Energy Cost Model Research Articles

The Green Flexible Job-Shop Scheduling Problem Considering Cost, Carbon Emissions, and Customer Satisfaction under Time-of-Use Electricity Pricing

Exploration of the green flexible job-shop scheduling problem is essential for enterprises aiming for sustainable practices, including energy conservation, emissions reduction, and enhanced economic and social benefits. While existing research has predominantly focused on carbon emissions or energy consumption as green scheduling objectives, this paper addresses the broader scope by incorporating the impact of variable energy prices on energy cost. Through the introduction of an energy cost model based on time-of-use electricity pricing, the study formulates a multi-objective optimization model for green flexible job-shop scheduling. The objectives include minimizing cost, reducing carbon emissions, and maximizing customer satisfaction. To prevent premature convergence and maintain population diversity, an enhanced genetic algorithm is employed for solving. The validation of the algorithm’s effectiveness is demonstrated through specific examples, providing decision results for optimal scheduling under various weight combinations. The research outcomes hold substantial practical value as they can significantly reduce energy expenses, lower carbon emissions, and elevate customer satisfaction while safeguarding production efficiency. This contributes to enhancing the market competitiveness and green brand image of businesses.

An approach for cost and configuration optimization of horizontal axis wind turbine (HAWT)

In addition to the environmental advantage of wind turbines, the modern wind turbine design is aiming to become more competitive by minimizing the cost of energy (COE). When evaluating any change to the design of a wind turbine, it is critical that the designer evaluates the impact of the design change on the system cost and performance. A typical problem when start a wind turbine design project is to determine the optimum configuration and operation parameters to minimize COE. In this article an optimization approach is adopted using COE as objective function with the rotor size, power rating and rated rotational velocity (RPM) as design variable. The cost of energy model of the National Renewable Energy Laboratory (NREL) is adopted with modifications to include the main components load level effects on the COE. An aerodynamic blade design is developed and used as base for evaluating the rotor performance. The blade is scaled to present different rotor size and operating conditions for each power rating. Analysis tool is developed to consider coupled interactions between power rating and the rotor size. The Blade Element Momentum (BEM) technique is used to evaluate the effect of configuration and operational conditions on the wind turbine load levels and the expected annual energy production (AEP). These are used as inputs for the proposed COE model in addition to main parameters presenting the manufacturing technology and site conditions. The COE model is based on several elements such initial capital cost (ICC), balance of station (BOS), operations and maintenance (O&M), levelized replacement cost (LRC), AEP and design load levels. A pattern search technique is adopted for the optimization and the approach is illustrated by means of a principle test examples for two types of turbines platform one for low wind onshore site and another for high wind offshore site.

Spatial and thermal aware methods for efficient workload management in distributed data centers

Geographically distributed data centers provide facilities for users to fulfill the demand of storage and computations, where most of the operational cost is due to electricity consumption. In this study, we address the problem of energy consumption of cloud data centers and identify key characteristics of techniques proposed for reducing operational costs, carbon emissions, and financial penalties due to service level agreement (SLA) violations. By considering computer room air condition (CRAC) units that utilize outside air for cooling purposes as well as temperature and space-varying properties, we propose the energy cost model which takes into account temperature ranges for cooling purposes and operations of CRAC units. Then, we propose spatio-thermal-aware algorithms to manage workload using the variation of electricity price, locational outside and within the data center temperature, where the aim is to schedule the incoming workload requests with minimum SLA violations, cooling cost, and energy consumption. We analyzed the performance of our proposed algorithms and compared the experimental results with the benchmark algorithms for metrics of interest including SLA violations, cooling cost, and overall operations cost. Modeling, experiments, and verification conducted on CloudSim with realistic data center scenarios and workload traces show that the proposed algorithms result in reduced SLA violations, save between 15% to 75% of cooling cost and between 3.89% to 39% of the overall operational cost compared to the existing solutions.

Optimal Feedback Control for the Proportion of Energy Cost in the Upper-Arm Reaching Movement.

