Hydrogen station location optimization based on multiple data sources
Introduction
Since fossil fuels are non-renewable and can cause serious environmental pollution, people are looking for alternative fuels, including hydrogen, ethanol, biodiesel, natural gas, or electricity. As a universal energy source and secondary energy source, hydrogen energy [1] is becoming increasingly significant, and the hydrogen industry is developing rapidly all over the world. The promotion of hydrogen energy includes hydrogen fuel vehicles and hydrogen stations. There are two types of hydrogen energy vehicles, the hydrogen internal combustion engine (HICEV) [2] and the hydrogen fuel cell vehicle (FCEV) [3]. At present, the faster development of the two is the FCEV.
In Germany, the government built a new station every two weeks on average in 2018 and a total of 100 hydrogen filling stations will be built by 2019 [4]. In California, the existing hydrogen stations are concentrated in Los Angeles, Sacramento and San Francisco [5], and hydrogen highway with stations spaced 20 miles apart is going to be built [6]. In Japan, FCEV is launched with high cruising range [7]. Compared to Germany, Japan and other countries, hydrogen energy has greater development potential in China. In the next decade, China plans to deploy more hydrogen stations and public hydrogen service vehicles [8], and in 2020, there will be 100 hydrogen stations and 5000 public hydrogen service vehicles. While in 2030, the number of hydrogen stations and public hydrogen service vehicles will achieve 300 and 50,000. Beijing's 2018 new energy vehicle policy announces that the government will subsidize FCEV for a long time. Before the promotion of FCEVs, a network of hydrogen stations needs to be preliminarily established [9].
During the fast development of hydrogen energy, the location planning of hydrogen station is a critical issue [10]. The high investment cost of the hydrogen station hinders the construction of the hydrogen refueling station [11]. Further promotion of hydrogen energy requires the current hydrogen station network to reach a certain scale. Therefore, the location of the hydrogen station is not only related to its long-term revenue profit, but also plays an important role in the promotion process of “hydrogen economic” [12]. In addition, it is necessary to consider a variety of factors during the location planning.
There are several models for facility location, including GIS models, operations research models [13], flow-capturing models [14], multi-objective optimization models, etc. Factors affecting the location of a hydrogen station include travel distance, fuel demand, traffic flow and so on [15]. The goal of site selection is based on these factors, which include minimizing the number of facilities and minimizing the travel distance or travel time, maximizing the traffic flow captured, maximizing the demand points covered, etc. There is no uniform standard for deciding which model outperforms other models, instead, it depends on our measurement perspective [16].
Section snippets
Related work
At present, various countries around the world are accelerating the construction of hydrogen stations. However, the construction cost and operation cost of the hydrogen station are much higher than those of the traditional gas station, which has become a research problem for many scholars. There have been related researches for the location of hydrogen stations at home and abroad.
The process of promoting hydrogen stations and FCEVs can refer to the promotion process of New Zealand gas-filled
Study area
The government divides Beijing into several regions according to their regional functions: core districts of capital function (Dongcheng District and Xicheng District), urban function development districts (Haidian District, Fengtai District, Chaoyang District and Shijingshan District), new urban development districts (5 districts) and ecological preservation development districts (5 districts). In summary, we select core districts of capital function and urban function development districts
Estimation of demand
The locations of the stations are related to the demand. Some analyses of the demand for hydrogen stations are based on the census tracts. While in California, besides the census tract, Sacramento also uses traffic analysis zones (TAZs) [38] for demand estimation. Traffic modelers in Sacramento have identified areas with roughly homogeneous travel characteristics as TAZs. However, Beijing does not have a similar regional division besides the census tract. So finally we further subdivide each
Model description
In terms of the station's location, there are some differences between the hydrogen station, EV charging station and petrol-refueling station. The locations of EV charging stations have to plan with the structure of the current power grid. And some drivers tend to charge electric cars in their homes rather than at public charging stations due to the long charging time. Existing petrol-refueling station distribution is a competitive distribution, which means there are several petrol-refueling
Model solution
Most of the location problems are classified as NP-Hard problems, which only have exponential algorithm complexity. Large instances of such problems cannot be solved with exact algorithms, and effective approximation algorithms for such problems must be sought.
The hydrogen station location problem can be classified as an integer programming problem. The feasible domain of integer programming problem is discrete. Therefore, it is necessary to combine some heuristic algorithms to optimize the
Result analysis
In this section, the results analysis includes analysis of the proposed solution, analysis of the number of hydrogen stations, and analysis of the coverage distance of hydrogen station, which is compared with the California area.
Conclusion
In account of the high construction cost and lack of network infrastructures, the hydrogen station cannot be deployed in the same way as the existing petrol-refueling station network. It is particularly significant to optimize the location of the hydrogen station. Combined with the researches on the location of the hydrogen station in California, our model is further optimized for Beijing, including the demand estimation and distance measurement. The proposed model is involved with multiple
Acknowledgments
This work was supported by the Beijing Natural Science Foundation (L171010) and the State Grid Corporation of China (520940180016).
References (41)
- et al.
Photo-electrochemical hydrogen generation from water using solar energy. materials-related aspects
Int J Hydrogen Energy
(2002) - et al.
The hydrogen-fueled internal combustion engine: a technical review
Int J Hydrogen Energy
(2006) - et al.
Gis-based scenario calculations for a nationwide German hydrogen pipeline infrastructure
Int J Hydrogen Energy
(2013) - et al.
Hydrogen energy stations: along the roadside to the hydrogen economy
Util Policy
(2005) - et al.
A green hydrogen economy
Energy Policy
(2006) - et al.
Near-term analysis of a roll-out strategy to introduce fuel cell vehicles and hydrogen stations in shenzhen China
Appl Energy
(2017) Initiating hydrogen infrastructures: preliminary analysis of a sufficient number of initial hydrogen stations in the us
Int J Hydrogen Energy
(2003)- et al.
Supporting green urban mobility–the case of a small-scale autonomous hydrogen refuelling station
Int J Hydrogen Energy
(2019) - et al.
Quantitative risk assessment on 2010 expo hydrogen station
Int J Hydrogen Energy
(2011) An empirical analysis on the adoption of alternative fuel vehicles: the case of natural gas vehicles
Energy Policy
(2007)
The maximal covering location problem
The p-median problem: a survey of metaheuristic approaches
Eur J Oper Res
An optimal approach for a set covering version of the refueling-station location problem and its application to a diffusion model
Int J Sustain Trans
Hydrogen refueling station siting of expressway based on the optimization of hydrogen life cycle cost
Int J Hydrogen Energy
The p-center flow-refueling facility location problem
Transp Res Part B Methodol
Refueling station location problem with traffic deviation considering route choice and demand uncertainty
Int J Hydrogen Energy
Incorporating institutional and spatial factors in the selection of the optimal locations of public electric vehicle charging facilities: a case study of beijing, China
Transp Res C Emerg Technol
The fuel-travel-back approach to hydrogen station siting
Int J Hydrogen Energy
Optimal location of wireless charging facilities for electric vehicles: flow-capturing location model with stochastic user equilibrium
Transp Res C Emerg Technol
The flow-refueling location problem for alternative-fuel vehicles
Soc Econ Plan Sci
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