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Vortrag im Rahmen des Klima- und Umwelt-Tags der Marktgemeinde Laxenburg am 23. Mai 2023 Der Vortrag basiert auf Graphiken des 6. Sachstandsbericht des IPCC (AR6) #WissenSchafftKlimaschutz

Background: The transition to a carbon neutral society such as that envisaged in the European Union Green Deal requires careful and comprehensive planning. Integrated assessment models (IAMs) and energy system models (ESMs) are both commonly used for policy advice and in the process of policy design. In Europe, a vast landscape of these models has emerged and both kinds of models have been part of numerous model comparison and model linking exercises. However, IAMs and ESMs have rarely been compared or linked with one another. Methods: This study conducts an explorative comparison and identifies possible flows of information between 11 of the integrated assessment and energy system models in the European Climate and Energy Modelling Forum. The study identifies and compares regional aggregations and commonly reported variables We define harmonised regions and a subset of shared result variables that enable the comparison of results across the models. Results: The results highlight similarities and differences on final electricity demand, electricity supply and hydrogen across three levels of aggregation. However, the differences between the regional aggregation of the models limit detailed analysis. Conclusions: This first-of-its-kind comparison and analysis of modelling results across model type boundaries provides modellers and policymakers with a better understanding of how to interpret both IAM and ESM results. It also highlights the need for community standards for region definitions and information about reported variables to facilitate future comparisons of this kind.

Vortrag im Rahmen von „Klimawissen Online aus 1. Hand“ organisiert vom Umwelt-Bildungs-Zentrum Steiermark https://www.ich-tus.steiermark.at/klimawissen

Wissenschaftliche Grundlagen und Handlungsoptionen zur Klimakrise in Österreich Vortrag anlässlich der Eröffnung des "Klimaforums", einer interaktiven Ausstellung des "Klimadashboard" im Designforum Wien

Vortrag am 5. Oktober 2022im Rahmen der Online-Veranstaltungsreihe "Klimaschutz und Klimawende“ der Evangelischen Kirche in Österreich in Kooperation mit Scientists for Future. https://evang.at/projekte/schoepfung2022/#eventId=14450740&eventName=klimawandel-wissenschaftliche-grundlagen-und-personliche-handlungsoptionen-i-online-veranstaltungsreihe-klimaschutz-und-klimawende

Lessons & insights from supporting the IPCC process and Horizon 2020 projects at the research theme “Scenario Services & Scientific Software” of the IIASA Energy, Climate, and Environment program

What: Approximately 40 participants – with expertise spanning energy, computer science, weather and climate research -– joined a week-long Energy-Climate data “hackathon” in June 2021. It was hosted by the Universities of Oxford and Reading in partnership with the UK Met Office as part of a series of themed hackathons supported by the Met Office and held in the run-up to the UN COP26 conference. Six projects were initiated and developed by teams over the course of the week, supported by access to state-of-the-art computational resources on the UK’s CEDA-JASMIN service, and stimulated by keynote speakers from industry and academia. The hackathon concluded with teams presenting their outputs to a panel of invited experts. Several teams plan to build on their hackathon success in publications, ongoing collaborations and research funding proposals. When: 18th May (half-day “scoping” event) & 21st-25th June 2021 (main hackathon) Where: Online via Zoom and Gather.Town, supported by Slack communication channels Affiliations: Initiated by: University of Oxford Dr Sarah Sparrow, Professor David Wallom, Professor Tim Woollings, & University of Reading Professor David Brayshaw, Dr Hannah Bloomfield, In partnership with the Met Office, the UK’s national meteorological service, and with support from the UK’s CEDA-JASMIN service and Gurobi optimization software.

The core objective of this work is to downscale the cost-effective heat supply of different European decarbonization scenarios generated by the aggregate model GENeSYS-MOD from the national to the community level in Austria and thus disclose the heat density of district heating in 2050. We assume that district heating encompasses geothermal, synthetic gas, hydrogen, waste, and large-scale heat pumps as renewable heat sources. The results determine district heating in 68 Austrian communities in Austria in 2050, which corresponds to 6% of the total number of communities. We find that GENeSYS-MOD results are capable in covering local trends in district heating since high shares of the projected heat densities at the local levels achieve values indicating economic viability. Further research should follow on how locally determined district heating and heat densities could be returned into more aggregate models, such as GENeSYS-MOD, in the sense of a feedback loop. That allows refining assumptions in the large-scale upper-level models, which in turn will increase the plausibility and realism of pathways at the European level.