The minimum expected energy cost model, which has been proposed as one of the optimization principles for movement planning, can reproduce many characteristics of the human upper-arm reaching movement when signal-dependent noise and the co-contraction of the antagonist's muscles are considered. Regarding the optimization principles, discussion has been mainly based on feedforward control; however, there is debate as to whether the central nervous system uses a feedforward or feedback control process. Previous studies have shown that feedback control based on the modified linear-quadratic gaussian (LQG) control, including multiplicative noise, can reproduce many characteristics of the reaching movement. Although the cost of the LQG control consists of state and energy costs, the relationship between the energy cost and the characteristics of the reaching movement in the LQG control has not been studied. In this work, I investigated how the optimal movement based on the LQG control varied with the proportion of energy cost, assuming that the central nervous system used feedback control. When the cost contained specific proportions of energy cost, the optimal movement reproduced the characteristics of the reaching movement. This result shows that energy cost is essential in both feedforward and feedback control for reproducing the characteristics of the upper-arm reaching movement.

Multi-objective optimization of the cathode catalyst layer micro-composition of polymer electrolyte membrane fuel cells using a multi-scale, two-phase fuel cell model and data-driven surrogates

Polymer electrolyte membrane fuel cells (PEMFCs) are considered a promising alternative to internal combustion engines in the automotive sector. Their commercialization is mainly hindered due to the cost and effectiveness of using platinum (Pt) in them. The cathode catalyst layer (CL) is considered a core component in PEMFCs, and its composition often considerably affects the cell performance (Vcell) also PEMFC fabrication and production Cstack costs. In this study, a data-driven multi-objective optimization analysis is conducted to effectively evaluate the effects of various cathode CL compositions on Vcell and Cstack. Four essential cathode CL parameters, i.e., platinum loading (LPt), weight ratio of ionomer to carbon (wtI/C), weight ratio of Pt to carbon (wtPt/c), and porosity of cathode CL (εcCL), are considered as the design variables. The simulation results of a three-dimensional, multi-scale, two-phase comprehensive PEMFC model are used to train and test two famous surrogates: multi-layer perceptron (MLP) and response surface analysis (RSA). Their accuracies are verified using root mean square error and adjusted R2. MLP which outperforms RSA in terms of prediction capability is then linked to a multi-objective non-dominated sorting genetic algorithm II. Compared to a typical PEMFC stack, the results of the optimal study show that the single-cell voltage, Vcell is improved by 28 mV for the same stack price and the stack cost evaluated through the U.S department of energy cost model is reduced by $5.86/kW for the same stack performance.

Edge-Enhanced Attentions for Drone Delivery in Presence of Winds and Recharging Stations

Existing variants of vehicle routing problems have limited capabilities in describing real-world drone delivery scenarios in terms of drone physical restrictions, mission constraints, and stochastic operating environments. To that end, this paper proposes a specific drone delivery problem with recharging (DDP-R) characterized by directional edges and stochastic edge costs subject to wind conditions. To address it, the DDP-R is cast into a Markov decision process over a graph, with the next node chosen according to a stochastic policy based on the evolving observation. An edge-enhanced attention model (AM-E) is then suggested to map the optimal policy via the deep reinforcement learning (DRL) approach. The AM-E comprises a succession of edge-enhanced dot-product attention layers and is designed with the aim of capturing the heterogeneous node relationship for DDP-Rs by incorporating adjacent edge information. Simulations show that edge enhancement facilitates the training process, achieving superior performance with less trainable parameters and simpler architecture in comparison with other deep learning models. Furthermore, a stochastic drone energy cost model in consideration of winds is incorporated into validation simulations, which provides a practical insight into drone delivery problems. In terms of both nonwind and windy cases, extensive simulations demonstrate that the proposed DRL method outperforms state-of-the-art heuristics for solving DDP-Rs, especially at large sizes.

ETAP: Energy-aware Timing Analysis of Intermittent Programs

Energy harvesting battery-free embedded devices rely only on ambient energy harvesting that enables stand-alone and sustainable IoT applications. These devices execute programs when the harvested ambient energy in their energy reservoir is sufficient to operate and stop execution abruptly (and start charging) otherwise. These intermittent programs have varying timing behavior under different energy conditions, hardware configurations, and program structures. This article presents Energy-aware Timing Analysis of intermittent Programs (ETAP), a probabilistic symbolic execution approach that analyzes the timing and energy behavior of intermittent programs at compile time. ETAP symbolically executes the given program while taking time and energy cost models for ambient energy and dynamic energy consumption into account. We evaluate ETAP by comparing the compile-time analysis results of our benchmark codes and real-world application with the results of their executions on real hardware. Our evaluation shows that ETAP’s prediction error rate is between 0.0076% and 10.8%, and it speeds up the timing analysis by at least two orders of magnitude compared to manual testing.

