A method for model predictive control of an electric-thermal integrated energy system considering a pipe network model

Abstract

In order to fully tap the heat pipe network heat storage adjustment ability, the application proposes a kind of electric heat integrated energy system model predictive control method considering pipe network model. Based on the virtual decomposition method of pipe, the simulation dynamic model of pipe network is established, which is used to calculate the delay time of pipe network. Compared with the traditional rounding method, the method can effectively avoid the error accumulation problem caused by pipe segmentation. The day-ahead scheduling strategy of electric heat integrated energy system is formulated. The day-ahead scheduling model of electric heat integrated energy system is established based on the virtual decomposition method of pipe. The error between the estimated output value of power grid tie line and energy storage and the day-ahead scheduling value is minimized as the target, and the day-ahead rolling optimization model of electric heat integrated energy system is established. The real-time control strategy of integrated energy system is obtained by solving the model. The scheme not only makes the operation of integrated energy system more stable, but also reduces the fluctuation of system and power grid exchange power, improves the stability of power grid operation, and improves the accommodation capacity of new energy.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of integrated energy system operation and control, and particularly relates to an electric-thermal integrated energy system model prediction method considering a pipe network model. BACKGROUND

[0002] With the increasing depletion of non-renewable resources and the increasingly serious global environmental problems, in recent years, large-scale access of renewable energy with uncertainty to the power grid has put higher requirements on system flexibility. Integrated energy systems are unified planning and operation according to the complementary characteristics of various energies, and have the characteristics of flexible scheduling, but due to the high coupling of various energies in the integrated energy system, the operation of the integrated energy system is more complex.

[0003] In the existing general pipe model, the pipe transmission delay is generally expressed as the number of time intervals, and the traditional delay calculation method needs to round off at each pipe through which the medium passes, which may cause error accumulation. It cannot meet the requirements of scheduling.

[0004] In the existing prediction model, the prediction of new energy output and load at a large time scale is still inaccurate, which will cause the electricity generated by new energy to be unable to be consumed in time, and will also cause power fluctuations of the grid tie line. SUMMARY

[0005] In view of the above problems, the present application provides an electric-thermal integrated energy system model prediction control method considering a pipe network model.

[0006] In order to achieve the purpose of the present application, an electric-thermal integrated energy system model prediction control method considering a pipe network model is provided, comprising the following steps:

[0007] S1: establishing a pipe network dynamic model based on a pipe virtual decomposition method;

[0008] S2: formulating an electric-thermal integrated energy system day-ahead scheduling model based on the pipe network dynamic model;

[0009] S3: obtaining a grid tie line power and an energy storage estimated output value of the electric-thermal integrated energy system based on the electric-thermal integrated energy system day-ahead scheduling model, and solving an electric-thermal integrated energy system day-ahead rolling optimization model based on the grid tie line power and the energy storage estimated output value, and continuously correcting the output power of the adjustable device in the solving process, and the solving process is an electric-thermal integrated energy system day-ahead real-time control strategy.

[0010] Further, the pipe network dynamic model specifically comprises:

[0011] Pipe node flow balance:

[0012] where P, i, in and out represent pipe, pipe number, inflow node and outflow node, respectively, m k,t represents the mass flow rate in the kth pipe at time t, and Ω represents the set of node pipes.

[0013] Temperature mixing:

[0014] where, and represent the water temperature of the outflow pipe and the mixed water temperature, respectively;

[0015] Pipe energy loss:

[0016]

[0017] λ i = πd i μ;

[0018] where C w represents the specific heat capacity of water in the pipe, μ represents the pipe heat loss coefficient, d i represents the pipe diameter, L i represents the pipe length, T a represents the ambient temperature, m i,t represents the mass flow rate of i pipe, represents the initial temperature of i pipe, represents the end temperature of o pipe;

[0019] Pipe transmission delay:

[0020] First, the temperature loss of the main pipe without virtual decomposition is calculated by the above pipe temperature loss model, and then the heat is converted into power loss, as follows:

[0021] where, represents the power loss of the pipe, represents the end temperature of the main pipe, represents the initial temperature of the main pipe, m 1,t represents the mass flow rate of the main pipe;

[0022] Second, since the mass flow rate of the main pipe is equal to the sum of the mass flow rates of the branch pipes, the power loss of each virtual pipe is solved by the mass flow rate weight ratio, as follows:

