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Showing 1 results for Hourly Acceptance Function
Agent-Based Modeling of Day-Ahead Real Time Pricing in a Pool-Based Electricity Market
Sh. Yousefi, M. Parsa Moghaddam, V. Johari Majd,
Volume 7, Issue 3 (9-2011)
Abstract
In this paper, an agent-based structure of the electricity retail market is presented based on which day-ahead (DA) energy procurement for customers is modeled. Here, we focus on operation of only one Retail Energy Provider (REP) agent who purchases energy from DA pool-based wholesale market and offers DA real time tariffs to a group of its customers. As a model of customer response to the offered real time prices, an hourly acceptance function is proposed in order to represent the hourly changes in the customer’s effective demand according to the prices. Here, Q-learning (QL) approach is applied in day-ahead real time pricing for the customers enabling the REP agent to discover which price yields the most benefit through a trial-and-error search. Numerical studies are presented based on New England day-ahead market data which include comparing the results of RTP based on QL approach with that of genetic-based pricing.
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© 2022 by the authors. Licensee IUST, Tehran, Iran. This is an open access journal distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license.