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Cloud consumers have access to an increasingly diverse range of resource and contract options, but lack appropriate resource scaling solutions that can exploit this to minimize the cost of their cloud-hosted applications. Traditional approaches tend to use homogeneous resources and horizontal scaling to handle workload fluctuations and do not leverage resource and contract heterogeneity to optimize cloud costs. In this paper, we propose a novel opportunistic resource scaling approach that exploits both resource and contract heterogeneity to achieve cost-effective resource allocations. We model resource allocation as an unbounded knapsack problem, and resource scaling as an one-step ahead resource allocation problem. Based on these models, we propose two scaling strategies: (a) delta capacity optimization, which focuses on optimizing costs for the difference between existing resource allocation and the required capacity based on the forecast workload, and (b) full capacity optimization, which focuses on optimizing costs for resource capacity corresponding to the forecast workload. We evaluate both strategies using two real world workload datasets, and compare them against three different scaling strategies. The results show that our proposed approach, particularly full capacity optimization, outperforms all of them and offers in excess of 70 percent cost savings compared to the traditional scaling approach. Refereed/Peer-reviewed

This article proposes a novel capacity optimization configuration method of battery energy storage system (BESS) considering the rate characteristics in primary frequency regulation to improve the power system frequency regulation capability and performance. By analyzing the charge or discharge rate characteristics of BESS, combined with the equivalent conversion method of the action time at different rates, the capacity optimization configuration method of BESS for frequency regulation considering the rate characteristic is proposed. Taking typical peak load working condition of a certain power grid as an example, the capacity configuration of three batteries (BESS 1, BESS 2 and BESS 3) considering the rate characteristics under different control methods is compared and analyzed. The results show that rate characteristics take most advantages of the corresponding control method and the high rate battery, and BESS requires a much lower capacity configuration for the same effect in grid frequency regulation. This indicates that the proposed optimal configuration method for BESS considering rate characteristics in frequency regulation can effectively reduce the configuration capacity, and this helps to realize the economic potential of BESS.

Hybrid energy system is one main mean to deal with the energy crisis and its capacity optimization is a key to obtain an economical and reliable supply of energy. In this paper, the capacity optimization of a novel hybrid system composed of wind turbine, concentrated solar plant and electric heater is modeled as a multi-objective two-stage stochastic problem, where capacity optimization minimizing the life cycle cost (LCC) and energy management strategy optimization minimizing the loss of power supply probability (LPSP) are integrated. The scenario-based approach is applied to reflect the random characteristics of wind and solar resources. The Pareto set of the problem is obtained by non-dominated sorting genetic algorithm (NSGA-II), whose effectiveness is validated by algorithm comparisons with multi-objective particle swarm optimization (MOPSO) and multi-objective evolutionary algorithm based on decomposition (MOEA/D). Furthermore, techno-economic comparisons with a reference hybrid energy system without electric heater are performed to investigate economic benefits of the electric heater. The comparison results show that the electric heater is beneficial for a lower LCC which is reduced by 1.21%, 1.34%, 1.68%, 3.14%, 4.94% and 4.55% respectively when the LPSP is 0%, 1%, 2%, 3%, 4% and 5%.

Fault current-limiting dynamic voltage restorers (FCL-DVRs), also named as dual-functional DVRs, involving both the FCL and the voltage compensation (VC) function, have attracted more attention. However, in case of the FCL-DVR operating in VC mode, either the high capacity of storage system in the dc-link or the oversizing of power converter is needed, and in case of the FCL-DVR operating in FCL mode both the high dc-link voltage and the oversizing of power converter are needed. Thus, in order to overcome these drawbacks, an improved FCL-DVR, which consists of an LC filter coupled with series coupling capacitor (LCC) at the ac side, is proposed in this article. The improved FCL-DVR using energy-optimized control strategy can not only perform the dual functions well, but also achieve the capacity optimization of FCL-DVR key components. The operating principle and performance of the proposed FCL-DVR under FCL and VC mode are elaborated, respectively. The optimal design of LCC and selection of FCL branch are studied in detail to ensure high stability and reliability for the proposed FCL-DVR operating in both modes. The simulation and experimental results clearly validate the effectiveness of the proposed topology and method.

This paper develops a novel demand side management (DSM) approach to incorporate in optimal sizing of solar photovoltaic (PV), wind turbine (WT), and battery storage (BS) for a standalone household. The DSM strategy is based on the state-of-charge level of battery and day-ahead forecasts of solar insolation and wind speed. The core of the DSM is a fuzzy logic method which decides for efficient load shifting and/or load curtailment. The day-ahead forecasting errors, obtained by an artificial neural network technique, are considered not only in the DSM strategy but also in maintaining an operating reserve. The battery capacity degradation is calculated using the Rainflow counting algorithm to obtain a realistic battery model and estimate its lifetime. A typical household in South Australia (SA) is considered as a case study. Three different configurations (PV-BS, WT-BS, and PV-WT-BS) of the electricity supply system are optimized using the proposed method. It is found that the PV-WT-BS system is the best configuration that provides the lowest cost of electricity for both with and without applying the proposed DSM strategy. Comparison of the results of the best system configuration with an actual system in SA and two recently published articles indicates that the proposed method is very effective in lowering the electricity cost with zero-emission. Refereed/Peer-reviewed

Configuration and operation are key to the successful deployment of a renewable energy integrated microgrid. Considering that wind generator, photovoltaic array, and storage battery may belong to different investors in market-oriented operation modes, game theory can be used to tackle the conflict between the overall optimal operation of the microgrid and the maximum profit of each investor. To handle the tradeoff among multiple investors and the uncertainties in wind and solar power, this article develops a new two-layer game model, where a planned output game and a capacity allocation game are considered. The inner layer is a master–slave game to optimize the planned renewable energy output, which is used to predetermine the operation strategy of the microgrid under the initial capacity allocation. In the outer layer, based on the optimized planned output obtained from the inner layer, a noncooperative game and a cooperative sequential game dominated by wind–photovoltaic cooperative alliance are formed to determine the optimal capacity allocations. The equilibrium solution, achieved from the iterative optimization between inner and outer layers, determines the optimal capacity allocation of the microgrid. The effectiveness of the proposed model is verified using a case study of a wind/photovoltaic/battery/gas microgrid.