[0023] m 2,t +m 3,t +···+m n,t = m 1,t ;

[0024]

[0025] where i = 1, ···, n, denotes the end temperature of the virtual pipe, denotes the start temperature of the virtual pipe, m n,t denotes the mass flow of the virtual pipe;

[0026] Then, the length of each virtual pipe is solved according to the time that the medium flows through the virtual pipe and the main pipe that is not virtually decomposed, like the formula:

[0027] where p, A1…A n and L1…L n' respectively denote the density of liquid water in the pipe, the cross-sectional area of the pipe, and the length of the pipe,

[0028] Finally, the length of the virtual pipe is added to the length of the corresponding branch pipe to obtain the new length of the pipe, and then the time delay is calculated, like the formula:

[0029]

[0030] where At denotes the time interval, m i,t At denotes the mass flow of water injected into the pipe at t time, assuming that the water quality flowing in at t time has water flowing out at t+n i,t At time, S i,t denotes the mass flow of water injected into the pipe at t time to t+n i,t At time, n i,t denotes the maximum number of delay time intervals, denotes the number of delay time intervals obtained.

[0031] Further, the electric heat comprehensive energy system day-ahead scheduling model specifically comprises:

[0032] The objective function of the day-ahead scheduling stage is expressed as:

[0033]

[0034] where T denotes the total number of total time periods of day-ahead optimization scheduling, N DG and N bat respectively denote the total number of distributed power sources and storage batteries, c DG and c bat respectively denote the cost of gas turbine unit operation and the maintenance cost of storage batteries, P t bat denotes the charging and discharging power of energy storage at t time, P t bat is a positive value, indicating that the storage battery discharges to the microgrid, P t batis negative, it means the microgrid charges the battery, and respectively represent the price of purchasing and selling electricity to the main grid at time t, P t DG , P t buy and P t sell respectively represent the output power of the gas turbine, the power of purchasing electricity from the main grid and the power of selling electricity to the main grid at time t.

[0035] Further, the day-ahead scheduling model of the integrated energy system according to the application is subject to the following constraints:

[0036] Power balance constraint:

[0037] wherein P t grid,C , P t DG,C , P t bat,C respectively represent the tie-line power of the refrigeration center, the output power of the gas turbine and the output power of the battery at time t, P t PV,C represents the power generation power of the roof photovoltaic of the refrigeration center at time t, P t W represents the power consumption of the water-cooled refrigeration equipment, P t IS represents the power consumption of the ice storage system, represents the power consumption of the auxiliary equipment;

[0038] The output power constraint of the gas turbine specifically includes:

[0039] For the upper and lower limit constraints of the gas turbine, the following formula is used: P DG,C,min ≤ P t DG,C ≤ P DG,C,max ;

[0040] wherein P DG,C,min and P DG,C,max respectively represent the upper limit and the lower limit of the output power of the gas turbine;

[0041] In order to ensure the service life of the distributed power supply, the ramping power of the gas turbine is limited, and the following formula is used:

[0042]

[0043] wherein ΔP DG,C,min and ΔP DG,C,max respectively represent the upper limit and the lower limit of the ramping power of the gas turbine, represents the actual power of the gas turbine at the previous time;

[0044] Operating constraints of the energy storage device, such as formula:

[0045]

[0046] SOC t = SOC t-1 (1 - r self-dis ) - P t bat Δt / η dis E SB ;

[0047] Wherein, SOC t-1 represents the load power of the t-1 period, SOC t represents the load power of the t period, P t bat represents the battery charge / discharge power of the t period, which is positive when discharging and negative when charging; η ch represents the charging efficiency of the battery, η dis represents the discharging efficiency of the battery, r self-dis represents the self-discharge rate of the battery, E SB represents the installed capacity of the battery;

[0048] Power distribution network flow constraints:

[0049] V i -V j = z ij I ij ,

[0050]

[0051] Wherein, V i and V j respectively represent the voltage of i, i node, I ij and respectively represent the branch current and the conjugate of the current, z ij represents the branch impedance, S ij and S jk represent the branch power, s j represents the node injection power, and set B represents the set of all nodes in the network, and set E represents the set of all branches in the network;

[0052] Phase angle relaxation of the branch flow model, remove the voltage power phase angle, let l ij = |I ij | 2 , v j = |V j | 2 ;

