Energy assistants for prosumers to enable trading strategies on local electricity markets

Home assistants gained more attention as they provide guidance and can also be active in energy communities and assist prosumers by controlling appliances. Graphical interface and interaction with appliances via vocal commands have transformed home assistants into valuable tools. In this paper, we propose a methodology embedded within an Energy Assistant (EA) that incorporates algorithms for forecasting generation availability, recommending optimal time slots for load scheduling, optimally scheduling appliances, setting trading strategies in Local Electricity Markets (LEM), and controlling the operation of appliances. EA provides decision support for trading on LEM using reinforcement learning agents trained to optimize the price and quantities to maximize the trading probability and minimize the cost. The EA algorithms are embedded as a home assistant into an Alexa skill that has multiple intents corresponding to user requests. For simulations, 114 apartments, 7 houses with small Photovoltaic (PV) systems and a large PV system of 500 kW are considered. When 51 % of the households use EA, Self-Consumption Index increases from 0.72 to 0.85 and Grid Dependence Index reduces from 56 to 32. The average annual cost per apartment decreased from €96.62 to €76.60 after using EA for trading, indicating savings of 37.80 %.