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Demand Response Strategy Model Based on User Satisfaction 16th

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Demand Response Strategy Model Based on User Satisfaction

16th International Conference on intelligent Information Hiding and Multimedia Signal processing, IIH-MSP 2020 in conjunction with the 13th International Conference on Frontiers of Information technology, Applications and Tools, FITAT 2020

作者： Guo, Xiaoli Sun, Yuhan Feng, Li Qu, Chaoyang Sun, Tieli School of Computer Science of Northeast Electric Power University Jilin City132012 China College of Humanities and Sciences of Northeast Normal University Changchun City130117 China Jilin Engineering Technology Research Center of Intelligent Electric Power Big Data Processing Jilin City132012 China State Grid Xin Yuan Fengman Training Center Jilin City132018 China

ISBN: (纸本)9789813364196

The demand response model of household microgrid based on satisfaction of customers is proposed after the users’ habit of using electricity which is found from the historical electricity data and aims at the problems of the load diversity of the household microgrid and the improvement of user satisfaction in response process, combined with the demand response theory of the users and the thought of data mining. The demand response strategy model based on users’ satisfaction is designed with the research of demand response behavior integrated with user’s side experience. The model takes the user's demand responsiveness as the load constraint, takes user’s comprehensive satisfaction maximum as the demand response objective, and takes the bacterial colony chemotaxis hybrid algorithm to solve the model. Finally, the optimal load power consumption plan is obtained as the user’s demand response strategy. The experimental simulation results verify the energy-saving effect of the model and the effectiveness of the algorithm. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Energy conservation

Coordinated cyber-attack detection model of cyber-physical power system based on the operating state data link

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arXiv 2021年

作者： Wang, Lei Xu, Pengcheng Qu, Zhaoyang Bo, Xiaoyong Dong, Yunchang Zhang, Zhenming Li, Yang School of Electrical Engineering Northeast Electric Power University Jilin China Jilin Engineering Technology Research Center of Intelligent Electric Power Big Data Processing Jilin China Siping Power Supply Company of State Grid Jilin Electric Power Company Limited Siping China

Existing coordinated cyber-attack detection methods have low detection accuracy and efficiency and poor generalization ability due to difficulties dealing with unbalanced attack data samples, high data dimensionality, and noisy data sets. This paper proposes a model for cyber and physical data fusion using a data link for detecting attacks on a Cyber-Physical power System (CPPS). Two-step principal component analysis (PCA) is used for classifying the system’s operating status. An adaptive synthetic sampling algorithm is used to reduce the imbalance in the categories’ samples. The loss function is improved according to the feature intensity difference of the attack event, and an integrated classifier is established using a classification algorithm based on the cost-sensitive gradient boosting decision tree (CS-GBDT). The simulation results show that the proposed method provides higher accuracy, recall, and F-Score than comparable algorithms. © 2021, CC BY-NC-ND.

关键词： Decision trees

Method for extracting patterns of coordinated network attacks on electric power CPS based on temporal-topological correlation

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arXiv 2020年

作者： Wang, Lei Qu, Zhaoyang Li, Yang Hu, Kewei Sun, Jian Xue, Kai Cui, Mingshi School of Electric Engineering Northeast Electric Power University Jilin132012 China Jilin Engineering Technology Research Center of Intelligent Electric Power Big Data Processing Jilin132012 China State Grid Jilin Province Electric Power Supply Company Changchun130000 China EastInner Mongolia Electric Power Company Hohhot010000 China

In the analysis of coordinated network attacks on electric power cyber-physical system (CPS), it is difficult to restore the complete attack path, and the intent of the attack cannot be identified automatically. A method is therefore proposed for the extracting patterns of coordinated network attacks on electric power CPS based on temporal-topological correlation. First, the attack events are aggregated according to the alarm log of the cyber space, and a temporal-causal Bayesian network-based cyber attack recognition algorithm is proposed to parse out the cyber attack sequences of the same attacker. Then, according to the characteristic curves of different attack measurement data in physical space, a combination of physical attack event criteria algorithm is designed to distinguish the types of physical attack events. Finally, physical attack events and cyber attack sequences are matched via temporal-topological correlation, frequent patterns of attack sequences are extracted, and hidden multi-step attack patterns are found from scattered grid measurement data and information from alarm logs. The effectiveness and efficiency of the proposed method are verified by the testbed at Mississippi State University. Copyright © 2020, The Authors. All rights reserved.

