Double-layer planning method and system for industrial steam system and medium

Abstract

The invention provides a bilevel planning method for an industrial steam system, a bilevel planning system for the industrial steam system and a computer readable storage medium. The bilevel planning method for the industrial steam system comprises the following steps: acquiring operation parameters and user requirements of each energy device in the industrial steam system; the upper layer of the double-layer game model of the industrial steam system constructed based on the Stackelberg game theory optimizes the operation cost of each energy device, and determines the energy price through a real-time pricing mechanism; according to the energy price, a lower layer of the double-layer game model determines an energy demand through a demand response mechanism, and the lower layer takes user energy cost minimization as a target; and according to the energy demand determined by the demand response mechanism, adjusting and optimizing operation parameters of each energy device so as to realize Stackelberg equilibrium between the industrial steam system and the user.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of industrial steam systems, and in particular to a bi-level programming method for industrial steam systems, a bi-level programming system for industrial steam systems, and a computer-readable storage medium. BACKGROUND

[0002] In recent years, global energy consumption has surged and is expected to continue to rise. Over the past 40 years, hydrocarbons (HC) have dominated global energy consumption, with their use growing by 185% between 1970 and 2012. Given environmental degradation and energy shortages, industrial energy conservation and emission reduction is a key measure to address these challenges.

[0003] The chemical industry is a key factor in these problems. Industrial steam systems are the core of the energy infrastructure of the chemical industry. Industrial steam systems generate industrial steam through boilers and adjust the temperature and pressure through steam turbines and electric motors to meet the steam demand of the entire production process. However, the dependence on traditional fossil fuel steam production perpetuates the cycle of carbon emissions, exacerbating the trajectory of climate change and posing a serious risk to ecological integrity and human health. In energy system integration, solar and wind energy are recognized as favorable alternatives to fossil fuels. However, the intermittency of solar energy and the variability of wind energy result in frequent output fluctuations, potentially limiting the integration of these energies into steam systems, and the fluctuations and variability can also lead to energy surplus during integration.

[0004] In order to overcome the above-mentioned defects existing in the prior art, there is an urgent need in the art for a programming method for industrial steam systems, which can be used to optimize the integration of renewable energy in industrial steam systems while considering demand response. SUMMARY

[0005] The following presents a simplified summary of one or more aspects in order to provide a basic understanding of such aspects. This summary is not an extensive overview of all contemplated aspects, and is intended to neither identify key or critical elements of all aspects nor delineate the scope of any or all aspects. Its sole purpose is to present some concepts of one or more aspects in a simplified form as a prelude to the more detailed description that is presented later.

[0006] In order to overcome the above-mentioned defects existing in the prior art, the present application provides a bi-level programming method for industrial steam systems, a bi-level programming system for industrial steam systems, and a computer-readable storage medium, which can be used to optimize the integration of renewable energy in industrial steam systems while considering demand response.

[0007] In particular, the double-layer planning method for industrial steam system according to the first aspect of the present application comprises the steps of: obtaining operation parameters of each energy device in the industrial steam system and user demand; optimizing operation cost of each energy device based on upper layer of a double-layer game model of the industrial steam system constructed based on Stackelberg game theory, and determining energy price through real-time pricing mechanism; determining energy demand through demand response mechanism based on the energy price, the lower layer of the double-layer game model aiming to minimize user energy cost; and adjusting and optimizing operation parameters of each energy device according to the energy demand determined by the demand response mechanism, so as to realize Stackelberg equilibrium between the industrial steam system and the user.

[0008] Preferably, in an embodiment of the present application, the construction of the double-layer game model comprises the steps of: constructing a double-layer game model of the industrial steam system based on Stackelberg game theory, the upper layer of the double-layer game model being used to optimize operation cost of the energy device and determine energy price, the lower layer aiming to minimize user energy cost; determining upper layer strategy of the double-layer game model based on real-time pricing mechanism, and determining lower layer strategy of the double-layer game model based on demand response mechanism; and calibrating parameters of the double-layer game model based on historical data.

[0009] Preferably, in an embodiment of the present application, the double-layer problem of the double-layer game model is expressed as a single-layer planning problem with equilibrium constraints by using KKT conditions.

[0010] Preferably, in an embodiment of the present application, the calculation of the operation cost of the energy device comprises calculation of fuel cost, circulating water cost, purchased steam cost and power cost, and calculation of output power of wind turbine and solar collector.

[0011] Preferably, in an embodiment of the present application, the fuel cost C fuel is defined as:

[0012]

[0013] wherein P fuel is fuel price, is fuel flow rate of boiler B1, B2 and B3 at time t;

[0014] The circulating water cost C water is defined as:

[0015]

[0016] wherein M ldν1,in,t , M ldν2,in,t , M ldv3,in,tM is the steam load of each LDV (Letdown Value) device at time t for steam in ldν1,out,t M ldv2,out,t M ldv3,out,t P is the steam load of each ldv device at time t for steam out water P is the price of circulating water;

[0017] The purchase steam cost C steam is defined as:

[0018]

[0019] Where M is the steam load, M d,oss,t M d,ohs,t M d,oms,t M d,ols,t is the steam load of purchased super-high pressure steam, high pressure steam, medium pressure steam and low pressure steam at time t, P is the steam price corresponding to purchased super-high pressure steam, high pressure steam, medium pressure steam and low pressure steam;

[0020] The electricity cost C grid is defined as:

[0021]

[0022] Where P ele is the electricity price, P grid,t is the electricity quantity purchased from the power grid at time t.

[0023] Preferably, in an embodiment of the present application, the power output P w of the wind turbine is defined as:

[0024]

[0025] Where v is the wind speed, v c is the cut-in wind speed, v r is the rated wind speed, v f is the cut-out wind speed, P r is the rated power;

[0026] The thermal energy output Q shc of the solar collector is defined as:

[0027] Q shc = S shc × F R (G(τα)-U L (T in -T a )),

[0028] Where Sshc F is the area of the solar collector R G is the heat loss coefficient, G is the real-time light intensity, ta is the optical efficiency, U L T is the heat loss coefficient in T is the temperature of the fluid entering the solar collector a T is the ambient temperature.

[0029] Preferably, in an embodiment of the present application, the real-time pricing mechanism is implemented according to a price constraint defined as:

[0030]

[0031] P i,min ≤P i,t ≤P i.max ,

[0032] where P i,min and P i.max are the minimum and maximum prices of the i-th steam during the programming period, T is the time period, is the average price of the i-th steam in the market, the i-th steam includes the outsourced super-high pressure steam, high pressure steam, medium pressure steam and low pressure steam, corresponding to {ss, hs, ms, ls}.

