DE19510343C2 - Process for sequential feedforward control of a process - Google Patents

Description

The invention relates to a method for sequential Piloting a process according to the generic term of Claim 1. It relates in particular to a method for Optimize the current power plant use with a Power plant management system. With power plants are here all power generation units that are in their performance by specifying an active power let setpoint influence.

A process to be controlled P of the aforementioned type consists of n parallel sub-processes P₁, P₂,. . . P _n with an input variable u₁, u₂,. . . u _n and output variables y₁, y₂,. . . y _n . For the input variables there are individual restrictions of the form u _{i, min} u _i u _{i, max} and v _{i, min} v _i : = du _i / dt v _{i, max} . This results in the distances Sum u _i and Σ v _i from the summary limits summ u _{i, min} , Σ u _{i, max} and Σ v _{i, min} , Σ v _{i, max} , which are the total reserves for the sums Sum u _i and Σ v _i . From the individual restrictions, the output variable y of the process P arises in the form y = y₁ + y₂ +. . . y _n and process operation is evaluated by a cost function f, which results from the sum of partial cost functions f _i (u _i ). In order to achieve a desired output variable profile y _A (t), the subprocesses P _{i are} subjected to input variable profiles u _i (t). These courses u _i (t) are formed from the sum of the components u _{i, A (t)} and u _{i, B} (t), which are generated by two separate devices - namely a pilot control A and a controller B -. The controller B reacts to a predetermined control variable e _B , which arises from deviations of the output variable y from the desired output variable curve y _A (t) and from faults. A future setpoint curve e _A (t), which generates the output variable curve y _A (t), is used for pilot control A.

The goals of power plant management systems that come from finally thermal power plants can be run Describe as follows: In the network control systems from Power utilities are for automation the use of power plants in their own supply area offers power plant management systems installed. For everyone too leading power plants generate power setpoints, which are then transferred to the power plants.

German laid-open specification 41 35 803 describes a distributed computer system for impressing special operations, in particular start-stop commands, on the power plant processes. The known control device represents a system for the joint operation and joint control of a power plant, which consists of a variety of individual power plants. According to this, the power plant should be operated by simple start, stop and load operation commands, structured by a central monitoring control device. The operating conditions to be taken into account according to the exemplary embodiment ( FIG. 4), however, only concern operating commands to the individual power plants, but not the optimization according to the invention.

The power plant management systems should include fol perform the following tasks:

• - Ensuring a balanced active power balance (performance balance) between generated and used performance in your own Coverage area, so no unwanted lei flows between the network partners ten;
• - Coordination and optimization of the power plant rate, so that the power plants economically and operated gently;
• - Establishing a balanced in the medium term Current account to the mean of the unwanted Power flows between the network partners small to keep.

To do the job, contain strength factory management systems the following key components ten:

• - A module for estimating the current load and for Forecasting load development;
• - A module for regulating the control current account in one's own supply area;
• - A module for coordinating and optimizing (Optimization module) of the current power plant sentence.

For the power plant use is determined by the Be type of drive determines which power plants are specified by default power setpoint can be controlled. Optimism The control module and control module deliver for each of these Power plants each have a power setpoint. The setpoints are sent either individually or as total setpoints to the Transfer power plants.

Power plant management systems are subject to their functions on the following restrictions and secondary conditions conditions:

• - The power setpoints are limited upwards and downwards for all power plants. The performance limits must not be violated by the setpoints. There is also a maximum positive and a maximum negative limit for the gradients of the power setpoints (maximum power changes; FIG. 1). These restrictions are usually different for each power plant.
• - The optimization module contains one by default Total setpoint. This total setpoint should, if which are not opposed to restrictions and it taking into account the forecast time changes is sensible by the opti module on the power plants be divided so that the sum of each Setpoints gives the total setpoint.
• - The optimization module should also ensure that a quick restoration of the balanced active power balance by the control module is possible at any time. The manipulated variable reserves should be symmetrically around zero so that a disturbance in the active power balance can always be corrected regardless of the sign ( Fig. 4). The size of the desired reserve that can be made available automatically (without taking reserve contracts into account) is based on the size of the expected disruptions (expected rules).

In German Offenlegungsschrift 43 15 317 a Guide device for the automatic control of force  plant blocks in an electrical power supply system stem described, in particular through measures to Lei power increase and frequency support. By the controller should be a predetermined level of reliability of the Ener Gie and power supply adhered to and unnecessary high reserve power can be avoided.

