- Email: [email protected]
Exploring the 2 °C target with 100 percent renewable energy under uncertainty in climate sensitivity
Available online at www.sciencedirect.com Available online at www.sciencedirect.com
ScienceDirect ScienceDirect Available onlineatat www.sciencedirect.com Available www.sciencedirect.com Energyonline Procedia 00 (2018) 000–000 Energy Procedia 00 (2018) 000–000
ScienceDirect ScienceDirect
www.elsevier.com/locate/procedia www.elsevier.com/locate/procedia
Energy Procedia 158 Energy Procedia 00(2019) (2017)3417–3420 000–000 www.elsevier.com/locate/procedia
10th th
International Conference on Applied Energy (ICAE2018), 22-25 August 2018, Hong Kong, 10 International Conference on Applied Energy China(ICAE2018), 22-25 August 2018, Hong Kong, China
Exploring 2 target 100 renewable energy Exploring the the 2 °C °C target with with 100 percent percent energy under under The 15th International Symposium on Districtrenewable Heating and Cooling uncertainty in climate sensitivity uncertainty in climate sensitivity Assessing the feasibility of using the heat demand-outdoor c Rieko Yasuokaaa, Koji Tokimatsub,c b,c *, and Masahiro Nishioc Rieko Yasuoka for , Koji *, and Masahiro Nishio temperature function a Tokimatsu long-term district heat demand forecast
Systems Research Center, Co. Ltd, KY Bldg., 3-16-7, Toranomon, Minato, Tokyo, 105-0001, Japan Systems Research Center, Co. 4259 Ltd, KY Bldg., 3-16-7, Toranomon, Minato, Tokyo, 105-0001, Japan Tokyo Institute of Technology, Nagatsuta, Midori-ku, Yokohama, Kanagawa, 226-8503, Japan a,b,c a a b c c c Tokyo Institute of Technology, 4259 Nagatsuta, Midori-ku, Yokohama, Kanagawa, 226-8503, Japan Japan National Institute of Advanced Industrial Science and Technology, 1-1-1, Higashi, Tsukuba, Ibaraki, 305-8567, c National Institute of Advanced Industrial Science and Technology, 1-1-1, Higashi, Tsukuba, Ibaraki, 305-8567, Japan a IN+ Center for Innovation, Technology and Policy Research - Instituto Superior Técnico, Av. Rovisco Pais 1, 1049-001 Lisbon, Portugal b Veolia Recherche & Innovation, 291 Avenue Dreyfous Daniel, 78520 Limay, France c Département Systèmes Énergétiques et Environnement - IMT Atlantique, 4 rue Alfred Kastler, 44300 Nantes, France Abstract a. a. b b
I. Andrić
*, A. Pina , P. Ferrão , J. Fournier ., B. Lacarrière , O. Le Corre
Abstract
Renewable energy has gained main issues in energy policy globally due to its rapid expansion after the Fukushima daiichi Renewable energyThe hasParis gained main issues in energy policy globally studies due to toitsmeet rapidtheexpansion after the Fukushima daiichi nuclear accident. agreement triggered to publish numerous 2 °C or well-below target. However, Abstract nuclear accident. The Paris agreement triggered to publish numerous studies to meet the 2 °C or well-below target. However, little studies had carried out in achieving 100% renewable energy up to 2100 simultaneously aiming the target, to which we little studies in achieving 100% renewable energy up tomodels. 2100 simultaneously the target, to which we addressed in had this carried study byoutusing our cost-minimizing resource balance The models areaiming consisted from production of District heating networks are commonly addressed in the literature as models. one of the effective solutionsfrom for production decreasing of the addressed in this by using our cost-minimizing balance Themost models are consisted resources, land usestudy and land use changes, inter-regionalresource transportation, energy conversion (power, liquid fuels, gas), production greenhouse gasuse emissions from the building sector. These systems require highconversion investments whichliquid are returned through the heat resources, land and land use changes, inter-regional transportation, energy (power, fuels, gas), production of materials, final demand, wood products, disposal of used products, and materials recycling. In order to address uncertainty in Due to thedemand, changedwood climate conditions andofbuilding renovation materials policies, recycling. heat demand in the future could decrease, ofsales. materials, final products, disposal used products, order to address uncertainty in climate sensitivity to meet the target, we integrated the latest version ofand simplified climate modelInby Nordhaus to our models. We prolonging the investment return period. climate sensitivity to meet the target, we integrated the latest version of simplified climate model by Nordhaus to our models. We clarified that the temperature rise calculated by the simplified climate model ranged widely due to uncertainty