Showing papers in "IEEE Systems Journal in 2023"

Power Distribution Expansion Planning in the Presence of Wholesale Multimarkets

Abstract: By deregulating the countries' electricity industry, the market players must adapt their performance to the deregulated environment. In this article, a risk-based stochastic optimization framework is proposed to model the participation of distribution system operator (DSO) in distribution expansion planning (DEP) problems in the presence of electricity wholesale multimarkets. Based on the proposed methodology in this article, the DSOs will be able to invest in the DEP by taking the benefits and imposed risks of the existing electricity multimarkets, i.e., forward contract, day-ahead, and balancing markets, to manage the risks of long-term uncertainties. The DSOs, who are the only retail suppliers in a specific region, can cover their DEP costs by offering fair retail prices to the retail consumers. Besides, DSOs could form diverse long- and short-term portfolios from the different electricity markets to procure the forecasted real loads and loss power of the network over the planning horizon. The diverse portfolio of the suppliers and a fair retail price can satisfy the DSOs to consider the multimarkets in the DEP process. Furthermore, the risk of uncertainties is modeled using the method of conditional value at risk (CVaR). Based on the obtained results, the DSO can reduce network loss costs by procuring power from markets rather than increasing the installed branches' ampacity by considering multimarkets and market price uncertainties. Besides, the obtained profit of DSO is increased by 12.16% in the proposed electricity multimarket environment.

Resilient Bipartite Consensus of Second-Order Multiagent Systems With Event-Triggered Communication

Abstract: This article investigates the resilient bipartite consensus problem for continuous-time second-order multiagent systems in the presence of totally bounded malicious nodes under signed digraphs. An event-based resilient impulsive algorithm is employed, which cannot only mitigate the malicious nodes’ influence on the convergence of normal ones but also reduce the communication loads of agents. A necessary and sufficient condition related to the network topology is established for solving resilient bipartite consensus by using system transformation. A numerical simulation illustrates the effectiveness of the result.

Distributed Adaptive-MPC Type Optimal PMS for PV-Battery Based Isolated Microgrid

Abstract: In the absence of an external grid, supplying quality power to critical loads in renewables, i.e., solar photovoltaics (PV)-battery-based self-sustained isolated microgrid (MG) system is quite troublesome. Because of involved generation arbitrage and variable load conditions, a robust power management scheme is essential. In this article, a distributed strategy with an adaptive model predictive control (MPC) principle is proposed. It considers all possible uncertainties, viz., intermittent generation as time-varying solar irradiance, variable load profile, alongside maintaining smooth charging/discharging cycle of storage units and shedding of noncritical loads. For this purpose, a novel comprehensive small-signal state-space model of MG with PV-battery systems having dedicated parallel RLC loads and a low-voltage network is considered, which accounts for inverter dc-bus link dynamics. The controller tracks the maximum power point of PV and keeps the MG power balance intact for both active and reactive powers by considering interconnected bus dynamics. The viability of the proposed scheme is verified by performing extensive testing, viz., variable solar irradiance profile, continuous small and large variations of loads, shedding of the noncritical loads, multiple topology subsystems, comparative study with state-of-art MPC, and smooth transition of battery charging/discharging cycles using MATLAB platform over 24 and 48 h duration.

Resilient Bipartite Consensus of Second-Order Multiagent Systems With Event-Triggered Communication

Abstract: This article investigates the resilient bipartite consensus problem for continuous-time second-order multiagent systems in the presence of totally bounded malicious nodes under signed digraphs. An event-based resilient impulsive algorithm is employed, which cannot only mitigate the malicious nodes’ influence on the convergence of normal ones but also reduce the communication loads of agents. A necessary and sufficient condition related to the network topology is established for solving resilient bipartite consensus by using system transformation. A numerical simulation illustrates the effectiveness of the result.

