Energy Usage Costs Research Articles

Evaluation and comparative analysis of the mixed traffic flow on urban roads considering the green cost

Evaluating vehicles’ energy consumption and emissions is significant to ease energy shortages and prevent environmental pollution. This study focuses on evaluating energy consumption and emissions of the mixed traffic flow with human-driven and autonomous vehicles from both economic and environmental perspectives. The proposed Green Cost Model is comprised of the energy consumption models and emissions models, taking energy recovery of electric vehicles and specific power of fuel vehicles into consideration. Based on the Energy, Economic and Environment theory, the fuel price, electricity price and carbon taxation are utilized to analyze the cost of energy usage and emissions. The feasibility and effectiveness of the Green Cost Model are verified by simulations in several typical traffic scenarios. The simulation results indicate the green cost of both fuel powered and electric vehicles. In different scenarios, the electric vehicles have less green cost than fuel vehicles, and the total green cost of the fleet decreases as the penetration of autonomous battery electric vehicles (ABEV) increases. In addition, lower average green cost and higher service level are obtained by taking green cost into account in the intersection timing scheme. Finally, scientific and rational policy implications are put forward for the government, drivers and automakers.

BIM to BEM Transition for Optimizing Envelope Design Selection to Enhance Building Energy Efficiency and Cost-Effectiveness

This article focuses on developing an energy simulation model through Building Information Modeling (BIM) and Building Energy Modeling (BEM)to optimize energy in building design in Vietnam. Reducing the energy consumption in buildings will help reduce operating costs, impact the environment, and increase the efficiency of buildings. However, there is limited research on buildings with complex structures and configurations, detailed surface design, and envelope construction, especially in simulating details through BIM. The author proposes converting from BIM to BEM to simulate energy in buildings and optimize the factors related to building construction in the envelope design of the building. These factors include wall cladding materials, mirror materials, the window ratio on the walls, and other details. This study has effectively created an energy model for a public building, allowing for the calculation of the Energy Intensity Index (EUI) and annual energy costs for various scenarios. By altering factors associated with the design and construction process, the system has the potential to decrease both energy intensity and usage costs for the building. The study results will help designers and building managers improve and enhance energy efficiency in building projects.

Centrifuge-Free Separation of Solution-Exfoliated 2D Nanosheets via Cross-Flow Filtration.

Solution-processed graphene is a promising material for numerous high-volume applications including structural composites, batteries, sensors, and printed electronics. However, the polydisperse nature of graphene dispersions following liquid-phase exfoliation poses major manufacturing challenges, as incompletely exfoliated graphite flakes must be removed to achieve optimal properties and downstream performance. Incumbent separation schemes rely on centrifugation, which is highly energy-intensive and limits scalable manufacturing. Here, cross-flow filtration (CFF) is introduced as a centrifuge-free processing method that improves the throughput of graphene separation by two orders of magnitude. By tuning membrane pore sizes between microfiltration and ultrafiltration length scales, CFF can also be used for efficient recovery of solvents and stabilizing polymers. In this manner, life cycle assessment and techno-economic analysis reveal that CFF reduces greenhouse gas emissions, fossil energy usage, water consumption, and specific production costs of graphene manufacturing by 57%, 56%, 63%, and 72%, respectively. To confirm that CFF produces electronic-grade graphene, CFF-processed graphene nanosheets are formulated into printable inks, leading to state-of-the-art thin-film conductivities exceeding 104 S m-1 . This CFF methodology can likely be generalized to other van der Waals layered solids, thus enabling sustainable manufacturing of the diverse set of applications currently being pursued for 2D materials.

