Terms Of Energy Cost Research Articles

Efficient on-demand spectrum sensing in sensor-aided cognitive radio networks

Spectrum Sensing as a Service (SSaS) has recently emerged as a promising solution to realize dynamic spectrum access networks (aka, cognitive radio networks (CRNs)). This paradigm relies on a dedicated infrastructure wireless sensor network, operated by a spectrum sensing service provider S3P entity, for spectrum monitoring. Wireless users which are interested in knowing the status of a given spectrum band make a request to the S3P to perform that monitoring on its behalf. In this work, we study the problem of optimizing the selection of sensors needed to serve a given request such that the total cost (consumed energy) is minimized. We consider a spatial and temporal scheduling problem, in which, the energy levels at sensors, the maximum transmission range of secondary user (SUs), and the required monitoring time are taken into account. The problem is formulated as an Integer Linear Program (ILP). Then a sub-optimal greedy algorithm is proposed with two variations. Thorough evaluation shows that the proposed algorithm performs very well with respect to the optimal solution. Finally, results illustrate that the first algorithm variation is better in terms of energy cost, and the second variation allows for serving requests with a lower number of nodes.

Exploiting Multi-Verse Optimization and Sine-Cosine Algorithms for Energy Management in Smart Cities

Due to the rapid increase in human population, the use of energy in daily life is increasing day by day. One solution is to increase the power generation in the same ratio as the human population increase. However, that is usually not possible practically. Thus, in order to use the existing resources of energy efficiently, smart grids play a significant role. They minimize electricity consumption and their resultant cost through demand side management (DSM). Universities and similar organizations consume a significant portion of the total generated energy; therefore, in this work, using DSM, we scheduled different appliances of a university campus to reduce the consumed energy cost and the probable peak to average power ratio. We have proposed two nature-inspired algorithms, namely, the multi-verse optimization (MVO) algorithm and the sine-cosine algorithm (SCA), to solve the energy optimization problem. The proposed schemes are implemented on a university campus load, which is divided into two portions, morning session and evening session. Both sessions contain different shiftable and non-shiftable appliances. After scheduling of shiftable appliances using both MVO and SCA techniques, the simulations showed very useful results in terms of energy cost and peak to average ratio reduction, maintaining the desired threshold level between electricity cost and user waiting time.

Design and analysis of optimal pre-cooling in residential buildings

Existing pre-cooling strategies provide a means of shifting or reducing the peak demand and/or energy cost in residential buildings. However, majority of them are rule-based and therefore may not be optimal in terms of cost saving, leaving room for improvement. In this paper, an integer linear programming problem that accounts for the thermal properties of a specific home, HVAC system capacity, utility rate structure, and weather conditions and makes use of a home thermal model is formulated. This problem determines the HVAC on/off control signal that minimizes the 24-h energy cost while maintaining thermal comfort and calculates the corresponding optimal indoor air temperature. The model is constructed using home thermal properties identified via data training in real-time. Through simulation, the energy performance of the proposed optimal pre-cooling strategy is investigated and compared with three rule-based operation strategies from the literature. It is found that the optimal strategy requires the least energy consumption without sacrificing thermal comfort. The superb energy performance of the optimal strategy is attributed to a longer runtime of the HVAC system in cool outdoor air conditions and to the elimination of deadband in HVAC operation, which is required by the rule-based strategies, to allow the indoor air temperature to stay near the thermal comfort upper bound as much as possible. In terms of energy cost, the rule-based operation strategies require $3.52, $1.90, and $2.79, respectively, while the optimal strategy only requires $1.52. These figures represent a saving of 56.82%, 20.00%, and 45.52%, respectively. The results suggest that the optimal strategy is indeed significantly more effective than the existing rule-based operation strategies.

Plasma Technology in Cold Storage for Shelf Life Extension of Fresh Fish

Low temperature storage or cold storage to prevent bacteria and microorganisms from growing on food. Maintaining the quality of fresh fish when stored above freezing temperatures requires special treatment to maintain the quality and prolong the storage. The use of ozone as disinfectant could be a solution to the decreasing quality of fresh fish. The combination of ozone technology with cold storage gives a great advantage in storing fish in fresh conditions. The use of ozone generating plasma technology in cold storage offers a space for increased energy efficiency and economic value by using three time less energy use intensity than cold storage without plasma technology, which means that the plasma technology in cold storage provides a three-fold benefit in terms of energy costs per kilogram of fresh fish per day. A prolonged shelf life as well as a decrease of energy use intensity is very important in food distribution chain, especially for fresh fish that are vulnerable to damage.

