Optimizing Appliance Scheduling for Solar Energy Management Using Metaheuristic Algorithms

2026-06-11

AI Summary

This study addresses the mismatch between solar power generation and household electricity demand, which limits renewable energy utilization, compounded by user behavior that complicates appliance scheduling. To tackle this challenge, the authors propose an optimization framework enabling multi-day continuous scheduling. By integrating Iterated Local Search (ILS) and Simulated Annealing (SA), the approach jointly accounts for solar irradiance forecasts, battery state of charge, inverter constraints, and appliance operational requirements. It dynamically reschedules appliance start times while preserving user convenience and effectively handles carry-over tasks that span consecutive days. Experimental results demonstrate that, in a purely solar-powered setting, the proposed method significantly enhances renewable energy utilization without compromising system feasibility or user experience.

Abstract

Renewable energy is essential for meeting future energy demands; however, solar energy generation, which occurs only during daylight hours often does not align with household consumption patterns. Appliances such as cookers, washing machines, and dryers are typically operated according to user preferred schedules rather than solar energy availability, creating a scheduling optimization problem. The objective is to determine optimal appliance start times to maximize renewable energy utilization while minimizing user inconvenience and adhering to system constraints. This paper presents a metaheuristic approach using Iterated Local Search (ILS) and Simulated Annealing (SA) to optimize appliance start times, while considering appliance operating durations, power consumption, inverter limit, battery state of charge constraints, and solar generation forecasts. Unlike most existing work, the scheduling is extended beyond a single day to accommodate unfinished tasks from previous days (spillover), ensuring operational continuity and enabling sequential operation across multiple days. Experimental results show that the sequential multi-day scheduling framework effectively manages system constraints while ensuring user convenience under exclusive solar generation. These findings also open opportunities for future research on multi-objective trade-offs between investment in equipment of various sizes, return on that investment, and user satisfaction.

Problem

• solar energy management
• appliance scheduling
• renewable energy utilization
• user inconvenience
• multi-day scheduling

Innovation

• metaheuristic algorithms
• multi-day scheduling
• solar energy management
• spillover tasks
• appliance scheduling