International Transactions on Electrical Energy Systems最新文献

This study proposes an advanced optimization technique for maximum power point tracking (MPPT) in wind turbines (WTs) based on a permanent magnet synchronous generator (PMSG), which is crucial for maximizing energy extraction under varying wind conditions. Several MPPT strategies have been evaluated and compared, including neural networks (NNs), sliding mode control (SMC), the Whale Optimization Algorithm (WOA), and the Cuckoo Search Algorithm (CSA), to determine the most effective approach for optimizing power output and improving system efficiency. Emphasis is placed on identifying techniques that not only enhance energy capture but also reduce the complexity and cost of wind energy systems. To achieve this, the study introduces a novel hybrid algorithm that integrates the strengths of both WOA and CSA, leveraging their complementary exploration and exploitation capabilities. The proposed method aims to deliver improved tracking accuracy and faster convergence to the optimal power point. The algorithms were tested using a real wind profile from Djelfa, Algeria, a region characterized by semiarid climate and varied topography, to simulate realistic operational scenarios, providing accurate assessments of each MPPT strategy under true environmental conditions. The results obtained through MATLAB/Simulink simulations demonstrate that the newly developed hybrid WO–CSA strategy consistently outperformed others, delivering approximately 140 W more power than CSA and about 230 W more than WOA and NN at a wind speed of 10 m/s, while the SMC strategy exhibited the lowest performance, generating roughly 750 W less power compared to WOA and NN. By developing the new algorithm, the study contributes to the development of more efficient and reliable WT technologies.

As the proportion of building energy consumption in total energy consumption continues to rise, traditional energy scheduling strategies and building load regulation methods are improved to reduce energy consumption and enhance the flexibility of building scheduling. In this study, a two-stage optimization strategy for energy-efficient buildings incorporating electric vehicles (EVs) based on user satisfaction is proposed. First, a source–load–storage coordinated energy optimization system for buildings, including photovoltaic (PV) generation, energy storage systems (ESSs), EVs, light-emitting diode (LED) lights, and heating, ventilation, and air conditioning (HVAC), is established. Second, the satisfaction levels of users with multiple flexible loads are used as indicators of comfort to dynamically adjust energy consumption in buildings. Then, a multiobjective energy optimization model is formulated to minimize daily operational costs while simultaneously maximizing user satisfaction, with an emphasis on balancing comfort and economic efficiency. Third, a two-stage energy optimization model of day-ahead and intraday is constructed to reduce the impact of source–load forecasting errors on the operation of building energy systems, and an incentive demand response strategy is introduced to guide users to participate in scheduling in the intraday stage. Finally, different cases are created to test the effectiveness of the proposed strategy. The overall simulation results validate the proposed approach with operational cost reduction of 12.9% while maintaining a user satisfaction level above 0.95 and grid volatility reduction of 7.56% as compared to the traditional energy optimization strategy.

This paper proposes a multiobjective medium-term optimal scheduling model of the cascade hydro–photovoltaic (PV)–pumped storage system to increase the renewable energy accommodation capacity of the system. The uncertainties of natural water inflow and PV power output have been formulated using the information gap decision theory (IGDT), and the proposed multiobjective model is solved with the ε constraint method. A case study of a test system including 410 MW cascade hydro, 70 MW pumped storage, and 60 MW PV shows that the proposed model reduced solar curtailment rate from 22.65% to 0.23% compared to the conventional hydro–PV system, and the IGDT-based model avoids risk from the uncertainties of natural water inflow and PV power output effectively.

