High-energy-consumption enterprise and new energy dynamic cooperation power insurance and supply risk early warning system

Abstract

The invention relates to a high-energy-consumption enterprise and new energy dynamic collaboration power supply insurance risk early warning system, which is characterized in that basic data can be acquired by using a data acquisition module, and a graph curve drawing module is matched with a first modeling component; establishing an annual / monthly / daily electric quantity probability model, an annual / monthly maximum / small output probability model and a typical daily output curve probability distribution model according to the basic data; the data analysis module can be used for researching the annual / monthly / daily power balance characteristics of the enterprise load of the high-energy-consumption industry and the new energy output of the regional power grid, and then a county power grid power quantity space-time probability balance analysis model is constructed through the second modeling component; a power grid power supply insurance risk early warning model for dynamic coordination of a high-energy-consumption enterprise and new energy can be constructed through a risk probability modeling method on the basis of a county power grid power quantity space-time probability balance analysis model by using a third modeling component; the method has the advantages of reasonable design, safety and high efficiency.

Description

Technical Field

[0001] The present invention belongs to the technical field of power risk warning, and specifically relates to a power supply risk warning system that dynamically coordinates high-energy-consuming enterprises and new energy sources. Background Art

[0002] For a long time, in terms of power supply security, although a lot of technical research and application have been carried out on both the power supply side and the grid side, with the continuous growth of power load and the continuous improvement of the penetration rate of new energy, the regional power grid has gradually shown new characteristics such as dual uncertainty of source and load, difficulty in balancing supply and demand in different time periods, and increased peak-to-valley differences. The power supply and demand has gradually shifted from overall balance to overall tension, facing the overall situation of "tight balance of the entire network, local gaps, and difficult supply security". If extreme weather or external electricity is lower than expected, the power supply gap will further increase, and the power supply security faces multiple challenges. For industrial county power grids dominated by high-energy-consuming enterprises, the problems of source-load time-based supply and demand balance and power supply security they face are more prominent; therefore, in order to solve the above problems, it is necessary to develop a reasonably designed, safe and efficient power supply security risk early warning system that dynamically coordinates high-energy-consuming enterprises and new energy. Summary of the Invention

[0003] The purpose of the present invention is to overcome the shortcomings of the existing technology and provide a reasonably designed, safe and efficient power supply risk warning system that dynamically coordinates high-energy-consuming enterprises and new energy sources.

[0004] The purpose of the present invention is achieved as follows: a power supply risk early warning system for dynamic collaboration between high-energy-consuming enterprises and new energy sources, comprising a data acquisition module, a graphic curve drawing module, a first modeling component, a data analysis module, a second modeling component and a third modeling component, wherein the data acquisition module can monitor and collect basic data; The graphic curve drawing module can draw the annual / monthly / daily power consumption curve, the annual / monthly maximum / minimum load curve and the typical daily load curve based on the data collected and obtained by the data acquisition module and through a method combining data drive and mechanism analysis; The first modeling component can establish a yearly / monthly / daily power probability model, a yearly / monthly maximum / minimum output probability model, and a typical daily output curve probability distribution model through a spatiotemporal probability modeling method and relying on the curve graph drawn by its internal graphic curve drawing module; The data analysis module can use a spatiotemporal dynamic probability balance analysis method based on the spatiotemporal probability distribution characteristics of renewable energy power generation and renewable energy output, and combine the load power demand and power consumption of high-energy-consuming industry enterprises with consideration of multi-spatial scale distribution characteristics, to study the annual / monthly / daily power balance characteristics of high-energy-consuming industry enterprise load and regional power grid renewable energy output with consideration of multi-spatial scale distribution characteristics; The second modeling component can construct a spatiotemporal probability balance analysis model for county power grid electricity based on the load power consumption characteristics of various types of high-energy-consuming industries and their spatiotemporal dynamic balance characteristics with the output of new energy sources, using a spatiotemporal dynamic probability balance analysis method; The third modeling component can be based on the spatiotemporal probability balance analysis model of the county power grid, adopt the confidence interval analysis method of spatiotemporal probability characteristics and the load-side collaborative optimization control model under safety constraints to construct a county power grid layered and zoned power supply risk warning model based on spatiotemporal probability confidence intervals. Then, in response to the risk of failure events of new energy power stations, the risk probability modeling method is adopted to construct a power grid power supply risk warning model for dynamic coordination between high-energy-consuming enterprises and new energy.

