Optimal configuration method and system for steam heat supply system in coupled green electricity industrial park

Abstract

The invention relates to an optimal configuration method for a steam heat supply system in a coupled green electricity industrial park. The method comprises the following steps: S1, based on a basic energy flow structure of a system, establishing a dynamic model of the steam heating system; s2, on the basis of the established dynamic model of the steam heating system, establishing and solving a dynamic hydraulic analysis model of the steam heating system; and S3, according to a solving result of the steam dynamic hydraulic analysis model, establishing a time-sharing steam price optimization model of the steam heating system of the coupled green electricity industrial park based on a demand response theory, and carrying out solving to obtain an optimal time-sharing steam price scheme of the system. And S4, on the basis of the system optimal time-sharing steam price scheme, constructing a system double-layer optimization configuration model, and optimizing and coupling the power-to-heat equipment configuration capacity and scheduling method of the steam heat supply system of the green electric power industrial park. The method provides a new theoretical support and practical path for optimized operation of the industrial comprehensive energy system, and has important economic and environmental benefits.

Description

Technical Field

[0001] The present invention belongs to the field of operation optimization of industrial parks, and specifically relates to a method and system for optimizing the configuration of a steam heating system in a green-electricity-coupled industrial park. Background Art

[0002] In China's industrial park energy consumption, industrial steam heating accounts for about 70% of my country's total industrial heating demand. As a key energy consumption link, the steam heating system is facing the challenge of transforming from a traditional high-carbon emission system to a green industrial integrated energy system. In the future, low-carbon industrial parks will present the randomness of both source and load, and it is urgent to integrate source and load resources and give full play to the complementary characteristics of flexible resources. Therefore, the demand response optimization research of industrial park steam heating systems has important guiding significance for promoting the green and low-carbon transformation of my country's industrial park steam heating systems. Summary of the invention

[0003] The present invention aims to provide a method and system for optimizing the configuration of a steam heating system coupled with a green power industrial park, so as to solve the two-sided randomness between the source and the load in the steam heating system, thereby realizing the effective integration of the source and load resources. The present invention carries out demand response management in the traditional steam heating system, adjusts the user-side load by time-of-use steam prices, reduces the peak-to-valley difference of the user load, and improves the flexibility of the system. On this basis, the system electricity substitution transformation is carried out to promote the consumption of renewable energy and reduce the system carbon emissions. Furthermore, based on the optimization pricing results of the time-of-use steam price, a two-layer optimization configuration model taking into account the time-of-use steam price is established, which reduces the system operation and configuration costs.

[0004] In order to solve the above technical problems, the present invention provides a method for optimizing the configuration of a steam heating system in a green power industrial park, comprising the following steps:

[0005] Step S1, establishing a dynamic model of the steam heating system based on the basic energy flow structure of the system;

[0006] Step S2, based on the established dynamic model of the steam heating system, establish and solve a dynamic hydraulic analytical model of the steam heating system;

[0007] Step S3, according to the solution result of the steam dynamic hydraulic analytical model, based on the demand response theory, a time-sharing steam price optimization model of the steam heating system of the coupled green electricity industrial park is established and solved to obtain the optimal time-sharing steam price solution of the system;

[0008] Step S4, based on the optimal time-sharing steam price scheme of the system, a system double-layer optimization configuration model is constructed to optimize the configuration capacity and scheduling method of the electric heat conversion equipment of the steam heating system coupled with the green power industrial park.

[0009] In the above technical solution, further, in step S1, a dynamic model of the steam heating system is established based on the basic energy flow structure of the system, and the specific steps include:

[0010] Step S11, constructing mass, momentum and energy equations for steam transportation according to graph theory principles;

[0011] Step S12, constructing a correlation matrix describing the topological structure of the steam heating system of the industrial park;

[0012] Step S13, constructing a dynamic model of the steam heating system based on the results of S11 and S12.

[0013] Furthermore, the mass, momentum and energy equations of steam transport in step S11 are respectively:

[0014]

[0015] in:

[0016]

[0017] In addition, the momentum equation and energy equation can be expanded as follows:

[0018]

[0019] Where: subscript s represents steam; Us and Zs represent the specific internal energy vector and gravitational potential energy vector of steam respectively; t represents time; ρ s is the steam density, kg / m 3 ;v s is the steam velocity, m / s; p s is the steam pressure, MPa; u s is the specific internal energy of steam, kJ / kg; g s is the gravitational acceleration of steam, m / s 2 θ s is the steam pipe inclination angle, °; λ s is the steam resistance coefficient; D s is the diameter of the steam pipe, m; K s is the steam heat transfer coefficient, kW / (m 2 ·K); T s is the steam fluid temperature, K; T s,us is the ambient temperature, K.

[0020] Furthermore, the step S12 is specifically as follows:

[0021] For a steam network with l nodes, m pipe sections (branches), and k closed loops, the node-pipeline association matrix is:

[0022]

[0023] Its matrix element a ij is defined as:

[0024]

[0025] Since the node flow in the closed network is conserved, we can get:

[0026] AR=Q

[0027] Where: R is the flow vector in each pipe section, R = [R1, R2, ..., R m ] T ; Q is the net mass flow vector of each node, Q = [Q1, Q2, ..., Q m ] T , generally take the outflow of the node as a positive value and the inflow of the node as a negative value.

[0028] The loop-link association matrix is:

[0029]

[0030] Its matrix element b ij is defined as:

[0031]

[0032] Since the pressure drop and temperature drop in the closed loop are 0, we can get:

[0033] BΔP=0

[0034] BΔT=0

[0035] Where: ΔP is the voltage drop matrix in the closed network, ΔP = [ΔP1, ΔP2, ..., ΔP m ] T , ΔP m represents the pressure drop of pipe segment m in the closed network, ΔT is the temperature drop matrix in the closed network, ΔT=[ΔT1,ΔT2,…,ΔT m ] T , ΔT m Represents the temperature drop of pipe segment m in the closed network.