Forecast-Assisted Service Function Chain Dynamic Deployment for SDN/NFV-Enabled Cloud Management Systems

Abstract: Software-defined network (SDN) and network function virtualization (NFV) are acknowledged as the most promising technologies to effectively allocate resource for network service. A service function chain (SFC), which can deploy virtualized network functions (VNFs) and chain them with associated flows allocation, can be used to represent each network service owing to the introduction of the SDN/NFV technology. Co-hosted applications on multiple Internet of Things terminals have dynamic and time-varying service requirements, in order to allocate network resources optimally and meet the end-to-end delay requirements of services, sufficient strategies are required to satisfy the continuously changing service demands. In this article, a deep learning model that combines the multitask regression layer above the graph neural networks is first presented to predict the future resource requirements of each VNF instance. The SFC deployment problem is then solved using the integer nonlinear programming (INLP) approach, and a novel prediction-assisted Viterbi algorithm is presented to overcome the scalability problem of the INLP approach. According to the simulation findings, the proposed deep model provides a minimum of a 6.2% improvement in prediction accuracy over baseline prediction models, and the proposed SFC deployment strategy has been demonstrated to deliver better performance in terms of acceptance ratio and revenue, compared to the current passive deployment algorithms.

Distributed Adaptive-MPC Type Optimal PMS for PV-Battery Based Isolated Microgrid

Abstract: In the absence of an external grid, supplying quality power to critical loads in renewables, i.e., solar photovoltaics (PV)-battery-based self-sustained isolated microgrid (MG) system is quite troublesome. Because of involved generation arbitrage and variable load conditions, a robust power management scheme is essential. In this article, a distributed strategy with an adaptive model predictive control (MPC) principle is proposed. It considers all possible uncertainties, viz., intermittent generation as time-varying solar irradiance, variable load profile, alongside maintaining smooth charging/discharging cycle of storage units and shedding of noncritical loads. For this purpose, a novel comprehensive small-signal state-space model of MG with PV-battery systems having dedicated parallel RLC loads and a low-voltage network is considered, which accounts for inverter dc-bus link dynamics. The controller tracks the maximum power point of PV and keeps the MG power balance intact for both active and reactive powers by considering interconnected bus dynamics. The viability of the proposed scheme is verified by performing extensive testing, viz., variable solar irradiance profile, continuous small and large variations of loads, shedding of the noncritical loads, multiple topology subsystems, comparative study with state-of-art MPC, and smooth transition of battery charging/discharging cycles using MATLAB platform over 24 and 48 h duration.

Physical Layer Security for RIS-Aided Wireless Communications With Uncertain Eavesdropper Distributions

Abstract: In this article, the physical layer security (PLS) is investigated for reconfigurable intelligent surface (RIS)-aided wireless communication systems, where one RIS is deployed to assist the communications between a pair of transmitter (Alice) and receiver (Bob), under a passive eavesdropper (Eve) attack. For the Eve, different from the bounded channel state information uncertainty model, the distribution of the Eve’s location is introduced into the wiretap link. In the proposed system, considering that the Eve can overhear signals transmitted from Alice or reflected by the RIS, two scenarios are studied for RIS-aided secure communication systems: one is that the Eve distributes close to Alice without the RIS orientation, and the other is that the Eve locates close to Bob and in the presence of the RIS. After investigating the probability distribution functions of the Eve’s location and the wiretap link, the novel cumulative density functions (CDFs) of the received signal-to-noise ratios (SNRs) at the Eves are, respectively, derived for the two considered scenarios, taking into account the effects of RIS reflection coefficients, pathloss, and Eve’s location distribution. The closed-form expressions for the probability of the nonzero secrecy capacity and the ergodic secrecy capacity are obtained, providing insights into the impact of the Eve’s location uncertainty and the RIS design on the secrecy performance. Moreover, based on the derived CDFs for received SNRs at Eves, the secrecy outage probabilities are, respectively, analyzed. Specifically, under the constraint of the secrecy outage probability, the closed forms of the minimum required SNRs at Bob and the number of RIS elements are also obtained. Simulation and analytical results corroborate the derived expressions and reveal the tradeoff between the system’s energy efficiency and the number of RIS elements.

Weighted Sum-Rate Maximization for STAR-RISs-Aided Networks With Coupled Phase-Shifters

Abstract: In this letter, we investigate the optimization in a simultaneously transmitting and reflecting reconfigurable intelligent surface-enabled network with a coupled coefficient model, where the transmit beamforming (BF) and the transmitting and reflecting coefficients (TRCs) are jointly designed. Our goal is to maximize the weighted sum rate for multiple users, subject to the discrete coefficient constraint. To solve the nonconcave objective, we develop an alternating optimization method, where the BF can be obtained in a closed form by the bisection method, and the TRCs are solved by the elementwise optimization approach. Simulation results demonstrate the superiority of the proposed algorithm.

