Optimization control method, device and storage medium for multi-energy flow system including wind-heat unit

Abstract

The present invention discloses an optimization control method, device and storage medium for a multi-energy flow system including a wind-thermal unit, constructs a unified model of the multi-energy flow system, and the unified model includes a wind power generation model, an energy storage device model and an electric heating device model; according to the unified model, a multi-objective optimization control model of the multi-energy flow system is established; the multi-objective optimization control model of the multi-energy flow system is solved to obtain an optimization control scheme for the multi-energy flow system. The present invention constructs a unified model of the multi-energy flow system, and from the perspective of optimizing the performance of the wind-thermal unit, a unified model of the multi-energy flow system of the distributed hybrid energy supply system is established; based on the unified model, a multi-objective optimization model is established considering the multi-energy complementary characteristics, and solving the optimization model can alleviate the impact of the peak power consumption caused by cold and hot loads on the distribution network, and improve the comprehensive energy utilization efficiency of the distribution system.

Description

Technical Field

[0001] The present invention relates to the technical field of power system dispatching automation, and in particular to a multi-energy flow system optimization control method, device and storage medium for a wind-heat unit. Background Art

[0002] Energy is an important foundation for social and economic development. Improving the comprehensive utilization efficiency of energy is the key to resolving the contradiction between energy development and environmental crisis, and is an important way to achieve efficient, safe, clean and stable application of energy.

[0003] Cogeneration heating units are a clean and efficient way of heating, but they usually operate in a "heat-to-electricity" mode. During the winter heating period, the lack of flexibility in the unit operation seriously affects the consumption of renewable energy. Electric heating has a flexible operation mode, which promotes the consumption of renewable energy during the winter heating period.

[0004] The large-scale investment in cogeneration units has promoted the development of thermal power multi-energy flow systems. While improving efficiency, multi-energy flow systems will also make the original energy system more complicated. Therefore, higher requirements are placed on the configuration and control of thermal power multi-energy flow systems. Summary of the invention

[0005] The present invention aims to provide an optimization control method for a multi-energy flow system based on a wind-heat unit to address the problems existing in the configuration and control methods of a thermoelectric multi-energy flow system.

[0006] In order to achieve the above technical objectives, the present invention adopts the following technical solutions.

[0007] The present invention adopts the following technical solutions.

[0008] In one aspect, the present invention provides a method for optimizing and controlling a multi-energy flow system including a wind-heat unit, comprising:

[0009] Constructing a unified model of a multi-energy flow system, wherein the unified model includes a wind power generation model, an energy storage device model, and an electric heating device model;

[0010] Based on the unified model, a multi-objective optimization control model for multi-energy flow system is established;

[0011] The multi-objective optimization control model of the multi-energy flow system is solved to obtain the optimization control scheme of the multi-energy flow system.

[0012] Furthermore, the wind power generation model is expressed as follows:

[0013]

[0014] Where P0 is the rated power of the generator set, v maxis the wind speed at cut-out, v is the wind speed, V0 is the rated wind speed, P w is the output power of the generator set, v min is the wind speed at cut-in.

[0015] Furthermore, the wind power generation model adopts the wind energy calculation model of wind power generation under ideal conditions and is expressed as follows:

[0016]

[0017] Where: P wind is the output power of the generator set under ideal conditions, ρ is the air density, A is the swept area of ​​the fan blades, and v is the wind speed.

[0018] Furthermore, the energy storage device model includes an electric energy storage model and a thermal energy storage model, wherein the electric energy storage model is expressed as:

[0019]

[0020] Where E remain (t) is the remaining capacity of the energy storage at time T, E remain (t-1) is the remaining capacity of the energy storage at time t-1, σ is the self-discharge efficiency of the energy storage, μ in is the charging efficiency, P in (t) is the charging power at time t, P out (t) is the discharge power at time t, μ out is the discharge efficiency;

[0021] The thermal energy storage model is expressed as:

[0022]

[0023] Among them, H remain (t) is the remaining capacity of thermal energy storage at time t, H remain (t-1) is the remaining capacity of thermal energy storage at time t-1, γ is the heat dissipation loss rate of thermal energy storage, Q in (t) is the heat power absorbed at time t, Q out (t) is the heat release power at time t, is the heat absorption efficiency, For heat release efficiency.

