Towards integrating renewable electricity generation sources into the grid, an important facilitator is the energy flexibility provided by buildings' thermal inertia. Most of the existing research follows a single-step price- or incentive-based scheme for unlocking the flexibility potential of buildings. In contrast, this paper proposes a novel two-step design approach for better harnessing buildings' energy flexibility. In a first step, a robust optimization model is formulated for assessing the energy flexibility of buildings in the presence of uncertain predictions of external conditions, such as ambient temperature, solar irradiation, etc. In a second step, energy flexibility is activated in response to a feasible demand response (DR) request from grid operators without violating indoor temperature constraints, even in the presence of uncertain external conditions. The proposed approach is tested on a high-fidelity Modelica simulator to evaluate its effectiveness. Simulation results show that, compared with price-based demand-side management, the proposed approach achieves greater energy reduction during peak hours.
@article{arxiv.2312.05108,
title = {Unlocking Energy Flexibility From Thermal Inertia of Buildings: A Robust Optimization Approach},
author = {Yun Li and Neil Yorke-Smith and Tamas Keviczky},
journal= {arXiv preprint arXiv:2312.05108},
year = {2023}
}
Comments
8 pages, accepted to the 62nd IEEE Conference on Decision and Control (CDC 2023)