Multipurpose FCS Model Predictive Control of VSC-Based Microgrids for Islanded and Grid-Connected Operation Modes

Abstract: This article presents an enhanced control strategy for renewable energy resources connected to the grid through voltage-sourced converters (VSCs) in microgrids. The proposed scheme contains a voltage control loop with the minimum inverter switching, a power-sharing controller with the minimum inverter switching, a negative-sequence current controller, and a loop to identify the control system operation mode. All the controllers are designed using the multipurpose finite control set-model predictive control (FCS-MPC) strategy. Since these controllers use the dynamic current and VSC voltage, they can be applied in grid-connected and island operation modes and transferred between them. The method uses voltage–frequency control instead of power control for VSCs. One inverter controls voltage, and the other controls current. The conventional FCS-MPC is enhanced to reduce the computation power by eightfold. This improvement is significant because the maximum switching frequency is limited in practical implementations. Also, the superiority of the proposed multipurpose control scheme is proved theoretically. Simulation is implemented using MATLAB software and compared with methods in the literature. The simulation demonstrates that the presented control strategy is efficient, authentic, and compatible. The proposed method is also tested and validated in hardware experiments.

An Anonymous IoT-Based E-Health Monitoring System Using Blockchain Technology

Abstract: The Internet of Things (IoT) has made it possible for health institutions to have remote diagnosis, reliable, preventive, and real-time decision-making. However, the anonymity and privacy of patients are not considered in IoT. Therefore, this article proposes a blockchain-based anonymous system, known as GarliMediChain, for providing anonymity and privacy during COVID-19 information sharing. In GarliMediChain, garlic routing and blockchain are integrated to provide low-latency communication, privacy, anonymity, trust, and security. Also, COVID-19 information is encrypted multiple times before transmitting to a series of nodes in the network. To ensure that COVID-19 information is successfully shared, a blockchain-based coalition system is proposed. The coalition system enables health institutions to share information while maximizing their payoffs. In addition, each institution uses the proposed fictitious play to study the strategies of others in order to update its belief by selecting the best responses from them. Furthermore, simulation results show that the proposed system is resistant to security-related attacks and is robust, efficient, and adaptive. From the results, the proposed proof-of-epidemiology-of-interest consensus protocol has 15.93% less computational cost than 26.30% of proof-of-work and 57.77% proof-of-authority consensus protocol, respectively. Nonetheless, the proposed GarliMediChain system promotes global collaborations by combining existing anonymity and trust solutions with the support of blockchain technology.

An Anonymous IoT-Based E-Health Monitoring System Using Blockchain Technology

Abstract: The Internet of Things (IoT) has made it possible for health institutions to have remote diagnosis, reliable, preventive, and real-time decision-making. However, the anonymity and privacy of patients are not considered in IoT. Therefore, this article proposes a blockchain-based anonymous system, known as GarliMediChain, for providing anonymity and privacy during COVID-19 information sharing. In GarliMediChain, garlic routing and blockchain are integrated to provide low-latency communication, privacy, anonymity, trust, and security. Also, COVID-19 information is encrypted multiple times before transmitting to a series of nodes in the network. To ensure that COVID-19 information is successfully shared, a blockchain-based coalition system is proposed. The coalition system enables health institutions to share information while maximizing their payoffs. In addition, each institution uses the proposed fictitious play to study the strategies of others in order to update its belief by selecting the best responses from them. Furthermore, simulation results show that the proposed system is resistant to security-related attacks and is robust, efficient, and adaptive. From the results, the proposed proof-of-epidemiology-of-interest consensus protocol has 15.93% less computational cost than 26.30% of proof-of-work and 57.77% proof-of-authority consensus protocol, respectively. Nonetheless, the proposed GarliMediChain system promotes global collaborations by combining existing anonymity and trust solutions with the support of blockchain technology.

