Multi-dimensional evaluation method for output characteristics of new energy power generation base containing matched energy storage

Abstract

The invention belongs to the field of planning and new energy evaluation in a power system, and particularly relates to a multi-dimensional evaluation method for output characteristics of a new energy power generation base containing matched energy storage, which comprises the following steps of: 1, researching a new energy evaluation index system in combination with the influence of random fluctuation characteristics of output of new energy after large-scale grid connection on planning evaluation, so as to obtain a new energy evaluation result; constructing a new energy routine evaluation index system basis; 2, adding new energy output power, an electric quantity index, a dynamic and static characteristic index and a multi-time scale evaluation index, and constructing a new energy multi-dimensional evaluation system; 3, additional indexes for describing the external output characteristic change of the new energy after the matched energy storage is built are added; according to the method, the influence of the randomness and volatility of the output of the large-scale new energy base on planning evaluation after grid connection is comprehensively considered, the influence of high-proportion new energy access on the power system is reflected, and the method has higher adaptability to operation analysis of the large-scale new energy power generation base access to the power system.

Description

Technical Field

[0001] The present invention belongs to the field of planning and new energy assessment in power systems, and specifically is a multi-dimensional evaluation method for output characteristics of a new energy power generation base with supporting energy storage. Background Art

[0002] In recent years, with the gradual reduction of fossil energy and the intensification of environmental problems, the construction of new energy power generation projects is booming. my country is currently actively promoting new energy projects such as wind power generation and solar power generation. However, the output of new energy is characterized by randomness, intermittency and volatility, and is greatly affected by factors such as seasons and climate. The large-scale grid connection of new energy has essentially changed the real-time balanced power supply mode, bringing certain challenges to the safe and stable operation of the power system.

[0003] After a period of rapid development, the phenomenon of wind and solar power abandonment in new energy has gradually become prominent, and development is facing bottlenecks. In this context, energy storage is seen as a key solution to the development bottlenecks of new energy such as photovoltaics and wind power. Energy storage technology can achieve smooth output, eliminate the peak and valley difference between day and night, and adjust the peak and frequency as well as spare capacity to ensure that new energy power generation is smoothly and safely connected to the grid, effectively reducing the phenomenon of wind and solar power abandonment.

[0004] As the basis for power system planning and operation, the resource indicator system for renewable energy output has attracted widespread attention from experts and scholars. With the large-scale access of wind power and photovoltaic power generation to the power grid, the analysis of their output characteristics is gradually deepening. However, the current evaluation methods for renewable energy power generation are relatively scattered, especially for renewable energy power generation with supporting energy storage. There is no recognized, mature, and effective evaluation indicator system for renewable energy output characteristics.

[0005] To this end, the present invention provides a multi-dimensional evaluation method for the output characteristics of a new energy power generation base with supporting energy storage. Summary of the invention

[0006] In order to make up for the deficiencies of the prior art, at least one technical problem raised in the background technology is solved.

[0007] The technical solution adopted by the present invention to solve the technical problem is: a multi-dimensional evaluation method for output characteristics of a new energy power generation base with supporting energy storage described in the present invention specifically includes the following steps:

[0008] Step 1: Combined with the impact of random fluctuation characteristics of the output of renewable energy after large-scale grid connection on planning and evaluation, the new energy evaluation index system is studied to build the foundation of the conventional new energy evaluation index system;

[0009] Step 2: On the basis of the conventional evaluation index system, add new energy output power and electricity indicators, dynamic and static characteristic indicators, and multi-time scale evaluation indicators to build a multi-dimensional evaluation system for new energy;

[0010] Step 3: On the basis of the evaluation index system in step 2, additional indicators are added to describe the changes in the external output characteristics of new energy after the investment in supporting energy storage, so as to achieve a multi-dimensional evaluation of the output characteristics of the new energy power generation base; among them, the additional indicators include the volatility smoothing rate, the reduction rate of the peak-to-valley difference of new energy output, and the reduction rate of the peak-to-valley difference of the system.

[0011] Preferably, in step 1, the basis of the conventional evaluation index system for new energy is constructed, specifically:

[0012] S1: Construct a conventional indicator system for new energy wind power output;

[0013] S2: Construct conventional evaluation indicators for new energy photovoltaic output.

