Simulation-Based Optimization for Policy Incentives and Planning of Hybrid Microgrids

TL;DR

This paper introduces a simulation-based optimization framework using MSPSA to improve policy incentives and microgrid planning for remote communities, significantly reducing costs and promoting renewable energy adoption.

Contribution

It presents a novel stochastic simulation approach integrating weather uncertainty into microgrid design and policy optimization, achieving substantial cost reductions.

Findings

68.1% reduction in total costs for case study microgrid

Effective integration of weather uncertainty in microgrid planning

Demonstrated cost savings with MSPSA algorithm

Abstract

Transitioning to renewable power generation is often difficult for remote or isolated communities, due to generation intermittency and high cost barriers. Our paper presents a simulation-based optimization approach for the design of policy incentives and planning of microgrids with renewable energy sources, targeting isolated communities. We propose a novel framework that integrates stochastic simulation to account for weather uncertainty and system availability while optimizing microgrid configurations and policy incentives. Utilizing the mixed-variable Simultaneous Perturbation Stochastic Approximation (MSPSA) algorithm, our method demonstrates a significant reduction in Net Present Cost (NPC) for microgrids, achieving a 68.1% reduction in total costs in a case study conducted on Popova Island. The results indicate the effectiveness of our approach in enhancing the economic viability of microgrids while promoting cleaner energy solutions. Future research directions include refining uncertainty models and exploring applications in grid-connected microgrids.