针对中国西北地区新能源消纳问题,该文聚合风力发电、光伏发电、光热电站、电储能装置组成虚拟电厂（VPP）,提出一种基于鲁棒随机优化理论的新能源虚拟电厂多时间尺度优化调度策略。首先对风力发电、光伏发电、光热电站与电储能装置进行数学描述,在此基础上建立VPP多时间尺度优化调度模型。在日前调度层中,以VPP运行效益最大为目标,依据风光日前预测出力建立日前优化调度模型;在时前调度层中,以VPP运行成本最小为目标,根据风光时前预测出力建立时前调度修正模型。同时,为了衡量风电、光伏发电出力不确定性对系统的运行影响,建立VPP随机优化调度模型。仿真结果验证该模型可提高运行效益与新能源消纳能力。
Abstract
For the consumption problem of new energy in northwest China, the paper integrates the wind power, photovoltaic power, concentrating solar power plant and electric energy storage devices to form a virtual power plant （VPP）, and proposes a multi-time scale stochastic optimization scheduling strategy of new energy virtual power plant based on robust stochastic optimization theory. First, mathematical description of the wind power photovoltaic power, concentrating solar power plant and electric energy storage devices is established. Based on that, the VPP multi-time scale optimization scheduling model is established. In the day-ahead scheduling layer, the paper takes the VPP operation benefit maximization as the goal, and establishes the day-ahead optimal scheduling model based on day-ahead forecasting output power of the wind power and photovoltaic power. In the hour-ahead scheduling layer, operation costs minimum is taken as the goal, and the day-ahead scheduling correction model is established by forecasting the current output of the wind power and photovoltaic power. Simultaneously, a VPP random scheduling model is established by application of measuring the impact of output uncertainty of wind power and photovoltaic power on system operation. The results show that the model can improve operating profit and new energy consumption capacity.
关键词
新能源 /
不确定性 /
虚拟电厂 /
运行成本 /
多时间尺度 /
鲁棒随机优化
Key words
new energy /
uncertainty /
virtual power plant /
operating cost /
multi-time scale /
robust stochastic optimization
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
参考文献
[1] KARDAKOS E G, SIMOGLOU C K, BAKIRTZIS A G.Optimal offering strategy of a virtual power plant:a stochastic bi-level approach[J]. IEEE transactions on smart grid, 2016, 7(2): 794-806.
[2] 王振浩, 许京剑, 田春光, 等. 计及碳交易成本的含风电电力系统热电联合调度[J]. 太阳能学报, 2020, 41(12): 245-253.
WANG Z H, XU J J, TIAN C G, et al.Combined heat and power scheduling strategy considering carbon trading cost in wind power system[J]. Acta energiae solaris sinica, 2020, 41(12): 245-253.
[3] NOSRATABADI S M, HOOSHMAND R A, GHOLIPOUR E.A comprehensive review on microgrid and virtual power plant concepts employed for distributed energy resources scheduling in power systems[J]. Renewable and sustainable energy reviews, 2017, 67: 341-363.
[4] NAVAL N, SÁNCHEZ R, YUSTA M. A virtual power plant optimal dispatch model with large and small-scale distributed renewable generation[J]. Renewable energy, 2020, 151: 57-69.
[5] SHABANZADEH M, SHEIKH-EL-ESLAMI M K, HAGHIFAM M R. The design of a risk-hedging tool for virtual power plants via robust optimization approach[J]. Applied energy, 2015, 155: 766-777.
[6] 臧海祥, 余爽, 卫志农, 等. 计及安全约束的虚拟电厂两层优化调度[J]. 电力自动化设备, 2016, 36(8): 96-102.
ZANG H X, YU S, WEI Z N, et al.Safety-constrained two-layer optimal dispatch of virtual power plant[J]. Power automation equipment, 2016, 36(8): 96-102.
[7] NGUYEN H T, LE L B, WANG Z Y.A bidding strategy for virtual power plants with intraday demand response exchange market using stochastic programming[J]. IEEE transactions on industry applications, 2018, 54(4): 3044-3055.
[8] ZAPATA J, VANDEWALLE J, DHAESELEER W.A comparative study of imbalance reduction strategies for virtual power plant operation[J]. Applied thermal engineering, 2014, 71(2): 847-857.
[9] 刘思源, 艾芊, 郑建平, 等. 多时间尺度的多虚拟电厂双层协调机制与运行策略[J]. 中国电机工程学报, 2018, 38(3): 753-761.
