Electric heating integrated energy system operation optimization method based on model complexity disassembly

Abstract

The invention discloses an electric heating integrated energy system operation optimization method based on model complexity disassembly. The method comprises the following steps: establishing an electric heating integrated energy system operation problem objective function and original operation constraints thereof; firstly, the complexity of a heat supply network model is concerned, a heat supply network adopts a quantity regulation operation mode, a power grid adopts a second-order cone tidal current model, renewable energy output is represented by predicted power, and a heat supply network hydraulic condition decision and a power grid second-order cone model phase angle reference value are obtained; secondly, attention is paid to the complexity of a power grid model, a heat supply network adopts a quality regulation operation mode, a power grid adopts an improved second-order cone power flow model, renewable energy output adopts a small amount of scene modeling after quantity reduction, and a power grid reference operation point and an equipment start-stop state decision are obtained; and finally, attention is paid to uncertainty modeling complexity, a heat supply network adopts a quality regulation operation mode, a power grid adopts a linear power flow model, renewable energy output adopts uncertainty scene modeling before quantity reduction, and an electric heating integrated energy system operation decision scheme is obtained. The decision-making efficiency of the operation scheme of the integrated energy system is improved while the operation safety of the system is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of integrated energy system operation optimization, and in particular to an electric-thermal integrated energy system operation optimization method based on model complexity decomposition. Background Art

[0002] The intensification of global warming and energy crisis has made the transformation of traditional energy systems more urgent. The electric-thermal integrated energy system is an important means to improve the energy structure and enhance the flexibility of system operation through the complementary and coordinated optimization between electric energy and thermal energy. However, the transmission characteristics of electric and thermal energy flows are significantly different, the dynamic and static processes of the network influence each other, and the electric-thermal system itself presents non-convex characteristics, which makes the system operation decision model problem more complicated. In addition, the output uncertainty problem brought about by the large-scale access of a high proportion of distributed renewable energy also brings challenges to the scheduling of IEHS, threatening the safe and economic operation of the electric-thermal integrated energy system.

[0003] In the study of the operation problems of the electric-thermal integrated energy system, the accuracy of the operation model has a great influence on the operation results. At present, the electric-thermal integrated energy system faces two difficult-to-handle complexities: the nonlinearity of the electric-thermal network model and the uncertainty modeling of renewable energy. It is difficult to ensure the rationality of the hydraulic decision-making, thermal decision-making, and power grid flow decision-making in the operation of the electric-thermal integrated energy system, as well as the feasibility and economy of the system operation. The linearization model of the power grid model and the second-order cone model, the quantity regulation and quality regulation mode of the heat network each have their own advantages and disadvantages, but there are few operation plans that combine the advantages of these models. In addition, in the operation problems of the complex electric-thermal integrated energy system, in order to ensure that the model is solvable, there are few accurate descriptions of uncertainty. Therefore, it is crucial to find effective methods to coordinate the conflict between uncertainty modeling and network modeling in the operation of the electric-thermal integrated energy system and fill the research gap in this area. Summary of the invention

[0004] The purpose of the present invention is to provide an operation optimization method for an electric-thermal integrated energy system based on model complexity decomposition, which decomposes and coordinates the complexity of the heat network model, power grid model and uncertainty modeling of the integrated energy system through step-by-step decision-making, realizes efficient operation optimization under the operation characteristics of the complex integrated energy system, ensures the safe operation of the system, and greatly reduces the optimization solution time. The technical solution adopted by the present invention is as follows.

[0005] On the one hand, the present invention provides an operation optimization method of an electric-thermal integrated energy system based on model complexity decomposition, comprising:

[0006] Obtain the operation optimization model of the electric-thermal integrated energy system and its original operation constraints;

[0007] Focusing on the complexity of the heat network model: the heat network adopts the quantity regulation operation mode, the power grid adopts the second-order cone power flow model, the renewable energy output is expressed by predicted power, the first decision model is constructed, the first decision model is solved, and the hydraulic condition decision of the heat network and the phase angle reference value of the second-order cone model of the power grid are obtained;

[0008] Focusing on the complexity of the power grid model: the heat network adopts a quality regulation operation mode with fixed hydraulic conditions, the power grid adopts an improved second-order cone power flow model, and the renewable energy output adopts a small number of scenarios with a reduced number of scenarios to build a second decision model. The second decision model is solved to obtain the hot start point of the power grid operation and the start and stop state decision of the equipment;

[0009] Focusing on the complexity of uncertainty modeling: the heat network adopts a quality regulation operation mode with fixed hydraulic conditions, the power grid adopts a linearized power flow model based on the hot start point of the power grid operation, and the renewable energy output adopts uncertainty scenario modeling before the quantity is reduced. A third decision model is constructed and solved to obtain the operation decision plan of the electric and thermal integrated energy system.

[0010] Optionally, the objective function of the operation optimization model of the electric-thermal integrated energy system is expressed as:

[0011] min C 0-1 +C g +C r +C cut +C CHP (1)

[0012] in,

[0013]

[0014]

[0015]

[0016]

[0017] In the formula, C 0-1 is the start-up and shutdown cost of conventional generators, is the start and stop action variable of generator i at time t; is the start and stop cost parameter of generator i; C g For the cost of electricity generation, is the operating cost parameter of generator i, is the active power of generator i at time t; C r is the regulation cost of the generator, is the cost parameter for adjusting generator i up and down, is the regulating power of generator i at time t, and when For positive time, when When it is negative, C cut is the system load shedding cost, is the load shedding cost parameter of node i, is the load power of node i at time t, is the load shedding ratio of node i at time t; C CHP is the cost of the combined heat and power unit, are the electrical and thermal powers of CHP unit i at time t, respectively; is the cost parameter of the combined heat and power unit.

[0018] Optionally, the original operation constraints include hydraulic constraints, thermal constraints and power constraints of a thermal network composed of a hydraulic model and a thermal model, wherein:

[0019] The hydraulic constraints include each heat network pressure head constraint, water pump power constraint and pipeline mass flow constraint;

[0020] The thermal constraints include heat source node thermal power balance constraints, cross node thermal power balance constraints, supply and return water temperature constraints, and pipeline thermal energy constraints within the scheduling period;

[0021] Power constraints include generator constraints, system load shedding ratio constraints, grid node power constraints and grid flow constraints.

