SSLR 1.4 User Guide An Optimization and Simulation Application for Unit Commitment and Economic Dispatch

Transcription

1 SSLR 1.4 User Guide An Optimization and Simulation Application for Unit Commitment and Economic Dispatch Arktos Project, April 2012 Anthony Papavasiliou, University of California, Berkeley Shmuel S. Oren, University of California, Berkeley Spyros Boutsis, University of Athens, Greece

2 Contents Introduction... 4 Requirements... 4 System... 4 CPLEX... 4 Java Virtual Machine... 4 Example System... 5 Getting Started... 6 Mac OS Installation... 6 Windows Installation... 6 Launch Options... 7 Input Data... 9 Thermal Plants Technical and economic characteristics Generator Failure Samples Generator Failure Scenarios Loads Wind Generators Wind Generator Profiles Wind Production Samples Wind Production Scenarios Imports Non- Wind Renewable Resources Electric Network Zones Buses Transmission lines Line Failure Samples Line Failure Scenarios Import constraints Electricity Prices Day- Ahead Electricity Prices... 20

3 Real- Time Electricity Price Forecast Error Samples Real- Time Electricity Price Forecast Error Scenarios Scenarios Execution Options Deterministic Unit Commitment Reserve Policy Run Selection Stochastic Unit Commitment Run Selection Lagrangian Relaxation Parameter Settings Perfect Forecast Running the Model Output Output Tables Pasting Data to Excel Acknowledgements... 32

4 Introduction SSLR is an optimization and simulation application for unit commitment and economic dispatch. The application simulates the daily operations of a system for eight representative day types over an entire year with an hourly time step. Although the application has been developed with a focus on performing large- scale wind integration studies, it can also be used for simulating unit commitment and economic dispatch for systems that rely purely on thermal resources. The model includes: Generator ramping constraints Generator minimum and maximum capacity limits Generator minimum up and down times Transmission network constraints (load flows are modeled using a DC approximation of Kirchhoff s laws) Generator and transmission line failures Real- time price uncertainty Requirements System SSLR runs on Windows and Mac OS. CPLEX SSLR uses CPLEX in the background. Researchers can download CPLEX from the IBM academic initiative website: Visit the following url: Select Get software & access. Select Download from the Software Catalog. Log in with your IBM ID and password. Under Find search by text enter cplex and choose IBM ILOG CPLEX Optimization Studio Academic Research Edition V12.3 Multiplatform Multilingual eassembly. Download the appropriate version depending on your system. The software runs on Windows and MacOS. The IBM Download Director will create a new folder, named IBM, in your system. You will need 3 subdirectories from the installed folder: The license file, located in /IBM/ILOG/ilm/access.ilm The CPLEX jar files, located in /IBM/ILOG/ CPLEX_Studio_Academic123/cplex/lib The CPLEX library path, located in /IBM/ILOG/ CPLEX_Studio_Academic123/cplex/bin/x86-64_darwin9_gcc4.0 (in case you have a 32 bit system the latter directory should be x86_darwin9_gcc4.0) Java Virtual Machine The application runs on Java. Users can download the Java Virtual Machine from the Oracle website:

5 Example System The software is distributed with an example system that is described in Papavasiliou, Oren and O Neill, Reserve Requirements for Wind Power Integration: A Scenario- Based Stochastic Programming Framework, IEEE Transactions on Power Systems, Vol. 26, No. 4, November The paper can be found here: The system represents a reduced model of the California Independent System Operator interconnected with the Western Electricity Coordinating Council. The example system is continuously updated according to the newest version of SSLR. Please contact Anthony Papavasiliou at anthony.papavasiliou@gmail.com for a zip file of the most recent version of the example system.

