Method for predicting influence of multi-energy micro-grid carbon emission reduction intensity on electric energy quality

Abstract

The invention discloses a method for predicting the influence of multi-energy micro-grid carbon emission reduction intensity on electric energy quality, and the method comprises the steps: defining an influence index of the influence of the multi-energy micro-grid carbon emission reduction intensity on the electric energy quality, and the influence index comprises an influence parameter; measuring an influence parameter to obtain an influence parameter measurement value; according to the influence parameter measurement value, determining a time sequence and an influence factor of the influence parameter measurement value on the influence of the multi-energy micro-grid carbon emission reduction intensity on the electric energy quality in the next time period; determining an influence parameter prediction value of the next time period according to the time sequence and the influence factor; and adjusting the charging and discharging power of the energy storage battery of the multi-energy micro-grid or the power of the distributed wind power according to the influence parameter predicted value. The method is performed according to the steps. A very accurate and widely accepted method can be established to quantify the specific influence of the carbon emission reduction intensity on the electric energy quality so as to ensure that the multi-energy microgrid can utilize sufficient energy conversion to carry out optimized operation treatment.

Description

Technical Field

[0001] The present invention relates to the technical field of carbon emission reduction in multi-energy microgrids, and specifically to a method for predicting the impact of carbon emission reduction intensity in multi-energy microgrids on power quality. Background Art

[0002] In recent years, in order to achieve sustainable development of mankind, countries around the world have formulated carbon reduction strategies to achieve the "dual carbon" goal. In this context, building a multi-energy microgrid that couples multiple energy forms of electricity, heat, and gas has become an important way to locally consume distributed new energy and reduce carbon emissions. Multi-energy microgrids effectively integrate distributed generators such as wind turbines and photovoltaic power generation, distributed energy conversion equipment such as gas turbines and power-to-gas power supply units, and distributed energy storage devices such as power storage devices, heat storage devices, and gas storage devices to promote the low-carbon and clean transformation of electricity and realize the conversion, transmission, storage, distribution and efficient utilization of electric energy.

[0003] However, the power quality of multi-energy microgrids is affected to a certain extent during the low-carbon operation. Due to the random output characteristics of new energy, the voltage and frequency of multi-energy microgrids are unstable, and the low-carbon and safe operation capabilities of multi-energy microgrids are reduced. Therefore, it is necessary to propose a method that can predict the impact index of the carbon emission reduction intensity of multi-energy microgrids on power quality, so as to ensure that the multi-energy microgrid can use sufficient energy conversion for optimized operation. Summary of the invention

[0004] The present invention provides a method for predicting the impact of carbon emission reduction intensity of a multi-energy microgrid on power quality, so as to solve the defect that there is no method in the prior art that can predict the impact index of carbon emission reduction intensity of a multi-energy microgrid on power quality.

[0005] On the one hand, the present invention provides a method for predicting the impact of carbon emission reduction intensity of a multi-energy microgrid on power quality, comprising:

[0006] Define an impact index of the carbon emission reduction intensity of a multi-energy microgrid on power quality, wherein the impact index includes an impact parameter, and the impact parameter is used to determine the impact of the carbon emission reduction intensity of a multi-energy microgrid on power quality;

[0007] Measuring the influencing parameter to obtain an influencing parameter measurement value;

[0008] Determining a time series according to the measured values ​​of the influencing parameters;

[0009] Determine, based on the measured values ​​of the influencing parameters, the influence factors of the measured values ​​of the influencing parameters on the influence of the carbon emission reduction intensity of the multi-energy microgrid on the power quality in the next time period;

[0010] Determine, based on the time series and the influencing factors, a predicted value of an influencing parameter of the carbon emission reduction intensity of the multi-energy microgrid on the power quality in the next time period;

[0011] According to the predicted value of the influencing parameter, the charging and discharging power of the multi-energy microgrid energy storage battery or the power of the distributed wind power is adjusted so that the multi-energy microgrid can optimize the operation process by utilizing sufficient energy conversion.

[0012] According to the method for predicting the index of impact of carbon emission reduction intensity on power quality of a multi-energy microgrid provided by the present invention, the charging and discharging power of the multi-energy microgrid energy storage battery or the power of distributed wind power is adjusted according to the predicted value of the impact parameter, including:

[0013] Normalizing the predicted value of the influencing parameter to obtain a normalized value;

[0014] Determine, based on the normalized value, a predicted impact index of the carbon emission reduction intensity of the multi-energy microgrid on the power quality in the next time period;

[0015] According to the predicted impact index, the charging and discharging power of the multi-energy microgrid energy storage battery or the power of the distributed wind power is adjusted.

[0016] According to the method for predicting the impact index of carbon emission reduction intensity of a multi-energy microgrid on power quality provided by the present invention, the charging and discharging power of the multi-energy microgrid energy storage battery or the power of distributed wind power is adjusted according to the predicted impact index, including:

[0017] When the predicted impact index is greater than or equal to a preset value, the distributed wind power power of the multi-energy microgrid is reduced or the power of the multi-energy microgrid energy storage battery is increased.

[0018] According to the method for predicting the index of impact of carbon emission reduction intensity of multi-energy microgrid on power quality provided by the present invention, the influencing parameters include at least one of the following parameters: cumulative distributed photovoltaic power generation, cumulative distributed wind power power generation, cumulative battery charge, cumulative battery discharge, maximum voltage and maximum frequency.