Coalition Formation of Microgrids with Distributed Energy Resources and Energy Storage in Energy Market

In recent years, significant efforts for improving the technical and the economic performance of smart grids have been applied with the presence of different players making decisions in these grids. This paper proposes a bi-level hierarchical structure for designing and planning distributed energy resources in home microgrids (H-MGs). Such a small-scale grid inside the market environment permits energy exchange among distributed energy resources and home microgrids through a pool market. In this paper, each home microgrid's planner maximizes the performance of distributed energy resources while exchanging energy with other H-MGs. The problem is formulated as a high-level problem decomposed into a set of low-level market clearing problems. The global optimal performance in terms of energy cost is met for a market structure (H-MGs, consumers, and retailers) at a Nash optimum point for the formulated scheduling game considering local and general constraints on the spot market. In general, the upper-level structure is based on H-MG generation competition for maximizing their individual and/or group income in the process of forming coalition with other H-MGs. In addition, the functionality of the lower-level of the hierarchical structure is governed by a market clearing based on the price response method by all the DERs enabling H-MGs to change the spot market strategic behavior. Prices are obtained as dual variables of power balance equations. This paper will investigate a set of bilevel games, including nonlinear programming problems solved through a complementary problem method. Using the binary theory, the bilevel hierarchical structure will be replanned using a nonlinear problem. Results prove that the proposed structure can increase the players's profit.

A novel energy-efficient framework (NEEF) for the wireless body sensor network

Increasing the availability of wireless body sensor network is obligatory to monitor the patient inside and outside the hospital environment. Major amount of energy dissipated in the sensor node is through the enormous switching transitions in the transceiving unit, and hence, it becomes essential to minimize the switching transitions to prolong the lifetime of the network. Also, the packet holding cost in terms of energy influences the network lifetime to a greater extent. The novel energy-efficient framework (NEEF) approach provides a solution for lifetime problem and availability problem in the network. The subject is modeled as finite state machine with normal, abnormal and above normal states. The packet in the cluster head is transmitted once it reaches the threshold level. The critical packet is hoped through high energy node to ensure safe data communication. The framework is evaluated with Fail Safe Fault Tolerant algorithm. The lifetime enhancement is achieved through reducing the switching loss of the transceivers circuit. The NEEF provides extended lifetime and throughput and the framework also promotes improved half-life period. The first dead node in case of NEEF framework is high. The NEEF ensures high availability and extended lifetime.

An Energy Community Implementation: The Unical Energy Cloud

Reducing greenhouse gas emissions, limiting the effects of climate change and decreasing the environmental, social and economic costs of energy production are some of the main issues related to the sustainable development of modern society. Energy communities, envisioned to enable local energy exchange between consumers and producers of renewable energy, represent a possible scenario towards a cleaner and sustainable energy system. In this paper, an energy community management model called Power Cloud and presented in previous papers is proposed for a real-world practical application at the University of Calabria. In particular, the implementation of the information and communication technology (ICT) architecture and other enabling technologies, such as the nanogrid and the smart energy box, are discussed in detail. The experiment results show that by adopting the Power Cloud management model it is possible to obtain significant savings in terms of energy cost, which provide benefit for a community, such as a university campus.

Application Task Allocation in Cognitive IoT: A Reward-Driven Game Theoretical Approach

In this study we consider the scenario of sensors belonging to different platforms and owned by different owners that join the efforts in an opportunistic way to improve the overall sensing capabilities in a given geographical area by forming clusters of nodes. The considered nodes have cognitive radio and exploit device-to-device communications. A solution is proposed which relies on a Cluster Head (CH) that guides the whole task allocation strategy. The addressed challenges are the following: i) collaborative spectrum sensing for effective communications within the cluster; ii) assignment of each request of sensing tasks to a single node in the cluster. The first challenge is addressed by proposing a collaborative sensing procedure where each node communicates to the CH the received signal energy of licensed users so that the latter makes a decision on the availability of the band by fusing the received information towards a minimisation of the uncertainty in detecting the free spectrum. The second challenge is addressed by proposing a non-cooperative Game theory based approach in which cluster nodes make effort to selfishly increase utility by winning the task. Each node takes part to the competition by considering two elements: the gain that is won for its contribution to sensing and for the execution of the task (in case it wins the competition); the cost in terms of energy to be consumed in case the task is executed. A Nash Equilibrium Point (NEP) is found for the aforementioned game in which each object has no incentive to deviate uni-laterally from the NEP. Extensive simulations are performed to evaluate the impact of probability of false alarm, utility function weighting factors and presence of licensed users on the cumulative system utility.

