Combined DR Pricing and Voltage Control using Reinforcement Learning based multi-agents and Load Forecasting
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Journal Title
Journal ISSN
Volume Title
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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Date
2022
Department
Ghulam Ishaq Khan Institute of Engineering Sciences and Technology
Department of Electrical Engineering and Automation
Power Systems and High Voltage Engineering
Department of Electrical Engineering and Automation
Power Systems and High Voltage Engineering
Major/Subject
Mcode
Degree programme
Language
en
Pages
11
130839-130849
130839-130849
Series
IEEE Access, Volume 10
Abstract
The demand for energy around the world continues to increase at a very high rate. To sufficiently supply this high demand, it is imperative to employ efficient methods so that the total costs for fulfilling such high demand in energy are minimized. To achieve this ambitious goal, this paper proposes a multi-agent reinforcement learning system for time of use pricing based combined demand response and voltage control. For this purpose, a long short term memory network is employed for day-ahead load forecasting in order to remove future uncertainties. The Q-learning algorithm is used which is a model free algorithm and hence, doesn't require the agent(s) to have prior knowledge of the environment. The role of reinforcement learning in this work is very important since it allows the agent(s) to determine their respective optimal behavior(s) autonomously without explicit training by the end user. To allow effective cooperation among multiple agents, each household is controlled by its own agent, whereas all the household agents are directed by a master agent or service provider. Accordingly, the voltage control agent serves the purpose of checking voltage level violations in the system and removing them through optimal decision making. The proposed system yields very good results, whereby, not only is the overall cost of electricity reduced, but voltage level violations are also removed from the entire system. The implementation of this mechanism reduces the total average aggregated load demand from 5.23 kW to 3.86 kW, while reducing the total aggregated average cost from 94.01 Rs to 60.80 Rs, thanks to the proposed effective multi-agent based system.Description
Publisher Copyright: Author
Keywords
Costs, demand response, Home appliances, Load forecasting, Load modeling, long short term memory, multi-agent system, Pricing, Reinforcement learning, Voltage control, voltage control
Citation
Khan , D A , Arshad , A , Lehtonen , M & Mahmoud , K 2022 , ' Combined DR Pricing and Voltage Control using Reinforcement Learning based multi-agents and Load Forecasting ' , IEEE Access , vol. 10 , pp. 130839-130849 . https://doi.org/10.1109/ACCESS.2022.3228836
