Gas cabinet regulation and control optimization method, device and equipment and storage medium

Abstract

According to the gas cabinet regulation and control optimization method and device, the equipment and the storage medium, the real-time operation data, the production plan information, the coal electricity historical production and consumption parameters, the cabinet capacity regulation and control constraint information and the electricity price data of the gas cabinet are obtained, and the first prediction is carried out based on the production plan information and the coal electricity historical production and consumption parameters; according to the long-period coal power generation and consumption prediction parameters and the electricity price data, a first cabinet capacity regulation and control target value is generated based on the cabinet capacity regulation and control constraint information, second prediction is carried out based on the real-time operation data, and short-period coal power generation and consumption prediction parameters are obtained; performing regulation and control optimization by taking the first cabinet capacity regulation and control target value as a regulation and control condition according to the short-period coal power production and consumption prediction parameter to obtain a second cabinet capacity regulation and control target value for regulation and control; according to the method, the regulation and control failure risk caused by prediction deviation is reduced through combination of long-term and short-term prediction, the manual intervention requirement is reduced, and the multi-dimensional regulation and control indexes are synchronized on the premise of meeting safety constraints.

Description

Technical Field

[0001] The present application relates to the field of production control, and in particular to a gas tank control optimization method, device, equipment and storage medium. Background Art

[0002] The steel production process generates a large amount of by-product gas (such as blast furnace gas, converter gas, and coke oven gas), which is typically stored in gasholders and used for power generation. Based on the peak-valley time-of-use electricity pricing policy, steel companies can utilize the storage space in their gasholders while implementing staggered production. This allows them to "charge during valley hours and discharge during peak hours" to achieve peak-time gas generation, further reducing the amount of electricity purchased during peak periods and ultimately lowering the cost of purchased electricity for steel production.

[0003] In the existing technology, the regulation of gas tanks mostly relies on manual experience or fixed strategies, and lacks adaptability to dynamic changes in production plans. Therefore, when the production plan is adjusted, the gas production and consumption fluctuate greatly. The existing methods are difficult to optimize the tank capacity in real time, which can easily cause gas leakage or insufficient power generation. In addition, the power generation plan is not flexibly adjusted in combination with the time-of-use electricity price strategy, and it is difficult to effectively reduce the peak power purchase. Secondly, the constraints such as the gas unit adjustment rate, the upper and lower limits of the tank capacity, and the power demand limit are not dynamically coordinated, which can easily lead to operational risks. In summary, the existing technology cannot maximize the effective regulation measures for gas tank peak power generation based on the gas volume, the effective buffer space of the gas tank, and the gas consumption capacity of the gas unit. Summary of the Invention

[0004] The purpose of the embodiments of the present invention is to provide a gas cabinet control optimization method, device, equipment and storage medium to solve the above problems.

[0005] The present invention provides a gas tank control optimization method, which includes: obtaining real-time operation data, production plan information, historical coal-fired power production and consumption parameters, tank capacity control constraint information and electricity price data of the gas tank, performing a first prediction based on the production plan information and historical coal-fired power production and consumption parameters to obtain long-term coal-fired power production and consumption prediction parameters; generating a first tank capacity control target value based on the long-term coal-fired power production and consumption prediction parameters and electricity price data and based on the tank capacity control constraint information; performing a second prediction based on the real-time operation data to obtain short-term coal-fired power production and consumption prediction parameters, and performing control optimization based on the short-term coal-fired power production and consumption prediction parameters with the first tank capacity control target value as a control condition to obtain a second tank capacity control target value, so as to control the gas tank.

[0006] In one embodiment of the present invention, the long-term coal-fired power generation and consumption prediction parameters include hourly gas production, gas consumption, production electricity consumption and non-gas power generation in the long period, and the gas production includes the prediction of blast furnace gas production, converter gas production and coke oven gas production; the blast furnace gas production is predicted by multiplying the planned blast furnace production by the unit molten iron gas production coefficient; the converter gas production is predicted by accumulating the instantaneous gas production at each moment in the converter production plan; the coke oven gas production is predicted by multiplying the planned coke oven production by the unit coke gas production coefficient; the gas consumption is predicted by summing the product of the planned production of each production line, the unit product gas consumption coefficient and the production mode correction coefficient; the production electricity consumption is predicted by summing the product of the planned production of each production line, the unit product electricity consumption coefficient and the production mode correction coefficient; the non-gas power generation is calculated by predicting the historical data and operating efficiency of waste heat power generation, blast furnace gas waste pressure turbine power generation and photovoltaic power generation.

[0007] In one embodiment of the present invention, according to the long-term coal-fired power production and consumption forecast parameters and electricity price data, and based on the cabinet capacity regulation constraint information, a first cabinet capacity regulation target value is generated, including: according to the electricity price data, with the lowest purchased electricity cost as the goal, establishing an optimal allocation model for gas power generation and purchased electricity, and constructing a gas cabinet tuning constraint based on the cabinet capacity regulation constraint information, the gas cabinet tuning constraint including gas system operation constraints constructed based on the gas cabinet control range, upper and lower limits of gas unit load regulation and regulation rate limit, and power system operation constraints constructed based on the constraints of power demand not exceeding the limit and self-generation not feeding back to the power grid; solving the optimization allocation model by a preset planning algorithm under the conditions of satisfying the gas cabinet tuning constraint to obtain multiple groups of feasible solutions; determining the first cabinet capacity regulation target value by the solution with the smallest sum of gas cabinet capacity deviations from the middle value and / or the smallest sum of gas unit load regulation amounts in adjacent time periods among the multiple groups of feasible solutions.

[0008] In one embodiment of the present invention, a second prediction is performed based on the real-time operating data to obtain short-period coal-fired power generation and consumption prediction parameters, including: performing a time series prediction of coal-fired power generation and consumption based on real-time production data to obtain short-period coal-fired power generation and consumption prediction parameters, wherein the short-period coal-fired power generation and consumption prediction parameters include the predicted values of gas production, gas consumption, production electricity consumption and non-gas power generation per minute within the short period.

[0009] In one embodiment of the present invention, control optimization is performed based on the short-cycle coal-fired power generation and consumption forecast parameters with the first cabinet capacity control target value as the control condition, including: constructing a supply and demand balance equation based on the short-cycle coal-fired power generation and consumption forecast parameters and the gas cabinet capacity and gas unit power generation, and the supply and demand balance equation is used to characterize the correlation between the gas unit power generation, cabinet capacity change and gas surplus or gap; minimizing the deviation between the cabinet capacity and the first cabinet capacity control target value is used as the optimization goal, and constructing control optimization constraints with cabinet capacity control constraint information and gas unit adjustment constraint information to solve the optimal power generation load of the gas unit, and the cabinet capacity deviation is used to characterize the deviation from the first cabinet capacity control target value; and controlling the gas cabinet according to the power generation load coordinated control instruction of the optimal gas unit corresponding to the second cabinet capacity control target value.

[0010] In one embodiment of the present invention, after constructing the supply and demand balance equation based on the short-cycle coal-fired power production and consumption prediction parameters and the gas tank capacity and gas unit power generation, the gas tank control optimization method also includes: obtaining the current load unit adjustment upper and lower limits and adjustment rate limit of the gas unit, the current gas tank capacity, the upper and lower limits of the tank capacity adjustment and the upper limit of the adjustment rate; inputting the current load of the gas unit, the adjustment rate limit and the current gas tank capacity, the upper and lower limits of the adjustment and the upper limit of the adjustment rate to adjust the power generation of the gas unit to compensate for the current tank capacity deviation; if the supply and demand balance equation is not balanced after compensation, the gas release amount or the production line coordinated control instruction is output, and the allowable power load range of the power system is simultaneously corrected; if the actual power load exceeds the corrected range, the load control instruction of the adjustable user is triggered to make the grid demand less than or equal to the maximum demand threshold.

[0011] In one embodiment of the present invention, the cabinet capacity regulation constraint information includes gas system constraints and power system constraints. The gas system constraints include the upper and lower limits of the gas cabinet capacity and the upper limit of the regulation rate, the upper and lower limits of the gas unit load regulation and the upper limit of the regulation rate. The power system constraints include the power demand not exceeding the limit and the self-generated power not being fed back.

[0012] An embodiment of the present invention also provides a gas tank control and optimization device, which includes: an operating parameter acquisition module for acquiring real-time operating data, production plan information, historical coal-fired power production and consumption parameters, cabinet capacity control constraint information and electricity price data of the gas tank; a first control execution module for performing a first prediction based on the production plan information and historical coal-fired power production and consumption parameters to obtain long-term coal-fired power production and consumption prediction parameters; generating a first cabinet capacity control target value based on the long-term coal-fired power production and consumption prediction parameters and electricity price data and based on the cabinet capacity control constraint information; a second control execution module for performing a second prediction based on the real-time operating data to obtain short-term coal-fired power production and consumption prediction parameters, and performing control optimization based on the short-term coal-fired power production and consumption prediction parameters with the first cabinet capacity control target value as the control condition to obtain a second cabinet capacity control target value to control the gas tank.

[0013] An embodiment of the present invention also provides an electronic device, comprising: one or more processors; a storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the electronic device implements the gas tank control optimization method as described in any one of the above embodiments.

[0014] An embodiment of the present invention further provides a computer-readable storage medium having computer-readable instructions stored thereon. When the computer-readable instructions are executed by a processor of a computer, the computer is caused to execute the gas tank control optimization method as described in any one of the above embodiments.

[0015] The gas tank control optimization method, device, equipment and storage medium in the embodiment of the present invention obtains the real-time operation data, production plan information, historical coal-fired power production and consumption parameters, tank capacity control constraint information and electricity price data of the gas tank, performs a first prediction based on the production plan information and historical coal-fired power production and consumption parameters, obtains long-term coal-fired power production and consumption prediction parameters, generates a first tank capacity control target value based on the long-term coal-fired power production and consumption prediction parameters and electricity price data and based on the tank capacity control constraint information, performs a second prediction based on the real-time operation data, obtains short-term coal-fired power production and consumption prediction parameters, and performs control optimization based on the short-term coal-fired power production and consumption prediction parameters with the first tank capacity control target value as the control condition, and obtains a second tank capacity control target value. The target value of capacity regulation is used to regulate the gas tank; this application maximizes the self-generated power output during the peak period of electricity prices through electricity price-sensitive regulation, directly reduces the dependence on high-priced external electricity, and dynamically optimizes the rhythm of gas storage and consumption to reduce the energy waste caused by gas release. Secondly, the combination of long-term and short-term forecasts avoids the limitations of single time scale forecasts, reduces the risk of regulation failure due to forecast deviations, and the dynamic coordination of constraints ensures that the optimization plan is always within the safe operation boundary, reduces the need for manual intervention, and can continuously learn and update through historical data to adapt to different production scenarios. On the premise of meeting safety constraints, it simultaneously optimizes multi-dimensional indicators such as power generation economy, energy utilization, and equipment life.

[0016] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The accompanying drawings are incorporated into and constitute a part of the specification, illustrating embodiments consistent with the present application and, together with the specification, serving to explain the principles of the present application. It is obvious that the drawings described below are merely some embodiments of the present application, and a person of ordinary skill in the art can derive other drawings based on these drawings without inventive effort. In the drawings:

[0018] Figure 1 is a schematic diagram of an exemplary system architecture shown in an exemplary embodiment of the present application;

[0019] Figure 2 This is a flow chart of a gas tank control optimization method shown in an exemplary embodiment of the present application;

[0020] Figure 3 This is a schematic diagram of a specific gas cabinet control optimization architecture shown in an exemplary embodiment of the present application;

[0021] Figure 4 This is a schematic diagram of a gas cabinet control and optimization device shown in an exemplary embodiment of the present application;

[0022] Figure 5 It is a structural diagram of a computer system of an electronic device shown in an exemplary embodiment of the present application. DETAILED DESCRIPTION

[0023] The following will describe embodiments of the present invention with reference to the accompanying drawings and specific embodiments. Those skilled in the art will readily appreciate the other advantages and benefits of the present invention from the disclosure herein. The present invention may also be implemented or applied through various other specific embodiments, and the details in this specification may be modified or altered based on different viewpoints and applications without departing from the spirit of the present invention. It should be understood that the preferred embodiments are intended only to illustrate the present invention and are not intended to limit the scope of protection of the present invention.

[0024] It should be noted that the illustrations provided in the following embodiments are merely schematic illustrations of the basic concept of the present invention. Therefore, the illustrations only show components related to the present invention and are not drawn according to the number, shape, and size of components in actual implementation. In actual implementation, the form, quantity, and proportion of each component may be changed arbitrarily, and the component layout may also be more complex.

[0025] In the following description, numerous details are discussed to provide a more thorough explanation of the embodiments of the present invention. However, it will be apparent to those skilled in the art that the embodiments of the present invention may be practiced without these specific details. In other embodiments, well-known structures and devices are shown in block diagram form rather than in detail to avoid obscuring the embodiments of the present invention.

[0026] The term "and / or" used in this application describes a relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A exists alone, A and B exist simultaneously, or B exists alone. The character " / " generally indicates that the associated objects are in an "or" relationship.

[0027] First, it's important to note that a gasholder is a device used to store converter gas, primarily used to collect and store the gas produced during the converter steelmaking process. Typically constructed in either vertical or horizontal configurations, it features high storage efficiency, excellent safety, ease of operation, and a compact footprint. The main components of a gasholder include the cabinet body, door, inlet pipe, exhaust pipe, vent pipe, and safety valve. During the converter steelmaking process, the gas produced undergoes purification processes such as cooling and dust removal before being fed into the converter gasholder via an exhaust fan for storage. The stored gas can be used in subsequent industrial production or burned as fuel. Converter gas is a byproduct of the converter production process in steelmaking. Its main components are hydrogen, carbon monoxide, and carbon dioxide. It has a high calorific value and low pollution levels, and is primarily used as a fuel for power generation, heating, and industrial production.

[0028] Figure 1 It is a schematic diagram of an exemplary system architecture shown in an exemplary embodiment of the present application.

[0029] Reference Figure 1 As shown, the system architecture may include a production database 101 and a computer device 102. The production database 101 includes at least parameters related to production processes such as gas tanks, gas tank regulating valves, and coal-fired power production monitoring. In the embodiment of the present application, the planned output and shutdown plan information of each production line, the energy consumption data of the performance module, and the data acquisition data of the production signal module are all stored in a data-type database. The calculation is performed based on the system configuration information, and the calculated prediction data and cabinet capacity control target data are stored in the time series database to ensure the efficiency of data reading and writing in the system calculation process. The computer device 102 can be at least one of a microcomputer, an embedded computer, an industrial computer, a network computer, etc. The computer device 102 obtains the real-time operation data, production plan information, historical coal-fired power production and consumption parameters, cabinet capacity control constraint information and electricity price data of the gas tank through the production database 101, and performs a first prediction based on the production plan information and historical coal-fired power production and consumption parameters to obtain long-term coal-fired power production and consumption prediction parameters. According to the long-term coal-fired power production and consumption prediction parameters and the electricity price data, and based on the cabinet capacity control constraint information, a first cabinet capacity control target value is generated. Based on the real-time operation data, a second prediction is performed to obtain short-term coal-fired power production and consumption prediction parameters. According to the short-term coal-fired power production and consumption prediction parameters, the control optimization is performed with the first cabinet capacity control target value as the control condition to obtain a second cabinet capacity control target value to control the gas tank.

[0030] Schematically, the computer device 102 obtains the real-time operation data, production plan information, historical coal-fired power production and consumption parameters, cabinet capacity control constraint information and electricity price data of the gas tank through the production database 101, performs a first prediction based on the production plan information and the historical coal-fired power production and consumption parameters, obtains long-term coal-fired power production and consumption prediction parameters, generates a first cabinet capacity control target value based on the long-term coal-fired power production and consumption prediction parameters and electricity price data, and based on the cabinet capacity control constraint information, performs a second prediction based on the real-time operation data, obtains short-term coal-fired power production and consumption prediction parameters, and performs control optimization based on the short-term coal-fired power production and consumption prediction parameters with the first cabinet capacity control target value as the control condition, and obtains a second cabinet capacity control target value. Standard value, in order to regulate the gas tank; this application maximizes the self-generated power output during the peak period of electricity prices through electricity price-sensitive regulation, directly reduces the dependence on high-priced external electricity, and dynamically optimizes the rhythm of gas storage and consumption, reducing the energy waste caused by gas release. Secondly, the combination of long-term and short-term forecasts avoids the limitations of single time scale forecasts, reduces the risk of regulation failure due to forecast deviation, and the dynamic coordination of constraints ensures that the optimization plan is always within the safe operation boundary, reduces the need for manual intervention, and can continuously learn and update through historical data to adapt to different production scenarios. On the premise of meeting safety constraints, it simultaneously optimizes multi-dimensional indicators such as power generation economy, energy utilization, and equipment life.

[0031] Figure 2 This is a flow chart of a gas tank control optimization method shown in an exemplary embodiment of the present application. The gas tank control optimization method can be executed by a computing processing device. The computing processing device can be Figure 1 The computer device 102 shown in FIG. Figure 3 This is a schematic diagram of a specific gas tank control optimization architecture shown in an exemplary embodiment of this application. Figure 2 and Figure 3 As shown, the flow chart of the gas cabinet control optimization method includes at least steps S210 to S250, which are described in detail as follows:

[0032] In step S210, the real-time operation data, production plan information, historical coal-fired power production and consumption parameters, cabinet capacity control constraint information, and electricity price data of the gas tank are obtained.

[0033] In one embodiment of the present application, the production planning information is a production line production-related plan for a long period of time formulated based on production demand, including but not limited to the hourly production plan, shutdown plan, converter furnace plan, heating furnace entry plan, and rolling plan of each production line in the future long period of time.

[0034] In step S220, a first prediction is performed based on the production plan information and the historical coal power production and consumption parameters to obtain long-term coal power production and consumption prediction parameters.

[0035] In one embodiment of the present application, the long-term coal-fired power generation and consumption prediction parameters include hourly gas production, gas consumption, production electricity consumption and non-gas power generation within a long period, and the gas production includes predictions of blast furnace gas production, converter gas production and coke oven gas production.

[0036] In one embodiment of the present application, the blast furnace gas production is predicted by multiplying the planned blast furnace production by the unit molten iron gas production coefficient; the converter gas production is predicted by accumulating the instantaneous gas production at each moment in the converter production plan; and the coke oven gas production is predicted by multiplying the planned coke oven production by the unit coke gas production coefficient.

[0037] In one embodiment of the present application, the gas consumption is predicted by summing the product of the planned output of each production line, the unit product gas consumption coefficient and the production mode correction coefficient; the production electricity consumption is predicted by summing the product of the planned output of each production line, the unit product electricity consumption coefficient and the production mode correction coefficient; the non-gas power generation is calculated by predicting the historical data and operating efficiency of waste heat power generation, blast furnace gas waste pressure turbine power generation and photovoltaic power generation.

[0038] Specifically, the prediction of the blast furnace gas production is represented by formula (1):

[0039]

[0040] in, represents the total output of blast furnace gas in the jth hour, represents the gas production of the i-th blast furnace in the j-th hour, represents the planned output of the i-th blast furnace in the j-th hour, It represents the gas output per unit of molten iron produced by the i-th blast furnace.

[0041] The prediction of converter gas production is represented by formula (2):

[0042]

[0043] in, represents the total output of converter gas in the jth hour, represents the gas production of the i-th converter in the j-th hour, It represents the instantaneous amount of gas produced at time k in the jth hour of the i-th converter, and the unrecovered period

[0044] The prediction of coke oven gas emission is represented by formula (3):

[0045]

[0046] in, represents the total production of coke oven gas in the jth hour, represents the gas production of the i-th coke oven in the j-th hour, represents the planned output of the i-th coke oven in the j-th hour, It represents the gas output per unit of coke produced by the i-th coke oven.

[0047] The prediction of gas consumption is represented by formula (4):

[0048]

[0049] Among them, g represents the type of gas, such as blast furnace gas, converter gas, coke oven gas, represents the consumption of gas g in the jth hour, represents the gas g consumption of production line i in hour j; p i,j represents the planned output of production line i in hour j, It represents the gas consumption g per unit of product produced by production line i, It represents the gas g consumption correction coefficient under different production modes of production line i.

[0050] The prediction of production electricity consumption is represented by formula (5):

[0051]

[0052] in, represents the total electricity consumption in j hours, represents the power consumption of production line i in hour j, It represents the power consumption per unit of product produced by production line i, Indicates the power consumption correction coefficient of production line i under different production modes.

[0053] The prediction of non-gas power generation is represented by formula (6):

[0054]

[0055] in, represents the non-gas power generation in j hours, represents the waste heat power generation in j hours, represents the power generation of blast furnace gas excess pressure turbine in j hours, represents the photovoltaic power generation in j hours.

[0056] In step S230, a first cabinet capacity regulation target value is generated according to the long-term coal power production and consumption forecast parameters and electricity price data, and based on the cabinet capacity regulation constraint information.

[0057] In one embodiment of the present application, based on the electricity price data and with the goal of minimizing the cost of purchased electricity, an optimal allocation model for gas-fired power generation and purchased electricity is established, and a gas tank tuning constraint is constructed based on the tank capacity control constraint information. The gas tank tuning constraint includes gas system operation constraints constructed based on the gas tank control range, upper and lower limits of gas unit load adjustment, and adjustment rate limit, as well as power system operation constraints constructed based on the constraints of no excessive power demand and no self-generated power fed back to the grid. The optimal allocation model is solved by a preset planning algorithm while satisfying the gas tank tuning constraint conditions, and multiple groups of feasible solutions are obtained. The first tank capacity control target value is determined by the solution with the smallest sum of gas tank capacity deviations from the median value and / or the smallest sum of gas unit load adjustment amounts in adjacent time periods among the multiple groups of feasible solutions.

[0058] Specifically, by establishing gas-to-electricity The relationship with the gas tank is expressed as formula (7):

[0059]

[0060] Where ξ represents the correction system for gas system flow measurement and gas tank capacity conversion;

[0061] Calculating gas power generation It is represented by formula (8):

[0062]

[0063] Obtain purchased electricity It is represented by formula (9):

[0064]

[0065] Among them, combined with the electricity price data, the time-sharing electricity price k j , with the goal of minimizing the cost of purchased electricity, can be represented by formula (10):

[0066]

[0067] In one embodiment of the present application, when a planning algorithm is used to solve a problem, two situations are considered separately:

[0068] If there is no solution, it means that there is an imbalance in the supply and demand of the gas-electricity system in the production plan, prompting a reverse adjustment of the production plan; if there is a solution, the first tank capacity control target value is determined by setting the minimum sum of the deviations of the gas tank capacity from the median value and / or the minimum sum of the load adjustment amounts of the gas units in adjacent time periods.

[0069] Among them, the gas tank middle capacity mode is that more gas tanks are close to the middle capacity under the optimal external power cost multiple solutions, that is,

[0070] Among them, the least adjustment of gas units is when the load adjustment of units in adjacent periods is the smallest under the optimal external power cost multiple solutions, that is,

[0071] Among them, the gas tank can be combined with the intermediate tank The multi-objective solution is formed with the minimum adjustment amount of the unit, that is,

[0072] calculate As the target value for gas tank capacity control at each hour, that is, the first target value for gas tank capacity control Among them, C1 and C2 are the control weight coefficients with more gas tanks close to the intermediate tank capacity and the smallest unit load adjustment amount in adjacent time periods, respectively.

[0073] It should be noted that the first prediction is made based on the production plan information and the historical production and consumption parameters of coal-fired power to obtain the long-term coal-fired power production and consumption prediction parameters, and the trigger condition for generating the first cabinet capacity regulation target value based on the cabinet capacity regulation constraint information is a change in the plan information or a scheduled update at the hour.

[0074] In step S240, a second prediction is performed based on the real-time operating data to obtain short-cycle coal-fired power generation and consumption prediction parameters, and control optimization is performed based on the short-cycle coal-fired power generation and consumption prediction parameters with the first cabinet capacity control target value as the control condition to obtain the second cabinet capacity control target value to control the gas tank.

[0075] In one embodiment of the present application, a second prediction is performed based on real-time operating data to obtain short-period coal-fired power generation and consumption prediction parameters, including performing a coal-fired power generation and consumption time series prediction based on real-time production data to obtain short-period coal-fired power generation and consumption prediction parameters, wherein the short-period coal-fired power generation and consumption prediction parameters include predicted values of gas production, gas consumption, production electricity consumption and non-gas power generation per minute within a short period.

[0076] Specifically, we use time series data prediction algorithm models such as autoregressive movement, neural network, and support vector machine to correct time series prediction data based on production plan and production performance signals, and predict the gas production time series per minute in the next hour. Gas consumption time series

[0077] '

[0078] Production power consumption time series E usem , Non-gas power generation time series

[0079] In one embodiment of the present application, control optimization is performed based on the short-cycle coal-fired power production and consumption forecast parameters with the first cabinet capacity control target value as the control condition, including constructing a supply and demand balance equation based on the short-cycle coal-fired power production and consumption forecast parameters and the gas cabinet capacity and the gas unit power generation, and the supply and demand balance equation is used to characterize the correlation between the gas unit power generation, cabinet capacity change and gas surplus or gap; minimizing the deviation between the cabinet capacity and the first cabinet capacity control target value is used as the optimization goal, and the control optimization constraint condition is constructed with the cabinet capacity control constraint information and the gas unit adjustment constraint information to solve the optimal power generation load of the gas unit, and the cabinet capacity deviation is used to characterize the deviation from the first cabinet capacity control target value; the gas cabinet is controlled according to the power generation load coordinated control instruction of the optimal gas unit corresponding to the second cabinet capacity control target value.

[0080] Specifically, the optimization goal is to minimize the tank capacity deviation and the total amount of unit regulation, that is, the gas tank capacity closest to the gas tank hourly tank capacity control target value is used as the short-term dynamic planning goal, which is represented by formula (11):

[0081]

[0082] With the load regulation of the gas unit as the main means of regulating the increase or decrease of the gas tank capacity, the balance of the gas system is represented by formula (12):

[0083]

[0084] The corresponding time series data of gas power generation and purchased electricity are calculated and represented by formulas (13) and (14):

[0085]

[0086] In one embodiment of the present application, it further includes obtaining the current load of the gas unit, the upper and lower limits of the unit adjustment and the adjustment rate limit, the current cabinet capacity of the gas tank, the upper and lower limits of the cabinet capacity adjustment and the upper limit of the adjustment rate; inputting the current load of the gas unit, the adjustment rate limit and the current cabinet capacity of the gas tank, the upper and lower limits of the adjustment and the upper limit of the adjustment rate to adjust the power generation of the gas unit to compensate for the current cabinet capacity deviation; if the supply and demand balance equation is not balanced after compensation, then outputting the gas release amount or the production line coordinated control instruction, and synchronously correcting the allowable power load range of the power system; if the actual power load exceeds the corrected range, then triggering the load control instruction of the adjustable user to make the grid demand less than or equal to the maximum demand threshold.

