Search

Reinforcement learning (RL) techniques have emerged as powerful tools for optimizing energy systems, offering the potential to enhance efficiency, reliability, and sustainability. This review paper provides a comprehensive examination of the applications of RL in the field of energy system optimization, spanning various domains such as energy management, grid control, and renewable energy integration. Beginning with an overview of RL fundamentals, the paper explores recent advancements in RL algorithms and their adaptation to address the unique challenges of energy system optimization. Case studies and real-world applications demonstrate the efficacy of RL-based approaches in improving energy efficiency, reducing costs, and mitigating environmental impacts. Furthermore, the paper discusses future directions and challenges, including scalability, interpretability, and integration with domain knowledge. By synthesizing the latest research findings and identifying key areas for further investigation, this paper aims to inform and inspire future research endeavors in the intersection of reinforcement learning and energy system optimization. [ABSTRACT FROM AUTHOR]

This paper reviews the current techniques used in energy management systems to optimize energy schedules into microgrids, accounting for uncertainties for various time frames (day-ahead and real-time operations). The current uncertainties affecting applications, including residential, commercial, virtual power plants, electric mobility, and multi-carrier microgrids, are the main subjects of this article. We outline the most recent modeling approaches to describe the uncertainties associated with various microgrid applications, such as prediction errors, load consumption, degradation, and state of health. The modeling approaches discussed in this article are probabilistic, possibilistic, information gap theory, and deterministic. Then, the paper presents and compares the current optimization techniques, considering the uncertainties in their problem formulations, such as stochastic, robust, fuzzy optimization, information gap theory, model predictive control, multiparametric programming, and machine learning techniques. The optimization techniques depend on the model used, the data available, the specific application, the real-time platform, and the optimization time. We hope to guide researchers to identify the best optimization technique for energy scheduling, considering the specific uncertainty and application. Finally, the most challenging issues to enhance microgrid operations, despite uncertainties by considering new trends, are discussed. [ABSTRACT FROM AUTHOR]

Fractional programming (FP) refers to a family of optimization problems that involve ratio term(s). This two-part paper explores the use of FP in the design and optimization of communication systems. Part I of this paper focuses on FP theory and on solving continuous problems. The main theoretical contribution is a novel quadratic transform technique for tackling the multiple-ratio concave–convex FP problem—in contrast to conventional FP techniques that mostly can only deal with the single-ratio or the max-min-ratio case. Multiple-ratio FP problems are important for the optimization of communication networks, because system-level design often involves multiple signal-to-interference-plus-noise ratio terms. This paper considers the applications of FP to solving continuous problems in communication system design, particularly for power control, beamforming, and energy efficiency maximization. These application cases illustrate that the proposed quadratic transform can greatly facilitate the optimization involving ratios by recasting the original nonconvex problem as a sequence of convex problems. This FP-based problem reformulation gives rise to an efficient iterative optimization algorithm with provable convergence to a stationary point. The paper further demonstrates close connections between the proposed FP approach and other well-known algorithms in the literature, such as the fixed-point iteration and the weighted minimum mean-square-error beamforming. The optimization of discrete problems is discussed in Part II of this paper. [ABSTRACT FROM AUTHOR]

In this paper, we investigated the day-ahead scheduling of network consisting of several distributed generation sources and connected loads. This scheduling is an optimization problem that has two objective functions, namely, operating costs and the cost caused by the emission of pollutants. In this scheduling, while introducing a new structure for micro-grid utilization, the possibility of exchanging power among networks and exchanging power between networks and the upstream distribution network is provided. The uncertainty of distributed wind generation sources and solar units has been investigated using appropriate probabilistic distributions. Incent-driven load-responsiveness management programs have been used as to encourage subscribers to reduce their power consumption during high-load hours. Whale optimization algorithm has been used for scheduling of network. [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 presents a novel method on the optimization of bi-objective Flexible Job-shop Scheduling Problem (FJSP) under stochastic processing times. The robust counterpart model and the Non-dominated Sorting Genetic Algorithm II (NSGA-II) are used to solve the bi-objective FJSP with consideration of the completion time and the total energy consumption under stochastic processing times. The case study on GM Corporation verifies that the NSGA-II used in this paper is effective and has advantages to solve the proposed model comparing with HPSO and PSO+SA. The idea and method of the paper can be generalized widely in the manufacturing industry, because it can reduce the energy consumption of the energy-intensive manufacturing enterprise with less investment when the new approach is applied in existing systems. [ABSTRACT FROM AUTHOR]

We propose an optimization-based power-constrained red-green–blue (RGB)-to-red-green–blue-white (RGBW) conversion algorithm for emissive RGBW displays. We measure the perceived color distortion using a color difference model in a perceptually uniform color space, and compute the power consumption for displaying an RGBW pixel on an emissive display. The central contribution of this paper is to formulate the optimization problem to minimize the color distortion subject to a constraint on the power consumption. Subsequently, we solve the optimization problem efficiently to convert an image in real time. Furthermore, based on the properties of the human visual system, we extend the proposed algorithm to image-dependent conversion that can preserve spatial detail in an input image. The simulation results show that the proposed algorithm provides a significantly less color distortion than the conventional methods, while providing a graceful tradeoff with the amount of power consumed. Specifically, it is shown that the power consumption can be reduced by up to 20%, while providing about 50% less color distortion than the conventional algorithms. In addition, a subjective evaluation on a real RGBW display is performed, which reveals the merits of the proposed image-dependent conversion for improving the perceptual quality over state-of-the-art techniques. [ABSTRACT FROM AUTHOR]

Terahertz (THz) frequency band has become one of the popular researches for the short-range broadband, high-rate data communication, reliability, and strong anti-interference ability, which can realize better performance for dense vehicular network scenario. In this paper, we introduce a cache based dense vehicular networks with THz frequency link and focus on the resource allocation optimization problem of subband and power allocation by maximizing energy efficiency (EE). In order to reduce the computational complexity, the original optimization problem is formulated as a mean-field game (MFG) problem, where each cache access point (cAP) can solve its local EE optimization problem by Dinkelbach-style method and Lagrangian dual method. Different access cells interact with MFG through mean subband power. Then a global MFG based EE optimization algorithm is proposed to associate the local strategy of each cell, which guarantee the globality of the optimized solutions. The numerical results suggest the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

