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Abstract This study introduces a strategy for the pre‐allocation and dispatch of mobile power sources (MPSs) within a power‐transportation coupled network, targeting efficient distribution network fault recovery under extreme weather conditions. The strategy incorporates the Dijkstra algorithm and blind number theory to analyse the MPS driving path and generate fault scenarios respectively, considering the effects of extreme weather on traffic capacity and distribution network fault rate. The MPS pre‐allocation model, formulated to minimize the load outage loss, MPS investment, and driving fuel cost, provides a foundation for optimization of the dispatch process. The dispatch optimization model adapts to dynamic information from the power‐transportation coupled network to make decisions on load restoration sequencing and MPS driving path. The effectiveness of the proposed strategy is demonstrated through an application to the IEEE 33‐node distribution system and a corresponding 33‐node transportation network.

This study introduces a strategy for the pre‐allocation and dispatch of mobile power sources (MPSs) within a power‐transportation coupled network, targeting efficient distribution network fault recovery under extreme weather conditions. The strategy incorporates the Dijkstra algorithm and blind number theory to analyse the MPS driving path and generate fault scenarios respectively, considering the effects of extreme weather on traffic capacity and distribution network fault rate. The MPS pre‐allocation model, formulated to minimize the load outage loss, MPS investment, and driving fuel cost, provides a foundation for optimization of the dispatch process. The dispatch optimization model adapts to dynamic information from the power‐transportation coupled network to make decisions on load restoration sequencing and MPS driving path. The effectiveness of the proposed strategy is demonstrated through an application to the IEEE 33‐node distribution system and a corresponding 33‐node transportation network. [ABSTRACT FROM AUTHOR]

Abstract Outage recovery is important for reducing the economic cost and improving the reliability of a distribution system (DS) in extreme weather and with equipment faults. Previous studies have separately considered network reconfiguration (NR) and dispatching mobile power sources (MPS) to restore the outage load. However, NR cannot deal with the scenario of an electrical island, while dispatching MPS results in a long power outage. In this paper, a resilient outage recovery method based on co-optimizing MPS and NR is proposed, where the DS and traffic network (TN) are considered simultaneously. In the DS, the switch action cost and power losses are minimized, and the access points of MPSs are changed by carrying out the NR process. In the TN, an MPS dispatching model with the objective of minimizing power outage time, routing and power generation cost is developed to optimize the MPSs’ schedule. A solution algorithm based on iteration and relaxation methods is proposed to simplify the solving process and obtain the optimal recovery strategy. Finally, numerical case studies on the IEEE 33 and 119-bus systems validate the proposed resilient outage recovery method. It is shown that the access point of MPS can be changed by NR to decrease the power outage time and dispatching cost of MPS. The results also show that the system operation cost can be reduced by considering power losses in the objective function.

Outage recovery is important for reducing the economic cost and improving the reliability of a distribution system (DS) in extreme weather and with equipment faults. Previous studies have separately considered network reconfiguration (NR) and dispatching mobile power sources (MPS) to restore the outage load. However, NR cannot deal with the scenario of an electrical island, while dispatching MPS results in a long power outage. In this paper, a resilient outage recovery method based on co-optimizing MPS and NR is proposed, where the DS and traffic network (TN) are considered simultaneously. In the DS, the switch action cost and power losses are minimized, and the access points of MPSs are changed by carrying out the NR process. In the TN, an MPS dispatching model with the objective of minimizing power outage time, routing and power generation cost is developed to optimize the MPSs' schedule. A solution algorithm based on iteration and relaxation methods is proposed to simplify the solving process and obtain the optimal recovery strategy. Finally, numerical case studies on the IEEE 33 and 119-bus systems validate the proposed resilient outage recovery method. It is shown that the access point of MPS can be changed by NR to decrease the power outage time and dispatching cost of MPS. The results also show that the system operation cost can be reduced by considering power losses in the objective function. [ABSTRACT FROM AUTHOR]

Inspired by the opportunities provided by the Industry 4.0 technologies for smarter, risk‐informed, safer, and resilient operation, control, and management of the lifeline critical networks, this study investigates mobility‐as‐a‐service for resilience delivery during natural disasters. Focusing on effective service restoration in power distribution systems, we introduce mobile power sources (MPSs) as the restoration technology of the future, the mobility of which can be harnessed for spatiotemporal flexibility exchange and effective response and recovery during disasters. We present automated decision‐making solutions that coordinate the MPSs utilization with repair crew (RC) schedules taking into account constraints in both energy and transportation networks. When integrated, the suggested technology aided by the proposed optimization models will have the potential to disrupt the current practice in boosting the resilience and operational endurance of the mission‐critical systems and services during disasters, ultimately resulting in an enriched social welfare and national security. [ABSTRACT FROM AUTHOR]

