Optimal Power Control Research Articles

Optimization of automatic power control of pulsed reactor IBR-2M in the presence of instability

The paper presents the main results of computational and experimental optimization of the automatic power control system (AС) of the IBR-2M pulsed reactor in the presence of a high level of oscillatory instability. Optimization of the parameters of the AС made it possible to significantly reduce the influence of random and deterministic oscillations of reactivity on the noise of the pulse energy, as well as to sharply reduce the manifestation of the oscillatory instability of the reactor. As a result, the safety and reliability of operation of the reactor has increased substantially.

Optimal Power Control for a Variable-Speed Generator Integrated in Series Hybrid Vehicle

In series hybrid electric vehicles, the fossil energy source is composed of an electrical generator driven by an internal combustion engine. Compared to constant speed control, variable speed control of the electrical generator improves the vehicle’s energetic efficiency and releases the battery load. Such control is extremely sensitive because of the nonlinearities. In a plug-in hybrid vehicle, in addition to the obvious inherent variable speed nonlinearity, the large range of battery voltage variation increases the complexity of the controller design. Moreover, in our case, to take advantage of the variable speed feature, the generator load has transient constraints because it must follow the vehicle load. In equivalent applications, for the electrical side, usually, there is a use of high-frequency control strategies based on the generator’s three phases current and voltage acquirement. This article investigates a low-frequency power control strategy through a model-based method for a specific power electronics vehicle architecture. Starting with an adaptive proportional, integrator, and derivative (PID)-based controller to tackle the nonlinearity issues, this article explains progressively the reasons and choices that lead to design a sliding hybrid fuzzy logic controller. The designed controller demonstrates good performance in power tracking and permits to reduce fuel consumption.

Energy-Efficient Optimization for Energy-Harvesting-Enabled mmWave-UAV Heterogeneous Networks.

Energy Harvesting (EH) is a promising paradigm for 5G heterogeneous communication. EH-enabled Device-to-Device (D2D) communication can assist devices in overcoming the disadvantage of limited battery capacity and improving the Energy Efficiency (EE) by performing EH from ambient wireless signals. Although numerous research works have been conducted on EH-based D2D communication scenarios, the feature of EH-based D2D communication underlying Air-to-Ground (A2G) millimeter-Wave (mmWave) networks has not been fully studied. In this paper, we considered a scenario where multiple Unmanned Aerial Vehicles (UAVs) are deployed to provide energy for D2D Users (DUs) and data transmission for Cellular Users (CUs). We aimed to improve the network EE of EH-enabled D2D communications while reducing the time complexity of beam alignment for mmWave-enabled D2D Users (DUs). We considered a scenario where multiple EH-enabled DUs and CUs coexist, sharing the full mmWave frequency band and adopting high-directive beams for transmitting. To improve the network EE, we propose a joint beamwidth selection, power control, and EH time ratio optimization algorithm for DUs based on alternating optimization. We iteratively optimized one of the three variables, fixing the other two. During each iteration, we first used a game-theoretic approach to adjust the beamwidths of DUs to achieve the sub-optimal EE. Then, the problem with regard to power optimization was solved by the Dinkelbach method and Successive Convex Approximation (SCA). Finally, we performed the optimization of the EH time ratio using linear fractional programming to further increase the EE. By performing extensive simulation experiments, we validated the convergence and effectiveness of our algorithm. The results showed that our proposed algorithm outperformed the fixed beamwidth and fixed power strategy and could closely approach the performance of exhaustive search, particle swarm optimization, and the genetic algorithm, but with a much reduced time complexity.

Handoff optimization for joint base station association and power control in uplink non‐orthogonal multiple access small cell networks

AbstractThis article investigates the handoff optimization in an uplink non‐orthogonal multiple access‐based small cell network (SCN) with successive interference cancellation that takes both the base station (BS) association and power control into consideration. Due to user mobility and varying communication environment, it is important to associate a user with the proper BS and control its transmit power by considering the user mobility for the SCNs. To overcome overly frequent re‐association and handoffs between BSs, a novel joint optimization of handoff, BS association, and total transmit power is presented that maximizes the system‐wide and handoff utilities and minimizes power consumption simultaneously. We define the handoff utility function by considering the BS association in each time frame with the previous time frame. Then, we formulate two inter‐related optimization problems under the minimum average‐rate and maximum allowable transmit power of mobile users (MUs) constraints. The designed optimization problems are combined through the weighted sum method into a single‐stage optimization problem. The transformed problem is solved by adopting the coalition formation game and primal decomposition theories. We design the coalition value function by collectively considering the handoff number of each BS in every time frame, transmit power of all MUs, and the number of associated MUs to every BS. The simulation results show the efficiency of the proposed handoff algorithm in comparison to another BS association algorithm in terms of the number of handoffs.