Intelligent-Reflecting-Surface-Aided Bidirectional Full-Duplex Communication System With Imperfect Self-Interference Cancellation and Hardware Impairments

Abstract: In this article, we combine two new technologies [full-duplex (FD) transmission and intelligent reflecting surface (IRS)] in a wireless communication system for investigation. Specifically, we evaluate the performance of an IRS-aided bidirectional FD communication system in a practical scenario where imperfect self-interference (SI) cancellation and hardware impairments (HIs) are taken into consideration. We successfully derive the closed-form expressions of ergodic capacity (EC) and symbol error rate (SER) of the IRS-aided FD-HI system over Rayleigh fading channels. We confirm the correctness of the derived expressions via Monte-Carlo simulations. To clarify the effects of residual SI and HIs, we compare the performance of the IRS-aided FD-HI system with that of the IRS-aided FD-ideal hardware (ID), half-duplex (HD)-HI, and HD-ID systems. Numerical results clarify a strong impact of residual SI and HIs on the EC and SER of the IRS-aided FD-HI system. Thus, the EC and SER of the IRS-aided FD-HI system go to the saturated values in a high signal-to-noise regime even with a large number of reflecting elements in the IRS. Therefore, depending on the residual SI and HI levels as well as the requirements about the EC and SER in practice, we can use appropriately the transmit power of terminals and number of reflecting elements in the IRS for enhancing the performance and saving the energy consumption of the IRS-aided FD-HI system.

Power Distribution Expansion Planning in the Presence of Wholesale Multimarkets

Abstract: By deregulating the countries' electricity industry, the market players must adapt their performance to the deregulated environment. In this article, a risk-based stochastic optimization framework is proposed to model the participation of distribution system operator (DSO) in distribution expansion planning (DEP) problems in the presence of electricity wholesale multimarkets. Based on the proposed methodology in this article, the DSOs will be able to invest in the DEP by taking the benefits and imposed risks of the existing electricity multimarkets, i.e., forward contract, day-ahead, and balancing markets, to manage the risks of long-term uncertainties. The DSOs, who are the only retail suppliers in a specific region, can cover their DEP costs by offering fair retail prices to the retail consumers. Besides, DSOs could form diverse long- and short-term portfolios from the different electricity markets to procure the forecasted real loads and loss power of the network over the planning horizon. The diverse portfolio of the suppliers and a fair retail price can satisfy the DSOs to consider the multimarkets in the DEP process. Furthermore, the risk of uncertainties is modeled using the method of conditional value at risk (CVaR). Based on the obtained results, the DSO can reduce network loss costs by procuring power from markets rather than increasing the installed branches' ampacity by considering multimarkets and market price uncertainties. Besides, the obtained profit of DSO is increased by 12.16% in the proposed electricity multimarket environment.

Multiobjective Energy Management of Multiple Pulsed Loads in Shipboard Integrated Power Systems

Abstract: Shipboard integratedpower systems (SIPSs) usually have multifaceted operational objectives in engineering scenarios, and many key tasks are performed by multiple pulsed power loads (PPLs) with a high power density. Owing to the complexity of the ocean environment, the optimization of SIPS comprehensive performance remains challenging. This article proposes a multiobjective energy management strategy of multiple PPLs in SIPSs. A multiobjective energy coordination model is formulated. By identifying typical operation modes of the SIPS based on discrepant priority, the PPL utility and maneuverability are incorporated into the comprehensive regulation objective in the form of a weighted sum. In the case of limited energy supply, the service duration is further considered in the optimization objective to meet the high survivability requirements. Reasonable methods are adopted to quantitatively evaluate different performances. Moreover, to improve SIPS survivability in the face of emergencies, a system-level energy allocation scheme with maneuverability enhancement is formulated to coordinate the power consumption of different functions. A particle swarm optimization algorithm with directional guidance and selective evaluation of the nonconvex constraint is proposed to solve the optimization problem effectively. Case studies on a typical SIPS under various working scenarios are presented to validate the effectiveness of the proposed method.

Multiobjective Energy Management of Multiple Pulsed Loads in Shipboard Integrated Power Systems

Abstract: Shipboard integratedpower systems (SIPSs) usually have multifaceted operational objectives in engineering scenarios, and many key tasks are performed by multiple pulsed power loads (PPLs) with a high power density. Owing to the complexity of the ocean environment, the optimization of SIPS comprehensive performance remains challenging. This article proposes a multiobjective energy management strategy of multiple PPLs in SIPSs. A multiobjective energy coordination model is formulated. By identifying typical operation modes of the SIPS based on discrepant priority, the PPL utility and maneuverability are incorporated into the comprehensive regulation objective in the form of a weighted sum. In the case of limited energy supply, the service duration is further considered in the optimization objective to meet the high survivability requirements. Reasonable methods are adopted to quantitatively evaluate different performances. Moreover, to improve SIPS survivability in the face of emergencies, a system-level energy allocation scheme with maneuverability enhancement is formulated to coordinate the power consumption of different functions. A particle swarm optimization algorithm with directional guidance and selective evaluation of the nonconvex constraint is proposed to solve the optimization problem effectively. Case studies on a typical SIPS under various working scenarios are presented to validate the effectiveness of the proposed method.

