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Voltage rise caused by high levels of distributed generation is manifesting as voltage regulation challenges for many electricity network service providers. In this environment it would be ideal to reduce supply voltage magnitudes, however, many network operators are hesitant to do so due to concerns related to consumer appliance performance at reduced supply voltage magnitudes. Voltage regulation requirements are defined by network standards and network service providers must ensure voltages remain within specified limits. Through an evaluation of domestic appliance performance when supplied at various voltage magnitudes, this paper examines the impact of varying voltage levels on residential appliances. Equipment energy demand, operation and actuation were monitored for each applied voltage magnitude. While no equipment failures were recorded, appliance behaviour varied significantly with applied voltage magnitude. Individual appliance conservation voltage reduction (CVR) factors have also been established. The results highlight the importance of good voltage regulation and provide substantiated appliance performance figures for future studies. The outcomes of this paper allow electricity network service providers to understand the implications of supply voltage magnitude on domestic appliance performance, whether it be understating of the impact of higher voltage magnitudes caused by distributed generation or implications of reducing voltage magnitudes to provide headroom for distributed generation integration. [ABSTRACT FROM AUTHOR]

Although smart home has received wide attention in recent years, numerous scholars focus more on energy optimization strategy than energy dispatch hardware device (named energy router). Meanwhile, this energy router should have several features, i.e., high renewable energy utilization, energy multi-port and low volume. Thus, this paper designs a nine-port energy router regarding smart home and proposes a multimode hierarchical management strategy for this energy router. First, for the multi-port demand of wind, solar, storage and utilization, this paper presents a nine-port energy router to improve the renewable energy consumption and power supply flexibility. In addition, to reduce the volume of the energy router, a non-isolated AC/DC hybrid topology is constructed through embedding the integrated power electronic converters, which achieves the miniaturization of the energy router. In order to improve the renewable energy utilization rate, the decentralized module control is proposed for the components of energy router to provide the voltage and frequency support for system, and realizes the power sharing of distributed generations (DGs). Furthermore, the power exchange control with three-mode switching is proposed to guarantee the global energy flow balance under complex conditions. Eventually, the feasibility of the energy router is verified by the simulation and experiments. [ABSTRACT FROM AUTHOR]

The paper proposed a novel intelligent transmission surface (ITS) aided transmitter in an intelligent reflection surface (IRS) assisted multiuser multiple-input multiple-output (MIMO) network. The ITS deployed in the transmitter architecture can reduce the power consumption in signal beamforming at the base station (BS), and the IRS can help the information transfer from the ITS-aided transmitter to the users. We first maximize the weighted sum rate (WSR) of the users by jointly designing the beamforming vector at the BS and the phase shifts of ITS and IRS. To solve this non-convex optimization problem, we propose an effective algorithm in which the Lagrangian dual transform, the alternative optimization (AO) algorithm and the quadratic transform (QT) method are adopted to simplify the objective function. Then, the bisection search and the alternating direction method of multipliers (ADMM) algorithm are considered to design the optimal beamforming vector and phase shifts of ITS and IRS, respectively. Furthermore, the paper explores the energy efficiency (EE) maximization problem to emphasize the value of the ITS-assisted transmitter in terms of power savings. Finally, we compare the simulation results to various state-of-the-art techniques to see how much better the proposed algorithm is in terms of WSR and EE. [ABSTRACT FROM AUTHOR]

The opening up of the railway to competition in Europe has been accompanied by many technical challenges never before seen in the railway field. One of these challenges concerns precise energy metering. As European railways have been a monopoly until now, the current systems can only measure the energy consumption of a single operator. In accordance with European law, the installation of an energy meter has been mandatory in new or refurbished rolling stocks since 2014. Nevertheless, these energy meters are still not fully reliable, with misreadings and missing readings being common occurrences. To address the problem of energy metering data reliability, this paper presents a method for validating the reading measurement based on a physical model and validates it in two different case studies. For the issue of energy metering data availability, different estimators are developed, and their performance are shown with the aim of calculating the missing consumptions for both case studies. Finally, the paper suggests how these methods may be implemented in other types of trains. [ABSTRACT FROM AUTHOR]

This paper presents a different Finite Control Set – Model Predictive Control (FCS-MPC) for grid-connected three-phase bidirectional power inverters. These are typically used in dc or ac renewable-based microgrids (MGs), where bidirectional operation and fast dynamic response is required. The bidirectional grid-connected inverters are an essential part of MG, which inject energy into the ac grid or demand energy from it. The dynamic behavior of the system is a major concern since the current can suddenly change depending on the hierarchical controller. This paper proposes a different cost function using sliding mode theory, which offers a good dynamic response, reduced computational burden, and a parameter-free control model. The operation principle of the proposed controller is given and evaluated using a Hardware-In-the-Loop (HIL) system, but also experimentally with a 1kW laboratory prototype. The final results demonstrate the advantages of using this approach in grid-connected three-phase bidirectional power inverters in terms of dynamic response and reduced computational burden, making this solution technically attractive and viable. [ABSTRACT FROM AUTHOR]