The security of wireless information transmission in large-scale multi-input and multioutput (MIMO) is the focus of research in wireless communication. Recently, a new artificial noise—SCO-AN which shows no orthogonality to the channel, is proposed to overcome the shortcomings of traditional artificial noise. In the previous research, the optimization function of SCO-AN is not convex, and its extremum cannot be obtained. Usually, nonconvex optimization algorithms or iterative relaxation algorithms are used to get the maximum value of the optimization objective function. Nonconvex optimization algorithms or iterative relaxation algorithms are greatly affected by the initial value, and the extremum cannot be obtained by a nonconvex optimization algorithm or iterative relaxation algorithm. In this paper, we creatively apply the strong law of large numbers to obtain the optimal value of the optimization function of SCO-AN under the condition of large-scale MIMO: the strong law of large numbers is applied to obtain the ergodic lower bound (ELB) expression of SC for SCO-AN. The power allocation (PA) problem of the SCO-AN system is discussed. We use a statistical method to get the formula for calculating the optimal power distribution coefficient of the SCO-AN system. The transmitter can use the optimal power ratio of PA to distribute the transmitted power without using the PA algorithm. The effect of imperfect channel state information is discussed. Through simulation, we found that more power should be generated for SCO-AN if the channel estimation is imperfect and the proposed method can achieve better security performance in the large-scale MIMO system.

This article presents an adaptive robust co-optimization for capacity allocation and bidding strategy of a prosumer equipped with photovoltaic system (PV), wind turbine (WT),and battery energy storage (BES). The uncertainties of load and PV/WT productions are modeled through controllable user-defined polyhedral uncertainty sets. The proposed co-optimization determines the optimal capacity of PV-WT-BES, while maximizing prosumer’s benefit by 1) optimal self-scheduling of PV-WT-BES, and 2) effective interactions with grid through optimal buying/selling bids under uncertainties. In previous min-max-min robust models,it was not possible to characterize bidding strategy binary variables as recourse decisions which was due to the use of duality theory in solving the inner max-min problem (duality theory is weak and nontractable in the presence of binary variables). In this study,block coordinate descent (BCD) method is used to solve the inner max-min problem by means of Taylor series instead of transforming it into a single-level max problem by duality theory. As a result, prosumer’s bidding status (indicated by binary variables)can be successfully modeled as recourse decisions, which make the obtained solutions more realistic and robust. Linearization of the dualized inner problem is also avoided as Lagrange multipliers are eliminated. A post-event analysis is developed to avoid over/under conservative solutions and to determine the optimal robust settings of the model. A comprehensive case study is conducted for an industrial prosumer. To illustrate the effectiveness of the proposed BCD robust model, its long-term performance is compared with conventional dual-based models in the literature. Results show 10%long-term cost reductions when using the proposed model under uncertainties. Refereed/Peer-reviewed

This article, with the intent of maximizing the cellular system coverage ratio, reveals the pseudometric structure of the solution space for antenna azimuth variables. First, we study the solution space of azimuth variables with equivalent and neighboring characteristics. A generalized improvement to coverage and capacity optimization (CCO) approaches is proposed only in the light of the pseudometric structure of the solution space. Then, we model the optimization problem as an unconstrained one and propose a pseudometric-driven evolutionary algorithm to optimize the coverage ratio. Experiments are carried out both in the ideal and the practical urban scenarios. Simulation results show that the proposed algorithm edges out other baseline algorithms belonging to evolutionary computation and swarm intelligence with respect to the convergence speed and the final coverage result. Furthermore, the pseudometric structure, as a fundamental topology on the azimuth solution space, provides a new lens to address other CCO issues concerning the antenna azimuth variables and, thus, provokes rethinking on the coverage optimization of cellular system in general.

This paper presents an PSO-based optimization methodology for estimating the capacities and initial SOC of an energy storage systems (ESSs) in a DC electric railway system. The proposed method calculates the optimal solution using the missing capacity caused by the limited storage capacity. The missing capacity can be estimated through continuous-powerflow analysis. In many previous studies, capacities was calculated by assuming the each ESS as an independent device. However, since each storage device affects the charging and discharging operation of each other, this assumption might affect the convergence characteristics. In this paper, to solve this problem, the missing capacity of the ESS at both sides is reflected by using the relating coefficient derived based on the electrical distance between storage devices. The case studies show that the most efficient operation without missing capacity is possible under the derived capacities and initial SOC.

Terahertz (THz) communications have been widely envisioned as a promising enabler to provide adequate bandwidth and achieve ultra-high data rates for sixth generation (6G) wireless networks. In order to mitigate blockage vulnerability caused by serious propagation attenuation and poor diffraction of THz waves, an intelligent reflecting surface (IRS), which manipulates the propagation of incident electromagnetic waves in a programmable manner by adjusting the phase shifts of passive reflecting elements, is proposed to create smart radio environments, improve spectrum efficiency and enhance coverage capability. Firstly, some prospective application scenarios driven by the IRS empowered THz communications are introduced, including wireless mobile communications, secure communications, unmanned aerial vehicle (UAV) scenario, mobile edge computing (MEC) scenario and THz localization scenario. Then, we discuss the enabling technologies employed by the IRS empowered THz system, involving hardware design, channel estimation, capacity optimization, beam control, resource allocation and robustness design. Moreover, the arising challenges and open problems encountered in the future IRS empowered THz communications are also highlighted. Concretely, these emerging problems possibly originate from channel modeling, new material exploration, experimental IRS testbeds and intensive deployment. Ultimately, the combination of THz communications and IRS is capable of accelerating the development of 6G wireless networks.