Background: Transparency, reproducibility and reusability of scientific analysisResearchers are now widely expected to share the data and source code of their work to foster transparency, reproducibility and reusability.Alas, the quality of data documentation and scientific software scripts can vary substantially. In many instances, metadata and information on the provenance of data are missing or incomplete, and source code often does not include a clear list of dependencies (including version information) or systems requirements. Finally, the source code does not include sufficient inline documentation to be easily understood. As a consequence, even though the data and related scripts may be released under an open-source license, analysis too often cannot be reproduced or adapted with reasonable effort by other researchers.The pyam packageThis talk presents the open-source Python package pyam for energy system scenario analysis and visualization. The aim of pyam is not to provide any ground-breaking new methods or analysis routines. Instead, it provides a reliable, well-tested interfacesimilar in feel & style to the widely used pandas package, but geared for data formats and applications often used in energy systems analysisand integrated assessment modelling.By using pyam for their scenario input data processing and analysis workflows, researchers can reduce standard tasks like unit conversion and data validation from a 5-minute effort to 30 seconds - and have the knowledge that their scripts won't break if pandas or another dependency change their APIs, because the pyam community will work to ensure forward-compatibility and continuity. As another side benefit, the pyam package will raise meaningful errors when input data doesn't make sense, whereas own ad-hoc scripts may fail silently or - even worse - return non-sensical values.Spatial, temporal and sectoral aggregation & downscaling featuresTo highlight the applicability of the pyam package for the EGU community and energy & climate modellers at large, this talk will focus on the features for spatial, temporal and sectoral aggregation and downscaling. The package include several often-used methods like weighting by proxy variables or deriving indicators based on minimum or maximum values of timeseries data.Building a communityThe pyam package follows best-practice of version control, continuous-integration and scientific-software documentation. This facilitates building on the package by other researchers. The community uses several tools for communication and discussion, including a Slack channel, an email list and a Github repository for issues & pull requests. And of course, we appreciate contributions by colleagues to extend the scope of features and methods based on their own use cases and requirements!More informationReadTheDocs: https://pyam-iamc.readthedocs.io/GitHub repo: https://github.com/iamconsortium/pyam

Global emissions scenarios play a critical role in the assessment of strategies to mitigate climate change and their related societal transformations. The current generation of scenarios, however, are criticized because they rely heavily on net negative CO2 emissions (NNCE) that result from allowing temperature limits to be temporarily exceeded. In this study we present a new set of emissions scenarios that exclude NNCE. We show that such scenarios require a more rapid near-term transformation with significant long-term gains for the economy (even without considering the benefits of avoided climate impacts). Scenarios that avoid temperature overshoot and NNCE are thus not only economically more attractive over the long term, they also involve lower climate risks. Our study further identifies possible alternative configurations of net-zero CO2 emissions systems and the distinct roles of different sectors and regions in order to balance emissions sources and sinks.

Low-carbon pathways consistent with the 2 °C and 1.5 °C long-term climate goals defined in the Paris Agreement are likely to induce substantial co-benefits for air pollution and associated health impacts. In this analysis, using five global integrated assessment models, we quantify the emission reductions in key air pollutants resulting from the decarbonization of energy systems and the resulting changes in premature mortality attributed to the exposure to ambient concentrations of fine particulate matter. The emission reductions differ by sectors. Sulfur emissions are mainly reduced from power plants and industry, cuts in nitrogen oxides are dominated by the transport sector, and the largest abatement of primary fine particles is achieved in the residential sector. The analysis also shows that health benefits are the largest when policies addressing climate change mitigation and stringent air pollution controls are coordinated. We decompose the key factors that determine the extent of health co-benefits, focusing on Asia: changes in emissions, urbanization rates, population growth and ageing. Demographic processes, particularly due to ageing population, counteract in many regions the mortality reductions realized through lower emissions.

Scenarios have supported assessments of the IPCC for decades. A new scenario ensemble and a suite of visualization and analysis tools is now made available alongside the IPCC 1.5 °C Special Report to improve transparency and re-use of scenario data across research communities.

