Coal gas system optimal dispatching method, device and equipment and storage medium

Abstract

The application provides a coal gas system optimal scheduling method, device and equipment and a storage medium. The method comprises the following steps: obtaining a long-period coal gas system scheduling result in a production day, and converting the long-period coal gas system scheduling result into a long-period coal gas system scheduling interval; taking the long-period coal gas system scheduling interval as a scheduling condition of short-period coal gas system scheduling, performing short-period coal gas system scheduling, and obtaining a short-period coal gas system scheduling result; inputting the short-period coal gas system scheduling result into a pipe network simulation model for first verification; when the result of the first verification is verified, performing coal gas optimal scheduling according to the verified short-period coal gas system scheduling result. The long-period coal gas system scheduling is combined with the short-period coal gas system scheduling, and then the coal gas optimal scheduling is performed according to the verified short-period coal gas system scheduling result, so that the problem of coal gas emission caused by overpressure in the pipe network can be prevented, and the safe production and low-carbon operation of the coal gas system can be ensured.

Description

Technical Field

[0001] This invention relates to the field of gas system management and control technology, and in particular to a gas system optimization scheduling method, apparatus, equipment and storage medium. Background Technology

[0002] With the rapid development of the steel industry, the steel sector faces enormous pressure to conserve energy and reduce emissions, making improved energy efficiency an inherent requirement for steel enterprises. In the steel smelting process, coal gas is an important by-product energy generated during production, including blast furnace gas, coke oven gas, and converter gas, playing a crucial role in balancing and regulating the enterprise's energy system. The coal gas system consists of a gas generation end, a consumption end, and intermediate transmission and distribution links. This multi-generation and multi-consumption system complicates gas scheduling. Improper gas scheduling can lead to excessive pressure fluctuations within the gas pipeline network and even gas venting, creating safety hazards or causing energy waste, thus affecting normal production at the consumption end.

[0003] Traditional gas dispatching strategies often rely solely on historical gas production and consumption data for short-term gas dispatching balance and optimization, without considering the impact of long-term production plans and schedules on current gas dispatching. During gas transmission, if gas dispatching is unreasonable, changes in local gas flow can challenge the matching pressure of gas pipelines, potentially leading to local pressure exceeding limits and gas venting. This makes it difficult to guarantee the production safety and economical, low-carbon operation requirements of the entire plant's gas system. Summary of the Invention

[0004] In view of the shortcomings of the prior art described above, the purpose of this invention is to provide a method, apparatus, equipment and storage medium for optimizing the scheduling of a gas system, which can solve the technical problem of gas venting caused by excessive internal pressure in the gas pipeline network due to unreasonable gas scheduling.

[0005] To address the above problems, this invention provides a method for optimizing the scheduling of a gas system, the method comprising:

[0006] Obtain the long-cycle gas system scheduling results within the production day, and convert the long-cycle gas system scheduling results into long-cycle gas system scheduling intervals;

[0007] The long-cycle gas system scheduling interval is used as the scheduling condition for the short-cycle gas system scheduling to perform short-cycle gas system scheduling and obtain the short-cycle gas system scheduling result.

[0008] The short-cycle gas system scheduling results are input into the pipeline network simulation model for the first verification, and the first verification results are obtained.

[0009] When the first verification result is successful, gas optimization scheduling is performed based on the successful short-cycle gas system scheduling result.

[0010] In one embodiment of the present invention, before obtaining the long-cycle gas system scheduling results within a production day, the method further includes:

[0011] Obtain historical unit output and unit consumption data, production volume plan and production schedule, and perform long-cycle gas system output and consumption prediction based on the historical unit output and unit consumption data, the production volume plan and the production schedule to obtain the long-cycle gas system output and consumption prediction result;

[0012] Based on the predicted production and consumption results of the long-cycle gas system, and combined with the long-cycle gas scheduling constraints and objective function, the long-cycle gas system is scheduled to obtain the scheduling results.

[0013] In one embodiment of the present invention, the long-cycle gas dispatching constraints include at least electricity price, shutdown and maintenance plan, gas balance, and adjustable upper and lower limits of the user end. The objective function of the long-cycle gas dispatching is the difference between the long-cycle internal and external electricity purchase costs and the long-cycle self-generated revenue, plus the sum of the long-cycle gas venting losses and the long-cycle fluctuation penalties.

[0014] In one embodiment of the present invention, obtaining the long-cycle gas system scheduling interval includes:

[0015] Obtain actual production and consumption data of the long-cycle gas system, compare the actual production and consumption data of the long-cycle gas system with the predicted production and consumption results of the long-cycle gas system, and calculate the accuracy of the predicted production and consumption results of the long-cycle gas system.

[0016] Based on the accuracy of the long-cycle gas system production and consumption prediction results, a probability distribution function of the accuracy of the long-cycle gas system production and consumption prediction results is constructed.

[0017] Analyze the probability distribution characteristics of the accuracy probability distribution function, and obtain the confidence interval of the long-cycle gas system production and consumption prediction results based on the preset confidence level.

[0018] Based on the confidence interval, the scheduling results of the long-cycle gas system are calculated to obtain the scheduling interval of the long-cycle gas system.

[0019] In one embodiment of the present invention, the long-cycle gas system scheduling interval is used as the scheduling condition for the short-cycle gas system scheduling to perform short-cycle gas system scheduling and obtain the short-cycle gas system scheduling result, including:

[0020] Obtain historical data on the production and consumption of the short-cycle gas system and process parameters of the gas production and consumption unit, and predict the production and consumption of the short-cycle gas system based on the historical data on the production and consumption of the short-cycle gas system and the process parameters of the gas production and consumption unit to obtain the prediction result of the production and consumption of the short-cycle gas system.

[0021] Based on the long-cycle gas system scheduling interval and the short-cycle gas system production and consumption prediction results, and combined with the short-cycle gas system scheduling constraints and objective function, the short-cycle gas system is scheduled to obtain the short-cycle gas system scheduling results.

[0022] In one embodiment of the present invention, the short-cycle gas scheduling constraints include at least the maximum and minimum gas production and consumption of the gas production and consumption unit, the gas regulation speed of the adjustable user end, and the range of gas holder positions. The objective function of the short-cycle gas scheduling is the difference between the electricity purchase cost inside and outside the short cycle and the self-generated revenue within the short cycle, plus the sum of the gas venting loss and the short-cycle fluctuation penalty within the short cycle.

