Comprehensive energy system staged optimization scheduling method fusing industrial waste heat

Abstract

The invention discloses a staged optimization scheduling method for an integrated energy system fusing industrial waste heat. The method comprises the following steps: forming an integrated energy system which takes electric energy and industrial waste heat as input and outputs cold energy and heat energy; setting an energy flow mechanism of the system in a summer cold supply stage and a winter heat supply stage; the adjacent enterprises serve as industrial waste heat sellers, the comprehensive energy system serves as an industrial waste heat purchaser, and master-slave game transaction of industrial waste heat of the two parties is achieved; in the day-ahead scheduling process, with optimal system operation cost and energy utilization rate as targets, establishing a system summer cold supply day-ahead optimal scheduling model and a system winter heat supply day-ahead optimal scheduling model; in the intra-day scheduling process, industrial waste heat transaction deviation, green power prediction deviation of a photovoltaic generator set and demand deviation of cold load and heat load are considered, demand response of system users is combined, and a system summer cold supply intra-day optimal scheduling model and a system winter heat supply intra-day optimal scheduling model are established by taking the minimum adjustment cost as a target.

Description

Technical Field

[0001] The present invention belongs to the technical field of integrated energy systems, and in particular relates to a phased optimization scheduling method for an integrated energy system integrating industrial waste heat. Background Art

[0002] The integrated energy system is a new type of integrated energy system that integrates multiple energy sources such as coal, natural gas, electricity, heat, and cold energy. It achieves coordinated and optimized energy scheduling through advanced physical information technology and innovative management models. Generally, the demand for cooling load is higher in the summer cooling stage, and the demand for heating load is higher in the winter heating stage. However, with the reduction in the output and rising prices of traditional fossil energy, as well as the improvement of environmental protection requirements, it is necessary to consider using more economical, more environmentally friendly, and more energy-saving energy to meet the load demand.

[0003] There are many industrial enterprises near the integrated energy system, which will produce different types of industrial waste heat during the production process, which is redundant and waste energy that is not used. The recycling of industrial waste heat is one of the main ways for the integrated energy system to achieve energy conservation and emission reduction, improve energy utilization and economy. In addition, the waste heat resources produced in the production process of various industries of industrial enterprises are abundant, but the amount of industrial waste heat is limited by the operation status of production equipment, enterprise production plan, market factors, etc. Therefore, how to apply the industrial waste heat of neighboring enterprises to the integrated energy system across entities, conduct reasonable transactions of industrial waste heat, and comprehensively consider the instability of industrial waste heat and other new energy output, transaction prices, etc., to achieve the optimal scheduling of the integrated energy system is an urgent problem to be solved. Summary of the invention

[0004] The technical problem to be solved by the present invention is to overcome the shortcomings of the prior art and provide a phased optimization scheduling method for an integrated energy system integrating industrial waste heat, so as to effectively utilize the industrial waste heat of neighboring enterprises and realize summer cooling and winter heating of the integrated energy system.

[0005] In order to solve the above technical problems, the technical solution of the present invention is:

[0006] The present invention provides a phased optimization scheduling method for a comprehensive energy system integrating industrial waste heat, comprising:

[0007] S1. Install photovoltaic power generation units, electric refrigerators, absorption refrigerators, heat exchange equipment, cold storage devices, heat storage devices, power storage devices and industrial waste heat recovery units, and connect them to the municipal power grid to form a comprehensive energy system with electricity and industrial waste heat as input and cold and heat energy as output;

[0008] S2. Set up a phased energy flow mechanism for the integrated energy system, including:

[0009] Input various types of industrial waste heat generated by the production operation of the integrated energy system near the enterprise into the system's industrial waste heat recovery unit for recovery and treatment;

[0010] In the summer cooling stage, the integrated energy system uses photovoltaic generators and municipal power grids to obtain electricity, and uses electricity to drive electric refrigerators for cooling; at the same time, the industrial waste heat recovery unit is used to obtain a type of industrial waste heat as a heat source to drive absorption refrigerators for cooling; and the cold storage device is used to store and release cold, and the electricity storage device is used to store and release electricity;

[0011] During the winter heating stage, the integrated energy system uses the industrial waste heat recovery unit to obtain various types of industrial waste heat, and outputs heat after heat exchange through heat exchange equipment; at the same time, the heat storage device is used to store and release heat;

[0012] S3. As the seller of industrial waste heat, the neighboring enterprise predicts the amount of industrial waste heat generated during production and operation, and then reports the industrial waste heat supply and transaction price to the integrated energy system as the buyer of industrial waste heat. The integrated energy system then provides feedback based on the demand values ​​of the system users' cooling load and heating load, the green electricity forecast value of the photovoltaic power generation unit, and the seller's industrial waste heat declaration information, thus realizing the master-slave game transaction of industrial waste heat between the two parties;

[0013] S4. In the day-ahead dispatching process, based on the results of the industrial waste heat trading between the two parties, the integrated energy system takes the optimization of system operation cost and energy utilization rate as the goal, establishes the day-ahead optimization dispatching model for summer cooling and the day-ahead optimization dispatching model for winter heating, and obtains the operation output strategy of each device in the system in the summer cooling and winter heating stages during the day-ahead dispatching process;

[0014] S5. During the intraday scheduling process, the deviation of industrial waste heat trading, the green electricity prediction deviation of photovoltaic power generation units, the demand deviation of cooling load and heating load, and the demand response of system users are taken into consideration. With the goal of minimizing the adjustment cost, an optimal scheduling model for the system's summer cooling and an optimal scheduling model for the system's winter heating are established to obtain the operating output strategy and demand response strategy of each device in the system during the summer cooling and winter heating stages during the intraday scheduling process.

[0015] Further, in S1, the absorption refrigerator is a hot water absorption refrigerator; the heat exchange equipment includes a water-water heat exchanger and a steam-water heat exchanger;

[0016] The model of the hot water absorption chiller is expressed as:

[0017] L AC (t) = η AC Q AC (t);

[0018] LAC,min ≤L AC (t)≤L AC,max ;

[0019] L AC (t) is the cooling capacity generated by the hot water absorption chiller at time t; Q AC (t) is the equivalent heat absorbed by the hot water absorption chiller from industrial waste heat at time t; η AC is the refrigeration performance coefficient of the hot water absorption chiller; L AC,max , L AC,min They are the upper and lower limits of hot water absorption cooling capacity, respectively;

[0020] The model of the electric refrigerator is expressed as:

[0021] L EC (t) = η EC E EC (t);

[0022] L EC,min ≤L EC (t)≤L EC,max ;

[0023] L EC (t) is the cooling capacity generated by the electric refrigerator at time t; E EC 9t) is the amount of electricity consumed by the electric refrigerator at time t; η EC is the refrigeration performance coefficient of the electric refrigerator; L EC,max , L EC,min They are the upper and lower limits of the cooling capacity of the electric refrigerator respectively;

[0024] The model of the heat exchange equipment is expressed as:

[0025] W HE (t) = η HE S HE (t);

[0026] W HE (t) is the heat generated by the heat exchange equipment at time t; S HE (t) is the equivalent thermal power of industrial waste heat input at time t; η HE is the conversion efficiency of the heat exchange equipment.

