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Multi-domainsdispatching method for power system considering energy storage

IOP Conference Series: Earth and Environmental Science 2021年第1期675卷

作者： Weichun Ge Debao Liu Tao Liu Shunjiang Wang Junfa Liu Lei Hu State Grid Liaoning Electric Power Co. Ltd. Shenyang 110006 China Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education (Northeast Electric Power University) Jilin 132012 China Anshan Power Supply Company of Liaoning Electric Power Co. Ltd. Anshan 114002 China

Reasonably arranging the matching of power sources is an effective mean to improve the capacity of new energy consumption. Using the dispatching characteristics of the transferable load of the energy storage systems can play an important role in the actual dispatch of the power system. From the perspective of flexible scheduling, a multi-domain scheduling method is proposed, which comprehensively considers the influence of factors such as load, wind power output random fluctuations and system adjustment capabilities, and redistributes it into three control domains. That is, the normal control domains with the goal of completely absorbing wind power, anomaly control domain where the energy storage systems needs to be used to absorb wind power, and the emergency control domain where there is no regulation ability and forced to abandon wind. To maximizing clean energy consumption, CPLEX software is used to optimize output and realize multi-domain green economic dispatch. The calculation example shows that this method can effectively improve the absorption capacity of clean energy.

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Transient Voltage Stability Assessment Method based on gcForest

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Journal of Physics: Conference Series 2021年第1期1914卷

作者： Sun Zhiyuan LI Mingpo Zhang Jie Hu Binjiang Qi Guo Zhu Yihua Guangxi Power Grid Electrical Power Research Institute Supported by the Science and Technology Project of Guangxi Power Grid (Contract No(***DL00006)). State Key Laboratory of HVDC Electric Power Research Institute CSG China Guangdong Provincial Key Laboratory of Intelligent Operation and Control for New Energy Power System CSG Key Laboratory of Power System Simulation

Aiming at the fact that the traditional method cannot quickly and accurately judge the power system transient voltage stability, and the deep neural network method needs many sample data sets and has a large number of hyperparameters, a power system transient voltage stability assessment method based on deep forest (gcForest) is proposed. gcForest effectively extracts features through ensemble learning, multi-layer feature transformation, and dynamically adjusting the number of model layers. Finally, this method is applied to transient voltage stability assessment of an electrolytic aluminium load-intensive area in Guangxi power Grid. The results show that the method has high accuracy, the feasibility and effectiveness of this method are verified. This method can assist dispatchers to judge the transient voltage stability of the power grid.

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Research on Parameter Identification Method of Generator Excitation system Based on Differential Evolution Algorithm

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IOP Conference Series: Materials Science and Engineering 2020年第1期752卷

作者： Nan Li Yuqun Liu Bin Geng Key Laboratory of Modern Power System Simulation and Control and Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin 132012 Jilin Province China College of Electrical Engineering Northeast Electric Power University Jilin 13012 Jilin Province China Jilin Electric Power Co. Ltd. Changchun 130021 Jilin Province China

In order to solve the problem of large parameter identification error caused by nonlinear links of excitation system being triggered easily when transient stability is under fault state, an improved differential evolution algorithm for system parameter identification is proposed by using the characteristic of artificial intelligence algorithm that the nonlinear link is approximated infinitely through optimization. The improvement of the algorithm solves the problems of slow convergence speed, poor fine optimization ability and easily to produce local optimum when classical artificial intelligence algorithm identifies the parameters of non-linear links. At the same time, in order to solve the problem of inaccurate parameters in the whole identification, a decomposition link identification strategy is proposed. The example analysis shows that the algorithm improves the convergence speed, avoids local optimum and improves the convergence accuracy. According to the proposed parameter identification strategy, the excitation system is decomposed and identified, which improves the accuracy of generator excitation system parameter identification, and provides an accurate model and method for power system stability analysis

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energy Optimization Dispatching of Microgrid in The Stand-alone Mode