In the acquaintance according to German Offenlegungsschrift 43 15 317 So a guide device is proposed that one via a data transmission element with everyone controlling power plant block connected first Steuerein unit which receives block-specific measured variables and delivers block-specific reference variables. To the blockspe Specific measured variables Mn include actual values of the block line steam and live steam pressure as well as valve positions and tempera measured in or on a turbine housing doors. In the block-specific measured variables Mn, con constant block-specific sizes and specifications does.

Conventional procedures for optimizing the power plant work on the basis of - in general quadratic - cost functions for each power plant. These cost functions approximate depending on the lei power plant's target fuel costs,  that arises in the power plant for the generation of the power hen.

The power limits of the power plants limit the search area in which all permissible power setpoints lie; the maximum permissible setpoint gradients of the power plants and the possible setpoint changes based on the current power setpoint, as shown in FIG. 6 for the case of two power plants, shows the search area S 1 to the search area S 2 that can be reached.

In order to ensure manipulated variable reserves for control at any time, fixed control bands are defined for selected power plants ( Fig. 2). These control bands tighten the restrictions for the optimization module by subtracting the associated fixed control bands from the power limits of the power plants (lowering the upper and increasing the lower power limits), so that (see FIG. 7) the search space S 2 is reduced. This means that a total of one performance band is reserved for general use by the control module.

In addition, depending on the process variant, the Re further restrictions for the optimization module sharpens by the maximum permissible setpoint gradients of the power plants to which a fixed set of rules is assigned net is for the optimization to a fixed proportion be limited by the permissible setpoint gradient. There ensures that the control module without to violate the maximum permissible setpoint gradients, at least the remaining portion of the setpoint graph  served. The sum is a summa nominal setpoint gradient for sole use reserved by the control module. That is also a quick balancing of the active power balance through the Control module within the defined reserves each currently possible.

The starting point for the optimization is the current lei division of services. The optimization determines in the part of the search space on the level at which all points that meet the specified total setpoint lie, the point that belongs to the lowest Ge leads to total costs. This point is the starting point for the next optimal power split.

To solve this optimization task are under different optimization algorithms used, first of all gig the step-by-step algorithm (a simplified gradual gradient method) or the so-called Euler-Lagrange process (global optimization process ren); see. see "Elektrizitätswirtschaft", 1970, 327 to 332; "etz-a", 1978, 416 to 421.

According to a knowledge on which the invention is based have the conventional optimization methods structure-related disadvantage that the optimizer results currently and on average over time from the are not optimal for the following reasons:

• - The gradient be described by the above restriction caused tightening of re restrictions enforces that the desired Gra  reserve reserves for the control module always right be served, even if instead of whose actually the optimization module this Reserves to meet the total claim would need.
• - The degrees of freedom of the optimization are determined by this tightening of the restrictions restricted if the clear for the regulation Total reserve held by the scheme currently is not needed.
• - The actually existing and therefore in principle usable manipulated variable reserve depends strongly on the Performance sharing through the optimization module from and can due to the tightening of the restriction ions are larger than required. If the brisk module on the use of the required reserve ven is limited, then such is a supra filling the reserve claims without advantage.
• - The provision of power reserves in the Power plants are made by selecting those Power plants to which a fixed set of rules is assigned is, and will not automatically be sent to each current power distribution adjusted. In particular is not guaranteed that the manipulated variable External reserve at the moment in terms of costs Optimization is kept ready.
• - The provision of manipulated variable reserves usually lowers additional costs, so that  cost-optimal performance distribution without ver harsher restrictions is cheaper. With the con conventional methods are not directly possible Lich, the reserves dynamically match the current one Adjust supply situation that the additional costs by providing reser ven actually only arise when needed (if the reserve is actually needed becomes)
• - The current optimization of the current force work for the next time step (Momentary optimization) does not necessarily lead to egg optimal power station in the long run rate because the forecast load development has no influence on the momentary optimization. This can be because of the limited setpoint gradients cause the sum required later target values are not reached or that the later performance allocations not cost-effective times are.
• -Additional information about known in advance Changes to timetables, target values, actual values, Performance limits or operating modes of individual Power plants are not used.

These shortcomings can be overcome with conventional optics Do not fix the lubrication process because the manipulated variables reserve a single force after a previous selection works are permanently assigned and a component for  Consider forecasted future changes is missing.

The invention is therefore based on the problem of an op Timing procedure for the optimal division of a lei to create setpoint values, especially for one Power plant park Setpoints for the individual power plants, taking into account restrictions regarding the individual setpoints and constraints and target positions (e.g. regarding the power plant management in total to determine); the goal of optimization is to Finding power sharing where both all Supplementary conditions and restrictions are met and the Sum value of all cost functions becomes minimal as well the optimization results currently and in time Means are optimal.

