Method and System for Coordinated Optimal Scheduling of Electrical-Gas Interconnected Systems

Abstract

The present invention provides a method and system for day-ahead coordinated optimization scheduling of an electrical-gas interconnected system, which acquires the operation parameter data of the power grid, the heat grid and the natural gas grid; controls the power grid to meet the power flow balance constraint and the output constraints of each unit, and adds the electric power input by the gas turbine in the energy hub; controls the heat grid to meet the heat supply balance and the output constraints of each unit, and the iteration of the power grid restricts the output heat power of the gas turbine in the energy hub; controls the natural gas grid to meet the flow balance constraint, the gas source output constraint and the node pressure constraint, and the power grid and the heat grid respectively restrict the gas supply demands of the gas turbine and the gas boiler in the energy hub; within the scheduling period, with the minimum operation cost as the optimization target, the optimal output combinations of each generator set, the energy hub and the gas source in the first time period are obtained, and then the iteration of the next time period is entered until the optimization of a scheduling period is completed; the present invention realizes the coordinated and refined optimization of the power grid, the heat grid and the natural gas network.

Description

Technical Field

[0001] The present invention relates to the technical field of optimal control of the electric-gas interconnected system, and particularly relates to a day-ahead collaborative optimal scheduling method and system for the electric-gas interconnected system. Background Technique

[0002] The statements in this part only provide the background technique related to the present invention and do not necessarily constitute the prior art.

[0003] The rapid economic development has increased the energy demand, and how to rationally and efficiently utilize energy has become a key issue. The energy system structure dominated by the traditional power grid is unreasonable, the consumption of highly polluting energy such as coal remains high, and the energy waste phenomenon is serious when various energies are converted into electric energy. At the same time, with the rapid development of distributed energy, the traditional energy network is difficult to offset the impact of a large number of distributed energy grid connections. As a bridge for the transition from fossil energy to new energy, natural gas not only has the advantages of cleanliness and high efficiency, but also has a fast adjustment speed and can be used for emergency peak shaving. In this context, the integrated electric-gas energy system (IEGS) has emerged.

[0004] In recent years, various studies have been conducted on the operation strategies of IEGS. Some researchers have established the physical model of IEGS and proposed economic dispatch and optimization strategies. Some researchers have proposed a general modeling method for the optimal scheduling of a combined cooling, heating and power microgrid. Some researchers have proposed a linearization method for the model and used the linearization method to solve the optimal energy flow distribution of the system. Some researchers have used mixed integer linear programming (MILP) to study the optimal scheduling strategy of IEGS. Some researchers have proposed a joint optimization strategy for IEGS, which considers the nonlinear characteristics of the natural gas pipeline and adopts a stochastic programming method to dispatch conventional units to adapt to the randomness of wind power prediction. Some researchers have considered the transmission delay and temperature loss of the heat network and used the heat storage of the pipeline to improve the wind energy receiving capacity of the system. Some researchers have studied the dynamic optimal operation strategy of the electric-gas integrated energy system based on the transient model of the natural gas network.

[0005] However, the inventor has found that the above studies all adopt the method of separately optimizing the power network and the natural gas network. Due to the constraints brought by network coupling not being fully considered, the collaborative optimization effect of the power network and the natural gas network is poor. Summary of the Invention

[0006] To address the deficiencies of the prior art, the present invention provides a method and system for day-ahead coordinated optimal scheduling of an electric-gas interconnected system. Based on the generalized Kirchhoff's law, the power equations of the power network and the flow equations of the natural gas network are listed, while considering the constraints of parameters such as line voltage and gas pressure. Secondly, various types of coupled units are abstracted into an energy hub model. The energy hub converts the input electricity and natural gas to meet the load demands of the power grid, heat network, and natural gas network, achieving coordinated and refined optimization of the power grid, heat network, and natural gas network.

[0007] To achieve the above object, the present invention adopts the following technical solutions:

[0008] The first aspect of the present invention provides a method for day-ahead coordinated optimal scheduling of an electric-gas interconnected system.

[0009] A method for day-ahead coordinated optimal scheduling of an electric-gas interconnected system includes the following processes:

[0010] Obtain the operation parameter data of the power grid, heat network, and natural gas network;

[0011] Control the power grid to meet the power flow balance constraint and the output constraints of each unit, and add the electric power input to the gas turbine in the energy hub; control the heat network to meet the heat supply balance and the output constraints of each unit, and the iteration of the power grid limits the output heat power of the gas turbine in the energy hub; control the natural gas network to meet the flow balance constraint, gas source output constraint, and node pressure constraint, and the power grid and heat network respectively limit the gas supply demands of the gas turbine and gas boiler in the energy hub;

[0012] Within the scheduling period, with the lowest operating cost as the optimization objective, obtain the optimal output combination of each generator set, energy hub, and gas source in the first time period, and then enter the iteration of the next time period until the optimization of one scheduling period is completed.

