Sabp A 023 PDF
Sabp A 023 PDF
Sabp A 023 PDF
Table of Contents
1 INTRODUCTION ............................................................................................................. 3
1.1 PURPOSE ......................................................................................................................... 3
1.2 SCOPE ............................................................................................................................. 3
1.3 INTENDED USERS .............................................................................................................. 3
1.4 RELATED DOCUMENTS ....................................................................................................... 3
2 GENERAL ....................................................................................................................... 4
2.1 DEFINITIONS..................................................................................................................... 4
2.2 GENERAL ASPECTS OF CO-GENERATION (CHP) SYSTEMS ......................................................... 4
2.3 CHP THERMAL EFFICIENCY CALCULATION.............................................................................. 5
2.4 ASSESSING THE BENEFITS OF THE CHP SYSTEMS ..................................................................... 6
3 CHP OPTIMIZATION MODEL DESCRIPTION ..................................................................... 7
3.1 MODEL STRATEGY ............................................................................................................. 7
3.2 POWER GENERATION UNITS OPTIMIZATION LEVEL.................................................................. 8
3.3 BOILERS OPTIMIZATION LEVEL ............................................................................................. 9
3.4 STEAM TURBINE VS. MOTOR OPTIMIZATION LEVEL............................................................... 13
3.5 REQUIREMENTS FOR NEW CHP MODEL DEVELOPMENT ........................................................ 14
3.6 MAIN FUNCTIONS ........................................................................................................... 16
3.7 MAIN FEATURES ............................................................................................................. 16
3.8 STEAM AND POWER DEMANDS INPUTS/CORRELATION EQUATIONS ......................................... 19
3.9 STEAM MASS AND ENERGY BALANCES ................................................................................ 22
3.10 OTHER MODEL COMPONENTS ........................................................................................... 24
APPENDIX A: ..................................................................................................................... 26
Page 2 of 44
Document Responsibility: Energy Systems Optimization Standards Committee SABP-A-023
Issue Date: 20 January 2016
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1 Introduction
Boiler load management, pump and compressor systems are all covered in previously
published best practices SABP-A-002 & SABP-A-008. However, both do not cover
combined heat and power (CHP) or cogeneration systems which primarily comprise of
steam and gas turbine systems. Thus, a best practice to address the CHP system should
be developed to support utility facilities to achieve optimum operation. The major part
of the best practice for the CHP system is the development of CHP models that reflect
the current mode of operation in each site. Once validated and verified against the
existing operating conditions, the models can then be used to assess various scenarios to
achieve optimum CHP plant operation/configuration.
1.1 Purpose
1.2 Scope
The CHP systems model typically consists of boilers, power generation units,
gas turbines, steam turbines and motors. Optimum operation of the model is
basically the optimum operation of the individual equipment. This Best Practice
manual focuses on determining the optimum load management for:
Boilers
Power generation units
Steam turbines
Motors
This Best Practice manual is intended for use by the utilities' operations,
engineering and management working in Saudi Aramco plants; who are
responsible for efficient operation of their facility.
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2 General
2.1 Definitions
Economic Dispatch: it's the process of allocating the load (demand) among
different generation units subject to minimum operating cost and satisfying all
system's constraints.
Unit commitment: It's the process of selecting the best equipment available
that can satisfy the required demand as well as satisfying all system's constraints
subject to the minimum operating cost.
There are two different types of CHP systems. One is the steam turbine based
CHP system; consisting of one or more boilers and steam turbines with the usual
controlled steam extraction(s) for process steam supply (illustrated in Figure 1).
The other type is a gas-turbine- based CHP systems ; consisting of one or more
gas turbines exhausting products of combustion through one or more heat-
recovery steam generators (HRSGs), which produce steam for the heat supply
(illustrated in Figure 2). Most of Saudi Aramco facilities or plants have
combination of both.
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Fuel Input
MW output
A CHP plant has the following major operational features: electric output in
kWh, heat output (Btu); and heat rate in (Btu/kWh). The CHP- plant heat rate
requires special definition. For a conventional power plant, heat rate represents
the heat consumption in (kcal. Btu) per kilowatt-hour of electric output.
