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Register a new userPeer-to-Peer Energy Sharing: A New Business Model Towards a Low-Carbon Future
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Added by
Yue Chen
Publication date
2021
fields
Informatics Engineering
and research's language is
English
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The development of distributed generation technology is endowing consumers the ability to produce energy and transforming them into prosumers. This transformation shall improve energy efficiency and pave the way to a low-carbon future. However, it also exerts critical challenges on system operations, such as the wasted backups for volatile renewable generation and the difficulty to predict behavior of prosumers with conflicting interests and privacy concerns. An emerging business model to tackle these challenges is peer-to-peer energy sharing, whose concepts, structures, applications, models, and designs are thoroughly reviewed in this paper, with an outlook of future research to better realize its potentials.
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Scalability and security problems of the centralized architecture models in cyberphysical systems have great potential to be solved by novel blockchain based distributed models.A decentralized energy trading system takes advantage of various sources and effectively coordinates the energy to ensure optimal utilization of the available resources. It achieves that goal by managing physical, social and business infrastructures using technologies such as Internet of Things (IoT), cloud computing and network systems. Addressing the importance of blockchain-enabled energy trading in the context of cyberphysical systems, this article provides a thorough overview of the P2P energy trading and the utilization of blockchain to enhance the efficiency and the overall performance including the degree of decentralization, scalability and the security of the systems. Three blockchain based energy trading models have been proposed to overcome the technical challenges and market barriers for better adoption of this disruptive technology.
Flexible load at the demand-side has been regarded as an effective measure to cope with volatile distributed renewable generations. To unlock the demand-side flexibility, this paper proposes a peer-to-peer energy sharing mechanism that facilitates energy exchange among users while preserving privacy. We prove the existence and partial uniqueness of the energy sharing market equilibrium and provide a centralized optimization to obtain the equilibrium. The centralized optimization is further linearized by a convex combination approach, turning into a multi-parametric linear program (MP-LP) with renewable output deviations being the parameters. The flexibility requirement of individual users is calculated based on this MP-LP. To be specific, an adaptive vertex generation algorithm is established to construct a piecewise linear estimator of the optimal total cost subject to a given error tolerance. Critical regions and optimal strategies are retrieved from the obtained approximate cost function to evaluate the flexibility requirement. The proposed algorithm does not rely on the exact characterization of optimal basis invariant sets and thus is not influenced by model degeneracy, a common difficulty faced by existing approaches. Case studies validate the theoretical results and show that the proposed method is scalable.
We study three capacity problems in the mobile telephone model, a network abstraction that models the peer-to-peer communication capabilities implemented in most commodity smartphone operating systems. The capacity of a network expresses how much sustained throughput can be maintained for a set of communication demands, and is therefore a fundamental bound on the usefulness of a network. Because of this importance, wireless network capacity has been active area of research for the last two decades. The three capacity problems that we study differ in the structure of the communication demands. The first problem is pairwise capacity, where the demands are (source, destination) pairs. Pairwise capacity is one of the most classical definitions, as it was analyzed in the seminal paper of Gupta and Kumar on wireless network capacity. The second problem we study is broadcast capacity, in which a single source must deliver packets to all other nodes in the network. Finally, we turn our attention to all-to-all capacity, in which all nodes must deliver packets to all other nodes. In all three of these problems we characterize the optimal achievable throughput for any given network, and design algorithms which asymptotically match this performance. We also study these problems in networks generated randomly by a process introduced by Gupta and Kumar, and fully characterize their achievable throughput. Interestingly, the techniques that we develop for all-to-all capacity also allow us to design a one-shot gossip algorithm that runs within a polylogarithmic factor of optimal in every graph. This largely resolves an open question from previous work on the one-shot gossip problem in this model.
Blockchain is increasingly being used as a distributed, anonymous, trustless framework for energy trading in smart grids. However, most of the existing solutions suffer from reliance on Trusted Third Parties (TTP), lack of privacy, and traffic and processing overheads. In our previous work, we have proposed a Secure Private Blockchain-based framework (SPB) for energy trading to address the aforementioned challenges. In this paper, we present a proof-on-concept implementation of SPB on the Ethereum private network to demonstrates SPBs applicability for energy trading. We benchmark SPBs performance against the relevant state-of-the-art. The implementation results demonstrate that SPB incurs lower overheads and monetary cost for end users to trade energy compared to existing solutions.
Random hashing is a standard method to balance loads among nodes in Peer-to-Peer networks. However, hashing destroys locality properties of object keys, the critical properties to many applications, more specifically, those that require range searching. To preserve a key order while keeping loads balanced, Ganesan, Bawa and Garcia-Molina proposed a load-balancing algorithm that supports both object insertion and deletion that guarantees a ratio of 4.237 between the maximum and minimum loads among nodes in the network using constant amortized costs. However, their algorithm is not straightforward to implement in real networks because it is recursive. Their algorithm mostly uses local operations with global max-min load information. In this work, we present a simple non-recursive algorithm using essentially the same primitive operations as in Ganesan {em et al.}s work. We prove that for insertion and deletion, our algorithm guarantees a constant max-min load ratio of 7.464 with constant amortized costs.
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