OPTIMAL PREVENTIVE AND CORRECTIVE ACTIONS
stability problems, preventive actions are required. If a feasible solution exists
to a given security control problem, then it is highly likely that other feasible
solutions exist as well. In this instance, one solution must be chosen from
among the feasible candidates. If a feasible solution does not exist (which is
also common), a solution must be chosen from the infeasible candidates.
Security optimization is a broad term to describe the process of selecting a
preferred solution from a set of (feasible or infeasible) candidate solutions. The
term Optimal Power Flow (OPF) is used to describe the computer application
that performs security optimization within an Energy Management System.
to a given security control problem, then it is highly likely that other feasible
solutions exist as well. In this instance, one solution must be chosen from
among the feasible candidates. If a feasible solution does not exist (which is
also common), a solution must be chosen from the infeasible candidates.
Security optimization is a broad term to describe the process of selecting a
preferred solution from a set of (feasible or infeasible) candidate solutions. The
term Optimal Power Flow (OPF) is used to describe the computer application
that performs security optimization within an Energy Management System.
Optimization in Security Control
To address a given security problem, an operator will have more than
one control scheme. Not all schemes will be equally preferred and the operator
one control scheme. Not all schemes will be equally preferred and the operator
will thus have to choose the best or “optimal” control scheme. It is desirable to
find the control actions that represent the optimal balance between security,
economy, and other operational considerations. The need is for an optimal
solution that takes all operational aspects into consideration. Security
optimization programs may not have the capability to incorporate all operational
considerations into the solution, but this limitation does not prevent security
optimization programs from being useful to utilities.
The solution of the security optimization program is called an “optimal
solution” if the control actions achieve the balance between security, economy,
and other operational considerations. The main problem of security
optimization seeks to distinguish the preferred of two possible solutions. A
method that chooses correctly between any given pair of candidate solutions is
capable of finding the optimal solution out of the set of all possible solutions.
There are two categories of methods for distinguishing between candidate
solutions: one class relies on an objective function, the other class relies on
rules.
1) The Objective Function
The objective function method assumes that it is possible to assign a
single numerical value to each possible solution, and that the solution with the
lowest value is the optimal solution. The objective function is this numerical
assignment. In general, the objective function value is an explicit function of
the controls and state variables, for all the networks in the problem.
Optimization methods that use an objective function typically exploit its
analytical properties, solving for control actions that represent the minimum.
The conventional optimal power flow (OPF) is an example of an optimization
method that uses an objective function.
The advantages of using an objective function method are:
• Analytical expressions can be found to represent MW production
costs and transmission losses, which are, at least from an economic
view point, desirable quantities to minimize.
• The objective function imparts a value to every possible solution.
Thus all candidate solutions can, in principle, be compared on the
basis of their objective function value.
• The objective function method assures that the optimal solution of
the moment can be recognized by virtue of its having the minimum
value.
Typical objective functions used in OPF include MW production costs
or expressions for active (or reactive) power transmission losses. However,
when the OPF is used to generate control strategies that are intended to keep the
power system secure, it is typical for the objective function to be an expression
of the MW production costs, augmented with fictitious control costs that
represent other operational considerations. This is especially the case whensecurity against contingencies is part of the problem definition. Thus when
security constrained OPF is implemented to support real-time operations, the
objective function tends to be a device whose purpose is to guide the OPF to
find the solution that is optimal from an operational perspective, rather than one
which represents a quantity to be minimized.
Some examples of non-economic operational considerations that a
utility might put into its objective function are:
• a preference for a small number of control actions;
• a preference to keep a control away from its limit;
• the relative preference or reluctance for preventive versus postcontingent
action when treating contingencies; and
• a preference for tolerating small constraint violations rather than
taking control action.
The most significant shortcoming of the objective function method is
that it is difficult (sometimes impossible) to establish an objective function that
consistently reflects true production costs and other non-economic operational
considerations.