However, this definition of heat rate is not applicable for CHP plants because it
does not account for heat output. The heat rate calculations are based on the
following definition:
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This heat-rate definition assigns all the benefits from the combined power and
steam generation to power production.
CHP system optimization for utilities plants can provide benefits in the
following areas;
More profitable management of site power and steam production
Improved monitoring and planning
Plan ahead versus reactive decision making
Early warning of equipment performance problems
Reduced occurrence of unscheduled down time
Optimized routine maintenance schedule.
The environmental impacts of air and water pollution and waste disposal
are very site-specific for CHP. This is a problem for some CHP plants
because the associated equipment (water treatment, air scrubbers, etc.)
required to meet environmental regulations adds to the cost of the
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The CHP model was found to be extremely useful in analyzing the interactions among
various utilities components. The interactions among these components could have
been very complex without constructing a reasonably accurate mathematical model.
The applications of the CHP model include the following:
Identifying opportunities for cost reduction through efficiency improvement
Developing an accurate energy cost accounting
Evaluating the energy cost impact of proposed process changes on the demand side
Evaluating various CHP options
Identifying load sharing strategies (e.g., switching between motors and turbine
drives)
The CHP model includes all the elements involved in the generation and distribution of
energy to drive the process and supporting infrastructure.
This section describes the methods used for optimizing the operation of the
boilers boilers load management, power generation units economic dispatch
and other equipment steam and motors based on the unit commitment and
economic dispatch techniques.
Due to the limitation of the existing excel solver, the model has three levels of
optimization as shown in Figure 3. It is intended that in the future this model
should be solved simultaneously.
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In this level, the objective function is to maximize the net profit from the
cogeneration units based on the purchased and exported power tariffs.
The constraints are the unit maximum and minimum operating range, and the
minimum level to run the HRSGs. This optimization level provides the whole
system with the optimum MW output and certain amount of steam. Figure 4,
shows a typical example of this level.
The selection of the units is based on the units' performance equations; those
equations relate the power and steam production with fuel consumption.
However, in most cases we dont have the independent instrumentation for
steam, power and fuel used by each unit. So, in the model we assumed that all
three units have the same efficiency curve. But if we could have the information
it would be much better to use the equation for each individual unit.
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For gas turbine generator CHP system, the efficiency is calculated as per the
equation:
The next optimization level is the boilers load management. The objective
function of this level is minimizing the operating cost of the boilers' system.
The constraints are the maximum and minimum levels of the each boiler; the
steam reserve is another main constraint to this optimization level.
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Steam reserve can be defined as the hot standby of steam required to provide the
minimum steam needed in case of a boiler failure during emergency situations.
The following illustration is an example:
A system of 5 boilers each has a capacity of 200 Mlb/h, if the facility requires
800 Mlb/h of steam, and all units are running as shown in Table 1 below:
From table above, steam demand has been met with total steam reserve of
200Mlb/h. if one unit is off, it will not be considered in the steam reserve.
The other constraint in this optimization level is the mass balance of the steam in
and out of each steam header.
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Boilers Model:
Equations:
1. Steam gen = optimization variable
2. LF (Load factor) = steam gen/ capacity
3. Blowdown = fraction x steam gen rate
4. Feedwater = stm gen + blowdown
5. Hs = f(P,T), from steam properties Steam table
6. Fuel input = Stm (Hs hBFW)/Fuel HHV
Boiler Curve:
There are two accepted methods for calculating boiler efficiency. One is the
heat balance method, and the other is the heat loss method. The heat balance
method is the one used in the CHP model and its very straightforward:
S ( H h)
B (4)
F HHV
Where,
S: steam flow rate (klb/hr)
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For each performance test, fuel vs. steam curve should be developed. As an
example, below graph shows the fuel vs. steam correlation equation. One can
notice that, there are two independent curves shown in the graph. The reason for
that is using two different types of fuel, which is based on the different BTU
content. In this case, the model has to account for both cases.
