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Title: Safe and Private Forward-trading Platform for Transactive Microgrids

Journal Article · · ACM Transactions on Cyber-Physical Systems
DOI:https://doi.org/10.1145/3403711· OSTI ID:1768837
 [1];  [2];  [1];  [1];  [2];  [1]
  1. Vanderbilt Univ., Nashville, TN (United States)
  2. Univ. of Houston, TX (United States)

Power grids are evolving at an unprecedented pace due to the rapid growth of distributed energy resources (DER) in communities. These resources are very different from traditional power sources, as they are located closer to loads and thus can significantly reduce transmission losses and carbon emissions. However, their intermittent and variable nature often results in spikes in the overall demand on distribution system operators (DSO). To manage these challenges, there has been a surge of interest in building decentralized control schemes, where a pool of DERs combined with energy storage devices can exchange energy locally to smooth fluctuations in net demand. Building a decentralized market for transactive microgrids is challenging, because even though a decentralized system provides resilience, it also must satisfy requirements such as privacy, efficiency, safety, and security, which are often in conflict with each other. As such, existing implementations of decentralized markets often focus on resilience and safety but compromise on privacy. In this article, we describe our platform, called TRANSAX, which enables participants to trade in an energy futures market, which improves efficiency by finding feasible matches for energy trades, enabling DSOs to plan their energy needs better. TRANSAX provides privacy to participants by anonymizing their trading activity using a distributed mixing service, while also enforcing constraints that limit trading activity based on safety requirements, such as keeping planned energy flow below line capacity. We show that TRANSAX can satisfy the seemingly conflicting requirements of efficiency, safety, and privacy. We also provide an analysis of how much trading efficiency is lost. Trading efficiency is improved through the problem formulation, which accounts for temporal flexibility, and system efficiency is improved using a hybrid-solver architecture. Lastly, we describe a testbed to run experiments and demonstrate its performance using simulation results.

Research Organization:
Vanderbilt Univ., Nashville, TN (United States)
Sponsoring Organization:
USDOE Advanced Research Projects Agency - Energy (ARPA-E); National Science Foundation (NSF)
Grant/Contract Number:
AR0000666; CNS-1647015; CNS-1818901; CNS-1840052
OSTI ID:
1768837
Report Number(s):
DOE-VANDERBILT-0000666-37
Journal Information:
ACM Transactions on Cyber-Physical Systems, Vol. 5, Issue 1; ISSN 2378-962X
Publisher:
Association for Computing Machinery (ACM)Copyright Statement
Country of Publication:
United States
Language:
English

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