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Optimal micro-grid battery scheduling within a comprehensive smart pricing scheme.

Mohammed Ashraf Ali1, Ahmad H Besheer2, Hassan M Emara3

  • 1Electrical Power Engineering Department, Faculty of Engineering, Cairo University, Giza, Egypt. m.a.elgendy@hotmail.com.

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Summary
This summary is machine-generated.

This study presents a new method for scheduling battery energy storage systems (BESS) in microgrids (MGs) to maximize revenue and minimize battery aging costs. The approach achieves significant cost savings and enhances operational efficiency.

Keywords:
Demand chargeDynamic programmingLiFePO4 degradationOptimal scheduleSmart grid

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Area of Science:

  • * Energy Systems Engineering
  • * Electrical Engineering
  • * Renewable Energy Integration

Background:

  • * Existing literature inadequately addresses the dual challenge of optimizing battery operating revenue and mitigating battery aging costs in microgrids.
  • * Microgrids (MGs) with smart grid attributes require sophisticated scheduling for battery energy storage systems (BESS) to manage fluctuating energy prices and demand charges.
  • * Battery degradation significantly impacts the long-term economic viability of BESS in MGs.

Purpose of the Study:

  • * To introduce a novel cost-benefit approach for scheduling BESS within smart grid-enabled MGs.
  • * To develop a high-speed BESS scheduling optimization algorithm that integrates a LiFePO4 battery degradation cost model.
  • * To enhance MG operational efficiency and achieve substantial cost savings by optimizing BESS operations.

Main Methods:

  • * Development of a comprehensive cost-benefit analysis framework for BESS scheduling.
  • * Application of dynamic programming to create a high-speed BESS scheduling optimization algorithm.
  • * Incorporation of a LiFePO4 battery degradation cost model into the optimization algorithm.
  • * Utilization of day-ahead forecasts for MG load profiles and photovoltaic output power.

Main Results:

  • * Achieved substantial monthly operational cost savings for the MG, ranging from 33.6% to 94.8%.
  • * Developed a BESS scheduling optimization algorithm with a fine-grained sampling interval of nine minutes and execution time under one minute.
  • * Demonstrated the algorithm's ability to predict optimal BESS power profiles a day in advance.
  • * Showcased real-time adaptability of BESS scheduling to dynamic grid fluctuations.

Conclusions:

  • * The proposed cost-benefit approach effectively optimizes BESS scheduling in MGs, balancing revenue generation with aging cost mitigation.
  • * The developed high-speed algorithm provides significant operational cost savings and enhances MG efficiency.
  • * This approach offers a superior solution compared to existing methods for BESS management in smart grid environments.