An Operation Optimization Method for Integrated Electric and Thermal Energy Systems Based on Model Complexity Decomposition

Abstract

This invention discloses a method for optimizing the operation of an integrated electric and thermal energy system based on model complexity decomposition. The method includes: establishing the objective function and original operating constraints of the integrated electric and thermal energy system operation problem; and constructing a decision model and solving decision schemes step by step: First, focusing on the complexity of the heating network model, the heating network adopts a quantity-regulated operation mode, the power grid adopts a second-order cone power flow model, and renewable energy output is represented by predicted power, resulting in the decision on the hydraulic conditions of the heating network and the reference value of the phase angle of the second-order cone model of the power grid; second, focusing on the complexity of the power grid model, the heating network adopts a quality-regulated operation mode, the power grid adopts an improved second-order cone power flow model, and renewable energy output is modeled using a small number of scenarios with reduced quantity, obtaining the power grid reference operating point and equipment start-up and shutdown state decisions; finally, focusing on the complexity of uncertainty modeling, the heating network adopts a quality-regulated operation mode, the power grid adopts a linearized power flow model, and renewable energy output is modeled using uncertain scenarios before quantity reduction, obtaining the operation decision scheme for the integrated electric and thermal energy system. This invention improves the decision-making efficiency of the integrated energy system operation scheme while ensuring system operation safety.

Description

Technical Field

[0001] This invention relates to the field of integrated energy system operation optimization technology, and in particular to an operation optimization method for an integrated electrothermal energy system based on model complexity decomposition. Background Technology

[0002] Global warming and the escalating energy crisis have made the transformation of traditional energy systems more urgent. Integrated power-heat (IEHS) systems, through the complementary and synergistic optimization of electrical and thermal energy, are an important means to improve the energy structure and enhance the operational flexibility of the system. However, the significant differences in the transmission characteristics of electrical and thermal energy flows, the mutual influence between dynamic and static processes in the network, and the inherent non-convexity of the IEHS itself further complicate the system operation decision-making model. Furthermore, the output uncertainty brought about by the large-scale integration of distributed renewable energy sources also poses challenges to the scheduling of IEHS systems, threatening the operational safety and economic efficiency of integrated power-heat (IEHS) systems.

[0003] In the study of the operation of integrated electrothermal energy systems, the accuracy of the operating model has a significant impact on the operational results. Currently, integrated electrothermal energy systems face two complexities: the nonlinearity of the electrothermal network model and the uncertainty modeling of renewable energy sources. These challenges make it difficult to guarantee the rationality of hydraulic, thermal, and power grid flow decisions, as well as the feasibility and economy of system operation. While linearized power grid models and second-order cone models, as well as quantitative and qualitative regulation modes of the heating network, each have their advantages and disadvantages, there are few operational schemes that combine the strengths of these models. Furthermore, in complex integrated electrothermal energy system operation problems, accurate characterization of uncertainties is rarely provided to ensure model solvability. Therefore, finding effective methods to reconcile the conflict between uncertainty modeling and network modeling in the operation of integrated electrothermal energy systems and filling this research gap is crucial. Summary of the Invention

[0004] The purpose of this invention is to provide an operation optimization method for integrated electrothermal energy systems based on model complexity decomposition. Through step-by-step decision-making, the complexity of the integrated energy system's heat network model, power grid model, and uncertainty modeling is decomposed and coordinated, achieving efficient operation optimization under the complex operating characteristics of the integrated energy system, ensuring safe system operation, and significantly reducing optimization solution time. The technical solution adopted by this invention is as follows.

[0005] On the one hand, this invention provides a method for optimizing the operation of an integrated electrothermal energy system based on model complexity decomposition, comprising:

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

[0007] Focusing on the complexity of the heating network model: the heating network adopts a quantity regulation operation mode, the power grid adopts a second-order cone power flow model, and the renewable energy output is represented by predicted power. A first decision model is constructed, and the first decision model is solved to obtain the hydraulic condition decision of the heating network and the phase angle reference value of the second-order cone model of the power grid.

[0008] Focusing on the complexity of the power grid model: a quality regulation operation mode with fixed hydraulic conditions is adopted for the heating network, an improved second-order cone power flow model is adopted for the power grid, and a small number of scenarios with reduced renewable energy output are modeled to construct a second decision model. The second decision model is solved to obtain the hot start point of power grid operation and the start-up and shutdown state decisions of equipment.

[0009] Focusing on the complexity of uncertainty modeling: the heating 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 quantity reduction. A third decision model is constructed, and the third decision model is solved to obtain the operation decision scheme of the integrated power and heat energy system.

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

[0011] (1)

[0012] in, (2)

[0013] (3)

[0014] (4)

[0015] (5)

[0016] (6)

[0017] In the formula, The start-up and shutdown costs of a conventional generator, , For the generator at time t Start and stop action variables; , For generator Start-up and shutdown cost parameters; For the cost of electricity generation, For generator Operating cost parameters For generator Active power at time t; For the regulation cost of the generator, , For generator Adjusting cost parameters up and down For generator The adjusted power at time t, and when When it is the right time, ,when When it is negative, ; For system load shedding costs, For nodes The load shedding cost parameters For nodes The load power at time t, For nodes The load shedding ratio at time t; For the cost of cogeneration units, , CHP units Electrical and thermal power at time t; , , , , , , Cost parameters for combined heat and power (CHP) units.

[0018] Optionally, the original operating constraints include hydraulic constraints, thermal constraints, and electrical constraints of the thermal network composed of the hydraulic model and the thermal model, wherein:

[0019] The hydraulic constraints include the head constraints of each heating network, the power constraints of the water pumps, and the mass flow constraints of the pipelines.

[0020] The thermal constraints include thermal power balance constraints at heat source nodes, thermal power balance constraints at intersection nodes, supply and return water temperature constraints, and pipeline thermal energy constraints within the scheduling cycle.

