An intelligent incentive-based demand response program for exhaustive environment constrained techno-economic...

Area of Science:

• Energy Systems Engineering
• Environmental Science
• Optimization Theory

Background:

• Microgrid energy management focuses on cost-effective scheduling of distributed energy resources, often using load-shifting techniques.
• Current approaches prioritize efficiency and economic factors, with pollutant reduction rarely being a primary objective.
• Existing methods often incorporate complex constraints like Plug-in Hybrid Electric Vehicle (PHEV) scheduling and battery lifespan assessment.

Purpose of the Study:

• To introduce an incentive-based demand response (IBDR) policy to reduce emissions and generation costs in microgrids.
• To promote load curtailment during peak hours through customer incentives.
• To achieve environmental and economic sustainability by balancing cost and emission reduction.

Main Methods:

• Implementation of an incentive-based demand response (IBDR) policy.
• Analysis of six scenarios with varying grid participation and electricity market pricing, including demand response programs.
• Utilization of the differential evolution algorithm for optimization.
• Application of a weighted economic emission dispatch algorithm.

Main Results:

• The IBDR strategy demonstrated suitability and effectiveness, leading to cost savings.
• Generating costs decreased by 10-13% with IBDR implementation.
• A 6-8% reduction in peak demand and a 4-5% improvement in load factor were observed.

Conclusions:

• The proposed IBDR strategy effectively reduces microgrid generation costs and emissions.
• The policy successfully balances economic and environmental objectives, enhancing microgrid sustainability.
• The study confirms the positive impact of IBDR on key performance indicators like peak demand and load factor.