用VAE-GAN模拟电价操控，揭示分布式能源协同市场中的潜在风险
VAE-GAN Based Price Manipulation in Coordinated Local Energy Markets
- 基于MADDPG的多智能体强化学习实现分布式能源用户实时交易决策
- 利用VAE-GAN生成操纵性电价，导致无发电能力的用户群体亏损
- 小规模市场更易被操控，大规模市场因协作涌现趋于稳定公平
本文提出一种协调异构分布式能源资源（DERs）参与本地能源市场（LEM）的机制，通过基于多智能体深度确定性策略梯度（MADDPG）的数据驱动、模型无关强化学习方法，使产消者能实时决定购电、售电或不行动，实现动态市场下的高效能源交易。同时，研究了一种基于变分自编码器-生成对抗网络（VAE-GAN）的电价操控策略，使电网运营商可调整价格信号，诱导产消者产生财务损失。结果显示，在对抗性定价下，尤其缺乏发电能力的产消者群体遭受损失，该现象在不同规模的LEM中均成立；随着市场规模扩大，交易趋于稳定，代理间涌现合作行为，公平性提升。
原文摘要 · Abstract (English)
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.
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