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

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

[0023] (7)

[0024] (8)

[0025] (9)

[0026] (10)

[0027] (11)

[0028] (12)

[0029] (13)

[0030] (14)

[0031] (15)

[0032] (16)

[0033] (17)

[0034] In the formula, A set of nodes in the heating network; A collection of heating network pipes; Let k be the pressure head at time t; , These are the upper and lower bounds of the pressure at node k, respectively; For pipelines The head loss due to the electric regulating valve at time t; For pipelines The water pump circulation power at time t The limit value; Representing nodes respectively , The initial pressure head; Pipeline caused by water pump The pressure head at time t; Indicates pipeline Indentation loss due to friction; For pipelines The drag coefficient; Let be the decision variable, representing the pipeline. Mass flow rate at time t; Indicates pipeline The circulating power of the water pump at time t; Pipeline caused by water pump The pressure head at time t; For water pump efficiency; , Let each be a set of pipes with inlet and outlet nodes k, respectively. A set of cross nodes in the heating network; , For pipelines Mass flow rate upper and lower bounds; and As an intermediate variable, it represents The proportion of the mass flow rate that flows into the pipeline during a given time period and is still in the pipeline at time t in the total mass flow rate of the pipeline. For pipelines Maximum transmission delay; For pipelines Length; For pipelines The cross-sectional area; This is the density of water.

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

[0036] (18)

[0037] (19)

[0038] (20)

[0039] (twenty one)

[0040] (twenty two)

[0041] (twenty three)

[0042] (twenty four)

[0043] (25)

[0044] (26)

[0045] In the formula, , For pipelines without considering heat loss The supply and return water outlet temperatures at time t; This is the specific heat capacity of water; Indicates pipeline exist Quality flow rate over a given period; , For pipelines exist The inlet temperatures of the supply and return water during different time periods; For pipelines The heat loss coefficient at time t; For pipelines Ambient temperature; , Pipeline derived from the simulation of the water block method The final outlet temperature of the supply / return water at time t; Indicates the heat transfer coefficient of the heating network pipeline; The heat supplied by heat source node k at time t; The heat supplied at time t for node k, which has a combined heat and power unit; A set of heat source nodes in a heating network; It is a collection of pipes connecting the heating network to the heat source nodes; For the set of load nodes in the heating network; , Water supply pipes The inlet and outlet temperatures at time t; Let k be the heat load at time t. , These are the return water pipes The inlet and outlet temperatures at time t; , For pipelines The inlet temperatures of the supply and return water at time t; , These are the upper and lower limits of the water supply pipeline temperature. , These are the upper and lower limits of the temperature in the return water pipe.

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

[0047] (27)

[0048] (28)

[0049] (29)

[0050] (30)

[0051] (31)

[0052] (32)

[0053] (33)

[0054] (34)

[0055] In the formula, For generator The start / stop status at time t; For generator The start / stop status at time t; , Generators The start / stop action state variables at time t; , Generators Minimum online time, minimum offline time; For the set of generator nodes; , For generator The merits of the upper and lower realms; , For generator The upper and lower bounds of the no-function; , For generator Active and reactive power at time t; , Generators The limits for adjusting the power up and down; For generator The adjusted power at time t; , Generators The upper and lower limits of climbing power; A set of power grid nodes; For nodes The load shedding ratio at time t; For power grid nodes The load shedding ratio limit; , Respectively, power grid nodes The voltage and phase angle decision variables at time t, and have ; branch road The power decision variable at time t; , They are nodes The upper and lower limits of the voltage; branch road Power limitation; For the collection of power grid branches; , For nodes Active and reactive load power at time t; Power output for renewable energy; For combined heat and power units The electric power at time t; This refers to the curtailment of renewable energy power. , branch road Active and reactive power at time t; , For power grid branch Its electrical conductivity and susceptivity.

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

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

[0058] (35)

[0059] Where A, B, and C are coefficient matrices; b and c are column vectors; These are the decision variables for the heating network capacity regulation model, including CHP units. Electric power at time t and thermal power ,pipeline heat loss coefficient Water supply and return pipelines Inlet and outlet temperatures , , , Water supply and return pipes Inlet and outlet thermal power , , , ,pipeline mass flow rate at time t Auxiliary variables , The pressure head of node k Pressure head loss in pipelines due to electric regulating valves ,pipeline The circulating power of the water pump ,pipeline Indentation loss due to friction ; These represent the parts of the corresponding decision variables involved in constraint i; It is a set of bilinear constraints, including equations (12), (17), (23) and the constraints after equivalent transformation of equations (15) and (18); It is the set of quadratic constraints for the heating network model, including equation (11);

[0060] S12, referencing intermediate variables , set The bilinear constraints are transformed into quadratic constraints, resulting in equation (36). All constraints of the heat network regulation model are transformed into quadratic constraint forms, resulting in equations (37)-(38):

[0061] (36)

[0062] (37)

[0063] (38)

[0064] S13, transform the convex constraint (37) into a convex relaxation model of second-order cone form, expressed as:

[0065] (39)

[0066] S14, introduce the McCormick envelope, transform the concave constraint (38) into the difference between a linear function and a convex function, introduce a relaxation variable into the objective function, relax the convex function, and obtain the penalty model;

[0067] S15, with Using these as iterative variables, the convex relaxation model and the penalty model are iterated to obtain the mass flow rate, pipeline pressure, and intermediate variables. The analytical solution, and the linear quantity-regulated heating network model.

[0068] Optionally, the second-order cone power flow model used for the power grid includes:

[0069] S16, Introducing auxiliary variables , and The AC power flow equation (34) is equivalently transformed into (40)-(43), and the expression is:

[0070] (40)

[0071] (41)

[0072] (42)

[0073] (43)

[0074] In the formula, Indicates the electrical conductance between nodes ij. Indicates the susceptance between nodes ij;

[0075] S17, perform a second relaxation on constraint (42), the expression is:

[0076] (44)

[0077] S18, in , The equilibrium point obtained at that location , Taylor expansion of constraint (43) yields the following expression:

[0078] (45);

[0079] The expressions for the second-order cone model of power flow in the power grid are (40)-(45).