[0022] Optionally, the hydraulic constraint is expressed as the following formula:

[0023]

[0024]

[0025]

[0026]

[0027]

[0028]

[0029]

[0030]

[0031]

[0032]

[0033]

[0034] In the formula, Φ n is the set of heating network nodes; Φ pipe It is the collection of heat network pipelines; k,t is the pressure head of node k at time t; are the upper and lower bounds of the pressure head of node k respectively; is the pressure head loss of pipeline l caused by the electric control valve at time t; is the circulating power of the pump in pipeline l at time t The limit value of They represent the initial pressure heads of nodes k1 and k2 respectively; is the pressure head in pipe l at time t caused by the water pump; Indicates the pressure head loss caused by friction in pipeline l; ε l is the resistance coefficient of the pipeline l; m l,t is the decision variable, which represents the mass flow rate of pipeline l at time t; represents the circulation power of the water pump in pipeline l at time t; is the pressure head in pipe l at time t caused by the water pump; is the pump efficiency; are the sets of pipes with the inlet and outlet as nodes k; Φ in is the set of cross-nodes of the heat network; are the upper and lower bounds of the mass flow rate in pipeline l; x l,t,t′ and l,t,t′ is an intermediate variable, which indicates the proportion of the mass flow rate flowing into the pipeline during the period tt′ that is still in the pipeline at time t in the total mass flow rate of the pipeline; T l is the maximum transmission delay of pipeline l; L l is the length of the pipeline l; S l is the cross-sectional area of ​​the pipe l; ρ is the density of water.

[0035] Optionally, the thermal constraint is expressed as the following formula:

[0036]

[0037]

[0038]

[0039]

[0040]

[0041]

[0042]

[0043]

[0044]

[0045] In the formula, is the outlet temperature of the supply and return water of pipe l at time t when heat loss is not considered; c is the specific heat capacity of water; m l,t-t′ represents the mass flow rate of pipeline l during the period tt′; is the supply and return water inlet temperature of pipe l during the period tt′; σ l,t is the heat loss coefficient of pipeline l at time t; is the ambient temperature of pipeline l; is the final outlet temperature of the supply / return water of pipe l at time t obtained by simulating the water block method; l It represents the heat transfer coefficient of the heating network pipeline; is the heat supply of heat source node k at time t; is the heat supply of node k with a cogeneration unit at time t; Φ source,n is the set of heat source nodes in the heat network; Φ source,p is the set of pipes connecting the heat network and the heat source node; Φ load,n is a collection of heat network load nodes; are the inlet temperature and outlet temperature of water supply pipe l at time t respectively; is the heat load of heat load node k at time t; are the inlet temperature and outlet temperature of the return pipe l at time t respectively; is the supply and return water inlet temperature of pipe l at time t; ω s.min ,ω s.max is the upper and lower limits of the water supply pipe temperature; ω r.min ,ω r.max The upper and lower limits of the return pipe temperature.

[0046] Optionally, the power constraint is expressed as the following formula:

[0047]

[0048]

[0049]

[0050]

[0051]

[0052]

[0053]

[0054]

[0055] In the formula, is the start and stop status of generator i at time t; is the start and stop status of generator i at time t; are the starting and stopping state variables of generator i at time t respectively; are the minimum online time and the minimum offline time of generator i respectively; Ω g is the set of generator nodes; are the upper and lower bounds of the active power of generator i; are the upper and lower bounds of reactive power of generator i; is the active and reactive power of generator i at time t; are the upward and downward power limits of generator i respectively; is the regulated power of generator i at time t; are the upper and lower limits of the ramp power of generator i; Ω bus is a set of power grid nodes; is the load shedding ratio of node i at time t; is the load shedding ratio limit of grid node i; v i,t ,θ i,t are the voltage and phase angle decision variables of grid node i at time t, and θ ij,t =θ i,t -θ j,t ;p ij,t is the power decision variable of branch ij at time t; are the upper and lower bounds of the voltage at node i, respectively; is the power limit of branch ij; Ω br It is a collection of power grid branches; is the active and reactive load power of node i at time t; Output power for renewable energy; is the electric power of cogeneration unit i at time t; The power abandoned by renewable energy; ij,t ,q ij,t is the active and reactive power of branch ij at time t; b ij , g ij is the conductance and susceptance of the grid branch ij.

[0056] Optionally, the heat network adopts a quantity regulation operation mode, including:

[0057] S11, based on the operation optimization model of the electric-thermal integrated energy system and its original operation constraints, the complexity of the heat network model is separated, and a heat network quantity regulation model after separation is constructed:

[0058] minc T α

[0059] stAα+Bβ+Cγ≤b

[0060] γ i =α i β i i∈Γ1

[0061] γ i =(α i ) 2 i∈Γ2 (35)

[0062] Among them, A, B, and C are coefficient matrices; b and c are column vectors; α, β, and γ are decision variables of the heat network regulation model, including the electric power of CHP unit i at time t. and thermal power Pipeline heat loss coefficient σ l,t ; Inlet and outlet temperatures of the supply and return pipe l Inlet and outlet heat power of the water supply and return pipe l The mass flow rate m in pipeline l at time t l,t , auxiliary variable x l.t.t′ ,y l.t.t′ , the pressure head s at node k k,t , the pressure head loss of the pipeline due to the electric control valve Circulation power of the water pump at pipe l Pipeline head loss due to friction α i , β i , γ i They represent the parts of the corresponding decision variables involved in constraint i; Γ1 is the set of bilinear constraints, including equations (12), (17), (23) and the equivalent transformation of equations (15) and (18); Γ2 is the set of quadratic constraints of the heat network model, including equation (11);

[0063] S12, reference to intermediate variable γ 1,i , γ 2,i The bilinear constraints of the set Γ1 are transformed into quadratic constraints to obtain equation (36). All constraints of the heat network regulation model are transformed into quadratic constraints to obtain equations (37)-(38):

[0064]

[0065] γ i≥(α i ) 2 ,i∈Γ2 (37)

[0066] γ i ≤(α i ) 2 ,i∈Γ2 (38)

[0067] S13, the convex constraint (37) is transformed into a convex relaxation model in the second-order cone form, expressed as:

[0068]

[0069] S14, introducing the McCormick envelope, transforming the concave constraint (38) into the difference between a linear function and a convex function, introducing a slack variable in the objective function, relaxing the convex function, and obtaining a penalty model;

[0070] S15, taking α, β, γ as iterative variables, iterating the convex relaxation model and the penalty model to obtain analytical solutions of mass flow, pipeline pressure and intermediate variables α, β, γ, and a linear quantity regulation heating network model.

