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Improved Proximal Policy Optimization Algorithm for Sequential Security-constrained Optimal power Flow Based on Expert Knowledge and Safety Layer

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Journal of Modern power systems and Clean Energy 2024年第3期12卷 742-753页

作者： Yanbo Chen Qintao Du Honghai Liu Liangcheng Cheng Muhammad Shahzad Younis State Key Laboratory of Alternate Electrical power System with Renewable Energy Sources and School of Electrical & Electronic Engineering North China Electric Power University School of Engineering Xining University Key Laboratory of Smart Operation of New Energy power System Ministry of Education Qinghai University China Electric power Research Institute National University of Sciences and Technology

In recent years, reinforcement learning(RL) has emerged as a solution for model-free dynamic programming problem that cannot be effectively solved by traditional optimization methods. It has gradually been applied in the fields such as economic dispatch of power systems due to its strong selflearning and self-optimizing capabilities. However, existing economic scheduling methods based on RL ignore security risks that the agent may bring during exploration, which poses a risk of issuing instructions that threaten the safe operation of power system. Therefore, we propose an improved proximal policy optimization algorithm for sequential security-constrained optimal power flow(SCOPF) based on expert knowledge and safety layer to determine active power dispatch strategy, voltage optimization scheme of the units, and charging/discharging dispatch of energy storage systems. The expert experience is introduced to improve the ability to enforce constraints such as power balance in training process while guiding agent to effectively improve the utilization rate of renewable energy. Additionally, to avoid line overload, we add a safety layer at the end of the policy network by introducing transmission constraints to avoid dangerous actions and tackle sequential SCOPF problem. Simulation results on an improved IEEE 118-bus system verify the effectiveness of the proposed algorithm.

关键词： Renewable energy sources Safety power systems Optimization power grids Energy storage Training

Resilient Smart power Grid Synchronization Estimation Method for System Resilience with Partial Missing Measurements

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CSEE Journal of power and Energy systems 2024年第3期10卷 1307-1319页

作者： Yi Wang Yanxin Liu Mingdong Wang Venkata Dinavahi Jun Liang Yonghui Sun School of Electrical and Information Engineering Zhengzhou UniversityZhengzhou 450001China Henan Engineering Research Center of power Electronics and Energy systems Zhengzhou 450001China Department of Electrical and Computer Engineering University of AlbertaEdmontonAB T6G 2V4Canada School of Engineering Cardiff UniversityCardiff CF243AAUK College of Energy and Electrical Engineering Hohai UniversityNanjing 210098China IEEE

With the increasing demand for power system stability and resilience,effective real-time tracking plays a crucial role in smart grid synchronization.However,most studies have focused on measurement noise,while they seldom think about the problem of measurement data loss in smart power grid synchronization.To solve this problem,a resilient fault-tolerant extended Kalman filter(RFTEKF)is proposed to track voltage amplitude,voltage phase angle and frequency dynamically.First,a threephase unbalanced network’s positive sequence fast estimation model is established.Then,the loss phenomenon of measurements occurs randomly,and the randomness of data loss’s randomness is defined by discrete interval distribution[0,1].Subsequently,a resilient fault-tolerant extended Kalman filter based on the real-time estimation framework is designed using the timestamp technique to acquire partial data loss information.Finally,extensive simulation results manifest the proposed RFTEKF can synchronize the smart grid more effectively than the traditional extended Kalman filter(EKF).

关键词： Dynamic state estimation Kalman filter partial missing measurements power systems smart grid synchronized measurements

Security-constrained Transmission Maintenance Optimization Considering Generation and Operational Risk Costs

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Journal of Modern power systems and Clean Energy 2024年第3期12卷 767-781页

作者： Dan Xu Zhi Cai Qian Cheng Guodong Huang Jingyang Zhou Junjie Tang Beijing Key Laboratory of Research and System Evaluation of power Dispatching Automation Technology China Electric Power Research Institute State Key Laboratory of power Transmission Equipment & System Security and New Technology Chongqing University