[0033] Preferably, in an embodiment of the present application, the constraints of the demand response mechanism are:

[0034] M d,ss,t =M fixed,ss,t +M flex,ss,t ,

[0035] M d,hs,t =M fixed,hs,t +M flex,hs,t ,

[0036] M d,ms,t =M fixed,ms,t +M flex,ms,t ,

[0037] M d,ls,t =M fixed,is,t +M flex,ls,t ,

[0038]

[0039] M d,i,min ≤M d,i,t ≤M d,i,max ,

[0040] where M d,ss,t , M d,hs,t , M d,ms,t , Md,ls,t M, M, M, M are the total demand of purchased super-high pressure steam, high pressure steam, medium pressure steam and low pressure steam at time t, M fixed,ss,t M, M, M, M fixed,hs,t M, M, M, M fixed,ms,t M, M, M, M fixed,ls,t M, M, M, M are the fixed demand of purchased super-high pressure steam, high pressure steam, medium pressure steam and low pressure steam at time t, M flex,ss,t M, M, M, M flex,hs,t M, M, M, M flex,ms,t M, M, M, M flex,ls,t M, M, M, M are the flexible demand of purchased super-high pressure steam, high pressure steam, medium pressure steam and low pressure steam at time t.

[0041] In addition, the above-mentioned bi-level planning system for industrial steam system provided by the second aspect of the present application comprises a memory and a processor. The memory has computer instructions stored thereon. The processor is connected to the memory and is configured to execute the computer instructions stored on the memory to implement the bi-level planning method for industrial steam system provided by any one of the above-mentioned embodiments.

[0042] In addition, the above-mentioned computer readable storage medium provided by the third aspect of the present application has computer instructions stored thereon. The computer instructions are executed by a processor to implement the bi-level planning method for industrial steam system provided by any one of the above-mentioned embodiments. BRIEF DESCRIPTION OF DRAWINGS

[0043] The above features and advantages of the present application can be better understood by reading the detailed description of embodiments of the present application in conjunction with the following drawings, in which: the components are not necessarily drawn to scale and components of similar or identical function or features can have the same or similar reference label.

[0044] Figure 1 The overall architecture diagram of the industrial steam system is shown;

[0045] Figure 2 The operation mechanism flow chart of the extraction steam turbine is shown;

[0046] Figure 3 The detailed architecture diagram of the industrial steam system integrating renewable energy is shown;

[0047] Figure 4 The flow chart of constructing a bi-level game model is shown according to some embodiments of the present application;

[0048] Figure 5 The bi-level game model framework diagram of the bi-level planning method for industrial steam system is shown according to some embodiments of the present application;

[0049] Figure 6A flow chart of a bi-level planning method for an industrial steam system according to some embodiments of the present application is shown;

[0050] Figure 7 A flow chart of a bi-level game decision between a utility steam system and a customer according to some embodiments of the present application is shown;

[0051] Figure 8 A result chart of a boiler flow interval parameter optimization according to some embodiments of the present application is shown;

[0052] Figure 9 A result chart of an extraction steam turbine inlet flow interval parameter optimization according to some embodiments of the present application is shown;

[0053] Figure 10 A result chart of an extraction steam turbine outlet flow interval parameter optimization according to some embodiments of the present application is shown;

[0054] Figure 11 A result chart of a pressure reducing valve inlet flow interval parameter optimization according to some embodiments of the present application is shown; and

[0055] Figure 12 A schematic diagram of a bi-level planning system for an industrial steam system according to some embodiments of the present application is shown.

[0056] Reference Signs:

[0057] S410-S430: Steps;

[0058] S610-S640: Steps;

[0059] 1200: Bi-level planning system for an industrial steam system;

[0060] 1210: Memory;

[0061] 1211: Computer readable storage medium; and

[0062] 1220: Processor. DETAILED DESCRIPTION

[0063] The present application is described in detail below with reference to the attached drawing figures and specific embodiments. It is noted that the aspects described below with reference to the drawing figures and specific embodiments are merely exemplary and should not be understood to limit the scope of the present application in any way.

[0064] In the description of the present application, it should be noted that unless otherwise explicitly specified and limited, the terms "mounting", "connection", "connecting" should be understood in a broad sense, for example, it can be fixed connection, or detachable connection, or integrally connected; it can be mechanical connection, or electrical connection; it can be directly connected, or indirectly connected through an intermediate medium, or the internal communication of two elements. For those skilled in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances.

[0065] In addition, "up", "down", "left", "right", "top", "bottom", "horizontal", "vertical" used in the following description should be understood as the orientation shown in the section and the related drawings. The relative terms are only for the convenience of description, and do not mean that the device described should be manufactured or operated in a particular orientation, so it should not be understood as a limitation on the present application.

[0066] It can be understood that although the terms "first", "second", "third" and the like are used herein to describe various components, regions, layers and / or parts, these components, regions, layers and / or parts should not be limited by these terms, and these terms are only used to distinguish different components, regions, layers and / or parts. Therefore, the first component, region, layer and / or part discussed below can be referred to as the second component, region, layer and / or part without departing from some embodiments of the present application.

[0067] As described above, an industrial steam system generates industrial steam through a boiler and adjusts the temperature and pressure through a steam turbine and an electric motor to meet the steam demand of the entire production process. However, the dependence on traditional fossil fuel steam production perpetuates the cycle of carbon emissions, exacerbating the trajectory of climate change and posing a serious risk to ecological integrity and human health. In energy system integration, solar and wind energy are recognized as favorable alternatives to fossil fuels. However, the intermittency of solar energy and the variability of wind energy result in frequent output fluctuations, potentially limiting the integration of these energy sources into the steam system, and the fluctuations and variability can also cause energy surplus during integration.

[0068] In order to overcome the above-mentioned defects existing in the prior art, the present application provides a bi-level planning method for an industrial steam system, a bi-level planning system for an industrial steam system, and a computer readable storage medium, which can be used to optimize the integration of renewable energy in an industrial steam system, while considering demand response.

[0069] Reference is made to Figure 1 , Figure 1 The overall architecture of the industrial steam system is shown.

[0070] As Figure 1As shown, the industrial steam system (ISS) includes energy providers, which include conventional energy (e.g., fuels and the power grid) and renewable energy (e.g., wind and solar).

[0071] The industrial steam system includes energy service systems, which include equipment serving conventional energy and equipment serving renewable energy. The equipment serving conventional energy includes traditional heat generators, such as Figure 1 boilers in the power grid. Based on the traditional heat generators, the industrial steam system can burn fuels in the conventional energy to generate high-temperature and high-pressure steam.