6 Getting Started To get started, download the installers ( setup.exe for Windows, SSLR.dmg for Mac). You can then follow the instruction of the installers. Mac OS Installation To install in Mac OS, the installer will prompt you to drop the SSLR application icon to the applications folder (see figure 1). The SSLR application will then appear in your dock. Figure 1: The SSLR installer in Mac OS Windows Installation Users can launch the Windows installer (see figure 2) and follow the instructions. Users can uninstall the application from their computer from the Start menu, under the Programs selection.

7 Figure 2: The SSLR installer for Windows Launch Options After the software launches, the user needs to specify the following in the System Options window shown in figure 3: Day type. The directory for the system data. The directory for the wind integration study 1. The CPLEX library path, located in /IBM/ILOG/CPLEX_Studio_Academicxxx/cplex/bin/x86-64_darwin9_gcc4.0 (for a 64 bit system) or x86_darwin9_gcc4.0 (for a 32 bit system). The CPLEX jar path, located in /IBM/ILOG/CPLEX_Studio_Academicxxx/cplex/lib/cplex.jar. The CPLEX license file, located in /IBM/ILOG/ilm/access.ilm. Allocated memory. The recommended value is at least 2048 MB for moderately sized systems (such as the example system that comes with SSLR). If there is not adequate memory allocated 1 In case the user is not performing a wind integration study, but instead focusing on other aspects of uncertainty (contingencies or real- time price fluctuations) then the wind folder refers to the folder where the case study data is stored. Within the wind folder, SSLR automatically generates eight subdirectories (one for each day type) where the output of the case study is stored.

8 to SSLR the application will crash in runtime. For most systems, a limit of no more than 4096 MB can be allocated to the application. The output of the software is directed to subfolders in the wind path, as described in the Execution Options and Output sections. The system path and wind path appear in the lower left of the software console. The same system path can be used for simulating the performance of a system under different wind integration scenarios. The wind data for the different wind integration scenarios should be included in different wind paths. In case the application encounters inconsistent data in the system or wind directories, the application warns the user during launch about the file that needs to be corrected and then shuts down. The user needs to correct or delete the inconsistent file and re- launch the application. Figure 3: The System Options window

9 Input Data The input data interface (figure 4) allows us to enter data about the system configuration and its components that are needed for the execution of the simulations. The interface is organized in eight sections: Data about the economic and technical characteristics of thermal plants, as well samples and scenarios of generator failures. Data about loads. Data about wind generators. Data about imports. Data about non- wind renewable resources. Data about the configuration and technical characteristics of the transmission network, as well as samples and scenarios of transmission line failures. Data about import constraints that are used for committing contingency reserves in the system. Data about day ahead and real- time electricity prices. Data about the scenarios considered in stochastic unit commitment. Figure 4: The Basic Data window The lower strip of the console displays the address of the system files, the address of the wind data files, and the chosen day type under study. The output directory of the optimization and simulations is determined from the wind directory and chosen day type (for details refer to the Output section). The lower right of the console also displays the chosen solver. Three varieties of unit commitment are available: Deterministic, Stochastic and Perfect Forecast. These are discussed in further detail in the Execution Options section.

