Distributionally robust optimization method and apparatus for electric-hydrogen-thermal integrated energy system

Abstract

A distributionally robust optimization method and apparatus for an electric-hydrogen-thermal integrated energy system, relating to the field of power system optimization operation. The method comprises: establishing a multi-agent optimization scheduling model of an electric-hydrogen-thermal integrated energy system; using a data driving-based distributionally robust optimization model to describe the source-load bilateral uncertainty of a power grid system agent optimization scheduling model, establishing a three-layer two-stage power balance model of a power grid system, and using a column and constraint generation algorithm for solving; and using an alternating direction method of multipliers to alternately iterate the three-layer two-stage power balance model of the power grid system, a hydrogen network system agent optimization scheduling model, and a heat network system agent optimization scheduling model, to realize distributed parallel solving of the multi-agent optimization scheduling model of the electric-hydrogen-thermal integrated energy system. Compared with the prior art, each agent scheduling scheme is solved by means of interactive iteration in the present method, thereby protecting the information privacy of each agent while improving the operation economy and reliability of an interconnected power grid.

Description

Electricity-hydrogen-heat integrated energy system distribution blue rod optimization method and device Technical Field

[0001] The present invention belongs to the field of optimized operation of power systems, and in particular relates to a distributed rod optimization method and device for an electric-hydrogen-heat integrated energy system. Background Art

[0002] With the development of the energy internet and the increasing penetration of renewable energy, the deep coupling of electricity, hydrogen, and thermal energy has improved the system's economy, flexibility, and reliability, but also increased the difficulty of scheduling. At the same time, the inherent randomness, intermittency, and volatility of renewable energy increase the uncertainty factors facing the system, increase the system's operational risks, and reduce the absorption rate of renewable energy. To address the above issues, scholars at home and abroad have also carried out relevant research on the optimized operation of the electricity-hydrogen-heat integrated energy system. Currently, most scholars use robust optimization models to deal with the uncertainty of renewable energy and model and solve the electricity-hydrogen-heat integrated energy system as a whole.

[0003] However, two issues remain to be addressed. First, traditional robust optimization models struggle to determine an appropriate uncertainty set. If the uncertainty set is too large, the scheduling strategy will be overly conservative; conversely, if it is too large, it will be overly aggressive. Second, modeling and solving electricity, hydrogen, and thermal energy as a whole is inconsistent with current social realities and fails to consider the individual benefits of different energy operators.

[0004] Summary of the Invention

[0005] In order to solve the problems existing in the above-mentioned prior art, the present invention proposes a distributed robust optimization method and device for an electric-hydrogen-heat integrated energy system, which can interactively iterate and parallelly solve the scheduling plans of each entity, while improving the operating economy and reliability of the interconnected power grid and protecting the privacy of the information of each entity.

[0006] The present invention adopts the following technical solutions:

[0007] A first aspect of the present invention provides a distributed robust optimization method for an electricity-hydrogen-heat integrated energy system, comprising:

[0008] Establish a multi-agent optimization and dispatching model for the electricity-hydrogen-heat integrated energy system, including the optimization and dispatching model for the power grid system, the optimization and dispatching model for the hydrogen network system, and the optimization and dispatching model for the heat network system;

[0009] A data-driven distributed robust optimization model is used to describe the uncertainty of both the source and load sides of the main optimization dispatch model of the power grid system. A three-layer two-stage power balance model is established and solved using a column constraint generation algorithm.

[0010] The alternating direction multiplier method is used to interactively iterate the three-layer two-stage power balance model of the power grid system, the main body optimization scheduling model of the hydrogen network system, and the main body optimization scheduling model of the heat network system to realize the distributed parallel solution of the multi-agent optimization scheduling model of the electricity-hydrogen-heat integrated energy system.

[0011] Furthermore, the multi-agent optimization scheduling model of the electricity-hydrogen-heat integrated energy system includes:

[0012] (1.1) Optimal dispatch model for power grid system entities

[0013] The goal of the grid system optimization dispatch model is to optimize the output of each device in the grid system and the electricity sales of hydrogen and heat production, so as to maximize the operating benefits of the grid system.

[0014] (1.2) Hydrogen network system optimization scheduling model

[0015] The goal of the hydrogen network system optimization scheduling model is to optimize the output of each device in the hydrogen network system, the amount of electricity purchased by the hydrogen production device, and the amount of hydrogen sold by the hydrogen fuel cell, so as to minimize the operating cost of the hydrogen network system.

