Conditions for Regional Frequency Stability in Power System Scheduling -- Part II: Application to Unit Commitment

TL;DR

This paper develops a linear constraint-based methodology for regional frequency stability in power system scheduling and demonstrates its application in a stochastic unit commitment model for the Great Britain system, highlighting regional inertia effects.

Contribution

It introduces a novel linear constraint formulation for regional frequency stability conditions and applies it to optimize unit commitment considering regional inertia and stability requirements.

Findings

Regional inertia distribution affects frequency stability.

Securing stability can lead to wind curtailment.

Reinforced transmission can mitigate stability constraints.

Abstract

In Part I of this paper we have introduced the closed-form conditions for guaranteeing regional frequency stability in a power system. Here we propose a methodology to represent these conditions in the form of linear constraints and demonstrate their applicability by implementing them in a generation-scheduling model. This model simultaneously optimises energy production and ancillary services for maintaining frequency stability in the event of a generation outage, by solving a frequency-secured Stochastic Unit Commitment (SUC). We consider the Great Britain system, characterised by two regions that create a non-uniform distribution of inertia: England in the South, where most of the load is located, and Scotland in the North, containing significant wind resources. Through several case studies, it is shown that inertia and frequency response cannot be considered as system-wide magnitudes in power systems that exhibit inter-area oscillations in frequency, as their location in a particular region is key to guarantee stability. In addition, securing against a medium-sized loss in the low-inertia region proves to cause significant wind curtailment, which could be alleviated through reinforced transmission corridors. In this context, the proposed constraints allow to find the optimal volume of ancillary services to be procured in each region.