Multi-objective optimization of gamified demand response for PV-integrated microgrids: a novel NSGA-III framework...

Area of Science:

• Energy Systems Engineering
• Renewable Energy Technologies
• Behavioral Economics

Background:

• Residential photovoltaic (PV) systems in microgrids increase renewable energy self-consumption but face challenges due to solar intermittency and demand-supply mismatches.
• Traditional demand response (DR) programs struggle with user engagement due to limited financial incentives and dynamic pricing effectiveness.

Purpose of the Study:

• To propose a novel gamification-driven demand response framework for PV-integrated microgrids.
• To optimize operational cost, renewable energy utilization, user participation, and load-shifting comfort simultaneously.
• To integrate behavioral adaptation modeling for a behaviorally-grounded microgrid scheduling approach.

Main Methods:

• Formulated a multi-objective optimization problem to balance competing objectives.
• Employed the Non-dominated Sorting Genetic Algorithm III (NSGA-III) for solving the optimization problem.
• Integrated behavioral adaptation modeling to capture dynamic household responses to gamification incentives.

Main Results:

• The proposed framework effectively optimizes operational costs, PV self-consumption, and user participation.
• Demonstrated improved load-shifting comfort preservation compared to conventional DR mechanisms.
• Successfully incorporated evolving user behavior, dynamic incentives, and social influence into the DR strategy.

Conclusions:

• Gamification-driven demand response, incorporating behavioral modeling, offers a more effective approach to demand-side flexibility in PV microgrids.
• This framework enhances user engagement and optimizes microgrid operations through a blend of financial and psychological incentives.
• The study highlights the potential of behaviorally-grounded strategies for sustainable and efficient microgrid management.