The increasing pace of urbanization has intensified the global demand for clean and decentralized energy systems, placing solar energy at the forefront of sustainable urban transitions. While prior studies have separately explored photovoltaic (PV) technologies, urban form, or energy policy frameworks, few have synthesized these dimensions into an integrated roadmap for solar adoption in smart cities. This study addresses that gap by introducing the policy–technology–morphology nexus (PTMN), a novel conceptual model developed through the cross-analysis of 120 peer-reviewed studies and urban case implementations. The PTMN framework unifies three essential pillars: policy instruments (e.g., feed-in tariffs, net metering), enabling technologies (e.g., AI-based solar mapping, smart grids, battery optimization), and urban morphological variables (e.g., building density, orientation, and shading).Through comparative tables and geospatial insights, the review reveals that morphology-sensitive design, when coupled with intelligent technologies and regulatory incentives, can enhance solar efficiency by up to 40% in selected cities such as Geneva, Stonehaven, and Shenzhen. Methodologically, the study integrates GIS-based assessments, deep learning approaches, and system-level classification typologies to map deployment scales, performance gaps, and policy relevance. Findings highlight the critical role of digital twins and smart storage integration in enabling equitable and scalable solar transitions. Limitations include the reliance on location-specific data and the absence of multicity dynamic simulations. Future research should focus on enhancing AI-driven predictive modeling for solar energy optimization, developing novel energy storage technologies, and fostering interdisciplinary collaborations among policymakers, engineers, and urban planners.

Electric vehicle (EV) technologies have become crucial in the current times as they are projected to be one of the major contributors to energy transition in global transportation and power system. They have been identified to offer social, technical, economic, and environmental benefits. Solar and hybrid EV chargers offer more significant advantages over grid-tied chargers. Despite the many advantages that EVs bring, there are also drawbacks associated with this technology. This paper therefore provides an extensive review on EV charging technologies and methods, international standards, and protocols. The work reviews solar power for EV charging stations, where grid-tied and off-grid systems are intensely examined. The system architecture and configuration, and charging station layouts are presented. An in-depth comparative review of charging technologies’ infrastructure Capital Expenditure (CAPEX) and Operational Expenditure (OPEX) cost analysis is examined. Eight global solar EV charging projects are closely analyzed and compared. From these case studies, lessons learnt and best practices are derived and a summary is provided. The challenges and future trends are also reviewed and presented in this work. The review presented in this work is useful to a wide range of individuals and groups, including but not limited to governments, potential buyers, policymakers, and researchers.

In day-ahead electricity markets with high renewable penetration, price prediction errors are prevalent. These errors significantly increase the downside risk of energy storage arbitrage, potentially diminishing profits or even causing sustained losses. To address the lack of effective downside protection for energy storage systems operating in highly uncertain environments, this paper proposes a reinforcement learning–based battery-dispatch method. The method is enhanced by three mechanisms to improve policy robustness and risk management capabilities. Residual injection disturbs predictive inputs to simulate various bias scenarios, guiding agents toward more conservative decision-making. Action hard projection maps outputs in real time onto feasible regions, ensuring physical feasibility and training stability. Teacher model behaviour cloning incorporates low-risk demonstrations based on actual prices, accelerating convergence and avoiding high-risk actions. The approach underwent long-term empirical validation using highly volatile data from the Germany–Luxembourg market for 2020–2024. Results indicate that, although the proposed method yields slightly lower average returns compared to the traditional prediction-and-optimization baseline, it significantly reduces maximum drawdowns, loss probability and profit volatility, thereby demonstrating robust downside-risk protection. This study validates reinforcement learning’s capacity for effective risk control in energy storage dispatch and provides a viable pathway for robust asset management in highly volatile electricity markets.

In this paper, a new high step-up converter based on a coupled inductor and switched capacitor cell circuits is presented. The proposed converter achieves zero-current switching (ZCS) turn-on for the switch and ZCS turn-off for all diodes, significantly reducing switching losses. Additionally, the voltage stress across the switch is greatly minimized, allowing the use of lower-cost switches with smaller on-resistance, which further contributes to improved efficiency. The converter’s operation is enhanced by its ability to increase voltage gain without increasing the duty cycle, thus achieving a large conversion ratio. Furthermore, the continuous input current and alleviation of diode reverse recovery are additional benefits. The operation modes, including over-resonance and below-resonance frequency modes, are discussed to analyze the converter’s performance and design limitations. Experimental results from a 200-W, 30–380 V prototype confirm the theoretical analysis, demonstrating the effectiveness of the proposed design.

