Optimising repair sequences for interdependent infrastructure resilience: a simulation model of power and transportation networks

Co-optimize recovery modeling for transportation and power network with multi-type mobile resources dispatching

Resilience Enhancement with Transportable Storage Systems and Repair Crews in Coupled Transportation and Distribution Networks

ABSTRACTBattery swapping and charging station (BSCS) is a developing domain for energy storage and electrical vehicles (EVs). An electric vehicle charging station can be combined with a microgrid (MG) and a road traffic transportation network (RTTN) for cost‐effective system operation to set up a new BSCS in a territory. This paper presents a new approach for the location and sizing of a BSCS constructed to solve the combined problem of high infrastructure cost, energy cost, and renewable energy support. The proposed model supports the participation between BSCS, MG, and RTTN for optimal location in the territory and power exchange between the MG and BSCS. This BSCS is connected with an electric network, photovoltaic (PV) units, wind turbines, and RTTN to offer auxiliary facilities and increased profit. The proposed optimization technique has been applied to a case study in Maharashtra, India, and it shows effectiveness by minimizing investment and operational cost along with location and sizing for the BSCS.

Integrated pricing strategy for coordinating load levels in coupled power and transportation networks

The alternative fuel vehicles (AFVs) which include electric vehicles (EVs) and natural gas vehicles (GVs) have gained much attention in recent years for delivering a clean and low-carbon urban mobility. The AFV development is promoting the coordination among electric power, natural gas, and transportation (ENT) networks. This paper proposes a coordinated planning method for investment on and operation of emerging urban energy infrastructures. The sites and sizes of refueling stations are optimized along with the expansion strategies for electric power distribution network (PDN) and natural gas distribution network (GDN). First, AFV refueling demands are analyzed according to the refueling station constraints and options, and transportation network (TN) constraints. Second, the coordinated planning model for PDN and GDN is formulated along with respective operation constraints in PDN, GDN, and TN. An efficient MILP solution method is developed for the proposed optimization problem. Finally, several case studies verify the effectiveness of the proposed model and its solution method. Compared with existing solutions, the proposed panning method offers a more accurate AFV refueling demand model with various refueling options. It is demonstrated that the coordinated planning can reduce investment and operation costs of urban energy infrastructures by exploring the pertinent interactions among constrained PDN, GDN, and TN.

Extreme events are greatly impacting the normal operations of microgrids, which can lead to severe outages and affect the continuous supply of energy to customers, incurring substantial restoration costs. Repair crews (RCs) are regarded as crucial resources to provide system resilience owing to their mobility and flexibility characteristics in handling both transportation and energy systems. Nevertheless, effectively coordinating the dispatch of RCs towards system resilience is a complex decision-making problem, especially in the context of a multi-energy microgrid (MEMG) with enormous dynamics and uncertainties. To this end, this paper formulates the dispatch problem of RCs in a coupled transportation and power-gas network as a decentralized partially observable Markov decision process (Dec-POMDP). To solve this Dec-POMDP, a hierarchical multi-agent reinforcement learning (MARL) algorithm is proposed by featuring a two-level framework, where the high-level action is used for switching decision-making between transportation and power-gas networks, and the lower-level action constructed via the multi-agent proximal policy optimization (MAPPO) algorithm is used to compute the routing and repairing decisions of RCs in the transportation and power-gas networks, respectively. The proposed algorithm also introduces an abstracted critic network by integrating the load restoration status, which captures the system dynamics and stabilizes the training performance with privacy protection. Extensive case studies are evaluated on a coupled 6-bus power and 6-bus gas network integrated with a 9-node 12-edge transportation network. The proposed algorithm outperforms the conventional MARL algorithms in terms of policy quality, learning stability, and computational performance. Furthermore, the dispatch strategies of RCs are analyzed and their corresponding benefits for load restoration are also evaluated. Finally, the scalability of the proposed method is also investigated for a larger 33-bus power and 15-bus gas network integrated with an 18-node 27-edge transportation network.

The resilience of critical infrastructure networks (CINs) after disruptions, such as those caused by natural hazards, depends on both the speed of restoration and the extent to which operational functionality can be regained. Allocating resources for restoration, a combinatorial optimal planning problem that involves determining which crews will repair specific network nodes and in what order, is complicated by several factors: the connectivity between the CIN and the road transportation network used for travel by repair crews, the enormous scale and nonlinear behavior of these networks, and the uncertainty in repair times. This paper presents a novel graph-based formulation that merges two interconnected graphs, representing crew and transportation nodes and power grid nodes, into a single heterogeneous graph. To enable efficient planning, graph reinforcement learning (GRL) is integrated with bigraph matching. GRL is utilized to design the incentive function for assigning crews to repair tasks based on the graph abstracted state of the environment, ensuring generalization across damage scenarios. Two learning techniques are employed: a graph neural network trained using Proximal Policy Optimization and another trained via Neuroevolution. The learned incentive functions inform a bipartite graph that links crews to repair tasks, enabling weighted maximum matching for crew-to-task allocations. An efficient simulation environment that pre-computes optimal node-to-node path plans is used to train the proposed restoration planning methods. An IEEE 8500-bus power distribution test network coupled with a 21 sq km transportation network is used as the case study, with scenarios varying in terms of numbers of damaged nodes, depots and crews. Results demonstrate the approach’s generalizability and scalability across scenarios, with learned policies providing 3-fold better performance than random policies, while also outperforming optimization-based solutions in both computation time (by several orders of magnitude) and power restored.

