The Integrated Design of a Novel Secondary Control and Robust Optimal Energy Management for Photovoltaic-Storage System Considering Generation Uncertainty

被引:4
|
作者
Xu, Shiyun [1 ]
Sun, Huadong [1 ]
Zhao, Bin [1 ]
Yi, Jun [1 ]
Hayat, Tasawar [2 ]
Alsaedi, Ahmed [2 ]
Dou, Chunxia [3 ]
Zhang, Bo [4 ]
机构
[1] China Elect Power Res Inst, Beijing 100192, Peoples R China
[2] King Abdulaziz Univ, Dept Math, Jeddah 009662, Saudi Arabia
[3] Nanjing Univ Posts & Telecommun, Inst Adv Technol, Nanjing 210023, Peoples R China
[4] Yanshan Univ, Inst Elect Engn, Qinhuangdao 066004, Hebei, Peoples R China
基金
中国国家自然科学基金;
关键词
uncertainly; PV; MG; secondary control; robust optimization; energy management; DROOP CONTROL; MICROGRIDS; COMMUNICATION; ELECTRICITY; GAS;
D O I
10.3390/electronics9010069
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Due to the generation uncertainty of photovoltaic (PV) power generation, it has been posing great challenges and difficulties in maintaining the stability, security, and reliability of PV-storage systems (one kind of microgrid). To overcome these challenges and difficulties, this paper is concerned with secondary control and robust energy management for PVs in a grid-connected microgrid (MG) considering uncertainty. In our designs, to maintain the stable operation of PVs in MG, a novel secondary control method combining an event-triggered finite time sliding mode controller (FTSMC) and consensus controllers is proposed. Furthermore, a robust optimization framework is established to minimize the total cost of grid-connected MG involving the operation cost of multi-battery Energy Storage Systems (BESSes) and the electricity purchased from the main grid. To eliminate the effects of PV uncertainty, the optimization problem with uncertain constraints is converted into a new optimization problem with only deterministic constraints by using the box theory to represent the PV outputs. In other words, the robust optimization strategy makes uncertain boundaries easier to be represented by setting all uncertain parameters into an uncertain domain involving all typical extreme cases. Then, a particle swarm optimization (PSO) method is employed to solve the newly converted optimization problem. Finally, the experimental results validate the effectiveness of the proposed integrated framework.
引用
收藏
页数:28
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