Showing papers in "IEEE Systems Journal in 2019"
TL;DR: The concept of Proof of Energy is proposed as a novel consensus protocol for P2P energy exchanges managed by DLT and an application of the proposed infrastructure considering a Virtual Power Plant aggregator and residential prosumers endowed with a new transactive controller to manage the electrical storage system is discussed.
Abstract: The unpredictability and intermittency introduced by Renewable Energy Sources (RESs) in power systems may lead to unforeseen peaks of energy production, which might differ from energy demand. To manage these mismatches, a proper communication between prosumers (i.e., users with RESs that can either inject or absorb energy) and active users (i.e., users that agree to have their loads changed according to the system needs) is required. To achieve this goal, the centralized approach used in traditional power systems is no longer possible because both prosumers and active users would like to take part in energy transactions, and a decentralized approach based on transactive energy systems (TESs) and Peer-to-Peer (P2P) energy transactions should be adopted. In this context, the Distributed Ledger Technology (DLT), based on the blockchain concept arises as the most promising solution to enable smart contracts between prosumers and active users, which are safely guarded in blocks with cryptographic hashes. The aim of this paper is to provide a review about the deployment of decentralized TESs and to propose and discuss a transactive management infrastructure. In this context, the concept of Proof of Energy is proposed as a novel consensus protocol for P2P energy exchanges managed by DLT. An application of the proposed infrastructure considering a Virtual Power Plant (VPP) aggregator and residential prosumers endowed with a new transactive controller to manage the electrical storage system is discussed.
285 citations
TL;DR: This paper focuses on data-driven methods for PdM, presents a comprehensive survey on its applications, and attempts to provide graduate students, companies, and institutions with the preliminary understanding of the existing works recently published.
Abstract: With the tremendous revival of artificial intelligence, predictive maintenance (PdM) based on data-driven methods has become the most effective solution to address smart manufacturing and industrial big data, especially for performing health perception (e.g., fault diagnosis and remaining life assessment). Moreover, because the existing PdM research is still in primary experimental stage, most works are conducted utilizing several open-datasets, and the combination with specific applications such as rotating machinery is especially rare. Hence, in this paper, we focus on data-driven methods for PdM, present a comprehensive survey on its applications, and attempt to provide graduate students, companies, and institutions with the preliminary understanding of the existing works recently published. Specifically, we first briefly introduce the PdM approach, illustrate our PdM scheme for automatic washing equipment , and demonstrate the challenges encountered when we conduct a PdM research. Second, we classify the specific industrial applications based on six algorithms of machine learning and deep learning (DL), and compare five performance metrics for each classification. Furthermore, the accuracy (a metric to evaluate the algorithm performance) of these PdM applications is analyzed in detail. There are some important conclusions: 1) the data used in the summarized literature are mostly from public datasets, such as case western reserve university (CWRU)/intelligent maintenance systems (IMS); and 2) in recent years, researchers seem to focus more on DL algorithms for PdM research. Finally, we summarize the common features regarding our surveyed PdM applications and discuss several potential directions.
266 citations
TL;DR: The novelty of this paper is experimental implementation and verification of FPSO-based hybrid MPPT as well as modified SVPWM inverter control has neither been discussed nor implemented before using dSPACE platform by the author's best review.
Abstract: Maximum power point trackers (MPPT) are required in order to obtain optimal photovoltaic power. To achieve this task, an intelligent fuzzy particle swarm optimization (FPSO) MPPT algorithm has been proposed in this paper. Also an inverter control strategy has been gated with a ripple factor compensation-based modified space vector pulse width modulation (SVPWM) method. The proposed system performance is verified under varying sun irradiance, partial shadow, and loading conditions. For load bus voltage regulation, the buck-boost Zeta converter is selected due to least ripple voltage output. The experimental responses verify the efficiency and improved system performance, which is realized through a MATLAB/Simulink interfaced dSPACE DS1104 real-time board. The proposed MPPT and inverter current controller provides high tracking efficiency and anti-islanding protection with superior dynamic control of the system performance by injecting sinusoidal inverter current to the utility grid. The novelty of this paper is experimental implementation and verification of FPSO-based hybrid MPPT as well as modified SVPWM inverter control has neither been discussed nor implemented before using dSPACE platform by the author's best review.
