Scheduling system for intelligent coupling of multiple zero-carbon energy sources

Abstract

The invention provides a scheduling system for intelligent coupling of multiple zero-carbon energy sources, which belongs to the technical field of optimal scheduling and is characterized in that the high-temperature side of a biomass gasification heat supply system is connected with a high-temperature heat storage spherical tank group, and an electrode heating system generates steam by using green electricity; the steam mixer is connected with the screw expansion generator, exhaust gas of the screw expansion generator is connected with the low-temperature side of the biomass gasification heat supply system for low-temperature side heat supply treatment, the screw expansion generator is used for power supply treatment, and the high-temperature heat storage spherical tank group is used for high-temperature side heat supply treatment; wherein the high-temperature heat storage spherical tank group on the high-temperature side connected with the biomass gasification heat supply system is not consistent with the high-temperature heat storage spherical tank group connected with the electrode heating system, so that the carbon emission in the heat supply process is reduced.

Description

Technical Field

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

[0002] Patent applications for invention CN202510023338.7 "A Remote Intelligent Management System for Zero-Carbon Energy" and CN202410722919.5 "Optimized Scheduling Method and Terminal for a Near-Zero-Carbon Energy Base Based on Multi-Energy Complementary Characteristics" both present scheduling processing models for zero-carbon multi-energy sources.

[0003] However, currently, zero-carbon energy mainly consists of wind power generation, solar power generation, hydropower generation, nuclear power generation, and biomass energy. Among them, hydropower and nuclear power are relatively large in scale and stable in operation, but there are problems such as geological condition limitations, ecological environment impacts, and nuclear leakage and nuclear waste treatment; the utilization of biomass energy has problems such as a narrow load regulation range, low efficiency, poor system stability under variable operating conditions, and excessive pollutant emissions; wind power generation and solar power generation are intermittent and volatile, and cannot independently meet the construction requirements of a new energy system. Therefore, for a zero-carbon energy system with multiple energy sources, due to the intermittency and volatility of new energy, it becomes an urgent technical problem to manage the operation mode of the coarse crusher of the biomass gasification heating system in response to the fluctuations of new energy, ensure the reliable operation of the biomass gasification heating system, and thus maintain the stability of heat supply processing when new energy fluctuates.

[0004] Therefore, 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 object of the present invention, the present invention adopts the following technical solutions: To achieve the above object of the invention, a scheduling system for intelligent coupling of multiple zero-carbon energy sources provided by the present application includes the following: A biomass gasification heating system, a high-temperature heat storage ball tank group, an electrode heating system, and a screw expansion generator; Wherein the high-temperature side of the biomass gasification heating system is connected to the high-temperature heat storage ball tank group, the electrode heating system generates steam using green electricity and is connected to the high-temperature heat storage ball tank group, the steam mixer is used to connect to the screw expansion generator, 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 supply processing, the screw expansion generator conducts power supply processing, and the high-temperature heat storage ball tank group conducts high-temperature side heat supply processing. Among them, the high-temperature heat storage ball tank group connected to the high-temperature side of the biomass gasification heating system is different from the high-temperature heat storage ball 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 heating treatment requirements on the low-temperature side, 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. Based on the high-temperature side heating ratio and the economically optimal goal, construct an AI model to determine the heating strategy on the low-temperature side, the heating strategy on the high-temperature side, and the strategy for power supply processing. During the power supply and heating processes, use the monitoring data of green electricity to determine the operating mode of the coarse crusher of the biomass gasification heating system.

[0006] The beneficial effects of the present invention are as follows: 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 heating treatment requirements on the low-temperature side, 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. Since the green electricity used in the high-temperature heat storage spherical tank group on the high-temperature side of the electrode heating system is unstable, by combining 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 heating treatment requirements on the low-temperature side, the difference in the matching degree between the heating treatment requirements on the low-temperature side and the biomass gasification heating system is realized, generating an operating strategy for the biomass gasification heating system. Furthermore, on the basis of ensuring the operating stability of the biomass gasification heating system, further combining the monitoring data of the high-temperature heat storage spherical tank group on the high-temperature side connected to the biomass gasification heating system, on the basis of meeting the heating treatment requirements on the high-temperature side, the operating stability of the high-temperature heat storage spherical tank group is ensured. At the same time, by optimizing the heating ratio of the high-temperature heat storage spherical tank group on the high-temperature side of the electrode heating system, the operating stability of the screw expansion generator is ensured.