[0071] Optionally, the second-order cone power flow model adopted for the power grid includes:

[0072] S16, introduce auxiliary variable M ij,t =v i,t v j,t cos(θ i,t -θ j,t ), N ij,t =v i,t v j,t sin(θ i,t -θ j,t )and The AC power flow equation (34) is equivalently transformed into (40)-(43), and the expression is:

[0073]

[0074]

[0075]

[0076]

[0077] In the formula, G ii represents the conductance between nodes ij, B ij represents the susceptance between nodes ij;

[0078] S17, perform secondary relaxation on constraint (42), the expression is:

[0079]

[0080] S18, in v i,t =1,θ i,t = 0, the equilibrium point Taylor expansion is performed on constraint (43), and the expression is as follows:

[0081]

[0082] The expressions of the second-order cone model of power grid flow are (40)-(45).

[0083] Optionally, the complexity of the power grid model is taken as the focus: the heat network adopts a quality regulation operation mode with fixed hydraulic conditions, the power grid adopts an improved second-order cone flow model, and the renewable energy output adopts a small number of scenarios with a reduced number of models, a second decision model is constructed, and the second decision model is solved to obtain the hot start point of the power grid operation and the start and stop state decision of the equipment, including:

[0084] S21, substituting the hydraulic condition decision of the heating network obtained by solving the first decision model into the nonlinear constraints (11)-(12), (15)-(19), (23) of the heating network to obtain a linear constraint containing only temperature, thereby obtaining the heating network quality regulation model;

[0085] S22, replace formula (1) with the following formula to obtain the objective function of the second decision model:

[0086]

[0087]

[0088] In the formula, The cost of adjusting the generator output to take into account the uncertainty of renewable energy output; is the load shedding cost. ξ represents the renewable energy prediction error; μ n is the probability of scene n; Ω sel A collection of scenarios for renewable energy output after the amount of reduction is reduced;

[0089] S23, the initial phase angle value of the second-order cone model of the power flow obtained by solving the first decision model is recorded as Then the second-order cone model of the power grid can be transformed into equation (45):

[0090]

[0091] The improved second-order cone power flow model is obtained.

[0092] Optionally, the uncertainty modeling complexity is taken as a focus: the heat network adopts a quality regulation operation mode with fixed hydraulic conditions, the power grid adopts a linearized power flow model based on the hot start point of the power grid operation, the renewable energy output adopts uncertainty scenario modeling before the quantity is reduced, a third decision model is constructed, and the third decision model is solved to obtain an operation decision plan for the electric and thermal integrated energy system, including:

[0093] S31, replace equation (1) in the objective function of the original electric-thermal integrated energy system operation optimization model with the following equation:

[0094]

[0095]

[0096] In the formula, Ω exp A collection of renewable energy output scenarios before reduction;

[0097] S32, the grid reference operating point obtained by solving the second decision model is recorded as The linearized power flow model based on the hot start point of the power grid operation is expressed as:

[0098]

[0099]

[0100]

[0101]

[0102]

[0103] In the formula, v ij,t =v i,t -v j,t and θ ij,t =θ i,t -θ j,t ; Formula (51) is the reference operating point The Taylor expansion approximation equation is performed on the voltage amplitude and phase angle product terms; is a constant parameter generated during the Taylor expansion approximation process.

[0104] Optionally, the objective function of the first decision model is formula (1)-(6), the linear constraints are (7)-(10), (13)-(14), (20)-(22), (24)-(26), (28)-(33), and the 0-1 variable constraints are (16) and (27); the initial value of the phase angle between the heating network hydraulic decision and the second-order cone model of the power grid flow is obtained by solving; the heating network hydraulic decision includes the pipeline mass flow Auxiliary variables Water pump circulation power Heat network node pressure head Pressure head loss in pipeline due to electric regulating valve Pipe pressure head caused by the pump and the pressure head loss caused by friction in the pipeline

[0105] The objective function of the second decision model is as follows: (2)-(6) and (46)-(47), sel For each scenario in the set, equations (7)-(26), (28)-(33) are used as linear constraints, equation (27) is used as a 0-1 variable constraint, and equations (40)-(44) and (48) are used as the optimization problem of the improved second-order cone power flow model of the power grid. The start-stop state of the power grid generator and the reference operating point of the power grid represented by the node voltage and phase angle are obtained by solving the problem.

[0106] The objective functions of the third decision model are (2)-(6), (49)-(50), and for Ω exp For each scenario in the set, equations (7)-(26) and (28)-(33) are used as linear constraints, and equations (51)-(55) are used as the optimization problem of the linearized model of power grid power flow hot start. The output of each device in the electric and thermal network is solved to form a decision plan for the operation of the electric and thermal integrated energy system.

[0107] Beneficial Effects

[0108] Under the premise of coordinating the accuracy of the heat network model, the power grid model and the uncertainty modeling, the present invention decomposes the complexity of the three, and constructs the decision-making model and solves the plan for the integrated energy system under different key focus objects in steps, which effectively coordinates the conflict between uncertainty modeling and network modeling in the operation of the electric-thermal integrated energy system, and can obtain a reasonable operation optimization plan for the electric-thermal integrated energy system based on the decomposition of the model complexity, while ensuring the safety of the system operation, avoiding the superposition of different complexities and the exponential increase of calculation difficulty. BRIEF DESCRIPTION OF THE DRAWINGS

[0109] Figure 1 Shown is a flow chart of the operation optimization method of the electric-thermal integrated energy system. DETAILED DESCRIPTION

[0110] The invention is further described below with reference to the accompanying drawings and specific embodiments.

[0111] Example 1

[0112] This embodiment introduces an operation optimization method of an electric-thermal integrated energy system based on model complexity decomposition. Figure 1 As shown, the operation optimization method of the electric-thermal integrated energy system of this embodiment includes:

[0113] Establish the objective function of the operation problem of the electric-thermal integrated energy system, and establish the original operation constraints of the model according to the structural characteristics of the network equipment of the electric-thermal integrated energy system;

[0114] The complexity of the separation heating network model: the heating network adopts the quantity regulation operation mode; the power grid adopts the second-order cone power flow model; the output of renewable energy is expressed by predicted power, and the hydraulic condition decision of the heating network and the phase angle reference value of the second-order cone model of the power grid are obtained;

[0115] The complexity of the separated power grid model: the heat network adopts the quality regulation operation mode with fixed hydraulic conditions; the power grid adopts the improved second-order cone power flow model; the renewable energy output adopts a small number of scenarios with a reduced number of scenarios; the hot start point of the power grid operation and the start and stop state decision of the equipment are obtained;

[0116] Separating uncertainty modeling complexity: The heat network adopts a quality regulation operation mode with fixed hydraulic conditions; the power grid adopts a linearized power flow model based on the hot start point of the previous node voltage phase angle; the renewable energy output adopts a large number of scenario modeling before the number is reduced, and an operation decision plan for the electric and thermal integrated energy system is obtained based on the decomposition of model complexity.

