Power Allocation Model Research Articles

The Choice of Values Behind Corporate Law: A Critique of Shareholder Primacy and a Response to China's Proposal

The theory of shareholder primacy, as an important value judgment in the U.S. corporate law on corporate governance model, has influenced the shaping of legal rules of corporate governance in different countries and regions of the world, as well as the revision of China's corporate law. The theoretical lineage of shareholder-centrism has its own peculiar soil of existence and should not be regarded as a universal value behind the norms of corporate law. Even in the U.S., shareholder primacy is highly controversial, and the direction it advocates is contradictory to the reality of corporate governance practices. China has not been affected by the shareholder primacy debate in the current revision of its corporate law, but has adopted a corporate power allocation model with Chinese characteristics, so as to achieve a balance between the efficiency of corporate governance and the protection of shareholders' interests.

Multi-dimensional resource allocation strategy for LEO satellite communication uplinks based on deep reinforcement learning

In the LEO satellite communication system, the resource utilization rate is very low due to the constrained resources on satellites and the non-uniform distribution of traffics. In addition, the rapid movement of LEO satellites leads to complicated and changeable networks, which makes it difficult for traditional resource allocation strategies to improve the resource utilization rate. To solve the above problem, this paper proposes a resource allocation strategy based on deep reinforcement learning. The strategy takes the weighted sum of spectral efficiency, energy efficiency and blocking rate as the optimization objective, and constructs a joint power and channel allocation model. The strategy allocates channels and power according to the number of channels, the number of users and the type of business. In the reward decision mechanism, the maximum reward is obtained by maximizing the increment of the optimization target. However, during the optimization process, the decision always focuses on the optimal allocation for current users, and ignores QoS for new users. To avoid the situation, current service beams are integrated with high- traffic beams, and states of beams are refactored to maximize long-term benefits to improve system performance.Simulation experiments show that in scenarios with a high number of users, the proposed resource allocation strategy reduces the blocking rate by at least 5% compared to reinforcement learning methods, effectively enhancing resource utilization.

Power Resource Allocation Algorithm for Dual-Function Radar–Communication System

In this paper, a power allocation algorithm of dual-function radar–communication system with limited power is proposed to obtain better overall system performance measured by the weighted summation of radar signal to interference plus noise ratio (SINR) and communication channel capacity. First, a power allocation model is established to maximize the radar SINR and communication channel capacity with limited transmitted power. Then, the Karush–Kuhn–Tucker (KKT) conditions are used to solve the optimal objective function under the condition that only radar SINR or communication channel capacity is considered, respectively. Finally, the optimal value is combined with the original model and transformed into a single objective optimization model, and the optimal power is obtained by solving the model through the iterative optimization algorithm. Simulation results show that, compared with other power allocation algorithms, the proposed algorithm can achieve better radar-communication integration performance under the same transmit power.

Secrecy performance evaluation and enhancement of vehicle-to-vehicle communications in the presence of big vehicles

Despite its significant role in providing and developing reliable and safe intelligent transportation services, the Internet of Vehicles faces several challenges and security threats, such as co-channel interference (CCI), node self-interference (SI), and eavesdropping. This paper investigates the secrecy performance of vehicular networks in terms of their secrecy outage probability (SOP) and average secrecy capacity (ASC) in the presence of an eavesdropper. The legitimate vehicles are assumed to operate in full-duplex mode. Hence, the impacts of CCI and SI are considered for practical modeling and performance evaluation. Moreover, the wireless communications among vehicles are assumed to be either subjected to Nakagami-m fading or Nakagami-N-Gamma shadowed fading due to the existence of a big vehicle and its location. Accordingly, closed-form mathematical expressions are derived for the SOP and the ASC. Furthermore, a power allocation (PA) model is proposed to improve the secrecy performance by maximizing the derived ASC. The numerical results validate the derived expressions by demonstrating the impact of the channel condition, the secrecy data rate threshold, the SI cancellation capability at the legitimate destination, the location of the eavesdropper, the location of the big vehicle, and the strength of shadowing on the SOP and the ASC. Also, it is shown that the proposed PA model can significantly enhance the secrecy performance.

