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The use of different time resolutions in OPERA: a hourly demand profiles; b transformed hourly demand profiles into profiles per time-slice; c optimization is executed using time-slices; d results on time-slice basis are transformed into hourly results for additional analysis in the post-processing stage
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This article introduces and describes OPERA, a new technology-rich bottom-up energy system optimization model for the Netherlands. We give a detailed specification of OPERA’s underlying methodology and approach, as well as a description of its multiple applications. The model has been used extensively to formulate strategic policy advice on energy...
Citations
... The final category features research that examines similar topics, but with a specific focus on a particular country or region. For instance, a number of studies examine the ability of different tools in proving insights specific to Ireland [18], Australia [19], Europe [20], and the United States [21] with regard to the macroeconomic impacts of environmental policies. ...
... ESMs often represent these aspects with detailed granularity, as exemplified by the IESA-Opt ESM, which comprises over 700 technologies distributed among multiple sectors [19]. Another example is the OPERA ESM, which represents industrial sector in about 104 subsectors [20]. To establish a link between the two models, the sectors and technologies in the ESM must be aggregated until they align with those of the macroeconomic model. ...
This research presents a thorough evaluation of macroeconomic modelling tools in the context of analysing industrial transformation. It emphasizes the need to link macroeconomic models with energy system models to accurately depict industrial transformation. The study begins with a broad survey of macroeconomic modelling tools. A detailed database of 61 tools is then compiled, providing a critical analysis of the tools' structures and features. From this broad spectrum, the focus is narrowed to Computable General Equilibrium (CGE) models. The study develops a multi-criteria analysis framework, applied specifically to four CGE modelling tools, which encompasses 19 criteria categorized under four main pillars: Industrial/Sectoral representation, Technological change, Employment, and Environment. This framework critically evaluates these tools' suitability in analysing industrial transformation, highlighting the diversity of their capabilities and limitations. Although the GEM-E3 model demonstrates a high level of alignment with the framework's criteria, none of the four tools achieves a full score in any category, indicating potential areas for improvement. The broader analysis of the database's tools reveals issues such as limited accessibility, inadequate representation of social aspects, and insufficient geographical coverage. Additionally, the study notes a general lack of transparent information concerning the full features of macroeconomic modelling tools in public literature. Concluding with recommendations for further research, the study underscores the complexities in macroeconomic modelling and the need for comprehensive tools that effectively address the multifaceted aspects of industrial transformation. Such advancements will assist in making informed decisions towards a transformation that is both environmentally and economically sustainable.
... Our modeling method is based on the use of the optimization model OPERA [34], which was developed in AIMMS 4.84 software [35], whereas our GIS model is based on QGIS 3.10 [36] and ArcMap 10.5 [37] software. OPERA is a Dutch-based national integrated energy system model, where regions within the country are inherent, i.e., hardlinked, to make it a regional model [4]. ...
... In addition to creating these land-use regions using OPERA, other onshore regions that were created in [4] were included, namely Drenthe, 1 Regarding time resolution, OPERA (and in this paper) uses a time slice approach where hours (within a year) having similar characteristics of energy demand and supply are grouped togethersee [34] for further details. ...
... In general, the distribution DH cost per unit length and losses are considerably higher than the transmission DH cost per unit length and losses. Another reason for the low DH penetration is heat savings associated 8 The OPERA model optimization details at the national level, including algorithm, design variables, and constraints, are presented in [34]. This is applicable for the electricity network. ...
Regional level energy system analyses and corresponding integrated modeling is necessary to analyze the impact of national energy policies on a regional level, while considering regional constraints related to energy infrastructure, energy supply potentials, sectoral energy demands, and their interactions. Nevertheless, current
literature on energy system analysis largely overlooks the regional level. In response, this study provided a systematic approach to refining and improving the spatial resolution of an existing regional energy system modeling framework. The methodology involved creating regions and nodes within the modeling framework
under categories corresponding to land use (cities and other regions), energy supply, and energy infrastructure. We established a unidirectional soft linking with geographical information system-based modeling results allocating spatially sensitive elements, such as renewable resources or heat demand. We provided a detailed breakdown of sectoral energy demand, supply options, and energy infrastructure for electricity and heat, including district heating (DH). This framework explicated regional differences in terms of demand–supply mismatch, supply options, and energy infrastructure. Our case study of the Dutch province of Groningen demonstrated clear differences compared to the previous crude regional model, with, e.g., an increased role of biomass (+460 % change) and decreased role of solar (- 59 %), while cities with high heat demand densities and/or compact structures exhibited serious DH penetration, ranging from 11 to 21 %. The systematic steps allow for the replication of the model in other regional analyses. Our framework is complementary for energy system analysis at the national and pan-European levels and can assist regional policymakers in decision-making.
