Intelligent database design for microgrids in low voltage distribution system-ii load and resource forecast

• Main Author: Low, Wan Ping
• Other Authors: Wang Peng
• Format: Final Year Project
• Language: English
• Published: 2014
• Subjects: DRNTU::Engineering::Electrical and electronic engineering::Electric power
• Online Access: http://hdl.handle.net/10356/60472
• Institution: Nanyang Technological University

Recent concerns of energy sustainability and the reliability of power supply to consumers have demonstrated the need to improve on the distribution of electricity to consumers. Microgrids have been developed to provide clean, reliable and cheaper electricity to consumers at a local level. Renewable energy such as solar energy can be used as the power source of a microgrid. It has been shown to have better sustainability as it will never run out thus microgrid will become a potential and promising advancement in the power system. The management of the microgrid, known as microgrid energy management system, is necessary for optimal operation of the microgrid. Short-term load forecasting and short-term solar power forecasting are one of the key tools to optimal microgrid operation. This project predicts the NTU’s laboratory load demand and the solar power output of NTU’s solar panel. Artificial neural network based forecast models are built to predict the day-ahead load and solar power. The forecast results and the accuracy of the forecast models are shown to be acceptable. In addition, correlation analysis is carried out to determine the models’ inputs. It can be seen that good forecast results also depends on the inputs. A graphic user interface is created to allow users to view the predicted results forecasted by the proposed models. This forecasting can be used by the planners and operators to ensure sufficient and reliable electricity supply to the consumers.