This paper introduces a novel cyberattack-resilient model designed for the optimal operation management of electrolysis-based hydrogen refueling stations (eHRSs) integrated with electric power systems. The optimization model aims to coordinate the scheduling of eHRSs to concurrently support both the transportation sector and the electric power utility. This includes fulfilling the hydrogen demand of electric mobility systems (e-Mobility) and enhancing the resilience of the electric grid by following ancillary service signals issued by the grid operator. Adaptive feasibility areas (FAs) are estimated using the operating parameters of the integrated transportation and power system to identify potential cyberattacks. A framework is developed wherein dispersed eHRSs are managed by an eHRS chain aggregator. The operating parameters of eHRSs are communicated between the individual stations and the eHRS chain aggregator. Additionally, the eHRS chain aggregator interfaces with the electric power utility operator to address the utility’s requirements. Various scenarios are modeled to assess the technical and financial impacts of cyberattacks on eHRS. The proposed model is employed to detect false data injection attacks and mitigate the adverse effects of cyberattacks on the integrated transportation and power system. Simulation studies are conducted to evaluate the effectiveness and practicality of the proposed model. The performance of the FA-based method is compared with traditional deep neural network models and data-driven methods, demonstrating 13.5% and 8.68% improvements, respectively in detection accuracy. In addition, the proposed model achieves a 19% reduction in training time.

Advanced stealthy cyberattacks are capable of infiltrating the cybersecurity layers of power grids and alter their operating conditions, resulting in adverse effects on the system performance. Detecting such Stealthy Infiltrated Cyberattacks (SICA) at the earliest opportunity becomes crucial in order to enable power system operators to implement appropriate corrective measures. To that end, this paper proposes the addition of a new cybersecurity layer for SICA after they have broken through existing cyberattack prevention layers. The paper develops the Feasibility Area (FA) as a classifier mechanism to detect SICA in the collected data of Power System State Variables (PSSV). The proposed detection layer consists of two computational stages. The first stage involves estimating the FA parameters through a historical window of data over a specified period of time, which is then inputted to the second stage. In the second stage, the position of each PSSV with respect to the estimated FA is assessed and utilized by the SICA detection mechanism to identify broken through attacks. A flag vector is created indicating the location of each PSSV with respect to the defined FA. The location of each PSSV and its pattern represented in the flag vector are utilized to identify the existence of SICA. Various SICA detection mechanisms using mathematical techniques and the Pattern Recognition Neural Network (PRNN) have been applied. The numerical results from the evaluation of the proposed FA approach demonstrate a promising performance in detecting the SICA using the proposed method.

Integration of power and gas systems has been recently proposed as a portfolio solution to deal with the sporadic availability of renewables and enhance the flexibility of power systems. In an integrated system, where critical operating information and control signals of both systems need to be communicated, the risk of cyber attack is intensified. In this article, we present a new model for the integration of power and gas systems using power-to-gas (PtG) and gas-fired generation (GfG) facilities. We demonstrate how the operation of the integrated system can be adversely impacted during cyber attacks that may not be detected using traditional methods. We propose two new detection schemes for false data injection attacks against the input and output signals of the PtG/GfG facility scheduler. In the first scheme, a supervised machine-learning technique, based on the convolutional neural network and wavelet transforms, is adopted to detect attacks on the information received by the facility scheduler. In the second scheme, a hybrid neural network is developed, based on an unsupervised learning technique, that requires no labeled training information to detect attacks on the output control signals issued by the scheduler. In both schemes, information acquired from local sensors and deterministic estimation methods is utilized for signal evaluation. The proposed schemes are incorporated into the facilities' scheduler to create a cyber-attack resilient scheduling model in an integrated power and gas grid. The efficacy and feasibility of the proposed model are evaluated via numerical studies using the IEEE30-bus power system integrated with the Belgian gas grid as the test bed using historical operating parameters.

