Energy Optimization Techniques Research Articles

A pareto strategy based on multi-objective optimal integration of distributed generation and compensation devices regarding weather and load fluctuations

In this study, we present a comprehensive optimization framework employing the Multi-Objective Multi-Verse Optimization (MOMVO) algorithm for the optimal integration of Distributed Generations (DGs) and Capacitor Banks (CBs) into electrical distribution networks. Designed with the dual objectives of minimizing energy losses and voltage deviations, this framework significantly enhances the operational efficiency and reliability of the network. Rigorous simulations on the standard IEEE 33-bus and IEEE 69-bus test systems underscore the effectiveness of the MOMVO algorithm, demonstrating up to a 47% reduction in energy losses and up to a 55% improvement in voltage stability. Comparative analysis highlights MOMVO's superiority in terms of convergence speed and solution quality over leading algorithms such as the Multi-Objective Jellyfish Search (MOJS), Multi-Objective Flower Pollination Algorithm (MOFPA), and Multi-Objective Lichtenberg Algorithm (MOLA). The efficacy of the study is particularly evident in the identification of the best compromise solutions using MOMVO. For the IEEE 33 network, the application of MOMVO led to a significant 47.58% reduction in daily energy loss and enhanced voltage profile stability from 0.89 to 0.94 pu. Additionally, it realized a 36.97% decrease in the annual cost of energy losses, highlighting substantial economic benefits. For the larger IEEE 69 network, MOMVO achieved a remarkable 50.15% reduction in energy loss and improved voltage profiles from 0.89 to 0.93 pu, accompanied by a 47.59% reduction in the annual cost of energy losses. These results not only confirm the robustness of the MOMVO algorithm in optimizing technical and economic efficiencies but also underline the potential of advanced optimization techniques in facilitating the sustainable integration of renewable energy resources into existing power infrastructures. This research significantly contributes to the field of electrical distribution network optimization, paving the way for future advancements in renewable energy integration and optimization techniques for enhanced system efficiency, reliability, and sustainability.

Towards Energy-Aware Federated Learning via Collaborative Computing Approach

This research delves into the consequences of the high complexity of on-device operations executed during the federated learning process. We investigate how the varying computational capabilities and battery levels among mobile devices can introduce performance disparities and influence training quality. Hence, in order to deal with these challenges, we propose EAFL+, a novel energy optimization technique, that focuses on managing power consumption in devices with limited battery capacity. EAFL+ is a cloud–edge–terminal collaborative approach that provides a new architectural design for achieving power-aware FL training by leveraging resource diversity and computation offloading. The innovative scheme enables the efficient selection of an approximately-optimal offloading target, from a set of Cloud-tier, Edge-tier, and Terminal-tier resources and achieves the best cost-quality tradeoff for the devices taking part in the FL system. Our evaluation shows EAFL+ can help conserve the devices’ energy participating in training, which improves the participation rates and increases the clients’ contributions, hence achieving higher accuracy and faster convergence. Through experiments on real datasets and traces in an emulated FL environment, EAFL+ reduces the drop-out of clients to zero and enhances accuracy by up to 24% and 9% compared to EAFL and Oort, respectively.

A Comprehensive Review of Microgrid Energy Management Strategies Considering Electric Vehicles, Energy Storage Systems, and AI Techniques

The relentlessly depleting fossil-fuel-based energy resources worldwide have forbidden an imminent energy crisis that could severely impact the general population. This dire situation calls for the immediate exploitation of renewable energy resources to redress the balance between power consumption and generation. This manuscript confers about energy management tactics to optimize the methods of power production and consumption. Furthermore, this paper also discusses the solutions to enhance the reliability of the electrical power system. In order to elucidate the enhanced reliability of the electrical system, microgrids consisting of different energy resources, load types, and optimization techniques are comprehensively analyzed to explore the significance of energy management systems (EMSs) and demand response strategies. Subsequently, this paper discusses the role of EMS for the proper consumption of electrical power considering the advent of electric vehicles (EVs) in the energy market. The main reason to integrate EVs is the growing hazards of climate change due to carbon emissions. Moreover, this paper sheds light on the growing importance of artificial intelligence (AI) in the technological realm and its incorporation into electrical systems with the notion of strengthening existing smart grid technologies and to handle the uncertainties in load management. This paper also delineates the different methodologies to effectively mitigate the probability of facing cyber-attacks and to make the smart grids invulnerable.

