Search Results (212)

The Dynamic Economic Dispatch (DED) problem underpins the cost-efficient and reliable operation of modern power systems, yet valve-point loading, ramp-rate coupling, and the growing share of intermittent wind, photovoltaic, and pumped-storage hydro (PSH) resources render it highly non-convex. Metaheuristic methods typically require large computational budgets and hand-crafted constraint-handling rules, whereas deep reinforcement learning agents rarely guarantee the feasibility of the schedules they produce. To address both limitations, this paper proposes a Two-Stage PPO–RLMPA framework that couples data-driven policy learning with a biomimetic metaheuristic search inspired by marine predator–prey dynamics. In the first stage, a Proximal Policy Optimization (PPO) agent is trained on a Markov Decision Process reformulation of DED in which a deterministic Safety Layer projects every raw action onto the feasible set defined by capacity, ramp-rate, and power-balance constraints, so the policy only observes physically viable transitions. In the second stage, the PPO dispatch is refined by the RLMPA module, a Marine Predators Algorithm (MPA) whose exploration–exploitation balance, Lévy-flight foraging, and Fish Aggregating Devices (FADs) attraction mechanisms emulate strategies documented in marine ecosystems; its step-size factor and FADs probability are further adapted online by a Deep Q-Network. This biomimetics-informed refinement translates predator–prey foraging intelligence into economically efficient thermal dispatch under valve-point non-convexity. Across 30 independent runs on ten- and twenty-unit benchmark systems with wind, PV, and PSH integration, the framework attains best costs of USD 368,763 and USD 737,348 on Test Systems 1 and 2, corresponding to reductions of approximately $1.1\%$ and $4.4\%$ over the CFCEP baseline, with zero post-repair constraint violations in every run. Full article

Reversible pump turbines (RPTs) play a key role in pumped hydro energy storage systems, where increasing grid flexibility requires frequent operation under off-design conditions. In turbine mode, deep partial load and no-load operation are often associated with severe flow instabilities, rotating stall, and strong rotor–stator interactions, which can limit operational flexibility and increase mechanical stress. Previous studies have shown that blade lean can influence hydrodynamic stability; however, its effect under no-load conditions remains insufficiently understood. In this work, the influence of runner blade lean on flow instabilities and rotor–stator interaction in a reversible pump turbine is numerically investigated. Two runner configurations, featuring a 0${}^{\circ }$ and a $-{15}^{\circ }$ blade lean angle, are analyzed through unsteady CFD simulations during the transition from deep partial load to no-load operation. The analysis focuses on flow field characteristics, blade loading, and the spectral content of pressure, torque, and radial forces. The results show that the negatively leaned runner significantly mitigates flow recirculation near the hub, reduces pressure and torque fluctuations, and strongly suppresses higher-order harmonic components associated with rotor–stator interaction. In particular, radial force amplitudes at blade-passing harmonics are substantially reduced under no-load conditions. These findings demonstrate that a negative blade lean improves hydrodynamic stability and reduces vibratory loads, contributing to the enhanced operational reliability of reversible pump turbines. Full article

The intermittent nature of renewable energy systems and the mismatch between power generation and load demand necessitate the integration of efficient energy storage systems (ESSs). Among large-scale energy storage technologies, pumped hydro-energy storage systems (PHESs) are widely recognized as one of the most cost-effective and longest-lifetime storage solutions under favorable geographical conditions. This study proposes and optimizes a hybrid renewable energy system (HRES) for the Wadi Baish region in Saudi Arabia as a real case study, where the significant elevation difference between the nearby mountains and the existing lake provides favorable conditions for PHES implementation. A nested optimization framework is developed to determine the optimal sizing and operation of the HRES components. The external optimization loop employs the non-dominated sorting genetic algorithm II (NSGA-II) to optimize system sizing, while the internal optimization loop uses mixed-integer linear programming (MILP) to optimally dispatch the PHES, battery energy storage system (BESS), and hydrogen energy storage system (HESS). In addition, demand-side management (DSM) is coordinated with the MILP dispatch strategy to improve system performance and reliability. The results show that the optimized system can supply a 10 MW average load with a renewable energy penetration of 98.7%. The proposed configuration achieves a total lifecycle cost of USD 231.37 million and avoids approximately 898.58 kt of CO₂ emissions over the project lifetime. PHES operates as the primary bulk energy storage technology due to its high storage capacity and low degradation characteristics. Furthermore, the degradation-aware model predicts battery replacement every 12 years and HESS replacement every 5 years. Compared with rule-based control, the MILP-based dispatch strategy reduces grid dependency by 87%. The coordinated DSM and MILP operation also reduces the levelized cost of energy to USD 0.066/kWh while improving overall system reliability. These findings demonstrate the importance of coordinated energy management and accurate degradation modeling in the optimal design and operation of renewable-based HRES configurations. Full article

