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.
The Global Centre for Risk and Innovation (GCRI)
Optimization 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.
GCRI · Established · service level R0
Energy system optimization is the use of mathematical programming to 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.
Optimization 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, optimization 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.
A model chooses generation, storage and transmission investments over decades to meet demand across many weather years, with reliability and emissions constraints stated.
Hourly scheduling of plants and storage against forecast demand and renewable output shows operating costs, reserve margins and curtailment under each scenario.
Crews, spare parts and switching operations are scheduled so that critical loads are restored first, subject to road access and network constraints.
Each term as this page uses it. Where a standard-setting body defines the term, the wording follows that body.
| Term | Definition |
|---|---|
| Capacity expansion model | An optimization model that chooses which generation, storage and network capacity to build over a planning horizon. |
| Unit commitment | Deciding which generating units to switch on and when, ahead of real time, to meet forecast demand at least cost. |
| Economic dispatch | Setting the output of committed units to meet demand at least cost within their limits. |
Where demand concentrates in cities and the rail, road and port corridors that move fuel and equipment, shown for South Africa as an example geography for this subject.
Data from the source register feeds the methods; calibration and an independent test gate the estimate and its limits; the decision stays with the competent authority.
Fig. 01Nodes carry their type, metrics and state; connectors show what feeds what; the gates are the validation steps; the timeline is one application. Design of the method's use, not a record of a run.
Datasets and records used by optimization 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 | OFFICIAL |
| Policy targets and constraints | Emissions and reliability limits | DECLARED |
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.
Systems Science and Modelling stewards optimization and operations research for the Consortium and reviews how it is applied across platforms.
The current service level is R0: information and enquiries. An enquiry receives a reference and names the next step; applications, payments, compute allocations and registrations open only when their team, terms and review capacity exist.