[0087] In one embodiment of the present application, the specific implementation of the above adjustment and compensation process includes:

[0088] Set first Under the constraints of the gas-electricity system, the solution is performed according to the mode with the least adjustment of the gas unit, that is,

[0089] If there is no solution, it means that the gas-electricity system is unbalanced and the constraints of the electricity and gas systems need to be decoupled:

[0090] A. In the gas system balance, first set Under the condition of meeting the gas system constraints, the short-term dynamic planning target is the gas tank capacity closest to the gas tank hourly capacity control target value. Solve the problem according to the mode with the least amount of gas unit regulation, that is,

[0091] If there is no solution, it means that the gas system is unbalanced. It is necessary to minimize the gas gap or release volume while satisfying the gas system constraints. Solve the problem and obtain the gas consumption time series data of the gas unit After substituting it into the gas system balance formula, the gas surplus / gap time series data is calculated. It is used to guide the coordinated regulation of the gas release tower and / or gas production and consumption units.

[0092] B. In power system balance, based on the time series data of gas unit power generation and non-gas power generation load sequence Under the constraints of the power system, the reasonable production power load range is calculated Combined with the production power load forecast time series Perform power system balancing:

[0093] like Then the power system is in balance and does not need to be regulated;

[0094] like It is necessary to carry out coordinated power regulation based on the period and amount of excess power, combined with the users with adjustable power load and their predicted power load.

[0095] In one embodiment of the present application, the cabinet capacity control constraint information includes gas system constraints and power system constraints.

[0096] Specifically, the gas system constraints include: upper and lower limits of gas tank capacity and upper limit of gas tank rate, upper and lower limits of gas unit load and upper limit of gas unit rate, that is,

[0097] The power system constraints include that the power demand does not exceed the limit and self-generated power does not return.

[0098] It should be noted that the constraint values at different time granularities need to be converted according to the time granularity.

[0099] The gas tank control optimization method, device, equipment and storage medium in the embodiment of the present invention obtains the real-time operation data, production plan information, historical coal-fired power production and consumption parameters, tank capacity control constraint information and electricity price data of the gas tank, performs a first prediction based on the production plan information and historical coal-fired power production and consumption parameters, obtains long-term coal-fired power production and consumption prediction parameters, generates a first tank capacity control target value based on the long-term coal-fired power production and consumption prediction parameters and electricity price data and based on the tank capacity control constraint information, performs a second prediction based on the real-time operation data, obtains short-term coal-fired power production and consumption prediction parameters, and performs control optimization based on the short-term coal-fired power production and consumption prediction parameters with the first tank capacity control target value as the control condition, and obtains a second tank capacity control target value. The target value of capacity regulation is used to regulate the gas tank; this application maximizes the self-generated power output during the peak period of electricity prices through electricity price-sensitive regulation, directly reduces the dependence on high-priced external electricity, and dynamically optimizes the rhythm of gas storage and consumption to reduce the energy waste caused by gas release. Secondly, the combination of long-term and short-term forecasts avoids the limitations of single time scale forecasts, reduces the risk of regulation failure due to forecast deviations, and the dynamic coordination of constraints ensures that the optimization plan is always within the safe operation boundary, reduces the need for manual intervention, and can continuously learn and update through historical data to adapt to different production scenarios. On the premise of meeting safety constraints, it simultaneously optimizes multi-dimensional indicators such as power generation economy, energy utilization, and equipment life.

[0100] The following describes an embodiment of the device of the present application, which can be used to implement the gas cabinet control optimization method described in the above embodiment of the present application. For details not disclosed in the embodiment of the device of the present application, please refer to the embodiment of the gas cabinet control optimization method described in the above embodiment of the present application.

[0101] Figure 4 This is a schematic diagram of a gas tank control optimization device shown in an exemplary embodiment of the present application. The device can be applied to Figure 1 The implementation environment shown in FIG. 1 is specifically configured in the computer device 102. The apparatus may also be applicable to other exemplary implementation environments and specifically configured in other devices. This embodiment does not limit the implementation environment to which the apparatus is applicable.

[0102] like Figure 4 As shown, the exemplary gas cabinet control and optimization device includes: an operating parameter acquisition module 401 , a first control execution module 402 , and a second control execution module 403 .

[0103] Among them, the operating parameter acquisition module 401 is used to obtain the real-time operating data, production plan information, historical coal-fired power production and consumption parameters, cabinet capacity control constraint information and electricity price data of the gas tank; the first control execution module 402 is used to make a first prediction based on the production plan information and historical coal-fired power production and consumption parameters to obtain long-term coal-fired power production and consumption prediction parameters; according to the long-term coal-fired power production and consumption prediction parameters and electricity price data, and based on the cabinet capacity control constraint information, a first cabinet capacity control target value is generated; the second control execution module 403 is used to make a second prediction based on the real-time operating data to obtain short-term coal-fired power production and consumption prediction parameters, and according to the short-term coal-fired power production and consumption prediction parameters, the first cabinet capacity control target value is used as the control condition to perform control optimization to obtain the second cabinet capacity control target value, so as to control the gas tank.

