The very high annual heat demand of greenhouses is the most critical factor that increases production costs. Conventional methods are generally used to obtain the optimum temperature required for greenhouses. In these systems, greenhouse air is heated by a boiler and pipe networks are connected to it, and in this way, most of the heat energy is transferred from the greenhouse ceiling to the atmosphere. In addition, in the greenhouse, not only the air but also the soil should be heated in order not to spoil the roots of the plants. The objective of this research is to provide sustainable heating for greenhouse applications. For this purpose, an innovative heating system has been designed for greenhouse heating by using of solar energy and heat pump technologies. In this study, a new approach was presented by designing a novelty heat pump flow for the heat required in the greenhouse. With this design, not only greenhouse air but also the soil will be heated and the best conditions for the development of plants will be provided. In the system, an ethylene glycol water mixture was used to prevent damage caused by freezing. In addition, it is designed to provide sustainability with an auxiliary heater when solar radiation is insufficient. It is highly recommended to apply this presented system for all greenhouse types.

Model predictive control has been widely used in the industry. This can control the multivariable system with constraints on input and output variables but it needs online computation solver, and creates the non-convex solution in nonlinear plant due to the parameter uncertainties. The online computational problem and non-convex solution of the model predictive control are achieved via neural network model predictive control. The paper explores the speed control of permanent magnet synchronous motor (PMSM) by using neural network model predictive control (NNMPC) technique. The multi-layer artificial neural network is used to identify the dynamics of PMSM. The set point speed tracking control of PMSM is identified by using neural network model predictive control strategy. By using the set of input and output data obtained from the system, the multi input-output feed-forward neural network model is created. Levenberg-Marquardt algorithm is used to train the process models of the PMSM. That provides future plant output for control optimization of the predictive control. The overall system is developed and tested in the MATLAB/Simulink. To evaluate the efficiency of the controller proposed, it is compared with a constrained model predictive controller through the studies of simulation. The overshoot and settling time of the speed response of the PMSM are measured and analyzed for NNMPC and constrained MPC.

The technological developments in wind energy field have reduced the investment and the operation costs. For this reason, wind farms have become more popular around the world. Increasing the share of wind energy in the market has led to the need for secure, inexpensive, and effective monitoring and control approaches. In the present work, various monitoring and control tools, which are cheap and easy to implement in wind farms using existing system data are proposed. The primary purpose of this study is to offer a new methodology, i.e. an artificial neural network (ANN) design with a novel training algorithm called Antrain ANN, in order to explore the early fault detection in a wind turbine. Our case problem is the fault detection for a wind turbine. For this issue, we used real data consisting of 873 samples with 12 inputs and one output. The models used in the work try to forecast fault occurrence before 10 minutes it happens. The proposed Antrain ANN algorithm is compared with Quick Propagation, Conjugate Gradient Descent, Quasi-Newton, Limited Memory Quasi-Newton, Online Backpropagation, and Batch Back Propagation algorithms, respectively. The results have shown that the proposed novel approach has better results in the correct classification rates than other algorithms except the Quasi-Newton and Limited Memory Quasi-Newton ones.

In 21th century demand for energy is rapidly increasing as the population is increased. To provide desirable living conditions and satisfy the food needs of overpopulated humanity getting harder and harder due to the limited energy and nutrient resources. Nutrient sources pass through many stages such as heating, cooling, drying and freezing which includes thermal and electrical energy needs before being presented to the consumer in the food industry. In the current situation, these two main energy requirements are largely met by fossil fuels which are not efficient, non-sustainable and not user friendly in terms of resource management. In this context, the possibilities of usage Photovoltaic-Thermal (PV/T) solar energy systems which will be able to eliminate these disadvantages, use solar energy as a source and can meet the thermal and electrical requirements of the facility continuously has been investigated and, a system was designed which can meet the energy needs according to different scenarios for different processes. It is aimed to produce food in low carbon-emission facilities by meeting both electrical and thermal energy from the solar energy which is a renewable and clean energy source.

In the present study, a new micro-power scaled electromagnetic (EM) harvester is designed and fabricated. The device has an innovative magnetic flux varying mechanism with two cylindrical Nb magnets and a central core moving inside the magnets back and forth. The system harvest electricity from the linear oscillations by the help of a spring attached at the bottom part of the core. The device requires only one spring and a second linear-laminated core closes the flux outside of the magnets in order to lower the reluctance of the system. The device is 6 cm in length and 2.4 cm in width in cylindrical geometry as a compact and stable geometry. The experimental verifications have proven that it can generate up to U = 7.76 mV output voltage depending on the oscillation frequency. The maximal output power has been measured as P= 32 mW for 44 Hz frequency with the resistive load RL = 0.2 Ohm. The power density p = 1.17 mW/cm3 has been obtained, experimentally.

