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Emerging multitenant cloud computing ecosystems allow multiple applications to share virtualized pool of computing and networking resources. As a result, such ecosystems are becoming increasingly prone to data privacy concerns (personal data leakages and unauthorized access). While cloud computing providers support robust security and privacy mechanisms (e.g., public key cryptography, firewalls, and virtual private networks, among many others), they lack mechanisms and frameworks to monitor, audit, and verify these data privacy concerns. The emergence of data protection regulations around the world, such as General Data Protection Regulation in Europe and the Data Protection Act in the U.K., further emphasizes the need to overcome these privacy limitations. In this article, a novel technique for monitoring, auditing, and verifying the operations carried out on a user’s personal data in cloud computing ecosystems is proposed. Our research methodology leverages distributed ledger technologies (e.g., blockchain and smart contracts) for developing an immutable recording technique, which transparently logs, monitors, and verifies the operations carried out on user data. Using a healthcare pharmacy scenario and extensive real-world experiments, we validate the feasibility of the proposed technique. The proposed work handles a large pool of requests ($>$ 13K) ensuring minimal latency ($\approx$ 50–60 ms) and overheads for three different service packages varied with respect to the number of actors and operations. [ABSTRACT FROM AUTHOR]

The European General Data Protection Regulation (GDPR) has had a far-reaching impact on data privacy and compliance for cloud providers. GDPR influences access to, storage, processing and transmission of personal data, requiring these operations to be verified by a cloud user through explicit consent prior to execution. GDPR rules implemented for such operations can be ambiguous and often open to interpretation, making manual verification a time consuming and error prone process for cloud providers. An encoding of GDPR rules is described, with each operation carried out using these rules recorded into a Blockchain for auditing purposes. Specifically, this work shows how some GDPR rules can appear as opcodes in smart contracts to verify the operations of providers on user data in a transparent and automatic way. An abstract model is designed to demonstrate how cloud providers can access and deploy such smart contracts through a Blockchain-based virtual machine. A case study is used to demonstrate how this approach can be used in practice. The case study uses a collection of design patterns and smart contracts to verify provider operations, including read, write, execution and transfer on user data. Validation is undertaken by deploying the smart contracts in a Blockchain test network to investigate the execution costs of GDPR compliance checking. [ABSTRACT FROM AUTHOR]

Fault localization in power distribution systems plays an important role in the resiliency of large-scale power distribution grids since its accuracy can influence the restoration time that needs to be minimized. In this article, we proposed a real-time deep learning algorithm to classify and localize the faults that occurred in the system based on measured data. To get the accurate fault location that may cause the potential fire, we extracted the feature vectors with the measured values of the distribution lines and applied a deep convolutional neural network classifier on them. Also, the presented method was able to detect the fault type, and compute its released energy that can contribute to wildfire based on the measured voltages and currents of the distribution nodes. Unlike prior data-driven methods, the proposed classifier was based on the single observability of the sensor measurement devices due to the limited access to the data and a limited number of measurement units. The performances of our scheme were validated through simulations of four types of faults in a real distribution test feeder under varying system observability, measurement attribute with and without distributed generation units. The proposed classifiers were able to determine the possible fire due to different fault types under 100% accuracy and find the possible fire location with an average accuracy of 85%. [ABSTRACT FROM AUTHOR]

This paper discusses a market-based pool strategy for a microgrid (MG) to optimally trade electric power in the distribution electricity market (DEM). The increasing penetration levels of distributed energy resources and MGs in distribution system (DS) stress distribution system operator and require higher levels of coordinated control strategies. The distribution system operator has limited visibility and control over such distributed resources. To reduce the complexity of the system and improve the efficiency of the electricity market operation, we propose a decentralized pool strategy for an MG to integrate with a distribution system through a market mechanism. A market-based interactions procedure between MGs and DS is developed for MGs as price makers to find an optimal bidding/offering strategy efficiently. To achieve a market equilibrium among all entities, we initially cast this problem as a bi-level programming problem, in which the upper level is an MG optimal scheduling problem and the lower level presents a DEM clearing mechanism. The proposed bi-level model is converted to a single mix-integer model, which is easier to solve. Uncertainties associated with MG's rivals’ offers and demands’ bids are considered in this problem. The solution results from a modified IEEE 33-Bus distribution system are presented and discussed. Finally, some conclusions are drawn and examined. [ABSTRACT FROM AUTHOR]