[0005] Furthermore, the basic data includes geographical distribution data of new energy sources within the power grid, output data of new energy sources, and power data at enterprise gateways.

[0006] Furthermore, the first modeling component can also establish a flexible scheduling model and a rigid control model for the load of high-energy-consuming enterprises through a mechanism modeling method.

[0007] Beneficial effects of the present invention: The present invention sets a data acquisition module, which can be used to monitor and collect basic data such as the geographical distribution data of new energy within the power grid, the output data of new energy and the power data of the enterprise gateway. By setting a graphic curve drawing module and a first modeling component, the basic data can be drawn by the graphic curve drawing module through a method combining data drive with mechanism analysis to draw annual / monthly / daily power consumption curves, annual / monthly maximum / minimum load curves and typical daily load curves. Afterwards, the first modeling component is used and the spatiotemporal probability modeling method is used to rely on the drawn curve graphs to establish annual / monthly / daily power probability models, annual / monthly maximum / minimum output probability models, and typical daily output curve probability distribution. Model; by setting a data analysis module and a second modeling component, using it and through the spatiotemporal dynamic probability balance analysis method, the annual / monthly / daily power balance characteristics of the high-energy-consuming industry enterprise load and the regional power grid new energy output with spatial scale distribution characteristics are studied, and then, through the second modeling component, a county power grid power and electricity spatiotemporal probability balance analysis model is constructed; by setting a third modeling component, it can be used to construct a county power grid layered and zoned power supply risk warning model based on the county power grid power and electricity spatiotemporal probability balance analysis model, and then, through the risk probability modeling method, a power supply risk warning model for the power grid with dynamic coordination between high-energy-consuming enterprises and new energy is constructed; in general, the present invention has the advantages of reasonable design, safety and efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 It is a structural flow chart of the present invention. DETAILED DESCRIPTION

[0009] The present invention will be further described below with reference to the accompanying drawings.

[0010] Example: Figure 1 As shown, a power supply risk warning system for dynamic coordination between high-energy-consuming enterprises and new energy sources includes a data acquisition module, a graphic curve drawing module, a first modeling component, a data analysis module, a second modeling component and a third modeling component, wherein the data acquisition module can monitor and collect basic data, and the basic data includes the geographical distribution data of new energy sources within the power grid, the output data of new energy sources and the power data of the enterprise gateway; the graphic curve drawing module can draw the annual / monthly / daily power consumption curve, the annual / monthly maximum / minimum load curve and the typical daily load curve according to the data collected and obtained by the data acquisition module and through a method combining data drive with mechanism analysis; the first modeling component can establish the annual / monthly / daily power probability model, the annual / monthly maximum / minimum output probability model and the typical daily output curve probability distribution model through the spatiotemporal probability modeling method and relying on the curve graph drawn by its internal graphic curve drawing module, and at the same time, can also establish the flexible scheduling model and rigid control model of the load of high-energy-consuming enterprises through the mechanism modeling method; the data analysis module can be based on the new energy The spatiotemporal probability distribution characteristics of energy generation and the spatiotemporal probability distribution characteristics of renewable energy output are taken into consideration, and the spatiotemporal dynamic probability balance analysis method is adopted to study the annual / monthly / daily power balance characteristics of the load of high-energy-consuming enterprises and the renewable energy output of the regional power grid considering the multi-spatial scale distribution characteristics. The second modeling component can be based on the load power consumption characteristics of multiple types of high-energy-consuming enterprises and their spatiotemporal dynamic balance characteristics with renewable energy output, and the spatiotemporal dynamic probability balance analysis method is adopted to construct a spatiotemporal probability balance analysis model for county power grid electricity. The third modeling component can be based on the spatiotemporal probability balance analysis model for county power grid electricity, and the confidence interval analysis method of spatiotemporal probability characteristics and the load-side collaborative optimization control model under safety constraints are adopted to construct a county power grid layered and zoned power supply risk warning model based on the spatiotemporal probability confidence interval. Afterwards, for the risk of failure events of new energy power stations, the risk probability modeling method is adopted to construct a power supply risk warning model for the power grid with dynamic coordination between high-energy-consuming enterprises and renewable energy.

