Micro Energy Management of Distributed Generations Inside Smart Micro Grids

A campus based smart micro grid model that combined power and heat is presented. In this study, optimal scheduling of distributed generations inside these smart micro grids is determined based on demand forecasting, solar power forecasting and wind power forecasting by artificial neural network (ANN). The proposed model is then evaluated through economical efficiency, environment efficiency and energy conservation efficiency. An optimization method, MINLP (Mixed Integral Non-linear Programming), is used and an intelligent method, GA (Genetic Algorithm), is used to solve the MINLP for optimal generation planning. This research is conducted to consider not only about scheduling of distributed energy but also take into consideration some types of renewable energy generator planning, such as photovoltaic generation, wind power generation, battery charge or discharge, and then even think about selling or buying power to or from main grid. The additional factors, such as heater, hot-water supply that called thermal demand is also considered. This study is believed to be useful for introducing smart micro grids in universities, factories, hospitals and other communities, either operate as stand alone or grid-connected, to operate with high efficiency in economic, environment and energy conservation. Keywords—micro-grid; smart-grid; energy management system; optimization; renewable energy