[0080] Optionally, the focus is on the complexity of the power grid model: a quality regulation operation mode with fixed hydraulic conditions is adopted for the heating network, an improved second-order cone power flow model is adopted for the power grid, and a small number of scenarios with reduced renewable energy output are used for modeling. A second decision model is constructed, and the second decision model is solved to obtain the power grid operation hot start point and equipment start-up and shutdown state decisions, including:

[0081] S21, Substitute 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 the linear constraints containing only temperature, and obtain the heating network quality regulation model;

[0082] S22, Substitute equation (1) with the following equation to obtain the objective function of the second decision model:

[0083] (46)

[0084] (47)

[0085] In the formula, To account for the generator output regulation costs that take into account the uncertainty of renewable energy output; This is the cost of load shedding. Indicates the forecast error for renewable energy; Let n be the probability of scenario n; A collection of scenarios for generating renewable energy, after the number has been reduced;

[0086] S23, the initial phase angle of the second-order cone power flow model obtained from solving the first decision model is denoted as... , Then, equation (45) in the second-order cone model of the power grid is transformed into:

[0087] (48)

[0088] An improved second-order cone power flow model was obtained.

[0089] Optionally, the focus is on the complexity of uncertainty modeling: a quality regulation operation mode with fixed hydraulic conditions is adopted for the heating network, a linearized power flow model based on the grid's hot start point is adopted for the power grid, and uncertainty scenario modeling is adopted for renewable energy output before quantity reduction. A third decision model is constructed, and the third decision model is solved to obtain the operation decision scheme of the integrated power and heat energy system, including:

[0090] S31, replace equation (1) in the objective function of the original integrated electric and thermal energy system operation optimization model with the following equation:

[0091] (49)

[0092] (50)

[0093] In the formula, A collection of scenarios where renewable energy sources contribute power before the reduction in quantity;

[0094] S32, the power grid reference operating point obtained from solving the second decision model is denoted as... , Then, the linearized power flow model based on the hot start point of the power grid operation is expressed as:

[0095] (51)

[0096] (52)

[0097] (53)

[0098] (54)

[0099] (55)

[0100] In the formula, and Equation (51) is at the reference operating point , Approximate the equation by Taylor expansion of the product of voltage magnitude and phase angle; , , , These are constant parameters generated during the Taylor expansion approximation process.

[0101] Optionally, 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 values ​​of the phase angle of the second-order cone model of the heating network hydraulic decision and the power flow of the power grid are obtained by solving the equation; the heating network hydraulic decision includes pipeline mass flow rate. Auxiliary variables Water pump circulation power Heat network node pressure head Pressure head loss in pipelines due to electric regulating valves Pipeline head caused by water pump and the pressure head loss caused by friction in the pipeline. ;

[0102] The objective functions of the second decision model are equations (2)-(6) and (46)-(47), for For each scenario in the set, equations (7)-(26) and (28)-(33) are used as linear constraints, equation (27) is used as 0-1 variable constraints, and equations (40)-(44) and (48) are used as optimization problems of the second-order cone power flow model for power grid improvement. The grid generator start-up and shutdown states and the grid reference operating point represented by node voltage and phase angle are obtained by solving the equations (40)-(44) and (48).

[0103] The objective functions of the third decision model are (2)-(6) and (49)-(50). For each scenario within the set, equations (7)-(26) and (28)-(33) are used as linear constraints, and equations (51)-(55) are used as optimization problems of the power grid flow heat start-up linearization model. The output status of each device in the electric heating network is obtained by solving the equations (7)-(26) and (28)-(33), and an electric heating integrated energy system operation decision scheme is formed.

[0104] Beneficial effects

[0105] This invention, while ensuring the accuracy of coordinating the heating network model, power grid model, and uncertainty modeling, decomposes the complexity of these three elements and constructs and solves integrated energy system decision models and solutions step by step for different key concerns. This effectively coordinates the conflict between uncertainty modeling and network modeling in the operation of integrated electric and heating energy systems, and can obtain reasonable optimization schemes for the operation of integrated electric and heating energy systems based on the decomposition of model complexity. While ensuring the safety of system operation, it avoids the superposition of different complexities and the exponential increase in computational difficulty. Attached Figure Description

[0106] Figure 1 The diagram shows a flowchart of the operation optimization method for an integrated electric and thermal energy system. Detailed Implementation

[0107] The following description, in conjunction with the accompanying drawings and specific embodiments, provides further details.

[0108] Example 1

[0109] This embodiment introduces a method for optimizing the operation of an integrated electrothermal energy system based on model complexity decomposition, such as... Figure 1 As shown, the method for optimizing the operation of the integrated electric and thermal energy system in this embodiment includes:

[0110] Establish the objective function for the operation problem of the integrated electric and thermal energy system, and establish the original operating constraints of the model based on the structural characteristics of the network equipment of the integrated electric and thermal energy system;

[0111] The complexity of the separate heating network model is as follows: the heating network adopts a quantity regulation operation mode; the power grid adopts a second-order cone power flow model; the output of renewable energy is represented 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;

[0112] Complexity of the power grid model: The heating 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 using a small number of scenarios with reduced quantity; and the power grid operation hot start point and equipment start-up and shutdown state decisions are obtained.

[0113] Separating uncertainty from modeling complexity: The heating 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 models before the quantity reduction, and obtains an operation decision scheme for the integrated electric and thermal energy system based on the decomposition of model complexity.

