International Journal of Electrical Power & Energy Systems最新文献

The inherent volatility in electricity prices exerts a significant impact on the dynamic nature of the electricity market, shaping the decision-making processes of its stakeholders. Precise Electricity Price Forecasting (EPF) plays a pivotal role in enabling energy suppliers to optimize their bidding strategies, mitigate transactional risks, and capitalize on market opportunities, thereby ensuring alignment with the true economic value of energy transactions. Hence, this study proposes an advanced deep learning model for forecasting electricity prices one day in ahead. The model leverages the synergistic capabilities of Convolutional Neural Networks (CNN) and bidirectional Long Short-Term Memory networks (BiLSTM), operating concurrently with an autoregressive (AR) component, denoted as CNN-BiLSTM-AR. The integration of the AR model alongside CNN-BiLSTM enhances overall performance by exploiting AR’s proficiency in capturing transient linear dependencies. Simultaneously, CNN-BiLSTM excels in assimilating spatial and protracted temporal features. Moreover, the research delves into the implications of incorporating hyperparameter optimization (HPO) techniques, such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Random Search (RS). The effectiveness of the model is evaluated using two distinct European datasets sourced from the UK and German electricity markets. Performance metrics, including Root Mean Square Error (RMSE) and Mean Absolute Error (MAE), serve as benchmarks for assessment. Finally, the findings underscore the notable performance enhancement achieved through the implementation of HPO methods in conjunction with the proposed model. Especially, the PSO-CNN-BiLSTM-AR model demonstrates substantial reductions in RMSE and MAE, amounting to 16.7% and 23.46%, respectively, for the German electricity market.

With power converters consisting of several components, the reliability of a power converter becomes a major concern. With a switched-capacitor (SC) integrated into multilevel inverter (MLI) topologies, the chances of faults is more due to a much larger number of components than the MLI topologies with multiple isolated dc voltage sources. In this article, several SC-based 5L-MLI topologies have been analyzed for their performance with open-circuit fault (OCF), and based on the analysis, three 5L fault-tolerant MLI topologies have been proposed. The analysis for the proposed topologies have been carried out in terms of power loss analysis using PLECS software and power quality analysis using MATLAB software for different fault case scenarios. Further, the proposed 5L topologies have been compared with a similar state-of-the-art. A hardware prototype has been developed, and results have been discussed for various operating conditions.

The planning of public charging stations is crucial for the growth of electric vehicles (EVs). To improve the accuracy of predicting EV charging demand in urban areas, we propose a charging decision-making model based on fuzzy logic. Meanwhile, the influence of private charging piles is considered to further enhance the accuracy of predicting public charging demand. To address the issue of optimization algorithms easily getting stuck in local optima due to the vast quantity of variables involved in the location and capacity planning process, this paper introduces a two-layer planning method. In specific, the upper-level location model optimizes the locations of charging stations, while the lower-layer capacity model determines the number of charging piles within each station, leading to a reduction in the number of variables for each layer. Moreover, through iterative exchange results between the upper-layer location model and lower-layer capacity model, the optimal solution can be attained. The simulation results demonstrate that the proposed method can simultaneously consider the perspectives of both EV drivers and charging station investors, while also enhancing the utilization rates of public charging piles.

Small-signal stability analysis is essential for the safe operation and parameter design of power electronic-dominated grids. Unbalanced conditions introduce more frequency-coupling terms, which are unneglectable in the stability assessment. This paper proposes a novel Linear Time-Periodic (LTP) theory-based stability analysis method and focuses on grid-connected voltage-source converters (VSCs) without improved unbalancing controls. Firstly, the analysis of fundamental solutions of the LTP system reveals that each LTP mode is characterized by a unique damping factor and multiple oscillation frequencies, defined by LTP eigenvalues and related transformation vectors, i.e., $({\lambda }_i,V_i\left(t\right))$. Then, a method for calculating $({\lambda }_i,V_i\left(t\right))$ is proposed using the characteristic matrix of the Harmonic State-Space (HSS) model. An iterative sorting method based on the time-domain interpretation of HSS eigenvalues/eigenvectors is proposed to determine accurate $({\lambda }_i,V_i\left(t\right))$ in the case of the minimum truncation order. Finally, generalized definitions and calculation methods of the widely used indicators for modal analysis are presented to assess system stability and guide the parameter design. Numerical and simulation results verify the proposed stability analysis method and indicate its advantages compared to the existing Floquet and HSS methods.

The widespread deployment of electric buses (EBs) offers substantial flexibility resources to support the service restoration and enhance the resilience of distribution system (DS). Therefore, this paper presents a comprehensive and collaborative service restoration strategy for resilient DS, considering the support of EBs. First, the restoration flexibility model for EBs is derived, which quantifies the restoration capability of EBs by dividing effective EB clusters. This model is integrated into the DS restoration process. Then, a two-step collaborative restoration method of DS is proposed with the aim of minimizing the load curtailment cost. The first step addresses the restoration planning problem to determine the dispatch decisions for EB clusters. Based on above planning solutions, the second step focuses on the real-time restoration problem of DS, obtaining optimal restoration results through coordinating EB clusters and other local resources. In this stage, the impact of natural disasters on the EB cluster dispatch in transportation system is considered, and a rolling optimization method is employed to deal with uncertainties from renewables, load, and EB cluster dispatch time. Finally, numerical simulations are conducted using the modified IEEE 33-bus distribution test system and a 29-node transportation system to verify the effectiveness of the proposed restoration method.