Selecting the right energy storage system (ESS) for grid integration is a high-stakes decision involving conflicting technical, economic, environmental, and risk criteria under deep uncertainty. The existing fuzzy multi-criteria decision-making (MCDM) methods either fail to capture neutral or abstaining expert judgments or treat evaluation criteria as independent, which is an unrealistic assumption in complex engineering decisions. To address both limitations simultaneously, this study develops four new aggregation operators by extending the Bonferroni mean (BM) into the q-rung picture fuzzy sets (q-RPFSs) framework: the q-RPFBM-based, q-RPFWBM-based, q-RPFGBM-based, and q-RPFWGBM-based operators. Unlike the existing q-RPFS operator families (Dombi, Frank, Fermatean, Yager, Maclaurin), which aggregate criteria independently, BM-based operators explicitly model pairwise interactions among criteria with a structurally distinct aggregation logic that is especially critical when criteria such as cost, risk, reliability, and environmental impact are mutually correlated. The theoretical validity of the operators is confirmed through proofs of idempotency, monotonicity, and boundedness. Applied to a comprehensive ESS selection problem for Türkiye (covering nine alternatives across nineteen sub-criteria and five main criteria, including an explicit risk dimension), the framework consistently identifies pumped hydro storage as the optimal choice. Sensitivity analyses under varying q, s, and t parameters, as well as perturbed criterion weights, confirm the robustness of this ranking. The proposed framework offers energy planners and decision-makers a principled and transparent tool for evaluating ESS under high uncertainty and criterion interdependence. Full article

Integrated energy systems (IES) are widely recognized as a key pathway toward carbon neutrality, enabling the coupling and coordinated optimization of electricity, heat, gas, and cooling. This review provides a structured, technology-oriented overview of IES based on a unified five-subsystem framework (production, conversion, transmission, storage, and consumption). It systematically covers: (1) renewable energy utilization—solar, wind, and geothermal—supported by a global spatial distribution map and representative top-performing commercial products; (2) energy cascade utilization, where combined heat and power/combined cooling, heating and power (CHP/CCHP) raises overall efficiency from approximately 35–40% to 70–90%; (3) multi-form energy storage—electrical, electrochemical, chemical, thermal, and mechanical—distinguishing short-term balancing (e.g., lithium-ion (Li-ion), flywheels, supercapacitors, with 85–95% round-trip efficiency) from long-duration and seasonal applications (e.g., pumped hydro, hydrogen/power-to-gas (P2G), redox flow batteries); and (4) forecasting, collaborative optimization, and the bidirectional integration of IES with smart grids and grid modernization. A strategic strengths, weaknesses, opportunities, and threats–Political, Economic, Sociological, Technological, Legal, and Environmental (SWOT–PESTLE) analysis is further presented to position IES within the global energy transition. The review highlights that IES and grid innovation are mutually enabling, and that realizing the full carbon-neutrality potential of IES requires coordinated progress in standardization, digitalization, long-duration storage, and cross-sector policy alignment. Full article

Against the backdrop of China’s dual carbon goals, cross-regional low-carbon power transmission from large-scale clean energy bases is a pivotal direction for energy transition. Formulating their power delivery curves requires precise alignment with the load demand characteristics of receiving provinces and the coordinated operation of hydropower, wind power, photovoltaic (PV) power, and pumped-storage hydropower (PSH). To address the limitations of existing methods, such as the lack of linearized modeling for core operational constraints, low solution efficiency and inadequate integration of multi-energy coupling constraints, this paper proposes a tailored linearized optimization modeling approach. By adopting auxiliary variables, binary variables and the Big M method, core constraints including PSH pumping power supply, stepwise power delivery and multi-energy coordinated operation are linearized. A monthly rolling linear optimization model is constructed with triple objectives: minimizing the renewable curtailment rate and the absolute error between delivery and load curves, and maximizing delivered electricity volume. Multi-objective coordinated optimization is realized via the linear weighted summation method, and the model is solved with the Gurobi solver. Case validation on an integrated hydro–wind–solar clean energy base in Southwest China and its corresponding receiving provincial power grid shows that the proposed method effectively improves the curve matching degree, controls the wind–PV curtailment rate within around 12% (engineering tolerance), and strictly meets engineering safety constraints such as PSH operation and HVDC transmission requirements. Comprehensive optimization of the three objectives is achieved when the weight coefficients for curtailment rate, load matching error and delivered electricity volume are set to 0.3–0.8, 0.1–0.2 and 0.1–0.6, respectively. This method resolves the problems of traditional nonlinear models being disconnected from engineering practice and low solution efficiency, providing a reliable technical reference for the refined dispatching of cross-regional power transmission and scientific formulation of power delivery curves for clean energy bases. Full article

