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 forecasting skill values between 0.65 and 0.79 when compared to the benchmark persistence method.
This paper proposed a new swarm-based optimization technique for tuning conventional proportional-integral (PI) controller parameters of a static var compensator (SVC) which controls a synchronous generator in a single machine infinite bus (SMIB) system. As one of the Flexible Alternating Current Transmission Systems (FACTS) devices, SVC is designed and implemented to improve the damping of a synchronous generator. In this study, two parameters of PI controller namely proportional gain, K<sub>P</sub> and integral gain, K<sub>I</sub> are tuned with a new optimization method called Whale Optimization Algorithm (WOA). This technique mimics the social behavior of humpback whales which is characterized by their bubble-net hunting strategy in order to enhance the quality of the solution. Validation with respect to damping ratio and eigenvalues determination confirmed that the proposed technique is more efficient than Evolutionary Programming (EP) and Artificial Immune System (AIS) in improving the angle stability of the system. Comparison between WOA, EP and AIS optimization techniques showed that the proposed computation approach gives better solution and faster computation time.
Electricity bill is one of the major operating expenses in most of the commercial buildings and industrial plants. Thus, the buildings’ energy management system is an essential element that should be utilized to optimize the energy usage and hence, contributes to carbon footprint reduction. To achieve this, one needs to first understand how the energy is being used in the buildings before any saving measures can be identified and proposed. Therefore, this paper presents the development of an Internet of Things (IoT) enabled device that can communicate with different digital energy meters through modbus protocol. The prototype has been successfully installed in three locations in the main campus of Universiti Teknikal Malaysia Melaka (UTeM). The proposed solution enables the campus-wide energy usage to be monitored and stored efficiently and economically as opposed to the capital-intensive SCADA system.
This study aims to quantify the transformer tap changer operations on distribution networks with large penetration of photovoltaic systems. The influence of solar generation variability on transformer tap changer is investigated by using five different solar variability day types collected in Malaysia with 1 min resolution. The case studies have been performed on a Malaysian representative distribution network. Results show that high solar variability day could increase the transformer's tap changes by 600% as compared to the clear sky day. The transformer's time delay setting plays a vital role in limiting the tap operations.
The rising cost and demand for energy have prompted the need to devise innovative methods for energy monitoring, control, and conservation. In addition, statistics show that 20% of energy losses are due to the mismanagement of energy. Therefore, the utilization of energy management can make a substantial contribution to reducing the unnecessary usage of energy consumption. In line with that, the intelligent control and optimization of energy management systems integrated with renewable energy resources and energy storage systems are required to increase building energy efficiency while considering the reduction in the cost of energy bills, dependability of the grid, and mitigating carbon emissions. Even though a variety of optimization and control tactics are being utilized to reduce energy consumption in buildings nowadays, several issues remain unsolved. Therefore, this paper presents a critical review of energy management in commercial buildings and a comparative discussion to improve building energy efficiency using both active and passive solutions, which could lead to net-zero energy buildings. This work also explores different optimum energy management controller objectives and constraints concerning user comfort, energy policy, data privacy, and security. In addition, the review depicts prospective future trends and issues for developing an effective building energy management system, which may play an unavoidable part in fulfilling the United Nations Sustainable Development Goals.
The impact of high PV penetration into the grid particularly at the distrib ution side has b een extensively studied. However, most of the availab le research focuses on North American style systems. This project aims to investigate the effect of high PV penetration at a residential area in a European-based distrib ution network, which is electricity supply system Malaysia is b ased on. The modeling is done using OpenDSS while the network model used is the IEEE European Low Voltage Test Feeder which consists of 55 loads representing a generic housing area. Each load point is then equipped with a 4 kW PV system-representing a typical size for a house installation. PV output variab ility is then introduced into the modeling using two sample days of actual irradiance variab ility ob tained from UTeM Malaysia; one for clear day and another for a high variab ility day. Voltage unb alance, voltage rise and reverse power flow were analyzed. One significant finding of this project is that voltage rise exceeds the standard of 1.05 pu during noon. Besides that, the high variab ility days significantly affect the mitigation measures required to manage reverse power flow.
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