[0023] In one embodiment of the present invention, the short-cycle gas system scheduling result is first verified to obtain a first verification result, including:

[0024] The short-cycle gas system scheduling results are input into the pipeline network simulation model to output the first pressure calculation value of any pipeline and node in the pipeline network.

[0025] Based on the maximum pressure value under normal operating conditions for any pipe and node in the pipeline network, the first calculated pressure value is compared with the maximum pressure value.

[0026] If the first calculated pressure value is less than or equal to the maximum pressure value, the first verification result is that the verification is passed; if the first calculated pressure value is greater than the maximum pressure value, the first verification result is that the verification is failed.

[0027] In one embodiment of the present invention, after the first verification result is "verification failed" if the first calculated pressure value is greater than the maximum pressure value, the method further includes:

[0028] The short-cycle gas scheduling constraints and the short-cycle gas scheduling objective function parameters are adjusted to optimize the short-cycle gas system scheduling and obtain the optimized scheduling result of the short-cycle gas system.

[0029] The short-cycle gas system optimization scheduling result is input into the pipeline model for a second verification to obtain a second verification result. If the second verification result is that the verification fails, the optimization and verification of the short-cycle gas system scheduling continues until the current verification result is that the verification passes. Then, the gas optimization scheduling is carried out according to the current short-cycle gas system optimization scheduling result.

[0030] In one embodiment of the present invention, obtaining the gas pipeline network simulation model includes:

[0031] An initialization simulation model of the gas pipeline network is established based on the actual gas pipeline network structure.

[0032] Obtain the measured flow rate and pressure values ​​of the gas production and consumption unit, and input the measured flow rate value into the initial pipeline network simulation model to obtain the second pressure calculation value of the gas production and consumption unit;

[0033] The second calculated pressure value is compared with the measured pressure value to obtain a first deviation between the second calculated pressure value and the measured pressure value. If the first deviation is greater than a first preset threshold, the parameters of the initial pipeline simulation model are corrected until the first deviation is less than or equal to the first preset threshold to obtain the pipeline simulation model.

[0034] or

[0035] Obtain the measured flow rate and pressure values ​​of the gas production and consumption unit, and input the measured pressure value into the initial pipeline network simulation model to obtain the calculated flow rate value of the gas production and consumption unit;

[0036] The calculated flow rate is compared with the measured flow rate to obtain a second deviation between the calculated flow rate and the measured flow rate. If the second deviation is greater than a second preset threshold, the parameters of the initial pipeline network simulation model are corrected until the second deviation is less than or equal to the second preset threshold to obtain the pipeline network simulation model.

[0037] This invention also provides a gas system optimization scheduling device, characterized in that the device comprises:

[0038] The conversion module is used to obtain the long-cycle gas system scheduling results within the production day and convert the long-cycle gas system scheduling results into a long-cycle gas system scheduling interval.

[0039] The short-cycle scheduling module is used to take the long-cycle gas system scheduling interval as the scheduling condition for short-cycle gas system scheduling, and to perform short-cycle gas system scheduling to obtain the short-cycle gas system scheduling result.

[0040] The verification module is used to input the short-cycle gas system scheduling results into the pipeline network simulation model for first verification and obtain the first verification result;

[0041] The gas optimization scheduling module is used to perform gas optimization scheduling based on the verified short-cycle gas system scheduling results when the first verification result is verified as passed.

[0042] This invention also provides an electronic device, including a processor, a memory, and a communication bus;

[0043] The communication bus is used to connect the processor and the memory;

[0044] The processor is used to execute a computer program stored in the memory to implement the method as described in any of the above embodiments.

[0045] This invention also provides a computer-readable storage medium having a computer program stored thereon, the computer program being used to cause the computer to perform the method as described in any of the above embodiments.

[0046] As described above, the gas system optimization scheduling method, apparatus, equipment, and storage medium provided by the embodiments of the present invention have the following beneficial effects:

[0047] By using the scheduling results of the long-cycle gas system as the scheduling condition for the short-cycle gas system, short-cycle gas system scheduling is performed to obtain the scheduling results. Finally, the short-cycle gas system scheduling results are verified using a pipeline network simulation model. Based on the verified short-cycle gas system scheduling results, the gas system is optimized for scheduling. This ensures the accuracy of the short-cycle gas system scheduling results and the operability and feasibility of the gas system scheduling decisions. It enables the gas production and consumption in the gas system to reach a real-time balance, avoiding the problem of gas venting due to excessive pressure inside the gas pipeline network. This provides a guarantee for the safe production and economical low-carbon operation of the entire plant's gas system. Attached Figure Description

[0048] Figure 1 This is a simulation model diagram of a portion of a gas system, illustrating an exemplary embodiment of this application;

[0049] Figure 2 This is a flowchart illustrating an exemplary embodiment of the gas system optimization scheduling method of this application;

[0050] Figure 3 This is a schematic diagram illustrating an exemplary real-time electricity price for a steel enterprise, as shown in an exemplary embodiment of this application.

[0051] Figure 4 This is a schematic diagram illustrating an exemplary real-time position of a gas holder, as shown in an exemplary embodiment of this application.

[0052] Figure 5 This is a schematic diagram illustrating an exemplary real-time load of a boiler, as shown in an exemplary embodiment of this application;

[0053] Figure 6 This is a flowchart illustrating a long-cycle gas system scheduling result conversion, as shown in an exemplary embodiment of this application.

[0054] Figure 7 This is a schematic diagram illustrating exemplary operating parameters of an arbitrary pipe segment and node, as shown in an exemplary embodiment of this application.

[0055] Figure 8 This is a flowchart illustrating an exemplary embodiment of the present application, showing a method for verifying the scheduling results of a short-cycle gas system;

[0056] Figure 9 This is a flowchart illustrating a specific gas system optimization scheduling method in an exemplary embodiment of this application;

[0057] Figure 10 This is a block diagram illustrating a gas system optimization scheduling device in an exemplary embodiment of this application;

[0058] Figure 11 This is a schematic diagram of the structure of an electronic device provided in one embodiment of this application. Detailed Implementation

[0059] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that, unless otherwise specified, the following embodiments and features described therein can be combined with each other.

