Tamer Khatib

Modeling of Daily Solar Energy on a Horizontal Surface for Five Main Sites in Malaysia

This paper presents models for global and diffuse solar energy on a horizontal surface for main five sites in Malaysia. The global solar energy is modeled using linear, nonlinear, fuzzy logic, and artificial neural network (ANN) models, while the diffuse solar energy is modeled using linear, nonlinear, and ANN models. Three statistical values are used to evaluate the developed solar energy models, namely, the mean absolute percentage error, MAPE; root mean square error, RMSE; and mean bias error, MBE. The results showed that the ANN models are superior compared with the other models in which the MAPE in calculating the global solar energy in Malaysia by the ANN model is 5.38%, while the MAPE for the linear, nonlinear, and fuzzy logic models are 8.13%, 6.93%, and 6.71%, respectively. The results for the diffuse solar energy showed that the MAPE of the ANN model is 1.53%, while the MAPE of the linear and nonlinear models are 4.35% and 3.74%, respectively. The accurate ANN models can therefore be used to predict solar energy in Malaysia and nearby regions.

A New Approach for Optimal Sizing of Standalone Photovoltaic Systems

This paper presents a new method for determining the optimal sizing of standalone photovoltaic (PV) system in terms of optimal sizing of PV array and battery storage. A standalone PV system energy flow is first analysed, and the MATLAB fitting tool is used to fit the resultant sizing curves in order to derive general formulas for optimal sizing of PV array and battery. In deriving the formulas for optimal sizing of PV array and battery, the data considered are based on five sites in Malaysia, which are Kuala Lumpur, Johor Bharu, Ipoh, Kuching, and Alor Setar. Based on the results of the designed example for a PV system installed in Kuala Lumpur, the proposed method gives satisfactory optimal sizing results.

Assessment of Artificial Neural Networks for Hourly Solar Radiation Prediction

This paper presents an assessment for the artificial neural network (ANN) based approach for hourly solar radiation prediction. The Four ANNs topologies were used including a generalized (GRNN), a feed-forward backpropagation (FFNN), a cascade-forward backpropagation (CFNN), and an Elman backpropagation (ELMNN). The three statistical values used to evaluate the efficacy of the neural networks were mean absolute percentage error (MAPE), mean bias error (MBE) and root mean square error (RMSE). Prediction results show that the GRNN exceeds the other proposed methods. The average values of the MAPE, MBE and RMSE using GRNN were 4.9%, 0.29% and 5.75%, respectively. FFNN and CFNN efficacies were acceptable in general, but their predictive value was degraded in poor solar radiation conditions. The average values of the MAPE, MBE and RMSE using the FFNN were 23%, −.09% and 21.9%, respectively, while the average values of the MAPE, MBE and RMSE using CFNN were 22.5%, −19.15% and 21.9%, respectively. ELMNN fared the worst among the proposed methods in predicting hourly solar radiation with average MABE, MBE and RMSE values of 34.5%, −11.1% and 34.35%. The use of the GRNN to predict solar radiation in all climate conditions yielded results that were highly accurate and efficient.

Solar Energy Prediction for Malaysia Using Artificial Neural Networks

This paper presents a solar energy prediction method using artificial neural networks (ANNs). An ANN predicts a clearness index that is used to calculate global and diffuse solar irradiations. The ANN model is based on the feed forward multilayer perception model with four inputs and one output. The inputs are latitude, longitude, day number, and sunshine ratio; the output is the clearness index. Data from 28 weather stations were used in this research, and 23 stations were used to train the network, while 5 stations were used to test the network. In addition, the measured solar irradiations from the sites were used to derive an equation to calculate the diffused solar irradiation, a function of the global solar irradiation and the clearness index. The proposed equation has reduced the mean absolute percentage error (MAPE) in estimating the diffused solar irradiation compared with the conventional equation. Based on the results, the average MAPE, mean bias error and root mean square error for the predicted global solar irradiation are 5.92%, 1.46%, and 7.96%. The MAPE in estimating the diffused solar irradiation is 9.8%. A comparison with previous work was done, and the proposed approach was found to be more efficient and accurate than previous methods.

A Novel Numerical Algorithm for Optimal Sizing of a Photovoltaic/Wind/Diesel Generator/Battery Microgrid Using Loss of Load Probability Index

This paper presents a method for determining optimal sizes of PV array, wind turbine, diesel generator, and storage battery installed in a building integrated system. The objective of the proposed optimization is to design the system that can supply a building load demand at minimum cost and maximum availability. The mathematical models for the system components as well as meteorological variables such as solar energy, temperature, and wind speed are employed for this purpose. Moreover, the results showed that the optimum sizing ratios (the daily energy generated by the source to the daily energy demand) for the PV array, wind turbine, diesel generator, and battery for a system located in Sohar, Oman, are 0.737, 0.46, 0.22, and 0.17, respectively. A case study represented by a system consisting of 30u2009kWp PV array (36%), 18u2009kWp wind farm (55%), and 5u2009kVA diesel generator (9%) is presented. This system is supposed to power a 200u2009kWh/day load demand. It is found that the generated energy share of the PV array, wind farm, and diesel generator is 36%, 55%, and 9%, respectively, while the cost of energy is 0.17u2009USD/kWh.

