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| | |source_download=https://github.com/switch-model/switch | | |source_download=https://github.com/switch-model/switch |
| | |text_description=Switch is a capacity-planning model for power systems with large shares of renewable energy, storage and/or demand response. It optimizes investment decisions for renewable and conventional generation, storage, hydro and other assets, based on how they would be used during a collection of sample days in many future years. The use of multiple investment periods and chronologically sequenced hours enables optimization and assessment of a long-term renewable transition based on a direct consideration of how these resources would be used hour-by-hour. The Switch platform is highly modular, allowing easy selection between prewritten components or addition of custom components as first-class elements in the model. | | |text_description=Switch is a capacity-planning model for power systems with large shares of renewable energy, storage and/or demand response. It optimizes investment decisions for renewable and conventional generation, storage, hydro and other assets, based on how they would be used during a collection of sample days in many future years. The use of multiple investment periods and chronologically sequenced hours enables optimization and assessment of a long-term renewable transition based on a direct consideration of how these resources would be used hour-by-hour. The Switch platform is highly modular, allowing easy selection between prewritten components or addition of custom components as first-class elements in the model. |
| − | |Primary purpose=design and assessment of high-renewable power systems
| |
| | |Primary outputs=optimal investment plans, hourly operational details, emissions, costs | | |Primary outputs=optimal investment plans, hourly operational details, emissions, costs |
| | |Support=contact authors via http://switch-model.org | | |Support=contact authors via http://switch-model.org |
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| | |processing_software=Python, any user-selected software | | |processing_software=Python, any user-selected software |
| | |External optimizer=glpk, cbc, cplex, gurobi, any Pyomo- (or AMPL-) compatible solver | | |External optimizer=glpk, cbc, cplex, gurobi, any Pyomo- (or AMPL-) compatible solver |
| − | |Primary purpose=design and assessment of high-renewable power systems
| |
| | |GUI=No | | |GUI=No |
| | |model_class=Power system capacity expansion, energy system | | |model_class=Power system capacity expansion, energy system |
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| | |math_modeltype=Optimization | | |math_modeltype=Optimization |
| | |math_modeltype_shortdesc=intertemporal mathematical optimization | | |math_modeltype_shortdesc=intertemporal mathematical optimization |
| − | |is_suited_for_many_scenarios=No | + | |math_objective=total cost or consumer surplus, including environmental adders |
| | + | |deterministic=stochastic treatment of hourly renewable variability; allocation of reserves for sub-hourly variability; scenarios or progressive hedging for uncertain annual weather or fuel or equipment costs |
| | + | |is_suited_for_many_scenarios=Yes |
| | |montecarlo=No | | |montecarlo=No |
| | + | |computation_time_minutes=20 |
| | + | |computation_time_hardware=single-threaded on 3.0 GHz Intel i7 CPU |
| | + | |computation_time_comments=computation time is roughly cubic with the spatial and temporal resolution selected; users typically adjust resolution to achieve 2-10 min solution time in testing phases, 10-60 min solution time for final optimizations |
| | + | |citation_references=J. Johnston, R. Henríquez, B. Maluenda and M. Fripp “Switch 2.0: a modern platform for planning high-renewable power systems,” Preprint, 2018. https://arxiv.org/abs/1804.05481 |
| | + | |report_references=Imelda, M. Fripp, and M. J. Roberts, “Variable pricing and the cost of renewable energy,” National Bureau of Economic Research, Cambridge, Massachusetts, NBER Working Paper No. 24712, Jun. 2018. http://www.nber.org/papers/w24712 |
| | + | |
| | + | M. Fripp, “Intercomparison between Switch 2.0 and GE MAPS models for simulation of high-renewable power systems in Hawaii,” Energy, Sustainability and Society, vol. 8, no. 1, p. 41, Dec. 2018, https://doi.org/10.1186/s13705-018-0184-x |
| | + | |
| | + | M. Fripp, “Making an Optimal Plan for 100% Renewable Power in Hawai‘i - Preliminary Results from the SWITCH Power System Planning Model,” University of Hawai‘i Economic Research Organization (UHERO), Honolulu, Hawai‘i, Working Paper No. 2016-1, Jan. 2016. |
