Electric power networking type resource intelligent prediction system

Abstract

The invention relates to the technical field of carbon emission management, in particular to an electric power networking type resource intelligent prediction system. Aiming at the problem that a method for accurately judging the correlation between the electric power carbon emission reduction and the electric energy quality does not exist at present, the invention provides the following technical scheme: the method comprises the following steps of: defining a correlation index of the electric power carbon emission reduction and the electric energy quality as XCIDto, and determining a correlation parameter of the electric power carbon emission reduction and the electric energy quality according to the definition of the correlation index of the electric power carbon emission reduction and the electric energy quality; and measuring the electric power carbon emission reduction and electric energy quality associated parameter, and establishing an electric power carbon emission reduction and electric energy quality associated parameter time sequence according to the obtained measured value. According to the method, the influence of the intensity of the carbon emission reduction of the network construction resources in the next time period on the voltage and frequency stability of the power grid can be accurately judged, and the multi-energy microgrid can be ensured to utilize sufficient energy conversion to carry out optimized operation processing.

Description

Technical Field

[0001] The present invention relates to the technical field of carbon emission management, and in particular to an intelligent prediction system for power grid-type resources. Background Art

[0002] As the scale of power systems continues to expand and the complexity increases, power grid construction faces unprecedented challenges. In modern power networks, the effective management and utilization of grid resources are crucial to ensure the stable and reliable operation of the power grid. However, there are many problems in the current situation.

[0003] On the one hand, traditional power resource forecasting methods often rely on simple statistical analysis and empirical models of historical data. For power grid resources, they are affected by the interaction of multiple complex factors, including but not limited to the intermittent nature of different energy sources (such as wind power and photovoltaics), dynamic changes in power loads, changes in grid topology, and uncertainty in power market transactions. These factors greatly reduce the accuracy of traditional forecasting methods, making it difficult to accurately grasp the real-time and future status of power grid resources.

[0004] On the other hand, the rapid development of the power system has led to an increasing number of grid-forming resources, such as the access of new types of resources such as distributed power sources, energy storage systems, and flexible loads. These resources have their own unique operating characteristics and control requirements, which further increases the complexity of resource forecasting. Traditional forecasting methods cannot fully consider the characteristics of these new resources, resulting in a lack of effective decision-making basis in the planning, scheduling, and operation process, which may cause safety issues such as voltage fluctuations and frequency instability in the power grid, and will also affect the optimal configuration and utilization efficiency of power resources. Summary of the invention

[0005] The purpose of the present invention is to address the problem in the background technology that there is currently no method for accurately determining the correlation between electricity carbon emission reduction and power quality, and to propose an intelligent prediction system for power grid-based resources.

[0006] The technical solution of the present invention is: an intelligent prediction system for power grid-type resources, comprising a power carbon emission reduction and power quality correlation index definition module, a power carbon emission reduction and power quality correlation parameter measurement module, a correlation parameter calculation module for different time periods, a first prediction value calculation module, a normalization processing module, and a second prediction value calculation module, wherein:

[0007] The power carbon emission reduction and power quality correlation index definition module is used to define the power carbon emission reduction and power quality correlation index as XCID to According to the definition of the power carbon emission reduction and power quality correlation index, the power carbon emission reduction and power quality correlation parameter variable is determined, and the power carbon emission reduction and power quality correlation index XCIDto The formula is:

[0008] A power carbon emission reduction and power quality related parameter measurement module is used to measure the power carbon emission reduction and power quality related parameter variables, and establish a power carbon emission reduction and power quality related parameter variable time series based on the obtained measurement values;

[0009] A module for calculating parameters associated with different time periods, calculating the influence factor of the parameters associated with carbon emission reduction and power quality on the parameters associated with carbon emission reduction and power quality in the next time period;

[0010] The first prediction value calculation module calculates T according to the time series of the power carbon emission reduction and power quality associated parameter variables and the influence factor of the power carbon emission reduction and power quality associated parameter variables on the power carbon emission reduction and power quality associated parameter variables in the next time period. m+1 The predicted values ​​of the parameters associated with power carbon emission reduction and power quality in the time period;

[0011] A normalization processing module performs normalization processing on the predicted value of the parameter variable associated with power carbon emission reduction and power quality to obtain a normalized value of the predicted value of the parameter variable associated with power carbon emission reduction and power quality;

[0012] The second prediction value calculation module calculates T using the normalized value of the prediction value of the parameter associated with the power carbon emission reduction and power quality. m+1 Prediction value of correlation index between electricity carbon emission reduction and power quality in different time periods Predict the correlation index between the electricity carbon emission reduction and the power quality.

