Search Results (1,206)

The development of renewable energy and the increasing demand for electricity underscore the importance of pumped storage for grid stability. Under low-flow pump operating conditions, pump-turbines frequently exhibit hump characteristics, causing severe hydraulic instability and strong pressure pulsations. This study investigates the formation of a hump using full-channel numerical simulations based on the Scale-Adaptive Simulation turbulence model. The numerical flow–head characteristics were validated against the available experimental H–Q data, while the pressure pulsation results were used for qualitative mechanism analysis. The results reveal three major mechanisms: pre-swirl and spiral backflow in the draft tube, non-uniform runner inflow, and vortex flow-induced separation in the wicket gates. An analysis of entropy production reveals that vortex dissipation is responsible for as much as 71% of hydraulic losses in the hump region. In order to mitigate these effects, four stabilizing fins were installed inside the draft tube. The simulations indicate that the fins possess the capability to inhibit swirl and backflow, confine the vortices within the fin–runner interface, improve inflow uniformity and reduce overall hydraulic losses. As a result, the structural modification significantly attenuates the pressure pulsation amplitudes at key monitoring points and visibly shortens the recovery periods. The region of the hump and positive slope of the performance curve are considerably reduced while the head near the region of the hump is increased. Although the intrinsic hump characteristic is still present, the fin-based flow-control strategy can effectively improve the performance and stability of the pump-turbine, which can guide the design and optimization of high-efficiency pumped-storage plants. Full article

Technological advances such as Vehicle-to-Grid (V2G) have the potential to support renewable energy integration and grid stability, but large-scale deployment depends on users’ willingness to participate, particularly in public charging environments. While prior research has examined V2G from technical feasibility and system-level perspectives, everyday public settings remain unexplored. This study investigates electric vehicle (EV) users’ willingness to engage in V2G services in public spaces, with a focus on incentives, expectations, and how participation aligns with existing routines and parking conditions. A mixed-method approach was applied, combining a survey of 544 car users with two waves of user-centered interviews. The survey data were analyzed using factor analysis and linear regression models, while the interview data were thematically analyzed. The results show that users’ evaluations of V2G are shaped by sustainability expectations, perceived efficiency, and uncertainties, and preferences for public V2G participation are strongly influenced by convenience, clarity of the offer, and perceived control. Home charging practices emerged as a key reference point shaping expectations of public V2G services. Across both methods, simple and transparent incentives, such as reduced charging or parking costs, were consistently preferred over more complex reward models, including point-based systems or dynamic energy trading. Concerns related to control over trips, battery degradation, trust in service providers, and added complexity remain important barriers to participation. The findings highlight the need for user-centered and socio-technical design of public V2G services that align with users’ everyday routines, parking conditions, and expectations to support broader adoption beyond the home context. Full article

In this paper, the dynamic modeling and integrated simulation of a ship microgrid system designed to enhance power quality and energy efficiency in electric propulsion vessels are proposed. The proposed system consists of a photovoltaic (PV) array, a battery energy storage system (BESS), a diesel generator, and a propulsion system, all of which are organically integrated through power conversion devices. To compensate for the intermittent nature of solar power, a control strategy featuring Maximum Power Point Tracking (MPPT) for the PV system and bidirectional DC/DC converter control for the battery was implemented. Specifically, a control logic to stabilize the system output in response to the fluctuating loads of the electric propulsion system was developed using PSCAD (v50) software. The simulation results demonstrate that the proposed control strategy maintains DC-link voltage deviation within ±1.8% and achieves a settling time of less than 0.8 s while optimizing propulsion efficiency (peak-shaving ratio 25–30%) under both constant and variable speed operating conditions. Battery SOC variation is limited to 18–88%, preventing overcharge or discharge. This research provides a foundational framework for the design of energy management systems (EMSs) and grid stability assessments for future eco-friendly electric propulsion ships. Full article