10 Thermal Plants In the Thermal Plants section users can enter data about the technical and economic characteristics of thermal plants. Users can also enter failure samples for running economic dispatch simulations and failure scenarios for running stochastic unit commitment. Users can use the add and minus button in the upper right of the screen to add or remove generators from the system. To update the information, use the save button in the upper left of the screen. A search box is available for locating data in the tables. Technical and economic characteristics In this section (figure 3) the user can enter the following information: Generator name. Generator bus: the node in the network where the generator is located. Startup cost ($): the cost incurred whenever a generator is started up. Minimum load cost ($/hr): a fixed cost incurred for each hour that a unit is operational (on). Heat rate (MMWh/MMBtu): the amount of electric energy produced per unit of fuel supplied to the thermal generator. Fuel price ($/MMBtu): the fuel price for a thermal unit which is multiplied by the heat rate in order to obtain the marginal cost of operation (constant marginal cost is assumed throughout the entire operating range). Fast generator (true/false): Fast generators are generators that can be started up or shut down in real time. Slow generators are committed in the day- ahead and their unit commitment schedule remains fixed during economic dispatch. Ramp up rate (MW/min): the limit on the rate of increase in power output from minute to minute (since this is an hourly model, the given ramp rate is internally multiplied by 60 within the application to yield an hourly ramp rate). Ramp down rate (MW/min): the limit on the rate of decrease in power output from minute to minute. Minimum up time (hrs): the minimum amount of hours a unit needs to stay on once it is started up. Minimum down time (hrs): the minimum amount of hours a unit needs to stay off once it is shut down. Non- spinning reserve limit (MW): the maximum amount of non- spinning reserve that can be provided by generators. Slow generators cannot provide non- spinning reserve due to their long response time. Consequently their non- spinning reserve limit is set to zero. Minimum run capacity (MW): the minimum production level of a unit once a unit is turned on. Maximum run capacity (MW): the maximum production level of a unit. Generator Failure Samples The failure samples (figure 4) are used for the economic dispatch simulation. For each generator, up to 30 failures are identified. The number of economic dispatch simulations is typically set to For a generator failure probability of 1%, no more than 30 failures are likely to occur. It is assumed that once

11 a generator fails, it remains off for the entire day. The data that is entered in this part of the graphical interface is stored in the day type subdirectory corresponding to the wind directory under consideration under the file name FailedGeneratorStates.txt. Consequently, users have the flexibility of specifying different samples of generator failures for different day types of different wind integration studies. If users wish to run the same generator failures for all day types, they can avoid re- entering identical generator failure data by copying the FailedGeneratorStates.txt to all day type subdirectories of a specific wind integration study. Figure 5: Technical and economic characteristics of thermal generators

12 Figure 6: Generator failure samples Generator Failure Scenarios The failure scenarios are used for stochastic unit commitment. The same comments apply as in the Generator Failure Samples section, the difference being that now the data is stored under a file named FailedGeneratorStatesIS.txt and used for stochastic unit commitment rather than Monte Carlo simulation. Loads In the loads section (figure 5) we insert data about each load in the network. Loads can be added or removed using the buttons in the upper right of the console. For each load we identify a bus that indicates its location in the network, as well as a 24- hour demand profile. This demand is assumed to be inelastic and the value of lost load is set to 5000$/MWh. The demand profile depends on the chosen day type. Changes made in the demand profile for a specific day type only affect the demand data files for the corresponding day ( Demandxx.txt, where xx corresponds to the day type under consideration) in the system folder type.

13 Figure 7: The load data Wind Generators In this section we enter both forecast data for wind generators in the system as well as wind power production samples that are used in the Monte Carlo simulations and the stochastic unit commitment algorithm. The wind data depends on the specific wind integration study that the user is performing, and is determined by the wind directory, discussed in the Getting started section. For each wind integration study we have a different wind directory and a different output directory. Wind Generator Profiles In this section (figure 6) the user adds or removes wind generators, using the buttons in the upper right of the console. For each wind generator the user needs to specify the bus where the generator is located, as well as an hourly profile that is used as a wind production forecast for the specific day type in the deterministic unit commitment algorithm. The wind production profile depends on the chosen day type. Changes made in the wind production profile for a specific day type only affect the wind data files ( WindProductionMax.txt ) for the corresponding day type.

14 Figure 8: Wind generator profiles Wind Production Samples The wind production samples (figure 7) are used for the Monte Carlo simulation of different unit commitment policies. The wind production sample files, named WindProductionSamples.txt are located in the appropriate day type subdirectory of the wind directory under consideration. Consecutive 24- hour samples are highlighted in alternating blue and yellow background. Wind Production Scenarios The wind production scenarios are used for the solution of the stochastic unit commitment algorithm. The same comments apply as in the Wind Production Samples section, with the difference that the data in this table is stored under a text file named WindProductionSamplesIS.txt.

