Hierarchical coordinated scheduling method and system for source-grid-load-storage multiple entities

Abstract

The present invention relates to the technical field of new energy. Disclosed are a hierarchical coordinated scheduling method and system for source-grid-load-storage multiple entities. The method comprises: acquiring the environment, power, and power generation data of a power station to obtain power supply data, acquiring power utilization data corresponding to different levels of an urban area, and acquiring energy storage data of a power storage device; acquiring a historical data set of the power station, the urban area and the power storage device, and analyzing the power supply data, the power utilization data and the energy storage data on the basis of the historical data set to obtain electrical energy estimation data; adjusting electrical energy supply on the basis of the electrical energy estimation data, and monitoring the adjusted power utilization data of different levels of the urban area to obtain secondary power utilization data; and invoking a power storage device of a peripheral area on the basis of the secondary power utilization data. The present invention can fully use participation of multiple energy sources and multiple entities to realize coordinated scheduling between source, grid, load and storage, thereby improving the operation efficiency of a power system. The present invention can perform overall optimization scheduling on different sources, grids, loads and storage, so that the energy supply and demand balance is more rational.

Description

A hierarchical and multi-entity collaborative scheduling method and system for source-grid-load-storage Technical Field

[0001] This invention relates to the field of new energy technology, specifically to a hierarchical and tiered multi-entity collaborative scheduling method and system for source-grid-load-storage. Background Technology

[0002] In recent years, with the increasing severity of the global energy crisis and environmental pollution, multi-entity coordinated dispatch technology involving multiple entities such as power sources, grids, loads, and energy storage (hereinafter referred to as multi-entity coordinated dispatch technology) has become an important direction for the modernization and transformation of power systems. This technology aims to achieve optimal allocation of energy supply and improve operational efficiency by efficiently integrating and optimizing various resources such as power sources, grids, loads, and energy storage devices.

[0003] In traditional power systems, the generation, transmission, distribution, and use of electricity are often operated independently. This approach lacks systematic optimization and cannot well adapt to the volatility and uncertainty of renewable energy sources such as wind and solar power. With the development of smart grid technology, the demand for integrated multi-source coordination and multi-level energy management is increasing, especially against the backdrop of large-scale integration of renewable energy and the rapid growth of new loads such as electric vehicles.

[0004] Multi-level collaborative scheduling technology achieves comprehensive analysis and efficient utilization of power supply data, electricity consumption data, and energy storage data through the coordinated operation of data acquisition modules, data processing modules, primary power supply adjustment modules, and secondary power supply adjustment modules. This method utilizes environmental data (such as light intensity and wind speed) and power data (including active and reactive power) to monitor and adjust the power supply in real time, while dynamically balancing the electricity demand according to different urban levels (such as special urban areas, residential areas, and industrial and commercial areas). Summary of the Invention

[0005] In view of the above-mentioned problems, the present invention is proposed.

[0006] Therefore, the technical problem solved by this invention is the problem of unreasonable regional power distribution.

[0007] To address the aforementioned technical problems, this invention provides the following technical solution: a hierarchical, multi-entity collaborative scheduling method for source-grid-load-storage systems, comprising the following steps:

[0008] The data acquisition module obtains environmental, power, and generation data from the power plant to obtain power supply data, electricity consumption data for different levels of the urban area, and energy storage data from energy storage devices. The data processing module obtains historical datasets from the power plant, urban area, and energy storage devices, and analyzes the power supply, electricity consumption, and energy storage data based on these historical datasets to obtain power estimation data. The primary power supply adjustment module adjusts the power supply to the power plant, urban area, and energy storage devices based on the power estimation data, monitors the adjusted electricity consumption data for different levels of the urban area, and obtains secondary power consumption data. The secondary power supply adjustment module calls upon energy storage devices in surrounding areas to supplement power based on the secondary power consumption data.

[0009] As a preferred embodiment of the hierarchical and graded source-grid-load-storage multi-entity collaborative scheduling method described in this invention, the obtained power data includes the data acquisition module associating the photovoltaic power station's illumination sensor and photovoltaic power generation equipment to obtain the photovoltaic power station's illumination intensity, photovoltaic power generation, photovoltaic active power, and photovoltaic reactive power.

[0010] The data acquisition module connects to the wind speed sensor and wind power generation equipment of the wind power station to obtain the wind speed, wind power generation, wind active power and wind reactive power of the wind power station.

[0011] The data acquisition module connects to the urban power grid to obtain electricity consumption data for special tiers, residential tiers, and industrial and commercial tiers.

[0012] The data acquisition module associates with energy storage devices to obtain the device type and stored capacity of the energy storage devices, thus obtaining energy storage data.

[0013] The data acquisition module uses light intensity and wind speed as environmental data, wind power active power, wind power reactive power, photovoltaic active power and photovoltaic reactive power as power data, wind power generation and photovoltaic power generation as power generation data, and environmental data, power data and power generation data as power source data.

[0014] The data acquisition module sends power data, power consumption data, and energy storage data to the data processing module.

[0015] As a preferred embodiment of the hierarchical and graded source-grid-load-storage multi-entity collaborative scheduling method described in this invention, the acquisition of historical datasets of power plants, urban areas, and energy storage equipment includes a data processing module obtaining historical average data of power plants, urban areas, and energy storage equipment over the past three years, as well as historical average environmental data, through a database to obtain historical datasets.

[0016] The data processing module calculates the average value of the historical datasets, then calculates the variance of the historical datasets, then calculates the standard deviation of the historical datasets, and finally calculates the covariance of the historical datasets.