Boiler #1
8
7
Fuel (MSCF/hr)
6
5
4
3
2
1
0
0 50 100 150 200 250 300 350
Stm (klb/hr)
Boiler -1; fuel in the x-axis and steam production in the Y-axis
But notice that, these equations are derived for one period and can't be
generalized, regular performance tests are required to update those equations.
Similarly for other boilers, the model uses these equations to find the optimum
operation for the boiler system.
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The last optimization level is the selection of steam turbine vs. motor serving the
same equipment pump or compressor or fan.
BHP
EMP (5)
Pin
where:
Pin: input power (Btu/hr)
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Input parameters = Pi, Ti, Po, steam flow in (Klb/h), power output rate (kwh/Klb).
Equations:
1. Power, kw = output rate x steam flow
2. Hs,o = Hs,i 3412/kw
3. To = f(Po, Hs,o), from steam props data base
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4 List of all Steam Turbines drivers and power generation (its rated HP and
actual)
5 List of pumps or compressors having flexibility and can operate using steam
turbine or motor
6 Develop performance curves for all major equipment such as boilers,
cogeneration units, gas turbines and large steam turbines (by trending PI data
for one year period)
For Boilers:
i. One year hourly trends for both steam output and fuel consumption of a
boiler
ii. Operating levels of steam (pressure and temperature)
iii. Save maximum and minimum operating limits of a boiler
iv. BD % and its operation (manual or automatic)
v. Cycle of concentration (cc)
vi. Fuel heating value
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The CHP model simulates numerous parameters in the plant. Some of the most
important functions are that the CHP model:
1 Shows steam production and consumption
2 Shows the electricity generated by either Gas Turbines (GT) or Cogeneration
Units (Cogen)
3 Calculates the Operational Cost (OC) under given input values
4 Calculates operational values under Islanding operation
5 Assists in evaluating proposed initiatives. For example, the effects of adding
a new Cogen unit.
The CHP model contains three main sections that are used most frequently:
The Plant View: Displays the illustration of the plant, and the equipment in
it. In addition, it allows the user to input some parameters such as
temperature, or steam demand for example. See Figure 8.
Figure 8 - The Plant View of the Shedgum Gas Plant CHP Model
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The Setup View: Displays the various parameters that are input and used
behind the scenes to simulate the plants operation. Here you can set the
values for the different pieces of equipment in the plant such as boilers,
Cogen units, GTs, pumps, and others. See Figure 9 below.
Figure 9 - The Setup View of the Shedgum Gas Plant CHP Model
Figure 10 - The Summary View of the Shedum Gas Plant CHP Model
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Figure 12, shows the main screen that is used by the model to run the
optimization solver. It shows all units, and the associated constraints.
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Figure 13, show the detailed CHP optimization model which is being used to
evaluate all CHP related initiatives. Also, it can be used as a simulator for the
plant for any future projects or expansions and gives appropriate
recommendations for the optimum operation mode for such conditions.
The model inputs are the steam and power demands. Those demands can be
taken directly from PI or derived from correlated equations. This section shows
the demands correlation equations.
Analyzing historical data, shows that the steam demand is affected by the
ambient temperature, the feed gas and its composition.
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The final correlation equation that is used by the model for steam is
shown below. The average error is about 3.5%
Where;
TF: Total feed (MBD)
AT: Ambient temperature (F)
C: Composition (C = ) where,
b0 0.087483
b1 14.53186 m1 -0.93786
b2 -6.6812 m2 -1.49584
b3 -0.78221 m3 -0.41518
Figure 14 below, shows the difference between the actual steam demand
(in Blue) and the correlated equation (in Pink) for calculating steam
demand.
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Figure 14 Calculated vs. Plant Actual Steam Demand for Year 2006
Where,
TF: Total feed (MBD)
AT: Ambient temperature (F)
b0 0.468681
b1 3.011369 m1 -1.93355
b2 -7.85182 m2 -0.61865
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80
MW Demand Actual MWh
70
MW Calculated
60
50
40
30
20
10
0
9/5 12/14 3/24 7/2 10/10 1/18 4/28 8/6 11/14 2/22
The CHP simulation model is based on both the material and energy balances
for all and each component of the CHP model (boilers, Cogen units, headers,
turbines, reducing stations, dearators, BD.., etc.). Figure 16 below shows the
summary of the material balance for a scenario. It shows that all balances are
closed and the system is fully balanced.
