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As distributed energy resources (DERs) are widely deployed, DC packetized power microgrids have been considered as a promising solution to incorporate DERs effectively and steadily. In this paper, we consider a DC packetized power microgrid, where the energy is dispatched in the form of power packets with the assist of a power router. However, the benefits of the microgrid can only be realized when energy subscribers (ESs) equipped with DERs actively participate in the energy market. Therefore, peer-to-peer (P2P) energy trading is necessary in the DC packetized power microgrid to encourage the usage of DERs. Different from P2P energy trading in AC microgrids, the dispatching capability of the router needs to be considered in DC microgrids, which will complicate the trading problem. To tackle this challenge, we formulate the P2P trading problem as an auction game, in which the demander ESs submit bids to compete for power packets, and a controller decides the energy allocation and power packet scheduling. Analysis of the proposed scheme is provided, and its effectiveness is validated through simulation.
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Decentralized Voltage Control with Peer-to-peer Energy Trading in a Distribution Network
Utilizing distributed renewable and energy storage resources via peer-to-peer (P2P) energy trading has long been touted as a solution to improve energy system’s resilience and sustainability. Consumers and prosumers (those who have energy generation resources), however, do not have 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 a multi-agent reinforcement learning (MARL) framework 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 framework can integrate physical network constraints to realize decentralized voltage control, hence ensuring physical feasibility of the P2P energy trading and paving ways for real-world implementations.
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- Award ID(s):
- 2129631
- PAR ID:
- 10552102
- Publisher / Repository:
- Proceedings of the 56th Hawaii International Conference on System Sciences
- Date Published:
- Subject(s) / Keyword(s):
- Distributed, Renewable, and Mobile Resources, multi-agent reinforcement learning, peer-to-peer trading, voltage control
- Format(s):
- Medium: X
- Location:
- 2023-01-03
- Sponsoring Org:
- National Science Foundation
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