Optimal economic scheduling of microgrids considering renewable energy sources based on energy hub model using demand response and improved water wave optimization algorithm

Abstract

Unpredictable nature of renewable energy sources puts power system operators in different conditions in terms of maintaining the system reliability. Microgrids (MGs) can provide an effective solution for the problems of grid-connected renewable energy sources due to their tuning capability and flexibility. In this study, a stochastic optimization strategy was proposed for participating in energy market operations considering demand response (DR). The results indicated the operating cost decreased when the MG implemented DR programs. Also, DR program could shift energy consumption from on- to off-peak hours and flatten the load curve. Therefore, a scheduling model was presented for the operation of energy carriers and reserves considering security constraints of power and natural gas grids in interconnected hubs as well as responsive load participation using the developed water wave optimization (WWO) algorithm. The objective function of this model aimed to minimize the operating cost of sources to supply electrical and thermal loads applied to the proposed MG. WWO is a meta-heuristic algorithm inspired by the behavior of water waves. The waves formed on the sea surface have complex, but interesting, relationships that can be used to solve optimization problems. In this algorithm, each problem solution is encoded as a water wave and undergoes changes in the problem search space based on three behaviors of propagation, refraction, and decay of water waves or problem solutions. The simplicity of the structure of this method, elitism and the ability to escape from local points due to the existence of different search operators and the mutation of generations have led to the use of this method. The results indicated a decline in operating costs through electrical and thermal responsive load participation and thermal energy storage system. Results of the proposed model revealed a correlation between the electricity price and natural gas consumption, indicating multi-carrier energy grids should be examined and optimized simultaneously.