[0104] An embodiment of the present application also provides an electronic device, comprising: one or more processors; a storage device for storing one or more programs, wherein when the one or more programs are executed by one or more processors, the electronic device implements the gas tank control optimization method provided in the above-mentioned embodiments.

[0105] Figure 5 This is a schematic diagram of the structure of a computer system of an electronic device according to an exemplary embodiment of the present application. Figure 5 The computer system 500 of the electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0106] like Figure 5 As shown, the computer system 500 includes a central processing unit (CPU) 501, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 502 or the program loaded from the storage portion into the random access memory (RAM) 503, such as executing the method in the above embodiment. In the RAM 503, various programs and data required for system operation are also stored. The CPU 501, ROM 502 and RAM 503 are connected to each other via a bus. An input / output (I / O) interface 505 is also connected to the bus 504.

[0107] The following components are connected to the I / O interface 505: an input section 506 including a keyboard, a mouse, etc.; an output section 507 including devices such as a cathode ray tube (CRT), a liquid crystal display (LCD), and a speaker; a storage section 508 including a hard disk; and a communication section 509 including a network interface card such as a LAN (Local Area Network) card or a modem. The communication section performs communication processing via a network such as the Internet. A drive is also connected to the I / O interface 505 as needed. Removable media 511, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., are installed in the drive 510 as needed so that computer programs read therefrom can be installed into the storage section 508 as needed.

[0108] In particular, according to an embodiment of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via the communication section 509, and / or installed from a removable medium 511. When the computer program is executed by the central processing unit (CPU) 501, the various functions defined in the system of the present application are executed.

[0109] It should be noted that the computer-readable medium shown in the embodiment of the present application can be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium can be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media can include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, which carries a computer-readable computer program. This propagated data signal can take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. A computer program embodied on a computer-readable medium may be transmitted using any suitable medium, including but not limited to wireless, wired, or any suitable combination thereof.

[0110] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. Among them, each box in the flowchart or block diagram can represent a module, program segment, or part of the code, and the above-mentioned module, program segment, or part of the code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0111] In the corresponding drawings of the above embodiments, connecting lines can represent the connection relationship between various components to represent more constituent signal paths (constituent_signalpath) and / or one or more ends of some lines have arrows to indicate the main information flow direction. The connecting lines are used as an identifier, not a limitation of the scheme itself, but the use of these lines in combination with one or more exemplary embodiments helps to connect circuits or logic units more easily. Any represented signal (determined by design requirements or preferences) can actually include one or more signals that can be transmitted in any direction and can be implemented with any appropriate type of signal scheme.

[0112] The units involved in the embodiments described in this application may be implemented by software or hardware, and the units described may also be set in a processor. In some cases, the names of these units do not constitute limitations on the units themselves.

[0113] Another aspect of the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the aforementioned method. The computer-readable storage medium may be included in the electronic device described in the above embodiments, or may exist independently and not be incorporated into the electronic device.

[0114] It should be noted that, although several modules or units of the device for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiment of the application, the features and functions of two or more modules or units described above can be concretized in one module or unit. On the contrary, the features and functions of one module or unit described above can be further divided into multiple modules or units to be concretized.

[0115] Through the description of the above embodiments, it is easy for those skilled in the art to understand that the example embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solution according to the embodiments of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a touch terminal, or a network device, etc.) to execute the method according to the embodiments of the present application.

[0116] It should be noted that the present application can be used in a wide variety of general-purpose or special-purpose computing system environments or configurations, such as personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above, and the like.

[0117] Those skilled in the art will readily conceive of other embodiments of the present application after considering the specification and practicing the embodiments disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present application that follow the general principles of this application and include common knowledge or customary techniques in the art that are not disclosed herein.

[0118] It should be understood that the above content is only a preferred exemplary embodiment of the present application and is not intended to limit the implementation scheme of the present application. Ordinary technicians in this field can easily make corresponding changes or modifications based on the main concept and spirit of the present application. Therefore, the scope of protection of the present application should be the scope of protection required by the claims.

Claims

1. A gas tank control optimization method, characterized in that: The gas tank control optimization method includes: Obtain real-time gas tank operation data, production plan information, historical coal-fired power production and consumption parameters, tank capacity control constraint information, and electricity price data; Performing a first prediction based on the production plan information and historical coal-fired power generation and consumption parameters to obtain long-term coal-fired power generation and consumption prediction parameters; generating a first cabinet capacity regulation target value based on the long-term coal-fired power production and consumption forecast parameters and electricity price data and based on the cabinet capacity regulation constraint information; A second prediction is made based on the real-time operating data to obtain short-cycle coal-fired power generation and consumption prediction parameters, and control optimization is performed based on the short-cycle coal-fired power generation and consumption prediction parameters with the first cabinet capacity control target value as the control condition to obtain the second cabinet capacity control target value for controlling the gas tank.

2. The gas tank control optimization method according to claim 1, characterized in that: The long-term coal-fired power generation and consumption forecast parameters include hourly coal gas production, coal gas consumption, production electricity consumption, and non-gas power generation within the long period. The coal gas production includes the forecast of blast furnace gas production, converter gas production, and coke oven gas production. The blast furnace gas production is predicted by multiplying the planned blast furnace production by the unit molten iron gas production coefficient; the converter gas production is predicted by accumulating the instantaneous gas production at each moment in the converter production plan; The coke oven gas output is predicted by multiplying the planned coke oven output by the unit coke gas output coefficient; The gas consumption is predicted by summing the product of the planned output of each production line, the unit product gas consumption coefficient and the production mode correction coefficient; The production power consumption is predicted by summing the product of the planned output of each production line, the unit product power consumption coefficient and the production mode correction coefficient; The non-gas power generation is calculated by predicting the historical data of waste heat power generation, blast furnace gas waste pressure turbine power generation and photovoltaic power generation and operating efficiency.