Integrating Photovoltaic Systems In Power System : Power Quality Impacts And Optimal Planning Challenges

This paper is an overview of some of the main issues in photovoltaic based distributed generation (PVDG). A discussion of the harmonic distortion produced by PVDG units is presented. The maximum permissible penetration level of PVDG in distribution system is also considered. The general procedures of optimal planning for PVDG placement and sizing are also explained in this paper. The result of this review shows that there are different challenges for integrating PVDG in the power systems. One of these challenges is integrated system reliability whereas the amount of power produced by renewable energy source is consistent. Thus, the high penetration of PVDG into grid can decrease the reliability of the power system network. On the other hand, power quality is considered one of the challenges of PVDG whereas the high penetration of PVDGs can lead to more harmonic propagation into the power system network. In addition to that, voltage fluctuation of the integrated PVDG and reverse power flow are two important challenges to this technology. Finally, protection of power system with integrated PVDG is one of the most critical challenges to this technology as the current protection schemes are designed for unidirectional not bidirectional power flow pattern.

Optimal Sizing of the Energy Sources in Hybrid PV/Diesel Systems: A Case Study for Malaysia

This article presents optimization of hybrid photovoltaic (PV)/diesel systems for five regions in Malaysia. The PV array, storage battery, and diesel generator capacities are the variables to be optimized, considering very low loss of load probability. In the proposed optimization method, a hybrid PV/diesel system model is developed using a long-term solar energy series and a defined load demand. The objective of the optimization problem is to minimize the system cost and to determine the optimal sizing of PV array, storage battery, and diesel generator in terms of its unit cost. As a result, for an optimal sizing of hybrid PV/diesel generator for Malaysia, the PV array capacity must be 1.12 times the load demand, the diesel generator capacity must be 0.3 times the load demand, and the optimum storage battery size must be 0.29 times the load demand. The proposed PV/diesel system is validated by comparing it with a benchmark system. This system supplies the load demand of a school located in the state of Sabah. The load demand of this school is 1012 kWh/day during working days and 812 kWh/day during weekends and holidays. This load is supplied by a 150 kW diesel generator and a 35 kWp PV array without backup batteries. The comparison is based on the lifetime cost of the system that leads to the kWh unit price.

Optimization of the Tilt Angle of Solar Panels for Malaysia

This article presents a method for optimizing the tilt angle of photovoltaic module/array installed in the five sites in Malaysia. The optimization method is based on the Liu and Jordan model for solar energy incident on a tilt surface considering monthly and seasonal tilt angles. The optimization results showed that a seasonal optimum tilt angle change is recommended for the peninsular Malaysia, while a monthly optimum tilt angle change is recommended for east Malaysia comprising the states of Sabah and Sarawak. By applying the monthly optimum tilt angle, the collected yields by the PV module/array in Kuala Lumpur, Johor Bharu, Ipoh, Kuching, and Alor Setar increased by 5.03, 5.02, 5.65, 7.96, and 6.13%, respectively. On the other hand, applying the seasonal optimum tilt angle for the same regions increased the collected yields by 4.54, 4.58, 5.70, 4.11, and 5.85%, respectively.

A software tool for optimal sizing of PV systems in Malaysia

This paper presents a MATLAB based user friendly software tool called as PV. MY for optimal sizing of photovoltaic (PV) systems. The software has the capabilities of predicting themetrological variables such as solar energy, ambient temperature and wind speed using artificial neural network (ANN), optimizes the PV module/ array tilt angle, optimizes the inverter size and calculate optimal capacities of PV array, battery, wind turbine and diesel generator in hybrid PV systems. The ANN based model for metrological prediction uses four meteorological variables, namely, sun shine ratio, day number and location coordinates. As for PV system sizing, iterative methods are used for determining the optimal sizing of three types of PV systems, which are standalone PV system, hybrid PV/wind system and hybrid PV/diesel generator system. The loss of load probability (LLP) technique is used for optimization in which the energy sources capacities are the variables to be optimized considering very low LLP. As for determining the optimal PV panels tilt angle and inverter size, the Liu and Jordan model for solar energy incident on a tilt surface is used in optimizing the monthly tilt angle, while a model for inverter efficiency curve is used in the optimization of inverter size.

On the monthly optimum tilt angle of solar panel for five sites in Malaysia

In this research, an optimization of monthly tilt angle for solar panels for five main sites in Malaysia is conducted. The optimization method is based on the Liu and Jordan model for solar energy incident on a tilt surface. As a result, applying the monthly optimum tilt angle, increases the collected yields of the PV module/array in Kuala Lumpur, Johor Bharu, Ipoh, Kuching and Alor Setar by 5.03%, 5.02%, 5.65%, 7.96% and 6.13%, respectively. Such recommendations help in increasing the productively of PV systems in Malaysia and nearby country.