CN117109345B - Optimal configuration method and device for high-temperature molten salt heat storage device of coupling thermal power generating unit - Google Patents
Optimal configuration method and device for high-temperature molten salt heat storage device of coupling thermal power generating unit Download PDFInfo
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- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
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Abstract
The application provides an optimal configuration method and device of a high-temperature molten salt heat storage device of a coupled thermal power unit, wherein a plurality of typical days of the thermal power unit are determined by a clustering algorithm according to the output value of the thermal power unit on each historical operation day; dividing the output time period of each typical day of the thermal power unit by using a small semi-trapezoid membership function according to the output values of the thermal power unit at different time points in each typical day; respectively controlling the molten salt heat storage device and the thermal power generating unit to operate for a preset duration according to a preset operation strategy of the power generation system; according to the output value of the coupling power generation system, solving a high-temperature fused salt heat storage capacity optimizing configuration mathematical model of the coupling thermal power unit, which considers the daily operation cost of the system and the output fluctuation of the thermal power unit, by utilizing a non-dominant sorting genetic algorithm and an approximate ideal solution sorting method to obtain the optimal energy storage capacity of the fused salt heat storage device. By adopting the method, the rationality and the suitability of the optimal energy storage capacity of the determined molten salt heat storage device are improved.
Description
Technical Field
The invention relates to the field of power plant operation management, in particular to an optimal configuration method and device of a high-temperature molten salt heat storage device of a coupled thermal power unit.
Background
In a part of large-scale power generation systems, a large-scale high-parameter molten salt heat storage device is generally arranged to be matched with other energy storage devices for power generation, for example, in the actual running process of the power generation system, whether the molten salt heat storage device is adopted for energy release or energy storage is selected according to the energy storage state of each power generation device, so that the power generation efficiency of the power generation system and the utilization rate of each power generation device are improved.
In the prior art, before power generation is performed by using a power generation system including a molten salt heat storage device, the optimal energy storage capacity of the molten salt heat storage device is generally determined according to human experience, and then the molten salt heat storage device is set according to the optimal energy storage capacity. However, when the method is adopted to determine the optimal energy storage capacity of the molten salt heat storage device, the determined optimal energy storage capacity of the molten salt heat storage device and the power generation system to which the determined optimal energy storage capacity of the molten salt heat storage device belongs cannot be well adapted due to the lack of human experiences or the influence of subjective factors, and the optimal energy storage capacity of the determined molten salt heat storage device and other power generation equipment in the power generation system are difficult to mutually cooperate to complete a high-efficiency power generation task, so that the rationality and the suitability of the determined optimal energy storage capacity of the molten salt heat storage device are reduced.
Disclosure of Invention
In view of the above, the invention aims to provide an optimal configuration method and device for a high-temperature molten salt heat storage device of a coupled thermal power generating unit, so as to improve the rationality and suitability of the optimal energy storage capacity of the determined molten salt heat storage device.
In a first aspect, an embodiment of the present application provides a method for optimally configuring a high-temperature molten salt heat storage device coupled to a thermal power generating unit, where the molten salt heat storage device is part of a power generation system, and the power generation system further includes the thermal power generating unit, and the method includes:
Determining a plurality of typical days of the thermal power unit by using a Canopy algorithm and a K-maens clustering algorithm according to the output value of the thermal power unit on each historical operation day;
For each typical day, dividing the output time period of the thermal power unit in the typical day by using a small semi-trapezoid membership function according to the output values of the thermal power unit in different time points in the typical day;
respectively controlling the molten salt heat storage device and the thermal power generating unit to operate for a preset duration according to a preconfigured power generation system operation strategy in each output time period of the typical day;
acquiring actual output values of the thermal power unit after the molten salt heat storage device and the thermal power unit operate for a preset time period according to a pre-configured operation strategy of a power generation system, and taking the actual output values as target output values of the power generation system;
and solving a mathematical model of the high-temperature molten salt heat storage capacity optimization configuration of the coupled thermal power generating unit according to the target output value to obtain the optimal energy storage capacity of the molten salt heat storage device.
Optionally, the determining a plurality of typical days of the thermal power generating unit according to the output value of the thermal power generating unit on each historical operation day by using a Canopy algorithm and a K-maens clustering algorithm includes:
clustering the output value of the thermal power unit on each historical operation day by using the Canopy algorithm to obtain a clustering center and a clustering number;
and inputting the clustering center and the clustering number into the K-maens clustering algorithm as input parameters to perform clustering processing to obtain a plurality of typical days of the thermal power unit.
Optionally, the force-exerting period is a valley period, or a peak period, or a flat period.
Optionally, the controlling the molten salt heat storage device and the thermal power generating unit to operate for a preset duration according to a preset operation strategy of the power generation system in each output time period of the typical day includes:
In the valley section of the typical day, controlling the output of the thermal power generating unit, and controlling the molten salt heat storage device to store energy for the preset time period;
Controlling the output of the thermal power unit and the energy storage and common output of the molten salt heat storage device to last for the preset time period in the peak section of the typical day;
And controlling the output of the thermal power generating unit in the flat period of the typical day, and continuing the preset duration.
Optionally, solving the mathematical model of the high-temperature molten salt heat storage capacity optimization configuration of the coupled thermal power generating unit according to the target output value to obtain the optimal energy storage capacity of the molten salt heat storage device, including:
Determining an optimization objective function of the optimal configuration mathematical model according to the target output value, wherein the optimization objective function is a function which takes the minimum typical daily operation cost of the system and the minimum output fluctuation of the coal-fired generator set as the capacity optimal configuration problem optimization objective;
And constraining the optimized objective function by using a preset constraint condition, and solving the optimal configuration mathematical model by using a non-dominant sorting genetic algorithm and an approximate ideal sorting method to obtain the optimal energy storage capacity.
In a second aspect, an embodiment of the present application provides an optimal configuration device for a high-temperature molten salt heat storage device coupled to a thermal power generating unit, where the molten salt heat storage device is part of a power generation system, and the power generation system further includes the thermal power generating unit, and the device includes:
the typical day determining module is used for determining a plurality of typical days of the thermal power unit by using a Canopy algorithm and a K-maens clustering algorithm according to the output value of the thermal power unit on each historical operation day;
The output time period dividing module is used for dividing the output time period of the thermal power generating unit on each typical day by using a small-sized semi-trapezoid membership function according to the output values of the thermal power generating unit on different time points in the typical day;
the operation control module is used for respectively controlling the molten salt heat storage device and the thermal power generating unit to operate for a preset time period according to a preset power generation system operation strategy in each output time period of the typical day;
The output value acquisition module is used for acquiring the actual output value of the thermal power unit after the molten salt heat storage device and the thermal power unit run for a preset time period according to a pre-configured operation strategy of the power generation system, and taking the actual output value as a target output value of the power generation system;
And the optimal energy storage capacity determining module is used for solving a mathematical model of the high-temperature fused salt heat storage capacity optimal configuration of the coupled thermal power generating unit according to the target output value to obtain the optimal energy storage capacity of the fused salt heat storage device.
Optionally, the typical day determining module is configured to determine, according to the output value of the thermal power unit on each historical operation day, a plurality of typical days of the thermal power unit by using a Canopy algorithm and a K-maens clustering algorithm, and specifically configured to:
clustering the output value of the thermal power unit on each historical operation day by using the Canopy algorithm to obtain a clustering center and a clustering number;
and inputting the clustering center and the clustering number into the K-maens clustering algorithm as input parameters to perform clustering processing to obtain a plurality of typical days of the thermal power unit.
Optionally, the force-exerting period is a valley period, or a peak period, or a flat period.
Optionally, the operation control module is configured to, when used for controlling the molten salt heat storage device and the thermal power generating unit to operate for a preset period of time according to a preconfigured operation strategy of the power generation system during each output time period of the typical day, specifically:
In the valley section of the typical day, controlling the output of the thermal power generating unit, and controlling the molten salt heat storage device to store energy for the preset time period;
Controlling the output of the thermal power unit and the energy storage and common output of the molten salt heat storage device to last for the preset time period in the peak section of the typical day;
And controlling the output of the thermal power generating unit in the flat period of the typical day, and continuing the preset duration.
Optionally, the optimal energy storage capacity determining module is configured to solve a mathematical model of the high-temperature molten salt heat storage capacity optimization configuration of the coupled thermal power generating unit according to the target output value, so as to obtain the optimal energy storage capacity of the molten salt heat storage device, and is specifically configured to:
Determining an optimization objective function of the optimal configuration mathematical model according to the target output value, wherein the optimization objective function is a function which takes the minimum typical daily operation cost of the system and the minimum output fluctuation of the coal-fired generator set as the capacity optimal configuration problem optimization objective;
And constraining the optimized objective function by using a preset constraint condition, and solving the optimal configuration mathematical model by using a non-dominant sorting genetic algorithm and an approximate ideal sorting method to obtain the optimal energy storage capacity.
The technical scheme provided by the application comprises the following beneficial effects:
determining a plurality of typical days of the thermal power unit by using a Canopy algorithm and a K-maens clustering algorithm according to the output value of the thermal power unit on each historical operation day; for each typical day, dividing the output time period of the thermal power unit in the typical day by using a small semi-trapezoid membership function according to the output values of the thermal power unit in different time points in the typical day; respectively controlling the molten salt heat storage device and the thermal power generating unit to operate for a preset duration according to a preconfigured power generation system operation strategy in each output time period of the typical day; acquiring actual output values of the thermal power generating unit after the molten salt heat storage device and the thermal power generating unit operate for a preset time period according to a preconfigured operation strategy of a power generation system; and solving a high-temperature fused salt heat storage capacity optimizing configuration mathematical model of the coupled thermal power unit, which considers the daily operation cost of the system and the output fluctuation of the thermal power unit, by utilizing a non-dominant sorting genetic algorithm and an approximate ideal solution sorting method according to the target output value, so as to obtain the optimal energy storage capacity of the fused salt heat storage device.
According to the method, the output time periods of a plurality of typical days of the thermal power unit are determined according to the historical output values of the thermal power unit in the power generation system, then the molten salt heat storage device and the thermal power unit are controlled in different time periods according to the operation strategy, the output values of the thermal power unit are respectively output, and finally the optimal energy storage capacity of the molten salt heat storage device is determined according to the output values of the thermal power unit and the mathematical model of the high-temperature molten salt heat storage capacity optimization configuration, so that the influence of human factors and subjective factors is avoided, and the optimal energy storage capacity of the molten salt heat storage device is determined according to the related operation data of the power generation system and other power generation equipment contained in the power generation system, so that the determined optimal energy storage capacity of the molten salt heat storage device can be mutually matched with the power generation system and other power generation equipment contained in the power generation system, and the rationality and the suitability of the determined optimal energy storage capacity of the obtained molten salt heat storage device are improved.
In order to make the above objects, features and advantages of the present invention more comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 shows a flowchart of an optimal configuration method of a high-temperature molten salt heat storage device coupled with a thermal power generating unit according to an embodiment of the present invention;
FIG. 2 is a flow chart of an exemplary day determination method provided in accordance with one embodiment of the present invention;
Fig. 3 shows a schematic structural diagram of an optimal configuration device for a high-temperature molten salt heat storage device coupled with a thermal power generating unit according to a second embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. The components of the embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the invention, as presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be made by a person skilled in the art without making any inventive effort, are intended to be within the scope of the present invention.
Example 1
In order to facilitate understanding of the present application, the following describes in detail the first embodiment of the present application with reference to the flowchart of the method for optimizing configuration of the high-temperature molten salt heat storage device of the thermal power generating unit according to the first embodiment of the present application shown in fig. 1.
The molten salt heat storage device provided by the application belongs to a part of a power generation system, and the power generation system further comprises a thermal power generating unit. From the aspects of cost, safety, technical maturity and peak regulation rate response requirement of the thermal power generating unit, the fused salt heat storage device has obvious advantages in selecting a double-tank heat storage system, and high-temperature fused salt and low-temperature fused salt are placed in different tanks. Because the heated temperature of the molten salt is limited by the lowest use temperature and the clamping point temperature of the molten salt, the use temperature range is limited, and the high-melting-point wide-temperature-range binary eutectic nitrate (60% NaNO 3-40% KNO 3) is selected in consideration of the fact that the steam temperature of a large thermal power generating unit for pushing a steam turbine to do work is 540-600 ℃, and the high-melting-point wide-temperature-range binary eutectic nitrate has a melting point of about 221 ℃ and has high thermal stability of 565 ℃. The power generation system consists of a thermal power unit and a fused salt heat storage device, and further comprises a main machine device such as a drum boiler or a direct-current boiler, a steam turbine, a generator and the like, and related auxiliary machine devices. The molten salt heat storage device comprises a high-temperature molten salt tank, a low-temperature molten salt tank, a preheater, an evaporator, a superheater and other heat exchange equipment. The output data of a thermal power generating unit for 24 hours is used as a planned output curve of the coupling system in the method, and the heat storage and release of the molten salt heat storage device are adjusted according to a certain control strategy, so that the total output power of the coupling system follows the planned output curve. The load-reducing heat storage process of the power generation system comprises the following steps: the main steam generated by the boiler not only meets the actual power consumption requirement of the power grid, but also enters the steam turbine to generate power, the residual high-pressure main steam enters the heat exchanger of the fused salt energy storage system to exchange heat with the fused salt, and the released steam is sent into the high-pressure heater through the pipeline. Load lifting and heat release processes of the power generation system: the molten salt in the hot salt tank replaces flue gas generated by boiler combustion to exchange heat with water of a water supply pump, steam is generated and sent into the boiler, and the unit output is increased while the running state of the unit is not changed. And the molten salt after heat exchange flows into a cold salt tank to wait for the heat accumulation process to heat the molten salt. The capacity optimization configuration method selects proper typical days from the historical day operation data, and performs capacity optimization configuration on the weighted typical days to acquire electricity requirements under different seasons and different time periods as far as possible.
Referring to fig. 1, fig. 1 shows a flowchart of an optimized configuration method of a high-temperature molten salt heat storage device coupled to a thermal power generating unit according to an embodiment of the present invention, where the method includes steps S101 to S104:
S101: and determining a plurality of typical days of the thermal power unit by using a Canopy algorithm and a K-maens clustering algorithm according to the output value of the thermal power unit on each historical operation day.
Specifically, the method adopts a density Canopy-K-means machine learning algorithm to cluster the historical daily operational output data. Firstly, carrying out coarse clustering on data by adopting a density Canopy algorithm, and then carrying out fine clustering by adopting a K-means algorithm so as to improve the clustering effect and obtain a proper typical solar power unit output curve.
Data set of output value of thermal power generating unit on each historical operation dayWhereinIs the first/>, in the yearOutput value of each historical operation day,/>For the historical operation days, setWherein/>In the first place of thermal power generating unitThe daily output curve is sampled at intervals of 30min at time points/>And taking 49 points every day according to the corresponding unit output value.
The density Canopy algorithm firstly calculates the density of the output value of the single solar thermal power unit：
;
;
;
Wherein: the Euclidean distance between the output of the thermal power generating unit on the ith day and the output of the thermal power generating unit on the jth day in the data set D; /(I) To/>Centered, the number of history sample days in the range with the distance average of all samples in the dataset D as radius,/>For/>Output values for each historical operating day; /(I)For/>/>, Of historical operating daysAnd outputting the power of the thermal power generating unit at the moment.
Taking the point with the highest density as the center of the first cluster, taking the point as the center, adding all points with Euclidean distances smaller than the distance average value of all samples in the data set D into the cluster, and deleting the points in the historical data set D to avoid calculation repetition.
Selecting a second cluster center, recalculating the density and the distance mean value in the residual historical data set of D after selecting the cluster center, selecting points meeting the conditions to add into the cluster, and deleting the points from the D. The above steps are repeated until the data set D is divided into several clusters. And taking the cluster center point as a cluster center of the next K-means algorithm.
Taking an initial clustering center and the number of clusters obtained by the density Canopy algorithm as input parameters of a K-means algorithm, then adopting the K-means algorithm to perform 'fine' clustering to obtain a final clustering result, dividing the historical unit output data into K types of typical output, calculating the average value of the historical output data in each type as unit output of each typical day to obtain K typical days, wherein K is a non-zero natural number.
S102: and for each typical day, dividing the output time period of the thermal power unit in the typical day by using a small semi-trapezoid membership function according to the output values of the thermal power unit in different time points in the typical day.
Specifically, after k typical days are obtained by clustering, the output value of each time point of each typical day is judgedThe likelihood of being in the peak-valley period determines the period interval of the peak, flat, valley.
Wherein: The maximum output value of the thermal power unit in the sampling point at each moment of the ith day; /(I) The output minimum value of the thermal power unit in the sampling point at each moment of the ith day; /(I)The smaller semi-trapezoidal membership function at the t moment of the i day represents the ratio relation of the output value at the t moment of the i day relative to the highest peak and the lowest valley of the day. Judging which period of peak period and valley Duan Huo flat period the moment belongs to by the membership degree of the output values at the moment before and after t and t,/>Then the moment is in the peak segment,/>The time period is in a valley time period, and if the time period is not satisfied, the time period is divided into a flat time period; Is a slightly small semi-trapezoid membership function at the time t-1 of the ith day,/> Is a slightly smaller semi-trapezoidal membership function at time t+1 on day i.
S103: and respectively controlling the molten salt heat storage device and the thermal power generating unit to operate for a preset duration according to a preconfigured power generation system operation strategy in each output time period of the typical day.
Specifically, according to the power generation characteristics of the thermal power generating unit and the heat storage capacity of the molten salt energy storage device, and by combining the characteristics of load change of the unit, the operation strategy of the power generation system in the method is as follows:
When the power generation system operates in the valley section, under the condition of not changing or slightly changing the operation working condition of the thermal power unit, the heat energy of steam in the thermodynamic system is extracted and stored in molten salt, so that the power generation power of the coupling system is reduced. By calculating the maximum heat accumulation amount of the typical solar valley Duan Rongyan energy storage device and the residual heat storage space of the current molten salt energy storage device, the load of the thermal power unit is not changed when the heat storage space is enough, and the load of the thermal power unit is reduced by a small margin when the heat storage space is insufficient.
When the power generation system operates at a peak section, the heat storage system is utilized to rapidly increase the load of the thermal power unit by releasing the stored heat to the thermal power unit under the condition of not greatly increasing the thermal power unit so as to increase the electric power output of the power generation system. Considering limited heat storage quantity, calculating heat required by compensation of the typical solar peak section molten salt heat storage device and releasable heat of the current molten salt energy storage device, if the releasable heat is insufficient to compensate all the peak sections, preferentially compensating peak time sections, and slightly increasing load of the thermal power unit.
When the power generation system operates in the normal period, the molten salt heat storage device is not called.
S104: and after the molten salt heat storage device and the thermal power generating unit run for a preset time according to a preconfigured power generation system operation strategy, the actual output value of the thermal power generating unit is obtained, and the actual output value is used as a target output value of the power generation system.
Specifically, the actual output value of the thermal power generating unit can be directly obtained through the related collecting equipment.
S105: and solving a mathematical model of the high-temperature molten salt heat storage capacity optimization configuration of the coupled thermal power generating unit according to the target output value to obtain the optimal energy storage capacity of the molten salt heat storage device.
Specifically, in consideration of uncertainty caused by the common influence of factors on the power generation requirement of the system, the method decides an optimal capacity configuration result under each typical day, and finally takes the minimum total expected daily operation cost under all typical days and the minimum fluctuation of the output of the thermal power unit as a high-temperature fused salt heat storage capacity optimal configuration mathematical model of the coupled thermal power unit, which takes the daily operation cost of the system and the fluctuation of the output of the thermal power unit into consideration, wherein the specific form of the high-temperature fused salt heat storage capacity optimal configuration mathematical model is as follows:
1. The daily operation cost of the system should be satisfied Minimum:
；
Wherein: k is the number of typical days of thermal power unit output; Probability of occurrence for each typical day; /(I) Is the running cost of the thermal power generating unit corresponding to the typical day,/>Is the peak regulating cost of the thermal power generating unit corresponding to the typical day,/>The method is the construction and operation cost of the fused salt energy storage system corresponding to the typical day.
Running cost of thermal power generating unitThe following should be satisfied:
；
Wherein: 、/> fuel cost and start-stop cost of thermal power generating unit respectively,/> For the output power of the thermal power generating unit at the moment t,/>For the running state of the thermal power generating unit at the moment t,/>Representing running,/>Indicating that the unit is shut down. Wherein:
；
Wherein: 、/>、/> is the cost coefficient of the unit,/> And T is the running period, and T is the total period length of the period.
Peak regulating cost of thermal power generating unitThe following should be satisfied:
when the thermal power generating unit operates in the basic peak shaving stage, the operation cost of the thermal power generating unit is mainly coal consumption cost. When the thermal power generating unit is in a deep peak shaving stage, additional unit wear cost is generated due to low cycle fatigue loss and creep loss. The wear cost of the thermal power generating unit at the time t is as follows:
；
Wherein: for the rotor fracturing cycle, the value is equal to the output power/>, of the thermal power unit at the moment t In relation, it can be calculated by a formula; /(I)Is the wear coefficient; /(I)The product of the cost of the machine purchasing and the actual running loss coefficient of the machine is the actual wear cost. Combustion stability is reduced when the unit runs under low load, combustion supporting cost in the forms of oil feeding, plasma ignition and the like can occur, but in recent years, technology is improved obviously, and combustion supporting cost is basically absent.
Due to the promotion of energy conservation and emission reduction requirements, when the output of the thermal power unit is low to a certain range, the working efficiency of environmental protection equipment is reduced, so that pollutant emission exceeds standard and is subjected to fine, and at the moment, the environmental additional cost of the thermal power unit is increasedThe method comprises the following steps:
；
Wherein: 、/> respectively, superscalar fines in unit volume of gas,/> 、/>Are respectively the emission standard in unit volume of gas,/>、/>Respectively, the discharge exceeding rate.
In conclusion, the two-stage deep peak regulation of the thermal power unit is considered, and the total process peak regulation cost of the thermal power unit at the moment t is consideredThe method comprises the following steps:
Wherein: Rated output of machine set,/> Is the lower limit of basic peak shaving output,/>、/>The lower limit of the first stage depth peak shaving output and the second stage depth peak shaving output are respectively adopted.
Cost of fused salt system construction and operation and maintenance：
；
；
Wherein: Is the annual average cost of the fused salt heat storage system,/> For the discount rate,/>Is per unit investment price,/>Is unit operation and maintenance cost,/>For the maximum service life of molten salt system,/>Is the capacity of the molten salt system; r takes 0.08,/>The value is 30.
2. The minimum fluctuation of the output force of the thermal power generating unit is satisfied:
the power difference between each moment and the previous moment of the thermal power generating unit is the lowest:
；
Wherein: the output fluctuation of the thermal power generating unit corresponding to the typical day; /(I) The output power of the thermal power generating unit at the moment t; /(I)The output power of the thermal power generating unit at the time t-1.