2) Rules
Rules are used in methods relying on expert systems techniques. A
rule-based method is appropriate when it is possible to specify rules for
choosing between candidate solutions easier than by modeling these choices via
an objective function. Optimization methods that use rules typically search for a
rule that matches the problem addressed. The rule indicates the appropriate
decision (e.g., control action) for the situation. The main weakness of a rulebased
approach is that the rule base does not provide a continuum in the solution
space. Therefore, it may be difficult to offer guidance for the OPF from the rule
base when the predefined situations do not exist in the present power system
state.
Rules can play another important role when the OPF is used in the realtime
environment. The real-time OPF problem definition itself can be ill
defined and rules may be used to adapt the OPF problem definition to the
current state of the power system.
find the control actions that represent the optimal balance between security,
economy, and other operational considerations. The need is for an optimal
solution that takes all operational aspects into consideration. Security
optimization programs may not have the capability to incorporate all operational
considerations into the solution, but this limitation does not prevent security
optimization programs from being useful to utilities.
The solution of the security optimization program is called an “optimal
solution” if the control actions achieve the balance between security, economy,
and other operational considerations. The main problem of security
optimization seeks to distinguish the preferred of two possible solutions. A
method that chooses correctly between any given pair of candidate solutions is
capable of finding the optimal solution out of the set of all possible solutions.
There are two categories of methods for distinguishing between candidate
solutions: one class relies on an objective function, the other class relies on
rules.
1) The Objective Function
The objective function method assumes that it is possible to assign a
single numerical value to each possible solution, and that the solution with the
lowest value is the optimal solution. The objective function is this numerical
assignment. In general, the objective function value is an explicit function of
the controls and state variables, for all the networks in the problem.
Optimization methods that use an objective function typically exploit its
analytical properties, solving for control actions that represent the minimum.
The conventional optimal power flow (OPF) is an example of an optimization
method that uses an objective function.
The advantages of using an objective function method are:
• Analytical expressions can be found to represent MW production
costs and transmission losses, which are, at least from an economic
view point, desirable quantities to minimize.
• The objective function imparts a value to every possible solution.
Thus all candidate solutions can, in principle, be compared on the
basis of their objective function value.
• The objective function method assures that the optimal solution of
the moment can be recognized by virtue of its having the minimum
value.
Typical objective functions used in OPF include MW production costs
or expressions for active (or reactive) power transmission losses. However,
when the OPF is used to generate control strategies that are intended to keep the
power system secure, it is typical for the objective function to be an expression
of the MW production costs, augmented with fictitious control costs that
represent other operational considerations. This is especially the case whensecurity against contingencies is part of the problem definition. Thus when
security constrained OPF is implemented to support real-time operations, the
objective function tends to be a device whose purpose is to guide the OPF to
find the solution that is optimal from an operational perspective, rather than one
which represents a quantity to be minimized.
Some examples of non-economic operational considerations that a
utility might put into its objective function are:
• a preference for a small number of control actions;
• a preference to keep a control away from its limit;
• the relative preference or reluctance for preventive versus postcontingent
action when treating contingencies; and
• a preference for tolerating small constraint violations rather than
taking control action.
The most significant shortcoming of the objective function method is
that it is difficult (sometimes impossible) to establish an objective function that
consistently reflects true production costs and other non-economic operational
considerations.
2) Rules
Rules are used in methods relying on expert systems techniques. A
rule-based method is appropriate when it is possible to specify rules for
choosing between candidate solutions easier than by modeling these choices via
an objective function. Optimization methods that use rules typically search for a
rule that matches the problem addressed. The rule indicates the appropriate
decision (e.g., control action) for the situation. The main weakness of a rulebased
approach is that the rule base does not provide a continuum in the solution
space. Therefore, it may be difficult to offer guidance for the OPF from the rule
base when the predefined situations do not exist in the present power system
state.
Rules can play another important role when the OPF is used in the realtime
environment. The real-time OPF problem definition itself can be ill
defined and rules may be used to adapt the OPF problem definition to the
current state of the power system.
