Modeling for insight using Tools for Energy Model Optimization and Analysis (Temoa)

Hunter, Kevin; Sreepathi, Sarat; DeCarolis, Joseph F.

doi:10.1016/j.eneco.2013.07.014

Cited by 97 publications

(64 citation statements)

References 23 publications

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“…Mirakyan and Guio miss a 'common agreement on typology of uncertainty' [6]. They propose a new framework that has a broader scope and a more detailed classification compared to uncertainties described by Pfenninger et al [3] and Hunter et al [5]. This framework for categorization of uncertainty incorporates energy system modelling, decision making and subsequent planning processes: (1) linguistic uncertainty, (2) knowledge or epistemic uncertainty, (3) variability or aleatory uncertainty, (4) decision uncertainty, (5) planning procedural uncertainty and (6) level of uncertainty.…”

Section: Uncertaintymentioning

confidence: 99%

“…Uncertainty in terms of energy system modelling can be classified into a number of types. Generally, literature has different scopes, approaches, and scientific backgrounds to classify uncertainty which results in different classification schemes [3,5,6]. Mirakyan and Guio miss a 'common agreement on typology of uncertainty' [6].…”

Section: Uncertaintymentioning

confidence: 99%

“…For example, models need to cover a high temporal and spatial resolution of distributed energy systems with renewable energies while simultaneously looking at long time horizons and large geographical areas [3]. Different types of uncertainty may also affect results obtained from energy system models [4][5][6]. Furthermore, energy is a driver of economic growth [7] and interacts with essential goods like water and food [8].…”

Section: Introductionmentioning

confidence: 99%

“…Several approaches for handling some of these challenge exist. Those range from addressing uncertainty [5], integrated modelling [17], model-linking [18][19][20] to transparent modelling [11,21].…”

Section: Introductionmentioning

confidence: 99%

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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: Uncertaintymentioning

confidence: 99%

Section: Uncertaintymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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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“…TIMES is possibly the most widely used general purpose ESM [7,14]. Alternative models following a similar structure such as TEMOA [16] or OSeMOSYS [17] are also considered.…”

Section: Energy System Models: Origin and Strengthsmentioning

confidence: 99%

Integrating Energy System Models in Life Cycle Management

Astudillo

Vaillancourt

Pineau

et al. 2018

Designing Sustainable Technologies, Products and Policies

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The energy supply chain is the backbone of industrialised societies, but it is also one of the leading causes of global environmental burden. Life cycle management (LCM) and life cycle assessment (LCA) are increasingly being used in combination with energy system optimisation models (ESOM) to better represent the energy sector and its dynamics, and facilitate better decision-making. The integration of ESOM and LCA can enable powerful analyses, but not without difficulties. In this chapter, we review studies linking a well-known bottom-up ESOM (TIMES) with LCA databases and identify the principal challenges and how they have been addressed. One of the main integration challenges is the identification of equivalent processes between life cycle inventories and ESOM databases: the mapping problem. Other concomitant issues such as double counting and parameter consistency have been identified and are also investigated.

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