Profit-guaranteed locational marginal price computation in non-convex electricity markets using sequential linear programming

The restructuring of the power industry led to the development of wholesale electricity markets, which introduced competition. However, non-convexities in these markets pose challenges for determining optimal market-clearing prices that can partially recover generators’ operating costs. Therefore, this paper proposes a locational marginal pricing scheme based on a primal–dual formulation for a market-clearing model that accounts for non-convexities related to fixed startup/shutdown and no-load costs, minimum generation, up/down time and incorporates the generator’s lost opportunity costs to encourage them to follow the independent system operator’s schedule. The proposed formulation generates uniform, revenue-adequate prices that deviate only slightly from the marginal prices of the conventional market-clearing model by incorporating startup cost recovery constraints and lost opportunity cost constraints. It is modeled as a mixed integer nonlinear programming problem, which is linearized using a sequential linear programming algorithm. Three case studies are presented to analyze the performance of the proposed formulation. The generated locational marginal pricing supports competitive equilibrium without requiring any side payments.