Search Results (51)

The grid volatility caused by the integration of wind and solar power poses challenges to power systems, where Pumped Storage Hydropower (PSH) plays an irreplaceable role. During start-up, shutdown, and mode transition of pump turbines, near-zero flow conditions frequently occur, leading to severe hydraulic instability, guide vane vibration, and abnormal noise. This review synthesizes field observations from multiple high-head pumped storage stations together with recent experimental, numerical, and theoretical studies. The review indicates that hydraulic instability is primarily associated with the coupled effects of clearance leakage flow, bi-stable flow, and Rotor–Stator Interaction (RSI). The review suggests that self-excited vibration, rather than forced resonance, dominates guide vane vibration and abnormal noise under near-zero flow conditions. Four mainstream regulation strategies are summarized, including Misaligned Guide Vanes (MGVs), start-up/shutdown sequence optimization, structural-parameter adjustment, and operating range avoidance. The applicability and limitations of each strategy are discussed. These findings provide support for the design and operation of high-head, large-capacity pump turbines. Full article

Morocco has emerged as a prominent renewable energy leader in North Africa and a significant contributor to the Middle East and North Africa (MENA) region through the extensive implementation of solar, wind, and hydropower technologies, supported by a sophisticated legal framework established since 2010. This study presents an integrated sustainability assessment of Morocco’s renewable energy transition through a structured comparative analysis of these three technologies across technical, economic, environmental, and socioeconomic dimensions. The methodology is built on a structured literature search and screening process covering 71 peer-reviewed articles and institutional reports published between 2010 and 2025. In order to facilitate the comparative sustainability assessment, sources were identified through Scopus, ScienceDirect, Web of Science, Google Scholar, and institutional repositories such as MASEN, IRENA, IEA, and the World Bank. Each technology was relatively evaluated using six sustainability criteria: energy security contribution, economic competitiveness, environmental sustainability, grid stability contribution, scalability potential, and socioeconomic impact. The assessment utilized quantitative indicators extracted from institutional databases and peer-reviewed literature with a standardized scoring framework and sensitivity analysis to determine the robustness of the technology rankings under alternative policy-priority scenarios. Technologies were comparatively scored on a standardized 1–5 scale based on quantitative proxies including installed capacity (MW), levelized cost of energy (USD/MWh), capacity factor (%), CO₂ avoided (tCO₂/GWh), jobs per MW, and dispatchability characteristics. A sensitivity analysis using five alternative weighting scenarios representing different policy priorities confirmed the robustness and consistency of the technology rankings. The comparative assessment indicates that solar energy achieved the highest scalability score (5/5), wind energy achieved the highest economic competitiveness score (5/5), and hydropower achieved the highest grid stability score (5/5), owing to its dispatchability and pumped-storage capability. The findings confirm that Morocco’s transition rests on technological complementarity rather than dominance by a single source. Structural challenges persist, including intermittency, storage insufficiency, grid modernization requirements, external financing dependency, and territorial disparities in socioeconomic benefits. The study proposes a replicable comparative sustainability assessment framework that can support renewable energy planning and policy decisions in Morocco and other emerging economies pursuing large-scale energy transitions. Full article

The large-scale integration of wind and solar power has significantly intensified peak-shaving pressure and operational risk in provincial power grids. Effectively leveraging the flexible regulation capability of hydropower to mitigate the uncertainty of wind and solar output is a promising approach to enhancing grid security and stability. To simultaneously improve the peak-shaving performance and risk resilience of hydro-wind-solar systems for a provincial power grid, this paper proposes a multi-objective short-term scheduling model that jointly minimizes the peak value of net load and the Conditional Value-at-Risk (CVaR) of flexibility shortage. Specifically, the residual peak load is used to quantify the system’s peak-shaving burden, while the average CVaR of upward/downward ramping deficits across all time periods characterizes the tail risk associated with insufficient flexibility. Historical wind and solar forecast error data are employed to generate representative uncertainty scenarios via Gaussian mixture model, and the Rockafellar–Uryasev formulation is adopted to accurately embed CVaR into a mixed-integer linear programming (MILP) framework. Furthermore, the normalized normal constraint (NNC) method is introduced to compute a well-distributed Pareto front. Numerical simulations based on a real-world hydro-wind-solar system in a provincial grid in Southwest China demonstrate that the proposed model can significantly reduce the peak load while effectively mitigating flexibility shortfall risk. The resulting Pareto front clearly reveals the trade-off between peak-shaving effectiveness and risk control, providing a scientific basis for day-ahead generation scheduling and coordinated dispatch of flexible resources. 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

