风-光-储联合参与绿电交易下多主体效益分配模型

doi:10.12204/j.issn.1000-7229.2023.05.013

摘要/Abstract

摘要：

储能的发展对我国新型电力系统建设工作和“双碳”目标的实现至关重要,而运行效益又是决定储能电力市场参与度的重要决定因素。针对风-光-储联合参与绿电交易场景,首先构建了以联合系统效益最大为目标函数的绿电交易模型;其次,考虑了各主体的负荷贡献、成本以及运营风险,构建了多主体效益分配模型;最后,以某园区实际数据进行了实例分析。结果表明,效益分配模型能够有效提升储能系统效益,促进风-光-储联合参与绿电交易,促进实现电力绿色价值。

关键词: 风-光-储联合, 效益分配, 敏感性分析, 绿电交易

Abstract:

The development of energy storage is crucial for the construction of China’s new power system and the realization of a dual-carbon energy goal, which is a crucial determinant of participation in the energy storage power market. To address the scenario of joint wind-solar-storage participation in green power trading, this study first constructs a green power-trading model with the objective function of maximizing the benefits of the joint system, followed by a multi-entity benefit allocation model that considers the load contribution, costs, and operational risks of each entity. Finally, an example analysis is conducted using actual data from a park. The results show that the benefit allocation model effectively enhances the benefits of the energy storage system, promotes the joint participation of wind-light storage in green power trading, and facilitates the realization of the green value of electricity.

Key words: wind-solar-storage combination, benefit distribution, sensitivity analysis, green power transaction

中图分类号:

TM715

王玉洁, 谭忠富, 鞠立伟, 汤广瑞. 风-光-储联合参与绿电交易下多主体效益分配模型[J]. 电力建设, 2023, 44(5): 134-140.

WANG Yujie, TAN Zhongfu, JU Liwei, TANG Guangrui. Multi-subject Benefit Distribution Model under the Union Participation of Wind-Solar-Storage in Green Power Transactions[J]. ELECTRIC POWER CONSTRUCTION, 2023, 44(5): 134-140.

图/表 10

图1 风-光-储联合系统供电结构

Fig.1 Power supply structure of the wind-solar-storage union system

表1 联合系统主要参数

Table 1 Main parameters of the union system

图2 园区负荷与风光出力

Fig.2 Power load of the industrial park and power supply of solar and wind systems

图3 风-光-储联合系统出力情况

Fig.3 Power output of the wind-solar-storage union system

图4 储能系统容量及存储功率情况

Fig.4 Change in energy storage system capacity and storage power with time

表2 绿电交易下各主体效益分配情况

Table 2 Benefit distribution of each subject under green electricity transaction 万元

表3 2种情景效益对比

Table 3 Comparison of benefits under two scenarios 万元

图5 不同价格变化率下多主体效益分配额对比注:价格变化率=变动价格/原价格。

Fig.5 Comparison of multi-entity benefit allocations under different rates of price change

图6 不同成本降低率下多主体效益分配额对比

Fig.6 Comparison of multi-entity benefit allocations for different rates of cost reduction

表4 不同修正系数组合下多主体效益分配额对比

Table 4 Comparison of multi-entity benefit allocations under different combinations of correction factors 万元