[0036] In addition, when the temporal changes of the state parameters of the steam are ignored, the mass equation of the steam transport in step S11 will be simplified to:

[0037]

[0038] Furthermore, the step S13: constructing a dynamic model of the steam heating system based on the results of S11 and S12, the specific method is as follows:

[0039] First, the following three assumptions are established:

[0040] (1) Assume that the fluid is a single-phase compressible fluid.

[0041] Based on actual engineering experience, steam transportation conditions can be classified into three categories according to pressure: low pressure, medium pressure, and high pressure. Among them, the pressure of low-pressure steam is generally 0.5-1.0MPa, and the steam supply temperature is 180-280℃; the pressure of medium-pressure steam is generally 1.1-3.3MPa, and the steam supply temperature is 260-320℃; the pressure of high-pressure steam is generally 3.4-10MPa, and the steam supply temperature is 290-360℃. Based on this, the dynamic modeling and simulation research of steam can be divided into three conditions: low pressure, medium pressure, and high pressure. The steam under the three conditions is generally superheated steam, and there is no condensation or hydrophobicity during the transportation process. Therefore, the steam can be assumed to be a single-phase compressible fluid, which is in line with the actual operation of the project.

[0042] (2) Simplification of some terms in the momentum equation. The five terms on the left side of the momentum equation in the formula proposed by S11 respectively describe the acceleration, convection effect, static effect, gravity effect and resistance effect of the energy fluid during pipeline transportation.

[0043] The steam transport velocity ranges from 0 to 50 m / s, which is much smaller than the speed of sound. The value of the second convection term approaches 0 and can be ignored.

[0044]

[0045] The squared velocity term in the resistance term is further approximated by incremental linearization:

[0046]

[0047] Where: is the base value of fluid velocity, taking the historical operating value, m / s; It is the fluctuation of actual flow velocity relative to the base value, m / s.

[0048] (3) Ignore the pipeline inclination angle and consider all pipelines to be horizontal. The steam heating pipeline network in industrial parks is generally an overhead pipeline or an underground pipeline, so the pipeline inclination angle is 0, and the vertical pipe section is not considered. The fourth term in the momentum equation and the third term in the energy equation become:

[0049] gρsinθ=0

[0050] gρvsinθ=0

[0051] Then, the partial differential equations describing the one-dimensional dynamic transport process of steam can be obtained, which is the dynamic model of the steam heating system:

[0052]

[0053] Where x represents the length direction vector of the pipeline, m; h s It represents specific enthalpy, kJ / kg.

[0054] Furthermore, in step S2, based on the established dynamic model of the steam heating system, a steam dynamic hydraulic analysis model is proposed and a solution method thereof is proposed, and the specific steps include: step S21, establishing a steam heating system dynamic hydraulic analysis model based on the dynamic model of the steam heating system established in step S1; step S22, solving the steam heating system dynamic hydraulic analysis model according to network topology constraints and boundary conditions.

[0055] Furthermore, in step S21, based on the dynamic model of the steam heating system established in S1, a dynamic hydraulic analytical model of the steam heating system is established by introducing a reference temperature assumption and an ideal gas assumption:

[0056]

[0057] Where: T b is the reference temperature, K; R g is the gas constant of steam, KJ / (kg·K); G is the mass flow rate, kg / s; A is the cross-sectional area of ​​the pipe, m 2 .

[0058] Furthermore, in step S22, the dynamic hydraulic analytical model of the steam heating system is solved according to the network topology constraints and boundary conditions, and the specific method is as follows:

[0059] Firstly, the obtained dynamic hydraulic analytical model of the steam heating system is converted into an ordinary differential equation in the complex domain by Fourier transform, and then solved by a single frequency process:

[0060]

[0061] Where: are the flow rate and pressure in the complex domain respectively; i is the imaginary unit; w is the frequency component.

[0062] Since the temperature T b is a fixed value, the analytical solution can be obtained:

[0063]

[0064] In the formula, is the analytical solution of the flow rate at a pipe length of l under a single frequency, is the analytical solution of the pressure at a pipe length of l at a single frequency, represents the initial flow in the complex domain, represents the reference pressure in the complex domain,

[0065] Then, based on the above single-frequency process solution process, according to the network topology constraints and boundary conditions, the multi-frequency results of the dynamic hydraulic analytical model of the steam heating system are solved. The specific method is:

[0066] The network topology constraint condition is to subdivide the node-pipeline association matrix A into the "node-outflow" pipeline matrix A _ and the node-inflow pipeline matrix A + . "Node-outflow" pipeline matrix A _ Keep the non-positive elements of A; the "node-inflow" pipeline matrix A + Keep the non-negative elements of A. (A) i,j ,(A - ) i,j ,(A + ) i,j Describe the relationship between the nodes in the i-th row and the pipelines in the j-th column in the three matrices respectively:

[0067]

[0068] The boundary conditions are flow constraint, closed-loop pressure drop constraint and head-end pressure constraint.

[0069] The model is solved by combining network topology constraints and boundary conditions. The specific method is as follows: in order to balance the solution accuracy and efficiency, the gas constant calculation, cutoff frequency calculation, solution time window application and flow rate base value correction method are introduced to simplify the dynamic hydraulic analytical model of the steam heating system and carry out iterative solution. Finally, the calculation of the iterative error is used as the convergence condition for the model solution.

