计及用户满意度的可调节负荷资源需求响应优化策略研究Research on Demand Response Optimization Strategy of Adjustable Load Resource Considering User Satisfaction
祁兵,陈淑娇,李彬,田珂
QI Bing,CHEN Shujiao,LI Bin,TIAN Ke
摘要(Abstract):
为提高需求侧资源灵活度,加强电网调节能力,基于多层级调控架构提出一种计及用户满意度的可调节负荷资源需求响应优化策略:通过层级间的协作和跨区聚合来高效完成需求响应业务,通过调整聚合商定价确保用户的满意度水平,同时提高负荷聚合商的收益。最后通过算例分析可以看出,所提出的需求响应优化策略在保证用户满意度的前提下,通过多层级协作完成了电网调控需求,并实现了负荷聚合商收益最大化的目标。
In order to improve the flexibility of demand side resources and strengthen the regulation ability of power grid,based on the multi-level regulation architecture, this paper proposes a demand response optimization strategy of adjustable load resource taking into account user satisfaction. On the basis of efficient completion of demand response business through inter-level collaboration and cross-regional aggregation, the satisfaction level of the users is ensured by adjusting aggregator pricing, and the revenue of load aggregators is improved. Finally, an example is given to verify that the proposed demand response optimization strategy completes the power grid regulation through multi-level cooperation on the premise of ensuring user satisfaction, and achieves the goal of maximizing the income of load aggregators.
关键词(KeyWords):
用户满意度;需求响应;跨区协作;优化控制;用户行为
user satisfaction;demand response;cross regional cooperation;optimized control;user behavior
基金项目(Foundation): 国家电网有限公司总部管理科技项目“分层分区的可调节负荷聚合与主动交互控制技术”(5100-202118394A-0-0-00)
作者(Author):
祁兵,陈淑娇,李彬,田珂
QI Bing,CHEN Shujiao,LI Bin,TIAN Ke
参考文献(References):
- [1]李彬,郝一浩,祁兵,等.支撑虚拟电厂互动的信息通信关键技术研究展望[J].电网技术,2022,46(5):1761-1770.LI Bin,HAO Yihao,QI Bing,et al.Research prospect of key information and communication technologies supporting virtual power plant interaction[J].Power System Technology,2022,46(5):1761-1770.
- [2]周然.2022年电力系统及新能源行业研究报告[R].北京:中国银河证券研究院,2021.
- [3]高赐威,李倩玉,李慧星,等.基于负荷聚合商业务的需求响应资源整合方法与运营机制[J].电力系统自动化,2013,37(17):9.GAO Ciwei,LI Qianyu,LI Huixing,et al.Demand response resource integration method and operation mechanism based on load aggregator business[J].Automation of Electric Power Systems,2013,37 (17):9.
- [4]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(2):694-704.LI Bin,CHEN Jingsheng,LI Dezhi,et al.Analysis and Prospect of key problems in implementing large-scale demand response in China[J].Power System Technology,2019,43(2):694-704.
- [5]杨旭英,周明,李庚银.智能电网下需求响应机理分析与建模综述[J].电网技术,2016,40(1):220-226.YANG Xuying,ZHOU Ming,LI Gengyin.Overview of demand response mechanism analysis and modeling under smart grid[J].Power System Technology,2016,40(1):220-226.
- [6]黄剑平,陈皓勇,林镇佳,等.需求侧响应背景下分时电价研究与实践综述[J].电力系统保护与控制,2021,49(9):178-189.HUANG Jianping,CHEN Haoyong,LIN Zhenjia,et al.Summary of research and practice of TOU price under the background of demand side response.[J].Power System Protection and Control,2021,49(9):178-189.
- [7]张钦.智能电网下需求响应热点问题探讨[J].中国电力,2013,46(6):85-90.ZHANG Qin.Discussion on hot issues of demand response under smart grid[J].Electric Power,2013,46(6):85-90.
- [8]郝洁,高赐威.基于需求侧竞价的安徽省激励型电力需求响应机制研究及应用[J].电力需求侧管理,2021,23(2):63-67.HAO Jie,GAO Ciwei.Research and application of incentive power demand response mechanism in Anhui Province Based on demand side bidding[J].Power Demand Side Management,2021,23(2):63-67.
- [9]曲欣瑶.基于分时电价的居民用户智能用电优化控制策略研究[D].杭州:浙江大学,2018.
- [10]李彬,卢超,曹望璋,等.基于区块链技术的自动需求响应系统应用初探[J].中国电机工程学报,2017,37(13):3691-3702.LI Bin,LU Chao,CAO Wangzhang,et al.Application of automatic demand response system based on blockchain technology[J].Proceedings of the CSEE,2017,37(13):3691-3702.
- [11]何德卫,张远雄,徐青甫,等.佛山工业企业分行业需求响应策略研究[J].电力需求侧管理,2017,19(3):35-38.HE Dewei,ZHANG yuanxiong,XU Qingfu,et al.Research on demand response strategy of Foshan Industrial Enterprises by industry[J].Power Demand Side Management,2017,19(3):35-38.
- [12]Beil I,Hiskens I,Backhaus S.Frequency Regulation From Commercial Building HVAC Demand Response[J].Proceedings of the IEEE,2016,104(4):1.
- [13]Alfaverh F,Dena M,Sun Y.Demand Response Strategy Based on Reinforcement Learning and Fuzzy Reasoning for Home Energy Management[J].IEEE Access,2020,8:39310-39321.