Co-Optimizing Bidding and Power Allocation of an EV Aggregator Providing Real-Time Frequency Regulation Service

The rapidly expanding scale of electric vehicle (EV) fleets and continuously decreasing battery costs are making vehicle-to-grid services a reality. In this paper, we study the interaction between the problems of an EV aggregator’s bidding in the regulation market and power allocation (i.e., determining the (dis)charging powers of the EVs in regulation deployment). Although the two problems are coupled, they are often regarded as decoupled and optimized separately for complexity issues. However, failing to consider the coupling of bidding and power allocation can lead to a decline in the profit of the EV aggregator (EVA). In this paper, we propose a framework for co-optimizing EVA bidding and power allocation in the regulation market. The bidding model is formulated as a stochastic programming problem with embedded power allocation in discretized regulation signal scenarios. To meet the solution time requirement for regulation deployment, we further propose a power allocation model that can be solved online. It utilizes the Lagrange multipliers from the bidding problem to ensure that the allocation results correspond to the optimal solution of the bidding problem. The effect of the proposed framework on improving EVA profits and reducing degradation costs is verified in the case study.

A Novel Allocation Strategy Based on the Model Predictive Control of Primary Frequency Regulation Power for Multiple Distributed Energy Storage Aggregators

As the amount of distributed energy storage (DES) in a power system continues to increase, it will not be long before there are multiple DES aggregators participating in frequency regulation, and the realization of their coordinated control is a critical topic of current research. This study focused on the primary frequency regulation (PFR) power allocation strategy among multiple DES aggregators participating in PFR. This study first calculated the PFR demand according to a system frequency response model of the power system with DESs. Next, a PFR power allocation model of DES aggregators was developed based on model predictive control. The objective of this model was to minimize the overall frequency regulation cost while satisfying all of the constraints of DESs. Finally, the distributed interior point method was used to solve the model rapidly. The correctness and effectiveness of the proposed model and algorithm were verified on two unified transmission and distribution systems with DES aggregators used to supply the PFR service. The results revealed that the proposed model could effectively allocate PFR power to the various types of energy storage, with the additional benefits of slowing down the shift in the state of charge for energy storage units and ensuring the continuity of energy storage participation in frequency regulation.

A novel method for increasing wind power output considering wind turbine failure

This work develops a complete process for power capture and power distribution. In our proposed approach, a power allocation model that considers wind turbine (WT) failure is presented using a new maximum power capture structure for wind farms (WFs). Specifically, the proposed approach adopts a hierarchical control strategy to maximize the wind power output from the WF. In the first level, a control strategy is employed between a centralized controller located in the substation and local controllers installed on each WT to maximize wind energy capture. Apart from this, we exploit a new competitive swarm optimizer with a local search (LSCSO) to implement the optimization process of maximum power capture at this level. In the second level, a new power allocation model that considers WT failure is presented, and the power distribution is optimized using a distributed split Bregman method (SBM). Finally, the proposed approach integrates the LSCSO-SBM method and yields improved wind-farm power optimization compared to traditional methods. Furthermore, this scheme has been applied to the Roy Billinton Test System to evaluate the reliability of the generating system, and the results demonstrate that the proposed approach can effectively reduce the impact of the wake effect and total electrical losses in WFs, especially the significant benefit of increasing the energy production of WFs and generating system reliability.

Allocation of power in NOMA based 6G-enabled internet of things using multi-objective based genetic algorithm

Abstract Sixth generation (6G)-enabled internet of things (IoT) requires significant spectrum resources to deliver spectrum availability for massive IoT’s nodes. But the existing orthogonal multiple access limits the full utilization of limited spectrum resources. The non-orthogonal multiple access (NOMA) exploits the potential of power domain to improve the connectivity for 6G-enabled IoT. An efficient quality of service (QoS) aware power allocation approach is required to enhance the spectral efficiency and energy of NOMA based 6G-enabled IoT nodes. The multi-objective genetic algorithm (MOGA) is used to resolve the non-convex problem by considering the successive interference cancellation (SIC), QoS, and transmission power. Extensive experiments are drawn by using the Monte Carlo simulation to evaluate the significant improvement of the proposed model. Experimental results indicate that the proposed power allocation model provides good performance of the NOMA based IoT network.