Self‐attention based generative adversarial network with Aquila optimization algorithm espoused energy aware cluster head selection in WSN

SummaryWSNs have a wide range of applications, and the effective Wireless Sensor Network (WSN) design includes the best energy optimization techniques. The nodes in wireless sensor networks run on batteries. The existing cluster head selection methods do not take into account the latency and rate of wireless network traffic when optimizing the node's energy constraints. To overcome these issues, a self‐attention based generative adversarial network (SabGAN) with Aquila Optimization Algorithm (AqOA) is proposed for Multi‐Objective Cluster Head Selection and Energy Aware Routing (SabGAN‐AqOA‐EgAwR‐WSN) for secured data transmission in wireless sensor network. The proposed method implements the routing process through cluster head. SabGAN classifiers are utilized to select the CH based on firm fitness functions, including delay, detachment, energy, cluster density, and traffic rate. After the selection of the cluster head, the malicious node gains access to the cluster. Therefore, the ideal path selection is carried out by three parameters: trust, connectivity, and degree of amenity. These three parameters are optimized under proposed AqOA. The data are transferred to the base station with the support of optimum trust path. The proposed SabGAN‐AqOA‐EgAwR‐WSN method is activated in NS2 simulator. Finally, the proposed SabGAN‐AqOA‐EgAwR‐WSN method attains 12.5%, 32.5%, 59.5%, and 32.65% higher alive nodes; 85.71%, 81.25%, 82.63%, and 71.96% lower delay; and 52.25%, 61.65%, 37.83%, and 20.63% higher normalized network energy compared with the existing methods.

Labelled dataset for Ultra-Low Temperature Freezer to aid dynamic modelling & fault detection and diagnostics

Ultra-low temperature (ULT) freezers are used to store perishable biological contents and are among the most energy-intensive equipment in laboratory buildings, biobanks, and similar settings. To ensure reliable and efficient operation, it is essential to implement data-driven fault detection and diagnostic algorithms, along with energy optimization techniques. This study presents labelled and long-term ULT-freezer performance dataset, the first of its kind, derived from 53 ULT freezers featuring two different control strategies. The dataset comprises high-resolution historical operation data spanning up to 10 years. More than 10 attributes are recorded from the freezing chamber and critical locations in the refrigeration systems. The dataset is labelled with regular events, such as door openings, as well as fault events obtained from 46 service reports. A scalable data pipeline, consisting of extraction, transformation, and loading processes, is developed to convert the raw data into a format ready for analysis. The dataset can be utilized to support the development of data-driven models and algorithms that advance the intelligent digital operation of ULT freezers.

Research on IoT-based hybrid electrical vehicles energy management systems using machine learning-based algorithm

Electric vehicles (EVs) are quickly becoming a staple of smart transportation in applications involving smart cities due to their ability to reduce carbon footprints. However, the widespread use of electric vehicles significantly strains the nation's electrical system. In-depth descriptions of the EV's energy management system (EMS) should highlight the vehicle's powertrain's vital role. The energy for propulsion in electric automobiles comes from a rechargeable battery. The safe and dependable operation of batteries in electric vehicles relies heavily on online surveillance and status estimations of charges. An energy management strategy (EMS) that considers the electric vehicle's battery and ultra-capacitor may lessen the vehicle's reliance on external power sources and extend the battery's lifespan. A machine learning-based mathematical dynamic programming algorithm is used in designing the energy management system to teach the system how to respond appropriately to various situations without resorting to predefined rules. Therefore, this research aims to use Machine Learning to create a Smart Energy Management System for Hybrid Electrical Vehicles (SEMS-HEV) with energy storage. Energy optimization techniques and algorithms are necessary in this setting to reduce expenses and length of charging and appropriately arrange the EV charging process to prevent bursts in the electrical supply that may impact the transmission network. To improve the performance of an energy management system, this study employs an IoT-based smart charging system for scheduling V2G connections for hybrid electrical vehicles. It allows for more precise and effective control and greater efficiency by enabling the system to learn from its surroundings.

