Power Optimization Scheme Research Articles

Multi‐type power generation planning method for power systems based on complex adaptive system theory

AbstractAiming at the problem of multi‐point power source layout planning for power systems, the output characteristics of a power system composed of wind power, photovoltaic power, hydropower, traditional thermal power, concentrated solar power and electrochemical energy storage are comprehensively analyzed. A power source multi‐point layout planning model for a power system based on complex adaptive system theory is proposed with a focus on the complementarity among these different energies and the combination of power optimization planning and complex adaptive system theory. With the minimum construction unit of various types of power sources as the ‘agent’, considering the interactions among agents and the accumulation of experiences, the behaviour rules of the model are constantly changed, the grid‐connected positions of various types of power sources are adjusted, and the optimal layout schemes for all kinds of power capacities for each node are obtained. In addition, an agent modelling method based on complex phenomena emergence with simple rules is proposed, which reveals the core idea of the complex adaptive system theory: adaptability creates complexity. Taking the new energy construction base in Northwest China as an example, the proposed method is verified to have a significant effect on improving energy consumption in the new system. Based on the current power capacity layout and the future grid structure in this area, the future power optimization scheme is determined, and it provides guiding significance for actual engineering construction.

Covert Millimeter Wave Communications Based on Beam Sweeping

The combination of millimeter wave (mmWave) and covert communications can ensure high security for some specific circumstances. Instead of considering the covertness only in the data transmission (DT) phase for mmWave massive MIMO systems, the covertness in both the beam alignment (BA) phase and the DT phase is investigated. As a common beam training method for mmWave massive multiple-input multiple-output (MIMO), beam sweeping is adopted to obtain the optimal beam pair in the BA phase. Aiming at maximizing the average effective achievable covert rate in the DT phase, a two-stage power optimization scheme is proposed subject to the covertness and power constraints in both the BA and the DT phases. Specifically, by introducing a covertness allocation factor, closed-form expressions of the transmit power in both phases are derived according to the likelihood function of the received signal. Simulation results demonstrate that the average covert rate of MIMO is larger than that of multiple-input single-output (MISO) although the BA success rate of the former is lower than that of the latter.

An Online Control Method of Reactive Power and Voltage Based on Mechanism–Data Hybrid Drive Model Considering Source–Load Uncertainty

The uncertainty brought about by the high proportion of distributed generations poses great challenges to the operational safety of novel distribution systems. Therefore, this paper proposes an online reactive power and voltage control method that integrates source–load uncertainty and a mechanism–data hybrid drive (MDHD) model. Based on the concept of a mechanism and data hybrid drive, the mechanism-driven deterministic reactive power optimization strategy and the stochastic reactive power optimization strategy are used as training data. By training the data-driven CNN–GRU network model offline, the influence of source–load uncertainty on reactive power optimization can be effectively assessed. On this basis, according to the online source and load predicted data, the proposed hybrid-driven model can be applied to quickly obtain the reactive power optimization strategy to enable fast control of voltage. As observed in the case studies, compared with the traditional deterministic and stochastic reactive power optimization models, the hybrid-driven model not only satisfies the real-time requirement of online voltage control, but also has stronger adaptability to source–load uncertainty.

Joint Pilot Spacing and Power Optimization Scheme for Nonstationary Wireless Channel: A Deep Reinforcement Learning Approach

Pilot-assisted channel estimation techniques are essential for wireless communication systems. Most studies focus on the estimation algorithms and interpolation techniques. However, the design of pilot pattern is often neglected. In this letter, we propose a joint pilot spacing and power optimization scheme based on deep reinforcement learning (DRL) to address the mismatch problem of pilot configuration for nonstationary wireless channel. First, we model the adaptive pilot design decision-making process as a Markov Decision Process (MDP) to reduce pilot overhead and power loss. Then a deep Q-network (DQN) based learning algorithm is proposed to optimize the spacing and power of pilots so as to maximize estimation performance while reducing system cost. Simulation results show that the performance of the proposed approach is better than conventional pilot configuration algorithms. Moreover, we analyze the key factors that affect the performance of the proposed scheme.

