Optimization of electricity consumption using thermal and battery energy storage systems in smart buildings

Abstract

Due to the variable electricity consumption pattern in buildings during the day, energy storage systems (ESS) are considered to be employed to store the energy and release it in peak hours to achieve peak load shaving, save cost, provide the demand load, and increase the power quality and stability. However, based on the limited capacity of ESSs and their limitations, it is challenging to meet peak load shaving criteria determined by utility companies. The novelty of this thesis is the employment of thermal energy storage system (TESS) alongside battery energy storage system (BESS) to compensate for BESS limitations and define the optimal charging/discharging schedule of TESS and BESS by optimization approaches. The proposed power management unit uses a thermal energy storage system (TESS) and a battery energy storage system (BESS) to store the energy in off-peak periods and discharge it in high load demands. The optimal charging/discharging schedule of TESS and BESS has an important role in achieving complete peak load shaving. Therefore, the charging/discharging schedules of TESS and BESS are formulated as an optimization problem. In the first framework, particle swarm optimization (PSO) is employed to obtain the optimal schedule due to its computational time efficiency. The mathematical approach is also applied to prove the convexity of the problem and the uniqueness of the solution. In the second framework and to validate the optimal solution by PSO, reinforcement learning (RL) is employed and results are compared. In this context, the optimization problem is formulated as Markov decision process (MDP) and then solved by Q-learning algorithm. To provide power reliability and stability, all types of loads including electrical plugged and thermal loads are considered to be supported by ESS during peak periods. Moreover, to model the building components and loads, grey-box modeling is adopted. The efficacy of the proposed framework is demonstrated by using real electric power consumption data of a campus building. Results show these proposed frameworks are capable of defining optimal charging/discharging of ESSs, saving cost, compensating for BESS limitations, and reducing its capacity.

Date	4 May 2023
Original language	American English
Awarding Institution	École de technologie supérieure
Supervisor	Louis-A. Dessaint (Supervisor)