[0024] Furthermore, the electric heating device model

[0025]

[0026] Where E G (t) is the power consumption at time t, Q G (t) is the heat load power, ρ G (t) is the heat dissipation rate of the boiler, Lx is the percentage of power loss, δ G is the electric heating damping coefficient.

[0027] Furthermore, the multi-objective optimization control model of the multi-energy flow system is expressed as follows:

[0028]

[0029] Where E remain (t) is the remaining capacity of the energy storage at time t, H remain (t) is the remaining capacity of thermal energy storage at time t, C1 and C2 are setting parameters, E G (t) is the power consumption at time t, ρ G (t) is the boiler heat dissipation rate, δ G is the electric heating damping coefficient;

[0030] The minimum power consumption per unit time E remain (t) as the remaining capacity of thermal energy storage H remain (t) constraint; wind power output power P w As E remain (t) Constraints for minimizing the remaining capacity of the energy storage system.

[0031] Furthermore, the multi-objective optimization control model of the multi-energy flow system is expressed as follows:

[0032]

[0033] Where E remain (t) is the remaining capacity of the energy storage at time T, H remain (t) is the remaining capacity of thermal energy storage at time t, C1 and C2 are setting parameters, E G (t) is the power consumption at time t, ρ G (t) is the boiler heat dissipation rate, δ G is the electric heating damping coefficient; P wind is the output power of the generator set under ideal conditions, ρ is the air density, A is the swept area of ​​the fan blades, and v is the wind speed;

[0034] The minimum power consumption per unit time E remain (t) as the remaining capacity of thermal energy storage H remain (t) constraint; the wind power output power P under ideal condition wind As E remain (t) Constraints for minimizing the remaining capacity of the energy storage system.

[0035] In the above technical solution, a hybrid particle swarm optimization algorithm is used to solve the multi-objective optimization control model of the multi-energy flow system.

[0036] On the other hand, the present invention also provides a multi-energy flow system optimization control device including a wind-heat unit, comprising: a unified model determination module, a multi-objective optimization control model determination module, and a solution optimization module;

[0037] The unified model determination module is used to construct a unified model of a multi-energy flow system, wherein the unified model includes a wind power generation model, an energy storage device model, and an electric heating device model;

[0038] The multi-objective optimization control model is used to establish a multi-objective optimization control model for a multi-energy flow system according to a unified model;

[0039] The solution optimization module is used to solve the multi-objective optimization control model of the multi-energy flow system to obtain the multi-energy flow system optimization control solution.

[0040] The present invention also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the steps of the method described in any possible implementation manner of the above technical solution are implemented.

[0041] The beneficial technical effects achieved by the present invention are as follows: the present invention constructs a unified model of the multi-energy flow system, and from the perspective of optimizing the performance of the wind-thermal unit, establishes a unified model of the multi-energy flow system of the distributed hybrid energy supply system; based on the unified model, considering the multi-energy complementary characteristics, a multi-objective optimization model is established, and solving the optimization model can alleviate the impact of the peak power consumption caused by cold and hot loads on the distribution network, with the goal of improving the comprehensive energy utilization efficiency of the distribution system. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 It is a schematic diagram of an optimization control method for a multi-energy flow system including a wind-heat unit provided in an embodiment. DETAILED DESCRIPTION

[0043] The present invention is further described below in conjunction with the accompanying drawings and specific embodiments.

[0044] The schematic diagram of the optimization control method of the multi-energy flow system including the wind thermal unit is as follows Figure 1 In order to achieve accurate analysis and flexible control of the multi-energy flow system and optimize the comprehensive energy utilization efficiency of the power distribution system, the embodiment of the present invention proposes a multi-energy flow system control optimization method containing a wind-heat unit. The multi-energy flow system includes three distributed energy sources: a wind-heat unit, an energy storage device, and an electric heating device. The unified model of the multi-energy flow system is as follows:

[0045] 1. Wind power generation model of wind thermal unit

[0046] The wind power generation model adopts the wind energy calculation model of wind power generation under ideal conditions and is expressed as follows:

[0047]

[0048] Where: P wind is the output power of the generator set under ideal conditions, ρ is the air density, A is the swept area of ​​the fan blades, and v is the wind speed.