Optimal Active Power Generation in Electrical Networks Using Distributed Fixed-Time Control of Multi-Agent Systems

This paper tackles the Economic Dispatch Problem (EDP) in power systems, with the objective of generating optimal active power at each source to minimize overall generation costs, while maintaining the power generation-demand equality and staying within the source generation limits. The main motivation behind this work is to create a power optimization strategy that accomplishes these goals while providing superior performance in terms of convergence speed and accuracy of the optimal solution. To this end, a fixed-time active power optimization protocol is proposed for power sources in electrical networks. Distributed and local fixed-time control laws of first-order multi-agent systems are developed for this purpose. This paper presents four technical contributions: i) minimizing active power generation costs in electrical network sources within a predetermined maximum settling time without violating their generation limits while ensuring generation-demand equality, ii) a distributed overall control system connected through a sparse communication network modeled as an undirected graph that avoids the pitfalls of centralized solutions, iii) a model-free optimization method that does not require previous knowledge of power system parameters, and iv) a continuous optimization law that provides smooth power variations. Simulations are conducted to demonstrate the effectiveness of the proposed approach.

245Gb/s P2MP Mobile Fronthaul Downstream Transmission Using Code-Division Multiplexing and Self-Homodyne Coherent Technologies

We have proposed and experimentally demonstrated a high-speed and point-to-multipoint (P2MP) mobile fronthaul (MFH) downstream system based on the code-division multiplexing (CDM) and self-homodyne coherent (SHC) technologies. We find that compared with the traditional 16-ary quadrature amplitude modulation (16-QAM) signal transmission, the change of phase noise between two demultiplexed symbols is much larger in the CDM-16QAM signal transmission because it is the accumulation of phase noise change of n multiplexed symbols. Therefore, a low-complexity pilot sequence-assisted phase noise estimation algorithm is proposed and designed to compensate for the phase noise caused by fiber mismatch length, which makes it possible to use a low-cost distributed feedback (DFB) laser. Besides, the spread signal's distribution in the frequency domain of each code sequence is different, leading to different transmission performances between different remote radio units (RRUs). A power optimization scheme is proposed to balance the transmission performances of different RRUs by multiplying each channel's signal by a normalized weight factor at the transmitter. The experimental results show that the proposed system could successfully support the transmission of 245 Gb/s 16QAM signal from baseband unit (BBU) to 7 RRUs (each RRU could have 2 antennas to transmit signals at 2 polarizations) over 10 km standard single-mode fiber (SSMF). The measured worst bit error rate (BER) value of 14 channels is 1.77e-3 which is below the hard-decision forward error correction (HD-FEC) threshold (3.8e-3).

An Efficient Power Optimization Approach for Fixed Polarity Reed–Muller Logic Circuits Based on Metaheuristic Optimization Algorithm

With the emergence of the multicore architecture and the increase of chip operating frequency, power optimization has become a key step of circuit logic synthesis. Aiming at the XNOR/OR circuits, with the goal of minimizing power, construct the optimal polarity fixed-polarity Reed–Muller (FPRM) circuits power optimization scheme. However, the power optimization for FPRM circuits is a multipeak combinatorial optimization problem, we first propose a metaheuristic optimization algorithm (MOA), which includes the global exploration optimizer, local deep exploitation optimizer, and initial population and uses the proposed differential evolution optimization, fierce wolf siege algorithm-based tabu search, and improved skew tent map to make the population evolve. Based on the proposed Huffman tree construction algorithm and MOA, we propose an efficient power optimization approach (EPOA) to find the minimum power FPRM circuit. Experimental results on the benchmark circuits confirm the effectiveness of EPOA.

Reactive Power Optimization of Active Distribution Network with Distributed Generation

Taking an active distribution network with distributed generation as an example, this paper designs a reactive power optimization scheme for the operation of a power grid to reduce the node loss in the operation of an active distribution network. The objective function of voltage optimization is established with the minimum active power loss as the optimization objective. According to the stable operation requirements of an active distribution network, the constraint conditions for the objective function are designed. The improved particle swarm optimization algorithm is introduced to search the particles in the effective workspace of the distribution network. Optimal position of the particles is located in this way. According to the solution set, a reactive power convergence model is constructed. According to the operation state of the active distribution network, the switching times of power equipment are set. The output value of the distributed generation in the power grid is taken as the reactive power optimization target to realize the distribution network’s dynamic reactive power optimization. Taking a large power generation enterprise in the pilot area as the research object, the application experiment is designed. The practice proves that this approach can act in reducing the loss of the distribution network.

