Plant-level multi-unit load optimal distribution and control method based on unit characteristic difference

Abstract

The invention discloses a plant-level multi-unit load optimal distribution method based on unit characteristic difference. The method comprises the following steps: establishing a plant-level digital twinborn model comprising a gas generation system, a gas storage system and a gas energy supply system; establishing a gas volume production prediction model of the gas generation system, obtaining a gas volume production prediction value in each time period, analyzing the gas volume production prediction value, the total consumed gas volume of the gas energy supply system in each time period and the gas storage and release amount of the gas storage system, and determining the total value of the gas volume of the gas energy supply system in each time period; simulation experiment analysis is carried out based on a plant-level digital twin model, load performance characteristic models of all boilers and turbines are established, and performance index differences of all the gas generator sets under all the consumed gas volumes are obtained; establishing an optimal distribution model of the gas volume of the upper-layer gas generator set, and optimally distributing the total value of the gas volume to each gas generator set; and establishing a lower-layer gas production-storage and release optimization model, and performing optimization control on a gas generation system and a gas storage system.

Description

Technical Field

[0001] The present invention belongs to the technical field of intelligent dispatching of coal gas generator sets, and in particular relates to a plant-level multi-unit load optimization distribution and control method based on unit characteristic differences. Background Art

[0002] At present, in order to cope with possible emergencies and save electricity purchase costs, large-scale steel mills, chemical plants and other high-energy-consuming enterprises will generally add equipment such as generators, and use resources such as coal gas generated during the production process to generate electricity and heat to meet the electricity and heat needs of the factory. This can reduce waste while effectively alleviating the energy burden within the factory.

[0003] However, the gas supply in the factory is closely integrated with the production process, and the total gas demand of all units is related to the electricity and heat demand in the factory. In this way, there will be an imbalance between the supply and demand of gas. Sometimes it is necessary to purchase part of the electricity online. In addition, the performance differences of each unit are also different, resulting in different power generation and heat production indicators of the units. How to use the gas supplied in the factory to meet the electricity and heat demand, consider the performance differences of each unit, optimize the gas allocation to each unit and optimize the supply and demand of gas, reduce the cost of purchasing electricity and improve the economic benefits of the whole plant are issues that need to be urgently addressed.

[0004] Based on the above technical problems, it is necessary to design a new plant-level multi-unit load optimization distribution and control method based on unit characteristic differences. Summary of the invention

[0005] The technical problem to be solved by the present invention is to overcome the shortcomings of the prior art and provide a plant-level multi-unit load optimization distribution and control method based on unit characteristic differences, which can take into account the performance differences of each unit and the supply and demand of gas, establish an upper-layer gas-fired power generation unit gas volume optimization distribution model and a lower-layer gas production-storage and release optimization model, and can optimize the total gas volume distribution to each gas-fired power generation unit and obtain the gas volume adjustment strategy and gas storage and release strategy of the gas production equipment through interactive iterative solutions of the upper and lower models, effectively utilize the gas generated in the plant for power generation and heat supply, save plant-level energy costs, and achieve supply and demand balance and maximize plant-level benefits.

[0006] In order to solve the above technical problems, the technical solution of the present invention is:

[0007] The present invention provides a plant-level multi-unit load optimization distribution and control method based on unit characteristic differences, which comprises:

[0008] S1. Establish a plant-level digital twin model including a gas generation system, a gas storage system, and a gas energy supply system; the gas energy supply system includes multiple gas generator sets, each of which is composed of a boiler and a steam turbine. With gas as input, part of the steam produced by the boiler is sent to the steam turbine to generate power, and at the same time, part of the steam is extracted from the steam turbine to meet the heat demand;

[0009] S2. Establish a plant-level electricity load and heat load demand forecasting model to obtain the combination of electricity load and heat load demand in each time period, and establish a relationship model between the total power generation steam flow, total extraction steam heating flow and the combination of electricity load and heat load demand of the gas energy supply system to obtain the total value of the power generation steam flow and extraction steam heating flow required to be output by the gas energy supply system in each time period;

[0010] S3. According to the total value of the power generation steam flow and the extraction steam heating flow required to be output in each time period, combined with historical operation data, a total gas consumption prediction model is established to obtain the total gas consumption of the gas energy supply system in each time period; and a gas output prediction model of the gas generation system is established to obtain the predicted value of the gas output of the gas generation system in each time period, and a comprehensive analysis is performed with the total gas consumption of the gas energy system in each time period and the gas storage and release capacity of the gas storage system to determine the total value of the gas volume of the gas energy supply system in each time period;

[0011] S4. Conduct simulation experiments and analysis based on the plant-level digital twin model, establish load performance characteristic models for each boiler and steam turbine, and obtain the performance index differences of each gas-fired power generation unit under various gas consumption volumes;

[0012] S5. With the goal of achieving the best operating economy of all gas-fired power generation units, the lowest gas consumption rate for steam production, and the lowest steam consumption rate for power generation and heating, and based on the differences in performance indicators of each gas-fired power generation unit under each gas consumption volume, a gas volume optimization allocation model for the upper gas-fired power generation unit is established to optimally allocate the total gas volume of the gas energy supply system at each time period to each gas-fired power generation unit;

[0013] S6. With the goal of minimizing the gas production cost and storage and release cost, a lower-level gas production-storage and release optimization model is established to optimize the control of the gas generation system and the gas storage system, and obtain the gas volume adjustment strategy and gas storage and release strategy of the gas production equipment.

[0014] Furthermore, in S1, a plant-level digital twin model including a gas generation system, a gas storage system, and a gas energy supply system is established, including:

[0015] Identify the physical entities within the plant, including the gas generation system, gas storage system, and gas energy supply system;

[0016] Establish the geometric model, physical model, behavior model and rule model of the physical entity in the factory, and after integrating the models, build a virtual entity mechanism model that is highly consistent with the actual physical entity in the factory;

[0017] Taking the virtual entity mechanism model as the main body, the residual of the virtual entity mechanism model is calculated using historical data. The input of the virtual entity mechanism model and the model residual result are preprocessed and used as the input and output of the machine learning network respectively. After training and learning, the digital twin data-driven model is obtained, and the residual of the virtual entity mechanism model is compensated and corrected.

[0018] The revised virtual entity mechanism model and the digital twin data-driven model are integrated to establish a plant-level digital twin model.

[0019] Furthermore, in S2, a relationship model is established between the total power generation steam flow, the total extraction steam heating flow, and the combination of the electric load and the heat load demand of the gas energy supply system, including:

[0020] Obtain historical operating data under plant-level supply and demand balance operating conditions, including the historical total power generation steam flow, total steam extraction heating flow, historical electrical load, thermal load, and the power generation steam flow output by each steam turbine, power generation power, exhaust steam pressure, exhaust steam temperature, thermal efficiency, thermal power, equivalent enthalpy drop, heating extraction steam flow, heating extraction steam pressure, and heating extraction steam temperature;

[0021] The feature extraction algorithm is used to extract features from historical operation data, and key operation data that affect power generation steam flow and steam extraction heating flow are selected;

[0022] The historical operation data fitting method is used to establish the relationship model between the total power generation steam flow, total extraction steam heating flow and the combination of electric load and heat load demand required by the gas energy supply system, which is expressed as:

[0023] (m g,t ,m h,t )=g(P e,t ,Q h,t ,E t );

[0024] m g,t is the total power generation steam flow in period t; m h,t is the total steam extraction heating flow rate during period t; P e,t is the electric load demand during period t; Q h,t is the heat load demand during period t; E t is the key operating data that affects the steam flow rate for power generation and the steam extraction flow rate for heating; g( ) is the power generation and heating characteristic equation of the gas energy supply system.

[0025] Further, the S3 includes:

[0026] Obtain the historical operation data of each boiler and each steam turbine, including the gas consumption, steam production, steam parameters, flue gas parameters, combustion efficiency of each boiler and the steam inlet flow, steam inlet pressure, steam inlet temperature, output power generation steam flow, steam extraction and heating flow of each steam turbine, as well as the total gas consumption of the gas energy supply system and the total values ​​of the system's output power generation steam flow and steam extraction and heating flow;

[0027] Based on the historical operation data of each boiler and each steam turbine, a prediction model for the total gas consumption of the gas energy supply system is established after training and learning using a machine learning algorithm.

[0028] The total value of the required output steam flow for power generation and steam extraction for heating in each time period is input into the established prediction model for the total coal gas consumption of the coal gas energy supply system to obtain the total coal gas consumption of the coal gas energy supply system in each time period;

[0029] Obtain the historical operation data of the gas generation system, establish a gas volume prediction model for the gas generation system, describe the relationship between gas production and operating parameters of related equipment, and then obtain the predicted value of gas volume output of the gas generation system in each future period;

[0030] When the predicted value of the gas output of the gas generation system in each period in the future is greater than the total gas consumption of the gas energy supply system, it means that the gas output is greater than the demand, and the excess gas is stored in the gas storage system to ensure that the total value of the gas volume in each period of the gas energy supply system is equal to the total gas consumption, or the excess electricity is sold online to ensure that the total value of the gas volume in each period of the gas energy supply system is equal to the predicted value of the gas output;

[0031] When the predicted gas output of the gas generation system in each future period is less than the total gas consumption of the gas supply system, it means that the gas output is less than the demand. The gas stored in the gas storage system is released to ensure that the total gas volume of the gas supply system in each period is equal to the predicted gas output value plus the released gas volume, or the total gas consumption is reduced by purchasing electricity online to ensure that the total gas volume of the gas supply system in each period is equal to the predicted gas output value.

[0032] Further, the S4 includes:

[0033] Set boiler load performance indicators, including boiler efficiency, gas consumption rate, boiler steam parameters, boiler load adjustment range, boiler load adjustment speed, and boiler heat loss;

[0034] Set turbine load performance indicators, including steam consumption rate, power generation efficiency, heating efficiency, heating steam extraction capacity, turbine load adjustment range, turbine load adjustment flexibility, and overload capacity;

[0035] Calculate the weights of various load performance indicators of boilers and steam turbines that affect load optimization distribution;

[0036] A simulation experiment is carried out based on the plant-level digital twin model. The same gas consumption volume is set as the input condition of the experiment. At the same time, the consistency of gas quality, pressure, and temperature parameters is considered. The boilers and steam turbines are compared under the same external conditions. At the same time, the operating data of each boiler and steam turbine are recorded, and the load performance indicators of each boiler and steam turbine are calculated. Combined with the calculated weights of each load performance indicator, the differences in load performance indicators of each boiler and steam turbine under the same gas consumption volume are evaluated and analyzed.

[0037] Furthermore, the weight calculation method includes a subjective weighting method, an objective weighting method and a combined weighting method.

