We study a stochastic model for the installation of renewable energy capacity under demand uncertainty and jump driven dynamics. The system is governed by a multidimensional Ornstein–Uhlenbeck (OU) process driven by a subordinator, capturing abrupt variations in renewable generation and electricity load. Installation decisions are modeled through control actions that increase capacity in response to environmental and economic conditions. We consider two distinct solution approaches. First, we implement a structured threshold based control rule, where capacity is increased proportionally when the stochastic capacity factor falls below a fixed level. This formulation leads to a nonlinear partial integro-differential equation (PIDE), which we solve by reformulating it as a backward stochastic differential equation with jumps. We extend the DBDP solver in Huré et al. (2020) to the pure jump setting, employing a dual neural network architecture to approximate both the value function and the jump sensitivity. Second, to benchmark the performance of the first approach, we propose a deep control algorithm that directly learns a state and time dependent feedback threshold policy by minimizing the expected cost functional using neural networks. This approach preserves the threshold based structure while allowing the threshold to adapt dynamically to the evolving system state, enabling more flexible and responsive interventions. Numerical experiments on calibrated data highlight the strengths of both methods. While the threshold based BSDE approach offers interpretability and tractability, the deep control strategy achieves improved performance through flexibility in capacity allocation. Together, these tools provide a robust framework for decision support in long term renewable energy expansion under uncertainty.