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Modern Power System Stability and Optimal Operating

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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F: Electrical Engineering".

Deadline for manuscript submissions: closed (15 November 2023) | Viewed by 14240

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Special Issue Editors

Prof. Dr. Jing Zhang

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Guest Editor

College of Electrical Engineering, Guizhou University, Guiyang, China
Interests: power system stability and operation; power system analysis; microgrid; renewable energy integrated power system

Dr. Bi Liu

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Guest Editor

College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Ya'an, China
Interests: uncertain analysis and control of renewable energy integrated power system

Prof. Dr. Rujing Yan

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Guest Editor

College of Electrical Engineering, Guizhou University, Guiyang, China
Interests: supply and demand coordination planning and scheduling of the integrated energy system

Dr. Xiao Xu

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Guest Editor

College of Electrical Engineering, Sichuan University, Chengdu, China
Interests: renewable energy based power system optimization

Special Issue Information

Dear Colleagues,

The integration of renewable resources and electric vehicles into power systems presents significant challenges in power grids with respect to stability and operation, as they are accompanied by complex uncertainties.

Unlike traditional power resources, which are operated to match the demand of the loads, renewable resources (e.g., PV, wind) are usually controlled to extract the maximal available power along with the corresponding solar radiation intensity and wind speed, thus resulting in uncertainties of grid power sources. Similarly, the vigorous promotion of electric vehicles by the government has spurred huge changes in power load structure, increasing the number of load categories and complicating the characteristic differences among power loads. Note that both of these may introduce complex uncertainties into power systems, making it difficult to ensure modern power system stability and operation using traditional technologies. To guarantee the stability and robust operation of modern power systems, it is still necessary to presuppose the effective handling of uncertainties and new characteristics due to the large-scale integration of renewable resources, inverters and varied load behavior. Therefore, addressing the aforementioned challenges has been one of the most exciting research topics in modern power systems, benefiting power system stability, safety, and reliability as well as supporting their economical and efficient operation.

This Special Issue welcomes the submission of both original research and review articles related to the above topics, particularly including but not limited to the following fields:

Stability analysis and optimal operation with respect to uncertainties;
Stability analysis and optimal operation based on data-driven and AI methods;
Robust stability control strategy and operating decision for varied conditions;
Renewable generation system control strategies;
Optimal operation of integrated energy systems;
Modeling of the power systems with integrated renewable energy sources and varied loads;
Analysis and modeling of renewable generation systems and load behavior;
The influence of demand-side responses and management on power system stability and operation;
Probabilistic analysis for modern power systems;
Wide-band oscillation and its stability control strategy;
State estimation frameworks for uncertain sources and loads integrated power systems.

Prof. Dr. Jing Zhang
Dr. Bi Liu
Prof. Dr. Rujing Yan
Dr. Xiao Xu
Guest Editors

Manuscript Submission Information

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Keywords

uncertainties
renewable energy
power system probabilistic analysis
state estimation
optimal operation
stability analysis and control
wide-band oscillation

Published Papers (10 papers)

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Research

13 pages, 2127 KiB

Open AccessArticle

Whole Life Cycle Cost Analysis of Transmission Lines Using the Economic Life Interval Method

by Wenhui Zeng, Jiayuan Fan, Wentao Zhang, Yu Li, Bin Zou, Ruirui Huang, Xiao Xu and Junyong Liu

Energies 2023, 16(23), 7804; https://0-doi-org.brum.beds.ac.uk/10.3390/en16237804 - 27 Nov 2023

Cited by 1 | Viewed by 1127

Abstract

With the large-scale construction and commissioning of transmission lines over the past two decades, the grid is facing a large-scale centralized decommissioning of transmission lines. The transmission line’s economic life is crucial to rationalizing its construction and reducing the grid’s development costs. Based on the minimum economic life calculation principle, the static and dynamic transmission line economic life calculation model is established, considering the whole life cycle for transmission line cost. The improved gray GM (1,1) model is applied to forecast cost data during the economic life assessment of transmission lines with fewer samples. Considering the cost uncertainty in life-cycle costing, the interval cost model based on the coefficient of variation wave amplitude is proposed to determine the economic life intervals under different guarantees by using the normal distribution probability density function, which reduces the influence of cost fluctuations on the economic life calculation error. The economic life analysis of a 500 kV transmission line is used as a case study to verify the model’s accuracy and effectiveness. The method shows the economic life intervals under different guarantee degrees based on the most probable economic life determination, which provides theoretical support for calculating the economic life elasticity of transmission lines. Full article

(This article belongs to the Special Issue Modern Power System Stability and Optimal Operating)