[0027] Furthermore, in S2, the various types of industrial waste heat generated when the integrated energy system is in the vicinity of the enterprise's production operation are input into the system's industrial waste heat recovery unit for recovery and treatment, including:

[0028] Establish corresponding waste heat transmission pipelines between neighboring enterprises and the integrated energy system according to different types of industrial waste heat, so as to transmit different types of industrial waste heat to the industrial waste heat recovery unit of the system;

[0029] The industrial waste heat recovery unit monitors and adjusts the temperature, pressure and flow parameters of different types of industrial waste heat to meet the operating parameters of the absorption chiller for refrigeration and the heat exchange equipment for heat exchange.

[0030] Furthermore, in S2, in the summer cooling stage, green electricity output by the photovoltaic power generation group is preferentially used, and when green electricity is insufficient, electricity is purchased from the municipal power grid to drive the electric refrigerator for cooling; at the same time, the electric refrigerator is used as a cooling supplementary device to consume electric energy for cooling when the cooling capacity of the absorption refrigerator is insufficient; and, excess green electricity and / or low-priced electricity from the municipal power grid is stored in an electricity storage device for releasing electric energy during the peak period of cooling demand; when the cooling supply is greater than the demand, the excess cooling capacity is stored in the cooling storage device for releasing cooling during the peak period of cooling demand;

[0031] During the winter heating stage, the corresponding type of industrial waste heat is obtained from the industrial waste heat recovery unit according to the type of heat exchange equipment, and the heat is output after heat exchange through the corresponding type of heat exchange equipment. At the same time, when the heat supply is greater than the demand, the excess heat is stored in the heat storage device for heat release during the peak period of heat consumption.

[0032] Furthermore, in S3, the industrial waste heat generated during production operation is predicted, including:

[0033] Obtain relevant data that affects the amount of industrial waste heat from nearby enterprises, including historical operating parameters of production equipment, raw material input, product output, industrial waste heat type, waste heat temperature, waste heat flow, external weather conditions, changes in enterprise market demand, and historical waste heat;

[0034] Clustering algorithms are used to process data and divide the production operations of neighboring enterprises into multiple operating conditions;

[0035] The data set under each operating condition is subjected to feature extraction using a variety of feature extraction algorithms to form a variety of data subsets under each operating condition;

[0036] For each data subset under each operating condition, a variety of machine learning algorithms are used for training and learning, and the industrial waste heat prediction models corresponding to the corresponding algorithms are established. After the performance of each prediction model is evaluated, the industrial waste heat prediction model with the best performance under each operating condition is selected to obtain the industrial waste heat prediction values ​​under various operating conditions of nearby enterprises.

[0037] Furthermore, the clustering algorithm is a K-means clustering algorithm; the feature extraction algorithm includes a LASSO algorithm and a LightGBM algorithm; and the machine learning algorithm includes a CatBoost algorithm and a BPNN algorithm.

[0038] Further, the S3 includes:

[0039] As the seller of industrial waste heat, nearby enterprises use the pre-established industrial waste heat prediction model to predict the industrial waste heat generated by the enterprise the next day, and consider market factors, cost factors and the preliminary purchase price given by the integrated energy system to preliminarily formulate the transaction price of different types of industrial waste heat;

[0040] Neighboring enterprises report to the integrated energy system the supply and transaction prices of different types of industrial waste heat for the next day;

[0041] In the summer cooling stage: the integrated energy system uses the pre-established user cooling load forecast model to obtain the cooling load forecast value for the next day, and uses the pre-established photovoltaic power generation forecast model to obtain the photovoltaic green power forecast value for the next day; as the purchaser of industrial waste heat, the integrated energy system comprehensively considers the different types of industrial waste heat supply and transaction prices, cooling load forecast values, photovoltaic green power forecast values, and grid dynamic electricity price factors reported by neighboring enterprises, and feeds back different types of industrial waste heat purchase quantities and purchase prices to neighboring enterprises; neighboring enterprises then adjust and optimize the reported transaction information based on the feedback information, and after continuously executing the transaction process and adjusting and optimizing, they formulate the final different types of industrial waste heat transaction quantities and transaction prices;

[0042] In the summer heating stage: the integrated energy system uses the pre-established user heat load forecasting model to obtain the heat load forecast value for the next day, preliminarily calculates the required different types of industrial waste heat, and then comprehensively considers the different types of industrial waste heat supply and transaction prices reported by neighboring enterprises, and the different types of industrial waste heat demand, and combines the industrial waste heat supply and demand relationship to feedback the purchase volume and purchase price of different types of industrial waste heat to neighboring enterprises; the neighboring enterprises then adjust and optimize the declared transaction information based on the feedback information, and after continuously executing the transaction process and adjusting and optimizing, formulate the final different types of industrial waste heat transaction volumes and transaction prices;

[0043] Among them, the integrated energy system is taken as the leader and the neighboring enterprises as the followers. The master-slave game transaction model of both parties is established with the optimal economic indicators of both parties. Through multiple iterations, both parties continuously adjust their industrial waste heat trading volume and price information until reaching the Nash equilibrium point acceptable to both parties, obtaining the optimal industrial waste heat trading volume and trading price strategy, and formulating the trading method.

[0044] Furthermore, in S4, taking the optimization of system operation cost and energy utilization as the goal, a day-ahead optimization scheduling model for summer cooling is established, which is expressed as:

[0045]

[0046] C h,buy,tCost of purchasing industrial waste heat for summer cooling; C e,buy,t is the cost of purchasing electricity from the municipal power grid; C AC,t is the operating cost of the absorption chiller; C EC,t is the operating cost of the electric refrigerator; C PV,t is the operating cost of the photovoltaic generator set; C s,t is the energy storage and discharge operation cost of the power storage device and the cold storage device; T is the dispatching period; L c,t is the cooling load produced by the system; E h,e,t The amount of energy consumed to cool the system, including industrial waste heat and electricity consumed for cooling;

[0047] The above-mentioned system winter heating day optimization scheduling model is established with the goal of optimizing the system operation cost and energy utilization rate, which is expressed as:

[0048]

[0049] C′ h,buy,t Cost of purchasing industrial waste heat for winter heating; C HE,t is the operating cost of the heat exchange equipment; L h,t is the heat load produced by the system; E h,t Industrial waste heat consumed for system heat generation;

[0050] The constraints of the day-ahead optimization scheduling model for summer cooling of the system include: operating constraints of absorption chillers, operating constraints of electric chillers, operating constraints of photovoltaic generators, operating constraints of power storage devices, operating constraints of cold storage devices and cold power balance constraints;

[0051] The operation output strategies of the various equipment in the system output by the optimization scheduling model of the summer cooling system before the day include: the cooling capacity output by the absorption chiller, the cooling capacity output by the electric chiller, the power generation of the photovoltaic generator set, the power purchase of the municipal power grid, the energy storage and discharge state and the energy storage and discharge of the power storage device and the cold storage device;

[0052] The constraints of the optimal scheduling model for winter heating of the system include: operating constraints of heat exchange equipment, operating constraints of heat storage device and thermal power balance constraints;

[0053] The operation output strategies of various devices in the system output by the day-ahead optimization scheduling model of the winter heating system include: the heat output by the heat exchange equipment, the heat storage and release state of the heat storage device, and the amount of heat stored and released.