We propose a new distribution locational marginal price (DLMP) model which is based on a linearized variant of the global energy balance formulation along with trust-region-based solution methodology. Compared to existing DLMP works in the literature, the proposed DLMP model has shown to depict the following features: 1) it decomposes into most general components, i.e., energy, loss, congestion, and voltage; 2) it presents market equilibrium conditions; and 3) it is capable of achieving an efficient flexibility resource allocation in local day-ahead distribution grid markets. The developed model is tested first on a benchmark IEEE 33-bus distribution grid and then on much larger grids with the inclusion of dispatch from flexible loads and distributed generators. [ABSTRACT FROM AUTHOR]

A parallel automated resilience-based restoration methodology is presented in the power system to minimize impact due to emergency power outages. In this power restoration process, a black start unit is assigned to a small region (i.e., a section) on an as-needed basis. A mixed integer nonlinear programming model is developed in order to optimally sectionalize the region of interest all the while maximizing the resiliency in terms of load shedding, restoration time, and network connectivity. For solving this large scale optimization model, a bi-level programming approach is proposed. This approach consists of two optimization levels. The sectionalization problem (upper level) is a mixed integer programming model and finds the optimal section set. The restoration problem (lower level) is a linear model and determines the dc optimal power flow and restoration time for the optimal section set identified in the upper level. We use the pre-emptive method of goal programming to deal with multiple conflicting objectives in the model. Our proposed solution approach outperformed mathematical programming with equilibrium constraints and found near optimal solutions. Numerical results and sensitivity analysis from two case studies (6- and 118-bus IEEE test systems) are further discussed to demonstrate the efficiency of the solution approach. [ABSTRACT FROM AUTHOR]

This paper addresses the microgrid expansion planning problem. In such a competitive electricity market, it will assist community microgrid (COMG) companies in deciding whether or not they should invest in microgrid installation. A two-stage stochastic optimization approach is proposed to eliminate the traditional centralized planning, which has led to competition among COMGs, generation companies (GENCOs), and transmission companies (TRANSCOs) for power delivery. The objective of the two-stage stochastic programming model is to maximize the expected revenue from these three power companies while ensuring the cost-effectiveness and reliability of the power system under uncertain factors, such as load growth and component outages. The proposed model is solved by decomposing the planning problem into two stages. The goal of the first stage is to maximize the profits of COMGs, GENCOs, and TRANSCOs; the goal of second stage is to minimize short-term operation cost considering uncertainty to enhance the reliability of the system. Computational results from two IEEE test systems are presented to analyze the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper reviews distributed/decentralized algorithms to solve the optimal power flow (OPF) problem in electric power systems. Six decomposition coordination algorithms are studied, including analytical target cascading, optimality condition decomposition, alternating direction method of multipliers, auxiliary problem principle, consensus+innovations, and proximal message passing. The basic concept, the general formulation, the application for dc-OPF, and the solution methodology for each algorithm are presented. We apply these six decomposition coordination algorithms on a test system, and discuss their key features and simulation results. [ABSTRACT FROM AUTHOR]

In this paper, the application of high reliability distribution system (HRDS) in the economic operation of a microgrid is studied. HRDS, which offers higher operation reliability and fewer outages in microgrids, is applied to looped networks in distribution systems. The microgrid model in this study is composed of distributed energy resources (DER) including distributed generation (DG), controllable loads, and storage. The microgrid would utilize the local DER as well as the main grid for supplying its hourly load economically which is subject to power quality and reliability requirements. The HRDS implemented at Illinois Institute of Technology (IIT) is used as a case study along with the local DER to increase the load point reliability and decrease the operation cost of the IIT microgrid. The availability of distribution lines, main grid supply, and microgrid generation is considered using the Markov chain Monte Carlo simulation in the microgrid scenarios. The reliability indices based on frequency and duration of outages are measured at the microgrid level and the load point level, and the potential system enhancements are discussed for improving the economic operation of the IIT microgrid. [ABSTRACT FROM PUBLISHER]

Presents corrections to the above named paper. [ABSTRACT FROM AUTHOR]