in climate The mainthat scope this paper isrise to assess the feasibility of using the heat demand outdoor temperature for heat demand clarified the oftemperature calculated by the simplified climate model –ranged widely due tofunction uncertainty in climate sensitivity, resulted in the energy structure changes to meet the target as well. forecast. The district of energy Alvalade, located in Lisbon (Portugal), used as a case study. The district is consisted of 665 sensitivity, resulted in the structure changes to meet the targetwas as well. buildings that vary in both construction period and typology. Three weather scenarios (low, medium, high) and three district Copyright © 2018 Elsevier Ltd. All rights reserved. renovation scenarios wereLtd. developed (shallow, obtained heat demand values were © 2019 The©Authors. Published by Elsevier Ltd. intermediate, deep). To estimate the error, Copyright Elsevier Allresponsibility rights reserved. International Conference on Applied Selection and2018 peer-review under of thelicense scientific committee of the 10th th International This is an open access the CC (http://creativecommons.org/licenses/by-nc-nd/4.0/) compared withpeer-review resultsarticle fromunder aunder dynamic heatBY-NC-ND demand model, previously developed and10 validated by the authors. Conference on Applied Selection and responsibility of the scientific committee of the Energy (ICAE2018). Peer-review under responsibility of the scientific committee of ICAE2018 – The of 10th International Conferencefor onsome Applied Energy. The results showed that when only weather change is considered, the margin error could be acceptable applications Energy (ICAE2018). (the error in annual demand was lower than 20% for all weather scenarios considered). However, after introducing renovation Keywords: 2°C target; 100% renewable energy; climate sensitivity; uncertainty scenarios,2°C thetarget; error100% valuerenewable increasedenergy; up toclimate 59.5%sensitivity; (depending on the weather and renovation scenarios combination considered). Keywords: uncertainty The value of slope coefficient increased on average within the range of 3.8% up to 8% per decade, that corresponds to the decrease in the number of heating hours of 22-139h during the heating season (depending on the combination of weather and renovation scenarios considered). On the other hand, function intercept increased for 7.8-12.7% per decade (depending on the coupled scenarios). The values suggested could be used to modify the function parameters for the scenarios considered, and improve the accuracy of heat demand estimations. © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and Cooling. * Corresponding author. Tel.: +81-45-924-5533; fax: +81-45-330-6302. * Corresponding Tel.: +81-45-924-5533; fax: +81-45-330-6302. E-mail address:author. [email protected] Keywords: Heat demand; Forecast; Climate change E-mail address: [email protected]
1876-6102 Copyright © 2018 Elsevier Ltd. All rights reserved. 1876-6102 Copyright © 2018 Elsevier Ltd. All of rights reserved. committee of the 10th International Conference on Applied Energy (ICAE2018). Selection and peer-review under responsibility the scientific Selection and peer-review under responsibility of the scientific committee of the 10th International Conference on Applied Energy (ICAE2018). 1876-6102 © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and Cooling. 1876-6102 © 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of ICAE2018 – The 10th International Conference on Applied Energy. 10.1016/j.egypro.2019.01.934
3418 2
Rieko Yasuoka et al. / Energy Procedia 158 (2019) 3417–3420 Author name / Energy Procedia 00 (2018) 000–000
1. Introduction Renewable energy dramatically had gained attentions to meet the stringent climate targets prevailed in the Paris agreements, rather than substitute for non-renewable energy resources. However, extreme and rosy scenarios such as 100% renewable energy have not been well addressed at the global level [1,2]; most are at local or national levels. Moreover, meeting such stringent targets as 2 °C or well-below are strongly depending on the climate sensitivity, leading to potentially significant changes in energy mix structure, to which current study contributes. The climate sensitivity is the equilibrium global mean temperature rise (GMTR) in response to the doubling atmospheric CO2 concentration. It is well known that it has an unsolidified huge range; 2.0-4.5 °C (median 3.0) in AR4 [3], 1.5-4.5 °C (median unspecified) in AR5 [4], and 2.2-3.4 °C (2.8) in a recent publication [5] narrowing