Joint Pilot and Data Power Control Optimization in the Uplink of User-Centric Cell-Free Systems

Joint pilot and data power control (JPDPC) is known to have a large impact on both the overall spectral/energy efficiency and fairness of cell-based systems. However, the impact of JPDPC on the inherent spectral/energy efficiency and fairness trade-off in cell-free (CF) systems is much less understood. In this letter, considering pilot contamination, user-centric clustering and multi-antenna access points, we formulate novel JPDPC problems in CF systems as distinct optimization tasks, whose objectives are maximizing the minimum spectral efficiency (SE), maximizing the total SE and maximizing the product of the individual signal-to-interference-plus-noise ratios. Since these problems are non-convex, we solve them by combining successive convex approximation and geometric programming. To the best of our knowledge, this is the first letter analyzing and optimizing JPDPC in user-centric CF systems. Our results indicate that JPDPC allows users to save more energy than the disjoint optimization of pilot and data powers when maximizing the minimum SE, while showing that JPDPC plays a crucial role in balancing between SE and fairness also in CF systems.

Robust Design of Multicell D2D Communication Under Partial CSI

We consider device-to-device (D2D) communication underlaid in a cellular network to share the uplink resource of cellular users (CUs). It is a key the emerging Internet of Things to support vehicle-to-everything communication networks. In a multicell scenario, both D2D pairs and CUs may cause significant significant intercell interference (ICI) to the neighboring cells. Furthermore, due to substantial signaling overhead, we assume only partial channel state information (CSI) of D2D links at the base station. We consider joint power control, beamforming, and CU-D2D matching problem, assuming partial CSI from D2D pairs under the general Nakagami fading model. We formulate a joint receive beamforming and robust power control optimization problem for a CU-D2D pair to expected sum rate under the power budget, while meeting the minimum SINR requirements and worst case ICI limits at neighboring cells in a probabilistic sense. We propose an efficient algorithm that combines an iterative D2D feasibility check and a ratio-of-expectation approximation. A performance upper bound is also developed for benchmarking. For multiple CUs and D2D pairs, due to orthogonal channelization within each cell, we first focus on the problem of joint power control and beamforming for a CU-D2D pair and show how our proposed solution can be leveraged to find a solution for this general problem. The complexity analysis of the proposed approach is also provided. Simulation results show that the proposed algorithm gives performance close to the upper bound.

A Novel Channel Model and Optimal Power Control Schemes for Mobile mmWave Two-Tier Networks

We present a unified system model and framework for the analytical performance study of two heterogeneous and physically-distinct, but coexisting, networks that work harmoniously at the same time, space, and frequency domains. The two-tier network model considered in this paper is an overlaying of femtocells on a macrocell. Overlaying femtocells improves the performance by offloading traffic from macrocells and providing spatial diversity. The mmWave channel model employed considers the number of clusters and rays within each cluster to vary due to the end-user mobility. This is a new and different model compared to the widely used channel models for mmWave two-tier networks. Optimal power control is formulated as a sum-rate maximization problem for downlink and uplink transmissions at two-tier networks and a power allocation scheme is proposed by following Shannon-Hartley theorem. A comprehensive and interesting performance investigation is provided, where it is shown that the upper bound on the number of admitted secondary users has a linear relationship with the outage probability threshold, logarithmic relationship with SINR and exponential relationship with channel gain factors. Simulation results show that the proposed scheme with algorithm 2 is very effective at managing the cross-tier interference and can outperform a competitive scheme from literature that is based on cognitive radio technology. The computational complexity analysis of proposed algorithms are also given, since the complexity of algorithm 2 can be a performance-complexity trade-off issue for systems with limited computation power and time requirements.

Adaptive Scheduling and Power Control for Multi-Objective Optimization in IEEE 802.15.6 Based Personalized Wireless Body Area Networks

Adaptive Scheduling and Power Control for Multi-Objective Optimization in IEEE 802.15.6 Based Personalized Wireless Body Area Networks

URLLC-Enabled by Laser Powered UAV Relay: A Quasi-Optimal Design of Resource Allocation, Trajectory Planning and Energy Harvesting

