A distributed solution method for combined dispatch of gas-electricity-water systems

Abstract

The application discloses a kind of gas-electricity-water system combined scheduling distributed solution method, comprising the following steps:1, obtain the initial value of boundary variable and Lagrange multiplier, obtain penalty parameter, correction factor and convergence accuracy;2, by sequentially solving natural gas, power and water supply system optimization model, update corresponding boundary variable;3, update Lagrange multiplier, and correct the boundary variable corresponding to power and water supply system;4, stop updating when boundary variable and Lagrange multiplier no longer change, obtain gas-electricity-water system combined scheduling scheme.As an independent operation subject, gas, electricity, water three systems exist respective privacy, unwilling to share all data information, while centralized solution needs all data information, at the same time, three systems cannot be independently solved, so as to guarantee each dispersed autonomy right.The application protects the data privacy of gas, electricity, water three systems to a great extent, and guarantees the dispersed autonomy right of each system.

Description

Technical Field

[0001] This invention relates to the field of optimization scheduling, and in particular to a distributed solution method for the joint scheduling of gas-electricity-water systems. Background Technology

[0002] Optimal gas-electricity-water energy flow is a fundamental tool for studying the coordinated operation and planning of natural gas-electricity-water supply coupled systems (hereinafter referred to as "gas-electricity-water systems"). It can break the existing model of separate planning and operation of gas, electricity and water systems, and provide important support for improving environmental benefits, optimizing scheduling decisions and reducing operating costs.

[0003] Currently, centralized methods are commonly used to solve for optimal gas-electricity-water energy flow. However, centralized solutions require collecting all data from the three subsystems (gas, electricity, and water). Furthermore, since these three subsystems cannot be solved independently, their decentralized autonomy cannot be guaranteed. At the same time, as independent operating entities, the gas, electricity, and water subsystems each possess their own privacy and are unwilling to share all data. Therefore, protecting the data privacy of these subsystems is of paramount importance. Summary of the Invention

[0004] To address the issues of data privacy and decentralized autonomy among the three subsystems of gas, electricity, and water in centralized solutions for optimal gas-electricity-water energy flow, this invention provides a decentralized solution method for joint scheduling of the gas-electricity-water system. This method can maintain the privacy of each of the three subsystems while ensuring decentralized autonomy of the subsystems.

[0005] To achieve the above objectives, the present invention employs the following technical means:

[0006] A distributed solution method for the joint scheduling of a gas-electricity-water system includes the following steps:

[0007] (1) Obtain the initial values ​​of boundary variables and Lagrange multipliers, and obtain the penalty parameter, correction factor and convergence accuracy;

[0008] (2) Update the boundary variables of the natural gas system by solving the optimization model of the natural gas system;

[0009] (3) Update the power system boundary variables by solving the power system optimization model;

[0010] (4) Update the boundary variables of the water supply system by solving the optimization model of the water supply system;

[0011] (5) Update the Lagrange multipliers;

[0012] (6) Correct the boundary variables corresponding to the power and water supply systems;

[0013] (7) Repeat steps 2-6 until the maximum relative deviation is less than the convergence accuracy;

[0014] (8) Output the calculation results, namely the gas-electricity-water system joint scheduling scheme.

[0015] The natural gas system optimization model aims to minimize cost. The boundary conditions of the dynamic natural gas model include: node gas load constraints, density and pressure constraints at natural gas supply nodes, mass flow rate constraints at each gas node, and upper and lower limits of natural gas mass flow rate and pressure within the pipeline. The partial differential equations describing the dynamic process are transformed into corresponding difference forms. The specific model is as follows:

[0016]