Strategic Energy Management and Carbon Footprint Reduction in University Campuses: A Comprehensive Review

This review paper offers a detailed analysis and summary of current studies related to energy management and carbon footprint reduction in university campuses. Using insights from different research papers, the review highlights the necessity for strategic and sustainable energy management approaches. The research reviewed explores a wide range of methodologies, including the examination of energy consumption, the identification of areas for improvement, the application of energy management and optimization techniques, and the use of renewable and sustainable energy strategies. It is evident from the review that successful energy management and carbon footprint reduction depend on energy management systems, integration of renewable energy, encouraging behavioural changes, and efficient resource use. The review paper is divided into six sections: understanding and enhancing energy consumption, Techniques for Energy Management and Optimization, random and systematic approaches to energy management, Energy Management Systems and their Applications, approaches for sustainable and renewable energy and carbon footprint reduction techniques. The review also identifies areas of future research in the field. It aims to serve as a valuable guide for university administrators, energy managers, and researchers seeking to implement sustainable energy practices and reduce carbon emissions on university campuses.

Analysis of solar-powered adsorption desalination systems: Current research trends, developments, and future perspectives

Growing population increases freshwater demand for all vital activities: industrial, agricultural, and drinking; thereby, desalination capacity has grown significantly. The development of new adsorption desalination technologies has been getting much attention recently. These systems can be driven by low-grade heat sources such as solar energy, using environmentally friendly working fluids to generate potable water. The present study conducted a bibliometric analysis to identify current trends, developments, and future perspectives in adsorption desalination systems based on the publications between 2011 and 2022. The analysis covers seven major research indicators: annual publication trends, major contributing countries, scientific mapping of research sources, analysis of keywords, highly cited papers, most contributing institutions and authors, and research gaps. The paper additionally characterized the yearly publication trends, emphasized significant phrases such as water filtration, evaporation, and environmental impact. Also identified major contributing countries such as China, Egypt, Saudi Arabia, India, and United Kingdom. Among the notable institutions mentioned are Sohag University, Ministry of Education in China, King Abdullah University. Finally, the analysis provided an outlook on future research directions, including investigating novel membranes and adsorbents, investigating hybrid desalination systems integrated with nanomaterials, and using energy and cost analysis modeling and optimization techniques.

A Survey of FPGA Optimization Methods for Data Center Energy Efficiency

This article provides a survey of academic literature about field programmable gate array (FPGA) and their utilization for energy efficiency acceleration in data centers. The goal is to critically present the existing FPGAs energy optimization techniques and discuss how they can be applied to such systems. To do so, the article explores current energy trends and their projection to the future with particular attention to the requirements set out by the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">European Code of Conduct for Data Center Energy Efficiency</i> . The article then proposes a complete analysis of over ten years of research in energy optimization techniques, classifying them by purpose, method of application, and impacts on the sources of consumption. Finally, we conclude with the challenges and possible innovations we expect for this sector.

Hybridization of Energy Optimization Technique for Cluster Based Routing using Various Computational Intelligence Methods in WSN

Approaches in WSN technology has determined by opportunity of tiny and inexpensive sensor nodes with adequacy of sensing multiple kinds of information processing and wireless communication. Network lifetime and energy efficiency are major indexes of WSN. Several clustering techniques are intended to extend the network lifetime but whereas there is an issue of incompetent Cluster Head (CH) election. To overcome this issue, an Integration of Novel Memetic and Brain Storm Optimization approach with Levy Distribution (IoNM-BSOLyD) has been proposed for clustering using fitness function. In the meanwhile, election of CH is done by utilizing fitness function, which incorporates following amplitude such as energy, distance to adjacent nodes, distance to BS, and network load. After clustering, routing techniques decides the detecting and pursuing the route in WSN. In this proposed work, a Water Wave Optimization with Hill Climbing technique (WWO-HCg) is introduced for routing purpose. This proposed methodology deals with ternary QoS aspect such as network delay, energy consumption, packet delivery ratio, network lifetime and security to select optimal path and enhance QoS as well. This proposed protocol provides better performance result than other contemporary protocols.

