Planning the defossilization of energy systems while maintaining access to abundant primary energy resources is a non-trivial multi-objective problem encompassing economic, technical, environmental, and social aspects. However, most long-term policies consider the cost of the system as the leading indicator in the energy system models to decrease the carbon footprint. This paper is the first to develop a novel approach by adding a surrogate indicator for the social and economic aspects, the energy return on investment (EROI), in a whole-energy system optimization model. In addition, we conducted a global sensitivity analysis to identify the main parameters driving the EROI uncertainty. This method is illustrated in the 2035 Belgian energy system for several greenhouse gas (GHG) emissions targets. Nevertheless, it can be applied to any worldwide or country energy system. The main results are threefold when the GHG emissions are reduced by 80%: (i) the EROI decreases from 8.9 to 3.9; (ii) the imported renewable gas (methane) represents 60 % of the system primary energy mix; (iii) the sensitivity analysis reveals this fuel drives 67% of the variation of the EROI. These results raise questions about meeting the climate targets without adverse socio-economic impact, demonstrating the importance of considering the EROI in energy system models.
Studying a large number of scenarios is necessary to consider the uncertainty inherent to the energy transition. In addition, the integration of intermittent renewable energy sources requires complex energy system models. Typical days clustering is a commonly used technique to ensure the computational tractability of energy system optimisation models, while keeping an hourly time step. Its capability to accurately approximate the full-year time series with a reduced number of days has been demonstrated (i.e., a priori evaluation). However, its impact on the results of the energy system model (i.e., a posteriori evaluation) is rarely studied and was never studied on a multi-regional whole-energy system. To address this issue, the multi-regional whole-energy system optimisation model, EnergyScope Multi-Cells, is used to optimise the design and operation of multiple interconnected regions. It is applied to nine diverse cases with different numbers of typical days. A bottom-up a posteriori metric, the design error, is developed and analysed in these cases to find trade-offs between the accuracy and the computational cost of the model. Using 10 typical days divides the computational time by 8.6 to 23.8, according to the case, and ensures a design error below 17%. In all cases studied, the time series error is a good prediction of the design error. Hence, this a priori metric can be used to select the number of typical days for a new case study without running the energy system optimisation model.
Emerging economies are experiencing significant growth, which implies a booming demand for energy, especially electricity. In order to meet the 2050 climate target, this growth will have to rely mainly on renewable energy. This contrasts with the fossil fuel-based growth experienced by developed countries. This paper analyses, for the case of Uganda, the difference between a fossil fuel-based energy development and leapfrogging to a renewable one. The analysis covers all energy sectors (electricity, heat and mobility) and shows that priority should be given to heat and mobility. Results show that the cheapest growth is based on fossil energies. Nevertheless, favouring renewable energy is not far from being competitive; not to mention the other positive impacts such as increasing energy sovereignty, increasing national employment and addressing climate change. The work estimates a penalty of 15-30 C/ton of CO 2 equivalent is sufficient to achieve the competitiveness of a highly sustainable society.
Planning the defossilization of energy systems by facilitating high penetration of renewables and maintaining access to abundant and affordable primary energy resources is a nontrivial multi-objective problem encompassing economic, technical, environmental, and social aspects. However, so far, most long-term policies to decrease the carbon footprint of our societies consider the cost of the system as the leading indicator in the energy system models. This paper is the first to develop a novel approach by adding the energy return on investment (EROI) in a whole energy system optimization model. We built the database with all EROI technologies and resources considered while keeping the core components of the model: open access, multi-energy carriers, short computational time, and accounting for all the energy sectors. In addition, moving away from fossil-based to carbon-neutral energy systems raises the issue of the uncertainty of low-carbon technologies and resource data. Thus, we conducted a global sensitivity analysis to identify the main parameters driving the variations in the EROI of the system. This novel approach can be applied to any energy system at the worldwide, country, or regional level. We use a real-world case study to illustrate the model: the 2035 Belgian energy system for several greenhouse gas emissions targets.The main results are threefold: (i) the EROI of the system decreases from 8.9 to 3.9 when greenhouse gas emissions are reduced by 5; (ii) the renewable fuels -mainly imported renewable gas -represent the largest share of the system primary energy mix due to the lack of endogenous renewable resources such as wind and solar; (iii) in the sensitivity analysis, the renewable fuels drive 67% of the variation of the EROI of the system for low greenhouse gas emissions scenarios.The decrease in the EROI raises questions about meeting the climate targets without adverse socio-economic impact. Most countries rely massively on fossil fuels, like Belgium, and they could encounter an EROI decline when shifting to carbon neutrality. Therefore, this study demonstrates the importance of considering other criteria, such as the EROI, in energy system models. It helps to nuance the cost-based results to better guide policy-makers in addressing the challenges of the energy transition.
To decrabonise the entire energy system, introduction of large shares of variable renewable electricity generation will be needed. Long term energy system planing models are useful to improve the understanding of the decarbonisation pathways but struggle to take into the account the short term variations associated with the increased penetration of variable renewables. This can generate misleading signals regarding the levels of flexibility required in the system. This paper addresses this gap by a innovative bi-directional soft-linking methodology between a long term whole-energy system planing model (EnergyScope) and a multi-sectoral unit commitment and power dispatch model (Dispa-SET). The proposed methodology assesses the integration of short term variability, sizes the flexibility needs and analyses its strengths, limitations and applicability. Results of this study show that convergence criteria of the bi-directional soft-linking are met within two iterations meaning that the newly proposed system is stable and reliable.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.