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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

Distributed Dispatch of Multiple Energy Systems Considering Carbon Trading

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CSEE Journal of Power and Energy Systems 2023年第2期9卷 459-469页

作者： Yue Xiang Mengqiu Fang Junyong Liu Pingliang Zeng Ping Xue Gang Wu College of Electrical Engineering Sichuan UniversityChengdu 610065China School of Automation Hangzhou Dianzi UniversityHangzhou 310018China state grid sichuan Technical Training Center Chengdu 610072China State Grid Sichuan Economic Research Institute Chengdu 610065China

As an integrated carrier of energy production,transmission,distribution,conversion,storage,and utilization,multiple energy systems(MESs)have significant low-carbon potential.This paper proposes a hierarchical distributed dispatch model of MESs considering carbon trading,which is composed of the lower autonomous operation level of each MES and the upper coordinated control level.Different carbon emission sources are considered,including combined heat and power(CHP)units,gas boilers,and power to gas(P2G)devices.The transactive control(TC)mechanism is used to solve the model by introducing a virtual price signal.In the case study based on a 3-MES system,the effectiveness of the proposed distributed method is proved by comparison with a centralized algorithm.Meanwhile,the impacts of different carbon prices on MESs with different resource endowments are analyzed from the aspects of scheduling results,carbon emissions,clean energy consumption rate,and comprehensive operating costs.

关键词： Carbon trading distributed optimal dispatch transactive control multiple energy systems

Optimal scheduling of wind-photovoltaic power-generation system based on a copula-based conditional value-at-risk model

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Clean Energy 2022年第4期6卷 550-556页

作者： Xin Ju Xiaomin Liu Shangke Liu Yangli Xiao state grid Ningxia economic and Technological research institute YinchuanNingxiaChina

Increasing the application of renewable energy in the power system is an effective way to achieve the goal of‘Dual Carbon’.At the same time,the high proportion of renewable energy connected to the grid endangers the safe operation of the power system.To solve this problem,this paper proposes the application of a copula function to describe the correlation between wind power and photovoltaic power,and reduce the uncertainty of power-system operation with a high proportion of renewable energy.In order to increase the robustness of the model,this paper proposes the application of the conditional value-at-risk theory to construct the objective function of the model and effectively control the tail risk of power-system operation costs.Through case analysis,it is found that the model proposed in this paper has strong practicality and economy.

关键词： renewable energy ‘Dual Carbon’targets copula-CVaR particle swarm optimization algorithm optimized scheduling

Decomposition analysis of CO_(2) emissions of electricity and carbon-reduction policy implication: a study of a province in China based on the logarithmic mean Divisia index method

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Clean Energy 2023年第2期7卷 340-349页

作者： Fuyuan Yang Xiaobin Yang Xueqin Tian Xinlei Wang Tong Xu state grid economic and Technological research institute Co. LtdBeijing102209China state grid Jiangxi Electric Power research institute State Grid Jiangxi Electric Power Co.LtdNanchangChina

As the proportion of electricity in final energy consumption gradually increases,CO_(2) emissions reduction actions in the power system will become the key to achieving China’s carbon-peak and carbon-neutrality goals.It is essential to analyse and quantify the driving forces of CO_(2) emissions from electricity generation in the fossil-rich area in China.This paper aims to identify the characteristics of CO_(2) emissions generated by electricity and provide a basis for formulating CO_(2)-reduction policies in power systems.First,we analyse the current state of CO_(2) emissions from electricity generation in Anhui Province that was dominated by fossil energy during the period 2010-19.Then,we apply the logarithmic mean Divisia index method to find the nature of the factors influencing the changes in CO_(2) emissions.Finally,we analyse the CO_(2)-reduction measures of each side of the source-network-load-storage of the power system in Anhui through a power-system carbon-reduction path analysis model proposed in this study and provide policy suggestions.The results showed the following.(i)CO_(2) emissions in Anhui Province continued to increase from 2010 to 2019 and the trend in the growth rate of CO_(2) emissions presented approximately a u-shaped curve.(ii)economic activity has always been the dominant factor driving the growth of electricity CO_(2) emis-sions.The increase in the proportion of renewable energy in power generation,the improvement in thermal power-conversion efficiency and the decrease in the intensity of power consumption are the three major driving factors for the reduction in CO_(2) emissions from power generation in Anhui.(iii)The CO_(2)-reduction measures of the power system are provided in each link of the source-network-load-storage,such as developing the photovoltaic industry and building energy storage,upgrading and transforming coal-fired power stations,redu-cing the loss rate of transmission lines on the grid side and improving the efficiency of the utilization of electricity on the user s

关键词： CO_(2)emissions LMDI electricity generation carbon-reduction measures

Optimal Scheduling of Residential Heating, Ventilation and Air Conditioning Based on Deep Reinforcement Learning

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Journal of Modern Power Systems and Clean Energy 2023年第5期11卷 1596-1605页

作者： Mingchao Xia Fangjian Chen Qifang Chen Siwei Liu Yuguang Song Te Wang School of Electrical Engineering Beijing Jiaotong UniversityBeijingChina state grid state Power economic research institute BeijingChina

Residential heating, ventilation and air conditioning(HVAC) provides important demand response resources for the new power system with high proportion of renewable energy. Residential HAVC scheduling strategies that adapt to realtime electricity price signals formulated by demand response program and ambient temperature can significantly reduce electricity costs while ensuring occupants' comfort. However, since the pricing process and weather conditions are affected by many factors, conventional model-based method is difficult to meet the scheduling requirements in complex environments. To solve this problem, we propose an adaptive optimal scheduling strategy for residential HVAC based on deep reinforcement learning(DRL) method. The scheduling problem can be regarded as a Markov decision process(MDP). The proposed method can adaptively learn the state transition probability to make economical decision under the tolerance violations. Specifically, the residential thermal parameters obtained by the leastsquares parameter estimation(LSPE) can provide a basis for the state transition probability of MDP. Daily simulations are verified under the electricity prices and temperature data sets, and numerous experimental results demonstrate the effectiveness of the proposed method.

