多时间尺度滚动优化在冷热电联供系统中的优化调度研究

doi:10.19912/j.0254-0096.tynxb.2021-1106

摘要/Abstract

摘要： 为解决可再生能源和用户负荷波动所造成的能量供需不平衡问题,该文提出多时间尺度优化运行方法。该方法建立“日前-日内滚动-实时调整”的三阶段优化模型来逐级优化机组出力,日前阶段考虑环境成本,日前优化以日运行成本最低为目标,日内滚动优化以滚动控制时域内购能成本和机组出力变化惩罚成本最低为目标,实时调整优化以设备功率总调整率最小为目标,最终得到设备的实时平滑出力计划。日前阶段比较不同的系统结构对CCHP系统日运行成本的影响,并确定了最佳的系统结构。仿真结果验证了所提策略的准确性,优化结果显示：多元储能模式能降低CCHP系统的运行成本;多时间尺度滚动优化不但能提高CCHP系统的运行经济性,而且能减小设备的功率波动,减小设备运行损耗。

关键词: 冷热电联供系统, 储能, 反馈, 多时间尺度优化, 波动率, 不确定性

Abstract: In order to solve the energy supply and demand imbalance caused by the fluctuation of renewable energy and user load, this paper proposes a multi-time-scale optimization operation method. This method establishes a three-stage optimization model of "day-ahead, day-in rolling, real-time adjustment" so as to optimize unit output step by step. Environmental cost is considered in the day-ahead phase, day-ahead optimization aims at the lowest daily operating cost, and day-in rolling optimization uses rolling control time domain purchases, which goal is to minimize the cost of energy and the penalty cost of unit output changes, and the real-time adjustment optimization aims to minimize the total adjustment rate of equipment power, and finally obtaining a real-time smooth output plan of the equipment. In the day-ahead stage,the effects of different system structures on the daily operating cost of combined cooling, heating and power （CCHP） system are compared,and the best system structure is determined. The simulation results verify the accuracy of the proposed strategy,and the optimization results show that：multi-energy storage mode can reduce the operating cost of CCHP system;multi-time-scale rolling optimization can not only improve the operation economy of CCHP system, but also reduce the power fluctuation and the operation loss of equipment.

Key words: combined cooling, heating and power system, energy storage, feedback, multi-time-scale optimization, volatility, uncertainty

中图分类号:

TK019

王智, 陶鸿俊, 蔡文奎, 张玲, 孟金祥. 多时间尺度滚动优化在冷热电联供系统中的优化调度研究[J]. 太阳能学报, 2023, 44(2): 298-308.

Wang Zhi, Tao Hongjun, Cai Wenkui, Zhang Ling, Meng Jinxiang. OPTIMAL SCHEDULING RESEARCH OF MULTI-TIME-SCALE ROLLING OPTIMIZATION IN CCHP SYSTEM[J]. Acta Energiae Solaris Sinica, 2023, 44(2): 298-308.

