| 311 | 0 | 336 |
| 下载次数 | 被引频次 | 阅读次数 |
在需求侧，调控储能、电动汽车等灵活性资源是提高配电系统灵活性的重要手段。为更准确地刻画灵活性资源集群的聚合灵活性，提出基于改进边界收缩算法的灵活性聚合方法。首先将灵活性资源刻画为等效储能模型，随后采用基于高维多面体的边界收缩算法来求解聚合等效模型的参数；在此基础上，通过调节等效储能模型的自放电系数来调节聚合等效模型外接多面体多个超平面的斜率，从而改变收缩后内近似投影多面体的形状；接着采用蒙特卡洛模拟法对原始聚合可行域进行采样，并计算聚合等效模型可行轨迹的样本覆盖率，将其作为方法近似效果的评估指标；最后，基于粒子群算法寻找模型具有最大内近似投影多面体时的自放电系数，从而获得等效储能的最优参数。算例结果表明，相比原方法，改进后的边界收缩算法能覆盖到更大的可行域空间，近似精度提高了13.54%，结果具有更低的保守性。
Abstract：On the demand side,regulation of energy storage,electric vehicles and other types of flexibility resources is an important regulator for improving the flexibility of the distribution system.In order to more accurately portray the aggregation flexibility of flexibility resource clusters,a flexibility aggregation method based on an improved boundary contraction algorithm is proposed. The flexibility resources are first inscribed into a equivalent energy storage model,followed by a boundary contraction algorithm based on high-dimensional polyhedral to solve the parameters of the aggregate equivalent model.On this basis,by adjusting the self-discharge coefficient of the equivalent energy storage model,the slopes of several hyperplanes of the external polyhedron of the polymerized equivalent model are made adjustable,thus changing the shape of the contracted internally connected polyhedral.Monte Carlo simulation is then used to sample the original aggregated feasible domains and calculate the sample coverage of the feasible trajectories of the aggregated equivalent model,which is used as an assessment of the effectiveness of the approximation. Finally,particle swarm optimization algorithm is used to find the self-discharge coefficients when the model has a maximum internally connected polyhedron,thus obtaining the optimal parameters for the equivalent energy storage.Example results show that the improved boundary contraction algorithm coverages to a larger space of feasible domains and improves the approximation accuracy by 13.54% compared to the original method,and the results have a lower conservatism.
[1]赵佩尧，李正烁，高晗，等.电-气-热综合能源系统协同调度优化研究综述[J].山东电力技术，2024,51(4):1-11.ZHAO Peiyao,LI Zhengshuo,GAO Han,et al. Review on collaborative scheduling optimization of electricity-gas-heat integrated energy system[J]. Shandong Electric Power,2024,51(4):1-11.
[2] HANIF S,MASSIER T,GOOI H B,et al.Cost optimal integration of flexible buildings in congested distribution grids[J]. IEEE Transactions on Power Systems,2017,32(3):2254-2266.
[3]许高秀，邓晖，房乐，等.考虑需求侧灵活性资源参与的国内外电力辅助服务市场机制研究综述[J].浙江电力，2022,41(9):1-13.XU Gaoxiu,DENG Hui,FANG Le,et al. A review of ancillary service market mechanism study at home and abroad considering flexible resources on demand side[J]. Zhejiang Electric Power,2022,41(9):1-13.
[4]冯昊，徐海栋，孙飞飞，等.计及需求侧响应能力的受端电网储能优化配置方法[J].浙江电力，2023,42(12):107-116.FENG Hao,XU Haidong,SUN Feifei,et al.An optimal allocation method of receiving-end power grid energy storage considering demand-side response capability[J]. Zhejiang Electric Power,2023,42(12):107-116.
[5] NAZIR M S,HISKENS I A,BERNSTEIN A,et al. Inner approximation of minkowski sums:a union-based approach and applications to aggregated energy resources[C]//2018 IEEE Conference on Decision and Control(CDC). IEEE,2018:5708-5715.
[6] YI Z K,XU Y L,GU W,et al. Aggregate operation model for numerous small-capacity distributed energy resources considering uncertainty[J]. IEEE Transactions on Smart Grid,2021,12(5):4208-4224.
[7] YI Z K,XU Y L,GU W,et al. A multi-time-scale economic scheduling strategy for virtual power plant based on deferrable loads aggregation and disaggregation[J]. IEEE Transactions on Sustainable Energy,2020,11(3):1332-1346.
[8]栗子豪，李铁，吴文传，等.基于Minkowski Sum的热泵负荷调度灵活性聚合方法[J].电力系统自动化，2019,43(5):14-21.LI Zihao,LI Tie,WU Wenchuan,et al. Minkowski sum based flexibility aggregating method of load dispatching for heat pumps[J].Automation of Electric Power Systems,2019,43(5):14-21.
