Representing node-internal transmission and distribution grids in energy system models

A recent article 'Burden of proof: A comprehensive review of the feasibility of 100% renewable-electricity systems' claims that many studies of 100% renewable electricity systems do not demonstrate sufficient technical feasibility, according to the criteria of the article's authors (henceforth 'the authors'). Here we analyse the authors' methodology and find it problematic. The feasibility criteria chosen by the authors are important, but are also easily addressed at low economic cost, while not affecting the main conclusions of the reviewed studies and certainly not affecting their technical feasibility. A more thorough review reveals that all of the issues have already been addressed in the engineering and modelling literature. Nuclear power, which the authors have evaluated positively elsewhere, faces other, genuine feasibility problems, such as the finiteness of uranium resources and a reliance on unproven technologies in the medium-to long-term. Energy systems based on renewables, on the other hand, are not only feasible, but already economically viable and decreasing in cost every year.

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“…Many studies that do not model the grid, do include blanket costs for grid expansion (e.g. from surveys such as [121][122][123]).…”

Section: Their Feasibility Criterion 3: Transmission and Distributionmentioning

confidence: 99%

“…Another study for Germany with 100% renewable electricity showed that grid expansion at transmission and distribution level would cost around 4-6 billion A C/a (with a big uncertainty range reaching from 1 to 12 billion A C/a) [123].…”

Section: Their Feasibility Criterion 3: Transmission and Distributionmentioning

confidence: 99%

Response to ‘Burden of proof: A comprehensive review of the feasibility of 100% renewable-electricity systems’

Brown

Bischof-Niemz

Blok

et al. 2018

Renewable and Sustainable Energy Reviews

385

220

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“…The analysis is performed in a "greenfield" approach. The transmission and distribution grid inside a region is considered according to [14] with specific grid values available in Table 19.…”

Section: Empirical Probability Analysis Within An Energy System Modelmentioning

confidence: 99%

“…The model is capable of making conclusions of grid expansion and curtailment of PV and wind energy in an optimized energy system. The region internal grid model is explained in [14]. The model uses two parameters to quantify the grid: start point of grid expansion in relation to peak load and specific cost per feed-in power of photovoltaics and wind turbines.…”

Section: Node-internal Transmission and Distribution Grid 821mentioning

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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“…This calculation provides a part of the basis to calculate the overall transmission grids efficiency, however no spatially georeferenced data was gathered but the calculations rather took place in a laboratory environment. Hess et al (2018) take a different look at the problem. Their hypothesis is that with the introduction of renewable energies and multiple new generation points within the grid, major grid expansions will take place, whose costs need to be considered when modelling the energy system.…”

Section: Introductionmentioning

confidence: 99%

A spatially explicit assessment of middle and low voltage grid requirements in Bavaria until 2050

et al. 2019

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The energy transition towards high shares of renewables and the continued urbanization process have a direct and strong impact on the shape and characteristics of the electricity transmission and distribution systems. At the continental and national scale, improved high voltage grids should allow the transmission and balance of electricity from hot-spots of variable renewable energy generation installations to demand centres. At the regional and municipal scale, the medium and low voltage grids should be capable of bringing sufficient electricity to users and allow the integration of distributed renewable generation installations. While data on the transmission systems is widely available, spatial and attribute data of the medium and mainly the low voltage grids are scarce. Additionally, while there are plenty of studies on the requirements of the grid to allow the energy transition, there is very little information on the necessary transformation of the grid due to changes generated by the expected urbanization process. This study relies on a data set that estimates the topology of the medium and low voltage grids of Bavaria (Germany) as well as data from the LUISA territorial modelling platform of the European Commission to calculate key figures of grid requirements depending on population and land use for the current case and the decades to come. Typologies of grid requirements are proposed based on a statistical analysis of population and land use data of each square kilometre of the federal state. These typologies are extrapolated to changes in the structure of settlements that are expected in the years 2030 and 2050. Results are presented using maps with expected absolute values of grid requirements and their temporal changes for each square kilometre of the project area. Grid requirements are expected to increase in cities and to be reduced in most of the rural areas. The largest changes are expected to take place in the suburbs of the major cities. Highlights for public administration, management and planning: • Medium and low voltage grid shapes and lengths are estimated for the entire federal state of Bavaria, Germany. • On average, distribution grid length requirements per person are between 13 and 16 times larger in rural regions than in city centres. • While city centres and suburbs expect an increase in grid requirements, the total grid length of Bavaria is expected to decrease in the near future. • Suburbs of large cities are not only expected to change steadily but also to show the largest changes in grid length requirements until 2050.

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Representing node-internal transmission and distribution grids in energy system models

Cited by 17 publications

References 10 publications

Response to ‘Burden of proof: A comprehensive review of the feasibility of 100% renewable-electricity systems’

Response to ‘Burden of proof: A comprehensive review of the feasibility of 100% renewable-electricity systems’

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

A spatially explicit assessment of middle and low voltage grid requirements in Bavaria until 2050

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