[0016] (1.3) Optimal scheduling model for the main heating network system

[0017] The goal of the main optimization scheduling model of the heating network system is to optimize the output of each device in the heating network system and the power purchase of the electric heating device, and minimize the main operating cost of the heating network system.

[0018] Furthermore, based on the structural characteristics of the distributed robust optimization model, a three-layer two-stage power balance model for the power grid system is established. A column constraint generation algorithm is used to iteratively solve the two-stage main and subproblems, where the subproblems can be solved in parallel, including:

[0019] (2.1) Three-layer two-stage power balance model

[0020] (2.1.1) The first stage model is the main optimization dispatch model of the power grid system, which is the first layer of the three-layer two-stage power balance model;

[0021] (2.1.2) The second-stage model includes the second and third layers of the three-layer two-stage power balance model. The second layer minimizes the expected adjustment cost of the output of each device in the power grid system under different source-load probability distributions. The third layer finds the probability distribution of the worst source-load discrete scenario based on the optimization results of the second layer.

[0022] Furthermore, the column constraint generation algorithm is as follows:

[0023] (2.2.1) Initialization: Set the number of iterations m = 1;

[0024] (2.2.2) Solve the main problem of the first stage and obtain the control variables of the main problem;

[0025] (2.2.3) Solve the second-stage sub-problems in parallel based on the control variables obtained from solving the main problem to obtain the probability distribution constraints of the worst-case source-load discrete scenario;

[0026] (2.2.4) Convergence judgment: If the convergence criterion is met, the problem converges and the iteration stops; otherwise, the probability distribution constraint of the worst source-load discrete scenario is added to the first-stage main problem, and the iteration continues, m = m + 1, and returns to (2.2.2).

[0027] Furthermore, the alternating direction multiplier method is used to interactively and iteratively solve the three-layer two-stage power balance model of the power grid system, the main optimization scheduling model of the hydrogen network system, and the main optimization scheduling model of the heat network system in parallel, including:

[0028] (3.1) Initialization: Set the number of iterations n = 1; initialize the average value of the transaction volume between the main body of the power grid system, the main body of the hydrogen network system, and the main body of the heat network system, that is, the average value of the coupling variable;

[0029] (3.2) Interactively solve the three-layer two-stage power balance model of the power grid system, the subject optimization scheduling model of the hydrogen network system, and the subject optimization scheduling model of the heat network system to obtain the scheduling plan of each subject and the amount of electricity traded between subjects;

[0030] (3.3) The main body of the power grid system, the main body of the hydrogen network system, and the main body of the heat network system exchange the latest transaction electricity, and recalculate the average value of the coupling variables and the algorithm parameters;

[0031] (3.4) Judgment: If the convergence criterion is met, the iteration stops and the optimal scheduling solution is obtained; otherwise, continue the iteration, n=n+1, and return to (3.2).

[0032] A second aspect of the present invention provides a device for optimizing distributed blue rods in an electric-hydrogen-heat integrated energy system, comprising:

[0033] The multi-agent model generation module for the electricity-hydrogen-heat integrated energy system is used to establish a multi-agent optimization scheduling model for the electricity-hydrogen-heat integrated energy system, including the main optimization scheduling model for the power grid system, the main optimization scheduling model for the hydrogen network system, and the main optimization scheduling model for the heat network system;

[0034] The power grid system's main source-load uncertainty solution module is used to describe the source-load uncertainty of the main power grid system's main optimization scheduling model using a data-driven distributed robust optimization model, establish a three-layer two-stage power balance model, and solve it using a column constraint generation algorithm;

[0035] The multi-agent model solving module of the electric-hydrogen-heat integrated energy system is used to interactively iterate the three-layer two-stage power balance model of the power grid system, the main body optimization scheduling model of the hydrogen network system, and the main body optimization scheduling model of the heat network system using the alternating direction multiplier method, to realize the distributed parallel solution of the multi-agent optimization scheduling model of the electric-hydrogen-heat integrated energy system.

[0036] The technical solution provided by the present invention has the following beneficial effects:

[0037] The present invention divides the power grid system, hydrogen network system and heat network system in the electric-hydrogen-heat integrated energy system into different entities, adopts a data-driven distributed robust optimization model to describe the source-load bilateral uncertainty of the power grid system entity model, and adopts the alternating direction multiplier method to interactively iterate and parallelly solve the scheduling scheme of each entity. While improving the operating economy and reliability of the interconnected system, it protects the privacy of the information of each entity, making the scheduling of the electric-hydrogen-heat integrated energy system more realistic.

