A method for analyzing and evaluating multi-energy complementary characteristics

Abstract

The present invention provides a method for analyzing and evaluating multi-energy complementary characteristics, and belongs to the research field of multi-energy complementarity of new energy. Aiming at the problem that the complementary characteristics of a multi-energy complementary system cannot be quantitatively and qualitatively evaluated in the analysis of multi-energy complementary characteristics, the present invention provides a method for analyzing and evaluating multi-energy complementary characteristics, which can not only quantitatively analyze the degree of complementarity and stability between different new energy outputs at any time scale, but also qualitatively analyze the complementary characteristics of the multi-energy complementary system. The construction of the method of the present invention is not limited to a specific multi-energy complementary system or a new energy output object, and can calculate the multi-energy complementary rate index and the multi-energy volatility index at all time scales according to actual conditions such as data and external environment. It has strong adaptability, low application threshold, and can be widely promoted and applied.

Description

Technical Field

[0001] The present invention belongs to the research field of new energy multi-energy complementarity, and more specifically, relates to a multi-energy complementarity characteristic analysis and evaluation method. Background Art

[0002] At present, quantitative evaluation of the complementary characteristics of renewable energy sources in multi-energy complementary systems is the key to designing, operating and optimizing multi-energy complementary systems. However, most of the existing quantitative description indicators of renewable energy complementarity are constructed based on correlation coefficients, and the degree of complementarity and the degree of fluctuation are often evaluated separately. There is a lack of a unified indicator mathematical model and complementarity evaluation system that can accurately and quantitatively describe the overall degree of complementarity and stability of different energy sources in complementary systems. This has become a key issue that needs to be urgently addressed in the design and operation of multi-energy complementary systems. Summary of the invention

[0003] The present invention discloses a method for analyzing and evaluating multi-energy complementary characteristics, and belongs to the research field of multi-energy complementarity of new energy. Aiming at the problem that the complementary characteristics of a multi-energy complementary system cannot be quantitatively and qualitatively evaluated in the analysis of multi-energy complementary characteristics, the present invention provides a method for analyzing and evaluating multi-energy complementary characteristics, which can not only quantitatively analyze the degree of complementarity and stability between different new energy outputs at any time scale, but also qualitatively analyze the complementary characteristics of the multi-energy complementary system. The construction of the method of the present invention is not limited to a specific multi-energy complementary system or a new energy output object, and can calculate the multi-energy complementary rate index and the multi-energy volatility index at all time scales according to actual situations (data, external environment, etc.), has strong adaptability, low application threshold, and can be widely promoted and applied.

[0004] To achieve the above object, the present invention provides a method for analyzing and evaluating multi-energy complementary characteristics, comprising:

[0005] S1. Multi-energy complementarity characteristic analysis and evaluation indicators, including the multi-energy complementarity rate index MECI used to evaluate the degree of complementarity and the multi-energy volatility index MEVI used to evaluate the degree of stability;

[0006] S2. The qualitative evaluation basis of the multi-energy complementarity characteristic analysis and evaluation method is the two-dimensional mapping of the multi-energy complementarity rate index MECI and the multi-energy volatility index MEVI;

[0007] S3. The value range of the multi-energy complementarity index MECI is (0,1). The larger the value, the stronger the degree of complementarity between different energy sources in the multi-energy complementarity system, and the smaller the value, the weaker the degree of complementarity between different energy sources in the multi-energy complementarity system.

[0008] S4. The value range of the multi-energy volatility index MEVI is (0,1). The larger the value, the stronger the fluctuation between different energy sources in the multi-energy complementary system, that is, the more unstable it is. The smaller the value, the weaker the fluctuation between different energy sources in the multi-energy complementary system, that is, the more stable it is.

[0009] Preferably, the calculation formula of the multi-energy complementation index MECI is as follows:

[0010]

[0011] Where DECI represents the complementary characteristic evaluation index of each pair of new energy sources in the same multi-energy complementary system.

