YUN Yun-yun1, DONG Hai-ying1,2, CHEN Zhao3, HUANG Rong3, DING Kun3
（1. School of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China；2. School of New Energy and Power Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China；3. Electric Power Research Institute of State Grid Gansu Electric Power Company, Lanzhou 730070, China）
Abstract： For the low utilization rate of photovoltaic power generation, taking a new energy power system constisting of concentrating solar power (CSP), photovoltaic power (PP) and battery energy storage system as an example, a multi-objective optimization scheduling strategy considering energy storage participation is proposed. Firstly, the new energy power system model is established, and the PP scenario generation and reduction frame based on the autoregressive moving average model and Kantorovich-distance is proposed. Then, based on the optimization goal of the system operation cost minimization and the PP output power consumption maximization, the multi-objective optimization scheduling model is established. Finally, the simulation results show that introducing energy storage into the system can effectively reduce the system operation cost and improve the utilization efficiency of PP.
Key words： new energy power system; multi-objective optimization; energy storage participation; operation cost; autoregressive moving average model
CLD number： TM734 doi： 10.3969/j.issn.1674-8042.2020.04.008
References
［1］Reddy S S. Optimal scheduling of thermal-wind-solar power system with storage. Renewable Energy, 2017, 101: 1357-1368.
［2］Ju L, Tan Z, yuan J, et al. A bi-level stochastic scheduling optimization model for a virtual power plant connected to a wind-photovoltaic-energy storage system considering the uncertainty and demand response. Applied Energy, 2016, 171: 184-199.
［3］Nguyen C, Lee H, Chun T. Cost optimized battery capacity and short-term power dispatch control for wind farm. IEEE Transactions on Industry Applications, 2015, 51(1): 595-606.
［4］Setlhaolo D, Xia X, Zhang J. Optimal scheduling of household appliances for demand response. Electric Power Systems Research, 2014, 116： 24-28.
［5］Faria P, Soares J, Vale Z, et al. Modified particle swarm optimization applied to integrated demand response and DG resources scheduling. IEEE Transactions on Smart Grid, 2013, 4(1)： 606-616.
［6］Zeng X T, Liu T Q, Li Q, et al. Short-term complementary optimal dispatch model of multi-source hybrid power system based on virtual power configuration strategy. Power System Technology, 2016, 40(05)： 1379-1386.
［7］Mazidi M, Zakariazadeh A, Jadid S, et al. Integrated scheduling of renewable generation and demand response programs in a microgrid. Energy Conversion and Management, 2014, 86： 1118-1127.
［8］Shi J J, Yuan T J, Saeed A K, et al. Unit commitment strategy considering cooperated dispatch of electric vehicles based on scheduling capacity and wind power generation . High Voltage Engineering, 2018, 44(10)： 3433-3440.
［9］Liu Y, Tan S, Jiang C. Interval optimal scheduling of hydro-pv-wind hybrid system considering firm generation coordination. IET Renewable Power Generation, 2017, 11(1)： 63-72.
［10］Gan W, Ai X M, Fang J K, et al. Coordinated optimal operation of the wind, coal, hydro,gas units with energy storage. Transactions of China Electrotechnical Society, 2017, 32(S1)： 11-20.
［11］Chen R Z, Sun H B, Guo Q L, et al. Reducing generation uncertainty by integrating csp with wind power： an adaptive robust optimization-based analysis. IEEE Transactions on Sustainable Energy, 2015, 6(2)： 583-594.
［12］Du E S, Zhang N, Hodge B M, et al. Economic justification of concentrating solar power in high renewable energy penetrated power systems. Applied Energy, 2018, 222： 649-661.
［13］Zakariazadeh A, Homaee O, Jadid S, et al. A new approach for real time voltage control using demand response in an automated distribution system. Applied Energy, 2014, 117(C)： 157-166.
［14］Perez R, Lorenz E, Pelland S, et al. Comparison of numerical weather prediction solar irradiance forecasts in the US, Canada and Europe. Solar Energy, 2013, 94(4)： 305-326.
［15］Dai L P, Wu W, Huang B W. Optimal energy scheduling for family grid-connected photovoltaic systems considering uncertainty. Power System Protection and Control, 2019, 47(3)： 48-55.
［16］Dong W L, Wang Q, Li Y. A coordinated dispatching model for a distribution utility and virtual power plants with wind/photovoltaic/hydro generators. Automation of Electric Power Systems, 2015, 39(9)： 75-81.
［17］Domínguez R, Conejo A J, Carrión M. Operation of a fully renewable electric energy system with CSP plants. Applied Energy, 2014, 119(12)： 417-430.
［18］ Guo H W, Pu L, Zhang Y X, et al. Optimization model for integrated complementary system of wind-pv-pump storage based on rough set theory. Journal of Zhejiang University (Engineering Science), 2019, 53(4)： 801-810.
计及储能参与的新能源发电系统多目标优化调度
贠韫韵1， 董海鹰1,2，陈 钊3， 黄 蓉3， 丁 坤3
（1. 兰州交通大学 自动化与电气工程学院， 甘肃 兰州 730070； 2. 兰州交通大学 新能源与动力工程学院， 甘肃 兰州 730070；3. 国网甘肃省电力公司电力科学研究院， 甘肃 兰州 730070）
摘 要： 针对新能源发电的消纳问题， 以光热发电、 光伏发电以及储能系统组成的新能源发电系统为研究对象， 提出了一种计及储能参与的新能源发电系统多目标优化策略。 首先建立了新能源发电系统模型， 并提出了基于自回归滑动平均模型与Kantorovich距离的光伏发电场景生成与缩减框架。 然后， 以系统运行成本最低及光伏发电并网功率最大为优化目标， 建立了多目标优化调度模型。 仿真结果表明， 将储能引入系统中可以有效降低系统的运行成本、 提高光伏发电的消纳功率。
关键词： 新能源发电系统； 多目标优化； 储能参与； 运行成本； 自回归滑动平均模型
引用格式：YUN Yun-yun, DONG Hai-ying, CHEN Zhao, et al. Multi-objective optimization scheduling for new energy power system considering energy storage participation. Journal of Measurement Science and Instrumentation, 2020, 11（4）： 365-372. ［doi： 10.3969／j.issn.1674-8042.2020.04.008］
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