[0072] The industrial steam system also includes extraction turbines (ET), which can be used to extract part of the steam from the boilers and reduce its pressure and temperature to meet the demand for different grades of steam.

[0073] In particular, please refer to Figure 2 , Figure 2 a flowchart showing the operation mechanism of the extraction turbine.

[0074] Figure 2 The inlet flow, extraction flow, and exhaust flow of the extraction turbine are shown, as well as how these flows are distributed and regulated in the extraction turbine system. By optimizing the distribution and regulation of these flows, the extraction turbine can efficiently convert the energy of the steam into mechanical or electrical energy while ensuring the stability and efficiency of the extraction turbine under different operating conditions. Table 1 shows the operating parameters of the extraction turbine in some embodiments, which can be obtained by fitting historical data.

[0075]

[0076] Table 1

[0077] Please continue to refer to Figure 1 , the industrial steam system also includes pressure reducing valves and electric motors, which are used to further regulate the pressure of the steam, and electric motors can be used to drive various equipment in the industrial steam system.

[0078] In addition, the industrial steam system further comprises an integrated renewable energy system, which includes wind turbines utilizing wind energy and solar collectors utilizing solar energy. The wind turbines and solar collectors can generate electricity and low-pressure steam to assist traditional energy devices, thereby improving the energy efficiency and environmental sustainability of the industrial steam system. Integrating renewable energy into the traditional industrial steam system can improve the energy self-sufficiency of the industrial steam system, reduce dependence on traditional fossil fuels, and significantly reduce carbon emissions. Through such integration, the industrial steam system can operate stably under different weather conditions, optimize energy conversion and distribution, and achieve economic and environmental sustainability of the industrial steam system.

[0079] The industrial steam system further comprises energy consumers, including industrial users and residential users, which use energy. The electricity and steam converted by the industrial steam system can be provided to the energy consumers at an energy price, and the supply and distribution of the industrial steam system can be further adjusted based on consumer strategies.

[0080] Further, please refer to Figure 3 , Figure 3 A detailed architecture diagram of the industrial steam system integrating renewable energy is shown.

[0081] Through the detailed architecture diagram shown in Figure 3 , it can be illustrated how to integrate wind energy and solar energy into the traditional industrial steam system. Renewable energy devices can work with traditional boilers, extraction steam turbines, electric motors, and pressure reducing valves. In the renewable energy devices, wind turbines are used to generate electricity, and solar collectors are used to generate low-pressure steam. The integration of these renewable energies not only improves the energy self-sufficiency of the system, but also reduces dependence on traditional fossil fuels and significantly reduces carbon emissions. Through such integration, the industrial steam system can operate stably under different weather conditions, optimize energy conversion and distribution, and achieve economic and environmental sustainability of the industrial steam system.

[0082] In some embodiments, the bi-level programming method for the industrial steam system provided by the present application can be implemented based on the architecture diagram shown in Figure 3 , which will be described in detail below in conjunction with Figure 3 .

[0083] In existing methods for managing uncertainty, robust planning usually prioritizes reliability and conservatism rather than economy. However, it is crucial to coordinate the interactions of a comprehensive industrial steam system with multiple stakeholders, and game theory is an effective tool for evaluating the interests of multiple stakeholders.

[0084] The double-layer planning method for industrial steam systems provided by the present application adopts a stochastic planning method to integrate contingency into the planning process, and can achieve a balance between reliability and economy. In the optimization of steam systems incorporating renewable energy, game theory is applied to balance the interests of various stakeholders, making the optimized industrial steam system more sustainable and user-centered, with significant economic and environmental benefits.

[0085] The working principle of the double-layer planning system for industrial steam systems will be described below in combination with some embodiments of the double-layer planning method for industrial steam systems. Those skilled in the art can understand that these embodiments of the double-layer planning method for industrial steam systems are only some non-limiting embodiments provided by the present application, which are intended to clearly demonstrate the main concept of the present application and provide some specific schemes for facilitating the public to implement, rather than to limit the overall functions or overall working modes of the double-layer planning system for industrial steam systems. Similarly, the double-layer planning system for industrial steam systems is also only a non-limiting embodiment provided by the present application, which does not limit the execution subject and execution order of each step in the double-layer planning method for industrial steam systems.

[0086] Please refer to Figure 4 , Figure 4 A flowchart for constructing a double-layer game model is shown according to some embodiments of the present application.

[0087] As Figure 4 shown, the double-layer planning system for industrial steam systems (hereinafter referred to as "double-layer planning system") can first perform step S410: based on the Stackelberg game theory, a double-layer game model for the industrial steam system is constructed, the upper layer of the double-layer game model is used to optimize the operating cost of the energy conversion equipment and determine the energy price, and the lower layer aims to minimize the user energy cost. Here, the industrial steam system integrates renewable energy.

[0088] In the double-layer game model constructed by the double-layer planning system, the upper layer is used to optimize the operating cost of the energy conversion equipment and determine the energy price. Here, the energy price can be determined by a real-time pricing mechanism, which will be described in detail later.

[0089] Accurate calculation of various operating costs can ensure efficient operation of the energy conversion equipment, and the operating costs can include fuel cost, circulating water cost, purchased steam cost and power cost. The calculation of these costs is crucial for optimizing the operation of the energy conversion equipment, which helps to reduce the overall operating cost and improve the efficiency of the industrial steam system.

[0090] Specifically, in the embodiment shown in Figure 3 , the double-layer planning system can calculate the fuel cost C fuel of the industrial steam system as follows:

[0091]

[0092] wherein P fuel is the fuel price, is the fuel flow rate of the boiler B1, B2, B3 at time t;

[0093] C water The definition of the circulating water cost C

[0094]

[0095] wherein M is the steam load, M ldv1,in,t , M ldv2,in,t , M ldv3,in,t is the steam load of the steam inlet of each LDV (Letdown Value) device at time t, M ldv1,out,t , M ldv2,out,t , M ldv3,out,t is the steam load of the steam outlet of each LDV device at time t, P water is the circulating water price;

[0096] The definition of the purchased steam cost C steam

[0097]

[0098] wherein M is the steam load, M d,oss,t , M d,ohs,t , M d,oms,t , M d,ols,t is the steam load of the purchased super-high pressure steam, high pressure steam, medium pressure steam and low pressure steam at time t, is the steam price corresponding to the purchased super-high pressure steam, high pressure steam, medium pressure steam and low pressure steam;

[0099] The definition of the electricity cost C grid

[0100]

[0101] wherein P ele is the electricity price, P grid,t is the electricity purchased from the power grid at time t.