Learning-Empowered Resource Allocation for Air Slicing in UAV-Assisted Cellular V2X Communications

Abstract: In this article, we propose a resource allocation scheme for air slicing in unmanned aerial vehicle (UAV)-assisted cellular vehicle-to-everything (V2X) communications. We consider a scenario, where multiple flexible UAVs are deployed as the aerial base station (BS) to assist terrestrial BS for providing service to vehicular users with the objective of maximizing the bandwidth efficiency while concurrently guaranteeing the transmission rate and the latency by adopting network slicing. Due to the uncertainty of the stochastic environment in the scenario, we formulate the optimization problem to be a stochastic game, which is an extension of game theory to Markov decision process-like environments for the case of multiple adaptive agents are involved to compete goals simultaneously. Nevertheless, the dynamic nature of both UAVs and vehicles pose the difficulty of perceiving and interacting with the unknown environment, the long short-term memory algorithm is used for extracting the features of the observation and making forecast on the mobility of UAVs and vehicles. Simulation results adduce the validity of the proposed scheme as compared with two benchmark schemes: Deep Q-network and deep deterministic policy gradient.

Learning-Empowered Resource Allocation for Air Slicing in UAV-Assisted Cellular V2X Communications

Abstract: In this article, we propose a resource allocation scheme for air slicing in unmanned aerial vehicle (UAV)-assisted cellular vehicle-to-everything (V2X) communications. We consider a scenario, where multiple flexible UAVs are deployed as the aerial base station (BS) to assist terrestrial BS for providing service to vehicular users with the objective of maximizing the bandwidth efficiency while concurrently guaranteeing the transmission rate and the latency by adopting network slicing. Due to the uncertainty of the stochastic environment in the scenario, we formulate the optimization problem to be a stochastic game, which is an extension of game theory to Markov decision process-like environments for the case of multiple adaptive agents are involved to compete goals simultaneously. Nevertheless, the dynamic nature of both UAVs and vehicles pose the difficulty of perceiving and interacting with the unknown environment, the long short-term memory algorithm is used for extracting the features of the observation and making forecast on the mobility of UAVs and vehicles. Simulation results adduce the validity of the proposed scheme as compared with two benchmark schemes: Deep Q-network and deep deterministic policy gradient.

Physical Layer Security for RIS-Aided Wireless Communications With Uncertain Eavesdropper Distributions

Abstract: In this article, the physical layer security (PLS) is investigated for reconfigurable intelligent surface (RIS)-aided wireless communication systems, where one RIS is deployed to assist the communications between a pair of transmitter (Alice) and receiver (Bob), under a passive eavesdropper (Eve) attack. For the Eve, different from the bounded channel state information uncertainty model, the distribution of the Eve’s location is introduced into the wiretap link. In the proposed system, considering that the Eve can overhear signals transmitted from Alice or reflected by the RIS, two scenarios are studied for RIS-aided secure communication systems: one is that the Eve distributes close to Alice without the RIS orientation, and the other is that the Eve locates close to Bob and in the presence of the RIS. After investigating the probability distribution functions of the Eve’s location and the wiretap link, the novel cumulative density functions (CDFs) of the received signal-to-noise ratios (SNRs) at the Eves are, respectively, derived for the two considered scenarios, taking into account the effects of RIS reflection coefficients, pathloss, and Eve’s location distribution. The closed-form expressions for the probability of the nonzero secrecy capacity and the ergodic secrecy capacity are obtained, providing insights into the impact of the Eve’s location uncertainty and the RIS design on the secrecy performance. Moreover, based on the derived CDFs for received SNRs at Eves, the secrecy outage probabilities are, respectively, analyzed. Specifically, under the constraint of the secrecy outage probability, the closed forms of the minimum required SNRs at Bob and the number of RIS elements are also obtained. Simulation and analytical results corroborate the derived expressions and reveal the tradeoff between the system’s energy efficiency and the number of RIS elements.

