【目的】为降低商业用户用能总成本,满足可再生能源电力消纳责任权重,提高新能源利用率,缓解配电变压器反向重过载,构建了价格型、准线型、基线型需求侧响应（demand response,DR）下的风光储自备电源配置策略,计及反向输电约束,对比评估其参与三类DR降低用能总成本的效果。【方法】以最小化用能总成本为目标,构建规划阶段自备电源配置、运行阶段负荷基线交替迭代的双层优化模型。上层模型考虑多条新能源预测曲线各时刻最大值,建立了自备电源配置模型,求解新能源容量、储能功率和标称放电时间;下层模型基于上层模型结果和多条新能源预测曲线概率,求解用能总成本的数学期望,并以输电曲线数学期望作为负荷基线反馈至上层模型,直至连续两次自备电源配置结果和负荷基线相同。按允许反向输送不超过20%的光伏发电量设定输电约束,并分析分时电价、基准激励价格和负荷准线的调整对经济性指标的影响。以我国沿海某省某商业综合体为例进行计算评估,从降低用能总成本方面验证策略的有效性。【结果】从数学期望看,相较于无自备电源的对照方案,采用所提模型方案的用能总成本降低2%~10%;参与基线型DR的相似度为98%~100%,显著高于参与准线型DR的82%~86%,参与基线型DR相比准线型DR用能总成本降低6%~10%。反向输送的光伏电量占比为0~10%。【结论】在满足省级电网可再生能源电力消纳责任权重需求下,所提策略求解的负荷基线和输电曲线相似度高,设定的反向输电约束增加了自备电源配置规模,显著降低了商业用户用能总成本。
Abstract
[Objective] In order to reduce total cost of energy consumption of commercials, meet the responsibility weight of renewable energy power consumption, improve the utilization rate of new energy, and alleviate reverse overload of distribution transformers, a configuration strategy of self-owned power sources including wind, solar and energy storage under time-of-use price curve, load guideline and load baseline demand response (DR) is constructed. The constraint of allowing reverse transmission is considered. The effectiveness of participating in three types of DR to reduce total cost of energy consumption is evaluated and compared. [Methods] Taking the minimum total cost of energy consumption as the target, a two-layer optimization model is established, including alternating iterations of self-owned power source configuration during the planning phase and load baseline during the operation phase. The upper model considers the maximum of multiple predicted new energy curves at any time, establishes a self-owned power source configuration model, and solves the capacity of new energy, energy storage, and nominal discharge time. The lower model calculates the mathematical expectation of total cost of energy consumption based on the outcome of the upper model and the probability of multiple predicted new energy curves. The mathematical expectation of transmission curves is used as the load baseline and fed back to the upper model until the results of two consecutive self-owned power source configuration and the load baseline are identical. Via setting the transmission constraint of allowing reverse transmission of not exceeding 20% of the generated electricity of photovoltaic, and adjusting time-of-use price, benchmark incentive price and load guideline, the influence on economic indicators is studied. Taking a certain large commercial complex in a coastal province of China as an example for calculation and evaluation, the effectiveness of the strategy is verified from the perspective of reducing total cost of energy consumption. [Results] From the perspective of mathematical expectation, compared with the control schemes without self-owned power sources, total costs of energy consumption utilizing the proposed model are reduced by 2% to 10%. The similarities when participating in baseline DR are 98% to 100%, which are significantly higher than 82% to 86% when participating in guideline DR. Compared to guideline DR, participating in baseline DR can reduce total cost of energy consumption by 6% to 10%. The proportions of reverse transmission of photovoltaic are 0% to 10%. [Conclusions] The proposed strategy in this paper satisfies the responsibility weight of renewable energy power consumption of provincial power grids, while achieving high similarity between the load baseline and transmission curve. The set constraint of allowing reverse transmission increases the scale of self-owned power sources, significantly reducing total cost of energy consumption of commercials.
关键词
风光储自备电源 /
用能总成本 /
DR /
反向输电 /
负荷准线 /
负荷基线
Key words
wind,photovoltaic and storage self-owned power sources /
total cost of energy consumption /
DR /
reverse transmission /
load guideline /
load baseline
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参考文献
[1] 张智刚, 康重庆. 碳中和目标下构建新型电力系统的挑战与展望[J]. 中国电机工程学报, 2022, 42(8): 2806-2818.
ZHANG Zhigang, KANG Chongqing.Challenges and prospects for constructing the new-type power system towards a carbon neutrality future[J]. Proceedings of the CSEE, 2022, 42(8): 2806-2818.