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21 pages, 3023 KiB

Open AccessArticle

Economic Evaluation Method of Modern Power Transmission System Based on Improved Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Best-Worst Method-Anti-Entropy Weight

by Wenhui Zeng, Jiayuan Fan, Zhichao Ren, Xiaoyu Liu, Shuang Lv, Yuqian Cao, Xiao Xu and Junyong Liu

Energies 2023, 16(21), 7242; https://0-doi-org.brum.beds.ac.uk/10.3390/en16217242 - 25 Oct 2023

Cited by 1 | Viewed by 799

Abstract

As the demand for power supply increases, the investment in the power transmission system constantly increases. An accurate economic evaluation of the power transmission system is essential for future investment decisions and management. Applying a single method in economic evaluation leads to excessive subjective consciousness and unreasonable weight allocation. The Euclidean distance in the traditional TOPSIS method only partially works on the condition that the criteria are linearly correlated. To solve these problems, an economic evaluation method based on improved TOPSIS and BWM-anti-entropy weight is proposed. For the assignment of weights, the method retains the advantages of subjective and objective weighting methods based on the Nash equilibrium, breaks through the limitation of utilizing a single method, which contributes to one-sided results, and enhances the scientific rigor and rationality of the comprehensive weighting process. Furthermore, based on comprehensive weights, the method improves the TOPSIS by introducing the Mahalanobis distance and Pearson correlation coefficients, which can eliminate the influence of linear correlation. Finally, ten 500 kV transmission and transformation projects are analyzed and ranked to verify the method’s feasibility. Empirical analysis shows that the method can effectively evaluate the economic benefits of the power transmission system. Full article

(This article belongs to the Special Issue Modern Power System Stability and Optimal Operating)

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14 pages, 1210 KiB

Open AccessArticle

Risk Reliability Assessment of Transmission Lines under Multiple Natural Disasters in Modern Power Systems

by Rongquan Fan, Wenhui Zeng, Ziqiang Ming, Wentao Zhang, Ruirui Huang and Junyong Liu

Energies 2023, 16(18), 6548; https://0-doi-org.brum.beds.ac.uk/10.3390/en16186548 - 12 Sep 2023

Cited by 1 | Viewed by 1132

Abstract

Climate change has led to more frequent extreme weather events, and various natural disasters have posed risks to the operation of transmission lines. Line failures caused by natural disasters are unpredictable and bring additional maintenance work. Therefore, this paper proposes a transmission line risk reliability assessment method that considers the combined effects of multiple natural disasters. This method establishes a theory of disaster risk quantification that considers the probability of the occurrence of the risk, the degree of the impact of the risk on the line, and the severity of the risk disaster. The risk weights for different natural disasters are calculated by combining a hierarchical analysis and entropy weighting methods. The example of a transmission line risk assessment under the combined effects of multiple natural disasters for a Sichuan region verifies the proposed method’s effectiveness. The results show that the method effectively assesses the operational risk to transmission lines under the combined effects of natural disasters. The assessment results can be used for disaster recovery and line risk prevention. Full article

(This article belongs to the Special Issue Modern Power System Stability and Optimal Operating)

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25 pages, 6307 KiB

Open AccessArticle

Short-Term Wind Power Prediction Based on CEEMDAN-SE and Bidirectional LSTM Neural Network with Markov Chain

by Yi Liu, Jun He, Yu Wang, Zong Liu, Lixun He and Yanyang Wang

Energies 2023, 16(14), 5476; https://0-doi-org.brum.beds.ac.uk/10.3390/en16145476 - 19 Jul 2023

Cited by 2 | Viewed by 1208

Abstract

Accurate wind power data prediction is crucial to increase wind energy usage since wind power data are characterized by uncertainty and randomness, which present significant obstacles to the scheduling of power grids. This paper proposes a hybrid model for wind power prediction based on complementary ensemble empirical mode decomposition with adaptive noise (CEEMDAN), sample entropy (SE), bidirectional long short-term memory network (BiLSTM), and Markov chain (MC). First, CEEMDAN is used to decompose the wind power series into a series of subsequences at various frequencies, and then SE is employed to reconstruct the wind power series subsequences to reduce the model’s complexity. Second, the long short-term memory (LSTM) network is optimized, the BiLSTM neural network prediction method is used to predict each reconstruction component, and the results of the different component predictions are superimposed to acquire the total prediction results. Finally, MC is used to correct the model’s total prediction results to increase the accuracy of the predictions. Experimental validation with measured data from wind farms in a region of Xinjiang, and computational results demonstrate that the proposed model can better fit wind power data than other prediction models and has greater prediction accuracy and generalizability for enhancing wind power prediction performance. Full article

