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.