[0013] The second aspect of the present invention provides a system for day-ahead coordinated optimal scheduling of an electric-gas interconnected system.

[0014] A system for day-ahead coordinated optimal scheduling of an electric-gas interconnected system includes:

[0015] A data acquisition module configured to: obtain the operation parameter data of the power grid, heat network, and natural gas network;

[0016] An electrical and thermal control module configured to: control the power grid to meet the power flow balance constraint and the output constraints of each unit, and add the electric power input to the gas turbine in the energy hub; control the heat network to meet the heat supply balance and the output constraints of each unit, and the iteration of the power grid limits the output heat power of the gas turbine in the energy hub; control the natural gas network to meet the flow balance constraint, gas source output constraint, and node pressure constraint, and the power grid and heat network respectively limit the gas supply demands of the gas turbine and gas boiler in the energy hub;

[0017] The scheduling optimization module is configured to: within a scheduling period, with the lowest operating cost as the optimization goal, obtain the optimal output combination of each generator set, energy hub, and gas source in the first time period, and then enter the iteration of the next time period until the optimization of a scheduling period is completed.

[0018] The third aspect of the present invention provides a computer-readable storage medium, on which a program is stored, and when the program is executed by a processor, the steps in the day-ahead collaborative optimization scheduling method of the electric-gas interconnected system as described in the first aspect of the present invention are implemented.

[0019] The fourth aspect of the present invention provides an electronic device, including a memory, a processor, and a program stored on the memory and executable on the processor. When the processor executes the program, the steps in the day-ahead collaborative optimization scheduling method of the electric-gas interconnected system as described in the first aspect of the present invention are implemented.

[0020] Compared with the prior art, the beneficial effects of the present invention are:

[0021] The day-ahead collaborative optimization scheduling method and system of the electric-gas interconnected system described in the present invention write the power equation of the power network and the flow equation of the natural gas network based on the generalized Kirchhoff's law, and at the same time consider the constraints of parameters such as the voltage and air pressure of the lines; secondly, various types of coupled units are abstracted into an energy hub model, and the energy hub converts the input electricity and natural gas to meet the load demands of the power grid, heat network, and natural gas network, realizing the collaborative and refined optimization of the power grid, heat network, and natural gas network. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The specification drawings constituting a part of the present invention are used to provide a further understanding of the present invention. The schematic embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention.

[0023] Figure 1 It is a schematic diagram of a typical EH model provided in Embodiment 1 of the present invention.

[0024] Figure 2 It is a schematic diagram of a collaborative optimization scheduling calculation strategy provided in Embodiment 1 of the present invention.

[0025] Figure 3 It is a schematic diagram of the principle of the generalized Kirchhoff's law provided in Embodiment 1 of the present invention.

[0026] Figure 4 It is a schematic diagram of a natural gas pipeline node model provided in Embodiment 1 of the present invention.

[0027] Figure 5 It is a schematic diagram of the output of each generator set in the power grid in Scenario 1 provided in Embodiment 1 of the present invention.

[0028] Figure 6 Output schematic diagram of each unit of the power grid in Scenario 2 provided in Embodiment 1 of the present invention.

[0029] Figure 7 Output schematic diagram of the energy hub in Scenario 1 provided in Embodiment 1 of the present invention.

[0030] Figure 8 Schematic diagram of cost comparison between two scenarios provided in Embodiment 1 of the present invention. Detailed implementation manners

[0031] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0032] It should be noted that the following detailed description is illustrative and is intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used in this embodiment have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present invention belongs.

[0033] It should be noted that the terms used herein are only for describing specific implementation manners and are not intended to limit the exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0034] In the case of no conflict, the embodiments in the present invention and the features in the embodiments can be combined with each other.

