A dispatching system for intelligent coupling of multiple zero-carbon energy sources

Abstract

The present invention provides a scheduling system for intelligent coupling of multiple zero-carbon energy sources, which belongs to the field of optimized scheduling technology, and specifically includes: the high-temperature side of the biomass gasification heating system is connected to the high-temperature heat storage spherical tank group, the electrode heating system uses green electricity to generate steam, and is connected to the high-temperature heat storage spherical tank group and the screw expansion generator using a steam mixer, the exhaust gas of the screw expansion generator is connected to the low-temperature side of the biomass gasification heating system for low-temperature side heat treatment, the screw expansion generator performs power supply treatment, and the high-temperature heat storage spherical tank group performs high-temperature side heat treatment, wherein the high-temperature heat storage spherical tank group on the high-temperature side connected to the biomass gasification heating system is inconsistent with the high-temperature heat storage spherical tank group connected to the electrode heating system, thereby reducing carbon emissions during the heating process.

Description

Technical Field

[0001] The present invention belongs to the technical field of optimized scheduling, and specifically relates to a scheduling system for intelligent coupling of multiple zero-carbon energy sources. Background Art

[0002] Invention patent applications CN202510023338.7 "A remote intelligent management system for zero-carbon energy" and CN202410722919.5 "Near-zero-carbon energy base optimization scheduling method and terminal based on multi-energy complementary characteristics" both provide zero-carbon multi-energy scheduling and processing models.

[0003] However, currently, zero-carbon energy sources are mainly wind power, solar power, hydropower, nuclear power, and biomass energy. Among them, hydropower and nuclear power are relatively large in scale and stable in operation, but they face challenges such as geological conditions, ecological and environmental impacts, and the disposal of nuclear waste caused by nuclear leakage. Biomass energy utilization has problems such as a narrow load regulation range, low efficiency, poor system stability under variable operating conditions, and excessive pollutant emissions. Wind power and solar power are intermittent and volatile, and cannot alone meet the construction needs of new energy systems. Therefore, for zero-carbon energy systems with multiple energy sources, the intermittent and volatile nature of new energy sources makes it urgent to solve the technical problems of how to specifically utilize the fluctuations of new energy sources to manage the operating mode of the coarse crusher of the biomass gasification heating system, ensure the reliable operation of the biomass gasification heating system, and thus ensure the stability of the heating process when new energy sources fluctuate.

[0004] Therefore, in order to solve the above technical problems, the present application provides a scheduling system for intelligent coupling of multiple zero-carbon energy sources. Summary of the Invention

[0005] To achieve the purpose of the present invention, the present invention adopts the following technical solutions:

[0006] To achieve the above-mentioned invention objectives, the present application provides a scheduling system for intelligently coupling multiple zero-carbon energy sources, including the following contents:

[0007] Biomass gasification heating system, high-temperature heat storage spherical tank group, electrode heating system, screw expansion generator;

[0008] The high-temperature side of the biomass gasification heating system is connected to a high-temperature thermal storage spherical tank group, the electrode heating system uses green electricity to generate steam, and is connected to the high-temperature thermal storage spherical tank group and a screw expansion generator using a steam mixer. The exhaust gas of the screw expansion generator is connected to the low-temperature side of the biomass gasification heating system for low-temperature side heating treatment, the screw expansion generator performs power supply treatment, and the high-temperature thermal storage spherical tank group performs high-temperature side heating treatment, wherein the high-temperature thermal storage spherical tank group on the high-temperature side connected to the biomass gasification heating system is inconsistent with the high-temperature thermal storage spherical tank group connected to the electrode heating system;

[0009] Based on the monitoring data of the high-temperature heat storage spherical tank group on the high-temperature side connected to the biomass gasification heating system and the monitoring data of the heat treatment demand on the low-temperature side, the high-temperature side heating ratio of the high-temperature heat storage spherical tank group of different heating systems on the high-temperature side is determined. Based on the high-temperature side heating ratio and the economic optimal goal, an AI model is constructed to determine the low-temperature side heating strategy, the high-temperature side heating strategy and the power supply treatment strategy. In the process of power supply and heating, the monitoring data of green electricity is used to determine the operation mode of the coarse crusher of the biomass gasification heating system.