Virtual power plants (VPPs) have emerged as a key solution for integrating distributed energy resources (DERs) into power systems, offering enhanced flexibility and supporting frequency and voltage stability. While traditional VPP models focus on static optimization and energy management, they fall short in capturing the dynamic responses required for transient stability analysis, especially in systems incorporating both grid-following (GFL) and grid-forming (GFM) inverters. The coexistence of GFM and GFL resources introduces complex, nonlinear interactions, which become even more challenging under topological reconfigurations or structural changes in the power systems. This paper proposes a neural network-based spatiotemporal model for dynamic VPP representation using graph convolutional networks (GCNs) and long short-term memory (LSTM) networks. The GCN captures both the static and dynamic structural topology of an 8-bus VPP system, while the LSTM models temporal behavior. The combined architecture effectively learns the interactions among inverter-based resources under various transient and reconfigured scenarios. High-fidelity Electro-Magnetic Transient (EMT) simulations validate the proposed method, demonstrating superior accuracy and better representation of dynamic behavior compared to conventional benchmark approaches. The framework provides a scalable solution for data-driven transient stability analysis, even under evolving system structures.

The design of underground cable systems must account for the risk of soil drying out due to heat dissipation, which can degrade cable performance and lead to environmental concerns. This study investigates a cost-effective cable rating methodology tailored to South African conditions, where native soils are used instead of engineered backfill. Using the IEC 60287 standard, an Excel-based calculation tool is developed to assess the effects of key installation parameters, including soil thermal resistivity, ambient soil temperature and cable laying depth. Soil samples from Sandton, South Africa, revealed thermal resistivity ranging from 0.596 K·m/W, at 14.5% moisture, to 3.72 K·m/W, at 0% moisture, resulting in current ratings from 518.34 A to 224.21 A. Worst-case conditions—high resistivity, increased depth, 1150 mm and elevated soil temperature, 28°C—reduced ampacity by over 45%. The findings underscore the need to incorporate site-specific soil data and worst-case assumptions into cable rating designs to prevent thermal degradation. The developed method offers a practical, locally optimised alternative for utilities in semiarid regions.

The consequences of fossil fuel consumption are increasingly evident through various climate anomalies and severe environmental impacts. Renewable energy sources have emerged as popular alternatives due to their zero-emission generation. However, the intermittent nature of renewables introduces uncertainty in the techno-economic operation of power systems. This article presents a novel adaptive penetration approach designed to maximize profit while minimizing tail-end risk for economic participation in the power market. The proposed adaptive strategy dynamically adjusts renewable energy penetration between 20% and 80%, based on real-time renewable energy availability. A Discrete-Time Markov Decision Process (DTMDP) is employed for decision-making and profit estimation, incorporating probabilistic renewable generation models and energy storage arbitrage operations. Profit and risk are evaluated over a 24-h horizon across twelve months, with tail-end risk quantified using Conditional Value at Risk (CVaR). This study models wind and solar energy generation probabilistically and integrates a two-stage energy storage arbitrage system. In the first stage, excess renewable generation is stored when supply exceeds demand, while in the second stage, stored energy is dispatched during power shortages. The IEEE 14-bus system with hybrid generation is used as the case study. The adaptive approach is compared with static renewable penetration levels of 20% and 80%. Results show that while 20% penetration yields lower tail risk, it also produces lower profits. Conversely, 80% penetration results in higher profits but comes with increased tail-end risk. Additionally, months with lower renewable energy probabilities, such as December, exhibited higher tail-end risk compared to months like July with higher renewable availability. The adaptive penetration strategy achieved higher profits than the 20% scenario while maintaining lower tail-end risk than the 80% scenario, demonstrating its effectiveness in balancing profitability and risk.