143 citations
TL;DR: This paper integrates sentiment analysis into a machine learning method based on support vector machine and takes the day-of-week effect into consideration to construct more reliable and realistic sentiment indexes.
Abstract: Investor sentiment plays an important role on the stock market. User-generated textual content on the Internet provides a precious source to reflect investor psychology and predicts stock prices as a complement to stock market data. This paper integrates sentiment analysis into a machine learning method based on support vector machine. Furthermore, we take the day-of-week effect into consideration and construct more reliable and realistic sentiment indexes. Empirical results illustrate that the accuracy of forecasting the movement direction of the SSE 50 Index can be as high as 89.93% with a rise of 18.6% after introducing sentiment variables. And, meanwhile, our model helps investors make wiser decisions. These findings also imply that sentiment probably contains precious information about the asset fundamental values and can be regarded as one of the leading indicators of the stock market.
130 citations
TL;DR: The optimal solution, provided for optimal scheduling of MG, prevents the MG from being exposed to high operational cost considering the undesired deviation of market power prices from the forecasted amount.
Abstract: Combined heat and power (CHP) units are able to produce electrical energy and supply heat demand, simultaneously. This paper aims to solve the optimal power and heat generation scheduling problem of integrated heat and power microgrid (MG) considering uncertainty associated with price of selling/purchasing power from power market. The studied MG includes various types of CHP units, a thermal plant, a boiler, electrical energy storage system, and heat buffer tank. The capability of the electrical energy exchange between MG and power market is considered in this paper, where the MG is able to sell/buy power to/from the market. The objective is to obtain the optimal set points of MG components in order to minimize the operational cost of the MG considering power market price uncertainty, which is handled using the robust optimization (RO) method. It is applied to optimize the robustness of the decision-making strategy. As a result, the optimal solution, provided for optimal scheduling of MG, prevents the MG from being exposed to high operational cost considering the undesired deviation of market power prices from the forecasted amount. The proposed method is simulated and the obtained solutions are reported and analyzed for confirming the performance of the proposed RO framework.
107 citations
TL;DR: This paper presents a traffic-aware position-based routing protocol for vehicular ad hoc networks (VANETs) suitable for city environments that uses an ant-based algorithm to find a route that has optimum network connectivity.
Abstract: This paper presents a traffic-aware position-based routing protocol for vehicular ad hoc networks (VANETs) suitable for city environments. The protocol is an enhanced version of the geographical source routing (GSR) protocol. The proposed protocol, named efficient GSR, uses an ant-based algorithm to find a route that has optimum network connectivity. It is assumed that every vehicle has a digital map of the streets comprised of junctions and street segments. Using information included in small control packets called ants, the vehicles calculate a weight for every street segment proportional to the network connectivity of that segment. Ant packets are launched by the vehicles in junction areas. In order to find the optimal route between a source and a destination, the source vehicle determines the path on a street map with the minimum total weight for the complete route. The correct functionality of the proposed protocol has been verified, and its performance has been evaluated in a simulation environment. The simulation results show that the packet delivery ratio is improved by more than 10% for speeds up to 70 km/h compared with the VANET routing protocol based on ant colony optimization (VACO) that also uses an ant-based algorithm. In addition, the routing control overhead and end-to-end delay are also reduced.
107 citations
TL;DR: It is shown that not only the MSD problem, but also its special case, termed the target coverage problem, are NP-hard.
Abstract: Recently, the problem of scheduling mobile sensors to cover all targets and maintain network connectivity such that the total movement distance is minimized, termed the mobile sensor deployment (MSD) problem, has received a great deal of attention. However, the complexity of the MSD problem remains unknown because no exact proof has been provided before. In this paper, we show that not only the MSD problem, but also its special case, termed the target coverage problem, are NP-hard.