Directional communication is helpful to improve the performance of millimeter Wave (mmWave) links. However, the dynamic nature of vehicular scenarios raises the complexity of directional mmWave vehicular communications. Also, a mmWave link is susceptible to blockages. Therefore, a mmWave vehicular communication system requires high environmental adaptability and context-awareness. Due to inadequate context information and insufficient beam settings in the existing related algorithm, it is difficult to pick out the set of beams with more reasonable widths and directions, which hinders the further promotion of network capacity in vehicular networks. Therefore, we propose an improved fast machine learning (IFML) algorithm to overcome this shortcoming. In order to improve network capacity while suppressing the additional beam search overhead, a partitioned search method is designed in the IFML. Also, in order to be robust to occasional fluctuations and timely adapt to significant changes in communication environments, the IFML adopts a flexible beam performance update approach based on adjustable weight coefficient. The simulation results show that the IFML significantly outperforms the existing related algorithm in terms of aggregate received data after a certain number of online learning time periods.

Large-scale electric vehicles (EVs) connected to the micro grid would cause many problems. In this paper, with the consideration of vehicle to grid (V2G), two charging and discharging load modes of EVs were constructed. One was the disorderly charging and discharging mode based on travel habits, and the other was the orderly charging and discharging mode based on time-of-use (TOU) price; Monte Carlo method was used to verify the case. The scheme of the capacity optimization of photovoltaic charging station under two different charging and discharging modes with V2G was proposed. The mathematical models of the objective function with the maximization of energy efficiency, the minimization of the investment and the operation cost of the charging system were established. The range of decision variables, constraints of the requirements of the power balance and the strategy of energy exchange were given. NSGA-II and NSGA-SA algorithm were used to verify the cases, respectively. In both algorithms, by comparing with the simulation results of the two different modes, it shows that the orderly charging and discharging mode with V2G is obviously better than the disorderly charging and discharging mode in the aspects of alleviating the pressure of power grid, reducing system investment and improving energy efficiency.

The increasing number of DC loads, such as electric vehicles (EVs), has resulted in micro-grid undergoing difficulty in satisfying the various demands of such loads. The study develops a multi-objective capacity optimization allocation model for hybrid micro-grid on the bases of users’ satisfaction and the orderly charging/discharging of EVs. The proposed model aims to reduce the cost of the capacity allocation of AC/DC hybrid micro-grid and improve users’ satisfaction. Particle swarm optimization algorithm is used to address the capacity optimization of hybrid micro-grid in EVs across scenarios. This study verified the rationality and effectiveness of the proposed capacity optimization model by comparing and analyzing the influence of capacity optimization on the orderly/disorderly charging/discharging of EVs and users’ satisfaction.

MIMO systems constitute a backbone of the fourth and fifth generations of wireless communication systems. The purpose of this paper is to introduce the involvement of conformal antenna arrays into MIMO systems. The Search Group Algorithm (SGA) is then used to further enhance the capacity of MIMO system employing conformal antenna arrays at both ends (Transmitter; Tx and Receiver; Rx). The results reveal that compared to the linear and 2D cases, conformal antenna arrays promise higher capacity values which motivates their employment in future MIMO communication systems.

The deployment, operation and maintenance of complex cellular networks are managed autonomously by multiple concurrently executing Self-Organizing Network (SON) functions with dedicated objectives, that can often negatively impact the functioning of each other. It is essential to avoid the blinkered view to their individual targets and consider a holistic approach towards identifying the best possible coordination between them, in order to achieve desired overall network gains while ensuring stable and robust network operation. The designing of appropriate SON-coordination mechanisms is quite challenging as it requires comprehensive modelling of all the complementing and conflicting interactions among them. This article discusses the application of Machine Learning based online Recommender Systems to model the dynamics between SON functions. To evaluate its applicability, in this work, the focus is to jointly implement two intertwined SON functions - Inter Cell Interference Coordination (ICIC) and Coverage and Capacity Optimization (CCO), to implicitly handle their conflicts and achieve the desired trade-off between coverage and capacity by optimizing a joint objective. The proposed cooperative learning and distributed configuration enforcement based ICIC-CCO coordinated SON solution has been evaluated on a system-level LTE network simulator with varied traffic distributions. It has been observed that the outage situations in the network are significantly reduced while still achieving high Signal-to-Interference Ratios (SIRs), even with reduced transmit power settings on several occasions.

In this paper, we present an algorithm for capacity optimization in intelligent reflecting surface (IRS)-based multiple-input multiple-output (MIMO) communication systems. To maximize the capacity of elements in IRS, we use augmented Lagrange method with the equivalent transformations on the covariance matrix and reflection matrix constraints. This results an adjustable phase shift on the incident signal. Furthermore, we reshape the complex-valued covariance matrix and reflection matrix to a vector for the ease of calculating partial derivatives to find the search direction. Then, the quasi-Newton updates and modified Broyden-Fletcher-Goldfarb-Shano (BFGS) method in the complex domain form are used to find the local minimum. Finally, numerical simulation results demonstrate that our proposed IRS-aided system using the algorithm performs better than the state-of-the-art and the conventional communication systems.

Cellular networks are bound to connect an ever-increasing number of subscribers. The issue of securing both sufficient capacity and reliable coverage remains to be resolved. This paper introduces the maximum coverage problem in wireless cellular networks and gets insight into the metric structure of the solution space for antenna orientation variables. We construct two metric spaces in mathematical views, in which both the deterministic search method (e.g., Nelder-Mead simplex algorithm) and the stochastic search method (e.g., Genetic Algorithm) have been fully discussed without using gradient information. Accordingly, we propose the improved deterministic and stochastic search methods to boost the coverage optimization procedure. Experiments show that the proposed algorithms not only obtain the close-to-optimal solution but greatly improve the convergence speed by reason that redundant exploration is avoided in the tailored solution spaces. Metric structure, as an essential topology in the antenna orientation solution space, therefore, provides a new perspective to settle other antenna orientation-related coverage and capacity optimization problems.