3. The gas tank control optimization method according to claim 1, characterized in that: Generating a first cabinet capacity regulation target value based on the long-term coal power production and consumption forecast parameters and electricity price data and cabinet capacity regulation constraint information includes: Based on the electricity price data, with the goal of minimizing the cost of purchased electricity, an optimal allocation model for gas-fired power generation and purchased electricity is established. Furthermore, based on the cabinet capacity control constraint information, gas tank tuning constraints are constructed. The gas tank tuning constraints include gas system operation constraints based on the gas tank control range, upper and lower limits of gas unit load regulation, and regulation rate limits, as well as power system operation constraints based on the constraints of not exceeding power demand limits and not feeding self-generated electricity back into the grid. Solving the optimization allocation model by a preset planning algorithm under the condition that the gas tank tuning constraints are satisfied, thereby obtaining multiple groups of feasible solutions; The first tank capacity control target value is determined by the scheme having the smallest sum of deviations of the gas tank capacity from the median value and / or the smallest sum of the gas unit load adjustment amounts in adjacent time periods among the multiple groups of feasible schemes.

4. The gas tank control optimization method according to claim 1, characterized in that: A second prediction is performed based on the real-time operation data to obtain short-term coal power production and consumption prediction parameters including: Based on real-time production data, a time series forecast of coal power generation and consumption is performed to obtain short-term coal power generation and consumption forecast parameters, which include the predicted values of gas production, gas consumption, production electricity consumption and non-gas power generation per minute within a short period.

5. The gas tank control optimization method according to claim 1, characterized in that: The control optimization is performed based on the short-cycle coal power production and consumption forecast parameters and the first cabinet capacity control target value as a control condition, including: A supply and demand balance equation is constructed based on the short-term coal-fired power generation and consumption prediction parameters, the gas tank capacity, and the power generation of the gas unit. The supply and demand balance equation is used to characterize the correlation between the power generation of the gas unit, the change in tank capacity, and the surplus or shortage of gas; Minimizing the deviation between the cabinet capacity and the first cabinet capacity control target value is the optimization goal. The control optimization constraint conditions are constructed based on the cabinet capacity control constraint information and the gas unit regulation constraint information to solve the optimal power generation load of the gas unit. The cabinet capacity deviation is used to represent the deviation from the first cabinet capacity control target value. The gas tank is regulated according to the power generation load coordinated regulation instruction of the optimal gas generator set corresponding to the second tank capacity regulation target value.

6. The gas tank control optimization method according to claim 5, characterized in that: After constructing a supply-demand balance equation based on the short-term coal-fired power generation and consumption forecast parameters, the gas tank capacity, and the power generation of the gas generator set, the gas tank control optimization method further includes: Obtain the current load of the gas unit, the upper and lower limits of the unit adjustment, and the adjustment rate limit; the current tank capacity of the gas tank, the upper and lower limits of the tank capacity adjustment, and the upper limit of the adjustment rate; Input the current load of the gas unit, the regulation rate limit, the current gas tank capacity, the upper and lower regulation limits, and the upper limit of the regulation rate to adjust the power generation of the gas unit to compensate for the current tank capacity deviation; If the supply-demand balance equation is not balanced after compensation, the gas release amount or production line coordinated control instruction is output, and the allowable power load range of the power system is simultaneously corrected; If the actual power load exceeds the corrected range, the load control instruction of the adjustable user is triggered to make the grid demand less than or equal to the maximum demand threshold.

7. The gas tank control optimization method according to any one of claims 1 to 6, characterized in that: The cabinet capacity regulation constraint information includes gas system constraints and power system constraints. The gas system constraints include the upper and lower limits of the gas cabinet capacity and the upper limit of the regulation rate, the upper and lower limits of the gas unit load regulation and the upper limit of the regulation rate. The power system constraints include the power demand not exceeding the limit and the self-generated power not being fed back.

8. A gas tank control and optimization device, characterized in that: The gas tank control and optimization device includes: The operating parameter acquisition module is used to obtain the real-time operating data of the gas tank, production plan information, historical coal and power production and consumption parameters, tank capacity control constraint information, and electricity price data; A first control execution module is configured to perform a first prediction based on the production plan information and the historical coal-fired power generation and consumption parameters to obtain long-term coal-fired power generation and consumption prediction parameters; and generate a first cabinet capacity control target value based on the long-term coal-fired power generation and consumption prediction parameters and electricity price data and based on the cabinet capacity control constraint information; The second control execution module is used to make a second prediction based on the real-time operation data to obtain short-cycle coal-fired power generation and consumption prediction parameters, and to perform control optimization based on the short-cycle coal-fired power generation and consumption prediction parameters with the first cabinet capacity control target value as the control condition to obtain the second cabinet capacity control target value to control the gas tank.

9. An electronic device, characterized in that: include: one or more processors; A storage device for storing one or more programs, which, when executed by the one or more processors, enables the electronic device to implement the gas cabinet control optimization method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that Computer-readable instructions are stored thereon, and when the computer-readable instructions are executed by a processor of a computer, the computer is caused to execute the gas tank control optimization method according to any one of claims 1 to 7.

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