[0011] When the present invention is in use, first, the data acquisition module can be used to monitor and collect basic data such as the geographical distribution data of new energy within the power grid, the output data of new energy and the power data of the enterprise gateway. Then, based on the basic data obtained, a method combining data drive and mechanism analysis is used to draw the annual / monthly / daily power consumption curve, the annual / monthly maximum / minimum load curve and the typical daily load curve through the graphic curve drawing module. Then, relying on the drawn curve graph, the first modeling component is used and the spatiotemporal probability modeling method is used to establish the annual / monthly / daily power probability model, the annual / monthly maximum / minimum output probability model and the typical daily output curve probability distribution model respectively; then, based on the spatiotemporal probability distribution characteristics of the new energy power generation and the spatiotemporal probability distribution characteristics of the new energy output, the data analysis module is used in combination with the load power demand and power consumption of high-energy-consuming industry enterprises considering the multi-spatial scale distribution characteristics, and the spatiotemporal dynamic probability balance analysis is adopted. An analysis method is adopted to study the annual / monthly / daily power balance characteristics of the loads of high-energy-consuming industry enterprises and the output of new energy in the regional power grid considering the multi-spatial scale distribution characteristics; finally, based on the power consumption characteristics of the loads of multiple types of high-energy-consuming industry enterprises and their spatiotemporal dynamic balance characteristics with the output of new energy, a spatiotemporal dynamic probability balance analysis method is adopted, and a spatiotemporal probability balance analysis model of the county power grid is constructed through the second modeling component; after completing the above operations, based on the spatiotemporal probability balance analysis model of the county power grid, a confidence interval analysis method of spatiotemporal probability characteristics and a load-side collaborative optimization control model under safety constraints are adopted, and a third modeling component is used to construct a hierarchical and partitioned power supply risk warning model for the county power grid based on spatiotemporal probability confidence intervals; finally, in response to the risk of failure events in new energy power stations, a risk probability modeling method is adopted to construct a power supply risk warning model for the power grid that dynamically coordinates high-energy-consuming enterprises with new energy; in general, the present invention has the advantages of reasonable design, safety and high efficiency.

[0012] The above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit the same. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be included in the scope of the claims of the present invention.

Claims

1. A power supply risk early warning system for dynamic collaboration between high-energy-consuming enterprises and new energy sources, comprising a data acquisition module, a graph curve drawing module, a first modeling component, a data analysis module, a second modeling component, and a third modeling component, characterized by: The data acquisition module can monitor and collect basic data; The graph curve drawing module can draw annual / monthly / daily power consumption curves, annual / monthly maximum / minimum load curves and typical daily load curves based on the data collected and obtained by the data acquisition module and through a method combining data drive and mechanism analysis; The first modeling component can establish a yearly / monthly / daily power probability model, a yearly / monthly maximum / minimum output probability model, and a typical daily output curve probability distribution model through a spatiotemporal probability modeling method and relying on the curve graph drawn by its internal graphic curve drawing module; The data analysis module can be based on the spatiotemporal probability distribution characteristics of renewable energy power generation and the spatiotemporal probability distribution characteristics of renewable energy output, and combined with the load power demand and power consumption of high-energy-consuming industry enterprises considering multi-spatial scale distribution characteristics, adopt a spatiotemporal dynamic probability balance analysis method to study the annual / monthly / daily power balance characteristics of high-energy-consuming industry enterprise load and regional power grid renewable energy output considering multi-spatial scale distribution characteristics; The second modeling component can construct a spatiotemporal probability balance analysis model for county power grid electricity based on the load power consumption characteristics of various types of high-energy-consuming industries and their spatiotemporal dynamic balance characteristics with the output of new energy sources, using a spatiotemporal dynamic probability balance analysis method; The third modeling component can be based on the spatiotemporal probability balance analysis model of the county power grid, adopt the confidence interval analysis method of spatiotemporal probability characteristics and the load-side collaborative optimization control model under safety constraints, to construct a county power grid layered and zoned power supply risk warning model based on spatiotemporal probability confidence intervals. Then, in response to the risk of new energy power station failure events, a risk probability modeling method is adopted to construct a power grid power supply risk warning model for dynamic coordination between high-energy-consuming enterprises and new energy.

2. The power supply risk early warning system for dynamic coordination between high-energy-consuming enterprises and new energy sources as claimed in claim 1 is characterized by: The basic data includes the geographical distribution data of new energy within the power grid, the output data of new energy and the power data at the enterprise gateway.

3. The power supply risk early warning system for dynamic coordination between high-energy-consuming enterprises and new energy sources as claimed in claim 1 is characterized by: The first modeling component can also establish a flexible scheduling model and a rigid control model for the load of high-energy-consuming enterprises through a mechanism modeling method.

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