Browsing by Author "Zhang, Gang"

Now showing 1 - 4 of 4
  • Thumbnail Image
    ItemOpen Access
    A fixed-point based distributed method for energy flow calculation in multi-energy systems
    (IEEE, 2020-01-15) Zhang, Gang; Zhang, Feng; Meng, Ke; Zhang, Xin; Dong, Zhaoyang
    Multi-energy flow calculation (M-EFC) is an essential tool for the coordinated analysis of strongly coupled electricity-gas-heating systems. However, the separate management of these subsystems poses a considerable challenge for designing a fast and reliable M-EFC method. In this paper, a fixed-point based distributed method is proposed for the M-EFC problem. The proposed method can preserve the autonomy of subsystems due to limited information exchange during the solution process. Moreo-ver, the fast and reliable convergence is achieved according to the proposed sufficient conditions based on the fixed-point theory. Besides, the proposed method is availa-ble for multi-energy systems (MES) with various coupling relationships and different structures of information ex-change. Simulations on a MES demonstrate that the pro-posed method has remarkable superiority compared to the unified Newton-Raphson method in computation time, accuracy and robustness against data loss.
  • Thumbnail Image
    ItemOpen Access
    Mobile emergency generator planning in resilient distribution systems: a three-stage stochastic model with nonanticipativity constraints
    (IEEE, 2020-06-19) Zhang, Gang; Zhang, Feng; Zhang, Xin; Wang, Zhaoyu; Meng, Ke; Dong, Zhao Yang
    Mobile emergency generators (MEGs) can effec-tively restore critical loads as flexible backup resources after power network disturbance from extreme events, thereby boosting the distribution system resilience. Therefore, MEGs are re-quired to be optimally allocated and utilized. For this purpose, a novel three-stage stochastic planning model is proposed for MEG allocation of resilient distribution systems in consideration of planning stage (PLS), preventive response stage (PRS) and emergency response stage (ERS). Moreover, the nonanticipativity constraints are proposed to guarantee that the MEG allocation decisions are dependent on the stage-based uncertainties. Specifically, in the PLS, the intensity uncertainty (IU) of disasters and the outage uncertainty (OU) incurred by a given disaster are considered with probability-weighted scenarios for the effective MEG allocation. Then, with the IU that can be observed in the PRS, the MEGs are pre-positioned in the consideration of OU. It is noted that the pre-position decisions should only correspond to the IU realizations, according to nonanticipativity constraints. Last, with the further realization of OU in the ERS, the MEGs are re-routed from the pre-position to the target location, so that the provisional microgrids can be formed to restore critical loads. The proposed planning model can be large-scale due to multiple sce-narios. Therefore, the progressive hedging algorithm (PHA) is customized to reduce the computational burden. The simulation results in 13 and 123 node distribution systems show the effec-tiveness and superiority of the proposed three-stage MEG plan-ning model over the traditional two-stage model.
  • Thumbnail Image
    ItemOpen Access
    A multi-disaster-scenario distributionally robust planning model for enhancing the resilience of distribution systems
    (Elsevier, 2020-05-26) Zhang, Gang; Zhang, Feng; Zhang, Xin; Wu, Qiuwei; Meng, Ke
    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.
  • Thumbnail Image
    ItemOpen Access
    Sequential disaster recovery model for distribution systems with co-optimization of maintenance and restoration crew dispatch
    (IEEE, 2020-05-12) Zhang, Gang; Zhang, Feng; Zhang, Xin; Meng, Ke; Yang Dong, Zhao
    To efficiently restore electricity customers from a large-scale blackout, this paper proposes a novel mixed-integer linear programing (MILP) model for the optimal disaster recovery of power distribution systems. In the proposed recovery scheme, the maintenance crews (MCs) are scheduled to repair damaged components, and the restoration crews (RCs) are dispatched to switch on the manual switches. Then, the MC and RC dispatch models are integrated into the disaster recovery scheme, which will generate an optimal sequence of control actions for distributed generation (DG), controllable load, and remote/ manual switches. Besides, to address the time scale related challenges in the model formulation, the technical constraints for system operation are investigated in each energization step rather than time step, hence the co-optimization problem is formulated as an “event-based” model with variable time steps. Consequently, the disaster recovery, MC dispatch and RC dispatch are properly cooperated, and the whole distribution systems can be restored step by step. Last, the effectiveness of the co-optimization model is validated in the modified IEEE 123 bus test distribution system.