[0060] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Therefore, the drawings only show the components related to the present invention and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0061] First, it should be noted that in a gas system, both gas generation and consumption occur at multiple points. There are multiple gas-generating points and multiple gas-consuming points. Gas is continuously produced at the gas-generating points and then supplied to the gas-consuming points through the gas pipeline network to ensure normal production at the consuming points. Please refer to [link / reference needed]. Figure 1 , Figure 1 This is a simulation model diagram of a portion of a gas system, illustrating an exemplary embodiment of this application. (See diagram below.) Figure 1 As shown, the shaded broken line from the upper left to the lower right of the figure represents the simulated local gas pipeline network used for gas transportation.

[0062] There are two types of user terminals in a gas system: adjustable terminals, where gas consumption can be adjusted without affecting production; and non-adjustable terminals, where the gas supply from the gas generator must match the gas consumption of that terminal, otherwise production at that terminal will be affected. In non-adjustable terminals, gas consumption changes with real-time production. When gas consumption decreases, the local pressure within the gas pipeline increases, potentially leading to gas venting. Conversely, when gas consumption increases, the local pressure decreases, affecting subsequent production at the user terminal. Therefore, to prevent gas venting and avoid impacting production at non-adjustable terminals due to their fluctuating production, a dynamic balance between gas generation and consumption must be maintained in real-time, keeping the local pressure within the entire gas pipeline network within a reasonable range. In a gas system, there are also gas holders used to store gas. Based on gas production and consumption forecasts, the positions of the gas holders and the gas consumption at the adjustable end can be controlled for gas scheduling, ensuring a real-time dynamic balance between gas generation and consumption. It should be understood that gas production and consumption forecasting pertains to the production system, i.e., the gas generation end and the non-adjustable consumption end, while gas scheduling pertains to the energy system, i.e., the gas holders and the adjustable consumption end.

[0063] If gas scheduling is not handled properly, it can still lead to excessive pressure fluctuations within the gas pipeline network and even gas venting, causing safety hazards or energy waste and affecting normal production at the user end. Therefore, developing a reasonable gas system scheduling strategy to ensure real-time balance between gas production and consumption is crucial to effectively prevent gas venting due to excessive pressure within the gas pipeline network, thus guaranteeing safe production and economical, low-carbon operation of the entire plant's gas system.

[0064] To address the aforementioned problems, this application provides a method for optimizing the scheduling of a gas system. It should be understood that this method can also be applied to other exemplary implementation environments and executed by devices in those environments; this embodiment does not limit the implementation environment to which the method is applicable. Please refer to... Figure 2 , Figure 2 This is a flowchart illustrating an exemplary embodiment of a gas system optimization scheduling method according to this application. Figure 2 As shown, in an exemplary embodiment, the gas system optimization scheduling method includes at least steps S201 to S204, which are described in detail below:

[0065] Step S201: Obtain the long-cycle gas system scheduling results within the production day, and convert the long-cycle gas system scheduling results into a long-cycle gas system scheduling interval.

[0066] It should be noted that a long cycle refers to a single production day, and the long-cycle gas system scheduling result represents the adjustable gas consumption and gas holder position at the user end each hour within that long cycle—a specific value. The long-cycle gas system scheduling range, on the other hand, represents the desired controllable gas consumption and gas holder position at each hour within that long cycle—a range of values. Converting the long-cycle gas system scheduling result into a long-cycle gas system scheduling range makes the long-cycle gas system scheduling more flexible.

[0067] In one embodiment, before obtaining the long-cycle gas system scheduling results for a production day, the method further includes:

[0068] Obtain historical unit output and unit consumption data, production volume plan and production schedule, and make long-term gas system output and consumption prediction based on historical unit output and unit consumption data, production volume plan and production schedule to obtain long-term gas system output and consumption prediction results.

[0069] Based on the production and consumption prediction results of the long-cycle gas system, and combined with the long-cycle gas scheduling constraints and objective function, the long-cycle gas system is scheduled to obtain the scheduling results.

[0070] The long-cycle gas system production and consumption forecast results represent the predicted gas production and consumption for each hour within the long cycle. In other words, it predicts how much gas will be produced and consumed each hour within the long cycle. Considering that production plans and scheduling affect the long-cycle gas system production and consumption forecast results, in addition to historical single-product production and consumption data, it is necessary to combine production plans and scheduling to make the forecast more accurate. Historical single-product production and consumption data refers to the historical gas production and consumption of a single product during production. Production plans refer to the total planned production volume for a production day, and scheduling refers to the specific arrangement of production volume for each hour within a production day. After obtaining the production and consumption prediction results of the long-cycle gas system, the predicted production and consumption of gas per hour within the long cycle can be used to determine the controllable gas consumption and gas holder position for each hour within the long cycle. At the same time, the controllable gas consumption and gas holder position must also take into account the influence of long-cycle gas scheduling constraints and the long-cycle gas scheduling objective function.

[0071] In one embodiment, the long-cycle gas dispatching constraints include at least electricity price, shutdown and maintenance plans, gas balance, and adjustable upper and lower limits of the user end. The objective function of the long-cycle gas dispatching is the difference between the long-cycle internal and external electricity purchase costs and the long-cycle self-generated revenue, plus the sum of the long-cycle gas venting losses and the long-cycle fluctuation penalties.

[0072] Long-cycle gas dispatching constraints include at least electricity price, shutdown and maintenance plans, gas balance, and adjustable usage limits. Electricity price refers to the hourly electricity price within a production day. Because electricity prices fluctuate in real time, peak, flat, and off-peak values ​​exist. Higher electricity prices allow for increased gas generation, effectively utilizing gas and saving production costs. Shutdown and maintenance plans are the shutdown and maintenance schedules at both the gas generation and usage ends during production. Shutdowns and / or maintenance at either the production or usage end disrupt the gas supply-demand balance, so these are considered in long-cycle dispatching. Gas balance ensures that gas production and consumption are balanced during production, preventing disruptions to production and pressure buildup within the pipeline network. Adjustable usage limits are the upper and lower limits of gas consumption per hour at the adjustable usage end. The objective function for long-cycle gas dispatching is the difference between the long-cycle internal and external electricity purchase costs and the long-cycle self-generated revenue, plus the sum of long-cycle gas venting losses and long-cycle fluctuation penalties. That is, long-cycle internal and external electricity purchase costs - long-cycle self-generated revenue + long-cycle gas venting losses + long-cycle fluctuation penalties, where the fluctuation penalty is the penalty cost incurred due to fluctuations in long-cycle gas dispatching. In long-cycle gas system dispatching, the goal is to minimize the result of the long-cycle gas dispatching objective function to reduce enterprise production costs.