3. The operation constraint conditions of the thermal power generating unit should be satisfied: upper and lower limit constraint of unit output:
In the middle of 、/>Respectively, the minimum active output and the maximum active output allowed by the machine set,/>The running state of the thermal power generating unit at the time t.
Unit variable load rate constraint:
；
Wherein: 、/> the maximum upward regulating speed and the maximum downward regulating speed of the output power of the unit are respectively, and the regulating speed limit can be according to/> The interval where is located changes,/>The running state of the thermal power generating unit at the time t-1.
And (3) unit start-stop constraint:
；
Wherein: 、/> the time period number of the machine set which is started and stopped is respectively; /(I) 、/>The time period number of the minimum start-up and stop of the unit is respectively;
unit rotation reserve constraint:
；
Wherein: the maximum uplink active output change rate of the unit in unit time is set; /(I) The change rate is the downlink active output; /(I)For upward rotation for standby; /(I)For downward rotation for standby; /(I)The load power at the time t-1; /(I)Is the period of operation.
4. The constraint condition of the fused salt heat storage device should be satisfied:
heat storage and release capacity constraints:
；
；
Wherein: For the heat storage amount at the time t of the heat storage device,/> The heat storage amount is the heat storage amount at the moment t-1 of the heat storage device;、/> the maximum heat accumulation and heat release power of the heat accumulation device are respectively obtained.
Charge-discharge state constraint:
；
；
Wherein: 、/> respectively represent the charge and discharge states of the molten salt heat storage system,/> AndThe heat storage device is in a heat charging and releasing state at the time t respectively, and the heat charging and releasing state constraint ensures that the system is only in one of three states of heat charging, heat releasing and no action.
Capacity constraint:
；
；
Wherein: the maximum heat storage capacity of the system is obtained; /(I) For the heat loss coefficient in the storage process, generally taking 5%; For t time heat storage power,/> And releasing the heat power for the time t.
Because the construction cost and the operation cost are low and the energy storage capacity is large, the optimization result can not achieve simultaneous optimization, a third-generation non-dominant genetic algorithm is selected to calculate the capacity optimization configuration model established in the foregoing, the Pareto front is solved according to the Pareto theory, decisions are made in the Pareto optimal solution set, and a group of solutions which are as close to the Pareto optimal domain as possible are found in a plurality of feasible solutions through continuous iteration:
(1) And initializing a population. And (3) taking all fused salt heat storage capacity configuration schemes as feasible domains of the thermal power generating unit within 24h, setting NSGA-III algorithm parameters, randomly generating an initial population P containing N individuals, and carrying out cross mutation operation on the initial parent population by using an adaptive mutation operator to obtain child populations with the same quantity. Mixing the parent population and the offspring population to obtain a new population S;
(2) Non-dominant ordering based on reference points. And sequencing the S population by adopting a rapid non-dominant mechanism, and selecting N excellent individuals to form a new generation evolutionary population Yt. Firstly, taking the optimal value of 2 objective functions in the step (4) as a reference to divide S into non-dominant layers F1, F2 and … with different grades, and adding the solutions of the non-dominant layers into a new population successively Until/>Is equal to N, or is greater than N for the first time. The last leading edge is assumed to be Fl if/>Is equal to N, then the initial population for the next iterationIf/>Is greater than N, and performs a reference point-based selection operation.
In order to ensure the diversity of solutions, NSGA-III algorithm introduces widely distributed reference points, essentially dividing grids in space to judge the crowding degree of each solution, and then screening solutions with smaller crowding degree to ensure the diversity of the population. After the reference points are set, taking the difference of the numerical scales of the objective functions into consideration, carrying out normalization processing on the difference, and changing the numerical scales of the objective functions into 0-1. Constructing a mapping relationship between individual population and reference points, namely a distance relationship, selecting the population closest to the reference points to be added into the populationUntil/>Is equal to N.
(3) Elite retention policy. By the time of the t-th generation,For population/>Is the most optimal individual. /(I)As a new generation group, ifThere is no ratio/>Preferred individuals, handle/>Added to/>As/>Is a subject of (a).
(4) And generating a Pareto optimal solution set. When the iteration number reaches a set value, the iterated population with N individuals is used as a Pareto optimal solution, and the values of the objective functions of the individuals in the Pareto optimal solution form an actual Pareto front closest to the theoretical front.
After the Pareto optimal solution set is generated, selecting an approximate ideal solution sorting method (TOPSIS) to select and determine an optimal solution among targets, wherein the method comprises the following specific steps of:
(1) N individuals in a Pareto optimal solution set generated through an NSGA-III algorithm represent N capacity configuration schemes, each configuration scheme corresponds to two objective functions, and because the objective functions of capacity optimization configuration designed by the method have the same trend, the smaller and better the daily running cost of the system and the fluctuation of the output of a thermal power unit are, the forward direction of the system is required to be unified into an extremely large index; the original data is required to be normalized to unify the dimensions among different variables, and numerical value differences are reduced. And normalizing the solution forward direction of each scheme under the daily operation cost of the first objective function system to be within a [0,1] interval by adopting a min-max normalization method:
；
Wherein: The minimum value of the daily operation cost of the system in each capacity optimizing configuration scheme is calculated; /(I) Maximum daily running cost of the system in each capacity optimizing configuration scheme; /(I)The 1 st objective function value for the i-th capacity allocation scheme. And the objective function can be obtained by the same method as the forward normalized value of each solution under the fluctuation of the output of the thermal power unit.
Constructing an initial judgment matrix A:
;
Wherein: the j-th objective function of the i-th capacity allocation scheme is normalized forward, i epsilon {1,2, …, N }, j epsilon {1,2};
(2) And constructing a weighted evaluation matrix. And selecting an entropy weight method for objective assignment depending on the discretization of the data to comprehensively evaluate the two objective functions, wherein the larger the discrete degree of the function value of the objective function under different capacity configuration schemes is, the larger the influence of the objective function on the final scheme selection is, and the higher the assigned weight is.
Determining weight corresponding to objective function through information redundancy：
Wherein,The j-th objective function value under the i-th scheme accounts for the proportion of the objective function values under all schemes; /(I)Is the information redundancy of the j-th objective function value.
Multiplying the forward normalized value of each objective function of each capacity allocation scheme in the initial judgment matrix A with the corresponding weight to obtain a weighted evaluation matrix：/>
;
Wherein,The j-th objective function forward normalized value for the i-th capacity allocation scheme is multiplied by its corresponding weight.
(3) Positive and negative ideal solutions are determined. Ideal solutionComposed of the maximum value of each column in the weighting matrix, negative ideal solution/>Then it consists of the minimum value for each column in the weighted evaluation matrix:
Wherein: And/> Respectively a maximum value and a minimum value of a first column of the weighting matrix; /(I)AndThe maximum and minimum values of the second column of the weighting matrix, respectively.
Determining Euclidean distance between each scheme and positive and negative ideal solutions:
；
；
Wherein: 、/> representing Euclidean distance from the ith capacity optimization configuration scheme to positive and negative ideal solutions respectively,/> The 1 st objective function forward normalized value for the i-th capacity allocation scheme is multiplied by its corresponding weight,/>The value normalized forward for the 2 nd objective function of the i-th capacity allocation scheme is multiplied by its corresponding weight.
(4) Calculating the proximity of each capacity optimization configuration scheme to the positive ideal scheme，/>Higher values indicate closer to the optimal solution, select/>The capacity optimization configuration scheme with the highest value is taken as the final scheme.
Finding out the maximum heat storage capacity of the system in the form of optimizing the configuration mathematical model for the heat storage capacity of the high-temperature molten saltAs the optimal energy storage capacity of the molten salt heat storage device.
In a possible implementation manner, fig. 2 shows a flowchart of a typical day determining method provided in an embodiment of the present invention, wherein the determining, according to the output value of the thermal power generating unit on each historical operation day, a plurality of typical days of the thermal power generating unit by using a copy algorithm and a K-maens clustering algorithm includes steps S201 to S202:
s201: and clustering the output value of the thermal power unit on each historical operation day by using the Canopy algorithm to obtain a clustering center and a clustering number.
Specifically, the Canopy algorithm is an unsupervised, fast-approximation "coarse" clustering algorithm. The classical K-maens clustering algorithm has high clustering precision, but has slow cluster obtaining speed and is greatly influenced by the K value. When clustering is carried out, aiming at the problem that the number of clusters and the initial cluster center cannot be determined by K-means and randomly selected, firstly, carrying out coarse clustering on data by adopting a density Canopy algorithm, and then carrying out fine clustering by adopting the K-means algorithm so as to improve the clustering effect and obtain a proper typical solar power unit output curve.
S202: and inputting the clustering center and the clustering number into the K-maens clustering algorithm as input parameters to perform clustering processing to obtain a plurality of typical days of the thermal power unit.
Specifically, an initial clustering center and the number of clusters obtained by a density Canopy algorithm are used as input parameters of a K-means algorithm, then the K-means algorithm is adopted to perform 'fine' clustering to obtain a final clustering result, historical unit output data are divided into K types of typical output, and the average value of the historical output data in each type is calculated to serve as unit output of each typical day to obtain K typical days.
In one possible embodiment, the force period is a valley period, or a peak period, or a plateau period.
Specifically, according to the power generation characteristics of the thermal power generating unit and the heat storage capacity of the molten salt energy storage device, and by combining the characteristics of the unit bearing load change, a reasonable system control strategy is specified so as to realize thermal decoupling and improve the peak regulation capacity and the load response rate of the unit.
In a possible embodiment, the controlling the molten salt heat storage device and the thermal power generating unit to operate for a preset period of time in each output time period of the typical day according to a preconfigured power generation system operation strategy respectively includes:
and in the valley section of the typical day, controlling the output of the thermal power generating unit, and controlling the molten salt heat storage device to store energy for the preset time.
Specifically, when the system operates in the valley section, under the condition of not changing or slightly changing the operation working condition of the thermal power boiler system, the heat energy of steam in the thermodynamic system is extracted and stored in molten salt, so that the power generation of the coupling system is reduced. By calculating the maximum heat accumulation amount of the typical solar trough heat accumulation device and the residual heat accumulation space of the current molten salt energy accumulation device, the boiler load is not changed when the heat accumulation space is enough, and the boiler load is reduced by a small margin when the heat accumulation space is insufficient.
And controlling the thermal power unit output and the energy storage and common output of the molten salt heat storage device to last for the preset time at the peak section of the typical day.
Specifically, when the system operates at a peak section, the heat storage system is utilized to rapidly increase the load of the unit by releasing the stored heat to the unit under the condition of not greatly increasing the load of the thermal power boiler so as to increase the electric power output of the system. Considering limited heat storage capacity, calculating heat required by compensation of the typical daily peak section molten salt heat storage device and releasable heat of the current molten salt energy storage device, if the releasable heat is insufficient to compensate all the peak sections, preferentially compensating peak periods, and slightly increasing boiler load.
And controlling the output of the thermal power generating unit in the flat period of the typical day, and continuing the preset duration.
Specifically, when the system operates in a normal period, the molten salt heat storage device is not called.
In a possible implementation manner, the solving the mathematical model of the high-temperature molten salt heat storage capacity optimization configuration of the coupled thermal power generating unit according to the target output value to obtain the optimal energy storage capacity of the molten salt heat storage device includes:
Determining an optimization objective function of the optimal configuration mathematical model according to the target output value, wherein the optimization objective function is a function which takes the minimum typical daily operation cost of the system and the minimum output fluctuation of the coal-fired generator set as the capacity optimal configuration problem optimization objective;
And constraining the optimized objective function by using a preset constraint condition, and solving the optimal configuration mathematical model by using a non-dominant sorting genetic algorithm and an approximate ideal sorting method to obtain the optimal energy storage capacity.
