VAE-GAN Based Price Manipulation in Coordinated Local Energy Markets

TL;DR

This paper presents a reinforcement learning-based local energy market model with a novel VAE-GAN price manipulation strategy that can induce financial losses in prosumers, highlighting vulnerabilities in market design.

Contribution

It introduces a model-free, multi-agent RL approach for prosumer coordination and a VAE-GAN based method for strategic price manipulation in local energy markets.

Findings

Adversarial pricing causes financial losses for prosumers without generation.

Market size increase leads to more stable trading and fairness.

Heterogeneous prosumer groups are differentially affected by price manipulation.

Abstract

This paper introduces a model for coordinating prosumers with heterogeneous distributed energy resources (DERs), participating in the local energy market (LEM) that interacts with the market-clearing entity. The proposed LEM scheme utilizes a data-driven, model-free reinforcement learning approach based on the multi-agent deep deterministic policy gradient (MADDPG) framework, enabling prosumers to make real-time decisions on whether to buy, sell, or refrain from any action while facilitating efficient coordination for optimal energy trading in a dynamic market. In addition, we investigate a price manipulation strategy using a variational auto encoder-generative adversarial network (VAE-GAN) model, which allows utilities to adjust price signals in a way that induces financial losses for the prosumers. Our results show that under adversarial pricing, heterogeneous prosumer groups, particularly those lacking generation capabilities, incur financial losses. The same outcome holds across LEMs of different sizes. As the market size increases, trading stabilizes and fairness improves through emergent cooperation among agents.