VAE-GAN Based Price Manipulation in Coordinated Local Energy Markets
This addresses market manipulation risks for prosumers in energy trading, but it is incremental as it builds on existing methods like MADDPG and VAE-GAN.
This paper tackles the problem of coordinating prosumers in local energy markets using a reinforcement learning approach, and investigates a price manipulation strategy that causes financial losses for prosumers, with results showing losses for heterogeneous groups and improved fairness as market size increases.
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.