Reserve Requirements in Ancillary Markets Using Consensus-Based Cooperative Model Considering Renewable Resources

TL;DR

This paper introduces a stochastic programming model for reserve commitment in power markets with high wind power, demonstrating improved performance over traditional reserve rules through scenario-based optimization.

Contribution

It presents a novel three-stage scenario-based programming model and a dual decomposition algorithm for reserve commitment considering renewable resources.

Findings

Stochastic programming policy outperforms traditional reserve rules.

Scenario generation method tested on a 118-bus system.

Model effectively incorporates wind power variability.

Abstract

From an economic point of view, a common criterion for assessing the merits of a reserve investment is its impacts on social welfare. The underlying assumption in using this criterion is that side payments may be used to distribute the social gains among all market players. In reality, however, since the impacts of an electricity reserve project on different players may vary, such side payments are rather difficult to implement. We present a three-stage scenario-based programming model for committing reserves in power markets with large amounts of wind power. We describe wind power generation in terms of a representative set of appropriately weighted scenarios, and we present a dual decomposition algorithm for solving the resulting stochastic program. We test our scenario generation methodology on a model of 118 bus system, and we show that the stochastic programming unit commitment policy outperforms common reserve rules.