Computation power maximization for mobile edge computing enabled dense network

High-density connection is among the natures of next-generation wireless communication systems. Meanwhile, various computation-intensive smart applications are becoming growing popularity with the technology boom. Thus, strong computation power will become a crucial requirement for wireless communication systems. Mobile edge computing provides a promising solution to this requirement by pushing a cloud-like computation capacity to the network edge. This study aims to maximize the computation power of a mobile edge computing enabled dense network. To this end, a computation bits maximization problem is formulated by jointly optimizing offloading decision and resource allocation. The problem is a mixed-variable nonlinear programming problem. First, by analyzing the feasibility of computation modes and constructing a user distribution strategy, the original problem is decoupled into two sub-problems, i.e., the assignment of the offloading users and resource allocation. The offloading users’ assignment is modeled as a restricted multiple knapsack problem, and a profit density is defined to maximize the overall profits of multiple knapsacks. Then, an improved differential evolution algorithm is developed to address the knapsack problem, in which mutation and repair operators are designed according to the profit density. Based on the optimal solution to the knapsack problem, the resource allocation sub-problem is solved by the corresponding calculation. Finally, extensive experiments are conducted to evaluate the performance of our scheme. Results show that: (1) our scheme provides superior computation power compared to that of benchmark schemes; (2) the performance gain of our scheme over benchmark schemes expands with growing connection density. Therefore, our scheme is an effective computation power optimization scheme.

Research on Reactive Power Optimization Strategy under the Intelligent Improvement Model of the Distribution Network

In order to improve the reactive power optimization effect of the distribution network, this paper combines the multiagent deep reinforcement learning algorithm to analyze the reactive power optimization strategy of the distribution network and constructs an intelligent optimization model. Moreover, the simulation models of power conversion elements, power transmission elements, control elements, and measurement elements in the platform are described, and the program structure and interactive functions are analyzed. In addition, this paper proposes a reactive power optimization method for distribution networks based on data-driven thinking. Finally, by using historical data and an artificial neural network, this paper extracts electrical quantity data such as load power and distributed power output and environmental data such as temperature and wind speed to perform multiagent analysis. The experimental verification shows that the reactive power optimization effect of the distribution network based on multiagent and multiagent deep reinforcement learning proposed in this paper is very good.

Performance Analysis of Relay-Aided NOMA Optical Wireless Communication System in Underwater Turbulence Environment

Non-orthogonal multiple access (NOMA) is a promising technology to improve spectrum utilization effectively for underwater optical wireless communications (UOWC). To exploit the benefits of NOMA in a turbulent environment, cooperative transmission has been introduced in the NOMA–UOWC network. The existing studies on NOMA suggest that relay selection and power optimization are the main factors affecting system performance. In this paper, a general NOMA node pairing method and two power optimization schemes for NOMA–UOWC are proposed, and both schemes are proven to be strictly quasi-convex. The two optimization schemes are solved by the BFGS algorithm and the particle swarm algorithm, respectively. The effectiveness of the proposed schemes are evaluated by our simulations, and the main factors affecting the relay-aided NOMA performance are derived.

Local Binary Patterns Based on Neighbor-Center Difference Image for Color Texture Classification with Machine Learning Techniques

This is a topic that receives a lot of interest since many applications of computer vision focus on the detection of objects in visually appealing environments. Information about an object’s appearance and information regarding the object’s motion are both used as crucial signals in the process of identifying and recognising any given item. This information is used to characterise and recognise the item. The identification of objects based solely on their outward appearance has been the subject of a substantial amount of research. However, motion information in the recognition task has received only a marginal amount of attention, despite the fact that motion plays an essential role in the process of recognition. In order to analyze a moving picture in a way that is both fast and accurate, it is required to make use of motion information in conjunction with surface appearance in a strategy that has been designed. Dynamic texture is a kind of visual phenomenon that may be characterised as a type of visual phenomenon that shows spatially repeated features as well as some stationary properties during the course of time by using methodologies that are associated with machine learning. The design of modern VLSI systems takes into consideration a larger chip density, which results in a processor architecture with several cores that are capable of performing a wide range of functions (multicore processor architecture). It is becoming more challenging to run such complicated systems without the use of electric power. In order to increase the effectiveness of power optimization strategies while maintaining system performance for text data extraction, it has been developed and put into practice power optimization strategies that are based on scheduling algorithms. Over the last twenty years, texture analysis has been an increasingly busy and profitable field of study. Today, texture interpretation plays a vital role in various activities ranging from remote sensing to medical picture analysis. The absence of tools to newline analyze the many properties of texture pictures was the primary challenge faced by the texture analysis approach. Texture analysis may be roughly categorised as texture classification, texture segmentation, texture synthesis, and texture synthesis. Texture categorization is useful in numerous applications, such as the retrieval of picture databases, industrial agriculture applications, and biomedical applications. Texture categorization relies on three distinct methods, namely, statistical, spectral, and structural methods. Statistical methods are based on the statistical characteristics of the image’s grey level. Features are collected using second order statistical order, autocorrelation function, and grey level co-occurrence matrix function.

