Research On Optimal Operation Method With Source-load Interaction Of Hybrid AC/DC Microgrid

According to the power supply mode of the internal main network, microgrid can be divided into AC microgrid, DC microgrid and hybrid AC/DC microgrid. Hybrid AC/DC microgrid can effectively integrate the advantages of AC microgrid and DC microgrid, and provide a more flexible power supply mode. Therefore, it not only can effectively reduce the equipment costs but also improve the operation and regulation level of the whole power supply and consume system. And it has become one of the development hotspots of microgrid technology. Moreover, with the construction of smart grid, in order to meet the diversified development of distributed generations, energy storage devices and loads, researches on optimal operation method with source-load interaction has been one of the difficulties in the research of AC/DC hybrid microgrid.This paper studies the optimal operation method of hybrid AC/DC microgrid with source-load interaction. The background and significance of this research are introduced. The development of hybrid AC/DC microgrid and the research status of the optimal operation and source-load interaction problem are summarized. The concept and characteristics of hybrid AC/DC microgrid are studied. The characteristics of the source and load in AC/DC hybrid microgrid and its influence on the operation of microgrid are analyzed. To gives full play to the complementary advantages of AC and DC power supply modes and the source-load interaction in hybrid AC/DC microgrid, selections of power supply modes for loads(i.e. connect to AC or DC bus) are optimized on the demand-side to improve power supply and consuming efficiency; and the outputs of distributed generations and energy storage devices are optimized on the generation-side. A random chance constrained bi-level source-load interaction optimization model is established aiming at minimizing power conversion loss and operating cost at the upper level and lower level of the optimization model respectively. The chaos search algorithm is introduced into the standard particle swarm optimization algorithm to propose the chaotic particle swarm optimization algorithm which is combined with the stochastic simulation and neural network to propose a hybrid intelligent algorithm for solving the above mentioned model. Finally, simulation results verify the effectiveness and feasibility of the proposed method.