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ردیف | عنوان | نوع |
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1 |
Boosting solar steam generation by structure enhanced energy management
افزایش تولید بخار خورشیدی توسط ساختار پیشرفته مدیریت انرژی -2020 Interfacial solar-steam generation is a promising and cost-effective technology for both
desalination and wastewater treatment. This process uses a photothermal evaporator to absorb
sunlight and convert it into heat for water evaporation. However solar-steam generation can be
somewhat inefficient due to energy losses via conduction, convection and radiation. Thus,
efficient energy management is crucial for optimizing the performance of solar-steam generation.
Here, via elaborate design of the configuration of photothermal materials, as well as warm and
cold evaporation surfaces, performance in solar evaporation was significantly enhanced. This
was achieved via a simultaneous reduction in energy loss with a net increase in energy gain from
the environment, and recycling of the latent heat released from vapor condensation, diffusive
reflectance, thermal radiation and convection from the evaporation surface. Overall, by using the
new strategy, an evaporation rate of 2.94 kg m−2 h−1, with a corresponding energy efficiency of
solar-steam generation beyond theoretical limit was achieved. Keywords: Solar-steam generation | photothermal | energy management | latent heat recycling | reduced graphene oxide | desalination |
مقاله انگلیسی |
2 |
Scaling laws of Mullins effect in nitrile butadiene rubber nanocomposites
مقیاس بندی قوانین اثر مولین در نانوکامپوزیت های لاستیکی بوتادین نیتریل-2020 Rubber nanocomposites experiencing cyclic deformation undoubtedly exhibit Mullins effect whose underlining
mechanisms are not yet clear. Herein this effect in nitrile butadiene rubber nanocomposites is systematically
investigated for revealing the influences of pre-strain interval, loading and unloading velocities, temperature,
filler type and content, as well as crosslinking agent. The results show that the recovery hysteresis energy and
accumulative softening energy of the nanocomposites can be superposed onto master curves as a function of
microscopic strain of the rubber phase, revealing that both involving the viscoelastic deformation of the rubber
phase. Especially the recovery hysteresis highly depending on temperature and loading and unloading velocities
is connected to the viscoelasticity of nonideally crosslinked rubber network in the nanocomposites. On the other
hand, the accumulative softening energy loss comes from recovery retardation of rubber chains and is somewhat
sensitive to the filler, temperature and crosslinking agent. The investigation would be instructive to clarify the
physical origin of Mullins effect to produce low dissipation rubber nanocomposites. Keywords: Mullins effect | Energy loss | Nanocomposites |
مقاله انگلیسی |
3 |
Prediction and management of solar energy to power electrochemical processes for the treatment of wastewater effluents
پیش بینی و مدیریت انرژی خورشیدی به قدرت فرآیندهای الکتروشیمیایی برای تصفیه پساب فاضلاب-2020 A highly versatile software tool able to predict and manage the solar power coming from photovoltaic
panels and to assess the environmental remediation of wastewater effluents has been developed. The
prediction software tool is made up of four modules. The first one predicts the solar radiation by a
phenomenological model. Secondly, an energy optimization algorithm manages the solar power towards
the third and fourth modules, an environmental remediation treatment (electrooxidation) and an energy
storage system (redox flow battery), respectively. The software tool is aimed to the best solar power
management to obtain the highest remediation treatment. Results shows a daily solar radiation prediction
with a high accuracy, attaining correlation coefficients of 0.89. Furthermore, the prediction of the
removal of an organochlorinated compound from a wastewater effluent at different time of the year was
studied. Different percentages of the total solar power are sent directly to the electrooxidation reactor
and to the redox flow battery. At non-solar production hours, the electrooxidation reactor is powered by
the redox flow battery in order to exploit the total solar power produced. The results show that, the
higher the solar radiation, the higher the power percentage that must be directly sent to the electrooxidation
treatment in order to attain the best energy management and the higher remediation. Thus, an
82.5% of the total solar power must be sent to the electrooxidation treatment in summer days in contrast
to the 25% that have to be powered in winter days to attain the highest removal of pollutant.
Consequently, it is important to evaluate the connection between devices to get the best green energy
management and the lower energy losses. Keywords: Energy management | Solar power | Green sources | Electrolysis | Redox flow batteries | Forecasting |
مقاله انگلیسی |
4 |
Optimum management of power and energy in low voltage microgrids using evolutionary algorithms and energy storage
مدیریت بهینه انرژی و برق در ریز شبکه های ولتاژ کم با استفاده از الگوریتم های تکاملی و ذخیره انرژی-2020 Microgrids are subsystems in which some loads and distributed energy resources are controlled in a coordinated
manner. In recent years, microgrids have been proposed as a solution to enhance critical infrastructures’ resilience
and the integration of distributed energy resources. There are many solutions on microgrid planning, as
well as some practical experience on microgrids’ implementation. However, choosing microgrid optimal control
strategy is strongly related to the individual structure, components and configuration of microgrid. Among
others, the advantages of microgrids include improved energy efficiencies, minimized operating costs and improved
environmental impacts. Achieving these targets necessitates optimal control of all energy components in
the microgrid. Main contribution of this paper are two control strategies of power and energy management for
synchronous microgrid operation, which have been analyzed for a specific low voltage microgrid configuration.