The article discusses the issue of modelling traffic flows and the transport network. Faced with an increase in the number of vehicles in road networks, the problem of congestion and the need to optimise traffic and adapt the transport infrastructure to changing demand are growing, especially in large cities. With this in mind, the authors of this publication developed a model of the road network in the north-eastern part of the Warsaw agglomeration based on the proposed algorithm. Two methods were used to optimise the distribution of traffic flows: the Nash equilibrium and the Stackelberg approach. The Nash equilibrium assumes the aim of achieving equal average times on all roads for each origin–destination (O-D) pair. This describes the state pursued by a decentralised system guided by the individual benefits of the traffic users. On the contrary, the Stackelberg approach aims to achieve optimal travel times for the entire system. The study was carried out for three scenarios that differed in the assumed traffic demand on the road network. The basic scenario assumed the average hourly traffic demand during the morning peak hour based on traffic measurements. On the other hand, the two alternative scenarios were developed as a 10% variation in traffic volumes from the baseline scenario. On the basis of the results, it was concluded that an increase in traffic volumes for all O-D pairs could result in a decrease in traffic volumes on some links of the road network. This means that the transport network is a complex system and any change in parameters can cause significant and difficult to predict changes. Therefore, the proposed approach is useful in terms of traffic forecasting for road networks under conditions of changing traffic flow volumes. Additionally, the total travel time for the entire system differed for each scenario by a percentage difference of 0.67–1.07% between the optimal solution according to the Nash equilibrium and the Stackelberg approach.

Repair crews (RCs) and mobile power sources (MPSs) are critical resources for distribution system (DS) outage management after a natural disaster. However, their logistics is not well investigated. We propose a resilient scheme for disaster recovery logistics to co-optimize DS restoration with 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.

The growing trend in electrical vehicle (EV) deployment has transformed independent power network and transportation network studies into highly congested interdependent network performance evaluations assessing their impact on power and transportation systems. Electrified transportation is highly capable of intensifying the interdependent correlations across charging service, transportation, and power networks. However, the evaluation of the complex coupled relationship across charging services, transportation, and power networks poses several challenges, including an impact on charging scheduling, traffic congestion, charging loads on the power grid, and high costs. Therefore, this article presents comparative survey analytics of large-scale EV integration’s impact on charging service network scheduling, transportation networks, and power networks. Moreover, price mechanism strategies to determine the charging fares, minimize investment profits, diminish traffic congestion, and reduce power distribution constraints under the influence of various factors were carried out. Additionally, the survey analysis stipulates the interdependent network performance index, ascertaining travel distance, route selection, long-term and short-term planning, and different infrastructure strategies. Finally, the limitations of the proposed study, potential research trends, and critical technologies are demonstrated for future inquiries.

Determining optimal location and size of PEV fast-charging stations in coupled transportation and power distribution networks considering power loss and traffic congestion

In road transportation networks, such as freeways, motorways or highways, variable speed limit (VSL) control is one of the most efficient strategies to substantially improve traffic flow and solve or reduce the congestion problem. One way to design control strategies applicable in real time is to represent the vehicle flows with a hybrid dynamic model. In this context, Batches Petri Nets (BPN) and their extensions, are well adapted for the modeling of such systems as they intend to model variable delays on continuous flows by adding special nodes, called batch nodes, to the continuous and hybrid Petri nets. Using BPN with controllable batch speed, this paper studies a portion of the A12 highway in The Netherlands. This hybrid representation leads to the evaluation of several real time VSL control laws in accordance with the accumulation front of vehicles.

Optimal operation for coupled transportation and power networks considering information propagation

Critical infrastructure systems such as power networks are prone to be subjected to various types of external attacks, which brings out the need for devising strategies to mitigate the consequences. Enhancing the resilience of a power network can be achieved through dispatching resources for recovery in an efficient and effective manner. Such resources are mobile energy storage systems to provide power for load demands and repair crews to restore the network to its functional state. As these resources are dispatched through a transportation network, it is important that the coupling between the power and transportation network be taken into consideration when planning for the operation schedules for dispatch. With the objective of fast recovery, a mixed-integer linear programming (MILP) model is established for the dispatchable resources and the coupled network for scheduling optimization under disruption scenarios. The improved framework was subjected to a case study using the IEEE 37 Node Test Feeder, where the transportation network was established based on its power network. A disruption scenario was devised using centrality measures and additional analyses were executed to investigate the effects of the depot and ESS facility locations. The results have shown that the framework can accommodate the coupled network and find the optimal scheduling of dispatched resources for recovery.

With the advancement of dual-carbon goals and the construction of new types of power systems, the proportion of electric vehicle charging stations (EVCSs) in the coupling system of power distribution and transportation networks is gradually increasing. However, the surge in charging demand not only causes voltage fluctuations and a decline in power quality but also leads to tension in the power grid load in some areas. The complexity of urban road networks further increases the challenge of charging station planning. Although laying out charging stations in areas with high traffic flow can better meet traffic demands, it may also damage power quality due to excessive grid load. In response to this problem, this paper proposes an optimized layout plan for electric vehicle charging stations considering the coupling effects of roads and electricity. By using section power flow to extract dynamic data from the power distribution network and comparing the original daily load curves of the power grid and electric vehicles, this paper plans reasonable capacity and charging/discharging schemes for EVCSs. It considers the impact of the charging and discharging characteristics of EVCSs on the power grid while satisfying the peak-shaving and valley-filling regulation benefits. Combined with the traffic road network, the optimization objectives include optimizing the voltage deviation, transmission line margin, network loss, traffic flow, and service range of charging stations. The Gray Wolf Optimizer (GWO) algorithm is used for solving, and the optimal layout plan for electric vehicle charging stations is obtained. Finally, through road–electricity coupling network simulation verification, the proposed optimal planning scheme effectively expands the charging service range of electric vehicles, with a coverage rate of 83.33%, alleviating users’ charging anxiety and minimizing the impact on the power grid, verifying the effectiveness and feasibility of the proposed scheme.