[0070] Furthermore, in step S3, according to the solution results of the steam dynamic hydraulic analysis model, a time-sharing steam price optimization model of the steam heating system of the coupled green electricity industrial park is established and solved based on the demand response theory to obtain the system's optimal time-sharing steam price solution, specifically including: step S31, time period division and user response characteristic analysis; step S32, based on the analysis results of step S31, a system time-sharing steam price optimization model is established and solved.

[0071] The step S31, time period division and user response characteristic analysis, is specifically performed as follows:

[0072] In the face of the adjustment of time-of-use steam prices, the park's steam users will respond according to their own steam demand and spontaneously transfer loads. There are three main types of load transfer: load transfer from peak hours to normal hours, load transfer from peak hours to valley hours, and load transfer from normal hours to valley hours.

[0073] Calculate the peak-to-valley load transfer rate of the i-th type of steam user in the park:

[0074]

[0075] Where: subscript pv represents peak-to-valley, ΔP pv is the peak-valley gas price difference, k pvi is the slope of the peak-to-valley load transfer linear region, a pvi and b pvi are the thresholds of the dead zone and linear zone of the peak-valley period gas price difference of the i-th user, It is the maximum value of the load transfer rate when the price difference between peak and valley periods reaches saturation and tends to be stable;

[0076] Calculate the flat-to-valley load transfer rate of the i-th type of steam user in the park:

[0077]

[0078] Where: fv represents flat-to-valley, ΔP fv is the Pinggu gas price difference, k fvi is the slope of the linear region of flat-valley load transfer, a fvi and b fvi are the thresholds of the dead zone and linear zone of the flat-valley period steam price difference of the i-th user, It is the maximum value of the load transfer rate when the electricity price difference between flat and valley periods reaches saturation and tends to be stable.

[0079] Under the influence of peak and valley time-of-use steam prices, considering the uncertainty of load transfer, the trapezoidal membership function μ(λ i ) to correct the load transfer rate:

[0080]

[0081] Where: and They represent the peak-to-flat, peak-to-valley, and flat-to-valley load transfer rates of user i after considering response uncertainty;

[0082]

[0083] Where: i is the user load; i1 ,λ i2 ,λ i3 and λ i4 is the membership parameter determined by the user response characteristics, where λ i1 represents the lower left corner of the trapezoidal membership function, i.e., the threshold at which the load transfer rate begins to increase significantly; λ i2 represents the upper left corner point of the trapezoidal membership function, i.e., the threshold value where the load transfer rate changes from increasing to stable; λi3 represents the lower right corner of the trapezoidal membership function, i.e., the threshold value at which the load transfer rate changes from stable to reduced; λ i4 It represents the upper right corner point of the trapezoidal membership function, which is the threshold value at which the load transfer rate is significantly reduced.

[0084] After considering the response uncertainty, the load of the i-th user in period t is expressed as:

[0085]

[0086] Where: L tp , L tf , L tv are the loads after considering the uncertainty in peak, flat and valley periods, L tp0 , L tf0 , L tv0 are the loads without considering uncertainty during peak, flat and valley periods, and are the average loads of the i-th user during the average peak period and the average loads during the normal period respectively; and They represent the peak-to-flat, peak-to-valley, and flat-to-valley load transfer rates of user i after considering response uncertainty; T p , T f and T v Represent peak, flat and valley time periods respectively.

[0087] Furthermore, the step S32 establishes and solves the time-sharing steam price optimization model of the steam heating system coupled with the green electricity industrial park, and the specific steps are as follows:

[0088] Step S321, setting the optimization objective function to the minimum peak-valley load difference and the minimum user steam purchase cost, expressed as:

[0089] (1) The peak-to-valley average load difference is minimal:

[0090] F1=min(L p -L v )

[0091] Where, L p and L v They are the average load after considering uncertainty in normal period and the average load after considering uncertainty in valley period.

[0092] (2) Steam users have the lowest total steam purchase cost:

[0093]

[0094] Where: L pt , L ft , L vtare the user loads during peak, flat and valley periods respectively; P p , P f , P v These are the time-of-day gas prices during peak, normal and valley periods respectively.

[0095] Step S322, analyzing the constraints:

[0096] (1) After the time-sharing steam price response, the total amount of steam used by users remains unchanged:

[0097]

[0098] (2) After the time-sharing gas price response, the user's gas purchase fee cannot increase, otherwise the user will refuse to participate in the response:

[0099]

[0100] (3) To avoid peak-valley inversion after load regulation, a reasonable upper limit of the peak-valley steam price ratio r is set. b With the lower limit r a :

[0101]

[0102] (4) In order to ensure that the time-sharing gas price response has a good participation rate, the upper and lower limits of the gas price in each period are constrained:

[0103] P pmin ≤P p ≤P pmax

[0104] P fmin ≤P f ≤P fmax

[0105] P vmin ≤P v ≤P vmax

[0106] Among them, P pmin and P pmax They represent the lower and upper limits of the gasoline price during normal periods; P fmin and P fmax They represent the lower and upper limits of the gas price during peak hours respectively; P vmin and P vmax They respectively represent the lower and upper limits of gasoline prices during off-peak hours.

[0107] Step S323, using NSGA-II algorithm to solve the model and obtain the optimal time-sharing steam price solution for the system.

[0108] Furthermore, step S4 combines the power system, steam heating system and steam user demand response of the industrial park to construct a two-layer optimization configuration model, and optimizes the configuration capacity and operation scheduling of the electric heat conversion equipment in the steam heating system of the coupled green electricity industrial park. The specific steps are as follows:

[0109] Step S41, the upper layer optimization configuration model takes minimizing the operation cycle cost as the optimization goal, and the calculation formula is:

[0110] F(x)=min(C C +C M +f(x)

[0111] Where: C C represents the equipment investment cost, yuan; C M represents the equipment maintenance cost, RMB; f(x) is the system operation cost, RMB.