This study assesses the technical feasibility of utilizing aggregated domestic freezers in Turkey as a distributed resource for frequency regulation. A dynamic thermal–electrical model was developed to simulate freezer responses under frequency deviation scenarios representative of real-world grid conditions. The modeled sample of 100,000 deep freezers (80 W each) can deliver approximately 3.2 MW of instantaneous down-regulation under a 40% initial duty cycle. Extrapolating to the estimated 4.7 million eligible freezers nationwide yields a total potential headroom of roughly 150–225 MW, depending on duty-cycle assumptions. The compressor duty cycle and allowable temperature range were identified as key factors influencing both regulation capacity and endurance. Although linear reference temperature control enabled effective participation in FCR-N within the simulated timeframes, it also led to cycle synchronization and peak loads following disturbances. Implementing strategies such as randomized reconnection delays could mitigate these effects. The wide availability of domestic freezers, minimal consumer impact, and broad geographic distribution suggest that this resource represents a promising complement to existing frequency regulation assets, particularly in enhancing grid stability amid increasing renewable energy penetration. Full article

The increasing penetration of intermittent renewable energy demands innovative solutions to maintain grid stability, resilience, and security in the body of smart cities. This paper presents a novel framework that redefines Bitcoin mining as a form of virtual energy storage, a flexible and controllable load capable of delivering large-scale demand response services, positioning it as a competitive alternative to traditional energy storage systems, including electrical, mechanical, thermal, chemical, and electrochemical storage solutions. By strategically aligning mining activities with grid conditions, Bitcoin mining can absorb excess electricity during periods of oversupply, converting it into digital assets, and reduce operations during times of scarcity, effectively emulating the behavior of conventional energy storage systems without the associated capital expenditures and material requirements. Beyond its operational flexibility, this paper explores the cyber–physical benefits of integrating Bitcoin mining into the power transmission systems as a defensive mechanism against false data injection (FDI) cyberattacks in smart city infrastructure. To achieve this goal, a decentralized and adaptive control strategy is proposed, in which mining loads dynamically adjust based on authenticated grid-state information, thereby improving system observability and hindering adversarial efforts to disrupt state estimation. In addition, to handle the proposed approach, this paper introduces a high-performance algorithm, a combination of quantum-augmented particle swarm optimization and wavelet-oriented whale optimization (QAPSO-WOWO). Simulation results confirm that strategic deployment of mining loads improves grid sustainability by utilizing curtailed renewables, enhances resilience by mitigating load-generation imbalances, and bolsters cybersecurity by reducing the impacts of FDI attacks. This work lays the foundation for a transdisciplinary paradigm shift, positioning Bitcoin mining not as a passive energy consumer but as an active participant in securing and stabilizing the future power grid in smart cities. Full article

Island microgrids are highly vulnerable to extreme weather, which threatens operational stability and post-disaster recovery. To address the challenge of widespread power outages caused by typhoons, a novel coordinated framework is proposed which optimizes electric ships as mobile power sources to enhance island microgrid resilience. By integrating a hybrid wind field model with an improved wind-resistant A* algorithm, the framework synergistically optimizes dynamic scenario-aware ship routing and distribution network reconfiguration. The problem is formulated as a mixed-integer second-order cone programming (MISOCP) model. Case studies based on real-world data from Hengsha Island, Shanghai, demonstrate that the proposed dynamic routing strategy significantly outperforms static approaches. Specifically, critical load recovery rates are improved by at least 29% during the navigation-restricted phase and total load curtailment costs are reduced by 31.6%. These findings reveal this significance of integrating spatiotemporal environmental dynamics into optimization frameworks, providing a robust decision-making tool for island grid operators to maintain power supply to critical loads under evolving disaster conditions. Full article

Interest in isolated electrical systems powered by renewable energy has driven the development of alternatives to traditional Wind–Diesel Systems (WDS) due to their unwanted emissions and regulatory constraints. In this context, clean and efficient hybrid architectures are needed to comply with regulations and ensure stable operation under variations in user load and wind generation. This paper proposes an integrated isolated hybrid system consisting of a fuel cell replacing the Diesel Generator (DG). To fulfil the role of the synchronous generator in the diesel-group, the fuel cell operates under a Grid-Forming (GFM) control scheme, acting as a virtual synchronous machine that establishes the system’s voltage and frequency. The main aim of the hybrid system is for the wind turbine to supply most of the active power to the loads, thereby minimising hydrogen consumption. A key challenge in these systems is maintaining power balance, particularly preventing reverse flows in the fuel cell system, which has less margin than the diesel generator. In this paper, a Dump Load (DL) quickly dissipates excess power and prevents reverse power conditions. Overall, the proposed system eliminates the need for diesel generation, thereby eliminating emissions while maintaining operational stability. Simulation results demonstrate the correct functioning of the system in the presence of significant variations in load and wind power generation. Full article