[0010] The beneficial effects of the present invention are:

[0011] The high-temperature side heating ratio of the high-temperature side heat storage tank group of different heating systems on the high-temperature side is determined based on the monitoring data of the high-temperature heat storage tank group on the high-temperature side connected to the biomass gasification heating system and the monitoring data of the heat treatment demand on the low-temperature side. Since the green electricity used by the high-temperature side heat storage tank group of the electrode heating system is unstable, the difference in the matching degree between the heat treatment demand on the low-temperature side and the biomass gasification heating system is achieved by combining the monitoring data of the high-temperature side heat storage tank group on the high-temperature side connected to the biomass gasification heating system and the monitoring data of the heat treatment demand on the low-temperature side. The operation strategy of the biomass gasification heating system is generated, and then, on the basis of ensuring the operation stability of the biomass gasification heating system, the monitoring data of the high-temperature side heat storage tank group on the high-temperature side connected to the biomass gasification heating system is further combined. On the basis of meeting the heat treatment demand on the high-temperature side, the operation stability of the high-temperature heat storage tank group is guaranteed. At the same time, the operation stability of the screw expansion generator is guaranteed by optimizing the heat supply ratio of the high-temperature side heat storage tank group on the high-temperature side of the electrode heating system.

[0012] Based on the high-temperature side heating ratio and the economic optimal goal, an AI model is constructed to determine the low-temperature side heating strategy, the high-temperature side heating strategy, and the power supply processing strategy. On the basis of ensuring the operating stability of the high-temperature heat storage spherical tank group, the economic optimal operation of the entire zero-carbon energy system is achieved, and the economy of heating and power supply processing is improved.

[0013] A further technical solution is that the biomass gasification heating system includes a coarse crusher, a biomass gasification furnace, a biomass gas boiler and a flue gas treatment system.

[0014] A further technical solution is that the electrode heating system includes an electrode boiler and an electric heating superheater.

[0015] A further technical solution is to further include a heat pump to transmit the preliminarily heated hot water to the electrode boiler through a conductivity adjustment device.

[0016] A further technical solution is that the monitoring data of the high-temperature heat storage spherical tank group includes operating temperature and heat storage capacity.

[0017] A further technical solution is that the monitoring data of the heat treatment demand on the low-temperature side includes the heat treatment demand on the low-temperature side at different times.

[0018] A further technical solution is that the low temperature side of the biomass gasification heating system is heated by a biomass gasification furnace.

[0019] Specifically, the low-temperature side heating strategy, the high-temperature side heating strategy, and the power supply processing strategy are determined, including:

[0020] The prediction model built with the LSTM algorithm takes historical heat usage data of heat users on the low-temperature side, historical heat usage data of heat users on the high-temperature side, and historical electricity usage data as input to determine the heat treatment demand on the low-temperature side, the heat treatment demand on the high-temperature side, and the electricity demand;

[0021] Determine the heating revenue and power supply revenue based on the heat treatment demand on the low-temperature side, the heat treatment demand on the high-temperature side, and the electricity demand, using the electricity price and the heat price;

[0022] Based on the biomass fuel cost of the biomass gasification heating system and the electricity cost of the electrode heating system connection, combined with the heating income and power supply income, with economic optimization as the goal, and combined with the high-temperature side heating ratio of the biomass spherical tank and the electric heating spherical tank, the low-temperature side heating strategy, high-temperature side heating strategy and power supply processing strategy are determined.

[0023] It can be understood that the nonlinear function of the LSTM algorithm is jointly constructed based on the sigmoid function and the tanh function, and its calculation formula is: Where T is the output of the nonlinear function, a1 and a2 are the outputs of the sigmoid function and the tanh function respectively, and K1 and K2 are weight coefficients, which are constants.

[0024] Other features and advantages will be described in the following description. The objectives and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description and drawings.

