基于机会约束的考虑N–1安全约束的储能优化配置方法
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摘要
为了解决公共储能在含高比例可再生能源输电网中选址定容的问题,提出了一种基于机会约束的虑及N–1安全约束的公共储能规划方法,用于计算公共储能的安装地点、额定容量和功率配置,在确保输电网安全运行的前提下,提升输电网的经济效益和新能源消纳比例。该方法综合考虑常规电厂、可再生能源电站和储能投资者三方利益,以输电网发电成本、弃风光惩罚最小和储能收益最大为目标函数,在考虑常规发电机组开、停状态和出力约束的同时考虑储能的充、放电状态及充、放电功率约束,在考虑输电网正常状态下安全约束的同时考虑输电网N–1状态下的安全约束。针对发输电可靠性测试系统IEEE RTS–96建立了仿真算例,以典型日负荷、风电和光伏预测出力数据作为依据,利用改进广义Benders分解法进行求解,并使用灵敏度分析法对优化结果进行对比分析,分析结果表明,所得储能优化方案可有效消除N–1故障时的支路潮流越限,能够保证输电网N–1状态下的安全运行,相较于不考虑N–1网络安全约束,本文方法所得优化结果虽然经济性略有降低,但是在线机组最大容量、负载率均衡度等安全指标均得到明显提高,提升了储能整合后输电网运行的安全性。所得储能规划方案实现了确保输电网N–1安全性的经济性最优,可用于指导对安全性要求较高的储能规划,具有一定的工程实用价值。
In order to solve the problems of energy storage location and capacity in transmission network with high percentage of renewable energy, an optimal method considering N–1 security constraints based on chance constraints was proposed. It can calculate the location, rated capacity and power of energy storage. On the premise of ensuring the security of the transmission network, the economic benefits of the transmission network and the proportion of new energy consumption should be improved. In this paper, the benefits of power plants, renewable plants and energy storage owners were considered. An object function was established to minimize the generating cost, wind-solar energy abandoning cost and maximize energy storage benefits. To acquire the optimal location, rated capacity and power of energy storage, many constraints were introduced,such as conventional generators on-off and power output, charge-discharge state and power of energy storage, N and N–1 security constraints of transmission network. According to the predicted data of typical daily load, wind power and photovoltaic, a simulation example was established for the reliability test system of generation and transmission IEEE RTS–96. The problem was solved by the improved generalized Benders decomposition method. Then, the sensitivity analysis method was used to compare and analyze the optimization results. The analysis results showed that the branch power flow overstepping could effectively eliminated when N–1 fault occurred. The security of the transmission network under N–1 state could be ensured. Compared with the method without considering N–1 security constraints, although slightly lower in economy, the maximum capacity of online units and load rate balance safety indicators are significantly improved, which accordingly improves the security of transmission network after energy storage integration. The proposed method can be used to guide energy storage planning with higher security requirements, and is worth to apply in engineering projects.
In order to solve the problems of energy storage location and capacity in transmission network with high percentage of renewable energy, an optimal method considering N–1 security constraints based on chance constraints was proposed. It can calculate the location, rated capacity and power of energy storage. On the premise of ensuring the security of the transmission network, the economic benefits of the transmission network and the proportion of new energy consumption should be improved. In this paper, the benefits of power plants, renewable plants and energy storage owners were considered. An object function was established to minimize the generating cost, wind-solar energy abandoning cost and maximize energy storage benefits. To acquire the optimal location, rated capacity and power of energy storage, many constraints were introduced,such as conventional generators on-off and power output, charge-discharge state and power of energy storage, N and N–1 security constraints of transmission network. According to the predicted data of typical daily load, wind power and photovoltaic, a simulation example was established for the reliability test system of generation and transmission IEEE RTS–96. The problem was solved by the improved generalized Benders decomposition method. Then, the sensitivity analysis method was used to compare and analyze the optimization results. The analysis results showed that the branch power flow overstepping could effectively eliminated when N–1 fault occurred. The security of the transmission network under N–1 state could be ensured. Compared with the method without considering N–1 security constraints, although slightly lower in economy, the maximum capacity of online units and load rate balance safety indicators are significantly improved, which accordingly improves the security of transmission network after energy storage integration. The proposed method can be used to guide energy storage planning with higher security requirements, and is worth to apply in engineering projects.
引文
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[20]Xiong Xiong,Ye Lin,Yang Rengang.Optimal allocation and economic benefits analysis of energy storage system on power demand side[J].Automation of Electric Power Systems,2015,39(17):42-48.[熊雄,叶林,杨仁刚.电力需求侧规模储能容量优化和经济性分析[J].电力系统自动化,2015,39(17):42-48.]
[21]Pandzic H,Wang Y,Qiu T,et al.Near optimal method for siting and sizing of distributed storage in a transmission network[J].IEEE Transactions on Power Systems,2015,30(5):2288-2300.
[2]Denholm P,Sioshansi R.The value of compressed air energy storage with wind in transmission constrained electric power systems[J].Energy Policy,2009,37(8):3149-3158.
