47,655 research outputs found
Privacy-Preserving Transactive Energy Management for IoT-aided Smart Homes via Blockchain
With the booming of smart grid, The ubiquitously deployed smart meters
constitutes an energy internet of things. This paper develops a novel
blockchain-based transactive energy management system for IoT-aided smart
homes. We consider a holistic set of options for smart homes to participate in
transactive energy. Smart homes can interact with the grid to perform vertical
transactions, e.g., feeding in extra solar energy to the grid and providing
demand response service to alleviate the grid load. Smart homes can also
interact with peer users to perform horizontal transactions, e.g., peer-to-peer
energy trading. However, conventional transactive energy management method
suffers from the drawbacks of low efficiency, privacy leakage, and single-point
failure. To address these challenges, we develop a privacy-preserving
distributed algorithm that enables users to optimally manage their energy
usages in parallel via the smart contract on the blockchain. Further, we design
an efficient blockchain system tailored for IoT devices and develop the smart
contract to support the holistic transactive energy management system. Finally,
we evaluate the feasibility and performance of the blockchain-based transactive
energy management system through extensive simulations and experiments. The
results show that the blockchain-based transactive energy management system is
feasible on practical IoT devices and reduces the overall cost by 25%.Comment: To appea
Efficient and Secure Energy Trading with Electric Vehicles and Distributed Ledger Technology
Efficient energy management of Distributed Renewable Energy Resources (DRER)
enables a more sustainable and efficient energy ecosystem. Therefore, we
propose a holistic Energy Management System (EMS), utilising the computational
and energy storage capabilities of nearby Electric Vehicles (EVs), providing a
low-latency and efficient management platform for DRER. Through leveraging the
inherent, immutable features of Distributed Ledger Technology (DLT) and smart
contracts, we create a secure management environment, facilitating interactions
between multiple EVs and energy resources. Using a privacy-preserving load
forecasting method powered by Vehicular Fog Computing (VFC), we integrate the
computational resources of the EVs. Using DLT and our forecasting framework, we
accommodate efficient management algorithms in a secure and low-latency manner
enabling greater utilisation of the energy storage resources. Finally, we
assess our proposed EMS in terms of monetary and energy utility metrics,
establishing the increased benefits of multiple interacting EVs and load
forecasting. Through the proposed system, we have established the potential of
our framework to create a more sustainable and efficient energy ecosystem
whilst providing measurable benefits to participating agents.Comment: Accepted at IEEE Virtual Conference on Communications (VCC) 202
On security and privacy of consensus-based protocols in blockchain and smart grid
In recent times, distributed consensus protocols have received widespread attention in the area of blockchain and smart grid. Consensus algorithms aim to solve an agreement problem among a set of nodes in a distributed environment. Participants in a blockchain use consensus algorithms to agree on data blocks containing an ordered set of transactions. Similarly, agents in the smart grid employ consensus to agree on specific values (e.g., energy output, market-clearing price, control parameters) in distributed energy management protocols.
This thesis focuses on the security and privacy aspects of a few popular consensus-based protocols in blockchain and smart grid. In the blockchain area, we analyze the consensus protocol of one of the most popular payment systems: Ripple. We show how the parameters chosen by the Ripple designers do not prevent the occurrence of forks in the system. Furthermore, we provide the conditions to prevent any fork in the Ripple network. In the smart grid area, we discuss the privacy issues in the Economic Dispatch (ED) optimization problem and some of its recent solutions using distributed consensus-based approaches. We analyze two state of the art consensus-based ED protocols from Yang et al. (2013) and Binetti et al. (2014). We show how these protocols leak private information about the participants. We propose privacy-preserving versions of these consensus-based ED protocols. In some cases, we also improve upon the communication cost
Resilient Microgrid Energy Management Algorithm Based on Distributed Optimization
This article proposes a fully distributed energy management algorithm for dc microgrids, resilient to different faults. Specifically, we employ distributed model-predictive control to deal with the uncertainty that characterizes the microgrid operation. The optimization problem is solved at each time step through a distributed optimization algorithm, which has three main advantages: 1) agents of the network require a small computational power; 2) local information is not shared among the network nodes, hence preserving a certain level of privacy; and 3) it is suitable for implementation in large-scale systems. The resilience property of the algorithm stems from additional constraints that are enforced in order to store in the system enough energy to sustain the microgrid in the case of utility grid or line fault. Simulation results show that the algorithm is suitable to schedule the operation of agents that are always connected to the microgrid (e.g., loads) as well as agents that may be connected and disconnected (e.g., electric vehicles)
A Federated learning model for Electric Energy management using Blockchain Technology
Energy shortfall and electricity load shedding are the main problems for
developing countries. The main causes are lack of management in the energy
sector and the use of non-renewable energy sources. The improved energy
management and use of renewable sources can be significant to resolve energy
crisis. It is necessary to increase the use of renewable energy sources (RESs)
to meet the increasing energy demand due to high prices of fossil-fuel based
energy. Federated learning (FL) is the most emerging technique in the field of
artificial intelligence. Federated learning helps to generate global model at
server side by ensemble locally trained models at remote edges sites while
preserving data privacy. The global model used to predict energy demand to
satisfy the needs of consumers. In this article, we have proposed Blockchain
based safe distributed ledger technology for transaction of data between
prosumer and consumer to ensure their transparency, traceability and security.
Furthermore, we have also proposed a Federated learning model to forecast the
energy requirements of consumer and prosumer. Moreover, Blockchain has been
used to store excess energy data from prosumer for better management of energy
between prosumer and grid. Lastly, the experiment results revealed that
renewable energy sources have produced better and comparable results to other
non-renewable energy resources.Comment: 14 figures, 7 tables, 15 page
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