关键词： Embedded systems

Localization of Dummy data Injection Attacks in power Systems Considering Incomplete Topological Information: A Spatio-Temporal Graph Wavelet Convolutional Neural Network Approach

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SSRN

SSRN 2023年

作者： Qu, Zhaoyang Dong, Yunchang Li, Yang Song, Siqi Jiang, Tao Li, Min Wang, Qiming Wang, Lei Bo, Xiaoyong School of Electrical Engineering Northeast Electric Power University Jilin China Jilin Electric Power Research Institute of State Grid Changchun China Northeast Electric Power Design and Institute Co. Ltd. China Power Engineering Consulting Group China Jilin Engineering Technology Research Center of Intelligent Electric Power Big Data Processing Jilin China

The emergence of new dummy data attacks seriously endangers power systems' safe and stable operation. Due to the injected data meeting the spatial distance concealment, it is difficult to accurately locate the attack location by existing methods, so a dummy data injection attack (DDIA) localization method based on a spatio-temporal graph wavelet convolutional neural network is proposed in this paper. First, the principle of the new DDIA for power systems is analyzed. Based on the injected data satisfying the constraints of consistency, virtuality, destructiveness, and finiteness, the mathematical model of DDIA under consideration of incomplete topological information and AC state estimation is designed from the attacker's perspective to compensate for the limitations of the DC flow-based attack data generation method that can be easily captured. Then, combining the a priori knowledge of grid topology, taking into account the temporal correlation of the measurement data and the topology-dependent features of the power grid, the temporal and spatial attention matrices are introduced to capture the spatio-temporal correlation of the attack adaptively, and the spatiotemporal features of DDIA are extracted jointly based on gated stacked causal convolution and graph wavelet sparse convolution, which improves the dynamic mining ability and computational efficiency of the spatiotemporal correlation in the neighborhood. Finally, based on the spatio-temporal graph neural network, a DDIA localization method is proposed to adapt to the topological structure changes of the power system. The correctness of the DDIA model is proved through the analysis of arithmetic cases. It is verified that the proposed localization method can detect and localize DDIA quickly and effectively and has superior accuracy, robustness and generalization ability. © 2023, The Authors. All rights reserved.

关键词： Convolution

Dynamic exploitation Gaussian bare-bones bat algorithm for optimal reactive power dispatch to improve the safety and stability of power system

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arXiv 2021年

作者： Qu, Zhaoyang Dong, Yunchang Mugemanyi, Sylvère Yu, Tong Bo, Xiaoyong Li, Huashun Li, Yang Rugema, François Xavier Bananeza, Christophe School of Electrical Engineering Northeast Electric Power University Jilin China Jilin Engineering Technology Research Center of Intelligent Electric Power Big Data Processing Jilin China Guangxi Power Grid Co. Ltd. Electric Power Research Institute Nanning China State Grid Jilin Electric Power Co. Ltd. Jilin Power Supply Company Jilin China School of Energy and Electrical Engineering Hohai University Nanjing China

In this paper, a novel Gaussian bare-bones bat algorithm (GBBBA) and its modified version named as dynamic exploitation Gaussian bare-bones bat algorithm (DeGBBBA) are proposed for solving optimal reactive power dispatch (ORPD) problem. The optimal reactive power dispatch (ORPD) plays a fundamental role in ensuring stable, secure, reliable as well as economical operation of the power system. The ORPD problem is formulated as a complex and nonlinear optimization problem of mixed integers including both discrete and continuous control variables. Bat algorithm (BA) is one of the most popular metaheuristic algorithms which mimics the echolocation of the microbats and which has also outperformed some other metaheuristic algorithms in solving various optimization problems. Nevertheless, the standard BA may fail to balance exploration and exploitation for some optimization problems and hence it may often fall into local optima. The proposed GBBBA employs the Gaussian distribution in updating the bat positions in an effort to mitigate the premature convergence problem associated with the standard BA. The GBBBA takes advantages of Gaussian sampling which begins from exploration and continues to exploitation. DeGBBBA is an advanced variant of GBBBA in which a modified Gaussian distribution is introduced so as to allow the dynamic adaptation of exploitation and exploitation in the proposed algorithm. Both GBBBA and DeGBBBA are used to determine the optimal settings of generator bus voltages, tap setting transformers and shunt reactive sources in order to minimize the active power loss, total voltage deviations and voltage stability index. The performance and effectiveness of the proposed algorithms are demonstrated on three power system test bus including the standard IEEE 14-bus, IEEE 57-bus and IEEE 118-bus. The results provided by the proposed algorithms have been compared with the results obtained by other algorithms reported in the literature. Simulation results show that