[0017] In the formula: C gas (t) represents the cost of natural gas; λ1 and λ3 are Lagrange multipliers; k is the iteration number; X g P GT X g P P2G These are the independent variables for the gas turbine and the electric-to-gas conversion equipment, respectively, in the optimal current flow subproblem of the natural gas system; Y p P GT Y p P P2G These are the independent variables for the gas turbine and the electric-to-gas conversion equipment, respectively, in the optimal power flow subproblem of the power system; P GT P represents the electrical power of the gas turbine. P2G c is the power consumed; η is the penalty parameter, η>0; i,gas (t) represents the price coefficient of natural gas; N gas M represents the number of gas turbines. i (t) is the mass flow rate of natural gas consumed by the gas turbine at time t; ρ is the density of natural gas; M is the mass flow rate of natural gas; p is the pressure of the natural gas pipeline; c is the temperature factor of natural gas; i is the starting point of the natural gas pipeline; j is the ending point of the natural gas pipeline; M i and M j These are the mass flow rates at endpoints i and j, respectively; ρ i and ρ j These are the natural gas densities at endpoints i and j, respectively; p i and p j These are the pipe pressures at endpoints i and j, respectively; Let be the average natural gas flow velocity in pipeline ij; d, L, and A be the diameter, length, and cross-sectional area of ​​pipeline ij, respectively; λ gas is the damping factor; x and Δt are the spatial interval and time interval, respectively; p0 and ρ0 are the pressure and density at the natural gas supply node, respectively; M max and M min These are the upper and lower limits of the mass flow rate of natural gas pipelines; p max and p minThese represent the upper and lower limits of natural gas pipeline pressure; η GT M represents the gas-to-electricity conversion efficiency of a gas turbine; GT M is the mass flow rate of natural gas consumed by the gas turbine. G-P2G For natural gas mass flow rate; η P2G The electro-gas conversion efficiency of the electro-gas conversion equipment.

[0018] The power system optimization model aims to minimize the power system cost and uses linear DistFlow equations to simulate the power flow of a radial distribution network. The specific model is as follows:

[0019]

[0020] In the formula: C ele (t) represents the cost of purchasing and selling electricity; C wcur λ1, λ2, λ3, λ4, and λ5 are Lagrange multipliers; k is the iteration number; Y p P GT Y p P P2G Y p P pump Z represents the independent variables for the optimal power flow subproblem in the power system, namely the gas turbine, the electric-to-gas conversion equipment, and the water pump; w P GT Z w P P2G Z w P pump The independent variables for the optimal tidal flow problem in the water supply system are the gas turbine, the electro-gas converter, and the water pump. η is the penalty parameter, η>0; c in (t)>0 and c out (t)>0 represents the power purchase price and power sales price of the distribution network, respectively, using time-of-use pricing; P + P(t) and P-(t) represent the power purchased by the distribution network from the main grid and the power sold by the distribution network to the main grid, respectively. G (t) represents the active power exchanged between the distribution network and the main network; c i,wcur (t) is the wind curtailment penalty coefficient, N wind P represents the number of wind turbine units. i,wcur (t) represents the curtailment power of the i-th wind turbine at time t, and Δt is the time interval; P i+1 (t) and Q i+1 (t) represent the active and reactive power transmitted from node i+1 to node i+2 in the distribution network, respectively; P i+1,L (t) and Q i+1,L (t) represent the active and reactive power of the load connected to node i+1 in the distribution network, respectively; P i+1,G (t) and Q i+1,G(t) represent the active and reactive power of the power source connected to node i+1 in the distribution network, respectively; x i and r i These represent the reactance and resistance between node i and node i+1, respectively; V0, V i and V i+1 The voltages at nodes 0, i, and i+1 are respectively, with node 0 referred to as the reference node; V i,max and V i,min V i The upper and lower limits.

[0021] The water supply system optimization model takes the minimum cost of the water supply system as the objective function, and includes constraints on water flow balance, head constraints at the outlet of the reservoir, head increment constraints of the variable speed pump, and head loss constraints of the pressure reducing valve. The specific model is as follows:

[0022]

