microgrids have emerged as a crucial solution for addressing environmental concerns, such as reducing greenhouse gas emissions and enhancing energy sustainability. By incorporating renewable energy sources like solar and wind, microgrids improve energy efficiency and offer a cleaner alternative to conventional power grids. Among various microgrid architectures, DC microgrids are gaining significant attention due to their higher efficiency, reduced reactive power losses, and direct compatibility with renewable energy sources and energy storage systems. However, DC microgrids face stability challenges, particularly due to the presence of constant power loads (CPLs), which exhibit negative incremental impedance characteristics. These loads can destabilize the system, leading to oscillations and performance degradation. This paper explores various control strategies designed to enhance the stability and dynamic response of DC microgrids, with a particular focus on interleaved boost converters (IBCs) interfaced with CPLs. Traditional control methods, including proportional–integral (PI) and sliding mode control (SMC), have shown limitations in handling dynamic variations and disturbances. To overcome these challenges, this paper proposes a novel RST-based control strategy for IBCs, offering improved stability, adaptability, and disturbance rejection. The efficacy of the RST controller is validated through extensive simulations tests, demonstrating competitive performance in maintaining DC bus voltage regulation and current distribution. Key performance indicators demonstrate competitive performance, including settling times below 40 ms for voltage transients, overshoot limited to ±2%, minimal voltage deviation from the reference, and precise current sharing between interleaved phases. The findings contribute to advancing the stability and efficiency of DC microgrids, facilitating their broader adoption in modern energy systems. Full article

The primary objective of this study is to assess the techno-economic feasibility of an innovative solar energy generation system with a rainwater collection feature to generate electrical energy and meet irrigation needs in agriculture. The proposed system is designed for an agricultural area (Gonyeli, North Cyprus) with high solar potential and limited rainfall. In the present study, global rainfall datasets are utilized to assess the potential of rainwater harvesting at the selected site. Due to the lack of the measured rainfall data at the selected site, the accuracy of rainfall of nine global reanalysis and analysis datasets (CHIRPS, CFSR, ERA5-LAND, ERA5, ERA5-AG, MERRA2, NOAA CPC CMORPH, NOAA CPC DAILY GLOBAL, and TerraClimate) are evaluated by using data from ground-based observations collected from the Meteorological Department located in Lefkoşa, Northern Cyprus from 1981 to 2023. The results demonstrate that ERA5 outperformed the other datasets, yielding a high R-squared value along with a low mean absolute error (MAE) and root mean square error (RMSE). Based on the best dataset, the potential of the rainwater harvesting system is estimated by analyzing the monthly and seasonal rainfall patterns utilizing 65 different probability distribution functions for the first time. Three goodness-of-fit tests are utilized to identify the best-fit probability distribution. The results show that the Johnson and Wakeby SB distributions outperform the other models in terms of fitting accuracy. Additionally, the results indicate that the rainwater harvesting system could supply between 31% and 38% of the building’s annual irrigation water demand (204 m³/year) based on average daily rainfall and between 285% and 346% based on maximum daily rainfall. Accordingly, the system might be able to collect a lot more water than is needed for irrigation, possibly producing an excess that could be stored for non-potable uses during periods of heavy rainfall. Furthermore, the techno-economic feasibility of the proposed system is evaluated using RETScreen software (version 9.1, 2023). The results show that household energy needs can be met by the proposed photovoltaic system, and the excess energy is transferred to the grid. Furthermore, the cash flow indicates that the investor can expect a return on investment from the proposed PV system within 2.4 years. Consequently, the findings demonstrate the significance of this system for promoting resource sustainability and climate change adaptation. Besides, the developed system can also help reduce environmental impact and enhance resilience in areas that rely on water and electricity. 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

Coastal regions are among the areas most affected by climate change, facing rising sea levels, frequent flooding, and accelerated erosion that place renewable energy infrastructures under serious threat. Solar farms, which are often built along shorelines to maximize sunlight, are particularly vulnerable to salt-induced corrosion, storm surges, and wind damage. These challenges call for monitoring solutions that are not only accurate but also scalable and privacy-preserving. To address this need, Q-MobiGraphNet, a quantum-inspired multimodal classification framework, is proposed for federated coastal vulnerability analysis and solar infrastructure assessment. The framework integrates IoT sensor telemetry, UAV imagery, and geospatial metadata through a Multimodal Feature Harmonization Suite (MFHS), which reduces heterogeneity and ensures consistency across diverse data sources. A quantum sinusoidal encoding layer enriches feature representations, while lightweight MobileNet-based convolution and graph convolutional reasoning capture both local patterns and structural dependencies. For interpretability, the Q-SHAPE module extends Shapley value analysis with quantum-weighted sampling, and a Hybrid Jellyfish–Sailfish Optimization (HJFSO) strategy enables efficient hyperparameter tuning in federated environments. Extensive experiments on datasets from Norwegian coastal solar farms show that Q-MobiGraphNet achieves 98.6% accuracy, and 97.2% F1-score, and 90.8% Prediction Agreement Consistency (PAC), outperforming state-of-the-art multimodal fusion models. With only 16.2 M parameters and an inference time of 46 ms, the framework is lightweight enough for real-time deployment. By combining accuracy, interpretability, and fairness across distributed clients, Q-MobiGraphNet offers actionable insights to enhance the resilience of coastal renewable energy systems. Full article