A Holistic Power Management Strategy of Microgrids Based on Model Predictive Control and Particle Swarm Optimization

Power control and optimization are both crucial for the proper operation of a microgrid. However, in existing research, they are usually studied separately. Active and reactive powers are either maintained to constant values at device level or optimized at system level without considering frequency and voltage control of distributed converters. In this article, a holistic power control and optimization strategy is proposed for microgrids. Specifically, a model predictive control incorporated with the droop method is developed at device level to achieve load sharing and flexible power dispatching among distributed energy resources, which is feasible for both islanded and grid-connected modes. In addition, an evolutionary particle swarm optimization algorithm is designed at system level to generate the optimal active and reactive power setpoints, which are then sent to the device level for controlling inverters. The proposed power optimization scheme is able to mitigate voltage deviations and minimize the operational cost of the microgrid. Comprehensive case studies and real-time simulator test are provided to demonstrate the feasibility and efficacy of the proposed power control and optimization strategy.

Real-time power optimization for application server clusters based on Mixed-Integer Programming

In the environment of peer competition and energy conservation, optimizing the deployment of application server clusters in real time according to actual workload conditions to reduce operating costs and energy consumption is an important issue that must be urgently addressed. In this paper, we propose a real-time power optimization strategy for application server clusters, and the optimization measures include CPU dynamic voltage/frequency scaling and server dynamic switching. First, the feasibility of defining variables for server types is proved, and appropriate variables are defined to describe the cluster power optimization as a mixed-integer programming (MIP) problem. Then, two solution methods are proposed: the exact method based on the Gurobi optimizer and the approximate method based on primary–secondary optimization and differential evolution with two mutations (PSODE). The former turns the MIP problem into a standard mixed-integer quadratic programming form by introducing intermediate variables and solves it using the Gurobi optimizer. The latter rewrites the MIP problem as a primary–secondary optimization problem and proposes a differential evolutionary-based solution algorithm for the primary optimization problem. The evolutionary process consists of two mutation operations, inter- and intraindividual mutations, which both use a heuristic policy to accelerate the evolution convergence. The test results reveal that the Gurobi-based method can quickly determine the global optimal deployment when the cluster size is small. The PSODE-based method can quickly determine the global optimal deployment or high-quality suboptimal deployment when applied to large-scale clusters.

Toward an intelligent community microgrid energy management system based on optimal control schemes

This article investigates a modelization of the energy management strategy of a grid-tied photovoltaic (PV), wind generator, and battery system. The model uses a smart metering system to collect data from various electrical components of a microgrid. The power optimization strategy is modeled under demand response, including time-of-use pricing to create a home's energy flow more efficiently. Two optimal control strategies are developed to coordinate energy expenses, expressed as the cost of minimizing electricity supplied by the utility grid and maximizing the opportunity for energy to sell to the grid. A nonlinear open-loop control and quadratic programming using model predictive control (MPC) are compared to assess the performance of the developed model. The design model takes into consideration the operational cost of batteries and degradation. The importance of the energy storage system is demonstrated in the context of energy conservation and gain on the demand side. MPC's capacity to specify the proper control and forecast future system reaction enables the proposed method to ensure significant daily energy and cost savings from 70.44 to 24.74 $ day−1 in the first scenario (without disturbances) and from 70.44 to 18.24 $ day−1 in the second case (with disturbances). In both methods, the net savings of greenhouse gas are approximately 288.10 (kgCO2-eq) and 338.87 (kgCO2-eq). These show better performance of the MPC compared to the linear model. Highlights Investigate the dynamic performance of an intelligent home energy management scheme. Implement a grid-tied photovoltaic, wind turbine, and energy storage system. Use intelligent metering to collect data from various electrical components of a smart home. Compare the energy saving from an open-loop algorithm and a model predictive control. Assess the topmost benefits of the optimal control method of grid-tied distributed energy resources.

A relay-based power optimization algorithm for platooning

As one of the advanced applications of C-V2X (cellular vehicle to everything), the platoon can effectively improve travel efficiency. Vehicles in the platoon must exchange information in real-time, and the platoon head needs to take a managerial role. However, in a high-density scenario, when platoon heads are assigned the same sub-channel for transmission, there will be interference between multiple platoons. If platoon heads use the traditional method of broadcast with fixed power, excessive power will cause interference to other platoons, and insufficient power will affect the communication quality in the platoon. As a result, an appropriate power allocation scheme is required. In this paper, a relay-based multi-platoon cooperative power optimization scheme is proposed to minimize total power consumption while meeting the requirements of delay and receiving signal-to-noise ratio. This scheme reduces the broadcast power required for each hop by selecting appropriate relay vehicles in the platoon, thereby reducing interference to other platoons and total power consumption. The simulation results show that the proposed scheme allows more platoon members than the traditional scheme while significantly lowering the total power consumption.

