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. Manuscript profile

Microgrids, with their ability to integrate renewable energy sources, play a crucial role in achieving sustainable and resilient energy systems. Effective planning and optimization of microgrids, particularly considering the inclusion of compressed air energy storage (CAES) systems, are essential for maximizing their benefits. This study proposes a novel approach, the Hybrid Artificial Neural Network-Modified Dragonfly Algorithm (HANN-MDA), for determining the optimum capacity of CAES in microgrid planning. The HANN-MDA method combines the learning capabilities of artificial neural networks with the optimization power of the modified dragonfly algorithm. The proposed method aims to minimize the overall cost of microgrid operation while considering the integration of renewable energy sources and the storage capabilities of CAES. Simulation results demonstrate the effectiveness of the HANN-MDA method in accurately determining the optimal CAES capacity, leading to improved microgrid performance and cost savings. The findings highlight the importance of considering CAES in microgrid planning and the potential of the HANN-MDA method for achieving efficient and economically viable microgrid designs. Manuscript profile