[0023] In the formula: C pump (t) represents the cost of the water pump; λ2, λ4, and λ5 are Lagrange multipliers; k is the number of iterations; Y p P GT Y p P P2G Y p P pump Z represents the independent variables for the optimal power flow subproblem in the power system, namely the gas turbine, the electric-to-gas conversion equipment, and the water pump; w P GT Z w P P2G Z w P pump The independent variables for the optimal tidal flow problem in the water supply system are the gas turbine, the electro-gas converter, and the water pump. η is the penalty parameter, η>0; h r It is the head of the water source; q Wd,i It is the water flow rate of the water load; d j Let (t) be the water load at node j at time t, and let L be the set of pipes. ij (t) represents the water flow rate from node i to node j in pipe ij at time t; h is the minimum head allowed for node j. j (t) represents the water head at each node at time t; Let the outlet head of reservoir k at time t be... Let h be the inlet head of reservoir k at time t, which is also the head of water node k. k (t), constant A k Let k be the cross-sectional area of ​​the reservoir. This refers to the maximum water storage height of the reservoir. This represents the water level in the reservoir at the end of this scheduling cycle. This is the initial water level for this scheduling cycle; R ij (t) represents the head loss caused by the pressure reducing valve connected between nodes i and j at time t; q w The water consumption of the gas turbine is considered as the water load at the gas turbine intake node; β GT M is the water consumption coefficient of the gas turbine; G-P2G P represents the mass flow rate of natural gas. P2G η represents the electrical power consumed. P2G The efficiency of the electro-gas conversion equipment; N pump P represents the number of water pumps. i,pump (t) represents the electrical power consumed by the i-th water pump at time t, and the electricity price c for the water pump. in (t) is obtained from the power grid; constant α W-P2G The value is 479.1 kg / m³ 3 ;q W-P2G Water consumption for the electro-gas conversion equipment; F Wp,ij Let q be the coefficient of friction of the water pipe. Wp,ij (t) is the water flow rate in the pipe, q max It is the maximum allowable water flow rate for the corresponding water pipe; ω ij (t) is the relative speed of the pump connected between nodes i and j, i.e., the ratio of the operating speed to the rated speed, ω ij,max This corresponds to the upper limit of the rotational speed, coefficient A. ij ≤0, B ij ≥0, and C ij ≥0 is the pump parameter evaluated at rated speed, η u For the pump efficiency, q ij,u,max It is the maximum allowable water flow rate of water pump ij.

[0024] The Lagrange multiplier update formula is as follows:

[0025]

[0026] The boundary variable correction formulas for the power and water supply systems are as follows:

[0027]

[0028] In the formula: v is the correction factor.

[0029] The value of v ranges from 0.9 to 0.95.

[0030] The convergence criterion is as follows:

[0031]

[0032] Where: ε k For the maximum relative deviation, the convergence accuracy ε0 > 0, and x is (X g PGT ,X g P P2G ), y is (Y p P GT ,Y p P P2G ,Y p P pump ), z is (Z w P GT Z w P P2G Z w P pump ).

[0033] The beneficial effects obtained by this invention are:

[0034] This invention solves the problems of data privacy and decentralized autonomy of the three subsystems of gas, electricity, and water caused by centralized solution of optimal gas-electricity-water energy flow. It provides a decentralized solution method for joint scheduling of gas-electricity-water system. This method can maintain the privacy of each of the three subsystems of gas, electricity, and water, and ensure the decentralized autonomy of the subsystems. Attached Figure Description

[0035] Figure 1 This is a flowchart illustrating a distributed solution method for the joint scheduling of a gas-electricity-water system as described in this invention. Detailed Implementation

[0036] As independent operating entities, the gas, electricity, and water systems each possess their own privacy and are unwilling to share all data information. Centralized solutions require all data information, and since the three systems cannot be solved independently, their decentralized autonomy cannot be guaranteed. To protect the data privacy of the gas, electricity, and water systems while ensuring their decentralized autonomy, this invention discloses a decentralized solution method for the joint scheduling of gas-electricity-water systems through the following embodiments.

[0037] This invention discloses a distributed solution method for the joint scheduling of a gas-electricity-water system. See [link to relevant documentation]. Figure 1 The workflow diagram shown includes the following steps:

[0038] Step 1: Obtain the initial values ​​of the boundary variables and Lagrange multipliers, and the maximum number of iterations k. max =400; Initial value of gas turbine independent variable X g P GT =Y p P GT =Z w P GT =0.5, initial value X of the independent variable of the electric-to-gas conversion equipment g P P2G =Y p PP2G =Z w P P2G =0.1, initial value of pump independent variable Y p P pump =Z w P pump =0.1; initial values ​​of Lagrange multipliers λ1=λ2=λ3=λ4=0.1, λ5=0.2. Obtain the penalty parameter, correction factor and convergence accuracy; where the penalty parameter is greater than zero, the correction factor ranges from [0.90,0.95], and the convergence accuracy is greater than zero and determined by the requirements of the actual project.

