艾永乐, 丁剑英, 刘群峰, 韩朝阳, 景亚丽, 李帅. 计及负荷需求响应的并网家用光伏系统协同优化调度[J]. 太阳能学报, 2021, 42(9): 104-114. DOI: 10.19912/j.0254-0096.tynxb.2019-0943
引用本文: 艾永乐, 丁剑英, 刘群峰, 韩朝阳, 景亚丽, 李帅. 计及负荷需求响应的并网家用光伏系统协同优化调度[J]. 太阳能学报, 2021, 42(9): 104-114. DOI: 10.19912/j.0254-0096.tynxb.2019-0943
Ai Yongle, Ding Jianying, Liu Qunfeng, Han Chaoyang, Jing Yali, Li Shuai. SYNERGETIC OPTIMAL SCHEDULING OF GRID-CONNECTED HOUSEHOLD PHOTOVOLTAIC SYSTEM CONSIDERING DEMAND RESPONSE FOR LOADS[J]. Acta Energiae Solaris Sinica, 2021, 42(9): 104-114. DOI: 10.19912/j.0254-0096.tynxb.2019-0943
Citation: Ai Yongle, Ding Jianying, Liu Qunfeng, Han Chaoyang, Jing Yali, Li Shuai. SYNERGETIC OPTIMAL SCHEDULING OF GRID-CONNECTED HOUSEHOLD PHOTOVOLTAIC SYSTEM CONSIDERING DEMAND RESPONSE FOR LOADS[J]. Acta Energiae Solaris Sinica, 2021, 42(9): 104-114. DOI: 10.19912/j.0254-0096.tynxb.2019-0943

计及负荷需求响应的并网家用光伏系统协同优化调度

SYNERGETIC OPTIMAL SCHEDULING OF GRID-CONNECTED HOUSEHOLD PHOTOVOLTAIC SYSTEM CONSIDERING DEMAND RESPONSE FOR LOADS

  • 摘要: 针对未充分利用负荷的需求响应而限制家用光伏系统经济调度问题,提出计及负荷需求响应的协同经济调度策略,实现源荷协同调度。首先,建立负荷需求响应模型,对家用负荷进行需求响应处理;其次,考虑光伏出力不确定性,构建计及用户售电收益、购电费用、政府补贴、运维费用和光伏系统建设成本的并网家用光伏系统优化调度模型;然后,提出以动态联络线传输功率为优化变量,结合拉丁超立方采样、场景削减及改进粒子群算法的优化方法求解该模型。最后,结合典型算例验证所提模型和方法的有效性。

     

    Abstract: To solve the economic dispatching problem of household photovoltaic(PV)system caused by not fully utilizing the demand response of loads,a synergetic economic dispatching strategy that takes into account the demand response of load is proposed,which achieves the synergetic scheduling of source and load. Firstly,the demand response model of the load is established to handle the household load. Secondly,considering the uncertainty of PV output,the optimal scheduling model of grid-connected household PV system is constructed,which takes into account the user’s power sold income,power purchased cost,government subsidy,operation and maintenance cost and the construction cost of PV system. Then,this model is solved by using dynamic grid-connected transmission power as optimization variable,combining with Latin hypercube sampling and scene reduction(LHSSR),improved particle swarm optimization algorithm. Finally,the simulation results show the correctness of the proposed model and method.

     

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