The study is about the development of stationary Hybrid Renewable Energy Systems (HRES) at a local scale by considering locally available renewable energy resources. The objective of microgrid development is to meet the present as well as future energy demand with high reliability and minimum cost while reducing greenhouse gas emission. The renewable energy resources, as well as needs, vary from region to region. These parameters are used to define an appropriate region to develop HRES at a local level. The HRES requires to fulfill the energy demand subject to certain limits or constraints with high penetration of renewable energy. The study describes the methodology to accurately predict the renewable energy potential to identify the ideal location for the installation of an energy system, and to maximize the use of the energy resources. The methodology adopted in the present study can also be applied to other regions, which have locally available renewable energy resources and energy demand; new regions can be defined for implementation of HRES.

The objective of wind farm layout optimization (WFLO) is to maximize the power generation with less cost. This paper proposes a program based on genetic algorithm for positioning turbines in a wind farm and studies the effect of wind direction on WFLO. Wind speed is measured at 28 locations in two southern states in India. GIS approach is used to identify the ideal location for a wind farm. Two different scenarios are taken for study; the first is constant wind speed with single direction and the second is constant wind speed with multiple wind directions. A wind farm of 2 km × 2 km is divided into grids of 10 × 10; each grid can have one or no turbine. The wind data of the past three years is taken for the optimization problem. The best solution would accommodate 19 turbines which can generate an average power of 183.55 MW with maximum 343.15 MW in November and a minimum 29.8 MW in May. A case study of wind farm layout optimization along with economical aspect is done in India.

Greenhouse gas emission from conventional energy system and growing demand for energy result in the use of renewable energies such as solar energy. This study aims at designing life-cycle assessment (LCA) of a standalone photovoltaic (PV) system to meet the electricity demand of building in the diverse climate of India using a theoretical and numerical approach. The electricity demand is estimated on the daily basis considering all the power consumption equipments and their operational hours. Then, the suitable components of the PV system are selected based on their specification and available rooftop space. In the LCA analysis, environmental as well as financial analysis of the PV system is performed. For the environmental assessment, carbon credits are evaluated, whereas internal rate of return (IRR), net present value (NPV), levelized cost of electricity (LCOE) and the payback period of the PV system are estimated for the financial evaluation with consideration of inflation rate and effective discount rate. The PV system is found to be economically feasible because the NPV of this project is positive. The entire solar PV system is also simulated using PVSyst software, and results such as economic feasibility, performance ratio, input/output diagram, incident energy and array output distribution are compared with the theoretical calculation. Moreover, losses at various stages, as well as the reliability of the PV system, are analyzed. The peak rating of the building came out to be 232.71 kW p . The energy payback time and LCOE for the PV system are 7.43 years and $0.076/kWh, respectively, with a life-cycle conversion efficiency of 0.069.

Wind energy potential in India has so far not been evaluated state wise. Moreover, the prediction and assessment of wind potential are difficult due to the complexity of its nature. Here, a parametric study is done for the better prediction of wind potential using generalized feed-forward with back-propagation neural networks. Effect of three meteorological parameters (pressure, relative humidity, and temperature) on the wind speed prediction is studied in southern states of India. The meteorological parameters taken here are monthly mean, measured at ground station. These data were obtained at 28 sites over a period of 20 years from the IMD, Pune. Three different architectures of artificial neural network model were designed, trained, and evaluated for the prediction of wind speed. All three models have been optimized for varying neurons in the hidden layer. To evaluate the developed artificial neural network model for test locations, mean absolute percentage error and mean squared error have been calculated. It was found that the model with relative humidity as input parameter and having six neurons in the hidden layer give better prediction of the wind speed. The correlation coefficients were higher than 0.96 and the mean absolute percentage error and mean squared error of all test locations is less than 2.5 and 0.0176, respectively, which show high reliability of the model for the prediction of the wind speed within the region of study. Predicted wind speed has been analyzed and used to create monthly mean maps using geographic information system technology.

Integrating renewable energy technologies in a single system is becoming more reliable to meet electrical demand of remote locations. Here, integration and the optimal use of various available energy resources in a stand-alone microgrid are investigated. An integrated renewable energy system (IRES) approach has been proposed and analyzed using homer software. Seven scenarios with different combinations of energy sources and storage systems have been investigated based on their levelized cost of energy (LCOE) supply and net present cost (NPC). The proposed IRES, which includes photovoltaic (PV), wind, and biogas, gives the least LCOE as $0.207/kW h without any policy intervention. This LCOE reduces to $0.12/kW h with policy intervention and consideration of carbon abetment cost. Moreover, sensitivity analysis has been carried out with variation in load, solar radiation, and wind speed. The NPC is found to be most sensitive to the variation of load and least sensitive to the variation of wind speed.