[0073] For example, in a gas-fired system, the boiler is the adjustable user, consuming gas for power generation. Taking peak-shaving-valley electricity price data as an example, under long-term gas dispatch constraints, more gas can be used to generate electricity when electricity prices are high, and less gas can be stored for power generation when electricity prices are low. That is, when electricity prices are high, the boiler's gas consumption can be controlled within a relatively large range, consuming more gas to generate more electricity; when electricity prices are low, the boiler's gas consumption can be controlled within a relatively small range, consuming less gas to generate more electricity. Please see [link to relevant documentation]. Figure 3 , Figure 3 This is a schematic diagram illustrating the real-time electricity transaction price for steel enterprises, as shown in an exemplary embodiment of this application. Figure 3 As shown, electricity prices are higher during the morning hours (6:00-9:00) and the evening hours (18:00-21:00), allowing boiler gas consumption to be controlled within a relatively large range, resulting in more electricity generation from higher gas consumption. At other times, boiler gas consumption can be controlled within a relatively small range, consuming less gas for electricity generation. By considering peak-valley electricity price data and rationally utilizing the peak-valley pricing mechanism, enterprises can effectively reduce their energy costs.

[0074] Please see Figure 4 , Figure 4 This is a schematic diagram illustrating the real-time location of an exemplary gas holder, as shown in an exemplary embodiment of this application. Figure 4 As shown, after combining peak-flat-valley electricity price data, the electricity price is relatively high during the morning period from 6:00 to 9:00 and the evening period from 18:00 to 21:00. The boiler's gas consumption can be controlled within a relatively large range, and more gas is consumed to generate electricity at more points. At this time, the gas stored in the gas holder will decrease. Therefore, the gas holder level gradually decreases during these two periods.

[0075] Please see Figure 5 , Figure 5 This is a schematic diagram illustrating an exemplary real-time boiler load, as shown in an exemplary embodiment of this application. Figure 5 As shown, after combining peak-flat-valley electricity price data, the electricity price is relatively high during the morning period from 6:00 to 9:00 and the evening period from 18:00 to 21:00. The boiler's gas consumption can be controlled within a relatively large range. More gas is consumed and more power is generated. Therefore, the boiler load reaches its peak during these two periods.

[0076] In one embodiment, obtaining the long-cycle gas system scheduling interval includes:

[0077] Obtain actual production and consumption data of long-cycle gas system, compare the actual production and consumption data of long-cycle gas system with the predicted production and consumption results of long-cycle gas system, and calculate the accuracy of the predicted production and consumption results of long-cycle gas system.

[0078] Based on the accuracy of the long-cycle gas system production and consumption prediction results, a probability distribution function of the accuracy of the long-cycle gas system production and consumption prediction results is constructed.

[0079] Analyze the probability distribution characteristics of the accuracy probability distribution function, and obtain the confidence interval of the long-cycle gas system production and consumption prediction results based on the preset confidence level.

[0080] Based on the confidence interval, the scheduling results of the long-cycle gas system are calculated to obtain the scheduling interval of the long-cycle gas system.

[0081] The actual production and consumption data for a long-cycle gas system represents the actual production and consumption of gas per hour within the long cycle. The predicted production and consumption results for a long-cycle gas system represent the predicted production and consumption per hour within the long cycle. Therefore, the actual production and consumption data for a long-cycle gas system can be compared with the predicted production and consumption results to determine the accuracy of the long-cycle gas system production and consumption results. Please refer to [link / reference]. Figure 6 , Figure 6 This is an exemplary embodiment of the present application illustrating a flowchart of long-cycle gas system scheduling result conversion. For example... Figure 6As shown, in step S601, firstly, based on the actual production and consumption data of the long-cycle gas system, the actual production and consumption of gas per hour within the long cycle are compared with the predicted production and consumption, and the accuracy rate of the long-cycle gas system production and consumption prediction results is calculated; in step S602, based on the accuracy rate of the long-cycle gas system production and consumption prediction results, a probability distribution function of the accuracy of the long-cycle gas system production and consumption prediction results is constructed; in step S603, the probability distribution characteristics of the accuracy probability distribution function are analyzed, and the confidence interval of the long-cycle gas system production and consumption prediction results is obtained according to the preset confidence level; in step S604, the scheduling results of the long-cycle gas system are calculated based on the confidence interval to obtain the long-cycle gas system scheduling interval.

[0082] For example, in the long-cycle gas system scheduling results, the gas holder position needs to be controlled at 180,000 m3 in a certain hour. Based on the statistical distribution of long-cycle gas prediction errors and the preset confidence level of 95%, a confidence interval is determined. Then, based on the confidence interval, the long-cycle gas system scheduling results are transformed into a long-cycle gas system scheduling range, that is, the gas holder position is controlled at 170,000 to 190,000 m3. At this time, the long-cycle gas system scheduling range is 170,000 to 190,000 m3, which means that controlling the gas holder position at 170,000 to 190,000 m3 can meet the error range that can be covered under a 95% probability of gas prediction.

[0083] Step S202: Use the long-cycle gas system scheduling interval as the scheduling condition for the short-cycle gas system scheduling, perform short-cycle gas system scheduling, and obtain the short-cycle gas system scheduling result.

[0084] It should be noted that the short-cycle gas system scheduling result refers to the adjustable gas consumption and gas holder position at any time within the short cycle. By using the long-cycle gas system scheduling interval as the scheduling condition for the short-cycle gas system, and combining long-cycle and short-cycle gas system scheduling, the long-cycle and short-cycle scheduling modes are linked, making gas scheduling more rational and optimizing the gas system scheduling.

[0085] In one embodiment, the long-cycle gas system scheduling interval is used as the scheduling condition for the short-cycle gas system scheduling. Short-cycle gas system scheduling is then performed to obtain the short-cycle gas system scheduling result, including:

[0086] Obtain historical data on the production and consumption of the short-cycle gas system and process parameters of the gas production and consumption unit, and predict the production and consumption of the short-cycle gas system based on the historical data on the production and consumption of the short-cycle gas system and process parameters of the gas production and consumption unit to obtain the prediction results of the production and consumption of the short-cycle gas system.

[0087] Based on the long-cycle gas system scheduling interval and the short-cycle gas system production and consumption prediction results, and combined with the short-cycle gas system scheduling constraints and objective function, the short-cycle gas system scheduling is carried out to obtain the short-cycle gas system scheduling results.