[0114] The technical concept of this embodiment is to decompose the complexity of the heating network model, power grid model, and uncertainty modeling while ensuring their accuracy. First, it focuses on handling the complexity caused by heating network quantity regulation. The power grid adopts a second-order cone power flow model with good global performance, ignoring the modeling of uncertainty factors to obtain the heating network hydraulic decision. Second, the heating network's quality regulation model under fixed hydraulic conditions still has sufficient flexibility and can be characterized as a linear model; however, the global and local accuracy of the power grid model cannot be obtained by fixing some conditions of the power flow model. Therefore, the heating network quality regulation model is used in subsequent decision-making steps. In the second step, the power grid still uses a second-order cone model, but its initial value of the phase angle Taylor expansion is improved, and renewable energy uncertainty is briefly considered under the premise of the quality regulation linear model. This yields the power network hot-start reference point and equipment start-up and shutdown decisions for system operation. Finally, using the information from the first two steps, a customized hot-start linear power flow model with high local accuracy can be constructed; simultaneously, the system equipment start-up and shutdown decisions have low sensitivity to renewable energy uncertainty factors. Therefore, after constructing the heating network quality regulation model, the power grid hot-start linear power flow model, and fixing the start-up and shutdown variable decisions, the modeling of renewable energy uncertainty is accurately considered. By breaking down the system as described above, the superposition of different complexities and the exponential increase in computational difficulty are avoided. Combining these three steps, a reasonable optimization scheme for the operation of the integrated electrothermal energy system based on model complexity decomposition can be obtained.

[0115] The specific implementation of this embodiment includes the following steps.

[0116] Step 1: Establish the objective function for the operation problem of the integrated electric and thermal energy system. Based on the structural characteristics of the network equipment of the integrated electric and thermal energy system, establish the original operating constraints of the model.

[0117] 1.1 Establish the objective function for the operation problem of the integrated electric and thermal energy system:

[0118] (1)

[0119] (2)

[0120] (3)

[0121] (4)

[0122] (5)

[0123] (6)

[0124] In the formula, These include generator start-up and shutdown costs, power generation costs, regulation costs, load shedding costs, and combined heat and power (CHP) costs. Among these, , For the generator at time t The start / stop action variable, when the generator When there is an activation action at time t Conversely, it is 0 when the generator... When a shutdown action occurs at time t Conversely, it is 0; , , , , Generators Power generation, regulation power, nodes Electrical load and electrical and thermal power of the CHP unit; , , , , , The parameters for generator start-up cost, shutdown cost, operating cost, up-regulation cost, down-regulation cost, and load shedding cost are as follows; the cost parameters for CHP are as follows: , , , , , , .

[0125] 1.2 Establishing hydraulic constraints for the integrated electrothermal energy system, consisting of a hydraulic model and a thermal model, of the thermal network:

[0126] The expressions for the pressure adjustment range of each node head, the electric regulating valve, and the upper and lower bounds of the water pump power are as follows:

[0127] (7)

[0128] (8)

[0129] (9)

[0130] In the formula, A set of nodes in the heating network; A collection of heating network pipes; , These are the upper and lower bounds of the pressure on node k, respectively; For pipelines The limit of the water pump's circulating power. , and Let be the decision variables, representing the head at node k, the head pressure loss in the pipeline due to the electric regulating valve, and the pipeline head pressure. The circulating power of the water pump.

[0131] The equation for pipeline pressure is expressed as follows:

[0132] (10)

[0133] In the formula, the left side represents the pressure between nodes k2 and k1; , The pipelines caused by the water pump Pressure head loss due to friction between the pressure head and the pipeline. Representing nodes respectively , The initial pressure head.

[0134] The expressions for pipeline head loss and circulating pump power are as follows:

[0135] (11)

[0136] (12)

[0137] In the formula, , Pipes The resistance coefficient and pump efficiency; Let be the decision variable, representing the pipeline. Mass flow rate.

[0138] The mass flow constraint equation is expressed as follows:

[0139] (13)

[0140] (14)

[0141] In the formula, (13) and (14) are respectively pipes Mass flow balance equation and upper and lower bound constraints for mass flow; A set of cross nodes in the heating network; , Let each be a set of pipes with inlet and outlet nodes k, respectively. , For pipelines Mass flow rate upper and lower bounds.

[0142] The relationship between intermediate variables and mass flow rate, and the conditions that their physical meaning must satisfy, are as follows:

[0143] (15)

[0144] (16)

[0145] In the formula, intermediate variables and Used to simulate the water block method, its physical meaning is: The mass flow rate that constantly flows into the pipe (or virtual pipe) The percentage of the total mass flow rate in the pipeline that is still in the pipeline (or virtual pipeline) at any given time. For pipelines Maximum transmission delay; , Pipes Length and cross-sectional area; This is the density of water.

[0146] Time and The relationship between the pipeline mass flow rate at any given time is as follows:

[0147] (17)

[0148] The meaning of this expression is: If If the mass flow rate flowing into the pipe has not yet completely flowed out, then The mass flow rate that flows into the pipe at any given time must be entirely within the pipe. The maximum transmission delay of pipe l; It is a collection of heating network pipes. and As an intermediate variable used to simulate the water block method, its physical meaning is... The percentage of the total mass flow in the pipe (or virtual pipe) at time t that is still in the pipe (or virtual pipe) at time t.

[0149] 1.3 Establish thermal constraints for the integrated electrothermal energy system, consisting of a thermal network composed of a hydraulic model and a thermal model.

[0150] The expression for the relationship between the inlet and outlet temperatures of a pipe, defined by the water block method, is as follows:

[0151] (18)

[0152] (19)

[0153] In the formula, (18) is the supply and return water pipe temperature relationship defined by the water block method, considering only the heat delay; (19) is the supply and return water pipe temperature relationship based on the heat delay, considering the heat loss of the pipe. c is the specific heat capacity of water; Indicates pipeline exist Quality flow rate over a given period; For pipelines The heat loss coefficient at time t; For pipelines Ambient temperature; , For pipelines exist The inlet temperature of the supply / return water at any given time; , For pipelines without considering heat loss The supply and return water outlet temperatures; , Pipeline obtained by the water block method The final outlet temperature of the supply and return water at time t.

[0154] The heat power balance expression for the heat source nodes in a thermal network is as follows:

[0155] (20)

[0156] In the formula, , These represent the heat supply at time t for heat source node k and node k equipped with CHP unit; This is the set of heat source nodes in the heating network.

