Inner-outer layer co-optimization of sizing and energy management for renewable energy microgrid with storage

Abstract

Conventional microgrid optimization schemes fall short in achieving global optimality for both sizing and scheduling aspects. In response to the demand for simultaneous optimization, this paper presents a novel inner-outer layer framework that includes an outer layer dedicated to sizing optimization and an inner layer focused on Energy Management System (EMS) operational optimization. This approach has a distinctive mechanism of two-layer interaction, where the outer layer focuses on the size assignment and renewal, while the inner layer is constrained by the size assignment and provides feedback on the corresponding operational cost to the outer layer. It enhances the capability of global optimization since the optimal sizes are searched within a fixed space. To establish the range of initial generation values, the framework commences with a pre-optimization phase. Based on the convexity nature of optimization problems, a combined solution methodology involving Rolling Horizon Optimization (RHO) and Particle Swarm Optimization (PSO) is implemented. The case study demonstrates that the algorithm exhibits a good convergence within 30 generations, reaching a global satisfactory solution with a population size of 50. Furthermore, our analysis of optimal sizes across diverse scenarios fills the research gap in understanding the relationship between optimal sizes and operational scenarios. These results underscore that the impact of the time-of-day tariff strategy is significant, and it is risky to determine the optimal sizes by rule-based strategies. Finally, the comparison to other co-optimization methods shows that the proposed framework can reduce the annual total cost by up to 23.1%, which highlights its greater capability of global optimization.