Roth-Erev Reinforcement Learning Approach for Smart Generator Bidding towards Long Term Electricity Market Operation Using Agent Based Dynamic Modeling. Issue 3 (7th February 2020)

• Record Type: Journal Article
• Authors: Purushothaman, Kiran; Chandrakala, Vijaya
• Is Part Of: Electric power components and systems. Volume 48:Issue 3(2020)
• Page Start: 256
• Page End: 267
• Publication Date: 2020-02-07
• Subjects: deregulated electricity market -- wholesale market -- Independent System Operator (ISO) -- agent based modeling -- Locational Marginal Price (LMP) -- Generation Company (GenCo) -- Load Serving Entity (LSE) -- Variant Roth-Erev (VRE) reinforcement learning -- DC Optimal Power Flow (DCOPF) -- Java Reinforcement Learning Module (JReLM); Electric machinery -- Periodicals; 621.3104205
• DOI: 10.1080/15325008.2020.1758840
• Languages: English
• ISSNs: 1532-5008

Abstract

Abstract: This article focuses towards agent based implementation of restructured power market with learning capabilities for generators. The market model considered for the analysis is wholesale market and through learning capability of the generator will confront self-sufficient smart generator to perform optimal bidding for a long term. The Agent Based Modeling for Electricity Systems (AMES) permits dynamic testing with learning traders. The whole electricity market is managed by the Independent System Operator (ISO) which computes the hourly Locational Marginal Price (LMP) and commitments of power exchange for day-ahead market operation. The bidding strategy of generators is trained using stochastic reinforcement learning algorithm (JReLM) developed under Java platform. For the analysis, agents are classified as market traders and Independent System Operator (ISO) linking to the IEEE 5-Bus system. The analysis is further extended to an IEEE-30 Bus system and the results demonstrate great potential of the agent based computational ability in the electricity market to help the generators to exercise more market power with optimal bidding than normal generators.