[0038] Furthermore, in S5, a gas quantity optimization allocation model for the upper gas generating set is established, including:

[0039] Based on the performance index differences of each gas-fired power generation unit under different gas consumption volumes, the total gas volume of the gas energy supply system in each period is defined as Z. coalgas_all,t , assume that n gas generating units have a preliminary gas distribution result in each period, which is defined as (S coalgas_x1,t ,S coalgas_x2,t ,…,S coalgas_xn,t );

[0040] The goal is to optimize the operating economy of all gas-fired generator sets, minimize the gas consumption rate for steam production, and minimize the steam consumption rate for power generation and heating, which can be expressed as:

[0041]

[0042] P e,i,t is the power generation of the i-th gas generator set in period t, is the self-use power generation; e P is the price of electricity purchased; c,i,t is the grid-connected power of the i-th gas-fired generator set in period t; c Q is the electricity price during the on-grid phase; h,i,t is the heat production of the i-th gas generator set in period t, is the self-use heat production; h P is the heat price for purchasing heat; f,buy,t is the amount of electricity purchased to meet the peak power load during period t; η f is the electricity purchase price; Z coalgas_all,t is the total gas consumption of the gas energy supply system; μ coalgas To absorb the gas price; S coalgas_xi,t D is the amount of gas consumed by the i-th gas-fired generator set in period t; i,t is the steam production of the i-th gas generator set in period t; Me,i,t is the steam inlet flow rate of the i-th gas generator set during period t; M h,i,t is the heating steam extraction flow rate of the i-th gas generator set during period t;

[0043] Set the operating constraints of the gas-fired power generation unit, including: turbine capacity constraints, extraction flow constraints, exhaust flow constraints, thermal load constraints, electrical load constraints, boiler capacity constraints, steam production constraints, and gas consumption constraints;

[0044] Based on the goals of optimal operating economy, minimum gas consumption rate for steam production and minimum steam consumption rate for power generation and heating of all gas-fired power generation units, and combined with the operating constraints of the gas-fired power generation units, an optimal gas allocation model for the upper-level gas-fired power generation units is established.

[0045] Further, the S6 includes:

[0046] With the goal of minimizing the gas production cost and storage and release cost, a lower-layer gas production-storage and release optimization model is established, which can be expressed as:

[0047]

[0048] C s,t , C x,t They are the production and operation cost of the gas generation system and the gas storage cost of the gas storage system during period t;

[0049] Solve the lower-layer gas production-storage-release optimization model to obtain the gas volume adjustment strategy and gas storage-release strategy of the gas production equipment.

[0050] Furthermore, the constraints of the lower-layer coal gas production-storage-release optimization model include: coal gas supply and demand balance constraints, coal gas generation system equipment production capacity constraints and coal gas storage system equipment capacity constraints.

[0051] Furthermore, the upper gas power generation unit gas quantity optimization allocation model and the lower gas production-storage-release optimization model are solved by using an algorithm combining a genetic algorithm and a simulated annealing algorithm.

[0052] The beneficial effects of the present invention are:

[0053] (1) The present invention establishes a plant-level digital twin model including a gas generation system, a gas storage system, and a gas energy supply system. The gas generation, storage, and usage conditions in the actual plant can be virtually mapped through digital twin technology, and the operating status of each plant-level device can be intuitively viewed. In addition, simulation analysis can be used to perform a preview analysis of each strategy plan, which is convenient for subsequent optimization control of each device.

[0054] (2) The present invention can obtain the total value of the power generation steam flow and steam extraction heat flow required to be output by the gas energy supply system in each time period according to the power load and heat load requirements, and then the total gas consumption required at the input end of the gas energy supply system in each time period. The total gas consumption required at the input end of the gas energy supply system can be reversely calculated from the load demand within the plant, which is convenient for subsequent gas supply and demand balance scheduling;

[0055] (3) The present invention can effectively analyze the predicted value of the gas output of the gas generation system and the total gas consumption of the gas energy supply system, and can combine the gas storage and release of the gas storage system to achieve the balance of gas supply and demand, and finally determine the total value of the gas volume of the gas energy supply system in each time period;

[0056] (4) The present invention establishes a load performance characteristic model for each boiler and steam turbine, and obtains the performance index difference of each gas-fired power generation unit under each gas consumption amount, so as to facilitate the subsequent allocation of gas amount to the power generation unit with good performance, so as to produce more electricity and heat, and other units can operate in an auxiliary manner;

[0057] (5) The present invention establishes an upper-layer gas generation unit gas quantity optimization allocation model and a lower-layer gas production-storage optimization model. Through interactive iterative solutions of the upper and lower models, the total gas quantity can be optimally allocated to each gas generation unit and the gas quantity adjustment strategy and gas storage strategy of the gas production equipment can be obtained. The gas generated in the plant can be effectively used for power generation and heat supply, saving plant-level energy costs, achieving supply and demand balance and maximizing plant-level benefits.

[0058] Other features and advantages will be described in the following description, and partly become apparent from the description, or understood by practicing the invention. The purpose and other advantages of the invention are realized and obtained by the structures particularly pointed out in the description and the drawings.

