Power Optimization Scheme Research Articles

Multi Objective Reactive Power Optimization of Distribution Network with Distributed Generation Power Uncertainty

Distributed generation will have a lot of adverse effects on the steadiness of the power system, resulting in the power wastage of the Distribution Network (DN) and the destruction of the reactive power balance of the power grid. It is essential to establish a dynamic reactive power optimization model to ameliorate the security, steadiness and economy of the DN. Based on this, this paper first analyses the connotation and typical characteristics of Distributed Generation (DG), then studies the connotation and typical characteristics of DG, and finally gives the multi-objective reactive power optimization strategy of DG DN.

Sub-Connected Structure Hybrid Precoding for Millimeter-Wave NOMA Communications

Sub-connected structure hybrid beamforming (SCS-HBF) exploiting both successful interference cancellation and zero-forcing (ZF) is tortured by frail performance when deployed for millimeter-Wave non-orthogonal multiple access (NOMA) system in a complete non-line of sight (NLOS) environment. Therefore, this letter articulates a sum-rate maximization for SCS-HBF-NOMA system built based on unique phased-ZF and dynamic power optimization schemes robust for both line of sight and NLOS environments. Radio frequency chain's diversity impact on the proposed scheme's performance is also analyzed.

Energy-Efficient Beamforming for Beamspace HAP-NOMA Systems

In this letter, we study an energy-efficient algorithm for beamspace high altitude platform with non-orthogonal multiple access (HAP-NOMA) systems over Rician fading channels. We first derive the non-line-of-sight (NLOS) theorem of beamspace HAP channel in large scale array regime according to the asymptotic property of the Toeplitz matrix. Then, we propose the beamspace HAP-NOMA scheme based on the derived NLOS theorem, which can be partitioned into two stages, i.e. user grouping scheme and power optimization scheme. The statistical channel state information (CSI) is only required in the proposed algorithm, which avoids acquiring instantaneous CSI. NOMA can serve users in each beam simultaneously to compensate for the loss of spectral efficiency due to the beamspace HAP. The digital precoder can be obtained by zero forcing (ZF) based on the equivalent channel. Numerical results show the performance enhancement of the proposed beamspace HAP-NOMA algorithm.

Beam Allocation and Power Optimization for Energy-Efficiency in Multiuser mmWave Massive MIMO System.

This paper studies beam allocation and power optimization scheme to decrease the hardware cost and downlink power consumption of a multiuser millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) system. Our target is to improve energy efficiency (EE) and decrease power consumption without obvious system performance loss. To this end, we propose a beam allocation and power optimization scheme. First, the problem of beam allocation and power optimization is formulated as a multivariate mixed-integer non-linear programming problem. Second, due to the non-convexity of this problem, we decompose it into two sub-problems which are beam allocation and power optimization. Finally, the beam allocation problem is solved by using a convex optimization technique. We solve the power optimization problem in two steps. First, the non-convex problem is converted into a convex problem by using a quadratic transformation scheme. The second step implements Lagrange dual and sub-gradient methods to solve the optimization problem. Performance analysis and simulation results show that the proposed algorithm performs almost identical to the exhaustive search (ES) method, while the greedy beam allocation and suboptimal beam allocation methods are far from the ES. Furthermore, experiment results demonstrated that our proposed algorithm outperforms the compared the greedy beam allocation method and the suboptimal beam allocation scheme in terms of average service ratio.

Joint UAV Position and Power Optimization for Accurate Regional Localization in Space-Air Integrated Localization Network

Accurate location estimation of Internet-of-Things (IoT) devices within an Area of Interest (AoI) is a challenging issue, especially in a global navigation satellite system (GNSS)-constrained environment. In this article, we present a space-air integrated localization network (SAILN) architecture to exploit the advantages of the unmanned-aerial-vehicle (UAV)-based localization through joint position and power optimization (JPPO) strategies. In SAILN, UAVs can utilize their flexible movement to obtain the line-of-sight (LOS) path with a high probability, thereby providing the potential IoT devices in the AoI with supplementary localization information. The JPPO of UAVs aims to improve the regional localization accuracy for the entire AoI, considering the no-fly-zone (NFZ) and the total energy constraint. We propose the average localization accuracy increment (ALAI) of the sampling points in the AoI as the metric to measure the performance of SAILN compared with that of only satellites, which is regarded as the objective to formulate the JPPO problems for UAV operations in both static and dynamic SAILN. The intractable problems can be resolved by the pure genetic algorithm (PGA) that has a low computational cost and unique features suiting the JPPO of UAVs. Then, by taking advantage of the ALAI convexity to the UAVs’ power, we propose a power reallocation-based two-step algorithm (PRTSA) to further explore an improved JPPO solution. Simulation results validate that the proposed PRTSA can obtain a higher localization accuracy for the entire AoI than the PGA and the other straightforward baselines.

