Adjustable Robust Low-Carbon Unit Commitment With Nonanticipativity by Linear Programming

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doi:10.1109/TNSE.2023.3281072

Adjustable Robust Low-Carbon Unit Commitment With Nonanticipativity by Linear Programming

2024-11-01

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Mohammad Shahidehpour

DOI：10.1109/TNSE.2023.3281072

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Thermal power generation is the main source of carbon emissions. Adopting low-carbon dispatching and developing renewable energy sources (RESs) are effective ways to reduce carbon emissions. This article constructs a mixed-integer programming (MIP) model of low-carbon full-scenario unit commitment with nonanticipativity. To overcome the curse of dimensionality problem caused by the use of a massive number of scenarios, we employ the adjustable robust optimization approach (AROA) to reformulate the full-scenario unit commitment as a deterministic robust model. In addition, we establish the precise adjustable robust convex hull of generation constraints in a higher dimensional space and generate a relaxed linear programming (LP) formulation of the original MIP model. Then, we design a heuristic method for obtaining a near-optimal feasible solution by converting a large-scale MIP into an LP model that can be solved in polynomial time. The numerical experiments presented in this article demonstrate the effectiveness and efficiency of the proposed method.

Keywords:

Carbon dioxide

Uncertainty

Costs

Optimization

Computational modeling

Linear programming

Stochastic processes

Low-carbon dispatching

adjustable robust optimization approach

convex hull

security-constrained unit commitment

Adjustable Robust Low-Carbon Unit Commitment With Nonanticipativity by Linear Programming

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