[0049] 2. Energy storage equipment model

[0050] The energy storage equipment of the multi-energy flow system mainly includes electric energy storage equipment and thermal energy storage equipment. The electric energy storage technology is mainly used to optimize the load in the peak and valley intervals of power load and improve the operation stability of the power system.

[0051] The electric energy storage optimization model is expressed as follows:

[0052]

[0053] The objective function E remain (t) is the remaining capacity of the energy storage at time t, σ is the self-discharge efficiency of the energy storage, P in (t) is the charging power at time t, P out (t) is the discharge power at time T, μ in is the charging efficiency, μ out is the discharge efficiency.

[0054] Thermal energy storage equipment mainly switches the thermal load between peak and valley periods to solve the problem of tight supply and demand during peak thermal load periods. The thermal energy storage optimization model is expressed as follows:

[0055]

[0056] The objective function H remain (t) is the remaining capacity of thermal energy storage at time t, γ is the heat dissipation loss rate of thermal energy storage, Q in (t) is the heat power absorbed at time t, Q out (t) is the heat release power at time t, is the heat absorption efficiency, For heat release efficiency.

[0057] 3. Electric heating device model

[0058] The heating equipment in the present invention mainly refers to an electric boiler, and the heat production of the electric boiler is related to the thermal efficiency.

[0059] The electric heating device model is expressed as follows:

[0060]

[0061] Objective function E G (t) is the power consumption at time t, Q G(t) is the heat load power, δ G is the electric heating damping coefficient, ρ G (t) is the heat dissipation rate of the boiler, L x is the percentage of power loss.

[0062] 4. Multi-energy flow control optimization

[0063] The multi-energy flow control problem can be regarded as a multi-objective optimization strategy problem. The goal of the multi-objective optimization algorithm is to coordinate the relationship between the unit modules so that the multi-objectives can reach the optimal solution set as much as possible. The definition of the multi-objective optimization problem is: let X be the decision vector, f(X) be the objective function, and the objective function contains n sub-objectives (for example, in other embodiments, the minimum unit heating cost of the wind thermal unit, the maximum heat storage economy, etc.), and the mathematical model of simply regarding the multi-energy flow control problem as a multi-objective optimization problem is:

[0064] f(X)=(f1(X), f2(X),…, f n (X)

[0065] g i (x)≥0, i=1, ..., m

[0066] x∈X

[0067] In this embodiment, E remain (t) The remaining capacity of the electric energy storage is minimum, H remain (t) Minimum remaining capacity of thermal energy storage and minimum power consumption per unit time of electric heating E G (t) is the objective function, and because the heat load power Q G (t) and the remaining capacity of thermal energy storage H remain (t) is linearly related, we can use the minimum power consumption per unit time E remain (t) As the remaining capacity of thermal energy storage H remain (t) constraint; Similarly, the wind power output power P under the ideal state can also be wind As E remain (t) The constraint condition of the minimum remaining capacity of the electric energy storage is as follows:

[0068]

[0069] Since the wind energy calculation model of wind power generation under ideal conditions is used to determine the output power of wind power generation, the influence of excessively high or low wind speed is not considered, which affects the accuracy of the model.

[0070] In other embodiments, it is further considered that the wind power generation power mainly depends on the natural wind speed, and the wind speed when the generator set reaches the rated power P0 is the rated wind speed. The wind speed model is:

[0071]

[0072]

[0073] When the wind speed is too high, the wind turbine needs to be cut out. The wind speed at the time of cut-out is v max ; The wind speed is too low, the wind turbine is switched on, and the wind speed at the time of switching on is v min ; The wind speed of the wind turbine at rated speed is v0; The wind power generation model is expressed as follows:

[0074]

[0075] Where P0 is the rated power of the generator set, v max is the wind speed at cut-out, v is the wind speed, V0 is the rated wind speed, P w is the output power of the generator set, v min is the wind speed at cut-in.

