工业园区参与调峰的电池储能-需求响应联合规划
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摘要
工业园区理论上具备很大的调峰潜力,但由于峰谷分布不规律、多方利益交互、规划与运行高度耦合等因素导致难以实现有效规划。文中旨在探索工业园区主动参与调峰的经济性与可行性,提出运用电池储能和需求响应作为两类主要调峰手段,构建了兼顾考虑设备配置与运行调度优化的双层规划优化模型。在经济性方面,对工业园区参与调峰过程中的各种成本效益进行量化建模,并分析了与电网公司、政府之间的利益交互。在不确定性处理方面,联合运用蒙特卡洛模拟法与K-means聚类法生成多个调峰场景以提高规划结果准确性。选取广东省某工业园区作为算例,验证了所提方法的可行性与有效性。
Industrial parks theoretically have great potential for peak shaving,whereas it is difficult to achieve effective planning due to the irregular distribution of peak and valley,multi-interest interaction,high coupling of planning and operation,etc.This paper aims at exploring the economy and feasibility of industrial parks participating in peak shaving via two main approaches,i.e.battery energy storage system(BESS)and demand response.A bi-level optimization model is built which considers both device configuration and economic dispatch optimization.Cost and benefit of industrial parks in the process of peak shaving are quantitatively modeling,and the interests interacting with the power supply companies and governments are analyzed.In terms of uncertainty processing,Monte Carlo simulation and K-means clustering method are jointly applied to form multiple peak shaving scenarios for improving the accuracy of planning results.Finally,the feasibility and effectiveness of the proposed method are verified on an industrial park in Guangdong Province of China.
Industrial parks theoretically have great potential for peak shaving,whereas it is difficult to achieve effective planning due to the irregular distribution of peak and valley,multi-interest interaction,high coupling of planning and operation,etc.This paper aims at exploring the economy and feasibility of industrial parks participating in peak shaving via two main approaches,i.e.battery energy storage system(BESS)and demand response.A bi-level optimization model is built which considers both device configuration and economic dispatch optimization.Cost and benefit of industrial parks in the process of peak shaving are quantitatively modeling,and the interests interacting with the power supply companies and governments are analyzed.In terms of uncertainty processing,Monte Carlo simulation and K-means clustering method are jointly applied to form multiple peak shaving scenarios for improving the accuracy of planning results.Finally,the feasibility and effectiveness of the proposed method are verified on an industrial park in Guangdong Province of China.
引文
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[6]邹波,文福拴,周盈,等.不完全信息情形下确定两部制输电价格的风险谈判模型[J].电力系统自动化,2015,39(22):59-67.ZOU Bo,WEN Fushuan,ZHOU Ying,et al.A CVaR-based negotiation model for determining two-part transmission prices under incomplete information[J].Automation of Electric Power Systems,2015,39(22):59-67.
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[10]黎静华,汪赛.兼顾技术性和经济性的储能辅助调峰组合方案优化[J].电力系统自动化,2017,41(9):44-50.DOI:10.7500/AEPS20170121003.LI Jinghua,WANG Sai.Optimal combined peak-shaving scheme using energy storage for auxiliary considering both technology and economy[J].Automation of Electric Power Systems,2017,41(9):44-50.DOI:10.7500/AEPS20170121003.
[11]LIZONDO D,ARAUJO P,WILL A,et al.Multiagent model for distributed peak shaving system with demand side management approach[C]//IEEE International Conference on Robotic Computing,April 10-12,2017,Taichuang,China:352-357.
[12]徐箭,曹慧秋,唐程辉,等.基于扩展序列运算的含不确定性需求响应电力系统优化调度[J].电力系统自动化,2018,42(13):152-160.DOI:10.7500/AEPS20170802005.XU Jian,CAO Huiqiu,TANG Chenghui,et al.Optimal dispatch of power system considering uncertainty of demand response based on extended sequence operation[J].Automation of Electric Power Systems,2018,42(13):152-160.DOI:10.7500/AEPS20170802005.
[13]欧阳婷.考虑需求响应资源的电网调峰模型与方法研究[D].北京:华北电力大学,2017.OUYANG Ting.Research on peak regulation model and method of power grid considering demand response resources[D].Beijing:North China Electric Power University,2017.
[14]李秀磊,耿光飞,季玉琦,等.主动配电网中储能和需求侧响应的联合优化规划[J].电网技术,2016,40(12):3803-3810.LI Xiulei,GENG Guangfei,JI Yuqi,et al.Integrated optimal planning of energy storage and demand side response in active power distribution network[J].Power System Technology,2016,40(12):3803-3810.
[15]ATZENI I,ORDONEZ L G,SCUTARI G,et al.Demandside management via distributed energy generation and storage optimization[J].IEEE Transactions on Smart Grid,2013,4(2):866-876.
[16]NGUYEN H K,SONG J B,HAN Z.Distributed demand side management with energy storage in smart grid[J].IEEETransactions on Parallel and Distributed Systems,2015,26(12):3346-3357.
