A Solution Strategy to the Unit Commitment Problem Incorporating Manifold Uncertainties

TL;DR

This paper presents a comprehensive approach to solving the unit commitment problem under multiple types of uncertainties in wind power and power grid, using evidence theory and an enhanced grey wolf optimizer, validated on IEEE and real power systems.

Contribution

It introduces a novel integrated framework combining evidence theory and an advanced optimization algorithm for uncertain unit commitment problems.

Findings

Effective handling of manifold uncertainties in power systems.

Successful application to IEEE 30-bus and 183-bus systems.

Improved solution quality and computational efficiency.

Abstract

The widespread uncertainties have made the interaction between wind power and power grid more complicated and difficult to model and handle. This paper proposes an approach for the solution of unit commitment (UC) problem incorporating multiple uncertainties that exist in both wind power and power grid inherently, consisting of probability, possibility, and interval measures. To handle the manifold uncertainties in a comprehensive and efficient manner, the evidence theory (ET) is applied to fuse these uncertain variables into Dempster-Shafer structure. Moreover, the power loss is introduced into power balance constraints, and the extended affine arithmetic (EAA) is employed to evaluate the uncertainty of power loss caused by the propagation of the aforementioned uncertainties. Regarding the mix-discrete nonlinear characteristics of the established optimization model, an enhanced grey wolf optimizer (GWO) algorithm is developed to solve the proposed model. Specifically, the corresponding commitment schedule is determined by a kind of binary grey wolf optimizer (BGWO), and the economic dispatch (ED) is settled by GWO. Finally, the IEEE 30-bus test system and a real-sized 183-bus China power system are studied to demonstrate the validity and scalability of the proposed model and method.