[0014] Preferably, conventional evaluation indicators for new energy photovoltaic output are constructed in S2, specifically:

[0015] 1) Seasonal attributes of new energy photovoltaic output;

[0016] 2) Obtain the maximum daily output of new energy photovoltaics:

[0017] By formula Get the maximum daily output of new energy photovoltaic Where t is a typical day divided into 24 identical and independent T periods, T = 24; P t S is the renewable energy photovoltaic output at time t; Ω is a set of 24 identical and independent time periods, t∈Ω={1,...,T};

[0018] 3) Obtain the average output of new energy photovoltaics:

[0019] By formula Get the average output of new energy photovoltaic power P S , where P t S is the new energy photovoltaic output at time t.

[0020] Preferably, in step 2, a multi-dimensional evaluation system for new energy is constructed, specifically:

[0021] S21: Constructing a new energy assessment system with multiple time scales:

[0022] S22: Construct dynamic and static characteristic indicators of new energy, which are divided into longitudinal error and transverse error. The longitudinal error describes the difference between the predicted result and the actual result in the vertical direction of a certain period of time, and the transverse error describes the difference between the predicted result and the actual result on the horizontal time axis;

[0023] S23: Establish indicators for the output power and electricity of new energy.

[0024] Preferably, a multi-time scale new energy evaluation system is constructed in S21, specifically:

[0025] 1) Build an evaluation system with time attributes to obtain the minimum output during peak hours, the maximum output during valley hours, and the daily peak-valley difference;

[0026] 2) Construct an evaluation system with seasonal attributes. New energy wind power and new energy photovoltaic power will change with weather conditions including temperature difference and sunrise and sunset time factors;

[0027] 3) Construct an intraday anti-peak regulation characteristic evaluation index, that is, the peak-to-valley difference change value of new energy intervention:

[0028] By formula Get the peak-to-valley difference change value of new energy access ΔP, where P t D is the load time series; P t W It is the time series of new energy wind power output;

[0029] 4) Construct weekly output characteristics.

[0030] Preferably, in S22, the dynamic and static characteristic indicators of new energy are constructed, specifically:

[0031] 1) Obtaining longitudinal error, where longitudinal error indicators include mean relative error, mean absolute error, root mean square error, and error frequency distribution;

[0032] 2) Obtain the lateral error using the correlation coefficient between the data to describe the correlation degree, through the formula The correlation coefficient cc is obtained; where Y1 is the measured power; Y2 is the predicted power; D is the variance; cov(Y 1 ,Y 2 ) is the covariance of Y1 and Y2.

[0033] Preferably, the output power and electricity quantity indicators of new energy are constructed, specifically:

[0034] 1) Construct a typical daily output curve of new energy to indicate the most likely output of new energy power stations on a typical day, reflect the output characteristics of new energy power stations, and provide information and guidance for the construction and operation of the power system;

[0035] 2) Construct a new energy curtailment rate indicator to indicate the ratio of the output of new energy to the maximum transmission capacity of the power system and the power consumption of the load;

[0036] 3) Construct a new energy output distribution characteristic index to describe the distribution law of new energy output and measure the balance of new energy output;

[0037] 4) Construct the volatility of new energy output and quantitatively analyze the volatility of new energy.

[0038] Preferably, the method for obtaining the suppressed volatility in step 3 is:

[0039] Combined with the output volatility index of new energy, through the formula Get the smooth volatility ξ; where ρ t is the actual new energy output fluctuation rate at time t; It is the fluctuation rate of the equivalent output after adding energy storage at time t.

[0040] Preferably, the method for obtaining the peak-to-valley difference reduction rate of new energy output in step 3 is:

[0041] By utilizing the characteristics of supporting energy storage, it absorbs energy when the output of new energy is large, and releases energy when the output of new energy is small or no output. The peak-to-valley difference reduction rate η of new energy output is obtained; where P t is the actual new energy output at time t; P t ES is the equivalent output after adding energy storage at time t; S is the installed capacity of new energy.

[0042] Preferably, the method for obtaining the system peak-to-valley difference reduction rate in step 3 is:

[0043] Combined with the peak-to-valley difference changes of new energy intervention, through the formula The system peak-to-valley difference reduction rate ΔP is obtained; where, is the peak-to-valley difference due to the introduction of new energy; ΔP ES The peak-to-valley difference is caused by the simultaneous intervention of new energy and supporting energy storage.

[0044] The beneficial effects of the present invention are as follows:

[0045] Based on the conventional evaluation system indicators of existing new energy power generation bases, this patent comprehensively considers the impact of the randomness and volatility of the output of large-scale new energy bases after grid connection on planning and evaluation, and constructs a multi-dimensional evaluation indicator system for the output characteristics of new energy power generation bases with supporting energy storage, which is used to reflect the impact of a high proportion of new energy access on the power system and the role of supporting energy storage therein.