[0102] Further, in order to effectively integrate renewable energy, the double-layer planning system also needs to accurately calculate the output power of wind turbines and solar collectors to consider the intermittency and uncertainty of wind energy and solar energy. Calculating the output power of wind turbines and solar collectors helps to optimize the use of renewable energy and ensure the stable operation of the industrial steam system under different weather conditions.​​

[0103] wind turbine power output P w The definition can be:

[0104]

[0105] where v is the wind speed, v c is the cut-in wind speed, v r is the rated wind speed, v f is the cut-out wind speed, P r is the rated power;

[0106] solar collector thermal energy output Q shc The definition can be:

[0107] Q shc = S shc × F R (G(τα)-U L (T in -T a )),

[0108] where S shc is the area of the solar collector, F R is the heat dissipation coefficient, G is the real-time light intensity, τα is the optical efficiency, U L is the heat loss coefficient, T in is the temperature of the fluid entering the solar collector, and T a is the ambient temperature.

[0109] In addition, in the bi-level game model constructed by the bi-level planning system, the lower layer aims to minimize the user's energy cost.

[0110] In this way, the bi-level planning system can construct a bi-level game model of the industrial steam system through step S410, the upper layer of the bi-level game model is used to optimize the operation cost of the energy equipment and determine the energy price, and the lower layer aims to minimize the user's energy cost.

[0111] Then, the bi-level planning system can continue to perform step S420: determining the upper layer strategy of the bi-level game model based on the real-time pricing mechanism, and determining the lower layer strategy of the bi-level game model based on the demand response mechanism.

[0112] Please combine Figure 5 , Figure 5 a bi-level game model framework diagram of the bi-level planning method for the industrial steam system provided according to some embodiments of the application is shown.

[0113] As Figure 5 shown, the bi-level game model is divided into an upper layer and a lower layer.

[0114] The upper layer (leader: industrial steam system) is responsible for optimizing the operating cost and price of the energy conversion equipment. The goal of the upper layer is to maximize the economic benefits of the industrial steam system through the set strategy, i.e., adjusting the operating parameters of the energy equipment and setting a reasonable energy price. The economic benefits can be considered based on steam profit, operating cost, and investment cost. Adjusting the operating parameters of the energy equipment specifically includes optimizing the operating parameters of the boiler, extraction turbine, and motor, thereby ensuring the balance between supply and demand of the industrial steam system and improving the overall efficiency and stability of the industrial steam system.

[0115] The lower layer (follower: industrial steam user) aims to minimize the user's energy cost. The lower layer allows users to adjust energy demand (strategy) according to the real-time price given by the industrial steam system through a demand response mechanism, thereby allowing users to reduce demand during peak periods and increase demand during off-peak periods. Through the demand response mechanism, the flexibility of the industrial steam system is improved, and the utilization efficiency of renewable energy is optimized, reducing the energy cost of the industrial steam user.

[0116] Renewable energy has uncertainty and intermittency. Renewable energy sources such as wind and solar energy have significant volatility (e.g., wind speed and light conditions change over time), which makes the power output of renewable energy unstable in time and difficult to directly match with high-reliability loads such as industrial steam systems. This imbalance between supply and demand is a core challenge in the operation and optimization of industrial steam systems.

[0117] To address the uncertainty and intermittency related to renewable energy, the bi-level planning system determines the upper layer strategy of the bi-level game model based on a real-time pricing mechanism and determines the lower layer strategy of the bi-level game model based on a demand response mechanism.

[0118] In some embodiments, in the upper layer strategy, the real-time pricing mechanism can dynamically adjust the price according to market demand and renewable energy supply to ensure the economic efficiency of the industrial steam system and the satisfaction of users.

[0119] Real-time pricing (RTP) is a price mechanism that dynamically adjusts energy consumption prices based on the current or predicted operating state of the energy system. When renewable energy output is high (i.e., supply exceeds demand), the bi-level planning system can set a lower price to attract users to consume energy; otherwise, it can increase the price to guide users to reduce energy consumption, thereby achieving load peak shifting and energy space reconstruction through price signals, improving the flexibility and economic efficiency of the industrial steam system.

[0120] Specifically, the real-time pricing mechanism can be implemented by setting price constraints. By setting price constraints, the bi-level planning system can ensure that the price fluctuates within a reasonable range, thereby achieving supply and demand balance. The price constraint can be defined as:

[0121]

[0122] P i,min ≤P i,t ≤P i.max ,

[0123] Among them, P i,min and P i.max These are the lowest and highest prices of the i-th level steam within the programming period, respectively, where T is the time period. This represents the average market price of Class i steam. Class i steam includes purchased ultra-high pressure steam, high pressure steam, medium pressure steam, and low pressure steam, corresponding to {ss, hs, ms, ls}.

[0124] In the lower-level strategy, the demand response mechanism allows users to adjust energy demand based on real-time prices, thereby reducing demand during peak hours and increasing demand during off-peak hours. This mechanism not only improves the flexibility of industrial steam systems but also optimizes the utilization efficiency of renewable energy.

[0125] Demand response (DR) mechanisms guide users to proactively adjust their energy consumption behavior based on factors such as the load status of the power or steam system and resource availability. This includes delaying, reducing, or shifting some energy demand, thereby decreasing load when resource supply is limited and increasing utilization efficiency when resources are abundant. This dynamic adjustment of demand to match supply significantly alleviates the supply-demand imbalance caused by fluctuations in renewable energy.

[0126] Here, the demand response mechanism can mitigate or offset the uncertainty of industrial steam system operation caused by the volatility of renewable energy by adjusting the energy consumption behavior on the demand side. For example, it can reduce load when wind and solar resources are insufficient and increase utilization when resources are abundant, thereby reducing volatility and supporting supply stability at the industrial steam system level.

[0127] Specifically, in Figure 3 In the illustrated embodiment, the constraints of the demand response mechanism can be:

[0128] M d,ss,t =M fixed,ss,t +M flex,ss,t ,

[0129] M d,hs,t =M fixed,hs,t +M flex,hs,t ,

[0130] M d,ms,t =M fixed,ms,t +M flex,ms,t ,

[0131] M d,ls,t =Mfixed,ls,t +M flex,ls,t ,

[0132]

[0133]

[0134] M d,i,min ≤M d,i,t ≤M d,i,max ,

[0135] wherein M d,s,t , M d,hs,t , M d,ms,t , M d,ls,t are the total demand of purchased super-high pressure steam, high pressure steam, medium pressure steam and low pressure steam at time t, M fixed,ss,t , M fixed,hs,t , M fixed,ms,t , M fixed,ls,t are the fixed demand of purchased super-high pressure steam, high pressure steam, medium pressure steam and low pressure steam at time t, M flex,ss,t , M flex,hs,t , M flex,ms,t , M flex,ls,t are the flexible demand of purchased super-high pressure steam, high pressure steam, medium pressure steam and low pressure steam at time t.

