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

Nanrui Gong, James C. Spall

TL;DR

This work addresses the challenge of providing affordable, reliable renewable energy to geographically isolated communities by integrating policy incentives with microgrid planning under weather- and reliability-driven uncertainty. It introduces a simulation-based optimization framework that uses mixed-variable MSPSA to jointly optimize microgrid configurations and low-emission incentives, leveraging stochastic simulations of solar, wind, and component reliability. In a Popova Island case study, the approach achieves a substantial NPC reduction (68.1%) and outperforms a PSO baseline, while demonstrating that policy incentives steer designs toward higher renewables and lower emissions. The framework offers a practical pathway for designing cost-effective, low-carbon microgrids for remote regions, with potential extensions to grid-connected contexts.

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.

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

TL;DR

This work addresses the challenge of providing affordable, reliable renewable energy to geographically isolated communities by integrating policy incentives with microgrid planning under weather- and reliability-driven uncertainty. It introduces a simulation-based optimization framework that uses mixed-variable MSPSA to jointly optimize microgrid configurations and low-emission incentives, leveraging stochastic simulations of solar, wind, and component reliability. In a Popova Island case study, the approach achieves a substantial NPC reduction (68.1%) and outperforms a PSO baseline, while demonstrating that policy incentives steer designs toward higher renewables and lower emissions. The framework offers a practical pathway for designing cost-effective, low-carbon microgrids for remote regions, with potential extensions to grid-connected contexts.

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.

Paper Structure

This paper contains 13 sections, 11 equations, 2 figures, 1 table.

Table of Contents

  1. Introduction
  2. Microgrid model
  3. Solar PV model
  4. Wind power model
  5. Microturbine model
  6. Battery storage model
  7. Low-emissions policy incentives
  8. Markov system reliability model
  9. Objective function
  10. Constrained MSPSA Algorithm
  11. Case Study
  12. Results
  13. Conclusion

Figures (2)

  • Figure 1: Comparison of MSPSA and PSO performance over 10 replicates
  • Figure 2: Comparison of planning outcome with and without policy incentives