In this paper, a fundamental study of energy efficiency (EE) optimization for coordinated multi-point (CoMP) simultaneous wireless information and power transfer (SWIPT) heterogeneous networks (HetNets) is provided. We aim to optimize the EE while satisfying certain quality-of-service requirements in regard to transmission rate and energy harvesting at both the macro cell and small cells. The corresponding joint beamforming and power allocation in the presence of intra- and inter-cell interference constitutes an EE maximization problem that is non-convex, and hence, very challenging to solve. In order to solve this problem, we propose to separate the beamforming design and power allocation processes. First, we adopt linear zero-forcing (ZF) beamforming to suppress the multi-user interference from both the energy harvesting users (EH-UEs) as well as the information decoding UEs (ID-UEs), thus transforming the HetNet under consideration to a virtual point-to-point system. An efficient power allocation algorithm is then developed to maximize the corresponding EE. On the other hand, the ZF strategy does not utilize the notion that interference benefits the EH-UEs. As a result, we propose a partial ZF approach by differentiating the EH-UEs and ID-UEs in order to further improve the EE. Our findings show that the EE can be significantly improved through the integration of CoMP-SWIPT in HetNets. [ABSTRACT FROM AUTHOR]

In this paper, an optimal hybrid (wind and grid)-hydrogen energy system (H-HES) is proposed using multi-objective non-dominated sorting algorithm (NSGA-II) optimization. The H-HES consists of the main energy system; wind-energy system (W-ES), which supplies a proton exchange membrane (PEM) electrolyzer via an energy management system (EMS) and a rectifier. In addition, the grid-energy system (G-ES) is available to support the W-ES to meet the PEM electrolyzer's energy demand, and the EMS facilitates control between the W-ES and G-ES. The W-ES is modelled using wind data from Wind Atlas South Africa (WASA) for six Renewable Energy Development Zones (REDZs) in South Africa and their appropriate wind turbine models. The selection of appropriate wind turbine models is guided by the optimal wind turbine variables obtained from NSGA-II corresponding to the optimal H-HES model. The optimal H-HES model is developed using two objective functions: cost of electricity and efficiency, which are minimized and maximized respectively and evaluated using NSGA-II available in Pymoo framework. NSGA-II successfully converges to a Pareto front, and the best solution for the H-HES cost of electricity and efficiency for each wind REDZ is determined by compromise programming; a multi-criteria decision-making technique available in Pymoo. From the optimal cost of electricity and efficiency solutions, optimal variables are successfully obtained for optimal modelling of the H-HES for each wind REDZ. [ABSTRACT FROM AUTHOR]

Most of the crude oil produced in China is highly viscous and easy to condense, and the pipeline transportation method is mostly heated. At present, frequency conversion technology is gradually applied to oil pump units. In order to find the best pipeline operation scheme, this paper is based on MATLAB programming software and established a corresponding mathematical model of oil pipeline energy consumption. This model innovatively takes the rotational speed of the variable frequency pump as one of the model optimization variables, calculating the pressure change and temperature change between stations of the pipeline, predicting the energy consumption of the pipeline and can guide the optimized operation of the pipeline, which has a high popularization and application value. An example of Qingtie Fourth‐line crude oil pipeline shows that among the genetic algorithm, particle swarm optimization, and simulated annealing algorithms for solving the optimization model, the particle swarm optimization algorithm has fast convergence speed, short optimization time, obtains the most optimal scheme, and can reduce energy consumption by 7.84%, which proves the practicability and creativity of the optimization model. [ABSTRACT FROM AUTHOR]

Rapid transit systems play a significant role in supporting rapid social and economic development in large cities all over the world. However, the systems consume a large amount of energy, which brings increasing environmental concerns. A number of energy-saving technologies have been studied on railways. However, few of the outcomes have been tested and evaluated in practice. This paper presents the development of a timetable optimization and trial test on a metro line to reach the full potential of the train regenerative braking system. To achieve this purpose, a timetable optimization algorithm has been developed, and a trial test of the optimal timetable has been arranged on a metro line for a whole day. In the test, all the trains running in the network were organized to operate in accordance with the optimal timetable. The trial test results indicate that by applying the optimal timetable, the regenerative braking energy utilization can be improved, thereby reducing the overall network energy usage. [ABSTRACT FROM AUTHOR]

In this paper, driving strategy optimization for a track is proposed for an energy efficient battery electric vehicle dedicated to the Shell Eco-marathon. A measurement-based mathematical vehicle model was developed to simulate the behavior of the vehicle. The model contains complicated elements such as the vehicle's cornering resistance and the efficiency field of the entire powertrain. The validation of the model was presented by using the collected telemetry data from the 2019 Shell Eco-marathon competition in London (UK). The evaluation of applicable powertrains was carried out before the driving strategy optimization. The optimal acceleration curve for each investigated powertrain was defined. Using the proper powertrain is a crucial part of energy efficiency, as the drive has the most significant energy demand among all components. Two tracks with different characteristics were analyzed to show the efficiency of the proposed optimization method. The optimization results are compared to the reference method from the literature. The results of this study provide an applicable vehicle modelling methodology with efficient optimization framework, which demonstrates 5.5% improvement in energy consumption compared to the reference optimization theory. [ABSTRACT FROM AUTHOR]