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Journal of Physics: Conference Series 2019年第1期1304卷

作者： Li Sun Shujian Zhao Chenglian Ma Jing Jiang Yuanzhu Meng Tianliang Yao Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education (Northeast Electric Power University) Jilin Jilin 132012 China Power Research Institute of State Grid Jilin Electric Power Co. Ltd. Changchun Jilin 130021 China Jilin Power Supply Company State Grid Jilin Electric Power Co. Ltd. Jilin Jilin 132001 China Gansu Electric Power Design Institute Limited Company of China Energy Construction Group Lanzhou 730050 China

A microgrid system in the stand-alone mode with photovoltaic, wind turbine, microturbine, fuel cell and energy storage unit is studied. Mathematical models of different distributed power supply and energy storage devices are established. The load demand of the system, the price of purchasing and selling electricity when interacting with the power grid and other constraints are considered. An economic model for optimizing stand-alone operation cost is established. According to the characteristics of the model, an improved genetic algorithm is selected to optimize the nonlinear system. The algorithm has better stability in solving the optimization problem of large state space dimension. The case study show that the proposed economic model is reasonable and the algorithm is effective.

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Economic Optimal Dispatch of Grid-connected Microgrid Based on Improved Genetic Algorithm

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Journal of Physics: Conference Series 2019年第1期1304卷

作者： Li Sun Shujian Zhao Chenglian Ma Naixin Chen Tianliang Yao Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education (Northeast Electric Power University) Jilin Jilin 132012 China Power Research Institute of State Grid Jilin Electric Power Co. Ltd. Changchun Jilin 130021 China Changchun Power Supply Company State Grid Jilin Electric Power Co. Ltd. Changchun Jilin 130000 China Gansu Electric Power Design Institute Limited Company of China Energy Construction Group Lanzhou 730050 China

Nowadays, the demand for energy is expanding, the fossil resources are on the verge of exhaustion, and the ecological environment is getting worse and worse. In this form, this paper studies the economic optimal dispatch of microgrid under the background of energy saving and emission reduction. Firstly, the mathematical model of microgrid composed of distributed power, energy storage devices and loads is established. Secondly, under the background of energy saving and emission reduction, the objective function of multi-objective optimal operation of microgrid is established considering both economic and environmental aspects. Finally, the improved Genetic Algorithm is used to solve the objective function. The results of optimization can reduce microgrid system generation cost.

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energy management of wind-PV-storage-grid based large electricity consumer using robust optimization technique

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Journal of energy Storage 2020年 27卷

作者： Dongmin Yu Tao Zhang Guixiong He Sayyad Nojavan Kittisak Jermsittiparsert Noradin Ghadimi Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education (Northeast Electric Power University) Jilin 132012 China Electric Power Research Institute of China Haidian District Beijing 100085 China Department of Electrical Engineering University of Bonab Bonab Iran Social Research Institute Chulalongkorn University Bangkok Thailand Young Researchers and Elite Club Islamic Azad University Ardabil Branch Ardabil Iran

Large electricity consumers (LEC) can purchase energy from various energy resources such as bilateral contracts, pool market, micro-turbines, battery storage systems, wind turbines, photovoltaic panels (PV). The uncertainty of market price leads to uncertainty in the total cost to the LEC. Therefore, in this article, the robust optimization (RO) technique is provided to investigate the uncertainty of the pool market price in the presented problem. Also, demand response program (DRP) is provided to decrease the purchased cost to the LEC as much as possible. According to the obtained results, without considering DRP, purchased cost is approximately $40,253.252 and $42,586.984, respectively in the risk-neutral strategy (ideal condition) and robust strategy (worst condition). Furthermore, the purchased cost is reduced nearly $36,945.362 and $39,789.267 in the risk-neutral and robust strategies with considering DRP. So, it can be concluded that the purchased cost to the LEC with considering DRP is reduced 8.2% and 6.5% in risk-neutral and robust strategies, respectively.