The ramping requirement in new power systems primarily stems from net load variations and forecast errors of renewable energy and load. Designing an equitable cost allocation mechanism for ramping services based on these factors facilitates incentives for generation and load to actively reduce ramping demands, thereby alleviating system ramping pressure. Accordingly, this paper proposes a fair ramping cost allocation mechanism based on the ramping responsibility coefficients of market participants. Under this mechanism, a market-oriented operation model for wind–hydro-storage joint operation is established to verify its effectiveness in market applications. First, a ramping cost allocation mechanism is constructed based on ramping responsibility coefficients. According to the responsibility coefficients of market participants for deterministic and uncertain ramping requirements, ramping costs are allocated to the corresponding contributors in proportion to the ramping demands caused by net load variations, load forecast deviations, and renewable energy forecast deviations. Specifically, for costs arising from renewable energy forecast errors, an allocation mechanism is designed based on the difference between the declared error range and the actual error. Second, within this allocation framework, hydropower and storage (including cascade hydropower and hybrid pumped storage) are utilized as flexible resources to mitigate wind power uncertainty and reduce its ramping costs. A two-stage day-ahead and real-time bi-level game model for wind–hydro-storage cooperative decision-making is developed. The upper level optimizes bilateral trading and market bidding strategies for wind–hydro-storage, while the lower level simulates the market clearing process. Through Stackelberg game modeling, joint optimal operation of wind–hydro-storage is achieved, ensuring mutual benefits. Finally, simulation results validate that the proposed ramping cost allocation mechanism can guide renewable energy to improve output controllability through economic signals. Furthermore, the bilateral trading and coordinated market participation of wind–hydro-storage realize win–win outcomes, reduce the ramping cost allocation for wind power by 23.10%, effectively narrow peak-valley price differences, and enhance market operational efficiency. Full article

Energy storage plays a key role in the energy transition by enabling the effective integration of variable renewable energy sources such as solar and wind power and by supporting the stability and flexibility of modern energy systems. The rapid development of energy storage technologies has become one of the pillars of sustainable energy management; however, it simultaneously raises environmental, material, and systemic challenges. This review analyses the environmental implications of energy storage development using an integrative perspective that combines technological, environmental, and system-level analysis. The paper examines major classes of energy storage technologies, including electrochemical, mechanical and physical, thermal energy storage, and chemical pathways within Power-to-X, with particular emphasis on their technical characteristics, maturity, and life cycle environmental performance. Lithium-ion battery systems typically achieve round-trip efficiencies of 85–92% and cycle lifetimes exceeding 5000 cycles, while flow batteries may exceed 10,000 cycles under stationary operating conditions. Mechanical storage technologies such as pumped hydro provide efficiencies of approximately 70–85% with operational lifetimes exceeding several decades. Key challenges related to critical raw material availability, recycling, end-of-life management, and ecosystem impacts are discussed, highlighting the importance of sustainable production and recovery strategies in supporting the circular economy. In addition, the review addresses the consequences of insufficient reuse of secondary materials and the growing relevance of digitisation and cyber resilience of energy storage systems as indirect contributors to environmental risk. The review also considers geopolitical aspects related to critical material supply chains and the cyber security of energy storage infrastructure, emphasising their growing importance for the resilience and environmental sustainability of future energy systems. The analysis indicates that further development of energy storage technologies will significantly influence not only power systems but also transport, industry, and heat sectors. The results emphasise that sustainable deployment of energy storage requires hybrid system architectures and policy frameworks that account for environmental performance, system flexibility, and long-term resilience in line with the principles of sustainable development. Full article