[0157] The formulas for calculating the heating power of the source and load nodes are as follows:

[0158] (twenty one)

[0159] (twenty two)

[0160] In the formula, It is a collection of pipes connecting the heating network to the heat source nodes; For the set of load nodes in the heating network; Represents the set of pipes whose exit is node k; , For water supply pipes The inlet and outlet temperatures at time t; , For return water pipe The inlet and outlet temperatures at time t; The heat supplied by heat source node k at time t; Let be the heat load of heat load node k at time t.

[0161] The formula for calculating the heat power of a pipeline is as follows:

[0162] (twenty three)

[0163] In the formula, c is the specific heat capacity of water; , Let be the supply and return water outlet temperatures of pipe l at time t; , Let be the inlet temperatures of the supply and return water in pipe l at time t; Let be the decision variable, representing the mass flow rate of pipe l at time t.

[0164] The heat power balance expression for the cross node is as follows:

[0165] (twenty four)

[0166] In the formula, , Let each represent a set of pipes with an inlet and an outlet at node k, respectively. It is the set of intersection nodes.

[0167] In a thermal network, all pipes flowing into the same node k have the same temperature, as expressed below:

[0168] (25)

[0169] The thermal constraints on supply and return water temperature limits and pipeline thermal energy limits during the scheduling cycle are expressed as follows:

[0170] (26)

[0171] 1.4 Establish power network constraints for the integrated electric and thermal energy system.

[0172] The generator start-stop constraint expression is as follows:

[0173] (27)

[0174] The above formula reveals the start-stop state of the generator. Its start-stop action , This relationship also limits the minimum online time of the generator. and offline time .when or Start or stop the generator at the appropriate time. or The generator does not change its operating state.

[0175] The expressions for the upper and lower bound constraints of the generator's active and reactive power are as follows:

[0176] (28)

[0177] In the formula, , , , Generators The realms of merit and incompetence, above and below.

[0178] The expressions for the upper and lower power limits of generator regulation are as follows:

[0179] (29)

[0180] In the formula, , Generators The upper and lower power limits.

[0181] The expressions for the upper and lower bounds of the generator's ramping power are as follows:

[0182] (30)

[0183] In the formula, , Generators The upper and lower limits of the climbing power.

[0184] The system load shedding ratio constraint expression is as follows:

[0185] (31)

[0186] In the formula, For power grid nodes The upper limit of the load shedding ratio.

[0187] The expressions for the upper and lower bounds of grid node voltage amplitude, phase angle, and branch power are as follows:

[0188] (32)

[0189] In the formula, , , For power grid nodes Voltage, phase angle decision variables, and branch power Decision variables; , For nodes Voltage upper and lower bounds; branch road Power limitation.

[0190] The active and reactive power balance expression for the power grid is as follows:

[0191] (33)

[0192] In the formula, , These are the output power of renewable energy and the curtailment power of renewable energy, respectively.

[0193] The power flow equations are expressed as follows:

[0194] (34)

[0195] In the formula, , For power grid branch Conductivity and susceptance; .

[0196] Step 2: Focus on the complexity of the heating network model: The heating network adopts a quantity regulation operation mode; the power grid adopts a second-order cone power flow model; the output of renewable energy is represented by predicted power, and the hydraulic condition decision of the heating network and the phase angle benchmark value of the second-order cone model of the power grid are obtained.

[0197] 2.1 Establishing constraints for the heating network capacity regulation model: The nonlinear constraints of the heating network model include bilinear constraints (12)(15)(17)(18)(23), exponential constraints (19) and quadratic constraints (11).

[0198] 2.1.1 Introducing Auxiliary Variables , , , , , Equations (15) and (18) can be transformed equivalently; due to the heat transfer coefficient of the heating network pipeline The heat loss coefficient equation (19) is very small, so it is linearly approximated. The constraints (7)-(26) of the heat network regulation model can be simplified to the following expression, defined as Model 1:

[0199] (43)

[0200] In the formula, A, B, and C are coefficient matrices; b and c are column vectors; These are the decision variables for the heating network capacity regulation model, including CHP units. Electric power at time t and thermal power ,pipeline heat loss coefficient Water supply and return pipelines Inlet and outlet temperatures , , , Water supply and return pipes Inlet and outlet thermal power , , , ,pipeline mass flow rate at time t Auxiliary variables , The pressure head of node k Pressure head loss in pipelines due to electric regulating valves ,pipeline The circulating power of the water pump Pressure head loss in pipelines due to friction ; These represent the parts of the corresponding decision variables involved in constraint i; It is the bilinear constraint index set of the heat network model, including (12) (17) (23) and the constraints after equivalent transformation of (15) (18); It is the set of secondary constraint indices for the heating network model, including (11).

[0201] 2.1.2 The index set of Model 1 above The bilinear constraints are transformed into quadratic constraints, as shown in (36); all constraints in Model 1 can be transformed into quadratic constraint forms, as shown in (37)-(38):

[0202] (36)

[0203] (37)

[0204] (38)

[0205] 2.1.3 The convex constraint (37) is transformed into a second-order cone form, as shown in the following expression:

[0206] (39)

[0207] From steps 2.1.2-2.1.3, the nonlinear model 1 can be transformed into a convex relaxation model;

[0208] 2.1.4 Introducing the McCormick envelope, the concave constraint (38) is transformed into the difference between a linear function and a convex function. Relaxation variables are introduced into the objective function to relax the above convex function, resulting in a penalty model based on 2.1.3.

[0209] 2.1.5 and Using these as iterative variables, the convex relaxation model (2.1.3) and the penalty model (2.1.4) are iterated to obtain the mass flow rate, pipeline pressure, and intermediate variables. The analytical solution yields a linear heat network regulation model.

[0210] 2.2 Establish constraints for the second-order cone power flow model of the power grid.

[0211] 2.2.1 Introducing Auxiliary Variables , and Then the AC power flow equation (34) can be equivalently transformed into (40)-(43), with the following expression:

[0212] (40)

[0213] (41)

[0214] (42)

[0215] (43)

[0216] 2.2.2 Perform a second relaxation on constraint (42), as shown in the following expression:

[0217] (44)

[0218] 2.2.3 At the equilibrium point , Taylor expansion of constraint (43) yields the following expression:

[0219] (45)

[0220] 2.3 Establish the first step of the decision-making model for the integrated energy system, namely the first decision-making model.