[0038] It should be stated that the above description and the following detailed description are exemplary and explanatory only and are not restrictive of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the present invention and, together with the description, serve to explain the principles of the present invention.

[0040] FIG1 is a flow chart of a method for optimizing the distribution of rods in an electric-hydrogen-heat integrated energy system according to the present invention;

[0041] FIG2 is a schematic diagram of a distributed blue rod optimization device for an electric-hydrogen-heat integrated energy system according to the present invention. DETAILED DESCRIPTION

[0042] The following clearly and systematically introduces the technical solution in this embodiment in conjunction with the accompanying drawings of the present invention. However, the embodiment described in the present invention is only a better example of the present invention. All relevant embodiments obtained by any person skilled in the art without making any creative work are within the scope of protection of the present invention.

[0043] FIG1 is a flow chart of a method for optimizing the distribution of a power-hydrogen-heat integrated energy system. As shown in FIG1 , the present invention provides a method for optimizing the distribution of a power-hydrogen-heat integrated energy system. The method includes the following steps:

[0044] Step 1: Establish a multi-agent optimization and dispatching model for the electricity-hydrogen-heat integrated energy system, including the optimization and dispatching model for the power grid system, the optimization and dispatching model for the hydrogen network system, and the optimization and dispatching model for the heat network system;

[0045] Step 2: A data-driven distributed robust optimization model is used to describe the uncertainty of the source and load sides of the main power grid system model. A three-layer two-stage power balance model is established and solved using a column constraint generation algorithm.

[0046] In step 3, the alternating direction multiplier method is used to interactively iterate the three-layer two-stage power balance model of the power grid system, the main body optimization scheduling model of the hydrogen network system, and the main body optimization scheduling model of the heat network system to realize the distributed parallel solution of the multi-agent optimization scheduling model of the electricity-hydrogen-heat integrated energy system.

[0047] From the above, it can be seen that the present invention divides the power grid system, hydrogen network system and heat network system in the electric-hydrogen-heat integrated energy system into different entities, adopts a data-driven distributed robust optimization model to describe the source-load bilateral uncertainty of the power grid system entity model, and adopts an interactive iterative method to solve the scheduling scheme of each entity in parallel. While improving the operating economy and reliability of the interconnected power grid system, it protects the privacy of the information of each entity, making the scheduling of the electric-hydrogen-heat integrated energy system more realistic.

[0048] In the specific implementation of step 1, a multi-agent optimization and scheduling model of the electricity-hydrogen-heat integrated energy system is established, including the main optimization and scheduling model of the power grid system, the main optimization and scheduling model of the hydrogen network system, and the main optimization and scheduling model of the heat network system.

[0049] Optimal dispatch model for power grid system entities

[0050] The goal of the power grid system optimization scheduling model is to optimize the output of each device in the power grid system and the electricity sales of hydrogen and heat production, so as to maximize the operating benefits of the power grid system.

[0051] 1) Objective function

[0052] C t OL =C OL P t OL (2)

[0053] Where: C E The total cost of dispatching the main body of the power grid system; is the fuel cost consumed by the traditional generator at time t; is the cost of starting and stopping the traditional generator at time t; is the energy storage cost of the electric energy storage equipment at time t; is the cost of wind and solar power curtailment by the main body of the power grid system at time t; is the load shedding cost of the main power grid system at time t; C is the electricity sales revenue of the power grid system at time t;OL Penalty coefficient for load abandonment by main body of power grid system; K is the load abandonment power of the main power grid system at time t; P The electricity sales price of the main power grid system; is the power consumption of the electrolytic hydrogen production device at time t; is the power consumption of the electric heating device at time t; is the output power of the hydrogen fuel cell at time t; N T Indicates the number of optimized scheduling periods.

[0054] 2) Renewable and energy models

[0055] The cost of curtailed renewable energy power includes wind power curtailment and solar power curtailment, and its cost function is:

[0056] Where: and are the predicted values ​​of photovoltaic and wind power on day t; and is the actual output value of photovoltaic and wind power on day t; C LV is the penalty coefficient for abandoned light; C LW is the wind curtailment penalty coefficient.

