Chance Constrained Optimal Power Flow with Curtailment and Reserves from Wind Power Plants

TL;DR

This paper presents a chance constrained optimal power flow model incorporating wind power curtailment and reserves, improving operational planning under renewable uncertainty.

Contribution

It introduces a novel mathematical framework using weighted chance constraints to model wind power control in day-ahead power system operations.

Findings

Reduced operational costs with wind control strategies

Enhanced system security through probabilistic constraints

Lower wind power curtailment in case study

Abstract

Over the past years, the share of electricity production from wind power plants has increased to significant levels in several power systems across Europe and the United States. In order to cope with the fluctuating and partially unpredictable nature of renewable energy sources, transmission system operators (TSOs) have responded by increasing their reserve capacity requirements and by requiring wind power plants to be capable of providing reserves or following active power set-point signals. This paper addresses the issue of efficiently incorporating these new types of wind power control in the day-ahead operational planning. We review the technical requirements the wind power plants must fulfill, and propose a mathematical framework for modeling wind power control. The framework is based on an optimal power flow formulation with weighted chance constraints, which accounts for the uncertainty of wind power forecasts and allows us to limit the risk of constraint violations. In a case study based on the IEEE 118 bus system, we use the developed method to assess the effectiveness of different types of wind power control in terms of operational cost, system security and wind power curtailment.