Mobile Emergency Generators Research Articles

Resilience enhancement of power distribution system using fixed and mobile emergency generators based on deep reinforcement learning

Growing extreme weather-related events have increased the need for a resilient emergency response in the Power Distribution Systems (PDSs). This paper proposes a novel restoration scheme that involves forming Micro-Grids (MGs) and dispatching Mobile Emergency Generators (MEGs) between them. Due to the PDS variability and extensive decision space, conventional model-based approaches aren't suitable for implementing the proposed scheme. Therefore, a model-free framework is designed to control the restoration process. The proposed problem is formulated as a Markov Decision Process (MDP) considering uncertainties associated with load profile, and renewable resources. Deep Q-Network (DQN), a fundamental algorithm in Deep Reinforcement Learning (DRL), is employed to solve the proposed MDP model. DQN algorithm can learn optimal control strategy by interacting with the simulated environment at the training phase and then deploy the learned policy in a real-world application. The main objective of the designed DQN model is to maximize the restored loads and ensure a high level of post-restoration reliability for Critical Loads (CLs). MATLAB software is considered as the simulated environment to perform power flow calculation and MEGs routing. The efficiency and applicability of the proposed method are evaluated by conducting experiments on the modified IEEE 13-bus, 37-bus, and 123-bus networks.

A multi-objective mixed integer linear programming approach for simultaneous optimization of cost and resilience of power distribution networks

In recent years, customers have experienced a significant increase in weather-related power outages. The power distribution network (PDN), a subset of the power system, in particular is more susceptible to extreme events. Therefore, ensuring the resilient and cost-effective operation of PDNs following extreme weather conditions poses a significant challenge for distribution network operators. This paper presents an approach for simultaneously optimizing the cost and load restoration for resilience enhancement of power distribution networks while determining the optimal positioning and generation levels of mobile emergency generators. The proposed method, in addition, employs a distribution network reconfiguration to improve the load restoration process, by optimally determining the status of switches. The multi-objective formulation involves the minimization of load shedding to increase the resilience of the system, while the other objective is formulated to minimize the cost of load restoration. A weighted sum method is employed to address the multi-objective mixed-integer linear programming (MILP) model. A set of non-dominated solutions determined using the proposed formulation provides opportunities to the distribution system operator in choosing a resilience improvement strategy according to the availability of the operational budget. The proposed model is implemented on 33-bus distribution system to validate the efficacy of the proposed model.

Optimal two-stage dispatch method of distribution network emergency resources under extreme weather disasters

In recent years, frequent extreme weather disasters have posed severe challenges to the safe and reliable power supply of distribution networks. In this paper, a resilience enhancement method is proposed for distribution networks, which establishes the full-phase analytical modeling of the distribution system defense and recovery process under extreme events, i.e., pre-event phase, degradation phase and restoration phase. Before a disaster strikes, multiple emergency resources are pre-allocated, including mobile emergency generators (MEGs) and repair crews and proactive islands are formed to improve the resistance of distribution networks to extreme weather disasters. After the disaster, real-time dispatching of MEGs and repair crews is coordinated with dynamic network reconfiguration, which considers the coupling processes among fault isolation, fault repair, and load restoration to improve the recovery ability of distribution networks. The IEEE 123-bus distribution system is used to verify the effectiveness and efficiency of the proposed method in enhancing the survivability of loads and the rapid recovery ability of distribution networks.

Distributed Coordinated Restoration of Transmission and Distribution Systems with Repair Crews and Mobile Emergency Generators

Distributed Coordinated Restoration of Transmission and Distribution Systems with Repair Crews and Mobile Emergency Generators

Resilience assessment of mobile emergency generator-assisted distribution networks: A stochastic geometry approach