Output performance analysis and power optimization of different configurations half-cell modules under partial shading

The performance of photovoltaic (PV) modules is influenced by environmental variables, especially shading. Partial shading makes the solar irradiance level of solar cells in PV modules uneven, thus reducing the performance of PV modules and even damaging the PV modules. In this work, the single diode model and the Bishop model are used to simulate the I–V characteristics of various half-cell modules in detail, and the results under normal and shading conditions are analyzed. Investigations of module configurations include a series-parallel-series connections half-cell module with 3 bypass diodes, one configuration with a series of 120 half-cells with 3 bypass diodes, and another configuration with a series of 120 half-cells with 5 bypass diodes. In the simulation, the working modes of the shaded half-cells can be clearly understood. Electrical performances of three modules in different shading scenarios are obtained. Through the practical shading experiment, the optimal output power of different configurations half-cell modules under two power optimization schemes is analyzed.

A Hierarchical Data–Driven Wind Farm Power Optimization Approach Using Stochastic Projected Simplex Method

In a wind farm, the interactions among the wind turbines through wakes can significantly reduce the power output of the wind farm. These together with the complex wind conditions make the power optimization problem of the wind farm very challenging. To address this problem, this article proposes a hierarchical data–driven power optimization scheme, which does not need a wake interaction model that can be rather difficult to develop due to the complex aerodynamics between the turbines. The proposed scheme consists of two steps: firstly the power optimization problem of the wind farm is divided into several optimization sub-problems to deal with the complex wind conditions based on the wind farm power efficiencies in different wind directions. Secondly, a data–driven stochastic projected simplex algorithm is developed to solve the power optimization sub-problems. The proposed algorithm can increase the power output of the wind farm by using measurement data only and has the ability to find the optimal solutions. Finally, simulation results show that the proposed scheme can efficiently improve the power output of the wind farm in different wind conditions compared with some benchmark methods.

Joint Power and QoE Optimization Scheme for Multi-UAV Assisted Offloading in Mobile Computing

Recent years, unmanned aerial vehicles (UAVs) have attracted much attention for providing intermediate relay to ground mobile user equipments (UEs) for their flexible mobility. UEs can offload computing-intensive task to mobile cloud computing (MCC) or mobile edge computing (MEC) for fast processing. However, with multi-UAV and ground mobile UEs in the system, heterogeneous performance requirement as well as fast-changing communication condition make the system more complicated. Meanwhile, both UEs and UAVs are battery-driven. How to optimize the energy efficiency for UEs' transmission and UAVs' position should be carefully considered. Since this is a non-convex and mixed-integer optimization problem, a heuristic joint power and quality of experience (HJPQ) algorithm is proposed in this article, where the UEs' offloading delay, MIMO channel, transmission power, as well as UAVs' placement are jointly optimized. The numeral simulations not only reveal the effectiveness of HJPQ, but also guarantee the great quality of experience (QoE) performance for UEs with different priorities. Furthermore, the comparison experiments with random assignment and deep deterministic policy gradient (DDPG) show the superiority of HJPQ in lower complexity, faster convergence, shorter offloading delay as well as higher energy efficiency.

Seawater Battery-Based Wireless Marine Buoy System With Battery Degradation Prediction and Multiple Power Optimization Capabilities

This paper presents a wireless marine buoy system based on the seawater battery (SWB), providing self-powered operation, power-efficient management, and degradation prediction and fault detection. Since conventional open circuit voltage (OCV) methods cannot be applied due to inherent cell characteristics of SWB, the coulomb counting (CC) method is adopted for the state of charge (SOC) monitoring. For the state of health (SOH), a variance-based detection scheme is proposed to provide degradation prediction and fault detection of the SWB. The self-powered operation is augmented by two proposed power optimization schemes such as multiple power management and three-step LED light control. A wireless buoy system prototype is manufactured, and its functional feasibility is experimentally verified, where its location and SOC are periodically monitored in a smartphone-based wireless platform.