[0054] Furthermore, in S5, establishing a system summer cooling daytime optimization scheduling model specifically includes:

[0055] The pre-established industrial waste heat prediction model is used to obtain the intraday prediction value of industrial waste heat, the pre-established user cold load prediction model is used to obtain the intraday prediction value of cold load, and the pre-established photovoltaic power generation prediction model is used to obtain the intraday prediction value of photovoltaic power generation. After comparing the corresponding prediction value and actual value with the actual value of industrial waste heat, the actual value of cold load and photovoltaic power generation, the industrial waste heat trading deviation, the green power prediction deviation of the photovoltaic power generation unit and the demand deviation of the cold load are analyzed and obtained; when the comprehensive deviation of the industrial waste heat trading deviation, the green power prediction deviation of the photovoltaic power generation unit and the demand deviation of the cold load is greater than the set first deviation interval range, the user side cold load demand response adjustment and equipment operation output adjustment are carried out to compensate for the deviation; with the goal of minimizing the adjustment cost, the system summer cooling daytime optimization scheduling model is established, which is expressed as:

[0056]

[0057] C i,t is the operating output adjustment cost of the ith equipment during summer cooling; C c,DR,t is the cooling load demand response adjustment cost on the user side during summer cooling; M is the total number of absorption chillers, electric chillers, photovoltaic generators, power storage devices and cold storage devices;

[0058] The establishment of the system winter heating daily optimization scheduling model specifically includes:

[0059] During the intraday scheduling process of the system in the winter heating stage: the intraday forecast value of industrial waste heat is obtained by using the pre-established industrial waste heat forecast model, and the intraday forecast value of heat load is obtained by using the pre-established user heat load forecast model, and the corresponding forecast value and actual value are compared with the actual value of industrial waste heat and the actual value of heat load, respectively, and the industrial waste heat transaction deviation and heat load demand deviation are analyzed; when the combined deviation of the industrial waste heat transaction deviation and the heat load demand deviation is greater than the set second deviation interval, the user side heat load demand response adjustment and equipment operation output adjustment are carried out to compensate for the deviation; with the goal of minimizing the adjustment cost, the system winter heating intraday optimization scheduling model is established, which is expressed as:

[0060]

[0061] C j,t is the operating output adjustment cost of the jth device during winter heating; C h,DR,t It is the heat load demand response adjustment cost on the user side during winter heating; N is the total number of heat exchange equipment.

[0062] Furthermore, the Pelican optimization algorithm is used to solve the system's summer cooling day-ahead optimization scheduling model, the system's winter heating day-ahead optimization scheduling model, the system's summer cooling intraday optimization scheduling model, and the system's winter heating intraday optimization scheduling model.

[0063] The beneficial effects of the present invention are:

[0064] On the one hand, the present invention can utilize different types of industrial waste heat from neighboring enterprises to meet the cooling load demand of the integrated energy system in the summer cooling stage and the heating load demand in the winter heating stage, thereby reducing the consumption of fossil energy, improving the utilization rate of industrial waste heat and enhancing the economy of neighboring enterprises and the integrated energy system; on the other hand, considering the transaction of industrial waste heat between neighboring enterprises and the integrated energy system, a master-slave game model is established to realize the master-slave game transaction of industrial waste heat between the two parties, thereby ensuring the scientific rationality of the transaction price and transaction volume of industrial waste heat; and, with the goal of optimizing the system operating cost and energy utilization rate, a system summer The day-ahead optimization scheduling model for seasonal cooling and the day-ahead optimization scheduling model for winter heating are used to obtain the day-ahead scheduling strategy. Various data deviations and load demand responses are taken into consideration. With the goal of minimizing the adjustment cost, the day-ahead optimization scheduling model for summer cooling and the day-ahead optimization scheduling model for winter heating are established to obtain the intraday scheduling strategy, which can improve the accuracy of the scheduling strategy. At the same time, the instability of industrial waste heat, green electricity of photovoltaic power generation units and load demand in the system is modeled, predicted and analyzed comprehensively for deviations, so as to realize the coordinated scheduling of various equipment and resources in the system and ensure the economy, environmental protection and supply-demand balance of the comprehensive energy system.

[0065] 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.

[0066] 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

[0067] 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.

[0068] Figure 1 This is a flow chart of a phased optimization scheduling method for a comprehensive energy system integrating industrial waste heat according to the present invention;

[0069] Figure 2 This is a schematic diagram of the structure of the comprehensive energy system integrating industrial waste heat according to the present invention. DETAILED DESCRIPTION

[0070] 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.

[0071] Example 1

[0072] like Figure 1 , Figure 2 As shown, this embodiment 1 provides a phased optimization scheduling method for a comprehensive energy system integrating industrial waste heat, including:

[0073] S1. Install photovoltaic power generation units, electric refrigerators, absorption refrigerators, heat exchange equipment, cold storage devices, heat storage devices, power storage devices and industrial waste heat recovery units, and connect them to the municipal power grid to form a comprehensive energy system with electricity and industrial waste heat as input and cold and heat energy as output;

[0074] S2. Set up a phased energy flow mechanism for the integrated energy system, including:

[0075] Input various types of industrial waste heat generated by the production operation of the integrated energy system near the enterprise into the system's industrial waste heat recovery unit for recovery and treatment;

[0076] In the summer cooling stage, the integrated energy system uses photovoltaic generators and municipal power grids to obtain electricity, and uses electricity to drive electric refrigerators for cooling; at the same time, the industrial waste heat recovery unit is used to obtain a type of industrial waste heat as a heat source to drive absorption refrigerators for cooling; and the cold storage device is used to store and release cold, and the electricity storage device is used to store and release electricity;

[0077] During the winter heating stage, the integrated energy system uses the industrial waste heat recovery unit to obtain various types of industrial waste heat, and outputs heat after heat exchange through heat exchange equipment; at the same time, the heat storage device is used to store and release heat;

[0078] S3. As the seller of industrial waste heat, the neighboring enterprise predicts the amount of industrial waste heat generated during production and operation, and then reports the industrial waste heat supply and transaction price to the integrated energy system as the buyer of industrial waste heat. The integrated energy system then provides feedback based on the demand values ​​of the system users' cooling load and heating load, the green electricity forecast value of the photovoltaic power generation unit, and the seller's industrial waste heat declaration information, thus realizing the master-slave game transaction of industrial waste heat between the two parties;

[0079] S4. In the day-ahead dispatching process, based on the results of the industrial waste heat trading between the two parties, the integrated energy system takes the optimization of system operation cost and energy utilization rate as the goal, establishes the day-ahead optimization dispatching model for summer cooling and the day-ahead optimization dispatching model for winter heating, and obtains the operation output strategy of each device in the system in the summer cooling and winter heating stages during the day-ahead dispatching process;

[0080] S5. During the intraday scheduling process, the deviation of industrial waste heat trading, the green electricity prediction deviation of photovoltaic power generation units, the demand deviation of cooling load and heating load, and the demand response of system users are taken into consideration. With the goal of minimizing the adjustment cost, an optimal scheduling model for the system's summer cooling and an optimal scheduling model for the system's winter heating are established to obtain the operating output strategy and demand response strategy of each device in the system during the summer cooling and winter heating stages during the intraday scheduling process.

