# Opportunities for Price Manipulation by Aggregators in Electricity Markets

arXiv: https://arxiv.org/abs/1606.06510 · HTML: https://mlsanity.com/paper/1606.06510/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jun 2016.

## Built on by (most cited first, 4 of 4)

- [Online Learning for Traffic Routing under Unknown Preferences](https://mlsanity.com/paper/2203.17150/) (2022)
- [Polymatrix Competitive Gradient Descent](https://mlsanity.com/paper/2111.08565/) (2021)
- [Online Learning for Equilibrium Pricing in Markets under Incomplete Information](https://mlsanity.com/paper/2303.11522/) (2023)
- [Harnessing Implicit Cooperation: A Multi-Agent Reinforcement Learning Approach Towards Decentralized Local Energy Markets](https://mlsanity.com/paper/2602.16062/) (2026)

## Abstract (arXiv)

Aggregators are playing an increasingly crucial role in the integration of renewable generation in power systems. However, the intermittent nature of renewable generation makes market interactions of aggregators difficult to monitor and regulate, raising concerns about potential market manipulation by aggregators. In this paper, we study this issue by quantifying the profit an aggregator can obtain through strategic curtailment of generation in an electricity market. We show that, while the problem of maximizing the benefit from curtailment is hard in general, efficient algorithms exist when the topology of the network is radial (acyclic). Further, we highlight that significant increases in profit are possible via strategic curtailment in practical settings.