This paper is regarding the design, modeling and simulation for reducing harmonics with passive LLCL filter in off-grid solar system. It is desired that current and voltage waveforms are to be in the sinusoidal form during energy generation from stand-alone solar systems. This condition can be provided by the most important one of the main factors which to determine the quality of electrical energy. Due to the harmonics produced by the non-linear loads, the waveform of the current and voltage is distorted from the sinusoidal form. The passive LLCL filter is designed and analyzed for mitigation of the total harmonic distortion for current (THDI) in the proposed off-grid PV system. The passive LLCL filter is practically installed between solar inverter and non-linear load. Simulation results are in a good compliance with the theoretical analysis. This study describes a design methodology of a LLCL filter for off-grid power system with a comprehensive study of how to mitigate the harmonics in off-grid solar system. The using of a LLCL filter mitigates the THDI that injected by a six pulse rectifier which is used as a non-linear load. The simulation result shows that the reduction of THDI from 89.89% to 3.257%. This paper attempts to show that the using of LLCL filter with a stand-alone solar system can highly improve the power quality of the system.

Hydrothermal systems play a significant role in electric energy systems as important power generation units, which have been studied in previous researches considering remarkable efforts. The optimal short-term hydrothermal scheduling (STHS) aims to attain optimal production scheduling of thermal and hydro plants for determining minimum operation cost of providing demand in the determined time interval. Different constraints should be studied in the solution of such issue containing limitations associated with water discharge, water storage, power production of plants, power balance of the system and water balance of hydro plants. In addition, valve impacts of thermal plants and complex hydraulic coupling of hydro plants are the other operational and technical constraints. In this study, the robust stochastic STHS is studied considering market price and demand uncertainties. Accordingly, robust optimization method is employed in this study to model price uncertainties. In addition, the uncertainties of load demand are handled using scenario-based modeling procedure. The scheduling problem of hydrothermal system is studied in a deregulated environment, where the hydrothermal system belongs to the private company and is capable to sell the surplus generated power to the market. Accordingly, the company aims to obtain maximum profit by selling power to market in addition to supplying power demand of the company. The introduced scheme for stochastic robust STHS is simulated and the provided solutions are investigated to verify the effectiveness of the scheme.

Axial flux machines are used frequently in wind energy applications due to their high power densities and efficiencies. However, the parametrical dependencies of those generators affect the generated power and efficiencies. For instance, turbine blade structures dimensions, change dramatically the speed and torque characteristics. Hence, this study includes a pursuit of optimization and the related design parameters for an axial generator in terms of its off grid operations. The analytical design relations and simulated data have shown that the geometric dimensions and morphology of the machine can be analyzed effectively under the finite element method and the optimization of the machine can be provided with a good accuracy by applying the response surface optimization. Cogging torque and power density formulated aim function gives a good optimization for the design. According to this approach, the general performance of the machine is maximized and the cogging torque and weight are kept to be decreased. Design variables are optimized by considering the generator size. Consequently, an optimal 4.8% increase in weight can yield to a 27.4% power enhancement in the machine.

This short review is based on an overview of the most recent works of the literature related to climatization in buildings. A total number of 40 relevant papers that have been published in the last years in prestigious international journals have been reviewed with the aim of showing the current state of the art in this field. It is very important as the new European regulations that will be applied in the next years in the construction of buildings, aiming to achieve nearly-Zero Energy Buildings (nZEBs), will require a multidisciplinary work in the different areas that affect the design of buildings. For this reason, it is relevant the envelope, the user behavior, the Heating, Ventilation and Air Conditioning applied (HVAC) and the influence of the meteorological conditions, among others. But apart from this, it would be very interesting any other alternative which helped to reach these targets. This article proposes the possibility of using Model Predictive Control (MPC) besides to Renewable Energies in order to optimize the energy management of air-conditioning in public and office buildings through a radiant floor by solar system and high-performance heat pump systems for their heating / cooling. In this paper, a short review is initially exposed where the different "Input data and information" are analyzed, to end up proposing an Operative for a Predictive Control using these Renewable Energies.

During the next decades, the electricity generation sector will know significant changes. This is mainly due to the increase in demand, with the limitation of traditional resources such as fossil fuels and the development of renewable energy particularly the proliferation of wind farms. Hydroelectric pumped-storage power plants, which store large amounts of energy and quickly compensate the power fluctuations, will then play a key role. It is therefore essential to find ways to fully optimize the performance of this type of plant. The main objective of this work is to study and choose the right place according to different criteria, topographical, geographical in order to install a pumped hydro station we try to size. All this is in order to ensure network stability. Such a study requires two main pumped hydro stations, namely the theoretical development and case study part. With the arming out of the three wind farms developed by Nareva holding, a major leader company in the domains of the energy and the environment, Akhfennir and Foum El Oued in the South of Morocco, and Haouma in the north of the country near to Tangier, these three parks, of a total capacity of 203 MW. Storing the surplus of the wind produced energy became a priority, hence we led a detailed study on six sites on various region of Morocco to install a hydraulic pumping station of a power of 50MW.The pumped hydro station of Abdelmoumen is situated in the upstream of the restraint of the dam Abdelmoumen on the Wadi Issen in approximately 70 kilometres in the Northeast of the city of Agadir. The study of the sizing of the pumped hydro station of Abdelmoumen led to us to the conception of a station of 50MW of power for a height difference of 800m separating both upper and lower reservoirs whose volumes are respectively 2 610 000 m3 and 749 700 m3. We conceived a Pelton turbine with a debit of turbinage of 8 m3/s, a pump spin-dries radial road of 2,4 m3 /s and penstock of 6 691m of 1m of diameter.