Management of Wind Power Variations in Electricity System Investment Models
Tóm tắt
Accounting for variability in generation and load and strategies to tackle variability cost-efficiently are key components of investment models for modern electricity systems. This work presents and evaluates the Hours-to-Decades (H2D) model, which builds upon a novel approach to account for strategies to manage variations in the electricity system covering several days, the variation management which is of particular relevance to wind power integration. The model discretizes the time dimension of the capacity expansion problem into 2-week segments, thereby exploiting the parallel processing capabilities of modern computers. Information between these segments is then exchanged in a consensus loop. The method is evaluated with regard to its ability to account for the impacts of strategies to manage variations in generation and load, regional resources and trade, and inter-annual linkages. Compared to a method with fully connected time, the proposed method provides solutions with an increase in total system cost of no more than 1.12%, while reducing memory requirements to 1/26’th of those of the original problem. For capacity expansion problems concerning two regions or more, it is found that the H2D model requires 1–2% of the calculation time relative to a model with fully connected time when solved on a computer with parallel processing capability.
Tài liệu tham khảo
IEA (2017) World energy outlook 2017. Report, International Energy Agency. URL https://www.iea.org/reports/world-energy-outlook-2017
Welsch M, Howells M, Hesamzadeh MR, Ó Gallachóir B, Deane P, Strachan N, Bazilian M, Kammen DM, Jones L, Strbac G, Rogner H (2015) Supporting security and adequacy in future energy systems: The need to enhance long-term energy system models to better treat issues related to variability. Int J Energy Res 39(3):377–396. https://doi.org/10.1002/er.3250
Pfenninger S, Hawkes A, Keirstead J (2014) Energy systems modeling for twenty-first century energy challenges. Renew Sustain Energy Rev 33:74–86. https://doi.org/10.1016/j.rser.2014.02.003
Göransson L (2014) The impact of wind power variability on the least-cost dispatch of units in the electricity generation system. PhD thesis, Chalmers University of Technology
Ringkjøb H-C, Haugan PM, Solbrekke IM (2018) A review of modelling tools for energy and electricity systems with large shares of variable renewables. Renew Sustain Energy Rev 96:440–459. https://doi.org/10.1016/j.rser.2018.08.002
Collins S, Deane JP, Poncelet K, Panos E, Pietzcker RC, Delarue E, Ó Gallachóir B (2017) Integrating short term variations of the power system into integrated energy system models: A methodological review. Renew Sustain Energy Rev 76:839–856. https://doi.org/10.1016/j.rser.2017.03.090
Haydt G, Leal V, Pina A, Silva CA (2011) The relevance of the energy resource dynamics in the mid/long-term energy planning models. Renew Energy 36(11):3068–3074. https://doi.org/10.1016/j.renene.2011.03.028
Wogrin S, Duenas P, Delgadillo A, Reneses J (2014) A new approach to model load levels in electric power systems with high renewable penetration. IEEE Trans Power Syst 29(5):2210–2218. https://doi.org/10.1109/TPWRS.2014.2300697
Lehtveer M, Mattsson N, Hedenus F (2017) Using resource based slicing to capture the intermittency of variable renewables in energy system models. Energ Strat Rev 18 https://doi.org/10.1016/j.esr.2017.09.008
Nahmmacher P, Schmid E, Hirth L, Knopf B (2016) Carpe diem: A novel approach to select representative days for long-term power system models with high shares of renewable energy sources. Energy 112(1):430–442. https://doi.org/10.1016/j.energy.2016.06.081
Mai T, Drury E, Eurek K, Bodington N, Lopez A, Perry A (2013) Resource planning model: An integrated resource planning and dispatch tool for regional electric systems. Report, National Renewable Energy Laboratory
Gils H-C (2016) Energy System Model REMix. Report, DLR, https://www.dlr.de/tt/Portaldata/41/Resources/dokumente/institut/system/Modellbeschreibungen/DLR_Energy_System_Model_REMix_short_description_2016.pdf
Gerbaulet C, Lorenz C (2017) dynELMOD: A dynamic investment and dispatch model for the future European electricity market. Technical report, Deutsches Institut für Wirtschaftsforschung (DIW), Berlin, www.diw.de; https://www.econstor.eu/bitstream/10419/161634/1/888201575.pdf
Frew BA, Jacobson MZ (2016) Temporal and spatial tradeoffs in power system modeling with assumptions about storage: An application of the POWER model. Energy 117:198–213. https://doi.org/10.1016/j.energy.2016.10.074
Tejada-Arango D, Domeshek M, Wogrin S, Centeno E (2018) Enhanced representative days and system states modeling for energy storage investment analysis. IEEE Trans Power Syst 33:6534–6544. https://doi.org/10.1109/TPWRS.2018.2819578
