Distributionally robust planning method for expressway hydrogen refueling station powered by a wind-PV system

Renewable energy production sources became very attractive in recent years due to environmental problems and their enormous potential. Many studies have been done on these systems alone and combined operation. Among these combined systems, a particular attention is made on the combined PV-Wind system in the literature due to the great sources complementarity and the resource availability in tropical countries as in Mali. Nowadays, PV-Wind system hybridization is an alternative solution to traditional and nuclear energy sources which are the most used to cover the worldwide energy consumption. This system represents an economic and environmental option. Their complementarity improves the service quality compared to PV or wind power single system. A maximum power point tracking controller is required for the electrical power generation profitability. This controller must take into account the sources of intermittent nature and the random variations of climatic parameters. The fuzzy logic controller operates with imprecise input values and can deal with nonlinear equations. This paper presents energy sources modeling and simulation results on fuzzy logic improving (FLI) for hybrid PV-Wind system power optimization. Firstly, a model of each generator is developed and the fuzzy logic rules are defined. Secondly, these models are designed under Matlab/Simulink in order to study their behavior in simulation. Finally, a comparative study is realized between the proposed control and the fuzzy logic controller (FLC) using a fixed gain. The obtained results show the proposed method effectiveness compared to FLC in terms of power optimization and oscillations damping after perturbation.

Review of researches on important components of hydrogen supply systems and rapid hydrogen refueling processes

A Novel Matheuristic based on bi-level optimization for the multi-Objective design of hydrogen supply chains

This study, from a consumer perspective, employs a discrete choice experiment and utilizes data from 507 survey responses collected in five cities across China. Through a mixed logit model, the study analyzes consumer preferences and heterogeneity regarding different attributes of hydrogen vehicles (HVs) and estimates consumers' willingness to pay for these attributes, providing guidance for future HV promotion policies. The results reveal that: (1) Price factors significantly influence consumers' purchasing decisions, with consumers showing a negative preference for vehicle purchase prices and a significant positive preference for government subsidies. (2) Convenience is a critical factor, as consumers prefer vehicles with longer driving ranges and denser hydrogen refueling station networks. (3) Environmental benefits positively influence purchasing decisions, as environmental attributes have become a key consideration for consumers when choosing HVs. (4) Significant heterogeneity exists in consumer preferences based on socio-economic factors.The findings offer practical insights for future HV promotion policies. Policymakers should consider consumer heterogeneity, optimize purchase subsidy policies, and accelerate hydrogen refueling infrastructure development to improve convenience. Moreover, differentiated policy strategies should target consumers with varying income and education levels, thereby promoting HV adoption, enhancing market penetration, and achieving the dual goals of economic viability and environmental benefits.

To ensure the continuous supply of power in remote areas utilizing renewable energy resources is significant. Hence, in this research, an effective energy management method for a small-scale hybrid wind-solar-battery-Ultra-capacitor-based microgrid is proposed. Hybrid Energy Storage System (HESS) has been presented in conjunction with wind and solar energy conversion technologies characterized by coupling of power electronic converters, neural networks, optimization, battery, controllers, and ultra-capacitor storage systems to attain the intended performance. The power balance is regulated via an energy management system by considering the fluctuation in load demand and renewable energy power generation. Further, the voltage controller utilizes the deep Siamese neural network (deep SNN) that effectively carries out energy management among the hybrid renewable energy sources. The smelling-based hunting optimization assists in optimal parameter tuning and training of the deep SNN for enhancing the HESS’s efficiency. In addition, the microgrid operates independently and offers a testing area for different energy management systems and testing scenarios. The proposed small-scale microgrid, which is based on renewable energy, can serve as a significant testing area for methods utilized in smart grid applications. The proposed SBHO model’s efficiency is determined by varying the voltage, current, and power of the wind, solar, battery, and ultra-capacitor measurements. The current capacity of the battery reaches −11.06A and the battery voltage reaches 259.831 V in 0.82 s. The DC load measurement utilizing the SBHO approach obtained a DC bus voltage of 357.11V, a load current of 3.348 A within 0.82 s, and in DC power load attained the 1195.58 W within 0.82 s. The battery’s SOC by applying the smelling-based hunting optimization is gradually increased to 50.008% in 0.82s.In terms of the PV measurement, the PV current, PV voltage, and PV power are obtained as 4.238A,241.08V, and 1021.64W for the SBHO approach which surpasses other competent techniques. The ultra-capacitor current ranges from 35A to 40A with reduced heavy discharge of SOC from 98% obtained on evaluating the performance. The output power of wind using a boost convert remains 3000 W with few harmonics.