| | + | http://www.uhero.hawaii.edu/assets/WP_2016-1.pdf. |
| | + | |
| | + | M. Fripp, “Switch: a planning tool for power systems with large shares of intermittent renewable energy,” Environmental Science & Technology, vol. 46, no. 11, pp. 6371–6378, Jun. 2012. http://dx.doi.org/10.1021/es204645c |
| | + | |
| | + | J. Nelson, J. Johnston, A. Mileva, M. Fripp, I. Hoffman, A. Petros-Good, C. Blanco, and D. M. Kammen, “High-resolution modeling of the western North American power system demonstrates low-cost and low-carbon futures,” Energy Policy, vol. 43, pp. 436–447, Apr. 2012. http://dx.doi.org/10.1016/j.enpol.2012.01.031 |
| | + | |
| | + | Max Wei et al., “Deep Carbon Reductions in California Require Electrification and Integration across Economic Sectors,” Environmental Research Letters 8, no. 1 (2013): 014038. |
| | + | |
| | + | John Larsen et al., “Transcending Oil: Hawaii’s Path to a Clean Energy Economy” (Oakland, California: Rhodium Group, April 19, 2018), https://rhg.com/wp-content/uploads/2018/04/rhodium_transcendingoil_final_report_4-18-2018-final.pdf. |
| | + | |
| | + | Ana Mileva et al., “SunShot Solar Power Reduces Costs and Uncertainty in Future Low-Carbon Electricity Systems,” Environmental Science & Technology 47, no. 16 (2013): 9053–60. |
| | + | |
| | + | Daniel L Sanchez et al., “Biomass Enables the Transition to a Carbon-Negative Power System across Western North America,” Nature Climate Change 5, no. 3 (February 2015): 230–34. |
| | + | Diego Ponce de Leon Barido et al., “Evidence and Future Scenarios of a Low-Carbon Energy Transition in Central America: A Case Study in Nicaragua,” Environmental Research Letters 10, no. 10 (2015): 104002, https://doi.org/10.1088/1748-9326/10/10/104002. |
| | + | |
| | + | Gang He et al., “SWITCH-China: A Systems Approach to Decarbonizing China’s Power System,” Environmental Science & Technology 50, no. 11 (June 7, 2016): 5467–73, https://doi.org/10.1021/acs.est.6b01345. |
| | + | |
| | + | Tatsuya Wakeyama, “Impact of Increasing Share of Renewables on the Japanese Electricity System - Model Based Analysis” (14th International Workshop on Large-Scale Integration of Wind Power into Power Systems as well as on Transmission Networks for Offshore Wind Power Plants, Brussels, Belgium: Energynautics GmbH, 2015). |
| | + | |
| | + | Juan-Pablo Carvallo et al., “Sustainable Low-Carbon Expansion for the Power Sector of an Emerging Economy: The Case of Kenya,” Environmental Science & Technology 51, no. 17 (September 5, 2017): 10232–42, https://doi.org/10.1021/acs.est.7b00345. |
| | + | |
| | + | |
| | + | |example_research_questions=identify least-cost combination of resources to reach 100% renewable power; calculate cost of achieving various renewable or carbon targets; select assets to minimize cost for a microgrid, possibly interacting with outside electricity supplier; calculate effect of price-responsive demand on consumer welfare while adopting renewable power |
| | + | |Comment on model validation=where technical detail is important, users should configure switch to reflect local operating rules and validate results against existing practices; Switch has also been validated against GE-MAPS in a Hawaii case study in production-cost mode (https://doi.org/10.1186/s13705-018-0184-x) |
| | + | |Specific properties=using selected samples of full days enables direct modeling of curtailment, storage, hydro and demand response in a multi-decade model; highly modular platform enables easy and structured customization for specific studies |
| | |Model input file format=No | | |Model input file format=No |
| | |Model file format=No | | |Model file format=No |
| | |Model output file format=No | | |Model output file format=No |
| | }} | | }} |
Revision as of 01:50, 31 January 2019
|
Switch
by University of Hawaii
Authors:
Matthias Fripp, Josiah Johnston, Rodrigo Henríquez, Benjamín Maluenda
Contact:
Matthias Fripp
|
Switch is a capacity-planning model for power systems with large shares of renewable energy, storage and/or demand response. It optimizes investment decisions for renewable and conventional generation, storage, hydro and other assets, based on how they would be used during a collection of sample days in many future years. The use of multiple investment periods and chronologically sequenced hours enables optimization and assessment of a long-term renewable transition based on a direct consideration of how these resources would be used hour-by-hour. The Switch platform is highly modular, allowing easy selection between prewritten components or addition of custom components as first-class elements in the model.
Based on Python, Pyomo. Using Python, any user-selected software for data processing.
