Applied Mechanics and Materials Vol. 785

Abstract: Datong area has abundant wind energy. Due to problem in large scale of wind power grid connection, this paper introduces virtual power plant concept. As for beginning, power source characteristics of the wind farm, pumped storage power station and the thermal power plant are taken for analysis. Three types of different power plants are chosen to represent the virtual power plant modeling as well as adopting the NSGA2 optimization. As a conclusion, this case study proved that virtual power plant can increase the benefits of each power plant and the wind power plant output power curve become smoother.

Abstract: This paper proposes hydropower potential sites for several islands located in the South China Sea. The islands depend mainly on diesel-fuel for electricity generation. As a result, the generating company exposed to diesel high and unpredictable market prices, high operation and maintenance costs, and possible risk of fuel spills. Therefore, reconnaissance studies were conducted through topographic maps and hydrological studies in order to determine the potential sites suitable for development of micro-hydro and pico-hydro generation. The studies conducted for 14 islands in the South China Sea with a total of 51 investigated sites. The result indicates that 24 sites identified to have micro-hydro potential and 22 sites have pico-hydro potential.

Abstract: Malaysia nowadays depends heavily on conventional energy resource for generating electricity and transportation. This will give negative impact and trigger other problems if not well managed. Therefore, it is very important to increase the use of renewable energy especially the tidal energy as the tidal energy is more predictable and reliable renewable energy source. A kinetic energy produced by the tidal differences will be used to generate electricity and involves the use of a tidal turbine. There are two approaches that are being used to harness the tidal energy for the generation of electricity which are barrage approach and tidal stream approach. This paper identify the potential region along west coast of peninsular Malaysia that has higher potential power generation for harnessing tidal energy and study which approach suitable to be applied. From the results, show that the highest potential power generation for harnessing tidal energy is at Pelabuhan Klang with the average power availability is 591.19kW while the lowest is at Tanjung Keling with the average potential power generation is 52.75kW.

Abstract: Renewable energy sources such as wind, solar, biomass, wave and tidal energy have become favourable alternatives to replace conventional generators due to excessive emission of harmful gaseous and depletion of natural resources. From all of the energy sources, solar photovoltaic is chosen in this paper because it is considered as an inexhaustible energy in Malaysia. Yet it is still in infancy stage due to high cost of implementation. This paper adopts a multi-state method for photovoltaic output power modelling which is incorporated with analytical technique to evaluate the capacity outage probability of a power system consisting both conventional and renewable generators. Consequently, this is to evaluate the system reliability index which is the Loss of Load Probability (LOLP). The results show that the presence of solar photovoltaic enhances the overall LOLP of the system.

Abstract: With the growing penetration of distributed energy resources on the network the operation regime has been changed from passive to active which brings a number of impacts to the network including voltage control, power flow management and protection issues etc. As one of the dominant impact, the voltage control strategy has become a key enabling technology for the deployment of renewable energy systems. This paper provides a comprehensive review on voltage control for active distribution networks, the efficiency and practicability have been studied in details.

Abstract: This paper proposes Artificial Neural Network (ANN) to determine adjusted baseline energy for quantifying energy savings from an energy efficiency program implemented in an office building. The input data to the ANN includes number of working days and cooling degree days (CDD) each month for one year period before implementation of the retrofitting program. On the other hand, output data is baseline energy use (i.e. energy use before retrofit). Since the input data to the network encompasses of 36 months set of data only, Bootstrap method is used to generate more input data without changing the input and output trend of the original data set. This is performed to increase validity of the training process. Once the optimum training parameters have been obtained, adjusted baseline energy is determined by feeding the number of working days and CDDs in the post-retrofit period (i.e. 12 months set of data) to the network. Energy savings is then calculated by comparing the adjusted baseline energy with the energy use after implementing the retrofit program. The performances of the ANN model are then compared with Multi-regression technique in term of R², Mean Absolute Percentage Error (MAPE), Mean Square Error (MSE) and Mean Absolute Deviation (MAD). Results show that the proposed ANN model has smaller errors and R² closer to one compare to Multi-regression technique.

Abstract: The use of cloud computing is increasing worldwide because users are now offered with IT utility services that consist of a pool of servers and switches that are fully interconnected. As such, cloud computing consumes huge amounts of power energy, leading to high operational costs and leaving carbon footprints in the environment. Thus, we are promoting the use of Green Cloud computing solutions. In this paper, we investigate how power management affects power saving in cloud computing by analyzing the architecture, topology, average load/server, and scheduling algorithms. We validated our result by using the GreenCloud simulator. Results reveal that changes in architecture, topology, average load/server, and scheduling algorithms have an impact on cloud computing, thus allowing for significant cost savings and indicating the high potential of improving energy efficiency under dynamic workload scenarios.

Abstract: Reducing energy consumption is one of the most important things for optimal operation of the multiple-chillers. The storage system for chilled-water is used for cooling demand and shifting load consumption at peak hours. The case study in this work is based on the existing system for chillers plant in an industrial building. This paper adopts Fuzzy Inference System, in order to adjust the operating points setting for a period of 24 hours to keep the cooling demand satisfied. This is based on a new strategy for the scheduled operation of the chillers. The results have shown the effectiveness for the energy savings and also satisfying the cooling demand. This proposed control system has been simulated in Matlab and the simulation result is compared with the measured data from the existing system.

Abstract: This paper presents the concept of International Performance Measurement and Verification Protocol (IPMVP) for determining energy saving at whole facility level for an office building in Malaysia. Regression analysis is used to develop baseline model from a set of baseline data which correlates baseline energy with appropriate independents variables, i.e. Cooling Degree Days (CDD) and Number of Working Days (NWD) in this paper. In determining energy savings, the baseline energy is adjusted to the same set condition of reporting period using energy cost avoidance approach. Two types of energy saving analyses have been presented in the case study; 1) Single linear regression for each independent variable, 2) Multiple linear regression for each independent variable. Results show that NWD has coefficient of determination, R² higher than CDD which indicates that NWD has stronger correlation with the energy use than CDD in the building. Finding also shows that the R2 for multiple linear regression model are higher than single linear regression model. This shows the fact that more than one component are affecting the energy use in the building.

Abstract: The usage of chemical battery is very critical nowadays because of their hazardous chemical contains that can cause pollution and need to dispose properly in order to avoid pollution towards the eco-system. Hence, the earth battery is an erstwhile concept designed to generate electricity by using two dissimilar electrodes buried in the ground or immersed in the sea. Although it has been proven to generate electricity, the voltage produced is quite low while the voltage intensity could not be sustained for a long period of time. This paper attempts to explore the potential of using organic wastes as additives in the soil of ground-based earth batteries to improve its performance. Three types of organic wastes were studied; Palm Oil Mill Effluent (POME), pineapple waste and lemon waste. Each organic waste is added into separate damped clay soil containers with a ratio of 20:80. Then a copper and an iron electrode are partially buried inside the soil and the electrical properties are measured by connecting a multimeter at the electrodes. The result shows that there is marginal improvement for all three organic wastes in terms of voltage compared to damped clay soil alone. The results also show that the voltage output of these samples could be sustained for seven days.