Optimizing Operation Scheduling in a Microgrid Considering Probabilistic Uncertainty and Demand Response Using Social Spider Algorithm

Subject Areas : International Journal of Smart Electrical Engineering

Amir  Mortazi ¹ (Department of Electrical Engineering, Yazd Branch, Islamic Azad University, Yazd, Iran.)
Seyedamin  Saeed ² (Department of Electrical Engineering, Yazd Branch, Islamic Azad University, Yazd, Iran)
Hamidreza  Akbari ³ (Department of Electrical Engineering, Yazd Branch, Islamic Azad University, Yazd, Iran.)

Received: 2022-10-05 Accepted : 2022-12-03 Published : 2023-07-01

Keywords: Optimization, Demand Response, Operation Scheduling, Probabilistic Uncertainty, Social Spider Algorithm,

Abstract :

The production of electrical energy from renewable sources has become an efficient solution to deal with the lack of fossil fuels, and prevent the emission of greenhouse gases and global warming. Due to the existence of different loads in terms of feeding priority, consumers can help the microgrid control center in optimizing the use of the microgrid and supplying energy to critical loads by providing the amount of load that can be interrupted or moved at different prices. Consumer pricing can reduce operating costs, especially when market prices are high. At the same time, with this method, consumers can economize on unimportant loads. In this paper, the effect of consumer pricing on the use of microgrids is analyzed considering the types of consumers and load priorities. The demand response program is achieved with the objective function of maximizing social welfare. on the other hand, the operation is principally concerned with flattening the load curve as much as possible. The flatter the load curve, the better the capacity installed in the network , and as a result, it postpones the development of generation and transmission. In this regard, an attempt is made to operate the microgrid in the presence of demand response, so that while increasing social welfare, the load curve is flat at an acceptable level. With these goals, the problem is formulated as a multi-objective objective function based on nonlinear programming GAMS optimization software used to solve the problem, and ε constraint will be used for multi-objective optimization.