Revealing the impact of renewable uncertainty on grid-assisted power-to-X: A data-driven reliability-based design optimization approach

Designing a reliable power-to-X (PtX) process that uses renewable energy and grid electricity as backup power is challenging due to the uncertain nature of renewables and potential limits on grid usage. To address this challenge, this paper proposes a data-driven, reliability-based optimization approach for designing a grid-assisted PtX process and understanding how the uncertainty of time-series renewable profile affects its design and optimal sizing. This approach uses generative adversarial networks (GANs) to capture the complex patterns of renewables in temporal and spatial dimensions, rather than simply treating uncertainty as a probabilistic range as in conventional methods. The optimization considers both the mean and variance of production cost as objectives, while also taking into account the level of grid energy penetration as a probabilistic constraint through the reliability analysis. A power allocation and performance evaluation model is developed and used in conjunction with the optimization to assess uncertainty propagation through the PtX process and evaluate the impact of renewable uncertainty on process performance. A case study involving the production of methanol on Jeju Island in South Korea demonstrates how the mean and variance of production cost and other performance indicators can be balanced in the reliable design of PtX processes under renewable energy uncertainty, providing insights for decision-making in such situations.

Hybrid Optimization Assisted Green Power Allocation Model for QoS-Driven Energy-Efficiency in 5G Networks

5G networks are progressing to offer services with high Quality of Service (QoS) features like high user mobility skills, higher data rates, high reliable communiqué, and lower latency. For this, numerous novel settings like integration of Software-Defined Networking (SDN) and Network Function Virtualization (NFV) should be implemented in the network system. Here, this paper plans to design an optimization-based power allocation model to increase the Effective Power Efficiency (EPE) that offers guaranteed QoS over “Single-Input Single-Output (SISO) and Multiple-Input Multiple-Output (MIMO)-channel oriented 5 G networks”. Moreover, the statistical QoS-based green power allotment system was also analyzed to increase the EPE over other MIMO networks. Particularly, this paper makes an attempt to make the power allocation optimal, which directly ensures the 5 G network energy efficiency when guaranteeing QoS. This is considered to be the optimization issue, which is solved by MS-DA. Our suggested approach can be applied to hybrid energy supply networks with various network topologies, such as 5 G networks, which are now the most extensively used, and is capable of achieving a good resolution through iterative convergence.

Consensus-based multi-converter power allocation strategy in battery energy storage system

Battery energy storage system (BESS) commonly consists of multiple power conversion systems (PCSs) under parallel operation, which are controlled by a centralized controller to realize power allocation. As the number of PCSs increases, the topology and communication structure of the BESS become more complex, reducing the ability of centralized controller to respond quickly and flexibly. An effective approach to address this problem is to develop a distributed power allocation strategy. Consensus theory, as an advanced distributed algorithm, is based on partial information interaction to achieve the state consistency of multiple agents. This paper presents a consensus-based strategy to realize efficient power allocation under distributed framework. The system convergence is analyzed under communication failure to demonstrate the robustness of the consensus-based control strategy. Considering state-of-charge (SOC) equalization, operation efficiency and battery life loss, the consensus factor of the consensus-based power allocation model is switched according to specific battery states and application scenarios. Finally, the effectiveness of the proposed strategy is validated by simulation and real-world experiment in peak shaving and mitigating wind farm fluctuation scenarios.

Power Allocation in Ultra-Dense Networks Through Deep Deterministic Policy Gradient

The existing reinforcement learning-based downlink power allocation (PA) schemes mostly consider the power optimization space as a discrete value space, however, their results will deviate from the optimal result in ultra-dense networks, and the deviation grows as the network size increases. This letter proposed a PA model based on deep deterministic policy gradient (DDPG), where policy-based power selection assisted with value-based evaluation is leveraged to explore the optimal result from a continuous power space. Specifically, this model uses two CNNs named actors to formulate continuous deterministic PA strategy function instead of discrete power distribution sampling, and designs another two CNNs named critics for PA strategy evaluation and actor CNNs’ update supervision. Additionally, to reduce the interference, a tunable service base station set is designed for each user and is considered for the model training. Experiments demonstrate the proposed DDPG-based PA model respectively reaches 116.2% and 95.9% sum-rate relative to the iterative algorithm in small and large-scale networks.