DeepOPF: A Feasibility-Optimized Deep Neural Network Approach for AC Optimal Power Flow Problems

Abstract: To cope with increasing uncertainty from renewable generation and flexible load, grid operators need to solve alternative current optimal power flow (AC-OPF) problems more frequently for efficient and reliable operation. In this article, we develop a deep neural network (DNN) approach, called DeepOPF, for solving AC-OPF problems in a fraction of the time used by conventional iterative solvers. A key difficulty for applying machine learning techniques for solving AC-OPF problems lies in ensuring that the obtained solutions respect the equality and inequality physical and operational constraints. Generalized a prediction-and-reconstruction procedure in our previous studies, DeepOPF first trains a DNN model to predict a set of independent operating variables and then directly compute the remaining ones by solving the power flow equations. Such an approach not only preserves the power-flow balance equality constraints but also reduces the number of variables to be predicted by the DNN, cutting down the number of neurons and training data needed. DeepOPF then employs a penalty approach with a zero-order gradient estimation technique in the training process toward guaranteeing the inequality constraints. We also drive a condition for tuning the DNN size according to the desired approximation accuracy, which measures its generalization capability. It provides theoretical justification for using DNN to solve AC-OPF problems. Simulation results for IEEE 30/118/300-bus and a synthetic 2000-bus test cases demonstrate the effectiveness of the penalty approach. They also show that DeepOPF speeds up the computing time by up to two orders of magnitude as compared to a state-of-the-art iterative solver, at the expense of $< $ 0.2% cost difference.

Joint Trajectory and Power Optimization for Jamming-Aided NOMA-UAV Secure Networks

Abstract: The combination of nonorthogonal multiple access (NOMA) technique and unmanned aerial vehicle (UAV) provides an effective solution for achieving massive connections and improving spectrum efficiency. However, the related security risk becomes serious due to the line-of-sight (LoS) channels involved and high transmit power for weaker users in NOMA-UAV networks. In this article, a UAV-assisted NOMA transmission scheme is proposed to achieve secure downlink transmission via artificial jamming, where a UAV flies straightly to serve multiple ground users in the presence of a passive eavesdropper. During the flight, only the closest NOMA users are chosen to connect with the UAV in each time slot to achieve high LoS probability. To balance the security and transmission performance, the tradeoff between the jamming power and the sum rate is investigated by jointly optimizing the power allocation, the user scheduling and the UAV trajectory. The formulated problem is mixed-integer and nonconvex due to the coupled variables. To address this, we first decompose the problem into two subproblems of power allocation and trajectory optimization. Then, they are transformed into convex ones via the first-order Taylor expansion. After that, an iterative algorithm is proposed to solve the convex problem. Finally, numerical results show that the security of the network is well enhanced and verify the effectiveness of the proposed scheme.

Learning-Aided Framework for Storage Control Facing Renewable Energy

Abstract: The Internet of Things (IoT) enables reliable and fast data collection and transmission, providing key infrastructure for power generation, distribution, and control in the smart grid. This IoT-enabled smart grid tackles challenges brought by renewable penetration in new ways: Accurate and real-time information allows for the application of artificial-intelligence-powered computation. We employ the deep learning framework and consider the problem of storage control facing uncertainties in renewable generation. We propose both model-based and model-free storage control frameworks to identify the value of information. For the first framework, opposing to most deep-learning-oriented research in the electricity sector, we use the one-shot load decomposition technique to encode structural information into the learning framework. The structural information refers to the fact that the one-shot load decomposition maintains the control strategy space. Based on this structural information, we develop the storage control policy by utilizing a deep learning framework for price and renewable prediction, which is the basis of our deep-learning-enabled storage control. For the model-free framework, we regard historical price and demand data as input and directly output the control actions. For each model, we further establish theoretical analysis on how the uncertainties in price and renewables influence the cost. Numerical evaluations illustrate the remarkable performance of our proposed frameworks and reveal the value of information.

An Efficient Energy-Saving Scheme Using Genetic Algorithm for 5G Heterogeneous Networks

Abstract: Energy-saving (ES) is becoming one of the most challenging tasks that fifth-generation (5G) tends to tackle. The problem of identifying the optimal set cells to be turned off is nondeterministic polynomial time-hard. In this research article, we use heuristic algorithms to save energy in 5G heterogeneous networks (HetNet). Our approach is based on turning off underutilized components of base stations to reduce energy consumption, while satisfying users’ requests. Basically, we elaborate a new mechanism providing ES for 5G networks. The proposed mechanism is based on genetic algorithm (GA) and is called ES based on GA in 5G (ESGA-5G). Bio-inspired GA and particle swarm optimization (PSO) algorithms stand for AI solutions that intelligently manage the operation of ES self-organized network mechanisms in 5G HetNet. The performance analysis of the proposed ESGA-5G approach illustrates its efficiency in terms of reducing the energy consumption. In particular, ESGA achieves a higher percentage of ESs compared to PSO algorithm, with a gap to optimality amounting to 28% for GA and 54% for PSO.