关键词： Gaussian distribution

Localization of Dummy data Injection Attacks in power Systems Considering Incomplete Topological Information: A Spatio-Temporal Graph Wavelet Convolutional Neural Network Approach

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arXiv 2024年

作者： Qu, Zhaoyang Dong, Yunchang Li, Yang Song, Siqi Jiang, Tao Li, Min Wang, Qiming Wang, Lei Bo, Xiaoyong Zang, Jiye Xu, Qi School of Electrical Engineering Northeast Electric Power University Jilin China Jilin Electric Power Research Institute of State Grid Changchun China Northeast Electric Power Design and Institute Co. Ltd. China Power Engineering Consulting Group China School of Computer Northeast Electric Power University Jilin China Jilin Engineering Technology Research Center of Intelligent Electric Power Big Data Processing Jilin China Department of Civil and Environmental Engineering The Hong Kong University of Science and Technology Hong Kong

The emergence of novel the dummy data injection attack (DDIA) poses a severe threat to the secure and stable operation of power systems. These attacks are particularly perilous due to the minimal Euclidean spatial separation between the injected malicious data and legitimate data, rendering their precise detection challenging using conventional distance-based methods. Furthermore, existing research predominantly focuses on various machine learning techniques, often analyzing the temporal data sequences post-attack or relying solely on Euclidean spatial characteristics. Unfortunately, this approach tends to overlook the inherent topological correlations within the non-Euclidean spatial attributes of power grid data, consequently leading to diminished accuracy in attack localization. To address this issue, this study takes a comprehensive approach. Initially, it examines the underlying principles of these new DDIAs on power systems. Here, an intricate mathematical model of the DDIA is designed, accounting for incomplete topological knowledge and alternating current (AC) state estimation from an attacker's perspective. This model aims to mitigate the vulnerabilities associated with direct current (DC) flow-based attack data generation methods that can be readily detected. Subsequently, by integrating a priori knowledge of grid topology and considering the temporal correlations within measurement data and the topology-dependent attributes of the power grid, this study introduces temporal and spatial attention matrices. These matrices adaptively capture the spatio-temporal correlations within the attacks. Leveraging gated stacked causal convolution and graph wavelet sparse convolution, the study jointly extracts spatio-temporal DDIA features. This methodology significantly enhances the dynamic correlation mining capability while improving computational efficiency in the neighborhood. Finally, the research proposes a DDIA localization method based on spatio-temporal graph

关键词： Convolution

The recommendation method of power selling packages based on the optimal feature subset of power trading users

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Journal of Computers (Taiwan) 2019年第2期30卷 14-26页

作者： Qu, Zhao-Yang Feng, Rong-Qiang Qu, Nan Liu, Yao-Wei Yan, Jia Qu, Chong School of Information Engineering Northeast Electric Power University Jilin132012 China Jilin Engineering Technology Research Center of Intelligent Electric Power Big Data Processing Jilin132012 China Maintenaue Company of Jiangsu Power Company NanJing210000 China State Grid Jilin Electric Power Supply Company Jilin130000 China Fushun Power Supply Company of State Grid Liaoning Electric Power Supply Company LiaoNing113001 China

By aiming at huge user groups in the power market and the difficult selection of power selling packages in the trading process, a recommendation method of power selling packages based on the optimal feature subset of power trading users was proposed. First of all, the Optimal Feature Subset of power Trading Users (OFSPTU) was defined, and the optimal subset discovery algorithm based on coverage rate with weighing increase was designed, the optimal user feature subset could be screened out reasonably from mass trading users. Next, the similar computing method of power selling packages based on attribute correlation was proposed, in the form of clustering and confirming package attribute weighing, the package similarity was calculated, obtaining the nearest neighbor of package projects. At last, the accurate recommendation of power selling packages based on the OFSPTU and package attribute was realized. The experiment shows that the effectiveness of OFSPTU and accuracy of the recommended algorithm. © 2019 Computer Society of the Republic of China. All rights reserved.

