A Data-Driven Policy for Addressing Deployability Issue of FMM FRPs: Resources Qualification and Deliverability

TL;DR

This paper introduces a data-driven policy for flexible ramping products (FRPs) that improves deployability and reliability in real-time markets by predicting suitable generators and considering transmission constraints.

Contribution

It presents a novel data-mining based approach to assign FRP awards, addressing deployability issues by integrating ramping response factors and transmission effects.

Findings

Enhanced reliability in real-time operation.

Reduced need for costly ad-hoc ramping actions.

Improved economic efficiency of FRP deployment.

Abstract

Intensified netload uncertainty and variability led to the concept of a new market product, flexible ramping product (FRP). The main goal of FRP is to enhance the generation dispatch flexibility inside real-time (RT) markets to mitigate energy imbalances due to ramp capability shortage. Generally, the FRP requirements are based on system-wide or proxy requirements, so the effect of FRP awards on the transmission line constraints is not considered. This can lead to FRP deployability issues in RT operation. This paper proposes a new FRP design based on a datadriven policy incorporating ramping response factor sets to address FRP deployability issue. First, a data-mining algorithm is performed to predict the ramp-qualified generators to create the data-driven policy. Then, the FRP awards are assigned to these units while considering effects of post-deployment of FRPs on the transmission line limits. Finally, the proposed data-driven policy is tested against proxy policy through an out-of-sample validation phase that (i) mimics the RT operation of the CAISO, and (ii) represents the expensive ad-hoc actions needed for procuring additional ramping capability to follow realized netload changes. The results show the effectiveness of the proposed data-driven policy from reliability and economic points of view