Open AccessDecentralised demand response market model based on reinforcement learning
Author(s) -
ShafieKhah Miadreza,
Talari Saber,
Wang Fei,
Catalão João P.S.
Publication year - 2020
Publication title -
iet smart grid
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.612
H-Index - 11
ISSN - 2515-2947
DOI - 10.1049/iet-stg.2019.0129
Subject(s) - reinforcement learning , demand response , market clearing , clearing , electricity , operator (biology) , computer science , set (abstract data type) , consumption (sociology) , electricity market , microeconomics , operations research , industrial organization , economics , artificial intelligence , engineering , social science , biochemistry , chemistry , programming language , finance , repressor , sociology , transcription factor , electrical engineering , gene
A new decentralised demand response (DR) model relying on bi‐directional communications is developed in this study. In this model, each user is considered as an agent that submits its bids according to the consumption urgency and a set of parameters defined by a reinforcement learning algorithm called Q‐learning. The bids are sent to a local DR market, which is responsible for communicating all bids to the wholesale market and the system operator (SO), reporting to the customers after determining the local DR market clearing price. From local markets’ viewpoint, the goal is to maximise social welfare. Four DR levels are considered to evaluate the effect of different DR portions in the cost of the electricity purchase. The outcomes are compared with the ones achieved from a centralised approach (aggregation‐based model) as well as an uncontrolled method. Numerical studies prove that the proposed decentralised model remarkably drops the electricity cost compare to the uncontrolled method, being nearly as optimal as a centralised approach.