... Similarly, the Electric Power Enterprise Control Simulator addresses the integration of variable renewable energy on multiple time-scales [42], [43]. These operational time-scale models, however, are not designed to explore the annual transformation pathways of the sustainable energy transition [26], [44], [45]. Finally, the development of multi-energy system models did not truly gain concerted attention until 2016 [46]. ...
As one of the most pressing challenges of the 21st century, global climate change demands a host of changes across four critical energy infrastructures: the electric grid, the natural gas system, the oil system, and the coal system. Unfortunately, these four systems are often studied individually, and rarely together as integrated systems. Instead, holistic multi-energy system models can serve to improve the understanding of these interdependent systems and guide policies that shape the systems as they evolve into the future. The NSF project entitled "American Multi-Modal Energy System Synthetic \& Simulated Data (AMES-3D)" seeks to fill this void with an open-source, physically-informed, structural and behavioral machine-learning model of the AMES. To that end, this paper uses a GIS-data-driven, model-based system engineering approach to develop structural models of the American Multi-Modal Energy System (AMES). This paper produces and reports the hetero-functional incidence tensor, hetero-functional adjacency matrix, and the formal graph adjacency matrix in terms of their statistics. This work compares these four hetero-functional graph models across the states of New York (NY), California (CA), Texas (TX), and the United States of America (USA) as a whole. From the reported statistics, the paper finds that the geography and the sustainable energy policies of these states are deeply reflected in the structure of their multi-energy infrastructure systems and impact the full USA's structure.
... For comparison with other renewables, we calculated the energy supply in the PJ (Table 15). For this, hourly solar irradiation for the whole of the Netherlands was averaged out, and full load hours (FLH) of rooftop PV and GBPV were explicitly calculated based on the OPERA Dutch energy system model [115]. ...
... GW. We used hourly wind speed at a hub height of 100 m for Groningen to convert capacity to energy potential, following a power velocity curve of a Vestas V100-1.8 wind turbine, based on [115]. We considered FLH corresponding to a standard single row of five turbines, resulting in a 48 PJ energy potential for the 2050 progressive scenario (Table 15). ...
Spatially sensitive regional renewables’ potentials are greatly influenced by existing land-use claims and related spatial and environmental policies. Similarly, heat particularly related to low-temperature demand applications in the built environment (BE) is highly spatially explicit. This study developed an analytical approach for a detailed spatial analysis of future solar PV, onshore wind, biomass, and geothermal and industrial waste heat potentials at a regional level and applied in the Dutch Province of Groningen. We included spatial policies, various spatial claims, and other land-use constraints in developing renewable scenarios for 2030 and 2050. We simultaneously considered major spatial claims and multiple renewable energy sources. Claims considered are the BE, agriculture, forest, nature, and network and energy infrastructure, with each connected to social, ecological, environmental, technical, economic, and policy-related constraints. Heat demand was further analyzed by creating highly granular demand density maps, comparing them with regional heat supply potential, and identifying the economic feasibility of heat networks. We analyzed the possibilities of combining multiple renewables on the same land. The 2050 renewable scenarios results ranged 2–66 PJ for solar PV and 0–48 PJ for onshore wind and biomass ranged 3.5–25 PJ for both 2030 and 2050. These large ranges of potentials show the significant impact of spatial constraints and underline the need for understanding how they shape future energy policies. The heat demand density map shows that future heat networks are feasible in large population centers. Our approach is pragmatic and replicable in other regions, subject to data availability.