the range. The goal of this paper is to address this uncertainty using a simplified climate model, tied with the GMTR leading to the energy mix structural changes. We borrowed the simplified climate model from the latest version of DICE (Dynamic Integrated model of Climate and the Economy) model, DICE-2013R [6]. Nordhaus had long been developed series models of DICE and RICE (Regional Integrated model of Climate and the Economy) from his first development in the beginning of 1990s. They had been fully documented; in which DICE-2013R manual [6] denotes its median level originally sets at 2.9 °C. Outline of our modeling and analysis is described in the next section, followed by results, conclusion, and future tasks. 2. Outline of our modeling and analysis Our global model is a cost minimization type to illustrate rational structure of energy technologies and resources. It balances demand and supply of resources for energy, mineral, biomass, and food formulated as dynamic linear programming over the time horizon in this century [7-10]. The models are consisted from production of resources, land use and its changes, inter-regional transportation, energy conversion (power, liquid fuels, gas), production of materials (e.g, ferrous and non-ferrous metals (i.e., aluminum, copper, lead, zinc), and limestone), final demand, wood products, disposal of used products, and materials recycling. The models incorporate bottom-up detailed technology options close to hundred meeting the exogenously given demand scenarios to provide a consistent structure for supplying the resources. CO2 emissions are tied from all the sectors via fossil fuel combustions as well as forestry changes, which are endogenously linked with the simplified climate model in DICE-2013R. Computation of GTMR is consisted from following four processes in the simplified climate model. First, as described above, is the anthropogenic CO2 emissions in geosphere from transformation in energy and land. Second is the carbon cycle, which links all together the CO2 emissions in geosphere with flow and stock of CO2 in atmosphere and hydrosphere including biosphere. Third is the radiative forcing, sum of those by carbon reservoir (i.e., the atmospheric CO2 concentration) via the carbon cycle and by exogenously given non CO2 GHGs. Finally, fourth is the temperature rise. The atmospheric radiative forcing linked with temperature in geosphere and in hydrosphere and heat dissipation in the deep ocean, then converted to the temperature rise via the climate sensitivity. From the exogenously given radiative forcing, we estimated non CO2 emissions by deriving functional forms based on those retrieved from the SSP database [11]. We originally set up energy (especially power) scenario achieving 100% renewable energy supply in 2100 (denoted hereafter Ren 100). The climate policy scenarios are business as usual (BAU) with no emission control of greenhouse gases (GHGs), and zero emissions scenario (net ZERO) by giving cumulative zero emissions constraints over time (i.e., from 2010 to 2150), allowing negative GHG emissions. We soak constraints on such as power sources, carbon capture and storage (CCS), and hydrogen in transport sector, so that GMTR arrived at the 2 °C target, under the conditions of climate sensitivity in the levels of median and their ranges described above. 3. Results Figure 1 shows net GHG emissions (in left axis) can be reduced from BAU (red dashed curve) to net ZERO (red solid one) by deploying CCS from gross GHG emissions in various sectors (showed in bar graphs), and corresponding GMTR (in right axis) for BAU (blue dashed) and net ZERO (blue solid) covered by their uncertainty range by bands. The range of climate sensitivity corresponds to the minimum and maximum levels at 2.2 °C and
Rieko Yasuoka et al. / Energy Procedia 158 (2019) 3417–3420 Author name / Energy Procedia 00 (2018) 000–000
3419 3
3.4 °C, respectively. We set those levels after clarifying the climate sensitivity allowed us to find feasible solutions in the our models, implying that no feasible solutions can be found in 4.5 °C to meet the 2 °C target.
BAU →
40
netZERO →
20
3 2 1
← netZERO
0
0
2010 2030 2030 2050 2050 2070 2070 2090 2090 2010
-1
Limestone
global mean temperature rise [degC]
global carbon balance (GtCO2eq/yr)
← BAU Ren100
60
-20
Non-CO2
4
80
material (FECCS) synfuel (BECCS) synfuel (FECCS) hydroge (BECCS) hydrogen (FECCS) heat (BECCS) heat (FECCS) power (BECCS) power (FECCS) combustion netZERO (Ren100) BAU (Ren100) netZERO (Ren100) BAU (Ren100)
Figure 1 global carbon balance and global mean temperature rise.