[0112] The capacity configuration of each device in the system has upper and lower limit constraints, and the calculation formula is:

[0113]

[0114] Where: C i and represents the capacity of device i, kW; device i includes wind turbines, photovoltaic units, electric boilers, and molten salt thermal storage equipment. They represent the upper and lower limits of the configuration capacity of device i respectively.

[0115] Combined with the optimal operation strategy obtained in the lower layer, the NSGA algorithm is used to solve the optimal configuration capacity of green power related equipment.

[0116] Step S42, the lower-level optimization scheduling model uses a deterministic algorithm to carry out system scheduling optimization (solving the minimum system operating cost) with the goal of economy, based on the green electricity-related equipment capacity given by the upper-level model and the system time-of-use steam price optimization pricing results of S3, under the constraints of the system demand-side user steam load, system topology, and system unit configuration on a typical day, and returns the optimization results to the upper layer, and obtains the optimal equipment configuration plan and operation strategy through loop iteration.

[0117] Among them, the minimum value of system operation cost is solved, and the objective function calculation formula is:

[0118] f(x)=min[C O +f env (x)]

[0119] Where: C O represents the equipment operating cost, yuan; f env (x) represents pollutant emission tax, RMB.

[0120] CO The calculation formula is:

[0121]

[0122] Where: F(t) represents the standard coal consumption in period t, kg; C coal (t) represents the standard coal price in period t, RMB / kg; M represents the number of electricity price types; E(t) represents the power consumption in period t, kW·h; C elec,k (t) represents the electricity price of the kth type of electricity in period t, in yuan / (kW·h).

[0123] Pollutant emission tax env (x) can be calculated based on the tax set by the state for various pollutants. By explicitly quantifying environmental benefits as economic costs, the environmental friendliness of the system can be more intuitively reflected. The calculation formula is:

[0124]

[0125] Where: P chp (t) represents the power of the cogeneration unit during period t, E chp (j) represents the emission coefficient of the jth pollutant of the cogeneration unit, g / (kW·h); n w represents the total number of pollutant types, δ(j) is an indicator function that takes 0 or 1. This invention only considers CO2, SO2, and NO X and CO, so when the jth pollutant is CO2, SO2, NO X When j is one of the four pollutants, CO or CO, δ(j) takes 1, and when j is other pollutants, it takes 0.

[0126] The present invention also provides a system for optimizing the configuration of a steam heating system in a coupled green electricity industrial park, and the system adopts the above method to achieve the optimized configuration of a steam heating system in a coupled green electricity industrial park.

[0127] The beneficial effects of the present invention are:

[0128] The present invention provides an optimization configuration method for a steam heating system coupled with a green power industrial park, which realizes the effective integration between source and load, and significantly improves the operation efficiency and flexibility of the steam heating system. This method is not only based on dynamic models and demand response theory, combined with time-sharing steam price optimization and a two-layer optimization configuration model, but also can adapt to the randomness and uncertainty of electric-to-heat equipment in industrial parks. This innovation provides strong support for promoting the green transformation of low-carbon industrial parks. By comprehensively considering operating costs, user response characteristics and environmental impacts, the system can promote efficient resource utilization and environmental protection while ensuring economy, thereby effectively coping with the uncertainty brought by renewable energy and improving the safety and stability of the energy system. The present invention provides a new theoretical support and practical path for the optimized operation of industrial integrated energy systems, which has important economic and environmental benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0129] Figure 1 It is a simplified flow chart of the method of the present invention;

[0130] Figure 2 The invention provides a double-layer optimization configuration framework for the steam heating system of a coupled green electricity industrial park taking into account the time-sharing steam price. DETAILED DESCRIPTION

[0131] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, which only illustrate the basic structure of the present invention in a schematic manner, and therefore only show the components related to the present invention.

[0132] like Figure 1 As shown, the present invention provides an optimization configuration method for a steam heating system coupled to a green power industrial park, comprising the following steps:

[0133] Step S1, establishing a dynamic model of the steam heating system based on the basic energy flow structure of the system;

[0134] Step S2, based on the established dynamic model of the steam heating system, establish and solve a dynamic hydraulic analytical model of the steam heating system;

[0135] Step S3, according to the solution result of the steam dynamic hydraulic analytical model, based on the demand response theory, a time-sharing steam price optimization model of the steam heating system of the coupled green electricity industrial park is established and solved to obtain the optimal time-sharing steam price solution of the system;

[0136] Step S4, based on the optimal time-sharing steam price scheme of the system, a system double-layer optimization configuration model is constructed to optimize the configuration capacity and scheduling method of the electric heat conversion equipment of the steam heating system coupled with the green power industrial park.

[0137] The step S1 specifically includes the following steps:

[0138] Step S11, constructing mass, momentum and energy equations for steam transportation according to graph theory principles;

[0139] Step S12, constructing a correlation matrix describing the topological structure of the steam heating system of the industrial park;

[0140] Step S13, constructing a dynamic model of the steam heating system based on the results of S11 and S12.

[0141] The mass, momentum and energy equations of steam transport in step S11 are:

[0142]

[0143]

[0144]

[0145] In addition, the momentum equation and energy equation can be expanded as follows:

[0146]

[0147] Where: subscript s represents steam; Us and Zs represent the specific internal energy vector and gravitational potential energy vector of steam respectively; t represents time; ρ s is the steam density, kg / m 3 ;v s is the steam velocity, m / s; p s is the steam pressure, MPa; u s is the specific internal energy of steam, kJ / kg; g s is the gravitational acceleration of steam, m / s 2 θ s is the steam pipe inclination angle, °; λ s is the steam resistance coefficient; D s is the diameter of the steam pipe, m; K s is the steam heat transfer coefficient, kW / (m 2 ·K); T s is the steam fluid temperature, K; T s,us is the ambient temperature, K.

