International Transactions on Electrical Energy Systems最新文献

The electric vehicle’s overall efficiency can be augmented through the implementation of the regenerative braking system (RBS), which serves to extend the drive range. This paper explores a method for further increasing said range by implementing a roof-mounted photovoltaic (PV) system in conjunction with RBS, resulting in an impressive 25.14% increase to the vehicle’s driving distance. The roof-mounted PV and regenerative braking work together to charge the dual battery pack present within the vehicle, following a specific sequence as dictated by its controller. Maximum energy is extracted from the PV module via employment of a particle swarm optimization (PSO)-based maximum power point tracking (MPPT) algorithm, while propulsion is provided through utilization of a brushless direct current (BLDC) motor driven by reduced switch voltage source inverter (VSI). The BLDC motor itself is controlled utilizing field-oriented control (FoC), with hybrid ANN-PID and Fuzzy-PID (HAP-FUP) controllers employed at the outer loop; furthermore, HAP-FUP controller performance was compared favorably against other top controllers such as model predictive controller (MPC), sliding mode controller (SMC), and conventional PID controller, ultimately demonstrating superior performance when it comes to reducing rise time and settling time of vehicle speed.

Distributed power supply has seen a significant rise because of the increasing demand for renewable energy. This rise has also presented various challenges, including independent operation and a lack of coordination among the sources. As a result of these challenges, these distributed power supplies are combined into a virtual power plant (VPP). This paper addresses these issues of interaction and coordination with a proposal for multitype VPP and modeling technology. First, it recommends establishing a peer-to-peer (P2P) transaction framework. Second, Nash bargaining cooperative game theory comes into play, which involves multiple stakeholders in establishing a P2P transaction framework among stakeholders. After that, this proposed model is solved using the alternating direction multipliers method (ADMM). In the end, the verification of this proposed model is achieved by the numerical example that ensures that the VPP alliance operates stably.

This article presents a high-gain step-up DC/DC converter based on the Cuk topology for renewable energy applications. The converter employs an auxiliary circuit to achieve less input current ripple, significantly reducing the input inductor size compared to conventional converters. Therefore, the inductor’s equivalent series resistance (ESR) will be substantially lowered to improve power efficiency. Introducing passive clamp capacitors further enhances efficiency by alleviating voltage stress on the main power switch. Comparative studies with other similar converters highlight the unique benefits of converter design. This converter uses a coupled inductor (CI) and a voltage multiplier circuit to achieve high voltage gains. The following article introduces the principle of operation for a proposed topology and analyzes voltage gain, voltage stress, and efficiency. A comprehensive comparison with the most recent counterparts is also included. Finally, a theoretical analysis is verified using a 200-W (30 V/310 V) sample prototype.

In response to escalating environmental concerns and the shortage of traditional energy resources, it is essential to enhance energy management within multienergy markets to facilitate the integration of renewable energy sources with end-users. To this end, our research focuses on the optimization of community-level integrated energy systems (CIESs) equipped with combined cooling, heating, and power (CCHP), utilizing a multiagent system (MAS) and a hierarchical Stackelberg game approach. The research begins with the development of an MAS-based optimization framework for game participants, followed by the establishment of a Stackelberg game model where the multienergy seller acts as the leader and the consumer as the follower. The model is thoroughly validated through the formulation and solution of a mixed-integer linear program (MILP)–based multiobjective optimization, which not only demonstrates effective strategies for optimizing power dispatch but also enhances economic returns and ensures fast game convergence. The results significantly validate the approach of maximizing the utilization of clean energy in energy transactions, clarifying the dynamic relationship between price fluctuations and load in the pricing mechanism. These insights are vital for promoting environmental sustainability and conserving resources, proving essential for future energy management practices.

Solar energy-based heating systems, which are capable of providing space heating as well as domestic hot water heating, are a promising alternative to conventional systems to achieve the status of reducing fossil energy consumption in residential buildings. Determining how suitable such systems are performing in Canada and which station is the most suitable in terms of energy-economic-environmental parameters are issues that have not been investigated so far. Considering that such results are very important for energy decision-makers and investors, therefore, in the present work, the provision of space heating and hot water heating on a residential scale in 10 Canadian provinces was done by Valentin TSOL v2021 R3 software. Then nine software output parameters along with three parameters of land price, the population of each station, and the natural disaster index were weighted using the AHP method. Finally, the results of the stations were ranked using five MCDM methods including AHP, TOPSIS, WASPAS, CRITIC, and GRA. The results of numerical simulations showed that the CO₂ emissions avoided parameter has the most weight, and the parameters solar contribution to DHW and boiler energy to DHW has the least weight. Also, the final ranking of each station showed that the most suitable station is Regina and the most unsuitable station is Victoria. By examining and analyzing the results, it was found that only based on the outputs of the Valentin TSOL v2021 R3 software, it is not possible to comment on finding appropriate and inappropriate stations, and the necessity of using ranking methods was observed more than before.

