Power System Research Articles

Metaheuristic optimization algorithms for multi-area economic dispatch of power systems: Part I—a comprehensive survey

Multi-area economic dispatch (MAED) provides an indispensable component for the security and economic operation of contemporary power systems. Over recent years, numerous metaheuristic optimization algorithms (MOAs) have surfaced for addressing the MAED problem. However, none of the literature to date conducted a comprehensive statistical research work on the MAED problem. In part I of this series, we present a comprehensive survey on this problem. (1) We collect all eleven reported MAED cases studied over the years. These cases have different structures, scales, and constraints. We illustrate the structures of all cases and provide their corresponding system parameters. (2) We collect all the MOA solution algorithms. These algorithms are inspired by different ways, and we categorize them in detail and review them comprehensively. (3) We list the detailed applications of MOAs on different cases and count the percentage of studies on each case. (4) Finally, we summarize the current research progress and point out the future research directions in terms of MAED models and solution methods, respectively. This survey provides an extensive overview of the MAED cases and its solution methods. It can provide applicable and reference suggestions for future research on the MAED problem.

Research on Automatic Identification of Power Big Data Anomalies Based on Improved Decision Tree

With the advancement of smart grid technology, the issue of power system network security has become increasingly critical. To fully utilize the power grid’s vast data resources and enhance the efficiency of anomaly detection, this paper proposes an improved decision tree (DT)-based automatic identification approach for anomalies in electric power big data. The method employs six-dimensional features extracted from the dimensions of volatility, trend, and variability to characterize the time series of power data. These features are integrated into a hybrid DT-SVM-LSTM framework, combining the strengths of DTs, support vector machines, and long short-term memory networks. Experimental results demonstrate that the proposed method achieves an accuracy of 96.8%, a precision of 95.3%, a recall of 94.8%, and an F1-score of 95.0%, outperforming several state-of-the-art methods cited in the literature. Moreover, the approach exhibits strong robustness to noise, maintaining high detection accuracy even under low signal-to-noise ratio conditions. These findings highlight the effectiveness of the method in efficiently detecting anomalies and addressing noise interference.

Microstructure and Corrosion Behaviors of Gas Tungsten Arc Welds for Borated Stainless Steel Using Various Filler Metals

In this study, the microstructure and corrosion behavior of gas tungsten arc (GTA) welds of borated stainless steel (BSS) with a boron content of 1.62 wt.% were investigated using various filler metals. The filler metals used in this study were 308L, 309L, and 310 without the B component. A small amount of the B component was observed in the weld metal (WM) of all specimens, even though none of the filler wires contained boron. This result was caused by the dilution of the B component from the BM into the WM by the welding heat. The segregation of boron in the WM resulted in Cr-depleted areas, which negatively affected the corrosion resistance of the welded specimens. The corrosion resistance of 308L WM with the highest fraction of B components was the most deteriorated, whereas 309L WM with the lowest boron content exhibited the best corrosion resistance. Using a filler metal without the B component is expected to effectively improve the weldability and corrosion resistance of BSS; however, it can also reduce the neutron absorption capacity. Therefore, for BSS to be used as a spent nuclear fuel storage container material, the boron content of the filler metal must be carefully considered. This study provides a foundation for research aimed at improving the development and applicability of filler metals in borated stainless steel and makes it competitive for application in fourth-generation nuclear power systems.

Volt/VAr Regulation of the West Mediterranean Regional Electrical Grids Using SVC/STATCOM Devices With Neural Network Algorithms