Escalation of extreme weather events represents substantial threat to power system infrastructure. Mobile emergency generators (MEGs) can form part of a flexible restoration strategy against such destructive events. However, with continued expansion of distribution networks, quantification of the impact of MEGs has become increasingly challenging owing to extreme-weather-event-induced uncertainties. In this paper, we propose a stochastic geometry-based method for assessing the impact of MEG deployment on distribution networks affected by extreme weather events through investigation of structural features. First, we propose a distance measure to represent the electrical connection between power grid components. Subsequently, we adopt the point process and Voronoi tessellation to describe the spatial distribution of power grid components and the service coverage provided by MEGs under different scenarios. Then, we propose a set of assessment metrics to evaluate the survivability of power grid components and the resilience of the entire distribution network under extreme weather events. Finally, we derive accurate analytical expressions for the distance distribution and resilience metrics, such as coverage probability and load shedding, enabling us to explore the relationship between MEG deployment decisions, structural features, and power grid resilience. The proposed method enables analytic assessment of the impact of MEG deployment on the resilience of distribution networks, and provides beneficial insights to help formulate efficient measures for enhancing resilience. Case studies demonstrated that the proposed method is accurate and efficient in dealing with network analysis and assessment problems for distribution networks under massive potential failure scenarios.

Stochastic pre‐disaster planning and post‐disaster restoration to enhance distribution system resilience during typhoons

AbstractIn recent years, extreme weather events, such as typhoons, have led to large‐scale power outages in distribution systems. As a result, developing strategies to bolster distribution system resilience has become imperative. This paper proposes a two‐stage stochastic programming model aimed at enhancing this resilience. Prior to a typhoon, the first stage establishes a comprehensive wind field model based on extreme value distribution for accurate wind speed predictions. Simultaneously, a refined stress–strength interference model is used to determine the likelihood of distribution line failures. Taking into account the uncertainty of line damage, repair crews and mobile emergency generators are then strategically positioned at staging depots. Following the typhoon, the second stage coordinates network reconfiguration, dispatches repair crews, and mobilizes mobile emergency generators to minimize load shedding and expedite repairs. This model was validated on the IEEE 33‐bus distribution system, coupled with a corresponding transportation network, utilizing data from the 2018 super typhoon Mangkhut'' in China. Simulations indicate that our approach can effectively reduce load shedding and power outage durations, thereby enhancing the resilience of distribution systems.

Two-stage mobile emergency generator dispatch for sequential service restoration of microgrids in extreme conditions

A fast and reliable sequential service restoration (SSR) methodology is necessary to enhance the resilience of the distribution system (DS) after an outage. However, traditional SSR strategies ignore the utilization of mobile emergency generators (MEGs) to reduce the system outage duration. This paper exploits the enhanced resilience of DSs by dispatching MEGs as backup sources to form microgrids (MGs) and sequentially restore out-of-service loads. A two-stage dispatching model is proposed for the SSR of MGs that takes into consideration the preventive control stage (PCS) and the emergency control stage (ECS). Moreover, SSR decisions are optimized depending on stage-based uncertainties. Specifically, in PCS, MEGs are pre-positioned prior to event strikes. Considering contingency uncertainty, a scenario-based stochastic model is proposed to minimize the expected outage duration of loads, and a progressive hedging algorithm is applied to improve computational efficiency. With the realization of contingency in ECS, MEGs are rerouted from pre-positions to target locations, and provisional MGs are sequentially formed to restore critical loads. Considering wind power uncertainty, a robust model is formulated, and the convex hull relaxation method is customized to reduce the computational complexity. The resulting SSR scheme can be adaptively and securely adjusted in response to random contingencies and wind power fluctuation. The effectiveness of the proposed method is validated by numerical simulations.