关键词： Large electricity consumers (LEC) Wind turbine Photovoltaic panel (PV) Demand response program (DRP) Robust optimization approach (RO)

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arXiv

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arXiv 2020年

作者： Li, Jiashuo Zhou, Sili Wei, Wendong Qi, Jianchuan Li, Yumeng Chen, Bin Zhang, Ning Guan, Dabo Qian, Haoqi Wu, Xiaohui Miao, Jiawen Chen, Long Liang, Sai Feng, Kuishuang Institute of Blue and Green Development Shandong University Weihai264209 State Key Laboratory of Coal Combustion School of Energy and Power Engineering Huazhong University of Science and Technology Wuhan430074 China School of International and Public Affairs Shanghai Jiao Tong University Shanghai200030 China State Key Joint Laboratory of Environment Simulation and Pollution Control School of Environment Beijing Normal University Beijing100875 China Laboratory of Anthropogenic Systems Ecology College of Engineering Peking University Beijing100871 China Department of Earth System Science Tsinghua University Beijing100871 China Institute for Global Public Policy Fudan University Shanghai200433 China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China Key Laboratory of Geographic Information Science Ministry of Education School of Geographic Sciences East China Normal University Shanghai200241 China

China has implemented retrofitting measures in coal-fired power plants (CFPPs) to reduce air pollution through small unit shutdown (SUS), the installation of air pollution control devices (APCDs) and power generation efficiency (PGE) improvement. The reductions in highly toxic Hg emissions and their related health impacts by these measures have not been well studied. To refine mitigation options, we evaluated the health benefits of reduced Hg emissions via retrofitting measures during China's 12th Five-Year Plan by combining plant-level Hg emission inventories with the China Hg Risk Source-Tracking Model. We found that the measures reduced Hg emissions by 23.5 tons (approximately 1/5 of that from CFPPs in 2010), preventing 0.0021 points of per-foetus intelligence quotient (IQ) decrements and 114 deaths from fatal heart attacks. These benefits were dominated by CFPP shutdowns and APCD installations. Provincial health benefits were largely attributable to Hg reductions in other regions. We also demonstrated the necessity of considering human health impacts, rather than just Hg emission reductions, in selecting Hg control devices. This study also suggests that Hg control strategies should consider various factors, such as CFPP locations, population densities and trade-offs between reductions of total Hg (THg) and Hg2+. Copyright © 2020, The Authors. All rights reserved.

关键词： Fossil fuel power plants

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Rotation Speed Recovery Strategy Based On Particle Swarm Optimization for Variable power Curve Optimization

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IOP Conference Series: Materials Science and Engineering 2018年第3期439卷

作者： Rui Liu Peng Song Xiaosheng Wang Yang Cui Zhongwei Lin State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources School of Control and Computer Engineering North China Electric Power University Beijing 102206 PR China phone: *** State Grid Jibei Electric Power Research Institute North China Electric Power Research Institute Co. Ltd. Beijing 100045 China Grid-connected Operation Technology for Wind-Solar Storage Hybrid System State Grid Corporation Key Laboratory Beijng 100045 China

With the increase of wind power capacity incorporated into the grid, the permeability of wind power increases continuously, which leads to the reduction of the equivalent inertia of the system. Rotor speed recovery will cause the secondary frequency dips when DFIG uses rotor inertial control to participate in system frequency modulation. In order to optimize the secondary frequency dips, this paper uses PSO (Particle Swarm Optimization) to optimize the Parameters of Variable power Functions on the basis of the previously proposed variable power curve strategy. This article simulates this strategy through setting up a system by Simulink. Simulate the results of optimization, compared with the traditional recovery strategy, validity of the proposed strategy is demonstrated through simulation and analysis.

