This repository was archived by the owner on Apr 24, 2025. It is now read-only.
This repository was archived by the owner on Apr 24, 2025. It is now read-only.
SILVER model submission #82
Description
Name
SILVER
Screenshots
No response
Focus Topic
SILVER (Strategic Integration of Large-capacity Variable Energy Resources) optimizes the asset dispatch for a user-defined electricity system configuration
Primary Purpose
SILVER (Strategic Integration of Large-capacity Variable Energy Resources) is a scenario-based electricity system model that explores the trade-offs among alternative balancing strategies for high variable renewable energy (VRE) electricity grids. SILVER optimizes the asset dispatch for a user-defined electricity system configuration that specifies demand response availability, generation assets, storage assets, and transmission infrastructure
Description
The SILVER model comprises four modules:
- The long-term scenario planning module: It aids the user in designing feasible and consistent electricity system scenarios, by ensuring that a set of minimum constraints are met.
2.The day-ahead network-constrained price-setting dispatch: This is the first stage of optimisation where the 24-h day�ahead economic dispatch solves for the network-constrained system marginal price. The day-ahead forecasted load curve is derived from published forecasted load schedules - The day-ahead unit commitment (UC) module: It minimizes the daily system costs over an entire 24-h period by imposing additional optimization constraints.
- Real-time optimal power flow: In the final optimization stage, the real-time OPF routine adjusts the day-ahead UC solution to account for differences between the forecasted and real-time VRE and load time series.
Mathematical Description
No response
Website
https://cme-emh.ca/inventory-model/silver/?lang=en
Documentation
https://gitlab.com/sesit/silver
Source
https://gitlab.com/sesit/silver
Year
2017
Institution
SESIT (https://sesit.cive.uvic.ca/)
Funding Source
No response
Publications
12
Publication List
- Codrington, L., Haghi, E., Moo Yi, K., & McPherson, M. (2022). Exploring Grassroots Renewable Energy Transitions: Developing a Community-Scale Energy Model. Transdisciplinary Journal of Engineering & Science, 13. https://doi.org/10.22545/2022/00215
- Xu, R., Seatle, M., Kennedy, C., & McPherson, M. (2023). Flexible electric vehicle charging and its role in variable renewable energy integration. Environmental Systems Research, 12(1), 11. https://doi.org/10.1186/s40068-023-00293-9
- Saffari, M., Crownshaw, T., & McPherson, M. (2023). Assessing the potential of demand-side flexibility to improve the performance of electricity systems under high variable renewable energy penetration. Energy, 272, 127133. https://doi.org/10.1016/j.energy.2023.127133
- Saffari, M., & McPherson, M. (2022). Assessment of Canada’s electricity system potential for variable renewable energy integration. Energy, 250, 123757. https://doi.org/10.1016/j.energy.2022.123757
- Saffari, M., McPherson, M., & Rowe, A. (2023). Evaluation of flexibility provided by cascading hydroelectric assets for variable renewable energy integration. Renewable Energy, 211, 55–63. https://doi.org/10.1016/j.renene.2023.04.052
- Seatle, M., Stanislaw, L., Xu, R., & McPherson, M. (2021). Integrated Transportation, Building, and Electricity System Models to Explore Decarbonization Pathways in Regina, Saskatchewan. Frontiers in Sustainable Cities, 3, 113. https://doi.org/10.3389/frsc.2021.674848
- Seatle, M., & McPherson, M. (2024). Residential demand response program modelling to compliment grid composition and changes in energy efficiency. Energy, 290, 130173.
- Stanislaw, L., Seatle, M., & McPherson, M. (2024). Quantifying the value of building demand response: Introducing a cross-sectoral model framework to optimize demand response scheduling. Energy Reports, 11, 2111-2126.
- 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
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
- It could allow user to define his/her system configuration in terms of a simulation start date, end date and hours of unit commitment.
- It allows user to choose a unique mathematical solver based on the license they have
- It runs on a provincial scale and user can choose a province to generate the simulation run for
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 CPLEX tool, a few packages (bokeh, future,ipdb,matplotlib,networkx,numpy,openpyxl,pandas,Pyomo,PyYAML) 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
XLSX, CSV
Input Data Description
generation and transmission fleet operational characteristics (could be existing, based on a ‘what-if’ scenario),operational costs, maintenance costs, operational constraints (generation, transmission), load profile, GHG emissions, network design, long-term scenario plans
Output Data Format
CSV with pre-processing scripts to convert it into pyam if a user-likes
Output Data Description
hourly operation of the electricity system, including associated costs, GHG emissions, generator output, transmission usage, asset capacity factors, variable renewable generation, operational costs
Contact Details
Interface, Integration, and Linkage
No response