Policy-based reserves for power systems

TL;DR

This paper proposes affine reserve policies using robust optimization to improve power system operation with high renewable variability, reducing costs and enabling new market reserve products.

Contribution

It introduces a novel affine policy framework for power reserves that accounts for prediction errors and their correlations, enhancing operational efficiency and market design.

Findings

Affine policies reduce operating costs in renewable-rich power systems.

The approach respects network constraints for all prediction error scenarios.

New reserve products for electricity markets are proposed based on these policies.

Abstract

This paper introduces the concept of affine reserve policies for accommodating large, fluctuating renewable infeeds in power systems. The approach uses robust optimization with recourse to determine operating rules for power system entities such as generators and storage units. These rules, or policies, establish several hours in advance how these entities are to respond to errors in the prediction of loads and renewable infeeds once their values are discovered. Affine policies consist of a nominal power schedule plus a series of planned linear modifications that depend on the prediction errors that will become known at future times. We describe how to choose optimal affine policies that respect the power network constraints, namely matching supply and demand, respecting transmission line ratings, and the local operating limits of power system entities, for all realizations of the prediction errors. Crucially, these policies are time-coupled, exploiting the spatial and temporal correlation of these prediction errors. Affine policies are compared with existing reserve operation under standard modelling assumptions, and operating cost reductions are reported for a multi-day benchmark study featuring a poorly-predicted wind infeed. Efficient prices for such "policy-based reserves" are derived, and we propose new reserve products that could be traded on electricity markets.