[0088] It should be noted that the short-cycle gas system production and consumption prediction results refer to the predicted production and consumption of gas at any given time within the short cycle. In other words, the gas system will predict the amount of gas produced and consumed in real time within a short cycle. It should be understood that a short cycle is several hours within a production day, which could be two, three, or four hours, etc. There is no restriction on the time period of the short cycle. Considering that the process parameters of the gas production and consumption units affect the prediction results of long-cycle gas production and consumption, in addition to using historical data on short-cycle gas system production and consumption, it is also necessary to combine the process parameters of the gas production and consumption units to make the short-cycle gas system production and consumption prediction results more accurate. The historical data on short-cycle gas system production and consumption includes the historical total production and consumption of gas within the short cycle, while the process parameters of the gas production and consumption units include the production parameters at each generation and consumption end, such as equipment parameters and production temperature.

[0089] After obtaining the short-cycle gas system production and consumption prediction results, the short-cycle gas system is scheduled by combining the long-cycle gas system scheduling interval, the short-cycle gas system production and consumption prediction results, the short-cycle gas scheduling constraints, and the short-cycle gas scheduling objective function. The short-cycle gas system scheduling result represents the adjustable gas consumption and gas holder position at any time within the short cycle. By combining the long-cycle and short-cycle gas system production and consumption predictions and scheduling, the gas system scheduling is further optimized. It should be understood that the long-cycle gas system production and consumption prediction and scheduling determines "how much to adjust" each hour, while the short-cycle gas system production and consumption prediction and scheduling determines "how to adjust" in real time.

[0090] In one embodiment, the short-cycle gas scheduling constraints include at least the maximum and minimum gas production and consumption of the gas production and consumption unit, the gas regulation speed at the adjustable end, and the range of gas holder positions. The objective function of the short-cycle gas scheduling is the difference between the electricity purchase cost within and outside the short cycle and the self-generated revenue within the short cycle, plus the sum of the gas release loss and the short-cycle fluctuation penalty within the short cycle.

[0091] The constraints for short-cycle gas scheduling are the maximum and minimum gas production and consumption of each gas production and consumption unit, the gas regulation rate at the adjustable end, and the range of gas holder positions. Specifically, the maximum and minimum gas production and consumption of each unit refer to the maximum and minimum gas production and consumption within a short cycle. The gas production and consumption of each unit can affect the pressure in the gas pipeline network in real time, thus influencing the short-cycle gas system scheduling. The gas regulation rate at the adjustable end is the rate at which the gas volume is changed based on the gas consumption at that end; this rate of adjustment affects the internal pressure of the gas pipeline network. The range of gas holder positions refers to the capacity range of the gas holder, i.e., how much gas it can store. The objective function for short-cycle gas dispatch is the difference between the cost of purchasing electricity within and outside the short cycle and the revenue from self-generated electricity within the short cycle, plus the sum of the gas emission loss and the short-cycle fluctuation penalty within the short cycle. That is, the cost of purchasing electricity within and outside the short cycle - the revenue from self-generated electricity within the short cycle + the gas emission loss + the short-cycle fluctuation penalty. In the dispatch of short-cycle gas systems, the goal is to minimize the result of the short-cycle gas dispatch objective function in order to reduce the enterprise's production costs.

[0092] Step S203: Input the short-cycle gas system scheduling results into the pipeline network simulation model for the first verification and obtain the first verification result.

[0093] After obtaining the short-cycle gas system scheduling results, the short-cycle gas system scheduling results are verified to obtain the first verification result, so as to determine whether the gas can be optimized and scheduled based on the short-cycle gas system scheduling results.

[0094] In one embodiment, a first verification is performed on the short-cycle gas dispatching results to obtain a first verification result, including:

[0095] Input the short-cycle gas system scheduling results into the pipeline network simulation model to output the first pressure calculation value of any pipeline and node in the pipeline network;

[0096] Based on the maximum pressure value under normal operating conditions of any pipeline and node in the pipeline network, the first pressure calculation value is compared with the maximum pressure value.

[0097] If the first calculated pressure value is less than or equal to the maximum pressure value, the first verification result is verification passed; if the first calculated pressure value is greater than the maximum pressure value, the first verification result is verification failed.

[0098] After obtaining the short-cycle gas system scheduling results, the results are input into the pipeline network simulation model. The simulation model outputs the first pressure calculation value for any pipe and node in the pipeline network. Since the maximum pressure value of any pipe and node in the pipeline network under normal operating conditions is known, the maximum pressure value can be compared with the first pressure calculation value. If the first pressure calculation value is less than or equal to the maximum pressure value, it indicates that the first pressure calculation value is not overpressured, and the first verification result is that the verification is passed. If the first pressure calculation value is greater than the maximum pressure value, it indicates that the first pressure calculation value is overpressured, and the first verification result is that the verification is failed.

[0099] Please see Figure 7 , Figure 7 This is a schematic diagram illustrating the operating parameters of an exemplary arbitrary pipe segment and node, as shown in an exemplary embodiment of this application. Figure 7 As shown, P is the pressure of the pipeline node, PD is the pressure drop of the gas pipeline, that is, the pressure difference between the two nodes of the gas pipeline, Q is the gas flow rate, and V is the gas flow velocity. The pressure calculation value of any pipeline and node in the pipeline network can be obtained through the pipeline network simulation model.

[0100] Step S204: When the first verification result is successful, gas optimization scheduling is performed based on the successful short-cycle gas system scheduling result.

[0101] When the first verification result is successful, it indicates that when optimizing the gas system scheduling based on the short-cycle gas system scheduling result, the pressure of any pipeline and node in the pipeline network is not over-pressured and will not cause the problem of gas diffusion. Therefore, the gas system can be optimized based on the verified short-cycle gas system scheduling result.

[0102] In one embodiment, after the first verification result is "verification failed" if the calculated first pressure value is greater than the maximum pressure value, the method further includes:

[0103] By adjusting the constraints and parameters of the short-cycle gas scheduling objective function, the short-cycle gas system scheduling is optimized, and the optimized scheduling results of the short-cycle gas system are obtained.

[0104] The short-cycle gas system optimization scheduling results are input into the pipeline network simulation model for a second verification. If the second verification result is that the verification fails, the optimization and verification of the short-cycle gas system scheduling continues until the current verification result is that the verification passes. Then, the gas optimization scheduling is carried out based on the current short-cycle gas system optimization scheduling results.

