Power Optimization Scheme Research Articles

Reflux Power Optimization of a Dual-Active Hybrid Full-Bridge Converter Based on Active Disturbance Rejection Control

The dual-active hybrid full-bridge (H-FDAB) DC–DC converter has great potential in medium-voltage high-power photovoltaic power station applications by introducing a three-level bridge arm to increase the output voltage range. However, its mathematical model and optimum modulation schemes have not been fully explored. Under the traditional PI control, the H-FDAB DC–DC converter will produce significant reflux power, which will lead to a decrease in converter efficiency and output voltage fluctuation. On this basis, this paper proposes a reflux power optimization strategy for an H-FDAB DC-DC converter based on active disturbance rejection control (ADRC). Firstly, the structure and power characteristics of the H-FDAB DC–DC converter are analyzed, and the relationship among the reflux power, the transmission power, and the phase shift angle is derived. Secondly, to reduce the complexity of the control calculation, upon the foundation of dual phase-shifting modulation, the Karush–Kuhn–Tucker (KKT) condition is used to solve for the phase shift angle that corresponds to the minimum reflux power. Simultaneously, we develop an ADRC loop utilizing an extended state observer (ESO) for the real-time estimation of system states. We also consider the sudden changes in input voltage, load switching, and transmission power fluctuations caused by reflux power optimization strategies as system disturbances and compensate for them accordingly. Finally, the experiments conclusively validate the designed control strategy’s correctness and feasibility.

Power Optimization and ARSs Selection Strategies in Downlink Cell-Free Multi-ARSs Communication Systems

Power Optimization and ARSs Selection Strategies in Downlink Cell-Free Multi-ARSs Communication Systems

Distributed energy storage participates in reactive power optimization strategy research of new distribution system

Abstract We studied the reactive power control strategy of distributed energy storage in distribution systems, improved reactive power support capacity, and enhanced system reliability and new energy carrying capacity. Firstly, the principles and methods of reactive power optimization in distribution networks are studied. Then, the principles and mechanisms of distributed energy storage participating in reactive power control in distribution networks are studied. Finally, the genetic algorithm optimization method for reactive power optimization in distribution networks is studied, including parameter setting, target selection, and genetic strategy improvement, providing support for fine control of reactive power and voltage in distribution systems and efficient operation. Through simulation analysis of the IEEE33 node system, it is shown that distributed energy storage can improve the reactive voltage level of the distribution system and promote the consumption of new energy.

Neural network‐based integrated reactive power optimization study for power grids containing large‐scale wind power

AbstractThe high uncertainty of wind power output greatly affects the rapid reactive power optimization of power systems. This paper proposes a neural network‐based comprehensive reactive power optimization method for large‐scale wind power grids, effectively addressing the challenges of rapid reactive power optimization in power systems. Firstly, by constructing typical wind‐power‐load scenarios, the generalization ability of the neural network is improved. Then, focusing on the comprehensive reactive power optimization problem after integrating typical wind‐power‐load scenarios into the system, the improved Harris hawks optimization algorithm (HHO) is compared with the particle swarm optimization algorithm and traditional HHO algorithm, highlighting its advantages. Finally, HHO is utilized for solving, thereby constructing a comprehensive reactive power optimization strategy tag set. Furthermore, through deep fitting of the neural network between the power grid operating state and the comprehensive reactive power optimization strategy, the computational complexity and decision‐making time of reactive power optimization are reduced.

Power optimization of photovoltaic modules under varying environmental conditions based on current equalization collaborating constant voltage control

Abstract The performance of photovoltaic modules is affected by environmental factors such as irradiance and temperature, which can lead to a decrease in output performance or even damage. This study proposes an improved formula for calculating the real maximum power of photovoltaic modules by analyzing the influence of irradiance and temperature. A simulation model is developed using PLECS software to simulate the global maximum power of photovoltaic modules under different environmental conditions, and the results are compared with the calculated real maximum power. A power optimization scheme for photovoltaic modules is then proposed based on current equalization and constant voltage control. This scheme employs a single-switch multi-winding forward-flyback converter to equalize the mismatched currents between cell strings, thereby enhancing the output performance. Traditional proportional integral controllers are utilized to achieve constant voltage control and obtain the real maximum power of photovoltaic modules. Simulation models are built in the PLECS simulation platform to evaluate the performance of a global maximum power point tracking scheme based on the traditional perturb and observe algorithm with current equalization, a segment perturb and observe algorithm without current equalization, and the proposed power optimization scheme. The simulation results demonstrate that the proposed constant voltage control has greater efficiency than the traditional perturb and observe algorithm. The proposed scheme achieves a significant improvement in efficiency, with a 27.87% increase compared to the segment perturb and observe algorithm without current equalization.