[0013] Optional, T1, T2, ..., T z ,...,T m is a time period of m fixed time intervals, where m is a natural number, m∈1,2,…, z is the zth time period, and z is a natural number, z∈{1,2,…,m}; is the cumulative power generation of distributed photovoltaics in the zth time period; Q PV,max For T1, T2, ..., T z ,...,T m The maximum value of the distributed photovoltaic cumulative power generation in these m time periods; is the cumulative power generation of distributed wind power in the zth time period, Q AEP,max For T1, T2, ..., T z ,...,T m The maximum value of the cumulative power generation of the distributed wind power in the m time periods; Q is the cumulative battery charge in the zth time period, BT,max For T1, T2, ..., Tz ,...,T m The maximum value of the accumulated charge of the battery in these m time periods; Q is the cumulative discharge of the battery in the zth time period, FB,max For T1, T2, ..., T z ,...,T m The maximum value of the cumulative discharge amount of the battery in these m time periods.

[0014] Optionally, the parameters associated with electricity carbon emission reduction and power quality include the cumulative distributed photovoltaic power generation, the cumulative distributed wind power generation, the cumulative battery charge, the cumulative battery discharge, the maximum voltage, and the maximum frequency within a specific time period.

[0015] Optionally, the method for measuring the parameter associated with the power carbon emission reduction and the power quality is: selecting m fixed time intervals T1, T2, ..., T z ,...,T m , in the multi-energy microgrid, the measured value of the distributed photovoltaic cumulative power generation of the multi-energy microgrid is obtained by measuring Measurement value of cumulative power generation of distributed wind power in multi-energy microgrid Measurement value of the cumulative charge of multi-energy microgrid batteries Measurement value of cumulative discharge of multi-energy microgrid batteries Measured value of maximum voltage of multi-energy microgrid Measured value of the maximum frequency of multi-energy microgrid

[0016] Optionally, the formula for the time series of parameters associated with power carbon emission reduction and power quality is:

[0017]

[0018] Optionally, the calculation of the influence factor of the parameter variable associated with power carbon emission reduction and power quality on the parameter variable associated with power carbon emission reduction and power quality in the next time period The formula is:

[0019] Q is the influence factor of the power carbon emission reduction and power quality associated parameter variable in the zth time period on the power carbon emission reduction and power quality associated parameter variable in the next time period; PV,min is the cumulative power generation of the distributed photovoltaic system at T1, T2, ..., T z ,...,T m The minimum value of the measured value of these m fixed time intervals; Q AEP,max is the cumulative power generation of the distributed wind power at T1, T2, ..., T z,...,T m The maximum value of the measured value of these m fixed time intervals; Q BT,max The cumulative charge capacity of the multi-energy microgrid battery at T1, T2, ..., T z ,...,T m The maximum value of the time period measurement of these m fixed time intervals; T z The cumulative discharge of the multi-energy microgrid battery in the time period is T1, T2, ..., T z ,...,T m The minimum value of the time period measurement of these m fixed time intervals.

[0020] Optionally, the formula for the predicted value of the parameter associated with power carbon emission reduction and power quality is:

[0021]

[0022] T m+1 The predicted value of the cumulative power generation of distributed photovoltaic power generation of the multi-energy microgrid in the time period; T m+1 The predicted value of the cumulative power generation of distributed wind power in the multi-energy microgrid during the time period; T m+1 The predicted value of the cumulative charge capacity of the multi-energy microgrid battery in the time period; T m+1 The predicted value of the cumulative discharge of the multi-energy microgrid battery in the time period; T m+1 The predicted value of the maximum voltage of the multi-energy microgrid in the time period; T m+1 The predicted value of the maximum frequency of the multi-energy microgrid during the time period.