[0148] The step S12 is specifically as follows:

[0149] For a steam network with l nodes, m pipe sections (branches), and k closed loops, the node-pipeline association matrix is:

[0150]

[0151] Its matrix element a ij is defined as:

[0152]

[0153] Since the node flow in the closed network is conserved, we can get:

[0154] AR=Q

[0155] Where: R is the flow vector in each pipe section, R = [R1, R2, ..., R m ] T ; Q is the net mass flow vector of each node, Q = [Q1, Q2, ..., Q m ] T , generally take the outflow of the node as a positive value and the inflow of the node as a negative value.

[0156] The loop-link association matrix is:

[0157]

[0158] Its matrix element b ij is defined as:

[0159]

[0160] Since the pressure drop and temperature drop in the closed loop are 0, we can get:

[0161] BΔP=0

[0162] BΔT=0

[0163] Where: ΔP is the voltage drop matrix in the closed network, ΔP = [ΔP1, ΔP2, ..., ΔP m ] T , ΔP m represents the pressure drop of pipe segment m in the closed network, ΔT is the temperature drop matrix in the closed network, ΔT=[ΔT1,ΔT2,…,ΔT m ] T , ΔT m Represents the temperature drop of pipe segment m in the closed network.

[0164] In addition, when the temporal changes of the state parameters of the steam are ignored, the mass equation of the steam transport in step S11 will be simplified to:

[0165]

[0166] The step S13: constructing a dynamic model of the steam heating system based on the results of S11 and S12, the specific method is as follows:

[0167] First, the following three assumptions are established:

[0168] (1) Assume that the fluid is a single-phase compressible fluid.

[0169] Based on actual engineering experience, steam transportation conditions can be classified into three categories according to pressure: low pressure, medium pressure, and high pressure. Among them, the pressure of low-pressure steam is generally 0.5-1.0MPa, and the steam supply temperature is 180-280℃; the pressure of medium-pressure steam is generally 1.1-3.3MPa, and the steam supply temperature is 260-320℃; the pressure of high-pressure steam is generally 3.4-10MPa, and the steam supply temperature is 290-360℃. Based on this, the dynamic modeling and simulation research of steam can be divided into three conditions: low pressure, medium pressure, and high pressure. The steam under the three conditions is generally superheated steam, and there is no condensation or hydrophobicity during the transportation process. Therefore, the steam can be assumed to be a single-phase compressible fluid, which is in line with the actual operation of the project.

[0170] (2) Simplification of some terms in the momentum equation. The five terms on the left side of the momentum equation in the formula proposed by S11 respectively describe the acceleration, convection effect, static effect, gravity effect and resistance effect of the energy fluid during pipeline transportation.

[0171] The steam transport velocity ranges from 0 to 50 m / s, which is much smaller than the speed of sound. The value of the second convection term approaches 0 and can be ignored.

[0172]

[0173] The squared velocity term in the resistance term is further approximated by incremental linearization:

[0174]

[0175] Where: is the base value of fluid velocity, taking the historical operating value, m / s; It is the fluctuation of actual flow velocity relative to the base value, m / s.

[0176] (3) Ignore the pipeline inclination angle and consider all pipelines to be horizontal. The steam heating pipeline network in industrial parks is generally an overhead pipeline or an underground pipeline, so the pipeline inclination angle is 0, and the vertical pipe section is not considered. The fourth term in the momentum equation and the third term in the energy equation become:

[0177] gρsinθ=0

[0178] gρvsinθ=0

[0179] Then, the partial differential equations describing the one-dimensional dynamic transport process of steam can be obtained, which is the dynamic model of the steam heating system:

[0180]

[0181] Where x represents the length direction vector of the pipeline, m; h s It represents specific enthalpy, kJ / kg.

[0182] In the step S2, based on the established dynamic model of the steam heating system, a steam dynamic hydraulic analytical model is proposed and a solution method thereof is provided. The specific steps include: step S21, establishing a steam heating system dynamic hydraulic analytical model based on the dynamic model of the steam heating system established in step S1; step S22, solving the steam heating system dynamic hydraulic analytical model according to network topology constraints and boundary conditions.

[0183] In step S21, based on the dynamic model of the steam heating system established in S1, a dynamic hydraulic analytical model of the steam heating system is established by introducing the reference temperature assumption and the ideal gas assumption:

[0184]

[0185] Where: T b is the reference temperature, K; R g is the gas constant of steam, KJ / (kg·K); G is the mass flow rate, kg / s; A is the cross-sectional area of ​​the pipe, m 2 .

[0186] In step S22, the dynamic hydraulic analytical model of the steam heating system is solved according to the network topology constraints and boundary conditions. The specific method is as follows:

[0187] Firstly, the obtained dynamic hydraulic analytical model of the steam heating system is converted into an ordinary differential equation in the complex domain by Fourier transform, and then solved by a single frequency process:

[0188]

[0189] Where: are the flow rate and pressure in the complex domain respectively; i is the imaginary unit; w is the frequency component.