[0007] Based on the high-temperature side heating ratio and the economically optimal goal, construct an AI model to determine the heating strategy on the low-temperature side, the heating strategy on the high-temperature side, and the strategy for power supply processing. On the basis of ensuring the operating stability of the high-temperature heat storage spherical tank group, the economically optimal operation of the entire zero-carbon energy system is realized, improving the economy of heating and power supply processing.

[0008] A further technical solution is that the biomass gasification heating system includes a coarse crusher, a biomass gasification furnace, a biomass gas-fired boiler, and a flue gas treatment system.

[0009] A further technical solution is that the electrode heating system includes an electrode boiler and an electric heating superheater.

[0010] A further technical solution is that it further includes a heat pump, and the preliminarily heated hot water is transmitted to the electrode boiler through a conductivity adjustment device.

[0011] A further technical solution is that the monitoring data of the high-temperature heat storage spherical tank group includes the operating temperature and the heat storage capacity.

[0012] A further technical solution lies in that the monitoring data of the heating treatment requirements on the low-temperature side includes the heating treatment demand on the low-temperature side at different times.

[0013] A further technical solution lies in that the low-temperature side of the biomass gasification heating system is subjected to heating treatment by a biomass gasification furnace.

[0014] Specifically, the determination of the low-temperature side heating strategy, the high-temperature side heating strategy, and the power supply treatment strategy specifically includes: Using a prediction model constructed by the LSTM algorithm, taking the historical heat consumption data of the heat users on the low-temperature side, the historical heat consumption data of the heat users on the high-temperature side, and the historical electricity consumption data as input quantities, to determine the heating treatment demand on the low-temperature side, the heating treatment demand on the high-temperature side, and the electricity consumption demand; According to the heating treatment demand on the low-temperature side, the heating treatment demand on the high-temperature side, and the electricity consumption demand, using the electricity price and the heat price, to determine the heating income and the power supply income; Based on the biomass fuel cost of the biomass gasification heating system and the electricity cost connected to the electrode heating system, and combined with the heating income and the power supply income, with the economic optimum as the goal, and combined with the high-temperature side heating ratio of the biomass storage tank and the electric heating storage tank, to determine the low-temperature side heating strategy, the high-temperature side heating strategy, and the power supply treatment strategy.

[0015] It can be understood that the non-linear 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 non-linear function, a1 and a2 are the outputs of the sigmoid function and the tanh function respectively, K1 and K2 are weight coefficients, and is a constant.

[0016] Other features and advantages will be described in the subsequent specification. The objectives and other advantages of the present invention are achieved and obtained by the structures specifically pointed out in the specification and the drawings.

[0017] To make the above objectives, features, and advantages of the present invention more obvious and understandable, the following specific preferred embodiments are given, and in conjunction with the accompanying drawings, the detailed description is as follows. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] By referring to the accompanying drawings and describing its exemplary embodiments in detail, the above and other features and advantages of the present invention will become more obvious; Figure 1 is a framework diagram of a scheduling system for intelligent coupling of multiple zero-carbon energy sources; Figure 2 is a flowchart of a method for determining the high-temperature side heating ratio of a high-temperature heat storage tank group; Figure 3 It is a flowchart for determining the strategies of low-temperature side heat supply strategy, high-temperature side heat supply strategy, and power supply processing. Detailed implementation manners

[0019] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of this specification. Obviously, the described embodiments are only a part of the embodiments of this specification, rather than all the embodiments. Based on the embodiments of this specification, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of this specification.