Energy efficient path planning for autonomous ground vehicles with ackermann steering

The autonomous ground vehicles have attracted a great deal of attention as viable solutions to a wide variety of military and civilian applications. However, the energy consumption plays a major role in the navigation of autonomous ground vehicles in challenging environments, especially if they are left to operate unattended under limited on-board power, such as planetary exploration, border patrol, etc. The autonomous ground vehicles are expected to perform more tasks more efficiently with limited power in these scenarios. Although plenty of research has developed an effective methodology for generating dynamically feasible and energy efficient trajectories for skid steering or differential steering vehicles, few studies on path planning for ackermann steering autonomous ground vehicles are available. In this study, an energy efficient path planning method with guarantee on completeness is proposed for autonomous ground vehicle with ackermann steering which is based on A∗ search algorithm. Firstly, the energy cost model is established for the autonomous ground vehicle using its kinematic constraints. Then, given the start and goal states, the energy-aware motion primitives are generated offline using the energy cost model to calculate the cost of each primary trajectory. Lastly, the energy efficient path planner is proposed and the analysis for completeness properties is given. The effectiveness of the proposed energy efficient path planner is verified by simulation over 150 randomly generated maps and real vehicle tests. The results show that a small increase in the distance of a path over the distance optimal path can result in a reduction of energy cost by nearly 26.9% in simulation and 21.09% in real test scenario for autonomous ground vehicles with ackermann steering.

An IoT-Based Energy Conservation Smart Classroom System

With the increase of energy consumption worldwide in several domains such as industry, education, and transportation, several technologies played an influential role in energy conservation such as the Internet of Things (IoT). In this article, we describe the design and implementation of an IoT-based energy conservation smart classroom system that contributes to energy conservation in the education domain. The proposed system not only allows the user to access and control IoT devices (e.g., lights, projectors, and air conditions) in real-time, it also has the capability to aggregate the estimated energy consumption of an IoT device, the smart classroom, and the building based on the energy consumption and cost model that we propose. Moreover, the proposed model aggregates the estimated energy cost according to the Saudi Electricity Company (SEC) rates. Furthermore, the model aggregates in real-time the estimated energy conservation percentage and estimated money-saving percentage compared to data collected when the system wasn't used. The feasibility and benefits of our system have been validated on a real-world scenario which is a classroom in the college of computer science and engineering, Taibah University, Yanbu branch. The results of the experimental studies are promising in energy conservation and cost-saving when using our proposed system.

Wind Turbine Energy Cost Optimisation Using Various Power Models

In modern times, the worldwide wind turbine installations have developed swiftly resulting in the decrease of green gas emissions. Though wind is a free gift of nature, it is expensive to harness this energy for useful applications like electricity generation. The cost of installation of the wind turbine at a particular station does not depend only on the wind resource, but also on the structure of the turbine and the energy conversion technology. The wind turbine Cost of Energy (CoE) is used to estimate the payback time for the return on the investment made by the wind farm owners for the turbine. Meticulous research is required to optimize the turbine CoE which will make wind a very competent source of energy. In this article, in order to minimize the wind turbine CoE, the wind speed is modelled using three different distributions namely, Dagum, Gamma and Weibull and the evaluation of the turbine Annual Energy Production (AEP) is carried out. Mathematical functions such as linear, quadratic and cubic have been used to model the wind power. For the cost analysis of the turbine, the price model which was established by United States, National Renewable Energy Laboratory (NREL) is employed. The comparative study of the proposed methodology have been done for six different stations. The turbine CoE model is an element of two factors, the rated power Pr of a turbine and the rated wind speed Vr of a turbine. Based on the results obtained, a broad recommendation to reduce the turbine CoE is presented. This study enables us to figure out the minimum turbine CoE among the three discussed mathematical distributions, the finest distribution for wind speed modelling and the optimum mathematical function for wind power modelling. The suitable size of the wind turbine also can be found by optimizing the rotor radius R of the turbine for each data.