[0059] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0061] Figure 1 This is a flow chart of a plant-level multi-unit load optimization distribution and control method based on unit characteristic differences of the present invention;

[0062] Figure 2 This is a schematic diagram of the plant-level multi-unit load optimization distribution and control structure of the present invention. DETAILED DESCRIPTION

[0063] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0064] Example 1

[0065] like Figure 1 , Figure 2 As shown, this embodiment 1 provides a plant-level multi-unit load optimization distribution and control method based on unit characteristic differences, which includes:

[0066] S1. Establish a plant-level digital twin model including a gas generation system, a gas storage system, and a gas energy supply system; the gas energy supply system includes multiple gas generator sets, each of which is composed of a boiler and a steam turbine. With gas as input, part of the steam produced by the boiler is sent to the steam turbine to generate power, and at the same time, part of the steam is extracted from the steam turbine to meet the heat demand;

[0067] S2. Establish a plant-level electricity load and heat load demand forecasting model to obtain the combination of electricity load and heat load demand in each time period, and establish a relationship model between the total power generation steam flow, total extraction steam heating flow and the combination of electricity load and heat load demand of the gas energy supply system to obtain the total value of the power generation steam flow and extraction steam heating flow required to be output by the gas energy supply system in each time period;

[0068] S3. According to the total value of the power generation steam flow and the extraction steam heating flow required to be output in each time period, combined with historical operation data, a total gas consumption prediction model is established to obtain the total gas consumption of the gas energy supply system in each time period; and a gas output prediction model of the gas generation system is established to obtain the predicted value of the gas output of the gas generation system in each time period, and a comprehensive analysis is performed with the total gas consumption of the gas energy system in each time period and the gas storage and release capacity of the gas storage system to determine the total value of the gas volume of the gas energy supply system in each time period;

[0069] S4. Conduct simulation experiments and analysis based on the plant-level digital twin model, establish load performance characteristic models for each boiler and steam turbine, and obtain the performance index differences of each gas-fired power generation unit under various gas consumption volumes;

[0070] S5. With the goal of achieving the best operating economy of all gas-fired power generation units, the lowest gas consumption rate for steam production, and the lowest steam consumption rate for power generation and heating, and based on the differences in performance indicators of each gas-fired power generation unit under each gas consumption volume, a gas volume optimization allocation model for the upper gas-fired power generation unit is established to optimally allocate the total gas volume of the gas energy supply system at each time period to each gas-fired power generation unit;

[0071] S6. With the goal of minimizing the gas production cost and storage and release cost, a lower-level gas production-storage and release optimization model is established to optimize the control of the gas generation system and the gas storage system, and obtain the gas volume adjustment strategy and gas storage and release strategy of the gas production equipment.

[0072] In this embodiment, in S1, a plant-level digital twin model including a gas generation system, a gas storage system, and a gas energy supply system is established, including:

[0073] Identify the physical entities within the plant, including the gas generation system, gas storage system, and gas energy supply system;

[0074] Establish the geometric model, physical model, behavior model and rule model of the physical entity in the factory, and after integrating the models, build a virtual entity mechanism model that is highly consistent with the actual physical entity in the factory;

[0075] Taking the virtual entity mechanism model as the main body, the residual of the virtual entity mechanism model is calculated using historical data. The input of the virtual entity mechanism model and the model residual result are preprocessed and used as the input and output of the machine learning network respectively. After training and learning, the digital twin data-driven model is obtained, and the residual of the virtual entity mechanism model is compensated and corrected.

[0076] The revised virtual entity mechanism model and the digital twin data-driven model are integrated to establish a plant-level digital twin model.

[0077] In actual applications, taking a steel plant as an example, according to different production processes, the gas generation system includes at least blast furnace gas equipment, coke oven gas equipment, and converter gas equipment; the gas storage system includes at least blast furnace gas storage cabinets, coke oven gas storage cabinets, and converter gas storage cabinets, which are used to store the excess gas produced by the corresponding gas equipment and release it during peak load periods. Using the gas energy supply system to utilize gas energy can not only maximize the recovery of gas resources and reduce the pollution of exhaust gas to the environment, but also improve the efficiency of power generation and heating.

[0078] Steel mills generally adopt the production process of "coking, sintering, ironmaking, steelmaking, and steel rolling". The coking process produces more than 20% of coke oven gas, the blast furnace ironmaking process produces nearly 30% of blast furnace gas, and the converter steelmaking process produces 5% of converter gas. Therefore, more than 50% of gas will be produced in the entire production process. Only a small part of these gases are used for the process itself, and there is still a large amount of surplus gas. These surplus gases are output through gas-fired generators including boilers and steam turbines to output electricity and heat energy to meet the electricity load and heat load requirements in the steel mill. If there is excess electricity, it can be sold online to obtain part of the electricity sales income, and gas-fired power generation reduces the steel mill's own electricity purchase and saves costs. In addition, according to the peak and valley periods of electricity load and heat load demand, the excess gas can be stored in the gas storage cabinet by adjusting the gas valve during the corresponding period. The gas is burned in the boiler, and the chemical energy released during the combustion process is utilized. In the process of heating the boiler, the chemical energy is converted into thermal energy. The water in the boiler is converted into steam after heating, and then the generator is driven by the steam turbine to achieve the purpose of power generation, and part of the steam is extracted from the steam turbine for heating.

[0079] In this embodiment, in S2, a relationship model between the total power generation steam flow, the total extraction steam heating flow and the combination of the electric load and the heat load demand of the gas energy supply system is established, including:

[0080] Obtain historical operating data under plant-level supply and demand balance operating conditions, including the historical total power generation steam flow, total steam extraction heating flow, historical electrical load, thermal load, and the power generation steam flow output by each steam turbine, power generation power, exhaust steam pressure, exhaust steam temperature, thermal efficiency, thermal power, equivalent enthalpy drop, heating extraction steam flow, heating extraction steam pressure, and heating extraction steam temperature;

[0081] The feature extraction algorithm is used to extract features from historical operation data, and key operation data that affect power generation steam flow and steam extraction heating flow are selected;

[0082] The historical operation data fitting method is used to establish the relationship model between the total power generation steam flow, total extraction steam heating flow and the combination of electric load and heat load demand required by the gas energy supply system, which is expressed as:

[0083] (m g,t ,m h,t )=g(P e,t ,Q h,t ,E t );

[0084] m g,t is the total power generation steam flow in period t; m h,t is the total steam extraction heating flow rate during period t; P e,tis the electric load demand during period t; Q h,t is the heat load demand during period t; E t is the key operating data that affects the steam flow rate for power generation and the steam extraction flow rate for heating; g( ) is the power generation and heating characteristic equation of the gas energy supply system.