[0057] Renewable energy output constraint: 0≤P t PV ≤P t PV,y (5) 0≤P t PW ≤P t PW,y (6)

[0058] 3) Traditional generator model

[0059] Traditional generator cost function:

[0060] Where: C O is the cost coefficient of the traditional generator; is the output of the traditional generator at time t.

[0061] Traditional generator start-up and shutdown costs:

[0062] Where: is the no-load cost of traditional generator sets; is the downtime cost of traditional generator sets; Cost of downtime for traditional generators; and is a 0-1 integer variable; and Respectively represent the start-up state and shutdown state of the traditional generator; This is the on / off state of a traditional generator.

[0063] Traditional generator start and stop constraints:

[0064] Where: and are the initial startup and shutdown time periods of the traditional generator, respectively; and are the minimum startup and shutdown time of the unit respectively; and They are the time the unit needs to be continuously started and shut down; Indicates the initial operating state of a traditional generator.

[0065] Traditional generator output upper and lower limit constraints:

[0066] Where: and They are respectively the maximum and minimum power of the traditional generator in the startup state.

[0067] Traditional generator ramping constraints:

[0068] Where: It is the maximum climbing power of traditional generator.

[0069] 4) Battery energy storage device model

[0070] Battery energy storage costs:

[0071] Where: C ES is the electricity storage cost coefficient; η CE is the charging efficiency of the electrochemical energy storage device; η DE is the discharge efficiency of the electrochemical energy storage device; is the charging power of the battery energy storage device at time t; is the discharge power of the battery energy storage device at time t;

[0072] Electrochemical energy storage output constraints:

[0073] Where: is the charge of electrochemical energy storage at time t; and The lower and upper limits of the storage capacity of the electrochemical energy storage device; and The upper limit of discharge and charge power for electrochemical energy storage devices; It is a 0-1 variable, indicating the charge and discharge status of the energy storage device; Δt is the scheduling time interval.

[0074] The energy storage period of the electric energy storage device is relatively short and needs to meet the energy balance within the dispatch period:

[0075] 5) Power balance constraints 0≤P t OL ≤P t L (twenty four)

[0076] Where: is the load power of the power grid system at time t.

[0077] Hydrogen network system main model

[0078] The goal of the main model of the hydrogen network system is to optimize the output of each device in the hydrogen network system, the amount of electricity purchased by the hydrogen production device and the amount of hydrogen sold by the hydrogen fuel cell, so as to minimize the main operating costs of the hydrogen network system.

[0079] 1) Objective function C t BE1 =K P (P t P2H -P t FC ) (26)

[0080] Where: C H The total cost of dispatching the main body of the hydrogen network system; The electricity purchase cost of the main body of the hydrogen network system at time t; is the energy storage cost of the hydrogen energy storage device at time t; C is the load abandonment penalty for the main body of the hydrogen network system at time t; HL The load abandonment penalty coefficient for the main body of the hydrogen network system; Indicates the amount of load shedding in the main body of the hydrogen network system.

[0081] 2) P2H device model H t P2H =P t F2H ηF2H Δt (28)

[0082] Where: is the hydrogen production rate of the electric hydrogen production device at time t; η P2H The conversion efficiency of electricity to hydrogen; It is the upper limit of hydrogen production of the electric hydrogen production device at time t.

[0083] 3) Hydrogen fuel cell model H t FC =P t FC η FC Δt (30)

[0084] Where: is the hydrogen consumption rate of the hydrogen fuel cell at time t; η FC is the hydrogen fuel cell conversion efficiency; is the upper limit of the hydrogen fuel cell power at time t.

[0085] 4) Hydrogen energy storage model

[0086] Cost function of hydrogen energy storage device:

[0087] Where: C HS is the hydrogen storage cost coefficient; η CH is the hydrogen storage efficiency of the hydrogen energy storage device; η DH The hydrogen release efficiency of the hydrogen energy storage device; is the hydrogen charging power of the hydrogen energy storage device at time t; is the hydrogen discharge power of the hydrogen energy storage device at time t;

[0088] Output constraints of hydrogen energy storage devices: H min ≤H t ≤H max (34)

[0089] Where: H t is the hydrogen storage capacity of the hydrogen energy storage device at time t; H min and H max The lower and upper limits of the total amount of hydrogen stored in the hydrogen energy storage device; and Set upper limits for hydrogen discharge and charging of hydrogen energy storage devices; It is a 0-1 variable, indicating the charging and discharging status of the hydrogen energy storage device.

[0090] 5) Hydrogen production and use balance constraints

[0091] Where: is the hydrogen loading rate.

