Data-Driven Continuous-Time Framework for Frequency-Constrained Unit Commitment

TL;DR

This paper introduces a continuous-time frequency-constrained unit commitment framework using Bernstein polynomials, enabling more accurate modeling of frequency dynamics and constraints in power systems, validated on a real-world island network.

Contribution

It presents a novel continuous-time formulation for unit commitment with frequency constraints, incorporating Bernstein polynomials and a data-driven frequency nadir constraint for improved accuracy.

Findings

Model effectively captures intra-hour frequency dynamics.

Validated on La Palma's network with timely solutions.

Reduces impact of power fluctuations on system frequency.

Abstract

The conventional approach to solving the unit commitment problem involves discrete intervals at an hourly scale, particularly when integrating frequency dynamics to formulate a frequency-constrained unit commitment. To overcome this limitation, a novel continuous-time frequency-constrained unit commitment framework is proposed in this paper. In this approach, Bernstein polynomials represent continuous variables in the unit commitment problem and enable the calculation of frequency response-related metrics such as the rate of change of frequency, quasi-steady-state frequency, and frequency nadir. Notably, startup and shut-down trajectories are meticulously considered, transforming the formulation into a fully continuous-time model and simplifying constraints related to variable continuity. To address the complexities associated with integrating the obtained non-linear frequency nadir constraint into a mixed-integer linear problem, an alternative data-driven frequency nadir constraint is proposed, which accurately constrains frequency nadir deviations throughout the time interval. To validate the proposed model, it is applied to the real-life network of the Spanish Island of La Palma. The results demonstrate the effectiveness of the proposed formulation, indicating that the model is solved timely while mitigating the impact of intra-hour real-time power fluctuations on system frequency.