[0039] Step 2: Update the boundary variables of the natural gas system by solving the optimization model of the natural gas system.

[0040] The boundary variable of the natural gas system is (X) g P GT ,X g P P2G The natural gas system optimization model aims to minimize cost. The boundary conditions of the dynamic natural gas model include: node gas load constraints, density and pressure constraints at natural gas supply nodes, mass flow rate constraints at each gas node, and upper and lower limits of natural gas mass flow rate and pressure within the pipeline. The partial differential equations describing the dynamic process are transformed into corresponding difference forms. The specific model is as follows:

[0041]

[0042] In the formula: C gas (t) represents the cost of natural gas; λ1 and λ3 are Lagrange multipliers; k is the iteration number; X g P GT X g P P2G Y represents the independent variables of the gas turbine and the electric-to-gas conversion equipment in the optimal power flow subproblem of the natural gas system; p P GT Y p P P2G For the optimal power flow subproblem in a power system, the independent variables are the gas turbine and the electric-to-gas conversion equipment; P GT P represents the electrical power of the gas turbine. P2G c is the power consumed; η is the penalty parameter, η>0; i,gas (t) represents the price coefficient of natural gas; N gas M represents the number of gas turbines. i (t) is the mass flow rate of natural gas consumed by the gas turbine at time t; ρ is the density of natural gas; M is the mass flow rate of natural gas; p is the pressure of the natural gas pipeline; c is the temperature factor of natural gas; i is the starting point of the natural gas pipeline; j is the ending point of the natural gas pipeline; M i and M jThese are the mass flow rates at endpoints i and j, respectively; ρ i and ρ j These are the natural gas densities at endpoints i and j, respectively; p i and p j These are the pipe pressures at endpoints i and j, respectively; Let be the average natural gas flow velocity in pipeline ij; d, L, and A be the diameter, length, and cross-sectional area of ​​pipeline ij, respectively; λ gas is the damping factor; x and Δt are the spatial interval and time interval, respectively; p0 and ρ0 are the pressure and density at the natural gas supply node, respectively; M max and M min These are the upper and lower limits of the mass flow rate of natural gas pipelines; p max and p min These represent the upper and lower limits of natural gas pipeline pressure; η GT M represents the gas-to-electricity conversion efficiency of a gas turbine; GT M is the mass flow rate of natural gas consumed by the gas turbine. G-P2G For natural gas mass flow rate; η P2G The electro-gas conversion efficiency of the electro-gas conversion equipment.

[0043] Step 3: Update the power system boundary variables by solving the power system optimization model.

[0044] The boundary variable of the power system is (Y) p P GT ,Y p P P2G ,Y p P pump The power system optimization model aims to minimize the power system cost. It uses linear DistFlow equations to simulate the power flow of a radial distribution network. The specific model is as follows:

[0045]

[0046] In the formula: C ele (t) represents the cost of purchasing and selling electricity; C wcur λ1, λ2, λ3, λ4, and λ5 are Lagrange multipliers; k is the iteration number; Y p P GT Y p P P2G Y p P pump Z represents the independent variables for the optimal power flow subproblem in the power system, namely the gas turbine, the electric-to-gas conversion equipment, and the water pump; w P GT Z w P P2G Z w P pumpThe independent variables for the optimal tidal flow problem in the water supply system are the gas turbine, the electro-gas converter, and the water pump. η is the penalty parameter, η>0; c in (t)>0 and c out (t)>0 represents the power purchase price and power sales price of the distribution network, respectively, using time-of-use pricing; P + P(t) and P-(t) represent the power purchased by the distribution network from the main grid and the power sold by the distribution network to the main grid, respectively. G (t) represents the active power exchanged between the distribution network and the main network; c i,wcur (t) is the wind curtailment penalty coefficient, N wind P represents the number of wind turbine units. i,wcur (t) represents the curtailment power of the i-th wind turbine at time t, and Δt is the time interval; P i+1 (t) and Q i+1 (t) represent the active and reactive power transmitted from node i+1 to node i+2 in the distribution network, respectively; P i+1,L (t) and Q i+1,L (t) represent the active and reactive power of the load connected to node i+1 in the distribution network, respectively; P i+1,G (t) and Q i+1,G (t) represent the active and reactive power of the power source connected to node i+1 in the distribution network, respectively; x i and r i These represent the reactance and resistance between node i and node i+1, respectively; V0, V i and V i+1 The voltages at nodes 0, i, and i+1 are respectively, with node 0 referred to as the reference node; V i,max and V i,min V i The upper and lower limits.