Multi-objective optimization of a hydrogen supply chain network: Wind and solid biomass as primary energy sources for the public transport in Sicily

Fuel cell electric vehicles (FCEVs) are rapidly growing owing to the increased public awareness of energy and environmental issues. Infrastructure for hydrogen production, transportation, and allocation is essential for FCEV promotion. For consumers, availability of few hydrogen refuelling stations is the primary concern. Currently, public charging infrastructure for battery electric vehicles, for example, fast charging station, has a more widespread coverage compared to infrastructure for hydrogen refuelling. With an electrolyser, the existing power grid can be used as an alternative to hydrogen supply and transportation infrastructure, which is still being developed. The authors propose a new conceptual combo station acting as both a hydrogen refuelling station and a fast charging station. This station can satisfy the requirement of FCEVs and battery electric vehicles, and even operate under a blackout situation. To quantify the value of coordinated operation, an optimal operation formulation of a combo station is developed. It is found that such a combo station is capable of saving land rents, reducing power network reinforcement cost, and boosting energy self‐balance capability under blackout condition.

Problem definition: Hydrogen fuel-cell vehicles (HFVs) have been proposed as a promising green transportation alternative. For regions experiencing renewable energy curtailment, promoting HFVs can achieve the dual benefit of reducing curtailment and developing sustainable transportation. However, promoting HFVs faces several major hurdles, including uncertain vehicle adoption, the lack of refueling infrastructure, the spatial mismatch between hydrogen demand and renewable sources for hydrogen production, and the strained power transmission infrastructure. In this paper, we address these challenges and study how to promote HFV adoption by deploying HFV infrastructure and utilizing renewable resources. Methodology/results: We formulate a planning model that jointly determines the location and capacities of hydrogen refueling stations (HRSs) and hydrogen plants as well as electricity transmission and grid upgrade. Despite the complexity of explicitly considering drivers’ HFV adoption behavior, the bilevel optimization model can be reformulated as a tractable mixed-integer second-order cone program. We apply our model calibrated with real data to the case of Sichuan, a province in China with abundant hydro resources and a vast amount of hydropower curtailment. Managerial implications: We obtain the following findings. (i) The optimal deployment of HRSs displays vastly different spatial patterns depending on the HFV adoption target. The capital city, a transportation hub, is excluded from the plan under a low target and only emerges as the center of HFV adoption under a high target. (ii) Promoting the HFV adoption can overall help reduce hydropower curtailment, but the effectiveness depends on factors such as the adoption target and the grid upgrade cost. (iii) Being a versatile energy carrier, hydrogen can be transported to various locations, which allows for strategic placement of HRSs in locations distinct from hydrogen plant sites. This flexibility offers HFVs greater potential cost savings and curtailment reduction compared with other alternative fuel vehicles (e.g., electric vehicles) under current cost estimates. Funding: W. Qi acknowledges the support from the National Natural Science Foundation of China [Grants 72242106, 72188101, and 72272014] and the Natural Sciences and Engineering Research Council of Canada [Grant RGPIN-2019-04769]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0381 .

Social cost-benefit assessment as a post-optimal analysis for hydrogen supply chain design and deployment: Application to Occitania (France)

Hydrogen is recognized as a key element of future low-carbon energy systems. For proper integration, an adequate delivery infrastructure will be required, to be deployed in parallel to the electric grid and the gas network. This work adopts an optimization model to support the design of a future hydrogen delivery infrastructure, considering production, storage, and transport up to demand points. The model includes two production technologies, i.e., steam reforming with carbon capture and PV-fed electrolysis systems, and three transport modalities, i.e., pipelines, compressed hydrogen trucks, and liquid hydrogen trucks. This study compares a multi-modality formulation, in which the different transport technologies are simultaneously employed and their selection is optimized, with a mono-modality formulation, in which a single transport technology is considered. The assessment looks at the regional case study of Lombardy in Italy, considering a long-term scenario in which an extensive hydrogen supply chain is developed to supply hydrogen for clean mobility. Results show that the multi-modality infrastructure provides significant cost benefits, yielding an average cost of hydrogen that is up to 11% lower than a mono-modality configuration.

Development and environmental impact of hydrogen supply chain in Japan: Assessment by the CGE-LCA method in Japan with a discussion of the importance of biohydrogen

A long-term capacity expansion planning model for an electric power system integrating large-size renewable energy technologies

Optimal strategies for production plan and carbon emission reduction in a hydrogen supply chain under cap-and-trade policy

Optimal design and techno-economic assessment of low-carbon hydrogen supply pathways for a refueling station located in Shanghai

Technical and economic analysis of a pilot-scale hydrogen system: From production to application