[0053]

[0054] where p and q represent the nodal injection power, P and Q represent the branch power flow, and r and x represent the branch resistance and reactance, respectively;

[0055] Second-order cone optimization conversion of the above formula is:

[0056]

[0057] Thermal equilibrium constraints:

[0058]

[0059] T CES,s,min ≤T t CES,s ≤T CES,s,max ;

[0060] where C w represents the specific heat capacity of water, m i,t represents the mass flow rate of water in the i-th pipe at time t, represents the output power of the energy station, T t CES,r and T t CES,s represent the end temperature of the return water pipe and the head temperature of the water supply pipe, respectively, T CES ,s,min and T CES,s,max represent the upper and lower limits of the water supply temperature, respectively, indicates that the refrigeration system is connected to the building;

[0061] Considering the constraints of the cold storage refrigeration system, specifically including:

[0062] The water-cooled refrigeration unit model is:

[0063]

[0064]

[0065] where P t W represents the electric power consumed by the water-cooled refrigeration equipment at time t, represents the operating state of the i-th equipment at time t, with the on / off states represented by 1 / 0, represents the refrigeration power provided by the water-cooled refrigeration equipment at time t, Ω W represents a set of water-cooled refrigeration equipment, N W represents the number of equipment, COP i W represents the performance coefficient of the refrigeration equipment, P W,CWP, P W,CP and P W,CT represent the rated power of ice-making water pump, refrigeration water pump and cooling tower, respectively;

[0066] Dual-purpose ice storage unit model:

[0067]

[0068] where, N D represents the number of dual-purpose ice storage devices, and represent the refrigeration power and ice-making power, respectively, and represent the power released by the ice storage tank and the total refrigeration power of the system, respectively;

[0069] The upper and lower limits of the output power of the dual-purpose ice storage system in two working modes are constrained, as shown in the formula:

[0070]

[0071] The device can only perform one working mode at the same time, as shown in the formula:

[0072]

[0073] where, Ω D represents a set of dual-purpose ice storage devices;

[0074] The relationship between the energy of the ice storage tank and the charging and discharging power, the energy limit of the ice storage tank, and the fact that the charging and discharging processes of the ice storage tank cannot be performed at the same time are shown in the specific model:

[0075]

[0076] where, represents the remaining energy in the ice storage tank, ε IT represents the heat loss rate of the ice storage tank;

[0077] The power consumption model of the ice storage system is:

[0078]

[0079] where, P t IS represents the power consumption of the ice storage system, COP i D,C and COP i D,I represent the performance coefficients of the system in refrigeration mode and ice-making mode, respectively, P D,CP , P D,CT , P EP and P IS,CWPrespectively represent the rated power of the refrigerated water pump, cooling tower, ethylene glycol pump and ice-making water pump in the system;

[0080] Heat supply pipe network constraints, specifically including:

[0081] Pipeline node flow balance constraints, temperature mixing constraints, pipeline energy loss constraints and pipeline transmission delays;

[0082] Load end energy balance constraints, expressed as:

[0083]

[0084] wherein, respectively represent the consumption power of indoor heat power and the refrigeration power injected into the indoor, ρ air represents the air density, C air represents the air specific heat capacity, V i represents the indoor volume, represents the indoor temperature at time t; in order to meet the calculation needs during scheduling, we discretize the differential equation, as follows:

[0085]

[0086] wherein, k i and F i the average heat dissipation coefficient and surface area of building i,

[0087] In order to ensure the comfort of users, set a reasonable temperature range and temperature ramp rate, as follows:

[0088]

[0089] -ΔT≤T i,t -T i,t-1 ≤ΔT;

[0090] wherein, T i,t represents the indoor temperature of the building at time t, T i,t-1 represents the indoor temperature of the building at time t-1, and ΔT represents the time interval.

[0091] Further, the intraday rolling optimization model of the electric-thermal integrated energy system specifically includes the following:

[0092] The grid tie line power and energy storage estimated output value are taken as the reference value of intraday scheduling, and the minimum error between the two and the preset energy storage day-ahead plan value is taken as the target;

[0093] According to the power balance constraint, the heat balance constraint and the operation constraint of the energy storage device in each period of the electric-thermal integrated energy system day-ahead scheduling model, the output variable Y f is calculated and obtained.