[2] 康重庆, 杜尔顺, 郭鸿业, 等. 新型电力系统的六要素分析[J]. 电网技术, 2023, 47(5): 1741-1750.
KANG Chongqing, DU Ershun, GUO Hongye, et al.Primary exploration of six essential factors in new power system[J]. Power System Technology, 2023, 47(5): 1741-1750.
[3] 国家发展改革委办公厅国家能源局综合司关于2025年可再生能源电力消纳责任权重及有关事项的通知[EB/OL].(2025-07-11)[2025-07-25]. https://www.ndrc.gov.cn/xxgk/zcfb/tz/202507/t20250711_1399141_ext.html.
[4] 国家发展改革委,国家能源局. 电力系统调节能力优化专项行动实施方案(2025—2027年)[EB/OL].(2024-12-20)[2025-07-15]. https://zfxxgk.ndrc.gov.cn/web/iteminfo.jspid=20468.
[5] 范帅, 危怡涵, 何光宇, 等. 面向新型电力系统的需求响应机制探讨[J]. 电力系统自动化, 2022, 46(7): 1-12.
FAN Shuai, WEI Yihan, HE Guangyu, et al.Discussion on demand response mechanism for new power systems[J]. Automation of Electric Power Systems, 2022, 46(7): 1-12.
[6] 李进鑫, 张沛超, 赵建立, 等. 基于负荷准线的5G基站虚拟电厂优化控制方法[J]. 电网技术, 2024, 48(6): 2356-2365.
LI Jinxin, ZHANG Peichao, ZHAO Jianli, et al.Optimal control of 5G base station virtual power plant based on customer directrix load[J]. Power System Technology, 2024, 48(6): 2356-2365.
[7] 王长浩, 高红均, 周文毅, 等. 考虑准线需求响应的高比例新能源电力系统调度优化[J]. 电网技术, 2024, 48(11): 4427-4435, I0004.
WANG Changhao, GAO Hongjun, ZHOU Wenyi, et al.High proportion new energy power system scheduling optimization considering customer directrix line demand response[J]. Power System Technology, 2024, 48(11): 4427-4435, I0004.
[8] 刘雁行, 乔如妤, 梁楠, 等. 基于负荷准线和深度强化学习的含电动汽车集群系统新能源消纳策略[J]. 上海交通大学学报, 2025, 59(10): 1464-1475.
LIU Yanhang, QIAO Ruyu, LIANG Nan, et al.Renewable energy consumption strategies of power system integrated with electric vehicle clusters based on load alignment and deep reinforcement learning[J]. Journal of Shanghai Jiao Tong University, 2025, 59(10): 1464-1475.
[9] 刘敦楠, 赵宁宁, 李鹏飞, 等. 基于“共享储能-需求侧资源”联合跟踪可再生能源发电曲线的市场化消纳模式[J]. 电网技术, 2021, 45(7): 2791-2800.
LIU Dunnan, ZHAO Ningning, LI Pengfei, et al.Market-oriented consumption model based on the joint tracking of renewable energy generation curve of“shared energy storage & demand side resources”[J]. Power System Technology, 2021, 45(7): 2791-2800.
[10] 徐博强, 赵建立, 张沛超, 等. 基于负荷准线和纳什谈判的高比例新能源消纳方法[J]. 电力系统自动化, 2023, 47(15): 36-45.
XU Boqiang, ZHAO Jianli, ZHANG Peichao, et al.High-proportion renewable energy accommodation method based on customer directrix load and Nash bargaining[J]. Automation of Electric Power Systems, 2023, 47(15): 36-45.
[11] 董雷, 倪佳炜, 张师铭, 等. 基于双重博弈的电价套餐设计方法[J]. 电网技术, 2024, 48(2): 688-700.
DONG Lei, NI Jiawei, ZHANG Shiming, et al.Designing of electricity price package method based on dual game[J]. Power System Technology, 2024, 48(2): 688-700.
[12] 张金良, 王朝阳, 郝宇. 考虑需求响应和共享储能的微网联盟两阶段优化调度[J]. 智慧电力, 2024, 52(7): 48-55.
ZHANG Jinliang, WANG Zhaoyang, HAO Yu.Microgrid alliance two-stage optimal scheduling considering demand response and shared energy storage[J]. Smart Power, 2024, 52(7): 48-55.
[13] 黄冬梅, 吕嘉欣, 时帅, 等. 计及需求响应的海岛微电网群优化运行研究[J]. 电力系统保护与控制, 2024, 52(9): 88-98.