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Input parameters = operating pressure and temp of the various steam headers,
process steam demands at the various pressure levels, steam flow rates to the
back-pressure and condensing turbines, condensate return rate (flow), makeup
water supply temp, estimated or allowable vent losses from LP header.
Calculation Sequence:
Assume a trial value for total steam generation in the boilers, and then calculate
the various parametric values from the top down by applying established
principles for steady-state material and energy balances.
1. PRV flow from HP to IP = sum (steam from boilers) - process demand
sum (turbine outflows)
2. Calculate BFW flow to DSH station in IP header by simultaneous material
and heat balance
3. PRV flow from IP to MP = sum (steam inflows) + DSH steam - process
demand sum (turbine outflows)
4. Calculate BFW flow to DSH station in MP header by simultaneous material
and heat balance
5. PRV flow from MP to LP = sum (steam inflows) + DSH steam - process
demand sum (turbine outflows)
6. Calculate BFW flow to DSH station in LP header by simultaneous material
and heat balance
7. Calculate flash vapor and net liquid blowdown flows from the BD flash
tank, by heat/mass balance.
8. Calculate steam and makeup water flows to the DA from DA model
9. BFW temp = DA temp + economizer duty /B
10. Vent flow from LP header to atmosphere = sum (steam inflows) + DSH stm
+ flash vapor from BD tank - process demand steam to DA
11. Heat recovery required against process hot streams = M x (DA feed temp
DI makeup water supply temp)
12. Calculate total steam generation required in boilers = sum (process
demands) + flow to condensing path of steam turbine + steam to
economizer + DA steam BD flash vapor sum (DSH flows) + LP vent to
atmosphere
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One note of caution the user should be careful to ensure that the assumptions
and data inputs do not result in infeasible solutions, such as reverse flows
(i.e., from lower to higher pressure) across the PRV, and violating capacity
constraints on the boilers and turbines.
Input parameters = Pi, Ti, Po, power output required, linked to process model,
turbine performance curve (from manufacturers data) that expresses the steam
flow rate as a function of power output for the given Pi, Ti, and Po.
Equations:
1. Steam flow = f(required power output)
2. Hs,o = Hs,i 3412/kw
3. To = f(Po, Hs,o), from steam props data base
Deaerator:
Input parameters = condensate flow and temp from process, condensate flow
and temp from condensing steam turbine, economizer duty, pressure of steam
used in economizer, DA operating pressure, temp of makeup water (after
preheating), vent vapor flow from DA
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Other Equipment
Similar models must be set up for the blowdown flash tank, desuperheating
stations, etc.
Revision Summary
12 March 2011 Reaffirmed the contents of the document, and reissued with editorial changes.
20 January 2016 Minor revision. 1. Title changed from In-House Combined Heat and Power Optimization
Model to Overview of In-House Combined Heat and Power Optimization Model,
2. The following sections were moved to Appendix CHP Optimization Model General
Background References, 3. Addition of SABP-A-060 in Related Document section,
4. Update old Excel Solver snapshots to new Excel 2013 snapshots, 5. Update old Excel
Solver feature description to new Excel 2013 features, and 6. Relocate sections relating to
optimization levels under the CHP Optimization Levels diagram to make the flow of the SABP
more easy for the end user.
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Appendix A:
Example: CHP Model for HGP Steam Turbine Generator Optimum Design
In the study, we considered all possible options / alternatives and used an optimization model to help
finding the optimum size of the STG for each alternative. In total, six (6) options were evaluated and
following table provides a summary of the evaluated options including business as usual case.
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BAU option:
This is the base case; there is no investment has been considered in this option. The energy balance will
be met by the reducing stations. Below figure, shows a snapshot of the HGP CHP optimization model
for this case, one can notice the zero outputs from STGs in orange color, this represents the new STGs.