Real-time power optimization for an air-coolant proton exchange membrane fuel cell based on active temperature control

Air-coolant proton exchange membrane fuel cells (PEMFCs) have the characteristics of compact structure and low cost, benefiting for lightweight applications such as aircrafts with limited volume. However, the air-coolant PEMFC has strong coupling between air supply and cooling resulting in difficultly analyzing the relationship between optimal power and stack temperature to cause low power efficiency. To effectively increase the output power of the air-coolant PEMFC under changeable environmental conditions, this article introduces a real-time power optimization strategy based on the active temperature control. To output the continuously maximum power, an improved temperature perturb and observe (P&O) is designed to obtain an optimal temperature reference. Considering the PEMFC is a highly nonlinear system, the super-twisting algorithm (STA)-based controller is developed to regulate the stack temperature. After the optimal temperature is determined, the STA controller may lead to the target temperature be well tracked with strong robustness under unknown external disturbances. To validate the proposed strategy, practical experiments were carried out in a 1-kW air-coolant PEMFC under different operation scenarios, and the results showed that the power was increased over 4% when the air-coolant PEMFC operated under relatively high load. Overall, the proposed strategy looks promising for power optimization of air-coolant PEMFCs.

A Decoupling Rolling Multi-Period Power and Voltage Optimization Strategy in Active Distribution Networks

With the increasing penetration of distributed photovoltaics (PVs) in active distribution networks (ADNs), the risk of voltage violations caused by PV uncertainties is significantly exacerbated. Since the conventional voltage regulation strategy is limited by its discrete devices and delay, ADN operators allow PVs to participate in voltage optimization by controlling their power outputs and cooperating with traditional regulation devices. This paper proposes a decoupling rolling multi-period reactive power and voltage optimization strategy considering the strong time coupling between different devices. The mixed-integer voltage optimization model is first decomposed into a long-period master problem for on-load tap changer (OLTC) and multiple short-period subproblems for PV power by Benders decomposition algorithm. Then, based on the high-precision PV and load forecasts, the model predictive control (MPC) method is utilized to modify the independent subproblems into a series of subproblems that roll with the time window, achieving a smooth transition from the current state to the ideal state. The estimated voltage variation in the prediction horizon of MPC is calculated by a simplified discrete equation for OLTC tap and a linearized sensitivity matrix between power and voltage for fast computation. The feasibility of the proposed optimization strategy is demonstrated by performing simulations on a distribution test system.

Dynamic power optimization for secondary wearable biosensors in e-healthcare leveraging cognitive WBSNs with imperfect spectrum sensing

The integration of cognitive radio with e-healthcare systems assisted by wireless body sensor networks (WBSNs) has been regarded as an enabling approach for a new generation of pervasive healthcare services, to provide differentiated quality of service requirements and avoid harmful electromagnetic interference to primary medical devices (PMDs) over the crowded radio spectrum. Due to the sharing spectrum bands with PMDs in e-healthcare scenario using cognitive WBSNs (CWBSNs), efficient transmit power control and optimization strategies for resource-constrained secondary wearable biosensors (SWBs) play a key role in controlling the inter-network interference and improving the energy efficiency. This paper investigates the problem of dynamic power optimization for SWBs in e-healthcare leveraging CWBSNs with practical limitations, e.g., imperfect spectrum sensing and quality of physiological data sampling. We develop a distributed optimization framework of dynamic power optimization via the theory of differential game, by jointly considering utility maximization and quality of physiological data sampling for every SWB, while satisfying the evolution law of energy consumption in SWB’s battery. With the non-cooperation and cooperation relations for all SWBs in mind, we transform the differential game model into two subproblems, namely, utility maximization problem and total utility maximization problem. Utilizing Bellman’s dynamic programming, we derive a non-cooperative optimal solution for power optimization as a Nash equilibrium point for the utility maximization problem posed by competitive scenario. By exploiting Pontryagin’s maximum principle, a cooperative optimal solution is obtained for the total utility maximization problem, wherein all SWBs fully cooperate to obtain the highest total utilities. Building upon the analytical results, the actual utility distributed to each SWB is compared between the non-cooperative and cooperative schemes. Extensive simulations show that the proposed optimization framework is indeed an efficient and practical solution for power control compared with the benchmark algorithm.

