- 中国高校优秀科技期刊
- 全国中文核心期刊
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| Citation: |
DONG Lun, HUANG Yuan, XU Xiao, et al. Energy Storage and Multi-user Demand Response Optimization Strategy Based on Data-driven and Customer Directrix Load[J]. Modern Electric Power. DOI: 10.19725/j.cnki.1007-2322.2024.0023
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In the context of a new power system, the high penetration of new energy units continuously weakens the regulation capacity on the generation side. Meanwhile, the adjustable resources on the demand side possess substantial potential for adjustment. There is a wide variety of load types on the demand side, each exhibiting complex electrical characteristics. Employing a data-driven approach can effectively address the challenges of precise modeling and scheduling management on the demand side. Therefore, an energy storage and multi-user demand response optimization strategy is proposed based on data-driven and customer directrix load. Firstly, an assessment is conducted on the adjustment potential of various adjustable resources on the demand side participating in demand response. Secondly, considering the response characteristics of the classified loads, the grid company provides differentiated rewards to different users participating in demand response. Thirdly, the demand response optimization model is transformed into a Markov Decision Process, shaping the load curve throughout the entire time period to closely align with the target directrix, thereby minimizing the overall operational cost of the grid. Finally, the experimental results indicate that the customer directrix load demand response not only significantly decreases the overall operational cost of the park's grid but also effectively accommodates new energy locally. Compared to different types of loads responding to the directrix separately, the effect of aggregating adjustable industrial, commercial, and residential loads with energy storage in a park microgrid within a region yields superior results in responding the directrix.
| [1] |
辛保安. 加快建设新型电力系统, 助力实现“双碳”目标[J]. 国家电网, 2021(08): 10−12. doi: 10.3969/j.issn.1673-4726.2021.08.008
XIN Baoan. Accelerating the construction of a new power system to facilitate the achievement of “dual carbon” goals[J]. State Grid, 2021(08): 10−12(in Chinese). doi: 10.3969/j.issn.1673-4726.2021.08.008
|
| [2] |
LI Peiyan, SUN Wei, ZHANG Zhengzhong, et al. Forecast of renewable energy penetration potential in the goal of carbon peaking and carbon neutrality in China[J]. Sustainable Production and Consumption, 2022, 34: 541−551. doi: 10.1016/j.spc.2022.10.007
|
| [3] |
李彬, 陈京生, 李德智, 等. 我国实施大规模需求响应的关键问题剖析与展望[J]. 电网技术, 2019, 43(02): 694−704.
LI Bin, CHEN Jingsheng, LI Dezhi, et al. Analysis and prospect of key issues in China's demand response for further large-scale implementation[J]. Power System Technology, 2019, 43(02): 694−704(in Chinese).
|
| [4] |
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. doi: 10.7500/AEPS20230324007
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(in Chinese). doi: 10.7500/AEPS20230324007
|
| [5] |
刘迪, 孙毅, 李彬, 等. 计及调节弹性差异化的产消群价格型需求响应机制[J]. 电网技术, 2020, 44(06): 2062−2070.
LIU Di, SUN Yi, LI Bin, et al. Price-based demand response mechanism of prosumer groups considering adjusting elasticity differentiation[J]. Power System Technology, 2020, 44(06): 2062−2070(in Chinese).
|
| [6] |
王韵楚, 张智, 卢峰, 等. 考虑用户行为不确定性的阶梯式需求响应激励机制[J]. 电力系统自动化, 2022, 46(20): 64−73. doi: 10.7500/AEPS20210907006
WANG Yunchu, ZHANG Zhi, LU Feng, et al. Stepwise incentive mechanism of demand response considering uncertainty of user behaviors[J]. Automation of Electric Power Systems, 2022, 46(20): 64−73(in Chinese). doi: 10.7500/AEPS20210907006
|
| [7] |
胡可心, 李康平, 刘春阳, 等. 动态分时电价下居民用户需求响应基线负荷预测方法[J/OL]. 电测与仪表: 1-11[2024-02-27].
HU Kexin, LI Kangping, LIU Chunyang, et al. A baseline load forecasting method for residential demand response under dynamic time-of-use electricity price [J/OL]. Electrical Measurement and Instrumentation: 1-11[2024-2-27].
|
| [8] |
范帅, 郏琨琪, 王芬, 等. 基于负荷准线的大规模需求响应[J]. 电力系统自动化, 2020, 44(15): 19−27. doi: 10.7500/AEPS20191107001
FAN Shuai, JIA Kunqi, WANG Fen, et al. Large-scale demand response based on customer directrix load[J]. Automation of Electric Power Systems, 2020, 44(15): 19−27(in Chinese). doi: 10.7500/AEPS20191107001
|
| [9] |
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(07): 1−12. doi: 10.7500/AEPS20210726010
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(07): 1−12(in Chinese). doi: 10.7500/AEPS20210726010
|
| [10] |
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. doi: 10.7500/AEPS20220307007
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(in Chinese). doi: 10.7500/AEPS20220307007
|
| [11] |
徐博强, 张沛超, 何光宇, 等. 基于主从博弈的热水器集群的负荷准线控制方法[J]. 中国电机工程学报, 2022, 42(21): 7785−7797.