[0035] Embodiment 1:

[0036] Aiming at the problem that the current power grid and natural gas grid are difficult to be unifiedly scheduled due to high coupling, Embodiment 1 of the present invention proposes a collaborative optimization model based on the generalized Kirchhoff's law to realize the unified solution calculation of the two parts of the network; at the same time, the influence of the coupling link on the optimal operation of the integrated electricity-gas system (IEGS) is reflected through the energy hub, and the energy coupling matrix of the source and load is established; this model fully considers the constraints of different network lines, units and network couplings, takes the economic index as the optimization goal, and obtains the optimal scheduling strategy for the coordinated operation of the power grid and natural gas network; the optimal scheduling of the IEEE 33-node power grid and the Belgian 20-node natural gas network under different scheduling scenarios is simulated and analyzed, and the results show that the scheduling strategy proposed by the model has important reference value for improving economic benefits and energy utilization efficiency.

[0037] Specifically, it includes the following processes:

[0038] S1: Modeling of IEGS with EH

[0039] S1.1: EH Model

[0040] The Energy Hub (EH) highly abstracts and classifies the energy supply and demand, and describes the energy coupling characteristics through a coupling matrix. In this embodiment, a typical EH model as shown in Figure 1 is established: the input end includes thermal power, wind power, and natural gas, and the energy transmission and conversion are realized through equipment such as gas turbines and gas boilers, and the output electrical energy and heat energy supply the load demand.

[0041] The mathematical model of EH is:

[0042]

[0043] In the formula, L e,t , L h,t are the electrical load and thermal load output by EH respectively; is the scheduling coefficient; are the efficiencies of gas turbine power generation, gas turbine heat recovery, and heat production by gas boilers respectively; are the electric power of the thermal power unit and the wind power unit input to EH at time t respectively; is the input flow rate of the natural gas source at time t.

[0044] S1.2: Scheduling Calculation Strategy

[0045] As shown in Figure 2 , in the collaborative optimization process, the system is first initialized, the node and branch parameters of each network are input, and the status of the units and the constraints of the network are initialized. The lowest operating cost is set as the optimization goal. In the iterative link, for the power grid, the power flow balance and constraints of the power grid need to be satisfied, and at the same time, the electric power input by the gas turbine in the energy hub is added. For the heat network, the heat supply balance of the entire network needs to be satisfied, and at the same time, the iterative process of the power grid limits the output heat power of the gas turbine in the energy hub. For the gas network, the flow balance of the natural gas network needs to be satisfied, and at the same time, the power grid and the heat network respectively limit the gas supply demands of the gas turbine and the gas boiler in the energy hub. After collaborative solution, the optimal output combination of each generator set, energy hub, and gas source under the cost target of the first time period is obtained, and then the system enters the iteration of the next time period until the optimization of a scheduling cycle is completed.

[0046] S2: IEGS Optimization Based on Generalized Kirchhoff's Law

[0047] Since there are significant differences in the operating conditions between the power transmission line and the natural gas transmission pipeline, it is difficult to carry out unified modeling and solution. Therefore, this embodiment proposes an IEGS model based on the generalized Kirchhoff's law to conduct unified analysis of the two parts of the network; at the same time, based on the optimization scheduling calculation strategy proposed above, with the goal of optimal economy, the optimal scheduling strategy of IEGS is sought.

[0048] S2.1: Objective Function

[0049] During one cycle, the operating cost of the system includes the coal consumption of thermal power units and the cost of purchased natural gas. Among them, the coal consumption of thermal power units is a quadratic function of the generated power, and the cost of purchased natural gas is related to the gas consumption and the real-time gas price.

[0050] The system operation can be described as:

[0051]

[0052] In the formula: Ω e and Ω g are the sets of thermal power units and gas sources in the system respectively; a i , b i , c i are the cost coefficients of the thermal power generation unit; ω i is the gas source price coefficient; and S i,t are the outputs of the thermal power units and gas sources in the system at time t respectively.

[0053] S2.2: Model Constraints

[0054] S2.2.1: Natural Gas Network Model Based on Generalized Kirchhoff's Law

[0055] In this embodiment, Kirchhoff's law is applied to the natural gas network, Figure 3 which represents the relationship between the flow rate and pressure in the natural gas pipeline network. The reference direction of the pipeline flow rate and the reference air pressure are specified, and the sum of the flow rates at any node is 0, and the pressure drop is 0 for any mesh loop.

[0056] For the high-pressure gas transmission pipeline k, the relationship between its flow rate f k and the pressures at both ends can be described as:

[0057]

[0058] Where:

[0059]

[0060]

[0061] In the formula, p0 and p i are the standard pressure and the node air pressure respectively; T0 and T ka are the standard temperature and the average temperature of the pipeline gas respectively; L k and D k are the length and diameter of the pipeline respectively; G is the gas specific gravity (air = 1.0, natural gas = 0.6); Z a is the average gas compressibility; ε is the pipeline efficiency.