Network models of energy markets have been beneficial for analyses and decision-making to tackle challenges related to the production, distribution and consumption of energy in its various forms. Despite the growing awareness of environmental and safety impacts of fuel transfer, such as emissions, spills and other harmful effects, existing energy models for various types of networks are yet to fully capture modal distinctions which are relevant to providing pathways to limiting these impacts. To address this deficit in detailed multimodal analyses, we have built on recent work to develop a partial-equilibrium model that incorporates the representation of multimodal fuel transfer within energy networks. In a novel application to the North American crude oil market, we also demonstrate that our model is a useful tool for exploring avenues for reducing the risks of light and heavy crude oil transportation across this region. The results we obtain indicate that a combined strategy of rail loading restrictions, pipeline deployments and a discontinuation of the oil export ban is most effective in reducing the transportation of crude oil by rail and thereby mitigating the associated risks.

The assessment of long-term greenhouse gas emissions scenarios and societal transformation pathways is a key component of the IPCC Working Group 3 (WG3) on the Mitigation of Climate Change. A large scientific community, typically using integrated assessment models and econometric frameworks, supports this assessment in understanding both near-term actions and long-term policy responses and goals related to mitigating global warming. WG3 must systematically assess hundreds of scenarios from the literature to gain an in-depth understanding of long-term emissions pathways, across all sectors, leading to various levels of global warming. Systematic assessment and understanding the climate outcomes of each emissions scenario, requires coordinated processes which have developed over consecutive IPCC assessments. Here, we give an overview of the processes involved in the systematic assessment of long-term mitigation pathways as used in recent IPCC Assessments1 and being further developed for the IPCC 6th Assessment Report (AR6). The presentation will explain how modelling teams can submit scenarios to AR6 and invite feedback to the process.Following discussions amongst IPCC Lead Authors to define the scope of scenarios desired and variables requested, a call for scenarios to support AR6 was launched in September 2019. Modelling teams have registered and submitted scenarios through Autumn 2019 using a new and secure online submission portal, from which authorised Lead Authors can interrogate the scenarios interactively.This analysis is underpinned by the open-source software pyam, a Python package specifically designed for analysis and visualisation of integrated assessment scenarios2. Submitted scenarios are automatically checked for errors and processed using a new climate assessment pipeline. The climate assessment involves infilling and harmonization3 of emissions data, then the scenarios are processed through Simple Climate Models, using the OpenSCM framework4, to give probabilistic climate implications for each scenario – atmospheric concentrations, radiative forcing and global mean temperature. The climate assessment accounts for updated climate sensitivity estimates from CMIP6 and WG1,s scenarios are categorized according to climate outcomes and distinguish between timing and levels of net-negative emissions, emissions peak and temperature overshoot. Scenarios are also categorized by other indicators, for consistent use across WG3 chapters, such as: population and GDP; Primary and Final energy use; and shares of renewables, bioenergy and fossil fuels.The automated framework also facilitates bolt-on analyses, such as estimating the population impacted by biophysical climate impacts5, and estimates of avoided damages with the social cost of carbon6.Upon publication of the WG3 AR6 report, all scenario data used in the WG3 Assessment will be publicly available on a Scenario Explorer, an online tool for interrogating and visualizing the data that supports the report. In combination, this framework brings new levels of consistency, transparency and reproducibility to the assessment of scenarios in IPCC WG3 and will be a key resource for the climate community in understanding the main drivers of different transformation pathways.Huppmman et al 2018, Nature Climate Change Gidden and Huppmann, 2019, Journal of Open Source Software Gidden et al 2018 Environ. Model. Softw Nicholls et al 2020 Byers et al 2018 Environmental Research Letters Ricke et al 2018 Nature Climate Change

The ambition of the openENTRANCE project is to develop and establish an open, transparent and integrated modelling platform for assessing low-carbon transition pathways of the European energy system. In this context, the open source energy system model GENeSYS-MOD is one of the core models having been developed enabling quantitative scenario pathway studies of the future European energy system. The four quantitative studies presented in the openENTRANCE project and in this paper build upon the four storylines developed at the beginning of the openENTRANCE project. A storyline is a narrative describing possible future trajectories (pathways) of the energy transition. Storylines should be understood as possible future developments of the European energy system, which could occur equally without having a preference for one of them. Three of the storylines, and subsequently quantified scenario pathway studies in openENTRANCE comply with the (European fraction of the) 1.5 ∘C global temperature increase limit. The fourth one approaches the 2.0 ∘C limit. The quantified scenario pathway results not only show the needs of the fully open energy system model GENeSYS-MOD to find feasible solutions of the underlying analytical optimization problem, but more importantly highlight what needs to be done in the future European energy system if we seriously intend to limit global warming! This is a post-peer-review, pre-copyedit version of an article. Locked until 14/10-2021 due to copyright restrictions. The final authenticated version is available online at: http://dx.doi.org/10.1007/s00502-020-00832-7

