Application and practice of the situation awareness system for the community level virtual power plant

doi:10.3969/j.issn.2097-0706.2023.01.009

Abstract

Abstract:

Situation awareness technology is an important guarantee for the improvement of state observability and system stable operation. In order to further participate in electricity market regulation，a community level virtual power plant system should be of excellent external characteristics. Therefore，it is particularly necessary for power plants to response to emergencies intelligently and rapidly by efficient and timely means. For a community level virtual power plant system，the most important thing is the establishment of a real-time awareness system based on situation awareness technology. The construction of a typical community level integrated energy system's situation awareness system is expounded，and the analysis results can provide references for relevant researches.

Key words: community, virtual power plant, situation awareness, integrated energy, intelligent energy

CLC Number:

TK01：TM62

SONG Zhenhui, MA Conggan, WANG Zhao. Application and practice of the situation awareness system for the community level virtual power plant[J]. Integrated Intelligent Energy, 2023, 45(1): 75-81.

Figures/Tables 1

References 20

[1]	中华人民共和国国务院. 国务院关于印发2030 年前碳达峰行动方案的通知[EB/OL].(2021-10-26)[2022-12-04]. https://www.gov.cn/zhengce/content/2021-10/26/content_5644984.htm.
[2]	北京市人民政府. 北京市碳达峰实施方案[Z]. 2022.
[3]	山西省能源局. 虚拟电厂建设与运营管理实施方案[Z]. 2022.
[4]	葛磊蛟, 李元良, 陈艳波, 等. 智能配电网态势感知关键技术及实施效果评价[J]. 高电压技术, 2021, 47(7):2269-2280.
	GE Leijiao, LI Yuanliang, CHEN Yanbo, et al. Key technologies of situation awareness and implementation effectiveness evaluation in smart distribution network[J]. High Voltage Engineering, 2021, 47(7):2269-2280.
[5]	GE L J, LIU J H, WANG B, et al. Improved adaptive gray wolf genetic algorithm for low-cost and high reliability of photovoltaic intelligent edge terminal optimal configuration[J]. Computer and Electrical Engineering, 2021, 95:107394. doi: 10.1016/j.compeleceng.2021.107394
[6]	葛磊蛟, 秦羽飞, 刘嘉恒, 等. 基于相似日与BA-WNN相结合的分布式光伏数据虚拟采集方法[J]. 电力自动化设备, 2021, 41(6):8-14.
	GE Leijiao, QIN Yufei, LIU Jiaheng, et al. Virtual acquisition method of distributed photovoltaic data based on similarity day and BA-WNN[J]. Electric Power Automation Equipment, 2021, 41(6):8-14.
[7]	葛磊蛟, 廖文龙, 王煜森, 等. 数据不足条件下基于改进自动编码器的变压器故障数据增强方法[J]. 电工技术学报, 2021, 36(S1):84-94.
	GE Leijiao, LIAO Wenlong, WANG Yusen, et al. Data augmentation method for transformer fault based on improved auto-encoder under the condition of insufficient data[J]. Transactions of China Electrotechnical Society, 2021, 36(S1):84-94.
[8]	葛磊蛟, 王守相, 瞿海妮. 智能配用电大数据存储架构设计[J]. 电力自动化设备, 2016, 36(6):194-202.
	GE Leijiao, WANG Shouxiang, ZHAI Haini. Design of storage framework for big data of SPDU[J]. Electric Power Automation Equipment, 2016, 36(6):194-202.
[9]	葛磊蛟, 王守相, 张明, 等. 智能用电条件下用户用能管理与服务平台[J]. 电力自动化设备, 2015, 35(3):152-156.
	GE Leijiao, WANG Shouxiang, ZHANG Ming, et al. User energy management and service platform under intelligent power consumption[J]. Electric Power Automation Equipment, 2015, 35(3):152-156.
[10]	谢涛, 石雪梅, 钟成元. 基于态势感知驱动的配电网投资决策体系架构设计探讨[J]. 电力与能源, 2018, 39(3):329-332.
	XIE Tao, SHI Xuemei, ZHONG Chenyuan. Design of distribution network investment decision system architecture based on situational awareness drive[J]. Power & Energy, 2018, 39(3):329-332.
[11]	刘自发, 谭雅之, 李炯, 等. 区域综合能源系统规划关键问题研究综述[J]. 综合智慧能源, 2022, 44(6):12-22. doi: 10.3969/j.issn.2097-0706.2022.06.002
	LIU Zifa, TAN Yazhi, LI Jiong, et al. Review on key points in the planning for a district-level integrated energy system[J]. Integrated Intelligent Energy, 2022, 44(6):12-22. doi: 10.3969/j.issn.2097-0706.2022.06.002
[12]	QIAO Z, GUO Q, SUN H, et al. Multi-time period optimized configuration and scheduling of gas storage in gas-fired power plants[J]. Applied Energy, 2018, 226:924-934. doi: 10.1016/j.apenergy.2018.06.033
[13]	DING Z, XIE L, LU Y. Emission-aware stochastic resource planning scheme for data center microgrid considering batch workload scheduling and risk management[J]. IEEE Transactions on Industry Applications, 2018, 54(6):5599-5608. doi: 10.1109/TIA.2018.2851516
[14]	GEIDL M, KOEPPEL G, FAVREPERRO D, et al. Energy hubs for the future[J]. Power & Energy Magazine IEEE, 2007, 5(1):24-30.
[15]	GUO M X, MU Y F, JIA H J, et al. Electric/thermal hybrid energy storage planning for park-level integrated energy systems with second-life battery utilization[J]. Advances in Applied Energy, 2021, 4:100064. doi: 10.1016/j.adapen.2021.100064
[16]	江婷, 赵雅姣. 基于燃气分布式的综合能源系统碳减排分析[J]. 综合智慧能源, 2022, 44(9):27-42. doi: 10.3969/j.issn.2097-0706.2022.09.004
	JIANG Ting, ZHAO Yajiao. Carbon emission reduction analysis for gas-based distributed integrated energy systems[J]. Integrated Intelligent Energy, 2022, 44(9):27-42. doi: 10.3969/j.issn.2097-0706.2022.09.004
[17]	王鑫, 陈祖翠, 卞在平, 等. 基于粒子群优化算法的智慧微电网风光储容量优化配置[J]. 综合智慧能源, 2022, 44(6):52-58. doi: 10.3969/j.issn.2097-0706.2022.06.006
	WANG Xin, CHEN Zucui, BIAN Zaiping, et al. Optimal allocation of a wind-PV-battery hybrid system in smart microgrid based on particle swarm optimization algorithm[J]. Integrated Intelligent Energy, 2022, 44(6):52-58. doi: 10.3969/j.issn.2097-0706.2022.06.006
[18]	YING C, THORSTEN K, NAZGUL Z, et al. Modeling and forecasting the dynamics of the natural gas transmission network in Germany with the demand and supply balance constraint[J]. Applied Energy, 2020, 278:115597. doi: 10.1016/j.apenergy.2020.115597
[19]	HU X, ZHANG H, CHEN D, et al. Multi-objective planning for integrated energy systems considering both energy efficiency and economy[J]. Energy, 2020, 197:117155. doi: 10.1016/j.energy.2020.117155
[20]	CAI W, MU Y, WANG C, et al. Distributional employment impacts of renewable and new energy:A case study of China[J]. Renewable and Sustainable Energy Reviews, 2014, 39:1155-1163. doi: 10.1016/j.rser.2014.07.136