Human Body Communication (HBC) is an alternative to radio wave-based Wireless Body Area Network (WBAN) because of its wide bandwidth leading to enhanced energy efficiency. Designing Modern HBC devices need the accurate electrical equivalent of the HBC channel for energy efficient communication. The objective of this paper is to present an improved lumped element-based detailed model of Galvanic HBC channel which can be used to explain the dependency of the channel behaviour on the internal body dependent parameters such as electrical properties of skin and muscle tissue layers along with the external parameters such as electrode size, electrode separation, geometrical position of the electrodes and return-path or parasitic capacitances. The model considers the frequency-dependent impedance of skin and muscle tissue layers and the effect of various coupling capacitances between the body and Tx/Rx electrodes to the Earth-Ground. A 2D planar structure of skin and muscle tissue layers is simulated using a Finite Element Method (FEM) tool to prove the validity of the proposed model. The effect of symmetry and asymmetry at the transmitter and receiver ends is also explained using the model. The model become very useful for fast calculation of Galvanic channel response without using any FEM tool. Experimental results show that the galvanic response is not only a function of channel length but also depends on the mismatch at the transmitter and receiver end. In case of a very high mismatch scenario, the channel behavior is dominated by the capacitive HBC, even for a galvanic excitation and termination. [ABSTRACT FROM AUTHOR]

Long short-term memory (LSTM) is a type of powerful deep neural network that has been widely used in many sequence analysis and modeling applications. However, the large model size problem of LSTM networks make their practical deployment still very challenging, especially for the video recognition tasks that require high-dimensional input data. Aiming to overcome this limitation and fully unlock the potentials of LSTM models, in this paper we propose to perform algorithm and hardware co-design towards high-performance energy-efficient LSTM networks. At algorithm level, we propose to develop fully decomposed hierarchical Tucker (FDHT) structure-based LSTM, namely FDHT-LSTM, which enjoys ultra-low model complexity while still achieving high accuracy. In order to fully reap such attractive algorithmic benefit, we further develop the corresponding customized hardware architecture to support the efficient execution of the proposed FDHT-LSTM model. With the delicate design of memory access scheme, the complicated matrix transformation can be efficiently supported by the underlying hardware without any access conflict in an on-the-fly way. Our evaluation results show that both the proposed ultra-compact FDHT-LSTM models and the corresponding hardware accelerator achieve very high performance. Compared with the state-of-the-art compressed LSTM models, FDHT-LSTM enjoys both order-of-magnitude reduction (more than $1000 \times$ 1000 × ) in model size and significant accuracy improvement (0.6% to 12.7%) across different video recognition datasets. Meanwhile, compared with the state-of-the-art tensor decomposed model-oriented hardware TIE, our proposed FDHT-LSTM architecture achieve $2.5\times$ 2. 5 × , $1.46\times$ 1. 46 × and $2.41\times$ 2. 41 × increase in throughput, area efficiency and energy efficiency, respectively on LSTM-Youtube workload. For LSTM-UCF workload, our proposed design also outperforms TIE with $1.9\times$ 1. 9 × higher throughput, $1.83\times$ 1. 83 × higher energy efficiency and comparable area efficiency. [ABSTRACT FROM AUTHOR]

As Graph Convolutional Networks (GCNs) have emerged as a promising solution for graph representation learning, designing specialized GCN accelerators has become an important challenge. An analysis of GCN workloads shows that the main bottleneck of GCN processing is not computation but the memory latency of intensive off-chip data transfer. Therefore, minimizing off-chip data transfer is the primary challenge for designing an efficient GCN accelerator. To address this challenge, optimization is initialized by considering GCNs as tiled matrix multiplication. In this paper, we optimize off-chip memory access from both the in- and out-of-tile perspectives. From the out-of-tile perspective, we find optimal tile configurations of given datasets and on-chip buffer capacity, then observe the dataflow across phases and layers. Inter-layer phase fusion dataflow with optimal tile configuration reduces data transfer of intermediate outputs. From the in-tile perspective, due to the sparsity of tiles, tiles have redundant data which does not participate in computation. Redundant data load is eliminated with hardware support. Finally, we introduce an efficient GCN inference accelerator, EGCN, specialized for minimizing off-chip memory access. EGCN achieves 41.9% off-chip DRAM access reduction, 1.49× speedup, and 1.95× energy efficiency improvement on average over the state-of-the-art accelerators. [ABSTRACT FROM AUTHOR]

This paper studies an unmanned aerial vehicle (UAV)-assisted wireless powered IoT network, where a rotary-wing UAV adopts fly-hover-communicate protocol to successively visit IoT devices in demand. During the hovering periods, the UAV works on full-duplex mode to simultaneously collect data from the target device and charge other devices within its coverage. Practical propulsion power consumption model and non-linear energy harvesting model are taken into account. We formulate a multi-objective optimization problem to jointly optimize three objectives: maximization of sum data rate, maximization of total harvested energy and minimization of UAV’s energy consumption over a particular mission period. These three objectives are in conflict with each other partly and weight parameters are given to describe associated importance. Since IoT devices keep gathering information from the physical surrounding environment and their requirements to upload data change dynamically, online path planning of the UAV is required. In this paper, we apply deep reinforcement learning algorithm to achieve online decision. An extended deep deterministic policy gradient (DDPG) algorithm is proposed to learn control policies of UAV over multiple objectives. While training, the agent learns to produce optimal policies under given weights conditions on the basis of achieving timely data collection according to the requirement priority and avoiding devices’ data overflow. The verification results show that the proposed MODDPG (multi-objective DDPG) algorithm achieves joint optimization of three objectives and optimal policies can be adjusted according to weight parameters among optimization objectives. [ABSTRACT FROM AUTHOR]

