Lightweight Blockchain Framework for Location-aware Peer-to-Peer Energy Trading

Peer-to-Peer (P2P) energy trading can facilitate integration of a large number of small-scale producers and consumers into energy markets. Decentralized management of these new market participants is challenging in terms of market settlement, participant reputation and consideration of grid constraints. This paper proposes a blockchain-enabled framework for P2P energy trading among producer and consumer agents in a smart grid. A fully decentralized market settlement mechanism is designed, which does not rely on a centralized entity to settle the market and encourages producers and consumers to negotiate on energy trading with their nearby agents truthfully. To this end, the electrical distance of agents is considered in the pricing mechanism to encourage agents to trade with their neighboring agents. In addition, a reputation factor is considered for each agent, reflecting its past performance in delivering the committed energy. Before starting the negotiation, agents select their trading partners based on their preferences over the reputation and proximity of the trading partners. An Anonymous Proof of Location (A-PoL) algorithm is proposed that allows agents to prove their location without revealing their real identity. The practicality of the proposed framework is illustrated through several case studies, and its security and privacy are analyzed in detail

Crossing Roads of Federated Learning and Smart Grids: Overview, Challenges, and Perspectives

Consumer's privacy is a main concern in Smart Grids (SGs) due to the sensitivity of energy data, particularly when used to train machine learning models for different services. These data-driven models often require huge amounts of data to achieve acceptable performance leading in most cases to risks of privacy leakage. By pushing the training to the edge, Federated Learning (FL) offers a good compromise between privacy preservation and the predictive performance of these models. The current paper presents an overview of FL applications in SGs while discussing their advantages and drawbacks, mainly in load forecasting, electric vehicles, fault diagnoses, load disaggregation and renewable energies. In addition, an analysis of main design trends and possible taxonomies is provided considering data partitioning, the communication topology, and security mechanisms. Towards the end, an overview of main challenges facing this technology and potential future directions is presented

A Systematic Literature Review of Peer-to-Peer, Community Self-Consumption, and Transactive Energy Market Models

Capper, T., Gorbatcheva, A., Mustafa, M. A., Bahloul, M., Schwidtal, J. M., Chitchyan, R., Andoni, M., Robu, V., Montakhabi, M., Scott, I., Francis, C., Mbavarira, T., Espana, J. M., & Kiesling, L. (2021). A Systematic Literature Review of Peer-to-Peer, Community Self-Consumption, and Transactive Energy Market Models. Social Science Research Network (SSRN), Elsevier. https://doi.org/10.2139/ssrn.3959620Peer-to-peer and transactive energy markets, and community or collective self-consumption offer new models for trading energy locally. Over the past 10 years there has been significant growth in the amount of academic literature and trial projects examining how these energy trading models might function. This systematic literature review of 139 peer-reviewed journal articles examines the market designs used in these energy trading models. The Business Ecosystem Architecture Modelling framework is used to extract information about the market models used in the literature and identify differences and similarities between the models. This paper identifies six archetypal market designs and three archetypal auction mechanisms used in markets presented in the reviewed literature. It classifies the types of commodities being traded, the benefits of the markets and other features such as the types of grid models. Finally, this paper identifies five evidence gaps which need future research before these markets can be widely adopted.publishersversionpublishe

P2PEdge : A Decentralised, Scalable P2P Architecture for Energy Trading in Real-Time

Author Contributions: Conceptualization, J.K., D.H.-S., R.N.A., B.S. and K.M.; Formal analysis, J.K., D.H.-S. and B.S.; Investigation, J.K.; Methodology, J.K.; Project administration, K.M.; Supervision, K.M. and D.H.-S.; Validation, J.K. and D.H.-S.; Visualization, J.K.; Writing—original draft, J.K.; Writing—review & editing, J.K., K.M., D.H.-S., R.N.A. and B.S. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding.Peer reviewedPublisher PD

Coordinated Trading of Capacity and Balancing Products in Multi-Area Local Flexibility Markets.

In a scenario with high penetration of renewable and distributed energy resources, Local Flexibility Markets (LFMs) emerge to enhance operation of distribution networks. They deal with new consumption patterns, flexibility, and storage systems to mitigate imbalances and congestions. In recent years, efforts toward the definition of stand-alone LFMs have been made, enabling energy trading in isolated systems. This paper present an alternative solution for congestion and imbalance mitigation using capacity and balancing flexibility products. Products prices are defined considering their intrinsic relation with traditional markets, what enhances compatibility and enables full deployment of this local structures. Besides of that, using the properties of an adaptive ADMM algorithm, the market clearing problem is solved under a Multi-Area setting while information privacy is preserved. The feasibility of the proposed approach is demonstrated on a radial network based on the IEEE 34 bus system, where the solution for the two-area LFM is found in four tens iterations. Furthermore, the scalability analysis provides shows a linear relation between the number of areas and the convergence.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech