WO1996029771A1 - Procede de commande pilote sequentielle d'un processus - Google Patents

Procede de commande pilote sequentielle d'un processus Download PDF

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Publication number
WO1996029771A1
WO1996029771A1 PCT/DE1996/000545 DE9600545W WO9629771A1 WO 1996029771 A1 WO1996029771 A1 WO 1996029771A1 DE 9600545 W DE9600545 W DE 9600545W WO 9629771 A1 WO9629771 A1 WO 9629771A1
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WO
WIPO (PCT)
Prior art keywords
power
setpoint
input variables
control
power plant
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Application number
PCT/DE1996/000545
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German (de)
English (en)
Inventor
Jens Albrecht
Harro Kiendl
Original Assignee
Vew Energie Ag
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Filing date
Publication date
Application filed by Vew Energie Ag filed Critical Vew Energie Ag
Publication of WO1996029771A1 publication Critical patent/WO1996029771A1/fr

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    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/0205—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric not using a model or a simulator of the controlled system
    • G05B13/026—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric not using a model or a simulator of the controlled system using a predictor

Definitions

  • the invention relates to a method for sequential pilot control of a process according to the preamble of claim 1. It relates in particular to a method for optimizing the current use of a power plant by a power plant management system.
  • Power plants are all power generation units that can be influenced in their performance by specifying an active power setpoint.
  • a process to be controlled P of the aforementioned type consists of n parallel sub-processes P 1 , P 2 , ... P n with input variables u 1 , u 2 , ... u n and output variables y 1 , y 2 , ... y n .
  • the subprocesses P i are subjected to input size profiles u i (t.
  • These profiles u i (t) are obtained from the Sur_ ⁇ e of the components U i, A (t) and u 1, B (t ), which are generated by two separate devices - namely a control A and a controller B.
  • the controller B reacts to a specified controlled variable eg, which arises from deviations of the output variable y from the desired output variable profile Y A (t) and from disturbances.
  • a future setpoint curve e A (t) which generates the output variable curve Y A (t), is used for pilot control A.
  • Power plant management systems are installed in the network control systems of energy supply companies to automate the use of the power plants in their own supply area. Power setpoints are generated for all power plants to be operated, which are then transferred to the power plants.
  • the 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 large number of individual power plants. According to this, the power plant should be able to 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 here according to the exemplary embodiment (FIG. 4) only concern operating commands to the individual power plants, but not the optimization according to the invention.
  • the power plant management systems perform other sweeping tasks: - Ensuring a balanced active power balance (power balance) between generated and consumed power in the own supply area, so that no unwanted power flows between the network partners occur;
  • power plant management systems include the following key components: - A module for estimating the current load and predicting the load development; - a module for regulating (regulation module) the active power balance in one's own supply area; - A module for coordinating and optimizing (optimization module) the current power plant use.
  • the operating mode determines which power plants are controlled by assigning a power setpoint.
  • the optimization module and control module deliver a power setpoint for each of these power plants.
  • the setpoints are transmitted to the power plants either individually or as total setpoints.
  • the optimization module contains a total setpoint as a default. Unless there are any restrictions to the contrary and it makes sense, taking into account the forecast changes in time, this total setpoint should be completely distributed to the power plants by the optimization module, so that the sum of the individual setpoints results in the total setpoint. - The optimization module is also intended to ensure that the control module can restore the balanced active power balance quickly at any time.
  • the manipulated variable reserves should be symmetrically around zero, so that regardless of the sign, a disturbance in the active power balance can always be corrected (Fig. 4).
  • the size of the desired and automatically available reserve (chne Consideration of reserve contracts) is based on the size of the expected disruptions (standard expectations).
  • German laid-open specification 43 15 317 describes a guide device for the automatic control of power plant blocks in an electrical power supply system, in particular by means of measures to increase performance and frequency support.
  • the control is intended to maintain a predetermined level of reliability of the energy and power supply and to avoid an unnecessarily high reserve power.
  • a guide device which comprises a first control unit, which is connected via a data transmission element to each power station block to be controlled, and which receives block-specific measured variables and outputs block-specific reference variables.
  • the block-specific measured variables Mn include actual values of the block power and the live steam pressure, as well as valve positions and temperatures measured in or on a turbine housing. In the block-specific measurement variable. Constant block-specific sizes and specifications are taken into account.
  • the restrictions for the optimization module are further tightened by limiting the maximum permissible setpoint gradients of the power plants, to which a fixed set of rules is assigned, for optimization to a fixed proportion of the permissible setpoint gradient. This ensures that the control module can use at least the remaining portion of the setpoint gradient without violating the maximum permissible setpoint gradient. All in all, a summary setpoint gradient is reserved for the sole use of the control module. This means that the regulation of the active power can be quickly compensated for by the control module within the defined reserve at any time.
  • the starting point for the optimization is the current power distribution. In the accessible part of the search space, the optimization on the level on which all the points that meet the predetermined total setpoint lie, is the point that leads to the lowest total costs. This point is the starting point for the next optimal division of performance.
