Search Results (952)

Achieving Egypt’s 2035 renewable electricity targets presents substantial operational and institutional challenges, compounded by limited electricity trade across the Middle East and North Africa (MENA) region. This study applies a PLEXOS-based simulation framework that integrates short-term economic dispatch with the Projected Assessment of System Adequacy (PASA) to evaluate the system-level impacts of economically dispatched cross-border interconnections with Saudi Arabia, Libya, Jordan, and Sudan. The analysis also incorporates domestic flexibility measures, including five-minute dispatch, dynamic reserve requirements, and battery energy storage systems (BESS). Scenarios with renewable energy penetration levels of up to 50% are assessed using Egypt’s 2023 power system as the baseline. The results demonstrate that transitioning from a static, hourly, standalone operating framework to an integrated flexibility configuration—combining five-minute dispatch, 8 GW of economically dispatched cross-border interconnection capacity, and 8 GWh of BESS—yields substantial system-wide benefits at 50% renewable penetration. Loss-of-Load Probability declines from 96.48% to zero, ensuring full system adequacy, while total operational costs decrease by more than 45%, corresponding to annual savings of approximately USD 1.04 billion. Renewable energy curtailment is reduced by over 98%, enabling nearly 15 TWh of additional clean electricity generation, and CO₂ emissions fall by 11.6 million tons (≈40%). In addition, the operating-reserve shadow price—an indicator of reserve scarcity—declines to near zero, underscoring the effectiveness of coordinated regional dispatch and domestic flexibility in mitigating scarcity conditions. These findings provide robust evidence that integrated operational, temporal, and spatial flexibility can significantly accelerate renewable energy integration while strengthening system adequacy. The proposed framework offers an actionable and scalable blueprint for policy coordination and market reform in Egypt, with broader relevance for emerging power systems across the MENA region. Full article

Electric-powered unmanned guidance vehicles provide surface taxiing guidance for arriving and departing aircraft within the airport movement area, enabling sustained safety under complex operational conditions and improving overall operational efficiency, particularly under low-visibility scenarios. In this context, how to design scientifically rigorous operational trajectories for the three phases of unmanned guidance vehicle operations—dispatch, guidance, and recovery—remains an open and important research problem. This study proposes a three-stage trajectory-planning method for unmanned guidance vehicles, including initial trajectory planning, conflict prediction, and conflict resolution. First, the Guidance Unit—composed of the unmanned guidance vehicle and the guided aircraft—is defined, and a standard speed-profile design model is established for this unit. Then, considering airport operational-safety constraints, a conflict prediction algorithm for the guidance process is developed, which identifies potential conflicts in guidance trajectory planning based on time-window overlap analysis. Subsequently, under operational safety constraints, an optimization model aiming to minimize the maximum guidance time is formulated, and a trajectory planning algorithm for unmanned guidance vehicles based on the improved A* algorithm is designed to generate conflict-free operational trajectories. Finally, a simulation study is conducted using a major airport in Southwest China as a case study. The results show that (1) the speed-profile design and airport operational-rule constraints affect the operational trajectories of unmanned guidance vehicles; (2) the proposed algorithm enables coordinated planning of both speed control and path selection, thereby improving overall operational efficiency by 43.65% compared with conventional operations, while ensuring conflict-free airport surface taxiing, due to the adoption of an improved A* trajectory-planning algorithm for unmanned guidance vehicles; (3) under the electric-powered guidance-vehicle scheme proposed in this study, the method achieves a 34.52% reduction in total energy consumption during the guidance phase compared with traditional Follow-Me guidance, enabling the simultaneous optimization of operational efficiency and energy consumption. Full article

Accurate forecasting of citywide electric vehicle (EV) charging load is critical for alleviating station-level congestion, improving energy dispatching, and supporting the stability of intelligent transportation systems. However, large-scale EV charging networks exhibit complex and heterogeneous spatio-temporal dependencies, and existing approaches often struggle to scale with increasing station density or long forecasting horizons. To address these challenges, we develop a modular spatio-temporal prediction framework that decouples temporal sequence modeling from spatial dependency learning under an encoder–decoder paradigm. For temporal representation, we introduce a global aggregation mechanism that compresses multi-station time-series signals into a shared latent context, enabling efficient modeling of long-range interactions while mitigating the computational burden of cross-channel correlation learning. For spatial representation, we design a dynamic multi-scale attention module that integrates graph topology with data-driven neighbor selection, allowing the model to adaptively capture both localized charging dynamics and broader regional propagation patterns. In addition, a cross-step transition bridge and a gated fusion unit are incorporated to improve stability in multi-horizon forecasting. The cross-step transition bridge maps historical information to future time steps, reducing error propagation. The gated fusion unit adaptively merges the temporal and spatial features, dynamically adjusting their contributions based on the forecast horizon, ensuring effective balance between the two and enhancing prediction accuracy across multiple time steps. Extensive experiments on a real-world dataset of 18,061 charging piles in Shenzhen demonstrate that the proposed framework achieves superior performance over state-of-the-art baselines in terms of MAE, RMSE, and MAPE. Ablation and sensitivity analyses verify the effectiveness of each module, while efficiency evaluations indicate significantly reduced computational overhead compared with existing attention-based spatio-temporal models. Full article