HUANG Dongmei, LÜ Jiaxin, SHI Shuai, et al.Optimal operation of island microgrid clusters considering demand response[J]. Power System Protection and Control, 2024, 52(9): 88-98.
[14] 侯慧, 何梓姻, 侯婷婷, 等. 大规模车网互动需求响应策略及潜力评估综述[J]. 电力系统保护与控制, 2024, 52(14): 177-187.
HOU Hui, HE Ziyin, HOU Tingting, et al.A review of demand response strategies and potential evaluation for large-scale vehicle to grid[J]. Power System Protection and Control, 2024, 52(14): 177-187.
[15] 王飞, 李美颐, 张旭东, 等. 需求响应资源潜力评估方法、应用及展望[J]. 电力系统自动化, 2023, 47(21): 173-191.
WANG Fei, LI Meiyi, ZHANG Xudong, et al.Assessment methods for demand response resource potential and their application and prospect[J]. Automation of Electric Power Systems, 2023, 47(21): 173-191.
[16] 王旭. 含不同类型负荷的区域电网优化调度策略研究[D]. 淮南: 安徽理工大学, 2023.
WANG Xu.Research on the optimal dispatching strategy of regional power grid with different types of loads[D]. Huainan: Anhui University of Science and Technology, 2023.
[17] 尹玉芬. 地区电力系统负荷特性分析与需求侧管理研究[D]. 广州: 华南理工大学, 2010.
YIN Yufen.Analysis of load characteristics and studies on demand-side management in regional power systems[D]. Guangzhou: South China University of Technology, 2010.
[18] 王晨语. 地区电网负荷特性分析及建模研究[D]. 北京: 华北电力大学, 2017.
WANG Chenyu.Research on load characteristic analysis and modeling of regional power grid[D]. Beijing: North China Electric Power University, 2017.
[19] 王浩丞, 高红均, 王仁浚. 计及需求响应的虚拟电厂日前市场交易策略研究[J]. 智慧电力, 2024, 52(7): 64-71.
WANG Haocheng, GAO Hongjun, WANG Renjun.Day-ahead market trading strategy of virtual power plant considering demand response[J]. Smart Power, 2024, 52(7): 64-71.
[20] 张凯瑞, 明昊, 高赐威. 基于CPSS视角的需求响应能力评估综述[J]. 电力科学与技术学报, 2024, 39(1): 28-46.
ZHANG Kairui, MING Hao, GAO Ciwei.A review of demand response capability assessment based on CPSS perspective[J]. Journal of Electric Power Science and Technology, 2024, 39(1): 28-46.
[21] 张小莲, 陈冲, 张仰飞, 等. 考虑电池运行状态的风电场储能容量优化配置[J]. 电力系统自动化, 2022, 46(18): 199-207.
ZHANG Xiaolian, CHEN Chong, ZHANG Yangfei, et al.Optimal configuration of wind farm energy storage capacity considering battery operation state[J]. Automation of Electric Power Systems, 2022, 46(18): 199-207.
[22] 南斌, 董树锋, 唐坤杰, 等. 考虑需求响应和源荷不确定性的光储微电网储能优化配置[J]. 电网技术, 2023, 47(4): 1340-1349.
NAN Bin, DONG Shufeng, TANG Kunjie, et al.Optimal configuration of energy storage in PV-storage microgrid considering demand response and uncertainties in source and load[J]. Power System Technology, 2023, 47(4): 1340-1349.
[23] 袁铁江, 张文达, 田雪沁, 等. 电夹点:完全能量目标下弃电最小的电源规划方法研究[J]. 中国电机工程学报, 2025, 45(8): 2925-2936.
YUAN Tiejiang, ZHANG Wenda, TIAN Xueqin, et al.Electrical pinch point: study on the power planning method for minimizing power abandonment under complete energy objective[J]. Proceedings of the CSEE, 2025, 45(8): 2925-2936.
[24] 丁双宁, 卢小龙, 孙志云, 等. 机场服务区光储配置和电能管理双层规划模型[J]. 电力科学与技术学报, 2024, 39(4): 222-233.
DING Shuangning, LU Xiaolong, SUN Zhiyun, et al.Two-layer planning model for photovoltaic and energy storage configuration and power management in airport service areas[J]. Journal of Electric Power Science and Technology, 2024, 39(4): 222-233.