[0019] According to the method for predicting the impact index of carbon emission reduction intensity on power quality of a multi-energy microgrid provided by the present invention, the definition formula of the impact index includes:

[0020]

[0021] Among them, XCID to is the impact index of the carbon emission reduction intensity of the multi-energy microgrid on the power quality; T1, T2, ..., T z ,...,T mis a time period of m fixed time intervals, where m is a natural number, m∈1,2,L, z is the zth time period, and z is a natural number, z∈{1,2,L,m};

[0022] Q PV,Tz is the cumulative power generation of distributed photovoltaics in the zth time period; Q pv,max is T1,T2,...,T z ,...,T m The maximum value of the distributed photovoltaic cumulative power generation in these m time periods;

[0023] Q AEP,Tz is the cumulative power generation of distributed wind power in the zth time period; Q AEP,max is T1,T2,...,T z ,...,T m The maximum value of the cumulative power generation of distributed wind power in these m time periods;

[0024] Q BT,Tz Q is the cumulative charge of the battery in the zth time period; BT,max is T1,T2,...,T z ,...,T m The maximum value of the battery's cumulative charge during these m time periods; Q FB,Tz is the cumulative discharge of the battery in the zth time period; Q FB,max is T1,T2,...,T z ,...,T m The maximum value of the battery's cumulative discharge during these m time periods.

[0025] According to the method for predicting the impact index of carbon emission reduction intensity on power quality of a multi-energy microgrid provided by the present invention, the definition formula of the time series includes:

[0026]

[0027] Among them, T1, T2, ..., T z ,...,T m is a time period of m fixed time intervals, m is a natural number, m∈1,2,L, z is the zth time period, and z is a natural number, z∈{1,2,L,m}; are the measured values ​​of the cumulative power generation of distributed photovoltaics in the 1st, 2nd, ..., zth, ..., mth time periods respectively; are the measured values ​​of the accumulated power generation of distributed wind power in the 1st, 2nd, ..., zth, ..., mth time periods respectively; are the measured values ​​of the cumulative battery charge in the 1st, 2nd, ..., zth, ..., mth time periods respectively; are the measured values ​​of the cumulative discharge of the battery in the 1st, 2nd, ..., zth, ..., mth time periods respectively;

[0028] are the measured values ​​of the maximum voltage in the 1st, 2nd, ..., zth, ..., mth time periods respectively; They are

[0029] The measured values ​​of the maximum frequency in 1, 2, ..., z, ..., m time periods.

[0030] According to the method for predicting the impact index of carbon emission reduction intensity on power quality of a multi-energy microgrid provided by the present invention, the definition formula of the impact factor includes:

[0031]

[0032] in, Q' is the factor affecting the parameters of carbon emission reduction intensity of multi-energy microgrid on power quality in the zth time period on the parameters of carbon emission reduction intensity of multi-energy microgrid on power quality in the next time period; PV,min is the cumulative power generation of distributed photovoltaics at T1, T2, ..., T z ,...,T m The minimum value of the measured values ​​of these m fixed time intervals; is the measured value of the cumulative power generation of distributed photovoltaics in the zth time period; Q' AEP,max is the cumulative power generation of distributed wind power at T1, T2, ..., T z ,...,T m The maximum value of the time period measurement of these m fixed time intervals; is the measured value of the cumulative power generation of distributed wind power in the zth time period; Q' BT,max is the cumulative charge 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; is the measured value of the battery's cumulative charge in the zth time period;

[0033] Q' FB,min is the cumulative discharge of the multi-energy microgrid battery at T1, T2, ..., T z ,...,T m The minimum value of the measured values ​​of these m fixed time intervals; is the measured value of the cumulative discharge of the battery in the zth time period;

[0034] According to the method for predicting the impact index of carbon emission reduction intensity on power quality of a multi-energy microgrid provided by the present invention, the definition formula of the predicted value of the influencing parameter includes:

[0035]

[0036] in, It is T m+1 Predicted value of the cumulative power generation of distributed photovoltaics in multi-energy microgrids in a certain time period; It is T m+1 Predicted value of cumulative power generation of distributed wind power in multi-energy microgrids during time periods; It is T m+1 Predicted value of the cumulative charge of multi-energy microgrid batteries in a time period;

[0037] It is T m+1 Predicted value of cumulative discharge of multi-energy microgrid batteries in time period; It is T m+1 The predicted value of the maximum voltage of the multi-energy microgrid in the time period; It is T m+1 The predicted value of the maximum frequency of the multi-energy microgrid in the time period, are the measured values ​​of the cumulative power generation of distributed photovoltaics in the 1st, 2nd, ..., zth, ..., mth time periods respectively;

[0038] are the measured values ​​of the accumulated power generation of distributed wind power in the 1st, 2nd, ..., zth, ..., mth time periods respectively; are the measured values ​​of the cumulative battery charge in the 1st, 2nd, ..., zth, ..., mth time periods respectively;

[0039] are the measured values ​​of the cumulative discharge of the battery in the 1st, 2nd, ..., zth, ..., mth time periods respectively; are the measured values ​​of the maximum voltage in the 1st, 2nd, ..., zth, ..., mth time periods respectively; are the measured values ​​of the maximum frequency in the 1st, 2nd, ..., zth, ..., mth time periods respectively.

[0040] According to the method for predicting the impact index of carbon emission reduction intensity of a multi-energy microgrid on power quality provided by the present invention, the predicted value of the influencing parameter is normalized to obtain a definition formula of the normalized value including:

[0041]