[0012] Preferably, the mathematical model of DECI in the calculation formula of the multi-energy complementarity index MECI is as follows:

[0013]

[0014] In the formula, in the process of calculating the pairwise energy complementarity, ZOD represents the non-power-off period of the compensated energy, and NZOD represents the power-off period of the compensated energy; W ZOD is the proportion of ZOD time periods in the overall sample of compensated energy output process; W NZOD is the proportion of NZOD time periods in the overall sample of the compensated energy output process; T energy1 It is expressed as the total number of time periods of the overall sample of the compensated energy output process; CDSS energy [t] represents the time series of the output coefficient change rate of energy in the NZOD period, which is obtained by calculating the derivative at any time by the numerical central difference method; SCDSS[t] represents the time series of the sum of the complementary dual energy output coefficient change rates, ε i Expressed as different multiples of the standard deviation of the SCDSS[t] sequence, Expressed as their respective ε i The weight value, R(ε i ) is expressed as the proportion of time periods in the SCDSS[t] sequence that are within the stable threshold interval, represented by ε i The stable threshold interval represented by represents the interval of strong complementarity, that is, the two-dimensional plane with values ​​close to both sides of 0. i ) value is larger, which means that there are more periods in the output time period in which the sum of the complementary energy output change rate is close to 0, that is, it falls within the stable threshold range, which indicates that the SCDSS[t] sequence is closer to good complementarity; SPOCS[t] represents the compensation energy output coefficient sequence. In the ZOD period, the output coefficient of the compensation energy should be closer to 1 more stably to reflect the excellent complementarity; μ i Expressed as different output coefficients, Expressed as μi The weight value, R(μ i ) is expressed as the proportion of time periods in the sequence that are in the stable threshold interval; CC pearson ,CC kendall ,CC spearman Represents the Pearson, Kendall, and Spearman correlation coefficients between the change rates of two energy output processes; CC spearman R i and S i Represents sequence CDSS energy1 [t] and CDSS energy2 [t] is the rank of the sequence; i is a simple counter; STD() represents the standard deviation of the sequence in brackets; D() represents the square of the standard deviation of the sequence in brackets.

[0015] Preferably, the mathematical model of the multi-energy volatility index MEVI is as follows:

[0016]

[0017] Where FE(i,j) represents the count of fluctuation events, and X=ΠY{Z} represents a counting function. If the condition Z in X=ΠY{Z} holds, then X in X=ΠY{Z} is equal to Y in X=ΠY{Z}, otherwise it is equal to 0. Therefore, the formula of FE(i,j) represents the condition if P e Yes (P i ,…,P e ), then FE(i,j) is equal to 1, otherwise it is equal to 0; according to the definition of FE(i,j) and X=ΠY{Z}, it is obvious that: and Then FE(i,j) can be regarded as a fluctuation event; ρ m Expressed as the volatility of the mth volatility event, P j -P i It is expressed as the difference between the final output and the initial output of this fluctuation event, t j -t i It represents the difference between the end time and the beginning time of this volatility event; EFE represents the effective volatility event, EDLE represents m (Effective Differences of Local Extrema) is expressed as the extreme difference of effective fluctuation events. Only when the difference between the end output and the initial output of a fluctuation event is P j -P iWhen it is greater than the specified volatility threshold ε, this volatility event FE(i,j) is regarded as an effective volatility event EFE(i,j,ε), and then its effective extreme value difference EDLE is calculated. m (i,j), and finally get the corresponding volatility ρ m ; The volatility index SEVI of an energy output process time series is equal to the ensemble average of its effective volatility events.

[0018] Preferably, when the multi-energy complementarity index MECI is greater than 0.5, the system is strong complementarity SC (Strong Complementarity), otherwise it is weak complementarity WC (Weak Complementarity); when the multi-energy volatility index MEVI is less than 0.5, the system is a strong stable complementary system S (Stability), otherwise it is a weak stable system I (Instability). Therefore, in two-dimensional space, the multi-energy complementary system is characterized into 16 different types, and the value range and description of the specific types are as follows Figure 2 As shown in Table 1 below:

[0019] Table 1. Description of the multi-energy complementary characteristics evaluation system

[0020]

[0021]

[0022] Preferably, the evaluation acquisition process is specifically as follows:

