Abstract
The Unit commitment (UC) problem in power systems has been studied for a long time; however, many new challenges have emerged in the UC problem with the increasing penetration of renewable generation which is intermittent and uncertain. Compared with the common uncertainty modeling methods including stochastic programming and robust optimization, in this paper, we develop a data-driven distributionally robust chance-constrained (DDRC) UC model. The proposed two-stage UC model focuses on the commitment decision and dispatch plan in the first stage, and considers the worst-case expected cost for possible power imbalance or re-dispatch in the second stage. To capture the uncertainty of wind power distribution, a distance-based ambiguity set is designed which can be constructed in a data-driven manner. Based on the ambiguity set, the original complicated UC problem is reformulated to a tractable optimization problem which is then solved by the column-and-constraint generation (C&CG) algorithm. The performance of the the proposed approach is validated by case studies with different test systems including the IEEE 6-bus test system, modified IEEE 118-bus system and a practical-scale system, especially the value of data in controlling the conservativeness of the problem.
Highlights
As a critical application in power system operation, unit commitment (UC) problem has been studied for a long time which aims to reduce system cost and improve reliability by optimal scheduling of generation units
NUMERICAL RESULTS To validate the performance of the proposed approach, case studies based on IEEE test systems and a practical system are conducted
Different from the moment-based ambiguity set, we constructed a distance-based ambiguity set to capture the uncertainty of wind power distribution, and this set can be derived in a data-driven environment
Summary
As a critical application in power system operation, unit commitment (UC) problem has been studied for a long time which aims to reduce system cost and improve reliability by optimal scheduling of generation units. It is imperative to incorporate associated uncertainties into UC problems with renewable generation so that more reliable solutions can be attained. Stochastic programming is a traditional method to deal with data uncertainty and was first investigated to solve uncertain UC problems [3]. It is assumed that the probability distributions of random variables are known in stochastic programming methods, and the objective is to minimize the expected total system cost. Stochastic programming methods suffer from heavy computational burden as substantial scenarios are required to comprehensively represent the probability distribution
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