Probabilistic optimization of planning and operation of networked microgrids with renewable energy resources considering demand response programs

Recently, demand response programs (DRPs) have been introduced as a potential solution to effectively enhance both energy systems and consumers participating in DRP due to the reduction in investment and energy cost of systems as well as the energy cost of consumers. This paper proposes an optimal framework for microgrid planning that integrates renewable energy sources, storage systems, uncertainty, and DRPs. Not only the lifetime and uptime of renewable resources and storage systems are considered by optimizing the life cycle costs, but also the rated power of devices with discrete values are integrated into the model by binary variables. The uncertain parameters are modeled by probability density functions then they are divided into states by the clustering technique. Scenarios are generated by the scenario matrix and then the number of scenarios is reduced to 10 to decrease the computational burden. A case study with a test system demonstrates the effectiveness of the proposed method and the potential of DRPs to avoid device investment and reduce energy costs.