Power Optimization Strategy of DC Microgrid Based on Droop Control

As an efficient way to consume new energy, DC microgrid has received extensive and international attention. However, the control of the DC microgrids has not taken into account the difference in operating cost of micro sources, resulting in low operating efficiency. Therefore, this paper establishes an economic model for the DC microgrid, with the goal of minimizing operating cost, the optimal operating condition of the system is solved. On this basis, a secondary power optimization control strategy is proposed. Based on the simulation platform, the feasibility of the proposed strategy is verified under different operating conditions. The results show that the proposed power optimization strategy can effectively reduce the system operating cost by about 1.49%.

Adaptive Reactive Power Optimization in Offshore Wind Farms Based on an Improved Particle Swarm Algorithm

To address the reactive power optimization control problem in offshore wind farms (OWFs), this paper proposes an adaptive reactive power optimization control strategy based on an improved Particle Swarm Optimization (PSO) algorithm. Firstly, an OWF multi-objective optimization control model is established, with the total sum of voltage deviations at wind turbine (WT) terminals, active power network losses, and reactive power margin of WTs as comprehensive optimization objectives. Innovatively, adaptive weighting coefficients are introduced for the three sub-objectives, enabling the weights of each optimization objective to be adaptively adjusted based on real-time operating conditions, thus enhancing the adaptability of the reactive power optimization model to changes in operating conditions. Secondly, a Uniform Adaptive Particle Swarm Optimization (UAPSO) algorithm is proposed. On one hand, the algorithm initializes the particle swarm using a uniform initialization method; on the other hand, it improves the particle velocity update formula, allowing the inertia coefficient to adaptively adjust based on the number of iterations and the fitness ranking of particles. Simulation results demonstrate the following: (1) Under various operating conditions, the proposed adaptive multi-objective reactive power optimization strategy can ensure the stability of node voltages in offshore wind farms, reduce active power losses, and simultaneously improve reactive power margins. (2) Compared with the traditional PSO algorithm, UAPSO exhibits an approximately 10% improvement in solution speed and enhanced solution accuracy.

Advancements in VLSI low-power design: Strategies and optimization techniques

As production technology advances, integrated circuits are increasing in size, leading to a corresponding rise in power consumption if not properly optimized. Consequently, the optimization of integrated circuit power consumption has gained paramount significance. This paper provides an overview of the theoretical and research developments in Very Large Scale Integration (VLSI) low-power design. Initially, the paper delves into the components of VLSI power consumption, elucidating the origins of various power consumption types and the factors influencing their magnitude. Subsequently, existing power reduction technologies are examined, including transistor-level optimization, gate-level optimization, and system-level power optimization. The principles, applicable power consumption types, as well as their respective advantages and drawbacks are analysed. The paper also introduces methods for evaluating VLSI power consumption and summarizes the characteristics, advantages, and disadvantages of high-level power estimation and low-level power estimation. Ultimately, it underscores the importance of considering multiple power optimization strategies during VLSI design and discusses research approaches for achieving low power consumption. This comprehensive exploration contributes to the enhancement and optimization of VLSI design efforts.

Artificial Intelligence Driven Power Optimization in IOT-Enabled Wireless Sensor Networks

The widespread use of Wireless Sensor Networks (WSN) in Internet of Things (IoT) causes energy efficiency issues. This paper proposes an AI-based solution to this problem. The propose an AI-Driven Power Optimization framework for IoT-enabled WSN using Deep Q-Network (DQN) and Dynamic Voltage and Frequency Scaling (DVFS). These techniques can adapt to changing network conditions and reduce power consumption when used together. Sensor nodes provide environmental parameters, battery status, and network behavior data to the AI-driven framework DQN is implemented after data preprocessing to learn and make power management decisions using reinforcement learning. Neural network-driven agent operates in a state and action space. It optimizes energy use with rewards. Real-time hardware power adjustment is done using DVFS. DVFS precise control and AI-driven decision-making create a comprehensive power optimization strategy. AI adapts to new challenges and optimizes network lifespan by improving its power management policies. Experimental implementations of the proposed framework show significant energy savings, network lifespan extension, and QoS improvements. AI-Driven Power Optimization in IoT-enabled WSN is proven effective and flexible. This study shows the potential of AI, specifically DQN and DVFS, in IoT-enabled WSN. This AI-Driven Power Optimization framework addresses energy efficiency and improves IoT sensor networks. AI improves power optimization in IoT-enabled WSN making IoT deployments more sustainable and resilient.