Resilience enhancement of distribution network under typhoon disaster based on two-stage stochastic programming

The reliability of power supply in distribution network is vulnerable to extreme weather events such as typhoon. Pre-event preparation can effectively mitigate the deterioration of system resilience. Therefore, we propose a distribution network resilience enhancement decision-making framework which is formulated as a two-stage stochastic mixed-integer linear programming (SMILP) model. The first stage invests coordinately in four strategies, including hardening lines, installing distributed generators (DG), allocating mobile emergency generators (MEG), and deploying switches, etc. And the goal at the first stage is to minimize the investment cost of resilience enhancement strategies. The objective at the second stage is to ensure the minimum expected recourse operation cost of the comprehensive strategies for all typical scenarios. The proposed model can combine distribution network long-term resilience planning with short-term post-event recovery. Furthermore, in order to address the uncertain problems of wind field, line damage and load fluctuations under typhoon disaster, this paper proposes the following solutions. For wind field uncertainty, a detailed wind field model considering time transition, sea-land transition and extreme value distribution is established to deal with wind speed prediction. For line damage uncertainty, an improved stress-strength interference model is set up. And for the load fluctuation uncertainty, scenario generation using load random multipliers is applied to address load uncertainty. The proposed framework is tested in IEEE 33-bus distribution system using historical data from 2018 super typhoon “Mangkhut” in China and demonstrates the SMILP model can significantly reduce the post-event expected recourse operation cost and meanwhile improve the distribution network resilience.

A three-stage resilient dispatch of mobile emergency generators in a distribution system against hurricanes

Mobile emergency generators (MEGs) have been widely used to enhance the resilience of distribution systems. However, the traditional two-stage MEG dispatch strategies focus on only the prepositioning and reallocation before and after hurricanes, neglecting the resilient operation during hurricanes. Therefore, this paper proposes a novel three-stage MEG dispatch model to enhance the resilience of distribution systems. To improve the survivability of distribution systems during hurricanes, the fault isolation operations by remote-controlled switches (RCSs) and forced cut-off of wind power are coordinated with MEG dispatch, which is prepositioned as a preparedness measure before hurricanes. Based on the realization of fault scenarios during hurricanes, MEG reallocation and fault isolation by manual switches (MSs) are conducted to boost the restoration of the distribution system after hurricanes. Considering the uncertainties of wind power cut-off and line faults, the proposed method is incorporated into a stochastic dispatch model, which takes the conditional value-at-risk (CVaR) as an objective function to improve robustness in extreme cases. In addition, the computational burden with multiple scenarios is reduced by the modified progressive hedging algorithm (PHA). Finally, the proposed method is validated on a modified IEEE 33-bus distribution system. Simulation results show that the survivability and restoration of the distribution system can be enhanced by the proposed approach.

Hybrid Stochastic-Robust Service Restoration for Wind Power Penetrated Distribution Systems Considering Subsequent Random Contingencies

Service restoration (SR) is essential to recovering distribution systems with outages when extreme weather conditions occur. However, the occurrence of long-duration extreme weather events may cause a restored system to become susceptible to subsequent random contingencies. To address this challenge, this paper proposes a microgrid-based SR approach for recovering critical loads in wind power penetrated distribution systems incorporating the impacts from subsequent random contingencies. A hybrid stochastic-robust optimization model is developed considering both the subsequent random contingencies that may occur and the uncertainty of wind power generation. The expected load shedding is minimized by locating mobile emergency generators, dynamically adjusting microgrid (MG) topologies, and dispatching power sources. To ensure the tractability of the optimization, the original problem is reformulated and decomposed into a master problem and several robust optimization subproblems based on the column-and- constraint generation approach. In addition, the master problem, which is in the format of stochastic optimization is solved by an improved progressive hedging algorithm to accelerate optimization. The proposed method is validated on the modified IEEE distribution systems and the simulation results show the effectiveness of the proposed SR method in reducing security violation risk and reducing the computational complexity.

Scheduling of Separable Mobile Energy Storage Systems With Mobile Generators and Fuel Tankers to Boost Distribution System Resilience

Mobile energy resources (MERs) have been shown to boost DS resilience effectively in recent years. In this paper, we propose a novel idea, the separable mobile energy storage system (SMESS), as an attempt to further extend the flexibility of MER applications. “Separable” denotes that the carrier and the energy storage modules are treated as independent parts, which allows the carrier to carry multiple modules and scatter them independently throughout the DS. The constraints for scheduling SMESSs involving carriers and modules are derived based upon the interactive behavior among them and the DS. In addition, the fuel delivery issue of feeding mobile emergency generators (MEGs), which was usually bypassed in previous studies involving the scheduling of MEGs, is also considered and modeled. SMESSs, MEGs, and fuel tankers (FTs) are then jointly routed and scheduled, along with the dynamic DS reconfiguration, for DS service restoration by integrating them in a mixed-integer linear programming (MILP) model. Finally, the test is conducted on the modified IEEE test systems, and results verify the effectiveness of the model in boosting DS resilience.