[0046] The multi-dimensional evaluation method for the output characteristics of a new energy power generation base with supporting energy storage described in the present invention can characterize a set consisting of a conventional core evaluation indicator system of an existing new energy power generation base and several other derived indicators thereof, and characterize the change law of the output characteristics of a multi-dimensional new energy power generation base according to the method of time scale derivation and statistical processing.

[0047] On the basis of the index system establishment method, a multi-dimensional index system framework for the output characteristics of new energy power generation bases is constructed, and power and electricity indicators, dynamic and static characteristic indicators, and multi-time scale evaluation indicators are added. The multi-dimensional index evaluation method of the present invention can more comprehensively evaluate the regional resource level of the new energy power generation base and provide guidance for power grid planning.

[0048] On the basis of the conventional evaluation index system of new energy, according to the supporting energy storage policy of new energy power generation base, relevant indicators describing the changes in output characteristics of new energy power generation base after considering supporting energy storage are added. The present invention adds a new energy supporting energy storage evaluation method, which can better coordinate the development of energy storage system and new energy, and reduce the impact of intermittent output of new energy on the power system.

[0049] Through the index system, based on the historical data of new energy in the assessment area, the new energy index including supporting energy storage is evaluated for the assessment area. The present invention can evaluate the output characteristics of the new energy power generation base using statistical methods based on historical data.

[0050] The multi-dimensional evaluation method for the output characteristics of a renewable energy power generation base with supporting energy storage proposed in the present invention has better rationality. Compared with the traditional conventional new energy evaluation index system, this method has stronger adaptability to the operation analysis of large-scale renewable energy power generation bases connected to the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] The present invention will be further described below in conjunction with the accompanying drawings.

[0052] Figure 1 It is a typical weekly new energy wind power output curve of the present invention;

[0053] Figure 2 This is a typical weekly new energy photovoltaic output curve of the present invention;

[0054] Figure 3 It is the predicted output and actual output diagram of the new energy wind power of the present invention;

[0055] Figure 4 It is the predicted output and actual output diagram after correction of the new energy wind power output of the present invention;

[0056] Figure 5 It is the predicted output and actual output diagram of the new energy photovoltaic of the present invention;

[0057] Figure 6 It is the predicted output and actual output diagram after correction of the new energy photovoltaic output of the present invention;

[0058] Figure 7 is the output distribution characteristic index of the photovoltaic power station of the present invention;

[0059] Figure 8 It is the probability density diagram of the fluctuation rate of the new energy wind power output of the present invention;

[0060] Fig. 9 This is the typical daily output curve of new energy wind power and supporting energy storage combined with the present invention;

[0061] Fig.10 It is the typical daily combined output curve of new energy photovoltaic and supporting energy storage of the present invention;

[0062] Fig.11 It is the cumulative probability distribution diagram of the output fluctuation rate of new energy wind power of the wind outlet wind farm #1s of the present invention;

[0063] Fig.12 It is a method flow chart of a multi-dimensional evaluation method for output characteristics of a new energy power generation base with supporting energy storage according to the present invention. DETAILED DESCRIPTION

[0064] In order to make the technical means, creative features, objectives and effects achieved by the present invention easy to understand, the present invention is further explained below in conjunction with specific implementation methods.

[0065] Embodiment 1

[0066] A multi-dimensional evaluation method for output characteristics of a new energy power generation base with supporting energy storage according to an embodiment of the present invention specifically comprises the following steps:

[0067] Step 1: When applying the model proposed in the present invention, it is necessary to first consider the impact of the random fluctuation characteristics of the output of new energy after a large proportion of grid connection on planning evaluation, study the new energy evaluation index system, and build the foundation of the new energy conventional evaluation index system:

[0068] Step 1.1: Establish a conventional indicator system for new energy wind power output:

[0069] 1) Daily maximum / minimum value:

[0070]

[0071] Where:

[0072] It is the maximum daily output of renewable energy wind power;

[0073] It is the minimum daily output of renewable energy wind power;

[0074] P t W is the output value of new energy wind power at time t;

[0075] t is a typical day divided into 24 identical and independent T periods, T = 24;

[0076] Ω is a set of 24 identical and independent time periods, t∈Ω={1,...,T}.

