International Transactions on Electrical Energy Systems最新文献

The increasing penetration of electric vehicles (EVs) presents both challenges and opportunities for integrated transportation and power systems. This paper addresses the pricing issues of distribution networks and charging stations (CSs) simultaneously, proposing a bilevel noncooperative pricing methodology that considers traffic flow, power flow, and renewable energy integration. Key stakeholders—including distribution networks, CSs, and EVs—are thoroughly analyzed, with EV charging behavior modeled through a combination of charging probability, pricing, detour distance, and charging level. The upper-level model focuses on optimal economic scheduling and calculates locational marginal prices using a power flow trace method. Meanwhile, the lower-level model represents CS price adjustments as a noncooperative game, solved via a greedy algorithm. To validate this pricing methodology, an integrated traffic and power distribution network testbed based on the Dublin area was established. Results demonstrate that the proposed dynamic price of the game (DPG) significantly enhances the EV charging market environment compared to traditional time-of-use tariffs or flat rates. Notably, the DPG improves the profitability and service ratio of CSs located near wind farms, with daily profits for these stations increasing by an average of 17.55% and 17.03% compared to the other pricing mechanisms. Furthermore, the average daily utilization rate of these CSs rose by 7.08% and 6.42%. In terms of promoting renewable energy use and alleviating traffic congestion, the DPG also outperforms the other pricing strategies by effectively adjusting charging prices to influence EV drivers’ charging behavior. This dynamic pricing strategy is poised to be widely applicable in future integrated transportation and power systems with high levels of renewable energy penetration.

This paper proposes a multiagent reinforcement learning (MARL) approach to optimize energy management in a grid-connected microgrid (MG). Renewable energy resources (RES) and customers are modeled as autonomous agents using reinforcement learning (RL) to interact with their environment. Agents are unaware of the actions or presence of others, which ensures privacy. Each agent aims to maximize its expected rewards individually. A double auction (DA) algorithm determines the price of the internal market. After market clearing, any unmet loads or excess energy are exchanged with the main grid. The New Energy Lab (NEL) at Staffordshire University is used as a case study, including wind turbines (WTs), photovoltaic (PV) panels, a fuel cell (FC), a battery, and various loads. We introduce a model-free Q-learning (QL) algorithm for managing energy in the NEL. Agents explore the environment, evaluate state-action pairs, and operate in a decentralized manner during training and implementation. The algorithm selects actions that maximize long-term value. To fairly consider the algorithms for both customers and producers, a fairness factor criterion is used. QL achieves a fairness factor of 1.2643, compared to 1.2358 for MC. It also has a shorter training time of 1483 compared with 1879.74 for MC and requires less memory, making it more efficient.

With the large-scale entry of electric vehicles into the grid, the impact on the new power system with new energy as the main status is gradually expanding. Utilizing V2G technology to make vehicle–network interaction, a two-layer hybrid game energy management transaction method for multisource microgrid clusters is proposed. The upper layer constructs a microgrid group transaction model containing an energy management system based on a cooperative game; the lower layer constructs a master–slave game model with each microgrid as the leader and its interest as the objective function, and the follower EV aggregator adjusts the charging and discharging time according to the net power to strive for its maximum interest. The model is optimally solved by the CPLEX solver through simulation cases, and the results verify the effectiveness and superiority of the proposed two-layer hybrid game model.

The advantages of DC distribution over AC distribution, combined with greater penetration of photovoltaic (PV) systems, have enhanced the popularity of DC microgrids. With the intermittency of a PV system, power management in a DC microgrid is an issue, but it can be addressed by using a battery energy storage system (BESS) as a backup. The goal is to maintain a constant DC-link voltage while balancing demand and supply. The study establishes a hybrid control approach for a DC microgrid involving PV, BESS, and DC loads, utilizing both the PV system and the BESS. PV will operate as a primary voltage regulator, making BESS a secondary control, resulting in decreased battery consumption and extended battery life. To achieve this objective, a flexible power point tracking (FPPT) algorithm is suggested, which requires the PV to track the load profile by adaptively modifying its PV power output. The effectiveness of the devised control method is tested by running time domain simulations on several case studies. To assess the adapted system’s tolerance to seasonal changes, k-means clustering is utilized to generate a cluster of irradiance profiles. These clustering solar irradiance and load profiles were simulated for 24 h to illustrate the resilience of the devised control method.

This paper focuses on developing power management strategies for hybrid energy storage systems (HESSs) combining batteries and supercapacitors (SCs) with photovoltaic (PV) systems. The proposed control scheme is based on proportional-integral (PI) controllers optimized with particle swarm optimization (PSO) and duplicate particle swarm optimization (DPSO) algorithms. The aim is to reduce peak current and the energy management system’s response time while enhancing the system’s stability during the charging and discharging of the HSS under various operating conditions. A comparative study with other tuning methods is presented to demonstrate the effectiveness of the proposed DPSO algorithm in particle duplication, population diversity, and the convergence speed toward the global optimum, enhancing the overall system’s performance. The results demonstrate the feasibility and robustness of the PI − DPSO in providing quick and accurate responses even under variable load, variable solar irradiations, and variable temperature, thus enhancing the dynamic response of the SC and reducing battery stress, resulting in a longer lifespan for the HESS.