The model analysis considers the operation of new Cogen units, plant will only need to run one boiler at
the minimum load of 113 Klb/h. The summary of this case is shown in the table below.
BAU
Parameters unit
2018 2023 2027 2031 2035
Net Operating Cost MM$/yr 62 81 91 98 103
Utilities Fuel Cons. MMBtu/h 1803 1803 1803 1803 1803
Fuel Cogen MMBtu/h 1660 1660 1660 1660 1660
Plants Stm Sys Eff.% % 65% 64% 64% 64% 64%
Make-up Klb/h 145 147 145 145 145
Exported Pwr. MW 38 38 38 38 38
FinFan Excess Stm Klb/h 42 55 62 62 62
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BP-STG HP-MP
Parameters unit
2018 2023 2027 2031 2035
Net Operating Cost MM$/yr 60 78 88 94 99
Utilities Fuel Cons. MMBtu/h 1803 1803 1803 1803 1803
Fuel Cogen MMBtu/h 1660 1660 1660 1660 1660
Plants Stm Sys Eff.% % 65% 65% 64% 64% 64%
Make-up Klb/h 145 147 145 145 145
Exported Pwr. MW 42 42 42 42 42
FinFan Excess Stm Klb/h 28 40 48 48 48
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HP-LP-STG
Parameters unit
2018 2023 2027 2031 2035
Net Operating Cost MM$/yr 57 74 83 89 93
Utilities Fuel Cons. MMBtu/h 1803 1803 1803 1803 1803
Fuel Cogen MMBtu/h 1660 1660 1660 1660 1660
Plants Stm Sys Eff.% % 66.7% 66.1% 65.7% 65.7% 65.7%
Make-up Klb/h 145 147 145 145 145
Exported Pwr. MW 48 50 49 49 49
FinFan Excess Stm Klb/h 21 32 40 40 40
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In conclusion, the study recommends going with option-2 as it has been identified the optimum
configuration compared to the other options. Table 1-6 below, provides the final results of the different
options after using company economics. This option is recommending the installation of (11 MW) back
pressure steam turbine generator from HP directly to LP. The recommended option will yield to the
most optimum configuration with NPV of 47 MM$.
Table 1-6 Summary of STGs Options and Configurations Considered in the Analysis
STG- Cap. Cost Plants overall NPV (US$ MM)
# Options/STGs Configurations
MW MM$ Eff.%
0 BAU 0 0 63.6% 0
1 HP-MP-STG 4.2 23.5 64.4% (3.0)
2 HP-LP-STG 11.2 25.5 65.7% 47.4
3 HP-MP-STG + MP-LP-STG 11.2 39.0 65.7% 28.2
4 HP-MP-LP MS-STG 12.0 32.5 65.9% 45.0
5 HP-LP-Cond MS-STG 12.5 45.5 66.0% 34.2
6 HP-MP-LP-Cond MS-STG 13.2 51.0 66.1% 33.9
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Operating best practice of the utilities systems optimization composed of several areas that
accounts for the most economic savings. Refer to Best Practice SABP-A-060 that focuses on
Operational Modifications of CHP systems. It covers the following directions:
1. Minimize steam flow to Fin-Fan condensers
2. Optimize Steam Turbine operating load (switch ability of running steam turbines and
motors)
3. Boiler Load Management
4. Maximizing Cogeneration Operation
5. Maintain Operation within Optimized case
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Figures 18 and 19 show the Solver tool box and its input requirements. Depending on the
optimization problem, different mathematical searching options can be selected. Table 7
illustrates some main features of the Solver tool.
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Item Description
Specifies the objective cell that the user wants to set to a certain value,
Set Objective
maximize or minimize. This cell must contain a formula.
Change Options for All Solving Methods
For a constraint to be satisfied, the relationship between the Cell Reference and
Constraint
the Constraint value cannot be violated by more than this amount. The smaller
precision
the number, the higher the precision
Selection of Use Automatic Scaling check box causes the Solver to internally
Use Automatic rescale the values of variables, constraints and the objective to similar
Scaling magnitudes, to reduce the impact of extremely large or small values on the
accuracy of the solution process. This box is selected by default.