[0020] Embodiment 1 Specifically, as Figure 1 shown, a scheduling system for intelligent coupling of multiple zero-carbon energy sources includes the following: Biomass gasification heat supply system, high-temperature heat storage sphere tank group, electrode heating system, screw expansion generator; Among them, the high-temperature side of the biomass gasification heat supply system is connected to the high-temperature heat storage sphere tank group. The electrode heating system generates steam using green electricity and is connected to the high-temperature heat storage sphere tank group and the screw expansion generator through a steam mixer. The exhaust gas of the screw expansion generator is connected to the low-temperature side of the biomass gasification heat supply system for low-temperature side heat supply processing. The screw expansion generator performs power supply processing, and the high-temperature heat storage sphere tank group performs high-temperature side heat supply processing. The high-temperature heat storage sphere tank group connected to the high-temperature side of the biomass gasification heat supply system is different from the high-temperature heat storage sphere tank group connected to the electrode heating system; Based on the monitoring data of the high-temperature heat storage sphere tank group on the high-temperature side connected to the biomass gasification heat supply system and the monitoring data of the heat supply processing requirements on the low-temperature side, determine the high-temperature side heat supply ratio of the high-temperature heat storage sphere tank group of different heat supply systems on the high-temperature side. Based on the high-temperature side heat supply ratio and the economically optimal goal, construct an AI model to determine the low-temperature side heat supply strategy, high-temperature side heat supply strategy, and power supply processing strategy, and during the power supply and heat supply processes, determine the operation mode of the coarse crusher of the biomass gasification heat supply system using the monitoring data of green electricity.

[0021] Furthermore, the biomass gasification heat supply system includes a coarse crusher, a biomass gasification furnace, a biomass gas-fired boiler, and a flue gas treatment system.

[0022] Furthermore, the high-temperature heat storage sphere tank group connected to the high-temperature side of the biomass gasification heat supply system and the high-temperature heat storage sphere tank group connected to the electrode heating system can also be the same. When they are the same, it is not necessary to determine the high-temperature side heat supply ratio of the high-temperature heat storage sphere tank group of different heat supply systems on the high-temperature side.

[0023] Specifically, the low-temperature side heat supply is carried out by recovering the waste heat of the cooling water of the biomass gasification furnace in the biomass gasification heat supply system and recovering the exhausted steam output by the screw expansion generator.

[0024] Specifically, the electrode heating system includes an electrode boiler and an electric heating superheater.

[0025] It should be noted that a heat pump is also included, and the preliminarily heated hot water is transmitted to the electrode boiler through the conductivity adjustment device.

[0026] It can be understood that the monitoring data of the high-temperature heat storage spherical tank group includes the operating temperature and the heat storage capacity.

[0027] Furthermore, the monitoring data of the heat supply processing requirements on the low-temperature side includes the heat supply processing requirements on the low-temperature side at different times.

[0028] It can be understood that the low-temperature side of the biomass gasification heat supply system is heated by the biomass gasification furnace.

[0029] Specifically, as Figure 2 shown, the method for determining the high-temperature side heat supply ratio of the high-temperature heat storage spherical tank group is as follows: The high-temperature heat storage spherical tank group on the high-temperature side connected to the biomass gasification heat supply system and the high-temperature heat storage spherical tank group connected to the electrode heating system are respectively regarded as the biomass spherical tank and the electric heating spherical tank; Based on the monitoring data of the heat supply processing requirements on the low-temperature side, determine the heat supply processing requirements on the low-temperature side at different times, and determine the low-temperature side deviation at different times according to the deviation between the heat supply processing requirements and the rated operating power of the gasifier in the biomass gasification heat supply system; Based on the low-temperature side deviation at different times and combined with the monitoring data of the biomass spherical tank, determine the high-temperature side heat supply ratio of the biomass spherical tank and the electric heating spherical tank.

[0030] Furthermore, based on the low-temperature side deviation at different times and combined with the monitoring data of the biomass spherical tank, determine the high-temperature side heat supply ratio of the biomass spherical tank and the electric heating spherical tank, which specifically includes: Based on whether the low-temperature side deviation at different times is within the preset deviation range, regard the time as the internal time of the interval and the external time of the interval; When the number of external times of the interval within the preset time period is greater than the preset external time number threshold, the biomass gasification furnace of the biomass gasification heat supply system operates at the rated operating power, and the high-temperature side heat supply is carried out according to the biomass spherical tank. When the biomass spherical tank cannot meet the high-temperature side load demand, the electric heating spherical tank is then used for high-temperature side heat supply; When the number of external moments outside the interval within the preset time period is not greater than the preset external moment quantity threshold, the operating power of the biomass gasifier is determined based on the heat supply treatment demands of the low-temperature side at different moments, and the high-temperature side heat supply ratios of the biomass spherical tank and the electric heating spherical tank are determined based on the monitoring data of the energy storage temperature of the biomass spherical tank.