An Integrated Energy System (IES) integrates electricity, heat, and natural gas, optimizing energy use and management efficiency. These systems connect to a Virtual Power Plant (VPP) for demand response dispatch in the electricity market. However, the impact of VPP load on the IES is often overlooked, which can limit the IES’s effective market participation and stability. To address this issue, this study introduces a two-layer collaborative model to coordinate VPP scheduling for multiple IES units, aiming to improve collaboration efficiency. The upper level involves the VPP setting electricity prices based on load conditions, guiding IES units to adjust their market strategies. At the lower level, the model encourages integration and optimization of different energy types within the IES through enhanced energy interactions. Additionally, the application of the Shapley value method ensures fair benefit distribution among all IES members. This approach supports equitable economic outcomes for all participants in the energy market. The model employs a multi-strategy improved Dung Beetle Optimizer (FSGDBO) combined with commercial solver techniques for efficient problem-solving. Experimental results demonstrate that the model significantly enhances the VPP’s peak-shaving and valley-filling capabilities while preserving the economic interests of the IES alliances, thereby boosting overall energy management effectiveness. Full article

The increasing penetration of renewable energy introduces significant challenges to grid stability and economic performance due to the intermittent and non-dispatchable nature of solar and wind generation. These fluctuations contribute to grid congestion, frequency control issues, and price volatility, reducing revenue predictability for renewable producers. It is then clear that the challenge of energy transition can be addressed by making the introduction of renewable sources into the electricity grid sustainable. Battery Energy Storage Systems (BESSs) have emerged as a flexibility resource providing time-shifting, frequency and voltage support, congestion management, and energy arbitrage. In response, several Transmission System Operators (TSOs), such as Terna in Italy in cooperation with photovoltaic (PV) and wind power producers, have initiated flexibility projects. However, these projects are limited and should be accompanied by liberalization measures that allow BESSs to be economically sustainable only under market conditions. This study evaluates the techno-economic feasibility of utility-scale BESSs either integrated into large PV/wind farms or stand-alone for providing grid flexibility services and profit increase for the producers. Both market conditions and TSO incentives will be considered. A two-step mixed integer linear (MILP) optimization approach is employed: first, an optimization schedules BESS charge and discharge operations based on historical generation and market data; second, the Net Present Value (NPV) is maximized to determine optimal system sizing and profit. The model is validated through real case studies and sensitivity analyses including BESS degradation, market volatility, and regulatory factors. The developed model is ultimately applied to compare the study cases, and the analysis shows that, under specific conditions, the arbitrage of a stand-alone BESS can be as profitable as the incentives offered by TSOs. Full article

To address the challenges associated with energy decarbonization and economic operation in integrated energy systems (IESs), this paper proposes a collaborative optimal dispatch strategy for IES that considers multiple flexible loads under a carbon trading mechanism. First, a mathematical model of user-side loads is constructed according to the characteristics of flexible loads. Second, a comprehensive optimization framework is constructed by embedding the carbon trading mechanism into the IES operational model. The objective function minimizes the total operating costs, including energy purchase costs, fuel costs, carbon trading costs, operation and maintenance costs, compensation costs, and green certificate revenues. The CPLEX solver is then employed to solve the model. Finally, a case study is conducted to validate the proposed method. Simulation results demonstrate that the carbon trading mechanism effectively leverages the demand response capabilities and coordinates multiple resources, including electricity, heat, and storage, thereby achieving low-carbon economic operation of the system. Full article

Hydrogen-integrated decentralized energy systems (DIESs) promise communities higher renewable penetration, greater resilience, and sector coupling across electricity, heat, and mobility. AI supports forecasting, dispatch optimization, multi-asset coordination, and planning, yet designing AI-driven hydrogen communities is challenging because it spans physical infrastructure, cyber-control, and governance. This review (2020–2025) synthesizes design strategies for AI-enabled hydrogen DIESs, distilling architectural patterns, electricity–hydrogen co-optimization, uncertainty-aware operation, and digital-twin planning. It summarizes AI benefits (flexibility, efficiency, reduced curtailment) and recurring risks (forecast-optimization cascades, objective mismatch, data drift, safety and constraint breaches, digital-twin credibility gaps, cybersecurity and privacy issues, and weak reproducibility) and proposes a pragmatic roadmap prioritizing safety-aware control, standardized metrics, transparent assumptions, and community-appropriate governance. Full article