Energy-Efficient Parallel Computing: Challenges to Scaling

The energy consumption of Information and Communications Technology (ICT) presents a new grand technological challenge. The two main approaches to tackle the challenge include the development of energy-efficient hardware and software. The development of energy-efficient software employing application-level energy optimization techniques has become an important category owing to the paradigm shift in the composition of digital platforms from single-core processors to heterogeneous platforms integrating multicore CPUs and graphics processing units (GPUs). In this work, we present an overview of application-level bi-objective optimization methods for energy and performance that address two fundamental challenges, non-linearity and heterogeneity, inherent in modern high-performance computing (HPC) platforms. Applying the methods requires energy profiles of the application’s computational kernels executing on the different compute devices of the HPC platform. Therefore, we summarize the research innovations in the three mainstream component-level energy measurement methods and present their accuracy and performance tradeoffs. Finally, scaling the optimization methods for energy and performance is crucial to achieving energy efficiency objectives and meeting quality-of-service requirements in modern HPC platforms and cloud computing infrastructures. We introduce the building blocks needed to achieve this scaling and conclude with the challenges to scaling. Briefly, two significant challenges are described, namely fast optimization methods and accurate component-level energy runtime measurements, especially for components running on accelerators.

A Survey on Energy Optimization Techniques in UAV-Based Cellular Networks: From Conventional to Machine Learning Approaches

Wireless communication networks have been witnessing unprecedented demand due to the increasing number of connected devices and emerging bandwidth-hungry applications. Although there are many competent technologies for capacity enhancement purposes, such as millimeter wave communications and network densification, there is still room and need for further capacity enhancement in wireless communication networks, especially for the cases of unusual people gatherings, such as sport competitions, musical concerts, etc. Unmanned aerial vehicles (UAVs) have been identified as one of the promising options to enhance capacity due to their easy implementation, pop-up fashion operation, and cost-effective nature. The main idea is to deploy base stations on UAVs and operate them as flying base stations, thereby bringing additional capacity where it is needed. However, UAVs mostly have limited energy storage, hence, their energy consumption must be optimized to increase flight time. In this survey, we investigate different energy optimization techniques with a top-level classification in terms of the optimization algorithm employed—conventional and machine learning (ML). Such classification helps understand the state-of-the-art and the current trend in terms of methodology. In this regard, various optimization techniques are identified from the related literature, and they are presented under the above-mentioned classes of employed optimization methods. In addition, for the purpose of completeness, we include a brief tutorial on the optimization methods and power supply and charging mechanisms of UAVs. Moreover, novel concepts, such as reflective intelligent surfaces and landing spot optimization, are also covered to capture the latest trends in the literature.

IoT-Enabled Campus Prosumer Microgrid Energy Management, Architecture, Storage Technologies, and Simulation Tools: A Comprehensive Study

Energy is very important in daily life. The smart power system provides an energy management system using various techniques. Among other load types, campus microgrids are very important, and they consume large amounts of energy. Energy management systems in campus prosumer microgrids have been addressed in different works. A comprehensive study of previous works has not reviewed the architecture, tools, and energy storage systems of campus microgrids. In this paper, a survey of campus prosumer microgrids is presented considering their energy management schemes, optimization techniques, architectures, storage types, and design tools. The survey is comprised of one decade of past works for a true analysis. In the optimization techniques, deterministic and metaheuristic methods are reviewed considering their pros and cons. Smart grids are being installed in different campuses all over the world, and these are considered the best alternatives to conventional power systems. However, efficient energy management techniques and tools are required to make these grids more economical and stable.

A Machine Learning-Based Energy Optimization System for Electric Vehicles

The growing demand for sustainable and eco-friendly transportation has led to the widespread adoption of electric vehicles (EVs). However, the limited driving range of EVs and the need for frequent recharging remain significant challenges. To address these challenges, researchers have proposed various energy optimization techniques, including machine learning-based approaches. In this paper, proposed method of Smart EV energy optimization systems for EVs. The system uses machine learning algorithms to analyze and learn from historical driving data, such as the driving patterns, road conditions, weather, and traffic. Based on this analysis, the system predicts the energy consumption of the EV and optimizes the energy usage to minimize energy waste and extend the driving range.