[0023] Step 1, collecting the power output data of the multi-energy complementary power generation system, pre-processing the output data of different power sources, and obtaining the per-unit output process time series data;

[0024] Step 2, based on the per-unit output process time series data obtained after preprocessing, calculating the multi-energy complementary rate index MECI of the multi-energy complementary power generation system over the entire time;

[0025] Step 3, based on the per-unit output process time series data obtained after preprocessing, calculating the multi-energy volatility index MEVI of the multi-energy complementary power generation system over the entire time period;

[0026] Step 4, based on the per-unit output process time series data obtained after preprocessing, a sliding time window is used for calculation to obtain the distribution of the MECI and MEVI in two-dimensional space, and finally obtain the evaluation of the multi-energy complementary power generation system in the multi-energy complementary characteristic evaluation method system.

[0027] The beneficial effects of the present invention are:

[0028] The present invention provides a method for analyzing and evaluating the characteristics of multi-energy complementarity, which can not only quantitatively analyze the degree of complementarity and stability between different new energy outputs at any time scale, but also qualitatively analyze the complementary characteristics of the multi-energy complementary system. The construction of the method of the present invention is not limited to a specific multi-energy complementary system or a new energy output object, and can calculate the multi-energy complementarity rate index and the multi-energy volatility index at all time scales according to actual conditions (data, external environment, etc.), has strong adaptability, low application threshold, and can be widely promoted and applied. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 It is a schematic diagram of the calculation structure of the mathematical model of the multi-energy complementation rate index MECI in the present invention;

[0030] Figure 2 It is a schematic diagram of the range of values ​​of 16 different types determined by the present invention in two-dimensional space;

[0031] Figure 3 This is the per-unit output process diagram of the multi-energy complementary system at the XXsha substation node;

[0032] Figure 4 is an example of a complementary characteristic evaluation diagram of a multi-energy complementary system (24h);

[0033] Figure 5 This is an example of a complementary characteristic evaluation diagram of a multi-energy complementary system (48h). DETAILED DESCRIPTION

[0034] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0035] (1) Taking the water-solar multi-energy complementary power generation system under the XXsha substation node in the XXX River Basin as an example, the output data of different power sources are preprocessed;

[0036] In order to further explain the specific implementation methods of the present invention in detail, the output data of the hydro-photovoltaic multi-energy complementary power generation system under the XXsha substation node in the XXX River Basin was collected. Under this node, there are 2 hydropower stations and 3 photovoltaic power stations. The hydropower stations include Xia XX Hydropower Station and Qi XX Hydropower Station, with installed capacities of 150MW and 210MW respectively; the photovoltaic power stations include Bao XXsha Photovoltaic Power Station, Tian XXsha Photovoltaic Power Station, and Shang XXsha Photovoltaic Power Station, all with installed capacities equal to 20MW. The time range of the extracted power process data is from January 1, 2022 to March 1, 2023. The time scale is hourly. The overall time series length is 10197 time steps. The power station output data of some time lengths are shown in Table 2 below:

[0037] Table 2. Power plant output data for some periods of time (unit: MW)

[0038]

[0039] Then, the output per unit value is calculated according to the installed capacity of each power station. The calculation method is to divide the actual output by the installed capacity. After this processing, the value range of the output data can be controlled within 0 to 1, so that the output data can better reflect the characteristics of natural resources and facilitate subsequent calculations. The output per unit value of the multi-energy complementary system of the XX Sha substation node obtained after preprocessing is as follows Figure 3 shown.

[0040] (2) Based on the processed per-unit output process time series data, the multi-energy complementarity rate index MECI of the multi-energy complementary system over the entire time is calculated;

[0041] Based on Python 3.8 and three libraries, Pandas, Numpy, and Scipy, the mathematical model calculation code of the multi-energy complementarity index MECI is written. The mathematical model calculation structure is as follows Figure 1 As shown. Input the output per unit value time series Excel table, the first column of the table is time, and the second to sixth columns are the output per unit value time series of the corresponding time of the five power stations in the multi-energy complementary system. Then, after calculating the DECI values ​​between 10 pairs of different energy sources, the multi-energy complementary rate index MECI of the overall time of the sample data multi-energy complementary system is finally obtained by ensemble average, which is equal to 0.281.