ABSTRACTReactive power compensation is now a challenging issue in maintaining adequate power quality and improving the performance of the transmission system. Many researchers have focused on the behavior of voltage in the unbalanced state, and the majority of the studies have been done with STATCOM. In this study, the increasing power demand in power grids and the challenges associated with maintaining power quality and stability are discussed. The power system in the western Mediterranean region of Turkey is modeled with the DigSILENT Power Factory program using real data. It highlights the importance of reactive power compensation and introduces FACTS devices as a solution to control power factors and reactive power in the grid. In the realistic model, voltage and reactive power regulation is achieved by using SVC and STATCOM devices to ensure voltage stability. A load flow and short circuit analysis is performed on the designed transmission system for a number of critical scenarios. The modeled power system is optimized for the size and location of the FACTS devices by applying genetic algorithms (GAs) and particle swarm optimization (PSO) algorithms to the selected busbars of the FACTS devices, a strategy designed to significantly reduce system losses. The load values used in the heuristics differ from those used in other studies in that they use a realistic load profile that varies randomly and instantaneously over the long term, as opposed to a fixed load profile. All the comparative results show that the STATCOM controller can maintain the most significant reduction in technical losses and excellent performance in controlling the reactive power response under various outages. The effectiveness of the proposed methodology has been validated in various outage scenarios, and it has been shown that it will reduce the total cost of electricity generation in the western Mediterranean region on an annual basis. Considering the results of the Turkish National Transmission System, increasing voltage sensitivity and reactive power control can be beneficial in reducing power transmission costs and maintaining the balance between generation and consumption.

Optimal Scheduling of the Microgrid Based on the Dynamic Characteristics of the Natural Gas Pipeline Network and the Thermal Network Along with P2G-CCS

In the power system, the integration of power-to-gas and carbon capture systems (P2G-CCS) within the microgrid enables the conversion of electrical energy into hydrogen or methane while simultaneously capturing CO2 emissions from power generation units. This approach significantly mitigates carbon emissions and supports the transition to a low-carbon energy system. Concurrently, the dynamic properties of the gas–thermal network exert a critical influence on the flexibility of system scheduling and the regulation of multi-energy coupling. Hence, this paper puts forward an optimal configuration strategy for microgrids with consideration of the dynamic characteristics of the gas–thermal network. Firstly, mathematical models for the dynamic characteristics of the gas network and the heat network were established and incorporated into the microgrid system. Secondly, in conjunction with the P2G-CCS coupling system, an optimization scheduling strategy was formulated with the aim of minimizing the total operational costs of the power grid, the natural gas network, and the heat network. An enhanced African vultures optimization algorithm (AVOA) was put forward. In the end, by setting different scheduling scenarios for conducting a comparative analysis, an appropriate optimization configuration scheme was selected, and the validity of the proposed method was verified through simulation with the improved case study.

Estimation of Fuel Cell Power Demand on Commercial Vehicles Based on Improved Multiple Grey Prediction Method Considering Dynamic Time Window

A frequently large variation in load conditions is of great impact on the service life of fuel cells during the operation of fuel cell vehicles and further increases the maintenance cost of the system. This study proposed a method of power demand prediction of the proton exchange membrane fuel cell (PEMFC) on vehicles in the actual traffic environment. The hybrid power system topology of fuel cells and power battery on commercial vehicles is selected to build a fuel cell model, and the accuracy of the fuel cell model is verified. An improved multiple grey prediction method is then proposed to predict the power demand during the sampling period of the fuel cell while considering a dynamic time window in the prediction period. Comparisons were made between this proposed model and the other two prediction models as a single-step prediction and multi-step prediction. Data of CHTC-HT and field testing working conditions were used to evaluate these three prediction models in fuel cell power demand. Results showed that the multiple grey method showed a better prediction performance than the other models, indicated by the lowest error value of 16.944% under the CHTC-HT condition, the lowest error value of 2.169% under stable conditions with less variable load and 1.930% under dynamic conditions with frequent load changes in field testing. This study of the demand power prediction can be devoted to pre-tuning the fuel cell system to avoid performance degradation caused by unanticipated power fluctuation.