[0062] Based on the generalized Kirchhoff's law, for any node in the pipeline, its model is as Figure 4 shown. In the figure, f ji and f ij are the flow rates of the upstream pipeline and the downstream pipeline connected to node i, respectively; S i and L i are the flow rates of the gas source and the natural gas load connected to node i, respectively. The inflow section includes the upstream pipeline and the gas source, and the outflow section includes the downstream pipeline and the load.

[0063] According to the Figure 4 described node flow relationship, the node equations for the natural gas network are written as:

[0064] ∑ j|(i,j)∈A f ji +∑ i∈A S i =∑ j|(i,j)∈A f ij +∑ i∈A (f i_d +f i GT +f i GB ) (6)

[0065] In the formula, A is the set associated with node i; f i_d , f i GT , f i GB are the amounts of natural gas consumed by the gas load, gas turbine, and gas boiler, respectively.

[0066] Meanwhile, the natural gas network also includes gas source and node pressure constraints:

[0067] Lower and upper limits of gas source output:

[0068] S i,min ≤S i ≤S i,max (7)

[0069] Lower and upper limits of node pressure:

[0070] p i,min ≤p i ≤p i,max (8)

[0071] In the formula, S i,min and S i,max are the minimum and maximum gas source outputs connected to node i, respectively; p i,min and p i,max are the minimum and maximum pressures of node i, respectively.

[0072] S2.2.2: Power Grid

[0073] The power grid constraints include power balance constraints (9)-(10) and the output constraints of each unit (11)-(16):

[0074]

[0075]

[0076]

[0077]

[0078] U i,min ≤U i ≤U i,max (13)

[0079]

[0080]

[0081]

[0082] In the formula, and are the active power output and reactive power output of the thermal power unit at node i respectively; and are the active power output and reactive power output of the gas turbine unit at node i respectively; P i_Le and Q i_Le are the active power load and reactive power load at node i respectively; U i and U j are the voltage amplitudes of nodes i and j respectively; G ij and B ij are the conductance and susceptance between nodes i and j respectively; θ ij is the phase angle difference between the two nodes; and are the minimum and maximum active power outputs of the thermal power unit at node i respectively; and are the minimum and maximum reactive power outputs of the thermal power unit at node i respectively; U i,min and U i,max are the minimum and maximum voltage amplitudes of node i respectively; and are the minimum and maximum active power outputs of the wind power unit at node i respectively; and are the minimum and maximum active power outputs of the gas turbine unit at node i respectively; and They are the minimum and maximum reactive power outputs of the gas turbine unit at node i, respectively.

[0083] S2.2.3: Heat Network

[0084] Since both the power grid and the natural gas grid are interconnected over a large area, and the heat network is generally transmitted only within a small area due to its transmission and supply characteristics, in this embodiment, it is assumed that heat energy is transmitted within the energy hub, and only the overall heat supply balance is considered, without considering heat network interconnection. Therefore, the heat network constraints include heat supply balance (17) and unit output constraints (18)-(19):

[0085]

[0086]

[0087]

[0088] In the formula, and They are the minimum and maximum heat outputs of the gas turbine at node i, respectively; and They are the minimum and maximum heat outputs of the gas boiler at node i, respectively.

[0089] S3: Case Study

[0090] S3.1: Case Description

[0091] In this embodiment, an IEGS system composed of a modified IEEE 33-node power grid and a 20-node natural gas network in Belgium is selected, and the networks are connected through an energy hub. The IEEE 33-node power grid includes 3 conventional thermal power units and 2 wind turbine generator units; the 20-node natural gas network in Belgium includes 2 gas sources, 4 gas storage facilities and 24 pipelines. The energy limiter includes two types of energy coupling units, namely gas turbines and gas boilers, which are respectively connected to nodes 29, 30 and 31 of the power grid and nodes 6, 17 and 20 of the natural gas network.

[0092] The model considers the following two scenarios:

[0093] Scenario 1: The power system and the natural gas system are connected through an energy hub, and the operation of the two networks is restricted by the coupling link.

[0094] Scenario 2: The power system and the natural gas system operate independently.