Optimization Subject to Security Constraints
The conventional OPF formulation seeks to minimize an objective
function subject to security constraints, often presented as “hard constraints,” for
which even small violations are not acceptable. A purely analytical formulation
might not always lead to solutions that are optimal from an operational
perspective. Therefore, the OPF formulation should be regarded as a framework
in which to understand and discuss security optimization problems, rather than
as a fundamental representation of the problem itself.1) Security Optimization for the Base Case State
Consider the security optimization problem for the base case state
ignoring contingencies. The power system is considered secure if there are no
constraint violations in the base case state. Thus any control action required will
be corrective action. The aim of the OPF is to find the optimal corrective action.
When the objective function is defined to be the MW production costs,
the problem becomes the classical active and reactive power constrained
dispatch. When the objective function is defined to be the active power
transmission losses, the problem becomes one of active power loss
minimization.
2) Security Optimization for Base Case and Contingency States
Now consider the security optimization problem for the base case and
contingency states. The power system is considered secure if there are no
constraint violations in the base case state, and all contingencies are manageable
with post-contingent control action. In general, this means that base case control
action will be a combination of corrective and preventive actions and that postcontingent
control action will be provided in a set of contingency plans. The
aim of the OPF is then to find the set of base case control actions plus
contingency plans that is optimal.
Dealing with contingencies requires solving OPF involving multiple
networks, consisting of the base case network and each contingency network.
To obtain an optimal solution, these individual network problems must be
formulated as a multiple network problem and solved in an integrated fashion.
The integrated solution is needed because any base case control action will
affect all contingency states, and the more a given contingency can be addressed
with post-contingency control action, the less preventive action is needed for
that contingency.
When an operator is not willing to take preventive action, then all
contingencies must be addressed with post-contingent control action. The
absence of base case control action decouples the multiple network problems
into a single network problem for each contingency. When an operator is not
willing to rely on post-contingency control action, then all contingencies must
be addressed with preventive action. In this instance, the cost of the preventive
action is preferred over the risk of having to take control action in the postcontingency
state. The absence of post-contingency control action means that
the multiple network problem may be represented as the single network problem
for the base case, augmented with post-contingent constraints.
Security optimization for base case and contingency states will involve
base case corrective and preventive action, as well as contingency plans for
post-contingency action. To facilitate finding the optimal solution, the objectivefunction and rules that reflect operating policy are required. For example, if it is
preferred to address contingencies with post-contingency action rather than
preventive action, then post-contingent controls may be modeled as having a
lower cost in the objective function. Similarly, a preference for preventive
action over contingency plans could be modeled by assigning the postcontingent
controls a higher cost than the base case controls. Some
contingencies are best addressed with post-contingent network switching. This
can be modeled as a rule that for a given contingency, switching is to be
considered before other post-contingency controls.
3) Soft Constraints
Another form of security optimization involves “soft” security
constraints that may be violated but at the cost of incurring a penalty. This is a
more sophisticated method that allows a true security/economy trade-off. Its
disadvantage is requiring a modeling of the penalty function consistent with the
objective function. When a feasible solution is not possible, this is perhaps the
best way to guide the algorithm toward finding an “optimal infeasible” solution.
4) Security versus Economy
As a general rule, economy must be compromised for security.
However, in some cases security can be traded off for economy. If the
constraint violations are small enough, it may be preferable to tolerate them in
return for not having to make the control moves. Many constraint limits are not
truly rigid and can be relaxed. Thus, in general, the security optimization
problem seeks to determine the proper balance of security and economy. When
security and economy are treated on the same basis, it is necessary to have a
measure of the relative value of a secure, expensive state relative to a less
secure, but also less expensive state.