[0117] The technical concept of this embodiment is: under the premise of coordinating the accuracy of the heat network model, the power grid model, and the uncertainty modeling, the complexity of the three is disassembled. First, the complexity caused by the heat network quantity regulation is dealt with. The power grid adopts a second-order cone flow model with good global effect, ignores the modeling of uncertainty factors, and obtains the heat network hydraulic decision. Secondly, the quality regulation model of the heat network under fixed hydraulic conditions is still flexible enough and can be characterized as a linear model; while the global and local accuracy of the power grid model cannot be obtained by fixing some conditions of the power flow model; therefore, the heat network quality regulation model is used in the subsequent decision steps. The power grid still adopts the second-order cone model in the second step, but improves its phase angle Taylor expansion initial value, and briefly considers the uncertainty of renewable energy under the premise of the quality regulation linear model, thereby obtaining the hot start reference point and equipment start-stop decision of the power network running the system. Finally, through the information of the first two decision steps, a customized hot start linear flow model with high local accuracy can be constructed; at the same time, the system equipment start-stop decision is less sensitive to the uncertainty factors of renewable energy. Therefore, after constructing the heat network quality regulation model, the power grid hot start linear flow model and fixing the start-stop variable decision, the uncertainty modeling of renewable energy is accurately considered. Through the above decomposition, the superposition of different complexities and the exponential growth of computational difficulties are avoided. Combining the above three steps, a reasonable operation optimization scheme of the electric-thermal integrated energy system based on model complexity decomposition can be obtained.

[0118] The specific implementation of this embodiment includes the following steps.

[0119] Step 1: Establish the objective function of the operation problem of the electric-thermal integrated energy system, and establish the original operation constraints of the model according to the structural characteristics of the network equipment of the electric-thermal integrated energy system.

[0120] 1.1 Establish the objective function of the operation problem of the electric and thermal integrated energy system:

[0121] min C 0-1 +C g +C r +C cut +C CHP (18)

[0122]

[0123]

[0124]

[0125]

[0126]

[0127] In the formula, C 0-1 , C g , C r , C cut , C CHP They are the start-up and shutdown cost of the generator, the power generation cost, the regulation cost, the load shedding cost and the CHP cost of the combined heat and power unit. is the start-stop action variable of generator i at time t. When generator i is started at time t, Otherwise, it is 0. When generator i stops at time t, Otherwise it is 0; are the generating power, regulating power of generator i, the electrical load of node i, and the electrical and thermal power of the CHP unit; are the startup cost, shutdown cost, operation cost, up-regulation cost, down-regulation cost and load shedding cost parameters of the generator; the cost parameters of CHP are

[0128] 1.2 Establish hydraulic constraints of the thermal network consisting of hydraulic model and thermal model of the electric and thermal integrated energy system:

[0129] The expressions for the pressure head at each node, the pressure adjustment range of the electric regulating valve, and the upper and lower limits of the water pump power are as follows:

[0130]

[0131]

[0132]

[0133] In the formula, Φ n is the set of heating network nodes; Φ pope It is a collection of heat network pipelines; are the upper and lower bounds of the pressure head at node k respectively; is the circulating power limit of the pump at pipe l. k,t , and are decision variables, representing the pressure head at node k, the head pressure loss in the pipeline due to the electric regulating valve, and the circulation power of the water pump at pipeline l.

[0134] The pipeline pressure equation is as follows:

[0135]

[0136] In the formula, the left side represents the pressure between nodes k2 and k1; are respectively the pressure head of the pipeline caused by the water pump and the pressure head loss caused by the friction of the pipeline. They represent the initial pressure heads of nodes k1 and k2 respectively.

[0137] The expressions of pipeline pressure head loss and circulating pump electric power are as follows:

[0138]

[0139]

[0140] In the formula, ε l , are the resistance coefficient of pipe l and the efficiency of the water pump respectively; m l,t is the decision variable, which represents the mass flow rate of pipeline l.

[0141] The mass flow constraint equation is expressed as follows:

[0142]

[0143]

[0144] Where, (13) and (14) are the mass flow balance equation and the upper and lower bounds of mass flow in pipeline l, respectively; Φ in is the set of cross-nodes of the heat network; are the sets of pipes whose inlet and outlet are nodes k; are the upper and lower bounds of the mass flow rate in pipeline l.

[0145] The relationship between the intermediate variable and the mass flow rate and the conditions satisfied by its physical meaning are as follows:

[0146]

[0147]

[0148] In the formula, the intermediate variable x l,t,t′ and l,t,t′ It is used to simulate the water block method. Its physical meaning is the proportion of the mass flow rate flowing into the pipeline (or virtual pipeline) at time tt′ that is still in the pipeline (or virtual pipeline) at time t in the total mass flow rate of the pipeline. l is the maximum transmission delay of pipeline l; L l , S l are the length and cross-sectional area of ​​the pipe l respectively; ρ is the density of water.

[0149] The relationship between the pipeline mass flow rate at time t-(t′+1) and time tt′ is as follows:

[0150]

[0151] The meaning of this formula is: if the mass flow rate flowing into the pipeline at time t-(t′+1) has not completely flowed out, then the mass flow rate flowing into the pipeline at time tt′ must be completely in the pipeline. l is the maximum transmission delay of pipeline l; Φ pipe It is the collection of heat network pipelines. l,t,t′ and l,t,t′ It is an intermediate variable used to simulate the water block method. Its physical meaning is the proportion of the mass flow rate flowing into the pipeline (or virtual pipeline) at time tt′ that is still in the pipeline (or virtual pipeline) at time t in the total mass flow rate of the pipeline.

[0152] 1.3 Establish thermal constraints of the thermal network consisting of hydraulic model and thermal model of the electric-thermal integrated energy system.

[0153] The relationship expression of the pipe inlet and outlet temperature defined by the water block method is as follows:

[0154]

[0155]

[0156] Where, (18) is the temperature relationship of the supply and return water pipes defined by the water block method considering only the thermal delay; (19) is the temperature relationship of the supply and return water pipes considering the heat loss of the pipes on the basis of the thermal delay. c is the specific heat capacity of water; m l,t-t′ represents the mass flow rate of pipeline l during the period tt′; σ l,t is the heat loss coefficient of pipeline l at time t; is the ambient temperature of pipeline l; is the supply / return water inlet temperature of pipe l at time tt′; is the outlet temperature of supply and return water of pipe l when heat loss is not considered; It is the final outlet temperature of supply and return water of pipe l at time t obtained by water block method.

