COPPER model submission #83
Description
Name
COPPER
Screenshots
No response
Focus Topic
COPPER is a multi-period, optimization-based capacity expansion model specifically designed for the Canadian context. It analyzes Canada’s electricity system transition under various carbon management policies.
Primary Purpose
COPPER is a dynamic (multi-period), optimization-based electricity system planning (ESPM) that co-optimizes investment in thermal generation, renewable generation, transmission expansion, storage technologies, and operation and main�tenance (O & M) of these assets. It uses a mixed-integer linear programming formulation to solve for this.
Description
COPPER uses a mixed-integer linear programming formulation to explore optimal mixes for the future of Canada’s electricity system under various uncertainties. The linear formulation guarantees that the model will converge to an optimal solution, while the integer formulation allows the model to make binary decisions such as the complete development of a hydroelectric asset or not (rather than giving incremental options). COPPER optimization formulation is built based on the CREST formulation, where the objective function minimizes total system planning and operation costs over the planning period.
COPPER's optimization has two inherent limitations that need to be considered when analyzing the results. First, COPPER explores the system-wide least-cost solutions from the point of view of a central operator. Second, the results of an optimization model might be biased specifically when there are uncertainties in the objective function co-efficients
Mathematical Description
Objective function: The objective function minimizes total system planning and operation costs over the planning period.
Min total system costs = InvestmentCost + MaintenanceCost+ ProductionCost + CarbonCost
Constraints: • The hourly balance of demand and supply in each balancing area
• Planning reserve margin in each balancing area
• Thermal unit constraints including maximum generation, minimum and maximum capacity factor (CF), and ramp-rate
• Hydroelectric constraints for run-of-river, small and large reservoir
facilities including operational and development constraints
• Renewable energy maximum generation and land-use constraints
• Transmission and energy storage constraints
Website
https://cme-emh.ca/inventory-model/copper/?lang=en
Documentation
https://gitlab.com/sesit/copper
Source
https://gitlab.com/sesit/copper
Year
2022
Institution
SESIT (https://sesit.cive.uvic.ca/)
Funding Source
No response
Publications
7
Publication List
- Arjmand, R., & McPherson, M. (2022). Canada’s electricity system transition under alternative policy scenarios. Energy Policy, 163, 112844. https://doi.org/10.1016/j.enpol.2022.112844
- Arjmand, R., Monroe, J., & McPherson, M. (2023). The role of emerging technologies in Canada’s electricity system transition. Energy, 278, 127836. https://doi.org/10.1016/j.energy.2023.127836
- Miri, M., & McPherson, M. (2024). Demand response programs: Comparing price signals and direct load control. Energy, 288, 129673. https://doi.org/10.1016/j.energy.2023.129673
- Miri, M., Saffari, M., Arjmand, R., & McPherson, M. (2022). Integrated models in action: Analyzing flexibility in the Canadian power system toward a zero-emission future. Energy, 261, 125181. https://doi.org/10.1016/j.energy.2022.125181
- McPherson, M., Rhodes, E., Stanislaw, L., Arjmand, R., Saffari, M., Xu, R., Hoicka, C., & Esfahlani, M. (2023). Modeling the transition to a zero emission energy system: A cross-sectoral review of building, transportation, and electricity system models in Canada. Energy Reports, 9, 4380–4400. https://doi.org/10.1016/j.egyr.2023.02.090
- McPherson, M., Monroe, J., Jurasz, J., Rowe, A., Hendriks, R., Stanislaw, L., Awais, M., Seatle, M., Xu, R., Crownshaw, T., Miri, M., Aldana, D., Esfahlani, M., Arjmand, R., Saffari, M., Cusi, T., Toor, K. S., & Grieco, J. (2022). Open-source modelling infrastructure: Building decarbonization capacity in Canada. Energy Strategy Reviews, 44, 100961. https://doi.org/10.1016/j.esr.2022.100961
- Jahangiri, Z., Judson, M., Yi, K. M., & McPherson, M. (2023). A Deep Learning Approach for Exploring the Design Space for the Decarbonization of the Canadian Electricity System. Energies, 16(3), 1352. https://doi.org/10.3390/en16031352
Use Cases
No response
Infrastructure Sector
- Atmospheric dispersion
- Agriculture
- Biomass
- Buildings
- Communications
- Cooling
- Ecosystems
- Electric
- District heating
- Forestry
- Health
- Hydrogen
- Individual heating
- Land use
- Liquid fuels
- Natural Gas
- Transportation
- Water
Represented Behavior
- Earth Systems
- Employment
- Built Infrastructure
- Financial
- Macro-economy
- Micro-economy
- Policy
- Social
Modeling Paradigm
- Analytics
- Data
- Discrete Simulation
- Dynamic Simulation
- Equilibrium
- Engineering/Design
- Optimization
- Visualization
Capabilities
No response
Programming Language
- C – ISO/IEC 9899
- C++ (C plus plus) – ISO/IEC 14882
- C# (C sharp) – ISO/IEC 23270
- Delphi
- GAMS (General Algebraic Modeling System)
- Go
- Haskell
- Java
- JavaScript(Scripting language)
- Julia
- Kotlin
- LabVIEW
- Lua
- MATLAB
- Modelica
- Nim
- Object Pascal
- Octave
- Pascal Script
- Python
- R
- Rust
- Simulink
- Swift (Apple programming language)
- WebAssembly
- Zig
Required Dependencies
Our model will need one of the mathematical solver tools (CPLEX, GLPK or CBC), a few packages (openpyxl, ipykernel,pandas, pyaarrow,pyomo) and some standadrd system installations like Anaconda/Miniconda
What is the software tool's license?
MIT License (MIT)
Operating System Support
- Windows
- Mac OSX
- Linux
- iOS
- Android
User Interface
- Programmatic
- Command line
- Web based
- Graphical user
- Menu driven
- Form based
- Natural language
Parallel Computing Paradigm
- Multi-threaded computing
- Multi-core computing
- Distributed computing
- Cluster computing
- Massively parallel computing
- Grid computing
- Reconfigurable computing with field-programmable gate arrays (FPGA)
- General-purpose computing on graphics processing units
- Application-specific integrated circuits
- Vector processors
What is the highest temporal resolution supported by the tool?
Hours
What is the typical temporal resolution supported by the tool?
None
What is the largest temporal scope supported by the tool?
Years
What is the typical temporal scope supported by the tool?
None
What is the highest spatial resolution supported by the tool?
Region
What is the typical spatial resolution supported by the tool?
None
What is the largest spatial scope supported by the tool?
Country
What is the typical spatial scope supported by the tool?
None
Input Data Format
CSV
Input Data Description
Power system data, costs data, weather data, demand data
Output Data Format
CSV with pre-processing scripts to convert it into pyam if a user-likes
Output Data Description
Investment decisions, generation mix, transmission expansion, emission, location of REs
Contact Details
Interface, Integration, and Linkage
No response