Research On FPGA-based Co-optimization Of Distribution Network Containing Wind-solar-storage

Currently,active distribution network containing wind energy,solar energy and energy storage have been received more and more attention.Co-optimization of the wind energy,solar energy and energy storage is essential to improve the reliability of the distribution network.The shorter the time to solve the problem of the optimization,the higher the reliability of the distribution network.Field Programmable Gate Array(FPGA)can be used to implement optimization algorithms.The advantage of the FPGA-based implementation of optimization algorithms is the great acceleration of the computational speed of distribution network co-optimization.Therefore,it is essential to study the FPGA-based co-optimization of distribution network containing the wind energy,solar energy and energy storage.Firstly,various optimization algorithms are analyzed in this paper.Genetic Algorithm was chosen to do the optimization calculation of the distribution network.To facilitate hardware implementation,the function of Genetic Algorithm is divided into six modules,such as control module,random number generation module,memory module,initialization module,genetic operation module(selection,crossover,variation),and adaptation calculation module.Each functional module of the Genetic Algorithm is described by using Verilog-HDL.Functional simulation and timing simulation are performed to each module using VIVADO software.Six modules are integrated together to perform the functional simulation and timing simulation.The proposed Algorithm was downloaded to the FPGA development board to verify the correctness.Secondly,a hardware structure of fast forward-backward sweep power flow algorithm based on FPGA was proposed.The hardware structure is divided into five functional modules,such as global control module,forward calculation module,backward calculation module,judgment convergence module,and network loss calculation module.Each functional module of the fast forward-backward sweep power flow algorithm is described by using Verilog-HDL.Functional simulation and timing simulation are performed to each module using VIVADO software.Five modules are integrated together to perform the functional simulation and timing simulation.The proposed fast forward-backward sweep power flow algorithm was downloaded to the FPGA development board to verify the correctness.The proposed fast forward-backward sweep power flow algorithm is validated using IEEE-33 node test system.Compare the calculation results of FPGA development board and the calculation results of MATLAB software to show the correctness of the calculation based on FPGA.The calculation time of fast forward-backward sweep power flow algorithm based on FPGA is 376.96μs,which is nearly two orders of magnitude higher compared with the calculation time of MATLAB of 42.07ms.Therefore,the computational speed of fast forward-backward sweep power flow algorithm based on FPGA is significantly improved.Finally,the proposed Genetic Algorithm based on FPGA and the proposed fast forward-backward sweep power flow algorithm based on FPGA are integrated together to perform the Co-optimization of the distribution network containing wind energy,solar energy and energy storage.Considering the constraints of the distribution network operation,loss of the distribution network is chosen as the objective function,the location and capacity of DGs are optimized.The proposed Algorithm is validated using IEEE-33 node test system and IEEE-69 node test system.The test results verify the effectiveness of the proposed algorithm.The proposed algorithm can significantly reduce the loss of the distribution network by optimizing the location and capacity of DGs including wind energy,solar energy and energy storage devices.Compared with the MATLAB-based Genetic Algorithm,FPGA-based Genetic Algorithm is significantly faster.Therefore,this paper proposes a new hardware method for the fast optimization of the distribution network containing wind energy,solar energy and energy storage.