To address the key problems of low-frequency oscillation and insufficient regulation accuracy at the Point of Common Coupling (PCC) in hydro–wind–photovoltaic hybrid systems, which are caused by the randomness of wind and photovoltaic output, the water-hammer effect of hydropower units, and multi-source power coupling, a joint control strategy based on Fractional-Order Proportional Integral Derivative (FOPID) and Co-evolutionary Multi-objective Particle Swarm Optimization (CMOPSO) is proposed. First, a small-signal transfer function model of the system covering photovoltaic inverters, doubly fed induction generators (DFIGs), hydropower units and voltage-source converter-based high-voltage direct current (VSC-HVDC) converter stations is established to accurately characterize the water-hammer effect and multi-source dynamic coupling characteristics. Second, a Caputo-type FOPID controller is designed. Compared with traditional integer-order controllers with limited tuning flexibility, the FOPID controller utilizes its five degrees of freedom to address specific multi-source coupling challenges. This precisely compensates for the non-minimum phase lag caused by the water-hammer effect in hydropower units via the fractional derivative link, and effectively smooths the impact of stochastic wind–solar fluctuations on PCC voltage through the memory characteristics of the fractional integral link. This multi-parameter regulation mechanism prevents a trade-off between response speed and overshoot suppression, achieving effective decoupling of complex multi-source dynamic interactions. Third, a dual-objective optimization framework with the Integral of Time-weighted Absolute Error (ITAE) and Oscillatory Disturbance Risk Index (ODRI) as the objectives is constructed. The multi-population co-evolution mechanism of the CMOPSO algorithm is adopted to solve the Pareto-optimal solution set, realizing the coordinated optimization of dynamic response accuracy and oscillation instability risk. Finally, comparative simulations are carried out on the Simulink platform with traditional PI/FOPI controllers and optimization algorithms such as Multi-objective Particle Swarm Optimization based on the Decomposition/Simple Indicator-Based Evolutionary Algorithm (MPSOD/SIBEA). The results show that the proposed strategy can effectively suppress low-frequency oscillations in the range of 0~30 Hz. Compared with the traditional PI controller, the PCC voltage overshoot is reduced by more than 40%, the oscillation decay time is shortened by 33%, the ITAE and ODRI indices are decreased by 12.58% and 2.47%, respectively, and the stability of DC bus voltage is significantly improved. Its robustness and comprehensive control performance are superior to existing methods, providing an efficient and stable control scheme for power electronics-dominated complex new energy grid-connected systems. Full article

In recent years, cascaded hydropower (CHP) has been extensively leveraged to enhance the grid-connected penetration of photovoltaic (PV) generation. However, the inherent stochasticity and volatility of high-penetration PV often lead to significant renewable curtailment. To address this challenge, this paper proposes a coordinated day-ahead and intra-day scheduling model that incorporates curtailment risk assessment. The proposed framework employs a two-stage optimization architecture: the day-ahead stage establishes a baseline dispatch schedule with the objective of maximizing total energy production, while the intra-day stage refines this plan through multi-scenario optimization that explicitly accounts for curtailment risk. This synergistic mechanism achieves the objective of “maximizing day-ahead economic benefits and ensuring intra-day renewable accommodation”. Case studies on a specific river basin demonstrate the effectiveness of the proposed model. Simulation results indicate that, compared to conventional energy-maximization approaches, the proposed model significantly reduces intra-day curtailment rates and substantially enhances the integrated accommodation capacity of the hydro–solar hybrid system. Full article

