A technico-economic analysis based on integrated modeling, simulation, and optimization approach is used in this study to design an off grid hybrid solar PV/Fuel Cell power system. The main objective ...is to optimize the design and develop dispatch control strategies of the standalone hybrid renewable power system to meet the desired electric load of a residential community located in a desert region. The effects of temperature and dust accumulation on the solar PV panels on the design and performance of the hybrid power system in a desert region is investigated. The goal of the proposed off-grid hybrid renewable energy system is to increase the penetration of renewable energy in the energy mix, reduce the greenhouse gas emissions from fossil fuel combustion, and lower the cost of energy from the power systems. Simulation, modeling, optimization and dispatch control strategies were used in this study to determine the performance and the cost of the proposed hybrid renewable power system. The simulation results show that the distributed power generation using solar PV and Fuel Cell energy systems integrated with an electrolyzer for hydrogen production and using cycle charging dispatch control strategy (the fuel cell will operate to meet the AC primary load and the surplus of electrical power is used to run the electrolyzer) offers the best performance. The hybrid power system was designed to meet the energy demand of 4500 kWh/day of the residential community (150 houses). The total power production from the distributed hybrid energy system was 52% from the solar PV, and 48% from the fuel cell. From the total electricity generated from the photovoltaic hydrogen fuel cell hybrid system, 80.70% is used to meet all the AC load of the residential community with negligible unmet AC primary load (0.08%), 14.08% is the input DC power for the electrolyzer for hydrogen production, 3.30% are the losses in the DC/AC inverter, and 1.84% is the excess power (dumped energy). The proposed off-grid hybrid renewable power system has 40.2% renewable fraction, is economically viable with a levelized cost of energy of 145 $/MWh and is environmentally friendly (zero carbon dioxide emissions during the electricity generation from the solar PV and Fuel Cell hybrid power system).
•Off grid hybrid solar PV/Fuel Cell system can be solution for remote community.•The hybrid system optimization enhances the cost for residential community.•Location conditions (temperature and dust) affect the PV system performance.•PV/Fuel Cell/electrolyzer system offers very good penetration of renewable.
Forecasting wind speed has become one of the most attractive topics to researchers in the field of renewable energy due to its use in generating clean energy, and the capacity for integrating it into ...the electric grid. There are several methods and models for time series forecasting at the present time. Advancements in deep learning methods characterize the possibility of establishing a more developed multistep prediction model than shallow neural networks (SNNs). However, the accuracy and adequacy of long-term wind speed prediction is not yet well resolved. This study aims to find the most effective predictive model for time series, with less errors and higher accuracy in the predictions, using artificial neural networks (ANNs), recurrent neural networks (RNNs), and long short-term memory (LSTM), which is a special type of RNN model, compared to the common autoregressive integrated moving average (ARIMA). The results are measured by the root mean square error (RMSE) method. The comparison result shows that the LSTM method is more accurate than ARIMA.
In this paper, an adaptive sliding mode speed control algorithm with an integral-operation sliding surface is proposed for a variable speed wind energy experimental system. In the control design, an ...estimator is designed to compensate for the uncertainties and the unknown turbine torque. In addition, the bound of the sliding mode is investigated to deal with uncertainties. The stability of the system can be guaranteed in the sense of the Lyapunov stability theorem. The laboratory size DC generator wind energy system is controlled using a buck-boost DC-DC converter interface. The control system is validated by experimentation and results demonstrate the achievement of favorable speed tracking performance and robustness against parametric variations and external disturbances.
The recent rapid and sudden growth of solar photovoltaic (PV) technology presents a future challenge for the electricity sector agents responsible for the coordination and distribution of electricity ...given the direct dependence of this type of technology on climatic and meteorological conditions. Therefore, the development of models that allow reliable future prediction, in the short term, of solar PV generation will be of paramount importance, in order to maintain a balanced and comprehensive operation. This article discusses a method for predicting the generated power, in the short term, of photovoltaic power plants, by means of deep learning techniques. To fulfill the above, a deep learning technique based on the Long Short Term Memory (LSTM) algorithm is evaluated with respect to its ability to forecast solar power data. An evaluation of the performance of the LSTM network has been conducted and compared it with the Multi-layer Perceptron (MLP) network using: Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE) and Coefficient of Determination (R 2 ). The prediction result shows that the LSTM network gives the best results for each category of days. Thus, it provides reliable information that enables more efficient operation of photovoltaic power plants in the future. The binomial formed by the concepts of deep learning and energy efficiency seems to have a promising future, especially regarding promoting energy sustainability, decarburization, and the digitization of the electricity sector.