5) Infeasibility
If a secure state cannot be achieved, there is still a need for the least
insecure operating point. For OPF, this means that when a feasible solution
cannot be found, it is still important that OPF reach a solution, and that this
solution be “optimal” in some sense, even though it is infeasible. This is
especially appropriate for OPF problems that include contingencies in their
definition. The OPF program needs to be capable of obtaining the “optimal
infeasible” solution. There are several approaches to this problem. Perhaps the
best approach is one that allows the user to model the relative importance of
specific violations, with this modeling then reflected in the OPF solution. This
modeling may involve the objective function (i.e., penalty function) or rules, or
both.
function subject to security constraints, often presented as “hard constraints,” for
which even small violations are not acceptable. A purely analytical formulation
might not always lead to solutions that are optimal from an operational
perspective. Therefore, the OPF formulation should be regarded as a framework
in which to understand and discuss security optimization problems, rather than
as a fundamental representation of the problem itself.1) Security Optimization for the Base Case State
Consider the security optimization problem for the base case state
ignoring contingencies. The power system is considered secure if there are no
constraint violations in the base case state. Thus any control action required will
be corrective action. The aim of the OPF is to find the optimal corrective action.
When the objective function is defined to be the MW production costs,
the problem becomes the classical active and reactive power constrained
dispatch. When the objective function is defined to be the active power
transmission losses, the problem becomes one of active power loss
minimization.
2) Security Optimization for Base Case and Contingency States
Now consider the security optimization problem for the base case and
contingency states. The power system is considered secure if there are no
constraint violations in the base case state, and all contingencies are manageable
with post-contingent control action. In general, this means that base case control
action will be a combination of corrective and preventive actions and that postcontingent
control action will be provided in a set of contingency plans. The
aim of the OPF is then to find the set of base case control actions plus
contingency plans that is optimal.
Dealing with contingencies requires solving OPF involving multiple
networks, consisting of the base case network and each contingency network.
To obtain an optimal solution, these individual network problems must be
formulated as a multiple network problem and solved in an integrated fashion.
The integrated solution is needed because any base case control action will
affect all contingency states, and the more a given contingency can be addressed
with post-contingency control action, the less preventive action is needed for
that contingency.
When an operator is not willing to take preventive action, then all
contingencies must be addressed with post-contingent control action. The
absence of base case control action decouples the multiple network problems
into a single network problem for each contingency. When an operator is not
willing to rely on post-contingency control action, then all contingencies must
be addressed with preventive action. In this instance, the cost of the preventive
action is preferred over the risk of having to take control action in the postcontingency
state. The absence of post-contingency control action means that
the multiple network problem may be represented as the single network problem
for the base case, augmented with post-contingent constraints.
Security optimization for base case and contingency states will involve
base case corrective and preventive action, as well as contingency plans for
post-contingency action. To facilitate finding the optimal solution, the objectivefunction and rules that reflect operating policy are required. For example, if it is
preferred to address contingencies with post-contingency action rather than
preventive action, then post-contingent controls may be modeled as having a
lower cost in the objective function. Similarly, a preference for preventive
action over contingency plans could be modeled by assigning the postcontingent
controls a higher cost than the base case controls. Some
contingencies are best addressed with post-contingent network switching. This
can be modeled as a rule that for a given contingency, switching is to be
considered before other post-contingency controls.
3) Soft Constraints
Another form of security optimization involves “soft” security
constraints that may be violated but at the cost of incurring a penalty. This is a
more sophisticated method that allows a true security/economy trade-off. Its
disadvantage is requiring a modeling of the penalty function consistent with the
objective function. When a feasible solution is not possible, this is perhaps the
best way to guide the algorithm toward finding an “optimal infeasible” solution.
4) Security versus Economy
As a general rule, economy must be compromised for security.
However, in some cases security can be traded off for economy. If the
constraint violations are small enough, it may be preferable to tolerate them in
return for not having to make the control moves. Many constraint limits are not
truly rigid and can be relaxed. Thus, in general, the security optimization
problem seeks to determine the proper balance of security and economy. When
security and economy are treated on the same basis, it is necessary to have a
measure of the relative value of a secure, expensive state relative to a less
secure, but also less expensive state.