Exploring potential future pathways for developing Asia’s energy consumption, CO2 emissions and infrastructure investment needs is essential to understanding how the countries of this rapidly growing region may contribute to the global climate targets set out in the 2015 Paris Agreement. To this end, this study employs the state-of-the-art global integrated assessment model MESSAGEix-GLOBIOM to investigate mid-century decarbonization strategies for developing Asia to 2050. Our results indicate that a radical change in the energy portfolio is required to reach the target of ‘well below’ 2°C. Specifically, our scenarios point to a rapid reduction of fossil fuel utilization, enhancement of low-carbon energy supply, and boosting of energy efficiency efforts. Such a transformation leads to a deep cut in CO2 emissions by 78% and 93% by 2050 in scenarios consistent with the 2°C and 1.5°C targets, respectively. Electricity generation and final energy consumption become dominated by low-carbon energy by 2050 in these scenarios. In terms of investment needs beyond a baseline scenario, the 2°C and 1.5°C pathways imply that the scale of low-carbon investment may need to double and triple, respectively. These increases would be partially offset by disinvestment in coal, oil and natural gas extraction and conversion infrastructure. Decarbonizing the energy system also impacts the capital needed for making progress on other sustainable development goals (SDGs), such as air pollution, clean water and food security.

To understand how global warming can be kept well-below 2°C and even 1.5°C, climate policy uses scenarios that describe how society could transform in order to reduce its greenhouse gas emissions. Such scenario are typically created with integrated assessment models that include a representation of the economy, and the energy, land-use, and industrial system. However, current climate change scenarios have a key weakness in that they typically focus on reaching specific climate goals in 2100 only. This choice results in risky pathways that delay action and seemingly inevitably rely on large quantities of carbon-dioxide removal after mid-century. Here we propose a framework that more closely reflects the intentions of the UN Paris Agreement. It focusses on reaching a peak in global warming with either stabilisation or reversal thereafter. This approach provides a critical extension of the widely used Shared Socioecononomic Pathways (SSP) framework and reveals a more diverse picture: an inevitable transition period of aggressive near-term climate action to reach carbon neutrality can be followed by a variety of long-term states. It allows policymakers to explicitly consider near-term climate strategies in the context of intergenerational equity and long-term sustainability.

The open-source Python package pyam provides a suite of features and methods for the analysis, validation and visualization of reference data and scenario results generated by integrated assessment models, macro-energy tools and other frameworks in the domain of energy transition, climate change mitigation and sustainable development. It bridges the gap between scenario processing and visualisation solutions that are "hard-wired" to specific modelling frameworks and generic data analysis or plotting packages. The package aims to facilitate reproducibility and reliability of scenario processing, validation and analysis by providing well-tested and documented methods for working with timeseries data in the context of climate policy and energy systems. It supports various data formats, including sub-annual resolution using continuous time representation and "representative timeslices". The pyam package can be useful for modelers generating scenario results using their own tools as well as researchers and analysts working with existing scenario ensembles such as those supporting the IPCC reports or produced in research projects. It is structured in a way that it can be applied irrespective of a user's domain expertise or level of Python knowledge, supporting experts as well as novice users. The code base is implemented following best practices of collaborative scientific-software development. This manuscript describes the design principles of the package and the types of data which can be handled. The usefulness of pyam is illustrated by highlighting several recent applications.

Constrained transmission capacity in electricity networks may give generators the possibility to game the market by specifically causing congestion and thereby appropriating excessive rents. Investment in network capacity can ameliorate such behavior by reducing the potential for strategic behavior. However, modeling Nash equilibria between generators, which explicitly account for their impact on the network, is mathematically and computationally challenging. We propose a three-stage model to describe how network investment can reduce market power exertion: a benevolent planner decides on network upgrades for existing lines anticipating the gaming opportunities by strategic generators. These firms, in turn, anticipate their impact on market-clearing prices and grid congestion. In this respect, we provide the first model endogenizing the trade-off between the costs of grid investment and benefits from reduced market power potential in short-run market clearing. In a numerical example using a three-node network, we illustrate three distinct effects: firstly, by reducing market power exertion, network expansion can yield welfare gains beyond pure efficiency increases. Anticipating gaming possibilities when planning network expansion can push welfare close to a first-best competitive benchmark. Secondly, network upgrades entail a relative shift of rents from producers to consumers when congestion rents were excessive. Thirdly, investment may yield suboptimal or even disequilibrium outcomes when strategic behavior of certain market participants is neglected in network planning.