[0094] Y f (k+i|k)=[p gird (k+i|k),SB(k+i|k)];

[0095] The vector Y is formed by selecting the power of the grid interconnection line and the preset day-ahead planned value of energy storage. da Then the vector Y da As the tracking and control target, it is represented as: Y da (k+i)=[p fird (k+i),SB(k+i)],

[0096] Establish a multi-input, multi-output state-space model:

[0097]

[0098] The vector X(k) = [P] is composed of gas turbine unit output, energy storage charging and discharging power, energy storage iterative equation, microgrid and external grid interconnection power exchange, and building temperature regulation charging and discharging energy. DG (k),P bat (k),SB(k),P grid (k),P building (k)] T As a state variable; the vector u(k) = [ΔP] is composed of the output increments of the dispatchable gas turbine unit, energy storage, and building indoor regulation. DG (k),ΔP bat (k),ΔP building (k)] is used as the control variable; the vector r(k) = [ΔP] is composed of the ultra-short-term predicted power increments of ordinary power load, cooling load, and photovoltaic power. load,C (k),ΔP PV [(k)] is used as the disturbance input; the vector y(k) = [P] consisting of tie-line switching power and energy storage SOC is used. grid (k),SB(k)] T As output variables, a, b, c, and d are represented as follows:

[0099]

[0100] Compared with the prior art, the present invention has the following beneficial technical effects:

[0101] The pipeline virtual decomposition method simulates the pipeline network dynamics, can reduce the error accumulation caused by the coupling use of pipelines of various sizes, and can reduce the problem of delay increase.

[0102] The present application is directed to the problem of inaccurate prediction information of renewable energy, outdoor temperature and power load, and the model predictive control method is used for real-time correction. The model predictive control takes the renewable energy and load prediction information in a future time window as disturbance input, and takes the tie-line power planning value in the future time window as tracking instruction, so that the rolling optimization is not only based on the model, but also fully utilizes the feedback information to form a closed-loop system. Compared with single-section real-time optimization scheduling, the model predictive control can predict the scheduling in a future period of time, develop a more reasonable control strategy, and effectively reduce the influence of inaccurate day-ahead prediction. BRIEF DESCRIPTION OF DRAWINGS

[0103] Figure 1 Fig. 1 is a flowchart of a model predictive control method of an electric-thermal integrated energy system considering a network management model according to an embodiment;

[0104] Figure 2 Fig. 3 is an improved IEEE33 node diagram according to an embodiment;

[0105] Figure 3 Fig. 4 is a structure diagram of an integrated energy system according to an embodiment;

[0106] Figure 4 Fig. 5 is a schematic diagram of a pipeline virtual decomposition method according to an embodiment;

[0107] Figure 5 Fig. 6 is a principle diagram of model predictive control according to an embodiment;

[0108] Figure 6 Fig. 7 is a flowchart of an integrated energy system optimization based on model predictive control according to an embodiment;

[0109] Figure 7 Fig. 8 is a curve diagram of a running result without optimization according to an embodiment;

[0110] Figure 8 Fig. 9 is a curve diagram of a running result after optimization according to an embodiment. DETAILED DESCRIPTION

[0111] In order to make the purpose, technical scheme and advantages of the present application clearer, the present application will be further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and do not limit the present application.

[0112] Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are they necessarily mutually exclusive of one another. As will be apparent to those of ordinary skill in the art, embodiments described herein can be combinable with other embodiments.

[0113] Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are they necessarily mutually exclusive of one another. As will be apparent to those of ordinary skill in the art, embodiments described herein can be combinable with other embodiments. Figure 1 Figure 1 Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are they necessarily mutually exclusive of one another. As will be apparent to those of ordinary skill in the art, embodiments described herein can be combinable with other embodiments.

[0114] S1: establishing a pipeline network dynamic model based on a pipeline virtual decomposition method;

[0115] S2: formulating an electric-thermal integrated energy system day-ahead scheduling model based on the pipeline network dynamic model. The electric-thermal integrated energy system day-ahead scheduling model takes economy as the goal, and takes power balance constraints, gas turbine output constraints, energy storage device operation constraints, distribution network power flow constraints, heat balance constraints, refrigeration system constraints considering cold storage, heating pipe network constraints, and load end energy balance constraints as constraint conditions.