15 Figure 9: Wind production samples Imports In this section (figure 8), users enter data regarding imports to the system. The data format is identical to that discussed in the Loads section. Users add and remove resources using the buttons in the upper right of the console. For each resource the user enters the bus where the resource is located, as well as an hourly profile that is specific for the chosen day type. Changes made in the resource profile for a specific day type only affect the data files ( ImportProductionxx.txt, where xx is the chosen day type) for the corresponding day type. Non- Wind Renewable Resources In this section users enter data regarding non- wind renewable resources in the system such as hydro producers and geothermal producers. SSLR does not optimize the dispatch of hydro resources, and instead assumes the availability of these resources as fixed at a zero marginal cost. The data format is identical to that discussed in the Loads section. Users add and remove resources using the buttons in the upper right of the console. For each resource the user enters the bus where the resource is located, as well as an hourly profile that is specific for the chosen day type. Changes made in the resource profile for a specific day type only affect the data files ( REProductionxx.txt, where xx is the chosen day type) for the corresponding day type.

16 Figure 10: Import profiles Electric Network In the electric network section the user specifies the configuration of buses and transmission lines in the system, the technical characteristics of lines as well as samples of line failures used in the Monte Carlo simulations of economic dispatch and in stochastic unit commitment. Zones Zones (figure 9) are added and removed in this section using the buttons in the upper right of the console. Zones define aggregations of buses in which the locational marginal price is identical.

17 Figure 11: The zones of the electric network Buses Buses (figure 10) are added and removed in this section using the buttons in the upper right of the console. In the second column the user specifies the zone to which a bus belongs from a dropdown list. Transmission lines In this section (figure 11) the user enters information regarding the transmission lines of the network. Kirchhoff s voltage and current laws are modeled using a linearized lossless DC network. The power flow equations are modeled on a directed arc network. The line data includes: The name of the line From bus: the bus adjacent to the line that the arc points out of To bus: the bus adjacent to the line that the arc points into Susceptance (p.u.): the per unit susceptance of a line Transmission capacity (MW): the thermal limit of a line s transmission capability Line Failure Samples In this section (figure 12) users enter line failure samples that are used in the Monte Carlo simulation of economic dispatch. For each line, up to 10 failures are identified. The number of economic dispatch simulations is typically set to For a line failure probability of 0.1%, no more than 10 failures are likely to occur. It is assumed that once a line fails, it remains off for the entire day. The data that is entered in this part of the graphical interface is stored in the day type subdirectory corresponding to the wind directory under consideration under the file name FailedLineStates.txt. Consequently, users have

18 the flexibility of specifying different samples of line failures for different day types of different wind integration studies. If users wish to run the same line failures for all day types, they can avoid re- entering identical generator failure data by copying the FailedLineStates.txt to all day type subdirectories of a specific wind integration study. Line Failure Scenarios The failure scenarios are used for stochastic unit commitment. The same comments apply as in the Line Failure Samples section, the difference being that now the data is stored under a file named FailedLineStatesIS.txt. Figure 12: The buses of the electric network

19 Figure 13: Transmission line data Figure 14: Line failure data Import constraints Import constraints are imposed for reliability purposes in deterministic unit commitment models. These constraints are defined ad hoc and limit the total amount of power flowing on certain sensitive sets of lines, named import groups. As a result, unit commitment is robust to line and generator failures. The

