Micro Grid Control Optimization with Load and Solar Prediction

Using renewable energy can save money and keep the environment cleaner. Installing a solar PV system is a one-time cost but it can generate energy for a lifetime. Solar PV does not generate carbon emissions while producing power. This thesis evaluates the value of being able to make accurate predictions in the use of solar energy. It uses predicted solar power and load for a system and a battery to store the energy for future use and calculates the operating cost or profit in several designed conditions. Various factors like a different place, tuning the capacity of sources, changing buy/sell schedule are considered to verify the results. Combining real battery cost makes this work more reliable from the existing system. The prediction error also considered while testing the results

26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 - Meeting Abstracts - Antwerp, Belgium. 15–20 July 2017

This work was produced as part of the activities of FAPESP Research,\ud Disseminations and Innovation Center for Neuromathematics (grant\ud 2013/07699-0, S. Paulo Research Foundation). NLK is supported by a\ud FAPESP postdoctoral fellowship (grant 2016/03855-5). ACR is partially\ud supported by a CNPq fellowship (grant 306251/2014-0)

Fast Design Optimization Method Utilizing a Combination of Artificial Neural Networks and Genetic Algorithms for Dynamic Inductive Power Transfer Systems

Multiple parameters with large nonlinear characteristics must be considered simultaneously to design the coil dimensions of static inductive power transfer (SIPT) systems. The design of dynamic inductive power transfer (DIPT) systems is more challenging due to the large number of parameters needed to be considered. In the conventional artificial neural network (ANN)-based design approach, optimal coil dimensions are found using ANN that has learned the nonlinear characteristics between coil dimensions and magnetic characteristics using the finite element method (FEM). However, this approach requires a large amount of training data, and it is difficult to reach an optimum design if there are many design criteria. In order to overcome these challenges, this paper proposes a design optimization method using two approaches: improving the time efficiency of ANN training data collection by superposing the magnetic fields from the coils and improving the input value of ANN using a genetic algorithm. Design results predicted by the ANN are compared with FEM simulation, circuit simulations, and experimental results to verify the validity of the proposed algorithm. The FEM and circuit simulation results and the ANN prediction results match with errors of 10.2% or less for all design requirements. Experimental results are provided for a 3 kW DIPT system with four transmitter coils and an automated test rail. Comparison results between ANN predicted values and experimental values match with an error of less than 12.7%