[3]Bose S,Gayme D F,Topcu U,et al.Optimal placement of energy storage in the grid[C]//Proceedings of the 51st IEEEConference on Decision and Control.Maui,Hawaii,2012:5605-5612.
[4]Zhao Jingjing,Xu Chuanlin,LüXue,et al.Optimization of micro-grid primary frequency regulation reserve capacity and energy storage system[J].Proceedings of the CSEE,2017,37(15):4324-4332.[赵晶晶,徐传琳,吕雪,等.微电网一次调频备用容量与储能优化配置方法[J].中国电机工程学报,2017,37(15):4324-4332.]
[5]Wang Chengshan,Yu Bo,Xiao Jun,et al.Sizing of energy storage systems for output smoothing of renewable energy systems[J].Proceedings of the CSEE,2012,32(16):1-8.[王成山,于波,肖峻,等.平滑可再生能源发电系统输出波动的储能系统容量优化方法[J].中国电机工程学报,2012,32(16):1-8.]
[6]Ge Le,Yuan Xiaodong,Wang Liang,et al.Capacity configuration of hybrid energy storage system for distribution network optimal operation[J].Power System Technology,2017,41(11):3506-3513.[葛乐,袁晓冬,王亮,等.面向配电网优化运行的混合储能容量配置[J].电网技术,2017,41(11):3506-3513.]
[7]Li Zhenkun,Chen Siyu,Fu Yang,et al.Optimal allocation of ESS in distribution network containing DG base on timingvoltage-sensitivity analysis[J].Proceedings of the CSEE,2017,37(16):4630-4640.[李振坤,陈思宇,符杨,等.基于时序电压灵敏度的有源配电网储能优化配置[J].中国电机工程学报,2017,37(16):4630-4640.]
[8]Song Bingbing,Gu Jie.Optimal locating and sizing of superconducting magnetic energy storage device for transfer capability improvement of power grid[J].Proceedings of the CSU-EPSA,2017,29(11):92-98.[宋柄兵,顾洁.考虑电网输电能力改善的超导储能装置优化选址定容[J].电力系统及其自动化学报,2017,29(11):92-98.]
[9]Makarov Y,Du P,Kintner M,et al.Sizing energy storage to accommodate high penetration of variable energy resources[J].IEEE Transactions on Sustainable Energy,2012,3(1):34-40.
[10]Solomon A,Kammen D,Callaway D.The role of largescale energy storage design and dispatch in the power grid:A study of very high grid penetration of variable renewable resources[J].Applied Energy,2014,134:75-89.
[11]Harsha P,Dahleh M.Optimal management and sizing of energy storage under dynamic pricing for the efficient integration of renewable energy[J].IEEE Transactions on Power Systems,2015,30(3):1164-1181.
[12]Qiu T,Xu B,Wang Y,et al.Stochastic multistage co-planning of transmission expansion and energy storage[J].IEEETransactions on Power Systems,2016,32(1):643-651.
[13]Wogrin S,Gayme D F.Optimizing storage siting,sizing,and technology portfolios in transmission constrained networks[J].IEEE Transactions on Power Systems,2015,30(6):3304-3313.
[14]Dvorkin Y,Fernandez-Blanco R,S.Kirschen D,et al.Ensuring profitability of energy storage[J].IEEE Transactions on Power Systems,2017,32(1):611-623.
[15]Wang Chaoqun,Wei Hua,Wu Siyuan.A decomposition coordination algorithm applied to hydro-thermal unit commitment problems with power flow constraints[J].Proceedings of the CSEE,2017,37(11):3148-3161.[汪超群,韦化,吴思缘.计及潮流约束的水火电力系统机组组合问题的分解-协调算法[J].中国电机工程学报,2017,37(11):3148-3161.]
[16]Pandzic H,Dvorkin Y,Qiu T,et al.Toward cost-eff icient and reliable unit commitment under uncertainty[J].IEEETransactions on Power Systems,2015,31(2):1-13.
[17]Lubin M,Dvorkin Y,Backhaus S.A robust approach to chance constrained optimal power flow with renewable generation[J].IEEE Transactions on Power Systems,2016,31(5):3840-3849.
[18]Bienstock D,Chertkov M,Harnett S.Chance-constrained optimal power flow:Risk-aware network control under uncertainty[J].Society for Industrial and Applied Mathematics,2014,56(3):461-495.
[19]Dunning I,Huchette J,Lubin M.JuMP:A modeling language for mathematical optimization[J].Mathematics,2015,59(2):1-26.
[20]Xiong Xiong,Ye Lin,Yang Rengang.Optimal allocation and economic benefits analysis of energy storage system on power demand side[J].Automation of Electric Power Systems,2015,39(17):42-48.[熊雄,叶林,杨仁刚.电力需求侧规模储能容量优化和经济性分析[J].电力系统自动化,2015,39(17):42-48.]
[21]Pandzic H,Wang Y,Qiu T,et al.Near optimal method for siting and sizing of distributed storage in a transmission network[J].IEEE Transactions on Power Systems,2015,30(5):2288-2300.