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Optimal operation of distributed generation and storage systems in microgrids under real-time pricing using

Emad M Ahmed1, Mehrdad Ahmadi Kamarposhti2, Hammad Alnuman3

  • 1Department of Electrical Engineering, College of Engineering, Jouf University, Sakaka, 72388, Saudi Arabia. emamahmoud@ju.edu.sa.

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Biogeography and genetic algorithms optimize microgrid energy management for cost savings. The biogeography algorithm proved more effective in reducing microgrid expenses and managing energy resources.

Keywords:
BBO algorithmConsumptionCost reductionLoad response programMicrogrid

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Area of Science:

  • Energy Systems Engineering
  • Artificial Intelligence
  • Optimization Techniques

Background:

  • Microgrids are increasingly incorporating distributed generation (DG) from electrical and thermal sources.
  • Effective energy management is crucial for optimizing DG and storage utilization, reducing consumption, and justifying investments.
  • Real-time pricing (RTP) necessitates advanced strategies for microgrid energy management.

Purpose of the Study:

  • To optimize the utilization of electrical and thermal DG resources within a microgrid to achieve cost savings under real-time pricing.
  • To compare the effectiveness of biogeography algorithms and genetic algorithms in microgrid energy management.
  • To reduce the overall energy supply costs for the microgrid.

Main Methods:

  • Utilized biogeography algorithms and genetic algorithms for optimizing DG resource allocation.
  • Incorporated electrical DG sources (solar panels, diesel generators, Battery energy storage) and thermal sources (boiler heat).
  • Included a combined heat and power (CHP) system for simultaneous heat and electrical energy generation.

Main Results:

  • The biogeography algorithm demonstrated superior performance in microgrid energy resource management.
  • The biogeography algorithm achieved greater cost reductions in the microgrid compared to the genetic algorithm.
  • Both algorithms aimed to minimize energy supply from the microgrid, with biogeography algorithms showing better results.

Conclusions:

  • Biogeography algorithms are highly effective for optimizing energy management in microgrids with diverse DG sources.
  • The proposed biogeography algorithm offers a more cost-effective solution for microgrid operation than genetic algorithms.
  • Load response strategies are essential for efficient thermal and electrical energy management in microgrids.