SILVER model submission

[mseatle]

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:

1. 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
2. The day-ahead unit commitment (UC) module: It minimizes the daily system costs over an entire 24-h period by imposing additional optimization constraints.
3. 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

1. 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
2. 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
3. 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
4. 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
5. 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
6. 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
7. Seatle, M., & McPherson, M. (2024). Residential demand response program modelling to compliment grid composition and changes in energy efficiency. Energy, 290, 130173.
8. 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.
9. 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
10. 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
11. 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
12. 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

1. It could allow user to define his/her system configuration in terms of a simulation start date, end date and hours of unit commitment.
2. It allows user to choose a unique mathematical solver based on the license they have
3. 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

modellingteam.sesit@uvic.ca

Interface, Integration, and Linkage

No response