Considering Multiple Uncertainties in Stochastic Security-Constrained Unit Commitment Using Point Estimation Method

TL;DR

This paper introduces a point estimation-based stochastic security-constrained unit commitment method to efficiently handle uncertainties like wind variability and load sensitivity, reducing computational complexity while maintaining accuracy.

Contribution

It proposes a novel point estimation approach for stochastic SCUC, replacing traditional scenario-based methods to lower computational costs in large-scale power systems.

Findings

Significant reduction in computational burden.

Maintains accuracy comparable to scenario-based methods.

Effective on both small and large power system models.

Abstract

Security-Constrained Unit Commitment (SCUC) is one of the most significant problems in secure and optimal operation of modern electricity markets. New sources of uncertainties such as wind speed volatility and price-sensitive loads impose additional challenges to this large-scale problem. This paper proposes a new Stochastic SCUC using point estimation method to model the power system uncertainties more efficiently. Conventional scenario-based Stochastic SCUC approaches consider the Mont Carlo method; which presents additional computational burdens to this large-scale problem. In this paper we use point estimation instead of scenario generating to detract computational burdens of the problem. The proposed approach is implemented on a six-bus system and on a modified IEEE 118-bus system with 94 uncertain variables. The efficacy of proposed algorithm is confirmed, especially in the last case with notable reduction in computational burden without considerable loss of precision.