The Hybrid Smart Energy Community (HySEC) model is an integrated framework for optimizing hybrid renewable energy systems, unifying BIM, IoT, and data-driven modeling, as an innovative approach for the energy transition. A Revit—Twinmotion BIM model, enriched with topographic, CAD, and real-image data, enhances spatial accuracy and stakeholder communication, while a digital–physical architecture linking sensors, gateways, edge devices, and cloud platforms enables decentralized peer-to-peer communication and real-time monitoring. The framework is applied to a smart energy community composed of a hydropower–wind–solar PV system serving six buildings (48.8 MWh/year), supported by high-resolution hourly Open-Meteo data. A NARX neural network trained on 8760 hourly observations achieves an MSE of 2.346 at epoch 16, providing advanced predictive capability. Benchmarking against HOMER demonstrates clear advantages in grid exports (15,130 vs. 8274 kWh/year), battery cycling (445 vs. 9181 kWh/year), LCOE (€0.09 vs. €0.180/kWh), IRR (9% vs. 6%), payback (8.7 vs. 10.5 years), and CO₂ emissions (−9.4 vs. 101 tons). These results confirm HySEC as a conceptually flexible solution that strengthens energy autonomy, supports heritage site rehabilitation, and promotes sustainable rural development. Full article

The global shift toward renewable energy systems raises major challenges related to the variability of solar and wind power and their poor alignment with electricity demand. This paper addresses energy adequacy, defined as the ability of an energy system to reliably meet demand by balancing generation, storage, transmission, and reserves for unforeseen events. Within this framework, energy storage systems are identified as strategic components, requiring a diversified and multi-scale set of solutions-from territorial to building scale-to respond to infrastructural constraints and user behaviour. The study adopts a multi-scalar and interdisciplinary methodology combining deductive and inductive approaches. The deductive analysis examines global, European, and Italian electricity systems, highlighting issues such as overcapacity and grid instability caused by the uncoordinated development of renewable generation and network infrastructures. The inductive approach focuses on existing storage technologies, with particular attention to two types of thermal energy storage selected for their simplicity, scalability, and replicability. Hydropower reservoirs are also considered due to their multifunctional role in energy balancing. Two case studies developed by the research group—a public building energy retrofit in Milan and a modular off-grid housing prototype—demonstrate how integrated storage solutions can enhance system flexibility. The results emphasize the necessity of a systemic design approach that combines storage technologies, adaptable energy use, and active user participation to ensure energy adequacy in scenarios with high renewable penetration. Full article

Hybrid renewable energy systems are increasingly important for enabling sustainable and resilient energy supply in rural smart communities, yet existing tools often lack the ability to integrate environmental variability, multi-technology interactions, and economic–environmental assessment in a unified framework. This study presents Hybrid Smart Micro Energy Community (HySMEC), a novel modeling approach that combines high-resolution meteorological data, technology-specific generation models, detailed demand characterization, and financial analysis to evaluate hybrid configurations of hydropower, solar PV, wind, battery storage, and grid interaction. Hourly simulations capture seasonal dynamics and system behavior under realistic technical efficiencies, investment costs, and emission factors, enabling a transparent assessment of energy flows, self-consumption, and grid dependence. The results show that hybrid systems can achieve competitive economic performance, low Levelized Costs of Energy, and significant CO₂ emission reductions across diverse rural community profiles, even when space or demand constraints are present. The analysis confirms the technical feasibility and environmental benefits of integrating multiple renewable sources with storage, highlighting the importance of self-consumption ratios in improving system profitability. Overall, HySMEC provides a robust and scalable tool to support data-driven design and optimization of distributed energy systems, offering valuable insights for researchers, planners, and decision-makers involved in sustainable rural energy development. Full article