  • step-by-step algorithm a simplified step-by-step gradient method
  • Euler-Lagrange method global optimization method
  • the conventional optimization methods have the disadvantage, due to the structure, that the optimization results are currently not optimal on average over the following reasons:
  • the reserve of power in the power plants is made available by selecting those power plants to which a fixed set of rules is assigned and is not automatically adapted to the current power distribution.
  • the provision of manipulated variable reserves generally causes additional costs, so that the cost-optimal allocation of services without cheaper restrictions is cheaper. With conventional methods, it is not possible to dynamically allocate the reserves to the current supply situation. adjust that additional cost by providing before. Reserves are only actually created when needed (if the reserve is actually needed).
  • the current optimization of the current power plant use for the next time step does not necessarily lead to an optimal use of the power plant in the longer term, because the forecast load development has no influence on the momentary optimization.
  • the invention is therefore based on the problem of creating an optimization method for optimally dividing a power setpoint, in particular for a power plant park, to determine setpoints for the individual power plants, taking into account restrictions with respect to the individual setpoints and secondary conditions, as well as target concepts (e.g. with regard to the power plant management as a whole) ;
  • the goal of optimization is to find the power split, with both all constraints and restrictions. are met and the total value of all cost functions becomes minimal and the optimization results are optimal at the moment as well as on average over time.
  • This division is determined in such a way that within a predetermined value range [u B, min ; u B, max] for the sum of the input variables u i, B, starting from the current sum ⁇ u i, B, with constant input variables u i, A and in compliance with the individual limitations of the input Great 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.
  • a power plant park's power distribution is to be found in which both all constraints and restrictions are met and the total value of all cost functions is minimal, and the optimization results are optimal at the moment and on average over time.
  • the most important tasks of the guiding device are - to define an operational plan and a reserve plan for primary control for each power plant block, - to determine dead time and rate of change for a block-specific minute reserve from block-specific measured variables and constant variables.
  • the method according to the invention can be used to solve the following generalized problem:
  • a given controller B which essentially reacts to deviations of the output variable y from a desired output variable profile y A (t) and to disturbances, generates the input variables u i, B.
  • a pilot control A is to generate the input quantities u i, A for the subprocesses P i such that firstly the output quantity of the process follows the output quantity curve y A (t) and secondly a cost function f which evaluates the operation of the process becomes minimal, and thirdly A reserve for the input quantities u i, B from the control system is kept available 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 method according to the invention is to optimize the current power plant use in a power plant management system.
  • the method according to the invention can be used cyclically, acyclically or cyclically with acyclic processing requirements.
  • the optimal power distribution which is to be produced according to the cycle or the course of the optimization process, is determined under the restrictions and additional conditions described.
  • the method according to the invention has the special feature, particularly when used for optimizing the use of power plants, that power reserve requirements are optimally met economically and predictable changes in input variables over time can be taken into account in advance when optimizing.
  • Fig. 1 the Scllwert Limiter. u i, min and u i, max one
  • FIG. 2 shows the setpoint limits of the optimization according to FIG. 1 with reserved control band RB i ;
  • Fig. 3 shows the setpoint limits of the optimization
  • FIG. 5 extended reserve conditions for FIG. 4;
  • Fig. 7 search spaces S 1 , S 2 with fixed control bands for both power plants;
  • the setpoint limits (power setpoint) of an individual power plant are defined as a function of time t.
  • the upper and lower power limits u i, max and u i, mix 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).
  • a control band RB i is additionally reserved at the upper and the lower power limit in accordance with FIG. 2, in order to ensure manipulated variable reserves for regulation at all times.
  • These rule bands tighten the restrictions for the optimization by restricting the lower and upper performance limits for the optimization u i, A, min and u i, A, max .
  • a feature of the invention is that the restricted performance limit for the optimization within a control band, which is generally applicable to the regulated power plants of a power plant park, is permeable for the individual power plant.
  • 4 and 5 relate to the representation of a summary rule band, with the total power in the ordinate and the time t in turn in the abscissa.
  • the control bands of the entirety of the power plant fleet are fully available for control, based on a current total control power.
  • a minimum control power change upwards and a minimum control power change downwards are required.
  • 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 S 1 in the case of two power plants. Power 1 of one power plant is plotted on the abscissa, power 2 of the other power plant is plotted on the ordinate.
  • the search area S 1 is shown , in which there are valid setpoints for both power plants which do not violate the performance limits.
  • the search space S 2 that can be reached within S 1 is shown, which contains valid setpoints after the optimization for both power plants, which additionally do not violate the maximum power changes.
  • levels of constant total power E 0 , E 1 and E 2 are shown schematically.
  • FIG. 7 shows the search space S1 and the achievable search space S 2 for fixed control bands RB 1 , RB 2 for both power plants, the power of the power plant 1 being shown on the abscissa and the power of the power plant 2 being on the ordinate is.
  • the predictive limitation of the search space S 2 to S 3 that can be reached in FIG. 8 is for the case schematically shown 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).
  • a finite number of paths can be generated as a combination if the power distributions are not optimized as before for intermediate forecast values.
  • the total setpoint E 2 is omitted in FIG. 11, the total setpoint E 1 in FIG. 12 and the total setpoints E 1 and E 2 in FIG. 13.
  • the levels of constant total power are drawn in dashed lines. This is discussed in more detail below.
  • Substitute power allocations that have not been optimized as before can be selected with the total setpoint so that the path length from the previous to the next is minimal via this substitute power part.
  • 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.
  • the drawing shows the path optimization from A to C 1 or C 2 via B in the event that t AB > t BC .
  • the process P relates to the process P with a pilot control A and a control or a controller B.
  • the process P consists of subprocesses P 1 , P 2 , ... P n .
  • the controller B to which the setpoint value eg is assigned, supplies input variables u i, B as part of the input variables u i mentioned .
  • the output variables y i of the sub-processes P i are combined to form the output variables y (t).
  • the starting point for determining the optimal power distribution for the next time step is either the current target value distribution, the current distribution of the existing actual power levels or a combination of ashamed divisions.
  • a minimum control speed is to be guaranteed for the control for the time required to reach the control range limits with this minimum control speed, starting from the current position in the control range (required possible control power change 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 applies equally to the regulation upwards and downwards. The control times are therefore dependent on the current reserve usage by the control module.
  • This strategic element means waiving 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.
  • a maximum permissible power component for the control can be assigned to each power plant instead of a fixed control band. This is not a fixed assignment of rule bands. awarded; for each power plant there is instead a permissible range of rules (Fig. 3. Power plants that are not involved in the control are not assigned a permissible power component for the control.
  • This additional flexibility can be can be used according to the optimization: Depending on the current total setpoint and the associated cost-optimal power distribution to the power plants, the required reserves are available in an optimally distributed manner.
  • the setpoint changes possible in both directions in the expanded power plant control bands per power plant are first estimated. These are the setpoint changes that are possible within the control times, based on the current power setpoint. Estimation models can also be used to estimate the power plant changes in power expected during these setpoint changes within the standard times.
  • the summary reserve results from the above from the sum of the possible setpoint changes (manipulated variable reserve) or the expected power changes (power reserve) of all power plants. This makes it possible to estimate how large the average position or performance reserve for a certain power distribution is over time.
  • the cost functions according to the invention are terms for punishing target values rungs and (especially discontinuous) gradient changes expanded.
  • a cost function for the unwanted exchange of services between the network partners, triggered by a missing total output in their own supply area, is supplemented.
  • Deviations from the desired total setpoint and deviations from the reserve requirement in the case of a power distribution are evaluated according to the invention with a punishment function which can only assume zero (if fulfilled) or positive function values (if not fulfilled). Together with a cost function that assigns costs to a power distribution depending on the desired total output, this results in a new cost function f for the entire permissible manipulated variable range.
  • the search space for optimization can thus be extended to the entire permissible manipulated variable range; it is not limited to the level of constant power at which the predetermined total setpoint is met.
  • predicted summation setpoints for the next few minutes are used in the form of time profiles or individual values for specific times in order to react to changes in a predictive manner.
  • load forecast There is a separate forecast for the change in power consumption (load forecast).
  • Uncontrollable performance Feeds that do not come from the power plants to be managed can also be forecast.
  • the summary results in predicted total setpoints for power plant use optimization (FIG. 9).
  • Power plant setpoints that are already fixed before the optimization can be enforced according to the invention by setting the permissible power limits of these power plants to these setpoints.
  • Power generation from power plants that cannot be influenced, e.g. in the arrival or departure are estimated for the next few minutes and interpreted as performance setpoints.
  • the power setpoints and thus also the power limits of the power plants, which are not involved in the optimization of the power plant use, are known in advance.
  • the future power limits of the other power plants can be estimated according to the invention, or they are known from information from the power plants, or they do not change. This provides forecasts for the future performance limits of all power plants, which can then be used in the preview together with the forecast total setpoints.
  • 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 in a certain time, or the prohibition of larger setpoints of the power plants if the predicted total setpoint is less than the current in a certain time .
  • 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 change in the total setpoint. 8 shows an example of the resulting restriction of the search space S 2 to the search space S 3 .
  • Predicted total setpoints are given for the next few minutes (FIG. 9). If, based on P 0 , the next optimal power distribution is determined within one time interval from one to the next forecast value P 1 , P 2 , P 3 , a path results in the search area (FIG. 10). If one or more of the forecast values P 1 , P 2 , P 3 are omitted in between on this path and the omitted forecast values are determined, the substitute power distribution for which the distance in the search area from the previous to the next power distribution is minimal (shortest route), but at the same time In this way, the current forecast value is met (shortest detour), then different paths are created in the search area.