The solution according to the invention is for the above genus described in the characterizing part of claim 1. Improvement gene and further embodiments of the invention are in specified the other claims.

In the above-mentioned method for sequential According to the invention, pilot control of one process becomes two Differentiated cases:

• (a) In the event that the next setpoint e _A for the precontrol A cannot be completely divided into the input variables u _i, A within a cycle time, without having to sum up with the current input variables u _{i, B based} on predetermined input variables u ′ _i the next input variables u _i violate the individual restrictions, the maximum possible input variables u _{i, A are} selected.
• (b) For the other case, for which the setpoint e _{A of} the pilot control A can be completely divided into the input variables u _i, A in one or more ways, a distribution is selected for which a total reserve for the control of the controller B is present. This division is determined so that within a given range of values [u _{B, min} ; u _{B, max} ] for the sum of the input variables u _{i, B} , starting from the current sum Σ u _{i, B,} with the input variables u _{i, A} remaining the same _, while observing the individual restrictions of the input variables u _i, the sum of the input variables u _{i, B} with the minimum rate of change v _{B, min reaches} the value u _{B, min} and with the maximum rate of change v _{B, max} the value u _{B, max} , and in addition the cost function f for the input variable distribution u _{i, A is} minimal.

The inventive method can be used to solve the use the following generalized problem:  

Given a process P, the processes from n parallel subprojects P _i with input variables u₁, u₂,. . . u _n and output variables y₁, y₂,. . . y _n exists. For the input quantities u _{i there} are individual restrictions of the form

given. The input variables consist of two parts u _{i, A} and u _{i, B} , which add up to the input variables u _i . The output variable y of the process P arises from the sum of the output variables y _{i of} all subprocesses P _i ; see. Fig. 15.

A given controller B, which reacts essentially to deviations of the output variable y from a desired output variable curve y _A (t) and to disturbances, generates the input variables u _{i, B.}

A pilot control A is to generate the input variables u _{i, A} for the subprocesses P _i so that firstly the output variable of the process follows the output variable curve y _A (t) and secondly a cost function f, which evaluates the operation of the process, becomes minimal, and thirdly _, a reserve is kept for the input variables u _{i, B} from the control system so that the control system can intervene quickly in the event of a deviation or malfunction without violating the individual restrictions.

A main application of the optimization according to the invention The procedure is in a power plant management system to optimize current power plant use. The method according to the invention can be zy cliché, acyclic or cyclic with acyclic bear Apply processing requirements. It is determined under the restrictions and constraints described optimal power split after the cycle or be made in the course of the optimization process should.

The method according to the invention specifically shows when used the special feature for optimizing the use of power plants on that power reserve requirements are economical optimally met and predictable timing Changes in input variables during optimization can be considered proactively.  

The invention will now be described with reference to FIG the enclosed drawing in case of an optimization he current power plant use of the nearer purifies. The drawing shows:

Fig. 1, the set point limits u _i, u _i and _{min, max} of a power plant and possible setpoint changes;

Fig. 2, the set point limits the optimization of Figure 1 in a reserved control band R _Bi.

Fig. 3 is the set point limits the optimization of Figure 1 in allowable control band areas.

Fig. 4 shows the summary rule of a power plant parks;

FIG. 5 extended reserve conditions for FIG. 4;

Fig. 6 on the accessible search space S₂ limited search space S₁ at two power plants;

Fig. 7 search spaces S₁, S₂ with fixed control bands for both power plants;

Fig. 8 is a foresighted restriction of the search space he can reach S₂ to S₃;

Figure 9 is the sum of a current power allocation P₀ and predicted total set values E₁, E₂, E₃.

Fig. 10 shows a path P₀ - P₁ - P₂ - P₃ in the search area S₁ with optimized power distributions P₁, P₂ and P₃;

Figure 11 shows the path P₀ - P₁ - P₂ - P₃ in the search space at S₁ exuberant total set point E₂.

Fig. 12, the path P₀ - P₁ - P₂ - P₃ in the search area S₁ with omitted total setpoint E₁;

Figure 13 shows the path P₀ - P₁ - P₂ - P₃ in the search area S₁ with omitted total setpoints E₁ and E₂;

Fig. 14 is a path optimization from A to C₁ or C₂ over B at t _AB <t _BC and

Fig. 15 shows a process P with feedforward control scheme A and B.

Referring to Figs. 1 to 3, the set point limits (power target value) of an individual power plant in dependence From t defined by the time. The upper and lower power limits u _{i, max} and u _{i, min are shown} as well as the maximum positive power change (maximum positive gradient) and the maximum negative power change (maximum negative gradient), each based on a current power setpoint u _i (O).