[0085] In this embodiment, S3 includes:

[0086] Obtain the historical operation data of each boiler and each steam turbine, including the gas consumption, steam production, steam parameters, flue gas parameters, combustion efficiency of each boiler and the steam inlet flow, steam inlet pressure, steam inlet temperature, output power generation steam flow, steam extraction and heating flow of each steam turbine, as well as the total gas consumption of the gas energy supply system and the total values ​​of the system's output power generation steam flow and steam extraction and heating flow;

[0087] Based on the historical operation data of each boiler and each steam turbine, a prediction model for the total gas consumption of the gas energy supply system is established after training and learning using a machine learning algorithm.

[0088] The total value of the required output steam flow for power generation and steam extraction for heating in each time period is input into the established prediction model for the total coal gas consumption of the coal gas energy supply system to obtain the total coal gas consumption of the coal gas energy supply system in each time period;

[0089] Obtain the historical operation data of the gas generation system, establish a gas volume prediction model for the gas generation system, describe the relationship between gas production and operating parameters of related equipment, and then obtain the predicted value of gas volume output of the gas generation system in each future period;

[0090] When the predicted value of the gas output of the gas generation system in each period in the future is greater than the total gas consumption of the gas energy supply system, it means that the gas output is greater than the demand, and the excess gas is stored in the gas storage system to ensure that the total value of the gas volume in each period of the gas energy supply system is equal to the total gas consumption, or the excess electricity is sold online to ensure that the total value of the gas volume in each period of the gas energy supply system is equal to the predicted value of the gas output;

[0091] When the predicted gas output of the gas generation system in each future period is less than the total gas consumption of the gas supply system, it means that the gas output is less than the demand. The gas stored in the gas storage system is released to ensure that the total gas volume of the gas supply system in each period is equal to the predicted gas output value plus the released gas volume, or the total gas consumption is reduced by purchasing electricity online to ensure that the total gas volume of the gas supply system in each period is equal to the predicted gas output value.

[0092] It should be noted that the predicted gas output value V of the gas generation system in each period in the future t As the supply value of gas volume, the total gas consumption of the gas energy supply system Lt As the demand value of gas volume, when the supply value is greater than the demand value: V t >L t , you can choose to transfer the excess gas volume V t -L t Stored in the gas storage system, the total gas volume Z of the gas energy supply system at each time period coalgas_all,t =L t You can also choose to use the excess gas V t -L t The gas generator sets can generate more electricity and sell it online to obtain some revenue. At this time, the total gas volume of the gas energy system at each time period is Z coalgas_all,t =V t ;

[0093] When the supply value is less than the demand value: V t <L t , you can choose to release the gas stored in the gas storage system. At this time, the total gas volume of the gas energy supply system in each period is Z coalgas_all,t =V t +B t You can also choose to purchase part of the electricity online to reduce the power load demand at the plant level, thereby reducing the demand for gas. At this time, the total gas volume of the gas energy supply system at each time period is Z coalgas_all,t =V t .

[0094] In this embodiment, S4 includes:

[0095] Set boiler load performance indicators, including boiler efficiency, gas consumption rate, boiler steam parameters, boiler load adjustment range, boiler load adjustment speed, and boiler heat loss;

[0096] Set turbine load performance indicators, including steam consumption rate, power generation efficiency, heating efficiency, heating steam extraction capacity, turbine load adjustment range, turbine load adjustment flexibility, and overload capacity;

[0097] Calculate the weights of various load performance indicators of boilers and steam turbines that affect load optimization distribution;

[0098] A simulation experiment is carried out based on the plant-level digital twin model. The same gas consumption volume is set as the input condition of the experiment. At the same time, the consistency of gas quality, pressure, and temperature parameters is considered. The boilers and steam turbines are compared under the same external conditions. At the same time, the operating data of each boiler and steam turbine are recorded, and the load performance indicators of each boiler and steam turbine are calculated. Combined with the calculated weights of each load performance indicator, the differences in load performance indicators of each boiler and steam turbine under the same gas consumption volume are evaluated and analyzed.

[0099] It should be noted that in addition to setting the same gas consumption volume as the input condition of the experiment, it is also possible to set the gas consumption volume from one volume to another, such as reducing or increasing the gas consumption volume, monitor the changes in the operating parameters of each boiler and turbine, evaluate the relevant load performance indicators, and identify equipment or indicators with poor performance.

[0100] In this embodiment, the weight calculation method includes a subjective weighting method, an objective weighting method and a combined weighting method.

[0101] It should be noted that the subjective weighting method includes AHP hierarchical analysis method and FAHP fuzzy hierarchical analysis method; the objective weighting method includes entropy method, principal component analysis method and CRITIC weight method; the combined weighting method is a combination of subjective weighting method and objective weighting method.

