A stochastic programming approach for electric vehicle charging station expansion plans

Kabli, Mohannad; Quddus, Abdul; Nurre, Sarah G.; Marufuzzaman, Mohammad; Usher, John M.

doi:10.1016/j.ijpe.2019.07.034

Cited by 35 publications

(18 citation statements)

References 45 publications

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“…Check the group restoration condition: if max i∈n T (AP Ti ) ≥ ∑n z j =1 |S j |, where n T and n z refer to the total number of tie and down zone, S is load demandSend accept proposal message to a ZTA that is included allowed power of AP T by FZA, and connect this ZTA to network end (15) If the group restoration condition is not possible, thus, multi ZTA connect to ADN based on limits (10)- (13) healthy and fault feeder, I vh , and I vf , are used in the proposed MAS-based RP as constraints Equations ( 12) and ( 13), respectively [35].…”

Section: Mas-based Rpmentioning

confidence: 99%

“…According to the literature review and Table 1, there are three main research gaps for the coordination between the flexible energy management and the self‐healing method: Based on the results of [25–32], EVs’ battery storage system is an important flexible source due to its high responsible speed, and it acts as a local power source that can control the different network variables in consumption points to obtain high reliability, security, stability and flexibility in ADN. But, it is noted that this capability of EVs is not considered in different researches such as [12–32]. In the many types of research, the energy management and restoration process of ADN have been investigated, separately [12–40].…”

Section: Introductionmentioning

confidence: 99%

“…But, it is noted that this capability of EVs is not considered in different researches such as [12–32]. In the many types of research, the energy management and restoration process of ADN have been investigated, separately [12–40]. In other words, the coordination of zone agents and RESs, as well as FSs with the distribution system operator (DSO), has not been considered such as works in [12–40].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Coordinated flexible energy and self‐healing management according to the multi‐agent system‐based restoration scheme in active distribution network

Bagherzadeh

Shayeghi

Pirouzi

et al. 2021

IET Renewable Power Gen

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This study presents the optimal model of the coordinated flexible energy and self‐healing management (C‐FE&SH‐M) in the active distribution network (ADN) including renewable energy sources (RESs), electric vehicles (EVs) and demand response program (DRP).The flexible energy management (FEM) is extracted using coordination between the RESs, EVs and DRP. The self‐healing method (SHM) is related to multi‐agent system‐based restoration process (MAS‐based RP) that finds the optimal restoration pattern at the fault condition according to the different zone agents (ZAs) distributing along with the network. This method minimizes the difference between energy cost and flexibility benefit related to the FEM part and difference between the number of switching operation and priority loads restored based on the SHM part. Also, this problem subjects to power flow equations, RESs and active loads constraints, restoration process formulation and system operation limits. Stochastic programming is used to model the uncertainty of loads, energy prices, RESs and EVs. Hereupon, the suggested strategy is implemented on the 33‐bus radial distribution network and it is solved by the crow search algorithm (CSA). Ultimately, the obtained results imply the high flexibility and security of the operation, incorporating the proposed strategy, and delineate the optimal restoration scheme for the ADN.

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Section: Mas-based Rpmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Coordinated flexible energy and self‐healing management according to the multi‐agent system‐based restoration scheme in active distribution network

Bagherzadeh

Shayeghi

Pirouzi

et al. 2021

IET Renewable Power Gen

View full text Add to dashboard Cite

show abstract

“…Variants of this algorithm have been used in recent studies with excellent results in terms of approximation of the exact solution and computational time on various kinds of problems. For instance, the PH has been used to solve problems regarding the social engagement paradigm [9], scheduling and planning in industry [25], suppliers selection [21], expansion plans for electric vehicle charging station [17], and fixed‐charge capacitated multicommodity network design [6]. Therefore, we decided to implement a generic version of the PH and to propose some interesting variants to improve its performance in several ways.…”

