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Energy dispatch in multi-user networks with limited energy resources can lead to competitive markets where individuals would place bids with the goal of maximizing their own profits. Game theoretic techniques are elegant mathematical candidates to solve such problems. However, they cannot guarantee the existence of equilibrium points. Even if such points are somehow reached, Pareto optimality of the solution cannot be ensured. In this paper, an alternative approach based on multi-objective optimization is proposed in order to maximize the “collective benefits” of a group of players. Instead of allowing individuals to compete for resources directly, a neutral third party finds a solution that is Pareto optimal and meets all constraints associated with individual users. A multi-microgrid industrial park is considered here, where the park is assumed to be equipped with a central controller that purchases energy from the grid and dispatches it among individual microgrids. When this energy is limited due to capacity constraints, each microgrid would try to receive a larger portion so as to increase its profit level. Naturally, these individual objectives are contradictory. As such, the problem is formulated as multi-objective optimization and solved using a modified approach based on goal programming to ensure the Pareto optimality of the overall solution. This way, the limited energy resource is shared in a way that maximizes the collective profit of the group, instead of individuals. The problem formulation is, then, extended by developing its robust counterpart so that model and parameter uncertainties can also be taken into account.
Choobineh et al. (Mon,) studied this question.