Ultra-reliable and low-latency (URLLC) traffic in the upcoming sixth-generation (6G) systems utilize short packets, signalling a distancing from traditional communication systems devised only to support long data packets based on Shannon&#x0027;s capacity formula. This poses a formidable challenge for system designers and network operators. Many URLLC scenarios involve infrastructure-less unmanned aerial vehicle (UAV)-assisted communications. One of the biggest challenges with UAVs is their limited battery capacity, which can cause abrupt disruption of UAV-assisted communications. To overcome these limitations, we consider URLLC-enabled <i>over-the-air</i> charging of UAV relay system using a laser transmitter. Furthermore, we formulate a non-convex optimization problem to minimize the total decoding error rate subject to optimal resource allocation, including blocklength allocation, power control, trajectory planning, and energy harvesting to facilitate URLLC in such systems. In this regard, given its lower complexity, a novel perturbation-based iterative method is proposed to solve the optimization problem. The proposed method yields optimal blocklength allocation and power control for the two transmission phases, i.e., from the source node to the UAV and from the UAV to the robot acting as a ground station. It also maps the UAV trajectory from the initial position to the final position, and the UAV completes the flight using the laser&#x0027;s harvested energy. It is shown that the proposed algorithm and fixed baseline scheme, named fixed blocklength (FB), yield a similar performance as the exhaustive search in terms of UAV energy consumption. In contrast, fixed trajectory (FT) delivers the worst performance. Simultaneously, the proposed method yields the best performance in terms of the lowest average overall decoding error compared to fixed baseline schemes, including FB and FT, showing the efficacy of the proposed technique.

Optimal Power and Frequency Control of Microgrid Cluster With Mixed Loads

In recent years, voltage and frequency regulation issues have been extensively discussed for the microgrid clusters (MGCs), as the high penetration of renewable energy resources (RES) might affect the continuous operation of the microgrid (MG). Furthermore, to enhance the MGC’s operation reliability, stability concerns need to be addressed. In this study, a residential MGC connected to a commercial MGC has been considered. A novel control scheme that combines both droop control and virtual inertia control is proposed. This control strategy relies on online measurements, and it can be adapted to different situations. At each iteration, the damping coefficient and droop coefficient are calculated this allows the system to switch as needed between the droop and the virtual inertia controller. This dynamic coefficient calculation allows plug and play capability, which provides the MGC with major flexibility in terms of the MGs operation and flexibility.

Optimal Frequency Reuse and Power Control in Multi-UAV Wireless Networks: Hierarchical Multi-Agent Reinforcement Learning Perspective

To overcome the problems caused by the limited battery lifetime in multiple-unmanned aerial vehicle (UAV) wireless networks, we propose a hierarchical multi-agent reinforcement learning (RL) framework to maximize the energy efficiency (EE) of UAVs by finding the optimal frequency reuse factor and transmit power. The proposed algorithm consists of distributed inner-loop RL for transmit power control of the UAV terminal (UT) and centralized outer-loop RL for finding the optimal frequency reuse factor. Specifically, the proposed algorithm adjusts these two factors jointly to effectively mitigate intercell interference and reduce undesired transmit power consumption in multi-UAV wireless networks. We show that, for this reason, the proposed algorithm outperforms conventional algorithms, such as a random action algorithm with a fixed frequency reuse factor and a hierarchical multi-agent Q-learning algorithm with binary transmit power controls. Furthermore, even in the environment where UTs are continuously moving based on the mixed mobility model, we show that the proposed algorithm can find the best reward when compared to conventional algorithms.

Optimal Power Management and Control of Hybrid Photovoltaic-Battery for Grid-Connected Doubly-Fed Induction Generator Based Wind Energy Conversion System

Optimal Power Management and Control of Hybrid Photovoltaic-Battery for Grid-Connected Doubly-Fed Induction Generator Based Wind Energy Conversion System

Constrained Optimal Average Power Control for Wireless Transmission

Constrained Optimal Average Power Control for Wireless Transmission

Investigation of a Multi-Strategy Ensemble Social Group Optimization Algorithm for the Optimization of Energy Management in Electric Vehicles

A multi-strategy ensemble social group optimization algorithm (ME-SGO) to improve the exploration for complex and composite landscapes through distance-based strategy adaption and success-based parameter adaption while incorporating linear population reduction is proposed. The proposed method is designed to achieve a better balance between exploration and exploitation with minimal tuning while overcoming the limitations of SGO. The proposed improved algorithm is tested and validated through CEC2019’s 100-digit competition, five engineering problems and compared against the standard version of SGO, four of its latest variants, five of the advanced state-of-the-art meta-heuristics, five modern meta-heuristics. Furthermore four complex problems on electric vehicle (EV) optimization namely, the optimal power flow problem with EV loading for IEEE 30 bus system (9 Cases) and IEEE 57 bus-system (9 cases) optimal reactive power dispatch with uncertainties in EV loading and intermittencies with PV and Wind energy systems for IEEE 30 bus system (25 scenarios), dynamic EV charging optimization (3 cases) and energy-efficient control of parallel hybrid electric vehicle (3 cases with 2 scenarios) covering the domains of power systems, energy and control optimization have been considered for validation through the proposed multi-strategy ensemble method and fifteen other state-of-the-art advanced and modern algorithms. The performance for the standard engineering problems and the EV optimization problems was excellent with good accuracy of the solutions and least standard deviation rates.

