How can renewable hydrogen compete with diesel in public transport? Robust design optimization of a hydrogen refueling station under techno-economic and environmental uncertainty
“…We assume that the compensation for grid reinjection is equal to the wholesale electricity price. Instead, the price for buying electricity is 2.5 times the wholesale electricity price (i.e., the wholesale electricity price corresponds to 40% of the price of purchasing electricity price from the grid, the remaining costs are related to taxes and transmission fees) 57 . Figure 6 A schematic of the system.…”
Section: Resultsmentioning
confidence: 99%
“… Parameter Value Unit Ref. CAPEX 1325 €/kW 60 OPEX 1.1 % of CAPEX 60 CAPEX 100 €/kW 57 OPEX 3 % of CAPEX 57 Discount rate 6 % 61 Inflation rate 2 % 62 …”
Despite the considerable uncertainty in predicting critical parameters of renewable energy systems, the uncertainty during system design is often marginally addressed and consistently underestimated. Therefore, the resulting designs are fragile, with suboptimal performances when reality deviates significantly from the predicted scenarios. To address this limitation, we propose an antifragile design optimization framework that redefines the indicator to optimize variability and introduces an antifragility indicator. The variability is optimized by favoring upside potential and providing downside protection towards a minimum acceptable performance, while the skewness indicates (anti)fragility. An antifragile design primarily enhances positive outcomes when the uncertainty of the random environment exceeds initial estimations. Hence, it circumvents the issue of underestimating the uncertainty in the operating environment. We applied the methodology to the design of a wind turbine for a community, considering the Levelized Cost Of Electricity (LCOE) as the quantity of interest. The design with optimized variability proves beneficial in 81% of the possible scenarios when compared to the conventional robust design. The antifragile design flourishes (LCOE drops by up to 120%) when the real-world uncertainty is higher than initially estimated in this paper. In conclusion, the framework provides a valid metric for optimizing the variability and detects promising antifragile design alternatives.
“…We assume that the compensation for grid reinjection is equal to the wholesale electricity price. Instead, the price for buying electricity is 2.5 times the wholesale electricity price (i.e., the wholesale electricity price corresponds to 40% of the price of purchasing electricity price from the grid, the remaining costs are related to taxes and transmission fees) 57 . Figure 6 A schematic of the system.…”
Section: Resultsmentioning
confidence: 99%
“… Parameter Value Unit Ref. CAPEX 1325 €/kW 60 OPEX 1.1 % of CAPEX 60 CAPEX 100 €/kW 57 OPEX 3 % of CAPEX 57 Discount rate 6 % 61 Inflation rate 2 % 62 …”
Despite the considerable uncertainty in predicting critical parameters of renewable energy systems, the uncertainty during system design is often marginally addressed and consistently underestimated. Therefore, the resulting designs are fragile, with suboptimal performances when reality deviates significantly from the predicted scenarios. To address this limitation, we propose an antifragile design optimization framework that redefines the indicator to optimize variability and introduces an antifragility indicator. The variability is optimized by favoring upside potential and providing downside protection towards a minimum acceptable performance, while the skewness indicates (anti)fragility. An antifragile design primarily enhances positive outcomes when the uncertainty of the random environment exceeds initial estimations. Hence, it circumvents the issue of underestimating the uncertainty in the operating environment. We applied the methodology to the design of a wind turbine for a community, considering the Levelized Cost Of Electricity (LCOE) as the quantity of interest. The design with optimized variability proves beneficial in 81% of the possible scenarios when compared to the conventional robust design. The antifragile design flourishes (LCOE drops by up to 120%) when the real-world uncertainty is higher than initially estimated in this paper. In conclusion, the framework provides a valid metric for optimizing the variability and detects promising antifragile design alternatives.
“…However, with an incipient industry that is regulated for industrial hydrogen production, several issues arise with regard to green hydrogen synthesis, transportation, distribution, and delivery to vehicles, that member states must regulate before widespread adoption into transportation. An evolution of hydrogen, as a competitive transportation fuel, compared to hydrocarbon based fuels, requires extensive networks of on-site production as transportation is cumbersome and cost-inefficient; renewable energy resources; carbon capture technology; and a wide-scale adoption expanded into networks for economies of scale to foster the development of "Hydrogen Valleys" [51].…”
The topic investigated in this article is a comparison, contrast, and integration effort of European strategies for sustainable development with the evolving market initiatives that are beginning to fuel the fourth industrial revolution. Several regulatory initiatives from continental bodies come into effect to radically change access to finances for business development, based on sustainability goals, and an analysis of the legislation and trends becomes essential for an effective pivot tactic in the face of adversity, as well as change management policies to pre-emptively adapt and perform. The general research question is “what the strategic tools are best employed to overcome the hurdles laid forth by the drastic changes legally required for a sustainable future?” The research methods include a quantitative analysis of norms, regulations, and legislation, including strategic initiatives circulated in the European Union governmental bodies, integrated with qualitative research of the literature. The study finds and draws synergies between national strategies that have recently been drafted or are currently evolving with sustainability-centric initiatives such as the hydrogen initiative, the nuclear initiative, the natural gas initiative, the renewables initiative, the synthetics, and biomass initiative, the ESG initiative, the digital initiative. The findings are to contribute to the business administration field by providing an appropriate image of the organizational design model in the sustainability era, and a strategy framework to build the optimum long-term vision founded on continental regulatory initiatives that have come into effect.
“…Diederik et al applied RDO on a wind‐ and solar‐powered hydrogen refueling system and a hydrogen‐ and diesel‐powered bus fleet, to optimize the levelized cost Of driving and carbon intensity. [ 24 ] He also performed a RDO on the natural variability of the levelized cost of exergy with Ward et al The results indicated that a PV–battery–heat pump configuration achieves higher robustness toward aleatory uncertainty than a PV–battery–gas boiler configuration. [ 25 ] However, RDO has strong randomness, resulting in excessively complex results.…”
Under the pressures of environmental pollution, solar energy and hydrogen have attracted widespread attention, thus promoting the development of photovoltaic–hydrogen integrated energy system. However, multiple uncertainties seriously interfere with the system's economic and stable operation. Therefore, this article proposes a temporal covariance‐involved distributionally robust optimization model for system scheduling considering multiple uncertainties from source, load sides, and market. First, the possible range of the uncertainties distribution is described by a novel moment‐based ambiguity set modified with temporal covariances. Second, the objective function is constructed as a min{sup} form to minimize the expected operation cost under the “worst‐case” distribution in the ambiguity set. Then, the constraints present a two‐stage structure. The first stage guarantees the operation feasibility under the forecasting scenario, and the second stage accounts for forecasting errors to formulate the rescheduling space for resisting uncertainties. Finally, the model is transformed into an easy‐to‐solve semidefinite programming. Simulation finds that the model 1) significantly reduces the average and standard deviation of system operation cost by 22.35% and 27.41%, respectively; 2) ensures 100% photovoltaic accommodation; and 3) maintains low conservativeness and complexity simultaneously. Above all, this study provides an effective approach for comprehensive ultilization of clean energies under multiple uncertainties.
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