A novel dynamic pricing model for a microgrid of prosumers with photovoltaic systems

Due to the growing demand for electricity and the increasing number of consumers who can produce energy (prosumers) using photovoltaic systems today, energy generated by prosumers can be utilized in the microgrid instead of selling it to the main utility grid. Pricing is one of the most important mechanisms for motivating prosumers to interact with each other in the microgrid. Many works have proposed different pricing models that mostly focus on optimizing prosumers’ behavior and energy usage costs. However, most of the proposed models require constant involvement of the end-user to adjust energy consumption profiles, which is not always possible in a real-world scenario. In this paper, a novel pricing model is presented with the aim of maximizing the utilization of energy generated in the microgrid and reducing the import of energy from the utility grid, whereas ensuring more beneficial prices for energy within the microgrid compared with the utility grid. Mathematical models based on the supply and demand ratio and prosumers’ absolute deviation from the predicted energy usage profiles are developed to determine the internal equilibrium price and the amount of energy each prosumer can buy and sell by interacting with the microgrid. To cover the energy transfer losses in the microgrid, a dynamic loss allocation mechanism is proposed. The proposed pricing model is validated using real energy usage profiles from 100 prosumers. The results show that the total energy usage cost can be decreased, whereas the amount of unused energy that is shared outside the microgrid is minimized.

Residential Building Energy Conservation in Mediterranean Climate Zone by Integrating Passive Solar and Energy Efficiency Design Strategies

Residential buildings have a list of functions, and one of the top priories is the thermal comfort of its occupants. Thermal comfort can be one of the measurements of successful building performance, and it can be addressed in various ways to provide a good quality indoor climate. The way to address the need for thermal comfort determines the consumption and conservation of energy. Adapting passive strategies and thermal mass to a building can reduce energy consumption significantly. Applying these strategies to a building does not need additional investment from the initial capital, and renovating a building to implement the strategy will cut the energy usage of a building and save the additional financing of new technology for heating and cooling, ventilation, and so on. In this study, two residential buildings located in North Cyprus with the same geographical location, climate zone, and different construction materials were studied. The study aimed to make a technical test to reduce energy consumption and improve energy conservation in the building by applying passive solar and energy efficiency design strategies (PSEEDS). The thermal mass has been used and discussed as an improbable energy technique alongside other insulation materials and shading devices. The article discusses and compares the energy consumption and thermal comfort of traditional Mediterranean houses with contemporary houses. The study uses 2D drawings, 3D modeling, and energy simulation programs to evaluate energy consumption in both real and assumed scenarios. The study finds that traditional Mediterranean homes, with their thick walls and built-in shading mechanisms, require less energy for heating and cooling than contemporary houses. The implementation of passive solar energy efficient design strategies (PSEEDS) significantly reduces energy consumption in both types of houses, but traditional Mediterranean houses still show more efficiency due to their higher thermal mass. The research demonstrates that improving windows, examining the orientation of the structure, altering shade devices, and decreasing infiltration rates can reduce overall energy consumption by up to 34.71% To sum up, in this study, PSEEDS, the performance of thermal mass, and pre-design measures significantly decrease energy consumption. In addition, implementing passive strategies during a renovation of a building also compensates for the investment and lifecycle cost of new technologies and energy usage.

Comparing energy profiles of different building types by determining their sensitivities

ABSTRACT The main purpose of this study is to understand the energy consumption profiles of different building types by identifying and comparing their energy consumption parameters with their sensitivities to each other while indicating the long-term energy consumption implications on energy cost and CO2 emissions. A duplex house, apartment building, convention center, and school building were selected to develop dynamic simulation models for the investigation. The annual electric and gas demand of buildings were calculated and compared based on their energy usage intensity (kWh/m2), emission intensity (kg CO2/m2), and cost intensity (USD/m2) with their sensitivity to selected parameters. Further, the 50-year longevity of the buildings that include the selected materials’ production and demolition stages were included to estimate the lifetime. Also, retrofitting strategies including renewable technology integration were developed for each building type separately based on their energy concerns. The average energy consumption of parameters from highest to lowest are space heating, area lights, equipment, space cooling, hot water, fans, and pumps, respectively. The critical primary energy consumption of the educational building and convention center is 43% and 38% from equipment and 30% from space heating for residential buildings. A large portion of the emission is allocated to energy consumption during the lifetime of buildings, mainly due to electricity consumption. The energy costs mainly are also electricity related. Although the effects of retrofit strategies are different depending on the energy behavior of buildings, the most effective scenario is envelope retrofitting, followed by lighting improvement.

