A Game Theoretical Randomized Method for Large-Scale Systems Partitioning

Abstract

In this paper, a game theory-based partitioning algorithm for large-scale systems (LSS) is proposed. More specifically, a game over nodes is introduced in a model predictive control framework. The Shapley value of this game is used to rank the communication links of the control network based on their impact on the overall system performance. A randomized method to estimate the Shapley value of each node and also an efficient redistribution of the resulting value to the links involved are considered to relieve the combinatorial explosion issues related to LSS. Once the partitioning solution is obtained, a sensitivity analysis is proposed to give a measure of its performance. Likewise, a greedy fine tuning procedure is considered to increase the optimality of the partitioning results. The full Barcelona drinking water network is analyzed as a real LSS case study, showing the effectiveness of the proposed approach in comparison with other partitioning schemes available in the literature.