Power generation (EJ/yr)
600 500 400 300 200 100 0
min median max
min median max
min median max
min median max
2030
2050
2070
2090
CHP electricity Ocean (OTEC) Ocean (wave, tidal) Wind (offshore) Wind (onshore) Solar (SPS) Solar (CSP) Solar (PV) Biomass IGCC with CCS Biomass IGCC Biomass with CCS Biomass Geothermal Hydropower (small) Hydropower (large) FBR LWR Hydrogen Methanol Gas with CCS Gas Oil with CCS Oil Coal IGCC with CCS Coal IGCC Coal with CCS Coal
Figure 2 Global power supply structure in the three climate sensitivity under the net ZERO scenario.
Figure 2 shows global power supply mix corresponding to the three climate sensitivities satisfying the 2 °C target. Regarding to the electricity supply in 2090, renewable power accounts for nearly 100% (except in the minimum climate sensitivity), the rest is hydrogen generation sourced by renewable energy. Gas fired power accounts for 25%
3420 4
Rieko Yasuoka et al. / Energy Procedia 158 (2019) 3417–3420 Author name / Energy Procedia 00 (2018) 000–000
in 2070 (closer to BAU) in the minimum climate sensitivity while gas and coal are zero in 2050 in the maximum climate sensitivity. 4. Conclusions and the way forward The main point of this study is clarifying the key role of climate sensitivity to meet the 2 °C target; that 4.5 °C climate sensitivity does not allow to meet the target while 3.4 °C provides feasible computational solution, and that the power mix structure under the 100% renewable energy scenario are greatly changes in levels of the climate sensitivities ranges from minimum (2.2 °C) to maximum (3.4 °C). It is expected that the energy mix structure under other energy scenarios (such as coal & nuclear, gas & renewables) will change greatly due to the climate sensitivity, which is addressed as the next step. Acknowledgements The authors give deepest thank to National Institute of Advanced Industrial Science and Technology (AIST) for supporting this work. References [1] Føyn THY, Karlsson K, Balyk O, Grohnheit PE. 2011, “A global renewable energy system: a modelling exercise in ETSAP/TIAM”. Appl Energy 88(2)526-534 [2] Hong, S., Bradshaw, C.J.A., Brook, B.W., 2015, “Global zero-carbon energy pathways using viable mixes of nuclear and renewables”, Appl Ener 143; 451-459 [3] Randall, D.A., R.A. Wood, S. Bony, R. Colman, T. Fichefet, J. Fyfe, V. Kattsov, A. Pitman, J. Shukla, J. Srinivasan, R.J. Stouffer, A. Sumi and K.E. Taylor, 2007: Cilmate Models and Their Evaluation. In: Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change [Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M.Tignor and H.L. Miller (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA. [4] Collins, M., R. Knutti, J. Arblaster, J.-L. Dufresne, T. Fichefet, P. Friedlingstein, X. Gao, W.J. Gutowski, T. Johns, G. Krinner, M. Shongwe, C. Tebaldi, A.J. Weaver and M. Wehner, 2013: Long-term Climate Change: Projections, Commitments and Irreversibility. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA. [5] Cox, P.M., Huntingford, C., Williamson, M.S. 2018, “Emergent constraint on equilibrium climate sensitivity from global temperature variability”, Nature 553: 319-322 [6] Nordhaus W., and Sztorc P., “DICE 2013R: Introduction and User’s Manual”, Second edition, October 31, 2013 [7] Tokimatsu K, Konishi S, Ishihara K, Tezuka T, Yasuoka R, Nishio M, 2016 “Global zero emissions scenarios: role of innovative technologies”, Appl. Ener 162: 1483-1493 [8] Tokimatsu K, Yasuoka R, Nishio M, 2017, “Global zero emissions scenarios: the role of biomass energy with carbon capture and storage by forested land use”, Appl. Ener 185: 1899-1906 [9] Tokimatsu, K., Watchmeister, H., McLellan, B., Davidsson, S., Murakami, S., Höök, M., Yasuoka, R., Nishio, M., 2017 “Energy modeling approach to the global energy-mineral nexus: A first look at metal requirements and the 2 C target”, Appl. Ener 207: 494-509 [10] Tokimatsu, K., Höök, M., McLellan, B., Watchmeister, H., Murakami, S., Yasuoka, R., Nishio, M., 2017 “Energy modeling approach to the global energy-mineral nexus: Exploring metal requirements and the well-below 2 °C target with 100 percent renewable energy”, Appl. Ener, accepted for publication [11] SSP Database (Shared Socioeconomic Pathways) - Version 1.1, October 2016. https://tntcat.iiasa.ac.at/SspDb/dsd?Action=htmlpage&page=series (accessed 10th May 10, 2018)
ScienceDirect ScienceDirect Available onlineatat www.sciencedirect.com Available www.sciencedirect.com Energyonline Procedia 00 (2018) 000–000 Energy Procedia 00 (2018) 000–000
