Strategic Integration of Distributed Generation in a Deregulated Power Market: An Agent-Based Approach

Abstract

In this study, the effects of agent-based modeling on deregulated electricity markets using AMES (Artificial Market Environment Simulator) have been investigated. It is shown how the Independent System Operator (ISO) intervenes to control market dynamics and maintain supply-demand balance through locational marginal pricing (LMP). This study uses a model called Agent-Based Model (ABM) to simulate generation companies (GenCo’s) and load-serving entities (LSEs) in order to enhance market efficiency and stability. The methodology encompasses both static and dynamic testing of GenCo’s, integrating learning algorithms to optimize bidding strategies. The results showed that allowing generators to learn from previous experiences enabled them to make better offers in real-time, thus leading to higher profits. Furthermore, it shows that where DG units are located in the power system as well as their size can significantly raise generator earnings. Finally, when generators have the ability for learning, their profits increased considerably under various scenarios compared with those examples where learning cannot be applied, which demonstrates how helpful strategic learning could be for market participants. Moreover, it looks into the consequences of power system faults such as line faults; it demonstrates that distributed generation can mitigate the impacts of power system interruptions and uphold load flow in order to enhance overall market resilience. It draws attention to the fact that agent-based models are a powerful analytical technique for assessing and enhancing deregulated electricity markets. Additionally, political and operational choices obtained from dynamic and strategic modeling approaches like these will be good for electrical power systems.