PSO-BP-Based Optimal Allocation Model for Complementary Generation Capacity of the Photovoltaic Power Station

To improve the operation efficiency of the photovoltaic power station complementary power generation system,an optimal allocation model of the photovoltaic power station complementary power generation capacity based on PSO-BP is proposed.Particle Swarm Optimization and BP neural network are used to establish the forecasting model,the Markov chain model is used to correct the forecasting error of the model,and the weighted fitting method is used to forecast the annual load curve,to complete the optimal allocation of complementary generating capacity of photovoltaic power stations.The experimental results show that thismethod reduces the average loss of photovoltaic output prediction,improves the prediction accuracy and recall rate of photovoltaic output prediction,and ensures the effective operation of the power system.

Active Power Allocation of Virtual Synchronous Generator Using Particle Swarm Optimization Approach

In recent years, the penetration of renewable energy sources (RES) is increasing due to energy and environmental issues, causing several problems in the power system. These problems are usually more apparent in microgrids. One of the problems that could arise is frequency stability issue due to lack of inertia in microgrids. Lack of inertia in such system can lead to system instability when a large disturbance occurs in the system. To solve this issue, providing inertia support to the microgrids by a virtual synchronous generator (VSG) utilizing energy storage system is a promising method. In applying VSG, one important aspect is regarding the set value of the active power output from the VSG. The amount of allocated active power during normal operation should be determined carefully so that the frequency of microgrids could be restored to the allowable limits, as close as possible to the nominal value. In this paper, active power allocation of VSG using particle swarm optimization (PSO) is presented. The results show that by using VSG supported by active power allocation determined by the method, frequency stability and dynamic stability of the system could be improved.

Antenna Selection and Power Allocation Design for 5G Massive MIMO Uplink Networks

Massive MIMO is one of the key technologies in future 5G communications which can satisfy the requirement of high speed and large capacity. This paper considers antenna selection and power allocation design to promote energy conservation then provide good quality of service(QoS) for the whole massive MIMO uplink network. Unlike previous related works, hardware impairment, transmission efficiency, and energy consumption at the circuit and antennas are involved in massive MIMO networks. In order to ensure the QoS, we consider the minimum rate constraint for each user and the system, which increases the complexity of power allocation problem for maximizing energy and spectral efficiency in massive MIMO system. To this end, a quantum-inspired social emotional optimization(QSEO) algorithm is proposed to obtain the optimal power control strategy in massive MIMO uplink networks. Simulation results assess the great advantages of QSEO which previous strategies do not have.

Joint Subcarrier and Power Allocation for Multi-UAV Systems

This paper investigates subcarrier and power allocation in a multi-UAV OFDM system.The study considers a practical scenario,where certain subcarriers are unavailable for dynamic subcarrier allocation,on account of pre-allocation for burst transmissions.We first propose a novel iterative algorithm to jointly optimize subcarrier and power allocation,so as to maximize the sum rate of the uplink transmission in the multiUAV OFDM system.The key idea behind our solution is converting the nontrivial allocation problem into a weighted mean square error(MSE) problem.By this means,the allocation problem can be solved by the alternating optimization method.Besides,aiming at a lower-complexity solution,we propose a heuristic allocation scheme,where subcarrier allocation and transmit power allocation are separately optimized.In the heuristic scheme,closedform solution can be obtained for power allocation.Simulation results demonstrate that in the presence of stretched subcarrier resource,the proposed iterative joint optimization algorithm can significantly outperform the heuristic scheme,offering a higher sum rate.

Genetic Based Approach for Optimal Power and Channel Allocation to Enhance D2D Underlaied Cellular Network Capacity in 5G

With the obvious throughput shortage in traditional cellular radio networks,Device-to-Device(D2D)communications has gained a lot of attention to improve the utilization,capacity and channel performance of nextgeneration networks.In this paper,we study a joint consideration of power and channel allocation based on genetic algorithm as a promising direction to expand the overall network capacity for D2D underlaied cellular networks.The genetic based algorithm targets allocating more suitable channels to D2D users and finding the optimal transmit powers for all D2D links and cellular users efficiently,aiming to maximize the overall system throughput of D2D underlaied cellular network with minimum interference level,while satisfying the required quality of service QoS of each user.The simulation results show that our proposed approach has an advantage in terms of maximizing the overall system utilization than fixed,random,BAT algorithm(BA)and Particle Swarm Optimization(PSO)based power allocation schemes.

