Intelligent Approaches to Support Demand Response in Microgrid Planning

Microgrids are facing several challenges by increasing the penetration level of distributed renewable energy resources (DRERs) and energy storage systems (ESSs). However, the cost of renewable generation and storage components is not yet affordable for a secure economic integration. Planning of microgrid to use the optimal capacity of components is, accordingly, an important stage to achieve the minimum cost. Demand response (DR) is an efficient solution to decrease the capacity of components while making microgrids more reliable. By changing the electricity consumption pattern of customers using DR programs, microgrid's load can better match DRERs generation and stored energy of ESSs. Application of DR in microgrids is, however, a complex program. This is because of the variety of available generation-storage components, massive data analysis of weather forecasts for DRER generation, electricity pattern prediction, and comfort level of consumers for DR application. This chapter investigates the application of intelligent approaches to support DR for microgrids planning. The planning problem is introduced by considering the available components, operation, and required data to solve the problem. The types of DR programs in microgrids are identified and explained. Data mining techniques are discussed to analyze the large amount of data in microgrid. The intelligent approaches to support DR and their applications and effects are described.