[0076] E remain (t) The remaining capacity of the electric energy storage is minimum, H remain (t) Minimum remaining capacity of thermal energy storage and minimum power consumption per unit time of electric heating E G (t) is the objective function, and because the heat load power Q G (t) and the remaining capacity of thermal energy storage H remain (t) is linearly related, we can use the minimum power consumption per unit time E remain (t) as the remaining capacity of thermal energy storage H remain (t) constraint; similarly, the wind power output power P w As E remain (t) The constraint condition of the minimum remaining capacity of the electric energy storage is as follows:

[0077]

[0078] The difficulty in solving the multi-objective optimization control model established above is that it may produce infeasible solutions, that is, the processing of constraints. A method based on an improved hybrid particle swarm optimization algorithm is used to solve nonlinear constraint programming. It has good stability, is simple and easy to implement, and is powerful and easy to master. It has universal applicability for solving multidimensional nonlinear and complex problems.

[0079] Since the multi-energy flow system considers more constraints, the problems are more complex and more difficult to solve, it is a typical mixed integer nonlinear programming problem. For this kind of problem, intelligent algorithms can usually be used to solve it. A method based on an improved hybrid particle swarm optimization algorithm to solve nonlinear constraint programming can be used. This algorithm uses a particle swarm optimization algorithm with a global search function to provide a better initial value, thereby improving the convergence speed and solution accuracy. Its mathematical model is as follows

[0080]

[0081] Where w represents the inertia weight; c1 and c2 are the historical optimal solutions p of the example tracking its own search. id (k) and track the optimal solution p of the particle swarm search id (k) is the weight coefficient; γ1 and γ2 are random numbers uniformly distributed in the interval [0,1]; α is the constraint factor, usually 1; v id (k+1), x id (k+1) respectively represent the flying speed and position of the particle with the ith digit d after the kth iteration update. The algorithm has fast convergence speed, high convergence accuracy, small number of iterations, and strong ability to jump out of the local optimal solution, which provides a new idea and method for solving the optimal power flow calculation of power system.

[0082] Secondly, considering the characteristics of renewable energy and local load demand, combined with the complementary characteristics of the transmission, storage and conversion of multiple energy flows, the distributed energy in the coordination system including wind thermal units, photovoltaic power generation devices and energy storage is coordinated.

[0083] The internal node power balance formula of the multi-energy flow system with distributed hybrid energy supply is established. The node power balance formula is as follows:

[0084]

[0085]

[0086] Formula (1) and formula (2) represent the wind turbine part and the electric boiler part respectively. Actual power generation of wind turbines; Wind power generation; Abandoned wind power; For power consumption; Electricity consumption of electric boilers; Electric boiler startup power; η inPower consumption efficiency, the node power balance formula is brought into the optimization algorithm as a condition; after the optimization algorithm, the system frequency can be guaranteed to be stable within the specified range; under the premise of minimizing the volatility of the user-side load curve, the wind power output power and thermal load power are used as constraints to ensure that the remaining capacity of heat and electricity storage is minimized, and then the system operating cost is minimized while considering the time-of-use electricity price.

[0087] In the specific embodiment of the present invention, the heat transfer and storage process in the thermal system is studied to study the resistance, capacitance and delay characteristics that are compatible with the operation characteristics of the power system. Considering the dynamic characteristics of energy conversion of the wind-heat unit, the distributed hybrid energy supply underlying data based on the distribution network is configured through the human-computer interaction subsystem, and the corresponding heat flow component model is established. Combined with the overall topological structure of the system, a unified model of the multi-energy flow control system including wind turbines, electric heating and thermal energy storage devices is constructed.

[0088] Optionally, basic collected data of wind-thermal units, distributed power generation and heat storage devices are normally acquired through a wired private network or a wireless public network under a protocol conversion device; the real-time library and commercial library of the distribution master station system platform store remotely sent data, and the human-machine interface displays the real-time telemetry and telesignaling data of the wind-thermal units; the distributed hybrid energy supply system provides rolling forecast scheme data through real-time data and historical data, according to coordinated optimization and multi-energy flow optimization control; the data interaction subsystem extracts the wind-thermal unit data in the database and sends it to the main grid system using the platform data service bus, a communication link is established between the main grid and the provincial dispatching based on the data center, and the data is reported to the provincial dispatching automatic control system, and the automatic control system establishes a multi-objective optimization control model of the multi-energy flow system according to a unified model; the multi-objective optimization control model of the multi-energy flow system is solved to obtain the multi-energy flow system optimization control scheme, and control instructions are generated according to the control scheme.

