Scheduling deferrable electric appliances in smart homes: a bi-objective stochastic optimization approach

Area of Science:

• Energy Management
• Operations Research
• Artificial Intelligence

Background:

• Cities increasingly rely on efficient electricity services for residential activities.
• Growing household electricity consumption necessitates improved demand-side management strategies.
• Sustainable resource usage requires optimized decision-making in energy consumption.

Purpose of the Study:

• To develop an optimization model for scheduling household deferrable appliances.
• To simultaneously minimize electricity costs and maximize user satisfaction.
• To incorporate a maximum power consumption constraint for households.

Main Methods:

• A stochastic optimization model was formulated to handle variable user preferences.
• Two algorithms were proposed: a simulation-optimization approach and a greedy heuristic.
• The methods were evaluated using real-world data across different household types.

Main Results:

• Both algorithms computed compromise solutions balancing cost and satisfaction.
• The simulation-optimization approach consistently outperformed the greedy heuristic.
• Reasonable computing times were achieved for generating trade-off solutions.

Conclusions:

• The proposed optimization model effectively addresses the trade-off between electricity cost and user satisfaction.
• The simulation-optimization method is a competitive and superior approach for household demand-side management.
• Efficient scheduling enhances sustainable energy usage in urban environments.