[0136] In addition, the demand response mechanism can also match the production of renewable energy with market demand through load forecasting, power generation forecasting and other ways, so as to realize flexible management and optimized distribution of energy. In this way, through the demand response mechanism, the double-layer planning system can optimize the scheduling of energy production in the integration of renewable energy, and ensure the efficient use and maximum consumption of renewable energy.

[0137] Through the demand response mechanism and real-time pricing strategy, the double-layer planning system can encourage users to actively adjust the energy use mode according to the energy supply and demand state, so that the industrial steam system load dynamically matches the renewable energy output. Through this flexibility adjustment, the supply fluctuation is smoothed to a certain extent, and the uncertainty brought by renewable energy is alleviated. Moreover, the double-layer planning system can increase the renewable energy consumption rate when wind energy and solar energy are sufficient, reduce system peak load and reserve capacity demand when renewable energy output is insufficient, while ensuring the cost control ability of the user side. Thus, the uncertainty and intermittency problems related to renewable energy are solved, and the adaptability of the entire industrial steam system to renewable energy fluctuations is improved, and the sustainability of the entire industrial steam system in terms of economy, flexibility and environmental friendliness is improved.

[0138] Thus, in the bi-level planning method for the industrial steam system provided by the present application, the upper layer of the bi-level game model represents the operator of the industrial steam system, which is responsible for formulating resource allocation and real-time pricing strategies; and the lower layer represents the user side, which optimizes its steam use behavior autonomously under the price incentive. Through the structure of the bi-level game model, the response behavior and strategy interaction under multi-agent game can be truly reflected, and the uncertainty and intermittency related to renewable energy can be solved by combining the comprehensive demand response mechanism and the dynamic real-time pricing strategy, thereby improving the adaptability of the entire industrial steam system to renewable energy fluctuations, optimizing energy conversion and distribution of the industrial steam system, balancing the interests of the industrial steam system and its users, and improving the economic and environmental sustainability of the industrial steam system.

[0139] Moreover, the comprehensive demand response mechanism involves consumers adjusting their consumption strategies according to real-time energy prices, which not only offsets the uncertainty of renewable energy, but also improves the potential economic feasibility of the entire energy system and promotes environmental sustainability goals. Further, the real-time pricing mechanism can not only reduce the impact of renewable energy variability on stakeholders by utilizing the flexibility of demand-side resources and dynamically adjusting the pricing structure according to real-time supply and demand dynamics, but also optimize resource utilization and reduce carbon footprint.

[0140] Through steps S410-S420, the bi-level planning system constructs a bi-level game model for the industrial steam system. In some embodiments, to facilitate solving, the bi-level planning system can utilize the KKT (Karush-Kuhn-Tucker) condition to restate the bi-level problem as a single-level planning problem with equilibrium constraints, thereby reducing the complexity of the bi-level problem and improving the solving efficiency.

[0141] Specifically, the bi-level planning system can introduce the KKT condition to express the optimality condition of the lower-level optimization problem as a constraint condition, so that the entire bi-level game model can be converted into a standard form of single-level optimization problem, thereby invoking the mathematical programming solver without losing the game characteristics.

[0142] Then, the bi-level planning system can perform step S430: calibrate the parameters of the bi-level game model based on historical data.

[0143] The bi-level planning system can calibrate the parameters of the bi-level game model using historical data to ensure the accuracy and reliability of the bi-level game model, and the calibration of these parameters helps to improve the prediction accuracy of the bi-level game model, thereby better adapting to actual operating conditions. Thus, the bi-level planning system constructs a bi-level game model for the industrial steam system integrating renewable energy. Understandably, the construction process of this bi-level game model can be completed by the bi-level planning system in an offline manner.

[0144] Please refer to Figure 6 , Figure 6 A flow chart of a bi-level planning method for an industrial steam system according to some embodiments of the present application is shown.

[0145] As shown in Figure 6 , the bi-level planning system can first perform steps S610-S620: obtaining the operating parameters of each energy device in the industrial steam system and the user demand. Then, the upper level of the bi-level game model of the industrial steam system constructed based on the Stackelberg game theory optimizes the operating cost of each energy device, and determines the energy price through the real-time pricing mechanism of the upper level.

[0146] The bi-level planning system can adjust and optimize the operating parameters of each energy device in the industrial steam system based on the constructed bi-level game model to ensure the balance between supply and demand of the industrial steam system. By accurately calculating the operating parameters of each device, the bi-level planning system can improve the overall efficiency and stability of the industrial steam system.

[0147] Specifically, in the embodiment shown in Figure 3 , the bi-level planning system can determine the operating parameters based on the models of the boiler, the extraction turbine, and the motor.

[0148] Here, the model of the boiler can be defined as:

[0149]

[0150] wherein, and correspond to the boiler inlet fuel flow and the boiler outlet steam flow, respectively. LHV and η b are the calorific value of the boiler fuel and the boiler efficiency, H ss is the enthalpy value of the super-high pressure steam, unit: kJ / kg, H b,w is the enthalpy value of the circulating water, f b,max is the maximum super-high pressure steam flow allowed to be output by the boiler, is the actual super-high pressure steam flow output by the boiler, ɑ b and β b are the boiler efficiency fitting parameters, α b and β b are used to reflect the influence of flow changes on efficiency, which can be obtained by fitting historical data.

[0151] The model of the extraction turbine (ET) can be defined as:

[0152]

[0153] wherein, P et,tis the total shaft power at time t. n represents the time discrete index, which is used for dynamic scheduling of the model, and N is the total number of time steps in the scheduling period. et,in,t i,et,ext,t et,exh,t are the inlet flow rate, extraction flow rate and extraction turbine exhaust flow rate, respectively, and H i,et,in i,et,ext et,exh are the actual refinery turbine inlet enthalpy, extraction enthalpy and exhaust enthalpy, respectively. P n,et,ext,t represents the net power supplied by the external power grid to the system, with a unit of kW, reflecting the power required to be supplemented by the power grid after meeting the load demand.

[0154] The actual refinery turbine inlet enthalpy, extraction enthalpy and exhaust enthalpy described above can be fitted by the least squares method according to a large amount of historical data. By fitting the operating parameters of the turbine through the least squares method, the accuracy of the double-layer game model in describing the operating state of the turbine can be ensured, which helps to improve the adaptability and prediction ability of the double-layer game model, thereby optimizing the operating efficiency of the industrial steam system.

