PLEXOS Hydro Stochastic Optimization
Table of Contents
Introduction
Power systems that have both hydroelectric and thermal generation require a systematic and coordinated approach to determine an optimal storage management. The goal of a hydrothermal planning tool is to minimize the expected thermal costs across the simulation period by dispatching hydro during highvalue periods as much as possible. These types of problems generally require stochastic analysis to deal with inflow uncertainty, which increases the mathematical size of the problem and hence are often cumbersome to solve. This white paper reviews the stochastic analysis algorithm in PLEXOS as applied to different hydrodominated power systems.
PLEXOS Hydro also provides the more traditional Stochastic Dual Dynamic Programming (the SDDP algorithm), which is adequately documented in academic literature.
Stochastic Optimization
When operating under uncertainty, a decisionmaker must make optimal choices throughout a decision horizon with incomplete information. The decision horizon includes many defined stages, in which each stage represents a point in time where the user either makes a decision, or the uncertainty partially or totally vanishes. The amount of information available to the decision maker is usually different from stage to stage. According to the number of stages in the optimization problem, we can distinguish between:
 Twostage stochastic problems.
 Multistage stochastic problems.
Twostage Stochastic Problems
Twostage applies to a decisionmaking problem in which decisions are made in two stages, and the existing uncertainty is represented by a set of scenarios or samples. It is a twostep process:
 Make a set of decisions.
 Profit from the outcome or clean up the mess (recourse actions if any).
The twostage stochastic optimization doesn’t allow the user to make revisions after revealing the uncertainty, so it has the following limitations:
 The plan for the entire horizon is determined before uncertainty is realized.
 Only a limited number of recourse actions (if available) can be taken afterwards.
 Limited use (shortterm only) for hydro optimization. Stochastic Hydro Multistage Optimization
Multistage Stochastic Problems
When information is slowly revealed over time, we need to adopt a multistage approach. The decisionmaking process for a multistage stochastic problem is:
 Make a decision for today (t) based on what I know today.
 Observe the outcome from stochastic process in time t.
 Increment t and repeat until the entire study horizon has been simulated and optimized.
This type of phased decisionmaking approach allows users to revise over time as more information regarding the uncertainties is revealed.
Multistage stochastic optimization has the following upsides and downsides:
 Upside: It is a better characterization of the dynamic planning process and provides more flexibility than the twostage model.
 Downside: Simulation size, memory and runtime increase exponentially as we add more stages.
For mediumlong term hydro optimization, the most suitable approach is multistage stochastic optimization, and that is the type of optimization problem covered in this paper.
Stochastic Tree Reduction for Multistage
When using multistage stochastic optimization, the user can generate many possible scenarios, as shown in Figure 1. For such a high number of scenarios, it is impossible to numerically obtain a solution for the multistage optimization problem. Different techniques have been introduced in the literature to help solve this problem and commonly involve either a scenario tree reduction or a more simplified tree solved with decomposition techniques.
In general we will have the number of scenarios are equal to:
Branches_perStage^{(nStages)}
For Example: 21 stages and 3 branches per node
3^{21} = 1.04605 x 10^{10} scenarios
The 'problem' with multistage stochastic optimization is the simulation size/memory/runtime increases exponentially as stages are added, quickly leading to problems that are unfeasible to solve.
PLEXOS offers a scenarioreduction technique called “hanging branches,” which is described in the following section.
Hanging Branches Representation
Assuming we have a multistage stochastic problem consisting of four stages and three branches per stage, we can represent the tree as shown in Figure 2.
Figure 2: Full multistage tree for 4 stages and 3 branches per stage. 3 4= 81 scenarios to explore.
We can reduce this full tree to solve an equivalent multistage problem. There are several ways to reduce the stochastic tree: in this paper we reduce the stochastic tree by organizing the branches as “full branches,” “hanging branches” or “death branches,” which are defined as:
 Full branch: Path to be explored.
 Hanging branch: Uncertainty associated to the full branch to be explored.
 Death branch: Path to be discarded to avoid dimensionality issues.
If the user reduces the tree in Figure 2, to in three full branches and two hanging branches per stage, Figure 3 is the resulting tree.
Figure 3: Full tree classified in full, hanging and death branches
By deleting the death branches from the tree in Figure 3, we have the following reduced tree:
INDEPENDENCY
It is important to note that the inflows at the beginning of each stage are known. This means we can solve each full branch independently, as shown in Figure 5.
Hanging Branches Reduction
By default, the weights for each branch are uniform. In order to avoid dimensionality issues in the number of hanging branches, the user can set the weights used in the objective function for those branches.
We developed a sample reduction algorithm for hanging branches to reduce the number of samples to a predefined smaller number, but the reduced samples are still a good approximation of the original problem. The reduction is based on rules that ensure only the samples that are similar to other samples or have small probabilities are combined. The methodology is illustrated in the following figure:
If we formulate 20 or 30 hanging branches per stage, the following weights are generated from root node (see Table 1).
Formulating the multistage problem in one single optimization problem produces very small weights for future branches. Commercial solvers can’t guarantee a solution outside the weight range 10^6 and 10^6.
However, multistage stochastic optimization says that at each stage, the decision maker can make a new decision because additional information is revealed. We solve this limitation in the same way a multistage stochastic problem is solved in real life: by using a “rolling horizon” approach, which splits the horizon in steps. The only required information we must pass between steps is initial storage and ending volumes.
Stage  N=20  N=30 
1 
0.05  0.0333 
2  0.0025  0.0011 
3  0.000125  3.7 𝑥 ^{−5} 
4  6.25𝑥 ^{−6}  1.23 𝑥 ^{−6} 
5  3.13𝑥 ^{−7}  4.12 𝑥 ^{−8} 
6  1.56𝑥 ^{−8}  1.37 𝑥 ^{−9} 
7  7.81𝑥 ^{−10}  4.57 𝑥 ^{−11} 
8  3.91𝑥 ^{−11}  1.52 𝑥 ^{−12} 
9  1.95𝑥^{−12}  5.08 𝑥^{− 14} 
10  9.77𝑥^{− 14}  1.69 𝑥^{− 15} 
The rolling horizon approach is designed to overcome the limitations of extremely small probabilities deep into the future. The method looks ahead to a given point in the future; the end volumes at that point are passed as initial volumes at the start of the next step.
For a horizon divided in multistep stochastics, the algorithm works as follows:
Step 1:
 Starting date is beginning of root node.
 Multistage tree formulated from start to a userdefined stage in the future where no more branches will be added to avoid weights issue.
First roll:
 Starting date is beginning of stage 1.
 End volumes in first solve are passed as initial volumes in roll 1.
 The past branches are not formulated because that part of the problem is already solved.
 More hanging branches are formulated when the weights provide information to the optimization solver.
Second roll:
 Starting date is beginning of stage 2.
 End volumes in first roll are passed as initial volumes in roll 2.
 The past branches are not formulated because that part of the problem is already solved.
 More hanging branches are formulated when the weights provide information to the optimization solver.
The problem continues rolling until it has fully explored the horizon.
Rolling Horizons
Multistage stochastic optimization needs a large number of scenarios, or samples, to represent the equivalent tree. The literature includes different methods for creating these samples, however the simplest method creates future samples based on history.
If we have historical records from 2000 to 2011, and the user wants to create samples from 2019 to 2030, the we need to generate the number of full branches as described in the following table:
Table:2 Historical Information
Simulated Year 

