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A novel approach for paper drying using airborne ultrasound technology is presented. A unique experimental setup is developed, and a systematic study is conducted using 23 factorial design of experiments and Analysis of Variance. Three controlling factors are considered in the experiments including the initial moisture content, basis weight and refining condition. The outcome of the experiments is compared to a previous work on direct-contact ultrasonic drying of paper. The results confirm that similar to direct-contact, for airborne ultrasonic drying, the basis weight/thickness of the sample is the most important factor in ultrasonic drying and it is followed by the effect of initial moisture content. Using linear regression model, a correlation for predicting the total time of ultrasonic drying is provided. Quality of the dried samples is evaluated, and the permeability measurements confirmed the effect of pore characteristics on ultrasonic drying. The analysis for energy consumption reveals that airborne ultrasonic drying is more efficient at higher moisture contents. [ABSTRACT FROM AUTHOR]

This paper addresses the question whether energy consumption variables and the disaggregation of output matter in the context of efficiency analysis of container terminals. While it is obvious that the energy consumption of refrigerated cargo is higher than the energy consumption of non-refrigerated cargo, this work investigates whether those differences show in an overall efficiency analysis of terminals. This would point to a potentially important input for efficiency measures, to be considered in future productivity and efficiency analysis of terminals. Starting with a discussion on theoretical concepts and variable selection for measuring the energy dimension of terminal efficiency, this is the first paper that applies data envelopment analysis (DEA) comparing results with and without energy consumption, as well as differentiating productive outputs (dry and reefer container handling). The results reveal how the output disaggregation leads to substantially different efficiency scores and are a first step to show the relevance of output disaggregation and the inclusion of the energy variables as inputs in container terminal efficiency studies. [ABSTRACT FROM AUTHOR]

This paper investigates the fixed-time consensus control for a class of multi-agent systems subject to unknown bounded disturbances under undirected topology. Firstly, the nonlinear controllers and the measurement errors are designed based on the hyperbolic tangent function to avoid the non-differential problem. Secondly, a distributed fixed-time control protocol is designed, which can realise the fixed-time convergence and improve the system convergence rate independent of the system's initial state. Thirdly, the dynamic event-triggered mechanism is proposed, such that the triggering frequency of events is reduced and communication energy consumption can be saved. The stability analysis is presented by employing the Lyapunov theory and the Zeno phenomenon is excluded. Finally, the simulation examples are presented to show the effectiveness and advantages of the proposed method in this paper. [ABSTRACT FROM AUTHOR]

This research investigates how supply chain finance (SCF) affects the investment efficiency of new energy firms by adopting a sample of Chinese listed new energy enterprises from 2011 to 2019. With the help of data envelopment analysis (DEA) combined with the Banker-Charnes-Cooper (BCC) model, we calculate efficiency values and analyze the regression results of the Tobit model, and find that SCF significantly boosts the investment efficiency of new energy corporations by improving their cash-holding position. The results of mechanism analysis also suggest that both supply chain concentration and digital finance positively magnify the relation between SCF and corporate efficiency. After comparing the impact in the new energy firms with different characteristics, we conclude that the SCF effect in promoting corporate investment efficiency is more prominent for non-stateowned new energy enterprises located in the western region that have low equity concentration. The conclusions of this paper shed new light on the fusion of the SCF business into building a new energy industry cluster. [ABSTRACT FROM AUTHOR]

The deregulation of the electricity market has been accompanied by the growing utilization of unpredictable renewable energy sources (RESs) such as solar, wind, and hydropower plants. Additionally, advancements in energy storage technologies and new energy demands have further contributed to this trend. As a result, the planning and operation of power systems are now surrounded by a higher level of uncertainty. In order to ensure the proper operation of power systems integrated with RESs, modern power systems are equipped with specific vital tools such as optimal power flow (OPF), which regulates generation and demand to achieve specific objectives. Hence, this paper conducts a comprehensive review of recently published research articles focusing on various solution strategies to address OPF problems in the presence of stochastic RESs and power demand. The review encompasses diverse solution methodologies, objective functions, constraints, and distinct techniques to simulate the stochastic behavior of RESs and dynamic loads. Additionally, the paper explores fundamental challenges, identifies critical research gaps, and highlights unexplored areas pertaining to optimal power system operation in the future. This review is essential for system operators who need to assess and pre-plan flexibility competency for their power systems to ensure practical and cost-effective operation under high RESs penetration. [ABSTRACT FROM AUTHOR]

Ride comfort is an important indicator to evaluate the dynamic performance of automotive vehicles. One method for improving ride comfort is to incorporate an active actuator into the passive suspension. However, the improvement is closely linked with the required actuation power and force. Increased actuation requirements may lead to higher energy consumption and a larger actuator size. To enhance the trade-off between ride comfort and active actuation requirements, a new design approach for searching for the optimal passive-active-combined suspension across many different mechanical component layouts incorporating an inerter is proposed in this paper. Compared with traditional designs where the suspension is limited to a few special layouts, via this approach the optimal passive part among all network possibilities with pre-determined numbers of each element type (springs, dampers and inerters), and optimal controller parameters of the parallel active actuator can be identified. Considering a quarter-car model, with a benchmark combined active-passive suspension in which the passive part is a spring-damper, the optimal inerter-based suspension can reduce the active force by more than 48% and regenerate 6 W more average power in the active part while achieving the same ride comfort. Note that in this case, two constraints are always considered: the road-holding ability (as indicated by the dynamic tyre load) and suspension travel of the identified suspension will not be worse than those of the benchmark suspension. The optimal trade-off obtained with this approach serves as a powerful tool in the automotive suspension design as it provides guidance on the following three aspects: the optimal ride comfort, the minimum power consumption and the feasibility of specific actuation requirements, where the second point is obtained in conjunction with the power conservation theorem proposed in this paper which proves that the total power consumed by the passive and active parts will not vary with changes in the passive part and active controller parameters. [ABSTRACT FROM AUTHOR]

This paper investigates a generalized Camassa–Holm equation with cubic nonlinearities (alias the mCH-Novikov equation), which is a generalization of some special equations. The mCH-Novikov equation possesses well-known peaked solitary waves that are called peakons. The peakons were proved orbital stable by Chen et al. in [Stability of peaked solitary waves for a class of cubic quasilinear shallow-water equations. Int Math Res Not. 2022;1–33]. We mainly prove the orbital stability of the multi-peakons in the mCH-Novikov equation. In this paper, it is proved that the sum of N fully decoupled peaks is orbitally stable in the energy space by using energy argument, combining the orbital stability of single peakons and local monotonicity of the method. [ABSTRACT FROM AUTHOR]