Dynamic reactive power optimization strategy for AC/DC hybrid power grid considering different wind power penetration levels

Dynamic reactive power optimization strategy for AC/DC hybrid power grid considering different wind power penetration levels

EdgeAISim: A toolkit for simulation and modelling of AI models in edge computing environments

To meet next-generation Internet of Things (IoT) application demands, edge computing moves processing power and storage closer to the network edge to minimize latency and bandwidth utilization. Edge computing is becoming increasingly popular as a result of these benefits, but it comes with challenges such as managing resources efficiently. Researchers are utilising Artificial Intelligence (AI) models to solve the challenge of resource management in edge computing systems. However, existing simulation tools are only concerned with typical resource management policies, not the adoption and implementation of AI models for resource management, especially. Consequently, researchers continue to face significant challenges, making it hard and time-consuming to use AI models when designing novel resource management policies for edge computing with existing simulation tools. To overcome these issues, we propose a lightweight Python-based toolkit called EdgeAISim for the simulation and modelling of AI models for designing resource management policies in edge computing environments. In EdgeAISim, we extended the basic components of the EdgeSimPy framework and developed new AI-based simulation models for task scheduling, energy management, service migration, network flow scheduling, and mobility support for edge computing environments. In EdgeAISim, we have utilized advanced AI models such as Multi-Armed Bandit with Upper Confidence Bound, Deep Q-Networks, Deep Q-Networks with Graphical Neural Network, and Actor-Critic Network to optimize power usage while efficiently managing task migration within the edge computing environment. The performance of these proposed models of EdgeAISim is compared with the baseline, which uses a worst-fit algorithm-based resource management policy in different settings. Experimental results indicate that EdgeAISim exhibits a substantial reduction in power consumption, highlighting the compelling success of power optimization strategies in EdgeAISim. The development of EdgeAISim represents a promising step towards sustainable edge computing, providing eco-friendly and energy-efficient solutions that facilitate efficient task management in edge environments for different large-scale scenarios.

Dynamic Reactive Power Compensation Optimization Strategy for Distribution Networks with Distributed Generators and Energy Storage Systems

To balance the output and load dynamic changes of distributed power generators (DG) and energy storage systems (ESS), a demand-side response-based dynamic reactive power optimization (RPO) strategy for distribution networks (DN) is proposed. This strategy adopts a phased optimization method to coordinate and optimize different types of reactive variables within the same period. By adjusting the states of parallel capacitor banks (SCBs) on load voltage transformers (OLTC), DGs, and ESS inverters, the RPO of the DN is carried out. Based on the characteristics of the model, an improved particle swarm optimization algorithm (IPSO) is used to solve different types of variables, and an IPSO based on tent chaotic mapping and levy flight strategy is proposed to improve the efficiency of collaborative solving. The simulation results show that the proposed strategy, considering the change of new energy output and demand side response, can reduce the solution scale while obtaining high satisfaction RPO results.

Event-triggered reactive power tracking optimization for second-level power fluctuations of renewables and stochastic loads

The power fluctuations of renewable energy sources and stochastic loads (RESL) makes it difficult for system operators to achieve optimal control. This paper proposes an event-triggered reactive power optimization strategy based on the event-triggered algorithm to rapidly track the optimal operating states for accommodating the second-level power fluctuations of RESL, with less computation and communication. Firstly, on a longer temporal scale, event-triggered multi-stage global optimization (MGO) is performed on a rolling basis to obtain the time-interval optimal operating states according to chance constraint programming, and the system operation security is improved by narrowing the voltage-feasible regions. Next, at each moment within the time interval, event-triggered local tracking optimization (LTO) is performed at each controller of RESL by rapidly regulating reactive power to adapt to its second-level active power fluctuations. The LTO is completed at the local level by using the sensitivity information from MGO, without running the optimal power flow program, to relieve the computation and communication burdens. Then, event-triggered LTOs performed at all controllers synchronously form the system-level real-time optimization. The unnecessary MGO and LTO are skipped by using the event-triggered algorithm when small power fluctuations are occurring. Finally, a case study on the IEEE-39 bus power system validates the feasibility of the proposed strategy.