ScienceDirect ScienceDirect
www.elsevier.com/locate/procedia www.elsevier.com/locate/procedia
Energy Procedia 158 Energy Procedia 00(2019) (2017)3417–3420 000–000 www.elsevier.com/locate/procedia
10th th
International Conference on Applied Energy (ICAE2018), 22-25 August 2018, Hong Kong, 10 International Conference on Applied Energy China(ICAE2018), 22-25 August 2018, Hong Kong, China
Exploring 2 target 100 renewable energy Exploring the the 2 °C °C target with with 100 percent percent energy under under The 15th International Symposium on Districtrenewable Heating and Cooling uncertainty in climate sensitivity uncertainty in climate sensitivity Assessing the feasibility of using the heat demand-outdoor c Rieko Yasuokaaa, Koji Tokimatsub,c b,c *, and Masahiro Nishioc Rieko Yasuoka for , Koji *, and Masahiro Nishio temperature function a Tokimatsu long-term district heat demand forecast
Systems Research Center, Co. Ltd, KY Bldg., 3-16-7, Toranomon, Minato, Tokyo, 105-0001, Japan Systems Research Center, Co. 4259 Ltd, KY Bldg., 3-16-7, Toranomon, Minato, Tokyo, 105-0001, Japan Tokyo Institute of Technology, Nagatsuta, Midori-ku, Yokohama, Kanagawa, 226-8503, Japan a,b,c a a b c c c Tokyo Institute of Technology, 4259 Nagatsuta, Midori-ku, Yokohama, Kanagawa, 226-8503, Japan Japan National Institute of Advanced Industrial Science and Technology, 1-1-1, Higashi, Tsukuba, Ibaraki, 305-8567, c National Institute of Advanced Industrial Science and Technology, 1-1-1, Higashi, Tsukuba, Ibaraki, 305-8567, Japan a IN+ Center for Innovation, Technology and Policy Research - Instituto Superior Técnico, Av. Rovisco Pais 1, 1049-001 Lisbon, Portugal b Veolia Recherche & Innovation, 291 Avenue Dreyfous Daniel, 78520 Limay, France c Département Systèmes Énergétiques et Environnement - IMT Atlantique, 4 rue Alfred Kastler, 44300 Nantes, France Abstract a. a. b b
I. Andrić
*, A. Pina , P. Ferrão , J. Fournier ., B. Lacarrière , O. Le Corre
Abstract
Renewable energy has gained main issues in energy policy globally due to its rapid expansion after the Fukushima daiichi Renewable energyThe hasParis gained main issues in energy policy globally studies due to toitsmeet rapidtheexpansion after the Fukushima daiichi nuclear accident. agreement triggered to publish numerous 2 °C or well-below target. However, Abstract nuclear accident. The Paris agreement triggered to publish numerous studies to meet the 2 °C or well-below target. However, little studies had carried out in achieving 100% renewable energy up to 2100 simultaneously aiming the target, to which we little studies in achieving 100% renewable energy up tomodels. 2100 simultaneously the target, to which we addressed in had this carried study byoutusing our cost-minimizing resource balance The models areaiming consisted from production of District heating networks are commonly addressed in the literature as models. one of the effective solutionsfrom for production decreasing of the addressed in this by using our cost-minimizing balance Themost models are consisted resources, land usestudy and land use changes, inter-regionalresource transportation, energy conversion (power, liquid fuels, gas), production greenhouse gasuse emissions from the building sector. These systems require highconversion investments whichliquid are returned through the heat resources, land and land use changes, inter-regional transportation, energy (power, fuels, gas), production of materials, final demand, wood products, disposal of used products, and materials recycling. In order to address uncertainty in Due to thedemand, changedwood climate conditions andofbuilding renovation materials policies, recycling. heat demand in the future could decrease, ofsales. materials, final products, disposal used products, order to address uncertainty in climate sensitivity to meet the target, we integrated the latest version ofand simplified climate modelInby Nordhaus to our models. We prolonging the investment return period. climate sensitivity to meet the target, we integrated the latest version of simplified climate model by Nordhaus to our models. We clarified that the temperature rise calculated by the simplified climate model ranged widely due to uncertainty in climate The mainthat scope this paper isrise to assess the feasibility of using the heat demand outdoor temperature for heat demand clarified the oftemperature calculated by the simplified climate model –ranged widely due tofunction