[0116] S3: obtaining electric grid tie-line power and energy storage estimated output values of the electric-thermal integrated energy system based on the electric-thermal integrated energy system day-ahead scheduling model, and solving an electric-thermal integrated energy system day-ahead rolling optimization model based on the electric grid tie-line power and energy storage estimated output values, and constantly correcting output power of adjustable devices in the solving process. This solving process is an electric-thermal integrated energy system day-ahead real-time control strategy.

[0117] The pipeline network dynamic model based on the pipeline virtual decomposition method in S1 specifically includes:

[0118] (1) pipeline node flow balance:

[0119]

[0120] In the formula, m k,t is the mass flow rate in the kth pipeline at time t;

[0121] (2) temperature mixing:

[0122]

[0123] In the formula, is the temperature at the end of the pipeline, is the mixed temperature;

[0124] (3) pipeline energy loss: ​

[0125]

[0126] λ i = πd i μ (4)

[0127] where C w and μ are the specific heat capacity of water in the pipe and the pipe heat loss coefficient, respectively. i , L i and T a are the pipe diameter, pipe length and ambient temperature, respectively.

[0128] (4) Pipe transmission delay: first, the temperature loss of the main pipe without virtual decomposition is calculated by the above pipe temperature loss model, and then the heat is converted into power loss, as follows:

[0129]

[0130] where: is the power loss of the pipe;

[0131] Secondly, since the mass flow rate of the main pipe is equal to the sum of the mass flow rates of the branch pipes, the power loss of each virtual pipe is solved by the mass flow rate weight ratio, as follows:

[0132] m 2,t +m 3,t +···+m n,t = m 1,t (6)

[0133]

[0134] Thirdly, the length of each virtual pipe is solved according to the fact that the time of the medium flowing through the virtual pipe is equal to the time of the medium flowing through the main pipe without virtual decomposition, as follows:

[0135]

[0136] where ρ, A, L represent the density of liquid water in the pipe, the pipe cross-sectional area and the pipe length, respectively.

[0137] Finally, the length of the virtual pipe is added to the length of the corresponding branch pipe to obtain the new pipe length, and then the time delay is calculated, as follows:

[0138]

[0139]

[0140] where Δt is the time interval, m i,t Δt represents the water quality injected into the pipe at time t, and it is assumed that the water quality flowing in at time t flows in at time t+ni,t Δt has water flow out. Where n i,t Can be obtained from the above formula, the parameter S i,t Indicates that the time t to t+n i,t Δt injected into the pipeline water quality.

[0141] The integrated energy system day-ahead scheduling model, comprising specifically:

[0142] Considering the working state of refrigeration equipment, system power purchase and sale, gas turbine output plan and battery charge and discharge state, the total operation cost of the system is minimized. The day-ahead scheduling objective function can be expressed as:

[0143]

[0144] In the formula: T is the total period of day-ahead optimization scheduling; N DG And N bat The total number of distributed power supply and battery respectively; c DG And c bat The cost of gas turbine unit operation and battery maintenance cost; P t bat The charge and discharge power of energy storage at t period, positive value indicates that the battery discharges to the microgrid, negative value indicates that the microgrid charges the battery; And Indicate the price of purchasing and selling electricity to the main grid at t time.

[0145] Power balance constraint, gas turbine output constraint, energy storage device operation constraint, power distribution network power flow constraint, heat balance constraint, refrigeration system constraint considering cold storage, heating pipe network constraint, load end energy balance constraint, comprising specifically:

[0146] The power balance constraint:

[0147]

[0148] In the formula: P t grid,C , P t DG,C , P t bat,C The tie line power of refrigeration center, gas turbine output and battery output; P t PV,C The power generation power of the roof photovoltaic of the refrigeration center at t time, P t W The power consumption of water-cooled refrigeration equipment, P t IS The power consumption of ice storage system, The auxiliary equipment power (pipeline pressure pump, etc.).

[0149] The gas turbine output constraint includes:

[0150] For the upper and lower limits of the gas turbine, as follows:

[0151] P DG,C,min ≤P t DG,C ≤P DG,C,max (14)

[0152] wherein P DG,C,min and P DG,C,max are the upper and lower limits of the output of the gas turbine, respectively;

[0153] In order to ensure the service life of the distributed power supply, the ramping power of the gas turbine is limited, as follows:

[0154]

[0155] wherein ΔP DG,C,min and ΔP DG,C,max are the upper and lower limits of the ramping of the gas turbine, respectively.

