Optimal energy management for industrial microgrids with high-penetration renewables

This paper presents a day-ahead optimal energy management strategy for economic operation of industrial microgrids with high-penetration renewables under both isolated and grid-connected operation modes.The approach is based on a regrouping particle swarm optimization(RegPSO)formulated over a day-ahead scheduling horizon with one hour time step,taking into account forecasted renewable energy generations and electrical load demands.Besides satisfying its local energy demands,the microgrid considered in this paper(a real industrial microgrid,“Goldwind Smart Microgrid System”in Beijing,China),participates in energy trading with the main grid;it can either sell power to the main grid or buy from the main grid.Performance objectives include minimization of fuel cost,operation and maintenance costs and energy purchasing expenses from the main grid,and maximization of financial profit from energy selling revenues to the main grid.Simulation results demonstrate the effectiveness of various aspects of the proposed strategy in different scenarios.To validate the performance of the proposed strategy,obtained results are compared to a genetic algorithm(GA)based reference energy management approach and confirmed that the RegPSO based strategy was able to find a global optimal solution in considerably less computation time than the GA based reference approach.

Short-term wind power forecasting using a double-stage hierarchical ANFIS approach for energy management in microgrids

Determination of the output power of wind generators is always associated with some uncertainties due to wind speed and other weather parameters variation,and accurate short-term forecasts are essential for their efficient operation.This can efficiently support transmission and distribution system operators and schedulers to improve the power network control and management.In this paper,we propose a double stage hierarchical adaptive neuro-fuzzy inference system(double-stage hybrid ANFIS)for short-term wind power prediction of a microgrid wind farm in Beijing,China.The approach has two hierarchical stages.The first ANFIS stage employs numerical weather prediction(NWP)meteorological parameters to forecast wind speed at the wind farm exact site and turbine hub height.The second stage models the actual wind speed and power relationships.Then,the predicted next day’s wind speed by the first stage is applied to the second stage to forecast next day’s wind power.The influence of input data dependency on prediction accuracy has also been analyzed by dividing the input data into five subsets.The presented approach has resulted in considerable forecasting accuracy enhancements.The accuracy of the proposed approach is compared with other three forecasting approaches and achieved the best accuracy enhancement than all.