Intelligent energy optimization in park-wide farming considering user's preferences

Area of Science:

• Energy Systems Engineering
• Artificial Intelligence
• Consumer Behavior

Background:

• The integration of agricultural production and household electricity demands intelligent solutions for optimizing power consumption.
• User participation in power management is crucial for grid stability and efficiency.

Purpose of the Study:

• To develop a multi-objective household intelligent power consumption optimization model.
• To enhance user comfort and minimize electricity expenditure by aligning with personal habits.

Main Methods:

• Established operating constraints for interruptible and non-interruptible loads based on appliance characteristics.
• Constructed an expenditure model incorporating photovoltaic surplus electricity sales.
• Developed a user comfort model using a three-layer index system and analytic hierarchy process for preference coefficients.
• Applied a multi-objective particle swarm optimization algorithm for optimization.

Main Results:

• The proposed model effectively balances economic factors and user comfort in household electricity consumption.
• Simulations demonstrated significant reductions in expenditure and increases in comfort levels during summer and winter.
• Achieved a 26.0% expenditure minimization and 27.5% comfort level increase in winter simulations.

Conclusions:

• The multi-objective optimization model provides a viable approach for intelligent household power management.
• Integrating user preferences and economic considerations leads to improved energy efficiency and user satisfaction.
• The model's effectiveness is validated through seasonal simulations, highlighting its practical applicability.