The extensive technological participation in our daily life and business world has transformed the technology model predominantly from last decade and it has created many directions for further research and development. In previous generations of technology, the major development was hardware side but now it is shifting to the smart and intelligent solutions based on AI. The new challenges have provoked and complexity is increasing for resource orchestration because of the nonstop increasing network and its applications. In cellular architecture, the backhaul network (intermediate network) is used to connect the radio and core network. The key technology is the point to point fixed links that is used in the wireless backhaul network. More than 60% radio base station are connected using this technology. Currently, the static approach is used for planning and optimization of the network which is now becoming very difficult as the network is increasing continuously and becoming more complex. The dynamic resource allocation method is proposed for the future capacity forecasting system which is founded on the factual employment of point to point links. Capacity is a crucial factor in wireless backhaul network so best capacity optimization can lead to a good frequency reuse and optimal use of other network resources. The development is based on three different models namely (1) Autoregressive (AR), (2) Seasonal Autoregressive Integrated Moving Average (SARIMA) and (3) Multi-Layer Perceptron (MLP) neural network. Root Means Squared Error (RMSE) and Means Absolute Percentage Error (MAPE) are performance criterion that are used to evaluate the models. When we compare the models' performance, the MLP results are most credible but it takes more time to converge than AR and SARIMA. By using the proposed estimation method, the static optimization will positively move to the dynamic (forecasted) optimization and the distribution of capacity utilization will be right skewed. Hence, the proposed system is efficient and has the ability to optimize the network according to the actual network’s capacity utilization. It will assist the network planner to perform more efficiently, resource distribution will be more balanced and the wastage of resources can be reduced.

Konteyner terminalleri konteyner yüklerinin dağıtıldığı, toplandığı, ulaştırma türünün değiştirildiği yerlerdir ve ülkelerin dış ticaret performansı için önemli birer ulaştırma yapılarıdır. Günümüzde konteyner birim yükünün ve türler arası ulaştırma olanaklarının gelişmesi farklı ulaştırma türlerinin kombine bir şekilde kullanılabilmesinin önünü açmıştır. Bu durum kombine yük taşımacılığında avantaj sağlayan demiryolu taşımacılığının önemini giderek daha da arttırmıştır. Bu gelişmelere rağmen bazı ülkelerde demiryolu taşımacılığı yerine karayolu taşımacılığının halen etkin bir şekilde kullanıldığı görülmektedir. Diğer ulaştırma türleri ile karşılaştırıldığında konteyner terminallerinde karayolu taşımacılığı, bağlantılı karayolu altyapısında trafik sıkışıklığı, gürültü kirliliği, hava kirliliği vb. trafik ve çevre sorunlarına yol açmaktadır. Bu problem özellikle limanların çevresinde ağır vasıtaların park etme arayışları ve konteyner terminallerinde yük almak ya da boşaltmak için kuyruğa girmesiyle ciddi trafik ve çevresel problemlere neden olabilmektedir. Bu çalışmada belirtilen bu problemlerin çözümünde katkı sağlayabilmek amacıyla tipik bir konteyner terminali için ağır vasıta park sahası tasarımı gerçekleştirilmiştir. Saha kapasitesi için öncül bir kapasite optimizasyon çalışması, simülasyon modeli desteği ile yapılmıştır. Park saha kapasitesinin belirlenmesi için bir maliyet optimizasyonu yöntemi geliştirilerek, simülasyon model çıktıları ile kapasite optimizasyonu hedeflenmiştir. Çalışma sonucunda simülasyon optimizasyon yöntemi ile ağır vasıtaların yol ağlarında ve limanlarda beklemelerini önleyebilmek amacıyla optimum park sahası kapasitesinin belirlenebildiği ortaya konmuştur.

In this paper, an Uncertainty-based Multi-disciplinary Design Optimization (UMDO) method combining with fuzzy theory and Multi-Discipline Feasible (MDF) method is developed for the conceptual design of a Hybrid Rocket Motor (HRM) powered Launch Vehicle (LV). In the method proposed, membership functions are used to represent the uncertain factors, the fuzzy statistical experiment is introduced to analyze the propagation of uncertainties, and means, standard deviations and credibility measures are used to delineate uncertain responses. A geometric programming problem is solved to verify the feasibility of the Fuzzy-based Multi-Discipline Feasible (F-MDF) method. A multi-disciplinary analysis of a three-stage HRM powered LV involving the disciplines of propulsion, structure, aerodynamics and trajectory is implemented, and the mathematical models corresponding to the F-MDF method and the MDF method are established. A two-phase optimization method is proposed for multi-disciplinary design optimization of the LV, including the orbital capacity optimization phase based on the Ziolkowski formula, and the scheme trajectory verification phase based on the 3-degree-of-freedom point trajectory simulation. The correlation coefficients and the quadratic Response Surface Method (RSM) based on Latin Hypercube Sampling (LHS) are adopted for sensitive analysis of uncertain factors, and the Multi-Island Genetic Algorithm (MIGA) is adopted as the optimization algorithm. The results show that the F-MDF method is applicable in LV conceptual design, and the design with the F-MDF method is more reliable and robust than that with the MDF method.

