Potential of Mobile Energy Hubs for Enhancing Resilience of Electricity Distribution Systems

In this paper, an operational framework is presented to improve electrical distribution network resilience based on the Mobile Energy Hubs (MEHs) concept. In fact, critical loads should be immediately islanded in a post-flood state and then recovered. Accordingly, this paper focuses on providing an effective management solution to enhance the functioning of electricity distribution systems with the objective of maximizing restoration of critical loads and minimizing their restoration time span based on MEH. To this end, MEHs are installed on trucks to deliver the required power for supplying the islanded critical loads in zones affected by a flood. Besides, in order to demonstrate a practical resilient structure, possible damage inflicted on other critical infrastructures is considered. Moreover, obstacles resulting from the destruction of the transportation infrastructure caused by a flood are overcome by using the shortest path algorithm (SPA). In this case, the optimization algorithm determines the shortest possible path for transporting the MEHs to supply critical loads in the least time aiming to improve the network resilience indicators. Finally, the proposed framework is studied in a standard test electricity distribution network. Simulations are carried out to evaluate the network resilience indicators of the proposed framework in obtaining a resilient distribution network during natural disasters.© 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed

Risk-based Probabilistic Quantification of Power Distribution System Operational Resilience

It is of growing concern to ensure the resilience in electricity infrastructure systems to extreme weather events with the help of appropriate hardening measures and new operational procedures. An effective mitigation strategy requires a quantitative metric for resilience that can not only model the impacts of the unseen catastrophic events for complex electric power distribution networks but also evaluate the potential improvements offered by different planning measures. In this paper, we propose probabilistic metrics to quantify the operational resilience of the electric power distribution systems to high-impact low-probability (HILP) events. Specifically, we define two risk-based measures: Value-at-Risk ($VaR_\alpha$) and Conditional Value-at-Risk ($CVaR_\alpha$) that measure resilience as the maximum loss of energy and conditional expectation of a loss of energy, respectively for the events beyond a prespecified risk threshold, $\alpha$. Next, we present a simulation-based framework to evaluate the proposed resilience metrics for different weather scenarios with the help of modified IEEE 37-bus and IEEE 123-bus system. The simulation approach is also extended to evaluate the impacts of different planning measures on the proposed resilience metrics.Comment: 12 pages, 11 figures, journa

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

Alocação de fontes renováveis em redes ilhadas

New opportunities enabled by renewable mobile power stations (RMPSs), in association with microgrids (MGs) ability to operate islanded to the main grid, presents a likely solution to ensure power system novel reliability requirements in face of the increasing number of severe disruptive events. In this perspective, this work proposes a novel intelligent RMPS allocation method to support MGs during scheduled islanded operation. In this operating mode, the isolated operation of a MG is beforehand planned to mitigate the effects caused by possible predictable contingencies, e.g. weather-related events, as well as programmed maintenance. For this, first a novel energy management strategy (EMS) based on dynamic programming specifically designed for MG scheduled islanded operation is proposed. Next, a particle swarm optimization (PSO) method modified to ensure a continuous search space in the face of discrete load shedding policies is developed to determine the adequate allocation of RMPS.The proposed method is simulated for different scenarios for a MG system considering local distributed generation capacity, electric vehicles’ penetration, load shedding based on priority groups, as well as technical and operational limits. The obtained results showcase the proposed approach's ability to meaningfully improve MG service capacity during scheduled islanded operation.Novas oportunidades possibilitadas pelas estações de energia móveis renováveis (RMPSs), em conjunto com a capacidade das microrredes (MGs) de operar ilhadas da rede principal, apresentam uma provável solução para garantir novos requisitos de confiabilidade do sistema de energia diante do crescente número de eventos disruptivos graves. Nesta perspectiva, este trabalho propõe um novo método inteligente de alocação de RMPS para dar suporte a MGs durante operações ilhadas programadas. Neste modo de operação, a operação ilhada de uma MG é planejada previamente para mitigar os efeitos causados por possíveis contingências previsíveis, por exemplo eventos relacionados ao clima, bem como a manutenção programada. Para isso, primeiro é proposta uma nova estratégia de gerenciamento de energia (EMS), baseada em programação dinâmica e projetada especificamente para operações ilhadas programadas de uma MG. Em seguida, é desenvolvido um método de otimização por enxame de partículas (PSO) modificado para garantir um espaço de pesquisa contínuo em face de políticas de corte de carga discretas para determinar a alocação adequada da RMPS.O método proposto é simulado para diferentes cenários de um sistema de uma MG, considerando a capacidade local de geração distribuída, a penetração de veículos elétricos, o corte de carga com base em grupos prioritários e os limites técnicos e operacionais. Os resultados obtidos mostram a capacidade da abordagem proposta de melhorar significativamente a capacidade de serviço de uma MG durante a operação ilhada programada