Ahmed Alabdulwahab

Since a single private cloud cannot satisfy the increasing computational requirements for executing multiple workflows simultaneously, hybrid clouds are often adopted to perform such execution. Multi-workflow scheduling is challenging as users may request various applications with different QoS requirements. This work proposes a Chaotic-nondominated-sorting Owl Search Algorithm (COSA) by combining an Owl Search Algorithm (OSA) with a Nondominated Sorting Genetic Algorithm II (NSGA-II) to schedule resource-constrained multiple workflows in hybrid clouds with makespan, cost and energy consumption minimized under the given deadline and budget constraints. First, a hierarchical evolving mechanism is designed to update the better half and worse half of population by NSGA-II and OSA, respectively to guarantee a good trade-off between exploration and exploitation. Second, a chaotic sequence is introduced to adaptively adjust OSA's step size during population evolution for better exploration. Third, we adopt a chaotic operator for searching around the resulting Non-Dominated Solutions (NDS) to improve COSA's local search ability. Experiments are conducted to compare COSA with four peers and the results show its superiority in the number of obtained NDS, diversity preservation and convergence towards the near optimal Pareto set. In particular, it can find at least 19% more NDS than its peers.

At present, most popular route navigation systems only use a few sensed or measured attributes to recommend a route. Yet the optimal route considered by drivers needs be based on multiple objectives and multiple attributes. As a result, these existing systems based on a single or few attributes may fail to meet such drivers’ needs. This work proposes a driver preference-based route planning (DPRP) model. It can recommend an optimal route by considering driver preference. We collect drivers’ preferences, and then provide a set of routes for their choice when they need. Next, we present an integrated algorithm to solve DPRP, which speeds up the search process for recommending the best routes. Its computation cost can be reduced by simplifying a road network and removing invalid sub-routes. Experimental results demonstrate its effectiveness.

People nowadays use the internet to project their assessments, impressions, ideas, and observations about various subjects or products on numerous social networking sites. These sites serve as a great source to gather data for data analytics, sentiment analysis, natural language processing, etc. Conventionally, the true sentiment of a customer review matches its corresponding star rating. There are exceptions when the star rating of a review is opposite to its true nature. These are labeled as the outliers in a dataset in this work. The state-of-the-art methods for anomaly detection involve manual searching, predefined rules, or traditional machine learning techniques to detect such instances. This paper conducts a sentiment analysis and outlier detection case study for Amazon customer reviews, and it proposes a statistics-based outlier detection and correction method (SODCM), which helps identify such reviews and rectify their star ratings to enhance the performance of a sentiment analysis algorithm without any data loss. This paper focuses on performing SODCM in datasets containing customer reviews of various products, which are (a) scraped from Amazon.com and (b) publicly available. The paper also studies the dataset and concludes the effect of SODCM on the performance of a sentiment analysis algorithm. The results exhibit that SODCM achieves higher accuracy and recall percentage than other state-of-the-art anomaly detection algorithms.

This paper presents a two-level transactive energy market framework, that enables energy trading among electric vehicle charging stations (EVCSs). At the lower level, the discharging capability of EVs and on-site PV generation are leveraged by individual EVCS for participating in the transactive trading with their peers. Once the lower-level trading is completed, EVCSs trade energy at the upper level through the power grid network managed by the distribution system operator (DSO). The upper-level market is cleared while satisfying the power distribution network constraints. A cooperative game-based model is proposed to model the energy trading among EVCSs. To this end, the asymmetric Nash bargaining method is applied to allocate the grand coalition’s payoff to each EVCS at the upper-level market, while a weighted proportional allocation method is used to allocate individual EVCS’s payoff to its respective EVs at the lower-level market. The upper-level market formulation is further decomposed into two subproblems representing an energy scheduling and trading subproblem which maximizes EVCS payoffs, and a bargaining subproblem which allocates EVCS payoffs. The effectiveness of the proposed framework for incentivizing transactive trades among EVs and EVCSs is validated in case studies.

This paper proposes a blockchain application for transacting energy and carbon allowance in networked microgrids (MGs). MGs submit trading energy and carbon allowance data to the centralized distribution system operator (DSO) operation, which would optimize the provision of energy and carbon allowance trading among MGs for satisfying power distribution network constraints. The hourly demand response along with onsite MG generation and the DSO’s trading exchanges with ISO are considered among market options to maximize MG payoffs and satisfy distribution network constraints. A cooperative game with externalities is applied to model the market behavior of networked MGs. A two-stage payoff allocation problem is devised to allocate the grand coalition payoff to participating MGs. A solution algorithm is proposed which consists of column-and-constraint generation (C&CG) and Karush-Kuhn-Tucker (KKT) conditions to solve the proposed two-stage market optimization problem with acceptable computational performance. Also, blockchain is applied to provide secure and effective transaction settlements and transparent distribution market operations in the proposed transactive energy and carbon allowance trading strategy. The proposed centralized transactive market is tested on a 4-MG system, the IEEE 33-bus system, and the IEEE 123-bus system. The numerical results show the effectiveness of the proposed method in incentivizing MGs to trade energy and carbon allowance while satisfying the distribution network constraints.

