Optimal Electricity Tariff Selection Considering Renewable Energy Sources, Electric Vehicles and Controllable Home Appliances

This paper investigates the capabilities of a decision support system for optimal electricity tariff selection considering renewable energy sources, electric vehicles, and controllable home appliances. The developed model is based on a standard mixed-integer linear programming (MILP) approach, which considers different scenarios for a typical smart home. The tariff selection strategy is applied over a selected week to reflect varying electricity tariffs in Portugal. The model accounts for time-variant energy consumption at a 15-minute resolution, consistent with smart meter recordings and real-world electricity pricing structures. The proposed system enables residential users to make informed decisions by optimizing electricity costs while considering the flexibility of energy resources within smart homes. By simulating various operational scenarios, the model demonstrates the effectiveness of MILP in identifying the most cost-efficient tariff plans under dynamic consumption patterns. The results highlight the potential for such a tool to support energy cost reduction, improve demand-side management, and contribute to smarter energy usage.