[0042] (3) Based on the processed per-unit output process time series data, the multi-energy volatility index MEVI of the multi-energy complementary system is calculated;

[0043] Based on Python 3.8 and three libraries, Pandas, Numpy, and Scipy, the mathematical model calculation code of the multi-energy volatility index MEVI is written. The mathematical model calculation structure is as follows Figure 1As shown. Input the output per unit time series Excel table, the first column of the table is time, and the second to sixth columns are the output per unit time series of the five power stations in the multi-energy complementary system at the corresponding time. Then calculate the volatility SEVI values ​​of the five different energy groups, and finally obtain the multi-energy complementary rate index MEVI of the overall time of the sample data multi-energy complementary system through ensemble average, which is equal to 0.154.

[0044] (4) Based on the processed per-unit output process time series data, the sliding time window is used to calculate and obtain the distribution of its MECI and MEVI in two-dimensional space, and finally the evaluation of the multi-energy complementary system in the multi-energy complementary characteristic evaluation system is obtained.

[0045] The mathematical model calculation code of the multi-energy volatility index MEVI and the mathematical model calculation code of the multi-energy complementary rate index MECI are written based on Python 3.8 and the three libraries of Pandas, Numpy, and Scipy, and the sliding time window calculation module is added. Input the output per unit value time series Excel table. The first column of the table is time, and the second to sixth columns are the output per unit value time series of the five power stations in the multi-energy complementary system at the corresponding time. Take 24h (one day) and 48h (two days) as two different sliding time windows, calculate the overall distribution of the complementary characteristics of the multi-energy complementary system under the overall time series, and substitute it into the multi-energy complementary characteristic evaluation system. Finally, it is concluded that the example multi-energy complementary system belongs to the WC-S-2 type of highly stable intermediate complementary system in the evaluation system.

[0046] By adopting the above technical solution disclosed in the present invention, the following beneficial effects are obtained: the present invention provides a method for analyzing and evaluating the characteristics of multi-energy complementarity, which can not only quantitatively analyze the degree of complementarity and stability between different new energy outputs at any time scale, but also qualitatively analyze the complementary characteristics of the multi-energy complementary system. The construction of the method of the present invention is not limited to a specific multi-energy complementary system or a new energy output object, and can calculate the multi-energy complementarity rate index and the multi-energy volatility index at all time scales according to actual conditions (data, external environment, etc.), has strong adaptability, low application threshold, and can be widely promoted and applied.

[0047] It will be easily understood by those skilled in the art that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for analyzing and evaluating multi-energy complementary characteristics, characterized in that: The evaluation acquisition process is as follows: Step 1, collecting the power output data of the multi-energy complementary power generation system, pre-processing the output data of different power sources, and obtaining the per-unit output process time series data; Step 2, based on the per-unit output process time series data obtained after preprocessing, calculating the multi-energy complementary rate index MECI of the multi-energy complementary power generation system over the entire time; Step 3, based on the per-unit output process time series data obtained after preprocessing, calculating the multi-energy volatility index MEVI of the multi-energy complementary power generation system over the entire time period; Step 4, based on the per-unit output process time series data obtained after preprocessing, a sliding time window is used to calculate to obtain the distribution of the MECI and MEVI in two-dimensional space, and finally obtain the evaluation of the multi-energy complementary power generation system in the multi-energy complementary characteristic evaluation method system; The calculation formula of the multi-energy complementation rate index MECI is as follows: In the formula, DECI represents the complementary characteristic evaluation index of each pair of new energy sources in the same multi-energy complementary system; The mathematical model of the multi-energy volatility index MEVI is as follows: Where FE(i,j) represents the count of fluctuation events, and X=ΠY{Z} represents a counting function. If the condition Z in X=ΠY{Z} holds, then X in X=ΠY{Z} is equal to Y in X=ΠY{Z}, otherwise it is equal to 0. Therefore, the formula of FE(i,j) represents the condition if P e Yes (P i ,…,P e ), then FE(i,j) is equal to 1, otherwise it is equal to 0; according to the definition of FE(i,j) and X=ΠY{Z}, it is obvious that: and Then FE(i,j) can be regarded as a fluctuation event; ρ m Expressed as the volatility of the mth volatility event, P j -P i It is expressed as the difference between the final output and the initial output of this fluctuation event, t j -t i It represents the difference between the end time and the beginning time of this volatility event; EFE represents the effective volatility event, EDLE represents m It is expressed as the extreme value difference of the effective fluctuation event. Only when the difference between the end output and the initial output of a fluctuation event is P j -P i When it is greater than the specified volatility threshold ε, this volatility event FE(i,j) is regarded as an effective volatility event EFE(i,j,ε), and then its effective extreme value difference EDLE is calculated. m (i,j), and finally get the corresponding volatility ρ m ; The volatility index SEVI of an energy output process time series is equal to the ensemble average of its effective volatility events.