[0105] After inputting the short-cycle gas system scheduling results into the pipeline network simulation model, the first calculated and predicted value of any pipeline and node within the pipeline network can be obtained. The maximum pressure value is compared with the first calculated pressure value. If it is determined that the first calculated pressure value is greater than the maximum pressure value, it indicates that the short-cycle gas system scheduling result does not meet the requirements and needs to be re-executed. This involves adjusting the short-cycle gas system scheduling constraints and objective function parameters, such as reducing the adjustment speed at the adjustable end and adjusting the parameters of the short-cycle fluctuation penalty. Then, the short-cycle gas system is optimized and scheduled again to obtain the optimized scheduling result. The optimized scheduling result is then verified a second time by inputting it into the pipeline network simulation model for simulation calculation. If the second verification result is still unsuccessful, the optimization and verification of the short-cycle gas system scheduling continues until the current verification result is successful, i.e., until the first calculated pressure value is less than or equal to the maximum pressure value. Then, the gas is optimized and scheduled according to the current optimized scheduling result.

[0106] Please see Figure 8 , Figure 8 This is a flowchart illustrating an exemplary embodiment of this application, showing a method for verifying the scheduling results of a short-cycle gas system. For example... Figure 8 As shown, step S801 involves adjusting the short-cycle gas scheduling constraints and the parameters of the short-cycle gas scheduling objective function; step S802 involves optimizing the short-cycle gas system scheduling; step S803 involves inputting the obtained short-cycle gas system optimization scheduling results into the pipeline simulation model for gas pipeline simulation calculations to obtain the first pressure calculation value for any pipeline and node in the pipeline network; step S804 involves determining whether the first pressure calculation value is greater than the maximum pressure value; if it is not greater, proceed to step S805 to optimize gas scheduling based on the current short-cycle gas system optimization scheduling results and end the process; if it is greater, return to step S801 and continue adjusting the short-cycle gas scheduling constraints and the parameters of the short-cycle gas scheduling objective function.

[0107] In one embodiment, obtaining the gas pipeline network simulation model includes:

[0108] An initialization simulation model of the gas pipeline network is established based on the actual gas pipeline network structure.

[0109] Obtain the measured flow rate and pressure values ​​of the gas production and consumption unit, and input the measured flow rate value into the initialization pipeline simulation model to obtain the second pressure calculation value of the gas production and consumption unit;

[0110] The calculated second pressure value is compared with the measured pressure value to obtain the first deviation between the calculated second pressure value and the measured pressure value. If the first deviation is greater than the first preset threshold, the parameters of the initial pipeline simulation model are corrected until the first deviation is less than or equal to the first preset threshold, and the pipeline simulation model is obtained.

[0111] or

[0112] Obtain the measured flow and pressure values ​​of the gas production and consumption unit, and input the measured pressure value into the initialization pipeline simulation model to obtain the calculated flow value of the gas production and consumption unit;

[0113] The calculated flow rate is compared with the measured flow rate to obtain a second deviation between the calculated and measured flow rates. If the second deviation is greater than a second preset threshold, the parameters of the initial pipeline simulation model are corrected until the second deviation is less than or equal to the second preset threshold, thus obtaining the pipeline simulation model.

[0114] The pipeline network simulation model is obtained through iterative training of an initialized pipeline network simulation model. First, an initial pipeline network simulation model is established based on the actual gas pipeline network structure. Then, the measured flow and pressure values ​​of the gas production and consumption units are obtained. It should be understood that the flow and pressure data of the gas production and consumption units can be mutually converted; that is, pressure data can be obtained from the flow data of the gas production and consumption units, and vice versa. Typically, only one measured value from either the flow or pressure gauge needs to be input into the initial pipeline network simulation model to obtain the simulation calculation value. The measured value from the other gauge is then compared with the simulation calculation value for model parameter verification and correction. Therefore, the measured flow value can be input into the initial pipeline network simulation model to obtain the second calculated pressure value of the gas production and consumption unit, and vice versa. When the measured flow rate is input into the initial pipeline network simulation model to obtain the second pressure calculation value of the gas production and consumption unit, the second pressure calculation value is compared with the measured pressure value. If the deviation of the second pressure calculation value from the measured pressure value is greater than the first preset threshold, it indicates that the second pressure calculation value obtained by initializing the pipeline network simulation model is inaccurate and the parameters of the initial pipeline network simulation model need to be corrected. When the deviation of the second pressure calculation value from the measured pressure value is less than or equal to the first preset threshold, the second pressure calculation value obtained by the current pipeline network simulation model can be considered relatively accurate, and the current pipeline network simulation model can be used as the final pipeline network simulation model. The same principle applies when the measured pressure value is input into the initial pipeline network simulation model to obtain the flow rate prediction value of the gas production and consumption unit, which will not be elaborated here.

[0115] It should be noted that when training the pipeline network simulation model, the input values ​​are the measured flow or pressure values ​​of the gas production and consumption units. The output values ​​include not only the predicted pressure or measured flow values ​​of the gas production and consumption units, but also the predicted pressure values ​​of any pipes and nodes in the pipeline network. This is because flow meters and pressure meters are only present at the gas production and consumption units. Therefore, the pipeline network simulation model is trained by comparing the measured flow and calculated flow values ​​or the measured pressure and calculated pressure values ​​of the gas production and consumption units. When applying the pipeline network simulation model, the model input values ​​are the gas consumption and gas holder position at any time within a short period of time at the adjustable end of the user. It should be understood that the adjustable end of the user belongs to the gas production and consumption unit, and the gas holder position can be equated with the gas production and consumption unit. The gas consumption and gas holder position at any time within a short period of time at the adjustable end of the user are the flow rate. The model output values ​​are the calculated pressure values ​​of any pipe and node in the pipeline network and the calculated pressure or flow rate values ​​of the gas production and consumption unit. The verification results of the short-term gas system scheduling results only need to be judged based on the calculated pressure values ​​of any pipe and node in the pipeline network.

