Stochastic Optimization of Integrated Energy System ConsideringDistribution Locational Marginal Price

doi:10.12204/j.issn.1000-7229.2020.12.014

Abstract

Abstract:

Integrated energy system (IES) focuses on renewable energy, which provides an effective carrier for multi-energy access and coupling utilization in the context of distribution system. Firstly, in order to promote the efficient energy conversion among multiple energy sources, the hybrid AC/DC microgrid structure is introduced, and the IES model based on energy hub is established. Then, according to the interaction between the upper distribution network and the lower energy hub, a two-level recursive optimization model is constructed. Distribution system operator (DSO) uses the flexibility of energy hub to improve the economic benefits of the system, while the energy hub optimizes the internal operation according to distribution locational marginal price (DLMP) and consumer load, thus forming the restriction and balance of interests between the distribution system and the energy hub. Meanwhile, considering the uncertainty of output of distributed renewable energy and consumers' energy consumption behavior, the multi-scenario stochastic optimal scheduling mode of IES based on hybrid AC/DC energy hub is proposed. Finally, the simulation results of modified IEEE 33-node system are used to verify the robustness and economy of the proposed model.

Key words: integrated energy system, energy hub, distribution system, distribution locational marginal price, stochastic optimization

CLC Number:

TM73

DU Wei, LIU Dong, LI Wenyun, CAO Min, CHEN Fei. Stochastic Optimization of Integrated Energy System ConsideringDistribution Locational Marginal Price[J]. ELECTRIC POWER CONSTRUCTION, 2020, 41(12): 141-151.