[0031] It can be understood that determining the high-temperature side heat supply ratios of the biomass spherical tank and the electric heating spherical tank based on the monitoring data of the energy storage temperature of the biomass spherical tank specifically includes: Dividing the energy storage temperature of the biomass spherical tank into multiple preset temperature intervals at equal intervals; Based on the deviation between the load demand of the high-temperature side and the preset recommended heat supply of the biomass spherical tank within different preset temperature intervals, determine the heat supply of the high-temperature side of the electric heating spherical tank.

[0032]

[0033] The above table shows the heat supply ratios of the high-temperature side, namely the biomass spherical tank and the electric heating spherical tank, under different conditions of the heat supply load of the low-temperature side.

[0034] In another possible embodiment, the method for determining the high-temperature side heat supply ratio of the high-temperature heat storage spherical tank group is as follows: Respectively regard the high-temperature heat storage spherical tank group on the high-temperature side connected to the biomass gasification heat supply system and the high-temperature heat storage spherical tank group connected to the electrode heating system as the biomass spherical tank and the electric heating spherical tank; Based on the monitoring data of the heat supply treatment demand of the low-temperature side, determine the heat supply treatment demands of the low-temperature side at different moments. According to the deviation between the heat supply treatment demand and the deviation of the gasifier of the biomass gasification heat supply system under the rated operating power, determine the low-temperature side deviation at different moments. Based on whether the low-temperature side deviation at different moments is within the preset deviation interval, regard the moment as an internal moment and an external moment of the interval; It should be further noted that before proceeding to the next step, it is also necessary to further determine whether there are external moments of the interval, whether the number of external moments of the interval meets the requirements, and whether the number of external moments of the interval whose deviation from the adjacent endpoint of the preset deviation interval is greater than the preset deviation threshold meets the requirements. Specifically, it can be determined whether the requirements are met by means of thresholds.

[0035] It can be understood that when there is no external time 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 an external time interval, it is also necessary to judge whether the number of external time intervals and the number of external time intervals with a deviation greater than the preset deviation threshold from the adjacent endpoints of the preset deviation interval both meet the requirements. When any one does not meet the requirements, the high-temperature side heating treatment can be directly carried out 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 then used for the high-temperature side heating treatment.

[0036] In addition, it should be noted that only when both meet the requirements can the next step be entered.

[0037] Determine the low-temperature side deviation coefficient based on the distribution data of the external time intervals within the preset time period and the deviations of different external time intervals from the adjacent endpoints of the preset deviation interval. It can be understood that the distribution data of the external time intervals within the preset time period includes the number of external time intervals within the preset time end and the corresponding time within the preset time period. Based on the determination of both the input and output quantities, the low-temperature side deviation coefficient can be determined using a neural network model or the like.

[0038] Furthermore, before entering the next step, it is also necessary to judge whether the distribution aggregation situation of the external time intervals within the preset time meets the requirements. Specifically, it is determined according to the number of unit time lengths with the number of external time intervals greater than the preset external time number threshold. When the number of unit time lengths with the number of external time intervals greater than the preset external time number threshold is relatively large, that is, greater than a certain threshold, it can be directly determined that the high-temperature side heating treatment needs to be carried out 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 then used for the high-temperature side heating treatment.

[0039] Based on the low-temperature side deviation coefficient and combined with the monitoring data of the biomass spherical tank, determine the high-temperature side heating ratio of the biomass spherical tank and the electric heating spherical tank.

[0040] Specifically, based on the low-temperature side deviation coefficient and combined with the monitoring data of the biomass spherical tank, determine the high-temperature side heating ratio of the biomass spherical tank and the electric heating spherical tank, which specifically includes: 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 heating treatment is carried out 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 then used for the high-temperature side heating treatment; When the deviation coefficient on the low-temperature side is not greater than the deviation coefficient threshold, the operating power of the biomass gasifier is determined based on the heat supply treatment requirements of the low-temperature side at different times, and the high-temperature side heat supply ratios of the biomass spherical tank and the electric heating spherical tank are determined based on the monitoring data of the energy storage temperature of the biomass spherical tank.