[0155] The model of the motor can be defined as:

[0156] P e,T = ε m p m ,

[0157] where p m is the rated power of the motor, ε m is a driving mode selection variable of the motor, which is a binary variable, and when ε m = 1, it indicates that the motor is driven by electricity, and when ε m = 0, it indicates that the motor is driven by steam.

[0158] Then, the double-layer planning system can perform steps S630-S640: according to the energy price, the lower layer of the double-layer game model determines the energy demand through the demand response mechanism. Based on the energy demand determined by the demand response mechanism, the operating parameters of each energy equipment in the industrial steam system are adjusted and optimized to ensure the supply and demand balance of the industrial steam system.

[0159] The energy demand can be determined based on the demand response mechanism. Moreover, the double-layer planning system can reduce the energy demand during peak periods and increase the energy demand during off-peak periods by adjusting and optimizing the operating parameters of each equipment, so as to optimize the utilization of renewable energy, thereby balancing the supply and demand of the industrial steam system.

[0160] ​​​​Thus, by the double-layer planning system for industrial steam system provided by the present application, the Stackelberg equilibrium between the industrial steam system and the users can be achieved. In some embodiments, the double-layer planning system can convert the double-layer game model into a single-layer model by introducing the KKT condition, and solve the single-layer model to achieve the equilibrium solution under the Stackelberg game, i.e., determine the optimal decision combination of the industrial steam system and the users under the pricing and response mechanism.

[0161] Further, in some embodiments, the double-layer planning method for industrial steam system provided by the present application can be verified by the actual industrial steam system by the person skilled in the art, which proves the effectiveness of the double-layer planning method. Compared with the traditional method, the double-layer planning method can improve the integration efficiency of renewable energy and reduce carbon dioxide emissions.

[0162] Please refer to Figure 7 , Figure 7 As a preferred embodiment, how the double-layer planning system realizes the decision through the double-layer game model in some embodiments will be described by Figure 7 .

[0163] Figure 7 A flow chart of the double-layer game decision between the utility steam system and the customers according to some embodiments of the present application is shown.

[0164] As Figure 7 shown, in the double-layer game model, the utility steam system as the leader first makes the decision to set the energy price and optimize the operation parameters of the energy conversion equipment. The customers as the followers adjust their energy demand according to the price and parameters set by the leader to minimize their own energy cost.

[0165] Specifically, in the upper-layer model constructed based on the leader, the input is the operation parameters of the utility steam system and the customer demand, and based on the decision variables, objective function and constraint conditions set in the upper-layer model, the steam system capacity, renewable energy system capacity and steam price are determined; in the lower-layer model constructed based on the follower, based on the steam system capacity, renewable energy system capacity and steam price output by the upper-layer model, based on the decision variables, objective function and constraint conditions set in the lower-layer model, the optimal strategy of the user to purchase steam is output. The double-layer planning system can judge whether the output optimal strategy of the user to purchase steam is optimal, when the output optimal strategy of the user to purchase steam is not optimal, the parameters are adjusted and the decision process is repeated; when the output optimal strategy of the user to purchase steam is optimal, the decision process is ended and the optimal strategy of the user to purchase steam is output.

[0166] In this, the upper model sets the decision variable as the public system capacity, the renewable energy capacity, and the steam price sales based on the optimization of the operating cost and the price of the energy conversion equipment, sets the objective function as maximizing the steam system profit, and sets the constraint condition as the energy conversion equipment constraint, the price constraint; correspondingly, the lower model sets the decision variable as the steam demand based on the minimization of the user energy cost as the target, sets the objective function as minimizing the user cost, and sets the constraint condition as the daily demand constraint.

[0167] Through the double-layer game model, the double-layer planning system can make the industrial steam system ensure the economy while optimizing the resource utilization, reducing the carbon emission, and improving the user satisfaction.

[0168] In order to illustrate the applicability of the double-layer planning method provided by the present application, a case study is carried out on a large industrial steam system located in a coastal area (including the surrounding industrial park). Case one adopts the double-layer planning method provided by the present application and considers demand response and real-time pricing for planning optimization; case two is a traditional method and is an industrial steam system planning without considering demand response and real-time pricing.

[0169] Under different optimization methods, the operating cost of the industrial steam system is shown in Tables 2 and 3. As shown in Table 2, compared with the traditional optimization method (case two), the double-layer planning method adopted in case one saves 1.03 million yuan of energy cost, but must pay 1.5 million yuan of penalty cost. It can be seen that case one sacrifices 0.47 million yuan of cost of the user.

[0170] Project name (RMB) Case 1 Case 2 Energy cost 1,903,600,000 1,904,630,000 Penalty cost 1,500,000 0 Total cost 1,905,100,000 1,904,630,000

[0171] Table 2

[0172] However, as shown in Table 3, for the industrial steam system, the double-layer planning method adopted in case one can increase 25.54 million yuan of revenue. It can be seen that in the optimization of the industrial steam system, the double-layer planning method considering demand response and real-time pricing can have a profound impact on the optimization of the energy system.

[0173] Project name (RMB) Case 1 Case 2 Fuel 921,480,000 921,515,000 Energy 85424600 81,979,300 Investment 32,101,300 28,334,700 Outsourced steam 0 28,656,900 Total profit 864,454,000 838,912,000

[0174] Table 3

[0175] Further, compared with single-level planning, the double-level planning can be used to balance the interests between the ISS system and the users. In order to verify the double-level planning method provided by the present application, four models are designed in the following examples. Model 1 constitutes an optimization framework focusing on the upper layer of the industrial steam system. Model 2 represents the double-level planning method provided by the present application which integrates dynamic pricing and demand response. Model 3 is a model focusing on the optimization of the lower layer users. Model 4 represents the conventional steam system optimization without introducing renewable energy and demand response mechanism. Table 4 shows the optimization results of different models.

[0176] Model Profit (RMB) Cost (RMB) Model 1 872,590,000 1,922,400,000 Model 2 864,454,000 1,905,100,000 Model 3 647,332,000 1,898,850,000 Model 4 773,645,604 1,906,318,274

[0177] Table 4

[0178] As shown in Table 4, compared with Model 4 which does not consider the introduction of renewable energy and demand response, the double-level planning method represented by Model 2 increases the profit of the steam system by 90,808,396 yuan and reduces the consumption bill of the users by 1,218,274 yuan. Therefore, the double-level planning method can increase the profit of the industrial steam system and reduce the cost of the users of the industrial steam system.