Show Iteration
Select the Show Iteration Results check box to see values of each trial solution
Results
Select the Ignore Integer Constraints check box to cause all integer, binary and
Solving with all different constraints to be ignored. In the Integer Optimality % box, type the
Integer maximum percentage difference Solver should accept between the objective
Constraints value of the best integer solution found and the best known bound on the true
optimal objective value before stopping.
Change Options for GRG Nonlinear Solving Method
Solving
Method: To solve smooth nonlinear optimization problem.
GRG Nonlinear
Specify in the Convergence box, the preferred amount of relative change in the
last five iterations before Solver stops with the message Solver converged to
Convergence
the current solution. Smaller values here usually mean that Solver will take
more time, but will stop at a point closer to the optimal solution.
In the Derivatives group box, Forward, estimates derivatives through forward
differencing, and Central estimates derivatives through central differencing.
Derivatives
Forward is the default choice. Central differencing yields more accurate
derivatives, but requires twice as many calculations at each new trial solution.
Select the Use Multistart check box to use the multistart method for global
optimization. Selecting this box and clicking Solve will run the GRG Nonlinear
method repeatedly, starting from different (automatically chosen) starting values
MultiStart for the decision variables. This process may find a better solution, but it will take
Options for more computing time than a single run of the GRG Nonlinear method.
Global
Optimization In the Population Size box, the minimum population size is 10; if the user
specifies a value less than 10 or leaves it blank, the multistart method uses a
population size of 10 times the number of decision variables, but no more than
200.
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Item Description
In the Random Seed box, type a positive integer number to be used as the
(fixed) seed for the random number generator used to generate candidate
starting points for the GRG Nonlinear method. If this is left blank, the random
number generator will use a different seed each time Solve is clicked, which
may yield a different (better or worse) final solution.
Select the Require Bounds on Variables check box to specify that the multistart
method should run only if lower and upper bounds are defined on all decision
variables in the Constraints list box. The multistart method is far more effective if
bounds are defined on all variables; the tighter the bounds on the variables, the
better the multistart method is likely to perform.
Solving
Method: To solve linear optimization problem.
Simplex LP
Change Options for Evolutionary Solving Method
Solving
Method: To solve non smooth optimization problem.
Evolutionary
In the Convergence box, type the maximum percentage difference in objective
values for the top 99% of the population that Solver should allow in order to stop
Convergence with the message Solver converged to the current solution. Smaller values
here normally mean that Solver will take more time, but will stop at a point closer
to the optimal solution.
In the Mutation Rate box, type a number between 0 and 1, the relative
frequency with which some member of the population will be altered or
mutated to create a new trial solution, during each generation or sub problem
Mutation Rate
considered by the Evolutionary method. A higher Mutation Rate increases the
diversity of the population and the chance that a new, better solution will be
found; but this may increase total solution time.
In the Population Size box, type the number of different points (values for the
decision variables) for the Evolutionary method to maintain at any given time in
its population of candidate solutions. The minimum population size is 10
Population Size
members; if a value less than 10 is specified in this box, or left blank, the
Evolutionary Solver uses a population size of 10 times the number of decision
variables in the problem, but no more than 200.
In the Random Seed box, type a positive integer number to be used as the
(fixed) seed for the random number generator used for a variety of random
choices in the Evolutionary method. If a number is entered here, the
Random Seed
Evolutionary method will use the same choices each time Solve is clicked. If this
box is left blank, the random number generator will use a different seed each
time Solve is clicked, which may yield a different (better or worse) final solution.
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Document Responsibility: Energy Systems Optimization Standards Committee SABP-A-023
Issue Date: 20 January 2016
Next Planned Update: TBD Overview of In-House Combined Heat and Power Optimization Model
Item Description
In the Maximum Time without Improvement box, type the maximum number of
Maximum Time
seconds you want the Evolutionary method to continue without a meaningful
without
improvement in the objective value of the best solution in the population, before
Improvement
it stops with the message Solver cannot improve the current solution.