(This article belongs to the Special Issue Modern Power System Stability and Optimal Operating)

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19 pages, 5975 KiB

Open AccessFeature PaperArticle

Optimal Configuration of Hybrid Energy Storage Capacity in a Microgrid Based on Variational Mode Decomposition

by Shuang Lei, Yu He, Jing Zhang and Kun Deng

Energies 2023, 16(11), 4307; https://0-doi-org.brum.beds.ac.uk/10.3390/en16114307 - 24 May 2023

Cited by 4 | Viewed by 1244

Abstract

The capacity configuration of the energy storage system plays a crucial role in enhancing the reliability of the power supply, power quality, and renewable energy utilization in microgrids. Based on variational mode decomposition (VMD), a capacity optimization configuration model for a hybrid energy storage system (HESS) consisting of batteries and supercapacitors is established to achieve the optimal configuration of energy storage capacity in wind–solar complementary islanded microgrids. Firstly, based on the energy mapping relationship between the frequency domain and time domain, the decomposition mode number

K

of VMD is determined based on the principle of minimum total mode aliasing energy. Then, considering the smoothing fluctuation characteristics of different energy storage components, the dividing point N of high frequency and low frequency in the unbalanced power between the source and load in the microgrid is selected to allocate charging and discharging power instructions for the battery and supercapacitor. Finally, taking the annual comprehensive cost of the HESS as the objective function, a hybrid energy storage capacity optimization configuration model is established, and the dividing point N is used as the optimization variable to solve the model in order to obtain the optimal configuration results. The case study results show that the proposed method is more economical and feasible than the existing energy storage configuration methods. Full article

(This article belongs to the Special Issue Modern Power System Stability and Optimal Operating)

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16 pages, 2695 KiB

Open AccessArticle

A Diagnosis Method of Power Flow Convergence Failure for Bulk Power Systems Based on Intermediate Iteration Data

by Gang Mu, Yibo Zhou, Mao Yang and Jiahao Chen

Energies 2023, 16(8), 3540; https://0-doi-org.brum.beds.ac.uk/10.3390/en16083540 - 19 Apr 2023

Viewed by 1646

Abstract

Power flow calculation is the foundation of security analyses in a power system, and the phenomenon of convergence failure is becoming more prominent with the expansion of the power grid. The existing convergence failure diagnosis methods based on optimization modeling and local feature recognition are no longer viable for bulk power systems. This paper proposes a diagnosis method based on intermediate iteration data and the identification of the transmission power congested channel. Firstly, the transmission power congestion index is constructed, and then a method for identifying transmission congestion channels is proposed. The reasons for convergence failure of the power flow are diagnosed from two aspects: excessive power to be transmitted and insufficient transmission capacity. Finally, with the aim of alleviating transmission channel congestion, a correction strategy for power flow injection space data was constructed, which generates relaxation schemes for operational variables. The effectiveness of the proposed strategy was verified using the simulation results of an actual provincial power grid and a standard example power system with 13,659 buses. The method proposed in this paper is entirely based on intermediate power flow iteration data, which avoids the complex modeling of the power flow adjustment and provides methodological support for power flow diagnosis in bulk power systems. Full article

(This article belongs to the Special Issue Modern Power System Stability and Optimal Operating)

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21 pages, 6671 KiB

Open AccessArticle

Multi-Microgrid Collaborative Optimization Scheduling Using an Improved Multi-Agent Soft Actor-Critic Algorithm

by Jiankai Gao, Yang Li, Bin Wang and Haibo Wu

Energies 2023, 16(7), 3248; https://0-doi-org.brum.beds.ac.uk/10.3390/en16073248 - 5 Apr 2023

Cited by 4 | Viewed by 1927

Abstract

The implementation of a multi-microgrid (MMG) system with multiple renewable energy sources enables the facilitation of electricity trading. To tackle the energy management problem of an MMG system, which consists of multiple renewable energy microgrids belonging to different operating entities, this paper proposes an MMG collaborative optimization scheduling model based on a multi-agent centralized training distributed execution framework. To enhance the generalization ability of dealing with various uncertainties, we also propose an improved multi-agent soft actor-critic (MASAC) algorithm, which facilitates energy transactions between multi-agents in MMG, and employs automated machine learning (AutoML) to optimize the MASAC hyperparameters to further improve the generalization of deep reinforcement learning (DRL). The test results demonstrate that the proposed method successfully achieves power complementarity between different entities and reduces the MMG system’s operating cost. Additionally, the proposal significantly outperforms other state-of-the-art reinforcement learning algorithms with better economy and higher calculation efficiency. Full article