[0042] Among them, T m+1 is the next time period of time m, where m is a natural number, m∈1,2,L; It is T m+1 Normalized value of the predicted value of the cumulative power generation of distributed photovoltaics in multi-energy microgrids in a certain time period; It is Tm+1 Normalized value of the forecast value of the cumulative power generation of distributed wind power in multi-energy microgrids in the time period; It is T m+1 Normalized value of the predicted value of the cumulative charge capacity of multi-energy microgrid batteries in the time period; It is T m+1 Normalized value of the predicted value of the cumulative discharge of multi-energy microgrid batteries in the time period; It is T m+1 Normalized value of the maximum voltage prediction value of the multi-energy microgrid in the time period; It is T m+1 Normalized value of the maximum frequency prediction value of the multi-energy microgrid in the time period; It is T m+1 Prediction value of the cumulative power generation of distributed photovoltaic multi-energy microgrid in the time period; Q' PV,max is T1,T2,...,T z ,...,T m The maximum value of the measured value of the cumulative power generation of the multi-energy microgrid distributed photovoltaic in the m fixed time intervals; Q' PV,min is T1,T2,...,T z ,...,T m The minimum value of the measured value of the distributed photovoltaic cumulative power generation of the multi-energy microgrid in these m fixed time intervals; It is T m+1 Prediction value of the cumulative power generation of distributed wind power in multi-energy microgrids during the time period; Q' AEP,min is 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' AEP,max is T1,T2,...,T z ,...,T m The maximum value of the measured value of the cumulative power generation of the distributed wind power in the multi-energy microgrid during these m fixed time intervals; It is T m+1 Prediction of the cumulative charge of multi-energy microgrid batteries in a certain time period; Q' BT,max is T1,T2,...,T z ,...,T m The maximum value of the cumulative charge measurement of the multi-energy microgrid battery during these m fixed time intervals; Q' BT,min is T1,T2,...,T z ,...,T m The minimum value of the measured value of the cumulative charge capacity of the multi-energy microgrid battery in the m fixed time intervals; It is T m+1Prediction value of the cumulative discharge of multi-energy microgrid batteries in a certain time period; Q' FB,max is T1,T2,...,T z ,...,T m The maximum value of the cumulative discharge measurement of the multi-energy microgrid battery during these m fixed time intervals; Q' FB,min is T1,T2,...,T z ,...,T m The minimum value of the measured value of the cumulative discharge of the multi-energy microgrid battery in the m fixed time intervals; It is T m+1 The predicted value of the maximum voltage of the multi-energy microgrid in the time period; U' CN,max is 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 is T1,T2,...,T z ,...,T m The minimum value of the maximum voltage measurement value of the multi-energy microgrid in the time period of these m fixed time intervals; It is T m+1 The predicted value of the maximum frequency of the multi-energy microgrid in the time period, f' CK,max is T1,T2,...,T z ,...,T m The maximum value of the maximum frequency measurement value of the multi-energy microgrid in these m fixed time intervals; f' CK,min is T1,T2,...,T z ,...,T m The minimum value of the maximum frequency measurement of the multi-energy microgrid during these m fixed time intervals.

[0043] According to the method for predicting the impact index of carbon emission reduction intensity of a multi-energy microgrid on power quality provided by the present invention, the definition formula for determining the predicted impact index of the impact of carbon emission reduction intensity of a multi-energy microgrid on power quality in the next time period according to the normalized value includes:

[0044]

[0045] in, It is T m+1 The predicted impact index of the carbon emission reduction intensity of the multi-energy microgrid in a time period on the power quality.

[0046] The method for predicting the index of influence of carbon emission reduction intensity on power quality of a multi-energy microgrid provided by the present invention monitors the distributed photovoltaic cumulative power generation, distributed wind power cumulative power generation, battery cumulative charge, battery cumulative discharge, voltage maximum value, and frequency maximum value in real time within a specific time period and predicts the predicted value of the parameters of influence of carbon emission reduction intensity on power quality of the multi-energy microgrid in the next time period, calculates the predicted value of the index of influence of carbon emission reduction intensity on power quality of the multi-energy microgrid according to the obtained monitoring parameters and the predicted value of the parameters for 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 utilize sufficient energy conversion for optimized operation processing, solves the problem that the power quality and carbon emission reduction requirements of the multi-energy microgrid cannot be met, and achieves the beneficial effect of ensuring that the multi-energy microgrid can utilize sufficient energy conversion for optimized operation processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0048] Figure 1 It is a flow chart of a method for predicting the impact index of carbon emission reduction intensity of a multi-energy microgrid on power quality provided by an embodiment of the present invention.

[0049] Figure 2 A flowchart of a method for predicting an index of impact of carbon emission reduction intensity on power quality in a multi-energy microgrid provided in another embodiment of the present invention. DETAILED DESCRIPTION

[0050] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0051] As mentioned above, with the access of a large number of new energy power generation units, the low-carbon operation of multi-energy microgrids has been affected to a certain extent, and the power quality has been affected to a certain extent. Due to the random output characteristics of new energy, the voltage and frequency of the multi-energy microgrid have become unstable, and the low-carbon and safe operation capabilities of the multi-energy microgrid have been reduced.

[0052] To address this problem, the present invention provides a method for predicting the index of impact of carbon emission reduction intensity of a multi-energy microgrid on power quality. When the power quality and carbon emission reduction requirements of the multi-energy microgrid cannot be met, the method can adjust the charging and discharging power of the multi-energy microgrid energy storage battery or the power of distributed wind power to meet the power quality and carbon emission reduction requirements of the multi-energy microgrid, thereby ensuring that the multi-energy microgrid can utilize sufficient energy conversion for optimized operation.

[0053] Combine the following Figure 1 and Figure 2 The present invention is described in detail.

[0054] Figure 1 This is a flow chart of a method for predicting the impact index of carbon emission reduction intensity on power quality of a multi-energy microgrid provided by an embodiment of the present invention. Figure 1 As shown, the execution subject of a method for predicting the impact index of carbon emission reduction intensity on power quality of a multi-energy microgrid provided by an embodiment of the present invention may be a controller, and the method mainly includes the following steps:

[0055] 101. Define an impact index of the carbon emission reduction intensity of a multi-energy microgrid on power quality, wherein the impact index includes an impact parameter, and the impact parameter is used to determine the impact of the carbon emission reduction intensity of a multi-energy microgrid on power quality;

[0056] 102. Measure the influencing parameter to obtain a measured value of the influencing parameter;

[0057] 103. Determine a time series according to the measured values ​​of the influencing parameters;

[0058] 104. Determine, based on the measured value of the influencing parameter, an influencing factor of the influencing parameter measured value on the impact of the carbon emission reduction intensity of the multi-energy microgrid on the power quality in the next time period;

[0059] 105. Determine, based on the time series and the influencing factors, a predicted value of an influencing parameter of the carbon emission reduction intensity of the multi-energy microgrid on the power quality in the next time period;

[0060] 106. According to the predicted value of the influencing parameter, the charging and discharging power of the multi-energy microgrid energy storage battery or the power of the distributed wind power is adjusted so that the multi-energy microgrid can optimize the operation by utilizing sufficient energy conversion.