Two-stage residential community energy management utilizing EVs and household load flexibility under grid outage event

To relieve the impact of the power grid outages on the residential sector, residential building energy management under grid outage events has been becoming the research hotspot in both the academic and industry. Considering the coordination of electric vehicles (EVs) and household load flexibility, this paper proposes a two-stage energy management approach for residential community under the planned outages. In the optimal scheduling stage, after receiving the information of the planned outage from the grid, the EVs’ charging–discharging power and community load curve reshaping schemes are optimal determined by residential community energy management system (CEMS), aiming at to minimize the total amount of unserved load of the whole community over the planned outage horizon. In the power allocation stage, with the formulated power allocation model, the determined residential community load curve from the above stage is then allocated to each house. The numerical test is finally conducted and the results validated the effectiveness and feasibility of the proposed approach.

Interference-aware modeling and analysis for the secrecy performance of cooperative vehicular relaying networks over mixed Nakagami-[formula omitted] and double Nakagami-[formula omitted] fading channels

Due to the massive wireless communications among numerous vehicular and static entities in the internet of vehicle networks, these networks became vulnerable to eavesdropping and interference. Thus, using physical-layer security techniques in vehicular networks can significantly enhance secrecy performance. This paper investigates the secrecy performance of cooperative vehicular relaying networks (CVRNs) over mixed Nakagami-m and double Nakagami-m fading channels in the presence of a single eavesdropper and co-channel interference. The relaying node is assumed to operate with different relaying schemes, namely, decode-and-forward, amplify-and-forward with variable gain, and amplify-and-forward with fixed gain. Hence, mathematical expressions for the average secrecy capacity (ASC) are obtained for the relaying schemes. Furthermore, a power allocation model is proposed to maximize the derived ASCs in terms of the source and relay nodes’ transmission powers, enhancing the relaying schemes’ secrecy performance. The results demonstrate the impact of channel condition and the strength of interference on the system secrecy performance. Moreover, the results show that the proposed power allocation model enhances the secrecy performance significantly compared to the equal power distribution model.

Automatic management of electrical loads in more electric aircraft

PurposeThis paper aims to automatically generate load shedding sequences due to insufficient power supply, to ensure flight safety and complete flight task.Design/methodology/approachIn this paper, a power allocation and load management model, including logical and physical submodels of the distribution system, is first established according to different requirements of the loads in different flight phase and the current total power supply. Then, an optimal load management scheme based on an improved ant colony algorithm is proposed to automatically generate load shedding sequences for both safety-critical and nonsafety critical loads, to achieve a reliable and safe power supply.FindingsTo verify the efficiency and feasibility of the algorithm, the proposed method is verified in a virtual simulation platform. Simulation result illustrates that the proposed algorithm is efficient and feasible.Practical implicationsThe proposed method can provide guidance on load power supply when the civil aircraft is under abnormal power supply situation.Originality/valueAn optimal load management scheme is proposed by considering different requirements of the loads in different flight phase and the current total power supply.

Power allocation model for residential homes using AI-based IoT

Conventional power transmission and distribution schemes have been completely transformed by the development of the Smart Grid (SG). Nearly every field has been impacted by technology and advancements, including smart grids. Electricity also isn't inexpensive to produce, so Smart Meters (SM) play a crucial role in controlling, managing, and performing efficiency to use the electricity successfully on the customer side, which would be referred to as demand side management (DSM). A Home Energy Management System (HEMS) has been proposed in the research study as a way of optimizing home appliances and saving as much money as possible. The Eagle Hard Optimization (EHO) method and our hybridization of the EHO technique have been implemented. EHO appears to be a Genetic Algorithm (GA) known as a Genetic EHO (GEHO). Compared to normal EHO and unplanned systems, simulation results showed that GEHO better planned the devices to lower the maximum price. Peak to Average Ration packs (PAR) has additionally been noted. Due to the planning of the largest amount of devices on both sides, GEHO& unforeseen has PAR equal, while EHO has a low PAR. Various Operation Time Intervals (OTI) were used to help in efficiency measures. With each of the three plans, capacity & costs money have indeed been thoroughly analyzed.