5) Infeasibility
If a secure state cannot be achieved, there is still a need for the least
insecure operating point. For OPF, this means that when a feasible solution
cannot be found, it is still important that OPF reach a solution, and that this
solution be “optimal” in some sense, even though it is infeasible. This is
especially appropriate for OPF problems that include contingencies in their
definition. The OPF program needs to be capable of obtaining the “optimal
infeasible” solution. There are several approaches to this problem. Perhaps the
best approach is one that allows the user to model the relative importance of
specific violations, with this modeling then reflected in the OPF solution. This
modeling may involve the objective function (i.e., penalty function) or rules, or
both.
The Time Variable
The preceding discussion assumes that all network states are based on
the same (constant) frequency, and all transient effects due to switching and
outages are assumed to have died out. While bus voltages and branch flows are,in general, sinusoidal functions of time, only the amplitudes and phase
relationships are used to describe network state. Load, generation, and
interchange schedules change slowly with time, but are treated as constant in the
steady state approximation. There are still some aspects of the time variable that
need to be accounted for in the security optimization problem.
1) Time Restrictions on Violations and Controls
The limited amount of time to correct constraint violations is a security
concern. This is because branch flow thermal limits typically have several
levels of rating (normal, emergency, etc.), each with its maximum time of
violation. (The higher the rating, the shorter the maximum time of violation.)
Voltage limits have a similar rating structure and there is very little time to
recover from a violation of an emergency voltage rating.
Constraint violations need to be corrected within a specific amount of
time. This applies to violations in contingency states as well as actual violations
in the base case state. Base case violations, however, have the added
seriousness of the elapsed time of violation: a constraint that has been violated
for a period of time has less time to be corrected than a constraint that has just
gone into violation.
The situation is further complicated by the fact that controls cannot
move instantaneously. For some controls, the time required for movement is
significant. Generator ramp rates can restrict the speed with which active power
is rerouted in the network. Delay times for switching capacitors and reactors
and transformer tap changing mechanisms can preclude the immediate
correction of serious voltage violations. If the violation is severe enough, slow
controls that would otherwise be preferred may be rejected in favor of fast, less
preferred controls. When the violation is in the contingency state, the time
criticality may require the solution to chose preventive action even though a
contingency plan for post-contingent corrective action might have been possible
for a less severe violation.
2) Time in the Objective Function
It is common for the MW production costs to dominate the character of
the objective function for OPF users. The objective function involves the time
variable to the extent that the OPF is minimizing a time rate of change. This is
also the case when the OPF is used to minimize the cost of imported power or
active power transmission losses. Not all controls in the OPF can be “costs” in
terms of dollars per hour. The start-up cost for a combustion turbine, for
example, is expressed in dollars, not dollars per hour. The costing of reactive
controls is even more difficult, since the unwillingness to move these controls is
not easily expressed in either dollars or dollars per hour. OPF technology
requires a single objective function, which means that all control costs must be
expressed in the same units. There are two approaches to this problem:• Convert dollar per hour costs into dollar costs by specifying a time
interval for which the optimization is to be valid. Thus control
costs in dollars per hour multiplied by the time interval, yield
control costs in dollars. This is now in the same units as controls
whose costs are “naturally” in dollars. This approach thus
“integrates” the time variable out of the objective function
completely. This may be appropriate when the OPF solution is
intended for a well-defined (finite) period of time.
• Regard all fixed control costs (expressed in dollars) as occurring
repeatedly in time and thus having a justified conversion into
dollars per hour. For example, the expected number of times per
year that a combustion turbine is started defines a cost per unit
time for the start-up of the unit. Similarly, the unwillingness to
move reactive controls can be thought of as reluctance over and
above an acceptable amount of movement per year. This approach
may be appropriate when the OPF is used to optimize over a
relatively long period of time.
• Simply adjust the objective function empirically so that the OPF
provides acceptable solutions. This method can be regarded as an
example of either of the first two approaches.
Using an Optimal Power Flow Program
OPF programs are used both in on-line and in off-line (study mode)
studies. The two modes are not the same.