Load imbalance among small and macro cells is a major challenge that undermines the gains of emerging ultradense heterogeneous networks (HetNets). Existing load balancing (LB) schemes have one common caveat which is operating in reactive mode i.e., cell parameters are tweaked reactively in accordance with the dynamics of cell loads. The inherent reactiveness of these LB schemes hinder in achieving promising quality of experience (QoE) gains from 5G and beyond. To cope with this issue, in this paper we propose a novel proactive load balancing framework “OPERA” empowered by mobility prediction paradigm for future ultra dense networks (UDNs). The pro-activeness of OPERA stems from its novel capability that instead of passively waiting for congestion indicators to be observed and then reacting to them, OPERA predicts future cell loads and then proactively optimizes key antenna parameters and cell individual offsets (CIOs) to preempt congestion before it happens. OPERA also incorporates capacity and coverage constraints and load aware association strategy for ensuring conflict free operation of LB and coverage and capacity optimization (CCO) self-organizing network (SON) functions. Simulation results show that compared to real network deployments settings and published state-of-the-art reactive schemes, OPERA can yield significant gain in terms of fairness in load distribution and percentage of satisfied users. Superior performance of OPERA on several fronts compared to current schemes stems from its following features: 1) It preempts congestion instead of reacting to it; 2) it actuates more parameters than any current LB schemes thereby increasing system level capacity instead of just shifting it among cells; 3) while performing LB OPERA simultaneously maximizes residual capacity while incorporating throughput and coverage constraints; 4) it incorporates a load aware association strategy for ensuring conflict free operation of LB and CCO SON functions; 5) the ahead of time estimation of cell loads allows ample time for heuristics search algorithms to find LB solutions with high gain.

A collaborative integration between cloud and edge computing is proposed to be able to exploit the advantages of both technologies. However, most of the existing studies have only considered two-tier cloud-edge computing systems which merely support vertical offloading between local edge nodes and remote cloud servers. This paper thus proposes a generic architecture of cloud-edge computing with the aim of providing both vertical and horizontal offloading between service nodes. To investigate the effectiveness of the design for different operational scenarios, we formulate it as a workload and capacity optimization problem with the objective of minimizing the system computation and communication costs. Because such a mixed-integer nonlinear programming (MINLP) problem is NP-hard, we further develop an approximation algorithm which applies a branch-and-bound method to obtain optimal solutions iteratively. Experimental results show that such a cloud-edge computing architecture can significantly reduce total system costs by about 34%, compared to traditional designs which only support vertical offloading. Our results also indicate that, to accommodate the same number of input workloads, a heterogeneous service allocation scenario requires about a 23% higher system costs than a homogeneous scenario.

This paper proposes a wind-photovoltaic-thermal energy storage hybrid power system with an electric heater, which adopts the idea of concentrated solar power plant but omits the expensive solar field, and utilizes the reformed power block from conventional small-scale thermal power plant. From the perspective of thermal power plant reconstruction, the hybrid system aims to minimize the levelized cost of energy and maximize the utilization rate of transmission channels. The capacity optimization is solved by multi-objective particle swarm optimization algorithm. Final optimal capacity is obtained by the Pareto front and decision-making method. Moreover, the probability of exceeding is introduced to correct the theoretical power output calculated by mathematical models and improve the practicality of simulation. Finally, by comparing wind-photovoltaic-thermal energy storage system, wind-photovoltaic-battery system and wind-photovoltaic system, it can be concluded that the proposed system effectively increases the utilization rate of transmission channels and presents a better reliability and economy performance.

Given the current situation of large-scale energy storage system (ESS) access in distribution network, a practical distributed ESS location and capacity optimization model is proposed. Firstly, a weighted voltage sensitivity is proposed to select the grid-connected node set of ESS. On this basis, the distributed ESS location model is established, which aims at reducing voltage deviation and active power loss of the distribution network. Then, an ESS partition method based on the improved flame propagation model is proposed, and the partition results are obtained by constructing the flammability of nodes, the wind direction of flame propagation, speed of flame propagation and other indicators. Based on partition results, the capacity optimization model is established with the maximum annual net income of energy stored in the partition as the objective function. Finally, the improved IEEE-33 bus distribution network is used to demonstrate the effectiveness and feasibility of the proposed model.

This paper presents the design and results of a model that uses household smart meter data, electric vehicle (EV) travel load and charging options, and multiple solar resource profiles, to make decisions on optimal combinations of photovoltaics (PV), battery energy storage systems (BESS) and EV charging strategies. The least-cost planning model is formulated as a stochastic mixed integer programming (MIP) problem that makes first stage decisions on PV/BESS investments, and recourse decisions on purchase/sell from/to the grid to minimize expected household electricity costs. The model undertakes a customer-centric optimization taking into consideration net metering policy, time-of-use grid pricing, and uncertainties around inter-annual variability of solar irradiance. The model adds to the existing literature in terms of stochastic representation of inter-annual variability of solar irradiance, together with BESS capacity optimization, and EV charging mode selection. Three case studies are presented: two for a residential house with and without EV load, and a third for a larger community facility. Results from the model for the first residential house case study are compared with commercially available software to show the impacts of an accurate load profile and different policy parameters. The stochastic feature of the model proves useful in understanding the impact of variability in solar resource profiles on PV sizing. Finally, simulations of alternative EV travel patterns and tariff policies that discourage charging during the evening peak show the efficacy of `super off-peak' pricing being introduced in the state of Maryland.

Air transportation as a public transport method is preferentially considered by passengers. However, this method will not only bring a congestion problem but also affect the passenger experiences in airport terminals during peak periods. Considering the fact that the capacities of airport terminals are proportional to their building costs, this paper proposes a scheme to calculate the minimum capacity for each airport terminal. The main purpose is to avoid the saturated congestion problems and reduce the waste of transition bus resources. For each airport terminal, its capacity is closely related with its inputs and outputs. The proposed scheme proves a relational expression for each airport terminal according to its inputs and outputs. Moreover, this work formally models the limited flow structures of flight process guidance systems by using finite capacity Petri nets and then verifies the correctness of the proposed relational expression. Finally, an example is presented to illustrate the proposed scheme.