Website / Documentation
Download
|
 Open Source
Apache License 2.0 (Apache-2.0)
Directly downloadable
Input data shipped
|
| Model Scope |
Model type and solution approach |
| Model class
|
Power system capacity expansion, energy system
|
| Sectors
|
electricity, gas, hydrologic, transport, end-use demand, carbon sequestration; user-extendable
|
| Technologies
|
Renewables, Conventional Generation, CHP
|
| Decisions
|
dispatch, investment
|
| Regions
|
|
| Geographic Resolution
|
buildings, microgrids, city, state, national or continental
|
| Time resolution
|
Hour
|
| Network coverage
|
transmission, distribution, AC load flow, DC load flow, net transfer capacities
|
|
| Model type
|
Optimization
|
|
|
intertemporal mathematical optimization
|
| Variables
|
|
| Computation time
|
20 minutes (computation time is roughly cubic with the spatial and temporal resolution selected; users typically adjust resolution to achieve 2-10 min solution time in testing phases, 10-60 min solution time for final optimizations)
|
| Objective
|
total cost or consumer surplus, including environmental adders
|
| Uncertainty modeling
|
stochastic treatment of hourly renewable variability; allocation of reserves for sub-hourly variability; scenarios or progressive hedging for uncertain annual weather or fuel or equipment costs
|
| Suited for many scenarios / monte-carlo
|
Yes
|
|
References
Scientific references
J. Johnston, R. Henríquez, B. Maluenda and M. Fripp “Switch 2.0: a modern platform for planning high-renewable power systems,” Preprint, 2018. https://arxiv.org/abs/1804.05481
Reports produced using the model
Imelda, M. Fripp, and M. J. Roberts, “Variable pricing and the cost of renewable energy,” National Bureau of Economic Research, Cambridge, Massachusetts, NBER Working Paper No. 24712, Jun. 2018. http://www.nber.org/papers/w24712
M. Fripp, “Intercomparison between Switch 2.0 and GE MAPS models for simulation of high-renewable power systems in Hawaii,” Energy, Sustainability and Society, vol. 8, no. 1, p. 41, Dec. 2018, https://doi.org/10.1186/s13705-018-0184-x
M. Fripp, “Making an Optimal Plan for 100% Renewable Power in Hawai‘i - Preliminary Results from the SWITCH Power System Planning Model,” University of Hawai‘i Economic Research Organization (UHERO), Honolulu, Hawai‘i, Working Paper No. 2016-1, Jan. 2016.
http://www.uhero.hawaii.edu/assets/WP_2016-1.pdf.
M. Fripp, “Switch: a planning tool for power systems with large shares of intermittent renewable energy,” Environmental Science & Technology, vol. 46, no. 11, pp. 6371–6378, Jun. 2012. http://dx.doi.org/10.1021/es204645c
J. Nelson, J. Johnston, A. Mileva, M. Fripp, I. Hoffman, A. Petros-Good, C. Blanco, and D. M. Kammen, “High-resolution modeling of the western North American power system demonstrates low-cost and low-carbon futures,” Energy Policy, vol. 43, pp. 436–447, Apr. 2012. http://dx.doi.org/10.1016/j.enpol.2012.01.031
Max Wei et al., “Deep Carbon Reductions in California Require Electrification and Integration across Economic Sectors,” Environmental Research Letters 8, no. 1 (2013): 014038.
John Larsen et al., “Transcending Oil: Hawaii’s Path to a Clean Energy Economy” (Oakland, California: Rhodium Group, April 19, 2018), https://rhg.com/wp-content/uploads/2018/04/rhodium_transcendingoil_final_report_4-18-2018-final.pdf.
Ana Mileva et al., “SunShot Solar Power Reduces Costs and Uncertainty in Future Low-Carbon Electricity Systems,” Environmental Science & Technology 47, no. 16 (2013): 9053–60.
Daniel L Sanchez et al., “Biomass Enables the Transition to a Carbon-Negative Power System across Western North America,” Nature Climate Change 5, no. 3 (February 2015): 230–34.
Diego Ponce de Leon Barido et al., “Evidence and Future Scenarios of a Low-Carbon Energy Transition in Central America: A Case Study in Nicaragua,” Environmental Research Letters 10, no. 10 (2015): 104002, https://doi.org/10.1088/1748-9326/10/10/104002.
Gang He et al., “SWITCH-China: A Systems Approach to Decarbonizing China’s Power System,” Environmental Science & Technology 50, no. 11 (June 7, 2016): 5467–73, https://doi.org/10.1021/acs.est.6b01345.
Tatsuya Wakeyama, “Impact of Increasing Share of Renewables on the Japanese Electricity System - Model Based Analysis” (14th International Workshop on Large-Scale Integration of Wind Power into Power Systems as well as on Transmission Networks for Offshore Wind Power Plants, Brussels, Belgium: Energynautics GmbH, 2015).
Juan-Pablo Carvallo et al., “Sustainable Low-Carbon Expansion for the Power Sector of an Emerging Economy: The Case of Kenya,” Environmental Science & Technology 51, no. 17 (September 5, 2017): 10232–42, https://doi.org/10.1021/acs.est.7b00345.
Example research questions
identify least-cost combination of resources to reach 100% renewable power; calculate cost of achieving various renewable or carbon targets; select assets to minimize cost for a microgrid, possibly interacting with outside electricity supplier; calculate effect of price-responsive demand on consumer welfare while adopting renewable power
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