[0025] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] The above and other features and advantages of the present invention will become more apparent by describing in detail example embodiments thereof with reference to the accompanying drawings;

[0027] Figure 1 It is a framework diagram of a dispatching system for intelligent coupling of multiple zero-carbon energy sources;

[0028] Figure 2 This is a flow chart of a method for determining the high-temperature side heat supply ratio of a high-temperature thermal storage spherical tank group;

[0029] Figure 3 This is a flow chart for determining the low-temperature side heating strategy, the high-temperature side heating strategy, and the power supply processing strategy. DETAILED DESCRIPTION

[0030] To help those skilled in the art better understand the technical solutions in this specification, the following will provide a clear and complete description of the technical solutions in the embodiments of this specification, in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of this specification, not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this specification without creative work should fall within the scope of protection of this specification.

[0031] Example 1

[0032] Specifically, such as Figure 1 As shown in FIG, a scheduling system for intelligent coupling of multiple zero-carbon energy sources includes the following:

[0033] Biomass gasification heating system, high-temperature heat storage spherical tank group, electrode heating system, screw expansion generator;

[0034] The high-temperature side of the biomass gasification heating system is connected to a high-temperature thermal storage spherical tank group, the electrode heating system uses green electricity to generate steam, and is connected to the high-temperature thermal storage spherical tank group and a screw expansion generator using a steam mixer. The exhaust gas of the screw expansion generator is connected to the low-temperature side of the biomass gasification heating system for low-temperature side heating treatment, the screw expansion generator performs power supply treatment, and the high-temperature thermal storage spherical tank group performs high-temperature side heating treatment, wherein the high-temperature thermal storage spherical tank group on the high-temperature side connected to the biomass gasification heating system is inconsistent with the high-temperature thermal storage spherical tank group connected to the electrode heating system;

[0035] Based on the monitoring data of the high-temperature heat storage spherical tank group on the high-temperature side connected to the biomass gasification heating system and the monitoring data of the heat treatment demand on the low-temperature side, the high-temperature side heating ratio of the high-temperature heat storage spherical tank group of different heating systems on the high-temperature side is determined. Based on the high-temperature side heating ratio and the economic optimal goal, an AI model is constructed to determine the low-temperature side heating strategy, the high-temperature side heating strategy and the power supply treatment strategy. In the process of power supply and heating, the monitoring data of green electricity is used to determine the operation mode of the coarse crusher of the biomass gasification heating system.

[0036] Furthermore, the biomass gasification heating system includes a coarse crusher, a biomass gasification furnace, a biomass gas boiler and a flue gas treatment system.

[0037] Furthermore, the high-temperature heat storage spherical tank group on the high-temperature side connected to the biomass gasification heating system and the high-temperature heat storage spherical tank group connected to the electrode heating system can also be consistent. When they are consistent, there is no need to determine the high-temperature side heating ratio of the high-temperature heat storage spherical tank groups of different heating systems on the high-temperature side.

[0038] Specifically, the low-temperature side heat is supplied by recovering the waste heat of cooling water of the biomass gasifier of the biomass gasification heating system and recovering the exhaust steam output by the screw expansion generator.

[0039] Specifically, the electrode heating system includes an electrode boiler and an electric heating superheater.

[0040] It should be noted that a heat pump is also included to transmit the preliminarily heated hot water to the electrode boiler through the conductivity adjustment device.

[0041] It can be understood that the monitoring data of the high-temperature thermal storage spherical tank group includes operating temperature and heat storage capacity.

[0042] Furthermore, the monitoring data of the heat treatment demand on the low temperature side includes the heat treatment demand on the low temperature side at different times.

[0043] It can be understood that the low temperature side of the biomass gasification heating system is heated by a biomass gasifier.

[0044] Specifically, such as Figure 2 As shown, the method for determining the high-temperature side heat supply ratio of the high-temperature thermal storage spherical tank group is:

[0045] The high-temperature heat storage spherical tank group on the high-temperature side connected to the biomass gasification heating system and the high-temperature heat storage spherical tank group connected to the electrode heating system are used as the biomass spherical tank and the electric heating spherical tank respectively;

[0046] Determining the heat treatment demand of the low-temperature side at different times based on the monitoring data of the heat treatment demand of the low-temperature side, and determining the low-temperature side deviation at different times based on the deviation between the heat treatment demand and the gasifier of the biomass gasification heating system at rated operating power;

[0047] Based on the low-temperature side deviation at different times and in combination with the monitoring data of the biomass spherical tank, the high-temperature side heating ratio of the biomass spherical tank and the electric heating spherical tank is determined.