[0102] In this embodiment, in S5, a gas quantity optimization distribution model for the upper gas generating set is established, including:

[0103] Based on the performance index differences of each gas-fired power generation unit under different gas consumption volumes, the total gas volume of the gas energy supply system in each period is defined as Z. coalgas_all,t , assume that n gas generating units have a preliminary gas distribution result in each period, which is defined as (S coalgas_x1,t ,S coalgas_x2,t ,…,S coalgas_xn,t );

[0104] The goal is to optimize the operating economy of all gas-fired generator sets, minimize the gas consumption rate for steam production, and minimize the steam consumption rate for power generation and heating, which can be expressed as:

[0105]

[0106] P e,i,t is the power generation of the i-th gas generator set in period t, is the self-use power generation; e P is the price of electricity purchased; c,i,t is the grid-connected power of the i-th gas-fired generator set in period t; c Q is the electricity price during the on-grid phase; h,i,t is the heat production of the i-th gas generator set in period t, is the self-use heat production; h P is the heat price for purchasing heat; f,buy,t is the amount of electricity purchased to meet the peak power load during period t; η f is the electricity purchase price; Z coalgas_all,t is the total gas consumption of the gas energy supply system; μ coalgas To absorb the gas price; S coalgas_xi,t D is the amount of gas consumed by the i-th gas-fired generator set in period t; i,t is the steam production of the i-th gas generator set in period t; Me,i,t is the steam inlet flow rate of the i-th gas generator set during period t; M h,i,t is the heating steam extraction flow rate of the i-th gas generator set during period t;

[0107] Set the operating constraints of the gas-fired power generation unit, including: turbine capacity constraints, extraction flow constraints, exhaust flow constraints, thermal load constraints, electrical load constraints, boiler capacity constraints, steam production constraints, and gas consumption constraints;

[0108] Based on the goals of optimal operating economy, minimum gas consumption rate for steam production and minimum steam consumption rate for power generation and heating of all gas-fired power generation units, and combined with the operating constraints of the gas-fired power generation units, an optimal gas allocation model for the upper-level gas-fired power generation units is established.

[0109] In this embodiment, S6 includes:

[0110] With the goal of minimizing the gas production cost and storage and release cost, a lower-layer gas production-storage and release optimization model is established, which can be expressed as:

[0111]

[0112] C s,t , C x,t They are the production and operation cost of the gas generation system and the gas storage cost of the gas storage system during period t;

[0113] Solve the lower-layer gas production-storage-release optimization model to obtain the gas volume adjustment strategy and gas storage-release strategy of the gas production equipment.

[0114] In this embodiment, the constraints of the lower-layer coal gas production-storage-release optimization model include: coal gas supply and demand balance constraints, coal gas generation system equipment production capacity constraints and coal gas storage system equipment capacity constraints.

[0115] In this embodiment, the upper gas power generation unit gas quantity optimization distribution model and the lower gas production-storage-release optimization model are solved by using an algorithm combining a genetic algorithm and a simulated annealing algorithm.

[0116] In practical applications, the algorithm solution steps using a combination of genetic algorithm and simulated annealing algorithm include:

[0117] 1) Initialize parameters and set genetic algorithm parameters, including population size, maximum number of genetic iterations, crossover probability and mutation probability, as well as simulated annealing algorithm parameters, including initial temperature, termination temperature and annealing coefficient;

[0118] 2) Select the initial population code, construct the initial population, and calculate the fitness value of each individual in the population;

[0119] 3) Perform genetic operations on the initial population at the initial temperature to obtain sub-populations and calculate the fitness value;

[0120] 4) Use the Metropolis mechanism of simulated annealing algorithm to perform chromosome selection operation and obtain suitable chromosomes to enter the next genetic iteration;

[0121] 5) Determine whether the genetic iteration has reached the maximum number of genetic iterations. If so, proceed to the next step; otherwise, go to step 3);

[0122] 6) Determine whether the termination temperature has been reached. If not, perform a cooling operation and go to step 3). Otherwise, the algorithm iteration ends and the optimal solution is output.

[0123] The Metropolis mechanism of the simulated annealing algorithm can get rid of the local optimal solution and introduce it into the genetic selection strategy of the genetic algorithm to select the new generation of population. The core of the simulated annealing algorithm lies in the Metropolis mechanism and the annealing process, which correspond to the inner loop and outer loop of the algorithm respectively.

[0124] It should be noted that the solution of the upper-level gas-fired power generation unit gas quantity optimization allocation model and the lower-level gas production-storage-release optimization model is carried out interactively and iteratively. The gas quantity regulation strategy and gas storage-release strategy of the gas production equipment output by the lower-level model affect the total gas quantity of the gas energy supply system, and further affect the gas quantity allocation results of each gas-fired power generation unit; conversely, the gas quantity allocation results of each gas-fired power generation unit output by the upper-level model affect the total gas quantity of the gas energy supply system, and further affect the gas quantity regulation strategy and gas storage-release strategy of the gas production equipment.

[0125] In several embodiments provided in the present application, it should be understood that the disclosed system and method can also be implemented in other ways. The system embodiments described above are merely schematic. For example, the flowcharts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the systems, methods and computer program products according to multiple embodiments of the present invention. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of the code, and a part of the module, program segment or 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 a different order from the order marked in the accompanying drawings. For example, two consecutive boxes 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 and / or flowchart, and the combination of boxes in the block diagram and / or flowchart can be implemented with a dedicated hardware-based system that performs a specified function or action, or can be implemented with a combination of dedicated hardware and computer instructions.

[0126] In addition, the functional modules in each embodiment of the present invention can be integrated together to form an independent part, or each module can exist separately, or two or more modules can be integrated to form an independent part. If the function is implemented in the form of a software functional module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, RandomAccess Memory), disk or optical disk and other media that can store program codes.