[0157] The thermal power balance expression of the heat source node of the thermal network is as follows:

[0158]

[0159] In the formula, are the heat supply of the heat source node k and the node k with CHP unit at time t respectively; Φ source,n It is the set of heat source nodes in the heat network.

[0160] The calculation expressions of heating power of source and load nodes are as follows:

[0161]

[0162]

[0163] In the formula, Φ source,p is the set of pipes connecting the heat network and the heat source node; Φ load,n is a collection of heat network load nodes; represents the set of pipelines whose exit is node k; are the inlet and outlet temperatures of the water supply pipe l at time t; are the inlet and outlet temperatures of the return pipe l at time t; is the heat supply of heat source node k at time t; is the heat load of heat load node k at time t.

[0164] The calculation formula of pipeline thermal power is as follows:

[0165]

[0166] Where c is the specific heat capacity of water; is the supply and return water outlet temperature of pipe l at time t; is the supply and return water inlet temperature of pipe l at time t; m l,t is the decision variable, representing the mass flow rate of pipeline l at time t

[0167] The cross node thermal power balance expression is as follows:

[0168]

[0169] In the formula, Respectively represent the pipeline set whose inlet and outlet are node k; Φ in is a set of intersection nodes.

[0170] The temperature of all pipes flowing into the same node k in the thermal network is the same, and the expression is as follows:

[0171]

[0172] The expressions of thermal constraints supply and return water temperature limit and pipeline heat energy limit within the scheduling period are as follows:

[0173]

[0174] 1.4 Establish power network constraints for the electric-thermal integrated energy system.

[0175] The generator start and stop constraint expression is as follows:

[0176]

[0177] The above formula reveals the start and stop status of the generator With its start-stop action The relationship between the generator and the minimum online time is limited. and offline time when or Start or stop the generator when or The generator does not change its operating state.

[0178] The upper and lower bounds of the active and reactive power of the generator are as follows:

[0179]

[0180] In the formula, are the upper and lower bounds of active and reactive power of generator i respectively.

[0181] The expressions for the upper and lower power limits of the generator regulation are as follows:

[0182]

[0183] In the formula, are the upward and downward power limits of generator i respectively.

[0184] The expressions for the upper and lower bounds of the generator climbing power are as follows:

[0185]

[0186] In the formula, are the upper and lower bounds of the climbing power of generator i respectively.

[0187] The system load shedding ratio constraint expression is as follows:

[0188]

[0189] In the formula, is the upper bound of the load shedding ratio of grid node i.

[0190] The expressions for the voltage amplitude, phase angle, and upper and lower bounds of the branch power at the power grid node are as follows:

[0191]

[0192] In the formula, v i,t ,θ i,t 、p ij,t are the voltage and phase angle decision variables of grid node i and the branch power ij decision variable; are the upper and lower bounds of the voltage at node i; is the power limit of branch ij.

[0193] The active and reactive power balance expressions of the power grid are as follows:

[0194]

[0195] In the formula, They are renewable energy output power and renewable energy abandoned power respectively.

[0196] The power flow equation of the power grid is as follows:

[0197]

[0198] Where b ij , g ij is the conductance and susceptance of the power grid branch ij; θ ij,t =θ i,t -θ j,t .

[0199] Step 2: Focus on the complexity of the heating network model: the heating network adopts the quantity regulation operation mode; the power grid adopts the second-order cone power flow model; the renewable energy output is expressed by predicted power, and the hydraulic condition decision of the heating network and the phase angle reference value of the second-order cone model of the power grid are obtained.

[0200] 2.1 Establish the constraints of the heat network quantity regulation model: The nonlinear constraints of the heat network model include bilinear constraints (12)(15)(17)(18)(23), exponential constraints (19) and quadratic constraints (11).

[0201] 2.1.1 Introducing auxiliary variables Equation (15) (18) is equivalently transformed; due to the heat transfer coefficient λ of the heating network pipeline lis very small, so the heat loss coefficient equation (19) is linearly approximated. The constraints (7)-(26) of the heat network regulation model can be simplified as follows, defined as model 1:

[0202]

[0203] Where A, B, and C are coefficient matrices; b and c are column vectors; α, β, and γ are decision variables of the heat network regulation model, including the electric power of CHP unit i at time t. and thermal power Pipeline heat loss coefficient σ l,t ; Inlet and outlet temperatures of the supply and return pipe l Inlet and outlet heat power of the water supply and return pipe l The mass flow rate m in pipeline l at time t l,t , auxiliary variable x l.t.t′ ,y l.t.t′ , the pressure head s at node k k,t , the pressure head loss of the pipeline due to the electric control valve Circulation power of the water pump at pipe l The pressure head loss caused by friction in the pipeline α i , β i , γ i They respectively represent the parts of the corresponding decision variables involved in constraint i; Γ1 is the bilinear constraint index set of the heat network model, including (12)(17)(23) and the constraints after equivalent transformation of (15)(18); Γ2 is the quadratic constraint index set of the heat network model, including (11).

[0204] 2.1.2 The bilinear constraints of the index set Γ1 of the above model 1 are transformed into quadratic constraints, as shown in (36); all constraints of model 1 can be transformed into quadratic constraints, such as (37)-(38):

[0205]

[0206] γ i ≥(α i ) 2 ,i∈Γ2 (54)

[0207] γ i ≤(α i ) 2 ,i∈Γ2 (55)

[0208] 2.1.3 Convert the convex constraint (37) into a second-order cone form, expressed as follows:

[0209]

[0210] By following steps 2.1.2-2.1.3, Model 1 of the nonlinear model can be transformed into a convex relaxation model;

[0211] 2.1.4 introduces the McCormick envelope, transforms the concave constraint (38) into the difference between a linear function and a convex function, introduces slack variables into the objective function, relaxes the above convex function, and obtains the penalty model based on 2.1.3.

[0212] 2.1.5 Taking α, β, γ as iterative variables, iterate the convex relaxation model in 2.1.3 and the penalty model in 2.1.4 to obtain the analytical solutions of mass flow, pipeline pressure and intermediate variables α, β, γ, and obtain the linear heat network quantity regulation model.

[0213] 2.2 Establish the constraints of the second-order cone power flow model of the power grid.