[0190] Since the temperature T b is a fixed value, the analytical solution can be obtained:

[0191]

[0192] In the formula, is the analytical solution of the flow rate at a pipe length of l under a single frequency, is the analytical solution of the pressure at a pipe length of l at a single frequency, represents the initial flow in the complex domain, represents the reference pressure in the complex domain,

[0193] Then, based on the above single-frequency process solution process, according to the network topology constraints and boundary conditions, the multi-frequency results of the dynamic hydraulic analytical model of the steam heating system are solved. The specific method is:

[0194] The network topology constraint condition is to subdivide the node-pipeline association matrix A into the "node-outflow" pipeline matrix A _ and the node-inflow pipeline matrix A + . "Node-outflow" pipeline matrix A _ Keep the non-positive elements of A; the "node-inflow" pipeline matrix A + Keep the non-negative elements of A. (A) i,j ,(A - ) i,j ,(A + ) i,j Describe the relationship between the nodes in the i-th row and the pipelines in the j-th column in the three matrices respectively:

[0195]

[0196] The boundary conditions are flow constraint, closed-loop pressure drop constraint and head-end pressure constraint.

[0197] The model is solved by combining network topology constraints and boundary conditions. The specific method is as follows: in order to balance the solution accuracy and efficiency, the gas constant calculation, cutoff frequency calculation, solution time window application and flow rate base value correction method are introduced to simplify the dynamic hydraulic analytical model of the steam heating system and carry out iterative solution. Finally, the calculation of the iterative error is used as the convergence condition for the model solution.

[0198] In the step S3, according to the solution results of the steam dynamic hydraulic analysis model, a time-sharing steam price optimization model of the steam heating system of the coupled green electricity industrial park is established and solved based on the demand response theory to obtain the optimal time-sharing steam price scheme of the system, which specifically includes: step S31, time period division and user response characteristic analysis; step S32, based on the analysis results of step S31, a system time-sharing steam price optimization model is established and solved.

[0199] The step S31, time period division and user response characteristic analysis, is specifically performed as follows:

[0200] In the face of the adjustment of time-of-use steam prices, the park's steam users will respond according to their own steam demand and spontaneously transfer loads. There are three main types of load transfer: load transfer from peak hours to normal hours, load transfer from peak hours to valley hours, and load transfer from normal hours to valley hours.

[0201] Calculate the peak-to-valley load transfer rate of the i-th type of steam user in the park:

[0202]

[0203] Where: subscript pv represents peak-to-valley, ΔP pv is the peak-valley gas price difference, k pvi is the slope of the peak-to-valley load transfer linear region, a pvi and b pvi are the thresholds of the dead zone and linear zone of the peak-valley period gas price difference of the i-th user, It is the maximum value of the load transfer rate when the price difference between peak and valley periods reaches saturation and tends to be stable;

[0204] Calculate the flat-to-valley load transfer rate of the i-th type of steam user in the park:

[0205]

[0206] Where: fv represents flat-to-valley, ΔP fv is the Pinggu gas price difference, k fvi is the slope of the linear region of flat-valley load transfer, a fvi and b fvi are the thresholds of the dead zone and linear zone of the price difference between the flat and valley periods for the i-th user, It is the maximum value of the load transfer rate when the electricity price difference between flat and valley periods reaches saturation and tends to be stable.

[0207] Under the influence of peak and valley time-of-use steam prices, considering the uncertainty of load transfer, the trapezoidal membership function μ(λ i ) to correct the load transfer rate:

[0208]

[0209] Where: and They represent the peak-to-flat, peak-to-valley, and flat-to-valley load transfer rates of user i after considering response uncertainty;

[0210]

[0211] Where: i is the user load; i1 ,λ i2 ,λ i3 and λ i4 is the membership parameter determined by the user response characteristics, where λ i1 represents the lower left corner of the trapezoidal membership function, i.e., the threshold at which the load transfer rate begins to increase significantly; λ i2 represents the upper left corner point of the trapezoidal membership function, i.e., the threshold value where the load transfer rate changes from increasing to stable; λ i3 represents the lower right corner of the trapezoidal membership function, i.e., the threshold value at which the load transfer rate changes from stable to reduced; λ i4It represents the upper right corner point of the trapezoidal membership function, which is the threshold value at which the load transfer rate is significantly reduced.

[0212] After considering the response uncertainty, the load of the i-th user in period t is expressed as:

[0213]

[0214] Where: L tp , L tf , L tv are the loads after considering the uncertainty in peak, flat and valley periods, L tp0 , L tf0 , L tv0 are the loads without considering uncertainty during peak, flat and valley periods, and are the average loads of the i-th user during the average peak period and the average loads during the normal period respectively; and They represent the peak-to-flat, peak-to-valley, and flat-to-valley load transfer rates of user i after considering response uncertainty; T p , T f and T v Represent peak, flat and valley time periods respectively.

[0215] The step S32 establishes and solves the time-sharing steam price optimization model for the steam heating system of the coupled green power industrial park, and the specific steps are as follows:

[0216] Step S321, setting the optimization objective function to the minimum peak-valley load difference and the minimum user steam purchase cost, expressed as:

[0217] (1) The peak-to-valley average load difference is minimal:

[0218] F1=min(L p -L v )

[0219] Where, L p and L v They are the average load after considering uncertainty in normal period and the average load after considering uncertainty in valley period.

[0220] (3) Steam users have the lowest total steam purchase cost:

[0221]

[0222] Where: L pt , L ft , L vt are the user loads during peak, flat and valley periods respectively; P p , P f , P vThese are the time-of-day gas prices during peak, normal and valley periods respectively.

[0223] Step S322, analyzing the constraints:

[0224] (1) After the time-sharing steam price response, the total amount of steam used by users remains unchanged:

[0225]

[0226] (2) After the time-sharing gas price response, the user's gas purchase fee cannot increase, otherwise the user will refuse to participate in the response:

[0227]

[0228] (3) To avoid peak-valley inversion after load regulation, a reasonable upper limit of the peak-valley steam price ratio r is set. b With the lower limit r a :

[0229]

[0230] (4) In order to ensure that the time-sharing gas price response has a good participation rate, the upper and lower limits of the gas price in each period are constrained:

[0231] P pmin ≤P p ≤P pmax

[0232] P fmin ≤P f ≤P fmax

[0233] P vmin ≤P v ≤P vmax

[0234] Among them, P pmin and P pmax They represent the lower and upper limits of the gasoline price during normal periods; P fmin and P fmax They represent the lower and upper limits of the gas price during peak hours respectively; P vmin and P vmax They respectively represent the lower and upper limits of gasoline prices during off-peak hours.