[0221] 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), (27) are 0-1 variable constraints; steps 1.1-1.5 replace the second-order cone flow regulation model of the heating network (11)-(12), (15), (17)-(19), (23); (40)-(45) replace the second-order cone flow model of the power grid (34). The initial phase angles of the heating network hydraulic decision and the second-order cone flow model of the power grid can be obtained by solving.

[0222] Step 3: Focus on the complexity of the power grid model: The heating 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 using a small number of scenarios with reduced quantity; the power grid is determined based on node voltage, operating hot start point and equipment start-up and shutdown status.

[0223] 3.1 Improve the objective function of the second-step decision model for the integrated electric and thermal energy system.

[0224] The objective function of the second-step decision model for the integrated electric and thermal energy system, Equation (1), is replaced as follows:

[0225] (46)

[0226] (47)

[0227] In the formula, The start-up and shutdown cost of a conventional generator; The cost of generating electricity using a conventional generator; Cost of combined heat and power units; To account for the generator output regulation costs that take into account the uncertainty of renewable energy output; This is the cost of load shedding. Indicates the forecast error for renewable energy; Let n be the probability of scenario n; A set of scenarios for generating power from renewable energy sources after the reduction in quantity.

[0228] 3.2 Establish constraints for the thermal network quality regulation model.

[0229] The hydraulic decisions obtained from solving the first step of the decision model include pipeline mass flow rate. Auxiliary variables Water pump circulation power Heat network node pressure head Pressure head loss in pipelines due to electric regulating valves Pipeline head caused by water pump Pressure head loss due to friction in pipelines Substituting the known data above, the original nonlinear constraints (11)-(12), (15)-(19), and (23) of the heat network quality regulation model (7)-(26) can be transformed into linear constraints containing only temperature decisions, thus obtaining the second-step heat network quality regulation model of the integrated energy system.

[0230] 3.3 Improve the constraints of the second-order cone model of the power grid.

[0231] The initial phase angle of the second-order cone power flow model obtained from the first step of running the model is denoted as: , Then, equation (45) in the second-order cone model of the power grid can be transformed into:

[0232] (48)

[0233] In the formula, A set of power grid nodes; Let be the phase angle decision variable for power grid node i at time t.

[0234] 3.4 Establish the second-step decision-making model for the integrated energy system.

[0235] The second decision model used in the second step of the integrated energy system decision-making process can be fully expressed as follows: the objective functions are (2)-(6) and (46)-(47). For each scenario within the set, (7)-(26) and (28)-(33) are used as linear constraints; (27) is used as a 0-1 variable constraint; and (40)-(44) and (48) are used to replace the second-order cone model of power flow in (34). The grid generator start-up and shutdown states can be obtained by solving the problem; the network reference operating point is represented by node voltage and phase angle, that is, the actual system operation is near this reference point.

[0236] Step 4: Focus on the complexity of uncertainty modeling: The heating 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 node voltage phase angle in the previous step; the renewable energy output adopts a more accurate scenario model before the quantity reduction, and obtains an operation decision scheme for the integrated electric and heat energy system based on the decomposition of model complexity.

[0237] 4.1 Improve the objective function of the third-step decision model for the integrated electric and thermal energy system.

[0238] The objective function of the third-step decision model for the integrated electric and thermal energy system, Equation (1), is replaced as follows:

[0239] (49)

[0240] (50)

[0241] In the formula, The start-up and shutdown cost of a conventional generator; The cost of generating electricity using a conventional generator; Cost of combined heat and power units; To account for the generator output regulation costs that take into account the uncertainty of renewable energy output; This is the cost of load shedding. Indicates the forecast error for renewable energy; Let n be the probability of scenario n; A collection of scenarios where renewable energy sources contribute power before the reduction in quantity.

[0242] 4.2 Establish constraints for the linearization model of power grid hot start.

[0243] The power grid reference operating point obtained by the second decision model is denoted as... , In a known system running at , When the local accuracy is good, the power flow linearization model exhibits good local accuracy and low model complexity. The power flow linearization model used is as follows:

[0244] (51)

[0245] (52)

[0246] (53)

[0247] (54)

[0248] (55)

[0249] In the formula, A set of power grid nodes; , Let be the active and reactive power of branch ij at time t; , Let voltage and phase angle be the decision variables for power grid node i at time t. and Equation (51) is given at the reference operating point. , Approximate the equation by Taylor expansion of the product of voltage magnitude and phase angle; , , , These are constant parameters generated during the Taylor expansion approximation process. , The active and reactive losses are respectively, and after linearization and approximation, they are as shown in equations (54)-(55).

[0250] 4.3 Establish the third-step decision-making model for the integrated energy system.

[0251] The third-step decision-making model for the integrated energy system can be fully expressed as follows: the objective functions are (2)-(6) and (49)-(50). For each scenario within the set, linear constraints (7)-(26) and (28)-(33) are used; and a linearized power flow thermal start-up model of (34) is replaced by (51)-(55). Solving this model yields an operational decision scheme for the integrated electric and thermal energy system based on the decomposition of model complexity.

[0252] Example 2

[0253] This embodiment introduces a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method for optimizing the operation of an integrated electrothermal energy system based on model complexity decomposition as described in Embodiment 1.

[0254] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0255] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0256] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work 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 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0257] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0258] The embodiments of the present invention have been described above with reference to the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of the present invention without departing from the spirit and scope of the claims. All of these forms are within the protection scope of the present invention.