[0156] The operation constraint of the energy storage device is as follows:

[0157]

[0158] SOC t = SOC t-1 (1-r self-dis )-P t bat Δt / η dis E SB (17)

[0159] wherein Δt is the difference between adjacent time periods; SOC t is the load power of the time period t; P t bat is the charging / discharging power of the battery in the time period t, which is positive when discharging and negative when charging; η ch is the charging efficiency of the battery; η dis is the discharging efficiency of the battery; r self-dis is the self-discharge rate of the battery; and E SB is the installed capacity of the battery.

[0160] The power flow constraint of the power distribution network is as follows:

[0161] V i -V j = z ij I ij ,

[0162]

[0163] where V i and V j represent the voltage of node i, I ij and represent the branch current and its conjugate, z ij represents the branch impedance, S ij and S jk represent the branch power, s j represents the node injection power, and sets B and E represent the set of all nodes and the set of all branches in the network, respectively.

[0164] The phase angle relaxation of the branch power flow model, removing the voltage and power phase angles, is given by l ij = |I ij | 2 , v j = |V j | 2 .

[0165]

[0166] where p and q are the node injection power and P and Q are the branch power flow, r and x are the branch resistance and reactance, respectively, and E represents the set of all branches in the network.

[0167] The second-order cone optimization conversion of the above equation is given by:

[0168]

[0169] The thermal equilibrium constraint is given by:

[0170]

[0171] T CES,s,min ≤ T t CES,s ≤ T CES,s,max (28)

[0172] where C w is the specific heat capacity of water, m i,t is the mass flow rate of water in the i-th pipe at time t, is the output power of the energy station, T t CES,r and T t CES,s are the end temperature of the return water pipe and the head temperature of the water supply pipe, respectively, T CES ,s,min and T CES,s,max are the upper and lower limits of the water supply temperature, respectively, indicates that the refrigeration system is connected to the building.

[0173] The constraints of the cold storage refrigeration system are considered, specifically including:

[0174] (1) The water-cooled refrigeration unit model is:

[0175]

[0176] P t W represents the electric power consumed by the water-cooled refrigeration equipment at time t, represents the operating state of the i-th equipment at time t, and the on / off states are represented by 1 / 0, represents the refrigeration power provided by the water-cooled refrigeration equipment at time t, Ω W represents a set of water-cooled refrigeration equipment, N W represents the number of equipment, COP i W represents the performance coefficient of the refrigeration equipment, P W,CWP , P W,CP , and P W,CT represent the rated power of the ice-making water pump, the refrigeration water pump, and the cooling tower, respectively;

[0177] (2) Dual-purpose ice storage unit model:

[0178]

[0179] N D represents the number of dual-purpose cold storage equipment, and represent the refrigeration power and ice-making power, respectively, and represent the power released by the ice storage tank and the total refrigeration power of the system, respectively;

[0180] The upper and lower limits of the output power of the dual-purpose ice storage system in two working modes are constrained, as shown in the following formula:

[0181]

[0182] The equipment can only perform one working mode at the same time, as shown in the following formula:

[0183]

[0184] N D is a set of dual-purpose ice storage equipment;

[0185] The relationship between the energy of the ice storage tank and the charging and discharging power, the energy limit of the ice storage tank, and the simultaneous charging and discharging process of the ice storage tank are as follows:

[0186]

[0187] wherein: represents the energy remaining in the ice storage tank, ε IT represents the heat loss rate of the ice storage tank;

[0188] The ice storage system power consumption model is:

[0189]

[0190] wherein: P t IS represents the electric power consumption of the ice storage system, COP i D,C and COP i D,I respectively represent the performance coefficients of the system in the cooling mode and the ice-making mode, P D,CP , P D,CT , P EP and P IS,CWP respectively represent the rated power of the chilled water pump, the cooling tower, the glycol pump and the ice-making water pump in the system.

[0191] The heat supply pipe network constraints include:

[0192] the pipe node flow balance constraint, the temperature mixing constraint, the pipe energy loss constraint and the pipe transmission delay.

