EN基于综合半梯云的不确定需求响应资源聚合调度策略
An Uncertain Demand Response Resource Aggregation Scheduling Strategy Based on Integrated Semi-trapezoidal Clouds
- 现代电力 2026年43卷第2期 页码：298-309
- 作者机构：
1. 海上风电技术教育部工程中心(上海电力大学), 杨浦区,上海市,200090
2. 上海电力大学 电气工程学院,上海市 杨浦区,200090
3. 国网浙江省电力有限公司兰溪市供电公司, 浙江省 兰溪市,321100
4. 国网北京市电力有限公司电力科学研究院, 北京市 丰台区,100000
- 作者简介：
[ "葛晓琳(1988)，女，教授，博士，研究方向为电力优化调度等，E-mail：gexiaolin2005@126.com" ]
[ "沈佳妮(1995)，女，硕士研究生，通信作者，研究方向为需求响应调频，E-mail：1090338246@qq.com" ]
[ "徐轶胜(1997)，男，硕士研究生，研究方向为海上风电优化规划，E-mail：xys1797064509@163.com" ]
[ "钱梦迪(1986)，女，研究方向为状态检测，E-mail: 437112831@qq.com" ]
- 基金信息：国家自然科学基金项目(52077130);上海市青年科技启明星计划(21QA1403500);上海绿色能源并网工程技术研究中心(13DZ2251900)。
DOI：10.19725/j.cnki.1007-2322.2023.0430
中图分类号： 2-
纸质出版：2026
- 稿件说明：
移动端阅览
葛晓琳, 沈佳妮, 徐轶胜, 等. 基于综合半梯云的不确定需求响应资源聚合调度策略[J]. 现代电力, 2026,43(2):298-309.
GE Xiaolin, SHEN Jiani, XU Yisheng, et al. An Uncertain Demand Response Resource Aggregation Scheduling Strategy Based on Integrated Semi-trapezoidal Clouds[J]. 2026, 43(2): 298-309.
葛晓琳, 沈佳妮, 徐轶胜, 等. 基于综合半梯云的不确定需求响应资源聚合调度策略[J]. 现代电力, 2026,43(2):298-309. DOI： 10.19725/j.cnki.1007-2322.2023.0430.
GE Xiaolin, SHEN Jiani, XU Yisheng, et al. An Uncertain Demand Response Resource Aggregation Scheduling Strategy Based on Integrated Semi-trapezoidal Clouds[J]. 2026, 43(2): 298-309. DOI： 10.19725/j.cnki.1007-2322.2023.0430.
摘要
需求响应(demand response，DR)作为一种激励机制，对于促进电力供需平衡意义重大。然而，需求响应的不确定性与多样性也导致实际与预期的需求负荷响应量存在较大偏差，为此提出一种基于综合半梯云模型的需求响应随机优化策略。首先，考虑到需求侧响应的实际参与程度与用户的意愿密切相关，建立logit-XGBoost用户意愿线性回归模型，剥离用户信息中与响应意愿相关性较低的行为信息，将用户参与响应的不确定意愿显性表达。随后，针对实际调度过程中无法获得的需求侧用户所有的信息，基于提取的用户意愿搭建综合半梯云模型，获得用户响应的数字特征，联合表征可削减、可中断、可平移三类需求响应用户收益与响应行为之间的不确定映射关系。接着，考虑需求侧资源分散性与多样性使得其可调度能力具有很大的局限，建立三类不确定需求侧资源的通用(virtual battery，VB)模型，量化需求响应资源出力，优化了需求侧调度模型的削峰填谷效果。最后，通过算例仿真验证该方法的有效性和可行性。
Abstract
As an incentive mechanism
demand response (DR) is of great importance to the promotion of the balance between power supply and demand. However
the uncertainty and diversity of DR also lead to large deviations between actual and expected DR response volumes. To this end
a stochastic optimization strategy for DR is proposed based on an integrated semi-trapezoidal cloud model. First
considering that the actual degree of participation in demand-side response is closely related to the user's willingness
a logit-XGBoost user willingness linear regression model is established to strip the behavioral information in the user's information that has a low correlation with the willingness to respond
and express the user's uncertain willingness to participate in the response explicitly. Subsequently
for all the information of demand-side users that cannot be obtained in the actual scheduling process
we build a comprehensive half-trapezoidal cloud model based on the extracted user willingness to obtain the numerical features of user responses
and jointly characterize the uncertain mapping relationship between the benefits and response behaviors of users of the three types of demand response: curtailable
interruptible
and pannable. Then
considering that the dispersion and diversity of demand-side resources make their dispatchability have great limitations
a generic virtual battery (VB) model of three types of uncertain demand-side resources is established to quantify the demand response resource output
and optimize the peak shaving and valley filling effects of the demand-side scheduling model. Finally
the effectiveness and feasibility of the method are verified by arithmetic simulation.
关键词
Keywords
references
CHEN Qixin, LÜ Ruike, GUO Hongye, et al . Demand response trading model of virtual power plant considering electricity consumption satisfaction degree and response quantity expectation[J ] . Electric Power Automation Equipment, 2023, 43(10): 23−37(in Chinese).
ZHOU Xingqiu, ZHENG Lingwei, YANG Lan, et al . Day-ahead optimal dispatch of an integrated energy system considering multiple uncertainty[J ] . Power System Technology, 2020, 44(7): 2466−2473(in Chinese).