LIU S Y, AI Q, ZHENG J P, et al.Bi-level coordination mechanism and operation strategy of multi-time scale multiple virtual power plants[J]. Proceedings of the CSEE, 2018, 38(3): 753-761.
[10] 吕梦璇, 娄素华, 刘建琴, 等. 含高比例风电的虚拟电厂多类型备用协调优化[J]. 中国电机工程学报, 2018, 38(10): 2874-2882, 3138.
LYU M X, LOU S H, LIU J Q, et al.Coordinated optimization of multi-type reserve in virtual power plant accommodated high shares of wind power[J]. Proceedings of the CSEE, 2018, 38(10): 2874-2882, 3138.
[11] PALANICHAMY P, SAMUEL A D, MURALI V.Descriptive statistical approach for the assessment of the output of a virtual power plant in a secondary distribution network[J]. IET generation, transmission & distribution, 2020, 14(11): 2191-2200.
[12] AL-AWAMI A T, AMLEH N A, MUQBEL A M. Optimal demand response bidding and pricing mechanism with fuzzy optimization: application for a virtual power plant[J]. IEEE transactions on industry applications, 2017, 53(5): 5051-5061.
[13] MOHAMMADREZA E, FARSHID K, MASOUD R.A two-stage stochastic programming framework for risk-based day ahead operation of a virtual power plant[J]. International transactions on electrical energy systems, 2020, 30(3): 1-12.
[14] 袁桂丽, 陈少梁, 刘颖, 等. 基于分时电价的虚拟电厂经济性优化调度[J]. 电网技术, 2016, 40(3): 826-832.
YUAN G L, CHEN S L, LIU Y, et al.Economic optimal dispatch of virtual power plant based on time-of-use power price[J]. Power system technology, 2016, 40(3): 826-832.
[15] 董海鹰, 房磊, 丁坤, 等. 基于热电联产运行模式的光热发电调峰策略[J]. 太阳能学报, 2019, 40(10): 2763-2772.
DONG H Y, FANG L, DING K, et al.Peak regulation strategy of CSP plants based on operation mode of cogeneration[J]. Acta energiae solaris sinica, 2019, 40(10): 2763-2772.
[16] DU E, ZHANG N, HODGE B M, et al.Economic justification of concentrating solar power in high renewable energy penetrated power systems[J]. Applied energy, 2018, 222: 649-661.
[17] 王振浩, 杨璐, 田春光, 等.考虑风电消纳的风电-电储能-蓄热式电锅炉联合系统能量优化[J]. 中国电机工程学报, 2017, 37(增刊): 137-143.
WANG Z H, YANG L, TIAN C G, et al.Energy optimization for combined system of wind-electric energy storage-regenerative electric boiler considering wind consumption[J]. Proceedings of the CSEE, 2017, 37(S1): 137-143.
[18] ZAKARIAZADEH A, HOMAEE O, JADID S, et al.A new approach for real time voltage control using demand response in an automated distribution system[J]. Applied energy, 2014, 117: 157-166.
[19] PRASAD A A, TAYLOR R A, KAY M.Assessment of direct normal irradiance and cloud connections using satellite data over Australia[J]. Applied energy, 2015, 143: 301-311.
[20] TAN Z F, JU L W, LI H H, et al.A two-stage scheduling optimization model and solution algorithm for wind power and energy storage system considering uncertainty and demand response[J]. International journal of electrical power & energy systems, 2014, 63: 1057-1069.
[21] 董文略, 王群, 杨莉. 含风光水的虚拟电厂与配电公司协调调度模型[J]. 电力系统自动化, 2015, 39(9): 75-81, 207.
DONG W L, WANG Q, YANG L.A coordinated dispatching model for a distribution utility and virtual power plants with wind/photovoltaic/hydro generators[J]. Automation of electric power systems, 2015, 39(9): 75-81, 207.
[22] XU T, ZHANG N.Coordinated operation of concentrated solar power and wind resources for the provision of energy and reserve services[J]. IEEE transactions on power systems, 2017, 32(2): 1260-1271.
[23] LOFBERG J.YALMIP:a toolbox for modeling and optimization in MATLAB[C]//2014 IEEE International Symposium on Computer Aided Control Systems Design, Taipei, Taiwan, 2014.
[24] SUN L L, LIU W, CHAI T Y.Tool sequence optimization based on three logical modes of robot control via cplex optimization[J]. Journal of Shanghai Jiaotong University (Science), 2011, 16(4): 436-440.
PDF(1757 KB)