参考文献

[1] GU W, WU Z, BO R, et al.Modeling, planning and optimal energy management of combined cooling, heating and power microgrid: a review[J]. International journal of electrical power and energy systems, 2014, 54: 26-37.
[2] CHENG S,WANG R, XU J Y, et al.Multi-time scale coordinated optimization of an energy hub in the integrated energy system with multi-type energy storage systems[J]. Sustainable energy technologies and assessments, 2021, 47(22): 101327.
[3] HOU W L, LIU Z X, MA L, et al.A real-time rolling horizon chance constrained optimization model for energy hub scheduling[J]. Sustainable cities and society, 2020, 62: 102417.
[4] LI X Z, WANG W Q, WANG H Y.Hybrid time-scale energy optimal scheduling strategy for integrated energy system with bilateral interaction with supply and demand[J]. Applied energy, 2021, 285: 116458.
[5] WEI S S, GAO X H, ZHANG Y, et al.An improved stochastic model predictive control operation strategy of integrated energy system based on a single-layer multi-timescale framework[J]. Energy, 2021, 235: 121320.
[6] ZHANG L, GAO Y, ZHU H B, et al.Bi-level stochastic real-time pricing model in multi-energy generation system: a reinforcement learning approach[J]. Energy, 2021, 239: 121926.
[7] QIU H F, GU W, XU Y L, et al.Multi-time-scale rolling optimal dispatch for AC/DC hybrid microgrids with day-ahead distributionally robust scheduling[J]. IEEE transactions on sustainable energy, 2018, 10(4): 1653-1663.
[8] ZHU Z Q, MA W G, LI M L, et al.Multi-time scale collaborative optimal dispatching strategy for island micro-grid with multi-distributed power generation[C]//2020 IEEE 4th Conference on Energy Internet and Energy System Integration (EI2), IEEE, Wuhan, China, 2020.
[9] YANG H Z, LI M L, JIANG Z Y, et al.Multi-time scale optimal scheduling of regional integrated energy systems considering integrated demand response[J]. IEEE access, 2020, 8: 5080-5090.
[10] GU W, WANG Z H, WU Z, et al.An online optimal dispatch schedule for CCHP microgrids based on model predictive control[J]. IEEE transactions on smart grid, 2017, 8(5): 2332-2342.
[11] SUN C, SU S, ZHOU Y F, et al.Multi-time scale coordinated optimal control method for ADN considering source load prediction error[C]//2021 6th Asia Conference on Power and Electrical Engineering(ACPEE), IEEE,Chongqing, China, 2021.
[12] MA H L, DU W, WANG Q, et al.Research on wind-hydro-thermal power system multi-time scale coordinated optimal dispatch[C]//2019 IEEE Innovative Smart Grid Technologies-Asia(ISGT Asia), IEEE, Chengdu, China,2019.
[13] YU J L, XIAO L H, SHI H F, et al.Active management of distribution network with high permeability distributed energy based on rolling optimization[C]//2018 2nd IEEE Conference on Energy Internet and Energy System Integration (EI2), IEEE, Beijing, China, 2018.
[14] HU J J, ZHOU H Y R, LI Y, et al. Multi-time scale energy management strategy of aggregator characterized by photovoltaic generation and electric vehicles[J]. Journal of modern power systems and clean energy, 2020, 8(4): 727-736.
[15] XUE X Z, AI X M, FANG J K, et al.Real-time schedule of integrated heat and power system:a multi-dimensional stochastic approximate dynamic programming approach[J]. International journal of electrical power & energy systems, 2022, 134: 107427.
[16] LIU H, ZHAO Y, GU C H, et al.Adjustable capability of the distributed energy system: definition,framework,and evaluation model[J]. Energy, 2021, 222: 119674.
[17] LUO Z, WU Z, LI Z Y, et al.A two-stage optimization and control for CCHP microgrid energy management[J]. Applied thermal engineering, 2017, 125: 513-522.
[18] 张靠社, 冯培基, 张刚, 等. 考虑机会约束的多能源微电网双层优化配置[J]. 太阳能学报, 2021, 42(8): 41-48.
ZHANG K S, FENG P J, ZHANG G, et al.Considering the opportunity constraints of multi-energy microgrid bi-level optimal configuration[J]. Acta energiae solaris sinica, 2021, 42(8): 41-48.
[19] 茅靖峰, 武剑, 张珍梦, 等. 基于AFPSO的孤岛型直流微网多目标优化研究[J]. 太阳能学报, 2021, 42(6):63-71.
MAO J F, WU J, ZHANG Z M, et al.Multi-objective optimization of islanded DC microgrid based on AFPSO[J]. Acta energiae solaris sinica, 2021, 42(6): 63-71.
[20] 肖浩, 裴玮, 孔力. 基于模型预测控制的微电网多时间尺度协调优化调度[J]. 电力系统自动化, 2016, 40(18): 7-14, 55.
XIAO H, PEI W, KONG L.Multi-time scale coordinated optimal scheduling of microgrid based on model predictive control[J]. Automation of electric power systems, 2016, 40(18): 7-14, 55.
[21] 王成山, 吕超贤, 李鹏, 等. 园区型综合能源系统多时间尺度模型预测优化调度[J]. 中国电机工程学报,2019, 39(23): 6791-6803, 7093.
WANG C S, LYU C X, LI P, et al.Multi-time scale model prediction and optimization scheduling of park-type integrated energy system[J]. Proceedings of the CSEE, 2019, 39(23): 6791-6803, 7093.
[22] 靳小龙, 穆云飞, 贾宏杰, 等. 集成智能楼宇的微网系统多时间尺度模型预测调度方法[J]. 电力系统自动化,2019, 43(16): 25-33.
JIN X L, MU Y F, JIA H J, et al.Multi-time scale model predictive scheduling method for microgrid system integrated with intelligent buildings[J]. Automation of electric power systems, 2019, 43(16): 25-33.
[23] 石可颂. 冷热电联供系统控制策略与优化调度研究[D]. 济南: 山东大学, 2015: 26.
SHI K S.Research on control strategy and optimal scheduling of CCHP system[D]. Ji’nan: Shandong University, 2015: 26.
[24] FOROUGH A B, ROSHANDE R.Lifetime optimization frame work for a hybrid renewable energy system based on receding horizon optimization[J]. Energy, 2018, 150: 617-630.
[25] MIAO Y Q, JIANG Q Y, CAO Y J.Battery switch station modeling and its economic evaluation in microgrid[C]//2012 IEEE Power and Energy Society General Meeting,IEEE, Beijing, China, 2012.