Research On Operation And Configuration Optimization Of Active Distribution Network Considering Multi-terminal SOP With Energy Storag

In recent years,the proportion of distributed power such as wind power generation and photovoltaic power generation in distribution network is increasing,and the uncertainty of its output brings many challenges to the stable operation of distribution network.Derived from the background analyzed above,soft open point(SOP)is new intelligent power distribution devices,it is based on full controlled type power electronic devices,able to quickly and accurately control the power flow of its own,to adjust the trend of the distribution of the whole distribution system,and then bring losses,voltage level to improve and a series of benefits.The ability of SOP integrated energy storage system in power flow control and voltage management is further enhanced,which brings new problems such as more difficult power flow control and how to optimize the configuration of energy storage and SOP capacity.In this paper,aiming at a series of optimal operation and capacity allocation planning problems caused by multi-terminal SOP integrated energy storage system connected to active distribution network,the operation and planning methods are studied.The main research work is as follows:1)Taking back-to-back voltage source converter as the research object,its mathematical model in d-q rotating coordinate system is analyzed,and its control strategy and operation constraints under different working conditions are described.Then,the mathematical models of multi-terminal SOP integrated energy storage system are further deduced based on the two-terminal SOP.The power flow optimization model of active distribution network is established and its solution process is described in detail.2)The emergence of SOP integrated energy storage system improves the utilization rate of equipment,enhances the ability of SOP to deal with transient disturbance and improve system power flow distribution.This paper proposes a power flow optimization strategy for active distribution networks with a multi-terminal SOP integrated energy storage system,establishes the SOP transmission of active power,reactive power as decision variables,multiple constraints distribution network of minimum energy loss,voltage deviation minimum multi-objective optimization mathematical model,adopting multi-objective particle swarm optimization algorithm to calculate the global optimal solution,The optimal access location of SOP is selected to reduce the total active power loss and improve the voltage level of active distribution network.Finally,the optimization effect of different types of SOP on the improved IEEE 33 node system is compared and analyzed in detail,and the effectiveness of the proposed optimization strategy is verified from two aspects of power flow optimization and economic benefit improvement of distribution network.3)Considering the investment and operation cost,the selection of SOP capacity and energy storage capacity becomes an issue worthy of study for the multi-terminal SOP integrated energy storage system proposed in this paper.Therefore,this paper proposes a bi-level optimization strategy for active distribution networks with a multi-terminal SOP integrated energy storage system for the purpose of optimizing the operation of active distribution networks and improving economic efficiency.A bi-level programming model of active distribution network considering multi-terminal SOP integrated energy storage system is established.The upper model takes the capacity of SOP and energy storage as decision variables,and the minimum annual comprehensive cost is the goal.The lower model takes the active power and reactive power at each time of SOP and the active power at each time of energy storage system as decision variables,and the total active power loss of active distribution network is the minimum.,and solved using standard particle swarm optimization algorithm and conic programming algorithm.Finally,the proposed bi-level optimization strategy is compared and analyzed with conventional power flow optimization on the modified IEEE 33-node system to verify its superiority.