The stability of solar-dependent energy systems is vital for urban sustainability, but it is increasingly threatened by compound energy risks (CERs), events where low photovoltaic generation coincides with high electricity demand. This study addresses a critical knowledge gap by disentangling the co-evolving structural and magnitude drivers of these events to identify their propagation pathways and the most vulnerable districts. To achieve this, a novel hybrid framework was developed to provide a high-resolution, spatiotemporal assessment of both risk dimensions across Singapore’s 41 districts. Structural risk was mapped by integrating an undirected co-occurrence network, quantified using Mutual Information (MI), with a directed influence network derived from Bayesian Network Theory (BNT). Concurrently, magnitude risk was assessed through a copula-based analysis of joint probabilities for historical and future climate conditions, using Singapore’s new V3 dataset under multiple Shared Socioeconomic Pathways (SSPs). The results reveal a significant shift in the compound energy risk landscape. Structurally, the network of risk propagation evolves from a historically diffuse configuration to a consolidated system dominated by clusters of 8 to 9 highly interconnected districts under the SSP245 scenario. Under the high-diffusion SSP585 scenario, this evolution is expanded by the addition of 4 more districts. At the same time, the magnitude of risk intensifies across identified hotspot districts. This synthesis uncovers a critical feedback dynamic: districts such as 29, 36, and 40 not only serve as key structural hubs but also experience sharp increases in event probability, with their return periods for extreme compound events collapsing from over 50 years historically to the 10–20-year range. This forms a self-reinforcing loop of systemic vulnerability. These findings indicate that Singapore’s energy security will become increasingly exposed to climate-driven risks that propagate through this consolidated network, requiring targeted spatial adaptation to ensure long-term grid sustainability. Full article

The global transition towards sustainable and resilient energy systems has emphasized the need for efficient utilization of renewable energy sources (RESs) and rapid electrification of transportation. However, smart grids must address the intermittency of solar and wind power while accommodating the growing demand from electric vehicles (EVs). Hence, in this paper, a data-driven energy management system (EMS) is proposed that combines multivariable forecasting, generation scheduling, and EV charging coordination in a dual-level decentralized framework to increase the efficiency, reliability, and scalability of modern power grids. First, short-term forecasts of solar irradiance, wind speed, and load demand are addressed via five machine learning models ranging from nonlinear to ensemble models. Accordingly, a unified CatBoost-based platform for forecasting these three variables is selected because of its better performance and accuracy. These forecasts are subsequently utilized in a mixed-integer linear programming (MILP) framework for optimal generation scheduling in the considered network, fulfilling load demand at reduced electricity and emission costs while maintaining grid stability. Finally, a priority-based scheme is proposed for charging/discharging coordination of the aggregated EVs, minimizing demand variability while fulfilling vehicles’ charging needs and maintaining their batteries’ lifetime. The superiority of the proposed method lies in integrating a multivariable forecasting pipeline, linear MILP generation scheduling, and battery-health-aware V2G coordination in a unified decoupled framework, unlike many recent frontier works that treat these capabilities independently. Simulation results, under different scenarios, confirm that the proposed intelligent EMS can significantly reduce operational fluctuations, satisfy load and EV demands, optimize RES utilization, and support system cost-effectiveness, sustainability, and resilience. Full article