Multigroup Multicast Downlink Cell-Free Massive MIMO Systems With Multiantenna Users and Low-Resolution ADCs/DACs

In this article, the effects of multiantenna users and low-resolution analog-to-digital converters/digital-to-analog converters (ADCs/DACs) on multigroup multicast downlink cell-free massive multi-input multi-output (MIMO) systems are explored. Leveraging on the distributed conjugate beamforming receiver and the generalized additive quantization noise model, we first derive a closed-form expression for the achievable sum spectral efficiency (SE). The resultant expression enables us to precisely quantify the effects of key parameters, such as the transmit power of access points (APs), the numbers of groups, users’ antennas, APs’ antennas, and quantization bits. Due to the joint influence of channel hardening property, multiantenna gain, and intergroup interference, the research becomes sophisticated particularly. The result shows that when the sum-SE is small, the multicast is better than the unicast and vice versa. Typically, it reveals that the full-resolution DAC can be replaced by 5-bit low-resolution DAC to achieve the same performance, and the additional antenna at users can effectively enhance the system performance. Then, according to a generated energy cost model, the formula of the energy efficiency (EE) is obtained. When the DAC quantization is equal to 2-bit, it can achieve the maximum sum EE. Finally, the simulation results are provided to verify the derived results.

Cooperative Scheduling Schemes for Explainable DNN Acceleration in Satellite Image Analysis and Retraining

The deep learning-based satellite image analysis and retraining systems are getting emerging technologies to enhance the capability of the sophisticated analysis of terrestrial objects. In principle, to apply the explainable DNN model for the process of satellite image analysis and retraining, we consider a new acceleration scheduling mechanism. Especially, the conventional DNN acceleration schemes cause serious performance degradation due to computational complexity and costs in satellite image analysis and retraining. In this article, to overcome the performance degradation, we propose cooperative scheduling schemes for explainable DNN acceleration in analysis and retraining process. For the purpose of it, we define the latency and energy cost modeling to derive the optimized processing time and cost required for explainable DNN acceleration. Especially, we show a minimum processing cost considered in the proposed scheduling via layer-level management of the explainable DNN on FPGA-GPU acceleration system. In addition, we evaluate the performance using an adaptive unlabeled data selection scheme with confidence threshold and a semi-supervised learning driven data parallelism scheme in accelerating retraining process. The experimental results demonstrate that the proposed schemes reduce the energy cost of the conventional DNN acceleration systems by up to about 40% while guaranteeing the latency constraints.

Optimal reaching trajectories based on feedforward control.

In human upper-arm reaching movements, the variance of the hand position increases until the middle of the movement and then decreases toward the endpoint. Such decrease in positional variance has been suggested as an evidence to support the hypothesis that our nervous system uses feedback control, rather than feedforward control, for arm reaching tasks. In this study, we computed the optimal trajectories based on feedforward control under several criteria for a one-link two-muscle arm model with considering the stochastic property of muscle activities in order to reexamine the hypothesis. The results showed that the feedforward control also represents the decrease in positional variance in the latter half of the movement when the control signal is planned under the minimum energy cost and minimum variance models. Furthermore, the optimal trajectory that minimizes energy cost represents not only the decrease in positional variance but also many other characteristics of the human reaching movements, e.g., the three-phasic activity of antagonistic muscle, bell-shaped speed curve, N-shaped equilibrium trajectory, and bimodal profile of joint stiffness. These results suggest that minimum energy cost model well expresses the characteristics of hand reaching movements, and our central nervous system would make use of not only a feedback control but also feedforward control.

Life Cycle Assessment and Multiobjective Optimization for Steam Cracking Process in Ethylene Plant.