The proliferation of hydrogen fueling stations as a critical infrastructure is necessary for the successful materialization of hydrogen-powered vehicles. Such fueling stations can, in part, utilize the renewable/inexpensive electricity, which would otherwise be curtailed, to generate and store hydrogen. The stored hydrogen can later be used to serve the transportation sector and straightforwardly yield profit for the operator of the stations. The available energy in the storage stations, however, would not be utilized effectively during offpeak hydrogen demand by the transportation sector. While hydrogen fueling stations are primarily contemplated as the suppliers to hydrogen vehicles, this paper shows how the storage capacity in each station can be exploited to provide operating reserve (OR) to an electricity market. To that end, this paper proposes a new supervisory-based model for the optimal scheduling of distributed hydrogen storage stations for 1) energy supply to hydrogen-powered vehicles; and 2) OR provision to an electricity market. As such, the economic feasibility of the investment in such stations would be further intensified due to extra financial settlements for the stations via joint applications. This paper, then, unveils a model that brings about more opportunities for the deployment of hydrogen fueling stations, thereby further inspiring the private investment in such an area by private sectors. The efficacy and feasibility of the proposed model are validated using numerical illustration conducted on a test system.

The existing distributed gas-fired generation (GfG) units have (partially) connected the power and gas distribution grids. In addition, as the emerging technology for conversion of renewable/surplus power to synthetic natural gas, i.e., power-to-gas (PtG) materializes, the foundation for a fully integrated power and gas distribution grid is more likely to be set. This could, in turn, bring about new opportunities for exploiting the natural gas distribution grid for mitigation of the existing and imminent issues in power distribution systems. To that end, this paper unveils a new model for optimal joint scheduling of PtG and GfG units in a power-gas embedded grid. The PtG-GfG facility is operated for arbitrage and loss alleviation as a regulation service to the power distribution system. The mathematical formulation of a new method for estimation of the loss reduction in the integrated grid is developed and embedded into the optimization problem. The efficacy and feasibility of the model is numerically validated on a test system. The results indicate that the proposed model can reliably estimate the loss reduction percentage and accordingly determine the scheduling setpoints to achieve the determined loss reduction. It is demonstrated that the model increases the profitability of investment in PtG-GfG facilities via extra financial settlements for the facility operator.

This paper proposes a new online-calibrated time series based model with the application to the day-ahead natural gas demand (GD) forecasting. A double-stage parallel process is developed for creating the forecasting model. The two stages include analysis of the temperature-independent and temperature-dependent components of the GD. The former stage is executed by online processing of the historical GD information considering the intertemporal variation of the GD. The latter stage, however, is conducted by exploiting the features of the GD information correlated with the ambient temperature. The forecast of the temperature is incorporated into the GD forecasting model through a correlation-based function. The model can generate the day-ahead GD forecast with both the hourly and intrahourly resolutions without compromising the forecast accuracy. The model is calibrated online using the historical GD and temperature information to achieve a higher forecast accuracy. The practical challenges associated with the industrial application of the model are also discussed. The application of the proposed model is numerically examined using real-world GD and temperature data, and the results are comprehensively studied. The outcomes reveal the efficacy and feasibility of the proposed model under various cases.

Summary The growth in computer and networking technologies over the past decades established cloud computing as a new paradigm in information technology. The cloud computing promises to deliver cost‐effective services by running workloads in a large scale data center consisting of thousands of virtualized servers. The main challenge with a cloud platform is its unpredictable performance. A possible solution to this challenge could be load balancing mechanism that aims to distribute the workload across the servers of the data center effectively. In this paper, we present a distributed and scalable load balancing mechanism for cloud computing using game theory. The mechanism is self‐organized and depends only on the local information for the load balancing. We proved that our mechanism converges and its inefficiency is bounded. Simulation results show that the generated placement of workload on servers provides an efficient, scalable, and reliable load balancing scheme for the cloud data center. Copyright © 2016 John Wiley & Sons, Ltd.