The first strategy reduces power and energy losses, thus improving the entire microgrid system’s efficiency. The
second minimizes operating costs. An evolutionary algorithm was developed to control the components of the
microgrid, including e.g. micro-sources and energy storage. The method of technical and economic energy
storage system sizing for microgrid optimal operation is also proposed. Keywords: Battery energy storage unit | Distributed generation | Evolutionary algorithm | Microgrid optimization | Power and energy management |
مقاله انگلیسی |
5 |
Thermal management of the waste energy of a stand-alone hybrid PV-windbattery power system in Hong Kong
مدیریت حرارتی انرژی زائد یک سیستم قدرتمند هیبریدی PV-بادگیر مستقل در هنگ کنگ-2020 This paper firstly investigated the thermal management of wasted energy from a stand-alone hybrid solar-windbattery
power system. The total dump load or waste power can be up to 50% of total system power yield, and
therefore waste energy management is urgent with high necessity. A new phase change material (PCM: Ba
(OH)2·8H2O) with high storage capacity is introduced for the thermal management of hybrid power system.
Different renewable energy configurations with different battery storage capacities are simulated and investigated.
For different scenarios, the ratio of the captured thermal energy from waste energy to total solar/
wind power output ranges from 24.45% to 72.48% regarding all system losses. The cases without battery bank
are featured by high thermal energy amount/ percentage (waste energy) and high power supply failure. Typical
results show that, the total yearly renewable power output is 173,877 kWh with only 51.99% directly for demand
load, and 57,672 kWh with 33.17% can be effectively stored in the thermal storage tank as heat, which can
supply about 136 people’ heat demand per year. Compared with the water tank, the PCM thermal storage tank
can save much space and land because of its high energy density. Appropriate thermal management of standalone
hybrid solar-wind-battery power systems is necessary and feasible. Keywords: Solar-wind-battery system | Dump load | Thermal energy | Heat | Energy losses |
مقاله انگلیسی |
6 |
A hierarchical energy management strategy for hybrid energy storage via vehicle-to-cloud connectivity
یک استراتژی مدیریت انرژی سلسله مراتبی برای ذخیره انرژی هیبریدی از طریق اتصال وسیله نقلیه به ابر-2020 In order to enhance energy efficiency and improve system performance, the road mobility system requires more
preview information and advanced methods. This paper proposes a novel hierarchical optimal energy management
strategy for electric buses with a battery/ultracapacitor hybrid energy storage system, to optimal split
the power and reduce the battery life degradation. This method is based on vehicle-to-cloud connectivity. In the
cloud platform, an optimal energy management strategy is developed using dynamic programming, where the
battery degradation cost and the electric cost are taken into consideration. In the vehicle level, a model predictive
control is developed to deal with the uncertainties, reduce the energy losses, and handle the system
constraints. The cost function of the model predictive control includes the ultracapacitor state of charge planning
and energy losses. In order to evaluate the effectiveness of the proposed method, a rule-based energy management
strategy is developed as the baseline approach. The China bus driving cycle and other six real bus
driving cycles recorded in China are used to validate the robustness of the proposed method. To be more realistic,
the random uncertainties up to 20% are included in all driving cycles. Furthermore, the time delay and
packet losses in communication are also considered. Simulation results show that the proposed method significantly
outperforms the rule-based method, and the average improvement could be over 40% in the studied
driving cycles. Keywords: Vehicle-to-cloud connectivity | Energy management | Model predictive control | Real-time optimization | Hybrid energy storage |
مقاله انگلیسی |
7 |
Efficiency in electromechanical drive motors and energy performance indicators for implementing a management system in balanced animal feed manufacturing
بهره وری در راندن موتورهای الکترومکانیکی و شاخص های عملکرد انرژی برای اجرای یک سیستم مدیریت در تولید متعادل خوراک دام-2020 Energy management for manufacturing animal feed requires efficiency in the electric motors that actuate
electro mechanisms consisting in chain conveyors (3548 kWh/day), elevators of buckets (2626 kWh/
day), types axis non-end screw (298 kWh/day) and conveyor bands (434 kWh/day); all with different
types of mechanical transmissions mainly reducers, chains and straps that as major consumers of electric
energy represent the greatest potential savings. On the other hand, energy performance indicators are
needed for assessing the production of feed and implementing an energy management system. In this
paper a methodology is applied to determine the operating efficiencies of the electrical motors to the
current load factors and adjusted to the actual operating conditions. The case study is a balanced animal