106 citations
TL;DR: A novel scheme termed layered orthogonal frequency division multiplexing with index modulation (L-OFDM-IM) to increase the spectral efficiency (SE) of OF DM-IM systems is proposed and results show that L-OFdm-IM outperforms the conventional OFDM- IM scheme.
Abstract: In this paper, we propose a novel scheme termed layered orthogonal frequency division multiplexing with index modulation (L-OFDM-IM) to increase the spectral efficiency (SE) of OFDM-IM systems. In L-OFDM-IM, all subcarriers are first divided into multiple layers, each determining the active subcarriers and their modulated symbols. The index modulation (IM) bits are carried on the indices of the active subcarriers of all layers, which are overlapped and distinguishable with different signal constellations so that the number of the IM bits is larger than that in traditional OFDM-IM. A low-complexity detection is proposed to alleviate the high burden of the optimal maximum-likelihood detection at the receiver side. A closed-form upper bound on the bit error rate, the achievable rate, and diversity order are derived to characterize the performance of L-OFDM-IM. To enhance the diversity performance of L-OFDM-IM, we further propose coordinate interleaving L-OFDM-IM (CI-L-OFDM-IM), which interleaves the real and imaginary parts of the modulated symbols over two different subchannels. Computer simulations verify the theoretical analysis, and results show that L-OFDM-IM outperforms the conventional OFDM-IM scheme. Moreover, it is also confirmed that CI-L-OFDM-IM obtains an additional diversity order in comparison with L-OFDM-IM.
95 citations
TL;DR: A novel anonymous attribute-based broadcast encryption which features the property of hidden access policy and enables the data owner to share his/her data with multiple participants who are inside a predefined receiver set and fulfill the access policy is provided.
Abstract: The sharing of personal data with multiple users from different domains has been benefited considerably from the rapid advances of cloud computing, and it is highly desirable to ensure the sharing file should not be exposed to the unauthorized users or cloud providers. Unfortunately, issues such as achieving the flexible access control of the sharing file, preserving the privacy of the receivers, forming the receiver groups dynamically, and high efficiency in encryption/decryption still remain challenging. To deal with these challenges, we provide a novel anonymous attribute-based broadcast encryption (A $^{2}$ B $^{2}$ E) which features the property of hidden access policy and enables the data owner to share his/her data with multiple participants who are inside a predefined receiver set and fulfill the access policy. We first suggest a concrete A $^{2}$ B $^{2}$ E scheme together with the rigorous and formal security proof without the support of the random oracle model. Then, we design an efficient and secure data sharing system by incorporating the A $^{2}$ B $^{2}$ E scheme, verifiable outsourcing decryption technique for attribute-based encryption, and the idea of online/offline attribute-based encryption. Extensive security analysis and performance evaluation demonstrate that our data sharing system is secure and practical.
90 citations
TL;DR: A deep review of the state of the art of smart DSSs is presented and the latest developments in intelligent systems to support decision-makers in health care are elaborated on.
Abstract: Medical activity requires responsibility not only based on knowledge and clinical skills, but also in managing a vast amount of information related to patient care. It is through the appropriate treatment of information that experts can consistently build a strong policy of welfare. The primary goal of decision support systems (DSSs) is to give information to the experts where and when it is needed. These systems provide knowledge, models, and data processing tools to help the experts make better decisions in several situations. They aim to resolve several problems in health services to help patients and their families manage their health care by providing better access to these services. This paper presents a deep review of the state of the art of smart DSSs. It also elaborates on the latest developments in intelligent systems to support decision-makers in health care. The most promising findings brought in literature are analyzed and summarized according to their taxonomy, application area, year of publication, and the approaches and technologies used. Smart systems can assist decision-makers to improve the effectiveness of their decisions using the integration of data mining techniques and model-based systems. It significantly improves the current approaches, enabling the combination of knowledge from experts and knowledge extracted from data.