[0017] As a preferred embodiment of the hierarchical and graded source-grid-load-storage multi-entity collaborative scheduling method of the present invention, the method of obtaining power estimation data includes: calculating the correlation coefficients in the historical datasets through the data processing module, calculating the regression coefficients in the historical datasets after obtaining the correlation coefficients, calculating the correction coefficients in the historical datasets after obtaining the regression coefficients, obtaining the regression equation, calculating the power estimation data after obtaining the regression equation, and sending the power estimation data to the first-level power supply adjustment module through the data processing module.

[0018] As a preferred embodiment of the hierarchical and graded source-grid-load-storage multi-entity collaborative scheduling method described in this invention, the adjustment of power supply to power plants, urban areas and energy storage equipment includes a primary power supply adjustment module that records the photovoltaic reactive power in the power data as nip and the wind reactive power as nwp.

[0019] In the electricity consumption data, the electricity consumption of special floors is denoted as Ua, the electricity consumption of residential floors is denoted as Ub, and the electricity consumption of industrial and commercial floors is denoted as Uc.

[0020] Read the device type from the energy storage data and record the stored energy as s.

[0021] The primary power supply adjustment module compares the stored capacity. If s ≥ (msi + msw), it means no adjustment of the functional relationship is needed. If s < (msi + msw), it means the power demand has increased. When s < (msi + msw), if s - (Ua + Ub + Uc) > 0, it means no adjustment of the functional relationship is needed. If s - (Ua + Ub + Uc) < 0, it means additional power is needed. If s - (Ua + Ub + Uc) = 0, it means the power demand has increased. When s - (Ua + Ub + Uc) = 0, the primary power supply adjustment module compares the useful power and adjusts the power station. The primary power supply adjustment module compares the useful power data and adjusts the power distribution.

[0022] Wherein, msi represents the estimated storage capacity of photovoltaic power generation, msw represents the storage capacity of wind power generation, Ua represents the electricity consumption of special layers in the electricity consumption data, Ub represents the electricity consumption of residential layers, and Uc represents the electricity consumption of industrial and commercial layers.

[0023] As a preferred embodiment of the hierarchical and graded source-grid-load-storage multi-entity collaborative scheduling method described in this invention, the acquisition of secondary power consumption data includes the re-acquisition of different levels of power consumption data by the primary power supply adjustment module after the power supply adjustment of power plants, urban areas and energy storage equipment is completed, denoted as Uaa, Ubb and Ucc.

[0024] The secondary power consumption data is calculated by the primary power supply adjustment module and expressed as: ΔUU=(mUi+mUw)-(Uaa+Ubb+Ucc)

[0025] Where mUi represents the estimated photovoltaic power generation and mUw represents the estimated wind power generation.

[0026] The primary power supply adjustment module sends the secondary power consumption data to the secondary power supply adjustment module.

[0027] As a preferred embodiment of the hierarchical and graded source-grid-load-storage multi-entity collaborative scheduling method of the present invention, the step of calling on the energy storage equipment in the surrounding area to supplement the power includes determining whether the secondary power consumption data ΔUU is greater than 0 through the secondary power supply adjustment module. If ΔUU≥0, it indicates that the power supply is balanced. If ΔUU<0, it indicates that additional power needs to be supplemented from the energy storage equipment in the surrounding area.

[0028] Another objective of this invention is to provide a hierarchical, multi-entity collaborative scheduling system for power generation, grid, load, and storage, which can monitor and dynamically schedule power resources in real time through intelligent data processing and optimization algorithms, thus solving the problems of uneven power resource allocation, low energy efficiency, and delayed response time in existing power systems.

[0029] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a hierarchical and graded multi-entity collaborative scheduling system for power generation, grid, load and storage, including a data acquisition module, a data processing module, a primary power supply adjustment module, a secondary power supply adjustment module and a database module.

[0030] The data acquisition module is used to acquire environmental data, power data, and power generation data of the power plant to obtain power supply data; acquire electricity consumption data corresponding to different floors in the urban area; and acquire energy storage data of energy storage devices.

[0031] The data processing module is used to acquire historical datasets of power plants, urban areas, and energy storage equipment, and analyze power supply data, electricity consumption data, and energy storage data based on the historical datasets to obtain energy estimation data.

[0032] The primary power supply adjustment module is used to adjust the power supply of power plants, urban areas and energy storage equipment based on power estimation data, monitor the adjusted power consumption data of different levels of the urban area, and obtain secondary power consumption data.

[0033] The secondary power supply adjustment module is used to call upon energy storage devices in the surrounding area to supplement power based on the secondary power consumption data.

[0034] The database module is used to store historical average data of power plants, urban areas and energy storage equipment, historical average environmental data, sunshine duration periods in different regions, maximum energy storage capacity of different energy storage equipment, maximum inverter coefficient of photovoltaic power, and maximum inverter coefficient of wind power.

[0035] A computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps of the hierarchical source-grid-load-storage multi-entity collaborative scheduling method described above.

[0036] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the hierarchical source-grid-load-storage multi-entity collaborative scheduling method described above.

[0037] The beneficial effects of this invention are as follows: This invention can fully utilize the participation of multiple energy sources and multiple entities to achieve coordinated scheduling among sources, grids, loads, and storage, thereby improving the operating efficiency of the power system. This invention can optimize the overall scheduling of different sources, grids, loads, and storage to make the energy supply and demand balance more reasonable, achieve optimal energy configuration and scheduling strategies, and the hierarchical and graded multi-entity coordinated scheduling method of sources, grids, loads, and storage can fully consider the volatility and uncertainty of renewable energy, achieve smooth absorption through energy storage and other means, and improve the utilization efficiency and absorption capacity of renewable energy. For different energy sectors and users, this invention can reduce the scheduling and operating costs of the power system through strategies such as coordinated scheduling and demand-side response. Attached Figure Description

[0038] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0039] Figure 1 is an overall flowchart of the hierarchical source-grid-load-storage multi-entity collaborative scheduling method provided in the first embodiment of the present invention.