关键词： Sales

Modeling method for the coupling relations of microgrid cyber-physical systems driven by hybrid spatiotemporal events

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arXiv 2021年

作者： Bo, Xiaoyong Chen, Xiaoyu Li, Huashun Dong, Yunchang Qu, Zhaoyang Wang, Lei Li, Yang School of Electrical Engineering Northeast Electric Power University Jilin132012 China Electrical and Information Engineering College Jilin Agricultural Science and Technology University Jilin132101 China Jilin Engineering Technology Research Center of Intelligent Electric Power Big Data Processing Jilin132012 China State Grid Jilin Electric Power Co. Ltd. Baishan Power Supply Company Baishan134300 China State Grid Jilin Electric Power Co. Ltd. Jilin Power Supply Company Jilin132011 China

The essence of the microgrid cyber-physical system (CPS) lies in the cyclical conversion of information flow and energy flow. Most of the existing coupling models are modeled with static networks and interface structures, in which the closed-loop data flow characteristic is not fully considered. It is difficult for these models to accurately describe spatiotemporal deduction processes, such as microgrid CPS attack identification, risk propagation, safety assessment, defense control, and cascading failure. To address this problem, a modeling method for the coupling relations of microgrid CPS driven by hybrid spatiotemporal events is proposed in the present work. First, according to the topological correlation and coupling logic of the microgrid CPS, the cyclical conversion mechanism of information flow and energy flow is analyzed, and a microgrid CPS architecture with multi-agents as the core is constructed. Next, the spatiotemporal evolution characteristic of the CPS is described by hybrid automata, and the task coordination mechanism of the multi-agent CPS terminal is designed. On this basis, a discrete-continuous correlation and terminal structure characteristic representation method of the CPS based on heterogeneous multi-groups are then proposed. Finally, four spatiotemporal events, namely state perception, network communication, intelligent decision-making, and action control, are defined. Considering the constraints of the temporal conversion of information flow and energy flow, a microgrid CPS coupling model is established, the effectiveness of which is verified by simulating false data injection attack (FDIA) scenarios. © 2021, CC BY-NC-ND.

关键词： Multi agent systems

Method for quantitative estimation of the risk propagation threshold in electric power CPS based on seepage probability

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arXiv 2018年

作者： Zhaoyang, Q.U. Zhang, Yu Nan, Q.U. Wang, Lei Yang, L.I. Dong, Yunchang College of Information Engineering Northeast Electric Power University Jilin132012 China Jilin Engineering Technology Research Center of Intelligent Electric Power Big Data Processing Jilin132012 China Maintenaue Company of Jiangsu Power Company Nanjing210000 China School of Electrical Engineering Northeast Electric Power University Jilin132012 China Energy Systems Division Argonne National Laboratory IL60439 United States

Because of the non-uniformity of the electric power CPS network and the dynamic nature of the risk propagation process, it is difficult to quantify the critical point of a cyber risk explosion. From the perspective of the dependency network, this paper proposes a method for quantitative evaluation of the risk propagation threshold of power CPS networks based on the percolation theory. First, the power CPS network is abstracted as a dual-layered network-directed unweighted graph according to topology correlation and coupling logic, and the asymmetrical balls-into-bins allocation method is used to establish a "one-to-many" and "partially coupled" non-uniform power CPS characterization model. Then, considering the directionality between the cyber layer and the physical layer link, the probability of percolation flow is introduced to establish the propagation dynamic equations for the internal coupling relationship of each layer. Finally, the risk propagation threshold is numerically quantified by defining the survival function of power CPS network nodes, and the validity of the proposed method is verified by the IEEE 30-bus system and 150-node Barabsi-Albert model. Copyright © 2018, The Authors. All rights reserved.

关键词： Solvents