uncertainty in climate sensitivity, resulted in the energy structure changes to meet the target as well. forecast. The district of energy Alvalade, located in Lisbon (Portugal), used as a case study. The district is consisted of 665 sensitivity, resulted in the structure changes to meet the targetwas as well. buildings that vary in both construction period and typology. Three weather scenarios (low, medium, high) and three district Copyright © 2018 Elsevier Ltd. All rights reserved. renovation scenarios wereLtd. developed (shallow, obtained heat demand values were © 2019 The©Authors. Published by Elsevier Ltd. intermediate, deep). To estimate the error, Copyright Elsevier Allresponsibility rights reserved. International Conference on Applied Selection and2018 peer-review under of thelicense scientific committee of the 10th th International This is an open access the CC (http://creativecommons.org/licenses/by-nc-nd/4.0/) compared withpeer-review resultsarticle fromunder aunder dynamic heatBY-NC-ND demand model, previously developed and10 validated by the authors. Conference on Applied Selection and responsibility of the scientific committee of the Energy (ICAE2018). Peer-review under responsibility of the scientific committee of ICAE2018 – The of 10th International Conferencefor onsome Applied Energy. The results showed that when only weather change is considered, the margin error could be acceptable applications Energy (ICAE2018). (the error in annual demand was lower than 20% for all weather scenarios considered). However, after introducing renovation Keywords: 2°C target; 100% renewable energy; climate sensitivity; uncertainty scenarios,2°C thetarget; error100% valuerenewable increasedenergy; up toclimate 59.5%sensitivity; (depending on the weather and renovation scenarios combination considered). Keywords: uncertainty The value of slope coefficient increased on average within the range of 3.8% up to 8% per decade, that corresponds to the decrease in the number of heating hours of 22-139h during the heating season (depending on the combination of weather and renovation scenarios considered). On the other hand, function intercept increased for 7.8-12.7% per decade (depending on the coupled scenarios). The values suggested could be used to modify the function parameters for the scenarios considered, and improve the accuracy of heat demand estimations. © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and Cooling. * Corresponding author. Tel.: +81-45-924-5533; fax: +81-45-330-6302. * Corresponding Tel.: +81-45-924-5533; fax: +81-45-330-6302. E-mail address:author. [email protected] Keywords: Heat demand; Forecast; Climate change E-mail address: [email protected]
1876-6102 Copyright © 2018 Elsevier Ltd. All rights reserved. 1876-6102 Copyright © 2018 Elsevier Ltd. All of rights reserved. committee of the 10th International Conference on Applied Energy (ICAE2018). Selection and peer-review under responsibility the scientific Selection and peer-review under responsibility of the scientific committee of the 10th International Conference on Applied Energy (ICAE2018). 1876-6102 © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and Cooling. 1876-6102 © 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of ICAE2018 – The 10th International Conference on Applied Energy. 10.1016/j.egypro.2019.01.934
3418 2
Rieko Yasuoka et al. / Energy Procedia 158 (2019) 3417–3420 Author name / Energy Procedia 00 (2018) 000–000
1. Introduction Renewable energy dramatically had gained attentions to meet the stringent climate targets prevailed in the Paris agreements, rather than substitute for non-renewable energy resources. However, extreme and rosy scenarios such as 100% renewable energy have not been well addressed at the global level [1,2]; most are at local or national levels. Moreover, meeting such stringent targets as 2 °C or well-below are strongly depending on the climate sensitivity, leading to potentially significant changes in energy mix structure, to which current study contributes. The climate sensitivity is the equilibrium global mean temperature rise (GMTR) in response to the doubling atmospheric CO2 concentration. It is well known that it has an unsolidified huge range; 2.0-4.5 °C (median 3.0) in AR4 [3], 1.5-4.5 °C (median unspecified) in AR5 [4], and 2.2-3.4 °C (2.8) in a recent publication [5] narrowing the range. The goal of this paper is to address this uncertainty using a simplified climate model, tied with the GMTR leading to the energy mix structural changes. We borrowed the simplified climate model from the latest version of DICE (Dynamic Integrated model of Climate and the Economy) model, DICE-2013R [6]. Nordhaus had long been developed series models of DICE and RICE (Regional Integrated model of Climate and the Economy) from his first development in the beginning of 1990s. They had been fully documented; in which DICE-2013R manual [6] denotes its median level originally sets at 2.9 °C. Outline of our modeling and analysis is described in the next section, followed by results, conclusion, and future tasks. 