Cyber Attacks in Transactive Energy Market-Based Microgrid Systems

Dasgupta, Rumpa; Sakzad, Amin; Rudolph, Carsten

doi:10.3390/en14041137

Cited by 23 publications

(7 citation statements)

References 58 publications

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“…A 2-layer DBN stacked through 2 RBMs holds a set of visible layers and 2 layers of hidden layers. If h (1) and h (2) denotes the state vectors of the hidden layer, v represents state vector of the visible layer, W (1) and W (2) indicates the matrix of the symmetrical weight, b (1) and b (2) signifies bias vectors of the hidden layers, and b (0) is the bias vectors of the visible layer.…”

Section: Load Prediction Using Dbn Modelmentioning

confidence: 99%

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Deep Learning Enabled Predictive Model for P2P Energy Trading in TEM

Shoab¹,

Alotaibi²

2022

Computers, Materials &Amp; Continua

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With the incorporation of distributed energy systems in the electric grid, transactive energy market (TEM) has become popular in balancing the demand as well as supply adaptively over the grid. The classical grid can be updated to the smart grid by the integration of Information and Communication Technology (ICT) over the grids. The TEM allows the Peerto-Peer (P2P) energy trading in the grid that effectually connects the consumer and prosumer to trade energy among them. At the same time, there is a need to predict the load for effectual P2P energy trading and can be accomplished by the use of machine learning (DML) models. Though some of the short term load prediction techniques have existed in the literature, there is still essential to consider the intrinsic features, parameter optimization, etc. into account. In this aspect, this study devises new deep learning enabled short term load forecasting model for P2P energy trading (DLSTLF-P2P) in TEM. The proposed model involves the design of oppositional coyote optimization algorithm (OCOA) based feature selection technique in which the OCOA is derived by the integration of oppositional based learning (OBL) concept with COA for improved convergence rate. Moreover, deep belief networks (DBN) are employed for the prediction of load in the P2P energy trading systems. In order to additional improve the predictive performance of the DBN model, a hyperparameter optimizer is introduced using chicken swarm optimization (CSO) algorithm is applied for the optimal choice of DBN parameters to improve the predictive outcome. The simulation analysis of the proposed DLSTLF-P2P is validated using the UK Smart Meter dataset and the obtained outcomes demonstrate the superiority of the DLSTLF-P2P technique with the maximum training, testing, and validation accuracy of 90.17%, 87.39%, and 87.86%.

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Section: Load Prediction Using Dbn Modelmentioning

confidence: 99%

“…It carries several benefits, like decreasing environment pollution, lower the power cost of DERs holders, minimize cost, etc. [1]. Furthermore, DERs owners could transform from consumer to prosumer by trading their excess energy production to other users and grids.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Enabled Predictive Model for P2P Energy Trading in TEM

Shoab¹,

Alotaibi²

2022

Computers, Materials &Amp; Continua

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“…In the digital transformation of energy industry, there are several potential risks, such as geomagnetic storms, malware, ransomware, phishing, botnets, etc. 48 For example, in 2015, Ukraine suffered a cyber-attack specifically on the power grid. The attacker used both the malware and the email phishing to obtain personnel credentials not only, but also attacked the substation's Supervisory Control and Data Acquisition system to prevent customers from getting the information about the outage.…”

Section: Security Risksmentioning

confidence: 99%

Strategic analysis and development plan design on digital transformation in the energy industry: A global perspective

Liu

2021

Int J Energy Res

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This paper conducts strategic analysis and development plan design of energy digital transformation from a global perspective. First, this paper analyzes the reasons for the digital transformation of the energy industry, and elaborates on the current status of the digital transformation of the energy industry. Second, this paper uses the "SWOT" model to conduct a strategic analysis of the digital transformation of energy. Finally, this paper uses the "IETB" model to design the development plan. The results showed that: (1) Energy digital transforma-

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“…Such links can be indeed targeted by cyber attacks with the purpose of taking advantages in the energy market [7]. With respect to the energy market in microgrids, we mention the work of Dasgupta et al [28] that identified potential cyber-attacks in such systems as well as implications to smart grids. On the same line, and conversely to what happens in microgrids, the production or storage of energy lies in the same physical infrastructure as consumers; thus, control communications in active buildings can be exchanged through the existing network channel of the building.…”

Section: Introductionmentioning

confidence: 99%

Incorporating Cyber Threat Intelligence into Complex Cyber-Physical Systems: A STIX Model for Active Buildings

2022

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Active buildings can be briefly described as smart buildings with distributed and renewable energy resources able to energise other premises in their neighbourhood. As their energy capacity is significant, they can provide ancillary services to the traditional power grid. As such, they can be a worthy target of cyber-attacks potentially more devastating than if targeting traditional smart buildings. Furthermore, to handshake energy transfers, they need additional communications that add up to their attack surface. In such a context, security analysis would benefit from collection of cyber threat intelligence (CTI). To facilitate the analysis, we provide a base active building model in STIX in the tool cyberaCTIve that handles complex models. Active buildings are expected to implement standard network security measures, such as intrusion-detection systems. However, to timely respond to incidents, real-time detection should promptly update CTI, as it would significantly speed up the understanding of the nature of incidents and, as such, allow for a more effective response. To fill this gap, we propose an extension to the tool cyberaCTIve with a web service able to accept (incursion) feeds in real-time and apply the necessary modifications to a STIX model of interest.

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Cyber Attacks in Transactive Energy Market-Based Microgrid Systems

Cited by 23 publications

References 58 publications

Deep Learning Enabled Predictive Model for P2P Energy Trading in TEM

Deep Learning Enabled Predictive Model for P2P Energy Trading in TEM

Strategic analysis and development plan design on digital transformation in the energy industry: A global perspective

Incorporating Cyber Threat Intelligence into Complex Cyber-Physical Systems: A STIX Model for Active Buildings

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