[0095] Select one hour as the time step and take 24 hours a day as the optimization period. At the same time, a linearization method is used to linearize the non-linear terms in the natural gas network constraints and the objective function, and the entire optimization model is converted into a large-scale mixed-integer linear programming problem. In this embodiment, Yamlip is used to build the model and the Cplex solver is called to calculate the results. The relevant parameters are shown in Table 1:

[0096] Table 1: Unit Parameters and Price Coefficients

[0097]

[0098] S3.2: Result Analysis

[0099] Under these two scenarios, the output of each unit is as Figure 5 、 Figure 6 and Figure 7 shown, and the cost comparison between the two scenarios is as Figure 8 shown.

[0100] Comparing Figure 5 and Figure 6 , the output of the thermal power unit in Scenario 1 is smoother. Under the constraint of the cost target, the system absorbs wind energy as much as possible, and during the peak period of power load, the gas turbine in the energy hub converts natural gas into electric energy, thus reducing the start-stop cost of the thermal power unit and smoothing the output curve.

[0101] Figure 7 shows the thermal energy output of the gas turbine and gas boiler in Case 1. According to the co-optimization method proposed in this embodiment Figure 2 , the thermal energy output of the gas turbine is limited by the electric output. At the same time, the sum of the thermal energy outputs of the gas turbine and gas boiler needs to meet the thermal energy balance in the energy hub, so most of the thermal load is provided by the gas boiler.

[0102] Figure 8 shows that the cost of Scenario 1 is $24,391.91 and the cost of Scenario 2 is $25,595.76. In Scenario 1, under the constraint of the cost target, the energy hub in the IEGS converts energy. At the same time, the gas turbine unit uses natural gas to generate electricity and recovers the remaining heat into the thermal load. Therefore, the energy utilization efficiency of Scenario 1 is higher. At the same time, according to the co-optimization strategy proposed in this embodiment, the output of the gas turbine is limited by the electric output. During the high thermal load period, the output of the gas turbine does not affect the absorption of wind energy. In Scenario 2, the thermal power unit is affected by the coal consumption rate, which limits the absorption of wind energy during the peak load period; at the same time, the flexibility and stability of the separately dispatched system are worse.

[0103] Example 2:

[0104] Embodiment 2 of the present invention provides a day-ahead collaborative optimization scheduling system for an electric-gas interconnected system, including:

[0105] A data acquisition module, configured to: acquire the operating parameter data of the power grid, the heat grid, and the natural gas grid;

[0106] An electric-thermal control module, configured to: control the power grid to meet the power flow balance constraint and the output constraints of each unit, and add the electric power input by the gas turbine in the energy hub; control the heat grid to meet the heat supply balance and the output constraints of each unit, and the iterative limit of the power grid on the output heat power of the gas turbine in the energy hub; control the natural gas grid to meet the flow balance constraint, the gas source output constraint, and the node pressure constraint, and the power grid and the heat grid respectively limit the gas supply demands of the gas turbine and the gas boiler in the energy hub;

[0107] A scheduling optimization module, configured to: within the scheduling period, with the lowest operating cost as the optimization goal, obtain the optimal output combination of each generator set, the energy hub, and the gas source in the first time period, and then enter the iteration of the next time period until the optimization of a scheduling period is completed.

[0108] The working method of the system is the same as the day-ahead collaborative optimization scheduling method for the electric-gas interconnected system provided in Embodiment 1, and will not be elaborated here.

[0109] Embodiment 3:

[0110] Embodiment 3 of the present invention provides a computer-readable storage medium, on which a program is stored, and when the program is executed by a processor, it implements the steps in the day-ahead collaborative optimization scheduling method for the electric-gas interconnected system as described in Embodiment 1 of the present invention.

[0111] Embodiment 4:

[0112] Embodiment 4 of the present invention provides an electronic device, including a memory, a processor, and a program stored on the memory and executable on the processor. When the processor executes the program, it implements the steps in the day-ahead collaborative optimization scheduling method for the electric-gas interconnected system as described in Embodiment 1 of the present invention.

[0113] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a hardware embodiment, a software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage and optical storage, etc.) containing computer-usable program code.

[0114] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, as well as the combination of flows and / or blocks in the flowchart and / or block diagram. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in one or more flows and / or blocks. Figure 1 one or more flows and / or blocks Figure 1 or means for implementing the functions specified in one or more blocks.

[0115] These computer program instructions can 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, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means that implement the functions specified in one or more flows and / or blocks. Figure 1 one or more flows and / or blocks Figure 1 or means for implementing the functions specified in one or more blocks.

[0116] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more flows and / or blocks. Figure 1 one or more flows and / or blocks Figure 1 or means for implementing the functions specified in one or more blocks.

