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Condition-based and predictive maintenance enable early detection of criticalsystem conditions and thereby enable decision makers to forestall faults andmitigate them. However, decision makers also need to take the operational andproduction needs into consideration for optimal decision-making when schedulingmaintenance activities. Particularly in network systems, such as power grids,decisions on the maintenance of single assets can affect the entire network andare, therefore, more complex. This paper proposes a two-level multi-agentdecision support systems for the generation maintenance decision (GMS) of powergrids in an electricity markets. The aim of the GMS is to minimize thegeneration cost while maximizing the system reliability. The proposed frameworkintegrates a central coordination system, i.e. the transmission system operator(TSO), and distributed agents representing power generation units that act tomaximize their profit and decide about the optimal maintenance time slots whileensuring the fulfilment of the energy demand. The objective function of agents(power generation companies) is based on the reward and the penalty that theyobtain from the interplay between power production and loss of production dueto failure, respectively. The optimal strategy of agents is then derived usinga distributed algorithm, where agents choose their optimal maintenance decisionand send their decisions to the central coordinating system. The TSO decideswhether to accept the agents' decisions by considering the market reliabilityaspects and power supply constraints. To solve this coordination problem, wepropose a negotiation algorithm using an incentive signal to coordinate theagents' and central system's decisions such that all the agents' decisions canbe accepted by the central system. We demonstrate the efficiency of ourproposed algorithm using a IEEE 39 bus system.

Multi-agent maintenance scheduling based on the coordination between central operator and decentralized producers in an electricity market