Specifically, referring to step S105, solving the mathematical model for optimizing the configuration of the heat storage capacity of the high-temperature molten salt of the coupled thermal power generating unit according to the target output value, to obtain a specific description of the optimal energy storage capacity of the molten salt heat storage device.
Example two
Referring to fig. 3, fig. 3 shows a schematic structural diagram of an optimizing configuration device of a high-temperature molten salt heat storage device coupled to a thermal power generating unit, where the molten salt heat storage device is part of a power generation system, and the power generation system further includes the thermal power generating unit, and the device includes:
the typical day determining module 301 is configured to determine, according to the output value of the thermal power unit on each historical operation day, a plurality of typical days of the thermal power unit by using a Canopy algorithm and a K-maens clustering algorithm;
the output time period dividing module 302 is configured to divide, for each of the typical days, an output time period of the thermal power generating unit on the typical day according to output values of the thermal power generating unit on different time points in the typical day by using a small-sized semi-trapezoid membership function;
the operation control module 303 is configured to control the molten salt heat storage device and the thermal power generating unit to operate for a preset duration according to a preset operation strategy of the power generation system during each output time period of the typical day;
The output value obtaining module 304 is configured to obtain an actual output value of the thermal power unit after the molten salt heat storage device and the thermal power unit operate for a preset time period according to a preconfigured operation strategy of the power generation system, and take the actual output value as a target output value of the power generation system;
And the optimal energy storage capacity determining module 305 is configured to solve a mathematical model of the high-temperature molten salt heat storage capacity optimization configuration of the coupled thermal power generating unit according to the target output value, so as to obtain the optimal energy storage capacity of the molten salt heat storage device.
In a possible implementation manner, the typical day determining module is used for determining a plurality of typical days of the thermal power generating unit according to the output value of the thermal power generating unit on each historical operation day by using a Canopy algorithm and a K-maens clustering algorithm, and is specifically used for:
clustering the output value of the thermal power unit on each historical operation day by using the Canopy algorithm to obtain a clustering center and a clustering number;
and inputting the clustering center and the clustering number into the K-maens clustering algorithm as input parameters to perform clustering processing to obtain a plurality of typical days of the thermal power unit.
In one possible embodiment, the force period is a valley period, or a peak period, or a plateau period.
In a possible embodiment, the operation control module is specifically configured to, when configured to control the molten salt heat storage device and the thermal power generating unit to operate in a preconfigured power generation system operation strategy for a preset period of time during each output time period of the typical day, respectively:
In the valley section of the typical day, controlling the output of the thermal power generating unit, and controlling the molten salt heat storage device to store energy for the preset time period;
Controlling the output of the thermal power unit and the energy storage and common output of the molten salt heat storage device to last for the preset time period in the peak section of the typical day;
And controlling the output of the thermal power generating unit in the flat period of the typical day, and continuing the preset duration.
In a possible implementation manner, the optimal energy storage capacity determining module is configured to solve a mathematical model of the high-temperature molten salt heat storage capacity optimization configuration of the coupled thermal power generating unit according to the target output value, so as to obtain the optimal energy storage capacity of the molten salt heat storage device, and is specifically configured to:
Determining an optimization objective function of the optimal configuration mathematical model according to the target output value, wherein the optimization objective function is a function which takes the minimum typical daily operation cost of the system and the minimum output fluctuation of the coal-fired generator set as the capacity optimal configuration problem optimization objective;
And constraining the optimized objective function by using a preset constraint condition, and solving the optimal configuration mathematical model by using a non-dominant sorting genetic algorithm and an approximate ideal sorting method to obtain the optimal energy storage capacity.
The optimal configuration device for the high-temperature molten salt heat storage device of the coupled thermal power generating unit provided by the embodiment of the invention can be specific hardware on equipment or software or firmware installed on the equipment and the like. The device provided by the embodiment of the present invention has the same implementation principle and technical effects as those of the foregoing method embodiment, and for the sake of brevity, reference may be made to the corresponding content in the foregoing method embodiment where the device embodiment is not mentioned. It will be clear to those skilled in the art that, for convenience and brevity, the specific operation of the system, apparatus and unit described above may refer to the corresponding process in the above method embodiment, which is not described in detail herein.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus and method may be implemented in other manners. The above-described apparatus embodiments are merely illustrative, for example, the division of the units is merely a logical function division, and there may be other manners of division in actual implementation, and for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some communication interface, device or unit indirect coupling or communication connection, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments provided in the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a usb disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
It should be noted that: like reference numerals and letters in the following figures denote like items, and thus once an item is defined in one figure, no further definition or explanation of it is required in the following figures, and furthermore, the terms "first," "second," "third," etc. are used merely to distinguish one description from another and are not to be construed as indicating or implying relative importance.
Finally, it should be noted that: the above examples are only specific embodiments of the present invention, and are not intended to limit the scope of the present invention, but it should be understood by those skilled in the art that the present invention is not limited thereto, and that the present invention is described in detail with reference to the foregoing examples: any person skilled in the art may modify or easily conceive of the technical solution described in the foregoing embodiments, or perform equivalent substitution of some of the technical features, while remaining within the technical scope of the present disclosure; such modifications, changes or substitutions do not depart from the spirit and scope of the corresponding technical solutions. Are intended to be encompassed within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. An optimal configuration method of a high-temperature molten salt heat storage device of a coupling thermal power generating unit is characterized in that the molten salt heat storage device is a part of a power generation system, the power generation system further comprises the thermal power generating unit, and the method comprises the following steps:
Determining a plurality of typical days of the thermal power unit by using a Canopy algorithm and a K-means clustering algorithm according to the output value of the thermal power unit on each historical operation day;
For each typical day, dividing the output time period of the thermal power unit in the typical day by using a small semi-trapezoid membership function according to the output values of the thermal power unit in different time points in the typical day;
respectively controlling the molten salt heat storage device and the thermal power generating unit to operate for a preset duration according to a preconfigured power generation system operation strategy in each output time period of the typical day;
acquiring actual output values of the thermal power unit after the molten salt heat storage device and the thermal power unit operate for a preset time period according to a pre-configured operation strategy of a power generation system, and taking the actual output values as target output values of the power generation system;
and solving a mathematical model of the high-temperature molten salt heat storage capacity optimization configuration of the coupled thermal power generating unit according to the target output value to obtain the optimal energy storage capacity of the molten salt heat storage device.