[0089] The automatic control system calculates the power grid section and sends control instructions to the data interaction system, and finally generates a planned data curve; the automatic control subsystem is based on the planned data and the rolling forecast plan data, combined with the current power grid section data and power grid frequency, and sends control commands to the wind thermal unit in real time. At the same time, it collects status data and real-time power data of the wind thermal unit, distributed power generation, and heat storage device, providing basic data for the next cycle optimization algorithm to achieve comprehensive supply of cold, hot, and electric energy in the region.

[0090] During the on-site trial operation phase, the control strategy is improved based on the actual system operation conditions and random natural conditions.

[0091] The present invention constructs a multi-energy flow optimization configuration model including wind power, energy storage, and electric heating systems to optimize the configuration of the distributed hybrid energy supply system. Considering the dynamic characteristics of the coupled complementary characteristics of multiple energy flows and the energy conversion efficiency of the wind-heat unit in the distributed hybrid energy supply system, it can meet the peak-shaving needs of the distribution network; at the same time, it takes into account the overall energy efficiency of the distributed hybrid energy supply system, and realizes the cogeneration of heat and power in the distributed power grid while meeting the needs of users, optimizes the overall control of the system, improves the system operation control level, alleviates the peak of electricity consumption of conventional cold and hot loads, and improves the comprehensive utilization efficiency of energy.

[0092] Corresponding to the above embodiments, the present invention also provides a multi-energy flow system optimization control device including a wind-heat unit, characterized in that it comprises: a unified model determination module, a multi-objective optimization control model determination module and a solution optimization module;

[0093] The unified model determination module is used to construct a unified model of a multi-energy flow system, wherein the unified model includes a wind power generation model, an energy storage device model, and an electric heating device model;

[0094] The multi-objective optimization control model is used to establish a multi-objective optimization control model for a multi-energy flow system according to a unified model;

[0095] The solution optimization module is used to solve the multi-objective optimization control model of the multi-energy flow system to obtain the multi-energy flow system optimization control solution.

[0096] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the system, device / module described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0097] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0098] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0099] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0100] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0101] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the enlightenment of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the purpose of the present invention and the claims, which all fall within the protection of the present invention.

Claims

1. An optimization control method for a multi-energy flow system including a wind-heat unit, characterized in that: include: Constructing a unified model of a multi-energy flow system, wherein the unified model includes a wind power generation model, an energy storage device model, and an electric heating device model; According to the unified model, a multi-objective optimization control model of a multi-energy flow system is established; wherein the multi-objective optimization control model of a multi-energy flow system is expressed as follows: Where E remain (t) is the remaining capacity of the energy storage at time t, H remain (t) is the remaining capacity of thermal energy storage at time t, C1 and C2 are setting parameters, E G (t) is the power consumption at time t, ρ G (t) is the boiler heat dissipation rate, δ G is the electric heating damping coefficient; The minimum power consumption per unit time E remain (t) as the remaining capacity of thermal energy storage H remain (t) constraint; wind power output power P w As E remain (t) Constraints on the minimum remaining capacity of the electric energy storage; The multi-objective optimization control model of the multi-energy flow system is expressed as follows: Where E remain (t) is the remaining capacity of the energy storage at time t, H remain (t) is the remaining capacity of thermal energy storage at time t, C1 and C2 are setting parameters, E G (t) is the power consumption at time t, ρ G (t) is the boiler heat dissipation rate, δ G is the electric heating damping coefficient; P wind is the output power of the generator set under ideal conditions, ρ is the air density, A is the swept area of ​​the fan blades, and v is the wind speed; The minimum power consumption per unit time E remain (t) as the remaining capacity of thermal energy storage H remain (t) constraint; the output power P of the generator set under the ideal state wind As E remain (t) Constraints on the minimum remaining capacity of the electric energy storage; The multi-objective optimization control model of the multi-energy flow system is solved to obtain the optimization control scheme of the multi-energy flow system.