With the large-scale integration of renewable energy, the traditional maintenance arrangement during the load valley period cannot satisfy the transmission demand of renewable energy generation. Simultaneously, in a market-oriented operation mode, the power dispatching control center aims to reduce the overall power purchase cost while ensuring the security of the power system. Therefore, a security-constrained transmission maintenance optimization model considering generation and operational risk costs is proposed herein. This model is built on double-layer optimization framework, where the upper-layer model is used for maintenance and generation planning, and the lowerlayer model is primarily used to address the operational security risk arising from the random prediction error and N-1 transmission failure. Correspondingly, a generation-maintenance iterative algorithm based on a defined cost feedback is included to increase solution efficiency. Generation cost is determined using long-term security-constrained unit commitment, and the operational risk cost is obtained using a double-layer N-1 risk assessment model. An electrical correlation coupling coefficient is proposed for the solution process to avoid maintenance of associated equipment simultaneously, thereby improving model convergence efficiency. The IEEE 118-bus system is used as a test case for illustration, and test results suggest that the proposed model and algorithm can reduce the total cost of transmission maintenance and system operation while effectively improving the solution efficiency of the joint optimization model.

关键词： Maintenance engineering Costs Optimization power systems Renewable energy sources power grids Load modeling

Estimation of Equivalent Model of Photovoltaic Array Using Unscented Kalman Filters

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Journal of Modern power systems and Clean Energy 2024年第3期12卷 819-827页

作者： M.A.González-Cagigal José A.Rosendo-Macías A.Gómez-Expósito the Department of Electrical Engineering University of Seville IEEE ENGREEN Laboratory of Engineering for Energy and Environmental Sustainability

This paper proposes the use of the unscented Kalman filter to estimate the equivalent model of a photovoltaic(PV) array, using external measurements of current and voltage at the inverter level. The estimated model is of interest to predict the power output of PV plants, in both planning and operation scenarios, and thus improves the efficient operation of power systems with high penetration of renewable energy. The proposed technique has been assessed in several simulated scenarios under different operating conditions. The results show that accurate estimates are provided for the model parameters, even in the presence of measurement noise and abrupt variations under the external conditions.

关键词： Mathematical models Estimation power systems power system dynamics Kalman filters Parameter estimation Voltage measurement

Interpreting the vulnerability of power systems in cascading failures using multi-graph convolutional networks

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Frontiers of Information Technology & Electronic Engineering 2022年第12期23卷 1848-1861页

作者： Supaporn LONAPALAWONG Changsheng CHEN Can WANG Wei CHEN State Key Lab of CAD&CG Zhejiang UniversityHangzhou 310058China China Electric power Research Institute Beijing 100192China College of Computer Science and Technology Zhejiang UniversityHangzhou 310058China

Analyzing the vulnerability of power systems in cascading failures is generally regarded as a challenging problem. Although existing studies can extract some critical rules, they fail to capture the complex subtleties under different operational conditions. In recent years, several deep learning methods have been applied to address this issue. However, most of the existing deep learning methods consider only the grid topology of a power system in terms of topological connections, but do not encompass a power system’s spatial information such as the electrical distance to increase the accuracy in the process of graph convolution. In this paper, we construct a novel power-weighted line graph that uses power system topology and spatial information to optimize the edge weight assignment of the line graph. Then we propose a multi-graph convolutional network(MGCN) based on a graph classification task, which preserves a power system’s spatial correlations and captures the relationships among physical components. Our model can better handle the problem with power systems that have parallel lines, where our method can maintain desirable accuracy in modeling systems with these extra topology features. To increase the interpretability of the model, we present the MGCN using layer-wise relevance propagation and quantify the contributing factors of model classification.

关键词： power systems Vulnerability Cascading failures Multi-graph convolutional networks Weighted line graph

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Distributed Robust Optimal Dispatch of Regional Integrated Energy systems Based on ADMM Algorithm with Adaptive Step Size

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Journal of Modern power systems and Clean Energy 2024年第3期12卷 852-862页

作者： Zhoujun Ma Yizhou Zhou Yuping Zheng Li Yang Zhinong Wei the College of Energy and Electrical Engineering Hohai University State Grid Jiangsu Electric power Co. Ltd. State Key Laboratory of Smart Grid Protection and Control NARI Group Corporation

This paper proposes a distributed robust optimal dispatch model to enhance information security and interaction among the operators in the regional integrated energy system(RIES). Our model regards the distribution network and each energy hub(EH) as independent operators and employs robust optimization to improve operational security caused by wind and photovoltaic(PV) power output uncertainties, with only deterministic information exchanged across boundaries. This paper also adopts the alternating direction method of multipliers(ADMM) algorithm to facilitate secure information interaction among multiple RIES operators, maximizing the benefit for each subject. Furthermore, the traditional ADMM algorithm with fixed step size is modified to be adaptive, addressing issues of redundant interactions caused by suboptimal initial step size settings. A case study validates the effectiveness of the proposed model, demonstrating the superiority of the ADMM algorithm with adaptive step size and the economic benefits of the distributed robust optimal dispatch model over the distributed stochastic optimal dispatch model.