A Three-Stage Resilient Dispatch of Mobile Emergency Generators in a Distribution System Against Hurricanes

A Three-Stage Resilient Dispatch of Mobile Emergency Generators in a Distribution System Against Hurricanes

Coordinating multiple resources for enhancing distribution system resilience against extreme weather events considering multi-stage coupling

Under extreme weather events with a certain warning time, the prevention and restoration of distribution systems (DSs) involve multiple processes, including pre-event prevention, DS degradation, and post-event fault isolation and service restoration. Multiple resources are coupled and interact with each other in the above processes, which makes it a complicated task to develop resilience enhancement strategies for DSs. In this paper, a multi-resource DS resilience enhancement method is proposed to assist DS operators in making decisions against extreme weather events. On one hand, in the proposed method, the prevention and restoration of DSs are formulated as multiple stages based on the physical process of DSs when affected by extreme weather events. On the other hand, distributed generations (DGs), mobile emergency generators (MEGs), remote-controlled switches (RCSs), manual switches (MSs) and operation crew teams are fully coordinated to enhance the resilience of DSs against extreme weather events. And multiple coupling relationships are considered, including the topology coupling relationships among each stage, the spatiotemporal coupling relationships of mobile resources, and the switching coupling relationships between MSs and operation crew teams. The proposed method is mathematically formulated as a mixed-integer linear programming (MILP) model. Several cases are introduced to verify the effectiveness of the proposed method. The results demonstrate that the degradation of the DS can be effectively reduced by the coordination of network pre-reconfiguration and resources pre-positioning before events. The recovery process can be significantly speeded up by the coordination of the network reconfiguration and the real-time dispatching of MEGs and operation crew teams after events.

Optimal planning of mobile emergency generators of resilient distribution system

Mobile emergency generators(MEGs) can accelerate disaster recovery and enhance the resiliency of the distribution system (SD). However, the resilience-oriented planning of MEGs has not been fully investigated. This paper proposes a method to achieve the resilience-oriented optimal plan of different kinds of MEGs via a two-stage stochastic optimization model. In the first stage, DS operators invest in various types of MEGs and allocate them in the second stage according to the line damage situation. A distributionally robust model based on confidence sets is used to reduce the number of scenario samples. The proposed model is transformed into a second-order cone program that commercial solvers can solve after convex relaxation. The case studies of a 33-bus test system demonstrate the effectiveness of the proposed method for scenario reduction and optimization of MEGs investment and allocation strategy.

Application of Mobile Energy Storage for Enhancing Power Grid Resilience: A Review

Natural disasters can lead to large-scale power outages, affecting critical infrastructure and causing social and economic damages. These events are exacerbated by climate change, which increases their frequency and magnitude. Improving power grid resilience can help mitigate the damages caused by these events. Mobile energy storage systems, classified as truck-mounted or towable battery storage systems, have recently been considered to enhance distribution grid resilience by providing localized support to critical loads during an outage. Compared to stationary batteries and other energy storage systems, their mobility provides operational flexibility to support geographically dispersed loads across an outage area. This paper provides a comprehensive and critical review of academic literature on mobile energy storage for power system resilience enhancement. As mobile energy storage is often coupled with mobile emergency generators or electric buses, those technologies are also considered in the review. Allocation of these resources for power grid resilience enhancement requires modeling of both the transportation system constraints and the power grid operational constraints. These aspects are discussed, along with a discussion on the cost–benefit analysis of mobile energy resources. The paper concludes by presenting research gaps, associated challenges, and potential future directions to address these challenges.