[0214] 2.2.1 Introducing auxiliary variable M ij,t =v i,t v j,t cos(θ i,t -θ j,t ),N ij,t =v i,t v j,t sin(θ i,t -θ j,t )and Then the AC power flow equation (34) can be equivalently transformed into (40)-(43), expressed as follows:

[0215]

[0216]

[0217]

[0218]

[0219] 2.2.2 Perform a second relaxation on constraint (42), the expression is as follows:

[0220]

[0221] 2.2.3 At the equilibrium point Taylor expansion is performed on constraint (43), and the expression is as follows:

[0222]

[0223] 2.3 The first step of establishing the decision model of the integrated energy system is the first decision model.

[0224] The first step decision model of the integrated energy system can be fully expressed as follows: the objective function is (1)-(6); (7)-(10), (13)-(14), (20)-(22), (24)-(26), (28)-(33) are linear constraints; (16) and (27) are 0-1 variable constraints; steps 1.1-1.5 are used to replace the second-order cone quantity regulation model of the heat network in (11)-(12), (15), (17)-(19), (23); (40)-(45) is used to replace the second-order cone model of the power grid flow in (34). The initial value of the phase angle between the hydraulic decision of the heat network and the second-order cone model of the power grid flow can be obtained by solving the problem.

[0225] Step 3: Focus on the complexity of the power grid model: the heat network adopts a quality regulation operation mode with fixed hydraulic conditions; the power grid adopts an improved second-order cone power flow model; the renewable energy output is modeled with a small number of scenarios with a reduced number; and the power grid is determined based on node voltage, operation hot start point, and equipment start and stop status decisions.

[0226] 3.1 Improve the objective function of the second-step decision-making model of the electric-thermal integrated energy system.

[0227] The objective function of the second-step decision-making model of the electric-thermal integrated energy system, formula (1) is replaced as follows:

[0228]

[0229]

[0230] In the formula, C 0-1 is the start-up and shutdown cost of conventional generators; C g is the power generation cost of conventional generators; C CHP is the cost of the combined heat and power unit; The cost of adjusting the generator output to take into account the uncertainty of renewable energy output; is the load shedding cost. ξ represents the renewable energy prediction error; μ n is the probability of scene n; Ω sel A collection of scenarios for renewable energy output with reduced quantities.

[0231] 3.2 Establish constraints for the thermal network quality regulation model.

[0232] The first step is to solve the decision model to obtain hydraulic decisions including pipeline mass flow Auxiliary variables Water pump circulation power Heat network node pressure head Pressure head loss in pipeline due to electric regulating valve Pipe pressure head caused by the pump and the pressure head loss caused by friction in the pipeline Substituting the above known data into the thermal network quality regulation model (7)-(26), the original thermal network nonlinear constraints (11)-(12), (15)-(19), and (23) can be transformed into linear constraints containing only temperature decisions, thus obtaining the second-step thermal network quality regulation model of the integrated energy system.

[0233] 3.3 Improve the constraints of the second-order cone model of the power grid.

[0234] The initial phase angle of the second-order cone model of the tidal current obtained by running the model in the first step is recorded as Then the equation (45) in the second-order cone model of the power grid can be transformed into:

[0235]

[0236] In the formula, Ω bus is the set of power grid nodes; θ i,t is the phase angle decision variable of grid node i at time t.

[0237] 3.4 Establish the second step decision model of the integrated energy system.

[0238] The second decision model used in the second step of the integrated energy system can be fully expressed as follows: The objective function is (2)-(6), (46)-(47). sel For each scenario in the set, (7)-(26), (28)-(33) are used as linear constraints; (27) is used as a 0-1 variable constraint; (40)-(44), (48) replace the power grid power flow second-order cone model in (34). The solution can obtain the start and stop states of the power grid generators; the network reference operating point represented by node voltage and phase angle, that is, the system actually operates near this reference point.

[0239] Step 4: Focus on the complexity of uncertainty modeling: the heat network adopts a quality regulation operation mode with fixed hydraulic conditions; the power grid adopts a linearized power flow model based on the hot start point of the previous node voltage phase angle; the renewable energy output adopts a more accurate scenario modeling before the quantity is reduced, and obtains an operation decision plan for the electric and thermal integrated energy system based on the decomposition of model complexity.

[0240] 4.1 Improve the objective function of the third step decision model of the electric and thermal integrated energy system.

[0241] The objective function of the third step decision model of the electric-thermal integrated energy system, formula (1) is replaced as follows:

[0242]

[0243]

[0244] In the formula, C 0-1 is the start-up and shutdown cost of conventional generators; C gis the power generation cost of conventional generators; C CHP is the cost of the combined heat and power unit; The cost of adjusting the generator output to take into account the uncertainty of renewable energy output; is the load shedding cost. ξ represents the renewable energy prediction error; μ n is the probability of scene n; Ω exp A collection of renewable energy output scenarios before reduction in quantity.

[0245] 4.2 Establish constraints for the linearized model of power grid hot start.

[0246] The grid reference operating point obtained by solving the second decision model is recorded as In known systems running on When the power flow linearization model is near, the local accuracy of the power flow linearization model is good and the model complexity is low. The power flow linearization model used is as follows:

[0247]

[0248]

[0249]

[0250]

[0251]

[0252] In the formula, Ω bus is the set of power grid nodes; p ij,t ,q ij,t is the active and reactive power of branch ij at time t; v i,t ,θ i,t is the voltage and phase angle decision variable of grid node i at time t; v ij,t =v i,t -v j,t and θ ij,t =θ i,t -θ j,t . (51) is the reference operating point The Taylor expansion approximation equation is performed on the voltage amplitude and phase angle product terms; is a constant parameter generated during the Taylor expansion approximation process. are the active and reactive losses respectively, which are linearized and approximated again as shown in equations (54)-(55).

[0253] 4.3 Establish the third step decision model of the integrated energy system.

[0254] The third step decision model of the integrated energy system can be fully expressed as follows: The objective function is (2)-(6), (49)-(50). exp For each scenario in the set, (7)-(26) and (28)-(33) are used as linear constraints; (51)-(55) replace the linearized model of power flow hot start in (34). The solution can obtain the operation decision plan of the electric-thermal integrated energy system based on the decomposition of model complexity.

[0255] Example 2

[0256] This embodiment introduces a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the operation optimization method of the electric-thermal integrated energy system based on model complexity decomposition as introduced in Example 1 is implemented.

[0257] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0258] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0259] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0260] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0261] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the enlightenment of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the purpose of the present invention and the claims, which all fall within the protection of the present invention.