ADAPTIVE HYBRID DIFFERENTIAL EVOLUTION PARTICLE SWARM OPTIMIZATION ALGORITHM FOR OPTIMIZATION DISTRIBUTED GENERATION IN DISTRIBUTION NETWORKS

The incorporation of Distributed Generation (DG) into the Radial Distribution System (RDS) aids in resolving power system issues such as power loss. However, finding the ideal location and size for DG is challenging yet crucial for maximizing these advantages. Inappropriate location and sizing of DG can negatively impact its benefits, highlighting the need for effective optimization methods. To address these challenges, metaheuristic methods like Particle Swarm Optimization (PSO), Differential Evolution (DE), and Firefly algorithms are particularly useful. This research aims to determine the best location and size of DG to minimize active power losses in the RDS. Despite the success of DE and PSO in previous works, a gap remains in achieving optimal convergence performance and computational efficiency. Building on the success of DE and PSO, this study presents a hybrid optimization approach, Adaptive Hybrid Differential Evolution and Particle Swarm Optimization (AHDEPSO), to reduce power loss in RDS. This approach combines the strengths of DE and PSO, enhancing exploration and exploitation capabilities while improving convergence performance. The effectiveness of this approach is demonstrated through testing on the IEEE 33 and IEEE 69 bus systems using MATLAB software, showing that AHDEPSO can achieve optimal computational time and best fitness function, being 38.3% faster than other algorithms. This hybrid approach offers a significant improvement over traditional methods, filling the research gap and providing a more efficient solution.

An Attentional Graph Neural Network-Based Fault Point Positioning Model for Low-Voltage Distribution Networks

With the rapid development of the smart grid, the fast and accurate fault location of low-voltage distribution networks has become the key to ensuring the stability and reliability of the power supply. This paper aims to explore and construct a fault location model of low-voltage distribution network based on an attention diagram neural network. First, this paper analyzes the current situation and challenges of fault location in low-voltage distribution network, and points out that traditional methods have limitations when processing large-scale and high-dimensional power system data. Subsequently, a graph neural network (GNN) is introduced for processing graph-structured network data, and combined with attention mechanisms. Thus, an innovative attention-graph neural network model (named as A-GNN) is proposed for the purpose. The model can make full use of the topology structure and node feature information in the power grid, and dynamically adjust the information aggregation weight between different nodes through the attention mechanism. This is expected to achieve efficient and accurate fault location. In the experimental part, we trained and tested the A-GNN model based on the real low-voltage distribution network dataset, and compared it with several prediction models. The experimental results show that the A-GNN model has higher accuracy and recall rate in fault location tasks, especially in complex fault scenarios.

Adaptive weight adjustment technology for reinforcement learning reward indicators based on the new-type power system

Current RL-based scheduling methods struggle with designing effective reward functions that balance cost reduction, safety and renewable energy integration. To tackle these issues, this paper proposes a novel hybrid optimization framework that combines Particle Swarm Optimization and Reinforcement Learning (PSO-RL), focusing on three key aspects: (1) Minimizing Generation Costs: Systematically reducing operational costs for economic efficiency. (2) Enhancing System Safety: Improving reliability by incorporating operational constraints. (3) Increasing Renewable Energy Proportion: Emphasizing the integration of renewable energy sources. The comprehensive mathematical model encapsulates intricate constraints and multi-objective characteristics of the new-type power system, featuring reward functions tailored for dynamic responsiveness. Empirical validations using the Grid2Op scheduler affirm the efficacy of our framework. Results highlight superior convergence speed and optimization precision of the PSO-RL approach over traditional RL methods. In summary, this study advances scheduling methodologies for the new-type power system, fostering renewable integration and smart grid technology evolution.

Assessing moisture content in XLPE power cables using frequency-dependent tangent delta measurements

Abstract The reliability of cross-linked polyethylene (XLPE)-insulated power cables, mostly used for electricity distribution and transmission in urban areas, is often compromised due to the lack of effective condition-based monitoring systems. This leads to difficulties in early fault detection and increased risk of unscheduled outages, and failures, particularly in cable end terminations. This research aims to develop a robust and accurate methodology for estimating moisture content in XLPE cable end terminations to enhance condition-based monitoring and ensure reliable power delivery. The study employed frequency domain spectroscopy to examine the dielectric properties of XLPE cable samples with varying moisture content levels. A novel predictive formula was derived to estimate moisture content using dielectric frequency response (DFR) supported by validation through sweep frequency response analysis (SFRA) tests. We conduct an experiment to estimate the moisture content and observe satisfactory agreement between the experimental and theoretical values. The proposed method achieved a high accuracy of 96%; the percentage error variation was minimal in moisture estimation, outperforming existing techniques. This study provides a practical and reliable approach for monitoring XLPE cable condition, particularly at end terminations. The proposed methodology ensures accurate moisture content estimation, enabling early fault detection and reducing the risk of unscheduled outages. This advancement has major implications for enhancing the reliability and efficiency of urban power systems.

