M. Chen
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Pathways that describe the optimal evolution of energy systems across multiple decades are important in energy system research and policy literature, with net-zero and similar climate policies being common drivers behind them. While there are many studies on aspects such as spatial and operational resolution, model features, and model transparency, there has been little attention on the methodological considerations of formulating pathway studies in mathematical optimisation terms, and how these methods have evolved over time. To address this, we conduct a systematic review of optimal pathway literature at or above the national level focusing on the following: i) the implications of model foresight choices, ii) end effects and related issues that may bias model outcomes, iii) trade-offs in model resolution, and iv) investment dynamics. We showcase how modellers have dealt with these aspects in a large sample of studies spanning multiple decades, and provide recommendations to both modellers and model users on identifying issues that can bias model results and how to improve upon them. In particular, we identify opportunities to better balance long-term anticipatory planning with high operational and spatial detail in models, and to improve the communication and systematic treatment of those mathematical design choices that potentially distort model decisions across time.
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Pathways that describe the optimal evolution of energy systems across multiple decades are important in energy system research and policy literature, with net-zero and similar climate policies being common drivers behind them. While there are many studies on aspects such as spatial and operational resolution, model features, and model transparency, there has been little attention on the methodological considerations of formulating pathway studies in mathematical optimisation terms, and how these methods have evolved over time. To address this, we conduct a systematic review of optimal pathway literature at or above the national level focusing on the following: i) the implications of model foresight choices, ii) end effects and related issues that may bias model outcomes, iii) trade-offs in model resolution, and iv) investment dynamics. We showcase how modellers have dealt with these aspects in a large sample of studies spanning multiple decades, and provide recommendations to both modellers and model users on identifying issues that can bias model results and how to improve upon them. In particular, we identify opportunities to better balance long-term anticipatory planning with high operational and spatial detail in models, and to improve the communication and systematic treatment of those mathematical design choices that potentially distort model decisions across time.
High-resolution energy system models, as powerful tools to represent energy systems in detail and assist energy transition planning, rarely account for economic disparity, unlike broader-scale tools such as integrated assessment models. In this study, by analysing net-zero European energy system designs through the lens of national gross domestic product (GDP) and household average income, we find that disparity-unaware high-resolution energy system models can produce results that are technically feasible but largely incompatible with economic realities. The investment in household heating technology may be disproportional to the income level of lower-income countries. Explicitly acknowledging economic disparity in such models reduces the danger of them proposing solutions which burden economically disadvantaged actors. Therefore, here, we explicitly include national GDP and household income disparity in a model for net-zero European energy system designs. We find that disparity-compatible systems are possible with a 1.1% total system cost increase compared to the least-cost ones. Unlike in disparity-unaware system designs, where energy infrastructure investments often reach over 20% of national GDP for some countries, we develop disparity-compatible designs which limit investments to below 5% in each country. Our results show that less affluent European countries may need substantial household-level financing to support their heating transition and to diversify their net-zero energy technology choices.
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High-resolution energy system models, as powerful tools to represent energy systems in detail and assist energy transition planning, rarely account for economic disparity, unlike broader-scale tools such as integrated assessment models. In this study, by analysing net-zero European energy system designs through the lens of national gross domestic product (GDP) and household average income, we find that disparity-unaware high-resolution energy system models can produce results that are technically feasible but largely incompatible with economic realities. The investment in household heating technology may be disproportional to the income level of lower-income countries. Explicitly acknowledging economic disparity in such models reduces the danger of them proposing solutions which burden economically disadvantaged actors. Therefore, here, we explicitly include national GDP and household income disparity in a model for net-zero European energy system designs. We find that disparity-compatible systems are possible with a 1.1% total system cost increase compared to the least-cost ones. Unlike in disparity-unaware system designs, where energy infrastructure investments often reach over 20% of national GDP for some countries, we develop disparity-compatible designs which limit investments to below 5% in each country. Our results show that less affluent European countries may need substantial household-level financing to support their heating transition and to diversify their net-zero energy technology choices.
Pathways that describe the optimal evolution of energy systems across multiple decades are important in energy system research and policy literature, with net-zero and similar climate policies being common drivers behind them. While there are many studies on aspects such as spatial and operational resolution, model features, and model transparency, there has been little attention on the methodological considerations of formulating pathway studies in mathematical optimisation terms, and how these methods have evolved over time. To address this, we conduct a systematic review of optimal pathway literature at or above national level focusing on the following: i) the implications of model foresight choices, ii) end effects and related issues that may bias model outcomes, iii) trade-offs in model resolution, and iv) investment dynamics. We showcase how modellers have dealt with these aspects in a large sample of studies spanning multiple decades, and provide recommendations to both modellers and model users on identifying issues that can bias model results and how to improve upon them. In particular, we identify opportunities to better balance long-term anticipatory planning with high operational and spatial detail in models, and to improve the communication and systematic treatment of those mathematical design choices that potentially distort model decisions across time.
...
Pathways that describe the optimal evolution of energy systems across multiple decades are important in energy system research and policy literature, with net-zero and similar climate policies being common drivers behind them. While there are many studies on aspects such as spatial and operational resolution, model features, and model transparency, there has been little attention on the methodological considerations of formulating pathway studies in mathematical optimisation terms, and how these methods have evolved over time. To address this, we conduct a systematic review of optimal pathway literature at or above national level focusing on the following: i) the implications of model foresight choices, ii) end effects and related issues that may bias model outcomes, iii) trade-offs in model resolution, and iv) investment dynamics. We showcase how modellers have dealt with these aspects in a large sample of studies spanning multiple decades, and provide recommendations to both modellers and model users on identifying issues that can bias model results and how to improve upon them. In particular, we identify opportunities to better balance long-term anticipatory planning with high operational and spatial detail in models, and to improve the communication and systematic treatment of those mathematical design choices that potentially distort model decisions across time.