Given the high demand for energy in the manufacturing industry and the increasing use of renewable but volatile energy sources, it becomes increasingly important to coordinate production and energy availability. With the help of incentive-based programmes, grid operators can incentivise consumers to adjust power demand in critical situations such that grid stability is not threatened. On the consumer side, energy-efficient scheduling models can be used to make energy consumption more flexible. This paper proposes a bi-objective job-shop scheduling problem with variable machine speeds that aims on minimising the total energy consumption and total weighted tardiness simultaneously. We use a genetic algorithm to solve the model and derive Pareto frontiers to analyse the trade-off between both conflicting objectives. We gain insights into how incentive-based programmes can be integrated into machine scheduling models and analyse the potential interdependencies and benefits that result from this integration. [ABSTRACT FROM AUTHOR]

The energy problem in green manufacturing has attracted enormous attention from researchers and practitioners in the manufacturing domain with the global energy crisis and the aggravation of environmental pollution. The distributed blocking flow shop scheduling problem (DBFSP) has considerable application scenarios in connection with its widespread application in the industry under the background of intelligent manufacturing. A multi-objective discrete differential evolution (MODE) algorithm is proposed to solve the energy-efficient distributed blocking flow shop scheduling problem (EEDBFSP) with the objectives of the makespan and total energy consumption (TEC) in this paper. The cooperative initialisation strategy is proposed to generate the initial population of the EEDBFSP. The mutation, crossover, and selection operators are redesigned to enable the MODE algorithm as applied to discrete space. A local search strategy based on the knowledge of five operators is introduced to enhance the exploitation capability of the MODE algorithm in the EEDBFSP. The non-critical path energy-efficient strategy is proposed to reduce energy consumption according to the specific constraints in the EEDBFSP. The effectiveness of each strategy in the MODE algorithm is verified and compared with the state-of-the-art algorithms. The numerical results demonstrate that the MODE algorithm is the efficient optimiser for solving the EEDBFSP. [ABSTRACT FROM AUTHOR]

Due to the non-deterministic and probabilistic qualities of renewable energy resources (RER), integrating these resources has posed a challenge for energy companies and government policy planners. Hybrid power systems require frequent voltage regulation to address the changes in renewable generation and facilitate voltage management. This leads to an increment in the operating time of the tap changer in the power transformer, thus resulting in accelerated degradation of the on-load tap changer (OLTC) device. The primary focus of this paper is to optimally manage the penetration level of renewable generation while maintaining the minimum time for proper operation of voltage regulation devices. Accordingly, the major contribution of this paper is to address the switching cycle issues of the on-load tap-changing transformer. Initially, a framework is proposed to optimize the location and size of renewable power generation using a multi-objective cuckoo search optimization algorithm. The main goal here is to reduce power losses and tap changer operations while improving the system voltage profile under different levels of renewable energy penetration. The optimization problem takes into consideration the fluctuating nature of wind speed and solar irradiance for sunny and cloudy days over 24 h. The framework has been applied to the IEEE 57-bus and 118-bus systems with different load levels. The performance of the proposed approach has been benchmarked by comparing it with the genetic optimization algorithm to identify the higher potential buses for renewable generation allocation. The cuckoo search algorithm (CSA) shows outstanding results in reducing the number of switching cycles to about (37 – 43) % whereas the genetic algorithm (GA) only (17 – 18). In this sense, the number of changes in tap position when using the CSA is 1956 for sunny days and 1763 for cloudy days compared to GA 2558 for sunny days and 2547 for cloudy days. The voltage profiles for both algorithms are maintained in the range of (1.06 and 0.94) per unit. The results affirm the effectiveness of the proposed approach in determining the optimal placement and size of RER with voltage profile improvement and reduction in the switching cycles of OLTC. [ABSTRACT FROM AUTHOR]

In view of water eutrophication, global energy crisis and the carbon neutrality policies, the ammonia recovery from wastewater with high efficiency and low energy consumption is crucial for preserving the ecological environment and achieving sustainable development. Transmembrane electro-chemisorption (TMECS), which integrates transmembrane chemisorption with electrochemical systems to reduce the addition of chemicals and improve recovery efficiency, is a promising technology for ammonia recovery from wastewater. Accordingly, this paper first reviews the recent advances in TMECS for ammonia recovery from wastewater. In particular, the technology principles, including ammonia stripping by cathodic base, ammonia recovery by TMECS with authigenic acid and base, and ammonia recovery by membrane cathode with in situ cathodic base are elucidated. Couplings of TMECS with other electrochemical systems, including electrodialysis, flow-electrode capacitive deionization, and electrochemical precipitation, are further summarized and compared. Finally, the challenges and prospects of the TMECS technology are addressed. [ABSTRACT FROM AUTHOR]

This paper provides new and useful insights into the performance and efficiency of geotechnical seismic isolation (GSI) systems with gravel-rubber mixtures containing 10%, 25% and 40% volumetric rubber content (VRC). Finite element numerical models were developed in OpenSees and subjected to sinusoidal ground motions with varying input base acceleration and frequency. The best seismic performance was attained for VRC = 40% at a frequency of 8 Hz. Using a newly developed GSI efficiency index, the energy dissipation efficiency of VRC = 40% was found to vary between good to excellent, while that of VRC = 10% and 25% is poor. [ABSTRACT FROM AUTHOR]

This paper presents a novel probabilistic data-driven approach to trip-level energy consumption estimation of battery electric vehicles (BEVs). As there are very few electric vehicle (EV) charging stations, EV trip energy consumption estimation can make EV routing and charging planning easier for drivers. In this research article, we propose a new driver behavior-centric EV energy consumption estimation model using probabilistic neural networks with model uncertainty. By incorporating model uncertainty into neural networks, we have created an ensemble of neural networks using Monte Carlo approximation. Our method comprehensively considers various vehicle dynamics, driver behavior, and environmental factors to estimate EV energy consumption for a given trip. We propose relative positive acceleration (RPA), average acceleration, and average deceleration as driver behavior factors in EV energy consumption estimation, and this paper shows that the use of these driver behavior features improves the accuracy of the EV energy consumption model significantly. Instead of predicting a single-point estimate for EV trip energy consumption, this proposed method predicts a probability distribution for the EV trip energy consumption. The experimental results of our approach show that our proposed probabilistic neural network with weight uncertainty achieves a mean absolute percentage error of 9.3% and outperforms other existing EV energy consumption models in terms of accuracy. [ABSTRACT FROM AUTHOR]