[0047] Step 4: Update the boundary variables of the water supply system by solving the water supply system optimization model.

[0048] The boundary variables of the water supply system are (Z) w P GT Z w P P2G Z w P pump The water supply system optimization model takes the minimum cost of the water supply system as the objective function, and includes constraints on water flow balance, head constraints at the outlet of the reservoir, head increment constraints of the variable speed pump, and head loss constraints of the pressure reducing valve. The specific model is as follows:

[0049]

[0050] In the formula: C pump (t) represents the cost of the water pump; λ2, λ4, and λ5 are Lagrange multipliers; k is the number of iterations; Y p PGT Y p P P2G Y p P pump Z represents the independent variables for the optimal power flow subproblem in the power system, namely the gas turbine, the electric-to-gas conversion equipment, and the water pump; w P GT Z w P P2G Z w P pump The independent variables for the optimal tidal flow problem in the water supply system are the gas turbine, the electro-gas converter, and the water pump. η is the penalty parameter, η>0; h r It is the head of the water source; q Wd,i It is the water flow rate of the water load; d j Let (t) be the water load at node j at time t, and let L be the set of pipes. ij (t) represents the water flow rate from node i to node j in pipe ij at time t; h is the minimum head allowed for node j. j (t) represents the water head at each node at time t; Let the outlet head of reservoir k at time t be... Let h be the inlet head of reservoir k at time t, which is also the head of water node k. k (t), constant A k Let k be the cross-sectional area of ​​the reservoir. This refers to the maximum water storage height of the reservoir. This represents the water level in the reservoir at the end of this scheduling cycle. This is the initial water level for this scheduling cycle; R ij (t) represents the head loss caused by the pressure reducing valve connected between nodes i and j at time t; q w The water consumption of the gas turbine is considered as the water load at the gas turbine intake node; β GT M is the water consumption coefficient of the gas turbine; G-P2G P represents the mass flow rate of natural gas. P2G η represents the electrical power consumed. P2G The efficiency of the electro-gas conversion equipment; N pump P represents the number of water pumps. i,pump (t) represents the electrical power consumed by the i-th water pump at time t, and the electricity price c for the water pump. in (t) is obtained from the power grid; constant α W-P2G The value is 479.1 kg / m³ 3 ;q W-P2G Water consumption for the electro-gas conversion equipment; F Wp,ij Let q be the coefficient of friction of the water pipe. Wp,ij (t) is the water flow rate in the pipe, q max It is the maximum allowable water flow rate for the corresponding water pipe; ω ij(t) is the relative speed of the pump connected between nodes i and j, i.e., the ratio of the operating speed to the rated speed, ω ij,max This corresponds to the upper limit of the rotational speed, coefficient A. ij ≤0, B ij ≥0, and C ij ≥0 is the pump parameter evaluated at rated speed, η u For the pump efficiency, q ij,u,max It is the maximum allowable water flow rate of water pump ij.

[0051] Step 5: Update the Lagrange multipliers.

[0052] The Lagrange multiplier update formula is as follows:

[0053]

[0054] Step 6: Correct the boundary variables corresponding to the power and water supply systems.

[0055] The boundary variable correction formulas for power and water supply systems are as follows:

[0056]

[0057] In the formula: v is the correction factor, and it is recommended to take a value in the range of [0.90, 0.95].

[0058] Step 7: Repeat steps 2-6 until the maximum relative deviation is less than the convergence accuracy.

[0059] Let the independent variable (X) be... g P GT ,X g P P2G (Y) is x, (Y) is y p P GT ,Y p P P2G ,Y p P pump ) is y, (Z) w P GT Z w P P2G Z w P pump If z is a given value, then the convergence criterion is as follows:

[0060]

[0061] Where: ε k For the maximum relative deviation, the convergence accuracy ε0 > 0.

[0062] The present invention has been described in detail above with reference to specific embodiments and exemplary examples; however, these descriptions should not be construed as limiting the present invention. Those skilled in the art will understand that various equivalent substitutions, modifications, or improvements can be made to the technical solutions and embodiments of the present invention without departing from the spirit and scope of the invention, and all such modifications and improvements fall within the scope of the present invention. The scope of protection of the present invention is defined by the appended claims.