85 citations
TL;DR: An overview of recent measurement models and approaches to establishing and enhancing SA in aviation environments and future research directions regarding SA assessment approaches are raised to deal with shortcomings of the existing state-of-the-art methods in the literature.
Abstract: Situation awareness (SA) is an important constituent in human information processing and essential in pilots’ decision making processes. Acquiring and maintaining appropriate levels of SA is critical in aviation environments as it affects all decisions and actions taking place in flights and air traffic control. This paper provides an overview of recent measurement models and approaches to establishing and enhancing SA in aviation environments. Many aspects of SA are examined including the classification of SA techniques into six categories, and different theoretical SA models from individual, to shared or team, and to distributed or system levels. Quantitative and qualitative perspectives pertaining to SA methods and issues of SA for unmanned vehicles are also addressed. Furthermore, future research directions regarding SA assessment approaches are raised to deal with shortcomings of the existing state-of-the-art methods in the literature.
TL;DR: A multisensor temporal prediction based wide-area control scheme for controlling the smart grid's voltage profile and the performance of the proposed technique in the presence of false-data-injection attacks shows promising results.
Abstract: Monitoring and control of electrical power grids are highly reliant on the accuracy of the digital measurements. These digital measurements reflect the precision of the installed sensors, which are vulnerable to the injection of unknown parameters in the form of device malfunction and cyberattacks. This may question the operational security and reliability of many cyberphysical infrastructure such as smart grid. To resolve this issue, a multisensor temporal prediction based wide-area control scheme is proposed in this paper. The feasibility of the designed scheme is verified in an advanced synchrophasor measurements based wide-area monitoring and control system (WAMCS). This WAMCS adopts a flexible ac transmission system device (the primary controller) for controlling the smart grid's voltage profile. The algorithm is validated in a real-time environment with an innovative software-in-the-loop testing setup. The performance of the proposed technique in the presence of false-data-injection attacks shows promising results.
TL;DR: This paper considers an uplink satellite multiterrestrial relay network, which employs a single-antenna user to communicate with a satellite via multiple decode-and-forward (DF) terrestrial relays, and derives closed-form expressions for the outage probability and throughput of the considered system.
Abstract: In this paper, we consider an uplink satellite multiterrestrial relay network, which employs a single-antenna user to communicate with a satellite via multiple decode-and-forward (DF) terrestrial relays. Due to the spectrum sharing policy, terrestrial relays are interfered by cochannel interference (CCI). For inevitable radio frequency (RF) front-end imperfections, the interconnected nodes of the whole network are always impaired by inherent hardware impairments (HIs). Specifically, in order to improve the overall system performance, a partial relay selection scheme is used to enhance the system performance by improving the spatial diversity. On this foundation, we have derived the closed-form expressions for the outage probability (OP) and throughput of the considered system, where both the satellite and terrestrial channels are under independent nonidentical distributions. To get better insights at high signal-to-noise-ratios (SNRs), the asymptotic behaviors for the system performance are also derived. From the asymptotic results, the impacts of CCI and the HIs on the system performance are quantitatively analyzed. Especially, the OP and throughput will have bounds when the system is under HIs. Monte Carlo (MC) simulation results corroborate the theoretical analysis and illustrate the joint effects of CCI and HIs on the considered system.
TL;DR: This paper proposes a novel controller for the secondary load frequency control of a shipboard microgrid based on the linear matrix inequality technique and the Lyapunov stability theory and several hardware-in-the-loop real-time simulations are performed to show the merits of the proposed method.
Abstract: This paper proposes a novel controller for the secondary load frequency control of a shipboard microgrid. The suggested controller is based on the linear matrix inequality technique and the Lyapunov stability theory. In the controller design procedure, the effects of the sensor-to-controller and controller-to-actuator delay communication links are considered, and a robust controller against the delay is proposed by utilizing a Lyapunov–Krasovskii functional. In addition, due to the practical space limitation of a ship, low-space, high-efficient renewable energy sources (RESs), and energy storage systems are considered. Furthermore, a diesel generator and a proton exchange membrane fuel cell are employed to effectively mitigate the frequency oscillations arise by the loads and RES power variations. Finally, to show the merits of the proposed method, several hardware-in-the-loop real-time simulations are performed. Comparison results illustrate the effectiveness of the proposed approach to the state-of-the-art methods.