AbstractGrain is the root of the people, and grain storage is a top priority. It is necessary to improve the efficiency of grain storage operations and reduce work intensity, so it is important to develop an automated or even intelligent grain leveling robot for the precise operation of grain silos. In this paper, we propose an area classification method based on target leveling height for the special working mode of truss-type grain leveling robot, simplify the 3D map to a 2D map, reduce the difficulty of path planning, and improve the working efficiency. Based on multi-level path planning and genetic algorithm to achieve the planning of the working path of the grain-leveling robot, it solves the problem of a large number of useless trips under the adoption of full-coverage operation. It saves a lot of time, improves grain-leveling efficiency, reduces energy consumption, optimizes the effect of grain-leveling, and helps realize the precise storage operation of grain silos. The experiment of rough leveling operation was carried out through the grain leveling robot prototype and the simulated experimental warehouse, and the results show that the height difference between the peak of the grain surface and the target leveling height is within 5 cm, which verifies that the path planning method in this paper is feasible, and shows that the grain leveling robot can complete the task of grain leveling and the effect is good. [ABSTRACT FROM AUTHOR]

Reducing the power consumption of oil production wells is one of the important directions of oil industry development in the context of transition of many fields to the late stages of operation. At the same time, there is often a need to limit hydrocarbon production, which is often accomplished by shutting down wells completely. In this paper, the authors propose an approach to improve the energy efficiency of the electric centrifugal pump unit through the use of periodic operation mode, which allows to reduce the specific power consumption of the electric centrifugal pump unit. Also, this mode can be considered as a way to regulate the well flow rate. According to the results of calculations it was obtained that the reduction of specific power consumption of the unit when operating at nominal frequency can reach 5% relative to the continuous mode of operation. An assessment was made of the effect of reducing the frequency of the supply voltage in a periodic mode on the parameters of the operation of the installation and it was found that the well flow rate increased by 1.43% with a reduction in specific power consumption by 1.64%. [ABSTRACT FROM AUTHOR]

This paper presents a novel method to compute various measures of effectiveness (MOEs) at a signalized intersection using vehicle trajectory data collected by flying drones. Specifically, this study investigates the use of drone raw data at a busy three-way signalized intersection in Athens, Greece, and builds on the open data initiative of the pNEUMA experiment. Using a microscopic approach and shockwave analysis on data extracted from real-time videos, we estimated the maximum queue length, whether, when, and where a spillback occurred, vehicle stops, vehicle travel time and delay, crash rates, and fuel consumption. The results of the various MOEs were found to be promising. We also demonstrated that estimating MOEs in real-time is achievable using drone data. Such models can track individual vehicle movements within street networks and thus allow the modeler to consider any traffic conditions, ranging from highly under-saturated to highly over-saturated conditions. [ABSTRACT FROM AUTHOR]

In the context of rapidly increasing energy demands and environmental concerns, optimizing energy management in commercial buildings is a critical challenge. Smart grids, empowered by advanced Energy Management Systems (EMS), play a pivotal role in addressing this challenge through Demand Side Management (DSM). However, the efficiency of DSM-based building EMS is often limited by the accuracy of load forecasting. This paper addresses this gap by exploring load forecasting models within DSM-based building EMS, focusing on a case study in a commercial building in Ankara, Turkey. Employing Deep Learning (DL) models for load forecasting, we provide inputs for rule-based controllers to enhance energy efficiency. Our major contribution is the development of the ANFIS-IC algorithm, aimed at maximizing demand response participation in commercial buildings. ANFIS-IC, integrating ANFIS controllers with LSTM-based load consumption forecasts, leads to a 33.14% reduction in energy consumption and a 39.22% decrease in energy costs, surpassing the performance of rule-based controllers alone which achieve reductions of 25.34% in energy consumption and 34.03% in energy costs. These findings not only highlight the potential of integrating rule-based controllers with deep learning algorithms but also underscore the importance of accurate load forecasting in improving the effectiveness of DSM-based building EMS. [ABSTRACT FROM AUTHOR]

Integrated energy system (IES) has become significant in the global energy revolution because of its unique advantage of promoting clean energy use and improving energy efficiency. However, its low-carbon operational optimization and capacity allocation challenges have sharply increased because, as its scale continuously grows, its inherent strongly coupled multi energy flow and high nonlinearity characteristics have become more prominent. This study proposes a distributed low-carbon operational optimization mechanism for an IES using integrated demand response (IDR) and ladder carbon trading (LCT). First, a low-carbon IES was modeled as a coupled model between energy operators (EO) that contain combined cooling, heating, and power systems (CCHP) and flexible users (FU) based on the Stackelberg game. Second, the operational optimization model was formulated as a source-load synergistic operation scheduling mechanism based on IDR and the LCT. Finally, the strategy of each stakeholder was obtained using a modified distributed algorithm of genetic algorithm nested quadratic programming (GA-QP). The effectiveness of the proposed model was verified through a case study. The economic benefits for each stakeholder improved, as confirmed through the experimental results. The energy cost of the FU decreased by 4.42%, and the revenue from energy sales of the EO increased by 11.9%; renewable energy was prioritized for consumption, with a consumption rate of 100%; the total output of the core devices for energy cascade utilization increased by 15.6%, significantly improving energy supply efficiency; the total carbon emissions decreased by 8.64%; and power and heat loss rates were 0% and 0.00059%, respectively. Highlights With the rapid development of renewable energy, The integrated energy system relies on its inherent operational advantages of high flexibility, high toughness, and multi energy coupling cascade utilization, demonstrates unique advantages in serving as a clean and renewable energy carrier and adapting to the development of distributed energy The energy cost of flexible users has decreased by 4.42%, and the revenue from energy sales of energy operators has increased by 11.9%; Renewable energy is prioritized for consumption, with a consumption rate of 100%; The total output of the core devices for energy cascade utilization has increased by 15.6%, significantly improving energy supply efficiency; The total carbon emissions decreased by 8.64%, both power and heat loss rates are 0%, the error rate of energy supply is 0.00059% Based on the above research, this paper is established a distributed low-carbon operation optimization model for Integrated energy system coupled the EO that contain CCHP and the FU based on Stackelberg game In view of the gradual deepening of the coupling degree of the IES, the increasingly obvious characteristics of multi-agent, the difficulty of centralized scheduling to protect the interests of each independent entity, and the inadequacy of the system's energy cascade utilization advantages, this paper establishes a bilateral distributed operation optimization model based on Game theory In response to the insufficient utilization of the advantages of multi energy coupling in existing research on IES, insufficient attention paid to the interaction between supply and demand on both sides of the system, and insufficient enthusiasm of users as independent entities to participate in system operation scheduling, this article adopts a integrated demand response as the load side operation scheduling strategy [ABSTRACT FROM AUTHOR]