[0081] In this embodiment, in S1, the absorption refrigerator is a hot water absorption refrigerator; the heat exchange equipment includes a water-water heat exchanger and a steam-water heat exchanger;

[0082] The model of the hot water absorption chiller is expressed as:

[0083] L AC (t) = η AC Q AC (t);

[0084] L AC,min ≤L AC (t)≤L AC,max ;

[0085] L AC (t) is the cooling capacity generated by the hot water absorption chiller at time t; Q AC 9t) is the equivalent heat absorbed by the hot water absorption refrigerator from industrial waste heat at time t; η AC is the refrigeration performance coefficient of the hot water absorption chiller; L AC,max , L AC,min They are the upper and lower limits of hot water absorption cooling capacity, respectively;

[0086] The model of the electric refrigerator is expressed as:

[0087] L EC (t) = η EC E EC (t);

[0088] L EC,min ≤L EC (t)≤L EC,max ;

[0089] LEC (t) is the cooling capacity generated by the electric refrigerator at time t; E EC 9t) is the amount of electricity consumed by the electric refrigerator at time t; η EC is the refrigeration performance coefficient of the electric refrigerator; L EC,max , L EC,min They are the upper and lower limits of the cooling capacity of the electric refrigerator respectively;

[0090] The model of the heat exchange equipment is expressed as:

[0091] W HE (t) = η HE S HE (t);

[0092] W HE (t) is the heat generated by the heat exchange equipment at time t; S HE 9t) is the equivalent thermal power of industrial waste heat input at time t; η HE is the conversion efficiency of the heat exchange equipment.

[0093] It should be noted that electric refrigerators are auxiliary cooling equipment in the integrated energy system. When the absorption refrigerator driven by industrial waste heat cannot meet the cold energy demand, the electric refrigerator can meet the additional cooling demand in the system. The working principle of the electric refrigerator is that the compressor is driven by electric energy, so that the internal refrigerant is liquefied and releases heat, and then the heat is transferred by evaporation and heat absorption. As the main component of industrial waste heat utilization, the absorption refrigerator is also a key equipment for the cascade utilization of system energy. The working fluid of the absorption refrigerator is generally lithium bromide, and the hot water type lithium bromide absorption refrigerator is usually the most widely used. The heat source of the absorption refrigerator is the industrial waste heat generated during the production and operation of the nearby enterprises, and the cooling is carried out through the physical property changes of the internal working fluid. The nearby enterprises refer to industrial production enterprises, such as steel, chemical, building materials and other enterprises, which are the main sources of industrial waste heat. The heat sources of industrial waste heat are various, such as steam, flue gas, hot water, etc.

[0094] In the present application, the number of heat exchange equipment is at least two, and they are two types of heat exchangers, which can utilize different types of industrial waste heat. In addition, the number of electric refrigerators is also multiple, which can fully guarantee the additional supply of cold capacity when the industrial waste heat is insufficient; the number of photovoltaic power generation units is multiple, which can provide more green electricity for the integrated energy system, meet the daily electricity needs of users, and reduce the cost and carbon emissions of purchasing electricity from the municipal power grid. In general, through the phased energy management and utilization of summer cooling and winter heating, the cascade utilization of industrial waste heat and the reasonable allocation of electric energy can be achieved, forming an integrated energy system that uses electric energy and industrial waste heat as input and outputs cold capacity in the summer cooling stage, and forms an integrated energy system that uses industrial waste heat as input and outputs heat in the winter heating stage.

[0095] The steam-water heat exchanger can exchange the heat contained in the steam into hot water, and the waste heat can be converted between different media through the heat exchanger; the water-water heat exchanger is a device that exchanges heat between two water flows of different temperatures; of course, depending on the type of industrial waste heat, it can also be a flue gas-water heat exchanger, which exchanges the heat contained in the high-temperature flue gas into hot water.

[0096] The model of the photovoltaic generator set is expressed as:

[0097]

[0098] P pv is the output power of the photovoltaic generator set; P pv,s is the rated power of the photovoltaic generator set; G C is the actual radiation intensity of the light; G STC is the light radiation intensity under standard conditions; k is the power temperature coefficient; T c is the actual temperature of the photovoltaic panel; T STC is the temperature of the photovoltaic panel under standard conditions;

[0099] The models of heat storage devices, electricity storage devices, and cold storage devices are similar, except for the different energy types. Taking the cold storage device as an example, the model of the cold storage device is expressed as:

[0100]

[0101] C CESS 9t) is the cold storage energy after the cold storage device stores and releases cold; C CESS (t-1) is the cold storage capacity at the end of the period (t-1); δ c is the loss rate of the cold storage device; are the conversion efficiency of the released cold and stored cold of the cold storage device respectively; is the input cooling power of the cold storage device during period t; is the output cooling power of the cold storage device in period t; Δt is the unit scheduling time; They are respectively the upper and lower limits of the cold storage device.

[0102] In this embodiment, in S2, the various types of industrial waste heat generated by the integrated energy system when the enterprise is in production and operation are input into the industrial waste heat recovery unit of the system for recovery and treatment, including:

[0103] Establish corresponding waste heat transmission pipelines between neighboring enterprises and the integrated energy system according to different types of industrial waste heat, so as to transmit different types of industrial waste heat to the industrial waste heat recovery unit of the system;

[0104] The industrial waste heat recovery unit monitors and adjusts the temperature, pressure and flow parameters of different types of industrial waste heat to meet the operating parameters of the absorption chiller for refrigeration and the heat exchange equipment for heat exchange.

[0105] In this embodiment, in S2, in the summer cooling stage, the green electricity output by the photovoltaic power generation group is preferentially used, and when the green electricity is insufficient, electricity is purchased from the municipal power grid to drive the electric refrigerator for cooling; at the same time, the electric refrigerator is used as a cooling supplementary device to consume electric energy for cooling when the cooling capacity of the absorption refrigerator is insufficient; and the excess green electricity and / or the low-priced electricity of the municipal power grid is stored in the power storage device for releasing electric energy during the peak period of cooling demand; when the cooling supply is greater than the demand, the excess cooling capacity is stored in the cooling storage device for releasing cooling during the peak period of cooling demand;

[0106] During the winter heating stage, the corresponding type of industrial waste heat is obtained from the industrial waste heat recovery unit according to the type of heat exchange equipment, and the heat is output after heat exchange through the corresponding type of heat exchange equipment. At the same time, when the heat supply is greater than the demand, the excess heat is stored in the heat storage device for heat release during the peak period of heat consumption.