[0116] Please see Figure 9 , Figure 9 This is a flowchart illustrating a specific gas system optimization scheduling method as shown in an exemplary embodiment of this application. Figure 9 As shown, firstly, a gas pipeline network model is constructed based on static data from the gas pipeline network data. Then, dynamic data of real-time production, consumption, flow, and pressure are used to verify the gas pipeline network model, obtaining a pipeline network simulation model. Next, based on the production plan and production schedule (i.e., the daily production plan and 24-hour production schedule shown in the figure), long-cycle gas system production and consumption prediction is performed, i.e., long-cycle energy production and consumption prediction in the figure. Long-cycle gas system scheduling is then performed based on peak-valley electricity price data, maintenance / shutdown plans, and other constraints, along with the long-cycle gas system scheduling objective function, i.e., long-cycle energy scheduling in the figure. Short-cycle gas system production and consumption prediction is then performed based on historical gas production and consumption data and gas production and consumption unit process parameters, i.e., short-cycle energy production and consumption prediction in the figure. Finally, short-cycle gas system scheduling is performed by combining the long-cycle gas system scheduling results, short-cycle gas system production and consumption prediction results, short-cycle gas system scheduling constraints, and short-cycle gas system scheduling objective function, i.e., short-cycle energy scheduling in the figure. Finally, the obtained short-cycle gas system scheduling results are input into the pipeline network simulation model for verification and optimization to obtain the final short-cycle gas system scheduling optimization results.

[0117] The gas system optimization scheduling method provided in the above embodiments uses the scheduling results of the long-cycle gas system as the scheduling conditions for the short-cycle gas system to perform short-cycle gas system scheduling, obtains the short-cycle gas system scheduling results, and finally verifies the short-cycle gas system scheduling results using a pipeline network simulation model. Based on the verified short-cycle gas system scheduling results, the gas system is optimized and scheduled, ensuring the accuracy of the short-cycle gas system scheduling results and the operability and feasibility of the gas system scheduling decisions. This ensures that the production and consumption of gas in the gas system are balanced in real time, avoids the problem of gas venting due to excessive pressure inside the gas pipeline network, and provides a guarantee for the safe production and economical low-carbon operation of the entire plant's gas system.

[0118] Please see Figure 10 , Figure 10 This is a block diagram illustrating a gas system optimization scheduling device in an exemplary embodiment of this application, as shown below. Figure 10 As shown, this embodiment provides a gas system optimization scheduling device 1000, which includes:

[0119] The conversion module 1001 is used to obtain the long-cycle gas system scheduling results within the production day and convert the long-cycle gas system scheduling results into a long-cycle gas system scheduling interval.

[0120] The short-cycle scheduling module 1002 is used to take the long-cycle gas system scheduling interval as the scheduling condition for the short-cycle gas system scheduling, and to perform short-cycle gas system scheduling to obtain the short-cycle gas system scheduling result.

[0121] The verification module 1003 is used to input the short-cycle gas system scheduling results into the pipeline simulation model for the first verification and obtain the first verification result.

[0122] The gas optimization scheduling module 1004 is used to perform gas optimization scheduling based on the verified short-cycle gas system scheduling results when the first verification result is verified as passed.

[0123] In this embodiment, the device is essentially configured with multiple modules to execute the methods in any of the above embodiments. The specific functions and technical effects can be referred to in the above embodiments, and will not be repeated here.

[0124] Please see Figure 11 The present invention also provides an electronic device 1100, including a processor 1101, a memory 1102 and a communication bus 1103;

[0125] Communication bus 1103 is used to connect processor 1101 and memory 1102;

[0126] The processor 1101 is used to execute a computer program stored in the memory 1102 to implement one or more methods as described in the above embodiments.

[0127] This invention also provides a computer-readable storage medium, characterized in that it stores a computer program thereon.

[0128] A computer program is used to cause a computer to perform any of the methods described in Embodiment 1 above.

[0129] This application also provides a non-volatile readable storage medium storing one or more modules (programs). When these modules are applied to a device, they enable the device to execute the instructions included in Embodiment 1 of this application.

[0130] It should be noted that the computer-readable medium described in this disclosure can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this disclosure, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in connection with an instruction execution system, apparatus, or device. In this disclosure, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0131] The aforementioned computer-readable medium may be included in the aforementioned electronic device; or it may exist independently and not assembled into the electronic device.

[0132] Computer program code for performing the operations of this disclosure can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0133] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of methods and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0134] The above embodiments are merely illustrative of the principles and effects of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in the present invention should still be covered by the claims of the present invention.

Claims

1. A method for optimizing the scheduling of a gas system, characterized in that, The method includes: Obtain the long-cycle gas system scheduling results within the production day, and convert the long-cycle gas system scheduling results into long-cycle gas system scheduling intervals; The long-cycle gas system scheduling interval is used as the scheduling condition for the short-cycle gas system scheduling to perform short-cycle gas system scheduling and obtain the short-cycle gas system scheduling result. The short-cycle gas system scheduling results are input into the pipeline network simulation model for the first verification, and the first verification results are obtained. When the first verification result is successful, gas optimization scheduling is performed based on the successful short-cycle gas system scheduling result. Before obtaining the long-cycle gas system scheduling results within a production day, the method further includes: obtaining historical single-unit output and single-unit consumption data, production volume plans, and production schedules; and performing long-cycle gas system output and consumption prediction based on the historical single-unit output and single-unit consumption data, the production volume plans, and the production schedules to obtain long-cycle gas system output and consumption prediction results; and performing long-cycle gas system scheduling based on the long-cycle gas system output and consumption prediction results, combined with long-cycle gas scheduling constraints and long-cycle gas scheduling objective functions, to obtain long-cycle gas system scheduling results. Obtaining the long-cycle gas system scheduling interval includes: acquiring actual production and consumption data of the long-cycle gas system; comparing the actual production and consumption data with the predicted production and consumption results of the long-cycle gas system; and calculating the accuracy rate of the predicted production and consumption results of the long-cycle gas system; constructing an accuracy probability distribution function for the predicted production and consumption results of the long-cycle gas system based on the accuracy rate of the predicted production and consumption results of the long-cycle gas system; analyzing the probability distribution characteristics of the accuracy probability distribution function; and obtaining the confidence interval of the predicted production and consumption results of the long-cycle gas system based on a preset confidence level; and calculating the scheduling results of the long-cycle gas system based on the confidence interval to obtain the long-cycle gas system scheduling interval. Using the long-cycle gas system scheduling interval as the scheduling condition for the short-cycle gas system scheduling, short-cycle gas system scheduling is performed to obtain the short-cycle gas system scheduling result. This includes: acquiring historical production and consumption data of the short-cycle gas system and process parameters of the gas production and consumption units; predicting the short-cycle gas system production and consumption based on the historical production and consumption data and the process parameters of the gas production and consumption units to obtain the short-cycle gas system production and consumption prediction result; and performing short-cycle gas system scheduling based on the long-cycle gas system scheduling interval and the short-cycle gas system production and consumption prediction result, combined with the short-cycle gas system scheduling constraints and the short-cycle gas system scheduling objective function to obtain the short-cycle gas system scheduling result.