India has an enormous potential for employing concentrated solar power (CSP) technology for energy generation. A great advantage of CSP is that it can be conveniently installed in parallel with existing fossil fuel power plants, which will enable us to increase the efficiency of the existing plants without making major changes in the plant setup and machinery. India has not yet utilised this resource to its full capacity. This paper discusses the status of CSP in India and the possible ways in which we can hybridize it with the existing conventional plants by simulating and optimizing different CSP technology and their economic feasibility. Further, the methodology of the system and different factors and aspects of the technology are discussed, that affect the performance and efficiency of the system. The various environment conditions like solar direct normal irradiance, wind-speed, land availability and economic factors like fixed cost expenses, inflation have been considered and areas suitable for CSP technology has been identified.

Prediction and assessment of solar radiation are necessary pre-requisites in developing solar technology. Here, an artificial neural network (ANN) model has been developed to predict potential of solar energy in the Southern part of India: Andhra Pradesh (AP) and Telangana State (TS), lie between 12 degrees 41' and 22 degrees N latitude and 77 degrees and 84 degrees 40'E longitude. Generalized feed-forward with back-propagation neural networks were considered using MATLAB. Three layered neural network with different architectures are designed and evaluated. For training and testing the network, geographical and meteorological data of 28 sites over a period of recent 22 years from the NASA geo-satellite database were taken. Geographical parameters (latitude, longitude and altitude), meteorological data (temperature, sunshine duration, relative humidity and precipitation) along with month were used as input data, whereas the mean solar radiation was used as the output of the network. All the parameters taken here are in the form of monthly mean. The ANN model has been evaluated for test locations by calculating mean absolute percentage error (MAPE). The correlation coefficients (R-value) between the output of model and the measured value of solar radiation is calculated. The R-value was more than 0.95, which show high reliability of the model for prediction of solar radiation anywhere within AP and TS. Solar radiation of major cities was predicted using developed model. Predicted solar radiation is analyzed and used to create monthly mean maps using GIS technology. These maps can be useful to estimate solar energy potential at any locations within AP and TS.

Prediction and assessment of wind speed are necessary prerequisites in the sitting and sizing of wind power applications. In this study, an artificial neural network (ANN) model was developed for prediction of wind energy potential in Andhra Pradesh (AP) and Telangana state (TS), India. ANN models are 'black-box' modelling technique, with capability to perform non-linear mapping of a multidimensional input space onto another multidimensional output space without the knowledge of the dynamics of the relationship between the input and output spaces. The geographical parameters (latitude, longitude and altitude) and the month of the year were used as input data, while the monthly mean wind speed was used as the output of the network. Geographical and meteorological data of 30 cities in AP and TS of 20 years (1995-2015) by the India meteorological department, Pune (IMD-Pune) database were used for the training and testing the network. The testing data were not used in the training of the network in order to give an indication of the performance of the system at unknown locations. Statistical error analysis in terms of mean absolute percentage error (MAPE) was conducted for testing data to evaluate the performance of ANN model.

Prediction and assessment of solar radiation is necessary prerequisite in the setting up and sizing of solar power applications. In this study, an artificial neural network (ANN) model was developed for prediction of solar energy potential in Andhra Pradesh (AP) and Telangana state (TS), India (lies between 12°41' and 22°N latitude and 77° and 84°40'E longitude). Standard multilayered, feed-forward, back-propagation neural networks with different architecture were designed using MATLAB. Geographical and meteorological data of 28 locations in AP & TS for period of recent 22 years from the NASA geo-satellite database were used for the training and testing the network. Geographical parameters (latitude, longitude and altitude), meteorological data (mean sunshine duration, mean temperature, mean wind speed, mean relative humidity and mean precipitation) and the month of the year were used as input data, while the monthly mean solar radiation was used as the output of the network. Statistical error analysis in terms of mean absolute percentage error (MAPE) was conducted for testing data to evaluate the performance of ANN model. The results show that the correlation coefficients between the ANN predictions and actual mean monthly global solar radiation intensities for training and testing datasets were higher than 95%, thus suggesting a high reliability of the model for evaluation of solar radiation in locations where solar radiation data are not available.

