Multiagent-based Market Simulator for the Wholesale Electricity Spot Market

As electricity markets develop into more complex structures, new modeling and simulation techniques are required to simulate the market and to identify strategic behavior that can profitably influence electricity prices. This study proposes an agent-based model to characterize and investigate the complex interaction between the physical network infrastructure of the power system and the economic behavior of the electricity market. Market clearing prices are determined by a single-sided auction where the profit-seeking behavior of a generation company is modeled using an autonomous adaptive agent capable of endogenously developing its own bidding strategies through a Roth-Erev reinforcement learning algorithm that determine their price and quantity offers in the hourly auction. The agent-based model in this study implements the generator-reported cost function as a piecewise linear function in order to more accurately represent bidding behavior and strategies. Market clearing can then be formulated as a DC optimal power flow problem that is solved using linear programming. An agent-based model developed for the Wholesale Electricity Spot Market (WESM) in the Philippines is able to show the effects on locational marginal prices and network congestion and losses, of the profit-seeking and strategizing behavior of the generation companies.