20 user defines import groups in the upper panel of figure 13. The import group maximum flow is the total amount of power that can flow over the set of lines defined in the import group. In the lower panel the user determines the polarity of each line in the import group. A 0 indicates that the specific line does not belong to the import group. A 1 indicates that flow on the line contributes towards the import constraint, whereas a - 1 indicates that flow on the line relieves the import constraint. Figure 15: Import constraint data Electricity Prices In this section the user enters data about the day- ahead and real- time electricity prices. Day- Ahead Electricity Prices In this section (figure 13), users enter data regarding day- ahead electricity prices. For each zone defined in the network there is a separate locational marginal price profile for the full day. Changes made in the day- ahead price data for a specific day type affect the DayAheadPrice.txt data file in the subdirectory of the corresponding day type. Real- Time Electricity Price Forecast Error Samples The real- time electricity price forecast error samples (figure 15) are used for the Monte Carlo simulation of different unit commitment policies. The sample files, named PFESamples.txt are located in the appropriate day type subdirectory of the wind directory under consideration. Consecutive 24- hour samples are highlighted in alternating blue and yellow background. Real- Time Electricity Price Forecast Error Scenarios The real- time electricity price forecast error scenarios are used for the solution of the stochastic unit commitment algorithm. The same comments apply as in the Real- Time Electricity Price Forecast Error

21 Samples section, with the difference that the data in this table is stored under a text file named PFESamplesIS.txt. Figure 16: The day- ahead electricity price data Figure 17: The real- time electricity price forecast error sample data

22 Scenarios In this section (figure 16) the user enters the following data about the scenarios that are used for stochastic unit commitment: A name for each scenario (the number of scenarios is fixed according to the setting that the user enters under Number of optimized SUC scenarios in the Execution Options section) The probability of each scenario The sample corresponding to the selected scenario. This setting determines which wind power production sample is chosen from the Wind Production Scenarios section of the data, which line failure sample is chosen from the Line Failure Scenarios section of the data and which generator failure sample is chosen from the Generator Failure Scenarios section of the data. Figure 18: The scenario data

23 Execution Options In the Execution Options section the user specifies what type of commitment policy is used as well as certain settings for the solver. The user can also specify whether to run unit commitment, economic dispatch or both. Deterministic Unit Commitment In this section (figure 16) the user adjusts the settings for deterministic unit commitment and simulation. Reserve Policy There are currently two types of deterministic unit commitment policies that are implemented by the software. Both policies are implemented by selecting Deterministic under the Unit commitment policy selection. The 3+5 rule has recently been proposed by the National Renewable Energy Laboratory for reserving generation capacity in order to deal with wind power supply uncertainty. This option is chosen by selecting 3+5 under Reserve policy. The 3+5 rule dictates that fast reserves are at least 3% of hourly forecast load plus 5% of forecast wind power supply for each hour of the day. The 3+5 rule strives to adapt hourly reserve commitment to the uncertainty introduced by the fluctuations of load and wind power supply. For further details, the user is referred to: A percent- of- peak- load rule. In this approach, total reserve is set equal to a certain percentage of forecast peak load for all hours of the day. This option is chosen by selecting Constant under Reserve policy and selecting a percentage under the % of peak load dropdown. A higher percentage selection results in the commitment of more reserves. Values ranging from 10 to 50 are reasonable for this setting.

24 Figure 19: Run configurations for deterministic unit commitment Run Selection The user has the following run options that can be selected under the Run dropdown: UC only determines a day- ahead unit commitment schedule for slow reserves in the output folder. No Monte Carlo simulation of real- time operations is performed. The schedule can be used in subsequent runs as input to the Monte Carlo simulations. If this option is selected, the user needs to specify a MIP gap for the unit commitment model. Recommended MIP gaps range between 0 and The smaller the MIP gap, the closer the solution is to optimality, at the expense of increasing the running time of the solver. UC and ED determines a day- ahead commitment schedule and subsequently runs a Monte- Carlo simulation of real- time economic dispatch against a number of wind production outcomes, transmission line failures and generator failures. The user needs to specify a MIP gap for both the unit commitment as well as economic dispatch models. Recommended MIP gaps range between 0 and The smaller the MIP gap, the closer the solution is to optimality, at the expense of increasing the running time of the solver. The user also needs to specify the number of samples for the Monte Carlo simulation. This selection needs to be consistent with the data provided under Generator Failure Samples in the Thermal Plants section, the Line Failure Samples in the Electric Network section, as well as the data in the Wind Production Samples section. If ED only is selected, a Monte Carlo simulation is performed against the currently selected policy. The user needs to specify the ED MIP gap and number of samples for the Monte Carlo simulation.