[0092] Heating network system main model

[0093] The main model of the heating network system aims to optimize the output of each device in the heating network system and the amount of electricity purchased by the electric heating device, and minimize the main operating cost of the heating network system.

[0094] 1) Objective function C t BE2 =K P P t Q (42)

[0095] Where: C Q The total cost of dispatching the main body of the heat network system; is the electricity purchase cost of the heating network system at time t; is the cost of the waste heat recovery device at time t; is the heat storage cost of the thermal energy storage device at time t; is the load abandonment penalty for the main body of the heating network system at time t; C is the power purchased by the heating network system at time t; QL It represents the penalty coefficient of load abandonment of the main body of the heating network system; Indicates the amount of load abandonment in the main heating network system.

[0096] 2) Electric heating device model

[0097] Where: is the heating power of the electric heating device at time t; η p is the conversion efficiency of the electric heating device; It is the upper limit of heating capacity of electric heating device.

[0098] 3) Heat recovery device model

[0099] Where: The efficiency of the heat recovery unit in recovering waste heat from the P2H unit; the efficiency of recovering waste heat from hydrogen fuel cells for heat recovery devices; Indicates the waste heat recovery capacity of the heat recovery device; Indicates the upper limit of waste heat recovery capacity of heat recovery device; CR Indicates the unit waste heat recovery cost coefficient.

[0100] 4) Thermal energy storage device constraints

[0101] Thermal energy storage costs:

[0102] Where: C QS is the heat storage cost coefficient; η CQ Charging efficiency of thermal energy storage device; η DQ is the discharge efficiency of the thermal energy storage device; is the charging power of the thermal energy storage device at time t; is the heat release power of the thermal energy storage device at time t.

[0103] Thermal energy storage output constraints:

[0104] Where: The amount of heat stored in the thermal energy storage device at time t; and The lower and upper limits of the amount of heat that can be stored in a thermal energy storage device; and Upper limits on the discharging and charging powers of thermal energy storage devices; It is a 0-1 variable, indicating the charging and discharging status of the thermal energy storage device.

[0105] The thermal energy storage device has a short storage period and needs to meet the energy balance within the scheduling period:

[0106] 5) Heat balance constraints between production and use

[0107] Where: is the heat load power.

[0108] In the specific implementation of step 2, a data-driven distributed robust optimization model is used to describe the uncertainty of the source and load sides of the main optimization scheduling model of the power grid system. A three-layer two-stage power balance model is established, and a column constraint generation algorithm is used to solve it.

[0109] The first-stage model of the three-layer two-stage power balance model is the main optimization scheduling model of the power grid system as described in equations (1)-(24) in step 1. The second-stage model includes the second and third layers of the three-layer two-stage power balance model. The second layer minimizes the expected adjustment cost of the output of each device in the power grid system under different source-load probability distributions. The third layer finds the probability distribution of the worst source-load discrete scenario based on the optimization results of the second layer. The objective function and constraints of the second-stage model are as follows:

[0110] 1) Objective function

[0111] Where: superscript s represents the variables of the second-stage model, superscript n represents the nth discrete scenario, and the specific meaning of each variable is the same as that of the first-stage model; N S represents the number of discrete scenarios; p n represents the probability of occurrence of the nth discrete scenario; ψ is the confidence set of scenario probabilities obtained based on historical data.

[0112] 2) Constraints 0≤P t PV,s,n ≤P t PV,y,s,n (60) 0≤P t PW,s,n ≤P t PW,y,s,n (61) C t O,s,n =C O P t GE,s,n (62) 0≤P t OL,s,n ≤P t L,s,n (73)

[0113] The column constraint generation algorithm defines the first-stage model as the main problem and the second-stage model as the subproblem, and solves the problem through iterative iteration of the main and subproblems. The specific steps are as follows.

[0114] 1) Initialization: Set the number of iterations m = 1.

[0115] 2) Solve the main problem of the first stage and obtain the control variables of the main problem.

[0116] Specifically, in the jth iteration of the main-subproblem, the compact form model of the main problem is shown in Equations (74) and (75):

[0117] Where: x is the first-stage control variable; X is the first-stage constraint condition, formula (2)-(24); a T is the constant matrix of the first stage objective function formula (1); η is the second stage objective function value to be optimized; y n,j Y is the second-stage control variable in the j-th iteration and the n-th scenario; n is the constraint condition of the second stage, equations (58)-(73); The probability distribution of the worst discrete scenario solved for the jth iterative subproblem; b T is the constant matrix of the second stage objective function (57).

