To expand the application of the technology in microgrid, estimating the demand of local loading and weather condition is a big issue. We can estimate the power generation of renewable energy like solar panels and wind turbines by collecting the climatological data.On the other hand, it is necessary to consider the capacity of the energy storage system which is already installed, and capacity of non-renewable generators etc. With all the data collected above, our algorithm will offer a suggestion for building a microgrid locally with the best economic benefit.
This exhibition our offered, which is using Particle Swarm Optimization as core calculation algorithm, can evaluate the optimal equipment configuration of renewable energy and non-renewable energy. With the hourly historical weather data, like solar radiation, duration of sunshine, wind speed etc., and other parameter like load usage, predetermined costs and generator device parameter as input values, in conjunction with the priority of different energy supplies, energy storage system limit etc., we use iteration to calculate relative optimal solution of multi-particle parameter and obtain the configuration capacity of each device to fit the best economic benefit. The advantage of our core algorithm is the speed of the calculation, so its efficiency is better than the pure analytic solution. The tool we chose is C＃ programming language that can package the algorithm into DLL file, and we use LabVIEW to design the human-machine interface and used it to call the DLL from C＃.