Volt/VAR Optimization: A Survey of Classical and Heuristic Optimization Methods

Reactive power optimization and voltage control is one of the most critical components of power system operation, impacting both the economy and security of system operation. It is also one of the most complex optimization problems, being highly nonlinear, and comprising both continuous and discrete decision variables. This paper presents the problem formulation, and a thorough literature review and detailed discussion of the various solution methods that have been applied to the Volt/VAR optimization problem. Each optimization method is described in detail, and its strengths and shortcomings are outlined. The review provides detailed information on classical and heuristic methods that have been applied to the Volt/VAR optimization problem. The classical methods reviewed include (i) first- and second-order gradient-based methods, (ii) Quadratic Programming, (iii) Linear Programming, (iv) Interior-Point Methods, (iv) and mixed-integer programming and decomposition methods. The heuristic methods covered include (i) Genetic Algorithm, (ii) Evolutionary Programming, (iii) Particle Swarm Optimization, (iv) Fuzzy Set Theory, and (v) Expert Systems. A comparative analysis of the key characteristics of the classical and heuristic optimization methods is also presented along with the review.

A Novel Method for Microgrid Stochastic Power Flow Calculation and Optimization Control Based on Multivariate Normal Distribution and Sensitivity Analysis

A Novel Method for Microgrid Stochastic Power Flow Calculation and Optimization Control Based on Multivariate Normal Distribution and Sensitivity Analysis

Bandit-Based Power Control in Full-Duplex Cooperative Relay Networks With Strict-Sense Stationary and Non-Stationary Wireless Communication Channels

Full-duplex relaying is an enabling technique of sixth generation (6G) mobile networks, promising tremendous rate and spectral efficiency gains. In order to improve the performance of full-duplex communications, power control is a viable way of avoiding excessive loop interference at the relay. Unfortunately, power control requires channel state information of source-relay, relay-destination and loop interference channels, thus resulting in increased overheads. Aiming to offer a low-complexity alternative for power control in such networks, we adopt reward-based learning in the sense of multi-armed bandits. More specifically, we present bandit-based power control, relying on acknowledgements/negative-acknowledgements observations by the relay. Our distributed algorithms avoid channel state information acquisition and exchange, and can alleviate the impact of outdated channel state information. Two cases are examined regarding the channel statistics of the wireless network, namely, strict-sense stationary and non-stationary channels. For the latter, a sliding window approach is adopted to further improve the performance. Performance evaluation highlights a performance-complexity trade-off, compared to optimal power control with full channel knowledge and significant gains over cases considering channel estimation and feedback overheads, outdated channel knowledge, no power control and random power level selection. Finally, it is shown that the sliding-window bandit-based algorithm provides improved performance in non-stationary settings by efficiently adapting to abrupt changes of the wireless channels.

Energy-efficient transmission policies for the linear quadratic control of scalar systems

This paper considers controlled scalar systems relying on a lossy wireless feedback channel. In contrast with the existing literature, the focus is not on the system controller but on the wireless transmit power controller that is implemented at the system side for reporting the state to the controller. Such a problem may be of interest, e.g., for the remote control of drones, where communication costs may have to be considered. Determining the power control policy that minimizes the combination of the dynamical system cost and the wireless transmission energy is shown to be a non-trivial optimization problem. It turns out that the recursive structure of the problem can be exploited to determine the optimal power control policy. As illustrated in the numerical performance analysis, in the scenario of a dynamics without perturbations, the optimal power control policy consists in decreasing the transmit power at the right pace. This allows a significant performance gain compared to conventional policies such as the full transmit power policy or the open-loop policy.

Reactive Power Optimization Control for Multi-Energy System Based on Pressure Limit of Hot and Gas Pipe Network

Reactive Power Optimization Control for Multi-Energy System Based on Pressure Limit of Hot and Gas Pipe Network

Optimization of the Structure of Power System Multi-Agent Control

The article provides an overview of the application of multi-agent control, provides a classification of multi-agent system architectures, and analyzes existing methods for constructing the structure of multi-agent control in the electric power industry. A method for constructing an optimal structure of multi-agent optimal control is proposed. A field of applications of proposed method for a multi-agent voltage and reactive power optimal control in a power system is discussed. The proposed method of optimal design of multi-agent power system control structure can be applied to various objective function of optimization. The application of multi-agent control for automatic voltage and reactive power control in the power system is currently being researched.