Maintaining operational efficiency in distributed solar energy systems requires intelligent coordination of inspection tasks and workforce resources to handle diverse fault conditions. This study presents a bi-level multi-objective optimization framework that addresses two tightly coupled problems: dynamic job scheduling and skill-based team formation. The job scheduling component assigns geographically dispersed inspection tasks to mobile teams while minimizing multiple conflicting objectives, including travel distance, tardiness, and workload imbalance. Concurrently, the team formation component ensures that each team satisfies fault-specific skill requirements by optimizing team cohesion and compactness. To solve the bi-objective team formation problem, we propose HMOO-AOS, a hybrid algorithm integrating six metaheuristic operators under an NSGA-II framework with an Upper Confidence Bound-based Adaptive Operator Selection. Experiments on datasets of up to seven instances demonstrate statistically significant improvements ($p<0.05$) in solution quality, skill coverage, and computational efficiency compared to NSGA-II, NSGA-III, and MOEA/D variants, with computational complexity $\mathcal{O}\left(G·N·(M+logN)\right)$ (time complexity), $\mathcal{O}(N·L)$ (space complexity). A cloud-integrated system architecture is also proposed to contextualize the framework within real-world solar inspection operations, supporting real-time data integration, dynamic rescheduling, and mobile workforce coordination. These contributions provide scalable, practical tools for solar operators, maintenance planners, and energy system managers, establishing a robust and adaptive approach to intelligent inspection planning in renewable energy operations. Full article

The transition toward low-carbon energy systems requires reliable tools for assessing renewable-based hydrogen production under real-world climatic and economic conditions. This study presents a novel probabilistic framework integrating the following three complementary elements: (1) a Photovoltaic Geographical Information System (PVGIS) for high-resolution, location-specific solar energy data; (2) Metalog probability distributions for advanced modeling of variability and uncertainty in photovoltaic (PV) energy generation; and (3) Levelized Cost of Hydrogen (LCOH) calculations to evaluate the economic viability of hydrogen production systems. The methodology is applied to three diverse European locations—Lublin (Poland), Budapest (Hungary), and Malaga (Spain)—to demonstrate regional differences in hydrogen production potential. The results indicate annual PV energy yields of 108.3 MWh, 124.6 MWh, and 170.95 MWh, respectively, which translate into LCOH values of EUR 9.67/kg (Poland), EUR 8.40/kg (Hungary), and EUR 6.13/kg (Spain). The probabilistic analysis reveals seasonal production risks and quantifies the probability of achieving specific monthly energy thresholds, providing critical insights for designing systems with continuous hydrogen output. This combined use of a PVGIS, Metalog, and LCOH calculations offers a unique decision-support tool for investors, policymakers, and SMEs planning green hydrogen projects. The proposed methodology is scalable and adaptable to other renewable energy systems, enabling informed investment decisions and improved regional energy transition strategies. Full article

The increasing uncertainty of distributed energy resources (DERs) challenges the secure and resilient operation of low-voltage distribution networks (LVDNs). Flexible interconnection via power-electronic devices enables controllable links among LVDAs, supporting capacity expansion, reliability, load balancing, and renewable integration. This paper proposes a two-stage robust optimization framework for flexible interconnection planning in LVDNs. The first stage determines investment decisions on siting and sizing of interconnection lines, while the second stage schedules short-term operations under worst-case wind, solar, and load uncertainties. The bi-level problem is reformulated into a master–subproblem structure and solved using a column-and-constraint generation (CCG) algorithm combined with a distributed iterative method. Case studies on typical scenarios and a modified IEEE 33-bus system show that the proposed approach mitigates overloads and cross-area imbalances, improves voltage stability, and maintains high DER utilization. Although the robust plan incurs slightly higher costs, its advantages in reliability and renewable accommodation confirm its practical value for uncertainty-aware interconnection planning in future LVDNs. Case studies on typical scenarios and a modified IEEE 33-bus system demonstrate that under the highest uncertainty the proposed method reduces the voltage fluctuation index from 0.0093 to 0.0079, lowers the autonomy index from 0.0075 to 0.0019, and eliminates all overload events compared with stochastic planning. Even under the most adverse conditions, DER utilization remains above 84%. Although the robust plan increases daily operating costs by about $70, this moderate premium yields significant gains in reliability and renewable accommodation. In addition, the decomposition-based algorithm converges within only 39 s, confirming the practical efficiency of the proposed framework for uncertainty-aware interconnection planning in future LVDNs. Full article