Alternative Construction using BIM in Old Educational Buildings

The current study analyzes historic educational buildings to discover the optimum combination of improvements for optimal energy cost. The analysis was conducted using BIM technology and associated programs such as Auto Desk Revit 2020 and Auto Desk Insight 360 to determine the optimal strategies by which the most applicable alternative construction materials and procedures are considered to obtain environmentally and economically sustainable old educational buildings. The analysis showed that several approaches may reduce the cost of electrical energy usage and generator fuel in aged schools. Optimal changes lead to such reductions. Altering wall and roof materials minimizes electrical energy usage. The findings reveal that altering the lighting efficiency (0.3 W/sf) in the best way decreases the cost of electrical energy consumption by about 65,575,500 Iraqi Dinars (ID), and reduces the cost of fuel energy consumption by approximately 510,000 ID.

An energy-efficient multi-objective integrated process planning and scheduling for a flexible job-shop-type remanufacturing system

This study considers an energy-efficient multi-objective integrated process planning and scheduling (IPPS) problem for the remanufacturing system (RMS) integrating parallel disassembly, flexible job-shop-type reprocessing, and parallel reassembly shops with the goal of realizing the minimization of both energy cost and completion time. The multi-objective mixed-integer programming model is first constructed with consideration of operation, sequence, and process flexibilities in the RMS for identifying this scheduling issue mathematically. An improved spider monkey optimization algorithm (ISMO) with a global criterion multi-objective method is developed to address the proposed problem. By embedding dynamic adaptive inertia weight and various local neighborhood searching strategies in ISMO, its global and local search capabilities are improved significantly. A set of simulation experiments are systematically designed and conducted for evaluating ISMO’s performance. Finally, a case study from the real-world remanufacturing scenario is adopted to assess ISMO’s ability to handle the realistic remanufacturing IPPS problem. Simulation results demonstrate ISMO’s superiority compared to other baseline algorithms when tackling the energy-aware IPPS problem regarding solution accuracy, computing speed, solution stability, and convergence behavior. Meanwhile, the case study results validate ISMO’s supremacy in solving the real-world remanufacturing IPPS problem with relatively lower energy usage and time cost.

Building energy management and Electric Vehicle charging considering battery degradation and random vehicles’ arrivals and departures

We study the energy management and Electric Vehicle (EV) charging optimization problem for a smart building integrating Renewable Energy Source (RES) considering battery degradation cost and random EVs’ arrivals/departures. Our design employs a general non-linear degradation cost model for lithium battery in which the degradation cost is a function of the Depth of Discharge (DoD). We develop a two-layer model predictive control (MPC) based framework to effectively deal with the uncertainties in EVs’ arrivals/departures and erroneous prediction of electricity load, RES energy, and energy price. Specifically, the upper layer with a longer optimization horizon aims to plan the energy for the EVs and ESS so that the total energy usage and battery degradation cost is minimized while satisfying the required energy levels of EVs before their departures. The lower layer optimizes the charging/discharging operations of the EVs and ESS over a shorter horizon following the energy planned by the upper layer. In extensive numerical studies, we study the impacts of different parameters on the achievable costs. Moreover, the proposed design is compared with different baselines using different cost models in the two layers and the one-layer MPC-based framework to demonstrate the efficacy of our proposed framework.

Operational optimization at the Nenäinniemi wastewater treatment plant's tertiary disc filter phosphorus removal installation to reduce chemical consumption.

Instrumentation, automation and control in municipal wastewater treatment can result in large resource, cost and energy savings. Feedforward and feedback control algorithms were implemented together with turbidity and phosphorus analysers to control the chemical dose at the tertiary stage of the Nenäinniemi wastewater treatment plant, consisting of coagulation, flocculation and microsieve filtration. This optimization lowered the coagulant dose by 70% and the polymer dose by 36% compared to manual adjustments of the chemical dosing. Effluent total phosphorus (TP) and total suspended solids (TSS) concentrations were lowered by 20-30%. With the control system in operation, the annual savings in coagulant and polymer were in the range of 100 and 1.4 tons, respectively. Conducting automated CIP on the media at an economical break-even interval of approximately 20 days was also important to further lower energy usage and operational costs.