[0118] 3) Solve the second-stage sub-problems in parallel based on the results of the main problem to obtain the probability distribution of the worst discrete scenario.

[0119] Specifically, the compact form model of the subproblem is shown in Equation (76):

[0120] Since the variable constraint ranges in the second stage of the distributed robust model subproblem are independent of each other across scenarios, Equation (76) can be solved in parallel.

[0121] 4) Convergence judgment: If the difference between η solved by the first-stage model and the objective function value of the second-stage model meets the convergence criterion, the problem converges and the iteration stops; otherwise, the probability distribution constraint (75) of the worst discrete scenario is added to the main problem, and the iteration continues, m = m + 1, and return to 2).

[0122] In the specific implementation of step 3, the alternating direction multiplier method is used to interactively iterate the three-layer two-stage power balance model of the power grid system, the main body optimization scheduling model of the hydrogen network system, and the main body optimization scheduling model of the heat network system to realize the distributed parallel solution of the multi-agent optimization scheduling model of the electricity-hydrogen-heat integrated energy system.

[0123] Specifically, the power grid, hydrogen grid, and heat grid interact with each other. When the power grid interacts with the hydrogen grid and the heat grid, respectively, it is considered the supply-side system, with the hydrogen grid and the heat grid each being the receiving-side system. When the hydrogen grid interacts with the heat grid, it is considered the supply-side system, with the heat grid being the receiving-side system. This paper uses the alternating direction multiplier method to perform a distributed parallel solution to the proposed multi-agent optimization scheduling model for the electricity-hydrogen-heat integrated energy system. The specific steps are as follows.

[0124] 1) Initialization: Set the number of iterations n = 1; initialize the average value of the coupling variable.

[0125] Specifically, the coupling variable between the power grid system and the hydrogen network system (i.e., the amount of electricity traded) is P t FC With P t P2H The coupling variable between the power grid system and the heat network system (i.e., the amount of electricity traded) is P t Q The coupling variable between the hydrogen network system and the heat network system (i.e., the amount of electricity traded) is P t FC With P t P2H .

[0126] 2) Interactively solve the three-layer two-stage power balance model of the power grid system, the main body optimization scheduling model of the hydrogen network system, and the main body optimization scheduling model of the heat network system to obtain the scheduling plan of each subject and the amount of electricity traded between subjects.

[0127] Specifically, each agent adds a corresponding penalty term to its objective function according to whether it is a supply-side system or a receiving-side system. During the nth iteration, the objective functions of the supply-side system and the receiving-side system are shown in Equations (77) and (78), respectively.

[0128] Where: the superscripts se and re represent the variables of the supply-end system and the receiving-end system, respectively; θ is the objective function of the original model, which refers to the three-layer two-stage power balance model of the power grid system, the main optimization scheduling model of the hydrogen network system, and the main optimization scheduling model of the heat network system; λ P,t is the Lagrange multiplier corresponding to the coupling variable; ρ P is the corresponding penalty factor; P t is the coupling variable; is the average value of the coupling variable obtained after the n-1th iteration.

[0129] 3) The main body of the power grid system, the main body of the hydrogen network system and the main body of the heat network system exchange the latest coupling variables and recalculate their average values ​​and algorithm parameters.

[0130] Specifically, the average value of the coupling variable is calculated according to formula (79), and the algorithm parameters are calculated according to formula (80):

[0131] 4) Judgment: If the convergence criterion is met, the iteration stops and the optimal scheduling solution is obtained; otherwise, the iteration continues, n=n+1, and returns to 2).

[0132] Specifically, the convergence criterion is shown in formula (81):

[0133] Where: gap F is the original residual, which indicates whether the coupling variable values ​​obtained by the connected subsystems are close enough, ε F is the original residual limit; gap D is the dual residual, which indicates whether the obtained result is stable, ε D is the dual residual limit.

Claims

1. A robust optimization method for an electricity-hydrogen-heat integrated energy system, characterized in that: include: Establish a multi-agent optimization dispatch model for the electricity-hydrogen-heat integrated energy system, including the optimization dispatch model for the power grid system, the optimization dispatch model for the hydrogen network system, and the optimization dispatch model for the heat network system; A data-driven distributed robust optimization model is used to describe the uncertainty of the source and load sides of the main optimization dispatch model of the power grid system. A three-layer two-stage power balance model is established and solved by a column constraint generation algorithm. The alternating direction multiplier method is used to interactively iterate the three-layer two-stage power balance model of the power grid system, the main body optimization scheduling model of the hydrogen network system, and the main body optimization scheduling model of the heat network system to realize the distributed parallel solution of the multi-agent optimization scheduling model of the electricity-hydrogen-heat integrated energy system.

