考虑需求响应的电/热/气云储能优化配置策略

doi:10.12204/j.issn.1000-7229.2022.03.010

电力建设 ›› 2022, Vol. 43 ›› Issue (3): 83-99.doi: 10.12204/j.issn.1000-7229.2022.03.010

• 面向新型电力系统的储能技术研究及应用·栏目主持李建林教授· • 上一篇下一篇

考虑需求响应的电/热/气云储能优化配置策略

丁曦¹(), 姜威¹(), 郭创新¹(), 奚增辉²(), 高洁²()

1.浙江大学电气工程学院,杭州市 310027
2.国网上海市电力公司,上海市 200122

收稿日期:2021-10-21 出版日期:2022-03-01 发布日期:2022-03-24
通讯作者: 郭创新 E-mail:dingxi@zju.edu.cn;13522212829@163.com;guochuangxin@zju.edu.cn;xizh@sh.sgcc.com.cn;gaoj@sh.sgcc.com.cn
作者简介:丁曦(1998),女,硕士研究生,主要研究方向为综合能源系统规划,E-mail: dingxi@zju.edu.cn;
姜威(1997),男,硕士研究生,主要研究方向为人工智能在综合能源系统配置中的应用,Email: 13522212829@163.com;
奚增辉(1974),男,硕士,主要研究方向为数字化转型,Email: xizh@sh.sgcc.com.cn;
高洁(1968),女,博士,主要研究方向为系统工程,Email: gaoj@sh.sgcc.com.cn。
基金资助:
国家自然科学基金项目(51877190);国家电网有限公司科技项目(52094021000A)

Optimal Configuration of Electricity-Heat-Gas CloudEnergy Storage Considering Demand Response

DING Xi¹(), JIANG Wei¹(), GUO Chuangxin¹(), XI Zenghui²(), GAO Jie²()

1. College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
2. State Grid Shanghai Municipal Electric Power Company, Shanghai 200122, China

Received:2021-10-21 Online:2022-03-01 Published:2022-03-24
Contact: GUO Chuangxin E-mail:dingxi@zju.edu.cn;13522212829@163.com;guochuangxin@zju.edu.cn;xizh@sh.sgcc.com.cn;gaoj@sh.sgcc.com.cn
Supported by:
Fund: National Natural Science Foundation of China(51877190);State Grid Corporation of China Research Program(52094021000A)

摘要/Abstract

摘要：

面向越来越开放的能源交易市场,为充分调动用户侧资源,提出了一种考虑需求响应(demand response, DR)的电/热/气云储能(cloud energy storage,CES)优化配置策略。建立含电/热/气云储能能源集线器(energy hub, EH)结构,从参与云储能商业模式的用户侧与云储能提供商出发,构建两主体双层优化模型。底层基于长短期记忆和贝叶斯神经网络的概率预测方法,刻画新能源出力的不确定性,建立考虑需求响应的用户侧云储能充放能模型,以用户总成本最小为目标优化决策用户侧充放能行为,并将决策信息传递到云储能提供商。顶层以云储能提供商的总成本最小为目标,集中优化决策实体储能功率和容量的配置问题。通过大M法对目标以及约束中的非线性部分进行松弛线性化,将其转化为混合整数线性规划模型。最后,建立4个典型应用场景,通过Matlab中的YALMIP工具箱调用CPLEX优化求解器对不同场景下的模型进行求解,联合对比在4种不同场景下的整体成本与收益,验证该策略在资源共享、节约系统整体成本等方面的优越性。

关键词: 云储能(CES), 需求响应(DR), 综合能源系统, 能源集线器(EH), 松弛线性化, 优化配置

Abstract:

In order to fully mobilize user-side resources in an increasingly open energy trading market, this paper proposes an optimal allocation strategy for electricity-heat-gas cloud energy storage (CES) considering demand response (DR). The proposed optimized configuration establishes an energy hub (EH) structure with electricity-heat-gas cloud energy storage, and a two-subject two-layer optimized model from the view of users and providers participating in the CES business model is established. The lower layer describes the uncertainty of new energy output according to the probability prediction method based on long-term and short-term memory and Bayesian neural network; a user-side CES charging and discharging model considering demand response, which is optimized aiming to minimize the user’s total cost, is established; and the decision information will be informed to the CES provider. The upper layer, aimed to minimize the investment and construction cost of CES providers, concentrates on optimizing the allocation of energy storage power and capacity of decision-making entities. The big M method is adopted to relax and linearize the nonlinear part of the objective and constraints, and then it is transformed into a mixed-integer linear optimization problem. Finally, four typical application scenarios are established. As to the verification of the superiority of the strategy, the CPLEX optimization solver is called through the YALMIP toolbox in Matlab to solve the models in different scenarios, and the overall costs and benefits are jointly compared.

