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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| Accès en ligne: | https://arxiv.org/abs/2407.21574 |
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| _version_ | 1866911267104489472 |
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| author | Palmer, Owen Radet, Hugo Camal, Simon Kazempour, Jalal Girard, Robin |
| author_facet | Palmer, Owen Radet, Hugo Camal, Simon Kazempour, Jalal Girard, Robin |
| contents | Green hydrogen production by water electrolysis using renewable electricity is considered essential for decarbonisation of certain sectors of the global economy, however development of the industry is lagging behind expectations due to the perceived financial risk for individual projects. This risk stems from a number of uncertainties, including future hydrogen demand, variable renewable energy sources, and volatile energy market prices. The interaction of these uncertainties is complex, yet the analysis of hydrogen projects is often carried out using simplified modelling that often omits uncertainty and/or energy hedging practices which are typical for intensive power consumers. In this study, we define a set of planning methods (planning policies) in order to compare the effectiveness of different modelling approaches. We propose a 2-stage market-focused stochastic program to represent a hydrogen producer supplying an industrial customer through a hydrogen offtake contract (a Hydrogen Purchase Agreement, or HPA). The model can be used to obtain equipment sizing decisions, as well as energy hedging decisions using Power Purchase Agreements (PPA's) and power futures. We find that for some HPA contract types, failure to use stochastic modelling can lead to planning decisions that result in 30% higher production costs during scenario stress-testing for the same project. This could lead to some projects being discarded by developers, incorrectly deemed to be unviable due to cost projections being too high. The results also show the importance of HPA contract volumetric obligations in limiting demand uncertainty. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_21574 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Hedging Hydrogen: Planning and Contracting Under Uncertainty for a Green Hydrogen Producer Palmer, Owen Radet, Hugo Camal, Simon Kazempour, Jalal Girard, Robin Optimization and Control Green hydrogen production by water electrolysis using renewable electricity is considered essential for decarbonisation of certain sectors of the global economy, however development of the industry is lagging behind expectations due to the perceived financial risk for individual projects. This risk stems from a number of uncertainties, including future hydrogen demand, variable renewable energy sources, and volatile energy market prices. The interaction of these uncertainties is complex, yet the analysis of hydrogen projects is often carried out using simplified modelling that often omits uncertainty and/or energy hedging practices which are typical for intensive power consumers. In this study, we define a set of planning methods (planning policies) in order to compare the effectiveness of different modelling approaches. We propose a 2-stage market-focused stochastic program to represent a hydrogen producer supplying an industrial customer through a hydrogen offtake contract (a Hydrogen Purchase Agreement, or HPA). The model can be used to obtain equipment sizing decisions, as well as energy hedging decisions using Power Purchase Agreements (PPA's) and power futures. We find that for some HPA contract types, failure to use stochastic modelling can lead to planning decisions that result in 30% higher production costs during scenario stress-testing for the same project. This could lead to some projects being discarded by developers, incorrectly deemed to be unviable due to cost projections being too high. The results also show the importance of HPA contract volumetric obligations in limiting demand uncertainty. |
| title | Hedging Hydrogen: Planning and Contracting Under Uncertainty for a Green Hydrogen Producer |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2407.21574 |