A two-stage resilience improvement planning for power distribution systems against hurricanes

This paper presents a novel planning strategy for distribution system planners (DSPs) to enhance the resilience of distribution systems confronting emergencies. The problem is formulated as a two-stage stochastic programming model under emergency and normal scenarios. The decisions on line hardening, distributed generation (DG) placement, mobile emergency generators (MEG) allocation, and tie switch placement are made in the first stage to maximize the system resilience. In the second stage, the operation costs of the system pertaining to the DSP power purchase from the upstream network, DG power production, and forced load shedding in emergency conditions are minimized to achieve a techno-economic compromise of investment costs and enhanced operation/resilience benefits over both planning and operation scales. Since access to dependable distribution functions for probabilistic approaches is a notable challenge in resilience studies, an uncertainty modeling approach is presented based on the thresholds for the line damage in the worst-case event. The proposed approach can drastically decrease the number of line damage scenarios for resilience studies. The efficiency and effectiveness of the new approach are validated on two distribution system testbeds with 33 and 118 nodes.

Co-Optimization of Supply and Demand Resources for Load Restoration of Distribution System Under Extreme Weather

To deal with the issue of the distribution system (DS) resilience enhancement under extreme weather events, this paper presents a new methodological framework for enhancing the load restoration of DS during a hurricane. As distinct from existing studies, this approach integrates mobile emergency generators (MEGs) and prosumer communities (PCs) incorporating combined heat and power (CHP) units, electric boilers (EBs), photovoltaic (PV) sources, and demand response (DR) resources and comprehensively explores the coordination and flexibility of supply-side and demand-side resources to boost the post-disaster DS recovery. The discussed problem is formulated by using a two-stage robust optimization model. In the first stage, the MEGs are pre-positioned prior to the hurricane with the objective of minimizing the outage cost of the load to promote the capability of DS to resist extreme disturbance. The second stage determines the real-time allocation of MEGs, output power of CHP units and EBs, and the power consumption of electric loads after the hurricane to maximize DS’s load recovery considering the worst-case of the uncertainty realization. Since the fragility analysis of network elements has an essential impact on the efficacy of the optimal strategy, we introduce the Z-number-based approach to scientifically determine the failure probability of components considering the effect of the aging of components and the credibility of the failure information obtained from the fragility. The effectiveness of the proposed methodology is examined based on an IEEE 123-bus distribution test system, and the obtained results confirm the validity of the proposed approach in actual implementations.

Using mobile emergency generators as reliability enhancers in distribution systems

Using mobile emergency generators as reliability enhancers in distribution systems

Mobile Emergency Generator Planning in Resilient Distribution Systems: A Three-Stage Stochastic Model With Nonanticipativity Constraints

Mobile emergency generators (MEGs) can effectively restore critical loads as flexible backup resources after power network disturbance from extreme events, thereby boosting the distribution system resilience. Therefore, MEGs are required 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 scenarios. 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 effectiveness and superiority of the proposed three-stage MEG planning model over the traditional two-stage model.

Distribution System Resilience Enhancement via Mobile Emergency Generators

Natural calamities always have been a serious threat to energy systems. In this regard, this paper constitutes a stochastic mixed integer linear programming (SMILP) model to enhance the resilience of power distribution systems to deal with disastrous events. In particular, the proposed model is developed to enhance both survivability and restoration capability of distribution systems. In this regard, to increase the preparedness of the power distribution system, the system operator reconfigures the network by utilizing remote-control switches (RCSs), manual switches (MSs), and distributed generations (DGs) before the natural calamity hits. The proposed model contemplates the traveling time of crew teams to the MS sites in the transportation system in order to achieve the best switching sequence. In addition, it pre-positions the crews and mobile emergency generators (MEGs) in staging locations to hasten the likely post-disturbance operations. To do so, likely post-event operations are included in the model using the scenario generation technique. To validate the performance of the developed model a distribution system is employed. The results of simulations confirm the effectiveness of the proposed approach in declining the interruption of electric energy for customers.