DESIGN A MONITORING SYSTEM OF A SINGLE-PHASE ELECTRICITY METER BASED ON THE INTERNET OF THINGS

This research aims to create systematic monitoring of a single-phase electricity meter that is accompanied by power monitoring features, the amount of electrical energy usage costs and relay control features using internet connections. The design has been carried out using PZEM-004T current and voltage sensors. The microcontrollers have been implemented by using ESP8266. This configuration is to reduce separate micro-controller. The embedded microcontroller and WiFi are made improvements from previous research. This embedded microcontroller is used to send data to the internet through WIFI. This monitoring system can then be accessed using the Cayenne interface. The test results of this tool show that the connection between the microcontroller and the Cayenne application works well through an internet connection. With this tool, users can see real-time data on the use of electrical energy and its cost, as shown in the cayenne dashboard. The voltage sensor test results show an average accuracy is 99.17%. The current sensor testing has an average accuracy rate of 96.9%. On average, the wattage delta between the cayenne dashboard and multimeter measurement is 2.16 watts.

An energy-efficient multi-objective scheduling for flexible job-shop-type remanufacturing system

This work considers a multi-objective scheduling issue for the remanufacturing system (RMS) including parallel disassembly/reassembly workstations and flexible job-shop-type reprocessing shops with the purpose of reducing completion time and energy usage by deciding the allocation/sequence of disassembly/reassembly jobs and determining the operation sequencing and workstation assignment of reprocessing jobs. A multi-objective mixed-integer programming model is first constructed to describe this scheduling problem mathematically. An improved grey wolf optimization (IGWO) algorithm with the global criterion (GC) multi-objective method and integrated float-number solution representation scheme is introduced to realize high-efficient scheduling. Various local neighborhood search strategies, random disturbance methods, and weighted distance updating mechanisms are integrated into IGWO to enhance its performance. A series of numeral instances are systematically designed and implemented to validate the effectiveness of IGWO. Finally, a case study is deployed to evaluate IGWO’s capability to address the practical remanufacturing scheduling problem. Experimental results reveal that the developed IGWO performs better than other existing methods in terms of solution accuracy, computing speed, solution stability, and convergence performance. Furthermore, the results of case study demonstrate IGWO’s superiority in solving the real-world remanufacturing scheduling problem in lower energy usage and time cost.

HunterPlus: AI based energy-efficient task scheduling for cloud–fog computing environments

Cloud computing is a mainstay of modern technology, offering cost-effective and scalable solutions to a variety of different problems. The massive shift of organization resource needs from local systems to cloud-based systems has greatly increased the costs incurred by cloud providers in expanding, maintaining, and supplying server, storage, network, and processing hardware. Due to the large scale at which cloud providers operate, even small performance degradation issues can cause energy or resource usage costs to rise dramatically. One way in which cloud providers may improve cost reduction is by reducing energy consumption. The use of intelligent task-scheduling algorithms to allocate user-deployed jobs to servers can reduce the amount of energy consumed. Conventional task scheduling algorithms involve both heuristic and metaheuristic methods. Recently, the application of Artificial Intelligence (AI) to optimize task scheduling has seen significant progress, including the Gated Graph Convolution Network (GGCN). This paper proposes a new approach called HunterPlus which examine the effect of extending the GGCN’s Gated Recurrent Unit to a Bidirectional Gated Recurrent Unit. The paper also studies the utilization of Convolutional Neural Networks (CNNs) in optimizing cloud–fog task scheduling. Experimental results show that the CNN scheduler outperforms the GGCN-based models in both energy consumption per task and job completion rate metrics by at least 17 and 10.4 percent, respectively.