2019  2020  2021  ...  2028  2029  2030  
Full Branches  1  2000  2001  2002  ...  2009  2010  2011 
2  2001  2002  2003  ...  2010  2010  2000  
3  2002  2003  2004  ...  2011  2000  2001  
...  ...  ...  ...  ...  ...  ...  ...  
...  ...  ...  ...  ...  ...  ...  ...  
12  2011  2000  2001  ...  2008  2009  2010  
...  ...  ...  ...  ...  ...  ...  ... 
This method is powerful and simple, as it preserves the spatial correlation between hydro storages and doesn’t require statistical analysis.
To generate the hanging branches, we can use the historical information as well. For this purpose, the sampling method selects a different year for the same position in time.
A weekly multistage problem starting from historical year 2000 could look as follows:
The hanging branches for full branch number 1 looks as follows:
Table 3: Hanging branches associated to full branch #1
Simulated Stage 

Stage 1  Stage 2  Stage 3  ...  Stage 10  Stage 11  ...  
Hanging Branches  1  Week 2_2001  Week 3_2001  Week 4_2001  ...  Week 11_2001  Week 12_2001  ... 
2  Week 2_2002  Week 3_2002  Week 4_2002  ...  Week 11_2002  Week 12_2002  ...  
3  Week 2_2003  Week 3_2003  Week 4_2003  ...  Week 11_2003  Week 12_2003  ...  
...  ...  ...  ...  ...  ...  ...  ...  
...  ...  ...  ...  ...  ...  ...  ...  
12  Week 2_2012  Week 3_2012  Week 4_2012  ...  Week 11_2012  Week 12_2012  ...  
...  ...  ...  ...  ...  ...  ...  ... 
Conclusion
PLEXOS is a powerful energy system modeling tool that allows users to simulate and optimize energy systems, including power generation, transmission, and distribution. One of the key features of PLEXOS is its ability to model with rolling horizons, making it particularly useful for solving multistage stochastic problems in energy systems.
PLEXOS is the globally recognized tool of choice when modeling multistage stochastic problems for five primary reasons.

Accuracy: PLEXOS allows users to model complex energy systems accurately, including renewable energy sources and energy storage systems. This accuracy is essential in multistage stochastic problems, where the outcomes of decisions made in one period can significantly impact the outcomes in future periods.

Flexibility: PLEXOS is a flexible tool that can handle different types of inputs, such as weather data, demand forecasts, and market prices. This flexibility enables users to adjust their strategies in response to environmental changes, which is essential when uncertainty is high.

Speed: PLEXOS is a fast tool that can handle large amounts of data, making it possible to run simulations quickly and efficiently. This speed is critical in multistage stochastic problems, where decisionmakers need to evaluate multiple scenarios to determine the best course of action.

Optimization: PLEXOS includes powerful optimization algorithms that allow users to find optimal solutions to complex energy system problems. These algorithms consider multiple objectives, such as minimizing costs and maximizing reliability, and can be used to evaluate different scenarios and strategies.

Visualization: PLEXOS includes a range of visualization tools that allow users to see the results of their simulations clearly and intuitively. This visualization is vital in multistage stochastic problems, where decisionmakers need to understand the impact of their decisions over time.
Using PLEXOS provides decisionmakers with the accuracy, flexibility, speed, optimization, and visualization tools they need to make informed decisions based on the available information while remaining flexible to industry changes.