While the vehicular network enables geographically distributed cooperative computation, its mature implementation has long been constrained due to the lack of an effective management platform. In this paper, employing the security and privacy attributes of blockchain, we propose a novel Blockchain-enabled Large-scale Parked Vehicular Computing (BLPVC) architecture to utilize the potential solar energy and vehicular computational resources in the outdoor parking lot. However, the uneven green power supply and random arrival time of electric vehicles compose the highly complex environment. Accordingly, in this paper, to handle the efficient utilization of the distributed resources by blockchain technology, we propose an integrated optimization framework which leverages the green energy utilization and service latency limit among the processes of block generation, task computing, and communication, whereas such a design leads to the mixed-timescale stochastic optimization problem. To this end, corresponding to the dynamic solar energy arrival, we propose a shaped deep deterministic policy gradient (DDPG) algorithm to accelerate the learning rate of computational frequency control in the short-term stage; while in the long-term stage, for the mixed-integer programming (MIP) of task offloading and blockchain parameters adjustment, a series of transformation is employed to preserve convexity. Finally, experiments are carried out on Python demonstrating that the proposed scheme achieves a balanced performance between service latency and distributed resources, while the battery depreciation cost is heavily reduced. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel optimization model for multiple Unmanned Aerial Vehicles (UAVs) working simultaneously as relays to help two set of ground users; namely relay users and device-to-device (D2D) users. The relay users are assumed out communication rages from each other and use the UAV link to for their data transmission. While the D2D users are assumed to be nearby users and use the UAV for managing the resources without being involved in the transmission. The goal of the paper is to operate the UAVs in an energy-efficient manner to support the different set of users by i) optimizing the available bandwidth and power allocations of the D2D links, and ii) acting as relays when needed to maintain the communication links between relay users. We formulate an optimization problem that maximizes the throughput-energy utility while respecting the resource availability including the UAVs’ energy consumption, UAV-user association, and trajectory constraints. Due to the non-convexity of the problem, we propose to solve it in three steps using Taylor series approximation. Firstly, we optimize the transmit power of the UAVs and users. Then, we optimize the bandwidth allocation for a given transmit power values. Finally, an efficient heuristic algorithm based on a recursive shrink-and-realign process is proposed to optimize the UAVs’ trajectories. The performance of the proposed method shows advantages in terms of average throughput compared to the fixed power and bandwidth solutions. [ABSTRACT FROM AUTHOR]

The combination of non-orthogonal multiple access (NOMA) and intelligent reflecting surface (IRS) is an efficient solution to significantly enhance the energy efficiency of the wireless communication system. In this paper, a downlink multi-cluster NOMA network is considered, where each cluster is supported by one IRS. This paper aims to minimize the transmit power by jointly optimizing the beamforming, the power allocation and the phase shift of each IRS. The formulated problem is non-convex and challenging to be solved due to the coupled variables, i.e., the beamforming vector, the power allocation coefficient and the phase shift matrix. To address this non-convex problem, an alternating optimization based algorithm is proposed. Specifically, the primal problem is divided into two subproblems for beamforming optimization and phase shifting feasiblity, where the two subproblems are solved iteratively. Moreover, to guarantee the feasibility of the beamforming optimization problem, an iterative algorithm is proposed to search the feasible initial points. To reduce the complexity, a simplified algorithm based on partial exhaustive search for this system model is also proposed. Simulation results demonstrate that the proposed alternating algorithm can yield a better performance gain than the partial exhaustive search algorithm, NOMA with random IRS phase shift scheme and OMA-IRS scheme. [ABSTRACT FROM AUTHOR]

This paper endeavors to explore the sustainable container shipping problem considering fuel bunker management and provide adequate recovery policies for countering disruption within maritime transportation. This paper addresses the environmental concerns related to fuel consumption and carbon emission within shipping operations and simultaneously presents strategies for countering disruption within the maritime transportation domain. Several studies addressed bunker fuel management strategies, but overlooked the need for integrating it with shipping operations. This paper aims to bridge this research gap by proposing a novel mathematical model and presenting a heuristic procedure combined with a variable neighborhood search algorithm for maximizing the shipping company's profitability, while addressing the vessel routing and scheduling decisions, container loading and unloading operations, selection of bunkering ports, and determining bunkered amount for heavy fuel oil and marine diesel oil. Recovery strategies such as port swapping and rescheduling of vessel route are considered to deal with disruptions related to weather adversities. An illustrative example is presented depicting the realistic scenario and providing results associated with ship routes, vessel speed, bunkering ports, bunkered amounts, fuel consumed by the vessel on each sailing leg, arrival and departure time of the ships, etc. Insights obtained from the analysis performed based on the fuel price, ship's bunkering capacity, adverse weather conditions on various routes, port closure, carbon tax, and fuel consumption provide useful information for shipping company managers. Managerial implications are presented with regard to the impact of fuel prices and carbon tax on shipping operation from the perspective of overall operational cost. Moreover, the results provide important policy insights for shipping company managers in terms of possessing alternate vessel route options for normal scenario and disrupted scenarios including weather adversities on sailing leg or port closure. [ABSTRACT FROM AUTHOR]

