International Transactions on Electrical Energy Systems最新文献

Due to the intermittent and fluctuating nature of wind power generation, it is difficult to achieve the desired prediction accuracy for wind power prediction. For this reason, this paper proposes a combined prediction model based on the Pearson correlation coefficient method, multivariate variational mode decomposition (MVMD), African vultures optimization algorithm (AVOA) for leader–follower patterns, convolutional neural network (CNN), long short-term memory (LSTM), and attention mechanism (AM). Firstly, the Pearson correlation coefficient method is used to filter out the meteorological data with a strong relationship with wind power to establish the wind power prediction dataset; subsequently, MVMD is used to decompose the original data into multiple subsequences in order to handle the meteorological data better. Thereafter, the African vultures algorithm is used to optimize the hyperparameters of the CNN-LSTM algorithm, and the AM is added to increase the prediction effect, and the decomposed subsequences are predicted separately, and the predicted values of each subsequence are superimposed to obtain the final prediction value. Finally, the effectiveness of the model is verified using data from a wind farm in Shenyang. The results show that the MAE of the established MVMD-AVA-CNN-LSTM-AM model is 2.0467, and the MSE is 2.8329. Compared with other models, the prediction accuracy is significantly improved, and it had better generalization ability and robustness, and better generalization and robustness.

This paper introduces a pioneering model for short-term planning of an energy hub (EH) that goes beyond traditional approaches by considering a comprehensive multicarrier energy system. The proposed model focuses on minimizing energy buffering costs while ensuring system operation and optimizing economic performance. The novelty of this study lies in its integrated approach, which simultaneously addresses operational efficiency, energy storage requirements, and overall system performance. The EH in this study is modeled as a prosumer within a day-ahead energy market, where both inflows and outflows of energy are optimized. The system’s capability to interact with upstream energy networks, including gas, heat, and electricity, is a critical aspect of the model. This interaction is managed through various technologies that enhance the hub’s ability to meet local demands efficiently. By employing an advanced improved particle swarm optimization (IPSO) algorithm, this model solves the complex multiobjective optimization problem inherent in EH management. The proposed model’s effectiveness is validated through extensive simulation on a test system, where its performance is compared with conventional heuristic optimization algorithms. The results demonstrate the superior efficiency and applicability of the IPSO algorithm, confirming that the proposed model offers a significant advancement in the field of sustainable energy management.

Electric vehicles (EVs) have caught significant attention in recent years due to their potential to reduce greenhouse gas emissions and dependency on fossil fuels. The reliability analysis of power electronic (PE) converters in EVs is crucial to improve their performance, cost-effectiveness, and long-term viability. In this paper, the lifetime estimation of IGBT in a hybrid EV unidirectional converter is evaluated based on control system impacts and statistical model uncertainties. For this purpose, a closed-loop model of a hybrid EV is developed in MATLAB using the Artemis mission profile to simulate the unidirectional converter output power. In the next step, the average model of the nonideal boost converter with Kharitonov’s controller is employed to calculate the IGBT losses. The robust controller is able to maintain converter model stability during long-term output power mission profile simulation. By applying the thermal impedance, the junction temperature profile of the switch is obtained, enabling lifetime analysis via rain flow (RF) and Miner’s rule methods. The results show that the controller selection considerably affects total consumed lifetime (TCL). Each controller can have a different TCL compared to other choices. Since the model coefficient for solder joint and bond wire failure mechanisms have been obtained based on the accelerated test results in the empirical method, considering the parameter statistical distribution and utilizing the Monte Carlo (MC) method can create a better view in the selection of IGBT and the converter design. Furthermore, based on the statistical results and the probability density function, it is feasible to determine how many percent of the IGBTs in the statistical community are damaged after a certain time. The B₁₀ parameter for the failure mechanisms of bond wire and solder is 11.2 and 450 years, respectively. This approach provides insights into risk assessment and design optimization.

Electricity and hydrogen have emerged as viable alternatives to traditional fossil fuels, playing a crucial role in clean and sustainable transportation solutions. The rapid growth of hydrogen vehicles (HVs) and electric vehicles (EVs) has significantly increased the demand for electricity–hydrogen hybrid charging stations (HCSs). Compared to the existing literature that predominantly focuses on optimal energy management from a system-level perspective, this paper explores power management in multiple HCSs and multienergy trading between HCSs and vehicles. In the proposed energy trading mechanism, the EVs and HVs are enabled to strategically submit their offer prices to maximize their utilities. Based on these prices, the aggregator allocates electricity and hydrogen and determines the final payments for the vehicles, aiming to maximize social welfare within the system, subject to the operational constraints of the HCSs. The theory of the Vickrey–Clarke–Groves (VCG) mechanism is employed to design the energy trading mechanism. Furthermore, we introduce the concept of information rents to address potential budget imbalances for the aggregator, enhancing the economic stability of the system. We also provide theoretical proofs for the properties of the proposed mechanism, which include truthfulness, individual rationality, and social welfare maximization. Simulation results demonstrate the effectiveness of the proposed mechanism and verify its three properties.

Combinations of technical advances in artificial intelligence of things (AIoT) are becoming increasingly fundamental constituents of smart houses, buildings, and factories in cities. In smart grids that ensure the resilient delivery of electrical energy to support cities, effective demand-side management (DSM) can alleviate ever-increasing electricity demand from customers in downstream grid sectors. Compared with the traditional intrusive load monitoring (ILM) approach used by energy management systems (EMSs), energy disaggregation, which is an EMS component instead of the ILM approach, can monitor relevant electrical appliances in a nonintrusive manner such that an effective DSM scheme can be achieved. In this study, a distributed horizontal federated learning (HFL)–based energy management framework that implements an active privacy-preserving and edge–cloud collaborative computing–based energy disaggregation algorithm for smart mains energy disaggregation to energy-efficient smart houses/buildings is proposed, and its preliminary implementation, in which active two-stage energy disaggregation considering edge–cloud collaborative computing for autonomous AI modeling is achieved under HFL preserving user data privacy, is demonstrated. In the proposed framework, edge computing that collaborates with the cloud to form edge–cloud computing can serve as converged computing from which load data gathered by distributed on-site edge devices for online load monitoring/smart energy disaggregation are globally consolidated through an artificial intelligence (AI) model in the cloud (cloud AI) and which the model that realizes global knowledge modeling is then deployed for global AI deployment at the edge (edge AI) via global knowledge sharing. In addition, edge–cloud collaboration based on HFL not only improves data privacy and data security but also enhances network traffic, as it exchanges AI model updates (model weights and biases) for global collaborative AI modeling. This is the promising achievement, instead of transmitting raw private real-time data to a centralized cloud server for traditional model training. Simulations are conducted and used to demonstrate the feasibility and effectiveness of the proposed framework for smart mains energy disaggregation as an illustrative application paradigm of the framework; the overall load classification rate can be improved by a maximum of approximately 11% as reported from simulation results.

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.