This study conducted a systematic review of 222 research articles (2014–2024) from the Web of Science Core Collection database to investigate the ecological and environmental impacts of pumped hydro storage (PHS). Utilizing CiteSpace 6.1R software for visual analysis, the research hotspots and evolutionary trends over the past decade were comprehensively examined. Key findings include the following: (1) Annual publication output exhibited sustained growth, with China contributing 29.7% of total publications, ranking first globally. (2) Research institutions demonstrated broad geographical distribution but weak collaborative networks, as the top 10 institutions accounted for only 21.6% of total publications, highlighting untapped potential for cross-regional cooperation. (3) Current research focuses on three domains: ecological–environmental benefit assessment, renewable energy synergistic integration, and power grid regulation optimization. Emerging trends emphasize multi-objective planning (e.g., economic–ecological trade-offs) and hybrid system design (e.g., solar–wind–PHS coordinated dispatch), providing critical support for green energy transitions. (4) Post-2020 research has witnessed novel thematic directions, including deepened studies on wind–PHS coupling and life-cycle assessment (LCA). Policy-driven renewable energy integration research entered an explosive growth phase, with PHS–photovoltaic–wind complementary technologies emerging as a core innovation pathway. Future research should prioritize strengthening institutional collaboration networks, exploring region-specific ecological impact mechanisms, and advancing policy–technology–environment multi-dimensional frameworks for practical applications. Full article

Under China’s dual-carbon strategic objectives, virtual power plants (VPPs) actively participate in the coupled electricity–carbon market through the optimized scheduling of distributed energy resources, simultaneously stabilizing grid operations and reducing carbon emissions. Photovoltaic (PV) generation, a cornerstone resource within VPP systems, introduces significant challenges in scheduling due to its inherent output variability. To increase the accuracy in the characterization of the PV output uncertainty, a weighted probability distribution of solar irradiance, based on historical irradiance data, is newly proposed. The leveraging rejection sampling technique is applied to generate solar irradiance scenarios that are consistent with the proposed weighted solar irradiance probability model. Further, a confidence interval-based filtering mechanism is applied to eliminate extreme scenarios, ensuring statistical credibility and enhancing practicability in actual dispatch scenarios. Based on the filtered scenarios, a novel dispatch strategy for the VPP operation in the electricity–carbon market is proposed. Numerical case studies verify that scenarios generated by the weighted solar irradiance probability model are capable of closely replicating historical PV characteristics, and the confidence interval filter effectively excludes improbable extreme scenarios. Compared to conventional normal distribution-based methods, the proposed approach yields dispatch solutions that are more closely aligned with the optimal dispatch of the historical irradiance data, demonstrating the improved accuracy in the probabilistic modelling of the PV output uncertainty. Consequently, the obtained dispatch strategy shows the improved capability to ensure the market revenue of the VPP considering the fluctuations of the PV output. Full article

Thermal energy storage (TES) is a crucial technology for mitigating energy supply–demand mismatches and facilitating the integration of renewable energy. This study proposes a novel horizontal phase change TES unit integrated with partially filled metal foam (MF) and fins, divided into six sub-regions (ε₁–ε₆) with graded pore parameters. A comprehensive numerical model is developed to investigate the synergistic heat exchange mechanism and energy storage performance. The results demonstrate that porosity in Porosity-1 (ε₁) and Porosity-2 (ε₂) regions dominates melting dynamics. Through multi-objective optimization, targeting both minimal energy storage time and maximal energy storage rate, an optimal configuration (Case TD) is derived after technical design. Case TD features porosity values ε₁ = ε₂ = ε₃ = ε₅ = ε₆ = 0.97 and ε₄ = 0.98, where the graded porosity distribution balances heat conduction efficiency and energy storage capacity. Compared to the uniform MF case (Case 1) and the fin-only case (Case 6), Case TD reduces TES time by 51.75% and 17.39%, respectively, while increasing the mean TES rate by 102.55% and 19.12%, respectively. This design minimizes the TES capacity loss (only decreasing by 2.14% compared to Case 1) while maximizing the energy storage density and improving the efficiency–cost trade-off of the phase-change material-based system. It provides a scalable solution for rapid-response TES applications in solar thermal power plants and industrial waste heat recovery. Full article