Safe and Private Forward-trading Platform for Transactive Microgrids

Abstract

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. Finally, we describe a testbed to run experiments and demonstrate its performance using simulation results.

DeepRing: Convolution Neural Network based on Blockchain Technology

“…The blockchain is a distributed, tamper-aware, and fault-tolerant database that links blocks together using cryptographic hash functions. Each block contains a hash of the previous block's header, creating an immutable chain of records [21]. A blockchain consists of two main components: a distributed database and a network of nodes as shown in Figure 2.…”

A Review of Cybersecurity Concerns for Transactive Energy Markets

“…However, transactive energy markets would operate at a significantly smaller scale than the blockchains being discussed here. In some implementations, distributed ledgers could operate at a scale as small as a portion of a neighborhood [4,29]. These ledgers would then contribute to a larger ledger that would consist of the local power distribution grid.…”

“…Many studies and reviews acknowledge an overall insufficient focus on privacy within the TE literature [11,26,29]. As an example, Lombardi et al [12] explicitly stated that they do not address privacy concerns in their proposal.…”

“…Chandra et al [11] noted that only a small subset of TE studies have focused on securing SM data, and further claim that privacy regarding smart contracts in P2P market settings has not been studied at all. Eisele et al [29] referenced the literature review of Andoni et al [50], who found that, among 47 DLT TE applications, privacy preservation was a key area that has yet to be addressed.…”

“…Indeed, the transaction history can be used to glean a great deal of information about prosumers, and thus most researchers agree that their identity should be obfuscated in some way in the market application [11,29,34].…”