Coupling techno-economic energy models with behavioral approaches

Fragnière, Emmanuel; Kanala, Roman; Moresino, Francesco; Reveiu, Adriana; Smeureanu, Ion

doi:10.1007/s12351-016-0246-9

Cited by 15 publications

(25 citation statements)

References 41 publications

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“…Social and behavioural factors are import to assess the adoption of renewable technologies [57,58] or the representation of consumer's real behaviour in classical energy models [59]. For example, social acceptance has a relevant impact on grid and wind power expansion [60,61].…”

Section: Interdisciplinary Modellingmentioning

confidence: 99%

Addressing Energy System Modelling Challenges: The Contribution of the Open Energy Modelling Framework (oemof)

Hilpert¹,

Günther²,

Kaldemeyer³

et al. 2017

Preprint

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These authors contributed equally to this work. AbstractThe process of modelling energy systems is accompanied by challenges inherently connected with mathematical modelling. However, due to modern realities in the 21st century, existing challenges are gaining in magnitude and are supplemented with new ones. Modellers are confronted with a rising complexity of energy systems and high uncertainties on different levels. In addition, interdisciplinary modelling is necessary for getting insight in mechanisms of an integrated world. At the same time models need to meet scientific standards as public acceptance becomes increasingly important. In this intricate environment model application as well as result communication and interpretation is also getting more difficult.In this paper we present the open energy modelling framework (oemof) as a novel approach for energy system modelling and derive its contribution to existing challenges. Therefore, based on literature review, we outline challenges for energy system modelling as well as existing and emerging approaches. Based on a description of the philosophy and elementary structural elements of oemof, a qualitative analysis of the framework with regard to the challenges is undertaken. Inherent features of oemof such as the open source, open data, non-proprietary and collaborative modelling approach are preconditions to meet modern realities of energy modelling. Additionally, a generic basis with an object-oriented implementation allows to tackle challenges related to complexity of highly integrated future energy systems and sets the foundation to address uncertainty in the future. Experiences from the collaborative modelling approach can enrich interdisciplinary modelling activities.Our analysis concludes that there are remaining challenges that can neither be tackled by a model nor a modelling framework. Among these are problems connected to result communication and interpretation.

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Section: Interdisciplinary Modellingmentioning

confidence: 99%

Addressing Energy System Modelling Challenges: The Contribution of the Open Energy Modelling Framework (oemof)

Hilpert¹,

Günther²,

Kaldemeyer³

et al. 2017

Preprint

View full text Add to dashboard Cite

show abstract

“…Social and behavioural factors are important to assess the adoption of renewable technologies [60,61] or the representation of consumers' real behaviour in energy models [62]. For example, social acceptance has a relevant impact on grid and wind power expansion [63,64].…”

Section: Interdisciplinary Modellingmentioning

confidence: 99%

A Qualitative Evaluation Approach for Energy System Modelling Software—Case Study Results for the Open Energy Modelling Framework (Oemof)

Hilpert¹,

Kaldemeyer²,

Wiese³

et al. 2017

Preprint

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Abstract:The research field of energy system analysis is dealing with increasingly complex energy systems and their respective challenges. Moreover, the requirement for open science has become a focal point of public interest. Both drivers have triggered the development of a broad range of (open) energy models and frameworks in recent years. However, there are hardly any approaches on how to evaluate these tools in terms of their capabilities to tackle energy system modelling challenges. This paper describes a first step towards a flexible evaluation of software to model energy systems. We propose a qualitative approach as an useful supplementary to existing model fact sheets and transparency checklists. We demonstrate the applicability by evaluating the newly developed "Open Energy Modelling Framework" with respect to existing challenges in energy system modelling. The case study results highlight that challenges related to complexity and scientific standards can be tackled to a large extent while the challenges of model utilization and interdisciplinary modelling are only tackled partially. However, the challenge of uncertainty remains for the most part unaddressed at present. Advantages of the evaluation approach lie in its simplicity, flexibility and transferability to other tools. Disadvantages mostly stem from its qualitative nature. Our analysis reveals that some challenges in the field of energy system modelling cannot be addressed by a software as they are on meta level like model result communication and interdisciplinary modelling.

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“…The family of models that were originally bottom-up includes, among others, MARKAL (Market Allocation) [8,9] and TIMES (The Integrated MARKAL-EFOM System) [10,11] developed under the aegis of the International domains [49][50][51]. Following this idea, [52] combined a classical energy model with the share of choice model. This approach, which combines a behavioural method with operational research, has however two drawbacks.…”

Section: Introductionmentioning

confidence: 99%

“…Thirdly, the market shares are input into the energy model using a specific modelling. Figure 1 schematizes the approach presented in this paper as opposed to the one proposed in [52]. Coupling techno-economic energy models with behavioural approaches.…”

Section: Introductionmentioning

confidence: 99%

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Combining Behavioral Approaches with Techno-Economic Energy Models: Dealing with the Coupling Non-Linearity Issue

Moresino

Fragnière

2018

Energies

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Consumer behaviour is often complex and even sometimes not economically rational. Wrongly, the first techno-economic energy planning models assumed the economic rationality hypothesis and, therefore, represented consumers' behaviour incorrectly. Nevertheless, the current trend is to couple these models with behavioural approaches that were specially developed to describe the real consumer choices. A novel approach was recently proposed, where a classical energy model is coupled with a share of choice model. This new approach has however two weaknesses. First, the share of choice increases the computational complexity as it necessitates additional binary variables for the modelling. Second, for complex models, the inclusion of the share of choice can lead to non-linearity and hence to severe computational problems. In the present paper, we propose to improve this method by externalizing the share of choice. Doing so, the number of binary variable will be reduced and the linearity property will be kept even for complex models.

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Coupling techno-economic energy models with behavioral approaches

Cited by 15 publications

References 41 publications

Addressing Energy System Modelling Challenges: The Contribution of the Open Energy Modelling Framework (oemof)

Addressing Energy System Modelling Challenges: The Contribution of the Open Energy Modelling Framework (oemof)

A Qualitative Evaluation Approach for Energy System Modelling Software—Case Study Results for the Open Energy Modelling Framework (Oemof)

Combining Behavioral Approaches with Techno-Economic Energy Models: Dealing with the Coupling Non-Linearity Issue

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Coupling techno-economic energy models with behavioral approaches

Cited by 15 publications

References 41 publications

Addressing Energy System Modelling Challenges: The Contribution of the Open Energy Modelling Framework (oemof)

Addressing Energy System Modelling Challenges: The Contribution of the Open Energy Modelling Framework (oemof)

A Qualitative Evaluation Approach for Energy System Modelling Software&mdash;Case Study Results for the Open Energy Modelling Framework (Oemof)

Combining Behavioral Approaches with Techno-Economic Energy Models: Dealing with the Coupling Non-Linearity Issue

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A Qualitative Evaluation Approach for Energy System Modelling Software—Case Study Results for the Open Energy Modelling Framework (Oemof)