[17]曾鸣,张平,王良,等.不确定条件下基于蒙特卡洛模拟的发电投资评估模型[J].电力系统自动化,2015,39(5):54-60.ZENG Ming,ZHANG Ping,WANG Liang,et al.Generation investment evaluation model under uncertainty based on Monte Carlo simulation[J].Automation of Electric Power Systems,2015,39(5):54-60.
[18]侯雨伸,王秀丽,张玥,等.考虑维度重要性的电力系统可靠性评估拟蒙特卡洛方法[J].电力系统自动化,2016,40(16):31-37.HOU Yushen,WANG Xiuli,ZHANG Yue,et al.Dimensional importance based quasi-Monte Carlo method for power system reliability evaluation[J].Automation of Electric Power Systems,2016,40(16):31-37.
[19]赵明宇,徐石明,高辉,等.基于模糊k-means算法的电动汽车应急供电策略[J].电力系统自动化,2016,40(5):91-95.ZHAO Mingyu,XU Shiming,GAO Hui,et al.Strategy of electric vehicle emergency power supply based on fuzzy k-means algorithm[J].Automation of Electric Power Systems,2016,40(5):91-95.
[20]蒋正邦,吴浩,程祥,等.基于多元聚类模型与两阶段聚类修正算法的变电站特性分析[J].电力系统自动化,2018,42(15):157-163.DOI:10.7500/AEPS20170815003.JIANG Zhengbang,WU Hao,CHENG Xiang,et al.Analysis of substation characteristics based on multivariate clustering model and two-stage clustering-correction algorithm[J].Automation of Electric Power Systems,2018,42(15):157-163.DOI:10.7500/AEPS20170815003.
[21]康重庆,杨高峰,夏清.电力需求的不确定性分析[J].电力系统自动化,2005,29(17):14-19.KANG Chongqing,YANG Gaofeng,XIA Qing.Analysis of the uncertainty of electric power demand[J].Automation of Electric Power Systems,2005,29(17):14-19.
[22]JING Rui,ZHU Xingyi,ZHU Zhiyu,et al.A multi-objective optimization and multi-criteria evaluation integrated framework for distributed energy system optimal planning[J].Energy Conversion&Management,2018,166(6):452-462.
[23]REN Hongbo,GAO Weijun.A MILP model for integrated plan and evaluation of distributed energy systems[J].Applied Energy,2010,87(3):1001-1014.
[A1]CALI?SKI T,HARABASZ J.A dendrite method for cluster analysis[J].Communications in Statistics,1974,3(1):1-27.
[2]胡朝阳,毕晓亮,王珂,等.促进负备用跨省调剂的华东电力调峰辅助服务市场设计[J].电力系统自动化,2019,43(5):175-182.DOI:10.7500/AEPS20180711007.HU Zhaoyang,BI Xiaoliang,WANG Ke,et al.Design of peak regulation auxiliary service market for East China power grid to promote inter-provincial sharing of negative reserve[J].Automation of Electric Power Systems,2019,43(5):175-182.DOI:10.7500/AEPS20180711007.
[3]马洪艳,韩笑,严正,等.鲁棒性驱动的含风电不确定性区域间调峰互济方法[J].电力系统自动化,2017,41(7):28-36.DOI:10.7500/AEPS20160909007.MA Hongyan,HAN Xiao,YAN Zheng,et al.Robustness driving reciprocal peak-regulation trading method of interregional grids containing wind power uncertainty[J].Automation of Electric Power Systems,2017,41(7):28-36.DOI:10.7500/AEPS20160909007.
[4]贾宏杰,王丹,徐宪东,等.区域综合能源系统若干问题研究[J].电力系统自动化,2015,39(7):198-207.JIA Hongjie,WANG Dan,XU Xiandong,et al.Research on some key problems related to integrated energy systems[J].Automation of Electric Power Systems,2015,39(7):198-207.
[5]张粒子,张伊美,叶红豆,等.可选择两部制电价定价模型及其方法[J].电力系统自动化,2016,40(3):59-65.ZHANG Lizi,ZHANG Yimei,YE Hongdou,et al.An optional two-part tariff pricing model based on the customers load characteristics[J].Automation of Electric Power Systems,2016,40(3):59-65.
[6]邹波,文福拴,周盈,等.不完全信息情形下确定两部制输电价格的风险谈判模型[J].电力系统自动化,2015,39(22):59-67.ZOU Bo,WEN Fushuan,ZHOU Ying,et al.A CVaR-based negotiation model for determining two-part transmission prices under incomplete information[J].Automation of Electric Power Systems,2015,39(22):59-67.
[7]NICK M,CHERKAOUI R,PAOLONE M.Optimal allocation of dispersed energy storage systems in active distribution networks for energy balance and grid support[J].IEEETransactions on Power Systems,2014,29(5):2300-2310.
[8]SHI Yuanyuan,XU Bolun,WANG Di,et al.Using battery storage for peak shaving and frequency regulation:joint optimization for superlinear gains[J].IEEE Transactions on Power Systems,2018,33(3):2882-2894.
