Abstract
Iterative Game Approach for Modeling the Behavior of Agents in a Competitive Flexibility Trading
Highlights
Our proposed flexibility management problem is solved in MATLAB using particle swarm optimization (PSO) for determining the optimal decision of each agent in each iteration
In this paper, we proposed a novel iterative game-based algorithm based on a complete interaction among agents for trading energy flexibility in the energy community
We considered three scenarios assigning different levels of freedom to end-users and the distribution system operator (DSO) in presence of both shiftable and interruptible loads for evaluating our proposed model
Summary
D UE to the increase in the utilization of renewable energies as variable and non-dispatchable resources, the power system is facing new problems related to the balance between demand and generation [1]. Our proposed iterative game-based approach has some advantages compared to other methods used in the literature It provides a competitive trading framework where all players have their decision variables and can solve their problems independently to find their optimal solutions. The autonomy of the followers in the bilevel optimization model is way less than the leader of the problem, as the leader can consider the objective function and constraints of the followers as a constraint in its optimization problem, to anticipate the logical actions of followers, while followers do not have a similar ability In this regard, our proposed approach considers a similar power for all agents to make and update their decisions and prevents monopoly in the transaction process to serve the players with a competitive trading platform. The DSO transacts flexibility with the RTEM and all agents as expressed in (10)
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