As the consumer electricity prices rise, European policymakers are increasingly focused on decarbonizing the power grid, which requires homeowners and local administrators to adopt renewable energy sources amidst a complex set of often conflicting objectives and constraints.
As the consumer electricity prices rise, European policymakers are increasingly focused on decarbonizing the power grid, which requires homeowners and local administrators to adopt renewable energy sources amidst a complex set of often conflicting objectives and constraints.
This paper introduces an innovative application of deep reinforcement learning (DRL) for long-term strategic planning of rooftop photovoltaic systems and battery energy storage within the residential sector, aiming to balance environmental and financial objectives considering the ever-evolving system condition and uncertainties inherent in the market.
The problem is modeled as a Markov Decision Process (MDP), facilitating sequential decision-making across 25 annual steps. The DRL environment incorporates a comprehensive set of variables identified through extensive literature review and market analysis. To account for their long-term dynamics, scenarios were simulated using appropriate stochastic and propabilistic processes for agent's training. A policy-based DRL agent is evaluated, exploring various residential and technological scenarios, including three single-family houses, different PV models and various optimisation scopes.
Moreover, a deployment workflow and a user interface are developed to support real-world decision-making applications. Furthermore, a separate DRL model is crafted to simulate battery management system's charging and discharging protocol.
The findings suggest that deep reinforcement learning offers a promising solution for addressing this complex problem. It offers enhanced flexibility in decision-making and helps mitigate investment risks.