Artificial neural network (ANN) based surrogate models have been widely used in place of high-fidelity simulation tools. Error metrics such as mean absolute error, and root mean squared error (RMSE) have been widely used as de facto criteria. However, whether the ANN-based surrogate model can adequately reproduce the interwoven relationships and nonlinear causalities between design variables and simulated outputs are often overlooked. In this regard, the authors designed a case study regarding four ANN-based surrogate models. It was found that despite all of the models having low RMSEs, the models failed to adequately predict the causal relationships between input variables and energy use. In other words, the surrogate models were not always capable of providing accurate assessments of expected energy use reduction as a result of design changes. In this paper, we present a workflow for validating whether a surrogate model can reproduce the causal relationships between inputs and outputs. [ABSTRACT FROM AUTHOR]

AbstractDrainage is a common practice in geotechnical engineering concerning dredged marine soils. Current drainage techniques, including surcharge preloading, vacuum preloading, and combined vacuum-surcharge preloading, have been proven to be effective in soft soil treatment, but are also criticized for their high energy consumption. This paper made a brief review on existing drainage techniques and proposed some prospects for the next-generation techniques in response to the public concern of sustainability. It is found that all conventional preloading techniques have been well studied from tests to modeling, and improved vacuum preloading tends to be used in combination with other techniques. Drainage techniques with lower energy consumption can be realized either by using renewable energy or designing biomimetic devices. The paper is expected to provide a comprehensive while concise report on recent advances in drainage techniques for dredged marine soils and in the meanwhile give an insight into the further development towards a more sustainable future. [ABSTRACT FROM AUTHOR]

At present, coal mine tunneling technology is developing in the direction of intelligence and unmanned. The piezoelectric vibration energy harvester can convert the vibration energy generated by the cutting part of roadheader into electrical energy and realize the energy self-sufficiency of the low-power wireless sensor. In practical applications, the vibration frequency of the cutting part of roadheader is different, and the working condition is complex, resulting in the energy recovery efficiency of the vibration energy harvester is not ideal. In order to improve its adaptability and reliability, a separated excitation broadband piezoelectric vibration energy harvester is proposed in this paper. [ABSTRACT FROM AUTHOR]

Occupant behavior, encompassing occupant numbers and actions, significantly contributes to the uncertainty of building energy performance simulation. By characterizing the regularity of occupant behavior, it becomes feasible to evaluate its impact on building energy consumption and mitigate disparities between simulated and measured outcomes. This paper presents a comprehensive model that integrates two key aspects of occupant behavior to predict and simulate energy performance in campus buildings. The model introduces two distinct categories of equipment, namely environment-related equipment (e.g. lighting, air conditioning) and time-related equipment (e.g. water dispenser, printer), to account for the diverse factors that influence occupant behavior. The impact of occupant perception on the environment is considered to enhance simulation accuracy. Furthermore, this study compares the proposed model with models that consider only occupant number or occupant energy use behavior individually to assess its effectiveness in capturing the complexities of occupant behavior in energy performance simulations. [ABSTRACT FROM AUTHOR]

The role of cartography in environmentally-sustainable development is twofold: first, useful and usable maps enable sense making and decision making about environmental processes, impacts, and protections; second, maps themselves, as digital tools, can be more energy efficient in minimizing our impact on the environment. In this paper, we elevate this energy awareness as a key design consideration for maps and introduce a novel conceptual framework for considering the carbon footprint of the content, form, and use of maps. We first systematically investigate how specific map design decisions impact the energy consumption of digital devices. We then discuss the possible ways that digital maps can be greener, outlining a series of 'big questions' about the content, form, and use context of maps needing future research to realize greener maps. Finally, we assert that green cartography requires not only novel techniques to design green maps but also greater attention to shaping individual, organizational, and social attitudes towards environmentally-friendly maps. [ABSTRACT FROM AUTHOR]

Given the future uncertainties regarding the availability of irrigation water in the Bengal Delta, planned water reuse in agriculture is a viable alternative. Using scenario planning, this paper depicted technological solutions to ensure the safe reuse of urban water in agriculture. A systematic literature review and expert interviews were conducted to identify the factors for appropriate technology selection. Results indicated that local conditions, resource recovery, energy consumption, initial investment and land availability are the notable factors for technology selection. Taking economic growth and demand for irrigation water, centralized (activated sludge) and decentralized technological (pond-based) solutions are envisioned to facilitate reuse. [ABSTRACT FROM AUTHOR]

This research introduces an innovative analytical framework for studying the behavior of electric vehicles (EVs) in power grids. The approach primarily examines the dynamic State of Charge (SOC) and energy reserves of EV batteries. By thoroughly examining patterns of EV charging demand and operational states (charging, discharging, idling, and waiting) across various time intervals, including day and night periods, this study aims to provide a comprehensive understanding of EV dynamic behavior in power networks. The proposed model goes beyond traditional assessments by incorporating human behavioral patterns and responsiveness to economic signals, specifically investigating the impact of electricity pricing on user behavior. The core analysis centers on exploring the Vehicle-to-Grid (V2G) concept, viewing EVs not only as energy consumers but also as potential contributors to grid stability and efficiency. The study delves into how the interplay of dynamic EV behavioral characteristics and owner responses to pricing influences the overall energy consumption curve of the grid, thereby shaping the capability of EVs in providing grid support services. To quantitatively validate the effectiveness of the model, a detailed case study is conducted, offering empirical evidence of its practical utility. The research findings are quantified, highlighting the significant implications for decision-makers, grid managers, and stakeholders in the energy sector. The paper emphasizes the main outcomes, demonstrating how the proposed analytical framework can empower stakeholders in making informed decisions about policy and infrastructure development. The robust methodological approach presented in this paper serves as a valuable tool for evaluating the impacts of adopting V2G, providing strategic insights for a more sustainable and economically viable paradigm [ABSTRACT FROM AUTHOR]

