Cost-Effective Design of Grid-tied Community Microgrid

TL;DR

The paper tackles designing cost-effective grid-tied community microgrids that balance economics, reliability, efficiency, and environmental impact. It introduces a multi-objective framework that couples HOMER Pro sizing with deep reinforcement learning to generate Pareto fronts and optimize operation under uncertainty, validated on the Central Tilba case with NASA solar/wind data. The optimal configuration (A5) achieves NPC $\$4.83$M, LCOE $\$0.208$/kWh, 100% reliability, and $\eta_{sys}=91.99\%$ with 418 kW PV, 123 kW wind, and 704 kWh BESS, eliminating the need for DGs. Sensitivity analyses show load and battery costs as major drivers of cost and emissions, highlighting the need for strategic energy management and potential DG-BESS hybrids or alternative long-duration storage for future microgrid design.

Abstract

This study aims to develop a cost-effective microgrid design that optimally balances the economic feasibility, reliability, efficiency, and environmental impact in a grid-tied community microgrid. A multi-objective optimization framework is employed, integrating HOMER Pro for system sizing with deep reinforcement learning (DRL). Sensitivity analyses are conducted to evaluate the system performance under varying load demand and renewable energy fluctuations, while an economic sensitivity assessment examines the impact of electricity prices and capital costs on the Levelized Cost of Energy (LCOE). The proposed microgrid configuration achieves high reliability, satisfying 100% of the load, even under adverse weather conditions. The proposed framework attains an efficiency of 91.99% while maintaining a carbon footprint of 302,747 kg/year, which is approximately 95% lower than that of the grid system. The economic analysis indicates a net present cost (NPC) of $4.83M with a competitive LCOE of $0.208/kWh. In addition, the operation cost is $201,473 per year with a capital investment of $1.42M, rendering it a financially viable alternative to conventional grid-dependent systems.This work can be valuable in identifying effective solutions for supplying reliable and cost-effective power to regional and remote areas.

Figures (6)

• Figure 1: Comparative Analysis of MG Design Approaches: (a) MG design prioritizing economic factors alone, (b) MG design integrating both economic and environmental considerations, (c) MG design that incorporates economic aspects alongside reliability and efficiency metrics, (d) MG design that merges economic considerations with environmental and reliability aspects, (e) MG design emphasizing economic and reliability factors, (f) Proposed MG design framework that synergistically combines economic, environmental, efficiency, and reliability factors for optimized performance. [Co $\rightarrow$Cost, Em$\rightarrow$Emission, Ef$\rightarrow$Efficiency, and Re$\rightarrow$Reliability].
• Figure 2: Hourly Electrical demand and electricity price ($), sellback price for the case srudy community.
• Figure 3: Daily Average Solar Radiation and Wind Speed for the Case Study Community.
• Figure 4: Trade-off Among Cost, Reliability, Efficiency, and Emissions .
• Figure 5: Multi-objective Trade-offs for Different Optimization Priorities.