Different from conventional reflecting-only metasurfaces, intelligent omni-surfaces (IOSs) are capable of reflecting and transmitting the received signals simultaneously. As such, users located at the both sides of IOSs can be served efficiently. In this paper, a novel IOS-enhanced aerial secure offloading system is proposed in the presence of multiple ground eavesdroppers. Specifically, the IOS is adopted to prevent information leakage, improve legitimate reception quality and expand the security deployment range of unmanned aerial vehicles (UAVs). To maximize the secrecy energy efficiency (SEE) of the considered system, a non-convex resource allocation problem is formulated by allocating computing frequency, determining offloading strategy, controlling transmit power, designing phase shifts, and deploying UAV locations. The formulated problem is non-trivial and challenging to be solved directly due to the highly coupled variables. To tackle this issue, the alternating optimization technique is invoked to decouple the original one into five subproblems, and then the computing and communication settings are alternatively optimized using a low complexity iterative algorithm. Finally, simulation results demonstrate that: i) In terms of the SEE performance, our IOS-enhanced aerial secure offloading system significantly outperforms the benchmark schemes; ii) Compared with reflecting-only surfaces, IOSs can take full advantage of the flexible deployment ability of UAVs, such that their secure offloading space can be considerably expanded. [ABSTRACT FROM AUTHOR]

The control strategy is an essential element in the improvement of electric motor energy efficiency through flux adaptation, according to the operating conditions. Therefore, the goal of this paper is to present an energy-efficient field-oriented control structure for squirrel-cage induction motors (IM), improving their energy efficiency and taking into consideration the core loss effect. For this purpose, optimal flux is computed in off-line calculations based on an improved IM dynamic model including core losses. These optimal values are stored in a torque-speed look-up table used to provide the flux reference of the control structure. Simulation and experimental results validate the proposed optimized field-oriented control showing the improvement in energy efficiency and the reduction in power losses. Experimental tests also confirm the improvement of the proposed method, compared to an industrial optimization technique and a state-of-art approach. [ABSTRACT FROM AUTHOR]

Non-orthogonal multiple access (NOMA) and backscatter communication are two emerging technologies for low-power communication. In this paper, we consider a NOMA backscatter system, where signals from two backscatter devices are multiplexed on a frequency resource block using NOMA in each time slot. Our objective is to maximize the average energy efficiency by optimizing backscatter device pairing, reflection coefficients of backscatter devices, and the transmit power of the reader. We formulate the average energy efficiency maximization problem subject to the minimum circuit power and the minimum data rate requirements of the backscatter devices, and the transmit power constraint of the reader. The formulated problem is nonconvex. To obtain a suboptimal solution for this problem, we use alternating optimization technique and decompose the problem into two subproblems. The subproblems are solved by using fractional programming, Dinkelbach’s algorithm, and successive convex approximation method. Simulation results show that our proposed algorithm converges quickly to a suboptimal solution. Our proposed algorithm outperforms several baseline algorithms, including the genetic algorithm, fixed device pairing scheme, conventional device pairing scheme, maximum transmit power allocation scheme, and random reflection coefficient selection scheme, in terms of the average energy efficiency. The optimality gap of our proposed algorithm is investigated by comparing with the optimal scheme, in which the optimal device pairing is obtained based on exhaustive search. [ABSTRACT FROM AUTHOR]

A wireless sensor network (WSN) includes more low-cost and less-power sensor nodes. All the sensor nodes are positioned in a particular area and form a wireless network by way of self-organizing. They has the ability to work normally at any of the special or wicked environ that people cannot close. However, the data transmission among nodes in an effective way is almost not possible due to various complex factors. Clustering is a renowned technique to make the transmission of data more effective. The clustering model divides the sensor nodes into various clusters. Every cluster in network has unique cluster head node, which send the information to other sensor nodes in cluster. In such circumstances, it is the key role of any clustering algorithm to choose the optimal cluster head under various constraints like less energy consumption, delay and so on. This paper develops a new cluster head selection model to maximize the lifetime of network as well as energy efficiency. Further, this paper proposes a new Fitness based Glowworm swarm with Fruitfly Algorithm (FGF), which is the hybridization of Glowworm Swarm Optimization (GSO) and Fruitfly Optimization algorithm (FFOA) to choose the best CH in WSN. The performance of developed FGF is compared to other existing methods like Particle swarm Optimization (PSO), Genetic Algorithm (GA), Artificial Bee Colony (ABC), GSO, Ant Lion Optimization (ALO) and Cuckoo Search (CS), Group Search Ant Lion with Levy Flight (GAL-LF), Fruitfly Optimization algorithm (FFOA) and grasshopper Optimization algorithm (GOA) in terms of alive node analysis, energy analysis and cost function and the betterments of proposed work is also proven. [ABSTRACT FROM AUTHOR]

In this paper, we consider a wireless powered multi-relay network in which a multi-antenna hybrid access point underlaying a cellular system transmits information to distant receivers. Multiple relays capable of energy harvesting are deployed in the network to assist the information transmission. The hybrid access point can wirelessly supply energy to the relays, achieving multi-user gains from signal and energy cooperation. We propose a joint optimization for signal beamforming of the hybrid access point as well as wireless energy harvesting and collaborative beamforming strategies of the relays. The objective is to maximize the network throughput subject to probabilistic interference constraints at the cellular user equipment. We formulate the throughput maximization with both the time-switching and power-splitting schemes, which impose very different couplings between the operating parameters for wireless power and information transfer. Although the optimization problems are inherently non-convex, they share similar structural properties that can be leveraged for an efficient algorithm design. In particular, by exploiting monotonicity in the throughput, we maximize it iteratively via customized polyblock approximation with reduced complexity. The numerical results show that the proposed algorithms can achieve close to optimal performance in terms of the energy efficiency and throughput [ABSTRACT FROM AUTHOR]

Computation of Loss of Load Probability (LOLP) is a challenge in Generation Expansion Planning (GEP) problem. This paper presents a dynamic GEP model considering LOLP as a reliability criterion. The objective function of the proposed dynamic GEP model includes the discounted total costs of investment cost, operation cost, and maintenance cost. The proposed LOLP constrained GEP problem is formulated as a mixed integer nonlinear programming (MINLP) problem. The MINLP formulation of GEP problem is then converted to a mixed integer linear programming (MIP) problem by applying several approximations to LOLP constraint. The utilized approximations are divided into two clusters for low and high order outages. A test case containing 12 types of installed and 5 types of candidate plants with a 14-year planning horizon is used to validate the efficiency of the proposed dynamic GEP problem. The developed MIP-based GEP model is solved using CPLEX solver and the obtained results are compared with the conventional LOLP-constrained GEP model. [ABSTRACT FROM AUTHOR]

