International Transactions on Electrical Energy Systems最新文献

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.

Brushless DC motor (BLDCM) drives often experience significant current and torque ripples, which negatively affect the overall performance. Moreover, the dynamic characteristics of BLDCMs such as low mechanical oscillations and tight speed regulation can introduce undamped AC components that destabilize the DC-link supply (DCLS). This study presents a novel compensation strategy to address DCLS instability in BLDCM drives. A quasiproportional-resonant compensator (QPRC) is proposed to enhance DCLS stability. This paper explores a QPRC-based stabilization approach for the DCLS in an in-front zeta converter (IFZC)–assisted BLDCM drive. The IFZC is mainly employed for voltage regulation while the QPRC is integrated to suppress the undamped AC signals in the DC link. A hardware prototype has been developed to validate the proposed control strategy. Experimental results confirm that the suggested stabilization strategy effectively enhances DC-link stability and improves the overall performance of the BLDCM drive.

Multienergy distribution network (MEDN) with high penetration photovoltaics (PVs) may suffer from sharp voltage fluctuations and increased network losses. Existing methods struggle to achieve voltage control due to challenges such as high interarea communication latency and difficulties in power flow modeling caused by low coverage of measurement devices. To address these issues, this paper proposes a multiagent deep reinforcement learning (MADRL) method to realize the collaborative optimization of controllable devices, including hybrid energy storage system (HESS) and PV inverters. Furthermore, under the framework of decentralized partially observable Markov decision processes (Dec-POMDP), we integrate cross-agent attention (CAA) and factored value networks to enhance perception capabilities and improve value function fitting. The proposed method explicitly assigns credit to agents and dynamically captures electrical coupling relationships between agents and buses. The improved IEEE 33-bus and IEEE 141-bus distribution systems were used as case studies to compare with mainstream MADRL. Experimental results demonstrate that after offline deployment, the agents achieve global voltage control based solely on limited local observations within each zone, without relying on a complete power flow model or interarea communication. The comparative experiments verify the effectiveness, robustness, and scalability of this method.

Bangladesh intends to build a 5 GW transmission super-highway from Moheshkhali to Madunaghat to Bhulta to evacuate a bulk amount of power. This study presents the particle swarm optimization (PSO) method for estimating line parameters. The optimum value for inductance is 0.3152 mH/km and capacitance is 3.57 × 10⁻² μF/km. A hexa-bundle conductors are designed using triangle properties. Three types of ACSR conductors such as Moose, Cardinal, and Tern are examined for the proposed EHVAC line. At 75°C, the line resistances of these three conductors are 0.0057, 0.0061, and 0.0074 Ω/km while line losses are 0.0006346, 0.0006791, and 0.0008238 MW/km. The conductor surface gradient is the permissible limit to suppress the audible noise. The value of surface gradient shows 7.4406 kV/cm for Tern conductor while using six conductors per bundle. The results indicate that the hexa bundle Moose, Cardinal, and Tern are promising for the proposed EHVAC line. The transmission capacity based on natural condition, normal condition, and emergency condition is examined for the proposed line. The natural loading (SIL) represents 6232.52 MW of the line for the optimal values of L and C. The corona loss is recorded as 0.422, 0.420, and 0.416 kW/km/phase when the line is made of Moose, Cardinal, and Tern conductors with subconductor spacing of 500 mm. The corona loss is insignificant in fair weather condition. The techno-economic analysis is presented using an economic model. The GDP-based long-term forecasting model is developed to compute the cost and benefit of the transmission system. Future cash flow is estimated using discounted cash flow method. The key economic parameters such as ENPV, EIRR, DPP ensure the economic viability of the high-voltage transmission line project. The life cycle cost of the proposed line is $1701.91 million, while the ENPV of the project is $1577.18 million. The results yield valuable information for the future 765 kV transmission line projects of Bangladesh Power’s grid.

Modern agricultural parks possess substantial photovoltaic (PV) resources, yet there is often hesitation to invest in PV and storage systems (PV–storage systems) due to economic considerations. This study introduces a method to boost the operational economic viability of agricultural parks with a high proportion of PV storage integration through cascaded fuzzy control. This strategy is designed to enhance the expected economic returns, thereby increasing the propensity to invest in PV-storage systems. The method involves a primary fuzzy controller, termed the “microgrid energy assessment module,” which uses a cloud model to determine the membership values based on the park’s PV power generation, load demand, and energy storage status. This assessment estimates the current energy status of the agricultural park microgrid. A secondary fuzzy controller, the “reference power transaction resolution module,” calculates the reference power transactions based on the energy status assessment provided by the primary controller and time-of-use (TOU) electricity pricing. In addition, this study leverages an adaptive genetic algorithm to optimize the fuzzy rule table, thereby refining the control strategy for economic improvement of the park. The park’s cloud-based controller can then utilize these reference power transactions, in conjunction with the storage system’s capacity constraints, to proactively manage the buying and selling of electricity, thus enhancing the park’s operational economic viability. Practical experiments conducted in an agricultural park in China, using an installed cloud controller, side sensor, and optical storage machine, demonstrate the feasibility of the proposed control method. Historical operational data simulation analysis further validates that the implementation of this method can significantly enhance the economic performance of agricultural parks with high PV storage integration. This facilitates faster recovery of investment costs, increased profitability, and supports the development of low-carbon, energy-autonomous agricultural parks.

Against the backdrop of the continuous development of ship informatization, joint scheduling for the entire fleet within a region brings numerous advantages. The optimization of scheduling problems for such a regional fleet, in addition to considering the number of orders completed, reducing operational costs, and further reducing carbon emissions, is also a key research point to address the increasingly severe climate change. This study establishes maritime scheduling strategies for container transport fleets considering energy management. It simulates a shipping company’s operations to meet freight demands among multiple ports. Utilizing reinforcement learning (RL) to choose the optimal scheduling strategy for each individual ship, the study ultimately derives the optimal operational plan for the shipping company. During the process of completing each navigation, every ship will attempt to control its voyage speed for reducing carbon emissions and operating costs. Double deep Q-learning (DDQN) is used to improve the performance of the RL algorithm, and an additional Q Rank network is used to reduce the action and state space. Ultimately, this paper validates the superiority of the model using a case study that includes multiple ports and ships.