TY - GEN
T1 - Optimal Energy Scheduling of Flexible Industrial Prosumers via Reinforcement Learning
AU - van den Bovenkamp, Nick
AU - Giraldo, Juan S.
AU - Salazar Duque, Edgar Mauricio
AU - Vergara, Pedro P.
AU - Konstantinou, Charalambos
AU - Palensky, Peter
N1 - KAUST Repository Item: Exported on 2023-08-14
PY - 2023/6/25
Y1 - 2023/6/25
N2 - This paper introduces an energy management system (EMS) aiming to minimize electricity operating costs using reinforcement learning (RL) with a linear function approximation. The proposed EMS uses a Q-learning with tile coding (QLTC) algorithm and is compared to a deterministic mixed-integer linear programming (MILP) with perfect forecast information. The comparison is performed using a case study on an industrial manufacturing company in the Netherlands, considering measured electricity consumption, PV generation, and wholesale electricity prices during one week of operation. The results show that the proposed EMS can adjust the prosumer's power consumption considering favorable prices. The electricity costs obtained using the QLTC algorithm are 99% close to those obtained with the MILP model. Furthermore, the results demonstrate that the QLTC model can generalize on previously learned control policies even in the case of missing data and can deploy actions 80% near to the MILP's optimal solution.
AB - This paper introduces an energy management system (EMS) aiming to minimize electricity operating costs using reinforcement learning (RL) with a linear function approximation. The proposed EMS uses a Q-learning with tile coding (QLTC) algorithm and is compared to a deterministic mixed-integer linear programming (MILP) with perfect forecast information. The comparison is performed using a case study on an industrial manufacturing company in the Netherlands, considering measured electricity consumption, PV generation, and wholesale electricity prices during one week of operation. The results show that the proposed EMS can adjust the prosumer's power consumption considering favorable prices. The electricity costs obtained using the QLTC algorithm are 99% close to those obtained with the MILP model. Furthermore, the results demonstrate that the QLTC model can generalize on previously learned control policies even in the case of missing data and can deploy actions 80% near to the MILP's optimal solution.
UR - http://hdl.handle.net/10754/693548
UR - https://ieeexplore.ieee.org/document/10202699/
U2 - 10.1109/powertech55446.2023.10202699
DO - 10.1109/powertech55446.2023.10202699
M3 - Conference contribution
BT - 2023 IEEE Belgrade PowerTech
PB - IEEE
ER -