This paper focuses on optimizing the laser engraving of acrylic plastics to reduce energy consumption and CO2 gas emissions, without hindering the production and material removal rates. In this context, the role of laser engraving parameters on energy consumption, CO2 gas emissions, production rate, and material removal rate was first experimentally investigated. Grey–Taguchi approach was then used to identify an optimal set of process parameters meeting the goal. The scan gap was the most significant factor affecting energy consumption, CO2 gas emissions, and production rate, whereas, compared to other factors, its impact on material removal rate (MRR) was relatively lower. Moreover, the defocal length had a negligible impact on the response variables taken into consideration. With this laser-process-material combination, to achieve the desired goal, the laser must be focused on the surface, and laser power, scanning speed, and scan gap must be set at 44 W, 300 mm/s, and 0.065 mm, respectively. [ABSTRACT FROM AUTHOR]

In hyperdense heterogeneous networks (HetNets), efficient intercell interference management is an important issue. In this paper, using recent advances in mean-field game (MFG) theory, we propose a novel game-theoretic approach for interference management in HetNets. The intercell interference management issue in HetNets is formulated as two nested problems: an overlay problem at the macrocell base station (MBS) level and an underlay problem at the small-cell base station (SBS) level. In the overlay problem, the MBS selects the optimal action first, which satisfies its associated users with a minimum amount of cross-layer interference, taking into account the reaction of the SBSs. The underlay problem is then formulated as a noncooperative game among the SBSs. The mean-field theory is exploited to help decouple a complex large-scale optimization problem into a family of localized optimization problems. Thus, each SBS can implement its policy by using only its local information and some macroscopic information. In addition to the achieved tradeoff between spectral efficiency (SE) and energy efficiency (EE), our approach can substantially reduce the communication overhead and convergence time of interference management in hyperdense HetNets. Simulation results are presented to show the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

Increasing spectrum-efficiency (SE) as well as energy-efficiency (EE) has attracted much attention recently due to the fact that the future wireless networks need to address the issues of high throughput and low power consumption. However, the objective for optimizing SE sometimes conflicts with the one for optimizing EE, and the methods for improving EE may result in a decrease in SE. In this paper, we consider the SE-EE tradeoff for cognitive radio (CR) networks with co-operative spectrum sensing (CSS). First, we formulate the general problem, and analyze two special cases: the SE maximization problem and the EE maximization problem. The SE and EE are optimized separately via joint optimization of sensing duration and final decision threshold in CSS. Based on the solutions of the two special cases, the general problem for SE-EE tradeoff is solved. Then, we consider the tradeoff of SE and EE from two perspectives: (1) maximizing EE while satisfying SE requirement; and (2) maximizing SE while satisfying EE requirement. Efficient algorithms for sensing strategy design are proposed for each scenario. Finally, we demonstrate the effectiveness of the proposed sensing strategies and illustrate the tradeoff between SE and EE via simulations. [ABSTRACT FROM AUTHOR]

Optimal control system has been considered as a valuable technique to enhance the reliability and security of wastewater treatment process (WWTP). However, due to the complex environments, it is difficult to realize the optimal control with changeable operational conditions in WWTP. To overcome this problem, an intelligent optimal control system with flexible objective functions (IOCS-FOF) was proposed in this paper. The main merits of this proposed IOCS-FOF were shown as follows. First, an optimal control system scheme was developed, where different time-scale operational objectives were detailed to describe the operational features. Second, a flexible objective strategy was designed to determine suitable objective functions, which could adapt to complex environments of WWTP. Third, a novel dynamic multiobjective optimization algorithm for flexible objectives (FO-DMOA) was developed to obtain the optimal solutions of IOCS-FOF. Finally, the proposed IOCS-FOF was applied in the benchmark simulation model No. 1 (BSM1) to confirm its technique capability. The results demonstrate that this proposed method can improve the operation performance of WWTP. [ABSTRACT FROM AUTHOR]

Energy efficiency (EE) is very crucial for future wireless communication systems, especially for cognitive radio networks (CRNs). The EE performance relies on channel state information (CSI) of channels. Besides, the interference from secondary users (SUs) to primary users (PUs) also closely depends on CSI in underlay CRNs. However, available works on EE usually assume that CSI is perfect, which is often inaccurate in practical systems. Thus, in this paper we investigate the robust EE maximization problem in underlay CRNs with multiple SUs and PUs. Assuming CSI error to be bounded, we consider that all channels lie in some bounded uncertainty regions. From the perspective of worst-case optimization, we formulate it as the max-min problem with infinite constraint, which is nontrivial even without this constraint. This is because that the outer-maximization problem is non-convex and the inner-minimization problem is a concave minimization problem known as NP-hard in general. We propose a scheme to handle this problem via the fractional programming and global optimization techniques. Particularly, we efficiently solve this problem in two special cases. Simulation results validate that our proposed scheme can improve the worst-case EE of SUs distinctly and strictly guarantee the quality-of-service (QoS) of PUs under all parameters' uncertainties. [ABSTRACT FROM AUTHOR]

Energy efficiency of three common hybrid automatic repeat request (HARQ) schemes, including Type I HARQ, HARQ with chase combining (HARQ-CC) and HARQ with incremental redundancy (HARQ-IR), is analyzed and joint power allocation and rate selection to maximize the energy efficiency is investigated in this paper. Unlike prior literature, time-correlated fading channels are considered and two widely concerned quality of service (QoS) constraints, i.e., outage and goodput constraints, are also considered in the optimization, which further differentiates this paper from prior ones. Using a unified expression of asymptotic outage probabilities, optimal transmission powers and optimal rate are derived in closed forms to maximize the energy efficiency while satisfying the QoS constraints. These closed-form solutions then enable a thorough analysis of the maximal energy efficiencies of various HARQ schemes. It is revealed that with low-outage constraint, the maximal energy efficiency achieved by Type I HARQ is $\frac{1}{4\ln 2}$ b/J, while HARQ-CC and HARQ-IR can achieve the same maximal energy efficiency as $\frac{\kappa _\infty }{4\ln 2}$ b/J, where $\kappa _\infty = 1.6617$. Moreover, time correlation in the fading channels has a negative impact on the energy efficiency, whereas large maximal allowable number of transmissions is favorable for the improvement of energy efficiency. The effectiveness of the energy-efficient optimization is verified by extensive simulations and the results also show that HARQ-CC can achieve the best tradeoff between energy efficiency and spectral efficiency among the three HARQ schemes. [ABSTRACT FROM AUTHOR]

