Profit-guaranteed locational marginal price computation in non-convex electricity markets using sequential linear programming
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- Non-convexities in electricity markets pose challenges in determining optimal market clearing prices, leading to partial recovery of the generators operating costs.
- We propose a locational marginal pricing scheme based on a primal-dual formulation for a market-clearing model, accounting 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 that encourages them to follow the ISO’s schedule.
- Our formulation generates uniform and revenue-adequate prices, deviating slightly from the marginal prices of the conventional market-clearing model to incorporate startup cost recovery constraints and lost opportunity cost constraints, without requiring any separate side payments and ensures market transparency.
- This non-convex formulation is modelled as a mixed integer nonlinear programming problem, which is linearised using the sequential linear programming algorithm.
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.
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