[0040] Figure 2 is an overall framework diagram of the hierarchical source-grid-load-storage multi-entity collaborative scheduling system provided in the second embodiment of the present invention. Detailed Implementation

[0041] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0042] Example 1

[0043] Referring to Figure 1, an embodiment of the present invention provides a hierarchical, multi-entity collaborative scheduling method for source-grid-load-storage systems, characterized in that:

[0044] S1: The data acquisition module acquires environmental data, power data, and power generation data of the power plant to obtain power data, acquires electricity consumption data corresponding to different floors in the urban area, and acquires energy storage data of the energy storage equipment.

[0045] Furthermore, the workflow of the data acquisition module is as follows:

[0046] The data acquisition module connects the solar sensors and solar power equipment of the photovoltaic power station to obtain the solar intensity, solar power generation, solar active power and solar reactive power of the photovoltaic power station.

[0047] The data acquisition module connects to the wind speed sensor and wind power generation equipment of the wind power station to obtain the wind speed, wind power generation, wind active power and wind reactive power of the wind power station.

[0048] The data acquisition module connects to the urban power grid to obtain electricity consumption data for special tiers, residential tiers, and industrial and commercial tiers.

[0049] The data acquisition module associates with energy storage devices to obtain the device type and stored capacity of the energy storage devices, thus obtaining energy storage data.

[0050] The data acquisition module uses light intensity and wind speed as environmental data; wind power, wind reactive power, photovoltaic active power, and photovoltaic reactive power as power data; wind power generation and photovoltaic power generation as power generation data; and environmental data, power data, and power generation data as power source data.

[0051] The data acquisition module sends power data, power consumption data, and energy storage data to the data processing module.

[0052] S2: The data processing module acquires historical datasets of power plants, urban areas, and energy storage equipment. Based on the historical datasets, it analyzes power supply data, electricity consumption data, and energy storage data to obtain energy estimation data.

[0053] Furthermore, the workflow of the data processing module is as follows:

[0054] Process A1: The data processing module records the light intensity as i, the wind speed as w, the photovoltaic active power as uip, the wind active power as uwp, the photovoltaic discharge as iw, and the wind power generation as ww in the environmental data.

[0055] Process A2: The data processing module obtains historical average data of power plants, urban areas, and energy storage equipment over the past three years, as well as historical average environmental data, through the database to obtain a historical dataset.

[0056] Historical data for the power plant includes:

[0057] Historical average photovoltaic active power, uip1, uip2, uip3.

[0058] Historical average photovoltaic power generation, iw1, iw2, iw3.

[0059] Historical average wind power, uwp1, uwp2, uwp3.

[0060] Historical average wind power generation, ww1, ww2, ww3.

[0061] Historical data of the urban area:

[0062] Historical average electricity consumption of special floors, Ua1, Ua2, Ua3.

[0063] Historical average residential electricity consumption, Ub1, Ub2, Ub3.

[0064] Historical average electricity consumption in the industrial and commercial sector, Uc1, Uc2, Uc3.

[0065] Historical data of energy storage equipment: historical average storage capacity, s1, s2, s3.

[0066] Historical average environmental data:

[0067] Historical average light intensity, i1, i2, i3.

[0068] Historical average wind speeds, w1, w2, w3.

[0069] Process A3: The data processing module calculates the average value of each historical dataset.

[0070] Historical average data of power plants:

[0071] Historical average active power of photovoltaic power Δuip, historical average active power of photovoltaic power Δiw, historical average active power of wind power Δuwp, historical active power of wind power Δww.

[0072] Historical average data for the urban area:

[0073] Historical average electricity consumption of special floors ΔUa, historical average electricity consumption of residential floors ΔUb, historical average electricity consumption of industrial and commercial floors ΔUc.

[0074] Average historical data of energy storage devices: average historical energy storage capacity Δs.

[0075] Historical environmental data averages: historical average light intensity Δi, historical average wind speed Δw.

[0076] Process A4: The data processing module calculates the variance of the historical datasets respectively.

[0077] (1) Variance of historical data of power plants:

[0078] Historical photovoltaic active power variance vuip:

[0079] Historical photovoltaic power generation variance (viw):

[0080] Historical wind-driven active power variance (vuwp):

[0081] Historical wind power generation variance vww:

[0082] (2) Variance of historical data in urban areas:

[0083] Historical special layer power consumption variance vUa:

[0084] Historical residential electricity consumption variance vUb:

[0085] Historical variance of electricity consumption in industrial and commercial sectors, vUc:

[0086] (3) Variance of historical data for energy storage devices:

[0087] Historical storage capacity variance vs:

[0088] (4) Variance of historical environmental data:

[0089] Historical light intensity variance vi:

[0090] Historical wind speed variance vw:

[0091] Process A5: The data processing module calculates the standard deviation of each historical dataset.

[0092] (1) Standard deviation of historical data for power plants:

[0093] Historical standard deviation of photovoltaic active power, sduip: sduip = (vuip) 2

[0094] Historical standard deviation of photovoltaic power generation, sdiw: sdiw = (viw) 2

[0095] Historical wind-driven active power standard deviation sduwp: sduwp = (vuwp) 2

[0096] Historical wind power generation standard deviation sdww: sdww = (vww) 2

[0097] (2) Standard deviation of historical data for urban areas:

[0098] Historical standard deviation of electricity consumption in special tiers, sdUa: sdUa = (vUa) 2

[0099] Historical standard deviation of residential electricity consumption, sdUb: sdUb = (vUb) 2

[0100] Historical standard deviation of electricity consumption in industrial and commercial sectors, sdUc: sdUc = (vUc) 2

[0101] (3) Standard deviation of historical data for energy storage devices:

[0102] Historical storage capacity standard deviation sds: sds = (vs) 2

[0103] (4) Standard deviation of historical environmental data:

[0104] Historical light intensity standard deviation sdi: sdi=(vi) 2

[0105] Historical wind speed standard deviation sdw: sdw = (vw) 2

[0106] Process A5: The data processing module calculates the covariance in the historical datasets respectively.