TL;DR: A generalized model for the residential load scheduling or load commitment problem (LCP) in the presence of renewable sources for any type of tariff is presented and Reinforcement learning (RL) is an efficient tool that has been used to solve the decision making problem under uncertainty.
Abstract: The significance and need of demand response (DR) programs is realized by the utility as a means to reduce the additional production cost imposed by the accelerating energy demand. With the development in smart information and communication systems, the price-based DR programs can be effectively utilized for controlling the loads of smart residential buildings. Nowadays, the use of stochastic renewable energy sources like photovoltaic (PV) by a small domestic consumer is increasing. In this paper, a generalized model for the residential load scheduling or load commitment problem (LCP) in the presence of renewable sources for any type of tariff is presented. Reinforcement learning (RL) is an efficient tool that has been used to solve the decision making problem under uncertainty. An RL-based approach to solve the LCP is also proposed. The novelty of this paper lies in the introduction of a comprehensive model with implementable solution considering consumer comfort, stochastic renewable power, and tariff. Simulation experiments are conducted to test the efficacy and scalability of the proposed algorithm. The performance of the algorithm is investigated by considering a domestic consumer with schedulable and nonschedulable appliances along with a PV source. Guidelines are given for choosing the parameters of the load.
TL;DR: In this article, the authors proposed a detection method of islanded operation mode for the large power synchronous generator-based DGs in bio-mass power plants, which can detect islanding phenomena with high accuracy, low detection time, very small number of non-detected zones and almost zero power balance.
Abstract: The question of the islanding operation of distributed generations (DGs) and that of the current inadmissible networking policy of islanding operations sets a demanding task for an efficient islanding detection, preferably from a single place, using either passive or active islanding-detection method. Islanding detection shall be faster than the fast automatic reclosing of the connected transmission line connected to the DG unit. This paper deals with the detection method of islanded operation mode for the large power synchronous generator-based DGs in bio-mass power plants. The proposed method has been applied to a practical 10-MW bio-mass power plant and experimentally tested to verify the simulation results. The rate of change of reactive power ( dQ/dt >) criterion is derived as a yardstick for islanding detection. The practical power plant model has been implemented to be simulated in DIgSILENT PowerFactory and MATLAB/Simulink software environments. Offline digital time-domain simulations and experimental tests on a practical bio-mass power plant reveals that the proposed method efficiently, and authentically detects islanding phenomena with high accuracy, low detection time, very small number of non-detected zones and, with almost zero power balance ( ΔP = 0).
TL;DR: This letter considers the deployment problem for a network of aerial drones to maximize the coverage of an area for surveillance and monitoring and develops a distributed optimization model and a coverage maximizing algorithm.
Abstract: This letter considers the deployment problem for a network of aerial drones to maximize the coverage of an area for surveillance and monitoring. A distributed optimization model is proposed and a coverage maximizing algorithm is developed. It is proved that the proposed algorithm converges to a local maximum in a finite number of steps. Simulations with a real dataset demonstrate the effectiveness of the algorithm.
TL;DR: A novel adaptive controller is proposed to mitigate the destructive effect of time-varying uncertain CPLs, aiming to adaptively modify the injecting current of the energy storage system.
Abstract: The performance of a DC microgrid (MG) might degrade because of the dynamics of constant power loads (CPLs). In this paper, a novel adaptive controller is proposed to mitigate the destructive effect of time-varying uncertain CPLs. A nonlinear disturbance observer is developed to estimate the instantaneous power of the CPLs. The estimated CPLs powers are then employed in a Takagi–Sugeno fuzzy-based model predictive control strategy, aiming to adaptively modify the injecting current of the energy storage system. The proposed approach is applied to a dc MG testbed that feeds one CPL. Experimental results show that the proposed adaptive controller is able to increase the stability margin and improve the transient response of the dc MG.