2. Outline of our modeling and analysis Our global model is a cost minimization type to illustrate rational structure of energy technologies and resources. It balances demand and supply of resources for energy, mineral, biomass, and food formulated as dynamic linear programming over the time horizon in this century [7-10]. The models are consisted from production of resources, land use and its changes, inter-regional transportation, energy conversion (power, liquid fuels, gas), production of materials (e.g, ferrous and non-ferrous metals (i.e., aluminum, copper, lead, zinc), and limestone), final demand, wood products, disposal of used products, and materials recycling. The models incorporate bottom-up detailed technology options close to hundred meeting the exogenously given demand scenarios to provide a consistent structure for supplying the resources. CO2 emissions are tied from all the sectors via fossil fuel combustions as well as forestry changes, which are endogenously linked with the simplified climate model in DICE-2013R. Computation of GTMR is consisted from following four processes in the simplified climate model. First, as described above, is the anthropogenic CO2 emissions in geosphere from transformation in energy and land. Second is the carbon cycle, which links all together the CO2 emissions in geosphere with flow and stock of CO2 in atmosphere and hydrosphere including biosphere. Third is the radiative forcing, sum of those by carbon reservoir (i.e., the atmospheric CO2 concentration) via the carbon cycle and by exogenously given non CO2 GHGs. Finally, fourth is the temperature rise. The atmospheric radiative forcing linked with temperature in geosphere and in hydrosphere and heat dissipation in the deep ocean, then converted to the temperature rise via the climate sensitivity. From the exogenously given radiative forcing, we estimated non CO2 emissions by deriving functional forms based on those retrieved from the SSP database [11]. We originally set up energy (especially power) scenario achieving 100% renewable energy supply in 2100 (denoted hereafter Ren 100). The climate policy scenarios are business as usual (BAU) with no emission control of greenhouse gases (GHGs), and zero emissions scenario (net ZERO) by giving cumulative zero emissions constraints over time (i.e., from 2010 to 2150), allowing negative GHG emissions. We soak constraints on such as power sources, carbon capture and storage (CCS), and hydrogen in transport sector, so that GMTR arrived at the 2 °C target, under the conditions of climate sensitivity in the levels of median and their ranges described above. 3. Results Figure 1 shows net GHG emissions (in left axis) can be reduced from BAU (red dashed curve) to net ZERO (red solid one) by deploying CCS from gross GHG emissions in various sectors (showed in bar graphs), and corresponding GMTR (in right axis) for BAU (blue dashed) and net ZERO (blue solid) covered by their uncertainty range by bands. The range of climate sensitivity corresponds to the minimum and maximum levels at 2.2 °C and
Rieko Yasuoka et al. / Energy Procedia 158 (2019) 3417–3420 Author name / Energy Procedia 00 (2018) 000–000
3419 3
3.4 °C, respectively. We set those levels after clarifying the climate sensitivity allowed us to find feasible solutions in the our models, implying that no feasible solutions can be found in 4.5 °C to meet the 2 °C target.
BAU →
40
netZERO →
20
3 2 1
← netZERO
0
0
2010 2030 2030 2050 2050 2070 2070 2090 2090 2010
-1
Limestone
global mean temperature rise [degC]
global carbon balance (GtCO2eq/yr)
← BAU Ren100
60
-20
Non-CO2
4
80
material (FECCS) synfuel (BECCS) synfuel (FECCS) hydroge (BECCS) hydrogen (FECCS) heat (BECCS) heat (FECCS) power (BECCS) power (FECCS) combustion netZERO (Ren100) BAU (Ren100) netZERO (Ren100) BAU (Ren100)
Figure 1 global carbon balance and global mean temperature rise.