  • 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 sections (FIG. 14).
  • any possible combination of intermediate forecast values is omitted in succession according to the invention.
  • those railways are used as alternatives to the railways constructed in this way, in which a compromise between optimal power distribution, power distribution in the shortest detour and complete omission 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 according to the invention.
  • the method according to the invention for optimizing the use of power plants works according to the above to a certain extent on the basis of an evaluation function which supplies a: function value for all permissible power distributions depending on the previous power distributors.
  • This evaluation function is made up of the sum of the extended cost functions of all power plants together.
  • a punishment function is used for non-fulfillment of the sums and reserve claim. A planned temporary non-fulfillment of these demands can be advantageous, for example, if these demands are better met in the future.
  • the permissible power limits and setpoint gradients of the power plants limit the search space in which all the permissible power setpoints are based on a given power distribution.
  • the optimization method according to the invention taking into account the predicted total setpoints based on a given power distribution, determines a future optimal path of the total setpoints within the search space for a specific time interval by means of a path optimization. If only one summation setpoint without further predicted summation setpoints is used in the optimization, the optimization according to the invention only determines the next optimal power distribution.
  • the optimization method works as follows: Starting from the given power distribution (as described above), a set of possible paths is generated in the search space for the specific time interval. To determine the support points of these railways, the optimal power distributions, based on a previous power distribution, and alternative power distributions are determined. According to the invention, there are two variants for the optimization of a power distribution based on a previous power distribution: a) First, the optimization on the level of constant power within the search space on which the predetermined total setpoint value is fulfilled determines the power distribution with the smallest value of the evaluation function.
  • the optimization determines the point P 1 on the level of constant power within the search space, which has the smallest punishment function value when the evaluation function deteriorates as little as possible compared to the starting point (step-by-step increase in the manipulated variable reserve taking into account the sum requirement) .
  • the optimization determines the point P within the search space for which the sum as a valuation function and punishment function is minimal, without first trying to meet only the predefined sum setpoint.
  • an iterative nutritional method is used to calculate the replacement power distributions for omitted total setpoints by the shortest detour.
  • a power distribution on the. shortest detour and a complete omission of the forecast value without replacement the optimal and the replacement service allocation for the shortest detour are calculated.
  • the one is selected as the optimal trajectory which has the smallest evaluation and punishment function values in a time-weighted average.
  • the first point of this path is the searched next optimal power distribution.
  • local, global and heuristic optimization methods are suitable for optimizing a power distribution, in particular also a modified step-by-step algorithm (a special step-by-step gradient method).
  • the method according to the invention is distinguished, among other things, by the following advantages when used to optimize the current use of power plants compared to conventional methods:
  • the manipulated variable reserve is kept available in a cost-optimal manner.
  • a larger manipulated variable reserve can be used than when assigning fixed control bands to selected power plants; this saves costs.
  • power plant management is becoming more economical.
  • the restrictions of the search space for the optimization are less restrictive, so that the optimization for the distribution of the total power can determine additional and cheaper power distributions.
  • the control quality of the power-frequency control improves according to the invention by a time-independent and uniform manipulated variable reserve.
  • the method according to the invention is characterized by a higher flexibility with changing control expectations.
  • the forward-looking restriction of the search space of the optimization according to the invention or the planning according to the invention of an optimal trajectory of the setpoints in the search space of the optimization avoids undesired flipping effects (opposite setpoint tendencies of individual power plants).
  • the setpoint curves of the individual power plants are calmed according to the invention by punishing gradients and gradient changes, so that there is a gentler power plant operation.
  • the optimization method according to the invention is particularly suitable together with a method for the central control of a power plant park, which is described in the simultaneously filed German patent application "Process for regulating the performance of a power plant park" by the same applicant (file number 40 713 K).
  • the degrees of freedom, parameters and specifications of the procedure can be used by external modules for monitoring, quality assessment or preview, e.g. for a change in the reserve requirements depending on the expected size of the power fluctuations and the control quality achieved for an adaptation of the extended box functions to punish changes too restless be value history. of individual power plants or for an automatic change of permitted power limits and gradients of individual power plants.
  • fuzzy quality measure which is linked to the process and which evaluates the process on-line and is used by the process operator or a direct feedback to intervene in the process.
  • the method according to the invention is particularly suitable for optimizing the use of power plants, preferably with a digital computer system, an automation system or a simulation system, parts of the method also being able to be implemented with different systems.