• A distribution network restoration model based on multi-source collaboration is proposed. • The risk of transportation route is considered in the dispatching process of mobile power sources. • A new multi-objective optimization method, ripple diffusion algorithm, is used. With the frequent occurrence of sudden natural disasters, there are more and more disaster events in distribution network, so the research on improving the resilience of distribution network after disaster has attracted increasing interest. Considering the diversity of emergency recovery resources in post-disaster distribution networks, this paper proposes a power grid resilience enhancement model based on multi-source coordination strategy, which comprehensively considers the synergistic effects of distribution line topology, distributed generator(DG) equipment and mobile power sources(MPS). In order to solve the problem that the scheduling strategy is difficult to implement due to the influence of transportation roads, this paper considers the risk problem of transportation lines on the basis of transportation distance factors, and uses ripple spreading algorithm(RSA) to calculate the Pareto front of transportation routes, which is used as an alternative set of transportation routes for MPS equipment. Finally, the IEEE 33-node model is used to verify the effectiveness of the proposed model, the importance of considering transport road risk for actual resource scheduling, and the advantages of RSA in terms of speed and accuracy. The results show that the model can effectively solve the optimal scheduling strategy according to the decision maker's risk preference. [ABSTRACT FROM AUTHOR]

Purpose. The research paper involves solving of the following tasks: 1) refinement of the mathematical model for determining the traction and dynamic, fuel and economic, environmental indicators of mobile energy facilities; 2) methodology development of theoretical studies of automatic control systems, static and dynamic characteristics automotive-tractor diesel with a gas turbine supercharger and a mobile power facility. Methodology. The work studies the working processes of vehicles and machine-tractor aggregates by mathematical simulation and the development of algorithms and programs for the calculation of these processes in actual operational conditions. The system of equations has been developed for theoretical research. It describes a nonlinear mathematical model of the automatic control system of an automotive-tractor diesel rotating frequency. In addition to the differential equations of the first and second order, equations are used in mathematical simulation of working processes of traction vehicles. These equations describe experimental characteristics of an automatic regulator, a high-pressure fuel pump, a turbocharger and an engine, as well as moments of engine mechanical losses and an external load. Findings. The developed mathematical model allows determining the effectiveness of new design, operational and technological developments, as well as various measures in order to improve the fuel-economic and environmental performances of vehicles and machine-tractor aggregates in operating conditions. Originality. For the first time, the mathematical model "Tractor driver – machine-tractor aggregate – road (field)" was developed. It allows conducting research of transport tractor aggregates by driving cycles, taking into account the processes of starting and speeding up the mobile-power sources with gear shift. Practical value. In conditions of a protracted economic crisis, in the absence of the necessary equipment, instruments, combustible and lubrication materials, comparative research of mobile power sources can be carried out thanks to the developed mathematical models with corresponding algorithms and programs that are brought to practical use. For these computer programs State Intellectual Property Service of Ukraine issued Certificates on registration of copyright No. 49285, No. 49286, No. 49287 of May 22, 2013.

Repair crews (RCs) and mobile power sources (MPSs) are critical resources for distribution system (DS) outage management after a natural disaster. However, their logistics have not been well investigated. We propose a resilient scheme for disaster recovery logistics to co-optimize DS restoration with the dispatch of RCs and MPSs. A novel co-optimization model is formulated to route RCs and MPSs in the transportation network, schedule them in the DS, and reconfigure the DS for microgrid formation coordinately, etc. The model incorporates different timescales of DS restoration and RC/MPS dispatch, the coupling of transportation and power networks, etc. To ensure radiality of the DS with variable physical structure and MPS allocation, we also model topology constraints based on the concept of spanning forest. The model is convexified equivalently and linearized into a mixed-integer linear programming. To reduce its computation time, preprocessing methods are proposed to pre-assign a minimal set of repair tasks to depots and reduce the number of candidate nodes for MPS connection. Resilient recovery strategies thus are generated to enhance service restoration, especially by dynamic formation of microgrids that are powered by MPSs and topologized by repair actions of RCs and network reconfiguration of the DS. Case studies demonstrate the proposed methodology. [ABSTRACT FROM AUTHOR]