[0117] Those of ordinary skill in the art can understand that all or part of the processes of implementing the methods in the above embodiments can be completed by instructing relevant hardware through a computer program. The program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above methods. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM), etc.

[0118] The above are only the preferred embodiments of the present invention and are not used to limit the present invention. For those skilled in the art, the present invention can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A day-ahead coordinated optimal scheduling method for an electric-gas interconnected system, characterized in that: It includes the following processes: Obtain the operating parameter data of the power grid, heat grid, and natural gas grid; Control the power grid to meet the power flow balance constraint and the output constraints of each unit, and add the electric power input to the gas turbine in the energy hub; control the heat grid to meet the heat supply balance and the output constraints of each unit, and the iteration of the power grid limits the output heat power of the gas turbine in the energy hub; control the natural gas grid to meet the flow balance constraint, gas source output constraint, and node pressure constraint, and the power grid and heat grid respectively limit the gas supply demands of the gas turbine and gas boiler in the energy hub; Within the scheduling period, with the lowest operating cost as the optimization goal, obtain the optimal output combination of each generator set, energy hub, and gas source in the first time period, and then enter the iteration of the next time period until the optimization of a scheduling period is completed; With the lowest operating cost as the optimization goal, it includes: Among them, and are the set of system thermal power generation units and the set of gas sources respectively; , , are the cost coefficients of the thermal power generation units; is the gas source price coefficient; and are respectively the outputs of the system thermal power generation units and gas sources during the period; The input of the energy hub includes thermal power, wind power, and natural gas. Through the transmission and conversion of energy, it outputs electric energy and heat energy to supply the load demand; The energy hub includes: Among them, , are the electrical load and thermal load output by the EH respectively; is the dispatching coefficient; , , are the efficiencies of gas turbine power generation, gas turbine heat recovery and gas boiler heat production respectively; , are respectively the electric power of the thermal power unit and the electric power of the wind power unit input to the EH during the is the input flow rate of the natural gas gas source during the 2. The day-ahead coordinated optimal scheduling method for an electric-gas interconnected system according to claim 1, characterized in that: In the natural gas grid, the sum of the flows at any node is 0, and for any mesh loop, the pressure drop is 0; The node equation of the natural gas grid is: Among them, is a set associated with the node ; , , are the gas load, the amounts of natural gas consumed by the gas turbine and the gas boiler respectively, and are respectively the flow rates of the upstream and downstream pipelines connected to the node, is the gas source connected to the node.

3. A day-ahead coordinated optimal scheduling system for an electric-gas interconnected system, adopting the day-ahead coordinated optimal scheduling method for an electric-gas interconnected system according to any one of claims 1-2, characterized in that: It includes: A data acquisition module, configured to: obtain the operating parameter data of the power grid, heat grid, and natural gas grid; An electric-thermal control module, configured to: control the power grid to meet the power flow balance constraint and the output constraints of each unit, and add the electric power input to the gas turbine in the energy hub; control the heat grid to meet the heat supply balance and the output constraints of each unit, and the iteration of the power grid limits the output heat power of the gas turbine in the energy hub; control the natural gas grid to meet the flow balance constraint, gas source output constraint, and node pressure constraint, and the power grid and heat grid respectively limit the gas supply demands of the gas turbine and gas boiler in the energy hub; A scheduling optimization module, configured to: within the scheduling period, with the lowest operating cost as the optimization goal, obtain the optimal output combination of each generator set, energy hub, and gas source in the first time period, and then enter the iteration of the next time period until the optimization of a scheduling period is completed.

4. The day-ahead coordinated optimal scheduling system for an electric-gas interconnected system according to claim 3, characterized in that: In the natural gas grid, the sum of the flows at any node is 0, and for any mesh loop, the pressure drop is 0; The node equation of the natural gas grid is: Among them, is a set associated with the node ; , , are the gas load, the amounts of natural gas consumed by the gas turbine and the gas boiler respectively, and are respectively the flow rates of the upstream and downstream pipelines connected to the node, is the gas source connected to the node.

5. A computer-readable storage medium having a program stored thereon, characterized in that, When the program is executed by a processor, it implements the steps in the day-ahead coordinated optimal scheduling method for an electric-gas interconnected system according to any one of claims 1-2.

6. An electronic device, comprising a memory, a processor, and a program stored on the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps in the day-ahead coordinated optimal scheduling method for an electric-gas interconnected system according to any one of claims 1-2.

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