Planair (Switzerland)

The decentralisation and unpredictability of new renewable energy sources require rethinking our energy system. Data-driven approaches, such as reinforcement learning (RL), have emerged as new control strategies for operating these systems, but they have not yet been applied to system design. This paper aims to bridge this gap by studying the use of an RL-based method for joint design and control of a real-world PV and battery system. The design problem is first formulated as a mixed-integer linear programming problem (MILP). The optimal MILP solution is then used to evaluate the performance of an RL agent trained in a surrogate environment designed for applying an existing data-driven algorithm. The main difference between the two models lies in their optimization approaches: while MILP finds a solution that minimizes the total costs for a one-year operation given the deterministic historical data, RL is a stochastic method that searches for an optimal strategy over one week of data on expectation over all weeks in the historical dataset. Both methods were applied on a toy example using one-week data and on a case study using one-year data. In both cases, models were found to converge to similar control solutions, but their investment decisions differed. Overall, these outcomes are an initial step illustrating benefits and challenges of using RL for the joint design and control of energy systems.

Sorption of water vapour in hygroscopic porous materials is associated with latent heat release and absorption. This phenomenon should be taken into account to achieve a better understanding of the coupled transfer of vapour and heat in hygroscopic porous materials. In this paper, water vapour adsorption and desorption in the longitudinal direction of spruce samples are studied. Neutron radiography is used to measure changes in moisture content and wireless thermocouples are used to measure temperature changes. During the adsorption and desorption experiments, large changes in moisture content and temperature are observed. A hygrothermal model is developed to simulate vapour and heat transfer during adsorption and desorption experiments. Generally, the numerical model predicts well the measured moisture and temperature changes. The large moisture change is due to the low vapour resistance factor in the longitudinal direction of the spruce samples. The latent heat associated with vapour adsorption is the cause of the large temperature changes. It was found that vapour permeability affects both vapour and heat transfer, while thermal conductivity only affects heat transfer.