Power generation (EJ/yr)
600 500 400 300 200 100 0
min median max
min median max
min median max
min median max
2030
2050
2070
2090
CHP electricity Ocean (OTEC) Ocean (wave, tidal) Wind (offshore) Wind (onshore) Solar (SPS) Solar (CSP) Solar (PV) Biomass IGCC with CCS Biomass IGCC Biomass with CCS Biomass Geothermal Hydropower (small) Hydropower (large) FBR LWR Hydrogen Methanol Gas with CCS Gas Oil with CCS Oil Coal IGCC with CCS Coal IGCC Coal with CCS Coal
Figure 2 Global power supply structure in the three climate sensitivity under the net ZERO scenario.
Figure 2 shows global power supply mix corresponding to the three climate sensitivities satisfying the 2 °C target. Regarding to the electricity supply in 2090, renewable power accounts for nearly 100% (except in the minimum climate sensitivity), the rest is hydrogen generation sourced by renewable energy. Gas fired power accounts for 25%
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in 2070 (closer to BAU) in the minimum climate sensitivity while gas and coal are zero in 2050 in the maximum climate sensitivity. 4. Conclusions and the way forward The main point of this study is clarifying the key role of climate sensitivity to meet the 2 °C target; that 4.5 °C climate sensitivity does not allow to meet the target while 3.4 °C provides feasible computational solution, and that the power mix structure under the 100% renewable energy scenario are greatly changes in levels of the climate sensitivities ranges from minimum (2.2 °C) to maximum (3.4 °C). It is expected that the energy mix structure under other energy scenarios (such as coal & nuclear, gas & renewables) will change greatly due to the climate sensitivity, which is addressed as the next step. Acknowledgements The authors give deepest thank to National Institute of Advanced Industrial Science and Technology (AIST) for supporting this work. References [1] Føyn THY, Karlsson K, Balyk O, Grohnheit PE. 2011, “A global renewable energy system: a modelling exercise in ETSAP/TIAM”. Appl Energy 88(2)526-534 [2] Hong, S., Bradshaw, C.J.A., Brook, B.W., 2015, “Global zero-carbon energy pathways using viable mixes of nuclear and renewables”, Appl Ener 143; 451-459 [3] Randall, D.A., R.A. Wood, S. Bony, R. Colman, T. Fichefet, J. Fyfe, V. Kattsov, A. Pitman, J. Shukla, J. Srinivasan, R.J. Stouffer, A. Sumi and K.E. Taylor, 2007: Cilmate Models and Their Evaluation. In: Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change [Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M.Tignor and H.L. Miller (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA. [4] Collins, M., R. Knutti, J. Arblaster, J.-L. Dufresne, T. Fichefet, P. Friedlingstein, X. Gao, W.J. Gutowski, T. Johns, G. Krinner, M. Shongwe, C. Tebaldi, A.J. Weaver and M. Wehner, 2013: Long-term Climate Change: Projections, Commitments and Irreversibility. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA. [5] Cox, P.M., Huntingford, C., Williamson, M.S. 2018, “Emergent constraint on equilibrium climate sensitivity from global temperature variability”, Nature 553: 319-322 [6] Nordhaus W., and Sztorc P., “DICE 2013R: Introduction and User’s Manual”, Second edition, October 31, 2013 [7] Tokimatsu K, Konishi S, Ishihara K, Tezuka T, Yasuoka R, Nishio M, 2016 “Global zero emissions scenarios: role of innovative technologies”, Appl. Ener 162: 1483-1493 [8] Tokimatsu K, Yasuoka R, Nishio M, 2017, “Global zero emissions scenarios: the role of biomass energy with carbon capture and storage by forested land use”, Appl. Ener 185: 1899-1906 [9] Tokimatsu, K., Watchmeister, H., McLellan, B., Davidsson, S., Murakami, S., Höök, M., Yasuoka, R., Nishio, M., 2017 “Energy modeling approach to the global energy-mineral nexus: A first look at metal requirements and the 2 C target”, Appl. Ener 207: 494-509 [10] Tokimatsu, K., Höök, M., McLellan, B., Watchmeister, H., Murakami, S., Yasuoka, R., Nishio, M., 2017 “Energy modeling approach to the global energy-mineral nexus: Exploring metal requirements and the well-below 2 °C target with 100 percent renewable energy”, Appl. Ener, accepted for publication [11] SSP Database (Shared Socioeconomic Pathways) - Version 1.1, October 2016. https://tntcat.iiasa.ac.at/SspDb/dsd?Action=htmlpage&page=series (accessed 10th May 10, 2018)