Combined Peak Reduction and Self-Consumption Using Proximal Policy Optimization

Autor:	Peirelinck, Thijs, Hermans, Chris, Spiessens, Fred, Deconinck, Geert
Rok vydání:	2022
Předmět:	Electrical Engineering and Systems Science - Systems and Control Computer Science - Machine Learning
Druh dokumentu:	Working Paper
DOI:	10.1016/j.egyai.2023.100323
Popis:	Residential demand response programs aim to activate demand flexibility at the household level. In recent years, reinforcement learning (RL) has gained significant attention for these type of applications. A major challenge of RL algorithms is data efficiency. New RL algorithms, such as proximal policy optimisation (PPO), have tried to increase data efficiency. Additionally, combining RL with transfer learning has been proposed in an effort to mitigate this challenge. In this work, we further improve upon state-of-the-art transfer learning performance by incorporating demand response domain knowledge into the learning pipeline. We evaluate our approach on a demand response use case where peak shaving and self-consumption is incentivised by means of a capacity tariff. We show our adapted version of PPO, combined with transfer learning, reduces cost by 14.51% compared to a regular hysteresis controller and by 6.68% compared to traditional PPO. Comment: Submitted to Elsevier Energy and AI
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2211.14831 Zobrazit plný text záznamu View this record from Arxiv