This paper presents a computation-efficient stochastic dynamic programming algorithm for solving energy storage price arbitrage considering variable charge and discharge efficiencies. We formulate the price arbitrage problem using stochastic dynamic programming and model real-time prices as a Markov process. Then we propose an analytical solution algorithm using a piecewise linear approximation of the value-to-go function. Our solution algorithm achieves extreme computation performance and solves the proposed arbitrage problem for one operating day in less than one second on a personal computer. We demonstrate our approach using historical price data from four price zones in New York Independent System Operator, with case studies comparing the performance of different stochastic models and storage settings. Our results show that the proposed method captures 50% to 90% of arbitrage profit compared to perfect price forecasts. In particular, our method captures more than 80% of arbitrage profit in three out of the four price zones when considering batteries with more than two-hour duration and realistic degradation cost. [ABSTRACT FROM AUTHOR]

In this paper, we aim to improve the connectivity, scalability, and energy efficiency of machine-type communication (MTC) networks with different types of MTC devices (MTCDs), namely Type-I and Type-II MTCDs, which have different communication purposes. To this end, we propose two transmission schemes called connectivity-oriented machine-type communication (CoM) and quality-oriented machine-type communication (QoM), which take into account the stochastic geometry-based deployment and the random active/inactive status of MTCDs. Specifically, in the proposed schemes, the active Type-I MTCDs operate using a novel Bernoulli random process-based simultaneous wireless information and power transfer (SWIPT) architecture. Next, utilizing multi-user power-domain non-orthogonal multiple access (PD-NOMA), each active Type-I MTCD can simultaneously communicate with another Type-I MTCD and a scalable number of Type-II MTCDs. In the performance analysis of the proposed schemes, we prove that the true distribution of the received power at a Type-II MTCD in the QoM scheme can be approximated by the Singh-Maddala distribution. Exploiting this unique statistical finding, we derive approximate closed-form expressions for the outage probability (OP) and sum-throughput of massive MTC (mMTC) networks. Through numerical results, we show that the proposed schemes provide a considerable sum-throughput gain over conventional mMTC networks. [ABSTRACT FROM AUTHOR]

Reconfigurable intelligent surface (RIS) is regarded as a promising technology with great potential to boost wireless networks. Affected by the “double fading” effect, however, conventional passive RIS cannot bring considerable performance improvement when users are not close enough to RIS. Recently, active RIS is introduced to combat the double fading effect by actively amplifying incident signals with the aid of integrated reflection-type amplifiers. In order to reduce the hardware cost and energy consumption due to massive active components in the conventional fully-connected active RIS, a novel hardware-and-energy efficient sub-connected active RIS architecture has been proposed recently, in which multiple reconfigurable electromagnetic elements are driven by only one amplifier. In this paper, we first develop an improved and accurate signal model for the sub-connected active RIS architecture. Then, we investigate the joint transmit precoding and RIS reflection beamforming (i.e., the reflection phase-shift and amplification coefficients) designs in multiuser multiple-input single-output (MU-MISO) communication systems. Both sum-rate maximization and power minimization problems are solved by leveraging fractional programming (FP), block coordinate descent (BCD), second-order cone programming (SOCP), alternating direction method of multipliers (ADMM), and majorization-minimization (MM) methods. Extensive simulation results verify that compared with the conventional fully-connected structure, the proposed sub-connected active RIS can significantly reduce the hardware cost and power consumption, and achieve great performance improvement when power budget at RIS is limited. [ABSTRACT FROM AUTHOR]

Mutualistic cooperative ambient backscatter communications (AmBC) have been proposed to improve the spectrum and energy efficiencies of Internet-of-Things (IoT) systems, where a primary link (from a primary transmitter to a primary receiver) and an AmBC link (from an IoT device to the same primary receiver) form a mutualism relationship. We note that hardware impairments (HIs), which are unavoidable in practical systems and may significantly affect the transmission rates of the primary and AmBC links and their mutualism relationships, have been largely ignored in the study of mutualistic cooperative AmBC networks. In this paper, we study a mutualistic cooperative AmBC network with HIs at all the active transceivers and a non-linear energy harvesting circuit at each IoT device. We derive closed-form rate expressions for both the AmBC and primary links and theoretically prove that the mutualism relationship between the AmBC and primary links is maintained under HIs, i.e., the rate of the primary link in the mutualistic cooperative AmBC network is still higher than that without the AmBC link. To maximize the weighted sum rate of all links in a cooperative AmBC network under HIs, we propose two resource allocation schemes for two scenarios with a single link and multiple AmBC links, respectively. For the single AmBC link case, we derive the optimal transmit power of the primary transmitter and the optimal power reflection coefficient of the IoT device in closed forms. For the scenario with multiple AmBC links, the weighted-sum-rate maximization problem is transformed into a convex one and solved with convex optimization tools. Computer simulations validate our theoretical results and that our proposed schemes outperform the benchmark schemes in terms of the weighted sum rate. [ABSTRACT FROM AUTHOR]