[0179] By comparing single-level planning and double-level planning, i.e., by comparing Model 1, Model 3 and Model 2, it can be seen that single-level planning has a significant impact on the benefits of the steam system and the energy consumption of the users. Compared with Model 1 which is a single-level optimization only for the upper layer, the double-level planning of Model 2 reduces the profit of the industrial steam system by 8.136 million yuan and at the same time reduces the energy bill of the users by 17,300,000 yuan. On the contrary, compared with Model 2 which is a double-level planning, Model 3 which is a single-level optimization only for the lower layer reduces the profit of the steam producer by 21712.2 million yuan, while the energy bill of the users is only saved by 6.25 million yuan. It can be seen that the double-level method is beneficial to both the steam system and the users, effectively balancing their profits and costs.

[0180] Please continue to refer to Figure 8 , Figure 8 A result diagram of the boiler flow interval parameter optimization provided according to some embodiments of the present application is shown.

[0181] In some embodiments, after the industrial steam system utilizes the double-level planning method for interval parameter optimization, the steam flow of the boiler BO1 (not shown) remains unchanged, with the inlet flow being stable at 35.02 t / h and the outlet flow being maintained at 170 t / h in each month of the year, indicating that the boiler BO1 serves as a base load device in the industrial steam system and runs stably without participating in the problem of responding to the uncertainty disturbance caused by renewable energy. In contrast, as shown in Table 4, the steam flow of the boiler BO2 (not shown) in Model 2 is 0 t / h in January, February, March, April, May, June, July, August, September, October, November and December, and the steam flow of the boiler BO3 (not shown) is 0 t / h in January, February, March, April, May, June, July, August, September, October, November and December. Figure 8As shown, the boiler BO2 and the boiler BO3 undertake the adjustment role of the interval parameter disturbance in the industrial steam system. Among them, the boiler BO2 is mainly responsible for fine tuning during the winter (January-February and November-December) to adapt to slight fluctuations in load; and the boiler BO3, as the main adjusting device, has a large fluctuation range of import and export steam flow throughout the year, reflecting its stronger flexibility and adjusting ability in uncertain scenarios. Through such division of labor and cooperation, the industrial steam system realizes the robustness of operation while ensuring the effective response of the optimization model to the interval parameter changes.

[0182] Please refer to Figure 9 and Figure 10 , Figure 9 The result figure of the extraction turbine inlet flow interval parameter optimization provided according to some embodiments of the present application is shown, Figure 10 The result figure of the extraction turbine outlet flow interval parameter optimization provided according to some embodiments of the present application is shown.

[0183] In some embodiments, the industrial steam system includes 4 extraction turbines, and the multi-period interval parameter optimization results of the extraction turbines after the industrial steam system is optimized by the bi-level programming method are as shown in Figure 9 and Figure 10 Different extraction turbine (ET) units undertake different roles in responding to uncertain disturbances. The upper limit flow of the extraction turbine ET1 fluctuates greatly throughout the year, showing a U-shaped trend, maintaining at a high level in winter (November-December, January-February) and dropping to about 227t / h in summer, so the extraction turbine ET1 undertakes the main role of adjusting in the peak load period in the industrial steam system. The lower limit flow of the extraction turbine ET1 remains stable throughout the year, always maintaining at 221.5t / h, indicating that the extraction turbine ET1 also undertakes the base load role of stable operation in the industrial steam system. The extraction turbine ET2 shows strong seasonal adjustment capacity. Its upper limit flow basically maintains at 200t / h throughout the year, but the lower limit flow drops to the lowest (about 177t / h) in May-July and rises to 195t / h in winter, indicating that the device adjusts the minimum operating load to respond to seasonal fluctuations in uncertainty. The extraction turbine ET3 has relatively small operation fluctuation, and the extraction turbine ET4 shows similar characteristics to the extraction turbine ET2, with the upper and lower limits of the overall flow sinking obviously in summer. In summary, the extraction turbine ET1 and the extraction turbine ET2 mainly undertake the annual main operating load, representing the base load support and main adjustment capacity of the system, while the extraction turbine ET3 and the extraction turbine ET4 play an auxiliary adjustment role, together guaranteeing the stability and economy of the system under interval parameter uncertainty.

[0184] Referring to Figure 11 , Figure 11 A result diagram of the inlet flow range parameter optimization of the pressure reducing valve is shown according to some embodiments of the present application.

[0185] In some embodiments, three pressure reducing valves LV1, LV2 and LV3 can be included in the industrial steam system, and in the optimization strategy, the pressure reducing valve LV1 and the pressure reducing valve LV2 are basically maintained in a state of not being used. Figure 11 The inlet flow range parameters of the pressure reducing valve LV3 are shown, and since the pressure reducing valve is mainly used to adjust the steam demand, when the steam demand is adjusted, the industrial steam system needs to adjust the pressure reducing valve control production, so the upper limit and the lower limit of the pressure reducing valve LV3 sometimes show opposite trends.

[0186] The above results show that the bi-level planning method provided by the present application simulates the interactive decision-making relationship between multiple stakeholders striving for the optimal target, and realizes the Stackelberg equilibrium between the ISS system and the user interests.

[0187] In some non-limiting embodiments, the above-mentioned bi-level planning method for the industrial steam system provided by the first aspect of the present application can be implemented through the above-mentioned bi-level planning system for the industrial steam system provided by the second aspect of the present application.

[0188] Referring to Figure 12 , Figure 12 A schematic diagram of the bi-level planning system for the industrial steam system according to some embodiments of the present application is shown.

[0189] As Figure 12 shown, the bi-level planning system for the industrial steam system 1200 can be configured with a memory 1210 and a processor 1220. The memory 1210 includes but is not limited to the above-mentioned computer readable storage medium 1211 provided by the third aspect of the present application, on which computer instructions are stored. The processor 1220 is connected to the memory 1210 and is configured to execute the computer instructions stored on the memory 1210 to implement the bi-level planning method for the industrial steam system provided by the first aspect of the present application.

[0190] Although the above-mentioned methods are illustrated and described as a series of actions for the sake of simplicity of explanation, it should be understood and appreciated that these methods are not limited by the order of actions, because according to one or more embodiments, some actions can occur in a different order and / or concurrently with other actions illustrated and described herein or not illustrated and described herein but can be understood by those skilled in the art.

[0191] Those skilled in the art will appreciate that information, signals, and data can be represented using any of a variety of different technologies and techniques. For example, data, instructions, commands, information, signals, bits, symbols, and chips that can be referenced throughout the above description can be represented by voltages, currents, electromagnetic waves, magnetic fields or particles, optical fields or particles, or any combination thereof.

[0192] Those skilled in the art will further appreciate that the various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the embodiments disclosed herein can be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Skilled artisans can implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.

[0193] The various illustrative logical blocks, modules, and circuits described in connection with the embodiments disclosed herein can be implemented or performed with a general purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general purpose processor can be a microprocessor, but in the alternative, the processor can be any conventional processor, controller, microcontroller, or state machine. A processor can also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration.

