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Simulation-Based Optimization for Policy Incentives and Planning of Hybrid Microgrids
- Program: Applied Mathematics and Statistics
- Course: Other
- Year: 2025
Project Description:
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.
Project Photo:
The diagram demonstrates the algorithm proposed and tested in this study, combining MCMC simulation with MSPSA optimization