Potential Game based Distributed Optimization of Modular Production Units We present a novel approach for distributed optimization of highly flexible, modular production units enabling plugandplay production with online optimization capabilities to adapt fast to changing production requirements. The approach is fully distributed in the sense that each production module to be optimized is equipped with its own optimization agent which local objective is the optimization of its own production objectives. To assure the necessary coordination between the agents, the resulting distributed optimization problem is designed using concepts of game theory. To this end, we model the production environment in terms of a potential game where each module is modeled as a player of the game. By assigning suitable utility functions to the players coordination of the agents behavior is achieved to find an optimal collective behavior. We apply the approach to a laboratory scale distributed bulk good production testbed with very encouraging results. In addition, due to the computational simplicity of the approach, an implementation in IEC61131 compatible code is possible allowing a direct implementation of the approach in existing production units.

RealTime Multiobjective Microgrid Power Management Using Distributed Optimization in an AgentBased Bargaining Framework In this paper, we propose a multiobjective power management procedure for microgrids (MGs). Through this procedure the power management problem is modeled as a bargaining game among different agents with different sets of objective functions. Nash bargaining solution (NBS) is employed to find the solution of the bargaining game. NBS lies on the Paretofront of the power management problem. Moreover, it introduces a unique and fair balance among the objective functions of different agents and removes the need to track the whole Paretofront in realtime. Distributed gradient algorithm is applied to find the NBS through a modular distributed decision framework without using a central control unit. In this way, the problem of data privacy of different parties within the MG is addressed. The proposed methodology has been tested through simulations on islanded and gridconnected MGs under different pricing scenarios (fixed versus timeofuse pricing).