25 Figure 20: Run configurations for stochastic unit commitment Stochastic Unit Commitment In this section (figure 18) the user adjusts the settings for stochastic unit commitment and simulation. Run Selection The same comments apply as in the Run Selection subsection of the Deterministic Unit Commitment section. Lagrangian Relaxation Parameter Settings The Lagrangian relaxation algorithm requires the following information: Number of Lagrangian relaxation iterations : more iterations increase the value of the dual function and therefore close the duality gap of the problem. Information regarding the duality gap is reported in the output files (see the Output section). Reasonable values for this selection range between 10 and 100. Step size (%) : this selection affects the step size of the subgradient algorithm in the dual space. Intuitively, setting this selection to a certain number p is telling the algorithm that if the peak of the dual function is estimated to be a certain distance d from the current point, then the algorithm should make a stride of p*d in the direction of steepest ascent. Therefore, small entries increase the chances of the algorithm to find a good dual solution, but require more iterations to find such a solution, whereas large values reduce the number of iterations but may cause the algorithm to oscillate around the optimal dual value. Reasonable values range between 0.5% and 10%. Upper bound estimate : this selection affects the stride of the algorithm and should be set as reasonably small as possible, without violating the true cost of the system. For the test system,

26 costs are known not to exceed 20 $M for any day type (for the most expensive day types these costs are about 12 $M), therefore a recommended setting is 20. If the user enters a number that is smaller than the cost of operating the system (e.g. 0 ), that will cause the solver to crash, whereas if the user enters an excessively large number (e.g. 1000) that will increase the step size of the algorithm excessively, causing the algorithm to oscillate around the dual optimal solution. Starting point of dual variables : this selection affects the starting point of the algorithm. If previous iterations have not been run, the user should set it to Zero, otherwise the user can set it to Previous and take advantage of the previous search effort of the algorithm. Search for feasible solutions : this selection instructs the algorithm whether or not to search for feasible solutions and provides an upper bound that can be used as a stopping criterion. If the user selects to look for feasible solutions, the running time of the algorithm approximately doubles. Number of optimized SUC scenarios : this selection determines how many scenarios are considered in the model (see Scenarios in the Input Data section). Number of total SUC scenarios : this selection determines the total number of wind production samples, generator failure samples and transmission line failure samples that are available for being considered as scenarios and should be consistent with the Wind Production Scenarios, Line Failure Scenarios and Generator Failure Scenarios entries in the Input Data section. Perfect Forecast The perfect forecast policy provides a best- case scenario as it assumes that generators are committed in the day ahead with advance knowledge of uncertainty. The results of running this model provide useful information regarding how well the deterministic and stochastic policies are performing relative to an idealized benchmark. Since the perfect foresight policy does not involve unit commitment in the day ahead, only Monte Carlo simulations of economic dispatch are performed. The ED MIP gap and number of samples are set as in figure 18. The run time of the perfect forecast policy is generally slower than the Monte Carlo economic dispatch simulation of the deterministic and stochastic unit commitment policies because the algorithm is solving a larger mixed integer linear programming model (since slow units in the perfect forecast policy do not have fixed unit commitment schedules). In order to cope with this, the user may want to increase the ED MIP gap, although this will lead to lower quality solutions for the perfect foresight policy.