A Review of Distribution Network Expansion Planning

Distribution Networks Expansion Planning (DNEP) is very complex and involves improving the system to meet the increasing demand using the most cost-effective strategy. Among the planned choices are the extension of substations, upgrade of distribution feeders, installation of additional Distributed Energy Resources (DERs), installation of Capacitor Banks (CBs) and many other methods. Distribution planners in contemporary networks must have faith in the reversibility of investments where Renewable Energy Resources (RERs) inject clean and cost-effective for DNEP to meet growing demand and environmental requirements. The comprehensive review of DNEP carried out in this paper covers all possible objective functions and problem constraints. With the rise of electric vehicles (EVs), there is a growing need to assess the impact of EV charging on distribution networks. Understanding how EV loads affect the network helps plan future expansions to efficiently accommodate the charging infrastructure. Integrating DERs, such as solar panels, wind turbines, and energy storage systems, is changing how electricity is generated, distributed, and consumed. Assessing the integration of DERs into distribution networks is crucial for optimal network planning and operation. In addition, CBs are essential for power factor correction and voltage regulation in distribution networks. Including CBs in expansion planning helps improve network efficiency, reliability, and overall power quality. By analyzing the impact of EV loads, DERs, and CBs in DNEP, researchers and planners can develop more accurate models and strategies for designing sustainable and resilient power systems to efficiently meet the growing energy demands.

Power Quality Improvement with Three-Phase Shunt Active Power Filter Prototype Based on Harmonic Component Separation Method with Low-Pass Filter

This work contributes to both Romania’s and the European Union’s energy policies by highlighting the research results obtained within the Dunarea de Jos University of Galati, but also through the technological transfer of this knowledge to the industry. In order to improve the power quality of the nonlinear loads connected to the electrical grid, a three-phase shunt active power filter prototype based on the Harmonic Component Separation Method with a Low-Pass Filter was used. The active power filter is connected at the Point of Common Coupling to compensate for individual loads or even all of them simultaneously. Therefore, active power filters can be used to compensate for the power factor and reduce the harmonic distortion of power supplies, or for processes subsequently connected to additional nonlinear loads, thus improving the energy efficiency. The shunt active power filter prototype is composed of the power side (three-phase insulated gate bipolar transistor bridge, DC link capacitor precharge system, inductive filter) and the control side (gate drive circuits, control subsystems, signal acquisition system). The filter control strategy is based on the principle of separating harmonic components with a low-pass filter, implemented by the authors on the industrial prototype. In this paper, the main technical features of the industrial shunt active power filter prototype are specified. The authors of this paper involved three cascaded control loops: the DC link voltage control loop, the shunt active power filter current control loop and the phase-locked loop. Both simulation and experimental results for the shunt-type active power filter prototype were obtained. By analyzing the obtained waveforms of the power supply source in two cases (with and without an active power filter), a decrease in the total harmonic distortion was demonstrated, both the voltage harmonic distortion factor THDu and the current harmonic distortion factor THDi in the case of the active power filter connection. By using the Field-Programmed Gate Array processing platform, the powerful computational speed features were exploited to implement the active shunt power filter control on an experimental test bench. Conducting source current harmonics mitigation increased the efficiency of the power system by decreasing the respective harmonic Joule losses. The energy-saving feature led to the increased added value of the parallel active power filter. Through the performed laboratory tests, the authors demonstrated the feasibility of the proposed control solution for the industrial prototype. In accordance with the European Union’s Research and Technological Development Policy, the development of an innovation ecosystem was taken into consideration. The unified and efficient integration of all the specific actors (enterprises, research institutes, universities and entrepreneurs) in innovation was achieved.