[0041] Furthermore, the green electricity includes the power generation of the photovoltaic device and the power generation of the wind power generation system.

[0042] Specifically, as Figure 3 shown, the determination of the low-temperature side heat supply strategy, the high-temperature side heat supply strategy, and the power supply treatment strategy is carried out, specifically including: Using a prediction model constructed by the LSTM algorithm, taking the historical heat consumption data of the heat users on the low-temperature side, the historical heat consumption data of the heat users on the high-temperature side, and the historical electricity consumption data as input quantities, to determine the heat supply treatment requirements on the low-temperature side, the heat supply treatment requirements on the high-temperature side, and the electricity consumption requirements; According to the heat supply treatment requirements on the low-temperature side, the heat supply treatment requirements on the high-temperature side, and the electricity consumption requirements, using the electricity price and the heat price, to determine the heat supply income and the power supply income; Based on the biomass fuel cost of the biomass gasification heat supply system and the power cost connected to the electrode heating system, and combined with the heat supply income and the power supply income, with the economic optimum as the goal, and combined with the high-temperature side heat supply ratios of the biomass spherical tank and the electric heating spherical tank, to determine the low-temperature side heat supply strategy, the high-temperature side heat supply strategy, and the power supply treatment strategy.

[0043] It can be understood that the non-linear function of the LSTM algorithm is jointly built based on the sigmoid function and the tanh function, and its calculation formula is: where T is the output of the non-linear function, a1 and a2 are the outputs of the sigmoid function and the tanh function respectively, K1 and K2 are weight coefficients, and is a constant.

[0044] Specifically, using the monitoring data of the green electricity to determine the operating mode of the coarse crusher of the biomass gasification heat supply system, specifically including: Using the monitoring data of the green electricity to determine the power generation of the green electricity in the recent preset time period, based on the power generation and the power consumption of the electrode heating system, to determine the moment when the power generation is less than the power consumption, and take it as the power generation deviation moment; When the proportion of the number of power generation deviation moments within the preset time period does not meet the requirement, that is, when it is greater than the threshold value, the operation mode of the coarse crusher of the biomass gasification heating system is controlled to be the maximum operation mode. In addition, if the proportion of the number of power generation deviation moments within the preset time period meets the requirement, that is, when it is not greater than the threshold value, the operation mode of the coarse crusher of the biomass gasification heating system is controlled to be the operation mode that can meet the requirements of the biomass gasification heating system. 0.35 is the threshold value in a specific possible embodiment of this application. The threshold value corresponding to the specific proportion of the number of power generation deviation moments can be determined according to the composition form of green power in the region where it is located. Specifically, if the power generation of wind power and photovoltaic power is relatively balanced, a smaller threshold value can be adopted. At this time, the fluctuation probability of green power is relatively large. When there is only one type of green power equipment or the power generation of green power equipment is relatively high, a larger threshold value can be adopted.

[0045] In addition, it should be noted that even if the number of power generation deviation moments within the preset time period meets the requirement, if the proportion of the number of power generation deviation moments within the preset time interval from the current moment is larger, it means that the number of power generation deviation moments is increasing at this time. Specifically, if the deviation amount 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 to 10 minutes from the current moment is more than 10, it means that the number of power generation deviation moments is increasing. Similarly, the operation mode of the coarse crusher of the biomass gasification heating system needs to be controlled to be the maximum operation mode. The deviation amount threshold and the preset time interval in the above steps can be dynamically adjusted according to the power generation within the preset time period. The greater the power generation of green power, the higher the operation stability of the system at this time. Therefore, it can be determined through a larger deviation amount threshold and a shorter preset time interval. In a possible embodiment, it can be determined according to the preset corresponding relationship between power generation, deviation amount threshold, and preset time interval.

[0046] 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 power, specifically including: Based on the monitoring data of green power, determine the power generation of green power within the recent preset time period. Based on the power generation and the power consumption of the electrode heating system, determine whether the power generation within the preset time period can meet the power consumption requirements. If so, control the operation mode of the coarse crusher of the biomass gasification heating system to be the operation mode that can meet the requirements of the biomass gasification heating system. If not, control the operation mode of the coarse crusher of the biomass gasification heating system to be the maximum operation mode.

