An agent-based FTR auction simulator

Continuous Reinforcement Algorithm and Robust Economic Dispatching-Based Spot Electricity Market Modeling considering Strategic Behaviors of Wind Power Producers and Other Participants

“…Santos et al [20] proposed an agent-based wholesale EM test bed (called MASCEM: multiagent simulator of competitive electricity markets) in which the variant Roth-Erev reinforcement learning (VRERL) algorithm was used to model the bidding behavior of the GenCOs agents. Similar researches on agent-based EM modeling can also be seen in [21][22][23][24][25][26][27][28], but none of researches in [19][20][21][22][23][24][25][26][27][28] is involved in considering wind and some other renewable power penetrations.…”

Day-Ahead Market Modeling for Strategic Wind Power Producers under Robust Market Clearing

“…If the convergence was verified, the market state and obtained action of every WPP would no longer change after enough training iterations. It should be noted that in the existing TBRL-based approaches [26][27][28][29][30][31]36], the action set of every agent is discrete and finite, and the optimality of an agent's final obtained action can be easily verified by using method mentioned in [31], which is to compare profits brought from all actions in this agent's action set while fixing the actions of other agents. However, in our proposed LSCAC-based approach, the action set of every agent is continuous.…”

“…If the convergence was verified, the market state and obtained action of every WPP would no longer change after enough training iterations. It should be noted that in the existing TBRL-based approaches [26][27][28][29][30][31]36], the action set of every agent is discrete and finite, and the optimality of an agent's final obtained action can be easily verified by using method mentioned in Before we analyze BMs and strategies for WPPs by using our proposed LSCAC-based day-head EM modeling approach, it should be tested first whether our proposed approaches under different BMs converge to dynamic stabilities after every WPP experiences enough iterations of on line training. If the convergence was verified, the market state and obtained action of every WPP would no longer change after enough training iterations.…”

“…In TBRL-based EM modeling approaches [26][27][28][29][30][31]36], both the state and action sets should be assumed as discrete, otherwise the problem of "curse of dimensionality" will be caused so as to significantly hinder agents from improving their profits. However, according to Section 3.1 and [41], in day-ahead EM with many strategic WPPs, both x i,T (∀i) and a i,T (∀i) are within the continuous, bounded and closed sets (spaces).…”