Section: Progressive Hedging‐based Heuristic Algorithmmentioning

confidence: 99%

Multiperiod transshipment location–allocation problem with flow synchronization under stochastic handling operations

2020

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The transshipment location–allocation problem consists of locating transshipment facilities (e.g., intermodal hubs) of a transportation network and allocating freight flows through them, from several origins to several destinations, to satisfy demand and supply constraints. The objective is to maximize the total net transportation utility given by the total shipping utility minus the total cost to locate the facilities. Moreover, flow synchronization at the facilities must also be ensured. Unfortunately, the flow synchronization depends on a broad set of unknown events, which could cause both unexpected reductions of the facility capacity and uncertain utility of handling operations. In this paper, we first want to evaluate how uncertainty on facility capacity and handling operations utility affects the transshipment location–allocation problem in terms of complexity, net gain, and optimal solutions. Moreover, we extend the problem from a single to a multi‐period setting to have a wider view of future scenarios realizations and consequently synchronize the flows by using different facilities on different periods. We propose a two‐stage stochastic programming formulation with recourse and analyze, over a ground set of instances, some well‐known economic indicators to derive managerial insights on the importance of addressing uncertainty for the problem. Finally, given the computational burden of solving the deterministic equivalent problem, we propose several heuristics based on progressive hedging and test their performance.

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“…A biobjective optimization model is proposed in Reference 16 to decide locations, capacities, and service types of EVCSs, which is solved by the Non‐dominated Sorting Genetic Algorithm‐II (NSGA‐II). The co‐optimization of EVCSs planning and power grid expansion is conducted in Reference 17, which reflects the strong dependency of EVCSs on sufficient power supply enabled by the expanded power grid. However, in the aforementioned publications, the detailed modeling of each individual EV, including routing selection and charging management, is not involved.…”

Section: Introductionmentioning

confidence: 99%

Optimal planning of charging stations for an electric vehicle fleet in c ar‐sharing business

Wang

Tang

Lai

et al. 2021

Int Trans Electr Energ Syst

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Summary With the increasing market share of electric vehicles (EVs), optimizing the planning decisions of charging stations becomes critical to support long‐distance traveling. Besides, the booming car‐sharing industry provides an alternative business model to enhance the utilization of vehicle resources. Recently, several car‐sharing enterprises have launched the collaborations with charging station construction companies to construct their own charging facilities, which calls for the co‐optimization of charging station planning and scheduling of EVs in car‐sharing business. Therefore, this paper develops an optimization model for electric vehicle charging stations placements and type selections, considering routing selection and charging management of an electric vehicle fleet in car‐sharing business. Specifically, a stochastic programming framework is exploited to incorporate a variety of operating scenarios, which reflect the uncertainties of riders' requirements, drivers' itineraries, traffic flow information, and electricity prices. The proposed model aims to improve the welfare of all riders and reduce total cost of fulfilling riders' pickup and delivery requirements simultaneously, in which determining the optimal locations and types of charging facilities is required. Moreover, portfolio management is integrated into the optimization model to mitigate investment risk. After a series of reformulation, the proposed optimization model can be recast into a mixed‐integer linear programming problem. Using two transportation networks, the effectiveness of the proposed model is validated in numerical studies. It finds that the optimal electric vehicle charging station planning strategy is determined according to the operator's preferences, electricity prices, and installation budgets. Moreover, diversity within operating scenarios yields the positive value‐of‐stochastic‐solution that demonstrates the necessity of implementing stochastic programming.

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A stochastic programming approach for electric vehicle charging station expansion plans

Cited by 35 publications

References 45 publications

Coordinated flexible energy and self‐healing management according to the multi‐agent system‐based restoration scheme in active distribution network

Coordinated flexible energy and self‐healing management according to the multi‐agent system‐based restoration scheme in active distribution network

Multiperiod transshipment location–allocation problem with flow synchronization under stochastic handling operations

Optimal planning of charging stations for an electric vehicle fleet in c ar‐sharing business

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