参考文献 23

[1]	任大伟, 肖晋宇, 侯金鸣, 等. 双碳目标下我国新型电力系统的构建与演变研究[J]. 电网技术, 2022, 46(10): 3831-3839.
	REN Dawei, XIAO Jinyu, HOU Jinming, et al. Construction and evolution of China’s new power system under dual carbon goal[J]. Power System Technology, 2022, 46(10): 3831-3839.
[2]	刁利, 李光, 宋雪莹, 等. 新能源分比例渗透下基于博弈的多主体效益综合评估方法[J]. 电力建设, 2022, 43(6): 43-55. doi: 10.12204/j.issn.1000-7229.2022.06.005
	DIAO Li, LI Guang, SONG Xueying, et al. Comprehensive evaluation method based on game for multi-entity benefit under the proportional penetration of new energy[J]. Electric Power Construction, 2022, 43(6): 43-55. doi: 10.12204/j.issn.1000-7229.2022.06.005
[3]	陈皓勇. “双碳”目标下的电能价值分析与市场机制设计[J]. 发电技术, 2021, 42(2): 141-150. doi: 10.12096/j.2096-4528.pgt.21008
	CHEN Haoyong. Electricity value analysis and market mechanism design under carbon-neutral goal[J]. Power Generation Technology, 2021, 42(2): 141-150. doi: 10.12096/j.2096-4528.pgt.21008
[4]	李笑蓉, 朱瑾, 石少伟, 等. 协调大规模风电汇聚外送的储能配置优化规划[J]. 智慧电力, 2022, 50(5): 69-76.
	LI Xiaorong, ZHU Jin, SHI Shaowei, et al. Energy storage optimal planning for coordinating large scale wind power aggregation and delivery[J]. Smart Power, 2022, 50(5): 69-76.
[5]	刘沅昆, 张维静, 张艳, 等. 面向新型电力系统的新能源与储能联合规划方法[J]. 智慧电力, 2022, 50(10): 1-8.
	LIU Yuankun, ZHANG Weijing, ZHANG Yan, et al. Joint planning method of renewable energy and energy storage for new-type power system[J]. Smart Power, 2022, 50(10): 1-8.
[6]	张淑兴, 马驰, 杨志学, 等. 基于深度确定性策略梯度算法的风光储系统联合调度策略[J]. 中国电力, 2023, 56(2): 68-76. doi: 10.11930/j.issn.1004-9649.202107065
	ZHANG Shuxing, MA Chi, YANG Zhixue, et al. Deep deterministic policy gradient algorithm based wind-photovoltaic-storage hybrid system joint dispatch[J]. Electric Power, 2023, 56(2): 68-76. doi: 10.11930/j.issn.1004-9649.202107065
[7]	SABOORI H, MOHAMMADI M, TAGHE R. Virtual power plant (VPP), definition, concept, components and types[C]// 2011 Asia-Pacific Power and Energy Engineering Conference. IEEE, 2011.
[8]	HROPKO D, IVANECKY J, TURCEK J. Optimal dispatch of renewable energy sources included in virtual power plant using accelerated particle swarm optimization[C]// 2012 ELEKTRO. IEEE, 2012: 196-200.
[9]	张粒子, 许通, 宋少群, 等. 电力市场中发电容量充裕性评估方法及保障机制[J]. 电力系统自动化, 2020, 44(18): 55-63.
	ZHANG Lizi, XU Tong, SONG Shaoqun, et al. Evaluation method and guarantee mechanism of power generation capacity adequacy in electricity market[J]. Automation of Electric Power Systems, 2020, 44(18): 55-63.
[10]	方八零. 混合可再生能源系统的多能互补及集成优化[D]. 长沙: 湖南大学, 2017.
	FANG Baling. Multi-energy complementarity and integrated optimal allocation for hybrid renewable energy system[D]. Changsha: Hunan University, 2017.
[11]	杜坤泽, 张铁岩. 基于混合储能的风光联合发电系统并网优化控制研究[C]// 第十六届沈阳科学学术年会论文集(理工农医). 2019: 374-379.
	DU Kunze, ZHANG Tieyan. Research on grid-connected optimization control of wind-solar combined power generation system based on hybrid energy storage[C]// Proceedings of the 16th Shenyang Scientific Annual Conference (Science, Engineering, Agriculture and Medicine). 2019: 374-379.
[12]	王昆能. 风光联合发电系统控制策略及优化配置研究[D]. 恩施: 湖北民族学院, 2018.
	WANG Kunneng. Research on control and optimal configuration of wind-solar complementary power generation system[D]. Enshi: Hubei University for Nationalities, 2018.