[25] 内蒙古自治区能源局. 内蒙古自治区分布式光伏项目开发建设管理实施细则[EB/OL].(2025-06-20)[2025-07-15]. https://nyj.nmg.gov.cn/zwgk/zfxxgkzl/fdzdgknr/zcwj_16462/202506/t20250625_2745546.html.
[26] 国家能源局. 分布式光伏发电开发建设管理办法[EB/OL].(2025-06-19)[2025-07-15]. https://www.gov.cn/gongbao/2025/issue_11946/202503/content_7015853.html.
[27] 山东省发展和改革委员会. 山东省分布式光伏发电开发建设管理实施细则[EB/OL].(2025-06-18)[2025-07-15]. http://fgw.shandong.gov.cn/art/2025/6/18/art_91410_10469655.html.
[28] 李晖, 刘栋, 秦继朔, 等. 考虑风光出力不确定性的新能源基地直流外送随机规划方法研究[J]. 电网技术, 2024, 48(7): 2795-2803.
LI Hui, LIU Dong, QIN Jishuo, et al.Stochastic planning method for UHVDC transmission of renewable energy power base considering wind and photovoltaic output uncertainties[J]. Power System Technology, 2024, 48(7): 2795-2803.
[29] 陆秋瑜, 马千里, 魏韡, 等. 基于置信容量的风场配套储能容量优化配置[J]. 电工技术学报, 2022, 37(23): 5901-5910.
LU Qiuyu, MA Qianli, WEI Wei, et al.Optimal configuration of energy storage parameters based on confidence capacity of wind farms[J]. Transactions of China Electrotechnical Society, 2022, 37(23): 5901-5910.
[30] 电力储能系统术语: GB/T 42313—2023[S] GB/T 42313—2023[S]. 北京: 中国标准出版社, 2023.
Terminology of electrical energy storage system: GB/T 42313—2023[S] GB/T 42313—2023[S]. Beijing: Standards Press of China, 2023.
[31] 吴雄, 贺明康, 何雯雯, 等. 考虑储能寿命的风-光-火-储打捆外送系统容量优化配置[J]. 电力系统保护与控制, 2023, 51(15): 66-75.
WU Xiong, HE Mingkang, HE Wenwen, et al.Optimal capacity of a wind-solar-thermo-storage-bundled power transmission system considering battery life[J]. Power System Protection and Control, 2023, 51(15): 66-75.
[32] 陕西省发展和改革委员会. 陕西省2022年新型储能建设实施方案(征求意见稿)[EB/OL].(2022-03-29)[2025-07-15]. http://www.china-sc.org.cn/zcfg/2356.html.
[33] 徐政, 程斌杰. 不同电压等级直流输电的适用性研究[J]. 电力建设, 2015, 36(9): 22-29.
XU Zheng, CHENG Binjie.Applicability study on DC transmission with different voltage levels[J]. Electric Power Construction, 2015, 36(9): 22-29.
[34] 刘增训, 游沛羽, 周勤勇. 适用高比例新能源系统广域消纳的输电技术研究综述[J]. 电力工程技术, 2020, 39(5): 59-70.
LIU Zengxun, YOU Peiyu, ZHOU Qinyong.Transmission technologies adapting to power systems with widely-consumed high-proportion renewable energy[J]. Electric Power Engineering Technology, 2020, 39(5): 59-70.
[35] 孟琰, 肖居承, 洪居华, 等. 计及需求响应不确定性的节点负荷准线: 概念与模型[J]. 电力系统自动化, 2023, 47(13): 28-39.
MENG Yan, XIAO Jucheng, HONG Juhua, et al.Nodal customer directrix load considering demand response uncertainty: concept and model[J]. Automation of Electric Power Systems, 2023, 47(13): 28-39.
[36] 中国电力企业联合会. 中国电力行业年度发展报告-2023[M]. 北京: 中国建材工业出版社, 2023.
[37] 北极星售电网. 全国各地2025年3月电网企业代理购电价格[EB/OL].(2025-02-26)[2025-07-15]. https://m.bjx.com.cn/mnews/20250226/1429171.shtml.
[38] 陈典, 陆润钊, 张健, 等. 实用化水火风光系统时序电力电量平衡方法[J]. 水力发电学报, 2023, 42(5): 25-34.
CHEN Dian, LU Runzhao, ZHANG Jian, et al.Sequential power and energy balance practical method for hydro-thermal-wind-solar power systems[J]. Journal of Hydroelectric Engineering, 2023, 42(5): 25-34.
基金
智能电网国家科技重大专项(2025ZD0805001)
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