XU Boqiang, ZHANG Peichao, HE Guangyu, et al. Stackelberg game-based control method for water heater cluster using customer directrix line[J]. Proceedings of the CSEE, 2022, 42(21): 7785−7797(in Chinese).
|
| [12] |
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. doi: 10.7500/AEPS20221130016
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(in Chinese). doi: 10.7500/AEPS20221130016
|
| [13] |
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: 1-10[2024-2-27].
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: 1-10[2024-2-27](in Chinese).
|
| [14] |
WANG Yu, LI Ke, LI Shuzhen, et al. A bi-level scheduling strategy for integrated energy systems considering integrated demand response and energy storage co-optimization[J]. Journal of Energy Storage, 2023, 66: 107508. doi: 10.1016/j.est.2023.107508
|
| [15] |
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/OL]. 电网技术: 1-11[2024-2-27].
LI Jinxin, ZHANG Peichao, ZHAO Jianli, et al. Optimal control method of 5G base station virtual power plant based on customer directrix load[J/OL]. Power System Technology: 1-11[2024-2-27].
|
| [16] |
OH Seongmun, KONG Junhyuk, YANG Yejin, et al. A multi-use framework of energy storage systems using reinforcement learning for both price-based and incentive-based demand response programs[J]. International Journal of Electrical Power & Energy Systems, 2023, 144: 108519.
|
| [17] |
杨梓俊, 丁小叶, 陆晓, 等. 面向需求响应的变频空调负荷建模与运行控制[J]. 电力系统保护与控制, 2021, 49(15): 132−140.
YANG Zijun, DING Xiaoye, LU Xiao, et al. Inverter air conditioner load modeling and operational control for demand response[J]. Power System Protection and Control, 2021, 49(15): 132−140(in Chinese).
|
| [18] |
陈启鑫, 吕睿可, 郭鸿业, 等. 面向需求响应的电力用户行为建模: 研究现状与应用[J]. 电力自动化设备, 2023, 43(10): 23−37.
CHEN Qixin, LV Ruike, GUO Hongye, et al. Electricity user behavior modeling for demand response: research status quo and applications[J]. Electric Power Automation Equipment, 2023, 43(10): 23−37(in Chinese).
|
| [19] |
杨斌, 陈振宇, 阮文骏, 等. 基于多种群协同进化遗传算法的智能小区需求响应调度策略[J]. 电力需求侧管理, 2019, 21(02): 10−14. doi: 10.3969/j.issn.1009-1831.2019.02.003
YANG Bin, CHEN Zhenyu, RUAN Wenjun, et al. Demand response dispatching strategy for intelligent community based on multi-group coevolution genetic algorithm[J]. Power Demand Side Management, 2019, 21(02): 10−14(in Chinese). doi: 10.3969/j.issn.1009-1831.2019.02.003
|
| [20] |
张丽, 刘青雷, 张宏伟. 基于改进二进制粒子群算法的家庭负荷优化调度策略[J]. 中国电力, 2023, 56(05): 118−128.
ZHANG Li, LIU Qinglei, ZHANG Hongwei. Home load optimization scheduling strategy based on improved binary particle swarm optimization algorithm[J]. China Electric Power, 2023, 56(05): 118−128(in Chinese).
|
| [21] |
孙毅, 刘迪, 李彬, 等. 深度强化学习在需求响应中的应用[J]. 电力系统自动化, 2019, 43(05): 183−191.
SUN Yi, LIU Di, LI Bin, et al. Application of deep reinforcement learning in demand response[J]. Automation of Electric Power Systems, 2019, 43(05): 183−191(in Chinese).
|
| [22] |
徐弘升, 陆继翔, 杨志宏, 等. 基于深度强化学习的激励型需求响应决策优化模型[J]. 电力系统自动化, 2021, 45(14): 97−103. doi: 10.7500/AEPS20200208001
XU Hongsheng, LU Jixiang, YANG Zhihong, et al. Decision optimization model of incentive demand response based on deep reinforcement learning[J]. Automation of Electric Power Systems, 2021, 45(14): 97−103(in Chinese). doi: 10.7500/AEPS20200208001
|
| [23] |
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/OL]. 上海交通大学学报: 1-31 [2024-02-27].
LIU Yanxing, QIAO Ruyu, LIANG Nan, et al. Renewable energy consumption strategies of the power system integrated with electric vehicle clusters based on load alignment and deep reinforcement learning[J/OL]. Journal of Shanghai Jiao Tong University: 1-31[2024-02-27].
|
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