RIS-Assisted UAV for Timely Data Collection in IoT Networks

Abstract: Intelligent transportation systems are thriving thanks to a wide range of technological advances, namely 5G communications, the Internet of Things (IoT), artificial intelligence, and edge computing. Meanwhile, in environments where direct communication can be impaired by, for instance, blockages, such as in urban cities, unmanned aerial vehicles (UAVs) can be considered as an alternative for providing and enhancing connectivity. In this article, we consider a time-constrained data gathering problem from a network of sensing devices and with assistance from a UAV. A reconfigurable intelligent surface (RIS) is deployed to further help in improving the connectivity and energy efficiency of the UAV. This integrated problem brings challenges related to the configuration of the phase shift elements of the RIS, the scheduling of IoT devices' transmissions, as well as the trajectory of the UAV. First, the problem is formulated with the objective of maximizing the total number of served devices each during its activation period. Owing to its complexity, we leverage deep reinforcement learning in our solution; the UAV trajectory planning is modeled as a Markov decision process, and proximal policy optimization is invoked to solve it. Next, the RIS configuration is then handled via block coordinate descent. Finally, extensive simulations are conducted to demonstrate the efficiency of our solution approach that, in many cases, outperforms other methods by more than 50%. We also show that integrating an RIS with a UAV in IoT networks can improve the UAV energy efficiency.

A Robust and Optional Privacy Data Aggregation Scheme for Fog-Enhanced IoT Network

Abstract: With the advances in fog computing, various users’ data, collected by smart devices in the Internet of Things (IoT), are published to facilitate services and efficiency. This leads to the users’ worry about security and privacy issues in fog-enhanced IoT. Although privacy protection can ease such worry, users need to pay some extra price, such as more computation costs. Besides, there are some users who want convenience rather than privacy. They prefer to publish their raw data to exchange for better convenience or benefit. In this article, an optional privacy-preserving data aggregation scheme based on BGN homomorphic encryption is proposed, without any trusted third party. In the proposed scheme, each user can choose either no privacy encryption or privacy encryption to upload its own data according to its own privacy sensitivity. With the help of the fog node, the control center can get the aggregated data of all smart devices and the single data of smart devices that choose the no privacy option. In addition, the proposed scheme is analyzed to achieve correctness, privacy preservation, and robustness. Experimental results and comparisons show the proposed scheme is satisfactory in terms of computation cost and communication overhead.

Reliability-Oriented Planning Framework for Smart Cities: From Interconnected Micro Energy Hubs to Macro Energy Hub Scale

Abstract: Energy hubs, as the prospect of future energy systems, are efficient and reliable frameworks for the generation, conversion, storage, and consumption of energy in multicarrier energy systems. Existing urban facilities in smart cities (SCs) can be considered as micro energy hubs (MIEHs), whereas the SC itself can be regarded as a macro energy hub incorporating its MIEHs. Moreover, the interconnection of MIEHs is particularly important to achieve high-reliability and economical smart city energy systems (SCESs). This article presents a framework for reliability-oriented expansion planning of multicarrier energy systems in SCESs. The proposed framework tends to improve energy not supplied and customer interruption cost indices for both heat and electrical loads, as well as the energy index of reliability. The proposed multicarrier energy system facilitates interoperability between different energy carrier systems to supply electricity and heat demand simultaneously. The interconnection of MIEHs is modeled through linearized alternating current optimal power flow and natural gas network constraints, whereas full interaction of the electricity and gas networks is realized by the power-to-gas systems and combined heat and power systems. The effectiveness of the proposed mixed-integer linear programming planning model is demonstrated in the Dättwil district (Switzerland) through four scenarios, analyzing the effect of MIEH interconnection, as well as the gas and electricity interaction within an MIEH. Simulation results confirm the effectiveness of the proposed method due to a significant reduction in total investment cost and a considerable improvement in reliability indices.