The intermittent nature of distributed energy resources (DERs) has introduced significant challenges in power system operations, particularly in terms of flexibility, efficiency, and market participation. Aggregating DERs into a virtual power plant (VPP) offers a promising solution to these challenges, but it requires effective strategies to manage the inherent uncertainties and optimize operations across multiple energy markets. This paper develops an optimal bidding strategy for an aggregated multienergy virtual power plant (MEVPP) participating in both the day-ahead (DA) energy market and the frequency regulation reserve market (FRRM). To effectively address these uncertainties, we propose a two-stage scenario-oriented stochastic optimization model that aims to maximize revenue and minimize operational costs by incorporating risk management strategies. Then, a novel fast forward selection and simultaneous reduction (FFS&SR) algorithm is proposed, which efficiently generates and refines scenarios, ensuring computational feasibility without compromising accuracy. The proposed VPP’s decision-making problem considers the VPP’s risk-averse nature, employing the conditional value at risk (CVaR) metric as a risk-aversion parameter. Simulation results conducted over a 24-h planning horizon validate the model’s performance, exhibiting superior performance in the bidding market scenarios. Furthermore, the numerical findings compare the risk-neutral VPP framework with the proposed risk-sensitive VPP strategy, revealing a trade-off between expected profit and CvaR, indicating that as the risk aversion parameter escalates, expected profits decline while CVaR value rises, underscoring the importance of risk management in VPP optimization.

Determining the optimal integration of renewable energy–based distributed generation units (DGUs) in electric distribution systems brings many positive technical and economic impacts and contributes significantly for improving the performance of the distribution system. The suitable installation of DGUs is always a challenging problem because the output behaviors of DGUs, specifically photovoltaic units (PVUs) and wind turbine units (WTUs) are strongly affected by stochastic natural conditions. In this study, the main purpose is to address optimal nonlinear constrained problems with three targets in the multiobjective function (MOF) for minimizing (1) total power loss, (2) voltage deviation, and (3) the cost of purchasing energy from the main grid considering the uncertainties of solar irradiance and wind speed in the actual region in Binh Thuan Province, Vietnam. This paper solves three problems related to optimal connection of DGUs in the distribution system considering constant power generation and consumption with unity power factor (PF) (Problem 1), constant power generation and consumption with optimal PF (Problem 2), and time-varying power generation and consumption with optimal PF (Problem 3). Besides, this research also proposes a novel meta-heuristic optimization algorithm called revised coyote optimization algorithm (RCOA) for addressing the optimization problems of the simultaneous integration of DGUs in IEEE 69-bus radial distribution system. The obtained results in above three problems are compared with the original and published methods to demonstrate the superior economic and technical benefits of the proposed method.

The conversion loss is the significant challenge due to the usage of multiple converters at different stages of a power distribution system. These stages include distribution of energy, energy storage, grid integration, and energy demand management. The conversion losses at each stage adversely impacts the performance of the power system, especially toward energy conservation if efforts are made toward it. To address this, a novel microgrid (MG) energy management scheme is introduced to mitigate conversion losses in distribution systems specifically under weak MG environment. This scheme employs a sophisticated control algorithm that assesses the potency of power available on the DC side before initiation of the conversion process. Conversion is executed only when available power meets the specific level. Otherwise, it is diverted and stored in a battery bank to prevent high losses. In this scenario, the AC loads are supplied by the utility grid while solar and battery bank catered the DC loads. The conversion process is selectively activated, prioritizing its use during indispensable circumstances. By optimizing conversion losses, this work reduces the energy prices by 1.95%. The proposed scheme guarantees economical deployment and affordability because of its effectiveness in a weak MG environment, thus promoting sustainable energy resources.

This paper aims to propose, study, and implement a non-isolated trans-inverse high step-up SEPIC-based DC-DC converter for photovoltaic applications. To increase the output voltage level, the presented configuration utilizes a three-winding coupled inductor and an improved voltage multiplier cell. However, unlike other coupled inductor-based DC-DC structures, the voltage gain could be enhanced by raising and lowering the secondary and tertiary winding turns ratio, respectively. Furthermore, a passive voltage clamp is employed to reduce the voltage stress on the switch and recover the energy stored in the leakage inductance of the coupled inductor. Hence, a switch with low RDS-ON could be used. Thanks to the soft switching performance of all diodes, their reverse recovery problem is eliminated. The outstanding merits of the converter such as continuous input current and high efficiency make the presented structure a promising solution for photovoltaic applications. In the end, the proposed converter is compared to different types of DC-DC converters to prove its advantages over the converters designed before. To confirm the converter’s performance and theoretical analysis, a 200 W laboratory prototype is implemented that steps up an input voltage of 25 V to an output voltage of 400 V at the switching frequency of 50 kHz. Experimental results are illustrated. At the end, the experimental results are presented to validate the analyses conducted.

Solar energy with hydropower power plants marks a significant leap forward in renewable energy innovation. The combination ensures a consistent power supply by merging the fluctuations of solar energy with the predictable storage provided by hydropower. This research aims to predict high solar irradiance on hydropower plants to maximize active power generation. A novel hybrid decomposed residual ensembling model for deep learning (SBL_TSRA_RW) using models such as autoregressive integrated moving average (ARIMA) and seasonal-trend decomposition using loess (STL) along with prediction and optimization models such as Bidirectional LSTM (Bi-LSTM), and Whale Optimization Algorithm (WOA) methods are used to predict the irradiances. Various forecasting methods, including STL-Bi-LSTM, SBL_TSA_R, SBL_TA_RS, and SBL_TSRA_R models, are assessed to determine their effectiveness in predicting solar radiation. The results show the accuracy of the proposed model, with RMSE and MAE values of 1.85 W/m² and 1.31 W/m², respectively. The proposed SBL_TSRA_RW model results are more accurate than the Bi-LSTM, STL-Bi-LSTM, SBL_TSA_R, SBL_TA_RS, and SBL_TSRA_R models, with RMSE value reductions of 517%, 217%, 151%, 98%, and 1%, respectively.