Allocation of Wind-Solar-Battery Under Electric Energy Substitution in Integrated Park Considering Demand Response

doi:10.12204/j.issn.1000-7229.2021.10.002

Abstract

Abstract:

Promoting the electric energy substitution of integrated park is an important measure to improve environmental problems. The electric energy substitution of integrated park is affected by distribution network transformation. The program of electric energy substitution to configure wind-solar-battery system is proposed. Firstly, the load characteristic model under electric energy substitution is established which involves the load models of electricity replacing heat, cooling and fuel oil. Secondly, the bi-level optimal model for wind-solar-battery capacity allocation is proposed considering the reliability of distribution network and the demand response, which is concluded distribution network layer and wind-solar-battery system layer. Finally, Cplex is used to solve the problem, and it is obtained the optimal planning scheme of the wing-solar-battery system considering the reliability of distribution network and the demand response in the electric energy substitution of the integrated park. In the system of IEEE 33-node system, the electric energy substitution load data of an actual park is used to perform simulation. The results show that the use of wind-solar-battery system for power supply and the consideration of load demand response can solve the demand for electric energy substitution load growth in the park and realize the wind-solar-battery system's optimal economic configuration.

Key words: electric energy substitution, wind-solar-battery system, economic cost, environmental benefits, energy storage configuration, demand response

CLC Number:

TM715

YANG Ying, LIU Youbo, HUANG Yuan, LIU Junyong, WANG Xiaodi. Allocation of Wind-Solar-Battery Under Electric Energy Substitution in Integrated Park Considering Demand Response[J]. ELECTRIC POWER CONSTRUCTION, 2021, 42(10): 9-18.

Figures/Tables 13

Fig.1 Energy system structure of an integrated park

Fig.2 Bilevel model under energy system in integrated park

Fig.3 Solving flowchart of the bi-level model

Fig.4 Modified 33-node distribution network

Fig.5 Load of electric energy substitution

Table 1 The basic parameters

Table 2 Table of time-of-use price

Fig.6 Daily load curve before and after DR

Table 3 Load characters before and after DR

Table 4 Configuration result of the wind-solar-battery system

Fig.7 Real-time operating power of system in different scenarios

Table 4 Utilization rate of wind energy storage equipment and daily investment cost