[0023] Optionally, the normalization formula is:

[0024]

[0025] T m+1 The normalized value of the predicted value of the distributed photovoltaic cumulative power generation of the multi-energy microgrid in the time period; T m+1 The normalized value of the predicted value of the cumulative power generation of distributed wind power in the multi-energy microgrid for the time period; T m+1 The normalized value of the predicted value of the cumulative charge capacity of the multi-energy microgrid battery in the time period; T m+1 Normalized value of the predicted value of the cumulative discharge of the multi-energy microgrid battery in the time period; T m+1 The normalized value of the predicted maximum voltage value of the multi-energy microgrid in the time period; T m+1 The normalized value of the maximum frequency prediction value of the multi-energy microgrid in the time period; Q AEP,min For T1, T2, ..., T z ,...,T m The minimum value of the measured value of the cumulative power generation of the multi-energy microgrid distributed wind power in the m fixed time intervals; Q BT,min For T1, T2, ..., T z ,...,T m The minimum value of the cumulative charge measurement value of the multi-energy microgrid battery in the m fixed time intervals; U CN,max For T1, T2, ..., T z ,...,T m The maximum value of the maximum voltage measurement value of the multi-energy microgrid in the m fixed time intervals; U CN,min For T1, T2, ..., T z ,...,T m The minimum value of the maximum voltage measurement value of the multi-energy microgrid in the m fixed time intervals; CK,max For T1, T2, ..., T z ,...,T m The maximum value of the maximum value of the multi-energy microgrid frequency measured during the m fixed time intervals; f CK,min For T1, T2, ..., T z ,...,T m The minimum value of the maximum value measured of the multi-energy microgrid frequency in the time period of these m fixed time intervals.

[0026] Optionally, the calculation T m+1 Prediction value of correlation index between electricity carbon emission reduction and power quality in different time periods The formula of the method is:

[0027]

[0028] If the predicted value of the correlation index between electricity carbon emission reduction and power quality is obtained If it is greater than or equal to 0.5, it is considered that the power carbon emission reduction in the next time period is closely related to the power quality, that is, the volatility of the voltage and frequency of the multi-energy microgrid becomes larger. At this time, the distributed wind power power of the multi-energy microgrid should be reduced or the power of the multi-energy microgrid energy storage battery should be increased;

[0029] Optionally, if the predicted value of the correlation index between carbon emission reduction and power quality is obtained If it is less than 0.5, it is considered that the electricity carbon emission reduction in the next time period is less correlated with the power quality, and the volatility of the multi-energy microgrid voltage and frequency becomes smaller. At this time, the current distributed wind power power of the multi-energy microgrid and the charging and discharging power of the multi-energy microgrid energy storage battery can be maintained.

[0030] In summary, the present application includes at least one of the following beneficial technical effects:

[0031] The present invention monitors the distributed photovoltaic cumulative power generation, distributed wind power cumulative power generation, battery cumulative charge, battery cumulative discharge, voltage maximum, and frequency maximum in a specific time period in real time and predicts the predicted value of the parameter associated with power carbon emission reduction and power quality in the next time period, and calculates the predicted value of the power carbon emission reduction and power quality correlation index based on the obtained monitoring parameter and the predicted value of the parameter in the next time period, and adjusts the charging and discharging power of the multi-energy microgrid energy storage battery or the power of distributed wind power in real time according to the calculation result, so as to ensure that the multi-energy microgrid can use sufficient energy conversion for optimized operation. The problem that the power quality and carbon emission reduction requirements of the multi-energy microgrid cannot be met is solved.

[0032] The present invention can accurately determine the impact of the intensity of carbon emission reduction of network resources in the next time period on the stability of grid voltage and frequency, and can ensure that the multi-energy microgrid can utilize sufficient energy conversion for optimized operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 The present invention is a flow chart of an intelligent prediction system for power grid resources. DETAILED DESCRIPTION

[0034] The technical solution of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments.

[0035] The components of the embodiments of the present invention generally described and shown in the drawings herein may be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention.

[0036] Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in the field without making any creative work shall fall within the scope of protection of the present invention.

[0037] In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicating the orientation or positional relationship, are based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first", "second", and "third" are used for descriptive purposes only, and cannot be understood as indicating or implying relative importance.

