A swarm-intelligent based load-shifting strategy for clean and economic microgrid operation

Area of Science:

• Electrical Engineering
• Energy Systems
• Artificial Intelligence

Background:

• Microgrid load demand fluctuates, leading to time-of-use (TOU) electricity pricing.
• Demand Side Management (DSM) is an economic strategy to manage load during peak and valley hours.
• Optimizing microgrids involves balancing generation costs and environmental impact.

Purpose of the Study:

• To propose a novel DSM approach using hybrid intelligence for LV grid-connected microgrids.
• To achieve a compromise between minimizing generation costs and reducing pollutant emissions from DERs.
• To evaluate the effectiveness of the DSM approach under various grid strategies and pricing models.

Main Methods:

• A hybrid intelligence technique was developed for the DSM approach.
• Five distinct scenarios examined different grid involvement strategies, power market pricing, and demand response mechanisms.
• Environmental constrained economic dispatch was employed to balance cost and emission.

Main Results:

• The proposed DSM technique demonstrated applicability and effectiveness in cost reduction.
• Customer participation of 30-40% in DSM led to a 10-13% reduction in generation costs.
• The environmental constrained economic dispatch provided a superior balance between generation cost and emissions.

Conclusions:

• The developed DSM approach is suitable for LV grid-connected microgrids.
• The hybrid intelligence technique effectively reduces generation costs and environmental impact.
• The proposed DSM policy, validated by statistical analysis, offers a superior solution compared to existing methods.