In a single power plant, a control band R _{Bi is} additionally reserved at FIG. 2 at the upper and the lower power limit in order to ensure control quantity reserves for regulation at all times. These rules tighten the restrictions for optimization by restricting the lower and upper performance limits for optimization u _{i, A, min} and u _{i, A, max} .

In Fig. 3, the limit of the reserved control band is drawn in dashed lines, because a feature of the invention is that the restricted performance limits for the optimization within a control band valid for the regulated power plants of a power plant park are permeable to the individual power plant.

FIGS. 4 and 5 refer to the representation of a summary control band, where the time t, in turn, are removed in the ordinate the sum of power and in the abscissa. The control bands for the entirety of the power plant fleet are fully available for control, based on current total control power. Within the control band, as shown in FIG. 5, a minimum control power change upward and a minimum control power change downward are required according to the invention. The change in performance should have its minimum size until the upper or lower limit of the control band is reached.

Fig. 6 shows the search area S₁ in the case of two power plants. The power 1 of one power plant is plotted on the abscissa, the power 2 of the other power plant is plotted on the ordinate. The search area S 1 is located, in which there are valid setpoints for both power plants which do not violate the performance limits. Based on the current power distribution P₀ of the total power E₀ between the two power plants, the search space S₂ that can be reached within S₁ is shown, in which the valid setpoints after optimization are for both power plants, which additionally do not violate the maximum power changes. Based on the current power distribution P₀ levels of constant total power E₀, E₁ and E₂ are shown schematically.

Fig. 7 shows the search area S₁ and the search space S₂ achievable with fixed control bands RB₁, RB₂ for both power plants, as well as in Fig. 6 on the abscissa the power of the power plant 1 and on the ordinate the power of the power plant 2 is shown. Similarly, the predictive limitation of the accessible search space S₂ to S₃ is schematically shown in Fig. 8 for the case that the setpoint change of the power plants involved, which is directed against the tendency of the total setpoint, is prohibited depending on the size of the total setpoint change. Fig. 9 shows an example of total setpoints on the ordinate as a function of time (abscissa).

Fig. 9 is based on the fact that based on the sum of the current power distribution P₀ sum setpoints E₁, E₂ and E₃, z. B. be forecast for the next few minutes. If the next optimal power distribution is determined from a power distribution to the next forecast value after a time interval in the performance representation of FIG. 8, the path P₀, P₁, P₂, P₃ shown in FIG. 10 results in the case of optimized power distributions on the levels constant total power E₀ to E₃.

According to FIGS. 11 through 13, a finite number of paths can be generated as a combination, if the power distributions are not as previously optimized for meantime Liche forecast values. In Fig. 11 the total setpoint E₂, in Fig. 12 the total setpoint E₁ and in Fig. 13 the total setpoints E₁ and E₂ are omitted. The levels of constant total power are shown in dashed lines. This is explained in more detail below.

Replacement performance allocations that are not as before were optimized with the total setpoint be chosen that the web length from the previous one to the next one about this replacement power distribution is minimal.

In the event that there are different times between the previous, the current len and the next power distribution, the length of the tracks is determined by the sum of the products of the individual lengths of the sections with the reciprocal of the associated time differences according to FIG. 14. From the drawing, the path optimization from A to C₁ or C₂ over B results in the case that t _AB <t _BC .

Fig. 15 relates to the process P with a pilot control A and a controller or a controller B. The process P consists of sub-processes P₁, P₂,. . . P _n . The pilot control A with the setpoint e _A supplies input variables u _{i, A} as part of the input variables u _i for the subprocesses P _i (i = 1, 2... N). The controller B, to which the setpoint e _B is assigned, supplies input variables u _{i, B} as part of the input variables u _{i mentioned} . The output variables y _{i of} the subprocesses P _i are combined to form the output variable curve y (t).

In the method according to the invention, the following are intended Strategy elements are used:

A. Starting point for the division of benefits

As a starting point for determining the optimal lei allocation of the next time step According to the invention, the current one is optionally used Setpoint distribution, the current distribution of the existing which actual benefits or a combination of both Divisions.

B. Extended summary reserve terms

In addition to a summary rule book (Power reserve) to balance the active power According to the invention, balance through the regulation is used further summary reserve condition:

Within the summary control band, a minimum control speed should be guaranteed for the time that is required to reach the control band limits with this minimum control speed, based on the current position in the control band (required possible change in control power within the control time). Before the control band limits are reached, this minimum control speed should not be undercut on average ( FIG. 5). The additional summary reserve condition is equally valid for the regulation upwards and downwards. The control times are therefore dependent on the current reserve usage by the control module. This strategy element means no individual gradient reserves for the regulation by the individual power plants. This leads to a dynamic manipulated variable reserve being available to the control according to the invention.