Key words: cloud energy storage(CES), demand response(DR), integrated energy system, energy hub(EH), relaxation linearization, optimize configuration

中图分类号:

TM715

丁曦, 姜威, 郭创新, 奚增辉, 高洁. 考虑需求响应的电/热/气云储能优化配置策略[J]. 电力建设, 2022, 43(3): 83-99.

DING Xi, JIANG Wei, GUO Chuangxin, XI Zenghui, GAO Jie. Optimal Configuration of Electricity-Heat-Gas CloudEnergy Storage Considering Demand Response[J]. ELECTRIC POWER CONSTRUCTION, 2022, 43(3): 83-99.

图/表 20

图1 云储能基本架构

Fig.1 Basic architecture of cloud energy storage

图2 电/热/气耦合云储能能源集线器结构

Fig.2 Structure of an electric-heat-gas coupling CES energy hub

图3 两主体充放能基础模型

Fig.3 Basic model of two-subject charging and discharging

图4 考虑需求响应的电/热/气云储能优化配置流程

Fig.4 Flow chart of optimal configuration ofelectric-thermal-gas CES considering demand response

图A1 电负荷曲线

Fig.A1 Cost comparison of CES providers indifferent scenarios

图A2 热负荷曲线

Fig.A2 Thermal load curve

图A3 气负荷曲线

Fig.A3 Gas load curve

图A4 典型日光伏出力曲线

Fig.A4 Typical photovoltaic output curve

图A5 风机总出力与热、气总负荷曲线

Fig.A5 Curve of total output of wind power generator and total load of heat and gas

表1 分时电价及时段划分

Table 1 Time-of-use electricity price and time division

表2 热-气功率换算

Table 2 Heat-gas power conversion

表A1 算例参数

Table A1 Example parameters

图5 光伏出力概率分布预测结果

Fig.5 Forecast results of probability distribution ofphotovoltaic generation power