[0048] Furthermore, based on the low-temperature side deviation at different times and in combination with the monitoring data of the biomass spherical tank, the high-temperature side heating ratio of the biomass spherical tank and the electric heating spherical tank is determined, specifically including:

[0049] Whether the low temperature side deviation at different moments is within a preset deviation range, the moments are regarded as the time inside the range and the time outside the range;

[0050] When the number of interval external moments within the preset time period is greater than the preset external moment number threshold, the biomass gasification furnace of the biomass gasification heating system is operated at the rated operating power, and the high-temperature side heat treatment is performed according to the biomass spherical tank. When the biomass spherical tank cannot meet the load demand on the high-temperature side, the electric heating spherical tank is used to perform the high-temperature side heat treatment;

[0051] When the number of external moments in the interval within the preset time period is not greater than the preset external moment number threshold, the operating power of the biomass gasification furnace is determined according to the heat treatment demand of the low-temperature side at different times, and the heat supply ratio of the high-temperature side of the biomass spherical tank and the electric heating spherical tank is determined according to the monitoring data of the energy storage temperature of the biomass spherical tank.

[0052] It is understandable that the high-temperature side heating ratio of the biomass spherical tank and the electric heating spherical tank is determined based on the monitoring data of the energy storage temperature of the biomass spherical tank, specifically including:

[0053] Dividing the energy storage temperature of the biomass spherical tank into a plurality of preset temperature intervals at equal intervals;

[0054] The heating capacity of the high temperature side of the electrically heated spherical tank is determined according to the deviation between the load demand of the high temperature side and the preset recommended heating capacity of the biomass spherical tank in different preset temperature ranges.

[0055]

[0056] The above table shows the heating ratio of the high-temperature side, i.e., the biomass spherical tank and the electric heating spherical tank, under different conditions of the low-temperature side heating load.

[0057] In another possible embodiment, the method for determining the high-temperature side heat supply ratio of the high-temperature thermal storage spherical tank group is:

[0058] The high-temperature heat storage spherical tank group on the high-temperature side connected to the biomass gasification heating system and the high-temperature heat storage spherical tank group connected to the electrode heating system are used as the biomass spherical tank and the electric heating spherical tank respectively;

[0059] Determine the heat treatment demand of the low-temperature side at different times based on the monitoring data of the heat treatment demand of the low-temperature side, determine the low-temperature side deviation at different times based on the deviation between the heat treatment demand and the gasifier of the biomass gasification heating system at rated operating power, and determine whether the low-temperature side deviation at different times is within a preset deviation range, and use the time as the time inside the range and the time outside the range;

[0060] It should be further explained that before entering the next step, it is necessary to further determine whether there are moments outside the interval, whether the number of moments outside the interval meets the requirements, and whether the number of moments outside the interval whose deviation from the adjacent endpoints of the preset deviation interval is greater than the preset deviation threshold meets the requirements. Specifically, whether the requirements are met can be determined by means of a threshold.

[0061] It can be understood that when there is no time outside the interval, the high-temperature side heating ratio of the biomass spherical tank and the electric heating spherical tank is directly determined by the monitoring data of the energy storage temperature of the biomass spherical tank. When there is a time outside the interval, it is also necessary to judge whether the number of time outside the interval and the number of time outside the interval whose deviation from the adjacent endpoint of the preset deviation interval is greater than the preset deviation threshold meet the requirements. When any one item does not meet the requirements, the high-temperature side heating treatment can be directly performed according to the biomass spherical tank. When the biomass spherical tank cannot meet the load demand on the high-temperature side, the electric heating spherical tank is used to perform the high-temperature side heating treatment.

[0062] In addition, it should be noted that if and only if all requirements are met, proceed to the next step.

[0063] Determine the low-temperature side deviation coefficient based on the distribution data of the time outside the interval within a preset time period and the deviation between different time outside the interval and the adjacent endpoints of the preset deviation interval;

[0064] It can be understood that the distribution data of the external moments of the interval within the preset time period includes the number of external moments of the interval within the preset time end and the corresponding moments within the preset time period. The low-temperature side deviation coefficient can be determined using a neural network model, etc. on the basis of the determination of both the input and output quantities.

[0065] Furthermore, before entering the next step, it is also necessary to determine whether the distribution and aggregation of the external moments of the interval within the preset time meet the requirements. The specific determination is based on the number of unit time lengths of the external moments of the interval that is greater than the preset external moment number threshold. When the number of unit time lengths of the external moments of the interval that is greater than the preset external moment number threshold is too large, that is, greater than a certain threshold, it can be directly determined that the high-temperature side needs to be heated according to the biomass spherical tank. When the biomass spherical tank cannot meet the load demand on the high-temperature side, the electric heating spherical tank is used to perform heating on the high-temperature side.

[0066] Based on the low-temperature side deviation coefficient and in combination with the monitoring data of the biomass spherical tank, the high-temperature side heating ratio of the biomass spherical tank and the electric heating spherical tank is determined.

[0067] Specifically, based on the low-temperature side deviation coefficient and in combination with the monitoring data of the biomass spherical tank, the high-temperature side heating ratio of the biomass spherical tank and the electric heating spherical tank is determined, specifically including:

[0068] When the low-temperature side deviation coefficient is greater than the deviation coefficient threshold, the biomass gasifier of the biomass gasification heating system is operated at the rated operating power, and the high-temperature side heat treatment is performed according to the biomass spherical tank. When the biomass spherical tank cannot meet the load demand on the high-temperature side, the electric heating spherical tank is used to perform the high-temperature side heat treatment;

[0069] When the low-temperature side deviation coefficient is not greater than the deviation coefficient threshold, the operating power of the biomass gasifier is determined based on the heat treatment demand of the low-temperature side at different times, and the high-temperature side heating ratio of the biomass spherical tank and the electric heating spherical tank is determined based on the monitoring data of the energy storage temperature of the biomass spherical tank.

[0070] Furthermore, the green electricity includes the power generated by photovoltaic devices and the power generated by wind power generation systems.

[0071] Specifically, such as Figure 3 As shown, the low-temperature side heating strategy, the high-temperature side heating strategy, and the power supply processing strategy are determined, specifically including:

[0072] The prediction model built with the LSTM algorithm takes historical heat usage data of heat users on the low-temperature side, historical heat usage data of heat users on the high-temperature side, and historical electricity usage data as input to determine the heat treatment demand on the low-temperature side, the heat treatment demand on the high-temperature side, and the electricity demand;

[0073] Determine the heating revenue and power supply revenue based on the heat treatment demand on the low-temperature side, the heat treatment demand on the high-temperature side, and the electricity demand, using the electricity price and the heat price;

[0074] Based on the biomass fuel cost of the biomass gasification heating system and the electricity cost of the electrode heating system connection, combined with the heating income and power supply income, with economic optimization as the goal, and combined with the high-temperature side heating ratio of the biomass spherical tank and the electric heating spherical tank, the low-temperature side heating strategy, high-temperature side heating strategy and power supply processing strategy are determined.

[0075] It can be understood that the nonlinear function of the LSTM algorithm is jointly constructed based on the sigmoid function and the tanh function, and its calculation formula is: Where T is the output of the nonlinear function, a1 and a2 are the outputs of the sigmoid function and the tanh function respectively, and K1 and K2 are weight coefficients, which are constants.