Technology Focus: Water Management (December 2022)

2022 has presented many challenges—not just inflation. Lowering water levels at Lake Mead and Lake Powell resulted in water crises in many states. Globally, many rivers dried, and that brought to surface over-5,000-year-old archeological artifacts that had been buried. An United Nations report says that nearly 4 billion people (approximately 60% of the world population) face water crises at least for a month every year. Despite all these admonishments, water—an essential raw material in industrial sector and a vital resource for human living—could be handled with more value and respect. The dollar value of water usage is much lower in the US compared with other regions, where it is more than twice its purchased value. The last 30 years or so have seen scientists and environmentalists apply conscientious use of technologies that focused on making water resources sustainable—chemistries that help us treat water, and intuitive and intelligent technologies that minimize need and maximize its purpose and benefit, remove contaminants, and ready it for repurposing. We can agree that awareness is universal, but when it comes to enforcing the actions needed to protect the resource as sustainable, questions about scalability and economic feasibility of many of the novel technologies arise. The three papers chosen for publication share different approaches to chemically managing high-salinity produced water through commercially viable approaches. The identification of processes to treat high salinity levels and the approaches to alter and control the process make the papers not only good for discussion but also practical for all players, big and small. The introduction of chemistries that are tagged and measurable positively influence discharge restrictions in different regions and could be exploited to stretch water reuse and the dollar value of water. The advent of smart sensors, probes, handhelds such as portable X-ray fluorescence and X-ray diffraction sensors, and machine learning make the treatment and reuse of high-saline water feasible to alleviate the water crisis. Technologies have advanced to lower the energy consumption and usage cost of membrane separation most commonly used in the water-reuse sector. The use of fresh water by the industry could be reduced significantly with the adaptation of some or all the smart technologies available today. The industry and the public are at a crossroads today, fighting for the right to use water. Governments are struggling to keep the balance and make the scarcest resource available to both sides. Let there not be another Akkadian empire that becomes extinct because of water. Recommended additional reading at OnePetro: www.onepetro.org. SPE 200171 Optimizing Waterflood Performance in Mature Fields Using Autonomous Inflow Control Valve Technology by Sufyan Shihab, Petroleum Development Oman, et al. SPE 209256 Improved Reverse-Osmosis Membranes for Treating Produced Water by Rich Franks, Nitto-Hydranautics, et al. SPE 204840 A Nano Method for a Big Challenge: Nanosilica-Based Sealing System for Water Shutoff by Ayman Almohsin, Saudi Aramco, et al.

Security Constrained P2P Energy Trading in Distribution Network: An Integrated Transaction and Operation Model

With the increasing penetration of distributed renewable energy, peer-to-peer (P2P) transactions among small scale consumers become possible. However, it remains challenging to maintain the operational security of distribution networks in P2P transactions. This paper proposes a novel integrated transaction and operation model to realize P2P transactions while ensuring the security constraints of distribution network simultaneously. The Vickrey-Clarke-Groves mechanism is used to solve the conflict between interests of prosumers in the P2P transactions and the operational requirements of distribution network. A zero-sum P2P settlement method is designed to quantify the expense among prosumers indicating their contributions to the safety operation of distribution network by controlling the flexible demand. A distributed algorithm based on sharing form alternating direction method of multipliers is developed for the P2P trading problem to derive transaction solutions of prosumers binding the power injection limits of connected buses. Numerical case studies based on IEEE 13-bus distribution network verify the effectiveness of the proposed model in motivating energy trading among prosumers against operational security violations of the distribution network and reducing the entire energy usage cost.

Battery Dispatching for End Users With On-Site Renewables and Peak Demand Charges—An Approximate Dynamic Programming Approach

Battery energy storage systems (BESSs) have the potential to reduce end users’ electricity bills by shifting their grid demand in response to price incentives. Some electricity retailers charge end users for their peak demand in the billing cycle regardless of the time of occurrence. Yet, using BESSs to reduce demand charges is challenging, since the net demand of end users can be highly uncertain due to low aggregation and on-site renewable generation. In this article, an approximate dynamic programming (ADP) methodology is developed to control a BESS to minimize an end user’s electricity bill, which includes both an energy charge and monthly peak demand charge. To address time-varying uncertainty, the net demand is modeled using a periodic autoregressive (PAR) model, which is then used to formulate a Markov Decision Process whose objective is to minimize the sum of energy, peak demand, and battery usage costs. A backward ADP strategy is developed which is enabled by new closed-form expressions for the probability distribution of the expected stage and tail costs when a radial basis function (RBF) value function approximation (VFA) is employed. The approach is applied to real net demand data from a small Australian residential community and compared to two benchmark policies: a lookup table VFA and a model predictive control (MPC) approach. The results show that the proposed approach reduces the average monthly peak demand by about 25% (yielding an 8% reduction in the electricity bill), whereas the best-performing benchmark policy reduced the peak demand by about 17%.