Power Allocation for Cooperative Communications in Non-Orthogonal Cognitive Radio Vehicular Ad-Hoc Networks

To achieve the better system performance for cooperative communication in non-orthogonal cognitive radio vehicular adhoc networks(CR-VANETs),this paper investigates the power allocation considering the interference to the main system in a controllable range.We propose a three-slot one-way vehicle system model where the mobile vehicle nodes complete information interaction with the assistance of other independent nodes by borrowing the unused radio spectrum with the primary networks.The end-to-end SNR relationship in overlay and underlay cognitive communication system mode are analyzed by using two forwarding protocol,namely,decode-and-forward(DF)protocol and amplify-and-forward(AF)protocol,respectively.The system outage probability is derived and the optimal power allocation factor is obtained via seeking the minimum value of the approximation of system outage probability.The analytical results have been confirmed by means of Monte Carlo simulations.Simulation results show that the proposed system performance in terms of outage under the optimal power allocation is superior to that under the average power allocation,and is also better than that under other power allocation systems.

OPTIMAL POWER ALLOCATION WITH AF AND SDF STRATEGIES IN DUAL-HOP COOPERATIVE MIMO NETWORKS

Dual-hop cooperative Multiple-Input Multiple-Output (MIMO) network with multi-relay cooperative communication is introduced. Power allocation problem with Amplify-and-Forward (AF) and Selective Decode-and-Forward (SDF) strategies in multi-node scenario are formulated and solved respectively. Optimal power allocation schemes that maximize system capacity with AF strategy are presented. In addition, optimal power allocation methods that minimize asymptotic Symbol Error Rate (SER) with SDF cooperative protocol in multi-node scenario are also proposed. Furthermore, performance comparisons are provided in terms of system capacity and approximate SER. Numerical and simulation results confirm our theoretical analysis. It is revealed that, maximum system capacity could be obtained when powers are allocated optimally with AF protocol, while minimization of system's SER could also be achieved with optimum power allocation in SDF strategy. In multi-node scenario, those optimal power allocation algorithms are superior to conventional equal power allocation schemes.

MULTICARRIER DS-CDMA WITH ADAPTIVE MODULATION AND POWER ALLOCATION

Adaptive modulation and power allocation is introduced into the multicarrier DS-CDMA system to improve the system performance and bandwidth efficiency. First, the systemdesign appropriate for adaptive modulation and power allocation is given, then the algorithmof adaptive modulation and power allocation is applied. Simulation results demonstrate greatperformance improvement compared with the fixed modulated one.

Optimal power allocation for amplify-and-forward in single relay non-cooperative systems

For a single-relay amplify-and-forward (AF) non-cooperative system,an optimal power proportionbetween source and relay is considered.Aiming to minimize end-to-end bit error rate (BER) and maximizeattainable rate,both large-scale path loss and small-scale Rayleigh fading are taken into account.Aclosed form expression to allocate power in optimal proportion at source is obtained.Simulation resultsshow that the proposed scheme to distribute power can minimize BER under any channel conditions.

Optimal Power Allocation Strategy for TBLAST Based 4G Systems

There is a big demand for increasing number of subscribers in the fourth generation mobile communication systems. However, the system performance is limited by multi-path propagations and lack of efficient power allocation algorithms in conventional wireless communication systems. Optimal resource allocation and interference cancellation issues are critical for the improvement of system performance such as throughput and transmission reliability. In this paper, a turbo coded bell lab space time system (TBLAST) with optimal power allocation techniques based on eigen mode, Newton and convex optimization method and carrier-interference-and-noise ratio (CINR) are proposed to improve link reliability and to increase throughput with reasonable computational complexity. The proposed scheme is evaluated by Monte-Carlo simulations and is shown to outperform the conventional power allocation scheme.

