于娜, 李宏伟, 葛延峰, 黄大为. 风荷组合场景下计及调峰效益的电锅炉和储热系统容量优化配置[J]. 现代电力, 2021, 38(1): 41-50. DOI: 10.19725/j.cnki.1007-2322.2020.0057
引用本文: 于娜, 李宏伟, 葛延峰, 黄大为. 风荷组合场景下计及调峰效益的电锅炉和储热系统容量优化配置[J]. 现代电力, 2021, 38(1): 41-50. DOI: 10.19725/j.cnki.1007-2322.2020.0057
YU Na, LI Hongwei, GE Yanfeng, HUANG Dawei. Optimal Capacity Configuration of Electric Boiler and Heat Storage System Considering Peak-shaving Benefit under Wind-Load Combination Scenario[J]. Modern Electric Power, 2021, 38(1): 41-50. DOI: 10.19725/j.cnki.1007-2322.2020.0057
Citation: YU Na, LI Hongwei, GE Yanfeng, HUANG Dawei. Optimal Capacity Configuration of Electric Boiler and Heat Storage System Considering Peak-shaving Benefit under Wind-Load Combination Scenario[J]. Modern Electric Power, 2021, 38(1): 41-50. DOI: 10.19725/j.cnki.1007-2322.2020.0057

风荷组合场景下计及调峰效益的电锅炉和储热系统容量优化配置

Optimal Capacity Configuration of Electric Boiler and Heat Storage System Considering Peak-shaving Benefit under Wind-Load Combination Scenario

  • 摘要: 储热系统具有良好的调峰特性,可以打破热电联产机组以热定电的刚性约束。合理的配置电锅炉和储热系统能够有效提高地区电网消纳风电的能力。综合考虑风荷不确定性,构建基于启发式矩匹配方法的风-荷组合场景,在此基础上,建立计及调峰效益的电锅炉和储热系统优化配置双层规划模型,上层以待规划热电厂年化调峰净收益最大为目标,下层以组合场景S下总燃料成本和弃风惩罚成本最小为目标,以电热系统相关约束作为约束条件,最后采用粒子群优化方法对该模型进行优化求解,获得最优电锅炉和储热系统配置方案。算例结果表明,合理配置电锅炉和储热系统,可以有效的提高风电消纳率以及整体收益。

     

    Abstract: By means of good peak load regulation characteristics of heat storage system, the rigid constraint of determining the generating capacity by the heating load in combined heat and power (CHP) generation units can be broken, and reasonable allocation of electric boilers and heat storage systems can effectively improve the capability of wind power accommodation in regional power grid. Firstly, comprehensively considering the uncertainty of wind load, a heuristic moment matching (HMM) based time series scenario of wind-load combination was constructed, and on this basis a bi-level programming model for optimal allocation of electric boilers and heat storage systems, in which the peak-shaving benefit was taken into account, was established. Secondly, for the upper level the maximized annualized earning of peak load regulation by the thermal power plant to be planned was taken as the objective, and for the lower level the minimized wind curtailment penalty cost and total fuel cost under composition scenario S was taken as the objective, and the relevant constraints of the CHP system were taken as constraint conditions. Finally, the particle swarm optimization (PSO) was utilized to optimize and solve the proposed model to obtain the optimal allocation plan of electric boilers and heat storage systems. Simulation results show that both wind power accommodation rate and the overall revenue can be effectively improved by rationally allocation of electric boilers and heat storage systems.

     

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