[0038] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0039] Example

[0040] like Figure 1 As shown, the present invention proposes an intelligent prediction system for power grid-type resources, a power carbon emission reduction and power quality correlation index definition module, a power carbon emission reduction and power quality correlation parameter measurement module, a correlation parameter calculation module for different time periods, a first prediction value calculation module, a normalization processing module, and a second prediction value calculation module, wherein the output end of the power carbon emission reduction and power quality correlation index definition module is electrically connected to the input end of the power carbon emission reduction and power quality correlation parameter measurement module, and the power carbon emission reduction and power quality correlation index definition module is used to define the power carbon emission reduction and power quality correlation index as XCID to , and determine the power carbon emission reduction and power quality correlation parameter according to the definition of the power carbon emission reduction and power quality correlation index. The power carbon emission reduction and power quality correlation index XCID to The formula is:

[0041]

[0042] Among them, T1, T2, ..., T z ,...,T m is a time period of m fixed time intervals, where m is a natural number, m∈1,2,…, z is the zth time period, and z is a natural number, z∈{1,2,…,m}; is the cumulative power generation of distributed photovoltaics in the zth time period; Q PV,max For T1, T2, ..., T z ,...,Tm The maximum value of the distributed photovoltaic cumulative power generation in these m time periods; is the cumulative power generation of distributed wind power in the zth time period, Q AEP,max For T1, T2, ..., T z ,...,T m The maximum value of the cumulative power generation of the distributed wind power in the m time periods; Q is the cumulative battery charge in the zth time period, BT,max For T1, T2, ..., T z ,...,T m The maximum value of the accumulated charge of the battery in these m time periods; Q is the cumulative discharge of the battery in the zth time period, FB,max For T1, T2, ..., T z ,...,T m The maximum value of the battery cumulative discharge in these m time periods. The parameters associated with power carbon emission reduction and power quality include the distributed photovoltaic cumulative power generation, distributed wind power cumulative power generation, battery cumulative charge, battery cumulative discharge, maximum voltage, and maximum frequency in a specific time period.

[0043] The power carbon emission reduction and power quality related parameter calculation module measures the power carbon emission reduction and power quality related parameter, and establishes the power carbon emission reduction and power quality related parameter time series according to the obtained measurement value. The method for measuring the power carbon emission reduction and power quality related parameter is: select m fixed time intervals T1, T2, ..., T z ,...,T m , in the multi-energy microgrid, the measured value of the distributed photovoltaic cumulative power generation of the multi-energy microgrid is obtained by measuring Measurement value of cumulative power generation of distributed wind power in multi-energy microgrid Measurement value of the cumulative charge of multi-energy microgrid batteries Measurement value of cumulative discharge of multi-energy microgrid batteries Measured value of maximum voltage of multi-energy microgrid Measured value of the maximum frequency of multi-energy microgrid The formula for the time series of parameters associated with power carbon emission reduction and power quality is:

[0044]

[0045] In this embodiment, the fixed time interval in the measurement process is set to 10 minutes, and 5 measurement time periods are selected, namely T1, T2, T3, T4, and T5 as 5 fixed time intervals; the measured value of the distributed photovoltaic cumulative power generation of the multi-energy microgrid is measured in the fixed time intervals T1, T2, T3, T4, and T5. The measured value of the cumulative power generation of the multi-energy microgrid distributed wind power is obtained in the fixed time interval time periods T1, T2, T3, T4, and T5. Measure the cumulative charge value of the multi-energy microgrid battery in the fixed time interval time period T1, T2, T3, T4, T5 The measured value of the cumulative discharge of the multi-energy microgrid battery in the fixed time interval time period T1, T2, T3, T4, T5 The maximum voltage value of the multi-energy microgrid is measured in the fixed time intervals T1, T2, T3, T4, T5 The maximum value of the multi-energy microgrid frequency is measured in the fixed time intervals T1, T2, T3, T4, T5 The time series of parameters associated with power carbon emission reduction and power quality is established based on all the above measured values ​​as follows:

[0046]

[0047] The calculation module of the associated parameters in different time periods calculates the influence factor of the parameters associated with carbon emission reduction and power quality on the parameters associated with carbon emission reduction and power quality in the next time period. The calculated power carbon emission reduction and power quality related parameter variables have an impact factor on the power carbon emission reduction and power quality related parameter variables in the next time period The formula is:

[0048]

[0049] in, Q is the influence factor of the power carbon emission reduction and power quality associated parameter variable in the zth time period on the power carbon emission reduction and power quality associated parameter variable in the next time period; PV,min is the cumulative power generation of the distributed photovoltaic system at T1, T2, ..., T z ,...,T m The minimum value of the measured value of these m fixed time intervals; Q AEP,max is the cumulative power generation of the distributed wind power at T1, T2, ..., T z ,...,T m The maximum value of the measured value of these m fixed time intervals; Q BT,maxThe cumulative charge capacity of the multi-energy microgrid battery at T1, T2, ..., T z ,...,T m The maximum value of the time period measurement of these m fixed time intervals; T z The cumulative discharge of the multi-energy microgrid battery in the time period is T1, T2, ..., T z ,...,T m The minimum value of the time period measurement of these m fixed time intervals.

[0050] In this embodiment, the influence factor of the parameter associated with power carbon emission reduction and power quality on the parameter associated with power carbon emission reduction and power quality in the next time period can be calculated as follows:

[0051] The first prediction value calculation module calculates T according to the time series of the power carbon emission reduction and power quality associated parameter variables and the influence factor of the power carbon emission reduction and power quality associated parameter variables on the power carbon emission reduction and power quality associated parameter variables in the next time period. m+1 The predicted value of the parameter associated with carbon emission reduction and power quality in the time period. The formula for the predicted value of the parameter associated with carbon emission reduction and power quality is:

[0052]

[0053] It is worth mentioning that T m+1 The predicted value of the cumulative power generation of distributed photovoltaic power generation of the multi-energy microgrid in the time period; T m+1 The predicted value of the cumulative power generation of distributed wind power in the multi-energy microgrid during the time period; T m+1 The predicted value of the cumulative charge capacity of the multi-energy microgrid battery in the time period; T m+1 The predicted value of the cumulative discharge of the multi-energy microgrid battery in the time period; T m+1 The predicted value of the maximum voltage of the multi-energy microgrid in the time period; T m+1 The predicted value of the maximum frequency of the multi-energy microgrid during the time period.

[0054] In this embodiment, the predicted value of the parameter associated with carbon emission reduction and power quality in the T6 time period is predicted according to the formula:

[0055]

[0056] In this scheme, The predicted value of the cumulative power generation of distributed photovoltaic power generation in the multi-energy microgrid during the T6 period; The predicted value of the cumulative power generation of distributed wind power in the multi-energy microgrid during the T6 period; is the predicted value of the cumulative charge capacity of the multi-energy microgrid battery in the T6 period; is the predicted value of the cumulative discharge of the multi-energy microgrid battery in the T6 period; is the predicted value of the maximum voltage of the multi-energy microgrid in the T6 period; It is the predicted value of the maximum frequency of the multi-energy microgrid in the T6 period.

[0057] The normalization processing module normalizes the predicted value of the parameter associated with power carbon emission reduction and power quality to obtain the normalized value of the predicted value of the parameter associated with power carbon emission reduction and power quality. The formula for the normalization processing is:

[0058]

[0059] T m+1 The normalized value of the predicted value of the distributed photovoltaic cumulative power generation of the multi-energy microgrid in the time period; T m+1 The normalized value of the predicted value of the cumulative power generation of distributed wind power in the multi-energy microgrid for the time period; T m+1 The normalized value of the predicted value of the cumulative charge capacity of the multi-energy microgrid battery in the time period; T m+1 Normalized value of the predicted value of the cumulative discharge of the multi-energy microgrid battery in the time period; T m+1 The normalized value of the predicted maximum voltage value of the multi-energy microgrid in the time period; T m+1 The normalized value of the maximum frequency prediction value of the multi-energy microgrid in the time period; Q AEP,min For T1, T2, ..., T z ,...,T m The minimum value of the measured value of the cumulative power generation of the multi-energy microgrid distributed wind power in the m fixed time intervals; Q BT,min For T1, T2, ..., T z ,...,T m The minimum value of the cumulative charge measurement value of the multi-energy microgrid battery in the m fixed time intervals; U CN,max For T1, T2, ..., T z ,...,T m The maximum value of the maximum voltage measurement value of the multi-energy microgrid in the m fixed time intervals; U CN,min For T1, T2, ..., T z ,...,Tm The minimum value of the maximum voltage measurement value of the multi-energy microgrid in the m fixed time intervals; CK,max For T1, T2, ..., T z ,...,T m The maximum value of the maximum value of the multi-energy microgrid frequency measured during the m fixed time intervals; f CK,min For T1, T2, ..., T z ,...,T m The minimum value of the maximum value measured of the multi-energy microgrid frequency in the time period of these m fixed time intervals.