NOMA Empowered Energy Efficient Data Collection and Wireless Power Transfer in Space-Air-Ground Integrated Networks

As the sixth generation network(6G)emerges,the Internet of remote things(IoRT)has become a critical issue.However,conventional terrestrial networks cannot meet the delay-sensitive data collection needs of IoRT networks,and the Space-Air-Ground integrated network(SAGIN)holds promise.We propose a novel setup that integrates non-orthogonal multiple access(NOMA)and wireless power transfer(WPT)to collect latency-sensitive data from IoRT networks.To extend the lifetime of devices,we aim to minimize the maximum energy consumption among all IoRT devices.Due to the coupling between variables,the resulting problem is non-convex.We first decouple the variables and split the original problem into four subproblems.Then,we propose an iterative algorithm to solve the corresponding subproblems based on successive convex approximation(SCA)techniques and slack variables.Finally,simulation results show that the NOMA strategy has a tremendous advantage over the OMA scheme in terms of network lifetime and energy efficiency,providing valuable insights.

多无人机辅助NOMA网络的联合无线资源分配算法

为了满足高容量需求和通信质量,提出了一种多无人机(Unmanned Aerial Vehicle, UAV)辅助下行非正交多址(Non-Orthogonal Multiple Access, NOMA)网络的联合定位、用户调度、用户配对和功率分配优化算法。在基站位置固定的网络场景下,推导出通用最优用户配对和功率分配策略闭式解。针对多小区场景,将无人机的位置优化问题转化为一个局部合作博弈问题,并证明所提博弈是一个精确的潜在博弈,它至少有一个纯策略纳什均衡(Pure Strategy Nash Equilibrium, PNE),且最优的纳什均衡(Nash Equilibrium, NE)能够最大化网络和速率。设计了一种集中分布式学习算法寻找最优PNE。仿真结果表明,该算法优于现有方案,显著提高了网络效用。

基于PSTSO多目标优化储能系统功率分配

为提高电池储能系统的功率分配合理性,提出基于状态优先的金枪鱼群优化PSTSO(priority of status tuna swarm optimization)算法的储能系统功率分配策略。首先设定了3个储能系统功率分配的评价指标,其次建立储能系统的运行成本、储能单元的健康状态SOH(state-of-health)损失、储能系统的荷电状态SOC(state-of-charge)一致性的数学模型,最后在满足系统功率平衡和SOC上、下限约束条件下,采用PSTSO算法进行功率分配。算例分析结果表明,所提策略可以有效减少电池单元充放电次数,降低电池单元的容量损耗,且保证储能系统的SOC一致性好。

基于FL的无蜂窝网络用户调度与功率分配策略

为解决无蜂窝网络系统中用户链路质量差异和通信、计算资源占用不平衡导致的联邦学习(FL)训练性能受限问题,设计了一个联合用户调度和功率分配的优化方案。首先,提出了一种低复杂度的基于资源优先的二次抽样用户调度(RPSS-US)算法,根据用户计算资源的可用性和链路质量选择用户,优先调度对系统容量和全局模型更新贡献较大的用户参与FL任务,提高整体训练性能。随后,提出了一种基于二分法的功率分配(BM-PA)算法,通过优化功率分配改善用户链路质量差异,以提高数据传输速率,减少FL任务的总体时延。通过交替迭代优化这2个子问题,实现系统性能的联合优化。仿真结果表明,相较于其他对比算法,所提出的算法下行吞吐量提升了47.19%,上行吞吐量提升了22.60%,FL任务时间消耗减少了57.33%,并在达到相同模型精度时的时间开销最小。

无人机辅助传感器网络中吞吐量与节点能量优化方法

无人机凭借其高机动性可以对传感节点进行无线数据采集和无线供能,然而无线数据采集与无线供能是相互耦合的,如何对两者的资源分配和无人机飞行轨迹进行优化是无人机辅助无线供能传感器网络性能的关键。为此,提出了一种联合优化方案,通过交替优化节点接入、无人机三维飞行轨迹和能量数据传输时间分配,最大化最小节点效用。针对无线供能传感器网络模型中最大化最小节点效用问题提出了一个联合优化方案。由于该问题为非凸问题,提出了一个最大化最小节点效用算法(Maximized the Minimum Node Utility Algorithm,MMNUA),采用块坐标下降法将原始问题转化成4个子问题,并利用连续凸近似技术在每次迭代时依次优化4个子问题,使最大化最小节点效用逐渐收敛。与基础无人机方案相比,MMNUA使系统性能分别提升了14.1%、567.5%和177.0%,有效提高了无人机的工作效率,同时所提算法具有良好的收敛性和较小的复杂度。