[0107] Procedure A51: Calculate the covariance ci1 of light intensity with respect to photovoltaic active power and the covariance cw1 of wind speed with respect to wind active power.

[0108] Procedure A52: Calculate the covariance ci2 of photovoltaic active power with respect to photovoltaic power generation, and the covariance cw2 of wind power active power with respect to wind power generation.

[0109] Procedure A53: Calculate the covariances cia3, cib3, and cic3 of photovoltaic power generation with respect to electricity consumption on the special floor, residential floor, and industrial and commercial floor, respectively.

[0110] Procedure A54: Calculate the covariances cwa3, cwb3, and cwc3 of wind power generation with respect to the electricity consumption of the special floor, residential floor, and industrial and commercial floor, respectively.

[0111] Procedure A55: Calculate the covariance ci4 of photovoltaic power generation with respect to energy storage; calculate the covariance cw4 of wind power generation with respect to energy storage.

[0112] Process A6: The data processing module calculates the correlation coefficients in the historical datasets respectively.

[0113] Procedure A61: Calculate the correlation coefficient ri1 ​​of light intensity with respect to photovoltaic active power, ri1=ci1 / (sdi*sduip).

[0114] Calculate the correlation coefficient rw1 of wind speed with respect to wind active power, rw1 = cw1 / (sdw*sduwp).

[0115] Procedure A62: Calculate the correlation coefficient ri2 of photovoltaic active power with respect to photovoltaic power generation, ri2=ci2 / (sduip*sdiw).

[0116] Calculate the correlation coefficient rw2 between wind active power and wind power generation, rw2=cw2 / (sduwp*sdww).

[0117] Procedure A63: Calculate the correlation coefficients ria3, rib3, and ric3 of photovoltaic power generation with respect to electricity consumption on special floors, residential floors, and industrial and commercial floors, respectively.

[0118] ria3=0.5*cia3 / (sdiw*sdUa).

[0119] rib3=0.5*cib3 / (sdiw*sdUb).

[0120] ric3=0.5*cic3 / (sdiw*sdUc).

[0121] Procedure A64: Calculate the correlation coefficients rwa3, rwb3, and rwc3 of wind power generation with respect to electricity consumption in special floors, residential floors, and industrial and commercial floors, respectively.

[0122] rwa3=0.5*cwa3 / (sdww*sdUa).

[0123] rwb3=0.5*cwb3 / (sdww*sdUb).

[0124] rwc3=0.5*cwc3 / (sdww*sdUc).

[0125] Procedure A65: Calculate the correlation coefficient ri4 of photovoltaic power generation with respect to energy storage, ri4 = 0.5 * ci4 / (sdiw * sds).

[0126] Calculate the correlation coefficient rw4 between wind power generation and energy storage, rw4 = 0.5 * cw4 / (sdww * sds).

[0127] Process A7: The data processing module calculates the regression coefficients in the historical datasets respectively.

[0128] Procedure A71: Calculate the regression coefficient bi1 of illuminance with respect to photovoltaic active power, bi1 = ri1 * (sduip / sdi).

[0129] Calculate the regression coefficient bw1 of wind speed with respect to wind active power, bw1 = rw1 * (sdww / sduwp).

[0130] Procedure A72: Calculate the regression coefficient bi2 of photovoltaic active power with respect to photovoltaic power generation, bi2 = ri2 * (sdiw / sduip).

[0131] Calculate the regression coefficient bw2 of wind power active power with respect to wind power generation, bw2=rw2*(sdww / sduwp).

[0132] Procedure A73: Calculate the regression coefficients bia3, bib3, and bic3 of photovoltaic power generation with respect to electricity consumption in special floors, residential floors, and industrial and commercial floors, respectively.

[0133] bia3=0.5*ria3*(sdUa / sdiw).

[0134] bib3=0.5*rib3*(sdUb / sdiw).

[0135] bic3=0.5*ric3*(sdUc / sdiw).

[0136] Procedure A74: Calculate the regression coefficients bwa3, ​​bwb3, and bwc3 of wind power generation with respect to electricity consumption in special floors, residential floors, and industrial and commercial floors, respectively.

[0137] bwa3=0.5*rwa3*(sdUa / sdww).

[0138] bwb3=0.5*rwb3*(sdUb / sdww).

[0139] bwc3=0.5*rwc3*(sdUc / sdww).

[0140] Procedure A75: Calculate the regression coefficient bi4 of photovoltaic power generation with respect to energy storage, bi4 = 0.5 * ri4 * (sds / sdiw).

[0141] Calculate the regression coefficient bw4 of wind power generation with respect to energy storage, bw4 = 0.5 * bw4 * (sds / sdww).

[0142] Process A8: The data processing module calculates the correction coefficients in the historical datasets to obtain the regression equations.

[0143] Procedure A81: Calculate the correction coefficient ai1 of illuminance with respect to photovoltaic active power, ai1 = Δuip - bi1 * Δi; the regression equation of illuminance with respect to photovoltaic active power is y = bi1 * x + ai1, where y represents photovoltaic active power and x represents illuminance.

[0144] Calculate the correction coefficient aw1 for wind speed with respect to wind active power, aw1 = Δuwp - bw1 * Δw; the regression equation for solar intensity with respect to photovoltaic active power is y = bw1 * x + aw1, where y represents wind active power and x represents wind speed.