Claims

1. A method for optimizing the operation of an electric-thermal integrated energy system based on model complexity decomposition, characterized in that: include: Obtain the operation optimization model of the electric-thermal integrated energy system and its original operation constraints; Focusing on the complexity of the heat network model: the heat network adopts the quantity regulation operation mode, the power grid adopts the second-order cone power flow model, the renewable energy output is expressed by predicted power, the first decision model is constructed, the first decision model is solved, and the hydraulic condition decision of the heat network and the phase angle reference value of the second-order cone model of the power grid are obtained; Focusing on the complexity of the power grid model: the heat network adopts a quality regulation operation mode with fixed hydraulic conditions, the power grid adopts an improved second-order cone power flow model, and the renewable energy output adopts a small number of scenarios with a reduced number of scenarios to build a second decision model. The second decision model is solved to obtain the hot start point of the power grid operation and the start and stop state decision of the equipment; Focusing on the complexity of uncertainty modeling: the heat network adopts a quality regulation operation mode with fixed hydraulic conditions, the power grid adopts a linearized power flow model based on the hot start point of the power grid operation, and the renewable energy output adopts uncertainty scenario modeling before the quantity is reduced. A third decision model is constructed and solved to obtain the operation decision plan of the electric and thermal integrated energy system.

2. The method according to claim 1, characterized in that: The objective function of the operation optimization model of the electric-thermal integrated energy system is expressed as: my C 0-1 +C g +C r +C cut +C CHP (1) in, In the formula, C 0-1 is the start-up and shutdown cost of conventional generators, is the start and stop action variable of generator i at time t; is the start and stop cost parameter of generator i; C g For the cost of electricity generation, is the operating cost parameter of generator i, is the active power of generator i at time t; C r is the regulation cost of the generator, is the cost parameter for adjusting generator i up and down, is the regulating power of generator i at time t, and when For positive time, when When it is negative, C cut is the system load shedding cost, is the load shedding cost parameter of node i, is the load power of node i at time t, is the load shedding ratio of node i at time t; C CHP is the cost of the combined heat and power unit, are the electrical and thermal powers of CHP unit i at time t, respectively; is the cost parameter of the CHP unit; The original operation constraints include hydraulic constraints, thermal constraints and power constraints of the thermal network composed of a hydraulic model and a thermal model, wherein: The hydraulic constraints include each heat network pressure head constraint, water pump power constraint and pipeline mass flow constraint; The thermal constraints include heat source node thermal power balance constraints, cross node thermal power balance constraints, supply and return water temperature constraints, and pipeline thermal energy constraints within the scheduling period; Power constraints include generator constraints, system load shedding ratio constraints, grid node power constraints and grid flow constraints.

3. The method according to claim 2, characterized in that: The hydraulic constraint is expressed as the following formula: In the formula, Φ n is the set of heating network nodes; Φ pipe It is the collection of heat network pipelines; k,t is the pressure head of node k at time t; are the upper and lower bounds of the pressure head of node k respectively; is the pressure head loss caused by the electric control valve in pipeline l at time t; is the circulating power of the pump in pipeline l at time t The limit value of They represent the initial pressure heads of nodes k1 and k2 respectively; is the pressure head in pipe l at time t caused by the water pump; Indicates the pressure head loss caused by friction in pipeline l; ε l is the resistance coefficient of the pipeline l; m l,t is the decision variable, which represents the mass flow rate of pipeline l at time t; represents the circulation power of the water pump in pipeline l at time t; is the pressure head in pipe l at time t caused by the water pump; is the pump efficiency; are the sets of pipes with the inlet and outlet as nodes k; Φ in is the set of cross-nodes of the heat network; are the upper and lower bounds of the mass flow rate in pipeline l; x l,t,t′ and l,t,t′ is an intermediate variable, which indicates the proportion of the mass flow rate flowing into the pipeline during the period tt′ that is still in the pipeline at time t in the total mass flow rate of the pipeline; T l is the maximum transmission delay of pipeline l; L l is the length of the pipeline l; S l is the cross-sectional area of ​​the pipe l; ρ is the density of water.

4. The method according to claim 3, characterized in that: The thermal constraint is expressed as the following formula: In the formula, is the outlet temperature of the supply and return water of pipe l at time t when heat loss is not considered; c is the specific heat capacity of water; m l,t-t′ represents the mass flow rate of pipeline l during the period tt′; is the supply and return water inlet temperature of pipe l during the period tt′; σ l,t is the heat loss coefficient of pipeline l at time t; is the ambient temperature of pipeline l; is the final outlet temperature of the supply / return water of pipe l at time t obtained by simulating the water block method; l It represents the heat transfer coefficient of the heating network pipeline; is the heat supply of heat source node k at time t; is the heat supply of node k with a cogeneration unit at time t; Φ source,n is the set of heat source nodes in the heat network; Φ source,p is the set of pipes connecting the heat network and the heat source node; Φ load,n is a collection of heat network load nodes; are the inlet temperature and outlet temperature of water supply pipe l at time t respectively; is the heat load of heat load node k at time t; are the inlet temperature and outlet temperature of the return pipe l at time t respectively; is the supply and return water inlet temperature of pipe l at time t; ω s.min ,ω s.max is the upper and lower limits of the water supply pipe temperature; ω r.min ,ω r.max The upper and lower limits of the return pipe temperature.

5. The method according to claim 4, characterized in that: The power constraint is expressed as the following formula: In the formula, is the start and stop status of generator i at time t; is the start and stop status of generator i at time t; are the starting and stopping state variables of generator i at time t respectively; are the minimum online time and the minimum offline time of generator i respectively; Ω g is the set of generator nodes; are the upper and lower bounds of the active power of generator i; are the upper and lower bounds of reactive power of generator i; is the active and reactive power of generator i at time t; are the upward and downward power limits of generator i respectively; is the regulated power of generator i at time t; are the upper and lower limits of the ramp power of generator i; Ω bus is a set of power grid nodes; is the load shedding ratio of node i at time t; is the load shedding ratio limit of grid node i; v i,t ,θ i,t are the voltage and phase angle decision variables of grid node i at time t, and θ ij,t =θ i,t -θ j,t ;p it,t is the power decision variable of branch ij at time t; are the upper and lower bounds of the voltage at node i, respectively; is the power limit of branch ij; Ω jr It is a collection of power grid branches; is the active and reactive load power of node i at time t; Output power for renewable energy; is the electric power of cogeneration unit i at time t; The power abandoned by renewable energy; ij,t ,q ij,t is the active and reactive power of branch ij at time t; b ij , g ij is the conductance and susceptance of the grid branch ij.