... These models are helpful to compare the impact of diverse technologies and evaluate the best future pathways to reduce GHG emissions and achieve energy goals [18]. The multisector approach is required to consider the multiple linkages of sectors that are part of the increasing complexity of future energy systems [3,35,36]. Covering all sectors in one single model may accelerate the analysis process, whereas delivering a consistent method of policy assessment across sectors [27]; therefore, this aspect represents one of the most relevant considerations in modelling planning. ...
... In addition, EnergyScope TD is another open-source ESOM with an hourly resolution (utilizing typical days), which makes the framework appropriate for evaluation of intermittent renewables integration, and whose compact mathematical formulation and computing efficiency are suitable for uncertainty applications [38]. However, it does not enable analyzing the transformation pathways, as in the OPERA model, which was specifically designed for The Netherlands [35]. ...
... Another critical element in this group is the fossil fuel prices due to their influence on the competitiveness of the conversion technologies [60] and the same demand behavior. On the supply side, different techniques can assess irradiation levels [61] and wind speeds [62] and estimate the potential of renewable energy [35], as well as the data on biomass and geothermal must be gathered to assess energy potentials. In addition, the existing and planned plant capacities must be determined as the starting point of energy mix configuration employing the national energy balance. ...
Energy planning is fundamental to ensure a sustainable, affordable, and reliable energy mix for the future. Energy system optimization models (ESOMs) are the accurate tools to guide decision-making in national energy planning. This article presents a systematic literature review covering the main ESOMs, the input and output data involved, the trends in scenario analysis for decarbonization pathways in national economies, and the challenges associated with energy system optimization modelling. The first part introduces the characterization of ESOMs, showing a trend in modelling focused on long-term, multisector, multiperiod, bottom-up, linear programming, and perfect foresight. Secondly, the analysis shows the intensive data requirements, including future demand profiles, fuel price projections, energy potentials, and techno-economic characteristics of technologies. This review also reveals that decarbonization pathways are the principal objective in energy system optimization modelling, including key drivers such as high-share renewable energy integration, energy efficiency increase, sector coupling, and sustainable transport. The last section presents ten challenges and their corresponding opportunities in research, highlighting the improvement of spatiotemporal resolution, transparency, the inclusion of social aspects, the representation of developing country features, and quality and availability data.
... Consequently, the calculated LCOEs do not correctly reflect the cost of system-wide constraints such as flexibility supply investments, operational constraints, cross-border electricity trade, and infrastructure limitations. Additionally, the ENCO report is criticized with four major drawbacks [19] : assuming high solar and wind costs, ignoring the meritorder curve, deviating from Dutch energy policies, and the absence of system-wide analyses. ...
... The TNO report (2022) concludes that nuclear power can play a complementary role with sun and wind to satisfy high electricity demands in long-term [18] . This TNO report used the OPERA optimization model [19] , which uses time-slices in comparison with hourly temporal resolution. Table 1 summarizes the reviewed studies' major methodological shortcomings and knowledge as follows: (1) The system-wide implica- tions of nuclear power in a transition to a net-zero energy system is barely discussed. ...
... Many of these assumed potentials influence transition costs, notably, potentials for renewable energy sources (including biomass) and CO 2 storage. The reference scenario bases the storylines of these potentials on the ENSPRESO reference scenario for biomass [44] and TNO's OPERA model reference scenario [19] . The prices for oil and coal are obtained by extrapolating the price estimates provided by Dutch energy outlook (KEV2020 [99] ). ...