This paper presents an on-line energy management strategy (EMS) based on trip condition prediction for commuter plug-in hybrid electric vehicles (P-HEVs). The purpose is to provide an on-line predictive control approach to minimize fuel consumption. Two pivotal contributions are provided to realize the purpose. First of all, we establish the trip condition prediction model by using back propagation neural network, to obtain the real-time vehicle-speed trajectory on-line. Particularly, both the genetic algorithm and particle swarm optimization algorithm are applied to improve the prediction accuracy of the trip condition prediction model. Next, to obtain an applicable EMS in real time, we propose a dynamic programming-based predictive control strategy. Finally, a simulation study is conducted for applying the proposed strategy to a practical trip path in the Beijing road network. The results show that the designed trip condition prediction model can effectively realize the on-line vehicle-speed prediction, and the prediction accuracy is more than 93%. In addition, compared to the offline global optimization EMS, although the proposed strategy makes the fuel consumption grow less than 5.2%, it can be implemented in real time. Moreover, compared with the existing real-time EMSs, it can further reduce the fuel consumption and emissions. It shows that the proposed EMS can provide an effective solution for commuter P-HEVs applying it on-line. [ABSTRACT FROM PUBLISHER]

Summary: Buildings around the world account for about one‐third of the energy consumption. Enough energy is required to maintain the comfort level for the occupants. Recently, the rise in global temperature resulting in climate change is associated with the comfort level for both outdoor and indoor of the buildings. Thus, providing acceptable comfort levels within buildings has become significant. The comfortable indoor environment of building requires energy for the operation of various appliances. A smart and energy efficient approach is the need of an hour to reduce energy consumption and attain comfortable indoor environment. The building energy and comfort management system (BECMS) model incorporating trade‐off between energy consumption and comfort has already been focused in previous studies. However, limited analyses have been observed in comparing the most efficient population‐based algorithm for BECMS. In this paper, a comparative study has been carried out using three different optimization techniques including multi‐objective genetic algorithm (MOGA), hybrid MOGA (HMOGA), and multi‐objective particle swarm optimization method (MOPSO) for optimal energy and comfort management in buildings. These optimization techniques have been widely employed to solve various optimization problems. The significant contribution of this article identifies the best suited algorithm in attaining best optimized solution of energy and comfort in a building. The comparative analysis of the three optimization techniques shows that MOPSO outperforms and attains maximum comfort level and higher energy savings. [ABSTRACT FROM AUTHOR]

In this paper we consider maximum-likelihood (ML) MIMO detection under one-bit quantized observations and binary symbol constellations. This problem is motivated by the recent interest in adopting coarse quantization in massive MIMO systems—as an effective way to scale down the hardware complexity and energy consumption. Classical MIMO detection techniques consider unquantized observations, and many of them are not applicable to the one-bit MIMO case. We develop a new non-convex optimization algorithm for the one-bit ML MIMO detection problem, using a strategy called homotopy optimization. The idea is to transform the ML problem into a sequence of approximate problems, from easy (convex) to hard (close to ML), and with each problem being a gradual modification of its previous. Then, our attempt is to iteratively trace the solution path of these approximate problems. This homotopy algorithm is well suited to the application of deep unfolding, a recently popular approach for turning certain model-based algorithms into data-driven, and performance enhanced, ones. While our initial focus is on one-bit MIMO detection, the proposed technique also applies naturally to the classical unquantized MIMO detection. We performed extensive simulations and show that the proposed homotopy algorithms, both non-deep and deep, have satisfactory bit-error probability performance compared to many state-of-the-art algorithms. Also, the deep homotopy algorithm has attractively low computational complexity. [ABSTRACT FROM AUTHOR]

This paper presents an energy-efficient downlink precoding scheme in a multi-cell Massive MIMO system. We approach the precoder design problem to maximize the system energy efficiency by jointly considering power control, interference management, antenna switching and user throughput in a cluster of base stations. This is computationally difficult as it requires solving a sparsity-inducing non-convex optimization problem, which is NP-hard. To alleviate the solution complexity, first a stochastic smooth approximation of zero-norm is applied in the antenna power management to enable fast, gradient-based algorithms. For efficient convergence, we develop a novel optimization algorithm combining augmented multiplier (AM) and quadratic programming (QP), and show how this scheme permits decentralized implementation by offloading parts of the computation to the individual base stations to reduce communication overhead. We provide theoretical proof that the proposed algorithm converges both locally and globally under realistic assumptions. Numerical results confirm that our method achieves higher energy efficiency with a superior convergence rate compared to different types of existing methods, and illustrate the relationship between energy efficiency performance and system design parameters. [ABSTRACT FROM AUTHOR]

This paper presents the way of identifying the influence of the tolerance of model pumps (TYPE 3PWR-SE) construction on the total efficiency. The identification of sensitive control dimensions (Value / Tolerance) of examined pumps has been made by means of the multi-valued logic and inductive decision trees. In order to confirm the obtained results, multiple-valued logic trees have been used in the paper according to algorithms for the minimization of individual logic functions. What is more, a specific monotonicity of values of the most key parameters has been determined for the investigated set of pumps having individual specification numbers. [ABSTRACT FROM AUTHOR]

An important objective in process design in the wood-processing industry is to make the system robust - or insensitive - to sources of variability that cannot be entirely controlled once the system is in use. Experimental designs can be used to solve some of these problems. The first techniques were introduced in the 1980s by Genichi Taguchi, a Japanese engineer. The objective of this paper is to apply advanced methods for optimization of cutting conditions using a robust design in the wood-processing industry according to energy efficiency criteria. By showing some numerical simulation results, the effectiveness of the proposed model is illustrated. [ABSTRACT FROM AUTHOR]