Power fluctuations and scheduling uncertainties caused by large-scale renewable energy grid integration have made the existing homogeneous energy storage solutions struggle in some cases to balance economic efficiency with dynamic response speed. To address the above challenge, this paper proposes a hybrid energy storage system integrating superconducting magnetic energy storage and hydrogen electric storage, and a corresponding dual-layer model predictive control energy management framework is therefore designed. This framework lies on its cross-timescale hierarchical coordination mechanism. Analytic validation in a typical high-fluctuation renewable microgrid scenario demonstrates that compared to conventional single-layer control strategies, the proposed management system reduced total operating costs by 55.5%, extended system stabilization time by 64.2%, decreased hydrogen storage system mode switching frequency by 59.9%, and simultaneously lowered computational burden by over 97%. This effectively enhanced power supply reliability and extended equipment service life. This innovative framework provides a practical solution for coordinated energy storage control in microgrids having a high ratio of renewable penetration. Full article

The generation from decentralized energy resources strongly depends on weather conditions, which causes fluctuations and degrades power grid quality. One of the most effective solutions in modern power systems to mitigate this issue is the use of energy storage systems (ESSs). These systems enhance the network performance by reducing power fluctuations. In this scope, and for frequency analysis, a model consisting of two interconnected microgrids was considered in this work. The frequency of these microgrids varies due to sudden changes in load or generation (or both). The frequency regulation was performed by an efficient load frequency controller (LFC). This regulation was essential and was employed to improve control performance, reduce the impact of load disturbances on frequency, and minimize power deviations in the power flow tie-lines. A fuzzy logic-based optimizer was installed in each microgrid to optimize the proposed proportional–integral–derivative (PID) controllers by generating their optimal parameters. The main objective of the LFC was to ensure zero steady-state error for system frequency and power deviations in the tie-lines. However, with the increasing integration of renewable energies and the intermittent nature of their production due to climate change, frequency fluctuations arise. To mitigate this issue, a coordinated AGC–PMS (automatic generation control–power management system) regulation with hybrid energy storage systems and interconnected microgrids was designed to enhance the quality and stability of the power network. This paper focuses on the load frequency control (LFC) technique applied to interconnected microgrids integrating renewable energy sources (RESs). It presents an optimization study based on artificial intelligence (AI) combined with the use of energy storage systems (ESSs) and high-voltage direct current (HVDC) transmission link for power management and control. The renewable energy sources used in this work are photovoltaic generators, wind turbines, and a solar thermal power plant. A hybrid energy storage system has been installed to ensure energy management and control. It consists of redox flow batteries (RFBs), a superconducting magnetic energy storage (SMES) system, electric vehicles (EVs), and fuel cells (FCs).The system behavior was analyzed through several case studies to improve frequency regulation and power management under renewable energy integration and load variation conditions. Full article

The increasing penetration of renewable energy sources reduces system inertia and introduces significant challenges for maintaining frequency stability in modern power grids. Battery Energy Storage Systems (BESS) have emerged as an effective solution for mitigating frequency deviations; however, existing studies typically recommend relocating BESS to the bus that is electrically furthest from the Center of Inertia (COI) to maximize frequency support. This paper investigates an alternative operational strategy in which the BESS remains co-located with the renewable energy source. A methodology combining COI-based electrical distance analysis and an artificial intelligence (AI)-driven dispatch framework is proposed to evaluate optimal BESS utilization without physical relocation. The AI model generates generator dispatch scenarios that are evaluated through dynamic simulations to assess the resulting system frequency nadir following disturbances. The proposed approach is validated using a modified IEEE nine-bus power system model. Simulation results demonstrate that, under specific generator dispatch conditions, maintaining the BESS at the renewable energy bus can achieve frequency-nadir performance comparable to relocating the BESS to the furthest bus from the COI. The analysis further identifies critical generator output ranges that influence frequency stability under different BESS placement scenarios. These findings suggest that optimized dispatch strategies can reduce the need for costly infrastructure relocation while maintaining effective frequency support in low-inertia power systems. Full article

