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

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.