A Hybrid Spatial Adaptive Modulation and Frequency Stochastic Approach for MU-MIMO-OFDM Systems in the Context of Underlay Cognitive Radios

In this paper, low complexity rate and power optimization schemes operating in the spatial and frequency domains are proposed in a cognitive radio (CR) setting involving multi-user multiple-input-multiple-output-orthogonal frequency division multiplexing (MU-MIMO-OFDM). Under the assumption of a perfect secondary channel state information (CSI) at the receiver, the presented architectures encompass two main stages. In the first one, spatial power waterfilling-like method is performed per each MIMO subchannel pertaining to each subcarrier of each secondary user (SU). The resulting allocated power per each eigen-channel is considered as the power budget in the second stage. In this latter, stochastic algorithm-based approach wherein the transmit parameters per each subcarrier of each SU are adapted such that to maximize the achievable sum-rate capacity of the SUs. Three different schemes are introduced in this work. First, the derivation of the continuous rate MU-MIMO-OFDM-CR version, referred to as C-MU-MIMO-OFDM-CR is presented. Obviously, this proposition is theoretical and is taken as a benchmark for the two remaining counterparts. The second proposition we called discrete-rate MU-MIMO-OFDM-CR, and briefly designated as D-MU-MIMO-OFDM-CR which is to round the provided allocated rate. Finally, the third modified solution, denoted as P-D-MU-MIMO-OFDM-CR proceeds in a similar way as the D-MU-MIMO-OFDM-CR alternative, but superimposes the non/over-used amount of power to the power budget in next iteration. The simulation results show that, compared to the discrete rate D-MU-MIMO-OFDM-CR solution, the P-D-MU-MIMO-OFDM-CR approach exhibits an approximate power gain of 1 dB when the SNR level is low, and of 5 dB at high SNR range.

User group-based pilot allocation and data power optimization in cell-free massive MIMO for coexistence of UAVs and ground users

To address the pilot contamination issue in cell-free massive multi-input multi-output (MIMO) systems supporting simultaneous communication between unmanned aerial vehicles (UAVs) and ground users (GUEs), a user group-based pilot allocation algorithm is proposed. Furthermore, the system performance is enhanced by optimizing the data power. Initially, the distances between users and pilots are calculated based on the principles of maximizing spectral efficiency and utilizing the large-scale fading matrix. Ground users and UAVs are randomly divided into two groups: primary users and shared users. The contamination intensity between users is measured based on the distance between users and pilots, and shared user pairs that are allocated the same orthogonal pilots are generated. Next, a user-centric approach is employed to select a subset of access points (APs) to serve the users. The scalable fractional power optimization strategy is combined as a power optimization scheme for uplink data transmission, providing a controllable trade-off between user fairness and average spectral efficiency. Simulation results demonstrate that the user group-based pilot allocation algorithm significantly improves the system’s overall spectral efficiency, and the fractional power optimization algorithm enhances the average spectral efficiency of the system.

Zero-Backflow Power Control Scheme of Dual Bridge Series Resonant DC–DC Converters With High-Accuracy Time Domain Modeling

In the fundamental harmonic approximation (FHA) modelling of dual bridge series resonant DC-DC converters (DBSRCs), the approximation error of the FHA model will lead to low accuracy of optimization schemes, which will reduce the efficiency of DBSRCs. In this paper, firstly, the time domain analysis (TDA) modelling of the DBSRC is adopted, which can describe the characteristics of the DBSRC accurately. Secondly, the extended phase shift (EPS) modulation based on the TDA modelling is discussed, and the backflow power model with the TDA of the DBSRC is developed. On this basis, a zero-backflow power optimization scheme is proposed. In addition, the effects of dead time are analyzed and the compensation method is proposed. The proposed backflow power optimization method with dead time compensation can achieve zero-backflow power and improve the efficiency of the DBSRC. Especially, the proposed method with TDA modelling is more excellent when DBSRC operates in wide voltage range conditions. Finally, a comprehensive experiment comparison of DBSRC under the minimum current trajectory (MCT) with FHA modelling and the proposed method with TDA modelling is discussed. The experimental results have verified the effectiveness of the proposed scheme.