2. The method for optimizing the distribution of electricity-hydrogen-heat integrated energy system according to claim 1, characterized in that: The multi-agent optimization scheduling model of the electricity-hydrogen-heat integrated energy system includes: (1.1) Optimal dispatch model of power grid system The goal of the optimization dispatching model of the power grid system is to optimize the output of each device in the power grid system and the sales volume of hydrogen and heat produced by electricity, so as to maximize the operating benefits of the power grid system. (1.2) Hydrogen network system optimization scheduling model The goal of the optimization scheduling model of the main body of the hydrogen network system is to optimize the output of each device in the hydrogen network system, the amount of electricity purchased by the electric hydrogen production device and the amount of hydrogen sold by the hydrogen fuel cell, so as to minimize the main operating cost of the hydrogen network system; (1.3) Optimal dispatch model of heating network system The goal of the main optimization scheduling model of the heating network system is to optimize the output of each device in the heating network system and the power purchase of the electric heating device, and minimize the main operating cost of the heating network system.

3. The method for optimizing the distribution of electricity-hydrogen-heat integrated energy system according to claim 1, characterized in that: According to the structural characteristics of the distributed robust optimization model, a three-layer two-stage power balance model of the power grid system is established, and the column constraint generation algorithm is used to iteratively solve the two-stage main and sub-problems, where the sub-problems can be solved in parallel, including: (2.1) Three-layer two-stage power balance model (2.1.1) The first stage model is the main optimization dispatch model of the power grid system, which is the first layer of the three-layer two-stage power balance model; (2.1.2) The second-stage model includes the second and third layers of the three-layer two-stage power balance model. The second layer minimizes the expected adjustment cost of the output of each device in the power grid system under different source-load probability distributions. The third layer finds the probability distribution of the worst source-load discrete scenario based on the optimization results of the second layer.

4. The method for optimizing the distribution of electricity-hydrogen-heat integrated energy system according to claim 3 is characterized in that: The contents of the column constraint generation algorithm are as follows: (2.2.1) Initialization: Set the number of iterations m = 1; (2.2.2) Solve the main problem of the first stage and obtain the control variables of the main problem; (2.2.3) Solve the second-stage sub-problems in parallel based on the control variables obtained from solving the main problem to obtain the probability distribution constraints of the worst source-load discrete scenario; (2.2.4) Convergence judgment: If the convergence criterion is met, the problem converges and the iteration stops; otherwise, the probability distribution constraint of the worst source-load discrete scenario is added to the main problem of the first stage, and the iteration continues, m=m+1, and returns to (2.2.2).

5. The method for optimizing the distribution of electricity-hydrogen-heat integrated energy system according to claim 1, characterized in that: The alternating direction multiplier method is used to interactively and iteratively solve the three-layer two-stage power balance model of the power grid system, the main optimization dispatch model of the hydrogen network system, and the main optimization dispatch model of the heat network system in parallel, including: (3.1) Initialization: Set the number of iterations n = 1; initialize the main body of the power grid system and the main body of the hydrogen network system The average value of the amount of electricity traded between the main bodies of the physical and thermal network systems, i.e. the average value of the coupled variables; (3.2) Interactively solve the three-layer two-stage power balance model of the power grid system, the subject optimization scheduling model of the hydrogen network system, and the subject optimization scheduling model of the heat network system to obtain the scheduling plan of each subject and the amount of electricity traded between subjects; (3.3) The main body of the power grid system, the main body of the hydrogen network system and the main body of the heat network system exchange the latest transaction electricity and recalculate the average value of the coupling variables and the algorithm parameters; (3.4) Judgment: If the convergence criterion is met, the iteration stops and the optimal scheduling solution is obtained; otherwise, continue the iteration, n=n+1, and return to (3.2).