The Application of Electric Vehicle Ordered Charging Technology in Ubiquitous Power Internet of Things

In recent years, the share of electric vehicles in the domestic automobile market is also increasing. When users meet the basic electricity consumption, they can use the electricity price given by the power company to reasonably allocate household energy and choose a more appropriate household energy layout, which can reduce energy consumption and save daily expenses. The purpose of this paper is to optimize the scheduling of household energy users, so as to improve the quality of power, save electricity expenditure, and obtain economic benefits. Based on the intelligent power optimization strategy and the environmental protection and energy conservation of electric vehicles, this paper analyzes the influence of V2G technology on the optimization strategy of household electricity consumption, and establishes the household electricity load model and electric vehicle model. For the whole system model, the scheduling time, power constraints and users’ habit of using electric vehicles as constraints, the final scheduling scheme is obtained through dynamic planning algorithm. Finally, the simulation results verify the correctness of the model coordination control optimization method established in this paper.

An Online Reactive Power-Optimization Strategy Based on Load-Curve Prediction and Segmentation

Due to fluctuating characteristics of loads, dynamic reactive power optimization over a certain time period is essential to provide effective strategies to maintain the security and economic operation of distribution systems. In operation, reactive power compensation devices cannot be adjusted too frequently due to their lifetime constraints. Thus, in this paper, an online reactive power optimization strategy based on the segmentation of multiple predicted load curves is proposed to address this issue, aiming to minimize network losses and at the same time to minimize reactive power-compensation device adjustment times. Based on forecasted time series of loads, the strategy first segments each load curve into several sections by means of thresholding a filtered signal, and then optimizes reactive power dispatch based on average load in each section. Through case studies using a modified IEEE 34-bus system and field measurement of loads, the merits of the proposed strategy is verified in terms of both optimization performance and computational efficiency compared with state-of-the-art methods.

Efficient energy harvesting scheme with power optimization strategies over cognitive radio networks

A wide growth of wireless communication and it's associated technologies innovates many innovative and robust systems. These kinds of innovations over wireless technologies highly depend on energy efficiency and power saving procedures over communication scenarios. These wirless technologies update its nature day-by-day based on the user needs, so a dynamic wireless communication medium is essential to tolerate and resolve this kind of robust needs. Cognitive Radio Networks, an intelligent dynamic programming scheme as well as which has powerful network configurations to choose the efficient wireless network channel for proceeding the communications without any interruptions. This kind of dynamicity and robustness requires more energy to manipulate the sensor node and routing details as well as their presence in every time to check the node availability for further communications. In this paper, we concentrate more on energy harvesting issues and power optimization procedures occurred over Cognitive Radio Networks. In this proposed system, the energy efficiency is preserved and power optimization over CR Networks is handled by means of three intelligent factors/strategies such as: (i) Efficient Route Reporter (ERR), (ii) Dynamic Channel Optimizer (DCO) and (iii) Power Efficiency Handler (PEH). These three strategies are collectively called as Dynamic Optimization Law over CR Networks (DOL-CRNs). By using these DOL-CRNs, we can easily manipulate the issues and provide best resolution to optimize the CR Networks to enhance the network lifetime, throughput, reduce the delay over CR Networks communication and maximize the frame success ratio. For all the proposed DOL-CRNs assures the efficiency of Cognitive Radio Networks as well as its communication robustness with the help of three associated strategies.

Physical Layer Key Generation Scheme Through Scrambling the Correlated Eavesdropping Channel

In this paper, we investigate the physical layer key generation scheme in most realistic scenarios where the correlation between the legitimate channel and the eavesdropping channel is considered. To degrade the correlation and improve the key generation capacity, a physical layer key generation scheme through scrambling the correlated eavesdropping channel is proposed. Firstly, the artificial noise is sent superimposed over pilots from the legitimate transmitter to confuse the eavesdropper. The artificial noise precoding matrix is randomly selected from decomposed unitary matrices orthogonal to the legitimate channel resulting in having no effect on legitimate channel. Then, the vector quantization algorithm and the winnow algorithm are respectively applied to quantize the channel characteristic and negotiate the generated random sequences to generate the final key. To further support the performance, we next present a power optimization scheme allocated to sending the artificial noise to maximize the generated key capacity under the total transmission power constraint. Finally, simulation results are presented to validate the effectiveness of our proposed scheme.