In this paper, we investigate security and energy efficiency of multicast communication in cognitive satellite terrestrial networks. To achieve high spectrum efficiency and low interference power transmissions in the cognitive terrestrial network, the rate splitting multiple access scheme (RSMA) is employed to prompt the massive access of the secondary users. In particular, we derive analytical and asymptotic closed-form expressions for secrecy outage probability of the satellite terrestrial network with eavesdropping, interfering, and imperfect channel state information. Hence, a beamforming (BF) scheme based on RSMA for secondary network is proposed to suppress eavesdropping, mitigate interference, and enhance energy efficiency of the proposed system model. Then, we transform non-convex optimization constraints into resoluble problems utilizing Taylor series expansion and successive convex approximation. Finally, simulation results corroborate the above theoretical analysis and highlight the superiority of the designed BF scheme on security and energy efficiency. [ABSTRACT FROM AUTHOR]

Responsive and highly controlled autonomous vehicles (AVs) on the road easily form platoons with small inter-vehicle gaps to increase traffic throughput and reduce energy consumption. Unlike human drivers, AVs typically employ discrete quantization signals and only generate finite discrete acceleration. Finite discrete acceleration easily causes unstable inter-vehicle gaps and inter-vehicle collisions, especially when there are inaccurate quantization parameters due to quantizer aging. Based on the backstepping technique, this paper proposes the cooperative constrained control algorithm under unknown quantization parameters for AVs with nonuniform input quantization and motion uncertainties. The control laws of each AV in the platoon only depend on the state information of its consecutive AVs. The position trajectories of consecutive AVs are constructed as inter-vehicle collision-free constraints to avoid emergency braking and inter-vehicle collisions. In addition, the consistent characteristic of two types of nonuniform quantizers (i.e., logarithmic quantizers and hysteresis quantizers) is proposed, and unknown disturbances in vehicle motion are compensated. The Lyapunov based local stability and string stability are proved. Numerical simulation shows that the control algorithm is applicable to AVs with either logarithmic quantizers or hysteresis quantizers. Compared with the previous control algorithms without considering input quantization and inter-vehicle collision avoidance, the control algorithm proposed reduces inter-vehicle spacing errors, avoids high acceleration, and avoids inter-vehicle collisions. [ABSTRACT FROM AUTHOR]

The dramatic increasing of data and demands for computing capabilities may result in excessive use of resources in cloud data centers, which not only causes the raising of energy consumption, but also leads to the violation of Service Level Agreement (SLA). Dynamic consolidation of virtual machines (VMs) is proven to be an efficient way to tackle this issue. In this paper, we present an Adaptive Deep Reinforcement Learning (DRL)-based Virtual Machine Consolidation (ADVMC) framework for energy-efficient cloud data centers. ADVMC has two phases. In the first phase, Influence Coefficient is introduced to measure the impact of a VM on producing host overload, and a dynamic Influence Coefficient-based VM selection algorithm (ICVMS) is proposed to preferentially choose those VMs with the greatest impact for migration in order to remove the excessive workloads of the overloaded host quickly and accurately. In the second phase, a Prediction Aware DRL-based VM placement method (PADRL) is further proposed to automatically find suitable hosts for VMs to be migrated, in which a state prediction network is designed based on LSTM to provide DRL-based model more reasonable environment states so as to accelerate the convergence of DRL. Simulation experiments on the real-world workload provided by Google Cluster Trace have shown that our ADVMC approach can largely cut down system energy consumption and reduce SLA violation of users as compared to many other VM consolidation policies. [ABSTRACT FROM AUTHOR]

GPUs are widely used to accelerate the training of machine learning workloads. As modern machine learning models become increasingly larger, they require a longer time to train, leading to higher GPU energy consumption. This paper presents GPOEO, an online GPU energy optimization framework for machine learning training workloads. GPOEO dynamically determines the optimal energy configuration by employing novel techniques for online measurement, multi-objective prediction modeling, and search optimization. To characterize the target workload behavior, GPOEO utilizes GPU performance counters. To reduce the performance counter profiling overhead, it uses an analytical model to detect the training iteration change and only collects performance counter data when an iteration shift is detected. GPOEO employs multi-objective models based on gradient boosting and a local search algorithm to find a trade-off between execution time and energy consumption. We evaluate the GPOEO by applying it to 71 machine learning workloads from two AI benchmark suites running on an NVIDIA RTX3080Ti GPU. Compared with the NVIDIA default scheduling strategy, GPOEO delivers a mean energy saving of 16.2% with a modest average execution time increase of 5.1%. [ABSTRACT FROM AUTHOR]