[0076] Specifically, the operation mode of the coarse crusher of the biomass gasification heating system is determined by using the monitoring data of green electricity, which specifically includes:

[0077] Determining the green electricity generation in a recent preset time period based on the green electricity monitoring data, and determining a time when the generation was less than the consumption based on the generation and the power consumption of the electrode heating system, and using the time as the generation deviation time;

[0078] When the proportion of the number of power generation deviation moments within the preset time period does not meet the requirements, that is, it is greater than the threshold, the operation mode of the coarse crusher of the biomass gasification heating system is controlled to the maximum operation mode. In addition, if the proportion of the number of power generation deviation moments within the preset time period meets the requirements, that is, it is not greater than the threshold, the operation mode of the coarse crusher of the biomass gasification heating system is controlled to an operation mode that can meet the needs of the biomass gasification heating system. 0.35 is a threshold value in a specific possible embodiment of the present application, where the threshold value corresponding to the specific proportion of the number of power generation deviation moments can be determined according to the composition of green electricity in the area. Specifically, if the power generation of wind power and photovoltaic power is relatively balanced, a smaller threshold value can be used. At this time, the probability of green electricity fluctuation is greater. When there is only one green electricity device or the power generation of the green electricity device is high, a larger threshold value can be used.

[0079] It should also be noted that even if the number of power generation deviation moments within the preset time period meets the requirements, if the number of power generation deviation moments within the preset time interval from the current moment accounts for a larger proportion, it means that the number of power generation deviation moments at this time is increasing. Specifically, if the deviation between the number of power generation deviation moments within 3 minutes from the current moment and the number of power generation deviation moments within 7 minutes to 10 minutes from the current moment is greater than 10, it means that the number of power generation deviation moments is increasing. It is also necessary to control the operation mode of the coarse crusher of the biomass gasification heating system to the maximum operation mode. The deviation threshold in the above steps and the preset time interval can be dynamically adjusted according to the power generation in the preset time period. The greater the green electricity generation, the higher the system operation stability at this time. Therefore, it can be determined by a larger deviation threshold and a shorter preset time interval. In a possible embodiment, it can be determined according to the preset correspondence between power generation, the deviation threshold and the preset time interval.

[0080] In another possible embodiment, the operation mode of the coarse crusher of the biomass gasification heating system is determined by using the monitoring data of green electricity, specifically including:

[0081] The green electricity monitoring data is used to determine the green electricity generation in the most recent preset time period. Based on the generation and the power consumption of the electrode heating system, it is determined whether the generation in the preset time period can meet the power consumption requirement. If so, the operation mode of the coarse crusher of the biomass gasification heating system is controlled to an operation mode that can meet the requirements of the biomass gasification heating system. If not, the operation mode of the coarse crusher of the biomass gasification heating system is controlled to the maximum operation mode.

[0082] The foregoing description of this specification describes specific embodiments. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in an order different from that described in the embodiments and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order shown or the sequential order to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0083] The foregoing description is merely one or more embodiments of this specification and is not intended to limit this specification. It will be apparent to those skilled in the art that various modifications and variations may be made to one or more embodiments of this specification. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of one or more embodiments of this specification are intended to be within the scope of the claims of this specification.