2. The optimization control method of a multi-energy flow system including a wind-heat unit according to claim 1 is characterized in that: The wind power generation model is expressed as follows: Where P0 is the rated power of the generator set, v max is the wind speed at cut-out, v is the wind speed, V0 is the rated wind speed, P w is the output power of the generator set, v min is the wind speed at cut-in.

3. The optimization control method of a multi-energy flow system including a wind-heat unit according to claim 1 is characterized in that: The wind power generation model adopts the wind energy calculation model of wind power generation under ideal conditions and is expressed as follows: Where: P wind is the output power of the generator set under ideal conditions, ρ is the air density, A is the swept area of ​​the fan blades, and v is the wind speed.

4. The optimization control method of a multi-energy flow system including a wind-heat unit according to claim 1 is characterized in that: The energy storage device model includes an electric energy storage model and a thermal energy storage model, wherein the electric energy storage model is expressed as: Where E remain (t) is the remaining capacity of the energy storage at time t, E remain (t-1) is the remaining capacity of the energy storage at time t-1, σ is the self-discharge efficiency of the energy storage, μ in is the charging efficiency, P in (t) is the charging power at time t, P out (t) is the discharge power at time t, μ out is the discharge efficiency; The thermal energy storage model is expressed as: Among them, H remain (t) is the remaining capacity of thermal energy storage at time t, H remain (t-1) is the remaining capacity of thermal energy storage at time t-1, γ is the heat dissipation loss rate of thermal energy storage, Q in (t) is the heat power absorbed at time t, Q out (t) is the heat release power at time t, is the heat absorption efficiency, For heat release efficiency.

5. The optimization control method of a multi-energy flow system including a wind-heat unit according to claim 1 is characterized in that: The electric heating device model is expressed as: Where E G (t) is the power consumption at time t, Q G (t) is the heat load power, ρ G (t) is the heat dissipation rate of the boiler, L x is the percentage of power loss, δ G is the electric heating damping coefficient.

6. The optimization control method for a multi-energy flow system including a wind-heat unit according to any one of claims 1 to 5, characterized in that: A hybrid particle swarm optimization algorithm is used to solve the multi-objective optimization control model of the multi-energy flow system.

7. An optimization control device for a multi-energy flow system including a wind-heat unit, characterized in that: include: Unified model determination module, multi-objective optimization control model determination module and solution optimization module; The unified model determination module is used to construct a unified model of a multi-energy flow system, wherein the unified model includes a wind power generation model, an energy storage device model, and an electric heating device model; The multi-objective optimization control model of the multi-energy flow system is expressed as follows: Where E remain (t) is the remaining capacity of the energy storage at time t, H remain (t) is the remaining capacity of thermal energy storage at time t, C1 and C2 are setting parameters, E G (t) is the power consumption at time t, ρ G (t) is the boiler heat dissipation rate, δ G is the electric heating damping coefficient; The minimum power consumption per unit time E remain (t) as the remaining capacity of thermal energy storage H remain (t) constraint; wind power output power P w As E remain (t) Constraints on the minimum remaining capacity of the electric energy storage; The multi-objective optimization control model of the multi-energy flow system is expressed as follows: Where E remain (t) is the remaining capacity of the energy storage at time t, H remain (t) is the remaining capacity of thermal energy storage at time t, C1 and C2 are setting parameters, E G (t) is the power consumption at time t, ρ G (t) is the boiler heat dissipation rate, δ G is the electric heating damping coefficient; P wind is the output power of the generator set under ideal conditions, ρ is the air density, A is the swept area of ​​the fan blades, and v is the wind speed; The minimum power consumption per unit time E remain (t) as the remaining capacity of thermal energy storage H remain (t) constraint; the output power P of the generator set under the ideal state wind As E remain (t) Constraints on the minimum remaining capacity of the electric energy storage; The multi-objective optimization control model is used to establish a multi-objective optimization control model of a multi-energy flow system based on a unified model; the solution optimization module is used to solve the multi-objective optimization control model of the multi-energy flow system to obtain an optimization control scheme for the multi-energy flow system.

8. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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