Claims

1. A distributed solution method for the joint scheduling of a gas-electricity-water system, characterized in that, Includes the following steps: (1) Obtain the initial values ​​of the boundary variables and Lagrange multipliers, and obtain the penalty parameters, correction factors and convergence accuracy; (2) Update the boundary variables of the natural gas system by solving the optimization model of the natural gas system; (3) Update the boundary variables of the power system by solving the power system optimization model; (4) Update the boundary variables of the water supply system by solving the optimization model of the water supply system; (5) Update the Lagrange multipliers; (6) Correct the boundary variables corresponding to the power and water supply systems; (7) Repeat steps 2-6 until the maximum relative deviation is less than the convergence accuracy; (8) Output the calculation results, i.e., the joint scheduling scheme of the gas-electricity-water system; The natural gas system optimization model aims to minimize cost. The boundary conditions of the dynamic natural gas model include: node gas load constraints, density and pressure constraints at natural gas supply nodes, mass flow rate constraints at each gas node, and upper and lower limits of natural gas mass flow rate and pressure within the pipeline. The partial differential equations describing the dynamic process are transformed into corresponding difference forms. The specific model is as follows: ， In the formula: C gas (t) represents the cost of natural gas; λ1 and λ3 are Lagrange multipliers; k is the number of iterations; X g P GT X g P P2G These are the independent variables for the gas turbine and the electric-to-gas conversion equipment, respectively, in the optimal current flow subproblem of the natural gas system; Y p P GT Y p P P2G These are the independent variables for the gas turbine and the electric-to-gas conversion equipment, respectively, in the optimal power flow subproblem of the power system; P GT P represents the electrical power of the gas turbine. P2G η is the power consumed; η is the penalty parameter, η > 0; c i,gas (t) represents the price coefficient of natural gas; N gas M represents the number of gas turbines. i (t) is the mass flow rate of natural gas consumed by the gas turbine at time t; ρ is the density of natural gas; M is the mass flow rate of natural gas; p is the natural gas pipeline pressure; c is the natural gas temperature factor; i is the starting point of the natural gas pipeline; j is the termination point of the natural gas pipeline; M i and M j These are the mass flow rates at endpoints i and j, respectively; ρ i and ρ j These are the natural gas densities at endpoints i and j, respectively; p i and p j These are the pipe pressures at endpoints i and j, respectively; Let be the average natural gas flow velocity in pipeline ij; d, L, and A be the diameter, length, and cross-sectional area of ​​pipeline ij, respectively; λ gas is the damping factor; x and Δt are the spatial interval and time interval, respectively; p0 and ρ0 are the pressure and density at the natural gas supply node, respectively; M max and M min These are the upper and lower limits of the mass flow rate of natural gas pipelines; p max and p min These are the upper and lower limits of natural gas pipeline pressure, respectively. η GT This indicates the gas-to-electricity conversion efficiency of a gas turbine. M GT M is the mass flow rate of natural gas consumed by the gas turbine. G-P2G This refers to the mass flow rate of natural gas. η P2G The efficiency of the electro-gas conversion equipment; The power system optimization model aims to minimize the power system cost and uses linear DistFlow equations to simulate the power flow of a radial distribution network. The specific model is as follows: ， In the formula: C ele (t) represents the cost of purchasing and selling electricity; C wcur λ1, λ2, λ3, λ4, and λ5 are Lagrange multipliers; k is the iteration number; Y p P GT Y p P P2G Y p P pump Z represents the independent variables for the optimal power flow subproblem in the power system, namely the gas turbine, the electric-to-gas conversion equipment, and the water pump; w P GT Z w P P2G Z w P pump The independent variables for the optimal tidal current problem of the water supply system are the gas turbine, the electric-to-gas conversion equipment, and the water pump; η is the penalty parameter, η > 0; c in (t) > 0 and c out (t) > 0 represent the power purchase price and power sales price of the distribution network, respectively, using time-of-use pricing; P + (t) and P − (t) represent the power purchased from the main grid by the distribution network and the power sold to the main grid by the distribution network, respectively. G (t) represents the active power exchanged between the