C. Extended Rulers

If necessary, according to the invention, a maximum permissible power component for the control can be assigned to each power plant instead of a fixed control band. This means that there is no fixed assignment of rule bands; for each power plant there is instead a permissible regulatory range ( Fig. 3). Power plants that should not be involved in the control are not assigned a permissible power share for the control. This additional flexibility can be used according to the invention through the optimization: depending on the current total setpoint and the associated cost-optimal power distribution among the power plants, the required reserves are available in an optimally divided manner.

D. Summary reserve estimate

To determine which total reserve for a given performance allocation actually exists is, according to the invention, first of all in the expansions power plant control tapes per power plant in both Directions of possible setpoint changes are estimated. These are the setpoint changes that occur within the Re gel times, based on the current power setpoint, possible are. With estimation models you can additionally those with these setpoint changes within the rule expected changes in output of the power plants estimate.

The summary reserve results from the above from the sum of the possible setpoint changes (Manipulated variable reserve) or the expected performance changes (power reserve) of all power plants. So that leaves estimate how big a particular lei is the corresponding control variable or output power reserve over time.

E. Extended cost function

Oscillating or strongly fluctuating setpoint trends for individual power plants are undesirable because they are one cause heavy material stress. To do this take into account the cost radio according to the invention terms around punishment for setpoint changes and (particularly discontinuous) gradient changes continues. It also adds to the individual Cost functions of the power plants a cost function for the unwanted exchange of services between the Ver  federal partners, triggered by a lack of aggregate credit in their own supply area, added.

F. Punitive function for failure to comply with the Total and reserve claim

Deviations from the desired total setpoint and deviation the reserve claim for a benefit division according to the invention with a Bestra assessment function that is only zero (if fulfilled) or positive function values (if not fulfilled) can take. Along with a cost function, the a division of benefits depending on the ge assigns the desired total output costs this allows a new cost function f for the whole range of manipulated variables. This allows the search space for optimization on the whole permissible position expand size range; he's not on one Level of constant power at which the predetermined total setpoint is met.

G. Forecasting the next optimization summation setpoints

In addition to the current total setpoint, inventions are made appropriately in the form of time courses or individual Values predicted sums for certain times setpoints used for the next few minutes before looking to respond to changes. For the aen a change in the power consumption is a separate pro forecast before (load forecast). Uncontrollable performance feeds that are not supplied by the power plant ken come, can also be predicted.  

The summary results in predicted total setpoints for power plant use optimization ( FIG. 9).

H. Forecast of future performance limits

Power plant setpoints already before optimization are certain can be forced according to the invention by the allowable power limits of these power plants are set to these setpoints. Uncontrollable Power generation from power plants, z. B. in the arrival or departure are for the next estimated minutes and as power setpoints interpreted. The power setpoints and therefore also the power limits of the power plants, the power plant deployment optimization are not involved so known in advance. The future performance limits zen of the other power plants can be inventively estimate, or they are informed by the Power plants known, or they do not change. In order to there are predictions for future performance limits of all power plants, which then together with the prognosti used sum setpoints used in the preview can be.

K. Predictive search space restrictions

It is assumed that predicted total setpoints are given for the next few minutes ( FIG. 9). In order to prevent the optimization of the respective current total setpoint, the total setpoints predicted for later are no longer or not optimally achieved, the search space of the optimization for the current total setpoint is restricted according to the present invention based on the previously admissible and achievable search space.

A hard search space restriction may be the prohibition of smaller setpoints of the power plants if the predicted total setpoint is greater than the current one in a certain time, or the prohibition of larger setpoints of the power plants if the predicted total setpoint is less than the current one in a certain time is. A soft search space restriction exists if the setpoint changes of the power plants, which are directed against the tendency of the total setpoint, are prohibited depending on the size of the total setpoint change. Fig. 8 shows an example of the resulting restriction of the search space S₂ on the search space S₃.

L. Construction of a family of tracks in the search area

Predicted total setpoints are given for the next few minutes ( FIG. 9). If starting from P₀ each Weil from one to the next forecast value P₁, P₂, P₃ the next optimal power distribution is determined within a time interval, a path results in the search area ( Fig. 10). Are omitted on this path one or more of the forecast values P₁, P₂, P₃ between and is omitted for missed forecast values, the replacement power distribution for which the path length in the search area from the previous to the next most power distribution minimal (shortest path), but at the same time In this way, the current forecast value is fulfilled (shortest detour), then different paths are created in the search area. In the event that there are times of different sizes between the previous, the current and the next power distribution, the length of the tracks is also determined according to the invention with the reciprocal of the associated time difference by the sum of the products of the individual lengths of the path sections ( FIG . 14).