Samothrace is a Greek island in the northern Aegean Sea. Though connected to the mainland grid and demonstrating strong wind potential, it is challenged by seasonal shortages in both electricity and potable water. This study assesses a Hybrid Renewable Energy System designed to meet local energy and water demands while maintaining economic viability. The system consists of 10 wind turbines (23.5 MW), a reverse osmosis desalination plant yielding 876,000 m³/year, and four alternative storage configurations: green hydrogen, pumped hydro, lithium-ion batteries, and a combined green hydrogen–pumped hydro option. Using identical climatic and demand data, system performance was simulated for the years 2011–2020. Wind generation reached 113,000 MWh annually, of which 81–84% was exported to the mainland. Potable water demand was met at a rate of 99% in all scenarios, with monthly production ranging from 17,500 m³ in February to almost 50,000 m³ in August, thus requiring 1.80% of wind output. Investment costs ranged from 34.4 M € to 39.8 M €; net present values remained around 75 M € for all scenarios. Results demonstrate that complete autonomy can be achieved; however, economic sustainability is maximized by leveraging the interconnection and sizing storage below full-autonomy levels. Full article

The global energy transition faces the critical challenge of intermittency in renewable sources, which causes grid imbalances and estimated annual losses of USD 42 billion. Within the framework of circular economy and sustainability, mechanical energy storage (MES) systems—such as compressed air energy storage (CAES) and flywheels—emerge as scalable, long-lived solutions (over 30 years), reducing dependence on fossil fuels by up to 94%. To provide a comprehensive assessment, this study applies a Technology–Economy–Policy (TEP) framework to differentiate the maturity and iteration rates of MES sub-technologies (CAES, flywheels, pumped hydro). Furthermore, it integrates core circular economy indicators—lifespan extension, material efficiency, and multi-vector synergy—to evaluate the sustainability impact of these systems. To assess their impact and evolution, a quantitative bibliometric methodology was applied, analyzing 706 documents from the Scopus database (2010–2025). The study employed tools such as R Studio (Bibliometrix), VOSviewer, and Plotly for co-occurrence mapping, cluster density analysis, and keyword burst detection. Results reveal exponential growth in research, fitted to a logistic model (R² = 0.969), with a projected productivity peak in 2032. A technological shift toward high-efficiency solutions, such as adiabatic CAES (75%) and flywheels (95%), is evident, with grid stability prioritized. Furthermore, artificial intelligence is already applied in 40% of new management models to optimize these hybrid systems. The analysis, which quantitatively identifies underexplored areas such as socio-technical integration and standardized testing protocols, concludes that integrating MES is essential for the sustainability and circularity of the power system, enabling synergy with other vectors such as green hydrogen and fostering scalable business models that strengthen the circular economy in the energy sector. Full article

With the integration of high-proportion renewable energy, the operation modes of the power system are becoming increasingly complex and diverse. The typical operation modes selected with manual experience cannot comprehensively represent system operating characteristics. To more accurately analyze system operating characteristics, an analysis method for power system operation modes based on autoencoder clustering is proposed. Compared to other clustering methods, the autoencoder clustering method can adapt to data of different types and structures, extract features and perform clustering in a reduced-dimensional space, and suppress noise in the data to a certain extent. First, multi-dimensional analysis metrics for power system operation modes are proposed. The metrics are used to evaluate system characteristics such as cleanliness, security, flexibility, and adequacy. The evaluation metrics for clustering are designed based on the metrics. Second, an operation mode analysis framework is constructed. The framework uses an autoencoder to extract implicit coupling relationships between system operation variables. The encoded feature vectors are used for clustering, which helps to find the internal similarities of the operation modes. Regulation resources such as pumped hydro storage are also considered in the framework. Finally, the proposed method is tested on the IEEE 39-node system. In the test, the comparison of clustering evaluation metrics and operation mode analysis errors shows that the proposed method has the best clustering performance and operation mode analysis effect compared to other clustering methods. The results prove that the proposed method can effectively extract the inner correlations and coupling relations of high-dimensional operating vectors, form consistent operation mode clusters, select typical operation modes, and accurately assess the characteristics and risks of the power system with high-proportion renewable energy integration. This paper helps to build a stronger power system that can integrate a higher proportion of renewable energy, replace fossil fuel generation, and contribute to a higher level of sustainable development. Full article