[0127] Based on the above ideal embodiments of the present invention, the relevant staff can make various changes and modifications without departing from the technical concept of the present invention through the above description. The technical scope of the present invention is not limited to the contents of the specification, and its technical scope must be determined according to the scope of the claims.

Claims

1. A plant-level multi-unit load optimization distribution and control method based on unit characteristic differences, characterized in that: It includes: S1. Establish a plant-level digital twin model including a gas generation system, a gas storage system, and a gas energy supply system; the gas energy supply system includes multiple gas generator sets, each of which is composed of a boiler and a steam turbine. With gas as input, part of the steam produced by the boiler is sent to the steam turbine to generate power, and at the same time, part of the steam is extracted from the steam turbine to meet the heat demand; S2. Establish a plant-level electricity load and heat load demand forecasting model to obtain the combination of electricity load and heat load demand in each time period, and establish a relationship model between the total power generation steam flow, total extraction steam heating flow and the combination of electricity load and heat load demand of the gas energy supply system to obtain the total value of the power generation steam flow and extraction steam heating flow required to be output by the gas energy supply system in each time period; S3. Based on the total value of the power generation steam flow and the extraction steam heating flow required to be output in each time period, combined with historical operation data, a total gas consumption prediction model is established to obtain the total gas consumption of the gas energy supply system in each time period; And establish a gas output prediction model for the gas generation system, obtain the predicted value of the gas output of the gas generation system in each period, and conduct a comprehensive analysis with the total gas consumption of the gas energy system in each period and the gas storage and release volume of the gas storage system to determine the total value of the gas volume of the gas energy system in each period; S4. Conduct simulation experiments and analysis based on the plant-level digital twin model, establish load performance characteristic models for each boiler and steam turbine, and obtain the performance index differences of each gas-fired power generation unit under various gas consumption volumes; S5. With the goal of achieving the best operating economy of all gas-fired power generation units, the lowest gas consumption rate for steam production, and the lowest steam consumption rate for power generation and heating, and based on the differences in performance indicators of each gas-fired power generation unit under each gas consumption volume, a gas volume optimization allocation model for the upper gas-fired power generation unit is established to optimally allocate the total gas volume of the gas energy supply system at each time period to each gas-fired power generation unit; S6. With the goal of minimizing the gas production cost and storage and release cost, a lower-level gas production-storage and release optimization model is established to optimize the control of the gas generation system and the gas storage system, and obtain the gas volume adjustment strategy and gas storage and release strategy of the gas production equipment.

2. The plant-level multi-unit load optimization distribution and control method according to claim 1 is characterized in that: In S1, a plant-level digital twin model including a gas generation system, a gas storage system, and a gas energy supply system is established, including: Identify the physical entities within the plant, including the gas generation system, gas storage system, and gas energy supply system; Establish the geometric model, physical model, behavior model and rule model of the physical entity in the factory, and after integrating the models, build a virtual entity mechanism model that is highly consistent with the actual physical entity in the factory; Taking the virtual entity mechanism model as the main body, the residual of the virtual entity mechanism model is calculated using historical data. The input of the virtual entity mechanism model and the model residual result are preprocessed and used as the input and output of the machine learning network respectively. After training and learning, the digital twin data-driven model is obtained, and the residual of the virtual entity mechanism model is compensated and corrected. The revised virtual entity mechanism model and the digital twin data-driven model are integrated to establish a plant-level digital twin model.

3. The plant-level multi-unit load optimization distribution and control method according to claim 1 is characterized in that: In S2, a relationship model between the total steam flow rate for power generation and total steam extraction flow rate for heating of the gas energy supply system and the combination of the electric load and the thermal load demand is established, including: Obtain historical operating data under plant-level supply and demand balance operating conditions, including the historical total power generation steam flow, total steam extraction heating flow, historical electrical load, thermal load, and the power generation steam flow output by each steam turbine, power generation power, exhaust steam pressure, exhaust steam temperature, thermal efficiency, thermal power, equivalent enthalpy drop, heating extraction steam flow, heating extraction steam pressure, and heating extraction steam temperature; The feature extraction algorithm is used to extract features from historical operation data, and key operation data that affect power generation steam flow and steam extraction heating flow are selected; The historical operation data fitting method is used to establish the relationship model between the total power generation steam flow, total extraction steam heating flow and the combination of electric load and heat load demand required by the gas energy supply system, which is expressed as: (m g,t ,m h,t )=g(P e,t ,Q h,t ,E t ); m g,t is the total power generation steam flow in period t; m h,t is the total steam extraction heating flow rate during period t; P e,t is the electric load demand during period t; Q h,t is the heat load demand during period t; E t It is the key operating data that affects the steam flow rate for power generation and the steam extraction flow rate for heating; g() is the power generation and heating characteristic equation of the gas energy supply system.