Accurate forecasting of multivariate time series is essential for energy management, grid optimisation, and policy planning. This study presents a hybrid deep learning and Transformer-based forecasting framework for predicting hourly electricity consumption across Turkey using nationwide data from Energy Exchange Istanbul (EPİAŞ) between 2018 and 2025. The dataset comprises 15 variables representing diverse energy sources and market indicators, including consumption, generation, and the market-clearing price (MCP). The proposed hybrid model integrates Long Short-Term Memory (LSTM), Bidirectional LSTM (BLSTM), and Gated Recurrent Unit (GRU) layers to capture both short- and long-term temporal dependencies, while a Transformer model leveraging multi-head self-attention mechanisms is used for comparison. All models were trained using standardised preprocessing, a 24 h lookback window, and optimised hyperparameters via GridSearchCV. Experimental results reveal that the hybrid model achieved the best overall performance, with MAE = 464.01, RMSE = 663.39, and R² = 0.9902, significantly outperforming the baseline and Transformer models. The Transformer demonstrated robust long-horizon learning capability (R² = 0.9257) but at a higher computational cost. These results confirm that combining multiple recurrent architectures enhances predictive accuracy and stability for large-scale, real-time energy forecasting. The proposed framework offers a reliable foundation for smart grid operations, demand prediction, and data-driven energy policy development. Full article

Italy’s abundant solar resources and its strategic Mediterranean location offer strong opportunities to accelerate the transition to a low-carbon energy system. This study presents a comparative techno-economic assessment of concentrating solar power (CSP) plants with 8 h of thermal energy storage (TES) and a 1 MW photovoltaic (PV) plant to evaluate their roles in exploiting Italy’s solar potential. The analysis covers four representative locations (Montalto, Val Basento, Ferrara, and Priolo) and examines solar availability, seasonal performance, capacity factor, electricity generation, land use, and levelized cost of electricity (LCOE). Both technologies show marked seasonal variability, with lower winter performance and summer peaks. Southern sites outperform the northern ones, with Priolo achieving the highest generation and Ferrara the lowest. CSP benefits from dispatchable operation enabled by TES, providing nearly constant rated output and summer capacity factors up to 78%, with annual production exceeding 4 GWh at the best site. In contrast, PV operates non-dispatchably, with capacity factors below 31% and annual generation between 1.47 and 1.72 GWh. The North–South performance gradient is stronger for CSP due to its dependence on direct normal irradiance. PV technology offers higher land use efficiency, producing over twice the energy per unit area compared to CSP technology, while CSP technology requires larger areas but ensures greater operational flexibility. Economically, PV technology achieves a lower LCOE, whereas CSP technology entails higher costs but adds value through dispatchability and improved grid integration. Overall, combining CSP and PV systems can enhance grid stability, reduce emissions, and strengthen Italy’s energy security, highlighting the importance of coordinated planning and investment in complementary solar technologies for decarbonization and for regions with similar climatic conditions. Full article

With hydropower supplying nearly 94% of Ethiopia’s electricity, the national power grid is extremely vulnerable to recurrent droughts and erratic rainfall. To mitigate this risk, this study examines the wind power potential across five specific locations: Arerti, Sela Dingay, Debre Berhan, Mega, and Gode. By combining on-site mast measurements with datasets from NASA and the Global Wind Atlas, we evaluated wind characteristics at industry-standard hub heights of 80 m and 100 m. The analysis focused on wind power density (WPD), Weibull stability parameters (k and c), and directional consistency. The results indicate that Gode and Mega are the premier choices for commercial development, showing average speeds above 8.5 m/s and power densities exceeding 500 W/m2 at the 100 m level. Gode stands out as the most reliable site, with a Weibull shape factor (k) of 2.8 and a scale factor (c) of 9.1 m/s. We modeled a standard 3 MW turbine while factoring in a 20% loss for real-world conditions; this yielded net annual energy productions of 9461 MWh (36% CF) for Gode, 9040 MWh (34.4% CF) for Mega, and 8619 MWh (32.8% CF) for Arerti. While Sela Dingay and Debre Berhan have lower initial yields, their feasibility improves significantly when using towers taller than 80 m. Wind rose data reveals that Gode and Arerti have highly unidirectional flows, which simplifies turbine micro-siting. Notably, Arerti provides a unique economic advantage due to its location right next to existing 132/230 kV transmission infrastructure and industrial load centers. Overall, these findings provide a definitive technical roadmap for Ethiopia to diversify its energy portfolio and meet its Climate-Resilient Green Economy (CRGE) objectives. Full article