关键词： Uncertainty Costs power systems Stochastic processes Renewable energy sources Optimization methods Adaptation models

A Fault Diagnosis Method of power systems Based on an Improved Adaptive Fuzzy Spiking Neural P systems and PSO Algorithms

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Chinese Journal of Electronics 2016年第2期25卷 320-327页

作者： WANG Jun PENG Hong TU Min Prez-Jimnez J. Mario SHI Peng School of Electrical and Information Engineering Xihua University Neijiang power Supply Company 5. Department of Computer Science and Artificial Intelligence University of Seville 6. School of Electrical and Electronic Engineeringthe University of Adelaide Sichuan Province Key Laboratory of power Electronics Energy-saving Technologies and Equipment Center for Radio Administration and Technology Development Xihua University

A new fault diagnosis method based on improved Adaptive fuzzy spiking neural P systems(in short,AFSN P systems) and Particle swarm optimization(PSO)algorithm is presented to improve the efficiency and accuracy of diagnosis for power systems in this paper. AFSN P systems are a novel kind of computing models with parallel computing and learning ability. Based on our previous works, this paper focuses on AFSN P systems inference algorithms and learning algorithms and builds the fault diagnosis model using improved AFSN P systems for diagnosing effectively. The process of diagnosis based on AFSN P systems is expressed by matrix successfully to improve the rate of diagnosis eminently. Furthermore, particle swarm optimization algorithm is introduced into the learning algorithm of AFSN P systems, thus the convergence speed of diagnosis has a big progress. An example of 4-node system is given to verify the effectiveness of this method. Compared with the existing methods, this method has faster diagnosis speed, higher accuracy and strong ability to adapt to the grid topology changes.

关键词： Fault diagnosis power systems Member Computing AFSN P systems Particle swarm optimization algorithm

Multi-time scale dynamics in power electronics-dominated power systems

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Frontiers of Mechanical Engineering 2017年第3期12卷 303-311页

作者： Xiaoming YUAN Jiabing HU Shijie CHENG State Key Laboratory of Advanced Electromagnetic Engineering and Technology School of Electrical and Electronic Engineering Huazhong University of Science and Technology Wuhan 430074 China

Electric power infrastructure has recently undergone a comprehensive transformation from electromagnetics to semiconductors. Such a development is attributed to the rapid growth of power electronic converter applications in the load side to realize energy conservation and on the supply side for renewable generations and power transmissions using high voltage direct current transmission. This transformation has altered the fundamental mechanism of power system dynamics, which demands the establishment of a new theory for power system control and protection. This paper presents thoughts on a theoretical framework for the coming semiconducting power systems.

关键词： power electronics power systems multi-time scale dynamics mass-spring-damping model self-stabilizing and en-stabilizing property multi-time scale power system stabilizer

Chaotic phenomena and small signal stability region of electrical power systems

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Science China Technological Sciences 2001年第2期44卷 187-199页

作者：余贻鑫贾宏杰王成山 Sch. Elec. Automat. and Ener. Eng. Tianjin University Tianjin 300072 China

In this paper, chaos phenomena and its influence on the power system small signal stability region (SSSR) are studied. We first review the studies on the SSSR, and point out that it is very important to make clear whether there exist some chaotic components on the boundary of the SSSR. Next, with some analytic skills of nonlinear dynamic system, we give a complete bifurcation diagram of a chaos existing in a sample power system, from a limit cycle (period-1) to chaotic state through cascading period-doubling bifurcation. The characteristics of the system energy varying in the continuous bifurcation are also shown. Thus a conclusion that chaos is always out of the Hopf bifurcation components (HB) on the SSSR’s boundary. Based on this conclusion and some further studies, we confirm that, from the viewpoint of power system engineering, we do not need to consider the existence of chaos in the SSSR and its boundary. Therefore greatly simplifying the study of SSSR. Moreover, some aspects of the attractive regi

关键词： power systems chaos small signal stability region nonlinear system.