Simple fabrication and integration of consecutive analysis system is a key feature for lab-on-a-chip (LOC) device. Soft-lithography method is used to fabricate a poly-dimethylsiloxane (PDMS) based microfluidic system which is faster and less expensive than other conventional methods such as etching glass and silicon. Nanogap was generated between two microchannels simply by the breakdown of PDMS layer using electric shock, without using any stateof-art method. The device consists of two parts: Micro-mixer and Preconcentration for detection system. The Micromixer is passive and planer, which is easy to fabricate, is used for mixing the protein with their fluorescent conjugate. Consecutively, Preconcentration of protein is done based on electrokinetic trapping trapping of protein near the nanogap. This device can be used to bring the concentration within the detection limit, because the sensitivity of detection system is still restricted to detect target analyte with low concentration in microfluidic system.

In this study, an integrated micro/nanofluidic system for protein analysis was presented. The device is comprised of a micromixer and a preconcentrator with a separation column. The integrated micromixer based on unbalanced split and cross collision of fluid streams is passive and planar, which is easy to fabricate and integrate to the microfluidic system. The preconcentrator has nanochannels formed by the electrical breakdown of polydimethylsiloxane (PDMS) membrane using a high electrical shock, without any nano-lithographic process. Micromixer and preconcentrator were used for sample preparation (tagging of protein for detection) and concentration of protein, consecutively. Proteins were electrokinetically trapped near the junction of micro/nanochannels.

We describe a novel design for a micromixer that generates vortical flow in a rectangular microchannel with tangentially aligned inlet channels. The mixing performance of the proposed vortex T-mixer with non-aligned inputs is compared to a simple T-mixer as a function of Reynolds number. The mixer generates the formation of mixing-inducing flows even at low Reynolds numbers as compared to a simple T-mixer. The vortex initially formed at the inlet of a rectangular microchannel increases the interfacial area of the fluid streams by stretching. The proposed vortex mixer is easy to fabricate and offers a tunable control for the generation of vortical flow based on Reynolds number.

We present here the effect of extracellular matrix (ECM) on the proliferation and physiology of HL-1 cardiac cells. HL-1 cell is from AT-1 mouse atrial cardiomyocyte tumor lineage. HL-1 cell can be serially passaged, yet they maintain the ability to contract which is a promising character of HL-1 cell for the cell based biosensors. HL-1 cells grow up on the ECM which can affect on the attachment and growth of HL-1. In this paper, we discuss HL-1 cell-ECM interactions with three different ECMs and non-treated surface. HL-1 cells are grown for 4 days after seeding then observed their attachment. Also they were immunostained by hoechst and EthD-1 for proliferation, phalloidin for F-actin, and DAPI for nuclei. Fibronectin was revealed as the proper ECM material for HL-1 cell culture. This study can provide basic information for understanding the cell-ECM interactions and growth of HL-1 cells.

In this study, an integrated micro-nanofluidic system for protein analysis was presented. The device is comprised of a micromixer and a preconcentrator with a separation column. The Integrated micromixer based on unbalanced split and cross collision of fluid streams is passive and planar, which is easy to fabricate and integrate to the microfluidic system. The preconcentrator has nanochannels formed by the electrical breakdown of PDMS membrane using high electric shock, without any nano-lithographic process. Micromixer and preconcentrator was used for sample preparation (tagging of protein for detection) and concentration of protein, consecutively. Proteins were electrokinetically trapped near the junction of micro/nanochannels.

본 연구에서는 세포의 성장속도, 배양표면 부착, 체내 특이성을 유지에 중요한 요소인 세포외 기질 물질에 따른 심근세포주(HL-1 cell)의 성장, 생존률 및 표현형 등을 분석했다. 서로 다른 5 가지의 세포외 기질 환경을 조성하여 세포 성장에 미치는 영향에 대하여 분석했다. 세포외 기질 물질이 처리된 배양 표면의 물리적인 형태는 원자현미경을 통하여 분석했으며, 세포의 표면부착, 성장 및 증식과 생존률 및 표현형은 역상형광현미경 및 면역염색법을 통해 분석했다. 본 연구를 통하여 서로 다른 세포외 기질 물질이 세포의 부착 및 성장 속도에 영향을 미치며, 심근세포의 특이성을 나타내는 단백질의 형성에도 차이를 보이는 것을 확인했다.