LUO Shuyu, LI Qi, YANG Yang, et al . Community integrated energy system optimal scheduling considering differentiated power-heat-hydrogen incentive based demand response[J ] . Electric Power Automation Equipment, 2023, 43(12): 214−221 (in Chinese).
LI Zhaoyu, AI Qian. Demand response estimation method of electricity consumption for residential customer under time of use price[J]. Electric Power Automation Equipment, 2023, 43(10): 127−133(in Chinese).
YANG H, LI M, JIANG Z, et al . Multi-time scale optimal scheduling of regional integrated energy systems consider ing integrated demand response[J ] . IEEE Access, 2020, 8: 5080−5090.
XIONG Xiaoping, YANG Hui, CAI Yiming. Robust optimal dispatch method of microgrid considering user endowment effect and environmental awareness uncertainty[J]. Proceedings of the CSEE, 2023, 43(21): 1−12(in Chinese).
ASTRIANI Y, SHAFIULLAH G, SHAHNIA F. Incentive determination of a demand response program for microgrids[J]. Applied Energy, 2021, 292: 116624.
CHEN Xiaolong, SUN Jia, ZHANG Junlin, et al . Soft open point siting scheme for distribution network with Stackelberg game between new energy accommodation and user-side demand response[J ] . Electric Power Automation Equipment, 2023, 43(2): 1−12(in Chinese).
ZHENG S L, SUN Y, LI B, et al . Stochastic programming model for incentive-based demand response considering complex uncertainties of consumers[J ] . IET Generation, Transmission Distribution, 2020, 14(20): 4488-4500.
CHEN Yuyang, SHAO Junjie, CHEN Jinjuzheng, et al . Demand response trading model of virtual power plant considering electricity consumption satisfaction degree and response quantity expectation[J ] . Electric Power Automation Equipment, 2023, 43(5): 226−234(in Chinese).
LI Xingsheng, DU Weixuan, LI Deyi. A new method for PSK/QAM modulation recognition of communication signals based on cloud model[J]. Measurement and Control Technology, 2003(3): 15−19(in Chinese).
GE Xiaolin, SHI Liang, LIU Ya, et al . Load forecasting of electric vehicles based on Sigmoid cloud model considering the uncertainty of demand response[J ] . Proceedings of the CSEE, 2020, 40(21): 6913−6925(in Chinese).
ZHANG Yuhua, WANG Cong, SUN Xiaopeng, et al . Multi-step backward semi-cloud model-based uncertainty modeling for price-based dem and response[J ] . Automation of Electric Power Systems, 2023, 47(1): 105−114(in Chinese).
HU Junjie, WANG Kunyu, AI Xin, et al . Transactive energy: an effective mechanism for balancing electric energy system[J ] . Proceedings of the CSEE, 2019, 39(4): 953−966(in Chinese).
WANG Chengshan, LI Peng, YU Hao. Development and characteristic analysis of flexibility in smart distribution network[J]. Automation of Electric Power Systems, 2018, 42(10): 13−21(in Chinese).
ZHOU Hailang, LIU Yipan, CHEN Yuguo. Demand side feasible region aggregation considering flexibility revenue[J]. Electric Power, 2022, 55(9): 56−63+155(in Chinese).
XIE D, WEN F, XUE Y, et al . Questionnaire designing, multi-agent modeling and analyzing of EV users' traveling willingness[C ] //International Conference on Advances in Power System Control. Hong Kong, China: IET, 2017.
LIN X X, JANAK S L, FLOUDAS C A. A new robust optimization approach for scheduling under uncertainty[J]. Computers Chemical Engineering, 2004, 28(6): 1069-1086.
WU Jiechen, AI Xin, HU Junjie, et al . Optimal dispatch of flexible resource on demand side considering uncertainties[J ] . Automation of Electric Power Systems, 2019, 43(14): 73−80+89(in Chinese).
RESTREPO J F, GALIANA F D. Unit commitment with primary frequency regulation constraints[J]. IEEE Transactions on Power Systems, 2005, 20(4): 1836−1842.
ZHU R, WEI H, BAI X, et al . Wasserstein metric based distributionally robust approximate framework for unit commitment[J ] . IEEE Transactions on Power Systems, 2019, 34(4): 2991−3001.
ZENG Jie, TONG Xiaoyang, FAN Jiale. Dynamic distributionally robust optimization of integrated electric-gas distribution system considering demand response uncertainty[J]. Power System Technology, 2022, 46(5): 1877−1888(in Chinese).
FAHRIOGLU M , ALVARADO F L . Using utility information to calibrate customer demand management behavior models[J]. IEEE Power Engineering Review, 2007, 21(4): 71-71.
CHEN Lianfu, ZHONG Haiwang, TAN Zhenfei, et al . Comprehensive evaluation of key technologies in power internet of things based on comprehensive similarity of cloud model[J ] . Journal of Shanghai Jiaotong University, 2024, 58(1): 19−29 (in Chinese).
WU Jiechen, AI Xin, ZHANG Yan, et al . Day-ahead optimal scheduling for high penetration of distributed energy resources in community under separated distribution and retail operational environment[J ] . Power System Technology, 2018, 42(6): 1709−1717(in Chinese).
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