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• Power-to-Gas (PtG) may offer flexibility to electricity markets with high shares of renewables. • This value is reduced when hydrogen produced by PtG is also demanded by other industries. • The benefits of this flexibility are lower than the costs of the PtG investments. • A carbon price of 150–700 euro/ton is required to make PtG investments socially beneficial. This paper analyzes the economic potential of Power-to-Gas (PtG) as a source of flexibility in electricity markets with both high shares of renewables and high external demand for hydrogen. The contribution of this paper is that it develops and applies a short-term (hourly) partial equilibrium model of integrated electricity and hydrogen markets, including markets for green certificates, while using a welfare-economic framework to assess the market outcomes. We find that strongly increasing the share of renewable electricity makes electricity prices much more volatile, while the presence of PtG reduces this price volatility. However, a large demand for hydrogen from outside the electricity sector reduces the impact of PtG on the volatility of electricity prices. In a scenario with a high external hydrogen demand, PtG can deliver positive benefits for some groups as it can provide hydrogen at lower costs than Steam Methane Reforming (SMR) during hours when electricity prices are low, but these positive welfare effects are outweighed by the fixed costs of PtG assets plus the costs of replacing a less expensive energy carrier (natural gas) with a more expensive one (hydrogen). Investments in PtG are profitable from a social-welfare perspective when the induced reduction in carbon emissions is valued at 150–750 euro/ton. Hence, at lower carbon prices, PtG can only become a valuable provider of flexibility when installation costs are significantly reduced and conversion efficiencies of electrolysers increased. [ABSTRACT FROM AUTHOR]

Highlights • The application of big data to energy economy modelling is a critical research gap. • Big data has the potential to reduce multiple blind spots in energy economy models. • Bottom-up hybrid models are a logical starting point for big data integration. • Moving from data scarcity to data abundance could completely transform the field. • We explore the research possibilities for revolutionising the state-of-the-art. Abstract Energy economy models are central to decision making on energy and climate issues in the 21st century, such as informing the design of deep decarbonisation strategies under the Paris Agreement. Designing policies that are aimed at achieving such radical transitions in the energy system will require ever more in-depth modelling of end-use demand, efficiency and fuel switching, as well as an increasing need for regional, sectoral, and agent disaggregation to capture technological, jurisdictional and policy detail. Building and using these models entails complex trade-offs between the level of detail, the size of the system boundary, and the available computing resources. The availability of data to characterise key energy system sectors and interactions is also a key driver of model structure and parameterisation, and there are many blind spots and design compromises that are caused by data scarcity. We may soon, however, live in a world of data abundance , potentially enabling previously impossible levels of resolution and coverage in energy economy models. But while big data concepts and platforms have already begun to be used in a number of selected energy research applications, their potential to improve or even completely revolutionise energy economy modelling has been almost completely overlooked in the existing literature. In this paper, we explore the challenges and possibilities of this emerging frontier. We identify critical gaps and opportunities for the field, as well as developing foundational concepts for guiding the future application of big data to energy economy modelling, with reference to the existing literature on decision making under uncertainty, scenario analysis and the philosophy of science. [ABSTRACT FROM AUTHOR]

Nowadays, global water shortages are an important challenge, due to the implications of water use in the agricultural, industrial, and energy sectors. In fact, one of the largest consumers of water is the energy sector, due to intensive water demand for cooling processes of fossil fuel power plants, or for the extraction of shale gas resources. Although there has been a 22% reduction in the use of water in the power sector from 2015 to 2020 (due to the increase of renewable energy), there is still a significant yearly volume of 47.5 trillion gallons needed for cooling of fuel based power plants in the United States. Therefore, water shortage generates integrated planning challenges among energy, water, and land systems when planning economy-wide transitions towards carbon neutrality. This paper evaluates carbon budgets consistent with those declared by the US Nationally Determined Contributions under water scarcity conditions in the context of energy–water–food nexus. The Global Change Analysis Model at the US state level (GCAM-USA) is used for this purpose. This model integrates different systems that allow us to study different mitigation strategies for these budgets for the US energy system. The results indicate that in order to achieve the US NDC, electrification of final demand sectors is required, reaching increased electricity supply of up to 90%. The mix of wind, solar, and CCS (gas and biomass) technologies that will supply that increased electricity demand depend on the level of water availability. Indeed, when water constraints are imposed, it is observed the that land sector is not able to supply biomass to the power sector, resulting in a 12% reduction of biomass based electricity participation. • Analysis of economy-wide carbon budgets under water scarcity conditions in the US. • Carbon budgets led to similar results towards 2050, however the transition differs. • Carbon budgets caused state-level increases in electricity generation up to 90%. • Water scarcity creates a lack of biomass supply from the land to the energy sector. • Coastal states increased desalination to more than 45% of its water demand by 2050. [ABSTRACT FROM AUTHOR]

In order to achieve stringent greenhouse gas emission reductions, a transition of our entire energy system from fossil to renewable resources needs to be designed. Such an energy transition brings two main challenges: most renewables generate variable electric energy, yet most demand is currently not electric (carrier mismatch) and does not always manifest at the same time as supply (temporal mismatch). Integrating multiple energy infrastructures can address both challenges by using the synergy between different energy carriers; building on existing infrastructure, while allowing a robust and flexible integration of the new. This paper proposes an optimization framework for long-term, multi-period investment planning of urban energy systems in an integrated manner. We formulate it as a mixed-integer linear program, combining a capacitated facility location with a multi-dimensional, capacitated network design problem. It includes generation and network expansion planning as well as interconnections between networks and storage infrastructure for each energy system. It can incorporate pathway effects like techno-economic developments, policy measures, and weather variations. The intended use is to support urban decision makers with long-term investment planning, though it can be tailored to fit other geographical or temporal scales. We demonstrate the model using two cases based on an average city in The Netherlands, which wants to reduce its CO 2 -emissions with 95% by 2050. In the first case, we include explicit carbon-emission constraints to study the effects of the carrier mismatch. In the second case, we implement interannual weather variations to analyze the temporal mismatch. The results give valuable insights into the energy transition design strategy for urban decision makers. They also show the future potential, as well as the computational challenges of the optimization framework. [Display omitted] • Generic optimization framework for energy transition design of urban infrastructures. • Full coupling of multiple energy infrastructures provides serious potential for robust and flexible solutions. • Multi-period investment planning allows explicit inclusion of social, techno-economical, and physical pathway effects. • Strict climate goals can be achieved and interannual weather variations balanced well. • Novel formulation of a capacitated facility location network design problem implies significant mathematical and computational complexity. [ABSTRACT FROM AUTHOR]