[0061] The method of the present invention monitors the influencing parameters in a specific time period in real time and predicts the predicted values ​​of the parameters affecting the power quality of the multi-energy microgrid carbon emission reduction intensity in the next time period, and calculates the predicted value of the impact index of the multi-energy microgrid carbon emission reduction intensity on the power quality based on the obtained monitoring parameters and the predicted values ​​of the parameters predicted for the next time period, and adjusts the charging and discharging power of the multi-energy microgrid energy storage battery or the power of the distributed wind power in real time based on the calculation results, ensuring that the multi-energy microgrid can use sufficient energy conversion for optimized operation. For the problem that the power quality and carbon emission reduction requirements of the multi-energy microgrid cannot be met, the method of the present invention can accurately judge the impact of the intensity of carbon emission reduction of the multi-energy microgrid in the next time period on the voltage and frequency stability of the power grid, and can ensure that the multi-energy microgrid can use sufficient energy conversion for optimized operation.

[0062] In order to refine and explain the steps of the present invention to ensure that the multi-energy microgrid can utilize sufficient energy conversion to optimize operation, the present invention also provides an additional embodiment. In this embodiment, if Figure 2 As shown, in an additional embodiment of the present invention, the execution subject of the method of the present invention may be a controller, and the specific steps are as follows:

[0063] 201: Define the impact index of carbon emission reduction intensity of multi-energy microgrid on power quality. The impact index includes impact parameters, which are used to judge the impact of carbon emission reduction intensity of multi-energy microgrid on power quality.

[0064] Specifically, in this embodiment, the influencing parameters described in step 201 at least include: the cumulative power generation of distributed photovoltaic Distributed wind power cumulative power generation Accumulated battery charge Accumulated battery discharge Maximum voltage And the maximum frequency At least one or more influencing parameters, including the same value, are used to determine the impact of the carbon emission reduction intensity of the multi-energy microgrid on the power quality. Then, based on these influencing parameters, we can calculate the impact index XCID to The formula for calculation is as follows:

[0065]

[0066] 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,L, z is the zth time period, and z is a natural number, z∈{1,2,L,m}; is the cumulative power generation of distributed photovoltaics in the zth time period; QPV,max is 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 is T1,T2,...,T z ,...,T m The maximum value of the cumulative power generation of distributed wind power in these m time periods; is the cumulative battery charge in the zth time period, Q BT,max is T1,T2,...,T z ,...,T m The maximum value of the battery's cumulative charge during these m time periods; is the cumulative discharge of the battery in the zth time period, Q FB,max is T1,T2,...,T z ,...,T m The maximum value of the battery's cumulative discharge during these m time periods.

[0067] 202: measuring the influencing parameter to obtain a measured value of the influencing parameter;

[0068] Specifically, in this embodiment, in step 202, according to the parameters defined in step 201, we select m fixed time intervals T1, T2, ..., T z ,...,T m , and measure the specific values ​​of these parameters according to these time periods, and the above-mentioned measured values ​​include at least one or more of the following: the measured value of the cumulative power generation of distributed photovoltaic Measurement value of distributed wind power cumulative power generation Measurement value of battery cumulative charge The measured value of the battery's cumulative discharge capacity The measured value of the maximum voltage and the measured value of the maximum frequency

[0069] 203: Determine a time series according to the measured value of the influencing parameter;

[0070] Specifically, in this embodiment, in step 203, according to the measured values ​​of the influencing parameters in step 202, a time series of the influencing parameters of the multi-energy microgrid carbon emission reduction intensity on the power quality is established. The specific formula is as follows:

[0071]

[0072] Then, according to the above measurement value annotation, correspondingly, These are the measured values ​​of the cumulative power generation of distributed photovoltaics in the 1st, 2nd, ..., zth, ..., mth time periods respectively; are the measured values ​​of the accumulated power generation of distributed wind power in the 1st, 2nd, ..., zth, ..., mth time periods respectively; are the measured values ​​of the cumulative battery charge in the 1st, 2nd, ..., zth, ..., mth time periods respectively; are the measured values ​​of the cumulative discharge of the battery in the 1st, 2nd, ..., zth, ..., mth time periods respectively; are the measured values ​​of the maximum voltage in the 1st, 2nd, ..., zth, ..., mth time periods respectively; are the measured values ​​of the maximum frequency in the 1st, 2nd, ..., zth, ..., mth time periods respectively.

[0073] 204: Determine, based on the measured value of the influencing parameter, an influencing factor of the influencing parameter measured value on the impact of the carbon emission reduction intensity of the multi-energy microgrid on the power quality in the next time period;

[0074] At the same time, in this embodiment, in step 204, according to the measured value of the influencing parameter in 202, the influencing factor of the carbon emission reduction intensity of the multi-energy microgrid on the power quality influencing parameter of the multi-energy microgrid carbon emission reduction intensity on the power quality influencing parameter in the next time period is calculated. The specific formula is as follows:

[0075]

[0076] in, Q' is the factor affecting the parameters of carbon emission reduction intensity of multi-energy microgrid on power quality in the zth time period on the parameters of carbon emission reduction intensity of multi-energy microgrid on power quality in the next time period; PV,min is the cumulative power generation of distributed photovoltaics at T1, T2, ..., T z ,...,T m The minimum value of the measured values ​​of these m fixed time intervals; is the measured value of the cumulative power generation of distributed photovoltaics in the zth time period; Q' AEP,max is the cumulative power generation of distributed wind power at T1, T2, ..., T z ,...,T m The maximum value of the time period measurement of these m fixed time intervals; is the measured value of the cumulative power generation of distributed wind power in the zth time period; Q' BT,max is the cumulative charge 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; is the measured value of the battery's cumulative charge in the zth time period; Q' FB,min is the cumulative discharge of the multi-energy microgrid battery at T1, T2, ..., T z ,...,T m The minimum value of the measured values ​​of these m fixed time intervals; is the measured value of the cumulative discharge of the battery in the zth time period;

