A Time Step Energy Process Model for Germany - Model Structure and Results

Martinsen, Dag; Krey, Volker; Markewitz, Peter; Vögele, Stefan

doi:10.15173/esr.v14i1.480

Cited by 35 publications

(23 citation statements)

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“…Energy system models are broadly applied in energy economics to assist in making investment decisions and/or the dispatching of power plants based on assumptions of the future energy system [1][2][3][4]. In the years to come, the complexity of the energy system will increase due to a raising share of renewable resources and the decentralisation of the electricity provision [5].…”

Section: Introductionmentioning

confidence: 99%

Reducing computing time of energy system models by a myopic approach

et al. 2013

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In this paper, the performance of the existing energy system model PERSEUS-NET is improved in terms of computing time. Therefore, the possibility of switching from a perfect foresight to a myopic approach has been implemented. PERSEUS-NET is a linear optimization model generating scenarios of the future German electricity generation system until 2030, whilst considering exogenous regional characteristics such as electricity demand and existing power plants as well as electricity transmission network restrictions. Up to now, the model has been based on a perfect foresight approach, optimizing all variables over the whole time frame in a single run, thus determining the global optimum. However, this approach results in long computing times due to the high complexity of the problem. The new myopic approach splits the optimization into multiple, individually smaller, optimization problems each representing a 5 year period. The change within the generation system in each period is determined by optimizing the subproblem, whilst taking into account only the restrictions of that particular period. It was found that the optimization over the whole time frame with the myopic approach takes less than one tenth of the computing time of the perfect foresight approach. Therefore, we analyse in this paper the advantages and draw-backs of a change in the foresight as a way of reducing the complexity of energy system models. For PERSEUS-NET it is found that the myopic approach with stable input parameters is as suitable as the perfect foresight approach to generate consistent scenarios, with the advantage of significantly less computing time.

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Section: Introductionmentioning

confidence: 99%

Reducing computing time of energy system models by a myopic approach

et al. 2013

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“…Some recent models have started to elaborate with iterative limited foresight (Martinsen, et al 2004, Nyqvist, 2005. These models do not find the optimal energy system from a social planner's perspective, but they are better suited at simulating market behaviour.…”

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confidence: 99%

Induced Technological Change in a Limited Foresight Optimization Model

2005

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in Induced Technological Change in a Limited Foresight Optimization Model SummaryThe threat of global warming calls for a major transformation of the energy system the coming century. Modeling technological change is an important factor in energy systems modeling. Technological change may be treated as induced by climate policy or as exogenous. We investigate the importance of induced technological change (ITC) in GET-LFL, an iterative optimization model with limited foresight that includes learningby-doing. Scenarios for stabilization of atmospheric CO2 concentrations at 400, 450, 500 and 550 ppm are studied. We find that the introduction of ITC reduces the total net present value of the abatement cost over this century by 3-9% compared to a case where technological learning is exogenous. Technology specific polices which force the introduction of fuel cell cars and solar PV in combination with ITC reduce the costs further by 4-7% and lead to significantly different technological solutions in different sectors, primarily in the transport sector.

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“…Model users can mimic non-cost barriers to technology diffusion by using hurdle rates and heterogeneous decision making [114], incorporating behavioural demand responses to higher energy prices using price elasticities of demand [115], or implementing myopic foresight (e.g. [116], [117]). This paper has illustrated one such approach in a system dynamic model that combines all of the above elements, but further research is clearly needed for these techniques to become widespread.…”

Section: Implications For Research and Science-policy Discoursementioning

confidence: 99%

Actors behaving badly: Exploring the modelling of non-optimal behaviour in energy transitions

2017

Energy Strategy Reviews

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There are real political and social barriers to climate mitigation that arise from multiactor dynamics and micro-economic decisions. Exploratory analysis that captures key uncertainties in the energy system, including behaviour, is crucial for policy design aimed at achieving ambitious greenhouse gas (GHG) mitigation targets. This paper explores the case for developing policy assessments that include non-optimal behaviour in energy systems modelling. A stochastic system dynamic model of the energy system that features multiple actors with differentiated behaviours is used to investigate energy transition pathways that deviate from strict economic rationality. The results illustrate the risks of basing GHG reduction strategies on analysis that overlooks key insights into decision making from fields such as behavioural economics and political science. KeywordsEnergy systems modeling; climate policy; decarbonization; behavioral economics; uncertainty; Highlights• Dynamic stochastic model of energy transitions used to explore actor behaviour • Uncertainty analysis explores impact of sub-optimal actor choices and policies • Behaviour could be as significant as the techno-economic drivers of transitions • Climate targets difficult to achieve without idealised assumptions about behaviour • Energy systems modelling must expand consideration of socio-political factors

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A Time Step Energy Process Model for Germany - Model Structure and Results

Cited by 35 publications

References 1 publication

Reducing computing time of energy system models by a myopic approach

Reducing computing time of energy system models by a myopic approach

Induced Technological Change in a Limited Foresight Optimization Model

Actors behaving badly: Exploring the modelling of non-optimal behaviour in energy transitions

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