Natural gas gained significant attention due to its low carbon emissions and competitive prices in North America relative to other energy sources. The Annual Energy Outlook 2015 projects the U.S. as a net exporter by 2017. Recently, Mexico launched its energy reform, aiming to expand domestic production by opening the market to private investors. The success or failure of these policy changes will impact the development of the natural gas market in North America. To analyze possible pathways of the Mexican energy reform, we develop the North American Natural Gas Model (NANGAM). NANGAM is a long-term partial equilibrium model that allows for endogenous infrastructure expansion and non-linear cost functions. NANGAM is calibrated using the most recent data available from U.S., Canadian, and Mexican sources. We find that, in order to reduce pipeline imports, Mexico depends on economic incentives that lower barriers to infrastructure investment and keep production costs at competitive levels. If reforms to guarantee these incentives are not successfully implemented, growing gas demand in Mexico will be satisfied by further supply from Texas and neighboring states. This will cause a ripple-effect of increasing production in other regions in the U.S. and a shift in trade flows across the continent.

The upheaval in global crude oil markets and the boom in shale oil production in North America brought scrutiny on the US export ban for crude oil from 1975. The ban was eventually lifted in early 2016. This paper examines the shifts of global trade flows and strategic refinery investments in a spatial, game-theoretic partial equilibrium model. We consider detailed oil supply chain infrastructure with multiple crude oil types, distinct oil products, as well as specific refinery configurations and modes of transport. Prices, quantities produced and consumed, as well as infrastructure and refining capacity investments are endogenous to the model. We compare two scenarios: an insulated US crude oil market, and a counter-factual with lifted export restrictions. We find a significant expansion of US sweet crude exports with the lift of the export ban. In the US refinery sector, more (imported) heavy sour crude is transformed. Countries importing US sweet crude gain from higher product output, while avoiding costly refinery investments. Producers of heavy sour crude (e.g. the Middle East) are incentivised to climb up the value chain to defend their market share and maintain their dominant position.

Energy systems support technical solutions fulfilling the United Nations’ Sustainable Development 2 Goal for clean water and sanitation (SDG6), with implications for future energy demands and greenhouse 3 gas emissions. The energy sector is also a large consumer of water, making water efficiency targets in4 grained in SDG6 important constraints for long-term energy planning. Here, we apply a global integrated 5 assessment model to quantify the cost and characteristics of infrastructure pathways balancing SDG6 tar6 gets for water access, scarcity, treatment and efficiency with long-term energy transformations limiting climate warming to 1.5 ◦ 7 C. Under a mid-range human development scenario, we find that approximately 8 1 trillion USD2010 per year is required to close water infrastructure gaps and operate water systems consistent with achieving SDG6 goals by 2030. Adding a 1.5 ◦ 9 C climate policy constraint increases these costs by up to 8 %. In the reverse direction, when the SDG6 targets are added on top of the 1.5 ◦ 10 C policy 11 constraint, the cost to transform and operate energy systems increases 2 to 9 % relative to a baseline 1.5 ◦ 12 C scenario that does not achieve the SDG6 targets by 2030. Cost increases in the SDG6 pathways 13 are due to expanded use of energy-intensive water treatment and costs associated with water conserva14 tion measures in power generation, municipal, manufacturing and agricultural sectors. Combined global spending (capital and operational expenditures) in the integrated SDG6-1.5 ◦ 15 C scenarios to 2030 on water and energy systems increases 92 to 125 % relative to a baseline scenario without 1.5 ◦ 16 C and SDG6 17 constraints. Evaluation of the multi-sectoral policies underscores the importance of water conservation 18 and integrated water-energy planning for avoiding costs from interacting water, energy and climate goals.

