Calculating stranded-asset cost; Assessing the impact of security-of-supply constraints, and environmental constraints, Maintenance: Optimizing the timing and duration of maintenance outages; Market assessment: Performing market benefit analysis for transmission and generation assets, Calculating market outcomes that account for both fixed and variable cost components, Calculating optimal trading strategies for a portfolio of generation and transmission assets including entrepreneurial interconnectors, Projecting pool prices under various scenarios of load growth and new entry, Determining the impact of market design decisions, and rule changes, As a real-time market-clearing engine, Analyzing generation and transmission constraints and calculating congestion rents. There are four different simulation options within Plexos. LT Plan: This performs the long-term expansion planning function. The purpose of the LT Plan model is to find the optimal combination of generation new builds and retirements and transmission upgrades (and retirements) that minimizes the net present value of the total costs of the system over a long-term planning
outages Maintenance Optimal planning of generator maintenance periods Transmission Optimal Power Flow Linearized DC optimal power flow (OPF) fully cooptimized with generation dispatch SCOPF Security constrained OPF Large-scale Systems Ability to model thousands of busbars and transmission lines DC Lines Optimization of DC lines Phase Shifters Optimization of phase shifters Reliability Monte Carlo modelling of line outages Interfaces and Generic Constraints Support for interfaces, generic constraints and transmission nomograms including shadow price reporting Nodal Pricing Calculation of locational marginal prices (LMP) LMP Decomposition Decomposition of LMP into congestion and marginal loss components with various slack bus options Hydro Energy Limits Multi-period energy limits (annual, monthly, quarterly, weekly, daily) Cascading Networks Long-term storage optimization, storage, interconnecting waterways and junctions Water Value Curves Multi-band water value functions for long-term storages Pump Storage Genuine optimization of pump storage resources Ancillary Services Cooptimization Complete cooptimization of ancillary services and generation dispatch Pricing Consistent energy and AS pricing outcomes AS Classes Spinning, regulation, and non-spinning reserves Emissions Constraints Generation dispatch optimally reflects emission constraints
Left click in the first row of the “Node” column in data grid. When you do this, you will see a menu appear on the right-side of the cell. Left click on it, and select “3”. The screen should appear as below. Add node 1 to line L12’s from node collection: Left click on “Lines” in the main tree. Left click on “Node From” in the membership tree. Left click in first row of “Line” column in data grid. When you do this, you will see a drop-down menu appear on the right-side of the cell. Left-click on it, and select “L12”. Left click in the first row of the “Node” column in data grid. When you do this, you will see a menu appear on the right-side of the cell. Left-click on it, and select “1”. Add node 2 to line L23’s from node collection: Repeat steps 14-c and d, except select L23 in step 14-c and select “2” in step 14-d. Add node 1 to line L13’s from node collection: Repeat steps 14-c and d, except select L13 in step 14-c and select “1” in step 14-d. The screen should appear as below. Add node 2 to line L12’s to node collection: Left click on “Lines” in the main tree. Left click on “Node To” in the membership tree.
Left click in first row of “Line” column in data grid. When you do this, you will see a drop-down menu appear on the right-side of the cell. Left-click on it, and select “L12”. Left click in the first row of the “Node” column in data grid. When you do this, you will see a menu appear on the right-side of the cell. Left-click on it, and select “2”. Add node 3 to line L23’s to node collection: Repeat steps 17-c and d, except select L23 in step 17-c and select “3” in step 17-d. Add node 3 to line L13’s to node collection: Repeat steps 17-c and d, except select L13 in step 17-c and select “3” in step 17-d. After steps 17, 18 and 19, the screen should appears as below. Set node properties: Left-click on “Nodes” in the main tree. You will see “Properties” of the nodes in the data grid. Click in the node 3 row under the “Is slack bus” column. When you do this, you will see a drop-down menu appear on the right-side of the cell. Left-click on it, and select “Yes”. You have now identified bus 3 as the system slack bus. Click in the node 1 row under the “Node participation factor” column in the data grid. Type 0. Repeat for the node 2 row. Repeat for the node 3
Set Planning horizon: Left click “Simulation” at the top of main tree, and then click on “Horizon” to set the planning horizon. An object named “default” will show up. Double-click “default” to edit its properties. In the lower part of the panel, set 24 steps of 1 hour in one day so that the model will run hour by hour. End case construction: You have completed constructing the case. If you click on “System” at the top of the main tree, and then click on the “Objects” tab at the top of the data grid, it should appear as below. Save: You should have a good, working Plexos case at this point, but you may have made some errors. Save the case from “File” on the menu bar. Execute: Execution at this point will result in a “ST Schedule” solution, which is the default solution method for the software. The “ST” is for “Short-term.” Essentially, the program runs a unit commitment over the time period. One may go to the wiki pages at and search on “ST Schedule” to learn more about this solution method. This will lead to the page at , where you can read the following information (yellow shading is added to highlight some statements). Short Term (ST) Schedule | | Description: ST Schedule simulation phase Detail:
The Plexos description of the LT Plan model formulation is below. LT Plan Formulation The capacity expansion problem is formulated in PLEXOS as a Mixed-Integer Linear Program (MILP or MIP for short). To illustrate the very core of this formulation, we develop the following simplified formulation. NOTE: The following description is illustrative only, and shows only the core of the LT Plan MIP formulation. Definitions Let us define the following variables: Variable Description Type GenBuildgy Number of generating units built in year yfor Generator g integer GenRetiregy Number of generating units retired in year yfor Generator g integer CapShorty Capacity shortfall in year y GenLoadgt Dispatch level of generating unit gin period t continuous USEt Unserved energy in dispatch period t continuous We also define the following parameters: Element Description Unit D Discount rate Lt Number of hours in dispatch period t hours BuildCostg Build cost for generator g $ RetireCostg Cost of retirement for generator g $ Pmax Maximum generating capacity of generator g MW Unitsg Existing number of generating units g PeakLoady Maximum power demand in year y MW
It is important in the LT Plan formulation that the model does not inappropriately consider the end of the planning horizon to also be the 'end of time'. In the above basic formulation we have implicitly assumed that LT Plan has an infinite horizon. In practice, the model will have a finite horizon such as 20 or 30 years. We therefore have to take account of so-called end year effects. Without some method for dealing with end-year effects, the model will in the last years of the horizon only build generators with low build costs (even if their marginal generation costs are high). This is because the average cost of generation between build year and the end of the planning horizon - including build costs - will be lower for such generators. We therefore assume that the last year of the horizon is repeated an infinite number of times. The objective function is expanded by the cost of the years after the final horizon year, which equals the sum of the present values of generation costs. Solution approach Problem Size Reduction The capacity expansion 'sub-problem' uses annual build/retire decisions. Thus the number of these elements in the formulation is relatively small. No problem size reduction is required to these elements. However, if all decisions are integer (as controlled by the option), then the problem can become very hard to solve. You can reduce the number of integers by selectively turning off integer optimization at the level using the property. LT Plan (by default) spans the whole simulation planning horizon in a single optimization 'step'. For realistically-sized power systems and long planning horizons this may lead to extremely big problems in the production cost sub-problem. To make this problem tractable, two problem size reduction techniques can be used: The production cost subproblem's chronology is reduced using the load duration curve (LDC) methodology (as in ). The optimization can be split into multiple steps using the option. LT Plan always uses a LDC approach. The controls for the level of detail in this method are , and .