To analyze the role of nuclear power in an integrated energy system, we used the IESA-Opt-N cost minimization model focusing on four key themes: system-wide impacts of nuclear power, uncertain technological costs, flexible generation, and cross-border electricity trade. We demonstrate that the LCOE alone should not be used to demonstrate the economic feasibility of a power generation technology. For instance, under the default techno-economic assumptions, particularly the 5% discount rate and exogenous electricity trade potentials, it is cost-optimal for the Netherlands to invest in 9.6 GWe nuclear capacity by 2050. However, its LCOE is 34 €/MWh higher than offshore wind. Moreover, we found that nuclear power investments can reduce demand for variable renewable energy sources in the short term and higher energy independence (i.e., lower imports of natural gas, biomass, and electricity) in the long term. Furthermore, investing in nuclear power can reduce the mitigation costs of the Dutch energy system by 1.6% and 6.2% in 2040 and 2050, and 25% lower national CO2 prices by 2050. However, this cost reduction is not significant given the odds of higher nuclear financing costs and longer construction times. In addition, with 3% interest rate value (e.g., EU taxonomy support), even high cost nuclear (10 B€/GW) can be cost-effective in the Netherlands. In conclusion, under the specific assumptions of this study, nuclear power can play a complementary role (in parallel to the wind and solar power) in supporting the Dutch energy transition from the sole techno-economic point of view.
... This report concludes that nuclear is not a cost-effective option for the Netherlands based on the results from two other reports: the Berenschot study [13] (already described) and a TNO scenario study [17]. The TNO scenario study used the OPERA optimization model [18] and shows no role of nuclear power in the Dutch energy mix. ...
... Many of these assumed potentials influence transition costs, notably, potentials for renewable energy sources (including biomass) and CO2 storage. The reference scenario bases the storylines of these potentials on the ENSPRESO reference scenario for biomass [44] and TNO's OPERA model reference scenario [18]. The prices for oil and coal are obtained by extrapolating the price estimates provided by Dutch energy outlook (KEV2020 [48]). ...
Intending to analyze the role of nuclear power in an integrated energy system, we used the IESA-Opt-N cost minimization model focusing on four key themes: system-wide impacts of nuclear power, uncertain technological costs, flexible generation, and cross-border electricity trade. We demonstrate that the Levelized Cost of Energy (LCOE) alone should not be used to demonstrate the economic feasibility of a power generation technology. For instance, under the default techno-economic assumptions, particularly the 5% discount rate and exogenous electricity trade potentials, it is cost-optimal for the Netherlands to invest in 9.6 GWe nuclear capacity by 2050. However, its LCOE is 34 €/MWh higher than offshore wind. Moreover, we found that nuclear power investments can reduce demand for variable renewable energy sources in the short term and higher energy independence (i.e., lower imports of natural gas, biomass, and electricity) in the long term. Furthermore, investing in nuclear power can reduce the mitigation costs of the Dutch energy system by 1.6% and 6.2% in 2040 and 2050, and 25% lower national CO2 prices by 2050. However, this cost reduction is not significant given the odds of higher nuclear financing costs and longer construction times. In addition, this study has shown that lower financing costs (e.g., EU taxonomy support) considerably reduce the relevance of nuclear cost uncertainties on its investments. Furthermore, we demonstrate that the economic feasibility of national nuclear power investments can vary considerably depending on the cross-border electricity trade assumptions. Additionally, we found that lowering the cost of small modular reactors has more impact on their economic feasibility than increasing their generation flexibility. In conclusion, under the specific assumptions of this study, nuclear power can play a complementary role (in parallel to the wind and solar power) in supporting the Dutch energy transition from the sole techno-economic point of view.
... The OPERA model and model modifications This chapter provides in Section 2.1 a brief description of the OPERA model and how it is used in this study. A more detailed description of the model can be found in (Scheepers, et al., 2020) and (van Stralen, 2021). An explanation of the model most recent modifications is given in Section 2.2. ...
... Therefore, sophisticated mathematical modeling tools are required to compute the system-wide influence of techno-economic decisions [16]. Moreover, Kalavasta (2020) criticized the ENCO report with four major drawbacks [19]: assuming high solar and wind costs, ignoring the merit-order curve, deviating from Dutch energy policies, and the absence of systemwide analyses. ...
... However, this conclusion is mainly based on the results from two other reports, namely, the Berenschot study [13] (already described) and a TNO scenario study [18]. The TNO scenario study used the OPERA optimization model [19] to determine the long-term cost-optimal energy system configuration. This study shows no role of nuclear power in the Dutch energy mix. ...