6. Electricity-hydrogen-heat integrated energy system distribution rod optimization device, characterized in that: include: The multi-agent model generation module of the electric-hydrogen-heat integrated energy system is used to establish the multi-agent optimization dispatching model of the electric-hydrogen-heat integrated energy system, including the main optimization dispatching model of the power grid system, the main optimization dispatching model of the hydrogen network system and the main optimization dispatching model of the heat network system; The power grid system main source and load uncertainty solving module is used to describe the source and load uncertainty of the main optimization scheduling model of the power grid system by using a data-driven distributed robust optimization model, establish a three-layer two-stage power balance model, and solve it using a column constraint generation algorithm; The multi-agent model solving module of the electric-hydrogen-heat integrated energy system is used to interactively iterate the three-layer two-stage power balance model of the power grid system, the main optimization scheduling model of the hydrogen network system and the main optimization scheduling model of the heat network system using the alternating direction multiplier method, so as to realize the distributed parallel solution of the multi-agent optimization scheduling model of the electric-hydrogen-heat integrated energy system.

7. The distributed optimization device for the electric-hydrogen-heat integrated energy system according to claim 6 is characterized in that: The multi-agent optimization scheduling model of the electricity-hydrogen-heat integrated energy system includes: (1.1) Optimal dispatch model of power grid system The main optimization dispatching model of the power grid system aims to optimize the output of each device in the power grid system and the power generation and hydrogen production. The amount of electricity sold for electric heating maximizes the operating benefits of the power grid system; (1.2) Hydrogen network system optimization dispatch model The goal of the optimization scheduling model of the main body of the hydrogen network system is to optimize the output of each device in the hydrogen network system, the amount of electricity purchased by the electric hydrogen production device and the amount of hydrogen sold by the hydrogen fuel cell, so as to minimize the main operating cost of the hydrogen network system; (1.3) Optimal dispatch model of heating network system The goal of the main optimization scheduling model of the heating network system is to optimize the output of each device in the heating network system and the power purchase of the electric heating device, and minimize the main operating cost of the heating network system.

8. The distributed optimization device for the electric-hydrogen-heat integrated energy system according to claim 6 is characterized in that: According to the structural characteristics of the distributed robust optimization model, a three-layer two-stage power balance model of the power grid system is established, and the column constraint generation algorithm is used to iteratively solve the two-stage main and sub-problems, where the sub-problems can be solved in parallel, including: (2.1) Three-layer two-stage power balance model (2.1.1) The first stage model is the main optimization dispatch model of the power grid system, which is the first layer of the three-layer two-stage power balance model; (2.1.2) The second-stage model includes the second and third layers of the three-layer two-stage power balance model. The second layer minimizes the expected adjustment cost of the output of each device in the power grid system under different source-load probability distributions. The third layer finds the probability distribution of the worst source-load discrete scenario based on the optimization results of the second layer.

9. The distributed rod optimization device for the electric-hydrogen-heat integrated energy system according to claim 7 is characterized in that: The contents of the column constraint generation algorithm are as follows: (2.2.1) Initialization: Set the number of iterations m = 1; (2.2.2) Solve the main problem of the first stage and obtain the control variables of the main problem; (2.2.3) Solve the second-stage sub-problems in parallel based on the control variables obtained from solving the main problem to obtain the probability distribution of the worst source-load discrete scenario; (2.2.4) Convergence judgment: If the convergence criterion is met, the problem converges and the iteration stops; otherwise Then, add the probability distribution constraint of the worst source-load discrete scenario to the main problem, continue iterating, m=m+1, and return to (2.2.2).

10. The device for optimizing the distribution of electric-hydrogen-heat integrated energy system according to claim 6, characterized in that: The alternating direction multiplier method is used to interactively and iteratively solve the three-layer two-stage power balance model of the power grid system, the main optimization dispatch model of the hydrogen network system, and the main optimization dispatch model of the heat network system in parallel, including: (3.1) Initialization: Set the number of iterations n = 1; initialize the average value of the transaction electricity between the main body of the power grid system, the main body of the hydrogen network system, and the main body of the heat network system, that is, the average value of the coupling variable; (3.2) Interactively solve the three-layer two-stage power balance model of the power grid system, the subject optimization scheduling model of the hydrogen network system, and the subject optimization scheduling model of the heat network system to obtain the scheduling plan of each subject and the amount of electricity traded between subjects; (3.3) The main body of the power grid system, the main body of the hydrogen network system and the main body of the heat network system exchange the latest transaction electricity and recalculate their average values ​​and algorithm parameters; (3.4) Judgment: If the convergence criterion is met, the iteration stops and the optimal scheduling solution is obtained; otherwise, continue the iteration, n=n+1, and return to (3.2).

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