The aim of this paper is to investigate agricultural sustainability as a collective issue involving multiple rather than individual farms. Through the utilization of energy consumption as a proxy, we propose a novel methodology that evaluates the impact of farm consolidations on agricultural sustainability while accounting for resource preferences. Our approach incorporates the ordered weighted averaging (OWA) operator within an inverse data envelopment analysis (IDEA) model to identify post-merger farms that meet preset efficiency targets. We employ a DEA cross-efficiency (DEA-CE) procedure to select merger plans that maximize agricultural sustainability for each preference scenario. By analysing a case study of 43 tomato greenhouse farms in Biskra, northern Algeria, our findings demonstrate that mergers can significantly enhance agricultural sustainability, surpassing the potential of individual farms by a factor of over 15. Additionally, the adoption of the most sustainable merger plan can lead to energy savings of more than 69%. Irrespective of the preference scenario, substantial energy savings in machinery, fertilizers, diesel, and electricity ranging from 22.92% to 73.73% were observed. These results emphasize the strategic role of merger processes in promoting agricultural sustainability and optimizing resource utilization. [ABSTRACT FROM AUTHOR]

Harvesting ambient vibration energy using piezoelectric elements is a promising energy harvesting technology. Energy extraction efficiency is the research focus. This paper presents a novel self-powered hybrid rectifier for piezoelectric energy harvesting. The purpose of this hybrid rectifier is to make the harvested power more efficient and ensure that the rectifier is self-powered. An optimized self-powered peak detection circuit consisting of discrete components reduces circuit losses. The theory and simulation of the hybrid rectifier are presented with a good agreement. The circuit is designed to help realize self-powered wireless sensor networks. [ABSTRACT FROM AUTHOR]

This paper tests the impact of model resolution and structure on the performance of Model Predictive Control (MPC) implementation in an unoccupied research house in Québec equipped with smart thermostats. Two low-order models and a high-order multi-zone model were calibrated with measured data, with the structure of the multi-zone model being generated automatically during the calibration procedure. The three models were used to apply real-time MPC to an experimental house in Québec using the established dynamic tariffs for morning and evening peaks. MPC with any of the three models successfully preheated the house before the demand-response events, outperforming the reference reactive controller, reducing cost and thermal discomfort. The high-order multi-zone model performed the best, reducing average cost of electricity by 55% and high-price energy consumption by 71%, compared to the low-order models, which achieved cost reductions of 40% and 44% and energy consumption reductions of 48% and 54% respectively. [ABSTRACT FROM AUTHOR]

In recent times, the research in sustainable energy resources was integrated with the control approaches of various hybrid renewable energy systems for balancing the electricity generation. The accelerated hybrid energy storage systems and steadily growing energy consumption make the variability and the predictable behavior occur in the renewable energy sources, which requires an energy management system. This paper aims to control hybrid renewable energy systems for power management. The systems consist of photovoltaic (PV) arrays, diesel generators, wind systems with boost converter DC-DC, and inverters. The optimum of the real-time process is less due to prediction errors of multiple renewable sources, which track the output voltage and load current. Model predictive control (MPC) is proposed for problem-solving time operation to address this issue and compensate for the prediction error. The major impact factors, such as optimal split power, stability, reliability of energy, and desired dynamic responses, have been identified by applying the MPC technique. A simulation model has been constructed using MATLAB/Simulink environment to confirm the effectiveness of the MPC technique model. [ABSTRACT FROM AUTHOR]

MANETs (Mobile AdHoc Networks) have major problems due to the resource-constrained nature of mobile nodes. MANET topology is highly uncertain due to the mobility of MANET nodes that affect the stability of interconnected links. This scenario results in an increased traffic overhead which affects the routing protocol performance and results in increased energy consumption. A routing scheme designed for MANET should be able to handle energy depletion issues and nodes’ mobility. This paper designs two novel schemes to enhance energy efficiency and network lifetime by performing clustering and routing in MANET. The Hybrid Tasmanian devil and Elephant herding optimization (HTDEHO) is used for clustering the MANET nodes to minimize the overhead, enhance the stability of the network topology, and reduce collision. The HTDEHO selects the optimal cluster head (CH) from a set of nodes based on their fitness value calculated by various parameters such as mobility-based node ranking, residual energy, distance, node degree, and optimal next hop CH selection. Dwarf mongoose optimization algorithm with Fuzzy variable (FDMO) algorithm selects optimal routing cost from CH to the base station based on various parameters such as throughput, delay, and distance. The efficiency of the proposed model is evaluated with different performance metrics such as energy consumption, throughput, end-to-end delay, and packet-dropping analysis. The performance of the proposed HTDEHO-FDMO classifier is compared with different techniques such as RDOAICRP, BFOA, MARP-HO, and FEKHO-QBA. Even after 170 iterations, the HTDEHO model retains a total of 10 alive nodes. [ABSTRACT FROM AUTHOR]

Current energy efficiency regulations have led to the development of heat pumps equipped with variable speed compressors (VSC) and electronic expansion valves (EEV). To reduce the amount of refrigerant charge, heat pump manufacturers are developing units without a liquid receiver. As a result, the amount of refrigerant charge has a direct impact on the unit performance and needs to be optimized. Previous studies have shown the existence of an optimum refrigerant charge that maximizes the COP at a given operating condition. However, with VSC it is not clear if the optimum charge at a given compressor speed is the adequate one at other speed values. This paper analyzes the effects of the refrigerant charge amount on the performance of a brine-to-water heat pump equipped with a VSC and an EEV. For a given compressor speed, the variation of the COP with the charge showed similar trends and an optimum refrigerant charge could be identified. The charge for the maximum COP changes slightly with the compressor speed. The seasonal coefficient of performance (SCOP) is proposed as a better metric for evaluating the optimum charge. Results show that the SCOP is quite stable with the refrigerant charge. [ABSTRACT FROM AUTHOR]

As commonly stated, we cannot manage what we do not measure. Understanding the flow of energy and its end-uses within a building is critical for energy management. Therefore, the lack of high resolution energy submetering is a significant barrier to efficient energy management in buildings. Despite this, many buildings still lack adequate submetering for their major end-uses because of the cost and practical restrictions. Energy disaggregation techniques aim at breaking down the bulk meter energy data into primary end-uses to gain insight into consumption patterns. However, high resolution, trustworthy BAS trend data is essential to develop reliable disaggregation techniques and capture unmeasured energy flow accurately. This paper explores a time series decomposition based method to disaggregate the total energy use into three major end uses namely lighting and plug loads, cooling, and heating energy use without BAS trend data. The results were compared with actual submetered data from ten office buildings in Ottawa, Canada for validation purposes. Specific insights into lighting and thermal scheduling, as well as hourly, daily, and monthly operational variations based on the de-composition components were discussed. The promising performance of the proposed method suggests that it could be used for quick and low cost auditing of commercial buildings with access to only the building's total energy use data. [ABSTRACT FROM AUTHOR]

