Analysis of the Belgian electricity mix used in environmental life cycle assessment studies: how reliable is the ecoinvent 3.1 mix?

Buyle, Matthias; Anthonissen, Joke; Bergh, Wim van den; Braet, Johan; Audenaert, Amaryllis

doi:10.1007/s12053-018-9724-7

Cited by 16 publications

(23 citation statements)

References 25 publications

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“…It would be beneficial to the field of CLCA to replicate this approach to other sectors in the ecoinvent database. Extending this work to other areas should be carefully tested as this method has been sometimes described in the literature as too simplistic and unable to capture certain complex dynamics (Mathiesen et al 2009;Buyle et al 2018). Furthermore, it is dependent on the availability of scenarios describing the future states of each specific sector which are, in many cases, not available.…”

Section: Discussionmentioning

confidence: 99%

The integration of long-term marginal electricity supply mixes in the ecoinvent consequential database version 3.4 and examination of modeling choices

Vandepaer

Treyer

Mutel

et al. 2018

Int J Life Cycle Assess

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Purpose The long-term marginal electricity supply mixes of 40 countries were generated and integrated into version 3.4 of the ecoinvent consequential database. The total electricity production originating from these countries accounts for 77% of the current global electricity generation. The goal of this article is to provide an overview of the methodology used to calculate the marginal mixes and to evaluate the influence of key parameters and methodological choices on the results. Methods The marginal mixes are based on public energy projections from national and international authorities and reflect the accumulated effect of changes in demand for electricity on the installation and operation of new-generation capacities. These newly generated marginal mixes are first examined in terms of their compositions and environmental impacts. They are then compared to several sets of alternative electricity supply mixes calculated using different methodological choices or data sources. Results and discussion Renewable energy sources (RES) as well as natural gas power plants show the highest growth rates and usually dominate the marginal mixes. Nevertheless, important variations may exist between the marginal mixes of the different countries in terms of their technological compositions and environmental impacts. The examination of the modeling choices reveals substantial variations between the marginal mixes integrated into the ecoinvent consequential database version 3.4 and marginal mixes generated using alternative modeling options. These different modeling possibilities include changes in the methodology, temporal parameters, and the underlying energy scenarios. Furthermore, in most of the impact categories, average (i.e., attributional) mixes cause higher impact scores than marginal mixes due to higher shares of RES in marginal mixes. Conclusions Accurate and consistent data for electricity supply is integrated into a consequential database providing a strong basis for the development of consequential Life Cycle Assessments. The methodology adopted in this version of the database eliminates several shortcomings from the previous approach which led to unrealistic marginal mixes in several countries. The use of energy scenarios allows the evolution of the electricity system to be considered within the definition of the marginal mixes. The modeling choices behind the electricity marginal mix should be adjusted to the goal and scope of individual studies and their influence on the results evaluated. Keywords CLCA. Ecoinvent. Life cycle inventory (LCI) database. Long-term marginal electricity supply mix 1 Using the IPCC Global Warming Potentials (GWP) characterization factors with a time horizon of 100 years and the calculation approach detailed in Mutel (2018).

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Section: Discussionmentioning

confidence: 99%

The integration of long-term marginal electricity supply mixes in the ecoinvent consequential database version 3.4 and examination of modeling choices

Vandepaer

Treyer

Mutel

et al. 2018

Int J Life Cycle Assess

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“…In this paper, data were gathered from the statistical database of the ENTSO-E, which collects data directly from the different TSOs, and ensures the quality and validation of data coming from real sources, according to [50]. Additionally, databases used under the LCI for electricity modeling in this research paper (i.e., GaBi R Software Professional Database and ecoinvent 3.1) are validated by external entities and publications [19,45].…”

Section: Discussionmentioning

confidence: 99%

“…The functional unit for the environmental impact assessment is defined as one kWh of electricity produced and delivered to the grid, which is in line with previous studies of the environmental impacts of electricity generation [19,[26][27][28][29][30]. The impact category chosen for the assessment was global warming potential (GWP), using the CML 2015 life cycle impact assessment method [48].…”