Figures/Tables 20

Fig.1 Basic structure of an AC/DC hybrid EH

Fig.2 Distribution system structure including EHs

Fig.3 Two-level recursive optimization model

Fig.4 Load, PV and the whole market prices

Fig.5 Power balance curve of EH optimization

Fig.6 Heating balance curve of EH optimization

Fig.7 Analysis of electricity price of distribution network node

Fig.8 AC/DC conversion power

Table 1 Case scenario of solution method

Fig.9 Comparison of electricity purchased by EH

Fig.10 Comparison of gas purchased by EH

Table 2 Comparison of optimization results of different scenarios

Table 3 Case scenario of uncertainty

Fig.11 Comparison of electricity purchased by EH considering uncertainty

Fig.12 Comparison of gas purchased by EH considering uncertainty

Table 4 Comparison of stochastic optimization results

Fig.A1 IEEE 33-node distribution network topology

Table A1 Unit parameters of the EH model

Fig.B1 Load data

Table B1 Uncertain parameters selected by consumers

References 27

[1]	别朝红, 王旭, 胡源. 能源互联网规划研究综述及展望[J]. 中国电机工程学报, 2017,37(22):6445-6462.
	BIE Zhaohong, WANG Xu, HU Yuan. Review and prospect of planning of energy Internet[J]. Proceedings of the CSEE, 2017,37(22):6445-6462.
[2]	孙宏斌, 郭庆来, 潘昭光. 能源互联网: 理念、架构与前沿展望[J]. 电力系统自动化, 2015,39(19):1-8.
	SUN Hongbin, GUO Qinglai, PAN Zhaoguang. Energy Internet: Concept, architecture and frontier outlook[J]. Automation of Electric Power Systems, 2015,39(19):1-8.
[3]	黄子硕, 何桂雄, 闫华光, 等. 园区级综合能源系统优化模型功能综述及展望[J]. 电力自动化设备, 2020,40(1):10-18.
	HUANG Zishuo, HE Guixiong, YAN Huaguang, et al. Overview and prospect of optimization model function for community-scale integrated energy system[J]. Electric Power Automation Equipment, 2020,40(1):10-18.
[4]	王伟亮, 王丹, 贾宏杰, 等. 能源互联网背景下的典型区域综合能源系统稳态分析研究综述[J]. 中国电机工程学报, 2016,36(12):3292-3306. doi: 10.13334/j.0258-8013.pcsee.152858 URL
	WANG Weiliang, WANG Dan, JIA Hongjie, et al. Review of steady-state analysis of typical regional integrated energy system under the background of energy Internet[J]. Proceedings of the CSEE, 2016,36(12):3292-3306.
[5]	刘怀东, 冯志强, 王锦桥, 等. 计及条件风险价值的综合能源系统经济调度[J]. 电网技术, 2018,42(5):1385-1392.
	LIU Huaidong, FENG Zhiqiang, WANG Jinqiao, et al. Economic dispatch of integrated energy systems considering conditional value-at-risk[J]. Power System Technology, 2018,42(5):1385-1392.
[6]	郝然, 艾芊, 朱宇超, 等. 基于能源集线器的区域综合能源系统分层优化调度[J]. 电力自动化设备, 2017,37(6):171-178.
	HAO Ran, AI Qian, ZHU Yuchao, et al. Hierarchical optimal dispatch based on energy hub for regional integrated energy system[J]. Electric Power Automation Equipment, 2017,37(6):171-178.
[7]	冯智慧, 吕林, 许立雄, 等. 高比例清洁能源渗透下的能量枢纽两阶段优化调度策略[J]. 电力建设, 2019,40(3):1-8.
	FENG Zhihui, LÜ Lin, XU Lixiong, et al. Two-stage optimal dispatch strategy for energy hub with high proportion clean energy penetration[J]. Electric Power Construction, 2019,40(3):1-8.
[8]	MOEINI-AGHTAIE M, ABBASPOUR A, FOTUHI-FIRUZABAD M, et al. Optimized probabilistic PHEVs demand management in the context of energy hubs[J]. IEEE Transactions on Power Delivery, 2015,30(2):996-1006. doi: 10.1109/TPWRD.2014.2348918 URL
[9]	YAZDANI-DAMAVANDI M, MOGHADDAM M P, HAGHIFAM M, et al. Modeling operational behavior of plug-in electric vehicles’ parking lot in multienergy systems[J]. IEEE Transactions on Smart Grid, 2016,7(1):124-135. doi: 10.1109/TSG.2015.2404892 URL
[10]	李宏仲, 房宇娇, 肖宝辉. 考虑广义储能的区域综合能源系统优化运行研究[J]. 电网技术, 2019,43(9):3130-3138.
	LI Hongzhong, FANG Yujiao, XIAO Baohui. Research on optimized operation of regional integrated energy system considering generalized energy storage[J]. Power System Technology, 2019,43(9):3130-3138.
[11]	徐业琰, 廖清芬, 刘涤尘, 等. 基于综合需求响应和博弈的区域综合能源系统多主体日内联合优化调度[J]. 电网技术, 2019,43(7):2506-2518.
	XU Yeyan, LIAO Qingfen, LIU Dichen, et al. Multi-player intraday optimal dispatch of integrated energy system based on integrated demand response and games[J]. Power System Technology, 2019,43(7):2506-2518.
[12]	仉梦林, 胡志坚, 王小飞, 等. 基于动态场景集和需求响应的二阶段随机规划调度模型[J]. 电力系统自动化, 2017,41(11):68-76.
	ZHANG Menglin, HU Zhijian, WANG Xiaofei, et al. Two-stage stochastic programming scheduling model based on dynamic scenario sets and demand response[J]. Automation of Electric Power Systems, 2017,41(11):68-76.