2. The method of claim 1, wherein determining a plurality of typical days of the thermal power plant using a Canopy algorithm and a K-means clustering algorithm based on the output values of the thermal power plant on each historical operating day comprises:
clustering the output value of the thermal power unit on each historical operation day by using the Canopy algorithm to obtain a clustering center and a clustering number;
And inputting the clustering center and the clustering number into the K-means clustering algorithm as input parameters to perform clustering processing to obtain a plurality of typical days of the thermal power generating unit.
3. The method of claim 1, wherein the force period is a valley period, or a peak period, or a plateau period.
4. A method according to claim 3, wherein the separately controlling the molten salt heat storage device and the thermal power plant to operate in a pre-configured power generation system operating strategy for a pre-set period of time during each output period of the typical day comprises:
In the valley section of the typical day, controlling the output of the thermal power generating unit, and controlling the molten salt heat storage device to store energy for the preset time period;
Controlling the output of the thermal power unit and the energy storage and common output of the molten salt heat storage device to last for the preset time period in the peak section of the typical day;
And controlling the output of the thermal power generating unit in the flat period of the typical day, and continuing the preset duration.
5. The method of claim 1, wherein solving the mathematical model of the optimal configuration of the high-temperature molten salt heat storage capacity of the coupled thermal power generating unit according to the target output value to obtain the optimal energy storage capacity of the molten salt heat storage device comprises:
Determining an optimization objective function of the optimal configuration mathematical model according to the target output value, wherein the optimization objective function is a function which takes the minimum typical daily operation cost of the system and the minimum output fluctuation of the coal-fired generator set as the capacity optimal configuration problem optimization objective;
And constraining the optimized objective function by using a preset constraint condition, and solving the optimal configuration mathematical model by using a non-dominant sorting genetic algorithm and an approximate ideal sorting method to obtain the optimal energy storage capacity.
6. An optimal configuration device of a high-temperature molten salt heat storage device of a coupling thermal power generating unit, which is characterized in that the molten salt heat storage device belongs to a part of a power generation system, the power generation system further comprises the thermal power generating unit, and the device comprises:
The typical day determining module is used for determining a plurality of typical days of the thermal power unit by using a Capopy algorithm and a K-means clustering algorithm according to the output value of the thermal power unit on each historical operation day;
The output time period dividing module is used for dividing the output time period of the thermal power generating unit on each typical day by using a small-sized semi-trapezoid membership function according to the output values of the thermal power generating unit on different time points in the typical day;
the operation control module is used for respectively controlling the molten salt heat storage device and the thermal power generating unit to operate for a preset time period according to a preset power generation system operation strategy in each output time period of the typical day;
The output value acquisition module is used for acquiring the actual output value of the thermal power unit after the molten salt heat storage device and the thermal power unit run for a preset time period according to a pre-configured operation strategy of the power generation system, and taking the actual output value as a target output value of the power generation system;
And the optimal energy storage capacity determining module is used for solving a mathematical model of the high-temperature fused salt heat storage capacity optimal configuration of the coupled thermal power generating unit according to the target output value to obtain the optimal energy storage capacity of the fused salt heat storage device.
7. The apparatus of claim 6, wherein the typical day determination module is configured to, when determining a plurality of typical days of the thermal power plant using a Canopy algorithm and a K-means clustering algorithm based on the output values of the thermal power plant on each historical operating day, specifically to:
clustering the output value of the thermal power unit on each historical operation day by using the Canopy algorithm to obtain a clustering center and a clustering number;
And inputting the clustering center and the clustering number into the K-means clustering algorithm as input parameters to perform clustering processing to obtain a plurality of typical days of the thermal power generating unit.
8. The apparatus of claim 6, wherein the force period is a valley period, or a peak period, or a plateau period.
9. The apparatus of claim 8, wherein the operation control module is configured to, when configured to control the molten salt heat storage device and the thermal power plant to operate with a pre-configured power generation system operation strategy for a preset period of time during each output time period of the typical day, respectively:
In the valley section of the typical day, controlling the output of the thermal power generating unit, and controlling the molten salt heat storage device to store energy for the preset time period;
Controlling the output of the thermal power unit and the energy storage and common output of the molten salt heat storage device to last for the preset time period in the peak section of the typical day;
And controlling the output of the thermal power generating unit in the flat period of the typical day, and continuing the preset duration.
10. The apparatus of claim 6, wherein the optimal energy storage capacity determination module is configured to, when configured to solve a mathematical model of an optimal configuration of a high-temperature molten salt heat storage capacity of a coupled thermal power generating unit according to the target output value, obtain the optimal energy storage capacity of the molten salt heat storage apparatus, specifically to:
Determining an optimization objective function of the optimal configuration mathematical model according to the target output value, wherein the optimization objective function is a function which takes the minimum typical daily operation cost of the system and the minimum output fluctuation of the coal-fired generator set as the capacity optimal configuration problem optimization objective;
And constraining the optimized objective function by using a preset constraint condition, and solving the optimal configuration mathematical model by using a non-dominant sorting genetic algorithm and an approximate ideal sorting method to obtain the optimal energy storage capacity.
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