Claims

1. A method for optimizing the operation of an integrated electrothermal energy system based on model complexity decomposition, characterized in that, include: Obtain the operation optimization model and its original operating constraints of the integrated electric and thermal energy system; Focusing on the complexity of the heating network model: the heating network adopts a quantity regulation operation mode, the power grid adopts a second-order cone power flow model, and the renewable energy output is represented by predicted power. A first decision model is constructed, and the first decision model is solved to obtain the hydraulic condition decision of the heating network and the phase angle reference value of the second-order cone model of the power grid. Focusing on the complexity of the power grid model: a quality regulation operation mode with fixed hydraulic conditions is adopted for the heating network, an improved second-order conical power flow model is adopted for the power grid, and a small number of scenarios with reduced renewable energy output are used for modeling. A second decision model is constructed, and the second decision model is solved to obtain the power grid operation hot start point and equipment start-up and shutdown state decisions. Among them, the fixed hydraulic conditions of the quality regulation operation mode are determined according to the hydraulic conditions of the heating network, and the improved second-order conical power flow model is obtained by substituting the phase angle reference value of the second-order conical power grid model into the branch phase angle decision variable constraint of the second-order conical power flow model. Focusing on the complexity of uncertainty modeling: the heating 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 start-stop variable decision is fixed according to the start-stop state decision of the equipment. The output of renewable energy adopts the uncertainty scenario model before the quantity reduction, constructs a third decision model, solves the third decision model, and obtains the operation decision scheme of the integrated power and heat energy system.

2. The method according to claim 1, characterized in that, The objective function of the operation optimization model for the integrated electrothermal energy system is expressed as follows: （1） in, (2) （3） （4） （5） （6） In the formula, The start-up and shutdown costs of a conventional generator, , For the generator at time t Start and stop action variables; , For generator Start-up and shutdown cost parameters; For the cost of electricity generation, For generator Operating cost parameters For generator Active power at time t; For the regulation cost of the generator, , For generator Adjusting cost parameters up and down For generator The adjusted power at time t, and when When it is the right time, ,when When it is negative, ; For system load shedding costs, For nodes The load shedding cost parameters For nodes The load power at time t, For nodes The load shedding ratio at time t; For the cost of cogeneration units, , CHP units Electrical and thermal power at time t; , , , , Cost parameters for combined heat and power units; The original operational constraints include hydraulic constraints, thermal constraints, and electrical constraints of the thermal network composed of hydraulic and thermal models, wherein: The hydraulic constraints include the head constraints of each heating network, the power constraints of the water pumps, and the mass flow constraints of the pipelines. The thermal constraints include thermal power balance constraints at heat source nodes, thermal power balance constraints at intersection nodes, supply and return water temperature constraints, and pipeline thermal energy constraints within the scheduling cycle. Power constraints include generator constraints, system load shedding ratio constraints, grid node energy constraints, and grid power flow constraints.

3. The method according to claim 2, characterized in that, The hydraulic constraint is expressed by the following formula: （7） （8） （9） （10） （11） （12） （13） （14） （15） （16） （17） In the formula, A set of nodes in the heating network; A collection of heating network pipes; Let k be the pressure head at time t; , These are the upper and lower bounds of the pressure at node k, respectively; For pipelines The head loss due to the electric regulating valve at time t; For pipelines The water pump circulation power at time t The limit value; Representing nodes respectively , The initial pressure head; Pipeline caused by water pump The pressure head at time t; Indicates pipeline Indentation loss due to friction; For pipelines The drag coefficient; Let be the decision variable, representing the pipeline. Mass flow rate at time t; Indicates pipeline The circulating power of the water pump at time t; Pipeline caused by water pump The pressure head at time t; For water pump efficiency; , Let each be a set of pipes with inlet and outlet nodes k, respectively. A set of cross nodes in the heating network; Indicates pipeline exist Quality flow rate over a given period; , For pipelines Mass flow rate upper and lower bounds; and As an intermediate variable, it represents The proportion of the mass flow rate that flows into the pipeline during a given time period and is still in the pipeline at time t in the total mass flow rate of the pipeline. For pipelines Maximum transmission delay; For pipelines Length; For pipelines The cross-sectional area; This is the density of water.

4. The method according to claim 3, characterized in that, The thermal constraint is expressed by the following formula: （18） （19） （20） （21） （22） （23） （24） （25） （26） In the formula, , For pipelines without considering heat loss The supply and return water outlet temperatures at time t; This is the specific heat capacity of water; , For pipelines exist The inlet temperatures of the supply and return water during different time periods; For pipelines The heat loss coefficient at time t; For pipelines Ambient temperature; , Pipeline derived from the simulation of the water block method The final outlet temperature of the supply / return water at time t; Indicates the heat transfer coefficient of the heating network pipeline; The heat supplied by heat source node k at time t; The heat supplied at time t for node k, which has a combined heat and power unit; A set of heat source nodes in a heating network; It is a collection of pipes connecting the heating network to the heat source nodes; For the set of load nodes in the heating network; , Water supply pipes The inlet and outlet temperatures at time t; Let k be the heat load at time t. , These are the return water pipes The inlet and outlet temperatures at time t; , For pipelines The inlet temperatures of the supply and return water at time t; , These are the upper and lower limits of the water supply pipeline temperature. , These are the upper and lower limits of the temperature in the return water pipe.

5. The method according to claim 4, characterized in that, The electrical constraint is expressed by the following formula: （27） （28） （29） （30） （31） （32） （33） （34） In the formula, For generator The start / stop status at time t; For generator The start / stop status at time t; , Generators The start / stop action state variables at time t; , Generators Minimum online time, minimum offline time; For the set of generator nodes; , For generator The merits of the upper and lower realms; , For generator The upper and lower bounds of the no-function; , For generator Active and reactive power at time t; , Generators The limits for adjusting the power up and down; For generator The adjusted power at time t; , Generators The upper and lower limits of climbing power; A set of power grid nodes; For nodes The load shedding ratio at time t; For power grid nodes The load shedding ratio limit; , Respectively, power grid nodes The voltage and phase angle decision variables at time t, and have ; branch road The power decision variable at time t; , They are nodes The upper and lower limits of the voltage; branch road Power limitation; For the collection of power grid branches; , For nodes Active and reactive load power at time t; Power output for renewable energy; For combined heat and power units The electric power at time t; This refers to the curtailment of renewable energy power. , branch road Active and reactive power at time t; , For power grid branch Its electrical conductivity and susceptivity.