[0107] In this embodiment, in S3, the industrial waste heat generated during production operation is predicted, including:

[0108] Obtain relevant data that affects the amount of industrial waste heat from nearby enterprises, including historical operating parameters of production equipment, raw material input, product output, industrial waste heat type, waste heat temperature, waste heat flow, external weather conditions, changes in enterprise market demand, and historical waste heat;

[0109] Clustering algorithms are used to process data and divide the production operations of neighboring enterprises into multiple operating conditions;

[0110] The data set under each operating condition is subjected to feature extraction using a variety of feature extraction algorithms to form a variety of data subsets under each operating condition;

[0111] For each data subset under each operating condition, a variety of machine learning algorithms are used for training and learning, and the industrial waste heat prediction models corresponding to the corresponding algorithms are established. After the performance of each prediction model is evaluated, the industrial waste heat prediction model with the best performance under each operating condition is selected to obtain the industrial waste heat prediction values ​​under various operating conditions of nearby enterprises.

[0112] In actual applications, different types of industrial waste heat in neighboring enterprises are affected by many factors. Some industrial enterprises are in production and operation for 24 hours, but at night, during holidays, production scheduling, and equipment maintenance, the production operation time and output will be reduced, or even short-term shutdown maintenance will be required. These will cause the industrial waste heat to be unstable during a certain period of time. In addition, the production and operation of different enterprises are also related to the scale of the enterprise, production process, market conditions, scheduling plans, and the off-season and peak season of the enterprise's output, which will directly lead to the output of industrial waste heat. In order to consider different types of industrial waste heat in neighboring enterprises, the production and operation of the enterprise can be divided into multiple operating conditions. Each operating condition represents the actual production and operation of the enterprise and the output of industrial waste heat. There will be relevant differences in the factors affecting the industrial waste heat under each actual operating condition.

[0113] In this embodiment, the clustering algorithm is a K-means clustering algorithm; the feature extraction algorithm includes a LASSO algorithm and a LightGBM algorithm; and the machine learning algorithm includes a CatBoost algorithm and a BPNN algorithm.

[0114] For the data sets under each operating condition, the data sets are divided into data set 1 and data set 2. The LASSO algorithm is used for data set 1 and the LightGBM algorithm is used for data set 2. The data features are screened and the data with higher importance are selected for prediction to reduce the data dimension and improve the accuracy of model prediction. Then the CatBoost algorithm and BPNN algorithm are used for training and learning for data set 1, and the CatBoost algorithm and BPNN algorithm are used for training and learning for data set 2 to establish industrial waste heat prediction models corresponding to various algorithms under the corresponding data sets. Finally, the root mean square error of the prediction model is evaluated, and the optimal prediction model under each operating condition is selected to predict the industrial waste heat.

[0115] In this embodiment, S3 includes:

[0116] As the seller of industrial waste heat, nearby enterprises use the pre-established industrial waste heat prediction model to predict the industrial waste heat generated by the enterprise the next day, and consider market factors, cost factors and the preliminary purchase price given by the integrated energy system to preliminarily formulate the transaction price of different types of industrial waste heat;

[0117] Neighboring enterprises report to the integrated energy system the supply and transaction prices of different types of industrial waste heat for the next day;

[0118] In the summer cooling stage: the integrated energy system uses the pre-established user cooling load forecast model to obtain the cooling load forecast value for the next day, and uses the pre-established photovoltaic power generation forecast model to obtain the photovoltaic green power forecast value for the next day; as the purchaser of industrial waste heat, the integrated energy system comprehensively considers the different types of industrial waste heat supply and transaction prices, cooling load forecast values, photovoltaic green power forecast values, and grid dynamic electricity price factors reported by neighboring enterprises, and feeds back different types of industrial waste heat purchase quantities and purchase prices to neighboring enterprises; neighboring enterprises then adjust and optimize the reported transaction information based on the feedback information, and after continuously executing the transaction process and adjusting and optimizing, they formulate the final different types of industrial waste heat transaction quantities and transaction prices;

[0119] In the summer heating stage: the integrated energy system uses the pre-established user heat load forecasting model to obtain the heat load forecast value for the next day, preliminarily calculates the required different types of industrial waste heat, and then comprehensively considers the different types of industrial waste heat supply and transaction prices reported by neighboring enterprises, and the different types of industrial waste heat demand, and combines the industrial waste heat supply and demand relationship to feedback the purchase volume and purchase price of different types of industrial waste heat to neighboring enterprises; the neighboring enterprises then adjust and optimize the declared transaction information based on the feedback information, and after continuously executing the transaction process and adjusting and optimizing, formulate the final different types of industrial waste heat transaction volumes and transaction prices;

[0120] Among them, the integrated energy system is taken as the leader and the neighboring enterprises as the followers. The master-slave game transaction model of both parties is established with the optimal economic indicators of both parties. Through multiple iterations, both parties continuously adjust their industrial waste heat trading volume and price information until reaching the Nash equilibrium point acceptable to both parties, obtaining the optimal industrial waste heat trading volume and trading price strategy, and formulating the trading method.

[0121] In practical applications, the transaction process between the integrated energy system and neighboring enterprises can be regarded as a master-slave game process. The upper leader of the master-slave game is the integrated energy system, and the lower followers are the neighboring enterprises. The transaction information given by the integrated energy system will affect the industrial waste heat supply of neighboring enterprises, and the industrial waste heat transaction information reported by neighboring enterprises will affect the decision of the integrated energy system to purchase industrial waste heat. The two will eventually reach an equilibrium solution in the game process.

[0122] As a leader, the integrated energy system mainly considers the following factors:

[0123] 1) Demand-driven: The integrated energy system is responsible for providing cooling and heating load services to users, so its demand for industrial waste heat is the dominant factor. The integrated energy system can decide how much industrial waste heat is needed based on user demand, the availability of other energy sources (such as photovoltaics, grid electricity, etc.), and the status of the energy storage system;

[0124] 2) Market influence: An integrated energy system usually has a large market influence, which can affect the energy supply pattern of the entire region. Therefore, it has the ability to set a certain price range and transaction conditions;

[0125] 3) Multi-energy synergy: The integrated energy system needs to coordinate multiple energy sources (such as industrial waste heat, photovoltaic power generation, grid power, etc.), so it needs a global perspective to optimize energy configuration to ensure the economy and stability of system operation.

