Use Cases from Around the World
With the advanced algorithms built into PLEXOS, you can run capacity expansion problems over the 10- to 30-year planning horizon, or any horizon timeline. With this solution, you can appropriately manage discounting and end-year effects, and it will meet your required LOLP target with new builds and/or restricting plant requirements. You can include LOLP and EENS targets by using a minimum capacity reserve requirement, which is commonly calculated based on reliability metrics such as LOLP and EENS.
- Demand-side participation
- Physical generation and load contracts
- Policies and regulations
- Multi-stage generation and transmission projects
- Transmission dynamics
- Capture operation constraints
- Across any type of generating technology
These modeling scenarios will give you confidence in your decisions for what, when and where to invest or retire your generation and transmission assets – across both electricity and gas.
Perform comprehensive modeling of the economics and technical limits of fossil-fired and renewable generation sources including:
- Heat rate curves
- Multi-fuel operation
- Minimum operating levels
- Minimum up and down times
- Ramping rates
- Start-up and shutdown profiles
- CCGT operating modes
- Emissions production and limits
You can also choose Monte Carlo for forced and planned outages, or stochastic modeling for wind and solar production.
Analyze and plan the impact of transmission operation and congestion costs. You can model optimal power flow (OPF) with losses because this solution is fully integrated with dispatch and unit commitment and expansion planning.
- Large interconnections of multiple AC and DC networks
- Loss modeling
- Security-constrained generator unit commitment (SCUC)
- DC lines and phase shifters
- Generic constraints and interface limits, wheeling charges
- Transmission aggregation and network reduction
- Monte Carlo simulation outages and optimal transmission switching
- Local Marginal Price (LMP) decomposition
- Financial Transmission Rights (FTR) evaluation
- Nodal pricing and decomposition into energy, congestion and marginal loss components
- Integration with the long-term plan to provide optimal transmission and generation expansion solutions
- Optimal placement of grid-connected energy storage systems
Ultimately run these models to identify optimal investment across transmission upgrades and retirements.
Whether you’re designing a new policy or navigating an existing regulatory landscape, you need a platform that can model life-like dynamics can give you actionable insights.
PLEXOS simulates planned and random outages in generation, transmission and gas pipeline resources in a powerful Monte Carlo simulation framework, and the solution includes a “Reserve” class of objects to specifically model various ancillary services. You can account for external constraints as well as internal constraints, such as crew limits.
- Takes forced outages into account
- Calculates LOLP, LOLE, EDNS and ENA indices
- Calculates the multi-area LOLP index using state space decomposition and Monte Carlo simulation
- Using the Security-Constrained Unite Commitment (SCUC) algorithm
- Calculated with contingency shift factors
- Supports “N-x” contingency analysis
PLEXOS has kept pace with disruptive and innovative advancements in energy storage, smart grids and electric vehicles. Real-world detail captured in sub-hourly simulations will provide insights into a low-carbon future, as back-up generation steps in to fill the gaps left behind from intermittency.
- Various autoregressive sampling models for wind speed, solar radiation and natural inflows
- Sub-hourly modeling – down to the 1 second level – to represent intermittent generation
- Multi-commodity optimization by integrating multiple models
- Co-optimize energy and ancillary services with renewables
- Interleaved simulation for optimizing unit commitment and economic dispatch of Day-Ahead and Real-Time markets
- Stochastic simulation for modeling uncertainties using Monte Carlo simulation or stochastic optimization
- Represent detailed real-world technical constraints
- Run simulations with forecast and actual data streams
Analyze a wide range of energy storage technologies for both short- and long-term modeling.
You can compute savings from production costs, congestion charges and reduction in losses.
- Carry out cost-benefit analysis of transmission and distribution network deferrals
- Assess potential benefits from reduced transmission congestion
- Model grid stabilization and transmission loss reduction
- Solve chronological unit commitment using sub-hourly resolution
- Identify and evaluate revenue stream opportunities in the energy and reserve market
- Model renewable energy smoothing, peak-shaving and load-levelling
Decision for long-term investment and short-term optimization are more insightful when hydro is co-optimized with your other resources. PLEXOS optimizes hydro dispatch while properly accounting for uncertainty of inflows and unknown future decisions.
- Integrate complex hydro systems with generation efficiency curves, head storage dependency, waterway flow delays, evaporation and other factors
- Perform deterministic and multi-stage stochastic solutions over any horizon
- Seamlessly integrate with the hydro-thermal coordination problem using hydro targets or future cost function decomposition
Unforeseen contingencies mean you must be ready to address imbalances in the market. Use PLEXOS to define ancillary services for co-optimisation with your energy dispatch, which you can calculate by various constraints such as:
- Regulation (frequency keeping) capability
- Fast response reserve (for various timeframes such as 6-60 seconds, or several minutes)
- Non-spinning (or replacement) reserves
The flexibility of this solution can be used to model any class of ancillary service where provision is made by:
- Setting aside spare capacity on running generators
- Interruptible load reserves including interruptible pumping load
- Synchronous condenser plant
- Plant on stand-by (non-spinning)
You can uncover tens of millions of dollars in saving from more efficient dispatch decisions. PLEXOS algorithms offer choices for unit commitment insights that can lower production costs.