2. A multi-energy complementary characteristics analysis and evaluation method according to claim 1, characterized in that: The mathematical model of DECI in the calculation formula of the multi-energy complementation rate index MECI is as follows: In the formula, when calculating the pairwise energy complementarity, ZOD represents the non-powered period of the compensated energy, and NZOD represents the powered period of the compensated energy; W ZOD is the proportion of ZOD time periods in the overall sample of compensated energy output process; W NZOD is the proportion of NZOD time periods in the overall sample of the compensated energy output process; T energy1 It is expressed as the total number of time periods of the overall sample of the compensated energy output process; CDSS energy [t] represents the time series of the output coefficient change rate of energy in the NZOD period, which is obtained by calculating the derivative at any time by the numerical central difference method; SCDSS[t] represents the time series of the sum of the complementary dual energy output coefficient change rates, ε i Expressed as different multiples of the standard deviation of the SCDSS[t] sequence, w εi Expressed as their respective ε i The weight value, R(ε i ) is expressed as the proportion of time periods in the SCDSS[t] sequence that are within the stable threshold interval, represented by ε i The stable threshold interval represented by represents the interval of strong complementarity, that is, the two-dimensional plane with values ​​close to both sides of 0. i ) value is larger, which means that there are more periods in the output time period in which the sum of the complementary energy output change rate is close to 0, that is, it falls within the stable threshold range, which indicates that the SCDSS[t] sequence is closer to good complementarity; SPOCS[t] represents the compensation energy output coefficient sequence. In the ZOD period, the output coefficient of the compensation energy should be closer to 1 more stably to reflect the excellent complementarity; μ i Expressed as different output coefficients, w μi Expressed as μ i The weight value, R(μ i ) is expressed as the proportion of time periods in the sequence that are in the stable threshold interval; CC pearson ,CC kendall ,CC spearman Represents the Pearson, Kendall, and Spearman correlation coefficients between the change rates of two energy output processes; CC spearman R i and S i Represents sequence CDSS energy1 [t] and CDSS energy2 [t] is the rank of the sequence; i is a simple counter; STD() represents the standard deviation of the sequence in brackets; D() represents the square of the standard deviation of the sequence in brackets.

3. A multi-energy complementary characteristic analysis and evaluation method according to claim 1, characterized in that: The value range of the multi-energy complementarity index MECI is (0,1). The larger the value, the stronger the degree of complementarity between different energy sources in the multi-energy complementary system, and the smaller the value, the weaker the degree of complementarity between different energy sources in the multi-energy complementary system. The value range of the multi-energy volatility index MEVI is (0,1). The larger the value, the stronger the fluctuation between different energy sources in the multi-energy complementary system, that is, the more unstable it is. The smaller the value, the weaker the fluctuation between different energy sources in the multi-energy complementary system, that is, the more stable it is.

4. A multi-energy complementary characteristics analysis and evaluation method according to claim 1, characterized in that: When the multi-energy complementarity index MECI is greater than 0.5, the system is a strong complementarity SC, otherwise it is a weak complementarity WC; when the multi-energy volatility index MEVI is less than 0.5, the system is a strong stable complementary system S, otherwise it is a weakly stable system I. Therefore, in two-dimensional space, the multi-energy complementary system is qualitatively divided into 16 different types, specifically include: 。

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