Reichenberg L, Siddiqui AS, Wogrin S (2018) Policy implications of downscaling the time dimension in power system planning models to represent variability in renewable output. Energy 159:870–877. https://doi.org/10.1016/j.energy.2018.06.160
Gabrielli P, Gazzani M, Martelli E, Mazzotti M (2018) Optimal design of multi-energy systems with seasonal storage. Appl Energy 219(1):408–424. https://doi.org/10.1016/j.apenergy.2017.07.142
Brown T, Schlachtberger D, Kies A, Schramm S, Greiner M (2018) Synergies of sector coupling and transmission reinforcement in a cost-optimised, highly renewable European energy system. Energy 160:720–739. https://doi.org/10.1016/j.energy.2018.06.222
Van der Hoven I (1956) Power spectrum of horizontal wind speed in the frequency range from 0.0007 to 900 cycles per hour. J Meteorol pp. 160–164
Guignard M, Kim S (1987) Lagrangean decomposition: A model yielding stronger Lagrangean bounds. Math Program 39(2):215–228. https://doi.org/10.1007/BF02592954
Larsson T, Patriksson M, Strömberg A-B (1996) Conditional subgradient optimization–Theory and applications. Eur J Oper Res 88(2):382–403. https://doi.org/10.1016/0377-2217(94)00200-2
Hiriart-Urruty J-B, Lemaréchal C (1993) Convex Analysis and Minimization Algorithms II: Advanced Theory and Bundle Methods, vol 306. Springer-Verlag, Berlin, Germany, p 346
Guignard M (2003) Lagrangean relaxation. TOP 11(2):151–228. https://doi.org/10.1007/BF02579036
Sørensen B (2017) Renewable Energy: Physics, Engineering, Environmental Impacts. Academic Press, Economics and Planning
Göransson L, Goop J, Odenberger M, Johnsson F (2017) Impact of thermal plant cycling on the cost-optimal composition of a regional electricity generation system. Appl Energy 197:230–240. https://doi.org/10.1016/j.apenergy.2017.04.018
Weber C (2005) Uncertainty in the electric power industry: methods and models for decision support. Springer, New York. https://www.springer.com/gp/book/9780387230474
Iverson KE (1962) A Programming Language. Wiley
Kjärstad J, Johnsson F (2007) The European power plant infrastructure–Presentation of the Chalmers energy infrastructure database with applications. Energy Policy 35(7):3643–3664. https://doi.org/10.1016/j.enpol.2006.12.032
IEA (2016) World energy outlook 2016. Report, International Energy Agency. https://www.iea.org/reports/world-energy-outlook-2016
Mone C, Hand M, Bolinger M, Rand J, Heimiller D, Ho J (2017) 2015 Cost of Wind Energy Review. Technical report NREL/TP-6A20-66861, National Renewable Energy Laboratory. https://www.nrel.gov/docs/fy17osti/66861.pdf
Jordan G, Venkataraman S (2012) Analysis of cycling costs in western wind and solar integration study. Report, National Renewable Energy Laboratory, Golden, Colorado, US
Persson J, Andgren K, Henriksson H, Loberg J, Malm C, Pettersson L, Sandström J, Sigfrids T (2012) Additional costs for load-following nuclear power plants—Experiences from Swedish, Finnish, German, and French nuclear power plants. Report 12:71, Elforsk, Stockholm, Sweden
Thunman H, Gustavsson C, Larsson A, Gunnarsson I, Tengberg F (2019) Economic assessment of advanced biofuel production via gasification using cost data from the GoBiGas plant. Energy Science and Engineering 7:217–229. https://doi.org/10.1002/ese3.271
Johansson V, Thorson L, Goop J, Göransson L, Odenberger M, Reichenberg L, Taljegård M, Johnsson F (2017) Value of wind power–Implications from specific power. Energy 126:352–360. https://doi.org/10.1016/j.energy.2017.03.038
ECMWF (2010) ERA-Interim u- and v-components of horizontal wind, surface solar radiation downward, skin temperature. Web Page Access Date: 2010-10-10, European Centre for Medium-Range Weather Forecasts. https://www.ecmwf.int/en/forecasts/datasets/browse-reanalysis-datasets
Lucchesi R (2012) File specification for MERRA products, GMAO Office Note No. 1. Report, Global Modeling and Assimilation Office, NASA. URL http://gmao.gsfc.nasa.gov/pubs/office_notes
Olauson J, Bergkvist M (2015) Modelling the Swedish wind power production using MERRA re-analysis data. Renew Energy 76:717–725. https://doi.org/10.1016/j.renene.2014.11.085
Nilsson K, Unger T (2014) Bedömning av en europeisk vindkraftpotential med GIS-analys. Report, PROFU
Norwood Z, Nyholm E, Otanicar T, Johnsson F (2014) A geospatial comparison of distributed solar heat and power in Europe and the US. PLoS One 9(12):1–31. https://doi.org/10.1371/journal.pone.0112442
Energistyrelsen (2012) Generation of electricity and district heating, energy storage and energy carrier generation and conversion: Technology data for energy plants. Report, Energistyrelsen, Copenhagen V, Denmark. ISBN: 978-87-7844-931-3