[0193] The load end energy balance constraint is:

[0194]

[0195] wherein respectively represent the consumption power of the indoor heat power and the chilled power injected into the indoor, ρ air represents the air density, C air represents the air specific heat capacity, V i represents the indoor volume, represents the indoor temperature at time t; in order to meet the calculation needs during scheduling, the differential equation is discretized, as follows:

[0196]

[0197] In order to ensure the comfort of users, it is necessary to set a reasonable temperature range and temperature ramping speed, as follows:

[0198]

[0199] -ΔT≤T i,t -T i,t-1 ≤ΔT (46)

[0200] The electric heat comprehensive energy system daily rolling optimization model specifically comprises:

[0201] The grid tie line power and the energy storage estimated output value are taken as reference values of daily scheduling, and the tie line power is ensured to follow the day-ahead scheduling plan value as much as possible in the daily scheduling process, therefore the grid tie line output power and the energy storage estimated output value are selected, and the minimum error between the two and the day-ahead plan value is taken as the target:

[0202] According to the power balance equation (power balance constraint and heat balance constraint) and the energy storage iteration equation (operation constraint of the energy storage device) of the electric heat comprehensive energy system day-ahead scheduling model, the output variable corresponding Y f is obtained: f (k+i|k)=[p gird (k+i|k),SB(k+i|k)];the vector Y da formed by the grid tie line output power and the preset energy storage day-ahead plan value is taken as the tracking control target; Y da is: da (k+i)=[p gird (k+i),SB(k+i)].

[0203] Since the space state model can control the process with multiple inputs and multiple outputs, and the state quantity and the input constraint are processed explicitly, the present application takes the grid-connected comprehensive energy system composed of a micro gas turbine, an energy storage device, photovoltaic, refrigeration load and ordinary power load as an example, and establishes the following multiple-input, multiple-output state space model:

[0204]

[0205] The vector X(k)=[P DG (k),P bat (k),SB(k),P grid (k),P building (k)] T is taken as the state variable; the vector u(k)=[ΔP DG (k),ΔP bat (k),ΔP building (k)] formed by the output increment of the schedulable gas turbine unit, the energy storage and the indoor regulation of the building is taken as the control variable; the vector r(k)=[ΔP load,C (k),ΔP PV (k)] formed by the power increment of the ordinary power load, the refrigeration load and the photovoltaic is taken as the disturbance input; the vector y(k)=[Pgrid (k),SB(k)] T As the output variable, wherein a, b, c, d respectively represent:

[0206]

[0207] The quadratic programming model described by the objective function can be solved by calling the quadratic programming quadprog function provided by the MATLAB optimization toolbox. After solving, the optimized control sequence of all unit output adjustments in the control time domain PDeltaT can be obtained, and only the control sequence of the first scheduling period from the current time is issued at this scheduling time. When the next scheduling period comes, repeat the above rolling optimization process.

[0208] In one embodiment, the improved IEEE33 node standard algorithm is used as Figure 2 , which contains 9 heat loads and 1 refrigeration center. The structure diagram of the refrigeration center is as shown in Figure 3 , the energy generated by the refrigeration center is supplied to each building through the heat pipe network, and the above pipeline virtual decomposition method is as shown in Figure 4 , the simulation time is 24 hours, and the step is 5 minutes. The principle diagram of the intraday real-time control strategy of the electric-thermal integrated energy system based on model predictive control proposed in the scheme is as shown in Figure 5 , the execution process is as shown in Figure 6 , and the simulation results are as shown in Figure 7 and Figure 8 . Compared with the results without optimization. Figure 7 is the power exchange curve diagram of the tie-line bus of the power distribution network without simulation and prediction control, Figure 8 is the power exchange curve diagram of the tie-line bus of the power distribution network optimized by the model predictive control proposed in the scheme. If the intraday rolling optimization based on model predictive control is not implemented, the tie-line power fluctuates around the planned value, and it is difficult to realize the stable operation of the power distribution network. Therefore, the present application can suppress the load fluctuation of the power grid caused by the large-scale uncertain new energy access.

[0209] With the increasingly prominent energy and environmental problems, gradually changing the energy structure and developing renewable energy have become a consensus. However, renewable energy such as solar energy and wind energy has the characteristics of randomness and intermittency, and its large-scale access to the power grid has brought great challenges to the operation and dispatching of the power system. Therefore, the present application is a kind of model predictive control method of electric-thermal integrated energy system considering pipe network model. The scheme can suppress the random fluctuation of power exchange power through optimization means, improve the stability of system operation, and improve the access capacity of new energy.