In order to solve the problem of voltage fluctuation caused by the grid integration of wind power cluster, a multitime scale reactive power and voltage optimal regulation method based on model predictive control (MPC) is proposed in this paper. In the day-ahead stage, the reduction of network active power loss is mainly achieved through the regulation of discrete reactive power compensation devices and generator units, focusing on the economic operation of the power system. In the intraday stage, considering the rapid response characteristics of continuous reactive power compensation devices, the optimal regulation aiming to minimize voltage control deviation is carried out under 15- and 5-min time scale based on the MPC algorithm with the adjustment of continuous reactive power compensation devices. In the feedback correction stage, a fast calculation method considering reactive power partitioning is adopted instead of solving the 5-min optimization model for 288 periods, avoiding excessive adjustment of reactive compensation devices. While effectively improving voltage fluctuations caused by wind power prediction deviation, it also alleviated the computational and communication burden on the system. Finally, the effectiveness of the proposed regulation method in this paper is verified with the improved IEEE-39 test case.

This paper presents our initial research findings on distribution system operator (DSO) functions in South Korea. The project team includes the Korean Electric Power Corporation, which is the only public utility company in South Korea. The planned development is based on a real distribution network and real-world environments. The project encompasses DSO–transmission system operator coordination and management of real-time distribution network congestion; this paper focuses on the latter aspect. For real-time congestion management, algorithms that handle all steps from database analysis to data export, including data preprocessing, topological configuration, power flow calculation, sensitivity analysis, and derivation of solutions to congestion, are developed. The distribution system equipment database of Korean Electric Power Corporation is used to develop a network congestion management system. When examining the database, many gaps between the theoretical and practical environments were found which are introduced in this paper. A practical distribution network database for South Korea is developed to narrow these gaps and to implement practical congestion management techniques. The remaining problems are introduced as future works.

Unlike the inverters in the traditional alternating-current (AC) microgrid, those in a microgrid with series microsource inverters (SMSI-MG) are connected to the power grid after being cascaded. The authors of this study first divide the control sections according to the degree of grid voltage dips and formulate a coordinated scheme to suppress fluctuations in the output powers of the SMSI-MG. For the section in which the degree of unbalanced grid voltage dips is relatively low, a current-limiting strategy that reduces the output power of the SMSI-MG through the coordinated control of the generator subunits (CCGU) is proposed. More active power can be provided by the SMSI-MG when the proposed strategy is used, than in the strategy that is based on changing the reference power, and the output reactive power of the SMSI-MG can be automatically changed with the degrees of dip and unbalance of the grid voltage. The Light Gradient-Boosting Machine (LightGBM) is used to establish a mapping relationship between the parameters characterizing overcurrent and the reduction quantity in output active power of the SMSI-MG to implement the CCGU-based current-limiting strategy. The complex collaborative control is simplified to improve the low-voltage ride through (LVRT) capability of the SMSI-MG.

The electric vehicle (EV) has been popular in recent years, which also brings huge challenges to the distribution network due to its energy instability. In order to consider the economic factors of dispatching these distributed renewable resources, the voltage variation is also important. A novel model-free method is put forward for collaborative management of EV resources of aggregators in the distribution network. The economic costs and physical network constraints for this energy management issue are considered at the same time. A Multiagent Deep Deterministic Policy Gradient (MADDPG) algorithm is applied to learn the cooperative energy control strategies. A transfer learning technique is used to fine-tune the trained policy when more aggregators join in the network. The proposed method can achieve close results to the traditional optimization methods, while it takes less than one second to take control actions, making it is more suitable for real-time online energy management. Compared to other advanced reinforcement learning (RL) models, numerical simulations conducted on IEEE test cases greatly illustrate the effectiveness and superiority of the proposed method.

This manuscript investigates the transformative shift in electricity generation and distribution towards distributed power networks, particularly microgrids, amid escalating energy demand and environmental concerns. Emphasizing a pioneering technoeconomic conservation voltage reduction–based demand response approach, the study integrates conservation voltage reduction as a controllable demand response method within distributed power networks, highlighting the developed droop control method for effective regulation. Conservation voltage reduction, a no-cost procedure for minimizing loss, is applied to reduce voltage during peak periods to conserve power, decrease active and reactive power losses through precise load modeling, and enhance consumption efficiency. The most significant challenge of this project is the simultaneous use of conservation voltage reduction with the uncertainties of distributed generation sources, resulting to reduce losses and ultimately lower operating costs, a topic not previously studied in existing literature. The contributions include introducing a novel approach based on droop control to manage resources and presenting a detailed control strategy tailored to distributed power networks for improving voltage stability with minimal costs. Importantly, the proposed method demonstrates superior accuracy, achieving up to an 18% improvement over existing methods. This research contributes to comprehensive solutions for optimizing energy consumption, enhancing grid stability, and adapting to the evolving distributed power systems landscape.