Utilizing distributed renewable and energy storage resources in local
distribution networks via peer-to-peer (P2P) energy trading has long been
touted as a solution to improve energy systems' resilience and sustainability.
Consumers and prosumers (those who have energy generation resources), however,
do not have the expertise to engage in repeated P2P trading, and the
zero-marginal costs of renewables present challenges in determining fair market
prices. To address these issues, we propose multi-agent reinforcement learning
(MARL) frameworks to help automate consumers' bidding and management of their
solar PV and energy storage resources, under a specific P2P clearing mechanism
that utilizes the so-called supply-demand ratio. In addition, we show how the
MARL frameworks can integrate physical network constraints to realize voltage
control, hence ensuring physical feasibility of the P2P energy trading and
paving way for real-world implementations.

使用多智能体强化学习 (MARL) 框架，通过特定的供需比例 P2P 结算机制，自动化处理消费者的太阳能光伏和能源储存资源的竞价和管理，以及实现电压控制，确保 P2P 能源交易的物理可行性，并为实际应用铺平道路。

网络化多智能体强化学习用于点对点能源交易

Networked Multiagent Reinforcement Learning for Peer-to-Peer Energy  Trading

To achieve desired carbon emission reductions, integrating renewable
generation and accelerating the adoption of peer-to-peer energy trading is
crucial. This is especially important for energy-intensive farming, like dairy
farming. However, integrating renewables and peer-to-peer trading presents
challenges. To address this, we propose the Multi-Agent Peer-to-Peer Dairy Farm
Energy Simulator (MAPDES), enabling dairy farms to participate in peer-to-peer
markets. Our strategy reduces electricity costs and peak demand by
approximately 30% and 24% respectively, while increasing energy sales by 37%
compared to the baseline scenario without P2P trading. This demonstrates the
effectiveness of our approach.

为了实现所需的碳排放减少，整合可再生能源发电和加快点对点能源交易的采用至关重要，尤其对于耗能大的农业如奶牛养殖。然而，整合可再生能源和点对点交易存在挑战。为了解决这个问题，我们提出了多智能体点对点奶牛养殖能源模拟器 (MAPDES)，使奶牛养殖场能够参与点对点市场。我们的策略能够将电力成本和峰值需求分别降低约 30% 和 24%，同时与无点对点交易的基准情景相比，能将能源销售量增加 37%。这证明了我们方法的有效性。

奶农中基于多智能体系统的点对点能源交易方法

A Multi-Agent Systems Approach for Peer-to-Peer Energy Trading in Dairy  Farming

The sharing-economy-based business model has recently seen success in the
transportation and accommodation sectors with companies like Uber and Airbnb.
There is growing interest in applying this model to energy systems, with
modalities like peer-to-peer (P2P) Energy Trading, Electric Vehicles (EV)-based
Vehicle-to-Grid (V2G), Vehicle-to-Home (V2H), Vehicle-to-Vehicle (V2V), and
Battery Swapping Technology (BST). In this work, we exploit the increasing
diffusion of EVs to realize a crowdsourcing platform called e-Uber that jointly
enables ride-sharing and energy-sharing through V2G and BST. e-Uber exploits
spatial crowdsourcing, reinforcement learning, and reverse auction theory.
Specifically, the platform uses reinforcement learning to understand the
drivers' preferences towards different ride-sharing and energy-sharing tasks.
Based on these preferences, a personalized list is recommended to each driver
through CMAB-based Algorithm for task Recommendation System (CARS). Drivers bid
on their preferred tasks in their list in a reverse auction fashion. Then
e-Uber solves the task assignment optimization problem that minimizes cost and
guarantees V2G energy requirement. We prove that this problem is NP-hard and
introduce a bipartite matching-inspired heuristic, Bipartite Matching-based
Winner selection (BMW), that has polynomial time complexity. Results from
experiments using real data from NYC taxi trips and energy consumption show
that e-Uber performs close to the optimum and finds better solutions compared
to a state-of-the-art approach

通过运用增长中的电动汽车资源， e-Uber 平台结合使用 V2G 和 BST 技术在空间众包环境下通过强化学习和反向拍卖理论为司机提供个性化的任务推荐，实现了共享乘车和能源交易，我们证明了任务分配优化问题是 NP 难的，引入了双分图匹配启发式算法 BMW 来解决该问题。