[13]	张宇帆, 艾芊, 郝然, 等. 基于机会约束规划的楼宇综合能源系统经济调度[J]. 电网技术, 2019,43(1):108-116.
	ZHANG Yufan, AI Qian, HAO Ran, et al. Economic dispatch of integrated energy system at building level based on chance constrained programming[J]. Power System Technology, 2019,43(1):108-116.
[14]	张津珲, 王旭, 蒋传文, 等. 新型城镇下含热电联产机组的配电网两阶段鲁棒优化调度[J]. 电力系统自动化, 2019,43(23):155-172.
	ZHANG Jinhui, WANG Xu, JIANG Chuanwen, et al. Two-stage robust optimization scheduling of distribution network with combined heat and power units in new-type towns[J]. Automation of Electric Power Systems, 2019,43(23):155-172.
[15]	王皓, 艾芊, 甘霖, 等. 基于多场景随机规划和MPC的冷热电联合系统协同优化[J]. 电力系统自动化, 2018,42(13):51-58.
	WANG Hao, AI Qian, GAN Lin, et al. Collaborative optimization of combined cooling heating and power system based on multi-scenario stochastic programming and model predictive control[J]. Automation of Electric Power Systems, 2018,42(13):51-58.
[16]	张思德, 胡伟, 卫志农, 等. 基于机会约束规划的电-气互联综合能源系统随机最优潮流[J]. 电力自动化设备, 2018,38(9):121-128.
	ZHANG Side, HU Wei, WEI Zhinong, et al. Stochastic optimal power flow of integrated power and gas energy system based on chance-constrained programming[J]. Electric Power Automation Equipment, 2018,38(9):121-128.
[17]	蒋超凡, 艾欣. 计及多能耦合机组不确定性的综合能源系统运行优化模型研究[J]. 电网技术, 2019,43(8):2843-2854.
	JIANG Chaofan, AI Xin. Integrated energy system operation optimization model considering uncertainty of multi-energy coupling units[J]. Power System Technology, 2019,43(8):2843-2854.
[18]	沈欣炜, 郭庆来, 许银亮, 等. 考虑多能负荷不确定性的区域综合能源系统鲁棒规划[J]. 电力系统自动化, 2019,43(7):34-45.
	SHEN Xinwei, GUO Qinglai, XU Yinliang, et al. Robust planning method for regional integrated energy system considering multi-energy load uncertainties[J]. Automation of Electric Power Systems, 2019,43(7):34-45.
[19]	WONG S, FULLER J D. Pricing energy and reserves using stochastic optimization in an alternative electricity market[J]. IEEE Transactions on Power Systems, 2007,22(2):631-638. doi: 10.1109/TPWRS.2007.894867 URL
[20]	高亚静, 李瑞环, 梁海峰, 等. 考虑间歇性电源与负荷不确定性情况下基于多场景技术的主动配电系统两步优化调度[J]. 中国电机工程学报, 2015,35(7):1657-1665. doi: 10.13334/j.0258-8013.pcsee.2015.07.013 URL
	GAO Yajing, LI Ruihuan, LIANG Haifeng, et al. Two step optimal dispatch based on multiple scenarios technique considering uncertainties of intermittent distributed generations and loads in the active distribution system[J]. Proceedings of the CSEE, 2015,35(7):1657-1665.
[21]	于晗, 钟志勇, 黄杰波, 等. 采用拉丁超立方采样的电力系统概率潮流计算方法[J]. 电力系统自动化, 2009,33(21):32-35, 81.
	YU Han, CHUNG Chiyong, WONG Kitpo, et al. A probabilistic load flow calculation method with Latin hypercube sampling[J]. Automation of Electric Power Systems, 2009,33(21):32-35, 81.
[22]	解蛟龙. 风/光/负荷典型场景缩减方法及在电网规划中的应用[D]. 合肥: 合肥工业大学, 2017.
	XIE Jiaolong. A typical reduced-scenario analysis method for wind, photoelectric, load scene and its application in power system planning[D]. Hefei: Hefei University of Technology, 2017.
[23]	徐青山, 李淋, 盛业宏, 等. 冷热电联供型多微网主动配电系统日前优化经济调度[J]. 电网技术, 2018,42(6):1726-1735.
	XU Qingshan, LI Lin, SHENG Yehong, et al. Day-ahead optimized economic dispatch of active distribution power system with combined cooling, heating and power-based microgrids[J]. Power System Technology, 2018,42(6):1726-1735.
[24]	QI C, WANG K Y, FU Y, et al. A decentralized optimal operation of AC/DC hybrid distribution grids[J]. IEEE Transactions on Smart Grid, 2018,9(6):6095-6105. doi: 10.1109/TSG.2017.2703582 URL
[25]	李媛, 冯昌森, 文福拴, 等. 含电动汽车和电转气的园区能源互联网能源定价与管理[J]. 电力系统自动化, 2018,42(16):1-10.
	LI Yuan, FENG Changsen, WEN Fushuan, et al. Energy pricing and management for park-level energy internets with electric vehicles and power-to-gas devices[J]. Automation of Electric Power Systems, 2018,42(16):1-10.
[26]	ZENG F D, BIE Z H, LIU S Y, et al. Trading model combining electricity, heating, and cooling under multi-energy demand response[J]. Journal of Modern Power Systems and Clean Energy, 2020,8(1):133-141. doi: 10.35833/MPCE.2018.000454 URL
[27]	NAJAFI-GHALELOU A, NOJAVAN S, ZARE K, et al. Robust scheduling of thermal, cooling and electrical hub energy system under market price uncertainty[J]. Applied Thermal Engineering, 2019,149:862-880. doi: 10.1016/j.applthermaleng.2018.12.108 URL