A Coordinated Frequency Regulation Strategy Integrating Power Generation, Energy Storage, and DC Transmission for Offshore Wind Power MMC-HVDC Transmission Systems

With the increasing proportion of renewable energy in power grids, the inertia level and frequency regulation capability of modern power systems have declined. In response, this paper proposes a coordinated frequency regulation strategy integrating power generation, energy storage, and DC transmission for offshore wind power MMC-HVDC transmission systems, aimed at improving the frequency stability of onshore power grids. First, considering the inability of the receiving-end MMC-HVDC converter station under constant DC voltage control to directly respond to AC system frequency variations, a frequency regulation method is developed based on constant DC voltage control. The approach employs DC voltage as a transmission signal to coordinate the responses of wind turbines and energy storage systems. Subsequently, based on the energy storage configuration of the onshore renewable energy aggregation station, a secondary frequency regulation strategy is proposed. This strategy integrates offshore wind power, MMC-HVDC transmission system, and energy storage systems, balancing AC frequency regulation and the recovery of the state of charge (SOC) of the energy storage system. Finally, the proposed method is tested on a modified IEEE 39-bus system, the results demonstrate that the minimum frequency value can be in-creased by 37.5%, the system frequency can be restored to the initial state after secondary FM, and the results demonstrate its effectiveness.

Resilient fractional-order fuzzy-based tilt control of renewable-dominated hybrid power system

Resilient fractional-order fuzzy-based tilt control of renewable-dominated hybrid power system

Research on optimization of power system load response strategy and cost–benefit analysis based on electricity price algorithm model

Abstract This article proposes an innovative framework that amalgamates deep reinforcement learning (DRL) with cost–benefit analysis (CBA). The enhanced actor–critic DRL algorithm simultaneously addresses short-term price fluctuations and long-term system benefits, facilitating optimization across multiple time scales. Furthermore, it establishes a dynamic, multidimensional CBA model that encompasses a comprehensive evaluation of economic, social, and technological benefits, employing a fuzzy comprehensive evaluation method for quantitative analysis. The integration of DRL and CBA forms a closed-loop system that continuously refines strategy optimization through real-time adjustments of the reward function and evaluation weights. Experimental results validate the efficacy of this approach.

Coordinated Frequency Control for Electric Vehicles and a Thermal Power Unit via an Improved Recurrent Neural Network

With the advancement of intelligent power generation and consumption technologies, an increasing number of renewable energy sources (RESs), smart loads, and electric vehicles (EVs) are being integrated into smart grids. This paper proposes a coordinated frequency control strategy for hybrid power systems with RESs, smart loads, EVs, and a thermal power unit (TPU), in which EVs and the TPU participate in short-term frequency regulation (FR) jointly. All EVs provide FR auxiliary services as controllable loads; specifically, the EV aggregations operate in charging mode when participating in FR. The proposed coordinated frequency control strategy is implemented by an improved recurrent neural network (IRNN), which combines a recurrent neural network with a functional-link layer. The weights and biases of the IRNN are trained by an improved backpropagation through time (BPTT) algorithm, in which a chaotic competitive swarm optimizer (CCSO) is proposed to optimize the learning rates. Finally, the simulation results verify the superiority of the coordinated frequency control strategy.

Boston Consulting Group Matrix-Based Equilibrium Optimizer for Numerical Optimization and Dynamic Economic Dispatch

Numerous optimization problems exist in the design and operation of power systems, critical for efficient energy use, cost minimization, and system stability. With increasing energy demand and diversifying energy structures, these problems grow increasingly complex. Metaheuristic algorithms have been highlighted for their flexibility and effectiveness in addressing such complex problems. To further explore the theoretical support of metaheuristic algorithms for optimization problems in power systems, this paper proposes a novel algorithm, the Boston Consulting Group Matrix-based Equilibrium Optimizer (BCGEO), which integrates the Equilibrium Optimizer (EO) with the classic economic decision-making model, the Boston Consulting Group Matrix. This matrix is utilized to construct a model for evaluating the potential of individuals, aiding in the rational allocation of computational resources, thereby achieving a better balance between exploration and exploitation. In comparative experiments across various dimensions on CEC2017, the BCGEO demonstrated superior search performance over its peers. Furthermore, in dynamic economic dispatch, the BCGEO has shown strong optimization capabilities and potential in power system optimization problems. Additionally, the experimental results in the spacecraft trajectory optimization problem suggest its potential for broader application across various fields.