关键词： Residential heating ventilation and air conditioning(HVAC) scheduling deep reinforcement learning leastsquares parameter estimation(LSPE)

Application research of compressed-air energy storage under high proportion of renewable energy

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Clean Energy 2022年第2期6卷 305-312页

作者： Bin Feng Bo Yu state grid Ningxia Electric Power Co.ltd YinchuanNingxiaChina state grid Ningxia economic and Technological research institute YinchuanNingxiaChina

China will strive to achieve a‘dual carbon’target:‘carbon peak’by 2030 and‘carbon-neutral’by 2060.In this context,improving the efficiency of renewable energy and reducing the use of thermal power are important ways to achieve the target.Clean,efficient and large-capacity energy-storage technology is the key to improving the utilization rate of renewable energy.First,this paper proposes to use compressed-air energy-storage technology instead of the old energy-storage technology to build an economical and environmentally friendly comprehensive energy park capacity optimization configuration model.Second,this paper uses the newly proposed improved chicken swarm optimization algorithm to solve the model,which is more accurate and faster.Finally,this paper analyzes a comprehensive energy park in north-west China.Through case analysis,it can be seen that the average utilization rate of renewable energy can reach 73.87%through the model proposed in this paper,while the average power-abandonment rate is only 9.32%.

关键词： renewable energy compressed-air energy storage power-abandonment rate chicken swarm optimization

Distribution Network Equipment Location and Capacity Planning Method Considering Energy Internet Attribute from the Perspective of Life Cycle Cost

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Energy Engineering 2022年第3期119卷 1059-1074页

作者： Qiang Li Lei Xue Xia Lin state grid Shanxi Electric Power Company economic and Technical research institute TaiyuanChina state grid Shanxi Electric Power Company TaiyuanChina

For facing the challenges brought by large-scale renewable energy having access to the system and considering the key technologies of energy Internet,it is very necessary to put forward the location method of distribution network equipment and capacity from the perspective of life cycle cost.Compared with the traditional energy network,the equipment capacity problem of energy interconnected distribution network which involves in electricity network,thermal energy network and natural gas network is comprehensively considered in this paper.On this basis,firstly,the operation architecture of energy interconnected distribution network is designed.Secondly,taking the grid connection location and configuration capacity of key equipment in the system as the control variables and the operation cost of system comprehensive planning in the whole life cycle as the goal,the equipment location and capacity optimization model of energy interconnected distribution network is established.Finally,an IEEE 33 bus energy mutual distribution grid system is taken for example analysis,and the improved chaotic particle swarmoptimization algorithm is used to solve it.The simulation results show that the method proposed in this paper is suitable for the equipment location and capacity planning of energy interconnected distribution network,and it can effectively improve the social and economic benefits of system operation.

关键词： Locating and sizing method distribution energy internetwork whole life cycle cost distribution network planning

Distributed optimization based on improved push-sum framework for optimization problem with multiple local constraints and its application in smart grid

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信息与电子工程前沿:英文版 2023年第9期24卷 1253-1260页

作者： Qian XU Chutian YU Xiang YUAN Mengli WEI Hongzhe LIU state grid Zhejiang economic research institute Hangzhou 310008China Beijing Yinshan Technology Co. Ltd.Beijing 100871China

In this paper,the optimization problem subject to N nonidentical closed convex set constraints is studied.The aim is to design a corresponding distributed optimization algorithm over the fixed unbalanced graph to solve the considered problem.To this end,with the push-sum framework improved,the distributed optimization algorithm is newly designed,and its strict convergence analysis is given under the assumption that the involved graph is strongly connected.Finally,simulation results support the good performance of the proposed algorithm.

关键词： Distributed optimization Nonidentical constraints Improved push-sum framework

Bi-level Multi-objective Joint Planning of Distribution Networks Considering Uncertainties

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Journal of Modern Power Systems and Clean Energy 2022年第6期10卷 1599-1613页

作者： Shouxiang Wang Yichao Dong Qianyu Zhao Xu Zhang Key Laboratory of Smart grid of Ministry of Education Tianjin UniversityTianjin300072China state grid Tianjin Electric Power Company economic and Technological research institute Tianjin 300171China state grid Tianjin Electric Power Company Tianjin300010China

With the increasing penetration of photovoltaics in distribution networks,the adaptability of distribution network under uncertainties needs to be considered in the planning of distribution systems.In this paper,the interval arithmetic and affine arithmetic are applied to deal with uncertainties,and an affine arithmetic based bi-level multi-objective joint planning model is built,which can obtain the planning schemes with low constraint-violation risk,high reliability and strong adaptability.On this basis,a bi-level multi-objective solution methodology using affine arithmetic based non-dominated sorting genetic algorithm II is proposed,and the planning schemes that simultaneously meet economy and adaptability goals under uncertainties can be obtained.To further eliminate bad solutions and improve the solution qualities,an affine arithmetic based dominance relation weakening criterion and a deviation distance based modification method are proposed.A 24-bus test system and a 10 kV distribution system of China are used for case studies.Different uncertainty levels are compared,and a sensitivity analysis of key parameters is conducted to explore their impacts on the final planning schemes.The simulation results verify the advantages of the proposed affine arithmetic based planning method.

关键词： Affine arithmetic adaptability multi-objective joint planning Pareto optimal front uncertainty