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Adjustable Robust Low-Carbon Unit Commitment With Nonanticipativity by Linear Programming
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DOI：10.1109/TNSE.2023.3281072.png)
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摘要
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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.
Keyword:
Carbon dioxide
Uncertainty
Costs
Optimization
Computational modeling
Linear programming
Stochastic processes
Low-carbon dispatching
adjustable robust optimization approach
convex hull
security-constrained unit commitment
期刊
I
IF:
7.9
论文数:
2.5K
被引数:
10.0K
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