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Unsupervised Learning for Non-intrusive Load Monitoring in Smart Grid Based on Spiking Deep Neural Network

Journal of Modern Power Systems and Clean Energy 2022年第3期10卷 606-616页

作者： Zejian Zhou yingmeng xiang Hao Xu Yishen Wang Di Shi Department of Electrical and Biomedical Engineering University of NevadaRenoNVUSA Global Energy Interconnection Research Institute North America(GEIRINA) San JoseCAUSA

This paper investigates the intelligent load monitoring problem with applications to practical energy management scenarios in smart grids.As one of the critical components for paving the way to smart grids’success,an intelligent and feasible non-intrusive load monitoring(NILM)algorithm is urgently needed.However,most recent researches on NILM have not dealt with practical problems when applied to power grid,i.e.,①limited communication for slow-change systems;②requirement of low-cost hardware at the users’side;and③inconvenience to adapt to new households.Therefore,a novel NILM algorithm based on biology-inspired spiking neural network(SNN)has been developed to overcome the existing challenges.To provide intelligence in NILM,the developed SNN features an unsupervised learning rule,i.e.,spike-time dependent plasticity(STDP),which only requires the user to label one instance for each appliance while adapting to a new household.To upgrade the feasibility in NILM,the designed spiking neurons mimic the mechanism of human brain neurons that can be constructed by a resistor-capacitor(RC)circuit.In addition,a distributed computing system has been designed that divides the SNN into two parts,i.e.,smart outlets and local servers.Since the information flows as sparse binary vectors among spiking neurons in the developed SNN-based NILM,the high-frequency data can be easily compressed as the spike times,and are sent to the local server with limited communication capability,whereas it is unable to handle the traditional NILM.Finally,a series of experiments are conducted using a benchmark public dataset.Meanwhile,the effectiveness of developed SNN-based NILM can be demonstrated through comparisons with other emerging NILM algorithms such as the convolutional neural networks.

关键词： Non-intrusive load monitoring(NILM) spiking neural network(SNN) smart grid unsupervised machine learning

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Bounded Rationality Based Multi-VPP Trading in Local Energy Markets:A Dynamic Game Approach with Different Trading Targets

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CSEE Journal of Power and Energy Systems 2023年第1期9卷 221-234页

作者： Hongjun Gao Fan Zhang yingmeng xiang Shengyong Ye Xuna Liu Junyong Liu College of Electrical Engineering Sichuan UniversityChengdu 610065China Global Energy Interconnection Research Institute North America San JoseCA 95134USA State Grid Sichuan Economic Research Institute Chengdu 610041SichuanChina

It is expected that multiple virtual power plants(multi-VPPs)will join and participate in the future local energy market(LEM).The trading behaviors of these VPPs needs to be carefully studied in order to maximize the benefits brought to the local energy market operator(LEMO)and each VPP.We propose a bounded rationality-based trading model of multiVPPs in the local energy market by using a dynamic game approach with different trading targets.Three types of power bidding models for VPPs are first set up with different trading targets.In the dynamic game process,VPPs can also improve the degree of rationality and then find the most suitable target for different requirements by evolutionary learning after considering the opponents’bidding strategies and its own clustered resources.LEMO would decide the electricity buying/selling price in the LEM.Furthermore,the proposed dynamic game model is solved by a hybrid method consisting of an improved particle swarm optimization(IPSO)algorithm and conventional largescale optimization.Finally,case studies are conducted to show the performance of the proposed model and solution approach,which may provide some insights for VPPs to participate in the LEM in real-world complex scenarios.

关键词： Bounded rationality different trading targets dynamic game local energy market virtual power plant

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Multi-objective Dynamic Reconfiguration for Urban Distribution Network Considering Multi-level Switching Modes

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Journal of Modern Power Systems and Clean Energy 2022年第5期10卷 1241-1255页

作者： Hongjun Gao Wang Ma yingmeng xiang Zao Tang Xiandong Xu Hongjin Pan Fan Zhang Junyong Liu College of Electrical Engineering Sichuan UniversityChengdu 610065China Iowa State University Ameslowa50010USA Key Laboratory of Smart Gridof Ministry of Education Tianjin UniversityTianjin 300072China

The increasing integration of photovoltaic generators(PVGs) and the uneven economic development in different regions may cause the unbalanced spatial-temporal distribution of load demands in an urban distribution network(UDN). This may lead to undesired consequences, including PVG curtailment, load shedding, and equipment inefficiency, etc. Global dynamic reconfiguration provides a promising method to solve those challenges. However, the power flow transfer capabilities for different kinds of switches are diverse, and the willingness of distribution system operators(DSOs) to select them is also different. In this paper, we formulate a multi-objective dynamic reconfiguration optimization model suitable for multi-level switching modes to minimize the operation cost, load imbalance, and the PVG curtailment. The multi-level switching includes feeder-level switching, transformer-level switching, and substation-level switching. A novel load balancing index is devised to quantify the global load balancing degree at different levels. Then, a stochastic programming model based on selected scenarios is established to address the uncertainties of PVGs and loads. Afterward, the fuzzy c-means(FCMs) clustering is applied to divide the time periods of reconfiguration. Furthermore, the modified binary particle swarm optimization(BPSO)and Cplex solver are combined to solve the proposed mixed-integer second-order cone programming(MISOCP) model. Numerical results based on the 148-node and 297-node systems are obtained to validate the effectiveness of the proposed method.

关键词： Binary particle swarm optimization(BPSO) dynamic reconfiguration multi-level switching mixed-integer second-order cone programming(MISOCP) urban distribution network(UDN)

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