Real-time power optimization based on PSO feedforward and perturbation & observation of fuel cell system for high altitude

The control technology of the air supply system is key to the performance and reliability of the fuel cell system (FCS). However, due to the low oxygen pressure in high altitude environment, the power consumption of the air compressor is large, and the output power of FCS is seriously degraded. To effectively increase the output power of the FCS under changeable altitude conditions, this article introduces a real-time power optimization strategy based on the particle swarm optimization (PSO) feedforward and perturbation and observation (P&O) algorithm. Considering the change of total entropy and loss of centrifugal air compressor with altitude, the working characteristics of centrifugal compressors at different altitudes are studied. Aiming at the long optimization time of the traditional P&O method and the misjudgment problem when the external conditions change drastically, the PSO feedforward-based P&O method is designed to adjust the air compressor voltage. The results show that the proposed strategy has real-time power optimization capability and can adapt to altitude change. The average setting time is 1.8 s when the operating condition changes and the power decreases to 9.1% at 3000 m altitude under a higher load (300 A).

IoT Orchestration-Based Optimal Energy Cost Decision Mechanism with ESS Power Optimization for Peer-to-Peer Energy Trading in Nanogrid

The Internet of things has revolutionized various domains, such as healthcare and navigation systems, by introducing mission-critical capabilities. However, the untapped potential of IoT in the energy sector is a topic of contention. Shifting from traditional mission-critical electric smart grid systems to IoT-based orchestrated frameworks has become crucial to improve performance by leveraging IoT task orchestration technology. Energy trading cost and ESS power optimization have long been concerns in the scientific community. To address these issues, our proposed architecture consists of two primary modules: (1) a nanogrid energy trading cost and ESS power optimization strategy that utilizes particle swarm optimization (PSO), with two objective functions, and (2) an IoT-enabled task orchestration system designed for improved peer-to-peer nanogrid energy trading, incorporating virtual control through orchestration technology. We employ IoT sensors and Raspberry Pi-based Edge technology to virtually operate the entire nanogrid energy trading architecture, encompassing the aforementioned modules. IoT task orchestration automates the interaction between components for service execution, involving five main steps: task generation, device virtualization, task mapping, task scheduling, and task allocation and deployment. Evaluating the proposed model using a real dataset from nanogrid houses demonstrates the significant role of optimization in minimizing energy trading cost and optimizing ESS power utilization. Furthermore, the IoT orchestration results highlight the potential for virtual operation in significantly enhancing system performance.

Downlink massive full dimension-multiple input multiple output downlink beamforming analysis at 3.5 GHz using coordinated ON-OFF switching

<p><span lang="EN-US">The long-term evolution and advancement (LTE-A) of the 5G wireless network depends critically on energy consumption. Many existing solutions focus on limiting power constraints and consequently system coverage. So, improving the antenna array elements of the base station (BS) can solve this issue. In this paper, introduce a coordinated ON-OFF switching method in the massive full dimensional multiple input multiple output (massive-FD-MIMO) system. It enhances the radiation pattern of the antenna array element by adjusting the angular power spectra at the BS. By the way, it allows to select the minimum number of antennas for effective beamforming toward specific user equipment’s (UEs). In this context, part of antenna element should be active mode and remining should be sleep mode at the time of signal beamforming. The multipath spatial profiles are decided the beamforming frequency band with minimize energy consumption. As part of the method, we used a conjugated beamforming with power optimization scheme to determine the individual antenna potential and fading channel condition, power optimization is performed. This method quality of service, reliability, energy consumption and data rate can all be evaluated by experimenting with different-sized antenna arrays such as 16×16, 32×32, 64×64 and 128×128.</span></p>

Multi-objective reactive power optimization strategy of power system considering large-scale renewable integration

To fully tap the abilities of renewables in reactive power optimization, this paper develops a detailed model for the power regulation capabilities of wind turbines and photovoltaic units and studies their impact on the power system’s operation. First, the power system model with renewables integration is established using AC power flow. The wind turbines and photovoltaic units are modeled in detail according to their topologies and operating characteristics, and then further simplified according to the feasible region. An improved DC power flow model is adopted to handle the non-linear characteristics of the power system. On this basis, a multi-objective reactive power optimization model is constructed to minimize the power generation cost, wind and solar power curtailment, and voltage offset. Finally, comparisons between two types of models in different scenarios are designed. Numerical simulations demonstrate that the participation of renewables in reactive power regulation can improve the operational economy and voltage stability of power systems.