The U.S. Department of Defense's National Defense Strategy prioritises the U.S. Navy's focus on strategic competitors, equipped with long-range weapons, significant anti-access/area denial (A2/AD) systems, and substantial cyber capabilities. This paper analyses the U.S. Navy's efforts to field an energy-resilient force able to support distributed operations in a globally contested logistics environment. Specifically, it examines operational energy strategies, focusing on energy supportability and demand reduction, against the backdrop of naval power and energy system developments. Moreover, it advocates for the application of updated performance metrics to spur technological innovation and policy formulation, advance a data-driven culture, and inform pathways to support a whole-of-government approach to achieve net-zero emissions by 2050. [ABSTRACT FROM AUTHOR]

Flame retardants (FRs) are commonly added to commercial products to achieve flammability resistance. Since most of them are not chemically bonded to the materials, they could be leached to the environment during the production and disposal cycle. These FRs were categorised based on their chemical nature, including brominated, organophosphorus-, mineral- and nitrogen-based. This review summarised the abiotic degradation reactions of these four classes of FRs, with a focus on thermal and photodegradation reactions in environmental and food matrices. Only 24 papers have reported related information on abiotic degradation reactions that could be useful for predicting possible degradation pathways, and most focused on brominated FRs. Most studies also investigated the thermal degradation of FRs under high temperatures (>400 °C), which exceeds the normal cooking temperature at 100–300 °C. For photodegradation, studies have used up to five times the energy typically used in UV radiation during food processing. It is recommended that future studies investigate the fate of these FRs in foods under more realistic processing conditions, to provide a more comprehensive picture of the estimated consumption of FRs and their degradation products from foods, and facilitate a better risk assessment of the use of these novel FRs. [ABSTRACT FROM AUTHOR]

The growing utilization of renewable energy sources (RES) that rely on inverters has adverse impacts on distribution systems, resulting in challenges such as power quality (PQ) issues and increased losses in the distribution network. One potential remedy for these issues involves the strategic allocation of open unified power quality conditioner (UPQC-O). This paper introduces a comprehensive two-layer integrated optimization model designed for the allocation of UPQC-O and RES in an active distribution network. The primary objective is to enhance energy efficiency, PQ, and economic benefits simultaneously. Furthermore, an innovative solver known as the multi-improved whale optimization method (multi-IWOA) has been implemented to address the optimization problem at hand. The efficacy of this approach was validated through testing on both the standard IEEE 69-bus radial distribution system and a real-world Indian 52-bus system. The evaluation results underscore the significance of optimally distributing UPQC-O resources while taking into consideration the integration of RES within the distribution system. It's worth noting that the proposed methodology achieved a substantial 56.72% cost reduction for the IEEE 69-bus radial distribution system and a significant 76.092% cost reduction for the Practical System Indian 52-bus system. This highlights the substantial advantages of the proposed methodology for the efficient management of UPQC-O and RES integration in distribution systems. [ABSTRACT FROM AUTHOR]

Residential energy policies for individual dwellings predominantly provide minimum design standards for thermal performance and energy efficiency on a square metre basis. This paper quantifies predicted dwelling energy use on a national scale to enable evaluation of the effectiveness of this policy model. Utilizing Australia as a case study, analysis of 580,956 thermal energy design certificates for new detached dwellings verifies that, although minimum energy efficiency design standards were achieved, predicted energy use for thermal comfort increased by 10.4% per dwelling and 5.6% nationally in 2022 compared to 2018. The study examines the impact of both the number and floor area of new dwellings on dwelling energy use and highlights the disparate jurisdictional outcomes resulting from inconsistent implementation of national policy across multi-state governance. To deliver reductions in household energy use it is imperative that residential energy policies target household energy use, rather than only energy efficiency, of new dwellings, of which floor area is a key determinant. Furthermore, to achieve global climate change objectives, there is a need for complementary policy mechanisms that consider the collective energy use of the residential dwelling sector. [ABSTRACT FROM AUTHOR]

Strategies of outside air reduction, fan speed adjustments, and temporary system shutdowns can be effective ways to maintain or improve the preservation quality of a collection environment while reducing the financial burden and carbon footprint of a collecting institution. However, current criteria guiding safe implementation of energy-saving strategies focus on temperature and RH alone, which ignores risks posed by pollutants. This research aims to address this by monitoring indoor and outdoor-generated pollutants at four collecting institutions before and during implementing energy-saving strategies. Alongside temperature and RH loggers, continuous pollution monitors were located within the mechanical system and collection spaces. Additionally, energy monitors were installed on the mechanical systems serving the collection spaces to quantify the energy consumption prior to and during operation modifications. Initial results indicate pollutant levels remain stable during the test periods, and did not exceed baseline concentrations. In this paper, the lessons learned from this field research are addressed and practical implications for optimizing sustainable environmental controls are given. [ABSTRACT FROM AUTHOR]

This paper investigates the asymmetric demand for clean energy for a panel of OECD countries. We construct a price index for clean energy based on information on the energy composition of electricity generation, and focus on the possibility of non-symmetric clean and dirty clean consumption due to rising and falling energy prices, and to positive income growth and negative income growth. Using an Autoregressive Distributed Lag (ARDL) model that encapsulates both long-run equilibrium among energy demands, energy prices and income, and short-run dynamics. We find evidence that clean energy demand adjusts more aggressively to a price decline than a price rise. In addition, we find that clean energy consumption is more responsive to negative income changes than to positive income changes. A climate policy implication is that policy should account for reversal effect of clean energy consumption during periods of economic downturn. [ABSTRACT FROM AUTHOR]

The drying process is a crucial stage in the thermal treatment of iron ore pellets in a straight grate, as it has a direct impact on pellet quality and energy consumption. Consequently, this paper develops a simulation model for the pellet bed drying process, which is validated through industrial data and enables the visualization of temperature and moisture distribution within the pellet bed. Based on the simulation model, an optimization model for the drying process is developed to minimize the specific energy consumption per tonne of green pellets. The constraints of the drying process are the moisture content requirements and burst temperature. A genetic algorithm is employed to optimize the parameters such as trolley speed, pellet bed thickness, air temperature, and air pressure. The results show that, with an absolute error criterion of 10 °C, the temperature calculation accuracy of the simulation model exceeds 86%, effectively describing the drying process in a straight grate sintering machine. Under yield limitations, the optimal cumulative energy consumption per tonne of green pellet is reduced by 1.50% during the drying process. Finally, a simulation and optimization system for the drying process in the straight grate is implemented in C++. This system not only generates essential data for designing the drying section in a straight grate but also lays a theoretical foundation for system optimization and optimal control. [ABSTRACT FROM AUTHOR]