In order to generate a finite number of paths, any possible combination of intermediate forecast values is omitted in succession according to the invention. There is a family of paths in the search area, in which both the step-by-step optimized path and the shortest path are included, taking into account the intermediate total setpoints E₀, E₁, E₂, E₃ ( Fig. 10 to 13). If an interim total setpoint E _{i cannot be reached} on a step-by-step optimized path, there are alternative paths according to the invention which come as close as possible to this total setpoint.

As an alternative to the so constructed lanes, such lanes are used according to the invention in which a compromise between optimal power distribution, power distribution by the shortest detour and complete replacement of the forecast value (E _i ) is chosen for the omitted intermediate points. The optimization can therefore take this circumstance into account in advance when determining the next power distribution.

The inventive method for optimizing the Power plant operation works according to the above on the basis of an evaluation function that dependent on all permissible power distributions of the previous division of benefits Function value delivers. This evaluation function sets derive from the sum of the expanded cost functions al power plants together. In addition, fiction according to a punishment function for the non-fulfillment of the Totals and reserve claim used. A planned one temporary non-fulfillment of these claims e.g. B. will be an advantage if this will result in the future these requirements are better met.

The permissible performance limits and setpoint gradients of the power plants limit the search space according to the invention, in which all permissible power setpoints based on given performance distribution.

The optimization method according to the invention determines un taking into account the forecast total target values based on a given power distribution a future optimal path of the sum setpoints in within the search space for a certain time interval through a path optimization. If only a total setpoint without further predicted total setpoints in the Optimization is used determines the invention Optimizing only the next optimal performance division.  

If a path optimization is carried out, the optimization method according to the invention works as follows:
Based on the given power distribution (as described above) for the specific time interval, a bevy of possible paths in the search space is generated. To determine the bases of these railways, the optimal power distributions based on a previous power distribution and substitute power distributions are determined.

For the optimization of a power distribution, out based on a previous division of benefits, there is two variants according to the invention:

• a) First, the optimization determines at the level constant performance within the search space the specified total setpoint is met, the Allocation of benefits with the smallest value of the Be scoring function. Starting from what is found Point P₀ then determines the optimization on the Level of constant performance within the search space the point P₁, the Ver as low as possible deterioration of the evaluation function compared to the Starting point for the smallest punishment function worth (gradually increasing the position size reserve taking into account the sum requirement).
• b) The optimization determines within the search space the point P for which the sum is used as an evaluation radio tion and punishment function is minimal without the first thing you try is only the given sum to meet the target value.

To calculate the alternative power allocations for omitted total setpoints on the shortest detour according to the invention, an iterative feeding process is used. With a compromise between optimal performance division, a division of benefits on the shortest Detour and a complete omission of the forecast values without replacement, the optimal and the replacement Service allocation calculated for the shortest detour. Of these lanes, the one is considered the optimal lane selected, the time-weighted average smallest evaluation and punishment function values owns. The first point of this path is the one you are looking for next optimal power distribution. For optimization a power distribution are suitable according to the invention local, global and heuristic optimization procedures, especially a modified step-by-step algo rithmus (a special gradual gradient process ren).

The inventive method is characterized in the Application to optimize the current power plant rate compared to conventional processes, among others rem by the following advantages:

• - The manipulated variable reserve is inventively in In contrast to conventional processes, it is cost-effective timally held ready. In particular, one larger manipulated variable reserve than with a closed order of fixed rule bands on selected Use power plants; this saves costs. The power plant management will be hosted at the same time  more sociable. The search space limitations are less restrictive for optimization, so that the optimization for dividing the sum additional and cheaper glue can determine the division of services.
• - The control quality of the power-frequency control improves according to the invention by a time-independent and even manipulated variable re serve. In addition, the invention excels due to greater flexibility with changing rule expectations because the reserve claims continuously meet the expected power fluctuations in supply have the area adjusted.
• - The predictive, inventive limitation kung the search space of the optimization or he Planning an optimal path according to the invention Avoids setpoints in the search space of the optimization undesired flip-over effects (opposite target trends of individual power plants). In addition is ensured according to the invention in advance that the reserve claims and the forecast Total setpoints completely or in the next few minutes are best met on average. If predicted total setpoints are not are available, summation setpoints are made in advance consciously over- or under-fulfilled.
• - The setpoint curves of the individual power plants are inventively punished by  Gradients and gradient changes calmed down that he is a gentler power plant operation gives.