[0126] As followers, neighboring enterprises mainly consider the following factors:

[0127] 1) Supply response: The neighboring enterprises are mainly responsible for providing industrial waste heat. They need to adjust their supply strategies according to the needs of the integrated energy system and the proposed transaction conditions;

[0128] 2) Cost and benefit: The neighboring enterprise needs to consider the waste heat cost generated in its own production process and the benefits it can obtain through the transaction. It will decide whether to accept the transaction and how much waste heat to provide based on the quotation of the integrated energy system;

[0129] 3) Flexibility: Neighboring companies usually have some flexibility to adjust production plans, thereby affecting the amount of waste heat generated. However, such adjustments need to be made without affecting normal production.

[0130] In the master-slave game trading model, the objective function of the upper integrated energy system is to minimize operating costs, including the cost of purchasing electricity from the municipal power grid, the cost of purchasing industrial waste heat, and the operating costs of various equipment; the objective function of the lower adjacent enterprises is to maximize profits, that is, the income obtained from selling industrial waste heat minus the collection and processing costs of related industrial waste heat. Through the master-slave game trading model, the integrated energy system and adjacent enterprises can jointly optimize the trading process of industrial waste heat and achieve a win-win situation.

[0131] The master-slave game transaction model is expressed as:

[0132] G={AE,IES,ρ price,t ,L wh,t ,F AE ,F IES};

[0133] AE and IES are participants in the master-slave game, close to enterprises and integrated energy systems; ρ price,t is the industrial waste heat transaction price at time t; L wh,t is the industrial waste heat trading volume at time t; F AE 、F IES They are the objective functions of neighboring enterprises and the objective function of the integrated energy system in the master-slave game respectively;

[0134] In the master-slave game transaction model, if the solutions and equilibrium solutions of the upper and lower models are (ρ price,t ,L wh,t ), (ρ * price,t ,L * wh,t ), the game reaches equilibrium when the following conditions are met at the same time:

[0135]

[0136] In this embodiment, in S4, with the goal of optimizing the system operating cost and energy utilization rate, a day-ahead optimization scheduling model for summer cooling is established, which is expressed as:

[0137]

[0138] C h,buy,t Cost of purchasing industrial waste heat for summer cooling; C e,buy,t is the cost of purchasing electricity from the municipal power grid; C AC,t is the operating cost of the absorption chiller; C EC,t is the operating cost of the electric refrigerator; C PV,t is the operating cost of the photovoltaic generator set; C s,t is the energy storage and discharge operation cost of the power storage device and the cold storage device; T is the dispatching period; L c,t is the cooling load produced by the system; E h,e,t The amount of energy consumed to cool the system, including industrial waste heat and electricity consumed for cooling;

[0139] The above-mentioned system winter heating day optimization scheduling model is established with the goal of optimizing the system operation cost and energy utilization rate, which is expressed as:

[0140]

[0141] C′ h,buy,t Cost of purchasing industrial waste heat for winter heating; C HE,t is the operating cost of the heat exchange equipment; L h,t is the heat load produced by the system; E h,t Industrial waste heat consumed for system heat generation;

[0142] The constraints of the day-ahead optimization scheduling model for summer cooling of the system include: operating constraints of absorption chillers, operating constraints of electric chillers, operating constraints of photovoltaic generators, operating constraints of power storage devices, operating constraints of cold storage devices and cold power balance constraints;

[0143] The operation output strategies of the various equipment in the system output by the optimization scheduling model of the summer cooling system before the day include: the cooling capacity output by the absorption chiller, the cooling capacity output by the electric chiller, the power generation of the photovoltaic generator set, the power purchase of the municipal power grid, the energy storage and discharge state and the energy storage and discharge of the power storage device and the cold storage device;

[0144] The constraints of the optimal scheduling model for winter heating of the system include: operating constraints of heat exchange equipment, operating constraints of heat storage device and thermal power balance constraints;

[0145] The operation output strategies of various devices in the system output by the day-ahead optimization scheduling model of the winter heating system include: the heat output by the heat exchange equipment, the heat storage and release state of the heat storage device, and the amount of heat stored and released.

[0146] In this embodiment, in S5, establishing a system summer cooling daytime optimization scheduling model specifically includes:

[0147] The pre-established industrial waste heat prediction model is used to obtain the intraday prediction value of industrial waste heat, the pre-established user cold load prediction model is used to obtain the intraday prediction value of cold load, and the pre-established photovoltaic power generation prediction model is used to obtain the intraday prediction value of photovoltaic power generation. After comparing the corresponding prediction value and actual value with the actual value of industrial waste heat, the actual value of cold load and photovoltaic power generation, the industrial waste heat trading deviation, the green power prediction deviation of the photovoltaic power generation unit and the demand deviation of the cold load are analyzed and obtained; when the comprehensive deviation of the industrial waste heat trading deviation, the green power prediction deviation of the photovoltaic power generation unit and the demand deviation of the cold load is greater than the set first deviation interval range, the user side cold load demand response adjustment and equipment operation output adjustment are carried out to compensate for the deviation; with the goal of minimizing the adjustment cost, the system summer cooling daytime optimization scheduling model is established, which is expressed as:

[0148]

[0149] C i,t is the operating output adjustment cost of the ith equipment during summer cooling; C c,DR,t is the cooling load demand response adjustment cost on the user side during summer cooling; M is the total number of absorption chillers, electric chillers, photovoltaic generators, power storage devices and cold storage devices;

[0150] The establishment of the system winter heating daily optimization scheduling model specifically includes:

[0151] During the intraday scheduling process of the system in the winter heating stage: the intraday forecast value of industrial waste heat is obtained by using the pre-established industrial waste heat forecast model, and the intraday forecast value of heat load is obtained by using the pre-established user heat load forecast model, and the corresponding forecast value and actual value are compared with the actual value of industrial waste heat and the actual value of heat load, respectively, and the industrial waste heat transaction deviation and heat load demand deviation are analyzed; when the combined deviation of the industrial waste heat transaction deviation and the heat load demand deviation is greater than the set second deviation interval, the user side heat load demand response adjustment and equipment operation output adjustment are carried out to compensate for the deviation; with the goal of minimizing the adjustment cost, the system winter heating intraday optimization scheduling model is established, which is expressed as:

[0152]

[0153] C j,t is the operating output adjustment cost of the jth device during winter heating; C h,DR,t It is the heat load demand response adjustment cost on the user side during winter heating; N is the total number of heat exchange equipment.

[0154] In this embodiment, the Pelican optimization algorithm is used to solve the system summer cooling day-ahead optimization scheduling model, the system winter heating day-ahead optimization scheduling model, the system summer cooling intraday optimization scheduling model and the system winter heating intraday optimization scheduling model.

[0155] 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.

[0156] 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 is essentially 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, and the computer software product is stored in a storage medium, including a number of 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, random access memory, disk or optical disk and other media that can store program codes.