[0145] Process A82: Calculate the correction coefficient ai2 of photovoltaic active power with respect to photovoltaic power generation, ai2=Δiw-bi2*Δuip; the regression equation of photovoltaic active power with respect to photovoltaic power generation is y=bi2*x+ai2, where y represents photovoltaic power generation and x represents photovoltaic active power.

[0146] The correction coefficient aw2 for wind power active power with respect to wind power generation is calculated as follows: bw2 = rw2 * (sdww / sduwp), aw2 = Δww - bi2 * Δuwp; the regression equation for photovoltaic active power with respect to photovoltaic power generation is y = bw2 * x + aw2, where y represents wind power generation and x represents wind power active power.

[0147] Process A83: Calculate the correction factors aia3, aib3, and aic3 for photovoltaic power generation with respect to electricity consumption in special floors, residential floors, and industrial and commercial floors, respectively.

[0148] aia3=ΔUa-bia3*Δiw; The regression equation of photovoltaic power generation with respect to the electricity consumption of the special layer is y=bia3*x+aia3, where y represents the electricity consumption of the special layer and x represents the photovoltaic power generation.

[0149] aib3=ΔUb-bib3*Δiw; The regression equation of photovoltaic power generation with respect to residential electricity consumption is y=bib3*x+aib3, where y represents residential electricity consumption and x represents photovoltaic power generation.

[0150] aic3=ΔUc-bic3*Δiw; The regression equation of photovoltaic power generation with respect to electricity consumption in the industrial and commercial layer is y=bic3*x+aic3, where y represents the electricity consumption in the industrial and commercial layer and x represents the photovoltaic power generation.

[0151] Procedure A84: Calculate the correction factors awa3, bwb3, and bwc3 for wind power generation with respect to the electricity consumption of special floors, residential floors, and industrial and commercial floors, respectively.

[0152] awa3=ΔUa-bwa3*Δww; The regression equation of wind power generation with respect to the electricity consumption of the special layer is y=bwa3*x+awa3, where y represents the electricity consumption of the special layer and x represents the wind power generation.

[0153] awb3=ΔUb-bwb3*Δww; The regression equation of wind power generation with respect to residential electricity consumption is y=bwb3*x+awb3, where y represents residential electricity consumption and x represents wind power generation.

[0154] awc3=ΔUc-bwc3*Δww; The regression equation of wind power generation with respect to the electricity consumption of the industrial and commercial layer is y=bwc3*x+awc3, where y represents the electricity consumption of the industrial and commercial layer and x represents the wind power generation.

[0155] Process A85: Calculate the correction coefficient ai4 for photovoltaic power generation with respect to energy storage, ai4 = Δs - bi4 * Δiw; the regression equation for photovoltaic power generation with respect to energy storage is y = bi4 * x + ai4, where y represents energy storage and x represents photovoltaic power generation.

[0156] Calculate the correction factor aw4 for wind power generation with respect to energy storage, aw4 = Δs - bw4 * Δww; the regression equation for wind power generation with respect to energy storage is y = bi4 * x + ai4, where y represents energy storage and x represents wind power generation.

[0157] Process A9: Calculate the estimated power consumption data.

[0158] Process A91: The data processing module substitutes the parameters from Process A1 into the regression equation of Process A8, and successively estimates the active power and power generation of the photovoltaic power station to obtain muip and miw; estimates the active power and power generation of the wind power station to obtain muwp and mww; estimates the photovoltaic power generation for power supply to the special floor, residential floor, and industrial and commercial floor to obtain mUai, mUbi, and mUci; estimates the wind power generation for power supply to the special floor, residential floor, and industrial and commercial floor to obtain mUaw, mUbw, and mUcw; and estimates the energy storage capacity msi of photovoltaic power generation and the energy storage capacity msw of wind power generation.

[0159] Process A92: The data processing module sends the estimation results of data process A91 as power estimation data to the first-level power supply adjustment module.

[0160] S3: The primary power supply adjustment module adjusts the power supply of power plants, urban areas and energy storage equipment based on power estimation data, monitors the adjusted power consumption data of different levels of the urban area, and obtains secondary power consumption data.

[0161] Furthermore, in process B1: the primary power supply adjustment module records the photovoltaic reactive power in the power data as nip and the wind power reactive power as nwp.

[0162] In the electricity consumption data, the electricity consumption of special floors is denoted as Ua, the electricity consumption of residential floors is denoted as Ub, and the electricity consumption of industrial and commercial floors is denoted as Uc.

[0163] Read the device type from the energy storage data and record the stored energy as s.

[0164] Process B2: The primary power supply adjustment module compares the stored energy.

[0165] Process B21: If s≥(msi+msw), it means that there is no need to adjust the energy supply relationship in this area, and the subsequent steps of process B21 are not executed; if s<(msi+msw), it means that the electricity demand in this area has increased, calculate s-(Ua+Ub+Uc), and execute process B22.

[0166] Process B22: If s-(Ua+Ub+Uc)>0, it means that no adjustment of the energy supply relationship is needed in this area; if s-(Ua+Ub+Uc)<0, it means that the area needs additional power, and the subsequent steps of process B22 are not executed; if s-(Ua+Ub+Uc)=0, it means that the power demand in this area has increased, and steps B3~B4 are executed.

[0167] Process B3: The primary power supply adjustment module compares the useful power and adjusts the power station.

[0168] Process B4: The primary power supply adjustment module compares the power usage data and adjusts the power distribution.

[0169] Process B5: After the power supply adjustment for power plants, urban areas and energy storage equipment is completed in processes B1 to B4 above, the first-level power supply adjustment module re-acquires the power consumption data of different levels in the region, which are recorded as Uaa, Ubb and Ucc.

