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

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