In heterogeneous wireless networks (HetNet), the multihomed user equipment (UE) utilizes multiple access options (AO) simultaneously and aggregate the offered resources from different AOs so as to support the same application, such as media streaming. Two important challenges of the resource optimization in this context are: 1) determining the connection results between UEs and AOs, and 2) determining the amount of radio resources that AOs should allocate to UEs. In this paper, we investigate the energy-efficient resource optimization for the HetNet with multihomed UEs. First, the energy-efficient resource optimization is formulated as an energy efficiency (EE) maximization problem. Second, the nonconcave objective function of EE maximization is converted by a fractional programming theory into a convex optimization problem. Considering that the connection results between UEs and AOs correspond to certain network topology configurations, the convex optimization problem is decomposed into a topology building problem and a resource allocation problem, which are proved as combination optimization and mixed integer nonlinear optimization, respectively. Then, the Markov approximation framework and continuity relaxation method are employed for solving the two problems, respectively. Finally, a joint topology building and resource allocation algorithm are proposed for optimizing the AOs radio resource energy efficiently. Simulation results validate the theoretical analysis of our proposed scheme. [ABSTRACT FROM PUBLISHER]

On-chip global interconnects are becoming speed and power bottlenecks in state-of-the-art chips. Low-swing signaling is used to improve delay performance and reduce power consumption. This paper first performs a delay analysis for different low-swing circuits based on the Asymptotic Waveform Evaluation (AWE). In addition, new delay metrics are presented and analyzed. The new delay metrics demonstrate that optimal designs can be obtained in low-swing signaling. To verify our analysis, a simulation environment is established. The simulation results indicate that the optimal designs can increase the 3dB bandwidth of a wire by more than 40% in resistively driven or capacitively driven 10mm global links. Thus, these optimal design methods can effectively improve the bandwidth of global wires. [ABSTRACT FROM AUTHOR]

Apart from spectral efficiency (SE), energy efficiency (EE) has become an important metric for future wireless communication systems. However, how to achieve balance between SE and EE for a multicell multiuser coordinated beamforming system remains an open problem. In this paper, we propose a new paradigm for EE–SE balancing, namely, using a new metric called resource efficiency (RE) as the objective function to achieve a reasonable tradeoff between SE and EE. The considered optimization problem is more complex than traditional problems and, in general, is NP-hard for finding the globally optimal solution. To obtain insight into the problem, we first equivalently transform the original problem into a parameterized subtractive form optimization problem with some auxiliary variables by using the fraction programming theory. Then, a successive convex approximation method and the classical second-order cone programming method are jointly used to solve the equivalent problem. Finally, an effective algorithm with proven convergence is proposed to obtain the solution to the considered RE optimization. In addition, the algorithm is further extended to consider imperfect channel state information (CSI) using the worst-case design. Extensive numerical simulations are given to validate the effectiveness of our algorithm. [ABSTRACT FROM AUTHOR]

The placement of Unmanned Aerial Vehicle (UAV) based aerial Base station (BS) has emerged as a viable solution to the contemplated high traffic demands of the future wireless networks. Nevertheless, the practical realization of UAVs for wireless coverage provisioning demands efficient utilization of its limited onboard energy reserves. This paper studies the power-domain Non-Orthogonal Multiple Access (NOMA) scheme for energy-efficient placement of the UAV-BS. The objective of maximized information bits per unit energy consumption of the UAV-BS is developed assuming a multi-Quality of Service (QoS) scenario. Also, the efficiency analysis is carried out considering the composite of energies consumed for the signal transmission and hover operation of the UAV-BS. The formulated Non-Linear Fractional Problem (NLFP) as a function of transmission power and altitude is solved by employing an alternating optimization technique based on a nested Dinkelbach structure. An investigation into the user pairing scheme also sheds new light on its effect upon the constrained feasible altitude range of the UAV-BS. Consequently, a computationally efficient approach is devised to render solution space based on the identified subset of user pairs. The numerical analyses of the proposed scheme signify a substantial increase in the energy efficiency of the NOMA based UAV-BS in comparison to the baseline scheme of Orthogonal Multiple Access (OMA). Thus, the improved energy efficiency enables enhanced coverage for the UAV-BS. [ABSTRACT FROM AUTHOR]

Cloud computing is the most prominent computing paradigm in the present era of information technology. However, data centers needed for hosting cloud services demand huge amount of electrical energy and release harmful gases to the atmosphere. To ensure a sustainable future, there is a need to focus on energy efficiency in cloud computing. Early literature pertaining to energy consumption in cloud computing is primarily focused on individual sub-domains like scheduling techniques, optimization, and green computing metrics. Research literature on cloud resource optimization is found to be the most discussed but less structured. This paper intends to provide a complete picture of energy efficiency in cloud computing. It also classifies heuristics-based optimization methods and the dynamic power management techniques. The survey shows the research trends based on regions, journals, conferences, etc., in the domain of energy efficiency in cloud computing. The study concludes with research issues and future research directions. [ABSTRACT FROM AUTHOR]

This paper investigates the fundamental energy efficiency–spectral efficiency (EE–SE) relationship in a multiple-input–multiple-output (MIMO) orthogonal frequency-division multiple-access (OFDMA) broadcast channel with a practical power model considering the power consumption due to the number of admitted users, as well as the number of active transmit antennas. However, with this power model, the EE–SE tradeoff optimization problem, which jointly optimizes the transmit covariance matrices while determining the optimal admitted user set and the active transmit antenna set, is nonconvex, and hence, it is extremely difficult to solve directly. As a result, we propose an algorithm that decouples the multicarrier EE optimization problem to a set of single-carrier EE optimization problems. For the single-carrier EE optimization problem, we first investigate the EE–SE tradeoff problem with a fixed admitted user set and transmit antenna set. Under this setup, we prove that the EE–SE relationship is a quasiconcave function. Furthermore, EE is proved to be either strictly decreasing with SE or first strictly increasing and then strictly decreasing with SE. Based on these findings, we propose a two-layer resource allocation algorithm to tackle the comprehensive EE–SE tradeoff problem. Meanwhile, since admitting more users and activating more transmit antennas can achieve a higher sum rate at the cost of larger transmit-independent power consumption, there exists a tradeoff between sum-rate gain and power consumption. We therefore study the user and antenna selection approach to further explore the optimal tradeoff. Both the optimal exhaustive search and the Frobenius-norm-based dynamic selection schemes are developed to further improve the achievable EE. To further reduce the computational complexity, a strategy that chooses a fixed admitted user set for all the subcarriers is developed. Simulation results confirm the theoretical findings and demonstrate that the proposed resource allocation algorithm can efficiently approach the optimal EE–SE tradeoff. [ABSTRACT FROM AUTHOR]