[0077] 205: Determine, based on the time series and the influencing factors, a predicted value of an influencing parameter of the carbon emission reduction intensity of the multi-energy microgrid on the power quality in the next time period;

[0078] Specifically, in this embodiment, in step 205, in combination with step 203 and step 204, according to the time series of the parameters affecting power quality by the carbon emission reduction intensity of the multi-energy microgrid and the influence factor of the parameters affecting power quality by the carbon emission reduction intensity of the multi-energy microgrid on the parameters affecting power quality in the next time period, T is calculated. m+1 The predicted value of the parameters affecting power quality by the carbon emission reduction intensity of the multi-energy microgrid in a certain time period is shown in the following formula:

[0079]

[0080] in, It is T m+1 Predicted value of the cumulative power generation of distributed photovoltaics in multi-energy microgrids in a certain time period; It is T m+1 Predicted value of cumulative power generation of distributed wind power in multi-energy microgrids during time periods; It is T m+1 Predicted value of the cumulative charge of multi-energy microgrid batteries in a time period; It is T m+1 Predicted value of cumulative discharge of multi-energy microgrid batteries in time period; It is T m+1 The predicted value of the maximum voltage of the multi-energy microgrid in the time period; It is T m+1 The predicted value of the maximum frequency of the multi-energy microgrid in the time period. Some of the above parameters have been explained in the previous article, so they will not be repeated here. The same applies below.

[0081] 206: According to the predicted value of the influencing parameter, the charging and discharging power of the multi-energy microgrid energy storage battery or the power of the distributed wind power is adjusted, so that the multi-energy microgrid can optimize the operation process by utilizing sufficient energy conversion;

[0082] Specifically, in this embodiment, in step 206, the charging and discharging power of the multi-energy microgrid energy storage battery or the power of the distributed wind power is adjusted according to the predicted value of the influencing parameter, so that the multi-energy microgrid can optimize the operation process by utilizing sufficient energy conversion, which is specifically manifested as follows: as shown in step 207,

[0083] 207: performing normalization processing on the predicted value of the influencing parameter to obtain a normalized value;

[0084] The predicted values ​​of the parameters affecting the power quality of the multi-energy microgrid carbon emission reduction intensity are normalized to obtain the normalized values ​​of the predicted values ​​of the parameters affecting the power quality of the multi-energy microgrid carbon emission reduction intensity. The definition formula of the obtained normalized value includes:

[0085]

[0086] Among them, T m+1 is the next time period of time m, where m is a natural number, m∈1,2,L; It is T m+1 Normalized value of the predicted value of the cumulative power generation of distributed photovoltaics in multi-energy microgrids in a certain time period; It is T m+1 Normalized value of the forecast value of the cumulative power generation of distributed wind power in multi-energy microgrids in the time period; It is T m+1 Normalized value of the predicted value of the cumulative charge capacity of multi-energy microgrid batteries in the time period; It is T m+1 Normalized value of the predicted value of the cumulative discharge of multi-energy microgrid batteries in the time period; It is T m+1 Normalized value of the maximum voltage prediction value of the multi-energy microgrid in the time period; It is T m+1 Normalized value of the maximum frequency prediction value of the multi-energy microgrid in the time period; It is T m+1 Prediction value of the cumulative power generation of distributed photovoltaic multi-energy microgrid in the time period; Q' PV,max is T1,T2,...,T z ,...,T m The maximum value of the measured value of the cumulative power generation of the multi-energy microgrid distributed photovoltaic in the m fixed time intervals; Q' PV,min is T1,T2,...,T z ,...,T m The minimum value of the measured value of the distributed photovoltaic cumulative power generation of the multi-energy microgrid in these m fixed time intervals; It is T m+1 Prediction value of the cumulative power generation of distributed wind power in multi-energy microgrids during the time period; Q' AEP,minis 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' AEP,max is T1,T2,...,T z ,...,T m The maximum value of the measured value of the cumulative power generation of the distributed wind power in the multi-energy microgrid during these m fixed time intervals; It is T m+1 Prediction of the cumulative charge of multi-energy microgrid batteries in a certain time period; Q' BT,max is T1,T2,...,T z ,...,T m The maximum value of the cumulative charge measurement of the multi-energy microgrid battery during these m fixed time intervals; Q' BT,min is T1,T2,...,T z ,...,T m The minimum value of the measured value of the cumulative charge capacity of the multi-energy microgrid battery in the m fixed time intervals; It is T m+1 Prediction value of the cumulative discharge of multi-energy microgrid batteries in a certain time period; Q' FB,max is T1,T2,...,T z ,...,T m The maximum value of the cumulative discharge measurement of the multi-energy microgrid battery during these m fixed time intervals; Q' FB,min is T1,T2,...,T z ,...,T m The maximum value of the measured value of the cumulative discharge of the multi-energy microgrid battery during these m fixed time intervals; It is T m+1 The predicted value of the maximum voltage of the multi-energy microgrid in the time period; U' CN,max is 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 is T1,T2,...,T z ,...,T m The minimum value of the maximum voltage measurement value of the multi-energy microgrid in the time period of these m fixed time intervals; It is T m+1 The predicted value of the maximum frequency of the multi-energy microgrid in the time period, f' CK,max is T1,T2,...,T z ,...,T mThe maximum value of the maximum frequency measurement value of the multi-energy microgrid in these m fixed time intervals; f' CK,min is T1,T2,...,T z ,...,T m The minimum value of the maximum frequency measurement of the multi-energy microgrid during these m fixed time intervals.