TL;DR: The background and motivation of the big data paradigm in smart power systems are provided, and then the major issues related to the architectures, the key technologies, and standardizations of big data analytics in smartPower systems are analyzed.
Abstract: The smart power systems are based upon information and communication technologies, which lead to a deluge of data originating from various sources. To address these challenges concerning accumulated voluminous data, big data analysis in smart power systems is inevitable. This article comprehensively surveys the literature related to the big data issues in smart power systems. The background and motivation of the big data paradigm in smart power systems are first provided, and then the major issues related to the architectures, the key technologies, and standardizations of big data analytics in smart power systems are analyzed. Also, the potential applications of big data in smart power systems based upon the state-of-the-art research are highlighted. Finally, the future issues and challenges of the big data issues in modern power systems are discussed.
TL;DR: A relay coordination scheme is proposed for the faulted section isolation in a dc ring bus microgrid and the maloperation of a relay due to the communication failure in the unit protection scheme can be averted by applying the proposed method using local data.
Abstract: A fast and accurate protection scheme for a dc microgrid enhances the service reliability and improves the power quality. A fault in any section of a dc microgrid results in oscillation in current, where its frequency is a function of fault position. The frequency and associated transient power of the first cycle of the oscillation are used for fault detection and faulted section identification, respectively. Based on the inverse-time transient power, a relay coordination scheme is proposed for the faulted section isolation in a dc ring bus microgrid. The maloperation of a relay due to the communication failure in the unit protection scheme can be averted by applying the proposed method using local data. The performance of the method is tested using data obtained from PSCAD/EMTDC simulations for numerous cases including bidirectional power flow situation, high fault resistance, and different fault types. The proposed algorithm is also validated on a scaled-down hardware setup in the laboratory.
TL;DR: It is proven that the consensus tracking control objective can be achieved by the designed control law, and a novel adaptive consensus tracking protocol is developed for the stochastic nonlinear multiagent systems.
Abstract: This paper addresses the event-triggered consensus tracking problem for a class of higher order stochastic nonlinear multiagent systems. First, we propose a new protocol design framework. Then, a novel adaptive consensus tracking protocol, under which the continuous communication is not required, is developed for the stochastic nonlinear multiagent systems. Using the Lyapunov functional approach and the stochastic theory, it is proven that the consensus tracking control objective can be achieved by the designed control law. The Zeno behavior was excluded by showing that there exists a lower bound for interevent instants. Finally, to verify the effectiveness of the given event-triggered control scheme, two numerical examples are provided.
TL;DR: A comprehensive survey of the major issues related to the architectures, the key technologies, and the requirements of the SG communication infrastructure are analyzed in this article.
Abstract: Advanced information and communication infrastructures are essential to successfully operate smart grids (SGs) and provide efficient, reliable, and sustainable electricity to the customers. After providing the background of the communication paradigm in SGs, a comprehensive survey of the major issues related to the architectures, the key technologies, and the requirements of the SG communication infrastructure are analyzed in this article. The role of cloud computing and Internet of Things in SGs is also discussed. Finally, the standardization, potential applications, and future fruitful research issues of the communication infrastructures of the SGs are classified and discussed.
TL;DR: The uneven cluster-based mobile charging (UCMC) algorithm for WRSNs is proposed, and an uneven clustering scheme and a novel charging path planning scheme are incorporated in the UCMC algorithm.