The demand for computing power has increased at a rate never seen before due to the quick development of artificial intelligence (AI) technologies and applications. Consequently, AI data centers, referring to computing facilities specifically designed for large-scale artificial intelligence workloads, have become one of the fastest-growing electricity consumers globally. Therefore, it is essential to understand the load characteristics of AI data centers and their impact on the grid. This paper provides a comprehensive review of the evolving energy landscape of AI data centers. Specifically, this paper (i) presents the energy consumption structure in AI data centers and analyzes the key workload features and patterns in four stages, emphasizing how high power density, temporal variability, and cooling requirements shape total energy use, (ii) examines the impacts of AI data centers for power systems, including impacts on grid stability, reliability and power quality, electricity markets and pricing, economic dispatch and reserve scheduling, and infrastructure planning and coordination, (iii) presents key technological, operational and sustainability challenges for AI data centers, including renewable energy integration, waste heat utilization, carbon-neutral operation, and water–energy nexus constraints, (iv) evaluates emerging solutions and opportunities, spanning grid-side measures, data-center-side strategies, and user-side demand-flexibility mechanisms, (v) identifies future research priorities and policy directions to enable the sustainable co-evolution of AI infrastructure and electric power systems. The review aims to support utilities, system operators, and researchers in maintaining reliable, resilient, and sustainable grid operation in the context of the rapid development of AI data centers. Full article

Sustained demand growth and the increasing share of renewable energy sources pose challenges for the operation of modern electrical systems. The variability in wind and solar photovoltaic generation causes temporary imbalances between supply and demand, requiring the incorporation of energy management and storage strategies to guarantee supply. In this context, the need arises to develop optimization models that allow for efficient energy dispatch, minimizing costs and promoting the appropriate use of both conventional and renewable resources. This study formulated a time block dispatch optimization model implemented in the IEEE 24-node system, integrating thermal, hydroelectric, photovoltaic, wind, and energy storage systems. The methodology was based on DC power flows and was developed in MATLAB R2024b, incorporating nodal balance constraints, transmission and generation capacity limits, as well as the operating conditions of the storage systems. The model allowed for the evaluation of both energy and economic performance, validating its behavior under conditions of peak demand and renewable variability. The results demonstrate that the inclusion of energy storage systems allows for a reduction in high-cost thermal generation, optimizing demand coverage with a greater share of renewable energy. An average storage efficiency of 85.5% was achieved, and total system costs were reduced by USD 40,392.39 per day, equivalent to annual savings of USD 14.75 million. Furthermore, power flows remained below 85% of transmission capacity, confirming the proper operation of the grid. In this sense, the model fulfills the proposed objectives and proves to be a tool for energy planning and the technical-economic integration of storage in electrical networks. Full article

The district heating system (DHS) can provide flexibility to the electrical power system (EPS) in the coordinated dispatch of an integrated power and heat system (IPHS). To exploit the energy storage capacity of the DHS and support the flexible IPHS operation, it is essential to characterize the flexible operation region (FOR) of the DHS. This paper proposes an FOR characterization method for the DHS, based on a Farkas-cut outer approximation algorithm (FCOAA). The FOR characterization is formulated as a polyhedral projection problem. Within the FCOAA-based framework, the feasible cut generation model is constructed as a bilinear programming problem, which is relaxed by using the normalized multiparametric disaggregation technique (NMDT) and converted into a mixed-integer linear programming (MILP) problem. Numerical simulations on a 6-bus/6-node IPHS are carried out to validate the proposed method, and key factors influencing the flexibility of the DHS are analyzed. Full article

This comprehensive review examines hierarchical control principles and frameworks for grid-connected microgrids operating in environments prone to load shedding and under demand response. The particular emphasis is on South Africa’s current electricity grid issues, experiencing regular planned and unplanned outages, due to numerous factors including ageing and underspecified infrastructure, and the decommissioning of traditional power plants. The study employs a systematic literature review methodology following PRISMA guidelines, analysing 127 peer-reviewed publications from 2018–2025. The investigation reveals that conventional microgrid controls require significant adaptation to address the unique challenges brought about by scheduled power outages, including the need for predictive–proactive strategies that leverage known load-shedding schedules. The paper identifies three critical control layers of primary, secondary, and tertiary and their modifications for resilient operation in environments with frequent, planned grid disconnections alongside renewables integration, regular supply–demand balancing and dispatch requirements. Hybrid optimisation approaches combining model predictive control with artificial intelligence show good promise for managing the complex coordination of solar–storage–diesel systems in these contexts. The review highlights significant research gaps in standardised evaluation metrics for microgrid resilience in load-shedding contexts and proposes a novel framework integrating predictive grid availability data with hierarchical control structures. South African case studies demonstrate techno-economic advantages of adapted control strategies, with potential for 23–37% reduction in diesel consumption and 15–28% improvement in battery lifespan through optimal scheduling. The findings provide valuable insights for researchers, utilities, and policymakers working on energy resilience solutions in regions with unreliable grid infrastructure. Full article