Section: Goal and Scopementioning

confidence: 99%

“…GHG emissions accounted by the electricity sector are calculated based on techniques that include absolute carbon emissions and average carbon intensity, as stated by Khan in [18]. This was the case in [19], which assessed the Belgian low-voltage electricity mix using life cycle assessment (LCA) approaches, resulting in average environmental impacts, to check the quality of the datasets from the European Network of Transmission System Operators for Electricity (ENTSO-E). Additionally, ecoinvent 3.1.…”

Section: Introductionmentioning

confidence: 99%

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The Potential Role of Flexibility During Peak Hours on Greenhouse Gas Emissions: A Life Cycle Assessment of Five Targeted National Electricity Grid Mixes

et al. 2019

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On the path towards the decarbonization of the electricity supply, flexibility and demand response have become key factors to enhance the integration of distributed energy resources, shifting the consumption from peak hours to off-peak hours, optimizing the grid usage and maximizing the share of renewables. Despite the technical viability of flexible services, the reduction of greenhouse gas emissions has not been proven. Traditionally, emissions are calculated on a yearly average timescale, not providing any information about peak hours’ environmental impact. Furthermore, peak-hours’ environmental impacts are not always greater than on the base load, depending on the resources used for those time periods. This paper formulates a general methodology to assess the potential environmental impact of peak-hourly generation profiles, through attributional life cycle assessment. This methodology was applied to five different countries under the INVADE H2020 Project. Evaluation results demonstrate that countries like Spain and Bulgaria could benefit from implementing demand response activities considering environmental aspects, enhancing potential greenhouse gas reductions by up to 21% in peak hours.

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“…Only [53] applies a prospective MEF with the linear regression method in a highly simplified manner. The authors use historical gradients of the linear fit for groups of power plants with the same energy carrier use and extrapolate these to the composition of power production in future scenarios for Belgium.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Prospective Average and Marginal GHG Emission Factors—Scenario-Based Method for the German Power System until 2050

Seckinger

Radgen

2021

Energies

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Due to the continuous diurnal, seasonal, and annual changes in the German power supply, prospective dynamic emission factors are needed to determine greenhouse gas (GHG) emissions from hybrid and flexible electrification measures. For the calculation of average emission factors (AEF) and marginal emission factors (MEF), detailed electricity market data are required to represent electricity trading, energy storage, and the partial load behavior of the power plant park on a unit-by-unit, hourly basis. Using two normative scenarios up to 2050, different emission factors of electricity supply with regard to the degree of decarbonization of power production were developed in a linear optimization model through different GHG emission caps (Business-As-Usual, BAU: −74%; Climate-Action-Plan, CAP: −95%). The mean hourly German AEF drops to 182 gCO2eq/kWhel (2018: 468 gCO2eq/kWhel) in the BAU scenario by the year 2050 and even to 29 gCO2eq/kWhel in the CAP scenario with 3700 almost emission-free hours from power supply per year. The overall higher MEF decreases to 475 and 368 gCO2eq/kWhel, with a stricter emissions cap initially leading to a higher MEF through more gas-fired power plants providing base load. If the emission intensity of the imported electricity differs substantially and a storage factor is implemented, the AEF is significantly affected. Hence, it is not sufficient to use the share of RES in net electricity generation as an indicator of emission intensity. With these emission factors it is possible to calculate lifetime GHG emissions and determine operating times of sector coupling technologies to mitigate GHG emissions in a future flexible energy system. This is because it is decisive when lower-emission electricity can be used to replace fossil energy sources.

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Analysis of the Belgian electricity mix used in environmental life cycle assessment studies: how reliable is the ecoinvent 3.1 mix?

Cited by 16 publications

References 25 publications

The integration of long-term marginal electricity supply mixes in the ecoinvent consequential database version 3.4 and examination of modeling choices

The integration of long-term marginal electricity supply mixes in the ecoinvent consequential database version 3.4 and examination of modeling choices

The Potential Role of Flexibility During Peak Hours on Greenhouse Gas Emissions: A Life Cycle Assessment of Five Targeted National Electricity Grid Mixes

Dynamic Prospective Average and Marginal GHG Emission Factors—Scenario-Based Method for the German Power System until 2050

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