Co-optimize dispatch and pricing in market clearing problem to balance conflicting market objectives

In electricity markets, dispatch instructions and price signals are typically derived from the primal and dual solutions of the market clearing models. The prevailing market clearing methods can be summarized as: determining dispatch instructions to maximize social welfare, and subsequently calculating prices to support a competitive equilibrium. However, this sequential approach may violate critical market operation requirements, such as consumer payment reduction and price caps. Inherent conflicts often exist between these economic properties and market operation requirements. Since some of these properties are jointly determined by dispatch and pricing decisions, the sequential optimization approach inherently limits the ability to realize the full coordination benefits. To address this gap, this paper proposes a novel market clearing method that co-optimizes dispatch instructions and price signals to balance conflicting market objectives. Firstly, this paper analyzes the conflict mechanism between the aforementioned properties and requirements under the prevailing market clearing paradigms. Secondly, a co-optimization market clearing method to trade off conflicting properties and requirements is proposed. The basic idea and general mathematical formulation of the proposed method are introduced. The distinctions and connections between the proposed method and existing methods are clarified. Thirdly, the application of the proposed method is explored in typical scenarios involving consumer payment reduction and price caps. Finally, numerical analysis demonstrates that the proposed method achieves a flexible balance between conflicting properties and requirements. For instance, it can significantly reduce consumer payments by 80.33 % while limiting increases in system operation costs to within 1.98 %; alternatively, it can reduce lost opportunity costs to zero under price caps at the expense of a 0.52 % increase in system operation costs.