Mobile power sources (MPSs), including electric vehicle fleets, truck-mounted mobile energy storage systems, and mobile emergency generators, have great potential to enhance distribution system (DS) resilience against extreme weather events. However, their dispatch is not well investigated. This paper implements resilient routing and scheduling of MPSs via a two-stage framework. In the first stage, i.e., before the event, MPSs are pre-positioned in the DS to enable rapid pre-restoration, in order to enhance survivability of the electricity supply to critical loads. DS network is also proactively reconfigured into a less impacted or stressed state. A two-stage robust optimization model is constructed and solved by the column-and-constraint generation algorithm to derive first-stage decisions. In the second stage, i.e., after the event, MPSs are dynamically dispatched in the DS to coordinate with conventional restoration efforts, so as to enhance system recovery. A novel mixed-integer programming model that resolves different timescales of MPS dispatch and DS operation, coupling of road and power networks, etc., is formulated to optimize dynamic dispatch of MPSs. Case studies conducted on IEEE 33-node and 123-node test systems demonstrate the proposed method’s effectiveness in routing and scheduling MPSs for DS resilience enhancement. [ABSTRACT FROM AUTHOR]

• A novel post-disaster repair approach is proposed. • RCs, MPSs, line switches, RESs and ESSs are cooperated. • The uncertainties of RES outputs and load demand is considered during repair. • A two-stage robust repair optimization model is established. Since extreme weather events usually severely damage the power grid and lead to widespread power outages, coordinating repair and dispatch resources, such as repair crews (RCs), mobile power sources (MPSs), renewable energy sources (RESs) and energy storage systems (ESSs), is an efficient method to restore the power supply for outage loads. However, the uncertainties existing in RES output and load demand pose significant challenges to the service restoration of distribution systems in terms of security constraints. To address these uncertainties, this paper proposes a robust restoration approach to maximize the restored load in a distribution system by coordinating RCs, MPSs, RESs, ESSs and network reconfiguration. The coordination problem is formulated as a two-stage robust optimization model to generate optimal dispatching and operation schemes of multiple resources with guaranteed robustness against uncertainties. Involving some unexpected changes in topologies and distributed sources during post-disaster repair, the robust coordination model is re-optimized to guarantee the efficiency of repair and operation scheme. Simulation results show that the repair and dispatch resources can be coordinated effectively to accommodate the RES output and load demand uncertainties while maximizing the amount of restored load for service restoration of the distribution system. [ABSTRACT FROM AUTHOR]

Energetic autonomy of a hydraulic-based mobile robot requires a power source capable of both hydraulic and electrical power generation. The hydraulic power is used for locomotion, and the electric power is used for the control computer, sensors and other peripherals. In addition, the power source must be lightweight and quiet. This study presents several designs of internal combustion engine-based power units. Each power unit is evaluated with a Ragone plot which shows its performance over a wide range of operation times. Several hydraulic–electric power units (HEPUs) were built and successfully demonstrated on the Berkeley lower extremity exoskeleton (BLEEX). The best-performing design of the HEPUs, based upon the Ragone plot analysis, is described in detail. This HEPU produces constant pressure hydraulic power and constant voltage electric power. The pressure and voltage are controlled on board the power unit by a computer. A novel characteristic of this power unit is its cooling system in which hydraulic fluid is used to cool the engine cylinders. The prototype power unit weighs 27 kg and produces 2.3 kW (3.0 hp) hydraulic power at 6.9 MPa (1000 p.s.i.) and 220 W of electric power at 15 V DC. [ABSTRACT FROM AUTHOR]

• Mobile emergency generator and mobile energy storage system meets the power demand of critical loads in emergency conditions. • Traffic congestion information is adopted to the proposed scheme considering the interdependent operation of coupled distribution transportation systems. • Coordinated operation of multiple types of mobile power sources and repair crews is addressed. • Distribution system constraints are modelled according to the linearized distflow equations in mixed integer quadratic programming framework. In this paper, a mixed integer quadratic programming (MIQCP) based post-event distribution restoration framework in response to high impact low probable events is propounded to maintain the power supply continuity of critical loads particularly in emergency conditions. Mobile power sources (MPSs) including mobile energy storage system (MESS) and mobile emergency generator (MEG) are considered as grid-support services in resiliency enhancement strategy due mainly to their spatiotemporal flexibility. Power system constraints are modelled according to the linearized DistFlow equations; on the other hand, traffic congestion is also considered while directing MPSs from staging locations to damaged areas. Repair crew dispatching schedule for maximizing the objective function is provided in a deterministic fashion. Numerical results based on the conducted case studies on modified version of the 15-bus distribution system show the capabilities of the model. Hereby power demand of all critical loads is served after an optimal allocation of MPS even though the damages are not completely cleared in the branches. Further, the decisions are close to what can be realistically expected in practical implementation, that fast-responded resources are exploited depending on traffic-related constraints and economic issues. [ABSTRACT FROM AUTHOR]