Efficient Demand Response Location Targeting for Price Spike Mitigation by Exploiting Price-demand Relationship
Yufan Zhang, Honglin Wen, Tao Feng, and Yize Chen

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…
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Taxonomy
TopicsSmart Grid Energy Management · Electric Power System Optimization · Optimal Power Flow Distribution