[9] ZHAO H T,WANG B,WANG X Y,et al. Active dynamic aggregation model for distributed integrated energy system as virtual power plant[J].Journal of Modern Power Systems and Clean Energy,2020,8(5):831-840.
[10] HAO H,WU D,LIAN J M,et al.Optimal coordination of building loads and energy storage for power grid and end user services[J].IEEE Transactions on Smart Grid,2018,9(5):4335-4345.
[11] MADJIDIAN D,ROOZBEHANI M,DAHLEH M A.Energy storage from aggregate deferrable demand:fundamental trade-offs and scheduling policies[J]. IEEE Transactions on Power Systems,2018,33(4):3573-3586.
[12] XU Z W,DENG T H,HU Z C,et al.Data-driven pricing strategy for demand-side resource aggregators[J]. IEEE Transactions on Smart Grid,2018,9(1):57-66.
[13] XU Z W,HU Z C,SONG Y H,et al.Risk-averse optimal bidding strategy for demand-side resource aggregators in day-ahead electricity markets under uncertainty[J]. IEEE Transactions on Smart Grid,2017,8(1):96-105.
[14] CHEN X,DALL’ANESE E,ZHAO C H,et al. Aggregate power flexibility in unbalanced distribution systems[J]. IEEE Transactions on Smart Grid,2020,11(1):258-269.
[15] SONG M,GAO C W,YAN H G,et al.Thermal battery modeling of inverter air conditioning for demand response[J]. IEEE Transactions on Smart Grid,2018,9(6):5522-5534.
[16] HAO H,SANANDAJI B M,POOLLA K,et al.Aggregate flexibility of thermostatically controlled loads[J]. IEEE Transactions on Power Systems,2015,30(1):189-198.
[17]边晓燕，孙明琦，董璐，等.计及灵活性聚合功率的源-荷分布式协调调度[J].电力系统自动化，2021,45(17):89-98.BIAN Xiaoyan,SUN Mingqi,DONG Lu,et al.Distributed sourceload coordinated dispatching considering flexible aggregated power[J].Automation of Electric Power Systems,2021,45(17):89-98.
[18]童宇轩，胡俊杰，杜昊明，等.基于虚拟电池模型的外逼近闵可夫斯基热泵负荷调节可行域聚合方法[J].电网技术（2023-11-15）[2024-06-04].DOI:10.13335/j.1000-3673.pst.2023.1580.TONG Yuxuan,HU Junjie,DU Haoming,et al. Feasible region aggregation method for load regulation of Minkowski heat pump based on external approximation of VB model[J]. Power System Technology(2023-11-15)[2024-06-04].DOI:10.13335/j.1000-3673.pst.2023.1580.
[19] WANG G R,LI Z S,WANG F.Enhanced sufficient battery model for aggregate flexibility of thermostatically controlled loads considering coupling constraints[J]. IEEE Transactions on Sustainable Energy,2021,12(4):2493-2496.
[20]吴界辰，艾欣，胡俊杰.需求侧资源灵活性刻画及其在日前优化调度中的应用[J].电工技术学报，2020,35(9):1973-1984.WU Jiechen,AI Xin,HU Junjie. Methods for characterizing flexibilities from demand-side resources and their applications in the day-ahead optimal scheduling[J]. Transactions of China Electrotechnical Society,2020,35(9):1973-1984.
[21]吴界辰，艾欣，胡俊杰，等.计及不确定因素的需求侧灵活性资源优化调度[J].电力系统自动化，2019,43(14):73-80.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.
[22] WANG S Y,WU W C.Aggregate flexibility of virtual power plants with temporal coupling constraints[J].IEEE Transactions on Smart Grid,2021,12(6):5043-5051.
[23] ZHAO L,ZHANG W,HAO H,et al. A geometric approach to aggregate flexibility modeling of thermostatically controlled loads[J]. IEEE Transactions on Power Systems,2017,32(6):4721-4731.
[24] TAN Z F,YU A,ZHONG H W,et al.Optimal virtual battery model for aggregating storage-like resources with network constraints[J].CSEE Journal of Power and Energy Systems,2024,10(4):1843-1847.
[25] SHI W B,LI N,XIE X R,et al. Optimal Residential Demand Response in Distribution Networks[J]. IEEE Journal on Selected Areas in Communications,2014,32(7):1441-1450.
[26] CHEN X,LI N.Leveraging two-stage adaptive robust optimization for power flexibility aggregation[J]. IEEE Transactions on Smart Grid,2021,12(5):3954-3965.
基本信息:
DOI：10.20097/j.cnki.issn1007-9904.2025.01.001
中图分类号:TM73
引用信息:
[1]翁亮涛,王思远,郑伟业等.基于改进边界收缩算法的灵活性资源聚合模型[J].山东电力技术,2025,52(01):1-11.DOI:10.20097/j.cnki.issn1007-9904.2025.01.001.
基金信息:
国家自然科学基金（52107094）; 新型电力系统运行与控制全国重点实验室开放基金课题（SKLD24KM04）~~