Capacity expansion planning
A model chooses generation, storage and transmission investments over decades to meet demand across many weather years, with reliability and emissions constraints stated.
GCRI · Energy Nexus
Optimisation for energy systems
Optimisation and operations research decide how energy systems are built and run at least cost within physical and policy limits: which plants to build, how to dispatch them, how much storage and transmission to add, and in what order to restore service after a failure.
01Register
Optimisation for energy systems on the record.
Method recordSTEM method
- MethodOptimisation and operations research
- Applied inEnergy Nexus
- Data4 inputs
- Checks3 validation steps
- Canonical pageSTEM Commons
02Application
What optimisation and operations research does here.
Optimisation and operations research develop mathematical methods for allocating resources and making decisions under constraints. They support planning and logistics in complex, interconnected systems. In energy, critical infrastructure and climate and hazards, optimisation supports prepositioning of supplies, network investment and recovery sequencing. Robust formulations help choose options that perform acceptably across many scenarios. The canonical description is on the STEM Commons.
Unit commitment and dispatch
Hourly scheduling of plants and storage against forecast demand and renewable output shows operating costs, reserve margins and curtailment under each scenario.
Restoration sequencing
Crews, spare parts and switching operations are scheduled so that critical loads are restored first, subject to road access and network constraints.
03Simulator
A cascade through interdependent networks.
A hazard footprint takes out co-located assets in every layer; fragmentation, dependency between layers and overload of the survivors then spread the failure. The 3D view separates the layers by depth. Teaching model with stated illustrative values, not a prediction.
Controls
- Assets per layer
- 18
- Hazard footprint
- 8% of area
- Assets that depend on another layer
- 60%
- Spare capacity (tolerance)
- 30%
Readouts
- Assets hit by the hazard
- 19%
- Assets still working
- 31%
- Amplification of the initial damage
- 3.7 times
- Steps to settle
- 2
Numbers, methods and sources
| Step | Power working (%) | Gas working (%) | Telecoms working (%) |
|---|---|---|---|
| 0 | 83 | 72 | 89 |
| 1 | 50 | 39 | 39 |
| 2 | 50 | 33 | 11 |
Methods
- Interdependent networks Buldyrev and others (2010), Catastrophic cascade of failures in interdependent networks, Nature 464
- Overload cascades Motter and Lai (2002), Physical Review E 66
- Betweenness load Brandes (2001), Journal of Mathematical Sociology 25
- Multilayer networks Kivela and others (2014), Journal of Complex Networks 2
- Infrastructure interdependencies Rinaldi, Peerenboom and Kelly (2001), IEEE Control Systems 21(6)
- Uncertainty fan (parameter Monte Carlo) Spiegelhalter, Pearson and Short (2011), Visualising uncertainty about the future, Science 333
- One-at-a-time sensitivity (tornado) Saltelli and others (2008), Global Sensitivity Analysis: The Primer, Wiley
Data
The model runs on the teaching values set by the controls. To calibrate it for a place, read it against these admitted layers and series, which this page’s map can draw:
- Major roads Natural Earth
- Railways Natural Earth
- Seaports Natural Earth
- Airports Natural Earth
- Urban extents Natural Earth
- Night lights tonight, outages (VIIRS Day/Night Band) NASA/NOAA VIIRS (Suomi NPP) · register nasa.gibs.context-live-layers
- Natural events (NASA EONET) NASA EONET · register nasa.eonet.events
Across the six pillars, led by Nexus Consortium
- Experiments Labs
- Built in Energy Nexus in Foundry
- Tested at Energy Nexus in Universe
- Codified in Energy and Critical Infrastructure in Standards
- Published in Energy Nexus in Reports
- Taught in Faculty of Infrastructure, Energy and Industry in Academy
04Data
What the method consumes.
Datasets and records used by optimisation and operations research on this platform, with the role each plays and the label its values carry. Sources are listed in the source register.
| Data | Role | Label |
|---|---|---|
| Technology cost and performance assumptions | Investment and operating costs | ESTIMATED |
| Hourly demand and weather-driven generation series | Operating conditions | MODELLED |
| Network topology and transfer limits | Transmission constraints | Published statistics |
| Policy targets and constraints | Emissions and reliability limits | DECLARED |
05Sector signature
The sector signature of optimisation for energy systems.
The sectors optimisation for energy systems touches and how strongly they depend on each other: what a study has to observe and model together.
Select corners · hover lines6 / 15 relations
Signature
Energy and its partners
Energy gives power for transport and telecoms to infrastructure (0.8), pumping and treatment to water (0.5) and fertiliser, machinery and cold chains to food (0.5).
W · E · I · F · H · B
- Sectors
- 4 of 6
- Relations
- 6 of 15
- Coupling
- 0.46 share of total
Strongest relations in play
- Energy → Infrastructure · power for transport and telecoms0.8
- Water → Energy · cooling water and hydropower0.6
- Water → Food · irrigation0.8
- Infrastructure → Water · pipes, dams and treatment works0.6
06Validation
How results are checked.
Solutions are checked for feasibility, tested through simulation and compared with historical decisions. Sensitivity analysis examines how results change with parameters.
| Check | How it is done |
|---|---|
| Operational feasibility | Every plan is re-run in an hourly operational simulation to confirm it meets demand and reserve requirements |
| Sensitivity and robustness | Results are tested across cost, weather and demand assumptions; robust options are reported beside the least-cost plan |
| Open model comparison | Results are compared with open energy system models used in published intercomparison studies |
Optimal solutions can be fragile when assumptions are wrong. Objectives may omit values that are hard to quantify.
07Methods and evidence
How optimisation for energy systems is modelled, measured and tested.
The model, its assumptions, its data and its uncertainty, in the form a scientist or engineer would check. Structures follow published methods. The values drawn are illustrative, not findings.
System model
The couplings between the systems in optimisation for energy systems: signs of influence, a reinforcing and a balancing loop, and cross-links where stress jumps.
FAIR guiding principles · GO FAIR and FORCE11W3C PROV · World Wide Web ConsortiumGuidance on consistent treatment of uncertainties · IPCCCEOS Analysis Ready Data · Committee on Earth Observation Satellites
08Connections
Topics that rely on this method.
09Stewardship
Who maintains the method.
Systems Science and Modelling stewards optimisation and operations research for the Consortium and reviews how it is applied across platforms.
10Connected
Where this page leads across the Consortium.
11Questions
Questions and answers.
What are the limits of optimisation and operations research in this field?
Optimal solutions can be fragile when assumptions are wrong. Objectives may omit values that are hard to quantify.
How are results checked?
Solutions are checked for feasibility, tested through simulation and compared with historical decisions. Sensitivity analysis examines how results change with parameters.
Are the values on this page real?
The data names, roles and checks are real descriptions of practice. The canvas shows a design of the method’s use, not a run; no result is reported here.
Where else is this method applied?
The canonical method page lists every platform that applies it.
ReportsContribute
Contribute evidence from this page.
Reports is the Consortium’s quarterly publication. What you know about this subject can become evidence, a case or a correction in the next edition, credited to you and published under CC BY 4.0 where it is your own work.
GCRIEnergy NexusGlobal Sector Risk and ResilienceStrategic SystemsScenario and Stress-Test
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