Investigating the Combination of Deep Learning for Channel Estimation and Power Optimization in a Non-Orthogonal Multiple Access System.

In a non-orthogonal multiple access (NOMA) system, the successive interference cancellation (SIC) procedure is typically employed at the receiver side, where several user’s signals are decoded in a subsequent manner. Fading channels may disperse the transmitted signal and originate dependencies among its samples, which may affect the channel estimation procedure and consequently affect the SIC process and signal detection accuracy. In this work, the impact of Deep Neural Network (DNN) in explicitly estimating the channel coefficients for each user in NOMA cell is investigated in both Rayleigh and Rician fading channels. The proposed approach integrates the Long Short-Term Memory (LSTM) network into the NOMA system where this LSTM network is utilized to predict the channel coefficients. DNN is trained using different channel statistics and then utilized to predict the desired channel parameters that will be exploited by the receiver to retrieve the original data. Furthermore, this work examines how the channel estimation based on Deep Learning (DL) and power optimization scheme are jointly utilized for multiuser (MU) recognition in downlink Power Domain Non-Orthogonal Multiple Access (PD-NOMA) system. Power factors are optimized with a view to maximize the sum rate of the users on the basis of entire power transmitted and Quality of service (QoS) constraints. An investigation for the optimization problem is given where Lagrange function and Karush–Kuhn–Tucker (KKT) optimality conditions are applied to deduce the optimum power coefficients. Simulation results for different metrics, such as bit error rate (BER), sum rate, outage probability and individual user capacity, have proved the superiority of the proposed DL-based channel estimation over conventional NOMA approach. Additionally, the performance of optimized power scheme and fixed power scheme are evaluated when DL-based channel estimation is implemented.

Online Condition Monitoring of Rotating Machines by Self-Powered Piezoelectric Transducer from Real-Time Experimental Investigations

This paper investigates self-powering online condition monitoring for rotating machines by the piezoelectric transducer as an energy harvester and sensor. The method is devised for real-time working motors and relies on self-powered wireless data transfer where the data comes from the piezoelectric transducer’s output. Energy harvesting by Piezoceramic is studied under real-time motor excitations, followed by power optimization schemes. The maximum power and root mean square power generation from the motor excitation are 13.43 mW/g2 and 5.9 mW/g2, which can be enough for providing autonomous wireless data transfer. The piezoelectric transducer sensitivity to the fault is experimentally investigated, showing the considerable fault sensitivity of piezoelectric transducer output to the fault. For instance, the piezoelectric transducer output under a shaft-misalignment fault is more than 200% higher than the healthy working conditions. This outcome indicates that the monitoring of rotating machines can be achieved by using a self-powered system of the piezoelectric harvesters. Finally, a discussion on the feasible self-powered online condition monitoring is presented.

Reactive Power Optimization Strategy of Power Grid Incorporating Renewable Energy Based on Interval Modeling

Reactive Power Optimization Strategy of Power Grid Incorporating Renewable Energy Based on Interval Modeling

FCTcon: Dynamic Control of Flow Completion Time in Data Center Networks for Power Efficiency

In the era of cloud computing, data center network (DCN) can consume a significant amount of power (e.g., 10 to 20 percent) in large-scale data centers. To reduce the power consumption of DCN, traffic consolidation has been recently proposed as an effective approach to reduce the number of DCN devices in use. However, existing consolidation approaches do not sufficiently consider the flow completion time (FCT) requirement. On one hand, missing the FCT deadlines can cause serious violation of service-level agreement, especially for delay-sensitive networking services, such as web search and E-commerce. On the other hand, keeping all the devices on to make FCTs much shorter than the desired requirements is unnecessary because 1) users may not be able to perceive the difference, and 2) such a greedy strategy can lead to unnecessarily high DCN power consumption and thus more electricity costs. In this paper, we propose FCTcon, a dynamic FCT control strategy for DCN power optimization. FCTcon is designed rigorously based on control theory to dynamically control the FCT of delay-sensitive traffic flows exactly to the requirements, such that the desired FCT performance is guaranteed while the maximum amount of DCN power savings can be achieved. Results from both hardware experiments and simulation evaluation demonstrate that, compared to the state-of-the-art DCN power optimization schemes, FCTcon can improve the DCN FCT performance, while achieving nearly the same or even more power savings. Consequently, FCTcon can result in more than 22.0 to 62.2 percent extra net profits for a data center with 50K servers. In addition, we further propose two extended designs of FCTcon to handle the coflow abstraction recently proposed for DCN, which successfully reduce the coflow deadline miss ratio by 12 to 15 percent.