[0087] Then, as shown in step 208:

[0088] 208: Determine, according to the normalized value, a predicted impact index of the carbon emission reduction intensity of the multi-energy microgrid on the power quality in the next time period;

[0089] The calculation formula is as follows:

[0090]

[0091] in, It is T m+1 The predicted impact index of the carbon emission reduction intensity of the multi-energy microgrid in a time period on the power quality.

[0092] 209: Finally, the predicted impact index is compared with the preset value. When the predicted impact index is greater than or equal to the preset value, the power of the multi-energy microgrid distributed wind power is reduced or the power of the multi-energy microgrid energy storage battery is increased. When the predicted impact index is less than the preset value, it remains unchanged.

[0093] That is, if the value obtained is greater than or equal to 0.5, it is determined that the carbon emission reduction intensity of the multi-energy microgrid in the next time period has a great impact on the power quality, and the volatility of the voltage and frequency of the multi-energy microgrid becomes larger. At this time, the distributed wind power of the multi-energy microgrid is reduced or the charging and discharging power of the multi-energy microgrid energy storage battery is increased;

[0094] If the predicted value is less than 0.5, it is determined that the carbon emission reduction intensity of the multi-energy microgrid in the next time period has little impact on 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 of the multi-energy microgrid and the charging and discharging power of the multi-energy microgrid energy storage battery are maintained.

[0095] In combination with the first two embodiments, in order to further explain the method of the present invention, the present invention also provides another embodiment, in which the constant 5 is assigned to m. Similarly, the execution subject of the method of the embodiment can be a controller, so we get the following steps:

[0096] 301~302 are the same as 201~202;

[0097] 303: Determine a time series according to the measured value of the influencing parameter;

[0098] Specifically, 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 measurement 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 interval time periods T1, T2, T3, T4, and T5. The maximum value of the multi-energy microgrid frequency is measured in the fixed time intervals T1, T2, T3, T4, T5 Based on all the above measured values, the time series of parameters affecting the power quality of the multi-energy microgrid carbon emission reduction intensity is established as shown in the following formula:

[0099]

[0100] 304: According to the measured value of the influencing parameter, determine the influence factor of the measured value of the influencing parameter on the influence of the carbon emission reduction intensity of the multi-energy microgrid on the power quality in the next time period. In this embodiment, through the calculation formula in 204, we can obtain the influence factor of the influencing parameter of the carbon emission reduction intensity of the multi-energy microgrid on the power quality in the next time period as follows:

[0101] 305: Determine, according to the time series and the influencing factors, a predicted value of an influencing parameter of the carbon emission reduction intensity of the multi-energy microgrid on the power quality in the next time period;

[0102] In this embodiment, the calculation formula is as follows:

[0103]

[0104] in, is the predicted value of the cumulative power generation of distributed photovoltaics in the multi-energy microgrid during the T6 period; It is 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 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; It is the predicted value of the maximum voltage of the multi-energy microgrid during the T6 period; It is the predicted value of the maximum frequency of the multi-energy microgrid in the T6 period.

[0105] 306: Same as 206;

[0106] 307: performing normalization processing on the predicted value of the influencing parameter to obtain a normalized value;

[0107] In this embodiment, when m=5, the T m+1 =T6, therefore, in this embodiment, the predicted value of the parameter affecting the power quality of the multi-energy microgrid carbon emission reduction intensity in the T6 time period is normalized as follows:

[0108]

[0109] in, It is the normalized value of the predicted cumulative power generation of distributed photovoltaic power generation of multi-energy microgrid in time period T6; It is the normalized value of the predicted cumulative power generation of distributed wind power in the multi-energy microgrid during the T6 period; It is the normalized value of the predicted value of the 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;

[0110] 308: Determine, according to the normalized value, a predicted impact index of the carbon emission reduction intensity of the multi-energy microgrid on the power quality in the next time period;

[0111] In this implementation, the predicted value of the impact index of carbon emission reduction intensity of multi-energy microgrid on power quality in the T6 period is for:

[0112]

[0113] 309: Finally, the predicted impact index is compared with the preset value. When the predicted impact index is greater than or equal to the preset value, the distributed wind power of the multi-energy microgrid is reduced or the power of the multi-energy microgrid energy storage battery is increased. When the predicted impact index is less than the preset value, the distributed wind power of the multi-energy microgrid or the power of the multi-energy microgrid energy storage battery is maintained unchanged.

[0114] If the value obtained is greater than or equal to 0.5, it is determined that the carbon emission reduction intensity of the multi-energy microgrid in the next time period has a great impact on the power quality, and the volatility of the voltage and frequency of the multi-energy microgrid becomes larger. At this time, the distributed wind power of the multi-energy microgrid is reduced or the charging and discharging power of the multi-energy microgrid energy storage battery is increased;

[0115] If the predicted value is less than 0.5, it is determined that the carbon emission reduction intensity of the multi-energy microgrid in the next time period has little impact on 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 of the multi-energy microgrid and the charging and discharging power of the multi-energy microgrid energy storage battery are maintained.

[0116] It should be understood that in various embodiments of the present invention, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0117] In addition, the terms "system" and "network" are often used interchangeably in this article. The term "and / or" in this article is only a description of the association relationship of the associated objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0118] It should be understood that in the embodiment of the present invention, "B corresponding to A" means that B is associated with A, and B can be determined according to A. However, it should also be understood that determining B according to A does not mean determining B only according to A, and B can also be determined according to A and / or other information.

[0119] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0120] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0121] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, or it can be an electrical, mechanical or other form of connection.

[0122] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the embodiments of the present invention.