Claims

1. A dispatching system for intelligent coupling of multiple zero-carbon energy sources, characterized by: Includes the following: Biomass gasification heating system, high-temperature heat storage spherical tank group, electrode heating system, screw expansion generator; The high-temperature side of the biomass gasification heating system is connected to a high-temperature thermal storage spherical tank group, the electrode heating system uses green electricity to generate steam, and is connected to the high-temperature thermal storage spherical tank group and a screw expansion generator using a steam mixer. The exhaust gas of the screw expansion generator is connected to the low-temperature side of the biomass gasification heating system for low-temperature side heating treatment, the screw expansion generator performs power supply treatment, and the high-temperature thermal storage spherical tank group performs high-temperature side heating treatment, wherein the high-temperature thermal storage spherical tank group on the high-temperature side connected to the biomass gasification heating system is inconsistent with the high-temperature thermal storage spherical tank group connected to the electrode heating system; Based on the monitoring data of the high-temperature heat storage spherical tank group on the high-temperature side connected to the biomass gasification heating system and the monitoring data of the heat treatment demand on the low-temperature side, the high-temperature side heating ratio of the high-temperature heat storage spherical tank group of different heating systems on the high-temperature side is determined. Based on the high-temperature side heating ratio and the economic optimal goal, an AI model is constructed to determine the low-temperature side heating strategy, the high-temperature side heating strategy, and the power supply treatment strategy. During the power and heat supply process, the green electricity monitoring data is used to determine the operating mode of the coarse crusher of the biomass gasification heating system; The method for determining the high temperature side heating ratio of the high temperature thermal storage spherical tank group is: The high-temperature heat storage spherical tank group on the high-temperature side connected to the biomass gasification heating system and the high-temperature heat storage spherical tank group connected to the electrode heating system are used as the biomass spherical tank and the electric heating spherical tank respectively; Determining the heat treatment demand of the low-temperature side at different times based on the monitoring data of the heat treatment demand of the low-temperature side, and determining the low-temperature side deviation at different times based on the deviation between the heat treatment demand and the gasifier of the biomass gasification heating system at rated operating power; Based on whether the low-temperature side deviation at different moments is within a preset deviation range, the moments are divided into moments within the range and moments outside the range; When the number of interval external moments within the preset time period is greater than the preset external moment number threshold, the biomass gasification furnace of the biomass gasification heating system is operated at the rated operating power, and the high-temperature side heat treatment is performed according to the biomass spherical tank. When the biomass spherical tank cannot meet the load demand of the high-temperature side, the electric heating spherical tank is used to perform the heat treatment on the high-temperature side; When the number of external moments in the interval within the preset time period is not greater than the preset external moment number threshold, the operating power of the biomass gasification furnace is determined according to the heat treatment demand of the low-temperature side at different times, and the heat supply ratio of the high-temperature side of the biomass spherical tank and the electric heating spherical tank is determined according to the monitoring data of the energy storage temperature of the biomass spherical tank.

2. The scheduling system for intelligent coupling of multiple zero-carbon energy sources according to claim 1, characterized in that: The biomass gasification heating system includes a coarse crusher, a biomass gasification furnace, a biomass gas boiler and a flue gas treatment system.

3. The scheduling system for intelligent coupling of multiple zero-carbon energy sources according to claim 1, characterized in that: The electrode heating system includes an electrode boiler and an electric heating superheater.

4. The scheduling system for intelligent coupling of multiple zero-carbon energy sources as claimed in claim 3 is characterized in that: A heat pump is also included to transmit the preliminarily heated hot water to the electrode boiler through the conductivity adjusting device.

5. The scheduling system for intelligent coupling of multiple zero-carbon energy sources as claimed in claim 1, characterized in that: The monitoring data of the high-temperature thermal storage spherical tank group includes operating temperature and heat storage capacity.

6. The scheduling system for intelligent coupling of multiple zero-carbon energy sources according to claim 1, characterized in that: The low-temperature side heat supply is provided by recovering the waste heat of cooling water of the biomass gasifier of the biomass gasification heating system and recovering the exhaust steam output by the screw expansion generator.

7. The scheduling system for intelligent coupling of multiple zero-carbon energy sources according to claim 1, characterized in that: The green electricity includes the power generated by photovoltaic devices and the power generated by wind power generation systems.

8. The scheduling system for intelligent coupling of multiple zero-carbon energy sources according to claim 1, characterized in that: Determine the low-temperature side heating strategy, high-temperature side heating strategy, and power supply processing strategy, specifically including: The prediction model built with the LSTM algorithm takes historical heat usage data of heat users on the low-temperature side, historical heat usage data of heat users on the high-temperature side, and historical electricity usage data as input to determine the heat treatment demand on the low-temperature side, the heat treatment demand on the high-temperature side, and the electricity demand; Determine the heating revenue and power supply revenue based on the heat treatment demand on the low-temperature side, the heat treatment demand on the high-temperature side, and the electricity demand, using the electricity price and the heat price; Based on the biomass fuel cost of the biomass gasification heating system and the electricity cost of the electrode heating system connection, combined with the heating income and power supply income, with economic optimization as the goal, and combined with the high-temperature side heating ratio of the biomass spherical tank and the electric heating spherical tank, the low-temperature side heating strategy, high-temperature side heating strategy and power supply processing strategy are determined.

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