[0235] Step S323, using NSGA-II algorithm to solve the model and obtain the optimal time-sharing steam price solution for the system.

[0236] The step S4 combines the power system, steam heating system and steam user demand response of the industrial park to construct a two-layer optimization configuration model, and conducts optimization analysis on the configuration capacity and operation scheduling of the electric heat conversion equipment in the steam heating system of the coupled green electricity industrial park. The specific steps are as follows:

[0237] Step S41, the upper layer optimization configuration model takes minimizing the operation cycle cost as the optimization goal, and the calculation formula is:

[0238] F(x)=min(C C +C M +f(x)

[0239] Where: C C represents the equipment investment cost, yuan; C M represents the equipment maintenance cost, RMB; f(x) is the system operation cost, RMB.

[0240] The capacity configuration of each device in the system has upper and lower limit constraints, and the calculation formula is:

[0241]

[0242] Where: C i and represents the capacity of device i, kW; device i includes wind turbines, photovoltaic units, electric boilers, and molten salt thermal storage equipment. They represent the upper and lower limits of the configuration capacity of device i respectively.

[0243] Combined with the optimal operation strategy obtained in the lower layer, the NSGA algorithm is used to solve the optimal configuration capacity of green power related equipment.

[0244] Step S42, the lower-level optimization scheduling model uses a deterministic algorithm to carry out system scheduling optimization (solving the minimum system operating cost) with the goal of economy, based on the green electricity-related equipment capacity given by the upper-level model and the system time-of-use steam price optimization pricing results of S3, under the constraints of the system demand-side user steam load, system topology, and system unit configuration on a typical day, and returns the optimization results to the upper layer, and obtains the optimal equipment configuration plan and operation strategy through loop iteration.

[0245] Among them, the minimum value of system operation cost is solved, and the objective function calculation formula is:

[0246] f(x)=min[C O +f env (x)]

[0247] Where: C O represents the equipment operating cost, yuan; f env (x) represents pollutant emission tax, RMB.

[0248] C O The calculation formula is:

[0249]

[0250] Where: F(t) represents the standard coal consumption in period t, kg; Ccoal (t) represents the standard coal price in period t, RMB / kg; M represents the number of electricity price types; E(t) represents the power consumption in period t, kW·h; C elec,k (t) represents the electricity price of the kth type of electricity in period t, in yuan / (kW·h).

[0251] Pollutant emission tax env (x) can be calculated based on the tax set by the state for various pollutants. By explicitly quantifying environmental benefits as economic costs, the environmental friendliness of the system can be more intuitively reflected. The calculation formula is:

[0252]

[0253] Where: P chp (t) represents the power of the cogeneration unit during period t, E chp (j) represents the emission coefficient of the jth pollutant of the cogeneration unit, g / (kW·h); n w represents the total number of pollutant types, δ(j) is an indicator function that takes 0 or 1. This invention only considers CO2, SO2, and NO X and CO, so when the jth pollutant is CO2, SO2, NO X When j is one of the four pollutants, CO or CO, δ(j) takes 1, and when j is other pollutants, it takes 0.

[0254] An embodiment of the present invention further provides a system for optimizing the configuration of a steam heating system in a coupled green electricity industrial park. The system adopts the above method to achieve the optimized configuration of a steam heating system in a coupled green electricity industrial park.

Claims

1. A method for optimizing the configuration of a steam heating system in a green power industrial park, characterized in that: The steps include: Step S1, establishing a dynamic model of the steam heating system based on the basic energy flow structure of the system; Step S2, based on the established dynamic model of the steam heating system, establish and solve a dynamic hydraulic analytical model of the steam heating system; Step S3, according to the solution result of the steam dynamic hydraulic analytical model, based on the demand response theory, a time-sharing steam price optimization model of the steam heating system of the coupled green electricity industrial park is established and solved to obtain the optimal time-sharing steam price solution of the system; Step S4, based on the optimal time-sharing steam price scheme of the system, a system double-layer optimization configuration model is constructed to optimize the configuration capacity and scheduling method of the electric heat conversion equipment of the steam heating system coupled with the green power industrial park.

2. The method for optimizing the configuration of the steam heating system in a coupled green electricity industrial park according to claim 1 is characterized in that: The step S1 includes: constructing mass, momentum and energy equations for steam transportation according to graph theory principles; and determining a dynamic model of a steam heating system based on the mass, momentum and energy equations for steam transportation and a topological structure of a steam heating system in an industrial park.

3. The method for optimizing configuration of steam heating system in coupled green electricity industrial park according to claim 2 is characterized in that: The step S2 includes: establishing a dynamic hydraulic analytical model of the steam heating system based on the dynamic model of the steam heating system established in S1, and analytically solving the dynamic hydraulic analytical model of the steam heating system in combination with network topology constraints and boundary conditions.

4. The method for optimizing configuration of steam heating system in coupled green electricity industrial park according to claim 3 is characterized in that: The step S3 comprises the following steps: Step S31, time period division and user response characteristic analysis; Step S32: Based on the analysis result of step S31, a time-sharing steam price optimization model for the steam heating system coupled with the green electricity industrial park is established and solved.

