Improving the representation of modal choice into bottom-up optimization energy system models – The MoCho-TIMES model

Tattini, Jacopo; Ramea, Kalai; Gargiulo, Maurizio; Yang, Christopher; Mulholland, Eamonn; Yeh, Sonia; Karlsson, Kenneth Bernard

doi:10.1016/j.apenergy.2017.12.050

Cited by 49 publications

(22 citation statements)

References 28 publications

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“…In addition to disaggregation, discount and hurdle rates, the TAM-Households model also makes use of budget constraints to reflect variations in the investment and consumption behaviour of different actors depending on their socio-economic circumstances [42]. Budget and time constraints are factors influencing investment and consumption behaviour of various actors in the model, and have additionally been considered, particularly for households [42,125] and transport [41,126]. This caps the available budget for investment and/or consumption for specific actors and thereby provides the cost-optimal technology choices by a more diverse share of actors.…”

Section: Model Integrationmentioning

confidence: 99%

Integrating Methods and Empirical Findings from Social and Behavioural Sciences into Energy System Models—Motivation and Possible Approaches

et al. 2020

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The transformation of the energy system is a highly complex process involving many dimensions. Energy system models help to understand the process and to define either target systems or policy measures. Insights derived from the social sciences are not sufficiently represented in energy system models, but address crucial aspects of the transformation process. It is, therefore, necessary to develop approaches to integrate results from social science studies into energy system models. Hence, as a result of an interdisciplinary discourse among energy system modellers, social scientists, psychologists, economists and political scientists, this article explains which aspects should be considered in the models, how the respective results can be collected and which aspects of integration into energy system models are conceivable to provide an overview for other modellers. As a result of the discourse, five facets are examined: Investment behaviour (market acceptance), user behaviour, local acceptance, technology innovation and socio-political acceptance. Finally, an approach is presented that introduces a compound of energy system models (with a focus on the macro and micro-perspective) as well as submodels on technology genesis and socio-political acceptance, which serves to gain a more fundamental knowledge of the transformation process.

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Section: Model Integrationmentioning

confidence: 99%

Integrating Methods and Empirical Findings from Social and Behavioural Sciences into Energy System Models—Motivation and Possible Approaches

et al. 2020

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“…Concerning the second approach, there are several methods to include behavior in E4 models. For instance, [11,45,46] emulate modal shift by integrating the concept of travel time budget and transport infrastructure, [47,48] introduce endogenous modal choice through modelling modal level of service and consumers' decisions, while [49] adopts substitution elasticities to enable modal shift. These approaches include different levels of transport behavior in E4 models and thus offer different capabilities, and require different data sources.…”

Section: Transport Behaviormentioning

confidence: 99%

“…Other studies differentiate between urban and non-urban transportation in national E4 models. One example is given by [47], which provides a modelling design characterizing transportation across the urban, suburban and rural areas for Denmark.…”

Section: Geographical Aggregation and System Boundariesmentioning

confidence: 99%

Energy Scenario Analysis for the Nordic Transport Sector: A Critical Review

et al. 2019

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Experiencing the highest growth in emissions since 1990 and relying mainly on oil, transport is considered the most complicated sector to decarbonize. Lately, the Nordic countries have shown remarkable success in reducing greenhouse gas (GHG) emissions, especially in the power and heat sector. However, when it comes to transportation, the greatest source of Nordic GHG emissions, stronger measures are needed. Relying on a rich and diversified portfolio of renewable sources and expertise, the Nordic countries could benefit from a common mitigation strategy by encompassing a larger variety of solutions and potential synergies. This article reviews studies addressing integrated energy and transport scenario analysis for the Nordic region as a whole. The studies targeted are those applying energy system models, given their extensive adoption in supporting scenario analysis. Most notable of these studies is the “Nordic Energy Technology Perspectives 2016” to which a special focus is dedicated. The article reviews the methodological choices and the research content of the selected literature. Challenges/limitations are identified in light of recent transport research, and categorized as: “transport behavior”, “breakthrough technologies”, “domestic energy resources” and “geographical aggregation and system boundaries”. Lastly, a list of suggestions to tackle the identified gaps is provided based on the existing literature.

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“…Based on these equations, the total fixed annual costs, K QPfix , are described in Equations (10) and (11).…”

Section: Analytical and Mathematical Approachmentioning

confidence: 99%

“…For the consideration of social factors, the incorporation of consumer behavior in energy system models represents an essential aspect. An approach to this is presented, for instance, in the TIMES (The Integrated MARKAL-EFOM System) model, which aims at extending the model details by integrating the heterogeneity of consumers [9,10]. On the other side, an example of uncertainties in the assumptions on technological developments is the future investment costs of technologies [11,12] Minimal changes to these assumptions on developments may induce marked differences in the resulting shares of technologies in the energy system.…”

Section: Introductionmentioning

confidence: 99%

Cost Uncertainties in Energy System Optimization Models: A Quadratic Programming Approach for Avoiding Penny Switching Effects

et al. 2019

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Designing the future energy supply in accordance with ambitious climate change mitigation goals is a challenging issue. Common tools for planning and calculating future investments in renewable and sustainable technologies are often linear energy system models based on cost optimization. However, input data and the underlying assumptions of future developments are subject to uncertainties that negatively affect the robustness of results. This paper introduces a quadratic programming approach to modifying linear, bottom-up energy system optimization models to take cost uncertainties into account. This is accomplished by implementing specific investment costs as a function of the installed capacity of each technology. In contrast to established approaches such as stochastic programming or Monte Carlo simulation, the computation time of the quadratic programming approach is only slightly higher than that of linear programming. The model’s outcomes were found to show a wider range as well as a more robust allocation of the considered technologies than the linear model equivalent.

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Improving the representation of modal choice into bottom-up optimization energy system models – The MoCho-TIMES model

Cited by 49 publications

References 28 publications

Integrating Methods and Empirical Findings from Social and Behavioural Sciences into Energy System Models—Motivation and Possible Approaches

Integrating Methods and Empirical Findings from Social and Behavioural Sciences into Energy System Models—Motivation and Possible Approaches

Energy Scenario Analysis for the Nordic Transport Sector: A Critical Review

Cost Uncertainties in Energy System Optimization Models: A Quadratic Programming Approach for Avoiding Penny Switching Effects

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