4. The plant-level multi-unit load optimization distribution and control method according to claim 1 is characterized in that: The S3 includes: Obtain the historical operation data of each boiler and each steam turbine, including the gas consumption, steam production, steam parameters, flue gas parameters, combustion efficiency of each boiler and the steam inlet flow, steam inlet pressure, steam inlet temperature, output power generation steam flow, steam extraction and heating flow of each steam turbine, as well as the total gas consumption of the gas energy supply system and the total values ​​of the system's output power generation steam flow and steam extraction and heating flow; Based on the historical operation data of each boiler and each steam turbine, a prediction model for the total gas consumption of the gas energy supply system is established after training and learning using a machine learning algorithm. The total value of the required output steam flow for power generation and steam extraction for heating in each time period is input into the established prediction model for the total coal gas consumption of the coal gas energy supply system to obtain the total coal gas consumption of the coal gas energy supply system in each time period; Obtain the historical operation data of the gas generation system, establish a gas volume prediction model for the gas generation system, describe the relationship between gas production and operating parameters of related equipment, and then obtain the predicted value of gas volume output of the gas generation system in each future period; When the predicted value of the gas output of the gas generation system in each period in the future is greater than the total gas consumption of the gas energy supply system, it means that the gas output is greater than the demand, and the excess gas is stored in the gas storage system to ensure that the total value of the gas volume in each period of the gas energy supply system is equal to the total gas consumption, or the excess electricity is sold online to ensure that the total value of the gas volume in each period of the gas energy supply system is equal to the predicted value of the gas output; When the predicted gas output of the gas generation system in each future period is less than the total gas consumption of the gas supply system, it means that the gas output is less than the demand. The gas stored in the gas storage system is released to ensure that the total gas volume of the gas supply system in each period is equal to the predicted gas output value plus the released gas volume, or the total gas consumption is reduced by purchasing electricity online to ensure that the total gas volume of the gas supply system in each period is equal to the predicted gas output value.

5. The plant-level multi-unit load optimization distribution and control method according to claim 1 is characterized in that: The S4 includes: Set boiler load performance indicators, including boiler efficiency, gas consumption rate, boiler steam parameters, boiler load adjustment range, boiler load adjustment speed, and boiler heat loss; Set turbine load performance indicators, including steam consumption rate, power generation efficiency, heating efficiency, heating steam extraction capacity, turbine load adjustment range, turbine load adjustment flexibility, and overload capacity; Calculate the weights of various load performance indicators of boilers and steam turbines that affect load optimization distribution; A simulation experiment is carried out based on the plant-level digital twin model. The same gas consumption volume is set as the input condition of the experiment. At the same time, the consistency of gas quality, pressure, and temperature parameters is considered. The boilers and steam turbines are compared under the same external conditions. At the same time, the operating data of each boiler and steam turbine are recorded, and the load performance indicators of each boiler and steam turbine are calculated. Combined with the calculated weights of each load performance indicator, the differences in load performance indicators of each boiler and steam turbine under the same gas consumption volume are evaluated and analyzed.

6. The plant-level multi-unit load optimization distribution and control method according to claim 5 is characterized in that: The weight calculation method includes subjective weighting method, objective weighting method and combined weighting method.

7. The plant-level multi-unit load optimization distribution and control method according to claim 1 is characterized in that: In S5, a gas quantity optimization distribution model for the upper gas generating set is established, including: Based on the performance index differences of each gas-fired power generation unit under different gas consumption volumes, the total gas volume of the gas energy supply system in each period is defined as Z. coalgas_all,t , assume that n gas generating units have a preliminary gas distribution result in each period, which is defined as (S coalgas_x1,t ,S coalgas_x2,t ,…,S coalgas_xn,t ); The goal is to optimize the operating economy of all gas-fired generator sets, minimize the gas consumption rate for steam production, and minimize the steam consumption rate for power generation and heating, which can be expressed as: P e,i,t is the power generation of the i-th gas generator set in period t, is the self-use power generation; e P is the price of electricity purchased; c,i,t is the grid-connected power of the i-th gas-fired generator set in period t; c Q is the electricity price during the on-grid phase; h,i,t is the heat production of the i-th gas generator set in period t, is the self-use heat production; h P is the heat price for purchasing heat; f,buy,t is the amount of electricity purchased to meet the peak power load during period t; η f is the electricity purchase price; Z coalgas_all,t The total gas consumption of the gas energy supply system; μ coalgas To absorb the gas price; S coalgas_xi,t D is the amount of gas consumed by the i-th gas-fired generator set in period t; i,t is the steam production of the i-th gas generator set in period t; M e,i,t is the steam inlet flow rate of the i-th gas generator set during period t; M h,i,t is the heating steam extraction flow rate of the i-th gas generator set during period t; Set the operating constraints of the gas-fired power generation unit, including: turbine capacity constraints, extraction flow constraints, exhaust flow constraints, thermal load constraints, electrical load constraints, boiler capacity constraints, steam production constraints, and gas consumption constraints; Based on the goals of optimal operating economy, minimum gas consumption rate for steam production and minimum steam consumption rate for power generation and heating of all gas-fired power generation units, and combined with the operating constraints of the gas-fired power generation units, an optimal gas allocation model for the upper-level gas-fired power generation units is established.

8. The plant-level multi-unit load optimization distribution and control method according to claim 1 is characterized in that: The S6 includes: With the goal of minimizing the gas production cost and storage and release cost, a lower-layer gas production-storage and release optimization model is established, which can be expressed as: C s,t , C x,t They are the production and operation cost of the gas generation system and the gas storage cost of the gas storage system during period t; Solve the lower-layer gas production-storage-release optimization model to obtain the gas volume adjustment strategy and gas storage-release strategy of the gas production equipment.

9. The plant-level multi-unit load optimization distribution and control method according to claim 8 is characterized in that: The constraints of the lower-layer gas production-storage-release optimization model include: gas supply and demand balance constraints, gas generation system equipment production capacity constraints and gas storage system equipment capacity constraints.

10. The plant-level multi-unit load optimization distribution and control method according to claim 1, characterized in that: The upper gas generator set gas quantity optimization distribution model and the lower gas production-storage-release optimization model are solved by using an algorithm combining a genetic algorithm and a simulated annealing algorithm.

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