5. The method for optimizing configuration of steam heating system in coupled green electricity industrial park according to claim 4 is characterized in that: The step S31 is specifically as follows: Calculate the peak-to-flat load transfer rate of the i-th type of steam user in the park: Where: subscript pf represents peak-to-average, ΔP pf is the peak-flat gas price difference, k pfi is the slope of the peak-to-average load transfer linear region, a pfi and b pfi are the thresholds of the dead zone and linear zone of the peak-average gas price difference of the i-th user, It is the maximum value of the load transfer rate when the price difference between peak and normal periods reaches saturation and tends to be stable; Calculate the peak-to-valley load transfer rate of the i-th type of steam user in the park: Where: subscript pv represents peak-to-valley, ΔP pv is the peak-valley gas price difference, k pvi is the slope of the peak-to-valley load transfer linear region, a pvi and b pvi are the thresholds of the dead zone and linear zone of the peak-valley period gas price difference of the i-th user, It is the maximum value of the load transfer rate when the price difference between peak and valley periods reaches saturation and tends to be stable; Calculate the flat-to-valley load transfer rate of the i-th type of steam user in the park: Where: fv represents flat-to-valley, ΔP fv is the Pinggu gas price difference, k fvi is the slope of the flat-valley load transfer linear region, a fvi and b fvi are the thresholds of the dead zone and linear zone of the price difference between the flat and valley periods for the i-th user, It is the maximum value of the load transfer rate when the price difference between flat and valley periods reaches saturation and tends to be stable; Under the influence of peak and valley time-of-use steam prices, considering the uncertainty of load transfer, the trapezoidal membership function μ(λ i ) to correct the load transfer rate: Where: and They represent the peak-to-flat, peak-to-valley, and flat-to-valley load transfer rates of user i after considering response uncertainty; Trapezoidal membership function μ(λ i ) is calculated as: Where: i is the user load; i1 ,λ i2 ,λ i3 and λ i4 is the membership parameter determined by the user response characteristics, where λ i1 represents the lower left corner of the trapezoidal membership function, i.e., the threshold at which the load transfer rate begins to increase significantly; λ i2 represents the upper left corner point of the trapezoidal membership function, i.e., the threshold value where the load transfer rate changes from increasing to stable; λ i3 represents the lower right corner of the trapezoidal membership function, i.e., the threshold value at which the load transfer rate changes from stable to reduced; λ i4 It represents the upper right corner point of the trapezoidal membership function, i.e., the threshold value at which the load transfer rate is significantly reduced; After considering the response uncertainty, the load of the i-th user in period t is expressed as: Where: L tp , L tf , L tv are the loads after considering the uncertainty in peak, flat and valley periods, L tp0 , L tf0 , L tv0 are the loads without considering uncertainty during peak, flat and valley periods, and are the average loads of the i-th user during peak hours and normal hours respectively; T p , T f and T v Represent peak, flat and valley time periods respectively.

6. The method for optimizing configuration of steam heating system in a coupled green electricity industrial park according to claim 5, characterized in that: The step S32 comprises: Step S321, implement user-side response in the steam heating system, set the optimization objective function to the minimum peak-valley load difference and the minimum user steam purchase cost, expressed as: (1) The peak-to-valley average load difference is minimal: F1=min(L p -L v ) Among them, L p and L v They are the average load after considering uncertainty in normal period and the average load after considering uncertainty in valley period respectively; (2) Steam users have the lowest total steam purchase cost: Where: L pt , L ft , L vt are the user loads during peak, flat and valley periods respectively; P p , P f , P v These are the time-of-day gas prices during peak, normal and valley periods respectively. Step S322, analyzing the constraints: (1) After the time-sharing steam price response, the total amount of steam used by users remains unchanged: (2) After the time-sharing gas price response, the user's gas purchase fee cannot increase, otherwise the user will refuse to participate in the response: (3) To avoid peak-valley inversion after load regulation, a reasonable upper limit of the peak-valley steam price ratio r is set. b With the lower limit r a : (4) In order to ensure that the time-sharing gas price response has a good participation rate, the upper and lower limits of the gas price in each period are constrained: P pmin ≤P p ≤P pmax P fmin ≤P f ≤P fmax P vmin ≤P v ≤P vmax Among them, P pmin and P pmax They represent the lower and upper limits of the gasoline price during normal periods; P fmin and P fmax They represent the lower and upper limits of the gas price during peak hours respectively; P vmin and P vmax They respectively represent the lower and upper limits of gasoline prices during off-peak hours. Step S323, using NSGA-II algorithm to solve the model and obtain the optimal time-sharing steam price solution for the system.

7. The method for optimizing configuration of steam heating system in a coupled green electricity industrial park according to claim 6, characterized in that: The step S4 combines the power system, steam heating system and steam user demand response of the industrial park to construct a two-layer optimization configuration model, and conducts optimization analysis on the configuration capacity and operation scheduling of the electric heat conversion equipment in the steam heating system of the coupled green electricity industrial park. The specific steps are as follows: Step S41, the upper configuration model takes minimizing the operating cycle cost as the optimization goal, combines the optimal operating strategy obtained by the lower layer, and uses the NSGA algorithm to solve the optimal configuration capacity of green power related equipment under the capacity configuration constraint. Step S42, the lower-level optimization scheduling model uses a deterministic algorithm to solve the minimum system operating cost based on the green electricity-related equipment capacity given by the upper-level model and the system optimal time-of-use steam price plan of S3, under the constraints of the user steam load on the system demand side on a typical day, the system unit configuration constraints, and the system topology structure constraints, and returns the optimization results to the upper layer, and obtains the optimal equipment configuration plan and operation strategy through loop iteration.

8. A system for optimizing the configuration of steam heating systems in a green power industrial park, characterized in that: The system uses the method described in any one of claims 1 to 7 to optimize the configuration of the steam heating system of the coupled green electricity industrial park.

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