(This article belongs to the Special Issue Modern Power System Stability and Optimal Operating)

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19 pages, 3582 KiB

Open AccessArticle

Ground Fault Detection Based on Fault Data Stitching and Image Generation of Resonant Grounding Distribution Systems

by Xianglun Nie, Jing Zhang, Yu He, Wenjian Luo, Tingyun Gu, Bowen Li and Xiangxie Hu

Energies 2023, 16(7), 2937; https://0-doi-org.brum.beds.ac.uk/10.3390/en16072937 - 23 Mar 2023

Viewed by 1383

Abstract

Fast and accurate fault detection is important for the long term, stable operation of the distribution network. For the resonant grounding system, the fault signal features extraction difficulties, and the existing detection method’s accuracy is not high. A ground fault detection method based on fault data stitching and image generation of resonant grounding distribution systems is proposed. Firstly, considering the correlation between the transient zero-sequence current (TZSC) of faulty and healthy feeders under the same operating conditions, a fault data stitching method is proposed, which splices the transient zero-sequence current signals of each feeder into system fault data, and then converts the system fault data into grayscale images by combining the signal-to-image conversion method. Then, an improved convolutional neural network (CNN) is used to train the grayscale images and then implement fault detection. The simulation results show that the proposed method has high accuracy and strong robustness compared with existing fault detection methods. Full article

(This article belongs to the Special Issue Modern Power System Stability and Optimal Operating)

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16 pages, 3387 KiB

Open AccessArticle

Ultra-Short-Term Prediction Method of Wind Power for Massive Wind Power Clusters Based on Feature Mining of Spatiotemporal Correlation

by Bo Wang, Tiancheng Wang, Mao Yang, Chao Han, Dawei Huang and Dake Gu

Energies 2023, 16(6), 2727; https://0-doi-org.brum.beds.ac.uk/10.3390/en16062727 - 15 Mar 2023

Cited by 3 | Viewed by 1421

Abstract

With the centralization of wind power development, power-prediction technology based on wind power clusters has become an important means to reduce the volatility of wind power, so a large-scale power-prediction method of wind power clusters is proposed considering the prediction stability. Firstly, the fluctuating features of wind farms are constructed by acquiring statistical features to further build a divided model of wind power clusters using fuzzy clustering algorithm. Then the spatiotemporal features of the data of wind power are obtained using a spatiotemporal attention network to train the prediction model of wind power clusters in a large scale. Finally, the stability of predictive performance of wind power is analyzed using the comprehensive index evaluation system. The results show that the RMSE of wind power prediction is lower than 0.079 at large-scale wind farms based on the prediction method of wind power proposed in this paper using experience based on the data of 159 wind farms in the Nei Monggol Autonomous Region in China and the extreme error is better than 25% for the total capacity of wind farms, which indicates high stability and accuracy. Full article

(This article belongs to the Special Issue Modern Power System Stability and Optimal Operating)

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13 pages, 2315 KiB

Open AccessArticle

Additional Compound Damping Control to Suppress Low-Frequency Oscillations in a Photovoltaic Plant with a Hybrid Energy Storage System

by Kanglin Dai, Wei Xiong, Xufeng Yuan, Huajun Zheng, Qihui Feng, Yutao Xu, Yongxiang Cai and Dan Guo

Energies 2022, 15(23), 9044; https://0-doi-org.brum.beds.ac.uk/10.3390/en15239044 - 29 Nov 2022

Viewed by 1056

Abstract

The use of the conventional dual closed-loop control strategy by photovoltaic (PV) plants with grid-connected inverters may weaken the damping of a power system, which may aggravate low-frequency oscillations (LFOs). This influence will become more severe as the penetration of PV plants increases. Therefore, it is necessary to incorporate damping controls into PV plants to suppress LFOs. This paper proposed an additional compound damping control (ACDC) system that combines additional damping control (ADC) for the inverter with ADC-based dynamic power compensation control (DPCC), allowing hybrid energy storage systems (HESSs) to suppress LFOs. First, the feasibility of suppressing low-frequency oscillations in PV plants is demonstrated by the torque method and a small signal model. Then, an additional damping controller is added to the active power control link of the PV inverter to enhance the damping abilities of the system. However, given that the damping performance of PV plants with only ADC is limited by the compensated power, PV plants require devices that can rapidly compensate for the damping power. Therefore, we added the HESS to the DC bus and proposed DPCC. Finally, a three-machine nine-node system for a PV plant was modeled and simulated in the PSCAD platform. The simulation results showed that the proposed control strategy could provide effective damping for interarea oscillation. Full article

(This article belongs to the Special Issue Modern Power System Stability and Optimal Operating)

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