[0194] The steps of a method or algorithm described in connection with the embodiments disclosed herein can be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module can reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor such that the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium can be integral to the processor. The processor and the storage medium can reside in an ASIC. The ASIC can reside in a user terminal. In the alternative, the processor and the storage medium can reside as discrete components in a user terminal.

[0195] In one or more exemplary embodiments, the functions described can be implemented in hardware, software, firmware, or any combination thereof. If implemented in software as a computer program product, the functions can be stored on or transmitted over as one or more instructions or code on a computer-readable medium. Computer-readable media includes both computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. A storage media can be any available media that can be accessed by a computer. By way of example, and not limitation, such computer-readable media can comprise RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer. Also, any connection is properly termed a computer-readable medium. For example, if the software is transmitted from a website, server, or other remote source using a coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL), or wireless technologies such as infrared, radio, and microwave, then the coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwave are included in the definition of medium. Disk and disc, as used herein, includes compact disc (CD), laser disc, optical disc, digital versatile disc (DVD), floppy disk and blu-ray disc where disks usually reproduce data magnetically, while discs reproduce data optically with lasers. Combinations of the above should also be included within the scope of computer-readable media.

[0196] The previous description of the disclosure is provided to enable any person skilled in the art to make or use the disclosure. Various modifications to the disclosure will be readily apparent to those skilled in the art, and the generic principles defined herein can be applied to other variations without departing from the spirit or scope of the disclosure. Thus, the disclosure is not intended to be limited to the examples described herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A bi-level programming method for industrial steam systems, characterized in that, The method comprises the steps of: obtaining operation parameters of each energy device in an industrial steam system and user demand; optimizing operation cost of the energy devices based on upper optimization of a bi-level game model of the industrial steam system constructed based on Stackelberg game theory, and determining energy price through a real-time pricing mechanism; determining energy demand through a demand response mechanism based on the energy price, the lower layer aiming to minimize user energy cost; and adjusting and optimizing operation parameters of the energy devices based on the energy demand determined by the demand response mechanism, so as to achieve Stackelberg equilibrium between the industrial steam system and the user.

2. The bi-level programming method of claim 1, wherein, The construction of the bi-level game model comprises the steps of: constructing a bi-level game model of the industrial steam system based on Stackelberg game theory, the upper layer of the bi-level game model being used to optimize operation cost of the energy devices and determine energy price, the lower layer aiming to minimize user energy cost; determining upper layer strategy of the bi-level game model based on a real-time pricing mechanism, and determining lower layer strategy of the bi-level game model based on a demand response mechanism; and calibrating parameters of the bi-level game model based on historical data. The method further comprises the step of:

3. The bi-level programming method of claim 2, wherein, expressing the bi-level problem of the bi-level game model as a single-level programming problem with equilibrium constraints by using the Kuhn-Tucker condition. The calculation of the operation cost of the energy devices comprises calculation of fuel cost, circulating water cost, purchased steam cost and power cost, and calculation of output power of wind turbines and solar collectors.

4. The bi-level programming method of claim 2, wherein, The real-time pricing mechanism is realized according to a price constraint defined as:

5. The bi-level programming method of claim 4, wherein, The fuel cost C fuel is defined as: where P fuel is the fuel price, is the fuel flow rate of the boilers B1, B2, B3 at time t; The cost C of the circulating water water is defined as: wherein M, M ldv1,in,t , M ldv2,in,t , M ldv3,in,t is the steam load of the steam inlet of each LDV (Letdown Value) device at time t, M ldv1,out,t , M ldv2,out,t , M ldv3,out,t is the steam load of the steam outlet of each LDV device at time t, P water is the circulating water price; The purchase steam cost C steam is defined as: wherein M is the steam load, M d,oss,t , M d,ohs,t , M d,oms,t , M d,old,t is the steam load of the purchased super-high pressure steam, high pressure steam, medium pressure steam and low pressure steam at time t, is the steam price corresponding to the purchased super-high pressure steam, high pressure steam, medium pressure steam and low pressure steam; The electricity cost C grid is defined as: where P ele is the electricity price, P grid,t is the amount of electricity purchased from the grid at time t.

6. The bi-level programming method of claim 4, wherein, The power output P of the wind turbine w is defined as: where v is the wind speed, v c is the cut-in wind speed, v r is the rated wind speed, v f is the cut-off wind speed, P r is the rated power; The thermal energy output Q of the solar collector shc is defined as: Q shc = S shc × F R (G(τa)-U L (T in -T a )), Wherein, S shc is the area of the solar collector, F R is the heat dissipation coefficient, G is the real-time light intensity, ta is the optical efficiency, U L is the heat loss coefficient, T in is the temperature of the fluid entering the solar collector, T a is the ambient temperature.

7. The bi-level programming method of claim 1, wherein, The constraint of the demand response mechanism is: P i,min ≤P i,t ≤P i.max , where P i,min and P i.max are the minimum and maximum price of the i-th grade of steam during the programming period, respectively, and T is the time period, is the average price of the i-th grade of steam on the market, the i-th grade of steam including the outsourced super-high pressure steam, high pressure steam, medium pressure steam and low pressure steam, corresponding to {ss, hs, ms, ls}.

8. The bi-level programming method of claim 1, wherein, The method comprises: M d,ss,t = M fixed,ss,t + M flex,ss,t , M d,hs,t = M fixed,hs,t + M flex,hs,t , M d,ms,t = M fixed,ms,t + M flex,ms,t , M d,ls,t = M fixed,ls,t + M flex,ls,t , M d,i,min ≤M d,i,t ≤M d,i,max , wherein M d,ss,t , M d,hs,t , M d,ms,t , M d,ls,t are the total demand of extra-high pressure steam, high pressure steam, medium pressure steam and low pressure steam, respectively, at time t, M fixed,ss,t , M fixed,hs,t , M fixed,ms,t , M fixed,ls,t are the fixed demand of extra-high pressure steam, high pressure steam, medium pressure steam and low pressure steam, respectively, at time t, M flex,ss,t , M flex,hs,t , M flex,ms,t , M flex,ls,t are the flexible demand of extra-high pressure steam, high pressure steam, medium pressure steam and low pressure steam, respectively, at time t.

9. A bi-level planning system for an industrial steam system, characterized by, a memory having computer instructions stored thereon; and a processor connected to the memory and configured to execute the computer instructions stored on the memory to implement the bi-level programming method for the industrial steam system according to any one of claims 1-8. The computer instructions, when executed by the processor, implement the bi-level programming method for the industrial steam system according to any one of claims 1-8.

10. A computer readable storage medium having stored thereon computer instructions, wherein, ​

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