Extreme circumstances in which a local distribution system is electrically isolated from the main power supply may not always be avoidable. Efforts must be made to keep the lights on for such an isolated distribution system (IDS) until reconnection to the main power source. In this paper, we propose a strategy to enhance IDS survivability by utilizing the coordination of two flexible approaches, namely, separable mobile energy storage systems (SMESSs), which construct non-wires links for energy transmission between the IDS and the external live power sources, and demand response (DR), which adjusts the internal electrical demand of the IDS to provide effective operating stress alleviation. Considering the uncertainty of renewable energy generation (REG) and loads, a two-stage robust optimization (RO) model involving the joint scheduling of these two approaches is constructed. The objective is to minimize the fuel consumption and the decreased and unserved demand under the worst-case scenario to endow the IDS with extended survivability. Finally, test is conducted and the results demonstrate the effectiveness of the proposed method in enhancing the survivability of IDS. [ABSTRACT FROM AUTHOR]

With the new feature of multi-energy coupling and the advancement of the energy market, Energy Internet (EI) has higher requirements for the efficiency and applicability of integrated energy response. This paper proposes an indirect multi-energy transaction (IMET) to promote multi-energy collaborative optimization in local energy market (LEM) and improve energy utilization through personalized responses from We-Energies (WEs). Firstly, an indirect customer-to-customer multi-energy transaction is modeled for local multi-energy coupling market which can satisfy privacy, preference and autonomy of users. The efficiency of energy matching can be promoted through the participation of conversion devices. In addition, multi-time scale hybrid trading mechanism is constructed with the consideration of the transmission speed of different energy sources. Meanwhile, energy transaction process is built as a Markov decision process (MDP) with deep reinforcement learning algorithm so that the system modeling error can be successfully avoided. Furthermore, a distributed training structure is utilized to obtain more experience for a wider range of scenarios. The results of numerical simulations demonstrate the performance of the proposed method. [ABSTRACT FROM AUTHOR]

Energy management strategy (EMS) is the key to the performance of fuel cell / battery hybrid system. At present, reinforcement learning (RL) has been introduced into this field and has gradually become the focus of research. However, traditional EMSs only take the energy consumption into consideration when optimizing the operation economy, and ignore the cost caused by power source degradations. It would cause the problem of poor operation economy regarding Total Cost of Ownership (TCO). On the other hand, most studied RL algorithms have the disadvantages of overestimation and improper way of restricting battery SOC, which would lead to relatively poor control performance as well. To solve these problems, this paper establishes a TCO model including energy consumption, equivalent energy consumption and degradation of power sources at first, then adopt the Double Q-learning RL algorithm with state constraint and variable action space to determine the optimal EMS. Finally, using hardware-in-the-loop platform, the feasibility, superiority and generalization of proposed EMS is proved by comparing with the optimal dynamic programming and traditional RL EMS and equivalent consumption minimum strategy (ECMS) under both training and unknown operating conditions. Results prove that the proposed strategy has high global optimality and excellent SOC control ability regardless of training or unknown conditions. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the effect of the strong time-varying transmission distance on the performance of the low-earth orbit (LEO) satellite-terrestrial transmission (STT) system. We propose a new analytical framework using finite-state Markov channel (FSMC) model and time discretization method. Moreover, to demonstrate the applications of the proposed framework, the performances of two adaptive transmissions, rate-adaptive transmission (RAT) and power-adaptive transmission (PAT) schemes, are evaluated for the cases when the transmit power or the transmission rate at the LEO satellite is fixed. Closed-form expressions for the throughput, energy efficiency (EE), and delay outage rate (DOR) of the considered systems are derived and verified, which are capable of addressing the capacity, energy efficiency, and outage rate performance of the considered LEO STT scenarios with the proposed analytical framework. [ABSTRACT FROM AUTHOR]

Most of the existing works in millimeter wave (mmWave) and non-orthogonal multiple access (NOMA) communications (mmWave NOMA) employs phased array (PA) antennas which yield only angle beamspace without range dependent, range-interference suppression and “dot-shaped” array resolution. In this paper, we propose a collocated transmitter (Tx) - receiver (Rx) mmWave NOMA system where frequency diverse array (FDA) antennas are utilized. FDA employs small frequency increments across the element indices which yields angle-range dependent beamspace. Herein, an equivalent radiate beamspace is designed to serve the users. Further, to ensure user equity, we formulate objective function to max-min achievable sum rate subject to power allocation and BS equivalent FDA transmit beamspace along the users. Since the problem is not convex, a boundary-compressed Bat meta-heuristic optimization algorithm is devised to provide a sub-optimal solution by optimizing the frequency increments to synthesis the BS beamspace along the users. The achievable secrecy rate, cost efficiency (CE), economic efficiency (ECE), and secrecy energy efficiency (SEE) are derived for the users. Both theoretical analysis and simulation results indicate that the proposed scheme would constitute an excellent nominee for 5G mmWave NOMA communication applications. [ABSTRACT FROM AUTHOR]