[0210] Any technical features in the above-described embodiments can be combined in any manner, and for the sake of brevity, not all possible combinations are described, however, it is understood that the scope of the present specification includes all possible combinations.

[0211] It should be noted that the terms "first", "second", and "third" in the embodiments of the present application are merely used to distinguish similar objects, and do not represent a specific order of the objects. Understandably, the terms "first", "second", and "third" can be interchanged in a specific order or sequence as long as the interchanging does not cause contradiction.

[0212] The terms "comprise" and "have" and any variations thereof in the embodiments of the present application are intended to cover non-exclusive inclusion. For example, a process, method, device, product, or apparatus that includes a series of steps or modules is not limited to the listed steps or modules, but can optionally further include steps or modules that are not listed, or can optionally further include other steps or modules inherent to the process, method, product, or apparatus.

[0213] The above-described embodiments only express several implementation manners of the present application, and the description is relatively specific and detailed, but it should not be understood as a limitation on the scope of the patent. It should be noted that for those skilled in the art, several modifications and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application should be subject to the appended claims.

Claims

1. A predictive control method for an integrated electrothermal energy system considering a pipeline network model, characterized in that, Includes the following steps: S1: Establish a dynamic model of the pipeline network based on the pipeline virtual decomposition method; S2: Develop a day-ahead scheduling model for the integrated electric and thermal energy system based on the aforementioned pipeline network dynamic model; S3: Based on the day-ahead scheduling model of the integrated electric and thermal energy system, obtain the grid tie line power and energy storage estimated output value of the integrated electric and thermal energy system. Then, based on the grid tie line power and energy storage estimated output value, solve the day-ahead rolling optimization model of the integrated electric and thermal energy system. In the solution process, continuously correct the output power of the adjustable equipment. This solution process is the day-ahead real-time control strategy of the integrated electric and thermal energy system. The dynamic model of the pipeline network specifically includes: Pipeline node flow balancing: Where P, i, in, and out represent the pipe, channel number, inflow node, and outflow node, respectively, and m k,t Let Ω represent the mass flow rate in the k-th pipe at time t, and let Ω represent the set of node pipes. Temperature mixing: in, and These represent the water temperature flowing out of the pipe and the water temperature after mixing; Pipeline energy loss: l i =πd i m; Among them, C w d represents the specific heat capacity of water in the pipe, μ represents the heat loss coefficient of the pipe, and d represents the specific heat capacity of water in the pipe. i L represents the pipe diameter. i T represents the length of the pipe. a Indicates ambient temperature, m i,t This represents the mass flow rate of pipe i. This indicates the temperature at the beginning of pipe i. This indicates the temperature at the end of pipe i; Pipeline transmission delay: First, the temperature loss of the main pipeline (without virtual decomposition) is calculated using the above pipeline temperature loss model, and then the heat is converted into power loss, as shown in the following equation: in, This indicates the power loss of the pipeline. Indicates the temperature at the end of the main pipe. Indicates the temperature at the beginning of the main pipeline, m 1,t Indicates the mass flow rate of the main pipeline; Secondly, since the mass flow rate of the main pipeline equals the sum of the mass flow rates of the branch pipelines, the power loss of each virtual pipeline can be calculated using the mass flow rate weighting ratio, as shown in the following equation: m 2,t +m 3,t +…+m n,t =m 1,t ; Where i = 1, ..., n, and n represents the number of branch pipes. This indicates the temperature at the end of the virtual pipe. Indicates the initial temperature of the virtual pipe, m n,t This represents the mass flow rate of the virtual pipeline; Then, based on the fact that the time it takes for the medium to flow through the virtual pipes is equal to the time it takes for the main pipe to be decomposed, the length of each virtual pipe is calculated, as shown in the following formula: Where ρ, A1…A n and L1…L n These represent the density of the liquid water in the pipe, the cross-sectional area of ​​the pipe, and the length of the pipe, respectively. Finally, the virtual pipe length is added to the corresponding branch pipe length to obtain the new pipe length, and then the time delay is calculated, as shown in the formula: Where Δt represents the time interval, assuming the water flowing in at time t has a quality of t+n i,t Water flows out at time Δt, S i,t This indicates the time from time t to t+n. i,t The mass of water injected into the pipe at time Δt, n i,t Indicates the maximum number of delay time intervals. This indicates the number of delay time intervals obtained.

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