[0047] The specific embodiments of the present specification have been described above. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve the desired result. Additionally, the processes depicted in the drawings do not necessarily require the particular order or sequential order shown to achieve the desired result. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0048] The foregoing is only one or more embodiments of the present specification and is not intended to limit the present specification. For those skilled in the art, various changes and modifications can be made to one or more embodiments of the present specification. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of one or more embodiments of the present specification shall be included within the scope of the claims of the present specification.

Claims

1. A scheduling system for the intelligent coupling of multiple zero-carbon energy sources, characterized in that, It includes the following: A biomass gasification heating system, a high-temperature heat storage spherical tank group, an electrode heating system, and a screw expansion generator; Among them, 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 generates steam using green electricity and is connected to the high-temperature heat storage spherical tank group and the screw expansion generator through 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 supply treatment. The screw expansion generator conducts power supply treatment, and the high-temperature heat storage spherical tank group conducts high-temperature side heat supply treatment. The high-temperature heat storage spherical tank group connected to the high-temperature side of the biomass gasification heating system is inconsistent with the high-temperature heat 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 supply treatment demand on the low-temperature side, determine the high-temperature side heat supply ratio of the high-temperature heat storage spherical tank group of different heat supply systems on the high-temperature side. Based on the high-temperature side heat supply ratio and the economically optimal goal, construct an AI model to determine the low-temperature side heat supply strategy, high-temperature side heat supply strategy, and power supply treatment strategy. During the power supply and heat supply processes, use the monitoring data of green electricity to determine the operation mode of the coarse crusher of the biomass gasification heating system.

2. The dispatching 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-fired 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 dispatching system for intelligent coupling of multiple zero-carbon energy sources according to claim 1, characterized in that, It also includes a heat pump that transmits the preliminarily heated hot water to the electrode boiler through a conductivity adjustment device.

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

6. The dispatching system for intelligent coupling of multiple zero-carbon energy sources according to claim 1, characterized in that, The low-temperature side heat supply is carried out by recovering the waste heat of the cooling water of the biomass gasification furnace of the biomass gasification heating system and the exhaust steam output of 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 method for determining the high-temperature side heat supply ratio of the high-temperature heat storage spherical tank group is as follows: Respectively regard the high-temperature heat storage spherical tank group connected to the high-temperature side of the biomass gasification heating system and the high-temperature heat storage spherical tank group connected to the electrode heating system as the biomass spherical tank and the electric heating spherical tank; Based on the monitoring data of the heat supply treatment demand on the low-temperature side, determine the heat supply treatment demand on the low-temperature side at different times, and determine the low-temperature side deviation amount at different times according to the deviation amount between the heat supply treatment demand and the vaporization furnace of the biomass gasification heating system under the rated operating power; Based on the low-temperature side deviation amount at different times and combined with the monitoring data of the biomass spherical tank, determine the high-temperature side heat supply ratio of the biomass spherical tank and the electric heating spherical tank.

8. 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 generation of photovoltaic devices and the power generation of wind power generation systems.

9. The dispatching system for intelligent coupling of multiple zero-carbon energy sources according to claim 1, characterized in that, To determine the low-temperature side heat supply strategy, high-temperature side heat supply strategy, and power supply treatment strategy, specifically include: Using a prediction model constructed by the LSTM algorithm, taking the historical heat consumption data of heat users on the low-temperature side, the historical heat consumption data of heat users on the high-temperature side, and the historical electricity consumption data as input quantities, to determine the heat supply treatment demand on the low-temperature side, the heat supply treatment demand on the high-temperature side, and the electricity consumption demand. According to the heat supply treatment demand on the low-temperature side, the heat supply treatment demand on the high-temperature side, and the electricity demand, and by using the electricity price and the heat price, determine the heat supply income and the power supply income; Based on the biomass fuel cost of the biomass gasification heat supply system and the power cost connected to the electrode heating system, and combined with the heat supply income and the power supply income, with the goal of economic optimization, and combined with the high-temperature side heat supply ratio of the biomass storage tank and the electric heating storage tank, determine the low-temperature side heat supply strategy, the high-temperature side heat supply strategy, and the power supply treatment strategy.

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