The widespread adoption of communication and control infrastructures will not only improve the microgrid system performance in normal conditions but also increase microgrid cybersecurity risks. Potential cyberattacks can deteriorate microgrid performances by corrupting and intercepting data exchanges among participating DERs, whereby microgrids deviate from desired operating conditions and stable microgrid operations are jeopardized. In this paper, a cross-layer control strategy is proposed to enhance the microgrid resilience against false data injection (FDI) and denial of service (DoS) attacks. On the one hand, the proposed control strategy will not interfere with microgrid normal operations when there are no cyberattacks. On the other hand, the proposed control strategy can effectively mitigate the impacts of FDI and DoS attacks on microgrids without relying on prompt detection and isolation of cyberattacks. The stability of the proposed control strategy is demonstrated using the Lyapunov theory under different scenarios, including without and with FDI and DoS attacks. The effectiveness of the proposed cross-layer resilient control strategy against cyberattacks is validated in a 12-bus microgrid system using time-domain PSCAD/EMTDC simulations.

This paper proposes a methodology for enhancing the power system flexibility, which can respond properly to contingencies in real-time operations. The proposed approach introduces a unified power flow controller (UPFC) in a three-stage security-constrained optimal power flow (SCOPF). The pre- and post-contingency system operation states are divided into three stages including the base case, post-contingency short-term, and post-contingency long-term periods. The UPFC applications re-route active power flow and provide reactive power to mitigate overloads and voltage violations when line outages occur in power systems. UPFC is adopted as a fast-response corrective control device during the post-contingency short-term period, which is coordinated with the conventional slow-response corrective control system during the post-contingency long-term period. A convex approach is applied to reformulate the original nonlinear nonconvex SCOPF problem into a second-order cone programming (SOCP) problem. A two-level algorithm using Benders decomposition and sequential cone programming (SCP) is applied to solve the large-scale SOCP problem. An improved covering cut bundle (CCB) strategy is proposed to accelerate the convergence of the Benders decomposition algorithm. Numerical results show the effectiveness of the proposed model and its solution technique for enhancing the power system flexibility.

This paper proposes a robust resilience enhancement method for a power distribution network in ice storms by the optimal routing of mobile de-icing devices (MDIDs) on congested transportation roads. We consider MDIDs as emergency vehicles, as other vehicles yield the right-of-way to MDIDs, and propose a de-icing schedule (DIS) to illustrate how MDIDs are routed on congested transportation roads. We further coordinate the DIS and MDID routing, which mitigate transportation routing congestions, with power distribution system operation, which considers distribution network reconfiguration and distributed energy resource dispatch. A two-stage robust model is proposed to manage the effects of ice storms forecast errors on both power distribution and urban transportation networks. The proposed model is reformulated as a mixed-integer second-order cone programming problem. The Benders decomposition and columnand-constraint generation algorithms are further utilized to solve the proposed MISOCP. Numerical results for the modified IEEE 33-bus 12-node, IEEE 123-bus 25-node, and 252-bus 80-node electricity-transportation systems show the effectiveness of the proposed model and solution technique for enhancing the power system resilience.

This paper proposes a two-level networkconstrained peer-to-peer (P2P) transactive energy for multimicrogrids (MGs), which guarantees the distribution power network security and allows MGs to trade energy with each other flexibly. At the lower level, a P2P transactive energy is employed for multi-MGs to trade energy with each other. A multi-leader multi-follower (MLMF) Stackelberg game approach is utilized to model the energy trading process among MGs. We prove the existence and the uniqueness of the Stackelberg equilibrium (SE) and provide the closed-form expression for SE. For privacy concerns, we provide several distributed algorithms to obtain SE. At the upper level, the distribution system operator (DSO) reconfigures the distribution network based on the P2P transactive energy trading results by applying the AC optimal power flow considering the distribution network reconfiguration. If there are any network violations, DSO requests trading adjustments at the lower level for network security. We reformulate the DSO operation problem in a mixed-integer second-order cone programming (MISOCP) model and ensure its solution accuracy. Numerical results for a 4-Microgrid system, a modified IEEE 33-bus and 123-bus distribution power system show the effectiveness of the proposed transactive model and its solution technique.

Networked microgrids (NMGs) provide a promising solution for accommodating various distributed energy resources (DERs) and enhancing the system performance in terms of reliability, resilience, flexibility, and energy efficiency. With the penetration of MGs, the communication-based distributed control is playing an increasingly important role in NMGs for coordinating a multitude of heterogeneous and spatially distributed DERs, which feature enhanced efficiency, reliability, resilience, scalability, and privacy-preserving as compared with conventional centralized control. This paper aims to provide a comprehensive survey of distributed control and communication strategies in NMGs. We provide thorough discussions and elaborate on: 1) Essential merits of MGs and NMGs, and their practical implementations; 2) Distributed communication network characteristics and specific operation objectives of NMGs; 3) Classifications of distributed control strategies in NMGs and their salient features; 4) Communication reliability issues concerning data timeliness, data availability, and data accuracy, and the development of countermeasures; 5) Advancements in several promising communication and computation technologies and their potential applications in NMGs.