[0170] Process B6: The primary power supply adjustment module calculates the secondary power consumption data ΔUU, ΔUU=(mUi+mUw)-(Uaa+Ubb+Ucc).

[0171] Process B7: The primary power supply adjustment module sends the secondary power consumption data to the secondary power supply adjustment module.

[0172] Furthermore,

[0173] The subsequent workflow of process B3 is as follows:

[0174] Process B31: The primary power supply adjustment module compares uip with muip and uwp with muwp. If uip ≥ muip and uwp ≥ muwp, it indicates that the power station is supplying power normally, and the subsequent steps of process B31 are not executed. If uip < muip, it indicates that the photovoltaic power station needs adjustment, and process B32 is executed. If uwp < muwp, it indicates that the wind power station needs adjustment, and process B33 is executed.

[0175] Process B32: The primary power supply adjustment module adjusts the photovoltaic power station.

[0176] Process B321: The primary power supply adjustment module reads the current system time.

[0177] Process B322: The primary power supply adjustment module obtains the location information of the photovoltaic power station by associating with it.

[0178] Process B323: The primary power supply adjustment module obtains the sunshine duration of the photovoltaic power station based on the location information and determines whether the system time is within the sunshine duration period. If not, it means that the photovoltaic power station cannot be adjusted, and the subsequent steps of process B323 are not executed. If it is, it means that the photovoltaic power station can be adjusted, and process B324 is executed.

[0179] Process B324: Record the system time as t, and record the start and end times of the illumination period as AT1 and AT2 respectively.

[0180] Process B325: Calculate the adjustment angle α of the photovoltaic power station's solar panels, α = 90 - (t - AT1) / (AT2 - AT1) * 180.

[0181] Process B325: The primary power supply adjustment module reads the maximum photoelectric inverter coefficient from the database, denoted as xia.

[0182] Process B326: The primary power supply adjustment module is associated with the photoelectric inverter to obtain the current photoelectric coefficient, denoted as xi.

[0183] Process B327: The first-level power supply adjustment module calculates the photoelectric coefficient adjustment value Δxi, Δxi=(1+nip / uip)*xi.

[0184] Process B328: The primary power supply adjustment module determines whether Δxi is less than xia. If Δxi ≤ xia, the angle of the photovoltaic panels in the photovoltaic power station is adjusted to α, and the photoelectric coefficient of the photovoltaic inverter is adjusted to Δxi. If Δxi > xia, the angle of the photovoltaic panels in the photovoltaic power station is adjusted to α, and the photoelectric coefficient of the photovoltaic inverter is adjusted to xia.

[0185] Process B33: Adjust the wind power station using the primary power supply adjustment module.

[0186] Process B331: The primary power supply adjustment module reads the maximum wind power inverter coefficient from the database, denoted as xwa.

[0187] Process B332: The primary power supply adjustment module is associated with the wind power inverter to obtain the current wind power coefficient, denoted as xw.

[0188] Process B333: The primary power supply adjustment module calculates the wind power coefficient adjustment value Δxw, Δxw=(1+nwp / uwp)*xw.

[0189] Process B334: The primary power supply adjustment module determines whether Δxw is less than xwa. If Δxw ≤ xwa, the photoelectric coefficient of the wind power inverter is adjusted to Δxi; if Δxw > xwa, the photoelectric coefficient of the wind power inverter is adjusted to xia.

[0190] The subsequent workflow of process B4 is as follows:

[0191] Process B41: The primary power supply adjustment module calculates the photovoltaic power consumption mUi=mUai+mUbi+mUci; and calculates the wind power consumption mUw=mUaw+mUbw+mUcw.

[0192] Process B42: The primary power supply adjustment module compares the values ​​of mUi+mUw and Ua+Ub+Uc. If mUi+mUw ≥ Ua+Ub+Uc, it indicates that the power supply in the region is sufficient. The primary power supply adjustment module reads the maximum storage capacity from the database according to the equipment type and records it as ss, then executes process B43; if mUi+mUw < Ua+Ub+Uc, it indicates that the power distribution in the region is unreasonable, then executes process B44.

[0193] Process B43: The primary power supply adjustment module calculates the storeable energy Δs, Δs = (mUi + mUw) - (Ua + Ub + Uc); it determines whether s + Δs ​​is less than ss. If s + Δs ​​≤ ss, then there is no extra power available for distribution to other regions in this area; if s + Δs ​​≤ ss, then there is extra power available for distribution to other regions in this area, and the extra power is hs, hs = (s + Δs) - ss.

[0194] Process B44: The primary power supply adjustment module adjusts the power distribution.

[0195] Process B441: The primary power supply adjustment module determines whether the ratio of mUai / mUaw is greater than 1. If it is greater than 1, it indicates that the wind power station is not supplying enough power, and process B33 is executed; if it is less than 1, it indicates that the photovoltaic power station is not supplying enough power, and process B32 is executed; if it is equal to 1, it compares whether the ratio of mUbi / mUbw is greater than 1, and proceeds to process B442.

[0196] Process B442: If mUbi / mUbw is greater than 1, it indicates that the wind power station is not supplying enough energy, and process B33 is executed; if mUbi / mUbw is less than 1, it indicates that the photovoltaic power station is not supplying enough energy, and process B32 is executed; if it is equal to 1, compare whether the ratio of mUci / mUcw is greater than 1, and proceed to process B443.

[0197] Process B443: If mUci / mUcw is greater than 1, it indicates that the wind power station is not supplying enough power, so proceed with process B33; if mUci / mUcw is less than 1, it indicates that the photovoltaic power station is not supplying enough power, so proceed with process B32; if it is equal to 1, it indicates that the power supply in the region is insufficient and additional power is needed.

[0198] S4: The secondary power supply adjustment module calls upon the energy storage equipment in the surrounding area to supplement power based on the secondary power consumption data.

