Merit order or unit-commitment: How does thermal power plant modeling affect storage demand in energy system models?

Cebulla, Felix; Fichter, Tobias

doi:10.1016/j.renene.2016.12.043

Cited by 49 publications

(17 citation statements)

References 40 publications

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“…The approach however seems sufficient, since the importance of the degree of detail in conventional power-plant modeling decreases with increasing share of RE generation. Moreover, as shown by Cebulla and Fichter [62], in highly renewable energy scenario for large observations areas (e.g. Europe) LP modeling for thermal power plants is sufficient when analyzing storage expansion.…”

Section: Electricity Generation From Fossil-fired and Nuclear Power-pmentioning

confidence: 98%

Electrical energy storage in highly renewable European energy systems: Capacity requirements, spatial distribution, and storage dispatch

Cebulla

Naegler

Pohl

2017

Journal of Energy Storage

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139

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One of the major challenges of renewable energy systems is the inherently limited dispatchability of power generators that rely on variable renewable energy (VRE) sources. To overcome this insufficient system flexibility, electrical energy storage (EES) is a promising option. The first contribution of our work is to address the role of EES in highly renewable energy system in Europe. For this purpose, we apply the energy system model REMix which endogenously determines both capacity expansion and dispatch of all electricity generation as well as storage technologies. We derive an EES capacity of 206 GW and 30 TWh for a system with a renewable share of 95%. An extensive sensitivity analysis shows that ESS requirements range from 126 GW and 16 TWh (endogenous grid expansion) to 272 GW and 54 TWh (low EES investment costs). As our second contribution, we show how the spatial distribution of EES capacity depends on the residual load, which-in turn-is influenced by regionally predominant VRE technologies and their temporal characteristics in terms of power generation. In this sense, frequent periods of high VRE excess require short-term EES, which naturally feature low power-related investment costs. In contrast, long-term EES with low energy-related costs are characteristic for regions where high amounts of surplus energy occur. This relationship furthermore underlines how EES capacity distribution is implicitly influenced technical potentials for VRE expansion.

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Section: Electricity Generation From Fossil-fired and Nuclear Power-pmentioning

confidence: 98%

Electrical energy storage in highly renewable European energy systems: Capacity requirements, spatial distribution, and storage dispatch

Cebulla

Naegler

Pohl

2017

Journal of Energy Storage

Self Cite

139

View full text Add to dashboard Cite

show abstract

“…Often, these analyses compare simplified linear programming approaches (ED) with more detailed mixed integer linear programming (UC) models for least cost power plant dispatch. As a result, such studies assess differences in power plant dispatch (e.g., in [42][43][44][45]) and, additionally, highlight effects on resulting metrics (e.g., storage requirements in [46] or marginal prices of electricity generation in [47,48]).…”

Section: Technological Aggregationmentioning

confidence: 99%

Classification and Evaluation of Concepts for Improving the Performance of Applied Energy System Optimization Models

et al. 2019

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Energy system optimization models used for capacity expansion and dispatch planning are established tools for decision-making support in both energy industry and energy politics. The ever-increasing complexity of the systems under consideration leads to an increase in mathematical problem size of the models. This implies limitations of today’s common solution approaches especially with regard to required computing times. To tackle this challenge many model-based speed-up approaches exist which, however, are typically only demonstrated on small generic test cases. In addition, in applied energy systems analysis the effects of such approaches are often not well understood. The novelty of this study is the systematic evaluation of several model reduction and heuristic decomposition techniques for a large applied energy system model using real data and particularly focusing on reachable speed-up. The applied model is typically used for examining German energy scenarios and allows expansion of storage and electricity transmission capacities. We find that initial computing times of more than two days can be reduced up to a factor of ten while having acceptable loss of accuracy. Moreover, we explain what we mean by “effectiveness of model reduction” which limits the possible speed-up with shared memory computers used in this study.

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“…The result is that the energy system would need more flexibility options than the model suggests. The flexibility of nuclear, coal and gas fired power plants and CCS is overestimated whereby the storage demand is underestimated [17]. In contrast, the flexibility of CSP is not underestimated because its components are designed to cover the demand flexible [18].…”

Section: Excursion: Empirical Probability Of Ccsmentioning

confidence: 99%

The empirical probability of integrating CSP and its cost optimal configuration in a low carbon energy system of EUMENA

Hess

2018

Solar Energy

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Ongoing cost reduction of low carbon energies allows an increasing implementation of such technologies for climate protection targets. How decreasing cost develop in the future is high-grade uncertain. Thus cost sensitivities analyses of an expert based cost-range are needed to show if specific technologies can be cost advantageous for an energy system. In this paper a low carbon and cost-optimal energy system of Europe, Middle East and North Africa (EUMENA) in the year 2050 is analysed with regard to concentrating solar power (CSP). Cost sensitivity analyses show how frequently an integration of CSP in the energy system is. This frequency is defined as empirical probability of technological integration (EPTI). An energy system model allows a suitable analysis framework with tangible competitive technologies such as renewable energies, nuclear power plants and carbon capture and storage (CCS). As a highlight the EPTI of an export of CSP from MENA to EU via point-to-point high voltage direct current (HVDC) transmission lines is analysed. Such CSP-HVDC power plants show an EPTI of up to 66%. CSP in MENA and southern EU show an average EPTI of 85%, nuclear power plants of 32% and CCS of 50%. The cost sensitivity analysis shows additionally the cost optimal configuration of CSP and CSP-HVDC.This clarifies the role of CSP and CSP-HVDC for the energy system as a dispatchable base and medium load power plant depending on the region inside EUMENA.

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Merit order or unit-commitment: How does thermal power plant modeling affect storage demand in energy system models?

Cited by 49 publications

References 40 publications

Electrical energy storage in highly renewable European energy systems: Capacity requirements, spatial distribution, and storage dispatch

Electrical energy storage in highly renewable European energy systems: Capacity requirements, spatial distribution, and storage dispatch

Classification and Evaluation of Concepts for Improving the Performance of Applied Energy System Optimization Models

The empirical probability of integrating CSP and its cost optimal configuration in a low carbon energy system of EUMENA

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