To the Theory of Filtration of High-viscosity Oil under Thermal Effects

A theoretical model of the reservoir development process with high-viscosity oil using the technology of paired horizontal wells is presented. Assuming that the system of two wells is replaced by one given well, through which the reservoir is heated simultaneously and the oil is extracted, numerical solutions have been obtained. The costs of heat for reservoir warming, the evolution of flow rate and the mass of pumped oil for the period under consideration are analyzed at different values of heating temperature, pressure drop and well induction period. The solutions obtained allow us to analyze the most favorable modes of exposure by thermal methods in terms of energy costs.

Optimal scheduling of PHEVs and D‐BESSs in the presence of DGs in a distribution system

Optimisation of cost and load flattening for a distribution network is attempted in this work. The objective function is described in terms of energy cost, C O 2 emissions, real power losses, and load flattening. The solution is envisaged in terms of hourly scheduling of distributed generations (DGs), distributed battery energy storage systems (D-BESSs), and plug-in hybrid electric vehicles (PHEVs). An investigation in the reformulation of the cost of energy is carried out to eliminate the solutions involving excessive charging and discharging of BESSs/D-BESSs and PHEVs. It is demonstrated that simultaneous optimisation of cost, C O 2 emissions, real power losses, and load flattening cannot be effectively solved as a weighted sum objective function. An e -constraint method is applied to obtain the optimal scheduling to achieve cost optimisation, load flattening, and minimisation of C O 2 emissions from the utility point of view. A case of decentralised multi-agent optimisation problem is also formulated and compared. It is observed that the combination of the scheduling of DGs, BESSs/D-BESSs, grid-to-vehicle/vehicle-to-grid can successfully be used to significantly reduce the system peak demand along with system cost, losses and C O 2 emissions.

Pareto Optimal Demand Response Based on Energy Costs and Load Factor in Smart Grid

Demand response for residential users is essential to the realization of modern smart grids. This paper proposes a multiobjective approach to designing a demand response program that considers the energy costs of residential users and the load factor of the underlying grid. A multiobjective optimization problem (MOP) is formulated and Pareto optimality is adopted. Stochastic search methods of generating feasible values for decision variables are proposed. Theoretical analysis is performed to show that the proposed methods can effectively generate and preserve feasible points during the solution process, which comparable methods can hardly achieve. A multiobjective evolutionary algorithm is developed to solve the MOP, producing a Pareto optimal demand response (PODR) program. Simulations reveal that the proposed method outperforms the comparable methods in terms of energy costs while producing a satisfying load factor. The proposed PODR program is able to systematically balance the needs of the grid and residential users.

RETRACTED ARTICLE: Optimal users based secure data transmission on the internet of healthcare things (IoHT) with lightweight block ciphers

The ever-growing advancement in communication innovation of modern smart objects carries with it a new era of improvement for the Internet of Things (IoT) based networks. The health care system is the best approach to store the patient’s health data online with high privacy. Ensuring the privacy and confidentiality of patient information in the cloud is of utmost importance; here, the enhanced security model of healthcare data gives rise to trust. For the secure communication, the healthcare data sensed by the IoT sensor network is encrypted by Lightweight SIMON block cipher. For improving the privacy of healthcare data among individuals, we implemented the share generation model. Then, share creation model, i.e., Chinese Remainder Theorem (CRT) is developed to generate the copy of every ciphertext based on the selected number of users and the data is shared among the optimal number of users. The selection of the users in IoHT is made by the metaheuristic algorithm called Hybrid Teaching and Learning Based Optimization (HTLBO). Then, we present healthcare service providers for giving the full scope of medical services to people enrolled in IoHT. The performance of Secure Data is approved through simulations in terms of energy cost, computation time, etc., of the proposed algorithms and the outcomes demonstrate that Secure Data can be efficient while applying for ensuring security chances in IoT-based healthcare systems.

Prediction model for the pressing process in an innovative forming joints technology for woodworking

To improve the efficiency of the joints formation, a new method of pressing in the longitudinal direction is proposed. This paper presents a predictive model for the pressing force depending on the state of the wood and the parameters of the pressed mortise. The most significant factors are the width of the mortise and the moisture content of the wood. Interestingly, the depth of the mortise formation is a less significant factor, which means that the pressing technology will allow to form a long glue line and accordingly high joint strength due to sufficient profile length. In the test range of factors, the best results in terms of energy costs are shown by a minimum mortise width of 4 mm. Further research should be devoted to the study of the formation of small width mortises (4 mm or less) and the investigation of their quality.