distribution network and the main network; c i,wcur (t) is the wind curtailment penalty coefficient, N wind P represents the number of wind turbine units. i,wcur (t) represents the curtailment power of the i-th wind turbine at time t, and Δt is the time interval; P i+1 (t) and Q i+1 (t) represent the active and reactive power transmitted from node i+1 to node i+2 in the distribution network, respectively; P i+1,L (t) and Q i+1,L (t) represent the active and reactive power of the load connected to node i+1 in the distribution network, respectively; P i+1,G (t) and Q i+1,G (t) represent the active and reactive power of the power source connected to node i+1 in the distribution network, respectively; x i and r i These represent the reactance and resistance between node i and node i+1, respectively; V0, V i and V i+1 The voltages at nodes 0, i, and i+1 are respectively, with node 0 referred to as the reference node; V i,max and V i,min V i The upper and lower limits; The water supply system optimization model takes the minimum cost of the water supply system as the objective function, and includes constraints on water flow balance, head constraints at the outlet of the reservoir, head increment constraints of the variable speed pump, and head loss constraints of the pressure reducing valve. The specific model is as follows: ， In the formula: C pump (t) represents the cost of the water pump; λ2, λ4, and λ5 are Lagrange multipliers; k is the number of iterations; Y p P GT Y p P P2G Y p P pump Z represents the independent variables for the optimal power flow subproblem in the power system, namely the gas turbine, the electric-to-gas conversion equipment, and the water pump; w P GT Z w P P2G Z w P pump The independent variables for the optimal tidal current problem of the water supply system are the gas turbine, the electric-to-gas conversion equipment, and the water pump; η is the penalty parameter, η > 0; h r It is the head of the water source; It is the water flow rate of the water load; d j Let (t) be the water load at node j at time t, and let L be the set of pipes. ij (t) represents the water flow rate from node i to node j in pipe ij at time t; h is the minimum head allowed for node j. j (t) represents the water head at each node at time t; Let the outlet head of reservoir k at time t be... Let h be the inlet head of reservoir k at time t, which is also the head of water node k. k (t), constant A k Let k be the cross-sectional area of ​​the reservoir. This refers to the maximum water storage height of the reservoir. This represents the water level in the reservoir at the end of this scheduling cycle. This is the initial water level for this scheduling cycle; R ij (t) represents the head loss caused by the pressure reducing valve connected between nodes i and j at time t; q w The water consumption of the gas turbine is considered as the water load at the gas turbine intake node; β GT M is the water consumption coefficient of the gas turbine; G-P2G P represents the mass flow rate of natural gas. P2G The electrical power consumed; η P2G The efficiency of the electro-gas conversion equipment; N pump P represents the number of water pumps. i,pump (t) represents the electrical power consumed by the i-th water pump at time t, and the electricity price c for the water pump. in (t) is obtained from the power grid; constant α W-P2G The value is 479.1 kg / m³ 3 ;q W-P2G Water consumption for the electro-gas conversion equipment; F Wp,ij Let q be the coefficient of friction of the water pipe. Wp,ij (t) is the water flow rate in the pipe, q max It is the maximum allowable water flow rate for the corresponding water pipe; ω ij (t) is the relative speed of the pump connected between nodes i and j, i.e., the ratio of the operating speed to the rated speed, ω ij,max It corresponds to the upper limit of the rotational speed, and the coefficient. , ,as well as These are the pump parameters evaluated at rated speed, η. u For the pump efficiency, q ij,u,max This is the maximum allowable water flow rate of water pump ij; The Lagrange multiplier update formula is as follows: ， The boundary variable correction formulas for the power and water supply systems are as follows: ， In the formula, v is the correction factor.

2. The distributed solution method for joint scheduling of a gas-electricity-water system according to claim 1, characterized in that: The value of v ranges from 0.9 to 0.

95.

3. The distributed solution method for joint scheduling of a gas-electricity-water system according to claim 1, characterized in that: The convergence criterion is as follows: ， Where: ε k For the maximum relative deviation, the convergence accuracy ε0 > 0, and x is (X g P GT , X g P P2G ), y is (Y p P GT , Y p P P2G ,Y p P pump ), z is (Z w P GT Z w P P2G Z w P pump ).

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