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Rotation Speed Recovery Strategy Based On Variable power Curve of Inertia control from DFIG Wind Turbine

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IOP Conference Series: Earth and Environmental Science 2018年第1期192卷

作者： R Liu P Song X S Wang Z W Lin State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources School of Control and Computer Engineering North China Electric Power University Beijing 102206 PR China State Grid Jibei Electric Power Research Institute North China Electric Power Research Institute Co. Ltd. Beijing 100045 China Grid-connected Operation Technology for Wind-Solar Storage Hybrid System State Grid Corporation Key Laboratory Beijng 100045 China

With the increase of wind power capacity incorporated into the grid, the permeability of wind power increases continuously, which leads to the reduction of the equivalent inertia of the system. A new strategy of recovering rotor speed that increases the output power by modifying the variable power curve is proposed in this paper, which decreases the drop of the active power. So as to reduce the secondary frequency dips caused by the sudden drop of the active power when the speed restores. The paper optimizes the secondary frequency dips by modifying the variable power curve continuously. This article simulates this strategy through setting up a system by Simulink. Compared with the traditional recovery strategy, validity of the proposed strategy is demonstrated through simulation and analysis.

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Near-real-time monitoring of global CO2 emissions reveals the effects of the COVID-19 pandemic (vol 11, 5172, 2020)

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NATURE COMMUNICATIONS 2020年第1期11卷 1-12页

作者： Liu, Zhu Ciais, Philippe Deng, Zhu Lei, Ruixue Davis, Steven J. Feng, Sha Zheng, Bo Cui, Duo Dou, Xinyu Zhu, Biqing Guo, Rui Ke, Piyu Sun, Taochun Lu, Chenxi He, Pan Wang, Yuan Yue, Xu Wang, Yilong Lei, Yadong Zhou, Hao Cai, Zhaonan Wu, Yuhui Guo, Runtao Han, Tingxuan Xue, Jinjun Boucher, Olivier Boucher, Eulalie Chevallier, Frederic Tanaka, Katsumasa Wei, Yiming Zhong, Haiwang Kang, Chongqing Zhang, Ning Chen, Bin Xi, Fengming Liu, Miaomiao Breon, Francois-Marie Lu, Yonglong Zhang, Qiang Guan, Dabo Gong, Peng Kammen, Daniel M. He, Kebin Schellnhuber, Hans Joachim Department of Earth System Science Tsinghua University Beijing China Laboratoire des Sciences du Climat et de l’Environnement LSCE CEA CNRS UVSQ Centre d’Etudes Orme de Merisiers Gif-sur-Yvette France Department of Meteorology and Atmospheric Science The Pennsylvania State University University Park USA Department of Earth System Science University of California Irvine Irvine USA Division of Geological and Planetary Sciences California Institute of Technology Pasadena USA Jet Propulsion Laboratory California Institute of Technology Pasadena USA Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control Collaborative Innovation Center of Atmospheric Environment and Equipment Technology School of Environmental Science and Engineering Nanjing University of Information Science & Technology Nanjing China Key Laboratory of Land Surface Pattern and Simulation Institute of Geographical Sciences and Natural Resources Research Chinese Academy of Sciences Beijing China Climate Change Research Center Institute of Atmospheric Physics Chinese Academy of Sciences Beijing China Key Laboratory of Middle Atmosphere and Global Environment Observation Institute of Atmospheric Physics Chinese Academy of Sciences Beijing China School of Environment Tsinghua University Beijing China School of Mathematical School Tsinghua University Beijing China Department of Mathematical Sciences Tsinghua University Beijing China Center of Hubei Cooperative Innovation for Emissions Trading System Wuhan China Faculty of Management and Economics Kunming University of Science and Technology Kunming China Economic Research Centre of Nagoya University Furo-cho Chikusa-ku Nagoya Japan Institut Pierre-Simon Laplace Sorbonne Université / CNRS Paris France Université Paris Dauphine Place du Maréchal de Lattre de Tassigny Paris France Center for Global Environmental Research National Institute for Environmental Studies Tsukuba Japan Center for Energy and Environmental Policy

A Correction to this paper has been published: https://***/10.1038/s41467-020-20254-5

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