Dual-Population Co-Evolution Multi-Objective Optimization Algorithm and Its Application

In the multi-objective optimization algorithm, the parameter strategy has a huge impact on the performance of the algorithm, and it is difficult to set a set of parameters with excellent distribution and convergence performance in the actual optimization process. Based on the MOEA/D algorithm framework, this paper construct an improved dual-population co-evolution MOEA/D algorithm by adopt the idea of dual-population co-evolution. The simulation test of the benchmark functions shows that the proposed dual-population co-evolution MOEA/D algorithm have significant improvements in IGD and HV indicators compare with three other comparison algorithms. Finally, the application of the LTE base station power allocation model also verifies the effectiveness of the proposed algorithm.

Joint Sensor Selection and Power Allocation Algorithm for Multiple-Target Tracking of Unmanned Cluster based on Fuzzy Logic Reasoning.

The unmanned aerial vehicle (UAV) cluster is gradually attracting more attention, which takes advantage over a traditional single manned platform. Because the size of the UAV platform limits the transmitting power of its own radar, how to reduce the transmitting power while meeting the detection accuracy is necessary. Aim at multiple-target tracking (MTT), a joint radar node selection and power allocation algorithm for radar networks is proposed. The algorithm first uses fuzzy logic reasoning (FLR) to obtain the priority of targets to radars, and designs a radar clustering algorithm based on the priority to form several subradar networks. The radar clustering algorithm simplifies the problem of multiple-radar tracking multiple-target into several problems of multiple-radar tracking a single target, which avoids complex calculations caused by multiple variables in the objective function of joint radar node selection and power allocation model. Considering the uncertainty of the target RCS in practice, the chance-constraint programming (CCP) is used to balance power resource and tracking accuracy. Through the joint radar node selection and power allocation algorithm, the radar networks can use less power resource to achieve a given tracking performance, which is more suitable for working on drone platforms. Finally, the simulation proves the effectiveness of the algorithm.

Energy Efficient Power Allocation Using Salp Particle Swarm Optimization Model in MIMO–NOMA Systems

Non-orthogonal multiple access (NOMA) acts as one of the promising multiple access technology for fifth generation communication systems, and has the capability of improving the performance of the system. It can be extended with multiple input multiple output (MIMO) systems with multi-users to produce considerable gains. The energy efficiency (EE) of MIMO–NOMA systems needs to be improved, for which the optimization method, termed as Salp Particle Swarm optimization for Power allocation (SPPA) model is proposed. The SPPA optimization model is obtained from the integration of Particle Swarm Optimization and Salp Swarm Algorithm and the proposed SPPA model schedules the user on the basis of EE and spectral efficiency in such a way that it offers an effective platform to provide the energy efficient power allocation. The scheduling is performed using the SPPA algorithm that prioritizes the users in an optimal way based on the objective function. The experimentation is performed using the evaluation metrics, such as Bit error ratio (BER), spectral power, achievable rate, and energy. The effectiveness of the proposed power allocation method is revealed through lower BER of 0.0004 and higher spectral power, achievable rate, and energy of 180.0013, 99.8578, and 19.6683, respectively.

Performance Analysis and Power Optimization for Spectrum-Sharing Mixed RF/FSO Relay Networks With Energy Harvesting

In this paper, the energy harvesting in mixed multiple-input-single-output radio frequency (RF)/free space optical (FSO) networks is studied. Assuming underlay mode, the secondary user (SU) source with limited battery communicates with its destination via a hybrid SU relay. The SU source harvests energy from both the hybrid relay and the primary network. The hybrid SU relay node is equipped with two antennas; one for energy transmission to the secondary user and the other for data reception. The SU source transmits its data over an RF link to the relay. Then, the relay decodes the SU data before retransmitting it to the SU destination over an optical link. The RF/FSO channels are assumed to follow Nakagami-m/Malaga-M fading models with pointing errors on the FSO link. Closed-form expressions for the exact outage probability, average bit error rate, and ergodic capacity are derived. For high signal-to-noise ratio values, closed-form expressions for the asymptotic outage probability and average bit error rate are derived. Based on the asymptotic results, a power allocation model is proposed to enhance the system outage performance. Some simulation and numerical results are employed to validate the derived expressions.