双碳目标下基于Stackelberg和合作博弈的虚拟电厂双层优化调度

在中国的双碳目标下,大规模新能源发电接入虚拟电厂时,将导致电力市场交易中的高额惩罚成本。为此,设计了虚拟电厂参与日前市场竞标的双层博弈模型,以双层收益最大化为目标对虚拟电厂进行调度优化。首先,利用场景生成与削减技术考虑风力发电商和电动汽车聚合商的不确定性,设计了由风力发电商、电动汽车聚合商和燃气轮机合作组成的虚拟电厂下层电量竞标模型,以及虚拟电厂控制中心的上层电价竞标模型。其次,基于两个子模型之间的主从递阶关系,利用卡罗需库-恩塔-克(KKT)条件将双层模型转化为一个混合互补问题,通过求解获得优化方案,并基于Shapley值法对合作方进行利润分配。数值算例表明,设计的模型能有效减少碳排放和降低风电惩罚成本,同时增加参与者的收益。

无人机辅助蜂窝网络的模式选择与资源优化

研究了无人机(UAV,unmanned aerial vehicle)与蜂窝网络共存下的无线通信资源分配和优化方案。为了提高网络频谱利用率,无人机用户以全双工(FD,full duplex)或半双工(HD,half duplex)设备对设备(D2D,device-to-device)技术复用蜂窝频谱资源接入网络。此外,构造了一个联合接入控制、模式选择、功率控制和资源分配优化问题最大化网络的整体吞吐量,并保证无人机用户和地面蜂窝用户的服务质量要求。为解决这个问题,首先利用凸优化的第一阶段方法分别对全双工和半双工两种设备对设备模式进行接入控制和可行性判定。然后,对可接入无人机用户对使用凸凹过程(CCCP,convex and concave procedure)迭代算法求解功率控制问题。利用该局部最优值,原优化问题可以简化为加权最大化问题。最后,通过库恩-芒克斯(KM,Kuhn-Munrkes)算法对最优信道资源进行匹配,获得系统的全局最优吞吐量值。数值结果表明,所提方案能显著提高系统性能。

配电网络电能质量综合治理设备优化配置策略

针对配电网络电能质量治理设备缺乏全局统筹配置的现状,提出了一种基于多目标粒子群优化算法对谐波、无功及三相不平衡治理设备的综合优化配置策略。分别采用有源电力滤波器抑制谐波、智能电容器补偿无功、换相开关治理三相不平衡,并以每类电能质量问题的治理效果和投入成本为优化对象,以满足相关电能质量标准为约束条件,通过多目标粒子群算法确定治理设备配置节点和相应投入容量的优化配置方案。建立了电能质量评估模型,搭建了基于IEEE-18节点的配电系统仿真模型,并分散接入谐波、无功和三相不平衡负载,模拟电能质量问题,通过仿真验证了所提出的治理设备综合优化配置策略的可行性和相较于传统电能质量治理方案在电能质量治理上的优越性。

计及激励型需求响应的低压配电网混合储能优化配置

分布式光伏高比例接入和再电气化与电能替代加剧了系统源、荷的波动性,传统低压配电网规划方法难以适应新型电力系统发展的要求。针对此问题,首先建立了计及激励型需求响应的低压配电网混合储能优化配置模型,然后,根据不同时间尺度下的储能设备和激励型需求侧响应资源的特点,提出运用改进的变分模态分解算法(variational mode decomposition,VMD)对净负荷曲线进行多尺度分解和组合重构,以系统总成本和有功功率波动值之和最小为目标,运用改进的鲸鱼优化算法对所提的优化配置模型进行求解。最后,通过算例验证所提方案的有效性。

多用户电力线通信自适应OFDM系统中的资源分配与交换优化

自适应正交频分复用(OFDM)是一种实现电力线多用户高速数据通信的技术,资源动态分配是这一技术的重要组成部分。文章针对电力线通信的限制条件,探讨了在最大总功率、各用户要求最小速率、各子载波分配最大功率和比特数的约束下,多用户在多子载波上自适应比特和功率分配的数学模型,提出一种新的速率自适应动态资源分配算法,其实现分为4个相互联系的阶段,依次是确定工作子载波、单用户资源分配、子载波和功率交换、比特交换和功率调整。在典型电力线信道环境下的仿真结果表明,该算法比已有的多用户频谱分配优化算法性能更优,能更好地满足多用户资源分配模型的目标要求。