In the realm of active distribution networks, the Inverter Interfaced Distributed Generator (IIDG) poses a challenge with its diverse non-linear fault outputs stemming from varied control strategies and objectives. This presents a dilemma, balancing computational precision and speed in short-circuit current calculation. To address this issue, a novel methodology is proposed, utilizing a Graph Attention Network (GAT)-based model. This model is designed for rapid and precise computation of IIDG fault outputs, thereby enhancing the efficiency of the iterative calculation process. Integration of the superposition theorem significantly boosts the efficiency of short-circuit current calculation in active distribution networks. Moreover, the proposed approach successfully overcomes common problems, such as reduced accuracy and non-convergence, often encountered due to the dynamic nature of network structures. The utility and adaptability of this method are illustrated through various examples, showcasing its ability to accommodate networks with unknown structures and increased branch circuits, while ensuring consistent and reliable computational results.

Virtual power plants (VPPs) are encountering multiple challenges due to market uncertainties and power network instability. In this paper, a novel probabilistic prediction-based multi-objective optimization framework for VPP is proposed to maximize operating profit while minimizing pollutant emissions and voltage deviations in the distribution network, which considers the uncertainties of wind power and electricity price. In this framework, the VPP that participates in the energy and ancillary service markets firstly aggregates the wind farms, the electric vehicle charging stations (EVCS), and the combined cooling, heating, and power subsystems to improve the utilization efficiency and operational flexibility of multiple energy sources. Then, a new Pareto optimizer, called multi-objective hybrid sand cat swarm optimization and strength firefly algorithm, is proposed to tackle the multi-objective optimization model of VPP. The proposed hybrid algorithm utilizes the advantages of sand cat swarm optimization and strength firefly algorithm mechanisms to facilitate local exploitation and global exploration. Finally, a new deep reinforcement learning probabilistic prediction approach based on quantile regression deep deterministic policy gradient is modeled to evaluate the uncertainties. The proposed models and methods have been thoroughly discussed on a modified distributed network. It is calculated that compared with the VPP without EVCS, the operating profit of the proposed VPP increases by 18.69%, and the emissions and voltage deviation of the proposed VPP are reduced by 3.42% and 10.44%, respectively. Experimental results also prove that the performance of the proposed Pareto optimizer and probabilistic prediction approach is superior to other benchmark techniques.

Inter-area frequency modes of electromechanical oscillations are decisive factors that influence the performance of electrical power systems. However, the accurate estimation of these frequencies remains a challenge due to the non-linear behavior of the response system following a disturbance and the strong coupling among the frequencies. This work describes a curve fitting and electromechanical frequency estimation algorithm, called Multi-method Piecewise Identification (MPI), based on a combination of the Matrix Pencil Method (MPM), the Prony method, and Subspace Identification (SSI), choosing the best approximation of an interval basis according to the model accuracy index (MAI) and the mean square error (MSE). The advantages and limitations of MPI were analyzed by computational analyses that were carried out with Python-based code that estimates the low frequencies for several ringdown signals, including oscillating signals from Australian and Brazilian systems that arise after large disturbances. To ensure robustness to random and noisy conditions, the method was tested with a synthetic signal with Gaussian white noise and time-variant frequencies, and compared to the methods mentioned above, the Hilbert–Huang Transform (HHT), and Eigensystem Realization Algorithm (ERA). The MPI has shown robust results and is capable of obtaining a better fitting for the signal than the other methods.

In today’s power systems operation, the dual challenge of optimizing economic load distribution while minimizing power plant emissions is pivotal. This challenge is accentuated by the pressing environmental concerns and the finite nature of fossil fuel reserves. In this context, renewable energy sources, notably wind power, have emerged as indispensable alternatives due to their cost-effectiveness and environmental compatibility. However, the inherent variability of wind velocity introduces uncertainty into power output, necessitating innovative approaches to address this complexity. To tackle this issue, we propose a scenario-based probabilistic approach that dynamically considers the slope rate of power output. By leveraging the Blue Whale multi-objective algorithm and employing the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) criterion, we identify significant solutions from the Pareto set across a spectrum of scenarios. Our method is rigorously evaluated across various systems and operational contexts, revealing its superiority over alternative algorithms. Specifically, our approach achieves lower objective function values, reduced standard deviation, and superior overall performance. These findings underscore the critical importance of efficient power system management in balancing environmental sustainability and economic viability. By embracing innovative methodologies, we can navigate the evolving energy landscape and contribute towards a more sustainable future.

For a few decades, operators of energy systems have sought to achieve appropriate frameworks due to energy crises and rapid growth in energy requirements. In this regard, this study presents a multi-objective optimization model for an energy hub (EH) designed to manage a diverse energy portfolio. The EH receives electricity, natural gas, hydrogen, seawater, and solar energy as inputs, aiming to satisfy electricity, heating, and freshwater demands at the output port while considering a limited available area. The model incorporates the selection of the optimal solar energy technology (photovoltaics, parabolic dish, or parabolic trough collector) through a comprehensive evaluation encompassing technical, economic, and environmental aspects. To achieve optimal scheduling of the EH’s production units, the model factors in forecasts of solar energy availability alongside electrical, heat, and water load demands. The evaluation of the EH’s performance is conducted through a multi-objective framework considering social welfare, CO₂ emissions, voltage stability margin (VSM), a newly proposed simplified fast temperature stability index (SFTSI), and a similarly novel simplified fast pressure stability index (SFPSI). The optimization problem is formulated within a MATLAB environment and solved using a multi-objective Archimedes optimization algorithm across five distinct case studies, each characterized by a varying designated area for solar energy generation. The effectiveness of the proposed model and optimization technique is validated through test systems, with the obtained results demonstrating significant improvements compared to a baseline scenario. These improvements include a 36.18% reduction in CO2 emissions, a 14.22% increase in total social welfare, and reductions in the average values of VSM, SFTSI, and SFPSI when incorporating all solar energy technologies.