This paper proposes the Hybrid desalination and power generation through parabolic trough collectors. This study aims to examine the feasibility, performance optimization, and economic viability of power-generation plant and hybrid-desalination using multi-stage flash (MSF) and reverse osmosis (RO) technologies assessed by using the parabolic trough collectors (PTC). The objective is to discover the posibilities of this innovative approach in addressing water scarcity and clean energy demands. The studies usually focus on several key aspects. Firstly, it resolves the technical feasibility of integrating MSF and RO technologies with parabolic-trough-collectors to optimize energy efficiency and freshwater production. This involves evaluating the system design, heat transfer mechanisms, and operational parameters.Secondly, performance optimization is a key area of investigation. By optimizing these parameters the aim is to maximize freshwater production while minimizing energy consumption. Thirdly, the economic viability of the hybrid plant will be assessed. This involves considering capital investment, conducting a comprehensive cost analysis, maintenance costs, and operational expenses. The study also evaluates the potential for revenue generation through the sale of excess electricity produced by the system. The potential outcomes of this research include demonstrating the technical-feasibility as well as economic-viability of the hybrid desalination and power generation plant. The findings may indicate that this approach can effectively address water scarcity challenges while contributing to clean energy production. The study may also highlight the environmental benefits of integrating renewable energy sources (RESs) and decreasing greenhouse gas emissions compared to traditional desalination methods.The proposed method is executed in MATLAB platform and compared to various existing approaches. [ABSTRACT FROM AUTHOR]

To improve the fuel cell durability of the hydrogen Electric Multiple Units, this paper proposes a novel multi-stack fuel cell hybrid system energy management strategy in consideration of fuel cell degradation. The top layer of the method distributes the power of fuel cells and lithium batteries reasonably according to the Equivalent Consumption Minimization Strategy, and adjusts the fuel cell power through the feedback power regulation module. The bottom layer distributes the power of different stacks according to the degradation degree. The proposed layering method improves the durability of fuel cells by reducing the fuel cell degradation degree. The hardware-in-the-loop (HIL) experiments results show that, compared with the traditional equivalent hydrogen consumption minimum energy management strategy, the proposed method is effective in lowering the degradation degree and operating pressure of the fuel cell by 25.77% and 35.73% respectively. [ABSTRACT FROM AUTHOR]

Renewable energy, sometimes called 'clean energy,' comes from natural processes or constantly renewed sources. For instance, wind and sunlight continue to move and shine, even though their availability depends on time and weather. In recent years, the high penetration of renewable energy sources, especially wind energy, has created new problems in power networks. One of the paramount concerns that we, as renewable energy specialists, must address is the inherent variability and unpredictability associated with the power generation of wind turbines. Using wind energy in microgrids with lower power and voltage levels can make them much more effective because wind energy is naturally variable. This issue specifies the need to perform a possible analysis of micro-grids that use wind energy to generate power. In this paper, the small signal stability of micro-grids will be studied under the influence of production uncertainty from wind energy. For this purpose, the method based on Gram-Charlier expansion is used as a possible numerical analysis method. Also, a method specific to micro-grids will be used to obtain the equations of the system's state, which provides excellent flexibility for modelling new components. [ABSTRACT FROM AUTHOR]

Energy demand is surging with the rise in population, economic development, and ever-increasing living standards. Due to sustainability and environmental issues, renewable energy sources have emerged as a credible option to meet this increased energy demand. However, it is plagued with the issue of variability and intermittency. Hybrid energy systems are proposed as a possible solution to this problem. The optimal sizing of hybrid energy systems ensures a reliable, efficient, and cost-effective power supply. Therefore, this paper discusses different hybrid energy systems in both on-grid and off-grid configurations, followed by the review of various sizing methodologies. The article also discusses various multi-criteria design indicators acting as decision variables, sensitivity variables, and constraints in different capacities while preparing the mathematical model of hybrid energy systems. As renewable resources and their based systems are inherently uncertain, it becomes imperative to characterize and model the uncertainty associated with such systems. Sincere efforts were made to understand various sources of uncertainty and how to characterize and model these uncertainties using different methodologies. The existing uncertainty modeling approaches were studied, compared, and analyzed. Further, the need for conducting sensitivity analysis and its usage in hybrid energy system design considering different sensitive parameters were also studied. [ABSTRACT FROM AUTHOR]

Energy generation and pollutant emissions are two faces of the same coin, as the current energy sources i.e., fossil energy, are still considered to be major sources of greenhouse gases (GHG). Therefore, shifting to cleaner energy sources imposes itself as an inevitable solution to reduce this environmental cost. In this paper, a hybrid system based on the multi-agent approach and Long Short-Term Memory (LSTM) neural networks to forecast the energy production and its carbon dioxide (CO2) emissions and simulate the potential emission reduction in case of switching to renewable energy sources is presented. The proposed system's architecture consists of combining LSTM models with the agent-based technology, where multiple LSTM forecasting models were trained to forecast the production of each type of the studied energies and then estimate the equivalent emitted CO2 and calculate the influence of the renewable energy inputs on the carbon emissions and the fossil fuels consumption. The simulation process consists of two phases: firstly, each forecasting agent uses a specific LSTM model to forecast short-term energy production. Secondly, these agents send the forecasted values to the coordination agent who is responsible for calculating the total CO2 emissions and the benefits of the renewable energy inputs. [ABSTRACT FROM AUTHOR]

Microwave drying (MWD) is an efficient dielectric drying method in food, with advantages such as volumetric heating, fast drying, safety, and good product quality. As a key indicator of a dryer's market value, energy efficiency is of concern to sellers and dryer manufacturers. This paper systematically reviewed the quantification methods and influencing factors of energy efficiency of microwave drying in food application from different perspectives. Mechanisms and possible improvements of these factors are highlighted. Future trends in improving the energy efficiency of MWD are proposed. Energy consumption of MWD depends on a variety of factors such as equipment structure, drying conditions (microwave power, frequency, temperature, and air velocity), material properties, and combined/hybrid drying technologies. The drying system can be effectively improved if these parameters are adjusted appropriately and taking the processing cost into consideration. Although a good product can be obtained by pretreatment or combined/hybrid drying method, it may consume more energy. Future research should develop artificial intelligence, renewable energy, and computational fluid dynamics technology to pave the way for large-scale application of MWD and reduce energy consumption. [ABSTRACT FROM AUTHOR]

