Behavior-aware energy management in microgrids using quantum-classical hybrid algorithms under social and demand...

Area of Science:

• Energy Systems Engineering
• Computational Optimization
• Behavioral Economics

Background:

• Modern microgrids face complexity due to unpredictable demand, distributed renewable energy sources, and user behavior.
• Existing optimization methods struggle with the dynamic and stochastic nature of these systems.

Purpose of the Study:

• To develop a hybrid quantum-classical optimization framework for demand-side energy management in microgrids.
• To integrate behavioral modeling principles from Social Cognitive Theory to enhance microgrid resilience and flexibility.
• To support distributed decision-making and adaptive prosumer behavior.

Main Methods:

• A hybrid framework combining Quantum Annealing and the NSGA-III algorithm for multi-objective optimization.
• Incorporation of Social Cognitive Theory principles (imitation, self-efficacy, social reinforcement) into the optimization model.
• Testing the framework on a peer-to-peer microgrid network case study.

Main Results:

• Demonstrated effectiveness in enhancing energy efficiency and reducing peak demand.
• Improved operational resilience of the microgrid network.
• Quantum-inspired model shows superior scalability and robustness compared to traditional methods like Mixed-Integer Programming.

Conclusions:

• The hybrid quantum-classical approach with behavioral modeling offers a powerful tool for intelligent microgrid control.
• Integrating quantum-inspired optimization with behavioral science advances socially-responsive energy management.
• This framework effectively handles trade-offs between cost, reliability, and demand response in complex microgrids.