Coordinated optimization of emergency power vehicles and distribution network reconfiguration considering the uncertain restoration capability of e-taxis

Network reconfiguration and emergency power vehicles (EPVs) dispatching are widely used in distribution networks for load restoration. However, their capabilities are limited by the allocated amounts of circuit breakers and EPVs. E-taxis can also participate in the restoration as a kind of mobile energy storage using the vehicle to grid (V2G) technology. However, the uncertainty of E-taxis should be considered in the restoration. To achieve better effectiveness of the restoration and fully utilize the capability of network reconfiguration, EPVs and E-taxis, this paper proposes a coordinated restoration optimization method considering the uncertain restoration capabilities of discharging stations with E-taxis. A joint probability distribution function is established based on Gaussian Mixture Model to describe the uncertainty of station discharging capabilities considering the correlation of user rationality, taxi state-of-charge and transportation status. Then, a bi-level programming model embedded with the chance constraint programming is developed to optimize the coordinated dynamic restoration scheme of the network reconfiguration and EPV dispatching, with the consideration of the mobility of EPVs during the restoration. Simulations studies are performed to verify the proposed method

A multi-disaster-scenario distributionally robust planning model for enhancing the resilience of distribution systems

Resilience oriented network planning provides an effective solution to protect the distribution system from natural disasters by the pre-planned line hardening and backup generator allocation. In this paper, a multi-disaster-scenario based distributionally robust planning model (MDS-DRM) is proposed to hedge against two types of natural disaster-related uncertainties: random offensive resources (ORs) of various natural disasters, and random probability distribution of line outages (PDLO) that are incurred by a certain natural disaster. The OR uncertainty is represented by the defined probability-weighted scenarios with stochastic programming, and the PDLO uncertainty is modeled as the moment based ambiguity sets. Moreover, the disaster recovery strategies of network reconfiguration and microgrid formation are integrated into the pre-disaster network planning for resilience enhancement in both planning and operation stages. Then, a novel primal cut based decomposition solution method is proposed to improve the computational efficiency of the proposed model. In particular, the equivalent reformulation of the original MDS-DRM is first derived to eliminate the PDLO-related variables. Then, the reformulation problem is solved by the proposed primal cut based decomposition method and linearization techniques. Finally, Simulation results are demonstrated for IEEE 13-node, 33-node and 135-node distribution systems to validate the effectiveness of the proposed method in enhancing the disaster-induced network resilience

A Virtual Space Vectors based Model Predictive Control for Three-Level Converters

Three-phase three-level (3-L) voltage source converters (VSC), e.g., neutral-point clamped (NPC) converters, T-type converters, etc., have been deemed to be suitable for a wide range of medium- to high-power applications in microgrids (MGs) and bulk power systems. Compared to their two-level (2-L) counterparts, adopting 3-L VSCs in the MG applications not only reduces the voltage stress across the power semiconductor devices, which allows achieving higher voltage levels, but also improves the quality of the converter output waveforms, which further leads to considerably smaller output ac passive filters. Various control strategies have been proposed and implemented for 3-L VSCs. Among all the existing control methods, finite-control-set model predictive control (FCS-MPC) has been extensively investigated and applied due to its simple and intuitive design, fast-dynamic response and robustness against parameter uncertainties. However, to implement an FCS-MPC on a 3-L VSC, a multi-objective cost function, which consists of a term dedicated specifically to control the dc-link capacitor voltages such that the neutral-point voltage (NP-V) oscillations are minimized, must be designed. Nevertheless, selecting proper weighting factors for the multiple control objectives is difficult and time consuming. Additionally, adding the dc-link capacitor voltages balancing term to the cost function distributes the controller effort among different control targets, which severely impacts the primary goal of the FCS-MPC. Furthermore, to control the dc-link capacitor voltages, additional sensing circuitries are usually necessary to measure the dc-link capacitor voltages and currents, which consequently increases the system cost, volume and wiring complexity as well as reduces overall reliability. To address all the aforementioned challenges, in this dissertation research, a novel FCS-MPC method using virtual space vectors (VSVs), which do not affect the dc-link capacitor voltages of the 3-L VSCs, was proposed, implemented and validated. The proposed FCS-MPC strategy has the capability to achieve inherent balanced dc-link capacitor voltages. Additionally, the demonstrated control technique not only simplifies the controller design by allowing the use of a simplified cost function, but also improves the quality of the 3-L VSC output waveforms. Furthermore, the execution time of the proposed control algorithm was significantly reduced compared to that of the existing one. Lastly, the proposed FCS-MPC using the VSVs reduces the hardware cost and complexity as the additional dc-link capacitor voltages and current sensors are not required, which further enhances the overall system reliability