A bilevel conic optimization model for routing and charging of EV fleets serving long distance delivery networks

Subramanian, Vignesh; Feijoo, Felipe; Sankaranarayanan, Sriram; Melendez, Kevin A.; Das, Tapas K.

doi:10.1016/j.energy.2022.123808

Cited by 8 publications

(5 citation statements)

References 36 publications

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“…The upper level obtains the routing decision by minimizing the function of charging cost and travel time. The routing decision is used in the lower level that solves the AC optimal power flow model, using second-order cone constraints, to determine nodal electricity prices [34]. Table 1 shows the different characteristics in previous works.…”

Section: Related Workmentioning

confidence: 99%

Dynamic Path-Planning and Charging Optimization for Autonomous Electric Vehicles in Transportation Networks

Tang

Zhang

et al. 2023

Applied Sciences

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With the growing popularity of autonomous electric vehicles (AEVs), optimizing their path-planning and charging strategy has become a critical research area. However, the dynamic nature of transport networks presents a significant challenge when ensuring their efficient operation. The use of vehicle-to-everything (V2X) communication in vehicular ad hoc networks (VANETs) has been proposed to tackle this challenge. However, establishing efficient communication and optimizing dynamic paths with charging selection remain complex problems. In this paper, we propose a joint push–pull communication mode to obtain real-time traffic conditions and charging infrastructure information (i.e., charging stations and energy segments). We also analyze the selection of relay vehicles in multi-hop communication routing, considering factors such as link stability, vehicle distance, and reputation values. Furthermore, we formulate a dynamic optimization problem based on real-time information to minimize travel and charging costs. Our proposed algorithm enables AEVs to obtain charging services from charging stations and conduct dynamic wireless charging via energy segments. We present a dynamic real-time A* algorithm to solve the path-optimization problem and a dynamic real-time charging selection algorithm based on dynamic path optimization when the state of charge is lower than the charging threshold. Extensive simulations demonstrate that the proposed joint push-pull communication mode can provide vehicles the up-to-date information and the developed optimization algorithms effectively reduce travel and charging costs.

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Section: Related Workmentioning

confidence: 99%

Dynamic Path-Planning and Charging Optimization for Autonomous Electric Vehicles in Transportation Networks

Tang

Zhang

et al. 2023

Applied Sciences

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“…The best-improving tour (T * ) is the next incumbent tour. T * is perturbed to create the next generation of 1 + n i giant tours (lines 9 and 15): the b pert pair of nodes within T * are mutated (pairwise exchange) to generate T, which is subsequently improved through local search (lines 11-12); and the remaining n i giant tours are constructed through a further exchange of b pairs of nodes within the updated T and performing local searches (line [15][16]. This is repeated until there is no more improvement in the solution for a consecutive nl number of generations (line 6).…”

Section: Ordering Heuristic: Hybrid (Iterated Local Search)mentioning

confidence: 99%

“…Indeed, as sales of EVs grow (14% of market share in 2022 [12]), the EV charging infrastructure becomes more stringent and a significant barrier [13]. Hence, the literature has analyzed the challenges that electromobility generates regarding the infrastructure associated with charging points and the increase in electrical demand, particularly if that new demand is not well managed [14,15]. The authors in [16] present a critical review of the effects of unmanaged EV charging and examine the benefits of smart charging and Vehicle-to-Grid (V2G) technology.…”

Section: Introductionmentioning

confidence: 99%

Power on the Go: A Solution to Address Electric Vehicle Charging Challenges

Kundu,

Feijoo,

Mesa

et al. 2023

Mathematics

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In this article, we propose a novel solution to address the current challenges related to charging an Electric Vehicle (EV). The Power on the Go: Single-drop and Double-drop problems allow an EV to be charged at a convenient location, where a service vehicle (drone), which is launched from a nearby charging station, can bring a compatible power bank at the request of the driver through an app. The objective is to reduce the cumulative wait time between service requests and the start of charging. We present mathematical formulations and two order-first split-second-based heuristic approaches for both problems. While the mathematical formulations can generate optimal solutions for small instances in a reasonable amount of time, the heuristics are fast and perform very well, with gaps < 5% for up to 20 node instances. Moreover, the formulations highlight the mean savings in wait time (29.37%) when the power bank can charge two EVs consecutively before a replacement is needed. The implications of the proposed model can be extensive, as we seek to overcome the availability and technological challenges of EV charging while advocating its adoption.

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“…introduced node power loss sensitivity and electricity price in the optimization goal to reduce network loss and charging costs. A novel interactive network model between the distribution network and EVs was proposed in, 14 which reduces the charging cost and has a smoothing effect on the load curve 15 . studied the travel route optimization problem of long‐distance EVs and jointly modeled the transportation network and power network.…”

Section: Introductionmentioning

confidence: 99%

“…A novel interactive network model between the distribution network and EVs was proposed in, 14 which reduces the charging cost and has a smoothing effect on the load curve. 15 studied the travel route optimization problem of long-distance EVs and jointly modeled the transportation network and power network. After converting the model into a multiobjective bilevel conic optimization model, it is solved, and finally, the corresponding charging strategy is obtained.…”

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confidence: 99%

Spatial optimization of integrated energy system including hydrogen electrolysis center based on mixed integer second‐order cone programming

Xiaoxin

Chen

2023

Energy Science & Engineering

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Hydrogen energy is playing an increasingly important role in integrated energy systems (IES) due to its multiple uses. Green hydrogen can be produced from electricity generated from renewable energy, reducing carbon emissions and environmental pollution. Previous work usually only considers electric vehicles (EVs) as flexible loads, and there are few studies on the production and application process of green hydrogen. In this paper, hydrogen electrolysis centers (HECs) and EVs are simultaneously schedulable loads, integrated into IES, and optimized. The distribution network model is established, and the optimization goal is to minimize network loss while ensuring the safe operation of the power grid. Through the power flow optimization method of mixed‐integer second‐order cone programming, the space optimization scheduling of EVs in the IES is carried out, and the optimal location of the HEC is reasonably planned and set. A simulation instance of IEEE 33 nodes is used to verify the proposed method. The final results show that this method can minimize network loss in the range of safe operation of the system for the spatial scheduling of electrolytic hydrogen centers and EVs. At the same time, it can also provide a reference for HEC addressing.

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A bilevel conic optimization model for routing and charging of EV fleets serving long distance delivery networks

Cited by 8 publications

References 36 publications

Dynamic Path-Planning and Charging Optimization for Autonomous Electric Vehicles in Transportation Networks

Dynamic Path-Planning and Charging Optimization for Autonomous Electric Vehicles in Transportation Networks

Power on the Go: A Solution to Address Electric Vehicle Charging Challenges

Spatial optimization of integrated energy system including hydrogen electrolysis center based on mixed integer second‐order cone programming

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