Abstract

L'invention concerne un procédé permettant d'optimiser la charge effective d'une centrale électrique au moyen d'un système de pilotage de centrales électriques. Selon ce procédé, une valeur nominale de puissance pour un parc de centrales électriques est répartie de façon optimale en valeurs nominales pour chaque centrale électrique, compte tenu des restrictions concernant chaque valeur nominale, des conditions secondaires et des objectifs concernant l'ensemble du système de pilotage des centrales électriques. A cet effet, un procédé de prévision, sur la base d'une fonction d'évaluation liée aux coûts, fournit une valeur fonctionnelle pour toutes les répartitions de puissance admissible en fonction des répartitions de puissance précédentes, ainsi qu'une fonction de pénalisation lorsque les exigences cumulées et de réserve ne sont pas satisfaites.
PCT/DE1996/000545 1995-03-22 1996-03-22 Procede de commande pilote sequentielle d'un processus WO1996029771A1 (fr)

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DE1995110343 DE19510343C2 (de) 1995-03-22 1995-03-22 Verfahren zur sequentiellen Vorsteuerung eines Prozesses
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DE19904974A1 (de) * 1999-02-06 2000-08-10 Abb Patent Gmbh Verfahren zum Betreiben eines Dampferzeugers unter Einsatz eines Freilastrechners
DE102011078195A1 (de) * 2011-06-28 2013-01-03 Siemens Aktiengesellschaft Sollwertanpassung bei einem Dampfkraftwerk
DE102012204218A1 (de) * 2012-03-16 2013-09-19 Siemens Aktiengesellschaft Leistungsregelung und/oder Frequenzregelung bei einem solarthermischen Dampfkraftwerk
DE102015218472A1 (de) 2015-09-25 2017-03-30 Siemens Aktiengesellschaft Verfahren und Vorrichtung zum Betreiben eines technischen Systems
DE102020204521B4 (de) * 2020-04-08 2023-02-16 Robert Bosch Gesellschaft mit beschränkter Haftung Verfahren zum dynamischen Setzen von Reglergrenzwerten für eine Schweißsteuerung und Schweißsteuerung

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EP0535382A1 (fr) * 1991-09-30 1993-04-07 STN Systemtechnik Nord GmbH Procédé pour l'exploitation économique d'un réseau d'île avec sources énergétiques renouvelables et circuit pour l'execution du procédé
DE4315317A1 (de) * 1993-05-07 1994-11-10 Siemens Ag Führungsgerät für Erzeuger elektrischer Energie

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JP2845606B2 (ja) * 1990-10-31 1999-01-13 株式会社東芝 発電プラント制御装置

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Publication number Priority date Publication date Assignee Title
EP0535382A1 (fr) * 1991-09-30 1993-04-07 STN Systemtechnik Nord GmbH Procédé pour l'exploitation économique d'un réseau d'île avec sources énergétiques renouvelables et circuit pour l'execution du procédé
DE4315317A1 (de) * 1993-05-07 1994-11-10 Siemens Ag Führungsgerät für Erzeuger elektrischer Energie

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