Energy management system for multi interconnected microgrids during grid connected and autonomous operation modes...

Area of Science:

• Electrical Engineering
• Computer Science
• Artificial Intelligence

Background:

• Power systems face challenges in performance and efficiency.
• Integration of distributed energy resources (DERs) offers a solution.
• Microgrids (MGs) can operate in grid-connected and autonomous modes, requiring sophisticated management.

Purpose of the Study:

• To enhance power system grid performance and efficiency.
• To develop an artificial intelligence (AI) based approach for economic dispatch and load management in linked microgrids.
• To reduce the operational costs of microgrids.

Main Methods:

• A day-ahead scheduling method for optimal set points of energy sources in MGs.
• A load management approach involving shifting controllable loads.
• Utilizing the one-to-one based optimizer (OOBO), an advanced meta-heuristic method, for optimization.

Main Results:

• The proposed methodology enables economical distribution in linked microgrids.
• Optimized scheduling and load shifting strategies were implemented.
• Daily operational costs were reduced by 1.6% in grid-connected mode and 0.47% in islanded operation mode.

Conclusions:

• The AI-based energy management strategy enhances system performance and efficiency.
• The OOBO algorithm effectively handles complex optimization challenges in energy scheduling and load shifting.
• The study presents a viable and efficient methodology for managing linked microgrids with DERs.