Aiming at the insufficient capture of temporal dependence and weak coupling of external factors in multivariate load forecasting, this paper proposes a Transformer model integrating timestamp-based positional embedding and convolution-augmented attention. The model enhances temporal modeling capability through timestamp-based positional embedding, optimizes local contextual representation via convolution-augmented attention, and achieves deep fusion of load data with external factors such as temperature, humidity, and electricity price. Experiments based on the 2018 full-year load dataset for a German region show that the proposed model outperforms single-factor and multi-factor LSTMs in both short-term (24 h) and long-term (cross-month) forecasting. The research results verify the model’s accuracy and stability in multivariate load forecasting, providing technical support for smart grid load dispatching. Full article

Electric vehicles (EVs) are central to low-carbon urban mobility, but range anxiety persists. In real fleet operations, vehicles are rarely discharged to low State-of-Charge (SOC), so the remaining driving range (RDR) labels are incomplete, hindering accurate RDR prediction and analysis of operating conditions. This paper proposes a label completion framework that reconstructs low SOC mileage and a hybrid mileage-factor-oriented residual regressor (MF-CMR) to learn mileage factors under SOC imbalance. Applied to one year of data from eight EVs in Guangzhou, China, the method achieves a mean absolute error of 0.88 and a coefficient of determination of 0.64, yielding completed trip-level RDR labels whose distribution centers around 241.73 km. Using the completed labels, a two-way analysis of variance (ANOVA) with ambient temperature and driving style as factors shows that temperature is the dominant determinant of RDR, while driving style exerts a secondary but substantial effect, with a significant interaction. Together, the label completion framework and the quantified impacts of temperature and driving style enable more reliable RDR estimation from fleet logs, offering a quantitative basis for dispatching policies, charging margins, and eco-driving guidance in EV fleet services involving long distance trips or low SOC deep discharge scenarios. Full article

The integration of high-penetration renewable energy sources (RESs) and electric vehicles (EVs) increases the risk of voltage fluctuations in distribution networks. Traditional static partitioning strategies struggle to handle the intermittency of wind turbine (WT) and photovoltaic (PV) generation, as well as the spatiotemporal randomness of EV loads. Furthermore, existing scheduling methods typically optimize EV active power or reactive compensation independently, missing opportunities for synergistic regulation. The main novelty of this paper lies in proposing a spatiotemporally coupled voltage-stability optimization framework. This framework, based on an hourly updated electrical distance matrix that accounts for RES uncertainty and EV spatiotemporal transfer characteristics, enables hourly dynamic network partitioning. Simultaneously, coordinated active–reactive optimization control of EVs is achieved by regulating the power factor angle of three-phase six-pulse bidirectional chargers. The framework is embedded within a hierarchical model predictive control (MPC) architecture, where the upper layer performs hourly dynamic partition updates and the lower layer executes a five-minute rolling dispatch for EVs. Simulations conducted on a modified IEEE 33-bus system demonstrate that, compared to uncoordinated charging, the proposed method reduces total daily network losses by 4991.3 kW, corresponding to a decrease of 3.9%. Furthermore, it markedly shrinks the low-voltage area and generally raises node voltages throughout the day. The method effectively enhances voltage uniformity, reduces network losses, and improves renewable energy accommodation capability. Full article

With the gradual rise of battery-powered ships, the high-power charging demand during berthing is poised to exacerbate the peak-to-valley difference in the port grid, possibly leading to grid congestion and logistical disruption. To address this challenge, this paper proposes a bi-level coordinated scheduling scheme across both logistical operations and energy flow dispatch. Initially, by developing a refined model for the dynamic power characteristics of quay crane (QC) clusters, the surplus power capacity that can be stably released through an orderly QC operational delay is quantified. Subsequently, a hybrid AC/DC ship–shore interconnection architecture based on a smart interlinking unit (SIU) is proposed to utilize the QC peak-shaving capacity and satisfy the increasing shore power demand. In light of these, at the logistics level a coordinated scheduling of berths, QCs, and ships charging is performed with the objective of minimizing port berthing operational costs. At the energy flow level, the coordinated delay in QC clusters’ operations and SIU-enabled power dispatching are implemented for charging power support. The case studies demonstrate that, compared with the conventional independent operational mode, the proposed coordinated scheduling scheme enhances the shore power supply capability by utilizing the QC peak-shaving capability effectively. Moreover, as well as satisfying the charging demands of electric ships, the proposed scheme significantly reduces the turnaround time of ships and achieves a 39.29% reduction in port berthing operational costs. Full article