Search

Flexible interconnection transformation of a distribution network using all-control power electronics technology will help to overcome the technical bottleneck of distributed generation (DG) penetration and high-quality power supply in the traditional distribution network. However, the flexible interconnection form that uses back-to-back power electronic converters to replace tie switches has disadvantages such as high cost and large footprint, so its mass promotion and application are bound to be limited. Therefore, a new topology of flexible interconnected distribution network with embedded DC system (FDN+EDC) with a better economy is proposed. Firstly, the topology of FDN+EDC is introduced to improve the controllability level of the distribution network under high percentage DG penetration, and is compared with the conventional flexible interconnection method. Secondly, in order to realize the coordinated optimal economic operation of source, network and load in the FDN+EDC, a dynamic reconfiguration model of switches status and continuous adjustment of flexible interconnection device (FID) are proposed. Then, to achieve a fast and globally optimal solution of the model, a hierarchical coordinated optimization strategy is designed based on an improved particle swarm algorithm for the optimization of discrete and continuous variables. Finally, the validity of the algorithm is verified, and an 88-node FDN+EDC is constructed to verify the advantages of the proposed embedded DC flexible interconnection topology and realize the joint optimal dispatch of network-source-load. [ABSTRACT FROM AUTHOR]

Both the steering and suspension systems have significant impacts on the dynamic performance of vehicles. Considering the coupling effect and aiming to improve the holistic performance of the full vehicle matching with mechanical elastic wheels (MEW), this paper carries out an integrated parameters optimization design of the electric power steering system and suspension systems. Comprehensive performance evaluation indices of the vehicle are formulated, including steering performance, ride comfort, and full vehicle stability. In order to deal with the multi-objective optimization design of the vehicle chassis, this paper puts forward an improved two-level multidisciplinary optimization method named hierarchical coordination optimization (HCO). The HCO method decomposes the optimization problem hierarchically with shared variables and local variables. Furthermore, consistency constraints and penalty functions are introduced into the objective functions of each optimizer to realize the decoupling between the system level and subsystem level. Therefore, system-level and discipline-level optimization can be carried out alternately, which not only simplifies the original optimization problem but also solves the coupling problem. Moreover, the particle swarm optimization algorithm is adopted to realize the entire optimization work. Finally, the optimization results of both vehicle chassis optimization design and numerical example are obtained, which show that HCO performs better than the linear weighted sum method. The simulation results reveal that the proposed optimization strategy can improve the performance of the vehicle equipped with MEW in multiple aspects. [ABSTRACT FROM AUTHOR]

With large-scale integration of distributed energy resources (DERs), distribution networks have turned into active distribution networks (ADNs). However, management risks and obstacles are caused by this in due to renewable energy generation (REG) forecasting errors. In this paper, a day-ahead active power scheduling method considering REG forecast errors is proposed to eliminate the risks, minimize the costs of distribution companies and achieve optimal power flow. A hierarchical coordination optimization model based on chance constrained programming is established to realize day-ahead optimal scheduling of active power in ADNs coordinated with network reconfiguration, achieving an optimal solution of network topologies and DER outputs. The hierarchical method includes three levels: the first level provides initial values, and multiple iterations between the second and third level are used to solve the multi-period mixed integer nonlinear optimization problem. The randomness due to REG forecast errors is tackled with chance constrained programming in the scheduling procedure. The hybrid particle swarm optimization algorithm is employed to solve the proposed model. Simulation results verify the validity of the proposed method with an improved 33 nodes distribution network. [ABSTRACT FROM AUTHOR]