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Integrated Energy Management for Operational Cost Optimization in Community Microgrids

Moslem Uddin, Huadong Mo, Daoyi Dong

TL;DR

The paper addresses cost-effective energy management for multi-energy community microgrids by proposing an integrated EMS that combines storage-based peak shaving with economic dispatch of diesel generators and grid-interaction strategies. The approach is validated via a rural Australian case study using MATLAB and HOMER Pro, showing substantial improvements: a 43% reduction in peak demand, a 14.21% lower LCOE, an 84.63% drop in operating costs, and renewable utilization rising to 92.3%, alongside a 196.4% reduction in CO2 emissions. The results demonstrate improved reliability during grid outages and near-doubling of renewable usage, underscoring the practical feasibility of deploying such EMS in community MGs. The work highlights the potential for cost-effective, resilient, and environmentally friendly energy management in rural and underserved settings, while calling for further analyses on economic scalability and urban applicability.

Abstract

This study presents an integrated energy management strategy for cost optimization in multi-energy community microgrids (MGs). The proposed approach combines storage-based peak shaving, economic dispatch of diesel generators, and efficient utilization of renewable energy sources to enhance energy management in community MGs. The efficacy of the energy management system (EMS) was validated through a simulation case study for a rural Australian community. The results demonstrate that the proposed EMS effectively reduces the peak energy demand by up to 43%, lowers operational costs by 84.63% (from $189,939/year to $29,188/year), and achieves a renewable energy utilization of 92.3%, up from 47.8% in the base system. Furthermore, the levelized cost of energy was reduced by 14.21% to $0.163/kWh. The strategy ensures an uninterrupted power supply during grid outages by utilizing DGs and battery energy storage systems. The environmental benefits included a 196.4% reduction in CO2 emissions and 100% reductions in CO, unburned hydrocarbons, and particulate matter. These findings validate the feasibility of the proposed EMS in achieving cost-effective, reliable, and sustainable energy management in community MGs. These findings contribute to the field by introducing a novel approach and demonstrating the practical feasibility of multi-energy MGs.

Integrated Energy Management for Operational Cost Optimization in Community Microgrids

TL;DR

The paper addresses cost-effective energy management for multi-energy community microgrids by proposing an integrated EMS that combines storage-based peak shaving with economic dispatch of diesel generators and grid-interaction strategies. The approach is validated via a rural Australian case study using MATLAB and HOMER Pro, showing substantial improvements: a 43% reduction in peak demand, a 14.21% lower LCOE, an 84.63% drop in operating costs, and renewable utilization rising to 92.3%, alongside a 196.4% reduction in CO2 emissions. The results demonstrate improved reliability during grid outages and near-doubling of renewable usage, underscoring the practical feasibility of deploying such EMS in community MGs. The work highlights the potential for cost-effective, resilient, and environmentally friendly energy management in rural and underserved settings, while calling for further analyses on economic scalability and urban applicability.

Abstract

This study presents an integrated energy management strategy for cost optimization in multi-energy community microgrids (MGs). The proposed approach combines storage-based peak shaving, economic dispatch of diesel generators, and efficient utilization of renewable energy sources to enhance energy management in community MGs. The efficacy of the energy management system (EMS) was validated through a simulation case study for a rural Australian community. The results demonstrate that the proposed EMS effectively reduces the peak energy demand by up to 43%, lowers operational costs by 84.63% (from 29,188/year), and achieves a renewable energy utilization of 92.3%, up from 47.8% in the base system. Furthermore, the levelized cost of energy was reduced by 14.21% to $0.163/kWh. The strategy ensures an uninterrupted power supply during grid outages by utilizing DGs and battery energy storage systems. The environmental benefits included a 196.4% reduction in CO2 emissions and 100% reductions in CO, unburned hydrocarbons, and particulate matter. These findings validate the feasibility of the proposed EMS in achieving cost-effective, reliable, and sustainable energy management in community MGs. These findings contribute to the field by introducing a novel approach and demonstrating the practical feasibility of multi-energy MGs.

Paper Structure

This paper contains 16 sections, 1 equation, 3 figures, 4 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Methods
  3. Test MG Design
  4. Proposed EMS
  5. PS Operation
  6. Grid Interaction Operations
  7. Case Study
  8. Simulation Setup
  9. Scenario-Based Performance Evaluation
  10. Results and Discussion
  11. Performance Evaluation
  12. Energy Storage Utilization
  13. Economic Benefits
  14. Environmental Benefits
  15. Scenario Analysis
  16. ...and 1 more sections

Figures (3)

  • Figure 1: A Model of the Proposed MG Framework.
  • Figure 2: Comprehensive overview of MG energy dynamics over a 24-hour period: (a) Load profile in conjunction with PV, wind and BESS generation profiles, showing renewable integration. (b) Comparison of original and managed load profiles, demonstrating the impact of EM strategies, including battery storage operations. (c) DG output, illustrating its role in stabilizing the MG during periods of low renewable generation. (d) Grid interactions depicting both import and export activities, reflecting the MG's capacity to adapt to fluctuating energy demands and supply conditions.
  • Figure 3: Analysis of battery and grid interactions over time. (a) Battery SOC depicting the percentage of charge throughout the simulation period. (b) Battery charging and discharging rates indicating power inflow and outflow from the battery. (c) Net battery power contribution illustrating the balance between charging and discharging. (d) Comparison of total grid demand and battery contribution, demonstrating the battery's role in meeting energy requirements.