[0123] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0124] Through the description of the above embodiments, it is clear to those skilled in the art that the present invention can be implemented in hardware, firmware, or a combination thereof. When software is used for implementation, the above functions can be stored in a computer-readable medium or transmitted as one or more instructions or codes on a computer-readable medium. Computer-readable media include computer storage media and communication media, wherein the communication media include any medium that facilitates the transmission of a computer program from one place to another. The storage medium can be any available medium that a computer can access. Taking this as an example but not limited to: a computer-readable medium can include RAM, ROM, EEPROM, CD-ROM or other optical disk storage, disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store a desired program code in the form of an instruction or data structure and can be accessed by a computer. In addition. Any connection can be appropriately a computer-readable medium. For example, if the software is transmitted from a website, a server or other remote source using a coaxial cable, an optical fiber cable, a twisted pair, a digital subscriber line (DSL), or wireless technologies such as infrared, radio, and microwaves, then the coaxial cable, optical fiber cable, twisted pair, DSL, or wireless technologies such as infrared, wireless, and microwaves are included in the fixing of the medium. As used in the present invention, disk and disc include compact disc (CD), laser disc, optical disc, digital versatile disc (DVD), floppy disk and blue-ray disc, wherein disk usually reproduces data magnetically, while disc uses laser to reproduce data optically. The above combination should also be included in the scope of protection of computer-readable media.

[0125] In short, the above is only a preferred embodiment of the technical solution of the present invention, and is not intended to limit the protection scope of the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for predicting the impact of carbon emission reduction intensity of multi-energy microgrids on power quality, characterized in that: include: Define an impact index of the carbon emission reduction intensity of a multi-energy microgrid on power quality, wherein the impact index includes an impact parameter, and the impact parameter is used to determine the impact of the carbon emission reduction intensity of a multi-energy microgrid on power quality; Measuring the influencing parameter to obtain an influencing parameter measurement value; Determining a time series according to the measured values ​​of the influencing parameters; Determine, based on the measured values ​​of the influencing parameters, the influence factors of the measured values ​​of the influencing parameters on the influence of the carbon emission reduction intensity of the multi-energy microgrid on the power quality in the next time period; Determine, based on the time series and the influencing factors, a predicted value of an influencing parameter of the carbon emission reduction intensity of the multi-energy microgrid on the power quality in the next time period; According to the predicted value of the influencing parameter, the charging and discharging power of the multi-energy microgrid energy storage battery or the power of the distributed wind power is adjusted so that the multi-energy microgrid can optimize the operation process by utilizing sufficient energy conversion.

2. The method according to claim 1, characterized in that The step of adjusting the charging and discharging power of the multi-energy microgrid energy storage battery or the power of the distributed wind power according to the predicted value of the influencing parameter includes: Normalizing the predicted value of the influencing parameter to obtain a normalized value; Determine, based on the normalized value, a predicted impact index of the carbon emission reduction intensity of the multi-energy microgrid on the power quality in the next time period; According to the predicted impact index, the charging and discharging power of the multi-energy microgrid energy storage battery or the power of the distributed wind power is adjusted.

3. The method according to claim 2, characterized in that The step of adjusting the charging and discharging power of the multi-energy microgrid energy storage battery or the power of the distributed wind power according to the predicted impact index includes: When the predicted impact index is greater than or equal to a preset value, the distributed wind power power of the multi-energy microgrid is reduced or the power of the multi-energy microgrid energy storage battery is increased.

4. The method according to any one of claims 1 to 3, characterized in that The influencing parameters include at least one of the following parameters: distributed photovoltaic cumulative power generation, distributed wind power cumulative power generation, battery cumulative charge capacity, battery cumulative discharge capacity, maximum voltage and maximum frequency.

5. The method according to claim 1, characterized in that The definition formula of the impact index includes: Among them, XCID to is the impact index of the carbon emission reduction intensity of the multi-energy microgrid on the power quality; T1, T2, ..., T z ,...,T m is a time period of m fixed time intervals, where m is a natural number, m∈1,2,L, z is the zth time period, and z is a natural number, z∈{1,2,L,m}; Q PV,Tz is the cumulative power generation of distributed photovoltaics in the zth time period; Q pv,max is T1,T2,...,T z ,...,T m The maximum value of the distributed photovoltaic cumulative power generation in these m time periods; Q AEP,Tz is the cumulative power generation of distributed wind power in the zth time period; Q AEP,max is T1,T2,...,T z ,...,T m The maximum value of the cumulative power generation of distributed wind power in these m time periods; Q BT,Tz Q is the cumulative charge of the battery in the zth time period; BT,max is T1,T2,...,T z ,...,T m The maximum value of the battery's cumulative charge during these m time periods; Q FB,Tz is the cumulative discharge of the battery in the zth time period; Q FB,max is T1,T2,...,T z ,...,T m The maximum value of the battery's cumulative discharge during these m time periods.

6. The method according to claim 1 or 5, characterized in that: The definition formula of the time series includes: Among them, T1, T2, ..., T z ,...,T m is a time period of m fixed time intervals, m is a natural number, m∈1,2,L, z is the zth time period, and z is a natural number, z∈{1,2,L,m}; are the measured values ​​of the cumulative power generation of distributed photovoltaics in the 1st, 2nd, ..., zth, ..., mth time periods respectively; are the measured values ​​of the accumulated power generation of distributed wind power in the 1st, 2nd, ..., zth, ..., mth time periods respectively; are the measured values ​​of the cumulative battery charge in the 1st, 2nd, ..., zth, ..., mth time periods respectively; are the measured values ​​of the cumulative discharge of the battery in the 1st, 2nd, ..., zth, ..., mth time periods respectively; are the measured values ​​of the maximum voltage in the 1st, 2nd, ..., zth, ..., mth time periods respectively; They are The measured values ​​of the maximum frequency in 1, 2, ..., z, ..., m time periods.