Pemanfaatan Potensi Energi Aliran Kanal Kalisolo I pada Pusat Pengembangan Sumber Daya Manusia Minyak dan Gas Bumi

Energy needs to improve services and operations in every office are needed, as well as infrastructure for oil and gas PPSDM infrastructure; Office, laboratory, housing and processing facilities are reported. Along with operational costs that increase in terms of energy costs, which include electricity, air and fuel oil. Oil and Gas PPSDM can request operational costs. The utilization of this energy can simultaneously participate in government programs to develop new renewable energy, as well as a pilot project for the community to use waste energy. To fulfill the need for clean water in Oil and Gas PPSDM, which on average every meeting requires water of 3,250 m3, using air from Bengawan Solo, which is processed in the air treatment section, by pumping and channeling it through the traffic channel to processing tanks. The air flow in this canal requires energy as a power plant. In this research, the installation of Kalisolo 1 Oil and Gas PPSDM Channel was installed for Hydrokinetic Power Plant (PLTHk) and / or Micro Hydro Power Plant (PLTMH) using the Open Flume Turbine. After measuring the object and analyzing it, on object I with a cross-sectional area of ​​1 mx 0.35 m, velocity of channel air flow 0.8 m / s, the power potential of 89.6 W. was obtained in object II with a cross-sectional area of ​​0, 4 mx 0.4 m, canal water flow velocity 0.4 m / s, obtained power potential of 5.12 W, if using MHP. While using the Micro Hydro Power Plant (PLTMH) using the Open Flume Turbine, taking into account the 2 m water head, on my object it can produce electric power of 494,424 W or 0.5 kW, and if the air head is 1 m, power can be generated electricity is 247,212 Watt or 0.25 kW, with generator efficiency 0.9 and turbine efficiency of 0.9, while in object II the Turbine Flume PLTMH cannot be regulated, because it is difficult to design head water.

Job-shop scheduling problem with energy consideration

These days, rising energy costs along with general concerns about major environmental issues (global warming, climate change), result in more and more strict production constraints for the industrial sector, which is known to be the first energy consumer and greenhouse gas emitter in the world. There is therefore a growing industrial need to address the problems of production systems related to energy aspects.In this paper, a job-shop scheduling problem with energetic aspects is considered. The objective is to minimize production costs in terms of energy, while respecting a power peak limitation, along with more traditional production constraints. Two integer linear programming models are proposed for the addressed problem. In order to evaluate and compare the performance of these formulations, computational experiments are presented and numerical results are discussed and analysed.

Modeling and Simulation of an Intelligent Hybrid Energy Source based on Solar Energy and Battery

Today, the world suffer from air pollution due to the presence of chemicals, particulate matters and biological materials in the air. All these elements have negative impacts on human health, environment and economy. Vehicle emissions are considered a major source of air pollution. Transportation is one of the main sources of air pollution around the world. Vehicle pollution takes two forms, the emission of greenhouse gases (global impact on the environment, global warming) and particulate emissions (local impact, deterioration of air quality). In order to tackle this problem, the solution is to encourage the development of hybrid vehicles.This paper will shed light on a new hybrid generator topology, equipped with a photovoltaic energy conversion system, a battery with its DC / DC converter is adopted to replace the fuel cells in order to solve the problems which are: high consumption with a high cost in terms of energy. Another contribution of this work consists in the use of fuzzy logic; indeed the use of the fuzzy logic system which is used as an autonomous control device. Fuzzy logic has been used primarily to adapt control parameters in order to improve the overall robustness of the system while maintaining its stability.

Multicriteria model of the process of crushing rock

The article deals with the modernization and adjustment of the fine chalk grinding process. The crushing process is an energy-consuming procedure, annually spent about 5% of all energy produced on Earth, including the energy of internal combustion engines. This indicates its great importance. In addition to the cost of electricity, large expenses go to repair the equipment. The greatest replacements are made on the main working parts of machines. In the course of substitutions a lot of time is spent, in order not to spend this rather important resource, it is necessary to approach this procedure from a scientific point of view. The organization and conduct of research on the replacement of the main working parts of crushers and mills will increase the productivity of the main equipment, improve the quality of the finished product and reduce production costs in terms of energy saving. Modernization and adjustment of technological equipment in order to improve the production process of fine chalk significantly increase the service life of the main equipment. For this purpose, it is proposed to conduct an active experiment. Before carrying out the experiment, it is necessary to set the model. The classical regression analysis is based on the assumption that the model type is a priori specified with accuracy to the parameters, and that an experiment has already been implemented that supplies the initial data for the regression construction. Hence, the problem is to choose the best method of data processing. In this paper, we propose a fundamentally new approach-automatic evaluation of the model options on a set of indicators, the calculation of which is based on a set of pareto-optimal variants of the model.The proposed method made it possible to identify two best alternatives out of 16384. Obviously, this approach can be easily modified for any other set of regression model quality criteria.