Table 6 Daily cost of the distribution network expansion and peak load

References 18

[1]	刘国静, 谈健, 李琥, 等. 基于Logistic模型的电能替代电量预测[J]. 电力工程技术, 2018, 37(6): 39-43.
	LIU Guojing, TAN Jian, LI Hu, et al. Forecast of electric energy substitution based on logistic model[J]. Electric Power Engineering Technology, 2018, 37(6): 39-43.
[2]	李鹏, 杨莘博, 李慧璇, 等. 计及多能源多需求响应手段的园区综合能源系统优化调度模型[J]. 电力建设, 2020, 41(5): 45-57.
	LI Peng, YANG Shenbo, LI Huixuan, et al. Optimization scheduling model of park integrated energy system considering multiple energies and multiple demand responses[J]. Electric Power Construction, 2020, 41(5): 45-57.
[3]	马益平. 考虑电动汽车调度的微电网混合储能容量优化配置[J]. 电力系统保护与控制, 2017, 45(23): 98-107.
	MA Yiping. Hybrid energy storage capacity optimization configuration for micro-grid considering EV scheduling[J]. Power System Protection and Control, 2017, 45(23): 98-107.
[4]	赵冬梅, 夏轩, 陶然. 含电转气的热电联产微网电/热综合储能优化配置[J]. 电力系统自动化, 2019, 43(17): 46-54.
	ZHAO Dongmei, XIA Xuan, TAO Ran. Optimal configuration of electric/thermal integrated energy storage for combined heat and power microgrid with power to gas[J]. Automation of Electric Power Systems, 2019, 43(17): 46-54.
[5]	郑国太, 李昊, 赵宝国, 等. 基于供需能量平衡的用户侧综合能源系统电/热储能设备综合优化配置[J]. 电力系统保护与控制, 2018, 46(16): 8-18.
	ZHENG Guotai, LI Hao, ZHAO Baoguo, et al. Comprehensive optimization of electrical/thermal energy storage equipments for integrated energy system near user side based on energy supply and demand balance[J]. Power System Protection and Control, 2018, 46(16): 8-18.
[6]	陈志杰, 李凤婷, 黄蓉. 计及需求响应的含风电电力系统旋转备用优化配置策略[J]. 电力系统保护与控制, 2020, 48(13): 117-122.
	CHEN Zhijie, LI Fengting, HUANG Rong. Spinning reserve optimal configuration strategy of a wind power system with demand response[J]. Power System Protection and Control, 2020, 48(13): 117-122.
[7]	陈纬楠, 胡志坚, 岳菁鹏, 等. 考虑微电网接入的主动配电网双层能量管理[J]. 南方电网技术, 2020, 14(7): 30-38.
	CHEN Weinan, HU Zhijian, YUE Jingpeng, et al. Bi-level energy management of active distribution network considering microgrid access[J]. Southern Power System Technology, 2020, 14(7): 30-38.
[8]	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.
[9]	方绍凤, 周任军, 许福鹿, 等. 考虑电热多种负荷综合需求响应的园区微网综合能源系统优化运行[J]. 电力系统及其自动化学报, 2020, 32(1): 50-57.
	FANG Shaofeng, ZHOU Renjun, XU Fulu, et al. Optimal operation of integrated energy system for park micro-grid considering comprehensive demand response of power and thermal loads[J]. Proceedings of the CSU-EPSA, 2020, 32(1): 50-57.
[10]	陈守军, 辛禾, 王涛, 等. 风电、蓄热式电锅炉联合供暖调度鲁棒优化模型[J]. 电力建设, 2016, 37(1): 103-109.
	CHEN Shoujun, XIN He, WANG Tao, et al. Heating operation scheduling robust optimization model for heat storage electric boiler combined with wind power[J]. Electric Power Construction, 2016, 37(1): 103-109.
[11]	黄文焘, 王萧博, 邰能灵, 等. 电热微网能量耦合转换模型与梯级优化利用方法[J]. 中国电机工程学报, 2020, 40(21): 6804-6815.
	HUANG Wentao, WANG Xiaobo, TAI Nengling, et al. Energy coupling conversion model and cascade utilization method for microgrid with heat and power system[J]. Proceedings of the CSEE, 2020, 40(21): 6804-6815.
[12]	STANČIN H, MIKULČIĆ H, WANG X, et al. A review on alternative fuels in future energy system[J]. Renewable and Sustainable Energy Reviews, 2020, 128: 109927. doi: 10.1016/j.rser.2020.109927 URL
[13]	王睿, 高欣, 李军良, 等. 基于聚类分析的电动汽车充电负荷预测方法[J]. 电力系统保护与控制, 2020, 48(16): 37-44.
	WANG Rui, GAO Xin, LI Junliang, et al. Electric vehicle charging demand forecasting method based on clustering analysis[J]. Power System Protection and Control, 2020, 48(16): 37-44.
[14]	周楠, 樊玮, 刘念, 等. 基于需求响应的光伏微网储能系统多目标容量优化配置[J]. 电网技术, 2016, 40(6): 1709-1716.
	ZHOU Nan, FAN Wei, LIU Nian, et al. Battery storage multi-objective optimization for capacity configuration of PV-based microgrid considering demand response[J]. Power System Technology, 2016, 40(6): 1709-1716.
[15]	门向阳, 曹军, 王泽森, 等. 能源互联微网型多能互补系统的构建与储能模式分析[J]. 中国电机工程学报, 2018, 38(19): 5727-5737.
	MEN Xiangyang, CAO Jun, WANG Zesen, et al. The constructing of multi-energy complementary system of energy Internet microgrid and energy storage model analysis[J]. Proceedings of the CSEE, 2018, 38(19): 5727-5737.
[16]	尹爱辉, 戎晓雪, 范莹莹, 等. 基于二阶锥优化的含风光储电网的动态最优潮流[J/OL]. 电测与仪表. [2021-08-01]. http://kns.cnki.net/kcms/detail/23.1202.TH.20201123.1840.022.html.
	YIN Aihui, RONG Xiaoxue, FAN Yingying, et al. Dynamic optimal power flow in distribution networks with wind/PV/storage based on second-order cone programming[J/OL]. Electrical Measurement & Instrumentation. [2021-08-01]. http://kns.cnki.net/kcms/detail/23.1202.TH.20201123.-1840.022.html.
[17]	黄亚峰, 朱玉杰, 穆钢, 等. 基于温度预报的户用电采暖负荷可调节能力评估[J]. 电网技术, 2018, 42(8): 2487-2493.
	HUANG Yafeng, ZHU Yujie, MU Gang, et al. Evaluation of adjustable capacity of household electrical heating load based on temperature forecast[J]. Power System Technology, 2018, 42(8): 2487-2493.
[18]	郭创新, 刘洞宇, 朱承治, 等. 电动汽车居民区充电负荷建模分析[J]. 电力自动化设备, 2020, 40(1): 1-9.
	GUO Chuangxin, LIU Dongyu, ZHU Chengzhi, et al. Modeling and analysis of electric vehicle charging load in residential area[J]. Electric Power Automation Equipment, 2020, 40(1): 1-9.