27 Figure 21: Run configurations for the perfect forecast policy

28 Running the Model Once the execution options are set, the user can run the software (either by hitting the play button on the top of the screen or under the Project tab). Depending on the execution settings, output is directed to an appropriate subfolder within the wind directory (see the next section, Output ). Once the user runs the simulation, a terminal appears that displays the progress of the simulation. The user can use the Close button on the terminal to interrupt the simulation. Once the simulation is complete, a popup window notifies the user. If the simulation does not complete successfully a popup window notifies the user. The user can then inspect the terminal to identify the reason for the failure of the simulation. Figure 22: Running the model

29 Output The output directories obey the following structure: A wind directory is determined by the user at the startup of the application. Within the wind directory, eight folders exist for each different day type: WinterWD, SpringWD, SummerWD, FallWD, WinterWE, SpringWE, SummerWE and FallWE. Within each day type, a different folder exists for each unit commitment policy under consideration. For example, the 3p5 folder corresponds to the NREL 3+5 reserve commitment rule. Within the directory of each policy the user can find the unit commitment schedule corresponding to the specific policy uda.out. Within the directory of each policy the user can also find a summary of economic dispatch simulations EDSummary.out, a breakdown of costs CostRecord.out as well as a folder Outcome with detailed records for each economic dispatch simulation. For the stochastic unit commitment algorithms, the following output data is also available and useful for monitoring the progress of the algorithm and changing execution options between consecutive runs: o CommitmentHist.out: a file that displays the evolution of the maximum run capacity of slow units in the system over the day over all iterations of the algorithm. o CostDecomposed.out: a file that displays the cost of the second- stage subproblems at each iteration. o CostFeasible.out: a file that displays the cost if the first- stage subproblems at each iteration. o LROutput.out: a file that displays the evolution of lower bounds, upper bounds, shed load and committed capacity at each iteration. o mu.out, nu.out: current values for the dual multipliers (used in the Previous selection of the starting point for Lagrangian relaxation, see Execution Options ). o wopt.out, zopt.out: the unit commitment and startup schedule, used by the stochastic unit commitment algorithm for communicating between iterations. o DecompositionCostComponents.out: the breakdown of costs to startup, minimum load, fuel and load shedding costs for each scenario for the current optimal feasible solution of the stochastic unit commitment algorithm. This information is useful in assessing the impact of each scenario on the expected cost of the stochastic unit commitment model and can help in guiding scenario selection. Output Tables The Output Data section of the application (figure 20) includes the following sections: Unit Commitment section. Economic Dispatch Summary section. Economic Dispatch Snapshot section. In the Unit Commitment section we are presenting the following information:

30 uda.out for Deterministic and Stochastic policies: : The unit commitment schedule of all units DecompositionCostComponent.out for Stochastic policies: the breakdown of costs for each scenario of the stochastic unit commitment LROutput.out for Stochastic policies: Lower and upper bounds for each iteration of the stochastic unit commitment algorithm In the economic dispatch section we are presenting the following results: In the Economic Dispatch Summary section we present the following results: * CostRecord.out: for all policies the breakdown of costs for each sample of the Monte Carlo simulations In the Economic Dispatch Snaposhot section, available for all policies, the user is prompted to enter the number of the specific Monte Carlo sample under consideration. The output that is presented is: ProductionDataxx.out: the production level of each generator for each hour, located in OutcomeDirectory/ProductionPerGenerator.out The breakdown of operating costs for each generator. This data is sourced from OutcomeDirectory/MinLoadCostPerGeneratorxx.out, OutcomeDirectory/StartupCostPerGeneratorxx.out and OutcomeDirectory/FuelCostPerGeneratorxx.out Figure 23: The 'Output data' section of the application Pasting Data to Excel It is possible to paste the content of the Basic Data and Output Data sections in Excel. The user can copy and paste the content of individual cells (by selecting individual cells), copy and paste the content

31 of consecutive rows (by holding down the shift button and selecting rows with the arrow buttons), or copy and paste the entire table (by selecting any of the table columns).

32 Acknowledgements This project has generously been supported by: The Center for Technology and Entrepreneurship Venture Lab competition (2010) The Sustainable Products and Solutions Program fellowship ( ) The Big Ideas Energy and Environmental Innovation competition (2008) The CITRIS IT for Society competition (2008)