Energy-Efficient Object Tracking Using Adaptive ROI Subsampling and Deep Reinforcement Learning

Recent innovations in ROI camera systems have opened up the avenue for exploring energy optimization techniques like adaptive subsampling. Generally speaking, image frame capture and read-out demand high power consumption. ROI camera systems make it possible to exploit the inverse relation between energy consumption and spatiotemporal pixel readout to optimize the power efficiency of the image sensor. To this end, we develop a reinforcement learning (RL) based adaptive subsampling framework which predicts ROI trajectories and reconfigures the image sensor on-the-fly for improved power efficiency of the image sensing pipeline. In our proposed framework, a pre-trained convolutional neural network (CNN) extracts rich visual features from incoming frames and a long short-term memory (LSTM) network predicts the region of interest (ROI) and subsampling pattern for the consecutive image frame. Based on the application and the difficulty level of object motion trajectory, the user can utilize either the predicted ROI or coarse subsampling pattern to switch off the pixels for sequential frame capture, thus saving energy. We have validated our proposed method by adapting existing trackers for the adaptive subsampling framework and evaluating them as competing baselines. As a proof-of-concept, our method outperforms the baselines and achieves an average AUC score of 0.5090 on three benchmarking datasets. We also characterize the energy-accuracy tradeoff of our method vs. the baselines and show that our approach is best suited for applications that demand both high visual tracking precision and low power consumption. On the TB100 dataset, our method achieves the highest AUC score of 0.5113 out of all the competing algorithms and requires a medium-level power consumption of approximately 4 W as per a generic energy model and an energy consumption of 1.9 mJ as per a mobile system energy model. Although other baselines are shown to have better performance in terms of power consumption, they are ill-suited for applications that require considerable tracking precision, making our method the ideal candidate in terms of power-accuracy tradeoff.

Energy and cost optimization mechanism for workflow scheduling in the cloud

Cloud computing has become one of the most important platforms for various applications like artificial intelligence, big data, the Internet of Things, and others, especially demand for cloud computing has exploded in recent years. Because of the increased demand for cloud computing, it has become more complex, which can lead to software or hardware failure. Due to the advanced infrastructure, failure may not be detected and repaired at a given timeline and will end up costing higher. In addition, the advent of cloud computing has another advantage over the massive spread of scientific work. Scientific workflow refers to a series of computations that enable data analysis in a distributed and systematic way; because this work flow has a large number of works, energy consumption is a major problem. Besides these issues, it also suffers from system reliability; in the response to these issues, several researchers have designed their mechanism, however they failed to understand cloud complex environment. Therefore, here we have designed a mechanism that efficiently reduces energy consumption, improve the fault tolerance to achieve reliability, performs operations in very less time, and optimize the cost in the workflow model. We have also demonstrated efficient energy optimization techniques by reducing task loads.

Energy-aware scheduling, compilation, and execution of hard-real-time multi-task Java programs

Most of the real-world embedded hard real-time applications are multi-task programs. Reduction in energy consumption for these systems is essential, especially if they are battery-powered. For multi-task applications, energy management could be considered in the offline or online scheduler. We developed an offline non-preemptive rate monotonic scheduler which generates an energy-optimized schedule judiciously using the idle intervals inside the rate monotonic schedule to either put the processor to sleep or slow down the previous task while satisfying the hard real-time constraints. In addition to the energy-aware scheduling, we implemented a new inter-task hardware-based run-time technique for energy management. We use three different methods of intra/inter-task and compile/run time energy management schemes to find out the optimum method for a multi-task hard-real-time Java program. Our results show that the offline energy-optimized schedule when used with intra/inter-task and compile/run time energy optimization techniques, gives around 35 to 40% energy reduction for the experimental multi-task program.