[13]	李彬, 杜亚彬, 曹望璋, 等. 考虑风光储互补与工作负载分配的数据中心优化调度[J]. 现代电力, 2022, 39(3): 356-363.
	LI Bin, DU Yabin, CAO Wangzhang, et al. Optimal scheduling of data center considering wind-solar-storage complementary and workload distribution[J]. Modern Electric Power, 2022, 39(3): 356-363.
[14]	李湃, 方保民, 祁太元, 等. 基于源-荷匹配的区域电网风/光/储容量配比优化方法[J]. 中国电力, 2022, 55(1): 46-54.
	LI Pai, FANG Baomin, QI Taiyuan, et al. Capacity proportion optimization of wind, solar power and battery energy storage system for regional power grid based on source-load matching[J]. Electric Power, 2022, 55(1): 46-54.
[15]	彭春华, 熊志盛, 张艺, 等. 基于多场景置信间隙决策的风光储联合鲁棒规划[J]. 电力系统自动化, 2022, 46(16): 178-187.
	PENG Chunhua, XIONG Zhisheng, ZHANG Yi, et al. Joint robust planning of wind-photovoltaic-energy storage system based on multi-scenario confidence gap decision[J]. Automation of Electric Power Systems, 2022, 46(16): 178-187.
[16]	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.
[17]	周立立, 向月, 延肖何, 等. 差异化市场下储能运营风险效益评估[J]. 太阳能学报, 2022, 43(3): 111-118. doi: 10.19912/j.0254-0096.tynxb.2020-0504
	ZHOU Lili, XIANG Yue, YAN Xiaohe, et al. Risk and benefit evaluation for energy storage under differentiated markets[J]. Acta Energiae Solaris Sinica, 2022, 43(3): 111-118. doi: 10.19912/j.0254-0096.tynxb.2020-0504
[18]	丁曦, 姜威, 郭创新, 等. 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. doi: 10.12204/j.issn.1000-7229.2022.03.010
	DING Xi, JIANG Wei, GUO Chuangxin, et al. Optimal configuration of electricity-heat-gas cloud energy storage considering demand response[J]. Electric Power Construction, 2022, 43(3): 83-99. doi: 10.12204/j.issn.1000-7229.2022.03.010
[19]	纪会争. 风光储联合发电调度管理与效益评价研究[D]. 北京: 华北电力大学, 2020.
	JI Huizheng. Research on dispatch management and benefit evaluation of wind-photovoltaic-energy storage hybrid power generation system[D]. Beijing: North China Electric Power University, 2020.
[20]	郭洋, 吴峰, 许庆强, 等. 风-光-储系统在不同运营模式下的最优容量配置策略及考虑网损补贴的经济性分析[J]. 智慧电力, 2019, 47(1): 26-33.
	GUO Yang, WU Feng, XU Qingqiang, et al. Optimal capacity allocation strategy for wind-PV-ES system with different operation modes and its economic analysis considering network loss subsidy[J]. Smart Power, 2019, 47(1): 26-33.
[21]	谭忠富, 宋艺航, 张会娟, 等. 大规模风电与火电联合外送体系及其利润分配模型[J]. 电力系统自动化, 2013, 37(23): 63-70.
	TAN Zhongfu, SONG Yihang, ZHANG Huijuan, et al. Joint delivery system of large-scale wind power and thermal power generation and its profit distribution model[J]. Automation of Electric Power Systems, 2013, 37(23): 63-70.
[22]	周兵兵, 林宏宇, 杨莘博, 等. 可再生能源多能互补系统多主体效益均衡模型[J]. 智慧电力, 2020, 48(1): 74-79.
	ZHOU Bingbing, LIN Hongyu, YANG Shenbo, et al. Multi-agent benefit equilibrium model for renewable energy in multi-energy complementary system[J]. Smart Power, 2020, 48(1): 74-79.
[23]	杜洋, 杨心刚, 郭灵瑜, 等. 双碳背景下配电网绿色发展与安全高效耦合评价[J]. 科学技术与工程, 2021, 21(30): 12973-12981.
	DU Yang, YANG Xingang, GUO Lingyu, et al. Coupling evaluation of green development and safety and efficiency of distribution network in context of double carbon[J]. Science Technology and Engineering, 2021, 21(30): 12973-12981.