- [14]Reka S S,Prakash V,Alhelou H H,et al.Real Time Demand Response Modeling for Residential Consumers in Smart Grid Considering Renewable Energy With Deep Learning Approach[J].IEEE Access,2021,56551-56562.
- [15]Yang G,Qian A.Demand-side Response Strategy of Multi-microgrids Based on an Improved Co-evolution Algorithm[J].CSEE Journal of Power and Energy Systems,2021,7(5):8.
- [16]Aghaei J,Barani M,Shafie-Khah M,et al.Risk-Con strained Offering Strategy for Aggregated Hybrid Power Plant Including Wind Power Producer and Demand Response Provider[J].IEEE Transactions on Sustainable Energy,2016,7(2):513-525.
- [17]Ak?n Ta?c?karaolu,Paterakis N G,Ozan Erdin?,et al.Combining the Flexibility from Shared Energy Storage Sys and DLC-based Demand Response of HVAC Units for Distribution System Operation Enhancement[J].IEEE Transactions on Sustainable Energy,2019,10(1):137-148.
- [18]Tan Z,Yang P,Nehorai A.An Optimal and Distributed Demand Response Strategy With Electric Vehicles in the Smart Grid[J].IEEE Transactions on Smart Grid,2014,5(2):861-869.
- [19]Yang Z,Ni M,Liu H.Pricing Strategy of Multi-Energy Provider Considering Integrated Demand Response[J].IEEEAccess,2020,8:149041-149051.
- [20]Li P,Wang H,Zhang B.A Distributed Online Pricing Strategy for Demand Response Programs[J].IEEE Transactions on Smart Grid,2019,10(1):350-360.
- [21]程元,饶尧,丁胜.工业领域电力需求侧可调节负荷潜力分析[J].能源工程,2023,43(1):72-78.CHENG Yuan,RAO Yao,DING Sheng.Potential analysis of power demand side adjustable load in industrial field[J].Energy Engineering,2023,43(1):72-78.
- [22]Wang F,Xiang B,Li K,et al.Smart Households′Aggregated Capacity Forecasting for Load Aggregators Under IncentiveBased Demand Response Programs[J].IEEE Transactions on Industry Applications,2020,56(2):1086-1097.
- [23]Li Z,Wang S,Zheng X,et al.Dynamic Demand Response Using Customer Coupons Considering Multiple Load Aggregators to Simultaneously Achieve Efficiency and Fairness[J].IEEETransactions on Smart Grid,2018,9(4):3112-3121.
- [24]许鹏,孙毅,张健,等.基于人工智能代理的负荷态势感知及调控方法[J].电力系统自动化,2019,43(3):178-186.XU Peng,SUN Yi,ZHANG Jian,et al.Load situation awareness and regulation method based on artificial intelligence agent.[J].Automation of Electric Power Systems,2019,43(3):178-186.
- [25]杨斌,陈振宇,黄奇峰,等.基于用户用能意愿及负荷特性的需求响应用户自动筛选策略[J].电力需求侧管理,2018,20(5):5-10.YANG Bin,CHEN Zhenyu,HUANG Qifeng,et al.Automatic screening strategy of demand response users based on user′s willingness to use energy and load characteristics[J].Power System Automation,2018,20(5):5-10.
- [26]唐巍,高峰.考虑用户满意度的户用型微电网日前优化调度[J].高电压技术,2017,43(1):140-148.TANG Wei,GAO Feng.Optimal Dispatching of Household Microgrids Considering User Satisfaction[J].High Voltage Engineering,2017,43(1):140-148.
- [27]王凌云,胡兴媛,李昇.基于多代理的实时电价机制下微网需求侧协同调控优化[J].电力系统保护与控制,2019,47(5):69-76.WANG Lingyun,HU Xingyuan,LI Sheng.Optimization of demand side collaborative regulation of microgrid based on multiagent real-time electricity price mechanism[J].Power system protection and control,2019,47(5):69-76.
- [28]马冲.基于用户用电特性的分时电价优化研究[D].北京:华北电力大学,2019.
- [29]龚诚嘉锐,林顺富,边晓燕,等.基于多主体主从博弈的负荷聚合商经济优化模型[J].电力系统保护与控制,2022,50(2):30-40.GONG Chengjiarui,LIN Shunfu,Bian Xiaoyan,et al.Economic optimization model of load aggregator based on multi-agent master-slave game[J].Power system protection and control,2022,50(2):30-40.
- [30]闫秀英,李忆言,杜伊帆,等.基于PCA-SOA-ELM的空调系统负荷预测[J].分布式能源,2022,7(2):56-63.YAN Xiuying,LI Yiyan,DU Yifan,et al.Load Prediction of Air Conditioning System Based on PCA-SOA-ELM[J].Distributed Energy,2022,7(2):56-63.
- [31]李琳玮,宁光涛,陈明帆,等.考虑需求响应和储能寿命约束的多类型电源协同调度[J].浙江电力,2021,40(12):27-36.LI Linwei,NING Guangtao,CHEN Mingfan,et al.Coordinated Scheduling of Multi-type Power Sources Considering Demand Response and Storage Life Constraints[J].Zhejiang Electric Power,2021,40(12):27-36.
- [32]周自强,王韵楚,颜拥,等.面向电网企业代理购电的行业精细化电价定价机制[J/OL].电力系统自动化:1-14[2023-05-12].http://kns.cnki.net/kcms/detail/32.1180.TP.20221201.1637.006.ht ml.