Zhengyu Chen
ABSTRACT
Port power, as one of the important scenarios to promote energy substitution, has the characteristics of great substitution potential and strong interaction ability. In order to explore the technology of port power participating in power grid regulation, a strategy of port power participating in power grid regulation based on multi-agent system (MAS) technology is proposed in this paper. Firstly, the analysis and modeling of the controllable resources are carried out from the power side and the power grid side of the port, that is, the interactive resources on both sides of the supply and demand are analyzed and modeled, which lays the foundation for the key technology research. Secondly, in order to reflect the economic and environmental benefits of energy substitution, and to take into account the benefits of port ships, ports, power grids and the government, the optimization based on MAS is proposed. Finally, Q-learning algorithm is used to solve the model, and an example is used to verify the effectiveness of the model and the control strategy.
- Saber A Y, Venayagamoorthy G K. Unit Commitment with Vehicle-to-Grid Using Particle Swarm Optimization[C]. PowerTech, 2009 IEEE Bucharest. IEEE, 2009, 1--8.Google Scholar
- Han S, Han S, Sezaki K. Development of an Optimal Vehicle-to-Grid Aggregator for Frequency Regulation[J]. IEEE Transactions on Smart Grid, 2010, 1(1), 65--72.Google ScholarCross Ref
- Asare-Bediako. B, Kling W. L, Ribeiro P. F. IEEE Multi-Agent System Architecture for Smart Home Energy Management and Optimization INNOVATIVE SMART GRID TECHNOLOGIES EUROPE (ISGT EUROPE)[C]. 2013.Google Scholar
- J. Hess, T. Schegner, P. Felsmann, C.GP IEEE TI Electrical Design of an Efficiency House Plus 2012 3RD IEEE PES INNOVATIVE SMART GRID TECHNOLOGIES EUROPE (ISGTEUROPE)[J]. 2012.Google Scholar
- Li Zuofeng. Research and Practice of Electric Energy Substitution in Jiangsu Province[J]. Power Management in Demand Side, 2016, 05, 1--3.Google Scholar
- Wu Zhihong, Han Jianfu. Electric Energy Substitution Evaluation Method Based on Fuzzy Multi-attribute Decision-making[J]. Power Demand Side Management, 2018.Google Scholar
- Xiao Hui. Electrical Lighting Technology[M]. Mechanical Industry Press, 2009.Google Scholar
- Lu N, Zhang Y. Design Considerations of a Centralized Load Controller Using Thermostatically Controlled Appliances for Continuous Regulation Reserves[J]. IEEE Transactions on Smart Grid, 2013, 4(2), 914--921.Google ScholarCross Ref
- Villanueva D, Feijóo A. Wind power distributions, A review of their applications[J]. Renewable & Sustainable Energy Reviews, 2010, 14(5), 1490--1495.Google ScholarCross Ref
- Riffonneau Y, Bacha S, Barruel F, et al. Optimal Power Flow Management for Grid Connected PV Systems With Batteries[J]. IEEE Transactions on Sustainable Energy, 2011, 2(3), 309--320.Google ScholarCross Ref
- Liu Dongqi, Wang Yaonan, Yuan Xiaofang. Collaborative Optimization Operation of Electric Eehicle Charging and Discharging and Wind/Thermal Power Generation System[J]. Transactions of the China Society of Electrical Engineering, 2017, 32(3), 18--26.Google Scholar
- Yu Yongsheng, Feng Yanhui, Jiang Hongxin, et al. Study on VCS-hvdc Wind Power Grid-connected Optimization Method Considering Wind Energy Randomness[J]. Power System and Clean Energy, 2016, 32(1), 75--84.Google Scholar
- Liu Siyuan, Ai Wei. Coordinated Optimization of Virtual Power Plant Based on Multi-Agent System[j]. Electrical Apparatus and Energy Efficiency Management Technology, 2017(3), 19--25.Google Scholar
- Lagorse J, Paire D, Miraoui A. A Multi-agent System for Energy Management of Distributed Power Sources[J]. Renewable Energy, 2010, 35(1), 174--182.Google ScholarCross Ref
- Sun Jiamin, Shi Tianyu, Wang Xuhui. Multi-Agent Hierarchical Dynamic Alliance Cooperation Mechanism[J]. Digital Technology & Applications, 2017(1), 112--112.Google Scholar
- Fanger P. O, Toftum J. Extension of the PMV Model to Non-air-conditioned Buildings in Warm Climates[J]. Energy & Buildings, 2002, 34(6), 533--536.Google ScholarCross Ref
- Yang R, Wang L. Multi-agent Based Energy and Comfort Management in a Building Environment Considering Behaviors of Occupants[C]. Power and Energy Society General Meeting, 2012 IEEE. IEEE, 2012, 1--7.Google Scholar
- He Chen, Peng Mingcheng. Research on Reliability Optimization Strategy of Microgrid Based on Q-Learning[J]. Electrical Apparatus and Energy Efficiency Management Technology, 2016(11), 48--53.Google Scholar
- Yin Linfei, Yu Tao. Design of Strong Robust Intelligent Generator Based on Deep Q-Learning[J]. Electric Power Automation Equipment, 2018, v.38; No.289(5), 19--26.FNM Surname (2018). Article Title. Journal Title, 10(3), 1--10.Google Scholar
Recommendations
Workload-adaptive process tuning strategy for power-efficient multi-core processors
ISLPED '10: Proceedings of the 16th ACM/IEEE international symposium on Low power electronics and designAs more devices are integrated with technology scaling, reducing the power consumption of both high-performance and low-power processors has become the first-class design constraint. Reducing power consumption while satisfying required performance is ...
Research on Control Strategy for Multi-port Power Electronics Transformer Based on Interphase Coupling MMC Topology
2019 22nd International Conference on Electrical Machines and Systems (ICEMS)In recent years, with the development of the power electronics technology, the power electronics transformer (PET) has been widely used in the hybrid AC/DC power grid. As the key equipment, the power electronics transformer needs to have multi ports to ...
Research on Container Terminal Scheduling System Based on Multi-agent
CASE '09: Proceedings of the 2009 IITA International Conference on Control, Automation and Systems Engineering (case 2009)In the near future, most container terminals will no longer be able to expand its surrounding land and will thus be unable to meet the storage requirements due to the boom in world trade. A solution is to improve utilization of the available resources. ...
Comments