An Efficient Computation for Energy Optimization of Robot Trajectory

Due to the wide distribution and high energy-saving potential of industrial robots, energy optimization techniques of industrial robots attract increasing attention. Dynamic time-scaling methods can optimize the energy consumption of robots only by stretching or shrinking reference trajectories in the time dimension. Dynamic time-scaling methods show advantages over other energy-saving techniques because time-scaling methods can achieve energy savings without hardware investment. Traditional dynamic time-scaling methods need to search the possible state transitions from the initial configuration to the final configuration to obtain the optimal solution. The integration of robot transient power has to be carried out for each possible state transition, which directly leads to intensive computation. Therefore, in this article, an efficient computation method for robot trajectory optimization is proposed. Based on parameter separation, an energy characteristic parameter model based on the dynamic time-scaling is developed, which describes the energy consumption during the state transition as a function of the scaling parameters. Based on the energy characteristic parameter model, the dynamic programming algorithm can replace the integral operation of transient power with the substitution calculations, which avoids intensive computation. Experimental results show that the proposed method can achieve 33.7% less energy consumption, and the computation time can be reduced by nearly 99%.

Energy Optimization Techniques in Underwater Internet of Things: Issues, State-of-the-Art, and Future Directions

The underwater internet of things (UIoT) has emerged as a booming technology in today’s digital world due to the enhancement of a wide range of underwater applications concerning ocean exploration, deep-sea monitoring, underwater surveillance, diver network monitoring, location and object tracking, etc. Generally, acoustic, infrared (IR), visible light (VL), radiofrequency (RF), and magnet induction (MI) are used as the medium of communication in order to transfer information among digitally linked underwater devices. However, each communication medium has its advantages and limitations: for example, the acoustic communication medium is suitable for long-range data transmission but has challenges such as narrow bandwidth, long delay, and high cost, etc., and the optical medium is suitable for short-range data transmission but has challenges such as high attenuation, and optical scattering due to water particles, etc. Furthermore, UIoT devices are operated using batteries with limited capacity and high energy consumption; hence, energy consumption is considered as one of the most significant challenges in UIoT networks. Therefore, to support reliable and energy-efficient communication in UIoT networks, it is necessary to adopt robust energy optimization techniques for UIoT networks. Hence, this paper focuses on identifying the various issues concerning energy optimization in the underwater internet of things and state-of-the-art contributions relevant to inducement techniques of energy optimization in the underwater internet of things; that provides a systematic literature review (SLR) on various power-saving and optimization techniques of UIoT networks since 2010, along with core applications, and research gaps. Finally, future directions are proposed based on the analysis of various energy optimization issues and techniques of UIoT networks. This research contributes much to the profit of researchers and developers to build smart, energy-efficient, auto-rechargeable, and battery-less communication systems for UIoT networks.

Delay Aware and Performance Efficient Workflow Scheduling of Web Servers in Hybrid Cloud Computing Environment

Background : To design an effective workflow scheduling optimization of web servers that will bring good trade-offs to meet the workflow task delay prerequisite and performance requirement. Methods: This study presents a delayaware and performance-efficient energy optimization (DAPEEO) technique for workflow execution in a heterogeneous environment (i.e., edge-cloud environment). This technique provides a workflow execution model which meets the application delay prerequisite and performance requirement. Findings: Our model has been designed to reduce the energy consumption, increase throughput, reduce computational cost and computational time to provide a delay-aware and performance-efficient workflow model for the web servers in hybrid cloud computing. Our model Delay Aware and Performance Efficient Workflow Scheduling of Web Servers in Hybrid Cloud Computing Environment (DAPEEO) has reduced the energy consumption by 4.217%, increased the throughput by 19.51%, and reduce the computational cost by 62.38% when compared with the existing Deadline-Constrained Cost Optimization for Hybrid Clouds (DCOH) models. Furthermore, the average energy consumption showed a reduction of 40.993% and 90.384% when compared with the DCOH and Self-Configuring and Self-Healing of Cloud-based Resources (RADAR) workload model respectively. Experiment outcome shows the DAPEEO technique achieves much superior energy efficiency, throughput and computation cost reduction when compared with the existing workflow execution model. Novelty: Existing model failed to balance reducing cost, and meeting workflow execution deadlines under a heterogeneous environment. On the other side, the DAPEEO is efficient in bringing trade-offs in reducing energy dissipation and meeting task deadlines with reduced cost under the edge-cloud computing model. Keywords: Cloud Computing; EdgeCloud; DAPEEO; Energy Efficiency; Throughput; Cost