6. The method according to claim 5, characterized in that: The heat network adopts a quantity regulation operation mode, including: S11, based on the operation optimization model of the electric-thermal integrated energy system and its original operation constraints, the complexity of the heat network model is separated, and a heat network quantity regulation model after separation is constructed: minc T α stAα+Bβ+Cγ≤b c i =a i b i i∈Γ1 c i =(a i ) 2 i∈Γ2 (35) Among them, A, B, and C are coefficient matrices; b and c are column vectors; α, β, and γ are decision variables of the heat network regulation model, including the electric power of CHP unit i at time t. and thermal power Pipeline heat loss coefficient σ l,t ; Inlet and outlet temperatures of the supply and return pipe l Inlet and outlet heat power of the water supply and return pipe l The mass flow rate m in pipeline l at time t l,t , auxiliary variable x l.t.t′ ,y l.t.t′ , the pressure head s at node k k,t , the pressure head loss of pipeline l due to the electric control valve Circulation power of the water pump at pipe l The pressure head loss caused by friction in the pipeline α i , β i , γ i They represent the parts of the corresponding decision variables involved in constraint i; Γ1 is the set of bilinear constraints, including equations (12), (17), (23) and the equivalent transformation of equations (15) and (18); Γ2 is the set of quadratic constraints of the heat network model, including equation (11); S12, reference to intermediate variable γ 1,i , γ 2,i The bilinear constraints of the set Γ1 are transformed into quadratic constraints to obtain equation (36). All constraints of the heat network regulation model are transformed into quadratic constraints to obtain equations (37)-(38): 4c 1,i =(a i +b i ) 2 4c 2,i =(a i -b i ) 2 (36) c i =c 1,i -c 2,i c i ≥(a i ) 2 ,i∈Γ2 (37) c i ≤(α i ) 2 ,i∈Γ2 (38) S13, the convex constraint (37) is transformed into a convex relaxation model in the second-order cone form, expressed as: S14, introducing the McCormick envelope, transforming the concave constraint (38) into the difference between a linear function and a convex function, introducing a slack variable in the objective function, relaxing the convex function, and obtaining a penalty model; S15, taking α, β, γ as iterative variables, iterating the convex relaxation model and the penalty model to obtain analytical solutions of mass flow, pipeline pressure and intermediate variables α, β, γ, and a linear heat network quantity regulation model.

7. The method according to claim 6, characterized in that: The second-order cone power flow model adopted for the power grid includes: S16, introduce auxiliary variable M ij,t =v i,t v j,t cos(θ i,t -θ j,t ), N ij,t =v i,t v j,t sin(θ i,t -θ j,t )and The AC power flow equation (34) is equivalently transformed into (40)-(43), and the expression is: In the formula, G ii represents the conductance between nodes ij, B ij represents the susceptance between nodes ij; S17, perform secondary relaxation on constraint (42), the expression is: S18, in v i,t =1,θ i,t = 0, the equilibrium point Taylor expansion is performed on constraint (43), and the expression is as follows: The expressions of the second-order cone model of power grid flow are (40)-(45).

8. The method according to claim 7, characterized in that: The complexity of the power grid model is taken as the focus: the heat network adopts a quality regulation operation mode with fixed hydraulic conditions, the power grid adopts an improved second-order cone flow model, and the renewable energy output adopts a small number of scenarios with a reduced number of models. The second decision model is constructed and solved to obtain the hot start point of the power grid operation and the start and stop state decision of the equipment, including: S21, substituting the hydraulic condition decision of the heating network obtained by solving the first decision model into the nonlinear constraints (11)-(12), (15)-(19), (23) of the heating network to obtain a linear constraint containing only temperature, thereby obtaining the heating network quality regulation model; S22, replace formula (1) with the following formula to obtain the objective function of the second decision model: In the formula, The cost of adjusting the generator output to take into account the uncertainty of renewable energy output; is the load shedding cost. ξ represents the renewable energy prediction error; μ n is the probability of scene n; Ω sel A collection of scenarios for renewable energy output after the amount of reduction is reduced; S23, the initial phase angle value of the second-order cone model of the power flow obtained by solving the first decision model is recorded as Then the second-order cone model of the power grid can be transformed into equation (45): The improved second-order cone power flow model is obtained.

9. The method according to claim 8, characterized in that: The uncertainty modeling complexity is taken as the focus: the heat network adopts the quality regulation operation mode with fixed hydraulic conditions, the power grid adopts the linearized power flow model based on the hot start point of the power grid operation, the renewable energy output adopts the uncertainty scenario modeling before the quantity reduction, and the third decision model is constructed. The third decision model is solved to obtain the operation decision plan of the electric and thermal integrated energy system, including: S31, replace equation (1) in the objective function of the original electric-thermal integrated energy system operation optimization model with the following equation: In the formula, Ω exp A collection of renewable energy output scenarios before reduction; S32, the grid reference operating point obtained by solving the second decision model is recorded as The linearized power flow model based on the hot start point of the power grid operation is expressed as: In the formula, v ij,t =v i,t -v j,t and θ ij,t =θ i,t -θ j,t ; Formula (51) is the reference operating point The Taylor expansion approximate equation is performed on the product of voltage amplitude and phase angle; is a constant parameter generated during the Taylor expansion approximation process.

10. The method according to claim 9, characterized in that: The objective function of the first decision model is equation (1)-(6), the linear constraints are (7)-(10), (13)-(14), (20)-(22), (24)-(26), (28)-(33), and the 0-1 variable constraints are (16) and (27); the initial value of the phase angle between the heat network hydraulic decision and the power grid flow second-order cone model is obtained by solving; the heat network hydraulic decision includes the pipeline mass flow Auxiliary variables Water pump circulation power Heat network node pressure head Pressure head loss in pipeline due to electric control valve Pipe pressure head caused by the pump and the pressure head loss caused by friction in the pipeline The objective function of the second decision model is as follows: (2)-(6) and (46)-(47), sel For each scenario in the set, equations (7)-(26), (28)-(33) are used as linear constraints, equation (27) is used as a 0-1 variable constraint, and equations (40)-(44) and (48) are used as the optimization problem of the improved second-order cone power flow model of the power grid. The start-stop state of the power grid generator and the reference operating point of the power grid represented by the node voltage and phase angle are obtained by solving the problem. The objective functions of the third decision model are (2)-(6), (49)-(50), and for Ω exp For each scenario in the set, equations (7)-(26) and (28)-(33) are used as linear constraints, and equations (51)-(55) are used as the optimization problem of the linearized model of power grid power flow hot start. The output of each device in the electric and thermal network is solved to form an operation decision plan for the electric and thermal integrated energy system.

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