Efficient Value Sharing in Large Energy Coalitions Considering Members Classification

Sharing values resulting from the purchase and sale of energy in large coalitions in smart grid systems has significant computational complexity and requires high calculation time. To reduce the problem-solving time, this paper proposes a novel clustering method designed to simplify the process of sharing values between players. This approach has been used for two methods, Shapley and worst-case minimization. Players are placed into specific clusters based on their roles and assets, which reduces the constraints and number of possible coalitions, thereby reducing the computation time in both methods. A case study involving 18 members of a community shows that these methods lead to a significant improvement in time and profit allocation.