The optimization method according to the invention is suitable especially together with a method for central Regulation of a power plant park in the same time German patent application "Procedure for Regulating the performance of a power plant park "the same Applicant (file number 40 713 K) is described. The Degrees of freedom, parameters and specifications of the process can be from external modules for monitoring, quality evaluation or preview can be used, e.g. B. for a Change in reserve claims depending on the expected size of the power fluctuations and the he aimed control quality for an adjustment of the extended Cost features to punish changes to restless setpoint curves of individual power plants or for an automatic change of allowed power limits and gradients of individual power plants.

For additional superordinate facilities for monitoring Research, quality assessment or foresight are available Process for pattern recognition, event recognition, Cluster analysis, neutron networks, fuzzy systems, simu lation systems or expert systems, in particular a fuzzy quality measure linked to the process, which the Process evaluated online and by process operator or egg ner direct feedback for intervention in the process is being used.  

Particular advantages result for the invention the use of positive and negative fuzzy rules and when using a parameterized fuzzy-in terfrequency filter, in which the parameter indicates whether the rules are interpreted harder or softer should, at the higher level for monitoring and evaluation (cf. the German patent publications 43 08 083 and 44 16 465).

The method according to the invention is particularly suitable to optimize power plant use, preferably with an egg a digital computer system, an automation system stem or a simulation system, with parts of the Procedure also with different systems reali are sizable.

Claims (22)

1. A method for sequential pilot control of a process Ses P, which consists of n parallel subprocesses P₁, P₂, P _n with input variables u₁, u₂,. . . u _n and output variables y₁, y₂,. . . y _n exists

• (a) where for the input quantities individual restrictions of the form consist,
• (aa) from which the distances Sum u _i and Σ v _i differ from the total limits Σ u _{i, min} , Σ u _{i, max} and Σ v _{i, min} , Σ v _{i, max} are the total reserves for the Sums Σ u _i and Σ v _i ,
• (ab) the output variable y of the process P in the form y = y₁ + y₂ +. . . y _n arises and
• (ac) the process operation is evaluated by a cost function f, which results from the sum of partial cost functions f _i (u _i ),
• (b) wherein the sub-processes P _i are applied with input waveforms u _i (t) to achieve a desired off gear body size y _A (t) and these patterns u _i (t) from the sum of the fractions produced by a pilot control device A and a controller B u _{i, A} (t) and u _{i, B} (t) are formed,
• (c) wherein the controller B reacts to a predetermined control variable e _B , which arises from disturbances and deviations of the output variable y from the desired output variable profile y _A (t), and
• (d) the pilot control device A uses a setpoint curve e _A (t) to be given, which assigns a setpoint e _{A to} the current point in time and future times, characterized in that
• (e) that in the event that the next setpoint e _A for the precontrol A cannot be completely divided into the input variables u _{i, A within} a cycle time, without starting with a total of the current input variables u _{i, B} predetermined input variables u ′ _i , the next input variables u _i violate the individual restrictions, the maximum possible input variables u _{i, A are} selected, and
• (f) that for the other case, for which the setpoint e _{A of} the pilot control A can be completely divided into the input variables u _{i, A} in one or more ways, a distribution is selected for which a sum marian reserve for the regulation of the Regulator B is present
• (g) that the latter division is determined such that within a predetermined range of values [u _{B, min} ; u _{B, max} ] for the sum of the input variables u _{i, B} , starting from the current sum Σ u _{i, B} , with constant input variables u _{i, A} while observing the individual restrictions of the input variables u _i
• (ga) the sum of the input variables u _{i, B} with the minimum rate of change v _{B, min reaches} the value u _{B, min} and with the maximum rate of change v _{B, max} the value u _{B, max} , and
• (gb) the cost function f for the division u _{i, A is also} minimal.