[0157] 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 phased optimization scheduling method for an integrated energy system integrating industrial waste heat, characterized in that: include: S1. Install photovoltaic power generation units, electric refrigerators, absorption refrigerators, heat exchange equipment, cold storage devices, heat storage devices, power storage devices and industrial waste heat recovery units, and connect them to the municipal power grid to form a comprehensive energy system with electricity and industrial waste heat as input and cold and heat energy as output; S2. Set up a phased energy flow mechanism for the integrated energy system, including: Input various types of industrial waste heat generated by the production operation of the integrated energy system near the enterprise into the system's industrial waste heat recovery unit for recovery and treatment; In the summer cooling stage, the integrated energy system uses photovoltaic generators and municipal power grids to obtain electricity, and uses electricity to drive electric refrigerators for cooling; at the same time, the industrial waste heat recovery unit is used to obtain a type of industrial waste heat as a heat source to drive absorption refrigerators for cooling; and the cold storage device is used to store and release cold, and the electricity storage device is used to store and release electricity; During the winter heating stage, the integrated energy system uses the industrial waste heat recovery unit to obtain various types of industrial waste heat, and outputs heat after heat exchange through heat exchange equipment; at the same time, the heat storage device is used to store and release heat; S3. As the seller of industrial waste heat, the neighboring enterprise predicts the amount of industrial waste heat generated during production and operation, and then reports the industrial waste heat supply and transaction price to the integrated energy system as the buyer of industrial waste heat. The integrated energy system then provides feedback based on the demand values ​​of the system users' cooling load and heating load, the green electricity forecast value of the photovoltaic power generation unit, and the seller's industrial waste heat declaration information, thus realizing the master-slave game transaction of industrial waste heat between the two parties; S4. In the day-ahead dispatching process, based on the results of the industrial waste heat trading between the two parties, the integrated energy system takes the optimization of system operation cost and energy utilization rate as the goal, establishes the day-ahead optimization dispatching model for summer cooling and the day-ahead optimization dispatching model for winter heating, and obtains the operation output strategy of each device in the system in the summer cooling and winter heating stages during the day-ahead dispatching process; S5. During the intraday scheduling process, the deviation of industrial waste heat trading, the green electricity prediction deviation of photovoltaic power generation units, the demand deviation of cooling load and heating load, and the demand response of system users are taken into consideration. With the goal of minimizing the adjustment cost, an optimal scheduling model for the system's summer cooling and an optimal scheduling model for the system's winter heating are established to obtain the operating output strategy and demand response strategy of each device in the system during the summer cooling and winter heating stages during the intraday scheduling process.

2. The method for optimizing the scheduling of integrated energy systems in stages according to claim 1 is characterized in that: In S1, the absorption refrigerator is a hot water absorption refrigerator; the heat exchange equipment includes a water-water heat exchanger and a steam-water heat exchanger; The model of the hot water absorption chiller is expressed as: L AC (t AC Q AC (t); L AC,min ≤L AC (t)≤L AC,max ; L AC (t) is the cooling capacity generated by the hot water absorption chiller at time t; Q AC (t) is the equivalent heat absorbed by the hot water absorption chiller from industrial waste heat at time t; η AC is the refrigeration performance coefficient of the hot water absorption chiller; L AC,max , L AC,min They are the upper and lower limits of hot water absorption cooling capacity, respectively; The model of the electric refrigerator is expressed as: L EC (t)=η EC AND EC (t)? L EC,min ≤L EC (t)≤L EC,max ; L EC (t) is the cooling capacity generated by the electric refrigerator at time t; E EC (t) is the power consumed by the electric refrigerator at time t; η EC is the refrigeration performance coefficient of the electric refrigerator; L EC,max , L EC,min They are the upper and lower limits of the cooling capacity of the electric refrigerator respectively; The model of the heat exchange equipment is expressed as: W HE (t)=η HE S HE (t); W HE (t) is the heat generated by the heat exchange equipment at time t; S HE (t) is the equivalent thermal power of industrial waste heat input at time t; η HE is the conversion efficiency of the heat exchange equipment.

3. The method for optimizing the scheduling of the integrated energy system in stages according to claim 1 is characterized in that: In S2, various types of industrial waste heat generated by the integrated energy system when the enterprise is in production and operation are input into the industrial waste heat recovery unit of the system for recovery and treatment, including: Establish corresponding waste heat transmission pipelines between neighboring enterprises and the integrated energy system according to different types of industrial waste heat, so as to transmit different types of industrial waste heat to the industrial waste heat recovery unit of the system; The industrial waste heat recovery unit monitors and adjusts the temperature, pressure and flow parameters of different types of industrial waste heat to meet the operating parameters of the absorption chiller for refrigeration and the heat exchange equipment for heat exchange.

4. The method for optimizing the scheduling of the integrated energy system in stages according to claim 1 is characterized in that: In S2, in the summer cooling stage, green electricity output by the photovoltaic power generation group is preferentially used, and when green electricity is insufficient, electricity is purchased from the municipal power grid to drive the electric refrigerator for cooling; at the same time, the electric refrigerator is used as a cooling supplementary device to consume electric energy for cooling when the cooling capacity of the absorption refrigerator is insufficient; and the excess green electricity and / or low-priced electricity from the municipal power grid is stored in the power storage device for releasing electric energy during the peak period of cooling demand; when the cooling supply is greater than the demand, the excess cooling capacity is stored in the cooling storage device for releasing cooling during the peak period of cooling demand; During the winter heating stage, the corresponding type of industrial waste heat is obtained from the industrial waste heat recovery unit according to the type of heat exchange equipment, and the heat is output after heat exchange through the corresponding type of heat exchange equipment. At the same time, when the heat supply is greater than the demand, the excess heat is stored in the heat storage device for heat release during the peak period of heat consumption.

5. The method for optimizing the scheduling of integrated energy systems in stages according to claim 1 is characterized in that: In S3, the industrial waste heat generated during production operation is predicted, including: Obtain relevant data that affects the amount of industrial waste heat from nearby enterprises, including historical operating parameters of production equipment, raw material input, product output, industrial waste heat type, waste heat temperature, waste heat flow, external weather conditions, changes in enterprise market demand, and historical waste heat; Clustering algorithms are used to process data and divide the production operations of neighboring enterprises into multiple operating conditions; The data sets under each operating condition are subjected to feature extraction using a variety of feature extraction algorithms to form a variety of data subsets under each operating condition; For each data subset under each operating condition, a variety of machine learning algorithms are used for training and learning, and the industrial waste heat prediction models corresponding to the corresponding algorithms are established. After the performance of each prediction model is evaluated, the industrial waste heat prediction model with the best performance under each operating condition is selected to obtain the industrial waste heat prediction values ​​under various operating conditions of nearby enterprises.

6. The method for optimizing the scheduling of the integrated energy system in stages according to claim 5 is characterized in that: The clustering algorithm is a K-means clustering algorithm; the feature extraction algorithm includes a LASSO algorithm and a LightGBM algorithm; the machine learning algorithm includes a CatBoost algorithm and a BPNN algorithm.