[0060] In this embodiment, the predicted values ​​of the parameters associated with power carbon emission reduction and power quality in the T6 time period are normalized as follows:

[0061]

[0062] in, It is the normalized value of the predicted value of the cumulative power generation of distributed photovoltaic power generation of multi-energy microgrid in the T6 period; It is the normalized value of the predicted cumulative power generation of distributed wind power in the multi-energy microgrid in the T6 period; is the normalized value of the predicted cumulative charge capacity of the multi-energy microgrid battery in the T6 period; It is the normalized value of the predicted value of the cumulative discharge of the multi-energy microgrid battery in the T6 period; It is the normalized value of the maximum voltage prediction value of the multi-energy microgrid in the T6 period; It is the normalized value of the maximum frequency prediction value of the multi-energy microgrid in the T6 period.

[0063] The second prediction value calculation module calculates T by using the normalized value of the prediction value of the parameter associated with the power carbon emission reduction and power quality. m+1 Prediction value of correlation index between electricity carbon emission reduction and power quality in different time periods The prediction of the correlation index between power carbon emission reduction and power quality is realized. The calculation T m+1 Prediction value of correlation index between electricity carbon emission reduction and power quality in different time periods The formula of the method is:

[0064]

[0065] If the predicted value of the correlation index between electricity carbon emission reduction and power quality is obtained If it is greater than or equal to 0.5, it is considered that the power carbon emission reduction in the next time period is highly correlated with the power quality, that is, the volatility of the voltage and frequency of the multi-energy microgrid becomes larger. At this time, the distributed wind power power of the multi-energy microgrid should be reduced or the power of the multi-energy microgrid energy storage battery should be increased; if the power carbon emission reduction and power quality correlation index prediction value is obtained If it is less than 0.5, it is considered that the electricity carbon emission reduction in the next time period is less correlated with the power quality, and the volatility of the multi-energy microgrid voltage and frequency becomes smaller. At this time, the current distributed wind power power of the multi-energy microgrid and the charging and discharging power of the multi-energy microgrid energy storage battery can be maintained.

[0066] In this embodiment, the predicted value of the correlation index between carbon emission reduction and power quality in the T6 period is calculated according to the formula: for:

[0067]

[0068] If the predicted value of the correlation index between electricity carbon emission reduction and power quality is obtained If it is greater than or equal to 0.5, it is considered that the power carbon emission reduction and power quality are closely related in the next period of time, and the volatility of the voltage and frequency of the multi-energy microgrid becomes larger. At this time, the distributed wind power power of the multi-energy microgrid should be reduced or the charging and discharging power of the multi-energy microgrid energy storage battery should be increased; if the predicted value of the power carbon emission reduction and power quality correlation index is obtained If it is less than 0.5, it is considered that the carbon emission reduction of electricity in the next time period is less correlated with the power quality, and the volatility of the voltage and frequency of the multi-energy microgrid becomes smaller. At this time, the current distributed wind power power of the multi-energy microgrid and the charging and discharging power of the multi-energy microgrid energy storage battery can be maintained.

[0069] The above specific embodiment is only an optional embodiment of the present invention. Based on the technical solution of the present invention and the relevant inspiration of the above embodiment, those skilled in the art can make various alternative improvements and combinations to the above specific embodiment.