In the version of ‘Supplementary Data 1’ originally published with this Article, the units for the ‘Capacity|Electricity|*’ variables in the ‘Non_Investment_Annual’ tab were incorrectly given as EJ/yr; they should have read GW. This has now been corrected. Also, some of the variables listed in the ‘Non_Investment_Variable_Defs’ were not required and have therefore been removed.

Scenarios that limit global warming to 1.5 °C describe major transformations in energy supply and ever-rising energy demand. Here, we provide a contrasting perspective by developing a narrative of future change based on observable trends that results in low energy demand. We describe and quantify changes in activity levels and energy intensity in the global North and global South for all major energy services. We project that global final energy demand by 2050 reduces to 245 EJ, around 40% lower than today, despite rises in population, income and activity. Using an integrated assessment modelling framework, we show how changes in the quantity and type of energy services drive structural change in intermediate and upstream supply sectors (energy and land use). Down-sizing the global energy system dramatically improves the feasibility of a low-carbon supply-side transformation. Our scenario meets the 1.5 °C climate target as well as many sustainable development goals, without relying on negative emission technologies. Achieving sustainable development goals while meeting the 1.5 °C climate target requires radical changes to how we use energy. A scenario of low energy demand shows how this can be done by down-sizing the global energy system to enable feasible deployment rates of renewable energy resources.

Low-carbon investments are necessary for driving the energy system transformation that is called for by both the Paris Agreement and Sustainable Development Goals. Improving understanding of the scale and nature of these investments under diverging technology and policy futures is therefore of great importance to decision makers. Here, using six global modelling frameworks, we show that the pronounced reallocation of the investment portfolio required to transform the energy system will not be initiated by the current suite of countries’ Nationally Determined Contributions. Charting a course toward ‘well below 2 °C’ instead sees low-carbon investments overtaking fossil investments globally by around 2025 or before and growing thereafter. Pursuing the 1.5 °C target demands a marked upscaling in low-carbon capital beyond that of a 2 °C-consistent future. Actions consistent with an energy transformation would increase the costs of achieving the goals of energy access and food security, but reduce the costs of achieving air-quality goals. The scale and nature of energy investments under diverging technology and policy futures is of great importance to decision makers. Here, a multi-model study projects investment needs under countries’ nationally determined contributions and in pathways consistent with achieving the 2 °C and 1.5 °C targets as well as certain SDGs.

International audience Scenarios showing future greenhouse gas emissions are needed to estimate climate impacts and the mitigation efforts required for climate stabilization. Recently, the Shared Socioeconomic Pathways (SSPs) have been introduced to describe alternative social, economic and technical narratives, spanning a wide range of plausible futures in terms of challenges to mitigation and adaptation1. Thus far the key drivers of the uncertainty in emissions projections have not been robustly disentangled. Here we assess the sensitivities of future CO2 emissions to key drivers characterizing the SSPs. We use six state-of-the-art integrated assessment models with different structural characteristics, and study the impact of five families of parameters, related to population, income, energy efficiency, fossil fuel availability, and low-carbon energy technology development. A recently developed sensitivity analysis algorithm2 allows us to parsimoniously compute both the direct and interaction effects of each of these drivers on cumulative emissions. The study reveals that the SSP assumptions about energy intensity and economic growth are the most important determinants of future CO2 emissions from energy combustion, both with and without a climate policy. Interaction terms between parameters are shown to be important determinants of the total sensitivities.

This article proposes a new electricity storage business model based on multiple simultaneously considered revenue streams, which can be attributed to different market activities and players. These players thus share electricity storage resources and compete to obtain the right to use them in a dynamic allocation mechanism. It is based on the design of anew periodically organized auction to allocate shared storage resources through physical storage rights between different market players and ac-companying applications. Through such a flexibility platform owners of flexible resources can commercialize their flexible capacity over different applications, while market players looking for additional flexibility can obtain this through a pay-per-use principle and thus not having to make long-term investment commitments. As such, they can quickly adapt their portfolio according to the market situation. Alternatively, through such an allocation mechanism players can effectively share storage re-sources. Players may be incentivized to participate as they can share the investment cost, mitigate risk, exploit economies of scale, overcome regulatory barriers, and merge time-varying and player-dependent flexibility needs. The mechanism allocates the limited storage resources to the most valuable application for each market-clearing, based on the competing players' willingness-to-pay. An illustrative case study is provided in which three players share storage resources that are allocated through a daily auction with hourly market-clearings.