图6 需求响应前后负荷对比

Fig.6 Load comparison before and after demand response

图7 云储能用户总充放能功率

Fig.7 Total charge and discharge power of CES users

图8 云储能提供商充放能功率

Fig.8 Charge and discharge power of CES provider

表A2 各参数优化结果

Table A2 Optimization results of each parameter

图9 不同场景下储能功率配置

Fig.9 Energy storage power configuration indifferent scenarios

图10 不同场景下储能容量配置

Fig.10 Energy storage capacity allocation indifferent scenarios

图11 不同场景下云储能提供商成本对比

Fig.11 Cost comparison of CES providers indifferent scenarios

参考文献 19

[1]	李政洁, 撖奥洋, 周生奇, 等. 计及综合需求响应的综合能源系统优化调度[J]. 电力系统保护与控制, 2021, 49(21):36-42.
	LI Zhengjie, HAN Aoyang, ZHOU Shengqi, et al. Optimization of an integrated energy system considering integrated demand response[J]. Power System Protection and Control, 2021, 49(21):36-42.
[2]	曾鸣, 刘英新, 周鹏程, 等. 综合能源系统建模及效益评价体系综述与展望[J]. 电网技术, 2018, 42(6):1697-1708.
	ZENG Ming, LIU Yingxin, ZHOU Pengcheng, et al. Review and prospects of integrated energy system modeling and benefit evaluation[J]. Power System Technology, 2018, 42(6):1697-1708.
[3]	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(12):3292-3305.
	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(12):3292-3305.
[4]	杨海柱, 李梦龙, 江昭阳, 等. 考虑需求侧电热气负荷响应的区域综合能源系统优化运行[J]. 电力系统保护与控制, 2020, 48(10):30-37.
	YANG Haizhu, LI Menglong, JIANG Zhaoyang, et al. Optimal operation of regional integrated energy system considering demand side electricity heat and natural-gas loads response[J]. Power System Protection and Control, 2020, 48(10):30-37.
[5]	CHEN Y B, ZHANG L X, XU P, et al. Electricity demand response schemes in China: Pilot study and future outlook[J]. Energy, 2021, 224:120042. doi: 10.1016/j.energy.2021.120042 URL
[6]	CHRYSIKOU V, ALAMANIOTIS M, TSOUKALAS L H. A review of incentive based demand response methods in smart electricity grids[J]. International Journal of Monitoring and Surveillance Technologies Research, 2015, 3(4):62-73. doi: 10.4018/IJMSTR.2015100104 URL
[7]	周孝信, 陈树勇, 鲁宗相, 等. 能源转型中我国新一代电力系统的技术特征[J]. 中国电机工程学报, 2018, 38(7):1893-1904, 2205.
	ZHOU Xiaoxin, CHEN Shuyong, LU Zongxiang, et al. Technology features of the new generation power system in China[J]. Proceedings of the CSEE, 2018, 38(7):1893-1904, 2205.
[8]	陈海生, 刘畅, 齐智平. 分布式储能的发展现状与趋势[J]. 中国科学院院刊, 2016, 31(2):224-231.
	CHEN Haisheng, LIU Chang, QI Zhiping. Developing trend and present status of distributed energy storage[J]. Bulletin of Chinese Academy of Sciences, 2016, 31(2):224-231.
[9]	李建林, 马会萌, 袁晓冬, 等. 规模化分布式储能的关键应用技术研究综述[J]. 电网技术, 2017, 41(10):3365-3375.
	LI Jianlin, MA Huimeng, YUAN Xiaodong, et al. Overview on key applied technologies of large-scale distributed energy storage[J]. Power System Technology, 2017, 41(10):3365-3375.
[10]	陶琼, 桑丙玉, 叶季蕾, 等. 高光伏渗透率配电网中分布式储能系统的优化配置方法[J]. 高电压技术, 2016, 42(7):2158-2165.
	TAO Qiong, SANG Bingyu, YE Jilei, et al. Optimal configuration method of distributed energy storage systems in distribution network with high penetration of photovoltaic[J]. High Voltage Engineering, 2016, 42(7):2158-2165.
[11]	慈松, 李宏佳, 陈鑫, 等. 能源互联网重要基础支撑: 分布式储能技术的探索与实践[J]. 中国科学: 信息科学, 2014, 44(6):762-773.
	CI Song, LI Hongjia, CHEN Xin, et al. The cornerstone of energy Internet: Research and practice of distributed energy storage technology[J]. Scientia Sinica (Informationis), 2014, 44(6):762-773.
[12]	LIU J K, ZHANG N, KANG C Q, et al. Cloud energy storage for residential and small commercial consumers: A business case study[J]. Applied Energy, 2017, 188:226-236. doi: 10.1016/j.apenergy.2016.11.120 URL
[13]	刘静琨, 张宁, 康重庆. 电力系统云储能研究框架与基础模型[J]. 中国电机工程学报, 2017, 37(12):3361-3371, 3663.
	LIU Jingkun, ZHANG Ning, KANG Chongqing. Research framework and basic models for cloud energy storage in power system[J]. Proceedings of the CSEE, 2017, 37(12):3361-3371, 3663.
[14]	郝木凯, 张伟, 董青卫, 等. 复杂因素下的多用户共享储能系统优化[J]. 信息与控制, 2020, 49(2):242-248. doi: 10.13976/j.cnki.xk.2020.9304
	HAO Mukai, ZHANG Wei, DONG Qingwei, et al. Optimization of multi-user shared energy storage system considering complex factors[J]. Information and Control, 2020, 49(2):242-248. doi: 10.13976/j.cnki.xk.2020.9304
[15]	匡熠, 王秀丽, 王建学, 等. 基于stackelberg博弈的虚拟电厂能源共享机制[J]. 电网技术, 2020, 44(12):4556-4564.
	KUANG Yi, WANG Xiuli, WANG Jianxue, et al. Virtual power plant energy sharing mechanism based on stackelberg game[J]. Power System Technology, 2020, 44(12):4556-4564.
[16]	孙偲, 陈来军, 邱欣杰, 等. 基于合作博弈的发电侧共享储能规划模型[J]. 全球能源互联网, 2019, 2(4):360-366.
	SUN Cai, CHEN Laijun, QIU Xinjie, et al. A generation-side shared energy storage planning model based on cooperative game[J]. Journal of Global Energy Interconnection, 2019, 2(4):360-366.
[17]	唐夏菲, 吴献祥, 任青青, 等. 利用云储能租赁服务的风电场储能容量优化配置[J]. 电力科学与技术学报, 2020, 35(1):90-95.
	TANG Xiafei, WU Xianxiang, REN Qingqing, et al. Optimized configuration of energy storage capacity of wind farms using cloud energy storage leasing services[J]. Journal of Electric Power Science and Technology, 2020, 35(1):90-95.
[18]	郭亦宗, 王楚通, 施云辉, 等. 区域综合能源系统电/热云储能综合优化配置[J]. 电网技术, 2020, 44(5):1611-1621.
	GUO Yizong, WANG Chutong, SHI Yunhui, et al. Comprehensive optimization configuration of electric and thermal cloud energy storage in regional integrated energy system[J]. Power System Technology, 2020, 44(5):1611-1621.
[19]	康重庆, 刘静琨, 张宁. 未来电力系统储能的新形态: 云储能[J]. 电力系统自动化, 2017, 41(21):2-8, 16.
	KANG Chongqing, LIU Jingkun, ZHANG Ning. A new form of energy storage in future power system: Cloud energy storage[J]. Automation of Electric Power Systems, 2017, 41(21):2-8, 16.

考虑需求响应的电/热/气云储能优化配置策略

Optimal Configuration of Electricity-Heat-Gas CloudEnergy Storage Considering Demand Response

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