Analysis of the Pricing Process in Electricity Market using Multi-Agent Model

Abstract

Many electric utilities worldwide have been forced to change their ways of doing business, from vertically integrated mechanisms to open market systems. We are facing urgent issues about how we design the structures of power market systems. In order to settle down these issues, many studies have been made with market models of various characteristics and regulations. The goal of modeling analysis is to enrich our understanding of fundamental process that may appear. However, there are many kinds of modeling methods. Each has drawback and advantage about validity and versatility. This paper presents two kinds of methods to construct multi-agent market models. One is based on game theory and another is based on reinforcement learning. By comparing the results of the two methods, they can advance in validity and help us figure out potential problems in electricity markets which have oligopolistic generators, demand fluctuation and inelastic demand. Moreover, this model based on reinforcement learning enables us to consider characteristics peculiar to electricity markets which have plant unit characteristics, seasonable and hourly demand fluctuation, real-time regulation market and operating reserve market. This model figures out importance of the share of peak-load-plants and the way of designing operating reserve market.

Economic evaluation of demand‐side energy storage systems by using a multi‐agent‐based electricity market

“…We use a multi-agent model of the electricity market. Multiagent systems make use of multiple agents that work out a group behavior while updating their knowledge, an approach that has already been applied to electricity market modeling [1,10]. Using the evaluation model, we examine the MCP variation in four cases: (1) the LSEs bid for power intended for customers without energy storage, (2) the LSEs bid for power intended for customers with energy storage installed, (3) the LSEs can control the operation of the customers' energy storage depending on the MCP, and (4) a mixed pattern of (2) and (3).…”

“…In this study, we estimate the MCP using a reinforcement learning model of a multi-agent system [9,10]. In reinforcement learning, the agents (in this case, the power producers and load service entities) are given rewards while taking trial-and-error actions.…”

“…As indicated by the LSE bidding strategies proposed in Section 3 [Eqs. (10) to (15)], the bid amount is related linearly to the price in cases (3) and (4). Therefore, the LSE profit in Eq.…”

“…An agent is aware of the results of the other agents' actions but unaware of their motivations, which may be interpreted as a model of human society [9]. Multi-agent models have been employed to investigate price formation in the power market and in different auction methods [1,10]. We may also envision other topics, such as the influence of DSM on market price formation from the viewpoint of LSEs.…”

Evaluation of costs for procuring reserve capacity in a deregulated power system using multi‐agent model

“…In this study, we perform simulations on the bidding behavior of market participants using multi-agent Q-learning [5][6][7], a numerical technique for finding the Nash equilibrium solution. Thus, we aim at quantitative estimation of how the number and share of market newcomers affect costs in the two reserve power procurement models.…”