[0199] The workflow of the secondary power supply adjustment module is as follows:

[0200] The secondary power supply adjustment module determines whether the secondary power consumption data ΔUU is greater than 0. If ΔUU≥0, it means that the power supply in the region has achieved a balance and no additional supplement is needed. If ΔUU<0, it means that the region needs to supplement the power from the energy storage equipment in the surrounding area to replenish the amount of |ΔUU|.

[0201] Example 2

[0202] Referring to Figure 2, an embodiment of the present invention provides a system for a hierarchical, multi-entity collaborative scheduling method for power generation, grid, load, and storage. The hierarchical, multi-entity collaborative scheduling system for power generation, grid, load, and storage includes a data acquisition module, a data processing module, a primary power supply adjustment module, a secondary power supply adjustment module, and a database module.

[0203] The data acquisition module is used to acquire environmental data, power data, and power generation data of the power plant to obtain power supply data; to acquire electricity consumption data corresponding to different floors in the urban area; and to acquire energy storage data of energy storage equipment.

[0204] The data processing module is used to acquire historical datasets of power plants, urban areas, and energy storage equipment. Based on the historical datasets, it analyzes power supply data, electricity consumption data, and energy storage data to obtain energy estimation data.

[0205] The primary power supply adjustment module is used to adjust the power supply of power plants, urban areas and energy storage equipment based on power estimation data, monitor the power consumption data of different levels of the urban area after adjustment, and obtain secondary power consumption data.

[0206] The secondary power supply adjustment module is used to call upon energy storage devices in the surrounding area to supplement power based on secondary power consumption data.

[0207] The database module is used to store historical average data of power plants, urban areas and energy storage equipment, historical average environmental data, sunshine duration periods in different regions, maximum energy storage capacity of different energy storage equipment, maximum inverter coefficient of photovoltaic power, and maximum inverter coefficient of wind power.

[0208] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0209] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0210] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0211] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0212] Example 3

[0213] In this embodiment, to verify the beneficial effects of the present invention, scientific demonstration is conducted through economic benefit calculations and simulation experiments. To verify the effectiveness and superiority of the "hierarchical and graded multi-entity collaborative scheduling method for power generation, grid, load, and storage" of the present invention, we designed a series of theoretical experiments, and conducted scientific demonstration through comparative analysis with traditional power dispatching technologies. By comprehensively utilizing historical data and real-time multi-source data, and achieving hierarchical and graded dynamic scheduling, the present invention can more accurately predict and adjust power supply and demand, especially demonstrating significant advantages in dealing with the volatility and randomness of renewable energy sources such as wind power and photovoltaics.

[0214] Traditional power dispatch methods (hereinafter referred to as traditional methods) are mainly based on static supply and demand forecasting and single resource optimization, lacking the ability to comprehensively analyze historical data and multi-source data. In traditional methods, power dispatch usually relies on simple historical average demand forecasting, and dispatch decisions mainly depend on real-time supply and demand conditions, without taking into account environmental data and the volatility of diverse resources. This leads to dispatch responses that are often lagging when facing renewable energy integration and complex load changes, and makes it difficult to effectively utilize distributed power sources and energy storage devices.

[0215] Table 1 shows the comparison results between our method and traditional methods.

[0216] Table 1 Comparison Results

[0217] Prediction accuracy test: By comprehensively utilizing historical data and real-time multi-source data, the method of this invention significantly improves the accuracy of electricity demand prediction, increasing it from 82% to 95% compared to the traditional method. This improvement makes electricity dispatching more precise and effectively reduces dispatching errors and unnecessary energy waste.

[0218] Dispatch response speed test: When demand changes suddenly, the dispatch response time of the method of this invention is reduced from 45 seconds of the traditional method to 30 seconds, which improves the dispatch response speed. This is of great significance for quickly balancing power grid supply and demand and improving system stability.

[0219] Energy storage device utilization efficiency test: Through hierarchical scheduling and accurate prediction, this invention increases the utilization rate of energy storage devices from 68% to 85%, which shows that this method can manage and utilize energy storage devices more effectively, and enhance the peak-shaving capacity and flexibility of the power grid.

[0220] Renewable energy utilization rate test: The method of this invention significantly improves the utilization rate of wind power and photovoltaic power generation, increasing the wind power utilization rate from 72% to 88% and the photovoltaic utilization rate from 75% to 90%. This result shows that the method can better adapt to the volatility of renewable energy, optimize its access and utilization, and promote the wider application of green energy.

[0221] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A layered hierarchical source network load storage multi-agent collaborative scheduling method, characterized in that, include: The data acquisition module obtains environmental data, power data, and power generation data of the power plant to obtain power data, obtains electricity consumption data corresponding to different floors of the urban area, and obtains energy storage data of the energy storage equipment. The data processing module acquires historical datasets of power plants, urban areas, and energy storage equipment. Based on these historical datasets, it analyzes power supply data, electricity consumption data, and energy storage data to obtain energy estimation data. The primary power supply adjustment module adjusts the power supply of power plants, urban areas and energy storage equipment based on power estimation data, and monitors the power consumption data of different levels of the urban area after adjustment to obtain secondary power consumption data. The secondary power supply adjustment module calls upon energy storage devices in the surrounding area to supplement power based on the secondary power consumption data.