1) On-line Optimal Power Flow
The solution speed of an on-line OPF should be high enough so that the
program completes before the power system configuration has changed
appreciably. Thus the on-line OPF should be fast enough to run several time per
hour. The values of the algorithm’s input parameters should be valid over a
wide range of operating states, such that the program continues to function as
the state of the system changes. Moreover, the application needs to address the
correct security optimization problem and that the solutions conform to current
operating policy.
2) Advisory Mode versus Closed Loop Control
On-line OPF programs are implemented in either advisory or closed
loop mode. In advisory mode, the control actions that constitute the OPF
solution are presented as recommendations to the operator. For closed loop
OPF, the control actions are actually implemented in the power system, typically
via the SCADA subsystem of the Energy Management System. The advisory
mode is appropriate when the control actions need review by the dispatcher
before their implementation. Closed loop control for security optimization is
appropriate for problems that are so well defined that dispatcher review of the
control actions is not necessary. An example of closed loop on-line OPF is theConstrained Economic Dispatch (CED) function. Here, the constraints are the
active power flows on transmission lines, and the controls are the MW output of
generators on automatic generation control (AGC). When the conventional
Economic Dispatch would otherwise tend to overload the transmission lines in
its effort to minimize production costs, the CED function supplies a correction
to the controls to avoid the overloads. Security optimization programs that
include active and reactive power constraints and controls, in contingency states
as well as in the base case, are implemented in an advisory mode. Thus the
results of the on-line OPF are communicated to the dispatchers via EMS
displays. Considering the typical demands on the dispatchers’ time and
attention in the control center, the user interface for on-line OPF needs to be
designed such that the relevant information is communicated to the dispatchers
“at-a-glance.”
3) Defining the Real-time Security Optimization Problem
As the power system state changes through time, the various aspects of
the security optimization problem definition can change their relative
importance. For example, concern for security against contingencies may be a
function of how secure the base case is. If the base case state has serious
constraint violations, one may prefer to concentrate on corrective action alone,
ignoring the risk of contingencies. In addition, the optimal balance of security
and economy may depend on the current security state of the power system.
During times of emergency, cost may play little or no role in determining the
optimal control action. Thus the security optimization problem definition itself
can be dynamic and sometimes ill defined.
the same (constant) frequency, and all transient effects due to switching and
outages are assumed to have died out. While bus voltages and branch flows are,in general, sinusoidal functions of time, only the amplitudes and phase
relationships are used to describe network state. Load, generation, and
interchange schedules change slowly with time, but are treated as constant in the
steady state approximation. There are still some aspects of the time variable that
need to be accounted for in the security optimization problem.
1) Time Restrictions on Violations and Controls
The limited amount of time to correct constraint violations is a security
concern. This is because branch flow thermal limits typically have several
levels of rating (normal, emergency, etc.), each with its maximum time of
violation. (The higher the rating, the shorter the maximum time of violation.)
Voltage limits have a similar rating structure and there is very little time to
recover from a violation of an emergency voltage rating.
Constraint violations need to be corrected within a specific amount of
time. This applies to violations in contingency states as well as actual violations
in the base case state. Base case violations, however, have the added
seriousness of the elapsed time of violation: a constraint that has been violated
for a period of time has less time to be corrected than a constraint that has just
gone into violation.
The situation is further complicated by the fact that controls cannot
move instantaneously. For some controls, the time required for movement is
significant. Generator ramp rates can restrict the speed with which active power
is rerouted in the network. Delay times for switching capacitors and reactors
and transformer tap changing mechanisms can preclude the immediate
correction of serious voltage violations. If the violation is severe enough, slow
controls that would otherwise be preferred may be rejected in favor of fast, less
preferred controls. When the violation is in the contingency state, the time
criticality may require the solution to chose preventive action even though a
contingency plan for post-contingent corrective action might have been possible
for a less severe violation.