Моделирование тепловой обработки семян масличных культур высокотемпературным теплоносителем

Из-за сложности экспериментального определения полей температур и влагосодержаний в единичных семенах масличных культур принципиально важным является разработка модели тепломассопереноса, которая позволила бы проводить анализ множества альтернативных вариантов аппаратурно-технологического оформления тепловой обработки и выбирать из них оптимальные с точки зрения энергетических затрат и показателей качества высушиваемого продукта. Цель работы сформулировать математическую модель тепловой обработки семян масличных культур сферической формы высокотемпературным теплоносителем, в качестве которого использовать перегретый пар атмосферного давления, на основе дифференциальных уравнений А.В. Лыкова и предложить ее аналитическое решение. Для решения модели приняты следующие упрощающие допущения: пренебрегается теплопередачей за счёт теплопроводности; не учитывается усадка и градиент давления; единичное зерно представляется в форме шара; теплофизические параметры зависят от температуры и влажности семян; начальное распределение температуры и влагосодержания в частице продукта постоянны. С учетом принятых допущений уравнения нестационарного тепломассопереноса приведены к безразмерному виду и рассмотрены в сферической системе координат с граничными и начальными условиями. Аналитическое решение задачи моделирования представлено модифицированными рядами Фурье. Установлено, что достаточно ограничиться двумя слагаемым ряда и получить приближённое решение с достаточной для инженерных расчетов точностью. Выполнена идентификация параметров модели по экспериментальным данным процесса тепловой обработки семян рапса перегретым паром атмосферного давления в барабанной сушилке с погрешностью моделирования 12-14%.

Solar tracking system with PID control of solar energy panels using servo motor

Parallel to the developing technology and the increasing population, the energy requirement is increasing day by day. Turkey's dependence on foreign sources of energy constitutes a significant budget in terms of energy costs. In this context, in Turkey, renewable energy sources with high potential are gaining importance. Fossil energy sources cause environmental pollution and threaten human health more and more every day. For this reason, the use of renewable energy sources should be encouraged and widespread. With a good energy needs analysis, producer costs will be reduced and efficiency will be increased.Solar energy is an efficient resource in electricity generation. Increase the efficiency of the solar panels used, increases the amount of electricity produced. However, the change in the angle of the sun's rays has an adverse effect on energy production. In this study, it was aimed to increase the efficiency of the solar panels by moving according to angle of sunlight. For this purpose, solar panel was driven by the servo motor and it was directed to the sunlight. Servo motor's PID control was implemented using the MATLAB program. The energy analysis made is given in the results.Solar energy is an efficient resource in electricity generation. Increase the efficiency of the solar panels used, increases the amount of electricity produced. However, the change in the angle of the sun's rays has an adverse effect on energy production.In this study, it was aimed to increase the efficiency of the solar panels by moving according to angle of sunlight. For this purpose, solar panel was driven by the servo motor and it was directed to the sunlight. Servo motor's PID control was implemented using the MATLAB program. The energy analysis made is given in the results.

Deep reinforcement learning-based joint task offloading and bandwidth allocation for multi-user mobile edge computing

The rapid growth of mobile internet services has yielded a variety of computation-intensive applications such as virtual/augmented reality. Mobile Edge Computing (MEC), which enables mobile terminals to offload computation tasks to servers located at the edge of the cellular networks, has been considered as an efficient approach to relieve the heavy computational burdens and realize an efficient computation offloading. Driven by the consequent requirement for proper resource allocations for computation offloading via MEC, in this paper, we propose a Deep-Q Network (DQN) based task offloading and resource allocation algorithm for the MEC. Specifically, we consider a MEC system in which every mobile terminal has multiple tasks offloaded to the edge server and design a joint task offloading decision and bandwidth allocation optimization to minimize the overall offloading cost in terms of energy cost, computation cost, and delay cost. Although the proposed optimization problem is a mixed integer nonlinear programming in nature, we exploit an emerging DQN technique to solve it. Extensive numerical results show that our proposed DQN-based approach can achieve the near-optimal performance.