COPPER model submission

[mseatle]

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

1. 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
2. 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
3. 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
4. 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
5. 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
6. 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
7. 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

[email protected]

Interface, Integration, and Linkage

No response