Both energy efficiency (EE) and spectral efficiency (SE) are two important design criteria that can hardly be achieved simultaneously. In this paper, we investigate the EE–SE tradeoff for a two-way relaying system with amplify-and-forward (AF) strategy. Applying a unified EE–SE tradeoff metric, a power-allocation problem is formulated as the joint EE and SE maximization problem with a tradeoff factor. In particular, only statistical channel-state information (CSI) is required, and the asymmetric traffic requirements are taken into consideration in our design. Through the exploitation of the theorem of nonlinear fractional programming, a unique closed-form optimum solution is obtained, and some useful insights are also achieved. It has been proved that the provided optimal solution is Pareto optimal for EE–SE optimization. Utilizing these results, one can flexibly make the tradeoff between EE and SE by simply setting the tradeoff factor. Simulation results highlight the effect of the proposed power-allocation scheme. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the energy consumption issue in cognitive radio (CR) networks. We aim to maximize the energy efficiency of the CR network while considering practical restrictions, including the power budget of the system, the interference thresholds of the primary users (PUs), the rate requirements of the secondary users, and the fairness among them. Particularly, due to the lack of explicit support from the PU system, perfect channel state information may not be acquired. Thus, the interference constraint is posed as chance-constrained form and tackled by Bernstein approximation. Then, we convert the optimization task into a quasi-convex problem via relaxing the integer variables, followed by a simple rounding technique to yield feasible subchannels assignment. We derive a fast algorithm to distribute power among subchannels by exploiting the structure of the power-allocation problem. Moreover, we give an efficient heuristic algorithm for subchannels assignment, which reduces the computation load dramatically. Simulation results show that both our proposed resource allocation schemes perform well in practical scenarios. The energy efficiency obtained by the integer subchannels assignment and the fast power distribution achieves more than 98% of the upper bound. On the other hand, the proposed heuristic subchannels assignment with optimal power allocation achieves a good tradeoff between computation complexity and energy efficiency. [ABSTRACT FROM AUTHOR]

In this paper, we propose an optimized, search based near-optimal mapping heuristic, named as ONMAP for mapping real time embedded application workloads on 2D based on-chip interconnection network platforms. ONMAP exploits NMAP, a well-known and fast nearest neighbor heuristic algorithm by using the modular exact optimization method. The proposed hybrid algorithm minimizes the on-chip inter-processor communication energy consumption and optimizes the interconnection network performance parameters. The algorithm inherits the constructive search based heuristic nature of the NMAP algorithm, as well as the property of exact optimization for mapping embedded applications on the target communication architecture. To verify the efficiency and effectiveness of the algorithm, we have compared the proposed algorithm with NMAP and random mapping algorithm under similar simulation environments and traffic conditions. The mapping results of the exemplary real world applications such as VOPD, PIP, MPEG4, MWD, MMS and WiFi-80211arx indicate that ONMAP algorithm is more efficient than its competitors for most of the performance parameters of the on-chip network designs. The algorithm successfully optimized the energy consumption, up to 20 % and 26% in comparison to NMAP and random algorithms, respectively. Similarly, the cost is optimized up to 10% and 60% as compared to NMAP and random mapping algorithms, respectively. [ABSTRACT FROM AUTHOR]

This paper proposes a new optimization tool based on the cross-entropy (CE) method to assess security-constrained optimal power flow (SCOPF) solutions. First, the corresponding SCOPF stochastic problem is defined so that the optimum solution is interpreted as a rare event to be reached by a random search. Second, the CE method solves this new problem efficiently by making adaptive changes to the probability density function according to the Kullback–Leibler distance, creating a sequence of density functions that guides the search in the direction of the theoretically degenerate density at the optimal point. Different types of density functions are tested in order to cope with discrete variables present in the SCOPF problem. Two test systems, namely the IEEE 57 bus and the IEEE 300 bus, are used to evaluate the effectiveness of the proposed method in terms of solution quality and computational effort. Comparisons carried out with reference algorithms in the literature demonstrate that the CE method is capable of finding better solutions for the SCOPF problem with fewer evaluations. [ABSTRACT FROM AUTHOR]

This paper is a research article for finding the optimal control of smart power substations for improving the network parameters and reliability. The included papers are the most essential and main studies in the field, which propose a different approach to reach the best performance in electrical power systems. The parameters for improvement are the ability for tracking of the reference signal, stabilizing the system, reducing the error in steady state and controlling the behavior in transient state. The research focuses with the reaching a better transient stability considering voltage and frequency dynamic parameters. The optimal model for the control is focused on minimizing energy consumption but maintaining the controllable parameters, exploring some optimization techniques to find the optimal control, with of aim of minimizing the response time, the energy consumption, and maximizing the reliability by means of improving the controller to be more robust. [ABSTRACT FROM AUTHOR]