With energy savings and emission reduction becoming national policies in recent years, the environmental impacts of industrial production are more and more critical. Most of the studies have concentrated on the environmental effects of the industrial production process. Little attention has been paid to the energy consumption and pollution emission in extracting, processing, and transporting the feedstock and other secondary materials. An integrated multiobjective optimization framework is proposed for the steam cracking process on the basis of a life cycle assessment and data-driven modeling methods. A multiobjective economic–environmental optimization model is developed on the basis of industrial and simulated data. A multiobjective optimization model combined with energy cost is also developed for comparative study. The nondominated sorting genetic algorithm-II is utilized to solve the problems, and the Pareto front is obtained. An industrial case study is carried out to indicate the effectiveness of the proposed method. The results show that the LCA-based method can better represent the environmental impacts in comparison with the standard energy cost model. Therefore, the proposed method can achieve a better tradeoff between economic benefits and environmental impacts for guiding ethylene production.

Multi-site and multi-objective optimization for wind turbines based on the design of virtual representative wind farm

The design optimization of wind turbines is an effective solution for reducing the generation cost of wind power and enhancing its market competitiveness. Because wind power projects often require a long construction period and large investment, designing a wind turbine suitable for multiple sites is economically feasible. In this study, a multi-site and multi-objective optimization framework was proposed. Based on the modified energy cost model at single wind farm level, the concept of a virtual representative wind farm (VRWF) was presented, wherein a weight calculation method to design the weights of multiple wind farms was proposed. To simultaneously maximize the annual energy production and minimize the annual production cost of the VRWF, the non-dominated sorting multi-objective harmony algorithm was improved and employed for conducting the optimization, using which the non-dominated solutions were obtained. Subsequently, the subjective and objective combined fuzzy membership function method was presented to determine the best parameters from non-dominated solutions. The proposed method was applied to a case involving three wind farms, with the results confirming its applicability. Compared with the single-site optimization, the proposed multi-site optimization reduced the overall energy cost by 0.4%, 0.8%, and 1.9%, respectively, indicating the necessity of employing multi-site and multi-objective optimization.

ИССЛЕДОВАНИЕ ХАРАКТЕРИСТИК СХЕМ РЕТРАНСЛЯЦИИ СИГНАЛА В БЕСПРОВОДНЫХ СЕТЯХ ДАТЧИКОВ

Wireless sensor networks (WSNs) are being actively implemented in various systems for remoteobservation and monitoring of distributed objects. WSNs have a number of undoubted advantages:flexibility, efficiency, relative cheapness, and the possibility of rapid deployment. However,the exchange of information and data is carried out in the WSN using wireless communicationchannels, which are subject to inevitable interference and noise, which leads to transmission errors and even to the loss of transmitted data packets. Another challenge, not fully resolved, is theuneven distribution of consumed energy within the WSN in the face of stringent requirements forenergy sources. Currently, there are two most widely used retransmission schemes in the loss oftransmitted data, namely, hop-by-hop and end-to-end. Most of the well-known studies devoted tothe issues of reliable data transmission in WSN using these schemes have been carried out experimentally.In addition, there are still no analytical methods for evaluating various reliabletransport solutions, which complicates the analysis of the proposed WSN. Therefore, the aim of theproposed work is the development of analytical methods and algorithms for studying the operatingcharacteristics of the signal relaying circuits in the WSN. Analytical methods are proposed forevaluating retransmission schemes in the WSN, based on a relatively new theoretical basis - thenetwork calculus for packet-switched networks, which is a tool for determining the size of the network.First, traffic, service and energy cost models are introduced. Based on these models andnetwork calculations, the maximum packet transmission delay and energy efficiency of the twomain types of retransmission schemes: hop-by-hop and end-to-end retransmission, are analyticallyestimated. According to the results of the experiment, the maximum latency and the maximumpower consumption of these two schemes are compared in several scenarios. In addition, the analyticallycalculated maximum delay is compared with the simulation results. With the proposedmethod, a suitable retransmission scheme can be selected based on the various requirements andconstraints to be set.