Abstract: Wireless rechargeable sensor networks (WRSN) have attracted considerable attention in recent years due to the constant energy supply for battery-powered sensor nodes. However, current technologies only enable the mobile charger to replenish energy for one single node at a time. This method has poor scalability and is not suitable for large-scale WRSNs. Recently, wireless energy transfer technology based on multi-hop energy transfer has made great progress. It provides fundamental support to alleviate the scalability problem. In this paper, the node energy replenishment problem is formulated into an optimization problem. The optimization objective is to minimize the number of non-functional nodes. We propose the uneven cluster-based mobile charging (UCMC) algorithm for WRSNs. An uneven clustering scheme and a novel charging path planning scheme are incorporated in the UCMC algorithm. The simulation results verify that the proposed algorithm can achieve energy balance, reduce the number of dead nodes, and prolong the network lifetime.
TL;DR: A user preference aware caching deployment algorithm is proposed for D2D caching networks that can achieve significant improvement on cache hit ratio, content access delay, and traffic offloading gain.
Abstract: Content caching in the device-to-device (D2D) cellular networks can be utilized to improve the content delivery efficiency and reduce traffic load of cellular networks. In such cache-enabled D2D cellular networks, how to cache the diversity contents in the multiple cache-enabled mobile terminals, namely, the caching deployment, has a substantial impact on the network performance. In this paper, a user preference aware caching deployment algorithm is proposed for D2D caching networks. First, the definition of the user interest similarity is given based on the user preference. Then, a content cache utility of a mobile terminal is defined by taking the transmission coverage region of this mobile terminal and the user interest similarity of its adjacent mobile terminals into consideration. A general cache utility maximization problem with joint caching deployment and cache space allocation is formulated, where the special logarithmic utility function is integrated. In doing so, the caching deployment and the cache space allocation can be decoupled by equal cache space allocation. Subsequently, we relax the logarithmic utility maximization problem, and obtain a low complexity near-optimal solution via a dual decomposition method. Compared with the existing caching placement methods, the proposed algorithm can achieve significant improvement on cache hit ratio, content access delay, and traffic offloading gain.
TL;DR: In this article, the design problem of observer-based fault-tolerant control is considered for three faulty cases: sensor only, actuator only, and both sensor and actuator faults.
Abstract: In this paper, the design problem of $\mathcal{H}_{\infty }$ observer-based fault-tolerant control is considered for three faulty cases: sensor only, actuator only, and both sensor and actuator faults. The observers are designed for estimating both states and faults. To reduce the fault effects on the system, virtual observers are first introduced. Based on the virtual observer, real observers are established because the virtual observers include unmeasurable information of the system. By using the estimated information from the observers, observer-based $\mathcal{H}_{\infty }$ fault-tolerant controllers are designed. A numerical example is provided to demonstrate the effectiveness of the proposed method.
TL;DR: This work presents an energy-efficient offloading decision mechanism and an offloading dispatcher to dispatch applications to the corresponding device by effectively managing the computation and communication resources of all devices in a fog computing environment.
Abstract: Fog computing has attracted considerable attention to meet the location awareness and real-time response requirements of various applications. However, compared to cloud computing, fog devices have relatively limited power supplies, computation resources, and communication resources, raising design challenges to meet real-time response requirements. To balance response time and energy consumption for multiple fog devices running multiple applications, this work presents an energy-efficient offloading decision mechanism and an offloading dispatcher to dispatch applications to the corresponding device by effectively managing the computation and communication resources of all devices in a fog computing environment. The offloading tasks are composed of several subtasks with an end-to-end deadline. To meet the response time requirements of such applications, a run-time scheduler with an end-to-end latency schedulability consideration is also presented. Evaluation results show considerable energy savings using this framework and a real platform study provides validation.
TL;DR: A categorization of proposed models, case studies, innovations, and solution methods of the most relevant works regarding TNEP is provided, providing a literary framework for TNEP specialists.
