• Energy Research

No description supplied 

Accurate building energy prediction is useful in various applications starting from building energy automation and management to optimal storage control. However, vulnerabilities should be considered when designing building energy prediction models, as intelligent attackers can deliberately influence the model performance using sophisticated attack models. These may consequently degrade the prediction accuracy, which may affect the efficiency and performance of the building energy management systems. In this paper, we investigate the impact of bi-level poisoning attacks on regression models of energy usage obtained from household appliances. Furthermore, an effective countermeasure against the poisoning attacks on the prediction model is proposed in this paper. Attacks and defenses are evaluated on a benchmark dataset. Experimental results show that an intelligent cyber-attacker can poison the prediction model to manipulate the decision. However, our proposed solution successfully ensures defense against such poisoning attacks effectively compared to other benchmark techniques.

Recent studies have shown how motion-based biometrics can be used as a form of user authentication and identification without requiring any human cooperation. This category of behavioural biometrics deals with the features we learn in our life as a result of our interaction with the environment and nature. This modality is related to changes in human behaviour over time. The developments in these methods aim to amplify continuous authentication such as biometrics to protect their privacy on user devices. Various Continuous Authentication (CA) systems have been proposed in the literature. They represent a new generation of security mechanisms that continuously monitor user behaviour and use this as the basis to re-authenticate them periodically throughout a login session. However, these methods usually constitute a single classification model which is used to identify or verify a user. This work proposes an algorithm to blend behavioural biometrics with multi-factor authentication (MFA) by introducing a two-step user verification algorithm that verifies the user’s identity using motion-based biometrics and complements the multi-factor authentication, thus making it more secure and flexible. This two-step user verification algorithm is also immune to adversarial attacks, based on our experimental results that show how the rate of misclassification drops while using this model with adversarial data.

With the proliferation of smart devices and revolutions in communications, electrical distribution systems are gradually shifting from passive, manually-operated and inflexible ones, to a massively interconnected cyber-physical smart grid to address the energy challenges of the future. However, the integration of several cutting-edge technologies has introduced several security and privacy vulnerabilities due to the large-scale complexity and resource limitations of deployments. Recent research trends have shown that False Data Injection (FDI) attacks are becoming one of the most malicious cyber threats within the entire smart grid paradigm. Therefore, this paper presents a comprehensive survey of the recent advances in FDI attacks within active distribution systems and proposes a taxonomy to classify the FDI threats with respect to smart grid targets. The related studies are contrasted and summarized in terms of the attack methodologies and implications on the electrical power distribution networks. Finally, we identify some research gaps and recommend a number of future research directions to guide and motivate prospective researchers.

This paper addresses the optimal pre-cooling problem for air conditioners (AC) used in Internet of Things (IoT)-enabled smart homes while ensuring that user-defined thermal comfort can be achieved. The proposed strategy utilises renewable energy generation periods and moves some of the air conditioning loads to these periods to reduce the electricity demand. In particular, we propose a multi-stage approach which maximises the utilisation of renewable energy at the first stage to satisfy air conditioning loads, and then schedules residual energy consumption of these loads to low price periods at the second stage. The proposed approach is investigated for the temperature and renewable generation data of NSW, Australia, over the period 2012–2013. It is shown that the approach developed can significantly reduce the energy consumption and cost associated with AC operation for nearly all days in summer when cooling is required. Specifically, the proposed approach was found to achieve a 24% cost saving in comparison to the no pre-cooling case for the highest average temperature day in January, 2013. The analysis also demonstrated that the proposed scheme performed better when the thermal insulation levels in the smart home are higher. However, the optimal pre-cooling scheme can still achieve reduced energy costs under lower thermal insulation conditions compared to the no pre-cooling case.

In this paper, two generalized algorithms are presented for sizing and allocating distributed generation (DG) units. To determine the size and location of a single DG unit, a heuristic method based on sensitivity analysis and quadratic curve fitting technique has been proposed. Another heuristic technique based on a loss improvement index has been introduced for allocation of multiple DG units. These studies are carried out using two IEEE test distribution systems which are multi-phase and unbalanced in nature. This analysis shows that the use of appropriate size and location of DG reduces total power loss in a distribution system significantly and hence improve the steady-state voltage profile. Methodologies described in this paper gives the distribution system planner an idea about the size and location DG unit which would be the most beneficial in terms of system efficiency and stability.

Abstract Recent studies have shown that the operational modules of an Energy Management System (EMS) are vulnerable to the anomalies that exist in an electric topological and configuration database (DB). In this paper, we focus on the security of EMS modules by detecting anomalies in an electric network DB. Firstly, we explain how an EMS's Optimal Power Flow (OPF) module can be exploited by accidental or deliberate changes in a power system model. As a defense mechanism, for the first time, we propose a graph comparison-based approach for identifying anomalies in an electric network DB. In this study, we formulate the problem as a Quadratic Assignment Problem (QAP) and use the Graduated Assignment algorithm to perform graph matching. To evaluate the effectiveness of the proposed method, we consider different test scenarios considering the IEEE benchmark 24-bus, 30-bus and 118-bus test systems. The results obtained from this analysis show that the proposed method successfully captures DB anomalies at very high detection rates with a smaller time complexity than those obtained from studies published in relevant literature.

Several recent studies have suggested Blockchain for Peer-to-Peer energy trading (P2P-ET) to achieve better security, privacy and fast payment settlement. Most of them however rely on either public Blockchains (which have low performance) or permissioned blockchains (which have low decentralization level and do not provide byzantine fault tolerance). Moreover, these solutions have limitations when capturing the business model of existing energy trading systems. This article proposes a Unified permissioned blockchain-based P2P-ET Architecture (UBETA) that integrates three different types of energy markets and provides a unified energy trading and payment settlement model. The UBETA system is based on an enterprise Ethereum Blockchain, known as Hyperledger Besu, and Istanbul Byzantine Fault Tolerance (IBFT) consensus algorithm. We compared the performance of the proposed IBFT-based system with three existing systems (i.e., Ethereum Clique, Ethereum Proof of Work and Hyperledger Fabric’s Raft) using specific performance metrics (i.e., read/write transaction latency, read/write transaction throughput and fail rate). The experiments were carried out on a network size of up to 60 nodes and a real energy trading data set from the Western Australian energy market was used. The experiment results indicate that the IBFT-based system has 15x lower latency and nearly 2x throughput compared to existing Proof of Work based P2P-ET solutions. Moreover, the system provides better scalability and success rate than existing Raft based P2P-ET systems: the fail rate of the IBFT-based system only increased by 11% while that of Raft increased by 20% when increasing the number of nodes from 20 to 60. In addition, the proposed unified energy trading model provides lower latency and reduces the number of blockchain transactions compared to the non-unified counterpart.