Despite the growing adoption of hybrid energy systems integrating solar photovoltaic (PV), pumped storage hydropower (PSH), and battery energy storage (BES), comprehensive studies on their dynamic stability and interaction mechanisms remain limited, particularly under weak grid conditions. Due to the high impedance of weak grids, ensuring stability across varied operating scenarios is crucial for advancing grid resilience and energy reliability. This paper addresses these research gaps by examining the interaction dynamics between PV, PSH, and BES on the DC side and the utility grid on the AC side. The study identifies operating-region-dependent instability mechanisms arising from negative incremental resistance behavior and weak grid interactions and proposes a virtual-impedance-based active damping control strategy to suppress poorly damped oscillatory modes. The proposed controller effectively reshapes the converter output impedance, shifts unstable eigenmodes into the left-half plane, and improves phase margins without requiring additional hardware components or introducing steady-state power losses. System stability is analytically assessed using root-locus, Bode, and Nyquist criteria within a developed small-signal state-space model, and further validated through large-signal real-time simulations on an OPAL-RT platform. The main contributions of this study are threefold: (i) a comprehensive stability analysis of a utility-scale grid-connected hybrid PV–PSH–BES system under weak grid conditions, (ii) identification of operating-region-dependent instability mechanisms associated with DC–link interactions, and (iii) development and real-time validation of a practical virtual-impedance-based active damping strategy for enhancing system stability and grid integration reliability. Full article

The rapid development of renewable energy has highlighted the issue of its accommodation, which has become a critical challenge for power grids with high renewable energy penetration. Accurately assessing a grid’s renewable energy accommodation capability is essential for ensuring power grid operational security, as well as for the rational planning and efficient operation of renewable energy sources and adjustable power resources. This paper adopts a long-term chronological power balance simulation approach, integrating the dynamic balance among multiple types of power sources, loads, and outbound transmission. Dispatch schemes suitable for different types of power sources, including hydropower, thermal power, wind power, solar power, and nuclear power, were designed based on their operational characteristics. Key operational constraints, such as output limits, staged water levels, pumping/generation modes of pumped storage, and nuclear power regulation duration, were considered. A refined analysis model for renewable energy accommodation in regional power grids was constructed, aiming to maximize the total accommodated renewable energy electricity. Using actual data from the Northeast China Power Grid in 2024, the model was validated, showing results largely consistent with actual accommodation conditions. Analysis based on next-year forecast data indicated that the renewable energy utilization rate is expected to decline to 90.6%, with the proportion of curtailment due to insufficient peaking capacity and grid constraints expanding to 8:2. Sensitivity analysis revealed a clear correlation between the renewable energy utilization rate and the scale of newly installed renewable capacity and energy storage. It is recommended to control the expansion of new renewable energy installations while increasing the construction of flexible power sources such as pumped storage and other energy storage technologies. Full article

In the context of Industry 4.0, digital transformation is reshaping global energy systems. Among the key enabling technologies, Digital Twin (DT)—a dynamic, virtual replica of physical systems—has emerged as a critical tool for improving the performance, reliability, and safety of clean energy infrastructure. In line with the United Nations Sustainable Development Goals (SDGs)—particularly SDG 7 (Affordable and Clean Energy) and SDG 11 (Sustainable Cities and Communities)—the integration of DTs presents unprecedented opportunities to enhance operational efficiency and support proactive decision making. This state-of-the-art review, focused on studies published in 2020–2025, summarizes applications of DTs across the energy value chain, encompassing a broad spectrum of sectors—including solar, wind, hydropower, hydrogen, geothermal, bioenergy, nuclear, and tidal energy—and their critical role in building-to-grid integration. It synthesizes foundational concepts, assesses the evolution of the DT from a predictive tool to a system-level risk-management platform, and provides a critical analysis of its impact. Furthermore, this review discusses the key challenges hindering widespread adoption, including the critical need for interoperability across systems, ensuring the cybersecurity of socio-technical infrastructure, and addressing the complexities of the human-in-the-loop problem. Key research gaps are identified to guide future innovation. Ultimately, this study underscores the transformative potential of DTs as essential tools for accelerating the digital transformation of the energy sector, offering a robust framework for both methodological development and practical deployment. Full article

