An Optimal Battery-Free Approach for Emission Reduction by Storing Solar Surplus in Building Thermal Mass

Abstract

Decarbonization in buildings calls for advanced control strategies that coordinate on-site renewables, grid electricity, and thermal demand. Literature approaches typically rely on demand side management strategies or on active energy storage, like batteries. However, the first solution often neglects carbon-aware objectives, and could lead to grid overload issues, while batteries entail environmental, end-of-life, and cost concerns. To overcome these limitations, we propose an optimal, carbon-aware optimization strategy that exploits the building's thermal mass as a passive storage, avoiding dedicated batteries. Specifically, when a surplus of renewable energy is available, our strategy computes the optimal share of surplus to store by temporarily adjusting the indoor temperature setpoint within comfort bounds. Thus, by explicitly accounting for forecasts of building energy consumption, solar production, and time-varying grid carbon intensity, our strategy enables emissions-aware load shifting while maintaining comfort. We evaluate the approach by simulating three TRNSYS models of the same system with different thermal mass. In all cases, the results show consistent reductions in grid electricity consumption with respect to a baseline that does not leverage surplus renewable generation. These findings highlight the potential of thermal-mass-based control for building decarbonization.

Figures (13)

• Figure 1: $\text{CO}_2$ Emissions from the building sector from 2010 to 2023 (left) and the share of buildings in global energy- and process-related emissions in 2023 (right). Source: reproduced from UNEPGlobalABC2024BeyondFoundations.
• Figure 2: Flowchart illustrating the stepwise development, implementation, and application of the proposed optimization strategy.
• Figure 3: Plant of each TRNSYS model. This Figure provides an overview of the energy plant implemented in each TRNSYS room.
• Figure 4: Heating and cooling model identification. This figure shows two representative validation periods, with winter data on the left and summer data on the right. The first two rows report the model's inputs, common to the three room configurations. The remaining rows compare the heating and cooling power predicted by the TRNSYS model ($P$) with that predicted by the corresponding surrogate model ($P_{\mathrm{pred}}$).
• Figure 5: Sensitivity analysis on hyperparameters. This figure shows how total yearly emissions reduction (left) and maximum daily setpoint deviation (right) change as a function of the sampling time and the prediction horizon for each room configuration. Optimal hyperparameters set is marked by a yellow star.