LiFi-Based D2D Communication in Industrial IoT

Abstract: This article analyzes the performance of light fidelity (LiFi)-based device-to-device (D2D) communication in industrial Internet-of-Things (IIoT). We present a comprehensive analysis of mobility management of D2D communication in industrial LiFi networks. Using the semiangle at half illuminance of the AP and D2D transmitting IIoT, a coverage model for the D2D communication range is derived. By adopting stochastic geometry, closed-form expressions for mode selection rate and residence time are derived as functions of the AP density, IIoT density, and velocity. The results have shown that high velocity and denser deployment cause a decrease in the average D2D residence time and an increase in the average D2D transition rate or vice versa. The proposed analytical models are then verified with Monte Carlo simulation results. The results provide system-level design insights.

Multi-Microgrids Operation With Interruptible Loads in Local Energy and Reserve Markets

Abstract: In the evolution of the power systems, a particular case is the presence of a number of microgrids (MGs) operated with mutual interconnection, but without connection to the main distribution system. The interconnected MGs form a structure in which the overall system operation and resource scheduling can be determined by considering centralized or decentralized approaches. This article introduces local energy and reserve markets (LERMs) in which the MG managers (MGMs) can meet their required energy and reserve with optimal scheduling of their resources, besides competing with the other MGs. To model such decision-making framework for MGMs, a bilevel optimization approach is developed in which the MGMs’ problem is modeled as the upper level problem and the LERMs clearing problem is modeled as the lower level problem. This model is transformed into a mathematical programming with equilibrium constraints (MPEC) using the primal-dual transformation. Then, the resulting MPEC for each MG is replaced with its Karush–Kuhn–Tucker conditions, obtaining an equilibrium problem with equilibrium constraints (EPEC) model. The nonlinear terms of the model are linearized through different approaches. Finally, the EPEC model is transformed into a mixed-integer linear problem considering the objective function of all MGMs. The model is applied to a test system with three interconnected MGs. Moreover, the sensitivity of the results to the probability of calling reserve is investigated.

Flicker Attenuation Using FACTS Device for DFIG-Based WECS Connected to Distribution Network

Abstract: Wind being stochastic in nature results in intermittent and fluctuating wind power sources. This affects the power quality during normal operation. Major causes for such changes are mainly due to tower shadow and wind shear effects (3p oscillations), which lead to active power changes and delay penetration of wind power into the grid. At times, due to lower short circuit ratios and grid impedance angle at the point of common coupling power quality concerns, such as voltage stability and flicker occur. This literature focuses on a method to mitigate flicker by reducing the grid current deviations using flexible ac transmission system (here, static synchronous series compensator) device under different modes. Due to changes in wind speed, the mechanical torque changes and as a result, its impact is visible on the grid current at the static synchronous series compensator. A detailed case-to-case study has been made for the grid-connected system for low as well as high wind speed conditions. The static synchronous series compensator has been used for capacitive series voltage injection as well as inductive series voltage injection. The proposed method attains a better reduction in flicker using static synchronous series compensator for inductive series voltage injection as seen using MATLAB/Simulink.

A Reservation-Based Vehicle-to-Vehicle Charging Service Under Constraint of Parking Duration

Abstract: Electric vehicle (EV) has been applied as the main transportation tool recently. However, EVs still require a long charging time and, thus, inevitably cause charging congestion. The traditional plug-in charging mode is limited by fixed location and peak hours. Therefore, a flexible vehicle-to-vehicle (V2V) charging mode is considered in this article. Here, parking lots (PLs) widely dispersed in cities are reused as a common place for V2V charging. EVs are divided into EVs as energy consumers and EVs as energy providers to form as vehicle-to-vehicle charging pairs (V2V-Pairs). In this article, we propose a V2V charging management scheme, which includes a distance-based V2V-Pair matching algorithm and a PL-selection scheme. As the occupation status at PLs is difficult to predict, to achieve high PL utilization and evenly PL selection, V2V charging reservation is introduced. Meanwhile, since EV drivers usually park at PLs within a limited duration, our proposed V2V charging scheme introduces the parking duration to optimize V2V charging under a temporal constraint. We simulate this V2V charging scheme under the Helsinki city scenario. The results prove that our proposed V2V charging scheme achieves great charging efficiency (minimized charging waiting time and maximized fully charging times).