Integrated hydro-wind-solar-storage (HWSS) bases are pivotal for advancing new power systems under the low carbon goals. However, the independent decision-making of diverse generation investors, coupled with limited transmission capacity, often leads to a dilemma in which individually rational decisions lead to collectively suboptimal outcomes, undermining overall benefits. To address this challenge, this study proposes a novel cooperative game-based method that seamlessly integrates grid congestion into capacity allocation and benefit distribution. First, a bi-level optimization model is developed, where a congestion penalty is explicitly embedded into the cooperative game’s characteristic function to quantify the maximum benefits under different coalition structures. Second, an improved Shapley value model is introduced, incorporating a comprehensive correction factor that synthesizes investment risk, congestion mitigation contribution, and capacity scale to overcome the fairness limitations of the classical method. Third, a case study of a high-renewable-energy base in Qinghai is conducted. The results demonstrate that the proposed cooperative model increases total system revenue by 20.1%, while dramatically reducing congestion costs and wind/solar curtailment rates by 86.2% and 79.3%, respectively. Furthermore, the improved Shapley value ensures a fairer distribution, appropriately increasing the profit shares for hydropower (from 28.5% to 32.1%) and energy storage, thereby enhancing coalition stability. This research provides a theoretical foundation and practical decision-making tool for the collaborative planning of HWSS bases with multiple investors. Full article

This study investigates the optimization of Pumped Storage Hydropower (PSH) integrated with Floating Photovoltaic (FPV) systems, with a focus on sustainable energy management. A nonlinear programming framework combined with scenario analysis was applied to a real hydropower system in Trentino, Italy. The optimization maximizes revenues through energy arbitrage while accounting for water resource and environmental objectives. Upgrading the traditional hydropower plant to PSH operation increases revenues by 4–8% over two hydrological years. Multi-objective optimization further reveals large gains in water availability, confirming PSH’s dual role as energy storage and water management infrastructure. Different FPV configurations analyzed show a 2–3% increase in photovoltaic energy yield due to the water-cooling effect, while the overall hybrid PSH–FPV integration mainly reduces grid dependency and pumping-related emissions, with near-complete decarbonization achievable under optimized scheduling. Overall, PSH provides the primary economic and operational advantage, while FPV strengthens sustainability, enabling resilient hydro–solar operation and contributing to renewable integration and decarbonization in future energy systems. Full article

As global decarbonization accelerates, the environmental and economic viability of hydrogen production largely depends on the evolving electricity supply mix. This study focused on alkaline water electrolysis (AWE) to identify the key factors affecting the competitiveness of green hydrogen. In this study, the temporal dynamics of grid transformation in Germany and the EU over a 20-year period (2025–2045) were addressed by developing a multi-objective optimization framework that integrates environmental impact analysis with machine-learning surrogate models to evaluate trade-offs between the carbon footprint and energy cost per kilogram of hydrogen. Grid-mix scenarios were generated via constrained Latin Hypercube Sampling under policy constraints, including coal phase-out and ≥80% renewables, screened for Pareto optimality, and clustered into distinct archetypes. The results indicated that cost-effective, low-carbon hydrogen production can be achieved through balanced portfolios that emphasize hydropower, biomass, and solar energy. Scenarios that minimize energy costs alone tend to breach environmental targets, whereas ultra-low-emission paths incur steep energy cost penalties. A representative scenario for 2034 (GWP = 24.57 kg CO₂-eq/kg H₂; Energy Cost = 9.47 €/kg H₂) demonstrated a realistic synergy between policy constraints, cost, and environmental impact. Full article