Machine-type communications (MTC) is a key technology for Internet-of-Things (IoT) services in 5G mobile communications and beyond. An essential design problem for an MTC network is the efficient and scalable data collection from low-power machine-type communication devices (MTCDs). This paper uses unmanned aerial vehicles (UAVs) to facilitate data collection from a clustered MTC network on the ground. The notion of artificial energy map (AEM) is introduced as a novel modeling technique for energy efficiency analysis, which is critical to the subject of investigation here considering the limited energy of battery-powered MTCDs and UAVs. The proposed design framework first determines the number of MTCD clusters $L_\mathrm{{opt}}$ according to a certain criterion. A greedy learning clustering (GLC) algorithm is then employed to divide the MTCDs into $L_\mathrm{{opt}}$ clusters. For each MTCD cluster, an AEM is constructed, and the optimal UAV hovering strategy within the cluster can be obtained accordingly. Finally, the UAV stations travel across the clusters and collect data from each cluster while hovering above it. This AEM-based modeling technique leads to a solution that can effectively improve the energy efficiency (EE) of UAV-enabled data collection. However, the MTCD clustering strategy, UAV hovering strategy, and UAV flying strategy all have impacts on the overall energy efficiency, which results in a coupled optimization problem that is difficult to solve. The GLC-AEM method is proposed to decouple the original EE optimization problem into sub-problems that can be handled easily by standard optimization techniques. Simulation results show that the GLC-AEM algorithm can be applied to UAV-enabled data collection scenarios with single and multiple UAV stations, and it can improve the overall EE effectively. Besides, the GLC-AEM algorithm shows good scalability and consistent performance in clustered MTC networks. The more MTCDs, the higher the achieved EE. [ABSTRACT FROM AUTHOR]

Drones have been attracting the attention of diverse industries thanks to their superior maneuverability. Logistics companies especially keep trying to utilize drones for fast delivery following the growing market size of e-commerce. Accordingly, methods for safely operating multi-drone have been researched, and many researchers have proposed various optimal or near-optimal routing methods. However, such methods have some problems that cause routing failures or huge routing computation time in a drone-dense space due to many collisions. In this paper, we propose a centralized framework that deals with enormous collisions and obtains collision-free paths rapidly. We first build a drone energy consumption model with a data-driven method using flight experiment data of a commercial drone to estimate the drone battery state-of-charge (SoC). Then, we develop a novel routing method that generates collision-free paths by considering both the congestion of the space and the SoC of each drone. The proposed method is inspired by the VLSI circuit routing method that connects all signal nets among thousands of logic components. Through numerous delivery routing simulations, we confirm that the proposed method achieves a maximum of 6 times higher routing success rate with a 10x faster runtime compared with the state-of-the-art optimal method. In addition, we validate that the proposed method is applicable to delivery routing problems with various drone battery capacities. [ABSTRACT FROM AUTHOR]

Adaptive bitrate streaming can confront dynamic environment and satisfy multifarious user requirements by encoding video files into different bitrate versions. It can significantly improve the user quality of experience (QoE). But at the same time, it can also lead to a further increase in video data volume. The interaction of massive video data tends to aggravate network congestion and increase transmission energy, which triggers new challenges for wireless networks in terms of delivery delay and energy consumption. In this paper, taking different video sizes and playback rate requirements into account, we aim to minimize the weighted sum of delivery delay and energy consumption for adaptive video streaming. The problem is formulated by jointly optimizing caching, computing and power allocation, which turns out to be a mixed integer nonlinear programming (MINLP) problem. To tackle it, a joint caching, computing and power allocation (JCCPA) algorithm is proposed. More specifically, the original problem is decomposed into two subproblems: 1) power allocation problem and 2) joint caching placement and computing decision problem. The power allocation problem is proved to be a quasi-convex problem, and the closed-form solution is derived. With the optimal transmit power, the joint caching placement and computing decision problem is further divided into two subproblems, which are solved in an iterative manner. Simulation results demonstrate that the proposed algorithms can effectively reduce the weighted sum of delivery delay and energy consumption (outperforms the baselines around 5%–12% within a certain range). [ABSTRACT FROM AUTHOR]

Reconfigurable intelligent surface (RIS) has been introduced as a promising emerging paradigm to design a smart radio environment for improving wireless capacity. It has the potential to reshape wave propagation via its tunable and low-cost reflecting elements. Therefore, it is regarded as a propitious solution that provides alternative paths for the propagation of high-frequency signals, which are vulnerable to blockages and suffer from penetration losses. Because of those features, RIS has been considered to be integrated into the wireless vehicular network (WVN), which is characterized by high mobility and high quality of service (QoS) requirements. Hence, similar to relaying systems, a vehicle needs to select the best available RIS that maximizes the signal-to-noise ratio (SNR) at the receiver. Inspired by that, in this paper, we introduce the partial RIS selection for enhancing capacity and reducing error rate. We derive closed-form expressions for the ergodic capacity, symbol error probability (SEP), and energy efficiency for a partial RIS selection design. Besides, we compare its performances to the partial relaying design while considering: decode-and-forward (DF) and amplify-and-forward fixed gain (AF). Moreover, we consider a wireless attack scenario, where an eavesdropper intends to overhear the transmitted signal at the receiver’s end. Then, we compare partial RIS to partial relay selection from this security perspective, where we derive closed-form expressions for the average secrecy capacity. Furthermore, we compare the partial RIS selection to a single RIS architecture scenario from the literature, where our solution outperforms the related existing work. [ABSTRACT FROM AUTHOR]