Abstract: Power systems must be prepared to match the current growing demand for electrical energy. In this paper, the transmission network plays an important role, delivering the electric power generated in conventional power plants to load centers. For the last 45 years, the transmission network expansion planning (TNEP) problem has been widely studied; nowadays, TNEP, combined with new challenges, is being highly investigated, as researchers aim to reach a better solution. This paper presents a complete review and classification of the most significant works to date, providing a literary framework for TNEP specialists. Hence, a categorization of proposed models, case studies, innovations, and solution methods of the most relevant works regarding TNEP is provided. In order to establish a complete background, not only traditional approaches, but also those involving maintenance, uncertainties in generation and demand, reliability, electricity markets, energy storage, and risk management in TNEP are highlighted. This framework can help planners to improve previous formulations and methods and can propose more efficient models to better exploit existing infrastructure and reduce costs of investment.
TL;DR: Considering versatile users’ quality of service (QoS) requirements on transmission delay and rate, coarse resource provisioning scheme and deep reinforcement learning-based autonomous slicing refinement algorithm are proposed and a shape-based heuristic algorithm for user resource customization is devised to improve resource utilization and QoS satisfaction.
Abstract: Network slicing has been introduced in fifth-generation (5G) systems to satisfy requirements of diverse applications from various service providers operating on a common shared infrastructure. However, heterogeneous characteristics of slices have not been widely explored. In this paper, we investigate dynamic network slicing strategies with mixed traffics in virtualized radio access network (RAN). Considering versatile users’ quality of service (QoS) requirements on transmission delay and rate, coarse resource provisioning scheme and deep reinforcement learning-based autonomous slicing refinement algorithm are proposed. Then, a shape-based heuristic algorithm for user resource customization is devised to improve resource utilization and QoS satisfaction. In principle, the DQN algorithm allocates only the necessary resource to slices to satisfy users’ QoS requirements. For fairness in comparison, we reserve all the unused resources back to the slices. In case there is a sudden change in user population in one slice, the algorithm provides isolation. To validate the advantage, system-level simulations are conducted. The results show that the proposed algorithm balances the satisfaction up to about 100% and resource utilization up to 80% against state-of-the-art solutions. The proposed algorithm also improves the performance of slices in mixed traffics against state-of-the-art benchmarks, which fail to balance satisfaction and resource utilization in some slices.
TL;DR: The proposed strategy addresses the challenges of renewable energy variability and forecast uncertainty using a two-stage decision process combined with a receding horizon approach and is able to produce reliable dispatch commands without considering probabilistic information from the forecasting system.
Abstract: This paper presents the mathematical formulation and architecture of a robust energy management system for isolated microgrids featuring renewable energy, energy storage, and interruptible loads. The proposed strategy addresses the challenges of renewable energy variability and forecast uncertainty using a two-stage decision process combined with a receding horizon approach. The first-stage decision variables are determined using a cutting-plane algorithm to solve a robust unit commitment; the second stage solves the final dispatch commands using a three-phase optimal power flow. This novel approach is tested on a modified International Council on Large Electric Systems (CIGRE) test system under different conditions. The proposed algorithm is able to produce reliable dispatch commands without considering probabilistic information from the forecasting system. These results are compared with deterministic and stochastic formulations. The benefits of the proposed control are demonstrated by a reduction in load interruption events and by increasing available reserves without an increase in overall costs.
TL;DR: An improved algorithm for commercial peak-load management using EVs, battery-energy-storage systems, and photovoltaic units is proposed, which uses the bidirectional vehicle-to-grid technique to utilize the energy from EVs in a parking lot.
Abstract: Electric vehicles (EVs) are getting popular as one of the effective solutions for increased energy efficiency in commercial systems This paper proposes an improved algorithm for commercial peak-load management using EVs, battery-energy-storage systems, and photovoltaic units It uses the bidirectional vehicle-to-grid technique to utilize the energy from EVs in a parking lot The proposed system has been tested in a real power distribution network in realistic load and weather conditions The financial benefit of the system is also investigated, and it is found that the industrial peak load can be reduced by 50%, and the energy cost can be reduced by up to 273% It also enhances the load factor by 9% The performance of the proposed control algorithm is compared with that of an artificial-neural-network-based technique and tested in a laboratory prototype From simulated and experimental results, it is found that the proposed approach provides substantial savings, while reducing the peak demand of the existing grids