This chapter presents two state‐of‐the‐art machine learning (ML)‐based techniques that tackle the coverage and capacity optimization (CCO) problem from each of the main aspects: configuring base‐station parameters to address current demand through a deep neural network architecture, where suitable configurations actions are taken on the basis of the inference from current network user geometry information; and enabling base‐station sleeping via a data‐driven approach by using deep reinforcement learning (RL), which leverages network traffic models to address the non‐stationarity in real‐world traffic. The chapter introduces a set of widely used ML techniques and provides an overview of their application to CCO problems in the wireless network domain. It then describes the used and achieved result of the deep RL approach in solving the problem of base‐station sleeping. The chapter also presents the application and evaluation of the multi‐agent deep neural network framework on the dynamic frequency reuse problem in mobile networks.

International audience

High degree of variability are introduced in a power distribution network due to the integration of decentralized renewable power sources (RES) that requires new solutions to assure the power quality with appropriate energy management. One solution is the integration of battery energy storage system (BESS) that plays a pertinent role in to mitigate the impact of RES, such as solar photovoltaics (PV). Nevertheless, considering the high cost of the BESS, an optimal sizing and placement is pertinent. In addition, the power quality degrades in terms of voltage and system losses with undersized or an oversized BESS. In this paper, an optimal BESS capacity optimization and optimal placement framework is developed based on voltage-to-load sensitivity technique which is tested and validated on a solar PV-integrated IEEE 8-bus distribution network. Based on the results, suitable BESS sizing and placement are identified that improves the voltage profile and maintains it under permissible limits with reduction of the system losses.

This work considers the joint transmit design of multiuser MIMO communications and MIMO radar. In particular, we propose to maximize the sum-rate capacity of multiuser MIMO communications under certain transmit correlation mismatch level for MIMO radar, and solve the maximization via convex optimization. Then, a practical transmit design scheme based on Tomlinson-Harashima precoding is proposed to approach the sum-rate capacity. The numerical results on the sum-rate capacity indicate that the average capacity under Rayleigh fading channel is the highest when radar intends to form a omnidirectional transmit beam. The bit error rate simulation shows that the proposed transmit design scheme is about 7dB from the theoretic capacity.

This article deals with the extremely difficult problem faced by a large number of smaller ports: how to enable small container terminals to simultaneously receive two ships of post-Panamax size, and at the same time provide effective transshipment operations on an individual terminal subsystem, which would enable the fast turnaround of the ship in port and at the same time provide the port the possibility to increase annual traffic. For this purpose, a simulation approach was used in a hypothetical small size container terminal. The performed simulations covered all the berth-yard-berth operations focusing on the correct allocation of transfer mechanization to ensure optimal results on the sea side of the terminal. The results obtained were used to define the key parameters on the basis of which a Decision Support Tool was created. The aim of the Decision Support Tool is to help port operators to identify their current problems quickly and effectively and to give them an insight into the measures that need to be implemented to accommodate two post-Panamax ships at the terminal at the same time.

In the United States, the principal reason behind flight delays is the airport capacity. Many major airports suffer from the incapability of satisfying all the demands. In this paper, we investigate the effects of the dynamic distribution of the airport's capacity on the total delay. Despite the relation between departure and arrival capacities, none of the previous works on air traffic flow management has addressed this problem. The proposed model considers departure and arrival capacities as decision variables. It minimizes the total delay and considers various management policies as ground and air holding, rerouting, and flight canceling. Our results revealed that adopting this approach might reduce the operation cost and minimize delays. Moreover, it precisely allocates the optimum needed capacity for the departing and landing flights.

Multimodal remote sensing data analysis can strongly improve the characterization of physical phenomena on Earth’s surface. Nonetheless, nonidealities and estimation imperfections between records and investigation models can limit its information extraction ability. Ensemble learning could be used to tackle these issues. Combining the information acquired by multiple weak classifiers can prevent the analysis of large scale heterogeneous datasets from being affected by overfitting and biasing. In this paper, we introduce an enhanced ensemble learning scheme where the information acquired by the weak classifiers is combined to optimize the maximum information extraction for the given system at a decision level. Using an asymptotic information theory-based approach, we define the capacity index associated with the maximum accuracy that can be achieved under optimal conditions for multimodal analysis. By selecting the decisions delivered by the different classifiers according to the capacity optimization, the performance of the ensemble learning scheme will be maximized.

Now that China has outlined its goals of “carbon peak and carbon neutrality”, the development of clean energy will accelerate, the connection between different energy systems will be closer, and the development prospects of the integrated energy service industry will be broader. Integrated energy services are promoting energy transformation and services. “Carbon peaking, carbon neutrality” and other aspects will also have multiple values and far-reaching significance. Before implementing integrated energy services, the top-level design of integrated energy system planning must be carried out, and how to achieve the optimal allocation of capacity in the field of integrated energy systems is an urgent problem to be solved in integrated energy system planning. This paper combines practical engineering experience and the latest theoretical research results to creatively introduce, for the first time, a comprehensive evaluation into the initial planning stage, and proposes, also for the first time, a three-level multi-element comprehensive energy system planning optimization method which combines multi-element requirements to carry out comprehensive energy system planning and optimization. The three-tier planning and optimization solution results in the optimal planning scheme of the integrated energy system, thereby making the scheme more specific and reliable. According to the demand data of an industrial park, this method was applied to complete a case analysis of integrated energy system planning, which verified the feasibility and effectiveness of the method. This method is easy to popularize, and it will guide the planning of integrated energy systems, promote integrated energy services, promote energy transition, and make positive contributions to achieve carbon neutrality as soon as possible.