feed manufacturing plant. The energy base line of the processes of higher energy consumption was
obtained as well as energy performance indicators of 10 kWh/t for the same productive levels (500 t/
day), representing a reduction of 364 000 kWh/year and, consequently, 15% of the entry recorded of
energy costs. This result is equivalent to generation costs at 120 t of oil/year, which means a saving of
6000 USD/year and 140.14 t of CO2 equivalent that is no longer emitted. Keywords: Balanced animal feed | Energy efficiency | Energy losses | Energy management systems | Energy performance indicators | Load factor |
مقاله انگلیسی |
8 |
Multi-agent microgrid energy management based on deep learning forecaster
مدیریت انرژی میکروگیدر چند عامل مبتنی بر پیشگویی یادگیری عمیق-2019 This paper presents a multi-agent day-ahead microgrid energy management framework. The objective is
to minimize energy loss and operation cost of agents, including conventional distributed generators,
wind turbines, photovoltaics, demands, battery storage systems, and microgrids aggregator agent. To
forecast market prices, wind generation, solar generation, and load demand, a deep learning-based
approach is designed based on a combination of convolutional neural networks and gated recurrent
unit. Each agent utilizes the designed learning approach and its own historical data to forecast its
required parameters/data for scheduling purposes. To preserve the information privacy of agents, the
alternating direction method of multipliers (ADMM) is utilized to find the optimal operating point of
microgrid distributedly. To enhance the convergence performance of the distributed algorithm, an
accelerated ADMM is presented based on the concept of over-relaxation. In the proposed framework, the
agents do not need to share with other parties either their historical data for forecasting purposes or
commercially sensitive information for scheduling purposes. The proposed framework is tested on a
realistic test system. The forecast values obtained by the proposed forecasting method are compared
with several other methods and the accelerated distributed algorithm is compared with the standard
ADMM and analytical target cascading. Keywords: Microgrid energy management system | Short-term forecasting | Deep learning | Convolutional neural networks | Gated recurrent unit | Alternating direction method of multipliers |
مقاله انگلیسی |
9 |
الگوریتم Krill herd برای محل بهینه تولید توزیع شده در سیستم توزیع شعاعی
سال انتشار: 2016 - تعداد صفحات فایل pdf انگلیسی: 14 - تعداد صفحات فایل doc فارسی: 48 تولید پراکنده (DG) به عنوان یک راه حل مناسب برای کنترل تلفات خط، ولتاژ گذر، ثبات ولتاژ و غیره به رسمیت شناخته شده و نشان دهنده یک عصر جدید برای سیستم های توزیع است. این مقاله در حال توسعه رویکردی برای قرار دادن DG به منظور به حداقل رساندن از دست دادن قدرت و انرژی فعال خطوط توزیع است، در حالی که ولتاژ باس و شاخص پایداری ولتاژ را در محدوده مشخص یک سیستم قدرت معین حفظ می کند.
بهینه سازی بر اساس محل مطلوب و اندازه بهینه از DG انجام شده است. این مقاله یک روش الگوریتم کریل گله جدید، کارآمد (KHA) را برای حل مشکل تخصیص بهینه DG شبکه های توزیع توسعه داده است. برای تست امکان سنجی و اثربخشی، الگوریتم KH ارائه شده بر روی 33 باس، 69-باس و 118 باس شبکه های توزیع شعاعی استاندارد تست شده است.
نتایج شبیه سازی نشان می دهد که نصب DG در محل مطلوب به طور قابل توجهی می تواند سبب کاهش از دست دادن قدرت سیستم برق توزیع شده شود. علاوه بر این، نتایج عددی، در مقایسه با دیگر الگوریتم های جستجوی تصادفی مانند الگوریتم ژنتیک (GA)، بهینه سازی ازدحام ذرات (PSO)، همراه GA و PSO (GA / PSO و عامل حساسیت از دست دادن شبیه سازی آنیلینگ (LSFSA)، نشان می دهد که KHA می تواند راه حل هایی با کیفیت بهتر را پیدا کند.
کلمات کلیدی: سیستم توزیع شعاعی | ژنراتور توزیع | کاهش تلفات | الگوریتم های تکاملی | الگوریتم کریل هرد | تکامل تفاضلی |
مقاله ترجمه شده |
10 |
Optimal allocation of stochastically dependent renewable energy based distributed generators in unbalanced distribution networks
تخصیص بهینه بر اساس ژنراتور انرژی های تجدید پذیر تصادفی وابسته به توزیع در شبکه های توزیع نامتعادل-2015 This paper proposes an algorithm for modeling stochastically dependent renewable energy based dis
tributed generators for the purpose ofproper planning ofunbalanced distribution networks. The proposed
algorithm integrate the diagonal band Copula and sequential Monte Carlo method in order to accurately
consider the multivariate stochastic dependence between wind power, photovoltaic power and the sys
tem demand. Secondly, an efficient algorithm based on modification ofthe traditional Big Bang-Big crunch
method is proposed for optimal placement ofrenewable energy based distributed generators in the pres
ence of dispatchable distributed generation. The proposed optimization algorithm aims to minimize the
energy loss in unbalanced distribution systems by determining the optimal locations ofnon-dispatchable
distributed generators and the optimal hourly power schedule ofdispatchable distributed generators. The
proposed algorithms are implemented in MATLAB environment and tested on the IEEE 37-node feeder.
Several case studies are done and the subsequent discussions show the effectiveness of the proposed
algorithms.
Keywords:
Big Bang-Big crunch
Copula
Monte Carlo
Renewable energy
Stochastic
Distributed Generation |
مقاله انگلیسی |