2. The method according to claim 1, characterized in that only selected input variables u _{i, B are used} for a contribution to the summary reserve for the B regulation. 3. The method according to claim 1 or 2, characterized in that the contributions of the input variables u _{i, B} , which make a contribution to the total reserve for the control B, to individual maximum values u _{i, B, min} , u _{i, B, max} , v _{i, B, min} and v _{i, B, max} be limited. 4. The method according to any one of claims 1 to 3, characterized in that partial cost functions f _i (u _i ) include or approximate the operating costs of the processes P _i . 5. The method according to any one of claims 1 to 4, characterized in that current or previous input variables u _i , current or previous output variables y _i and / or available current or previous states of the processes P _{i are} integrated into the partial cost functions f _i . 6. The method according to any one of claims 1 to 5, characterized in that both the value of a punishment function k, which assigns a clear function value to the difference between the sum of all input variables u _{i, A} and the setpoint value e _A , as well as the sum of the Partial cost functions f _{i are} minimized by choosing the distribution of the setpoint value e _A to the input variables u _{i, A} for which a new function f, which is a linear combination of the functions f _i and k, is minimal. 7. The method according to any one of claims 1 to 6, characterized in that in addition the value of a punishment function Be h, which assigns a unique function value to the difference between a measure for the existing total reserve and a measure for the required total reserve , by selecting that division of the setpoint value e _A into the input variables u _{i, A} for which a new function f, which is a linear combination of the functions f _i and h or f _i , k and h, is minimized. 8. The method according to any one of claims 1 to 7, characterized in that depending on the desired future target value e _A (t + T) for the pilot control A after a predetermined time T from the possible next divisions u _{i, A} , at which a change in the setpoint e _A within the time T no change in the input variables u _{i, A} opposite this change, starting from u ′ _{i, A} , the one with a minimal function value is selected ge. 9. The method according to any one of claims 1 to 7, characterized in that the setpoint e _{A of} the pilot control A at a fixed number of desired to future setpoints for a fixed time range T taking into account future individual restrictions for the input variables u _i is divided into the input variables u _{i, A in} such a way that the function f becomes minimal on average over the time range T by a group of sequences of input variables u _A = (u _{1, A} , u _{2, A} .... _{n, A} ) is constructed using all possible combinations, either by step-by-step procedure to determine the next division u _A or by determining the next division u _A by minimizing the path length between the previous division u _A and the subsequent division u _{A with a} meanwhile complete division of the setpoint e _A and consideration of the individual restrictions or with complete ignoring of the setpoint rtes e _A arise from the determination of the division u _A on the direct connection between the previous division u _A and the subsequent division u _A or from a compromise of these three possibilities, from which the first division u _{A of} the sequence of divisions u _A is selected that has the smallest functional value of all constructed partitions on a time-weighted average. 10. The method according to any one of claims 1 to 9, characterized in that the division u _A cyclically, acyclically or cyclically with acyclic machining requirements is determined if the division u ' _{A is} the result of the previous cycle or process run. 11. Use of the method according to one of claims 1 to 10 for optimizing the use of a power plant by a power plant management system, characterized in that the function e _A (t) corresponds to the load forecast at a current power plant use, the subprocesses P _{i are} power plants and the device B is the control power distribution of the setpoint from the power-frequency control. 12. Use of the method according to claim 11, characterized characterized that additional parent A  directions for monitoring, quality assessment or Foresight the degrees of freedom, parameters, specifications or strategies of the optimization process or be changed. 13. Power plant management system based on the procedure is optimized according to one of claims 1 to 12, characterized in that it is with a digital computer system, an automation system and / or a simulation system. 14. Power plant management system according to claim 13, characterized characterized in that parts of the process with under different or parallel systems are. 15. Power plant management system according to claim 13 or 14, characterized in that as additional over ordered facilities procedures of sample recognition tion, event detection, cluster analysis, neural Networks, fuzzy systems, simulation systems or Ex serving systems. 16. Power plant management system according to claim 15, characterized characterized that the additional parent Fuzzy controllers with positive and nega tive rules or parameterized inference filters contain. 17. The method, in particular according to one of claims 1 to 10, to optimize the current power plant use by a power plant management system with  one for the power plants in total gel tendency summary rule book that the rule sum limited up and down, and with for each led power plant individual gra service reserves for the positive and the negative Gradients of the power setpoints, thereby known records that as a starting point for the determination the optimal distribution of performance for each next time step the current division of the Setpoints and / or actual performance serves that in within the summary rule band regardless of the individual gradient reserves of the individual Power plant a minimum control speed for the time until the rule band is reached limit is set that everyone at the scheme power plant involved a maximum permissible lei Performance share for the scheme is assigned, and that the total setpoint trends for the services and the associated summation setpoint gradients for the individual power plants. 18. The method according to claim 17, characterized in that that looking ahead to changes in the totals of the Setpoint changes (total setpoint) in the form of Time courses or of individual values for be agreed timed predicted total setpoints for about the next few minutes. 19. The method according to claim 17 or 18, characterized records that already before the optimization existing power plant setpoints are enforced  that the allowable performance limits on this Setpoints are set. 20. The method according to any one of claims 17 to 19, there characterized in that the area (search space) optimization for the current total setpoint, based on the previously allowed and he accessible search space, foresightedly restricted becomes. 21. The method according to claim 20, characterized in that larger setpoints of the power plants are prohibited if the forecast total setpoint in egg less than the current time. 22. The method according to claim 20, characterized in that that the setpoint changes of the power plants, the ent directed against the trend of the total setpoint are dependent on the size of the total target changes in value are prohibited.