Low-Carbon Economic Dispatch of Bulk Power Systems Using Nash Bargaining Game

TL;DR

This paper introduces a low-carbon economic dispatch model for bulk power systems using Nash bargaining, balancing emission reduction and cost efficiency through a novel game-theoretic approach validated by real-world data.

Contribution

It proposes a Nash bargaining-based dispatch model that improves computational efficiency by dynamic weighting, addressing the trade-off between low-carbon goals and economic costs.

Findings

Effective trade-off between emissions and costs achieved

Enhanced computational efficiency through dynamic weights

Validated model with nationwide Chinese power system data

Abstract

Decarbonization of power systems plays a crucial role in achieving carbon neutral goals across the globe, but there exists a sharp contradiction between the emission reduction and levelized generation cost. Therefore, it is of great importance for power system operators to take economic as well as low-carbon factors into account. This paper establishes a low-carbon economic dispatch model of bulk power systems based on Nash bargaining game, which derives a Nash bargaining solution making a reasonable trade-off between economic and low-carbon objectives. Because the Nash bargaining solution satisfies Pareto effectiveness, we analyze the computational complexity of Pareto frontiers with parametric linear programming and interpret the inefficiency of the method. Instead, we assign a group of dynamic weights in the objective function of the proposed low-carbon economic dispatch model so as to improve the computational efficiency by decoupling time periods and avoiding the complete computation of Pareto frontiers. In the end, we validate the proposed model and the algorithm by a realistic nationwide simulation in mainland China.