Wind turbine power curve upgrades: methods for the assessment and test cases study

The research about wind turbine control and blade design optimization has flourished in the latest years and has provided the opportunity of diffusely updating the technology of operating wind turbines. Due to multivariate dependence of wind turbine power on ambient conditions and working parameters, it is complex to estimate the actual impact of power optimization strategies. This problem therefore calls for devoted operation data mining and statistical techniques, which are explored in the present work. In particular, two test cases of multi-MW wind turbines power upgrades are discussed: the former is a combined aerodynamic and control optimization, the latter is the optimization of the yaw control. The assessment of the upgrades impact is performed through the comparison between the post-upgrade measured production and a model estimate of the pre-upgrade production in the same conditions. The wind turbines nearby to the target upgraded ones are employed as references for the operation conditions and their working parameters are employed for a principal component regression of the power of the target wind turbine. The proposed method is general and, for the selected test cases, it arises that the aerodynamic and control optimization improves the Annual Energy Production of the order of the 3%, while the yaw control optimization provides a 1% AEP improvement.

Asymptotically Linear Analysis and Gate Probability Allocation Schemes in Probabilistic Circuits

As the feature size of CMOS technology rapidly shrinks into the very deep submicrometer, the power density is getting higher and the circuit's reliability is seriously impacted by thermal noise. In order to reduce power dissipation, decreasing the supply voltage is a promising way. However, soft errors and decreased supply voltage make circuits behave probabilistic. Fortunately, some applications can tolerate a certain level of operation errors. The tradeoff between energy and reliability provides an opportunity to save the power consumption in the circuit. An asymptotically linear analysis (ALA) strategy is proposed to model the reliability evaluation of combinational logic circuits. Under the assumption that the gate error probability is not more than 0.05, an asymptotic linear matrix A is constructed by using the first-order term of the output error probability. When propagating the gate error probability from the input to the output ports, the ALA strategy only requires one multiplication between the matrix A and the gate error probability vector. There are significant differences in the complexity of computation of the ALA scheme and the Bayesian network (BN) and the probability transfer matrix (PTM) schemes, while its relative error (RE) is no more than 0.08 on the benchmark of LGSynth91. Furthermore, three schemes are proposed to adjust the gate error probability to meet the constraint on the error probability of the probabilistic CMOS (PCMOS) circuit's outputs. The proposed schemes can be performed to search the gate error probability combinations in the logic and circuit design stage of VLSI. During the power optimization, the switching activity factor in the asymptotically linear region is approximately invariant. It makes the power optimal model convex. The energy-saving ratio of the proposed power optimization scheme is 5%-6% higher than the existing schemes. The time consumption of the proposed three schemes is one-thousandth of the time consumed by the BN and PTM schemes. All the three schemes are verified by conducting experiments on the ISCAS'85 and IWLS'15 benchmark circuits. The simulation results establish the feasibility of the ALA and the performance of the proposed schemes.

Energy-Efficient Resource Allocation for Secure Cognitive Radio Network With Delay QoS Guarantee

A joint resource allocation policy for an underlay multi-user secure cognitive radio network (CRN) adopting the time-division multiple-access scheme with the guarantee of the statistical delay quality-of-service is proposed in this paper. Through the problem formulation of the resource allocation for the multi-user secure CRN with perfect channel state information (CSI), the energy-efficient joint power and time optimization scheme is proposed, which aims at minimizing the β-fair cost functions subject to fairness, delay, and security constraints. Furthermore, considering the multi-user secure CRN with imperfect CSI, we also propose a robust resource allocation scheme and the tradeoff between the robustness and the achievable sum-power is analyzed. Numerical results show that the optimal resource allocation scheme with perfect CSI can effectively balance the tradeoff between the energy efficiency and the fairness and is superior to the other conventional resource allocation strategies. Moreover, the robust resource allocation policy with imperfect CSI makes the theoretical research much closer to the practical applications.

OmniCast: Wireless Pseudo-Analog Transmission for Omnidirectional Video

Wireless virtual reality (VR) applications that provide users extraordinary viewing experience are now drawing great attentions. Transmitting VR video via wireless channel to users’ head-mounted display devices efficiently with low latency is very important for many emerging VR applications. In this paper, we propose a pseudo-analog transmission framework named OmniCast, which provides graceful quality degradation and competitive performance for unpredictably varying wireless channels while featuring low latency, low complexity, and low energy cost. In particular, we analyze the influence of projection between the spherical representation and the 2-D plane representation, and derive a power optimization scheme to minimize the distortion on the sphere. In addition, we develop an approach to measure the efficiency of decorrelation transform in the spherical domain. Based on that, an appropriate transform option can be determined. Experimental results show that the proposed framework improves the transmission efficiency of omnidirectional videos, while achieving elegant quality degradation for channel fluctuation in a wide channel SNR range.