Select the Require Bounds on Variables check box to specify that the
Evolutionary method should run only if the lower and upper bounds have been
Require Bounds defined on all decision variables in the Constraints list box. The Evolutionary
on Variables method is far more effective if bounds are defined on all variables; the tighter the
bounds on the specified variables, the better the Evolutionary method is likely to
perform.
System Requirements
The first step is to install the model in your personal computer; there will be no restriction for installing
the model. To have the model operate, you need to follow the below steps:
1. STEP 1: Setting Up Solver Add-In (Excel)
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Document Responsibility: Energy Systems Optimization Standards Committee SABP-A-023
Issue Date: 20 January 2016
Next Planned Update: TBD Overview of In-House Combined Heat and Power Optimization Model
b. Choose Add-In from the Excel Options list, click on Go , then select the Solver
add-in check box from the list as shown below and click on OK.
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Document Responsibility: Energy Systems Optimization Standards Committee SABP-A-023
Issue Date: 20 January 2016
Next Planned Update: TBD Overview of In-House Combined Heat and Power Optimization Model
b. Choose Customize Ribbon from the Excel Options list, then select the Developer
check box from the right pane and click on OK.
a. After Steps 1 & 2, close the excel file and save it, then, open the file a gain
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Document Responsibility: Energy Systems Optimization Standards Committee SABP-A-023
Issue Date: 20 January 2016
Next Planned Update: TBD Overview of In-House Combined Heat and Power Optimization Model
c. Once the VBA window appears, go to the Tools tab and select References, then
chose Solver and then click ok
After going through steps 1, 2 and 3, the CHP model should work fine.
Steam properties table is required as part of the CHP excel model. By default, steam properties are
embedded in CHP template files. User is advised to acquire a copy of the template file from the Primary
Contact person listed on the cover page.
Model Features
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Document Responsibility: Energy Systems Optimization Standards Committee SABP-A-023
Issue Date: 20 January 2016
Next Planned Update: TBD Overview of In-House Combined Heat and Power Optimization Model
A cell color coding technique is applied throughout the model to make it easier to track the calculations
cells and differentiate input data cells from calculated ones. A summary of the color coding description
is shown in Table 8.
Model Strategy
The following section explains the strategy used by the model in solving both unit commitment and
economic dispatch problems:
a) The state of a unit is represented by 1 and 0 for On and Off respectively.
b) Going over all possible state combinations for the units, and filtering out some un wanted
combinations such as: combinations at which their maximum output is less than demand (i.e.,
demand is accounting for steam required, steam losses and minimum steam reserve)
c) From the selected options (combinations), the model selects the lowest operating cost option;
unit commitment problem is solved.
d) Then the economic dispatch model is applied to the best selected option in c; i.e., economic
dispatch model is basically allocating the demands steam and power on the available units so
that the total operating cost is the minimum.
Note:
There are 2 types of constraints used in the model: system requirements and equipment limitations
(i.e., steam out >0, and < unit capacity). Following is the summary:
a) Total power and steam generated must equal to the forecasted system demand (including losses)
b) Export or import power tariffs (parameter)
c) Systems steam and power reserve required
d) Maximum and minimum output limitations for each unit.
e) Energy and material balances flow IN/OUT must be equal.
Page 43 of 44
Document Responsibility: Energy Systems Optimization Standards Committee SABP-A-023
Issue Date: 20 January 2016
Next Planned Update: TBD Overview of In-House Combined Heat and Power Optimization Model
Appendix D - References
J D Kumana, CHP Models for Energy Optimization, invited paper presented at Pfizer
Process Engineering Conference, Boston, Ma (Oct 26th, 2000).
H. Asano, S. Sangai, E. Imamura, K. Ito, and R. Yokoyama, Impacts of Time of Use Rates on
the Optimal Sizing and Operation of Cogeneration Systems, IEEE Trans. Power Syst., vol. 7,
pp. 14441450, Nov. 1992.
B. K. Chen and C. C. Hong, Optimum Operation for a Back Pressure Cogeneration Systems
under Time of Use Rates, IEEE Trans. Power Syst., vol. 11, pp. 10741084, May 1996.
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