[9]尤毅,刘东,钟清,等.主动配电网储能系统的多目标优化配置[J].电力系统自动化,2014,38(18):46-52.YOU Yi,LIU Dong,ZHONG Qing,et al.Multi-objective optimization configuration for the energy storage system in the active distribution system[J].Automation of Electric Power Systems,2014,38(18):46-52.
[10]黎静华,汪赛.兼顾技术性和经济性的储能辅助调峰组合方案优化[J].电力系统自动化,2017,41(9):44-50.DOI:10.7500/AEPS20170121003.LI Jinghua,WANG Sai.Optimal combined peak-shaving scheme using energy storage for auxiliary considering both technology and economy[J].Automation of Electric Power Systems,2017,41(9):44-50.DOI:10.7500/AEPS20170121003.
[11]LIZONDO D,ARAUJO P,WILL A,et al.Multiagent model for distributed peak shaving system with demand side management approach[C]//IEEE International Conference on Robotic Computing,April 10-12,2017,Taichuang,China:352-357.
[12]徐箭,曹慧秋,唐程辉,等.基于扩展序列运算的含不确定性需求响应电力系统优化调度[J].电力系统自动化,2018,42(13):152-160.DOI:10.7500/AEPS20170802005.XU Jian,CAO Huiqiu,TANG Chenghui,et al.Optimal dispatch of power system considering uncertainty of demand response based on extended sequence operation[J].Automation of Electric Power Systems,2018,42(13):152-160.DOI:10.7500/AEPS20170802005.
[13]欧阳婷.考虑需求响应资源的电网调峰模型与方法研究[D].北京:华北电力大学,2017.OUYANG Ting.Research on peak regulation model and method of power grid considering demand response resources[D].Beijing:North China Electric Power University,2017.
[14]李秀磊,耿光飞,季玉琦,等.主动配电网中储能和需求侧响应的联合优化规划[J].电网技术,2016,40(12):3803-3810.LI Xiulei,GENG Guangfei,JI Yuqi,et al.Integrated optimal planning of energy storage and demand side response in active power distribution network[J].Power System Technology,2016,40(12):3803-3810.
[15]ATZENI I,ORDONEZ L G,SCUTARI G,et al.Demandside management via distributed energy generation and storage optimization[J].IEEE Transactions on Smart Grid,2013,4(2):866-876.
[16]NGUYEN H K,SONG J B,HAN Z.Distributed demand side management with energy storage in smart grid[J].IEEETransactions on Parallel and Distributed Systems,2015,26(12):3346-3357.
[17]曾鸣,张平,王良,等.不确定条件下基于蒙特卡洛模拟的发电投资评估模型[J].电力系统自动化,2015,39(5):54-60.ZENG Ming,ZHANG Ping,WANG Liang,et al.Generation investment evaluation model under uncertainty based on Monte Carlo simulation[J].Automation of Electric Power Systems,2015,39(5):54-60.
[18]侯雨伸,王秀丽,张玥,等.考虑维度重要性的电力系统可靠性评估拟蒙特卡洛方法[J].电力系统自动化,2016,40(16):31-37.HOU Yushen,WANG Xiuli,ZHANG Yue,et al.Dimensional importance based quasi-Monte Carlo method for power system reliability evaluation[J].Automation of Electric Power Systems,2016,40(16):31-37.
[19]赵明宇,徐石明,高辉,等.基于模糊k-means算法的电动汽车应急供电策略[J].电力系统自动化,2016,40(5):91-95.ZHAO Mingyu,XU Shiming,GAO Hui,et al.Strategy of electric vehicle emergency power supply based on fuzzy k-means algorithm[J].Automation of Electric Power Systems,2016,40(5):91-95.
[20]蒋正邦,吴浩,程祥,等.基于多元聚类模型与两阶段聚类修正算法的变电站特性分析[J].电力系统自动化,2018,42(15):157-163.DOI:10.7500/AEPS20170815003.JIANG Zhengbang,WU Hao,CHENG Xiang,et al.Analysis of substation characteristics based on multivariate clustering model and two-stage clustering-correction algorithm[J].Automation of Electric Power Systems,2018,42(15):157-163.DOI:10.7500/AEPS20170815003.
[21]康重庆,杨高峰,夏清.电力需求的不确定性分析[J].电力系统自动化,2005,29(17):14-19.KANG Chongqing,YANG Gaofeng,XIA Qing.Analysis of the uncertainty of electric power demand[J].Automation of Electric Power Systems,2005,29(17):14-19.
[22]JING Rui,ZHU Xingyi,ZHU Zhiyu,et al.A multi-objective optimization and multi-criteria evaluation integrated framework for distributed energy system optimal planning[J].Energy Conversion&Management,2018,166(6):452-462.
[23]REN Hongbo,GAO Weijun.A MILP model for integrated plan and evaluation of distributed energy systems[J].Applied Energy,2010,87(3):1001-1014.
[A1]CALI?SKI T,HARABASZ J.A dendrite method for cluster analysis[J].Communications in Statistics,1974,3(1):1-27.