7. The method according to claim 1 or 5, characterized in that: The definition formula of the impact factor includes: in, Q' is the factor affecting the parameters of carbon emission reduction intensity of multi-energy microgrid on power quality in the zth time period on the parameters of carbon emission reduction intensity of multi-energy microgrid on power quality in the next time period; PV,min is the cumulative power generation of distributed photovoltaics at T1, T2, ..., T z ,...,T m The minimum value of the measured values ​​of these m fixed time intervals; is the measured value of the cumulative power generation of distributed photovoltaics in the zth time period; Q' AEP,max is the cumulative power generation of distributed wind power at T1, T2, ..., T z ,...,T m The maximum value of the time period measurement of these m fixed time intervals; is the measured value of the cumulative power generation of distributed wind power in the zth time period; Q' BT,max is the cumulative charge 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; is the measured value of the battery's cumulative charge in the zth time period; Q' FB,min is the cumulative discharge of the multi-energy microgrid battery at T1, T2, ..., T z ,...,T m The minimum value of the measured values ​​of these m fixed time intervals; It is the measured value of the cumulative discharge of the battery in the zth time period.

8. The method according to claim 7, characterized in that The definition formula of the predicted value of the influencing parameter includes: in, It is T m+1 Predicted value of the cumulative power generation of distributed photovoltaics in multi-energy microgrids in a certain time period; It is T m+1 Predicted value of cumulative power generation of distributed wind power in multi-energy microgrids during time periods; It is T m+1 Predicted value of the cumulative charge of multi-energy microgrid batteries in a time period; It is T m+1 Predicted value of cumulative discharge of multi-energy microgrid batteries in time period; It is T m+1 The predicted value of the maximum voltage of the multi-energy microgrid in the time period; It is T m+1 The predicted value of the maximum frequency of the multi-energy microgrid in the time period, are the measured values ​​of the cumulative power generation of distributed photovoltaics in the 1st, 2nd, ..., zth, ..., mth time periods respectively; are the measured values ​​of the accumulated power generation of distributed wind power in the 1st, 2nd, ..., zth, ..., mth time periods respectively; are the measured values ​​of the cumulative battery charge in the 1st, 2nd, ..., zth, ..., mth time periods respectively; are the measured values ​​of the cumulative discharge of the battery in the 1st, 2nd, ..., zth, ..., mth time periods respectively; are the measured values ​​of the maximum voltage in the 1st, 2nd, ..., zth, ..., mth time periods respectively; are the measured values ​​of the maximum frequency in the 1st, 2nd, ..., zth, ..., mth time periods respectively.

9. The method according to claim 2, characterized in that: The normalization process is performed on the predicted value of the influencing parameter to obtain a definition formula of the normalized value including: Among them, T m+1 is the next time period of time m, where m is a natural number, m∈1,2,L; It is T m+1 Normalized value of the predicted value of the cumulative power generation of distributed photovoltaics in multi-energy microgrids in a certain time period; It is T m+1 Normalized value of the forecast value of the cumulative power generation of distributed wind power in multi-energy microgrids in the time period; It is T m+1 Normalized value of the predicted value of the cumulative charge capacity of multi-energy microgrid batteries in the time period; It is T m+1 Normalized value of the predicted value of the cumulative discharge of multi-energy microgrid batteries in the time period; It is T m+1 Normalized value of the maximum voltage prediction value of the multi-energy microgrid in the time period; It is T m+1 Normalized value of the maximum frequency prediction value of the multi-energy microgrid in the time period; It is T m+1 Prediction value of the cumulative power generation of distributed photovoltaic multi-energy microgrid in the time period; Q' PV,max is T1,T2,...,T z ,...,T m The maximum value of the measured value of the cumulative power generation of the multi-energy microgrid distributed photovoltaic in the m fixed time intervals; Q' PV,min is T1,T2,...,T z ,...,T m The minimum value of the measured value of the distributed photovoltaic cumulative power generation of the multi-energy microgrid in these m fixed time intervals; It is T m+1 Prediction value of the cumulative power generation of distributed wind power in multi-energy microgrids during the time period; Q' AEP,min is 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' AEP,max is T1,T2,...,T z ,...,T m The maximum value of the measured value of the cumulative power generation of the distributed wind power in the multi-energy microgrid during these m fixed time intervals; It is T m+1 Prediction of the cumulative charge of multi-energy microgrid batteries in a certain time period; Q' BT,max is T1,T2,...,T z ,...,T m The maximum value of the cumulative charge measurement of the multi-energy microgrid battery during these m fixed time intervals; Q' BT,min is T1,T2,...,T z ,...,T m The minimum value of the measured value of the cumulative charge capacity of the multi-energy microgrid battery in the m fixed time intervals; It is T m+1 Prediction value of the cumulative discharge of multi-energy microgrid batteries in a certain time period; Q' FB,max is T1,T2,...,T z ,...,T m The maximum value of the cumulative discharge measurement of the multi-energy microgrid battery during these m fixed time intervals; Q' FB,min is T1,T2,...,T z ,...,T m The minimum value of the measured value of the cumulative discharge of the multi-energy microgrid battery in the m fixed time intervals; It is T m+1 The predicted value of the maximum voltage of the multi-energy microgrid in the time period; U' CN,max is 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 is T1,T2,...,T z ,...,T m The minimum value of the maximum voltage measurement value of the multi-energy microgrid in the time period of these m fixed time intervals; It is T m+1 The predicted value of the maximum frequency of the multi-energy microgrid in the time period, f' CK,max is T1,T2,...,T z ,...,T m The maximum value of the maximum frequency measurement value of the multi-energy microgrid in these m fixed time intervals; f' CK,min is T1,T2,...,T z ,...,T m The minimum value of the maximum frequency measurement of the multi-energy microgrid during these m fixed time intervals.

10. The method according to claim 9, characterized in that The definition formula for determining the predicted impact index of the carbon emission reduction intensity of the multi-energy microgrid on the power quality in the next time period according to the normalized value includes: in, It is T m+1 The predicted impact index of the carbon emission reduction intensity of the multi-energy microgrid in a time period on the power quality.

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