Kyairul Azmi Baharin

Transmission losses allocation in deregulation electricity market

The restructuring of Electricity Supply Industry (ESI) all over the world that started mainly in the 20th century introduces an open electricity market for trading electricity between generators and suppliers in competitive environments. This transformation consists of two aspects that are related with each other; restructuring and privatization. However, due to this change, some problems and challenges have risen. One of it is the issue of power losses allocation. When electrical power is transmitted through a network, it will cause power losses. The generators must compensate this loss by generating more power. Under competitive electricity market environment, no generators would want to generate more to compensate this loss as it will increase their production cost. Logically both generators and consumers are supposed to pay for the losses because they both use the network and thus are responsible for the losses incurred. If there is no specified method to handle this problem, there is a probability that the Independent System Operator (ISO) which is a non-profit entity and does not have source of income will be responsible for this losses. However, if ISO paid for the losses, it is considered unfair. It is the market participants who should cover up the cost of losses. Thus, this analysis focuses on some existing allocating transmission losses and loss cost methods. The selected methods are pro rata, proportional sharing and novel pricing and these methods have been tested using simple bus network and the IEEE standard 14 test bus system.

Short-Term Forecasting Of Solar Photovoltaic Output Power For Tropical Climate Using Ground-Based Measurement Data

This paper highlights a new approach using high-quality ground measured data to forecast the hourly power output values for grid-connected photovoltaic (PV) systems located in the tropics. A case study using the 1-year database consisting of PV power output, global irradiance, module temperature, and other relevant variables obtained from Universiti Teknikal Malaysia Melaka is used to develop forecast models for three typical weather conditions—clear, cloudy, and overcast sky conditions. A machine learning method (Support Vector Regression—SVR) and an Artificial Neural Network method (nonlinear autoregressive) are used to produce the models and the results are compared with a benchmark model using the persistence method. Comparison with all the variables suggests that tilted global horizontal irradiance (GHItilt) and module temperature (Tmod) are the essential input variables to forecast the PV power output. It has also been observed that SVR performs well across all types of sky conditions, with the fore...

Hourly Photovoltaics Power Output Prediction for Malaysia Using Support Vector Regression

Reliable solar energy forecasting enables grid operators to manage the grid better as PV penetration level increases. This research explores the use of support vector regression to forecast hourly power output from a grid-connected PV system in Malaysia. Data is obtained from a grid-connected PV system that is equipped with a weather monitoring station. Three parameters are used as input to the forecast model; global irradiance, tilted irradiance and ambient temperature. Results were compared against a persistence model. The SVR model manages to forecast hourly power production with satisfactory accuracy.

Hourly irradiance forecasting in Malaysia using support vector machine

This paper investigates the use of support vector machine (SVM) to forecast hourly solar irradiance for a tropical country. The hourly irradiance data was obtained from Sepang Malaysia, recorded throughout 2011. The data is converted into corresponding clearness index values to facilitate model convergence. The forecast is tested against the standard multilayer perceptron (MLP) technique and persistence forecast. The evaluation metrics used to validate each models performance are mean bias error, root mean square error, mean absolute error/average, and Kolmogorov-Smirnov integral test. Results show that the SVM performs significantly better than the conventional MLP technique.

Influence of Single-Phase Solar Photovoltaic Systems on Total Harmonic Distortion: A Case Study

Applied Computing Technology (ACT), Universiti Tun Hussein Onn Malaysia (UTHM), Parit Raja, Batu Pahat, 86400 Johor, Malaysia. Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia (UTHM), Parit Raja, Batu Pahat, 86400 Johor, Malaysia. Department of Management Information Systems, College of Commerce and Business Administrations, Dhofar University, Salalah, Oman.

Passing-Cloud Effects of Solar Photovoltaic System on Distribution Network Voltages

The introduction of Feed-in Tariff (FiT) scheme has prompted an increasing number of grid-connected Photovoltaic (PV) systems installations in Malaysia. As a consequence, the network issues related to the PV systems integration need to be properly addressed. This includes the effect of solar irradiance intermittency which is caused by the passing-clouds. In this regard, this paper investigates the effect of passing-cloud on a standard IEEE 4 node test feeder, focusing on short term voltage drop analysis. Actual five-minute interval PV generation data in Melaka, Malaysia was used in the analysis. The network was analyzed by using the well-known OpenDSS tool. The network voltage impact of different PV penetration levels were investigated on both sunny and cloudy days. The results show that temporal voltage drop could occur on the network when there is a sudden drop of PV generation driven by passing-cloud. The percentage of voltage drop recorded was observed to be proportionate to the increment of PV penetration levels.