Coverage and lifetime are two paramount problems in wireless sensor networks (WSNs). In this paper, a method called Multiround Distributed Lifetime Coverage Optimization protocol (MuDiLCO) is proposed to maintain the coverage and to improve the lifetime in wireless sensor networks. The area of interest is first divided into subregions, and then the MuDiLCO protocol is distributed to the sensor nodes in each subregion. The proposed MuDiLCO protocol works in periods during which sets of sensor nodes are scheduled, with one set for each round of a period, to remain active during the sensing phase and thus ensure coverage so as to maximize the WSN lifetime. The decision process is carried out by a leader node, which solves an optimization problem to produce the best representative sets to be used during the rounds of the sensing phase. The optimization problem formulated as an integer program is solved to optimality through a Branch-and-Bound method for small instances. For larger instances, the best feasible solution found by the solver after a given time limit threshold is considered. Compared with some existing protocols, simulation results based on multiple criteria (energy consumption, coverage ratio, and so on) show that the proposed protocol can prolong efficiently the network lifetime and improve the coverage performance. [ABSTRACT FROM AUTHOR]

Fuel economy is the key performance for the vehicle besides emission, which has been compulsory controlled by the legislation. For the electronic controlled Diesel engines, the mentioned properties could be satisfied not only by engine design but also by engine performance tuning. Fuel economy may be influenced by many coupled factors, such as injection timing, speed, load and under the limitation of cylinder peak pressure and exhaust temperature. To achieve a high efficient calibration, a model based calibration was performed on a four cylinder electronic unit pump Diesel engine with exhaust gas recirculation. The objective of the study is to solve the complexity of the interactions among the engine running parameters and the best fuel economy performance in order to meet under the restriction of NOx emission performance. The study was carried out in four stages. First, the experiment design has been proposed to identify designed experiment operating points and weighting factors. Second, two-stage statistical engine responses and boundary models have been established. Third, the global optimization and European steady-state cycle operating point optimization have been carried out. Finally, the bench test has been conducted on the Diesel engine. The global operating points results show that the fuel consumption rate has decreased at most test operating points by model based calibration. The fuel consumption rate has decreased by 3.5%, and 13 mode cycle test results indicate that the proposed model based calibration method is effective and can improve the fuel efficiency by 2.72% compared with the traditional calibration. [ABSTRACT FROM AUTHOR]

Heterogeneous cellular networks (HCNs) are capable of meeting the explosive mobile-traffic demands. However, the conventional base station (BS) deployment strategy is unsuitable for supporting the often unpredictable nonuniform mobile-traffic demands, as governed by the large-scale user behavior (LUB). This results in the inefficient exploitation of the system's resources. In this paper, we develop an analytical framework for characterizing the achievable energy-spectral-efficiency (ESE) of HCNs, which explicitly quantifies the relationship between the network's ESE and the randomly time-varying LUBs as well as other network deployment parameters. Specifically, we model the quantitative impact of the geographical mobile-traffic intensity, the load migration factor, the users’ required service rate and the per-tier BS densities on the achievable ESE of the network, while considering the area-spectral-efficiency requirements. Importantly, a closed-form ESE expression is derived, which enables us to explicitly analyze the properties of the network's ESE. Furthermore, the optimal LUB-aware BS deployment strategy is proposed for maximizing the ESE under specific outage constraints. Using numerical simulations, we verify the accuracy of the analytical ESE expression and quantify the impact of several relevant system parameters on the achievable ESE. Furthermore, we evaluate the achievable ESE performance of the network under diverse time-varying LUB scenarios. Our work, therefore, provides valuable insights for designing future ultradense HCNs. [ABSTRACT FROM PUBLISHER]

Copyright of Revista Ingeniería Industrial is the property of Departamento de Ingenieria Industrial, Universidad del Bio-Bio and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this paper, we focus on the low-power scheduling problem with multiple threshold and/or supply voltage technologies in high-level synthesis. We propose a unified scheduling approach which is applicable to various optimization problems, including: 1) dynamic power and resource usage co-optimization; 2) leakage power optimization; and 3) dynamic power and leakage power co-optimization. To deal with different objectives with high flexibility, three problems are divided into two common subproblems including delay assignment and resource density variance minimization, then a vertex potential-based mobility allocation model is proposed to solve two subproblems simultaneously. Experimental results show that, for dynamic power and resource co-optimization, our scheduling approach produces optimum solutions for all six benchmarks with 15 groups of data; for leakage power optimization it also greatly excels the latest existing work, by 20% leakage power reduction and 52 times speedup. Besides, for dynamic and leakage power co-optimization, the Pareto solutions are studied. [ABSTRACT FROM PUBLISHER]

High-speed (HS) electrical machines provide high system efficiency, compact design, and low material consumption. Active magnetic bearings (AMBs) bring additional benefits to the HS system, such as elimination of the friction losses, reduced wear and maintenance, and a built-in monitoring system. HS drivetrains are usually designed for specific applications and require a high level of integration. This paper describes a design method of the HS electrical machine supported by AMBs, considering their mutual influence on the system performance. The optimization procedure, which takes into account both the electrical machine and bearing designs, is developed. The optimization is based on a multiobjective genetic algorithm. The selected optimization parameters include the AMB and machine dimensions. The optimization objectives cover the electrical machine performance and the rotordynamics. The results of the proposed optimization algorithm are implemented in the constructed 350-kW, 15 000-r/min induction machine with a solid rotor supported by AMBs. The prototype tests verify the design and optimization results. [ABSTRACT FROM AUTHOR]

Constant matrix multiplication (CMM), i.e., the multiplication of a constant matrix with a vector, is a common operation in digital signal processing. It is a generalization of multiple constant multiplication (MCM) where a single variable is multiplied by a constant vector. Like MCM, CMM can be reduced to additions/subtractions and bit shifts. Finding a circuit with minimal number of add/subtract operations is known as the CMM problem. While this leads to a reduction in circuit area it may be less efficient for power consumption or throughput. It is well studied for the MCM problem that a) reducing the adder depth (AD) leads to a reduced power consumption and b) pipeline resources have to be considered during optimization to enhance throughput without wasting area. This paper addresses the optimization of CMM circuits which considers both adder depth and pipelining for the first time. For that, a heuristic is proposed which evaluates the most attractive graph topologies. It is shown that the proposed method requires 12.5% less adders with min. AD and 38.5% less pipelined operations. Synthesis results for recent FPGAs show that these reductions also translate to superior results in terms of delay and power consumption compared to the state-of-the-art. [ABSTRACT FROM PUBLISHER]