- Linear programming
- Rounded relaxation
- Mixed integer programming
- Dynamic programming
With mixed integer programming and sub-hourly resolution, you can simulate real-world systems. Then automate PLEXOS to with in the background in a “real time” environment to regularly feed results to operators.
Accurately forecast price by accommodating for uncertain demand variables, plant failures, fuel shortages, renewable energy generation and transmission constraints. With PLEXOS, you can use the price-based unit commitment algorithms to set minimum profit targets, perform sensitivity analysis, and consistent with your market’s design. Bid, cost or other types of markets are all able to be represented within the universal architecture.
- Regional, zonal and nodal price forecasts
- eneration, fuel and revenue budgets
- Monte Carlo simulation to understand price volatility
- Asset valuation and risk assessment
Make investment decisions that have the greatest impact of risk. Whether you are designing an optimal portfolio or stress-testing an existing one, use PLEXOS for seamless multi-scenario planning to give you a wide array of outputs that will help you make a more informed decision.
PLEXOS models physical portfolios for your optimal dispatch strategy and financial portfolios for your optimal contacting. Choose to model deterministically or stochastically, and you can also integrate PLEXOS outputs with:
- Value at Risk (VAR)
- Earning at Risk (EAR)
- Conditional Value at Risk (CVaR)
Identify high-risk/low-cost or low-risk/high-cost solutions.
Whether you are assessing changes to the market rules or answering the question “what is the maximum amount of renewables that can be invested in this region,” your best decisions will be based on real-world simulations.
Stay on top of how the existing design is performing, how its participants are behaving and what risks lie ahead in terms of changing conditions and technological advancements. Monitoring the market and calculating competition indices with PLEXOS gives you robust analysis.
Optimize scarce resources across multiple maintenance events and time horizons to minimize your costs. In the case of planned maintenance, you can decide where to take outages based on where costs are the least or reserve levels are the best. In the case of forced outages, you can apply convergent, normal Monte Carlo or stochastic optimization.
In addition, maintenance events can be linked to other events. You can perform a run either deterministically or stochastically, and easily change your assumptions in seconds.
Impact the bottom line by minimizing your fuel and operational costs across your portfolio of assets. By performing optimization studies with PLEXOS, you can optimize dispatch subject to emissions, transmission or any other related constraints for the most true-to-life simulations.
- Operations: solve unit commitment and economic dispatch problems
- Generating asset evaluation: quantify the value, run simulations with and without them and calculate the production cost differences
- Budgeting: predict production cost, fuel consumption, traded energy, emissions production and more; support your decisions for rate design, fuel procurement, emission allowance allocation and more
- Long-term capacity expansion: determine optimal investment and retirement of the generation or transmission facility; meet projected load growth while complying with regulations and requirements
The transition to a lower carbon economy is driving the evolution of gas supply planning and power planning needs. Taking a whole-system view to optimize gas and electricity creates new insights and opportunities compared to the siloed approach. Use PLEXOS to master the management of fuels, capacity, and planning across your organization and deliver better financial performance.
- Value gas and electric storage options with dual fuel optimization
- Evaluate gas and electric contingencies as well as the reliability impacts on the wider system
- Calculate least cost opex and capex co-optimization for expansion and retirement
- Create full end-to-end LNG modeling and co-optimize with electricity
- Identify emergence of gas constraints with generation retirements
Bring together the fully optimized chain from gas production basin to the electricity delivered load.
PLEXOS is fully integrated to co-optimize electric power and water. The mode includes heat classes, which are useful for desalination, district heating and Combined Cycle Gas Turbine (CCGT). Now you can use PLEXOS to model production, transportation and consumption of heat resources. PLEXOS is already being used successfully to model desalination water plants; this latest functionality now reaches across any heat and water co-optimization facility such as water treatment plants and other heat-related operations.
Integrate detailed cascading hydro networks with generation efficiency curves, pumped storage, head storage dependency, waterway flow delay times, spillways and evaporation. With deterministic and multi-stage stochastic solutions, you can model over any horizon. PLEXOS also seamlessly integrates with short-term hydro-thermal coordination via hydro targets or future cost function decomposition.
With several functions at your fingertips, you can model the competition for best pricing and maximizing your profitability. Switch between or blend cost-minimization and price-driven maximization objective functions such as price-based unit commitment (PBUC).
Perform dynamic bidding on generation resources that reflect contract position, medium-term revenue requirements or Bertrand competition. In addition, model the Nash-Cournot equilibria to determine the expansion plan and production decisions that maximize your profits.
The comprehensive financial reporting is available to the generator, zone, region and company levels.