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Evaluating retrospective performance of energy system models in 31 European countries

Dr. Xin Wen, University of Geneva, Switzerland.

Dr. Xin Wen, University of Geneva, Switzerland.

- 1. RENEWABLE ENERGY SYSTEMS Evaluating retrospective performance of electricity system models in 31 European countries Xin Wen, Marc Jaxa-Rozen, and Evelina Trutnevyte Renewable Energy Systems Group, University of Geneva ETSAP semi-annual workshop 23–24 May 2022, Freiburg Grant no. 186834 (ACCURACY)
- 2. RENEWABLE ENERGY SYSTEMS 2 Retrospective modeling and model evaluation q Retrospective modeling can be used for model evaluation to see what can be learnt from how accurately the model captures the real-world transition and how we can improve models. q Example: Comparison of the cost-optimal scenario and the real- world transition in the UK. The research gaps q Retrospective modeling § Until now, there is barely any model-based retrospective assessment that has been done simultaneously for multiple countries to allow more generic findings. Source: Trutnevyte E. (2016) Energy Cost deviation: 16% Cost-optimal scenario Real-world transition The total installed capacity and total costs in UK q Model evaluation § Based on our literature review, there is no systematic summary of why certain accuracy indicators should be chosen. Background
- 3. RENEWABLE ENERGY SYSTEMS 3 Research questions q What accuracy indicators have been used in literature so far? q What will be the proper small suite of accuracy indicators for retrospective energy modeling considering the following properties: § Effectiveness: They provide all the essential and correct information on accuracy evaluation; § Robustness: They can be used for data covering diverse units and orders of magnitude; § Compatibility: They can be used to compare the results across countries; § Diversity: Each indicator differs from the other selected indicators and has its own added value. Background
- 4. RENEWABLE ENERGY SYSTEMS 4 D-EXPANSE model q D-EXPANSE (Dynamic version of EXploration of PAtterns in Near- optimal energy ScEnarios) (Trutnevyte, 2016) is a cost optimization-based national electricity system model with bottom-up structure, perfect foresight, customized technologies, representation of both capacity planning and operation, and endogenous demand function. q D-EXPANSE is typically used with Modeling to Generate Alternatives, but here we only apply cost optimization. q We exclude energy policies and emission targets in the model. Historic data of the national electricity system transitions in 31 European countries in 1990–2019 Jaxa-Rozen et al. (2022). Inputs Model outputs Installed capacity and annual generation of technologies: solar PV, onshore wind, hard coal, gas, etc. Electricity demand under price elasticity Total costs, investment costs, CO2 emissions Cost optimization-based national outputs 31 national D-EXPANSE models Method
- 5. RENEWABLE ENERGY SYSTEMS 5 Modeled vs. real-world transition in Austria Real-world transition Installed capacity (GW) Annual generation (TWh / year) Modeled cost-optimal scenario with endogenous demand Results
- 6. RENEWABLE ENERGY SYSTEMS 6 Modeled vs. real-world transition in Austria Real-world transition Modeled cost-optimal scenario with endogenous demand Installed capacity (GW) Annual generation (TWh / year) Electricity demand (TWh) Cumulative CO2 emissions (thousand tons) More oil and coal Less gas, onshore wind, biomass Results
- 7. RENEWABLE ENERGY SYSTEMS 7 Model evaluation: Quantifying the accuracy indicators for each model output Model errors 24 different accuracy indicators to quantify the errors between model outputs and real-world transition (One example: annual endogenous demand in Austria in 1990-2019) Results Minimum value Maximum value
- 8. RENEWABLE ENERGY SYSTEMS 8 Model evaluation: Correlation-based dissimilarity analysis Accuracy indicators 31 countries ´ 30 time slices ´ all relevant model outputs of each country = 28’800 samples of accuracy indicators Find the most effective and informative set of indicators to compare the accuracy of various quantities and countries sMPE: symmetric mean percentage error An indicator of direction and magnitude of deviation sMAPE: symmetric mean absolute percentage error An absolute indicator of magnitude of relative deviation without cancelation effect sMdAPE: symmetric median absolute percentage error A median indicator and hence it is more robust to outliers RMSLE: root-mean-squared logarithmic error A squared indicator of absolute deviation instead of relative ones Growth error: Growth error shows the errors in temporal scale The small set of indicators Results Asymmetric percentage errors Scale-dependent errors Not for negative quantities