An Economic Feasibility Study on Solar Installation for University Campus: A Case of Universiti Utara Malaysia

Transforming towards renewable energy consumption has become the main agenda for the country due to the drawback of consuming conventional resources. Climate change, energy security, and price fluctuation are the main factors contributing towards the penetration of renewable energy. Solar energy seems to be the most attractive due to unlimited source of energy, and fundamentally, its availability will never run out. This form of energy can also be used as an alternative in producing electricity. Malaysia has potential in developing solar energy for daily usage due to its location in the equatorial area and acquiring approximately 400–600 MJ/ m–2 of solar radiation every month. Universiti Utara Malaysia (UUM) is a public higher learning institution located in Sintok, Kedah. Sintok is a potential area for the installation of this solar system because the area has a longer peak sun hour rate than other areas. The aims of this study are: (1) to analyse the factors involved in solar installation, and (2) to examine the economic feasibility of solar installation at a university campus. This study used a qualitative approach, whereby the data were taken from interviews with the Development and Maintenance Department of UUM and Detrolic Solar Company. The solar implementation in UUM could reduce the cost of energy usage annually. Furthermore, UUM could gain revenue from the Net Energy Metering (NEM) 3.0 Government Ministries and Entities (GoMEn) incentive. Solar installation in the campus could hasten the effort of UUM moving towards a green and eco-friendly university campus. It is best believed that this effort could be a success if UUM is ready to move towards sustainable energy with solar consumption.

Budget-Constrained Rail Electrification Modeling Using Symmetric Traffic Assignment: A North American Case Study

We consider a budget constrained rail network electrification problem with associated changes in costs of energy usage (via path gradient and curvature), operations, and long-term maintenance. In particular, we consider that freight flows on such a network form a user equilibrium. Interactions between electric and diesel trains on the same corridor are represented with nonseparable link performance functions, which nevertheless have a symmetric Jacobian. This bi-level formulation is solved for the North American railroad network using a genetic algorithm (GA), incorporating domain-specific insights to reduce the number of solutions which must be considered. We analyze solution characteristics and decision-making implications. Results show that broad connectivity would be beneficial for most impact. Increasing demand shifts electrified corridors towards the more populous east and gulf coasts, while increased operational costs results in electrification of routes through mountainous terrains.

Secure Wireless Networks Based on Fuzzy Logic for Smart HVAC Systems in Small-Scale Industries

The traditional heating, ventilation, and air conditioning (HVAC) system depends on wired mechanical thermostats and regulators deployed at various points in the industrial environment for temperature monitoring. Due to ineffective dynamic changes according to the environment, the traditional HVAC system consumes more electrical energy. Next-generation wireless network (NGWN) systems play a vital role in improving the overall efficiency of the system by continuous monitoring and analysis. A small-scale industry using HVAC systems needs an active smart energy saver technique for maintaining an economical budget. This work uses MATLAB software to simulate and analyze the utilization of fuzzy-based NGWN to reduce the energy consumption requirements of small-scale manufacturing. In the model, a fuzzy rule is designed and applied to a small-scale enterprise divided into five sectors, one of which is temperature sensitive, with the goal of lowering the energy bill. The model employs fuzzy rules to enhance the NGWN, which minimizes the energy usage cost by 30% as compared with the traditional existing HVAC systems.

Smart home comfort and energy conservation using internet of things

This new work presents a home automation system which employs internet of things (IoT) technology. This technology streamlines the design with an accurate home automation system which guides against the issues of energy wastage and excessive expenditure on the present systems of the utility company and the consumers. The system deploys a 32-bit ESP8266 system on chip (SoC) wireless fidelity (Wi-Fi) module which is linked to some sensors through a microcontroller for data communication. User friendly remote access, operation and management of the system are achieved through a mobile/web graphic user interface application. Daniel hall of students' residence at Covenant University is the location chosen for the deployment of prototype designed, the overall system achieved approximately 17% savings in energy consumption which in turn reduces the cost of energy usage for the same comform level.