This paper proposes an optimized energy management strategy (EMS) for hybrid electric fuel cell/battery-based drones focusing on fuel economy while extending sources lifespans in persistent missions. An off-the-shelf drone fed by a 650 $\mathrm{W}$ fuel cell is selected as a case study, where the power splitting is conventionally managed by a simple rule-based method. Then, a multi-objective genetic algorithm is used to optimize the proposed EMS parameters considering three scenarios regarding battery state of charge, namely charge sustaining, depleting, and increasing. Therefore, advantages of rule-based strategy and genetic algorithm are combined in an online EMS to fit on drone applications. Extensive simulation results demonstrate that the proposed approach allows power sources to operate within their rated area, prolonging their service life, and leading to 5.1% of fuel consumption reduction. Thus, the autonomy will be increased depending on the carried hydrogen quantity, and the world record can be extended by about 37 min. It may also lead to benefit in the operating cost achieving 1450€ during one fuel cell stack lifecycle. In fleet tasks, the benefit can be further multiplied. [ABSTRACT FROM AUTHOR]

As a flexible and reliable way for distributed energy consumption and integration, the converters-dominated microgrid has attracted more and more attention recently. Owing to the low inertia and high nonlinearity of power converters, the islanded microgrid under large-signal disturbances is easily suffer to transient stability problems. To support the stable operation of microgrid, grid-forming converters are extensively investigated and applied. In this paper, an improved equal area criterion (IEAC) is proposed for the transient stability analysis of a simple microgrid equipped with both grid-following and grid-forming converters. Firstly, a simplified second-order model of the microgrid is established, which contains a nonlinear damping term relying on the power angle. Then, the limitation of conventional equal area criterion (EAC) for transient stability analysis is revealed, which completely ignores the unfavorable influence of negative damping and may lead to erroneous judgement. To fill this gap, an improved equal area criterion is proposed to derive the transient stability conditions for islanded microgrid, which could improve the estimation accuracy of stability boundaries. Moreover, impacts of system parameters and controller parameters on transient stability are studied, which provides useful guidelines for the parameter optimization of grid-forming and grid-following converters. Eventually, simulation and hardware-in-loop experiments are conducted to verify the effectiveness and superiority of the proposed method. [ABSTRACT FROM AUTHOR]

Deep neural network (DNN) inference and training produce a large amount of intermediate data. To achieve high energy efficiency, sufficient on-chip buffer is preferred to reduce the energy and time consuming off-chip DRAM access. However, SRAM buffer suffers from large area cost and high standby power due to its large cell size and high leakage current. Although embedded DRAM (eDRAM) offers higher memory density, its energy consumption is high due to frequent refresh operation, which is induced by the short refresh interval (40∼100μs). In this paper, a dual-mode buffer memory based on the CMOS compatible HfZrO2 ferroelectric material is proposed for DNN accelerators. It can operate in both volatile eDRAM mode and non-volatile ferroelectric RAM (FeRAM) mode. The functionality of the proposed dual-mode memory bit-cell design is verified using SPICE simulation with the multi-domain Preisach physical model. A data-lifetime-aware memory mode configuration protocol is proposed to optimize the buffer access energy for both DNN inference and training. Detailed circuitry and architectural support for the dual-mode memory are presented. For DNN training with ferroelectric-field-effect-transistor (FeFET) and SRAM-based compute-in-memory (CIM) accelerator, the proposed dual-mode buffer design improves the overall energy efficiency by 92.2%∼98.7%, 44.1%∼47.6%, 12.6%∼13.0% compared to baseline designs using SRAM buffer with the same buffer area, eDRAM and FeRAM with the same buffer capacity, respectively. For DNN inference with tensor-processing-unit (TPU)-like systolic array, the energy efficiency during computing is improved by 40.7%∼45.6%, 18.4%∼29.6% compared to the designs with eDRAM and FeRAM buffer, respectively. By storing the persistent data using the non-volatile mode, the energy efficiency of systolic array is improved by 2.3×∼5.5× over SRAM-based design when standby is frequent. The chip area overhead of the dual-mode buffer design is 5.2%, 4.1% and 7.2% for FeFET-based-CIM, SRAM-based-CIM and systolic-array-based accelerators using eDRAM buffer, respectively. [ABSTRACT FROM AUTHOR]

Renewable energy is an advisable choice to reduce fuel consumption and $\rm CO_{2}$ emission. Therein, wind energy and solar energy are the most promising contributors to reach this goal. Although the hybrid wind/solar system has been widely studied, the real-time current sharing based on their maximum capacities is rarely achieved in terms of seconds. Based on this, this paper proposes an accurate current sharing and voltage regulation approach in hybrid wind/solar systems, which is based on distributed adaptive dynamic programming approach. Firstly, the equivalent wind/solar model is built, which is an indispensable preprocessing to achieve the complementary between wind energy and solar energy. Therein, the wind energy and solar energy can output relative current according to their respective capacity ratio, which ensure the maximum utilization ratio of renewable energy source. Furthermore, current sharing and voltage regulation problem is switched into optimal control problem. Under this effect, each source agent aims to obtain the optimal control variable and achieve accurate current sharing/voltage regulation. Moreover, an adaptive dynamic programming approach based on Bellman principle is proposed. It can achieve accurate current sharing and voltage regulation. Finally, the simulation results are provided to illustrate the performance of the proposed adaptive dynamic programming approach. [ABSTRACT FROM AUTHOR]