With the increasing integration of high-proportion renewable energy, power systems are exhibiting low-inertia and low-damping characteristics, posing severe challenges to frequency stability. This paper proposes a coordinated supplementary frequency regulation strategy utilizing electrolytic aluminum (EA) loads and a hybrid energy storage system (HESS). Firstly, a system frequency response model is established, incorporating EA, electrochemical energy storage, pumped hydro storage, and conventional generation units. Secondly, an improved variable filter time constant controller is designed, supplemented by fuzzy logic, to achieve adaptive power allocation under different disturbance magnitudes. Concurrently, regulation intervals are defined based on the area control error (ACE), enabling a tiered response from source-grid-load resources. Simulation results demonstrate that under a severe disturbance of 0.05 p.u., the proposed strategy reduces the maximum frequency deviation from 0.198 Hz to 0.054 Hz, achieving a 72.7% performance improvement, and shortens the system settling time by 59.5%. Furthermore, the state of charge (SOC) of the electrochemical storage is successfully maintained within the range of [0.482, 0.505], effectively balancing frequency regulation performance and device lifespan. The findings demonstrate the effectiveness of the proposed strategy in enhancing the frequency resilience of low-inertia power grids. Full article

To address the power and energy balancing challenges faced by high-penetration renewable energy systems under long-term intermittent output conditions, this study proposes a multi-source, multi-timescale collaborative dispatch strategy (2MT-S) integrating wind, solar, hydro, thermal, and hydrogen energy resources. First, a long-term-to-day-ahead coupled scheduling framework is established based on intermittent output duration forecasts (3-day/10-day). By integrating seasonal hydrogen storage and pumped-storage hydroelectric plants, this framework achieves comprehensive coordination among electrochemical storage, thermal power, and other flexible resources. Second, a multi-time-horizon optimization model is developed to simultaneously minimize system operating costs and load curtailment costs. This model dynamically adjusts day-ahead scheduling boundary conditions based on long-term and short-term scheduling results, enabling cross-period resource complementarity during wind and photovoltaic generation troughs. Finally, comparative analysis on an enhanced IEEE 30-bus system demonstrates that compared to traditional day-ahead scheduling, this strategy significantly reduces renewable energy curtailment rates and load curtailment volumes during sustained low-generation periods, fully validating its significant advantages in enhancing power supply reliability and economic benefits. Full article

This research study examines a renewable energy system that has been designed to meet the water needs of Mykonos, a tourism-dependent island in Greece with high seasonal demand. The proposed system consists of 22 wind turbines of 2.3 MW each, 4 desalination units with a total capacity of 1400 m³/h and multiple pumped-hydro storage reservoirs with a total volume of 3,900,000 m³. Two operational scenarios were analyzed. Water production through desalination was prioritized in both scenarios; however, their difference lies in the way excess renewable energy has been allocated: that is either to storage or to electricity generation. The results indicate that water demand in Mykonos is almost fully met in both scenarios, reaching a coverage of 99.9%. However, there is a significant difference between the two scenarios regarding energy coverage, which corresponds to coverage rates of 73% and 79%, respectively. From an economic perspective, the marginal selling price of electricity is EUR/MWh 100 and the cost of desalinated water ranges from EUR/m³ 0.48 to 0.91 depending on the operating scenario. Overall, the results demonstrate nearly complete water autonomy in both scenarios, whereas the second scenario is proven optimal in terms of energy coverage. This approach proves that integrated water and energy management can lower fossil fuel use and improve sustainability on islands with strong seasonal variations. Full article

Pumped-storage power stations, as a critical resource for supporting secure and stable grid operation, typically adopt a ’single-tunnel-multiple-unit’ configuration, where hydraulic disturbance becomes a key operating condition affecting system security. Existing studies have primarily focused on the impact of the hydro-mechanical subsystem on the normally operating units, while the influence of the electrical subsystem on hydraulic disturbance has been insufficiently addressed. To bridge this gap, this study develops a coupled model of a grid-connected pumped-storage power station incorporating a detailed representation of the power system. The model comprehensively captures the multi-domain interactions among the hydraulic, mechanical, electrical, and grid subsystems, and its accuracy is validated using data from a physical model test platform. On this basis, the hydraulic transient responses under two modeling conditions—detailed grid representation and conventional simplified grid modeling—are systematically compared. Key parameters from the hydraulic, mechanical, and electrical domains are further examined to quantify their impacts on the dynamic characteristics of hydraulic disturbance. The results demonstrate that detailed grid modeling reveals novel characteristics of the hydraulic disturbance that cannot be simulated by the conventional model. Under the detailed model, the normally operating units compensate for the power deficit caused by the tripping unit, leading to reduced hydraulic pressure fluctuations and a significant increase in the maximum output of the operating units. Meanwhile, hydro-mechanical parameters strongly influence the transient behaviors of unit output and net head, whereas the guide vane regulation of the operating unit remains predominantly driven by grid-frequency deviations. Overall, this study enhances the understanding of hydraulic disturbance dynamics in grid-connected pumped-storage systems and provides important insights for ensuring their secure and stable operation. Full article