... Many of these assumed potentials influence transition costs, notably, potentials for renewable energy sources (including biomass) and CO2 storage. The reference scenario bases the storylines of these potentials on the ENSPRESO reference scenario for biomass [43] and TNO's OPERA model reference scenario [19]. Table 1 shows the assumed resource and technology potentials for the reference scenario. ...
Intending to analyze the role of nuclear power in an integrated energy system, we used the IESA-Opt-N cost minimization model focusing on four key themes: system-wide impacts of nuclear power, uncertain technological costs, flexible generation, and cross-border electricity trade. We demonstrate that the Levelized Cost of Energy (LCOE) alone should not be used to demonstrate the economic feasibility of a power generation technology. For instance, under the default techno-economic assumptions, particularly the 5% discount rate and exogenous electricity trade potentials, it is cost-optimal for the Netherlands to invest in 9.6 GWe nuclear capacity by 2050. However, its LCOE is 34 €/MWh higher than offshore wind. Moreover, we found that nuclear power investments can reduce demand for variable renewable energy sources in the short term and higher energy independence (i.e., lower imports of natural gas, biomass, and electricity) in the long term. Furthermore, investing in nuclear power can reduce the mitigation costs of the Dutch energy system by 1.6% and 6.2% in 2040 and 2050, and 25% lower national CO2 prices by 2050. However, this cost reduction is not significant given the odds of higher nuclear financing costs and longer construction times. In addition, this study has shown that lower financing costs (e.g., EU taxonomy support) considerably reduce the relevance of nuclear cost uncertainties on its investments. Furthermore, we demonstrate that the economic feasibility of national nuclear power investments can vary considerably depending on the cross-border electricity trade assumptions. Additionally, we found that lowering the cost of small modular reactors has more impact on their economic feasibility than increasing their generation flexibility. In conclusion, under the specific assumptions of this study, nuclear power can play a complementary role (in parallel to the wind and solar power) in supporting the Dutch energy transition from the sole techno-economic point of view.
... The aim of this study is to meet these needs by calculating energy scenarios with a cost-optimised energy system model, and to show the impact of social developments and policy choices on the development of the energy system and on overall energy system costs. Although the energy system model used for this study has already been described in the scientific literature [26], a low-emissions scenario study with this model has not been presented to the scientific community before. This study differs from other national-level low-carbon scenario studies in the completeness of the energy system coverage (including the use of feedstocks for industrial production and energy supply to international aviation and maritime transport), the coverage of all domestic greenhouse gases, a wide range of technology options and a high time resolution. ...
... It computes the costoptimal energy and GHG system configuration, under specific constraints, by minimizing an objective function that expresses the total system costs for a given future year. An extended model description with a detailed specification of OPERA's underlying set-up, assumptions, and methodology can be found in Ref. [26]. Two features that make OPERA especially useful for the purpose of the present study are: (1) it covers the complete energy system of the Netherlands and reflects all domestic emissions and types of greenhouse gases; (2) it simulates energy supply and demand on an hourly basis and allows for separately handling distinct sets of hours. ...
This paper presents two different scenarios for the energy system of the Netherlands that achieve the Dutch government's national target of near net-zero greenhouse gas emissions in 2050. Using the system optimisation model OPERA, the authors have analysed the technology, sector and cost implications of the assumptions underlying these scenarios. While the roles of a number of key energy technology and emission mitigation options are strongly dependent on the scenario and cost assumptions, the analysis yields several common elements that appear in both scenarios and that consistently appear under differing cost assumptions. For example, one of the main options for the decarbonisation of the Dutch energy system is electrification of energy use in end-use sectors and for the production of renewable hydrogen with electrolysers. As a result the level of electricity generation in 2050 will be three to four times higher than present generation levels. Ultimately, renewable energy – particularly from wind turbines and solar panels – is projected to account for the vast majority of electricity generation, around 99% in 2050. Imbalances between supply and demand resulting from this variable renewable electricity production can be managed via flexibility options, including demand response and energy storage. Hydrogen also becomes an important energy carrier, notably for transportation and in industry. If import prices are lower than costs of domestic production from natural gas with CCS or through electrolysis from renewable electricity (2.4–2.7 €/kgH2), the use of hydrogen increases, especially in the built environment.