In this paper, we propose an energy storage capacity optimization (ESCO) method for grid-connected microgrid systems (MSs) considering multiple time scale uncertainty coupling. First, an envelope model and a box uncertainty model are, respectively, employed to characterize the stochastic uncertainties and the forecasting error uncertainty of the renewable energy (RE) power supply and load demand. Based on the above two uncertainty characterization models, the energy balance ability indicator and the robustness coordination cost indicator are proposed, which are used to quantitatively analyze the MS energy balance state probability for different ESCO schemes and the economic cost of improving the robustness of the MS optimal operation, respectively. Then, an ESCO model, where the stochastic uncertainty and forecasting error uncertainty of the RE power supply and load demand are, respectively, considered in the long-time-scale investment decision of the energy storage capacity and the short-time-scale operation optimization of the energy storage system, is established, with the goal of realizing an MS that operates in a grid-friendly and economical manner. Furthermore, a new multi-objective compound differential evolution algorithm is designed to solve the energy storage capacity collaborative optimization model efficiently. Finally, simulations are conducted to verify the rationality and effectiveness of the proposed model and method.

This paper focuses on the planning and scheduling of restoration efforts provided by infrastructure networks in the aftermath of disruptive events. Two mathematical formulations are presented to assign restoration crews to disrupted components and maximize network resilience progress in any given time horizon. In the first formulation, the number of assigned restoration crews to each component can vary to increase the flexibility of models in the presence of different disruption scenarios. Along with considering the assumptions of the first formulation, the second formulation models the condition where the disrupted components can be partially active during the restoration process. We test the efficacy of our formulations on the realistic dataset of 400-kV French electric transmission network and 32 realistic size datasets illustrating scale-free, small-world, lattice, and random networks. The results indicate that the proposed formulations can be used for a wide variety of infrastructure networks and for real-time restoration process planning.

With the proliferation of enabling smart grid technologies, industries are increasingly looking to reduce their electricity costs through the adoption of renewable energy sources and efficient load management strategies. In this context, the realization of lower electricity costs requires the design of efficient energy management systems (EMSs). An EMS needs to factor in the unique operational requirements of the industry for which it is designed. Consequently, there has been a lot of research interest in designing EMSs for various industrial applications. However, the existing EMS formulations and models are not suitable for a shipyard drydock. Shipyard drydocks may be treated as grid-connected microgrids containing pump loads, interruptible loads, critical loads and heterogeneous generation sources. This paper proposes three modules, which can constitute a shipyard drydock energy management system (SEMS). A load forecasting module generates short term and medium term load forecasts using historical load demand data and ship arrival schedules as inputs. A multilayer feed-forward neural network with backpropagation is used to perform the load forecasting. A contracted capacity optimization module uses the medium term load forecast to find the optimal contracted capacity for the drydock. The short term load forecast and the optimal contracted capacity are used by an optimal scheduling module to reduce the total electricity cost incurred by the shipyard drydock. Case studies performed using data from a local shipyard demonstrate the efficacies of the proposed load forecasting and optimal scheduling modules in improving the accuracies of the load forecasts and reducing the overall electricity cost, respectively.

In this study, a multi-commodity network flow model is proposed to optimize the railroad capacity under temporary line blockage. The proposed model enables the assessment of residual railroad capac...

This paper considers networked nanogrids with an electric vehicle battery swapping station (BSS) as a cyber-physical energy management system (CPEMS) for enhancing the energy supply reliability, resilience, and economics. This paper discusses the architecture and control framework of the proposed CPEMS. A capacity optimization problem is formulated based on the mixed-integer linear programming and solved with a scenario-based approach to schedule the CPEMS generation and storage. Individual nanogrids and BSS share scheduled generation and storage resources and exploit diversities in supply and demand profiles to achieve mutual economic and security benefits. The paper considers the following three issues: 1) optimal sizing and scheduling of nanogrids and BSS can supply a sufficient storage capacity for smoothing out volatile renewable energy sources (RES); 2) surplus RES generation can be aggregated and stored in the BSS for supplying electric vehicles at peak hours; and 3) a networked nanogrid structure can share BSS resources for attaining considerable savings and higher resilience, without compromising each nanogrid's cyber-physical security. Numerical case studies demonstrate the applications of the proposed CPEMS to power system operation and control.

Optical transport networks are progressively being designed around reconfigurability. Operators require an infrastructure capable of carrying large amounts of data, but also able to deliver that data when and where needed. While centralized control plane architectures based on software-defined networking are pushing flexibility in automation and interoperability, many of these objectives are reliant on a hardware-enabled flexible data plane. At the same time, reduced operating margins imply that capacity planning through resource overprovisioning is increasingly unsustainable. Therefore, a natural tradeoff between cost and flexibility emerges in many aspects of optical transport network architectures. More complex and reconfigurable hardware, such as flexible-rate transponders or switching fabrics, can be pitted against cheaper purpose-built modules as contending alternatives for incrementally deploying networks. The added value of adaptability to changing conditions must be evaluated against its potential upfront cost and capacity overprovisioning risk. In this context, technoeconomic analysis based on multiperiod capacity optimization plays a pivotal role in identifying the target network scenarios for fixed and flexible hardware. In this paper, use cases where the cost/flexibility tradeoff emerges in optical transport scenarios are identified, such as in the design of line cards and multilayer grooming architectures, and multiperiod optimization frameworks based on integer linear programming (ILP) models are presented. More generally, this paper also discusses scalability issues affecting the use of ILPs for multiperiod capacity optimization, and proposes some simple design and modeling guidelines to help overcome them. These include either reformulating the models themselves, or identifying specific subproblems within the global framework that can be offloaded without undermining the validity of the results.