6. The method according to claim 5, characterized in that, The aforementioned quantity regulation operation mode for the heating network includes: S11, Based on the operation optimization model of the integrated electric and thermal energy system and its original operation constraints, the complexity of the heat network model is separated, and a separated heat network quantity regulation model is constructed: （35） Where A, B, and C are coefficient matrices; b and c are column vectors; These are the decision variables for the heating network capacity regulation model, including CHP units. Electric power at time t and thermal power ,pipeline heat loss coefficient Water supply and return pipelines Inlet and outlet temperatures , , , Water supply and return pipes Inlet and outlet thermal power , , , ,pipeline mass flow rate at time t Auxiliary variables , The pressure head of node k ,pipeline Pressure head loss due to the electric regulating valve ,pipeline The circulating power of the water pump Pressure head loss in pipelines due to friction ; These represent the parts of the corresponding decision variables involved in constraint i; It is a set of bilinear constraints, including equations (12), (17), (23) and the constraints after equivalent transformation of equations (15) and (18); It is the set of quadratic constraints for the heating network model, including equation (11); S12, referencing intermediate variables , set The bilinear constraints are transformed into quadratic constraints, resulting in equation (36). All constraints of the heat network regulation model are transformed into quadratic constraint forms, resulting in equations (37)-(38): （36） （37） （38） S13, transform the convex constraint (37) into a convex relaxation model of second-order cone form, expressed as: （39） S14, introduce the McCormick envelope, transform the concave constraint (38) into the difference between a linear function and a convex function, introduce a relaxation variable into the objective function, relax the convex function, and obtain the penalty model; S15, with Using these as iterative variables, the convex relaxation model and the penalty model are iterated to obtain the mass flow rate, pipeline pressure, and intermediate variables. The analytical solution and the linear heat network regulation model.

7. The method according to claim 6, characterized in that, The second-order cone power flow model used for the power grid includes: S16, Introducing auxiliary variables , and The AC power flow equation (34) is equivalently transformed into (40)-(43), and the expression is: （40） （41） （42） （43） In the formula, Indicates the electrical conductance between nodes ij. Indicates the susceptance between nodes ij; S17, perform a second relaxation on constraint (42), the expression is: （44） S18, in , The equilibrium point obtained at that location , Taylor expansion of constraint (43) yields the following expression: （45）； The expressions for the second-order cone model of power flow in the power grid are (40)-(45).

8. The method according to claim 7, characterized in that, Focusing on the complexity of the power grid model: a quality regulation operation mode with fixed hydraulic conditions is adopted for the heating network, an improved second-order cone power flow model is used for the power grid, and a small number of scenarios with reduced renewable energy output are used for modeling. A second decision model is constructed, and the second decision model is solved to obtain the power grid operation hot start point and equipment start-up and shutdown state decisions, including: S21, Substitute the decision of the heating network hydraulic conditions obtained by solving the first decision model into the nonlinear constraints (11)-(12), (15)-(19), (23) of the heating network to obtain the linear constraints containing only temperature, and obtain the heating network quality regulation model under the quality regulation operation mode with fixed hydraulic conditions. S22, Substitute equation (1) with the following equation to obtain the objective function of the second decision model: （46） （47） In the formula, To account for the generator output regulation costs that take into account the uncertainty of renewable energy output; For load shedding costs; Indicates the forecast error for renewable energy; Let n be the probability of scenario n; A collection of scenarios for generating renewable energy, after the number has been reduced; S23, the initial phase angle of the second-order cone power flow model obtained from solving the first decision model is denoted as... , Then, equation (45) in the second-order cone model of the power grid is transformed into: （48） An improved second-order cone power flow model was obtained.

9. The method according to claim 8, characterized in that, Focusing on the complexity of uncertainty modeling: The heating network adopts a quality regulation operation mode with fixed hydraulic conditions; the power grid adopts a linearized power flow model based on the grid's hot start point; and the start-stop variables are fixed based on the equipment start-stop state decisions. Renewable energy output is modeled using an uncertainty scenario before quantity reduction. A third decision model is constructed, and solving this third decision model yields the operation decision scheme for the integrated power and heat energy system, including: S31, replace equation (1) in the objective function of the original integrated electric and thermal energy system operation optimization model with the following equation: （49） （50） In the formula, A collection of scenarios where renewable energy sources contribute power before the reduction in quantity; S32, the power grid reference operating point obtained from solving the second decision model is denoted as... , Then, the linearized power flow model based on the hot start point of the power grid operation is expressed as: （51） （52） （53） （54） （55） In the formula, and Equation (51) is at the reference operating point , Approximate the equation by Taylor expansion of the product of voltage magnitude and phase angle; , , , These are constant parameters 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 values ​​of the phase angle of the second-order cone model of the heating network hydraulic decision and the power flow of the power grid are obtained by solving the equation; the heating network hydraulic decision includes pipeline mass flow rate. Auxiliary variables Water pump circulation power Heat network node pressure head Pressure head loss in pipelines due to electric regulating valves Pipeline head caused by water pump and the pressure head loss caused by friction in the pipeline. ; The objective functions of the second decision model are equations (2)-(6) and (46)-(47), for For each scenario in the set, equations (7)-(26) and (28)-(33) are used as linear constraints, equation (27) is used as 0-1 variable constraints, and equations (40)-(44) and (48) are used as optimization problems of the second-order cone power flow model for power grid improvement. The grid generator start-up and shutdown states and the grid reference operating point represented by node voltage and phase angle are obtained by solving the equations (40)-(44) and (48). The objective functions of the third decision model are (2)-(6) and (49)-(50). For each scenario within the set, equations (7)-(26) and (28)-(33) are used as linear constraints, and equations (51)-(55) are used as optimization problems of the power grid flow thermal start-up linearization model. The output of each device in the electric heating network is obtained by solving these problems, and an operation decision scheme for the integrated electric heating energy system is formed.

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