Energy trading model for optimal microgrid scheduling based on genetic algorithm

In this paper, a novel microgrid energy trading model (METM) is proposed to determine an optimal schedule of all available units over a planning horizon so as to meet all system, plant and unit constraints, as well as meet the load and ancillary service demands. As the optimization greatly depends on the power generation and the power output from renewable sources strongly depends on the weather, the forecast of power generation is required for METM. A neural network power forecasting is used to predict hourly power outputs. Depending on the forecast module, the METM utilizing genetic algorithm was developed to assist the microgrid scheduling which manages the micro sources and make good operation and trading decisions while meeting the constraints.