PLEXOS uses optimization to convert the properties and behaviors of a physical power system into mathematical problems that find the best course of action from a range of available options. By simply changing the inputs, you get another optimal course of action, and now you can compare those insights to choose a solution that gives you the best outcome.
Underneath it all, you can feel confident that the algorithms are calculated correctly, so you can focus on making changes, experiment with your variables and interpreting the results.
A Unified Platform
Consolidate your power system analyses in one tool.
PLEXOS uses the exact same core simulation code for long, medium and short-term simulations ensuring outcomes are completely consistent and automatically transferred between those phases.
PLEXOS is the only solution that unifies your market simulations across electric, water and gas energy systems: from wellhead to the city gate.
Flexibility and Customization
You can customize PLEXOS with a limitless collection of scenarios, customized constraints, conditional variables, physical elements, simulation horizon, duration of the simulation period, phases in the integration and model resolution.
This data-driven flexibility creates a dynamic solving engine: it adapts to your data to produce the right balance of detail and performance while also giving you ultimate scalability and razor-fine control.
When you need to be sure you can trust the results of the simulation, or when it needs to be checked by academics and outside auditors, PLEXOS gives you transparency across the equations so you can trace how the solutions were produced.
These results are also robust and transparent enough for litigation work.
Driving High Performance
Cloud and Distributed
You get exponentially faster results through by distributing your runs across machines.
Cloud computing gives you instant access to the best hardware for your simulations and allows you to control compute infrastructure costs.
Capture the full value chain from the gas production basin to the electricity load or further to water objects like a desalination plant through co-optimization modeling.
Leading utilities and ISOs use co-optimized models of electricity, gas and water to deliver better savings and higher returns to stakeholders.
High Resolution Modeling
You can change the resolution of your models to years, hours or seconds.
High levels of detailed granularity combined with mixed integer programming give you real-life constraints.
With scalability you can model a system of any size. For example, take a zonal model and run full nodal studies, or you take a short-term model and run it with a 30-year horizon.
Breadth of Functionality
Ancillary Services and Energy Co-optimization
Model reserve provisions that are co-optimized with generation dispatch and unit commitment down to a sub-hourly level.
PLEXOS models multiple reserve classes, and you get detailed treatment of start-up, shut-down, ramping and other constraint interaction minute-by-minute.
Battery Energy Storage
Model the impact of batteries on energy smoothing, peak shaving, ancillary services, capacity expansion planning or transmission congestion.
The battery class with its purpose built properties allows for the modelling of electric vehicles, CAES, thermal storage, Flywheels, secondary batteries and flowing electrolyte batteries.
Conditions and Variables
Create the most real-world models with a virtually infinite amount of conditions and variables.
Set up conditional relationships that trigger an action based on events. Define your input data accounting for forecast error and the relationships between variables.
You can run scenarios incorporating any type of generator technology.
There are no limits to the quantity and categories of generators you can model.
The graphical user interfaces are easy to use for almost any configuration, so you don’t need any special programming.
You can view large datasets in seconds as well as create custom reports to export to excel or link to SQL.
Whether planned or forced outages, you can optimize scarce resources across multiple maintenance events and time horizons to minimize your costs.
Multi-stage Stochastic Hydro Optimization
There is a faster and more robust way to model hydro dispatch and storage planning under uncertainty. Using “rolling horizon” methodology, you can handle many more detailed constraints and integer decision variables.
When you’re dispatching a portfolio of assets against a forecast set of commodity prices, PLEXOS will find the optimal dispatch, unit commitment and contract purchase decisions that maximize profit under uncertainty.
Use the advanced algorithms in PLEXOS to choose what, where and when to optimally change your assets.
Plan for investment, expansion or retirement considering generation and transmissions across electricity and gas systems.
Predict future energy prices with generator bidding or using competition modeling.
Bertrand, Residual Supply Index, Nash Cournot, Long Run Marginal Cost and more.
Simulate planned and random outages in generation, transmission and gas pipeline resources in a powerful Monte Carlo simulation framework.
You can account for external constraints as well as internal constraints, such as crew limits.
Stochastic and Monte Carlo Simulation
Deterministic models only give you a point-in-time estimate. Instead, model any number of iterations using Monte Carlo, however our stochastic optimization approach offers two-stage and multi-stage optimization such as unit commitment or expansion with respect to uncertainty.
Stochastic Unit Commitment
You can generate optimal decisions even in the face of uncertainties.
Stochastic unit commitment guides you in making more optimal unit commitment decisions for the next period given the arrival of unforeseen events.
You have choices for unit commitment decisions including linear programming, mixed integer programming, rounded relaxation and dynamic programming.
You can automate PLEXOS to work in the background in “real time” to regularly feed results to operators.