2. The gas system optimization scheduling method according to claim 1, characterized in that, The constraints of the long-term gas dispatching include at least electricity price, shutdown and maintenance plan, gas balance, and adjustable upper and lower limits of the user end. The objective function of the long-term gas dispatching is the difference between the long-term internal and external electricity purchase costs and the long-term self-generation revenue, plus the sum of the long-term gas venting losses and the long-term fluctuation penalties.

3. The gas system optimization scheduling method according to claim 1, characterized in that, The constraints of the short-cycle gas dispatching include at least the maximum and minimum gas production and consumption of the gas production and consumption unit, the gas regulation speed at the adjustable end, and the range of gas holder positions. The objective function of the short-cycle gas dispatching is the difference between the electricity purchase cost within and outside the short cycle and the self-generated revenue within the short cycle, plus the sum of the gas release loss and the short-cycle fluctuation penalty within the short cycle.

4. The gas system optimization scheduling method according to claim 1, characterized in that, The short-cycle gas system scheduling results are input into the pipeline network simulation model for the first verification, and the first verification results are obtained, including: The short-cycle gas system scheduling results are input into the pipeline network simulation model to output the first pressure calculation value of any pipeline and node in the pipeline network. Based on the maximum pressure value under normal operating conditions for any pipe and node in the pipeline network, the first calculated pressure value is compared with the maximum pressure value. If the first calculated pressure value is less than or equal to the maximum pressure value, the first verification result is that the verification is passed; if the first calculated pressure value is greater than the maximum pressure value, the first verification result is that the verification is failed.

5. The gas system optimization scheduling method according to claim 4, characterized in that, If the first calculated pressure value is greater than the maximum pressure value, and the first verification result is that the verification failed, the method further includes: The short-cycle gas scheduling constraints and the short-cycle gas scheduling objective function parameters are adjusted to optimize the short-cycle gas system scheduling and obtain the optimized scheduling result of the short-cycle gas system. The optimized scheduling result of the short-cycle gas system is input into the pipeline simulation model for a second verification to obtain a second verification result. If the second verification result is that the verification fails, the optimization and verification of the short-cycle gas system scheduling continues until the current verification result is that the verification passes. Then, the gas system is optimized and scheduled according to the current optimized scheduling result of the short-cycle gas system.

6. The gas system optimization scheduling method according to claim 1, characterized in that, The acquisition of the pipeline network simulation model includes: An initialization simulation model of the gas pipeline network is established based on the actual gas pipeline network structure. Obtain the measured flow rate and pressure values ​​of the gas production and consumption unit, and input the measured flow rate value into the initial pipeline network simulation model to obtain the second pressure calculation value of the gas production and consumption unit; The second calculated pressure value is compared with the measured pressure value to obtain a first deviation between the second calculated pressure value and the measured pressure value. If the first deviation is greater than a first preset threshold, the parameters of the initial pipeline simulation model are corrected until the first deviation is less than or equal to the first preset threshold to obtain the pipeline simulation model. or Obtain the measured flow rate and pressure values ​​of the gas production and consumption unit, and input the measured pressure value into the initial pipeline network simulation model to obtain the calculated flow rate value of the gas production and consumption unit; The calculated flow rate is compared with the measured flow rate to obtain a second deviation between the calculated flow rate and the measured flow rate. If the second deviation is greater than a second preset threshold, the parameters of the initial gas pipeline network simulation model are corrected until the second deviation is less than or equal to the second preset threshold, and the pipeline network simulation model is obtained.

7. A gas system optimization and scheduling device, characterized in that, The device includes: The conversion module is used to obtain the long-cycle gas system scheduling results within the production day and convert the long-cycle gas system scheduling results into a long-cycle gas system scheduling interval. The short-cycle scheduling module is used to take the long-cycle gas system scheduling interval as the scheduling condition for short-cycle gas system scheduling, and to perform short-cycle gas system scheduling to obtain the short-cycle gas system scheduling result. The verification module is used to input the short-cycle gas system scheduling results into the pipeline network simulation model for first verification and obtain the first verification result; The gas optimization scheduling module is used to perform gas optimization scheduling based on the verified short-cycle gas system scheduling results when the first verification result is verified as passed. The conversion module is specifically used to acquire historical unit output and unit consumption data, production volume plans, and production schedules, and to perform long-cycle gas system output and consumption prediction based on the historical unit output and unit consumption data, the production volume plans, and the production schedules to obtain long-cycle gas system output and consumption prediction results; based on the long-cycle gas system output and consumption prediction results, and in combination with long-cycle gas scheduling constraints and long-cycle gas scheduling objective functions, to perform long-cycle gas system scheduling to obtain long-cycle gas system scheduling results; The conversion module is further specifically used to acquire actual production and consumption data of the long-cycle gas system, compare the actual production and consumption data of the long-cycle gas system with the predicted production and consumption results of the long-cycle gas system, and calculate the accuracy rate of the predicted production and consumption results of the long-cycle gas system; based on the accuracy rate of the predicted production and consumption results of the long-cycle gas system, construct the accuracy probability distribution function of the predicted production and consumption results of the long-cycle gas system; analyze the probability distribution characteristics of the accuracy probability distribution function, and obtain the confidence interval of the predicted production and consumption results of the long-cycle gas system according to a preset confidence level; and calculate the scheduling results of the long-cycle gas system according to the confidence interval, thereby obtaining the scheduling interval of the long-cycle gas system. The short-cycle scheduling module is specifically used to acquire historical production and consumption data of the short-cycle gas system and process parameters of the gas production and consumption units, and to predict the short-cycle gas system production and consumption based on the historical production and consumption data and process parameters of the gas production and consumption units, thereby obtaining the short-cycle gas system production and consumption prediction result; and to perform short-cycle gas system scheduling based on the long-cycle gas system scheduling interval and the short-cycle gas system production and consumption prediction result, combined with the short-cycle gas scheduling constraints and the short-cycle gas scheduling objective function, thereby obtaining the short-cycle gas system scheduling result.

8. An electronic device, characterized in that, Includes processor, memory, and communication bus; The communication bus is used to connect the processor and the memory; The processor is configured to execute a computer program stored in the memory to implement the method as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, It contains computer programs. The computer program is used to cause the computer to perform the method as described in any one of claims 1-6.

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