A hybrid heuristic algorithm for optimal energy scheduling of grid-connected micro grids

The micro grids (MG) are small-scaled and restricted energy systems using distributed energy sources and storages. They can be operated in two different ways; grid-connected or islanded modes. The shifting between the modes depends on the volatility of demand. The use islanded mode is beneficiary as it helps minimizing the amount of power bought from main grid. It is not always possible unless a fertile field is found. This study proposes a hybrid heuristic approach for optimal management of MG considering regional conditions and constraints. For a power generating MG, the use of renewable resources in that region is as important as exchanging power with the main grid. MG is constructed in an industrial zone where the hourly power demand has to be matched. The aim is to schedule the power loads to minimize the amount of power taken from the main grid. To deal with this complex problem which contains power generation and consumption constraints, a versatile mathematical model must be established. The mathematical model needs to be integrated with a hybrid heuristic algorithm. Thus, a hybrid Genetic Algorithm (GA)-Simulated Annealing (SA) method is proposed for solution. The schedule is programmed using GA, while, parameters are optimized by using SA. In the application stage, a MG in Gebze is simulated with three factories as consumers, where, grid connection and a wind turbine together with photovoltaic panels are assumed to be in use.