Efficient Demand Response Location Targeting for Price Spike Mitigation by Exploiting Price-demand Relationship

TL;DR

This paper presents a novel method for selecting demand response locations to effectively mitigate price spikes by leveraging the price-demand relationship, formulated as a tractable mixed-integer linear program.

Contribution

It introduces a bilevel optimization framework with a piecewise linear approximation to efficiently determine DR locations and demand reductions for price mitigation.

Findings

Reduces wholesale prices to target levels effectively.

Demonstrates robustness against parameter inaccuracies.

Achieves over 50% reduction in computation time.

Abstract

Demand response (DR) leverages demand-side flexibility, offering a promising approach to enhance market conditions like mitigating wholesale price spikes. However, poorly chosen DR locations can inadvertently increase electricity prices. For that, we introduce a method to rigorously select DR locations and corresponding demand reductions. We formulate a bilevel program where the upper level determines the DR locations and demand reductions while ensuring the average nodal prices meet a predetermined target. The lower level tackles an economic dispatch (ED) problem and feeds the resulting nodal prices back to the upper level based on post-DR demands. This bilevel formulation presents challenges due to the lower-level non-convexity affecting the upper-level constraints on average nodal prices. To address this, we propose to replace the lower level with a piecewise linear function representing the price-demand relationship, solving iteratively for each linear segment. This results in a tractable mixed-integer linear program. An acceleration strategy is proposed to further reduce the computation time. Numerical studies demonstrate the ability of the proposed approach to reduce prices to a desired level. Besides, we empirically show that the proposed approach is robust against inaccurate system parameters and can reduce computation time by over 50%.