2. The hierarchical and graded source-grid-load-storage multi-entity collaborative scheduling method as described in claim 1, characterized in that: The obtained power data includes the data acquisition module associating the photovoltaic power station with the light sensor and photovoltaic power generation equipment to obtain the light intensity, photovoltaic power generation, photovoltaic active power and photovoltaic reactive power of the photovoltaic power station; The data acquisition module connects to the wind speed sensor and wind power generation equipment of the wind power station to acquire the wind speed, wind power generation, wind active power and wind reactive power of the wind power station; The data acquisition module is connected to the urban power grid to obtain electricity consumption data for special layers, residential layers, and industrial and commercial layers. The data acquisition module associates with energy storage devices to obtain the device type and stored capacity of the energy storage devices, thus obtaining energy storage data. The data acquisition module uses light intensity and wind speed as environmental data, wind active power, wind reactive power, photovoltaic active power and photovoltaic reactive power as power data, wind power generation and photovoltaic power generation as power generation data, and environmental data, power data and power generation data as power source data. The data acquisition module sends power data, power consumption data, and energy storage data to the data processing module.

3. The hierarchical and graded source-grid-load-storage multi-entity collaborative scheduling method as described in claim 2, characterized in that: The acquisition of historical datasets for power plants, urban areas, and energy storage equipment includes a data processing module obtaining historical average data and environmental historical average data for the past three years from a database to obtain historical datasets. The data processing module calculates the average value of the historical datasets, then calculates the variance of the historical datasets, then calculates the standard deviation of the historical datasets, and finally calculates the covariance of the historical datasets.

4. The hierarchical and graded source-grid-load-storage multi-entity collaborative scheduling method as described in claim 3, characterized in that: The process of obtaining power estimation data includes calculating the correlation coefficients in the historical datasets through the data processing module, calculating the regression coefficients in the historical datasets after obtaining the correlation coefficients, calculating the correction coefficients in the historical datasets after obtaining the regression coefficients, obtaining the regression equation, calculating the power estimation data after obtaining the regression equation, and sending the power estimation data to the first-level power supply adjustment module through the data processing module.

5. The hierarchical and graded source-grid-load-storage multi-entity collaborative scheduling method as described in claim 4, characterized in that: The adjustment of power supply to power plants, urban areas and energy storage equipment includes a primary power supply adjustment module that records the photovoltaic reactive power in the power data as nip and the wind reactive power as nwp. In the electricity consumption data, the electricity consumption of special floors is denoted as Ua, the electricity consumption of residential floors is denoted as Ub, and the electricity consumption of industrial and commercial floors is denoted as Uc. Read the device type from the energy storage data and record the stored energy as s; The primary power supply adjustment module compares the stored capacity. If s ≥ (msi + msw), it means no adjustment of the functional relationship is needed. If s < (msi + msw), it means the power demand has increased. When s < (msi + msw), if s - (Ua + Ub + Uc) > 0, it means no adjustment of the functional relationship is needed. If s - (Ua + Ub + Uc) < 0, it means additional power is needed. If s - (Ua + Ub + Uc) = 0, it means the power demand has increased. When s - (Ua + Ub + Uc) = 0, the primary power supply adjustment module compares the useful power and adjusts the power station. The primary power supply adjustment module compares the useful power data and adjusts the power distribution. Wherein, msi represents the estimated storage capacity of photovoltaic power generation, msw represents the storage capacity of wind power generation, Ua represents the electricity consumption of special layers in the electricity consumption data, Ub represents the electricity consumption of residential layers, and Uc represents the electricity consumption of industrial and commercial layers.

6. The hierarchical and graded source-grid-load-storage multi-entity collaborative scheduling method as described in claim 5, characterized in that: The obtained secondary power consumption data includes the primary power supply adjustment module re-acquiring the power consumption data of different levels after the power supply adjustment of power plants, urban areas and energy storage equipment is completed, and is denoted as Uaa, Ubb and Ucc. The secondary power consumption data is calculated by the primary power supply adjustment module and expressed as follows: ΔUU=(mUi+mUw)-(Uaa+Ubb+Ucc) Where mUi represents the estimated photovoltaic power generation and mUw represents the estimated wind power generation; The primary power supply adjustment module sends the secondary power consumption data to the secondary power supply adjustment module.

7. The hierarchical and graded source-grid-load-storage multi-entity collaborative scheduling method as described in claim 6, characterized in that: The process of calling upon energy storage devices in the surrounding area to supplement power includes determining whether the secondary power consumption data ΔUU is greater than 0 through the secondary power supply adjustment module. If ΔUU≥0, it indicates that the power supply is balanced. If ΔUU<0, it indicates that additional power needs to be supplemented from energy storage devices in the surrounding area, and the amount of power to be supplemented is |ΔUU|.

8. A system employing the hierarchical, graded source-grid-load-storage multi-entity collaborative scheduling method as described in any one of claims 1 to 7, characterized in that: It includes a data acquisition module, a data processing module, a primary power supply adjustment module, a secondary power supply adjustment module, and a database module; The data acquisition module is used to acquire environmental data, power data, and power generation data of the power plant to obtain power data; acquire electricity consumption data corresponding to different floors of the urban area; and acquire energy storage data of energy storage devices. The data processing module is used to acquire historical datasets of power plants, urban areas and energy storage equipment, and analyze power data, electricity consumption data and energy storage data based on the historical datasets to obtain energy estimation data. The primary power supply adjustment module is used to adjust the power supply of power plants, urban areas and energy storage equipment based on power estimation data, monitor the adjusted power consumption data of different levels of urban areas, and obtain secondary power consumption data. The secondary power supply adjustment module is used to call upon energy storage devices in the surrounding area to supplement power based on the secondary power consumption data; The database module is used to store historical average data of power plants, urban areas and energy storage equipment, historical average environmental data, sunshine duration periods in different regions, maximum energy storage capacity of different energy storage equipment, maximum inverter coefficient of photovoltaic power, and maximum inverter coefficient of wind power.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the hierarchical source-grid-load-storage multi-entity collaborative scheduling method as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the hierarchical source-grid-load-storage multi-entity collaborative scheduling method according to any one of claims 1 to 7.

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