In today's smart communities, small-scale energy systems are essential for sustainable development and efficient resource management. However, ensuring the confidentiality, safety, and accurate prediction of energy consumption patterns in energy trading is a major challenge. To address these issues, an innovative solution that synergistically combines two cutting-edge technologies: blockchain and machine learning is proposed. This paper unveils a novel approach that harmoniously merges blockchain with the Recalling-Enhanced Recurrent Neural Network (RERNN) to revolutionize energy trading systems called 'Blockchain-Enhanced Energy Trading with Recalling-Enhanced Recurrent Neural Network (BET-RERNN).' Data from IoT-enabled smart devices is securely stored in blockchain blocks, ensuring data integrity and immutability. Blockchain's decentralized nature creates a trust-less environment for energy trading, protecting the privacy and anonymity of participants while maintaining transparency. At the heart of our system lies the advanced machine-learning capabilities of the RERNN model. By processing the data stored on the blockchain, RERNN accurately predicts optimal power generation for small-scale energy systems, enabling smart communities to make informed decisions and optimize their energy consumption. The BET-RERNN scheme provides a plethora of strengths. First, participants can securely engage in energy trading without compromising sensitive information, fostering a more resilient and efficient market. Second, blockchain technology ensures that all energy-related data is protected from tampering and unauthorized access, ensuring system reliability and trust. An in-depth comparison of RERNN's performance to traditional General Regression Neural Network (GRNN) and Gradient Boost Decision Tree (GBDT) methods is conducted. To verify the strategy's effectiveness, MATLAB simulations are employed, demonstrating its real-world applicability and scalability. By combining blockchain and machine learning, a secure and privacy-preserving smart community is established, promoting sustainable energy practices for a greener future. [ABSTRACT FROM AUTHOR]

The main objective of this study is to model variable speed water-to-water heat pumps ( VSHP) and to examine the impact of the operation of such devices on ground heat exchanger sizing and energy consumption when they are used in ground-source applications. In the first part of the paper, a complete physics-based steady-state model of a variable-speed water-to-water heat pump is briefly presented. A performance map approach is also used by modifying an existing TRNSYS variable speed heat pump model to provide a minimum speed of operation and a better representation at part load. Simulation results over a heating season on a residential ground source VSHP indicate that the energy coverage (i.e., percentage of annual heat supplied by the heat pump) increases at a faster rate than the effect coverage (i.e., percentage of peak building heat supplied by the heat pump) for a small value of the effect coverage. For example, for effect coverage of 60%, the energy coverage is ∼ 93%. It is also shown that the normalized length of the ground heat exchanger varies linearly up to effect coverage of 60% where it is equal to 75% of the value encountered for effect coverage of 100%. [ABSTRACT FROM AUTHOR]

With the increasing penetration of renewable sources, the cogeneration integrated energy system (CIES) is much more efficient and flexible to achieve various types of energy supply for remote loads. Due to uncertainties in both sources and loads, the CIES should be carefully planned and optimised to keep it working at the suitable operation condition. Therefore, this paper proposes a multi-objective capacity allocation optimisation method for the islanded CIES, which is composed of renewable energy generation unit, electric-thermal conversion unit, electric storage, thermal storage and various loads. Firstly, three optimisation objectives are selected as typical indexes, including economic cost, solar energy utilisation, and life cycle carbon emission. An improved radar chart evaluation (RCE) solution is subsequently utilised for calculating a fitness function and building the multi-objective optimisation model. Besides, several compulsory constraints are supplemented to ensure the overall CIES performance satisfy energy supply requirement. As a mature and widely accepted algorithm, a genetic algorithm is chosen as an example to obtain the optimal capacity allocation scheme. Case study of an islanded CIES is provided on the basis of real data from the remote region of northwestern China, which successfully verifies the effectiveness of the proposed optimal capacity allocation method. [ABSTRACT FROM AUTHOR]

Based on particle size distribution characteristics, this paper explored the energy dissipation law in the coal ultrafine grinding process. By studying the fractal dimension of coal particle size distribution under the multi-domain fractal rule, the truncated particle size was introduced to characterize the energy consumption changes of coarse, intermediate, and fine particles. Combined with macro-energy consumption laws and three-piecewise fractal dimensions, a micro-energy consumption theory considering lattice fracture and agglomeration of ultrafine particles was proposed. On this basis, the energy consumption fractal dimension DE was proposed, and its theoretical calculation value was 2.021. The energy consumption formula of coal ultrafine grinding was established, in which energy consumption was characterized by the DEth power of the added surface area of coal particles. The DE obtained by the specific surface area and the adsorption equilibrium constant of pores were 2.285 and 2.564, respectively, which confirmed the reliability of the above theoretical analysis. [ABSTRACT FROM AUTHOR]

In view of the stochastic nature of wind and solar energy, maintaining quality power in grid-connected hybrid energy system is crucial for efficient energy utilization, especially in the presence of non-linear loads. This paper deals with the control and performance assessment of grid-integrated hybrid energy system that comprises of doubly fed induction generator (DFIG) and photovoltaic (PV) system including battery energy storage (BES) as a stand-by supply to meet utility and load demand. In order to maintain power balance and address various power quality issues encountered with hybrid renewable integration along the source and the grid/load end, a dual-layer variable-step size adaptive control is implemented for the grid-interfaced converter of the proposed system. The variable step-size based dual-layer least mean square (LMS) adaptive control functions by extracting weights of various control signals (stator and load current) where the step-size parameter adjusts according to the conditions that limit the maladjustment error and increase the rate of convergence. The DC-link voltage control realized by bidirectional BES control reduces the steady-state error and fluctuations in the voltage. An artificial bee colony (ABC) optimization is adopted for optimal gain tuning of the DC-link voltage controller as a result of which the battery performance and grid-side converter performance is enhanced with the increased power exchange to the grid. The system model is developed in MATLAB/Simulink and controller performance is evaluated for diverse operating conditions including steady-state, unbalanced non-linear load conditions, wind-speed and solar irradiation variation and specified grid power modes. [ABSTRACT FROM AUTHOR]