This paper presents a cascade second-order sliding mode control scheme applied to a permanent magnet synchronous motor for speed tracking applications. The control system is comprised of two control ...loops for the speed and the armature current control, where the command of the speed controller (outer loop) is the reference of the q-current controller (inner loop) that forms the cascade structure. The sliding mode control algorithm is based on a single input-output state space model and a second order control structure. The proposed cascade second order sliding mode control approach is validated on an experimental permanent magnet synchronous motor drive. Experimental results are provided to validate the effectiveness of the proposed control strategy with respect to speed and current control. Moreover, the robustness of the second-order sliding mode controller is guaranteed in terms of unknown disturbances and parametric and modeling uncertainties.
Among the various renewable energy generation systems, the solar photovoltaic occupies a leading position today due to its simple structure. However, increasing the efficiency of solar photovoltaic ...systems is a highly researched topic. In this study, possible connection failures in maximum power inverters and other failures, which decrease the efficiency in solar power plants, are examined. Furthermore, the possible consequences of these losses and their effects on the performance of solar power plants are explained. Some missing-failure processes were identified and corrected in the field analysis of the solar power plant in Turkey. Detected missing failures include connection failures of solar inverters, incorrect network configuration of camera system, fixing lighting time settings. The inverter string connection failure made during the projecting and assembly phase was eliminated and the maximum output was determined as 584.25 kW after the DC string arrangement. An increase of approximately 10% was achieved in production. In the project and application phase, the connection details of the inverters should be drawn and given to the field application personnel as a full-fledged project. In this way, incorrect connections that are not shown in the project and made in the field are prevented. This ensures that the installed power plant operates more efficiently, and the budgetary payback period of the investments made is shortened.
Forecasting wind speed is one of the most important and challenging problems in the wind power prediction for electricity generation. Long short-term memory was used as a solution to short-term ...memory to address the problem of the disappearance or explosion of gradient information during the training process experienced by the recurrent neural network (RNN) when used to study time series. In this study, this problem is addressed by proposing a prediction model based on long short-term memory and a deep neural network developed to forecast the wind speed values of multiple time steps in the future. The weather database in Halifax, Canada was used as a source for two series of wind speeds per hour. Two different seasons spring (March 2015) and summer (July 2015) were used for training and testing the forecasting model. The results showed that the use of the proposed model can effectively improve the accuracy of wind speed prediction.
Using renewable energy sources instead of fossil fuels is one of the best solutions to overcome greenhouse gas (GHG) emissions. However, in designing clean power generation microgrids, the economic ...aspects of using renewable energy technologies should be considered. Furthermore, due to the unpredictable nature of renewable energy sources, the reliability of renewable energy microgrids should also be evaluated. Optimized hybrid microgrids based on wind and solar energy can provide cost-effective power generation systems with high reliability. These microgrids can meet the power demands of the consuming units, especially in remote areas. Various techniques have been used to optimize the size of power generation systems based on renewable energy to improve efficiency, maintain reliability, improve the power grid’s resilience, and reduce system costs. Each of these techniques has shown its advantages and disadvantages in optimizing the size of hybrid renewable energy systems. To increase the share of renewable energies in electricity supply in the future and develop these new technologies further, this paper reviews the latest and most efficient techniques used to optimize green microgrids from an economical and reliable perspective to achieve a clean, economical, and highly reliable microgrid.
This article presents the modeling and optimization control of a hybrid water pumping system utilizing a brushless DC motor. The system incorporates battery storage and a solar photovoltaic array to ...achieve efficient water pumping. The solar array serves as the primary power source, supplying energy to the water pump for full-volume water surrender. During unfavorable weather conditions or when the photovoltaic array is unable to meet the power demands of the water pump, the battery discharges only at night or during inadequate solar conditions. Additionally, the photovoltaic array can charge the battery on its own when water distribution is not necessary, negating the need for external power sources. A bi-directional charge control mechanism is employed to facilitate automatic switching between the operating modes of the battery, utilizing a buck-boost DC–DC converter. The study incorporates a control system with loops for battery control and DC voltage control within the bidirectional converter. The water cycle algorithm adjusts four control parameters by minimizing an objective function based on tracking errors. The water cycle optimization is compared to other methods based on overshoot and settling time values to evaluate its performance, showcasing its effectiveness in analyzing the results.
This paper presents an improved estimation strategy for the rotor flux, the rotor speed and the frequency required in the control scheme of a standalone wind energy conversion system based on ...self-excited three-phase squirrel-cage induction generator with battery storage. At the generator side control, the rotor flux is estimated using an adaptive Kalman filter, and the rotor speed is estimated based on an artificial neural network. This estimation technique enhances the robustness against parametric variations and uncertainties due to the adaptation mechanisms. A vector control scheme is used at the load side converter for controlling the load voltage with respect to amplitude and frequency. The frequency is estimated by a Kalman filter method. The estimation schemes require only voltage and current measurements. A power management system is developed to operate the battery storage in the DC-microgrid based on the wind generation. The control strategy operates under variable wind speed and variable load. The control, estimation and power management schemes are built in the MATLAB/Simulink and RT-LAB platforms and experimentally validated using the OPAL-RT real-time digital controller and a DC-microgrid experimental setup.
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