Five-layer gradient structures for improved energy storage performances on the basis of sandwich framework

Abstract High energy storage performances in multilayer nanocomposites are crucial for the advancement of modern electronic and power systems. However, the large dielectric contrast between adjacent layers leads to an uneven electric field distribution, which hampers the potential for further increasing their energy storage performances. Herein, five-layer gradient-structured nanocomposites were designed and fabricated based on previous sandwich framework by introducing transition layers between the outmost insulation layer and the inner polarization layer. Meanwhile, the volume fraction of the polarization layer maintains the same in the two frameworks to minimize adverse effects on polarizations. Experimental and simulation results show that the transition layer is able to effectively reduce dielectric contrast that alleviates electric field distortion and increases two extra interlayer interfaces to prolong breakdown paths, leading to an improved Eb. In addition, Ni(OH)₂ nanosheets could further hinder the breakdown path to suppress leakage current and prevent premature breakdown. As a result, the optimal nanocomposite with x=60 achieves an energy density of 25.9 J/cm³ at 663.6 MV/m, with an efficiency of 80%. This approach provides a promising design for advanced dielectric nanocomposites.

Research on ACC Control Strategy for Electric Vehicles Considering Cut-in Behavior

Background: Current adaptive cruise control systems primarily focus on the main target vehicle in the primary lane. However, there may be a delay in updating their targets after a side vehicle merges. This delay can impede the effective utilization of driving data from merging side vehicles, resulting in delayed responses and an increased risk of collision. Objective: Aiming at the problem of untimely control of traditional adaptive cruise control systems in cut-in scenarios, the authors establish a parallel model and collision analysis and design the control strategy of adaptive cruise under parallel behavior to improve the safety, following, and comfort of ACC. Methods: Initially, the vehicle model provided by CarSim is utilized and the power system parameters are adjusted to match the dynamic performance index. The permanent magnet synchronous motor model is then simplified to create an electric vehicle model. Following this, the cruise control system is designed using a PID control method, incorporating a hierarchical control structure. Simultaneously, a cut-in model characterized by sinusoidal acceleration is established, and a collision avoidance strategy based on cut-in behavior is formulated. Building on this foundation, an adaptive cruise control strategy is developed that takes cut-in behavior into account. Results: A test scenario utilizing the CarSim and Simulink co-simulation platforms was developed to simulate and validate the conditions for mode switching and lane cut-in. The ACC system, considering cut-in behavior recognition, demonstrated an ability to control the lead vehicle approximately 1 second earlier than traditional ACC systems. Notably, the distance error improved from - 0.1039 m to 0.040 m, the absolute value of velocity error decreased from 2.65 m/s to 2.02 m/s, the absolute value of main vehicle acceleration reduced by 34%, and the jerk response in acceleration diminished by approximately 14%. Conclusion: The proposed multi-objective cooperative adaptive cruise control system, which considers cut-in behavior, is effective.

Innovative Capacitive Wireless Power System for Machines and Devices

This article deals with the design, development, and analysis of a wireless power transfer system prototype based on capacitive coupling. The system is intended for the continuous charging of a suspended mine drivetrain. It consists of a resonant inverter, primary and secondary matching circuits, a suitable capacitive coupler, and a rectifier with a load, ultimately serving as a battery charger for a mobile energy storage device. The system successfully achieved the target output voltage of 320 V and a charger output power of 2 kW at an operating frequency of 300 kHz. Additionally, the total system efficiency was at the level of 60%, ensuring that the RMS voltages on passive components remained below 3 kV.