- 9. RENEWABLE ENERGY SYSTEMS 9 Model evaluation: The small set of accuracy indicators to evaluate the average errors in 1990–2019 Results Source: Wen et al. (2022) sMPE: symmetric mean percentage error sMAPE: symmetric mean absolute percentage error sMdAPE: symmetric median absolute percentage error RMSLE: root-mean-squared logarithmic error
- 10. RENEWABLE ENERGY SYSTEMS 10 Model evaluation: The small set of accuracy indicators Results Cyprus Source: Wen et al. (2022) sMPE: symmetric mean percentage error sMAPE: symmetric mean absolute percentage error sMdAPE: symmetric median absolute percentage error RMSLE: root-mean-squared logarithmic error
- 11. RENEWABLE ENERGY SYSTEMS 11 Model evaluation: The small set of accuracy indicators Results Norway Croatia Source: Wen et al. (2022) sMPE: symmetric mean percentage error sMAPE: symmetric mean absolute percentage error sMdAPE: symmetric median absolute percentage error RMSLE: root-mean-squared logarithmic error
- 12. RENEWABLE ENERGY SYSTEMS 12 Model evaluation: The small set of accuracy indicators Results Germany Source: Wen et al. (2022) sMPE: symmetric mean percentage error sMAPE: symmetric mean absolute percentage error sMdAPE: symmetric median absolute percentage error RMSLE: root-mean-squared logarithmic error
- 13. RENEWABLE ENERGY SYSTEMS 13 Model evaluation: The small set of accuracy indicators Results sMPE: symmetric mean percentage error sMAPE: symmetric mean absolute percentage error sMdAPE: symmetric median absolute percentage error RMSLE: root-mean-squared logarithmic error Ireland Source: Wen et al. (2022)
- 14. RENEWABLE ENERGY SYSTEMS 14 Key findings Perspectives q For all countries, the cost-optimal scenarios show a similar tendency of: § Endogenous demand estimations with higher accuracy than emissions. § Underestimation of: o renewable technologies due to their high investment costs (energy policy and emission targets are excluded). o total installed capacity in most countries, except for Cyprus, Iceland and Norway. § Overestimation of nuclear power (Belgium, Spain, Hungary, Latvia, Netherlands, Poland, Slovenia). § The errors are smaller in countries with more centralized low-carbon generation since 1990, compared to countries with an increasing share of decentralized technologies deployment. e.g. France, Norway vs. Denmark. q This is a first step towards developing a model accuracy testbench to assess energy models and scenarios retrospectively in multiple dimensions. q The chosen five accuracy indicators are informative, effective, and complement each other q The retrospective modeling in multiple countries made it possible to systematically assess the applicability of these accuracy indicators. q The accuracy performance of the models does not only depend on the quality of data, but also on the structural formulation of the model.
- 15. RENEWABLE ENERGY SYSTEMS Thank you Xin Wen, Marc Jaxa-Rozen, and Evelina Trutnevyte Renewable Energy Systems, University of Geneva Website: www.unige.ch/res xin.wen@unige.ch ETSAP semi-annual workshop 23–24 May 2022, Freiburg Grant no. 186834 (ACCURACY)
- 16. RENEWABLE ENERGY SYSTEMS 16 References Trutnevyte E. (2016). Does cost optimization approximate the real-world energy transition? Energy 2016;106:182–93. https://doi.org/10.1016/j.energy.2016.03.038. Jaxa-Rozen M, Wen X, Trutnevyte E. (2022). Historic data of the national electricity system transitions in Europe in 1990–2019 for retrospective evaluation of models. Data in Brief. Under review. Jaxa-Rozen M, Wen X, Trutnevyte E. (2022). Historic data of the national electricity system transitions in Europe in 1990–2019 for retrospective evaluation of models [dataset]. DOI: 10.5281/zenodo.6338417. Wen X, Jaxa-Rozen M, Trutnevyte E. (2022). Accuracy indicators for evaluating retrospective performance of energy system models. Under review.

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