In multiple-input multiple-output wireless communication system, artificial noise (AN) is designed to be aligned into the null space of the legitimate channel so that it has no effect on the legitimate channel. Meanwhile, AN will reduce the secrecy capacity of the eavesdropping channel so that AN increase the secrecy capacity of the wireless communication system. Nevertheless, AN technology only pays attention to the CSI of the legitimate channel but does not pay attention to the CSI of the eavesdropping channel so that the secrecy capacity of the system can be further improved. In this paper, we analyze the performance of traditional AN. A secrecy capacity optimization artificial noise for the whole communication system to further improve the system's secrecy capacity is proposed. The secrecy capacity optimization artificial noise is considered as a kind of artificial noise which is disadvantageous to the legitimate receiver but more disadvantageous to the eavesdropper. Furthermore, we further analyzed the technical details of AN technology and found the key factor affecting the secrecy capacity. Meanwhile, the secrecy capacity optimization artificial noise is effectively designed by applying these conclusions and the key factors of secrecy capacity optimization artificial noise affecting the secrecy capacity of the wireless communication system are described. The analysis and simulation results in practical environments show that the proposed method has a good performance on improving the secrecy capacity.

Aiming at capacity optimization of an isolated microgrid, this paper establishes a bi-level capacity optimization model that considers load demand management (LDM) while comprehensively considering load and renewable generation uncertainties. The uncertainties in this paper are brought by the source and load on the same timescale, as well as by the different characteristics of uncertainty presented over different timescales. For long timescales, the problem of source/load random uncertainty is solved using the stochastic network calculus theory to meet the energy balance constraints. For short timescales, we primarily aim to resolve the problem of power balance at the operation level, considering the uncertainty of source/load prediction errors and the impact of LDM. Particularly, by controlling the interruptible and shiftable loads, the LDM can optimize load characteristics, reduce operation costs, and increase system stability. The bi-level optimization model established in this paper is analyzed with regard to energy and power balance constraints, and the proposed mixed integer linear programming (MILP) model is solved by utilizing the CPLEX solver to minimize the investment cost. A typical microgrid, comprising a wind turbine (WT), a photovoltaic panel (PV), a controllable micro generator (CMG), and an energy storage system (ESS), is taken as an example to study capacity optimization problems. The simulation results verify the rationality and effectiveness of the proposed model and method.

Wireless network resource allocation is an important issue for designing Internet of Things systems. In this paper, we consider the problem of wireless network capacity optimization that involves issues such as flow allocation, link scheduling, and power control. We show that it can be decomposed into a linear program and a nonlinear weighted sum-rate maximization problem for power allocation. Unlike most traditional methods that iteratively search the optimal solutions of the nonlinear subproblem, we propose to directly compute approximated solutions based on machine learning techniques. Specifically, the learning systems consist of both support vector machines (SVMs) and deep belief networks (DBNs) that are trained based on offline computed optimal solutions. In the running phase, the SVMs perform classification for each link to decide whether to use maximal transmit power or be turned off. At the same time, the DBNs compute an approximation of the optimal power allocation. The two results are combined to obtain an approximated solution of the nonlinear program. Simulation results demonstrate the effectiveness of the proposed machine learning-based algorithm.

This paper researches the capacity optimization problem of Iron-steel enterprises under the supply chain environment, builds up a mathematical model with a joint objective of maximize the capacity utilization rate of core enterprises, the demand satisfaction degree of downstream enterprises and the profit margin of downstream enterprises based on the minimum planned production amount required for each process constraints and demand expectation range constraints of downstream enterprises and so on. The PSO algorithm is applied and improved by heuristic rules in order to repair the infeasible solutions.

In this paper, the upper optimization, based on the dynamic model of heating network, solves the problem of pipe network failure caused by too low temperature, the lower layer optimizes the capacities of the devices at the heat sources with the objective of economy according to the results of upper layer. Through the case analysis, the result shows that after optimization, the difference between the pipeline temperature and standard temperature is reduced by 33%, the minimum temperature of the pipe is increased from 52.5 °C to 76.7 °C, and the problem of pipeline failure is effectively avoided. Compared to the traditional capacity optimization model, the capacity planning cost of the two heat sources is reduced by 3.37% and 4.14% respectively. The bi- level programming model proposed in this paper can effectively avoid the operation failure of pipe network, and has better economic benefits after considering the variable efficiency characteristics of energy device.

In this paper, we consider the physical layer security problem of the wireless communication system. For the multiple-input, multiple-output (MIMO) wireless communication system, secrecy capacity optimization artificial noise (SCO−AN) is introduced and studied. Unlike its traditional counterpart, SCO−AN is an artificial noise located in the range space of the channel state information space and thus results in a significant increase in the secrecy capacity. Due to the limitation of transmission power, making rational use of this power is crucial to effectively increase the secrecy capacity. Hence, in this paper, the objective function of transmission power allocation is constructed. We also consider the imperfect channel estimation in the power allocation problems. In traditional AN research conducted in the past, the expression of the imperfect channel estimation effect was left unknown. Still, the extent to which the channel estimation error impacts the accuracy of secrecy capacity computation is not negligible. We derive the expression of channel estimation error for least square (LS) and minimum mean squared error (MMSE) channel estimation. The objective function for transmission power allocation is non-convex. That is, the traditional gradient method cannot be used to solve this non-convex optimization problem of power allocation. An improved sequence quadratic program (ISQP) is therefore applied to solve this optimization problem. The numerical result shows that the ISQP is better than other algorithms, and the power allocation as derived from ISQP significantly increases secrecy capacity.

The development of hydrogen energy is one of the key paths to realize the clean and low-carbon transformation of the global energy system. Producing green hydrogen from renewable energy has broad prospects. This paper proposes a capacity optimization configuration model for island-operated microgrids coupled with wind/solar/green hydrogen systems, with the goal of minimizing the equivalent annual cost of the system's operating life. The model takes into account investment costs, operating costs, fuel costs, and value of lost loads, considering the operation constraint of microgrid, green hydrogen system and diesel engines. Taking annual wind speed, temperature, and light intensity as inputs, the optimal scheme for the type and capacity of the microgrid power source is obtained. Taking an island microgrid as an example for verification, the results show the effectiveness of the proposed optimal configuration method, which provides theoretical basis and technical support for the planning and design of island microgrid considering green hydrogen systems.