Simulation study on terminal layout in automated container terminals from efficiency, economic and environment perspectives

Terminal layout design is an important strategic decision to obtain efficient automated container terminals. Recently, several types of layout design are being implemented to realize side-loading operations, such as U-type and parallel layout design for bilateral-cantilever automated rail-mounted gantry cranes. However, it is lack of a comprehensive comparation with perpendicular layout design for non-cantilever gantry cranes, which allows end-loading operations. Besides, the impact of layout design on terminal performance, such as annual throughput, ship waiting time and operation efficiency of quay cranes, is still not well understood and needs to be estimated in the planning phases of automated terminals. Thus, this study conducts a detailed simulation research on different types of layout design to compare their terminal performance. Novel agent-based simulation models are developed, which incorporate side-loading and end-loading operations under different types of layout design. And simulation-based energy consumption and cost models are developed to evaluate their energy consumption and total cost. Results indicate that U-type automated terminals gain the lowest energy consumption and operation cost, and outperform those with perpendicular layout in terms of operation efficiency and waiting time in most cases. The valuable reference regarding layout design is identified for terminal operators to make strategic decisions.

Identifying practical sustainable retrofit measures for existing high-rise residential buildings in various climate zones through an integrated energy-cost model

Sustainable building retrofit can provide a great opportunity to reduce the energy consumption of existing buildings to commensurate the climate policies and enhance the building energy performance. Given it strategic importance, there is no shortage of research studies on sustainable building retrofit globally in particular the retrofit measures and technologies for different types of buildings. Despite that, sustainable building retrofit is climate and location specific, and decision-makers are still facing challenges to identify an optimum set of retrofit measures for different climatic conditions according to the trade-off between energy saving and retrofit cost. One reason for that is because of the lack of available information especially those related to the cost of sustainable building retrofit measures. This study aims to develop an integrated energy-cost model for selecting the optimal retrofit solutions for existing high-rise residential buildings in various climatic zones in China. Based on in-depth literature review and analysis of published reports, different alternative retrofit measures for wall, window, shading system, heating and cooling systems, and renewable energy technologies for buildings in different climate zones of China are identified. The retrofit costs related to the identified sustainable building retrofit measures are analyzed by referring to the local renovation techniques and cost in each zone through developing a set of equations. A total 28 practical sustainable building retrofit measures along with a set of retrofit options are identified. A deterministic decision framework is developed to facilitate decision-makers achieving a specific energy saving target and evaluating the associated costs based on the identified retrofit measures for high-rise residential buildings in the studied climatic zones. The practical implications of the deterministic decision framework are examined through the prototype buildings in climatic zones being studies. The findings not only actively support the selection of cost-effective sustainable retrofit measures for high-rise residential buildings in China, but the methodological framework and its implications are also equally applicable to other countries with diverse climatic characteristics and standards of living.

Research on Multiple Demand Response in Integrated Energy System Containing Power, Gas and Heat

Integrated demand response provides an important direction for realizing the bidirectional-interaction between supply and demand in the integrated energy system. In view of the differences in the time, space, cost and other aspects of users’ demand for multi-energy, a integrated demand response method based on the energy pricing strategy of the integrated energy system is proposed. Firstly, the characteristics of integrated demand response resources are analyzed, and various integrated demand response models are established. Secondly, based on the nodal energy price, considering the energy response potential, and taking the user energy cost as the optimization goal, an energy cost model that takes into account the load change of the user’s integrated demand response is established. Finally, an example is used to analyze the multi-energy load response under the integrated energy system nodal energy price strategy, which is beneficial to promote the rational use of energy by multi-energy users and reduce energy costs.

Energy Saving for Tissue Paper Mills by Energy-Efficiency Scheduling under Time-of-Use Electricity Tariffs

Environmental concerns and soaring energy prices have brought huge pressure of energy saving and emission reduction to tissue paper mills. Electricity is one of the main energy sources of tissue paper mills. The production characteristics of tissue paper mills make it easy to decrease energy cost by using time-of-use (TOU) electricity tariffs. This study investigates the bi-objective energy-efficiency scheduling of tissue paper mills under time-of-use electricity tariffs, the objectives of which are makespan and energy cost. First, considering the processing energy cost, setup energy cost, and transportation energy cost, an energy cost model of a tissue paper mill under TOU electricity tariffs is established. Second, the energy-efficiency scheduling model under TOU electricity tariffs is built based on the energy cost model. Finally, on the basis of decomposition and teaching–learning optimization, this study proposes a novel multi-objective evolutionary algorithm and further combined with the variable neighborhood search to solve the problem. The case study results demonstrate that our study of tissue paper mill energy saving is feasible, and the proposed method has better performance than the existing methods.