7. The method for optimizing the scheduling of the integrated energy system in stages according to claim 1 is characterized in that: The S3 includes: As the seller of industrial waste heat, nearby enterprises use the pre-established industrial waste heat prediction model to predict the industrial waste heat generated by the enterprise the next day, and consider market factors, cost factors and the preliminary purchase price given by the integrated energy system to preliminarily formulate the transaction price of different types of industrial waste heat; Neighboring enterprises report to the integrated energy system the supply and transaction prices of different types of industrial waste heat for the next day; In the summer cooling stage: the integrated energy system uses the pre-established user cooling load forecast model to obtain the cooling load forecast value for the next day, and uses the pre-established photovoltaic power generation forecast model to obtain the photovoltaic green power forecast value for the next day; as the purchaser of industrial waste heat, the integrated energy system comprehensively considers the different types of industrial waste heat supply and transaction prices, cooling load forecast values, photovoltaic green power forecast values, and grid dynamic electricity price factors reported by neighboring enterprises, and feeds back different types of industrial waste heat purchase quantities and purchase prices to neighboring enterprises; neighboring enterprises then adjust and optimize the reported transaction information based on the feedback information, and after continuously executing the transaction process and adjusting and optimizing, they formulate the final different types of industrial waste heat transaction quantities and transaction prices; In the summer heating stage: the integrated energy system uses the pre-established user heat load forecasting model to obtain the heat load forecast value for the next day, preliminarily calculates the required different types of industrial waste heat, and then comprehensively considers the different types of industrial waste heat supply and transaction prices reported by neighboring enterprises, and the different types of industrial waste heat demand, and combines the industrial waste heat supply and demand relationship to feedback the purchase volume and purchase price of different types of industrial waste heat to neighboring enterprises; the neighboring enterprises then adjust and optimize the declared transaction information based on the feedback information, and after continuously executing the transaction process and adjusting and optimizing, formulate the final different types of industrial waste heat transaction volumes and transaction prices; Among them, the integrated energy system is taken as the leader and the neighboring enterprises as the followers. The master-slave game transaction model of both parties is established with the optimal economic indicators of both parties. Through multiple iterations, both parties continuously adjust their industrial waste heat trading volume and price information until reaching the Nash equilibrium point acceptable to both parties, obtaining the optimal industrial waste heat trading volume and trading price strategy, and formulating the trading method.

8. The method for optimizing the scheduling of integrated energy systems in stages according to claim 1, characterized in that: In S4, taking the optimization of system operation cost and energy utilization as the goal, a day-ahead optimization scheduling model for summer cooling is established, which is expressed as: C h,buy,t Cost of purchasing industrial waste heat for summer cooling; C e,buy,t is the cost of purchasing electricity from the municipal power grid; C AC,t is the operating cost of the absorption chiller; C EC,t is the operating cost of the electric refrigerator; C PV,t is the operating cost of the photovoltaic generator set; C s,t is the energy storage and discharge operation cost of the power storage device and the cold storage device; T is the dispatching period; L c,t is the cooling load produced by the system; E h,e,t The amount of energy consumed to cool the system, including industrial waste heat and electricity consumed for cooling; The above-mentioned system winter heating day optimization scheduling model is established with the goal of optimizing the system operation cost and energy utilization rate, which is expressed as: C h ' ,buy,t Cost of purchasing industrial waste heat for winter heating; C HE,t is the operating cost of the heat exchange equipment; L h,t is the heat load produced by the system; E h,t Industrial waste heat consumed for system heat generation; The constraints of the day-ahead optimization scheduling model for summer cooling of the system include: operating constraints of absorption chillers, operating constraints of electric chillers, operating constraints of photovoltaic generators, operating constraints of power storage devices, operating constraints of cold storage devices and cold power balance constraints; The operation output strategies of the various equipment in the system output by the optimization scheduling model of the summer cooling system before the day include: the cooling capacity output by the absorption chiller, the cooling capacity output by the electric chiller, the power generation of the photovoltaic generator set, the power purchase of the municipal power grid, the energy storage and discharge state and the energy storage and discharge of the power storage device and the cold storage device; The constraints of the optimal scheduling model for winter heating of the system include: operating constraints of heat exchange equipment, operating constraints of heat storage device and thermal power balance constraints; The operation output strategies of various devices in the system output by the day-ahead optimization scheduling model of the winter heating system include: the heat output by the heat exchange equipment, the heat storage and release state of the heat storage device, and the amount of heat stored and released.

9. The method for optimizing the scheduling of integrated energy systems in stages according to claim 1, characterized in that: In S5, establishing the system summer cooling daytime optimization scheduling model specifically includes: The pre-established industrial waste heat prediction model is used to obtain the intraday prediction value of industrial waste heat, the pre-established user cold load prediction model is used to obtain the intraday prediction value of cold load, and the pre-established photovoltaic power generation prediction model is used to obtain the intraday prediction value of photovoltaic power generation. After comparing the corresponding prediction value and actual value with the actual value of industrial waste heat, the actual value of cold load and photovoltaic power generation, the industrial waste heat trading deviation, the green power prediction deviation of the photovoltaic power generation unit and the demand deviation of the cold load are analyzed and obtained; when the comprehensive deviation of the industrial waste heat trading deviation, the green power prediction deviation of the photovoltaic power generation unit and the demand deviation of the cold load is greater than the set first deviation interval range, the user side cold load demand response adjustment and equipment operation output adjustment are carried out to compensate for the deviation; with the goal of minimizing the adjustment cost, the system summer cooling daytime optimization scheduling model is established, which is expressed as: C i,t is the operating output adjustment cost of the i-th equipment during summer cooling; C c,DR,t is the cooling load demand response adjustment cost on the user side during summer cooling; M is the total number of absorption chillers, electric chillers, photovoltaic generators, power storage devices and cold storage devices; The establishment of the system winter heating daily optimization scheduling model specifically includes: During the intraday scheduling process of the system in the winter heating stage: the intraday forecast value of industrial waste heat is obtained by using the pre-established industrial waste heat forecast model, and the intraday forecast value of heat load is obtained by using the pre-established user heat load forecast model, and the corresponding forecast value and actual value are compared with the actual value of industrial waste heat and the actual value of heat load, respectively, and the industrial waste heat transaction deviation and heat load demand deviation are analyzed; when the combined deviation of the industrial waste heat transaction deviation and the heat load demand deviation is greater than the set second deviation interval, the user side heat load demand response adjustment and equipment operation output adjustment are carried out to compensate for the deviation; with the goal of minimizing the adjustment cost, the system winter heating intraday optimization scheduling model is established, which is expressed as: C j,t is the operating output adjustment cost of the jth device during winter heating; C h,DR,t It is the heat load demand response adjustment cost on the user side during winter heating; N is the total number of heat exchange equipment.

10. The method for optimizing the scheduling of the integrated energy system in stages according to claim 1, characterized in that: The Pelican optimization algorithm is used to solve the system's summer cooling day-ahead optimization scheduling model, the system's winter heating day-ahead optimization scheduling model, the system's summer cooling intraday optimization scheduling model, and the system's winter heating intraday optimization scheduling model.

Images