Abstract

This paper presents a methodology for solving unit commitment (UC) problem for thermal units integrated with wind power and generalized energy storage system (ESS).The ESS is introduced to achieve peak load shaving and reduce the operating cost. The volatility of wind power is simulated by multiple scenarios, which are generated by Latin hypercube sampling. Meanwhile, the scenario reduction technique based on probability metric is introduced to reduce the number of scenarios so that the computational burden can be alleviated. The thermal UC problem with volatile wind power and ESS is transformed to a deterministic optimization which is formulated as the mixed-integer convex program optimized by branch and bound-interior point method. During the branch and bound process, the best first search and depth first search are combined to expedite the computation. The effectiveness of the proposed algorithm is demonstrated by a ten unit UC problem.

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