Claims

1. An intelligent prediction system for power grid resources, characterized in that: It includes a power carbon emission reduction and power quality correlation index definition module, a power carbon emission reduction and power quality correlation parameter measurement module, a correlation parameter calculation module for different time periods, a first prediction value calculation module, a normalization processing module, and a second prediction value calculation module, which are connected in sequence. The power carbon emission reduction and power quality correlation index definition module is used to define the power carbon emission reduction and power quality correlation index as XCID to According to the definition of the power carbon emission reduction and power quality correlation index, the power carbon emission reduction and power quality correlation parameter variable is determined, and the power carbon emission reduction and power quality correlation index XCID to The formula is: A power carbon emission reduction and power quality related parameter measurement module is used to measure the power carbon emission reduction and power quality related parameter variables, and establish a power carbon emission reduction and power quality related parameter variable time series based on the obtained measurement values; A module for calculating parameters associated with different time periods, calculating the influence factor of the parameters associated with carbon emission reduction and power quality on the parameters associated with carbon emission reduction and power quality in the next time period; The first prediction value calculation module calculates T according to the time series of the power carbon emission reduction and power quality associated parameter variables and the influence factor of the power carbon emission reduction and power quality associated parameter variables on the power carbon emission reduction and power quality associated parameter variables in the next time period. m+1 The predicted values ​​of the parameters associated with power carbon emission reduction and power quality in the time period; A normalization processing module performs normalization processing on the predicted value of the parameter variable associated with power carbon emission reduction and power quality to obtain a normalized value of the predicted value of the parameter variable associated with power carbon emission reduction and power quality; The second prediction value calculation module calculates T using the normalized value of the prediction value of the parameter associated with the power carbon emission reduction and power quality. m+1 Prediction value of correlation index between electricity carbon emission reduction and power quality in different time periods Predict the correlation index between the electricity carbon emission reduction and the power quality.

2. According to claim 1, a power grid resource intelligent prediction system is characterized in that: T1,T2,...,T z ,...,T m is a time period of m fixed time intervals, where m is a natural number, m∈1,2,…, z is the zth time period, and z is a natural number, z∈{1,2,…,m}; is the cumulative power generation of distributed photovoltaics in the zth time period; Q PV,max For T1, T2, ..., T z ,...,T m The maximum value of the distributed photovoltaic cumulative power generation in these m time periods; is the cumulative power generation of distributed wind power in the zth time period, Q AEP,max For T1, T2, ..., T z ,...,T m The maximum value of the cumulative power generation of the distributed wind power in the m time periods; Q is the cumulative battery charge in the zth time period, BT,max For T1, T2, ..., T z ,...,T m The maximum value of the accumulated charge of the battery in these m time periods; Q is the cumulative discharge of the battery in the zth time period, FB,max For T1, T2, ..., T z ,...,T m The maximum value of the cumulative discharge amount of the battery in these m time periods.

3. The power grid resource intelligent prediction system according to claim 2, characterized in that: The parameters associated with electricity carbon emission reduction and power quality include the cumulative distributed photovoltaic power generation, the cumulative distributed wind power generation, the cumulative battery charge, the cumulative battery discharge, the maximum voltage, and the maximum frequency within a specific time period.

4. The power grid resource intelligent prediction system according to claim 3 is characterized in that: The method for measuring the parameters associated with power carbon emission reduction and power quality is as follows: m fixed time intervals T1, T2, ..., T z ,...,T m , in the multi-energy microgrid, the measured value of the distributed photovoltaic cumulative power generation of the multi-energy microgrid is obtained by measuring Measurement value of cumulative power generation of distributed wind power in multi-energy microgrid Measurement value of the cumulative charge of multi-energy microgrid batteries Measurement value of cumulative discharge of multi-energy microgrid batteries Measured value of maximum voltage of multi-energy microgrid Measured value of the maximum frequency of multi-energy microgrid 5. The power grid resource intelligent prediction system according to claim 4, characterized in that: The formula for the time series of parameters associated with power carbon emission reduction and power quality is:

6. The power grid resource intelligent prediction system according to claim 5, characterized in that: The calculated power carbon emission reduction and power quality related parameter variables have an impact factor on the power carbon emission reduction and power quality related parameter variables in the next time period The formula is: Q is the influence factor of the power carbon emission reduction and power quality associated parameter variable in the zth time period on the power carbon emission reduction and power quality associated parameter variable in the next time period; PV,min is the cumulative power generation of the distributed photovoltaic system at T1, T2, ..., T z ,...,T m The minimum value of the measured value of these m fixed time intervals; Q AEP,max is the cumulative power generation of the distributed wind power at T1, T2, ..., T z ,...,T m The maximum value of the measured value of these m fixed time intervals; Q BT,max The cumulative charge capacity of the multi-energy microgrid battery at T1, T2, ..., T z ,...,T m The maximum value of the time period measurement of these m fixed time intervals; T z The cumulative discharge of the multi-energy microgrid battery in the time period is T1, T2, ..., T z ,...,T m The minimum value of the time period measurement of these m fixed time intervals.

7. The power grid resource intelligent prediction system according to claim 6, characterized in that: The formula for the predicted value of the parameter associated with power carbon emission reduction and power quality is: T m+1 The predicted value of the cumulative power generation of distributed photovoltaic power generation of the multi-energy microgrid in the time period; T m+1 The predicted value of the cumulative power generation of distributed wind power in the multi-energy microgrid during the time period; T m+1 The predicted value of the cumulative charge capacity of the multi-energy microgrid battery in the time period; T m+1 The predicted value of the cumulative discharge of the multi-energy microgrid battery in the time period; T m+1 The predicted value of the maximum voltage of the multi-energy microgrid in the time period; T m+1 The predicted value of the maximum frequency of the multi-energy microgrid during the time period.

8. The power grid resource intelligent prediction system according to claim 7, characterized in that: The formula for the normalization process is: T m+1 The normalized value of the predicted value of the distributed photovoltaic cumulative power generation of the multi-energy microgrid in the time period; T m+1 The normalized value of the predicted value of the cumulative power generation of distributed wind power in the multi-energy microgrid for the time period; T m+1 The normalized value of the predicted value of the cumulative charge capacity of the multi-energy microgrid battery in the time period; T m+1 Normalized value of the predicted value of the cumulative discharge of the multi-energy microgrid battery in the time period; T m+1 The normalized value of the predicted maximum voltage value of the multi-energy microgrid in the time period; T m+1 The normalized value of the maximum frequency prediction value of the multi-energy microgrid in the time period; Q AEP,min For T1, T2, ..., T z ,...,T m The minimum value of the measured value of the cumulative power generation of the multi-energy microgrid distributed wind power in the m fixed time intervals; Q BT,min For T1, T2, ..., T z ,...,T m The minimum value of the cumulative charge measurement value of the multi-energy microgrid battery in the m fixed time intervals; U CN,max For T1, T2, ..., T z ,...,T m The maximum value of the maximum voltage measurement value of the multi-energy microgrid in the m fixed time intervals; U CN,min For T1, T2, ..., T z ,...,T m The minimum value of the maximum voltage measurement value of the multi-energy microgrid in the m fixed time intervals; CK,max For T1, T2, ..., T z ,...,T m The maximum value of the maximum value of the multi-energy microgrid frequency measured during the m fixed time intervals; f CK,min For T1, T2, ..., T z ,...,T m The minimum value of the maximum value measured of the multi-energy microgrid frequency in the time period of these m fixed time intervals.

9. The power grid resource intelligent prediction system according to claim 8, characterized in that: The calculation T m+1 Prediction value of correlation index between electricity carbon emission reduction and power quality in different time periods The formula of the method is: If the predicted value of the correlation index between electricity carbon emission reduction and power quality is obtained If it is greater than or equal to 0.5, it is considered that the electricity carbon emission reduction in the next time period is more closely related to the power quality, that is, the volatility of the voltage and frequency of the multi-energy microgrid becomes larger. At this time, the distributed wind power power of the multi-energy microgrid should be reduced or the power of the multi-energy microgrid energy storage battery should be increased.

10. The power grid resource intelligent prediction system according to claim 9, characterized in that: When the predicted value of the correlation index between power carbon emission reduction and power quality is obtained When it is less than 0.5, it is judged that the electricity carbon emission reduction in the next time period is less correlated with the power quality, and the volatility of the voltage and frequency of the multi-energy microgrid is reduced to maintain the current distributed wind power of the multi-energy microgrid and the charging and discharging power of the multi-energy microgrid energy storage battery.

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