2) Time in the Objective Function
It is common for the MW production costs to dominate the character of
the objective function for OPF users. The objective function involves the time
variable to the extent that the OPF is minimizing a time rate of change. This is
also the case when the OPF is used to minimize the cost of imported power or
active power transmission losses. Not all controls in the OPF can be “costs” in
terms of dollars per hour. The start-up cost for a combustion turbine, for
example, is expressed in dollars, not dollars per hour. The costing of reactive
controls is even more difficult, since the unwillingness to move these controls is
not easily expressed in either dollars or dollars per hour. OPF technology
requires a single objective function, which means that all control costs must be
expressed in the same units. There are two approaches to this problem:• Convert dollar per hour costs into dollar costs by specifying a time
interval for which the optimization is to be valid. Thus control
costs in dollars per hour multiplied by the time interval, yield
control costs in dollars. This is now in the same units as controls
whose costs are “naturally” in dollars. This approach thus
“integrates” the time variable out of the objective function
completely. This may be appropriate when the OPF solution is
intended for a well-defined (finite) period of time.
• Regard all fixed control costs (expressed in dollars) as occurring
repeatedly in time and thus having a justified conversion into
dollars per hour. For example, the expected number of times per
year that a combustion turbine is started defines a cost per unit
time for the start-up of the unit. Similarly, the unwillingness to
move reactive controls can be thought of as reluctance over and
above an acceptable amount of movement per year. This approach
may be appropriate when the OPF is used to optimize over a
relatively long period of time.
• Simply adjust the objective function empirically so that the OPF
provides acceptable solutions. This method can be regarded as an
example of either of the first two approaches.
Using an Optimal Power Flow Program
OPF programs are used both in on-line and in off-line (study mode)
studies. The two modes are not the same.
1) On-line Optimal Power Flow
The solution speed of an on-line OPF should be high enough so that the
program completes before the power system configuration has changed
appreciably. Thus the on-line OPF should be fast enough to run several time per
hour. The values of the algorithm’s input parameters should be valid over a
wide range of operating states, such that the program continues to function as
the state of the system changes. Moreover, the application needs to address the
correct security optimization problem and that the solutions conform to current
operating policy.
2) Advisory Mode versus Closed Loop Control
On-line OPF programs are implemented in either advisory or closed
loop mode. In advisory mode, the control actions that constitute the OPF
solution are presented as recommendations to the operator. For closed loop
OPF, the control actions are actually implemented in the power system, typically
via the SCADA subsystem of the Energy Management System. The advisory
mode is appropriate when the control actions need review by the dispatcher
before their implementation. Closed loop control for security optimization is
appropriate for problems that are so well defined that dispatcher review of the
control actions is not necessary. An example of closed loop on-line OPF is theConstrained Economic Dispatch (CED) function. Here, the constraints are the
active power flows on transmission lines, and the controls are the MW output of
generators on automatic generation control (AGC). When the conventional
Economic Dispatch would otherwise tend to overload the transmission lines in
its effort to minimize production costs, the CED function supplies a correction
to the controls to avoid the overloads. Security optimization programs that
include active and reactive power constraints and controls, in contingency states
as well as in the base case, are implemented in an advisory mode. Thus the
results of the on-line OPF are communicated to the dispatchers via EMS
displays. Considering the typical demands on the dispatchers’ time and
attention in the control center, the user interface for on-line OPF needs to be
designed such that the relevant information is communicated to the dispatchers
“at-a-glance.”
3) Defining the Real-time Security Optimization Problem
As the power system state changes through time, the various aspects of
the security optimization problem definition can change their relative
importance. For example, concern for security against contingencies may be a
function of how secure the base case is. If the base case state has serious
constraint violations, one may prefer to concentrate on corrective action alone,
ignoring the risk of contingencies. In addition, the optimal balance of security
and economy may depend on the current security state of the power system.
During times of emergency, cost may play little or no role in determining the
optimal control action. Thus the security optimization problem definition itself
can be dynamic and sometimes ill defined.
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