[0077] 2) Daily peak-to-valley difference:

[0078]

[0079] Where:

[0080] ΔP W The daily peak-to-valley difference of wind power output;

[0081] It is the maximum value of renewable energy wind power generation per day;

[0082] It is the minimum value of renewable energy wind power generation per day.

[0083] 3) Daily average:

[0084]

[0085] Where:

[0086] It is the daily average output of renewable energy wind power;

[0087] t is a typical day divided into 24 identical and independent T periods, T = 24;

[0088] P t W The new energy wind power output at time t.

[0089] 4) Median:

[0090] Arrange the new energy wind power output time series in the order of numerical value, that is, The median output of new energy wind power is expressed as:

[0091]

[0092] Where:

[0093] P W It is the median output of renewable energy wind power;

[0094] n is the new energy wind power output time series Pt W The number of items arranged in order of numerical value, n=24.

[0095] 5) Annual utilization hours of new energy wind power: the average full-load operating time of wind turbines in one year, and the proportion of wind turbine utilization hours in the 8,760 hours per year.

[0096]

[0097] Where:

[0098] T is the number of hours of utilization of new energy wind power;

[0099] P 电量 The annual power generation of renewable energy wind power;

[0100] P 装机 It is the total installed capacity of new energy wind power.

[0101] Step 1.2: Construct conventional evaluation indicators for new energy photovoltaic output:

[0102] 1) Seasonal attributes of new energy photovoltaic power generation: The output period of new energy photovoltaic power generation is limited from sunrise to sunset, and is greatly affected by seasonal characteristics. When analyzing the characteristic indicators of new energy photovoltaic power generation, seasonal attribute indicators are given and considered according to the three categories of "spring and autumn", "summer" and "winter".

[0103] 2) Maximum daily output of new energy photovoltaics:

[0104]

[0105] Where:

[0106] It is the maximum output of new energy photovoltaic in a day;

[0107] t is a typical day divided into 24 identical and independent T periods, T = 24;

[0108] P t S is the new energy photovoltaic output at time t;

[0109] Ω is a set of 24 identical and independent time periods, t∈Ω={1,...,T}.

[0110] 3) Average output of new energy photovoltaic power:

[0111]

[0112] Where:

[0113] The average output of new energy photovoltaics;

[0114] t is a typical day divided into 24 identical and independent T periods, T = 24;

[0115] P t S is the new energy photovoltaic output at time t.

[0116] Based on the construction of the conventional indicator system for new energy, refer to the attached figure to construct a multi-dimensional evaluation system for new energy with supporting energy storage.

[0117] Step 2: Build a multi-dimensional evaluation system for new energy:

[0118] Step 2.1: Construct a new energy assessment system with multiple time scales:

[0119] 1) Construct an evaluation system with time period attributes:

[0120] a) Minimum output during peak hours, i.e. the minimum output of renewable energy wind power during peak load hours:

[0121]

[0122] Where:

[0123] Ω 1 is the peak time range;

[0124] P t W The new energy wind power output at time t.

[0125] b) Maximum output during valley period, i.e. the maximum output of renewable energy wind power during valley load period:

[0126]

[0127] Where:

[0128] Ω 2 It is the valley period range;

[0129] P t W The new energy wind power output at time t.

[0130] c) Daily peak-to-valley difference, that is, the difference between the maximum and minimum renewable energy wind power output of the day:

[0131]

[0132] Where:

[0133] ΔP W It is the daily peak-to-valley difference;

[0134] Ω is the peak time period 1 Median output of renewable energy wind power in China;

[0135] Ω is the valley period 2 The median output of renewable energy wind power in China.

[0136] 2) Construct an evaluation system with seasonal attributes. New energy wind power and new energy photovoltaic power will change with factors including weather conditions including temperature difference, sunrise and sunset times, etc.

[0137] 3) Construct an intraday anti-peak regulation characteristic evaluation index, that is, the peak-to-valley difference change value of new energy intervention:

[0138]

[0139] Where:

[0140] ΔP is the peak-to-valley difference change of new energy access;

[0141] P t D is the load time series;

[0142] P t W It is the time series of renewable energy wind power output.

[0143] 4) Construct weekly output characteristics. Due to the changes in load within a week, especially when there are large users with significantly different electricity consumption characteristics on different days of the week, the daily output characteristics of new energy within the corresponding week will change with the changes in load on different days. When the regulation cycle characteristics of the new energy supporting energy storage are weekly regulation, the external output characteristics of new energy are more likely to change in a weekly cycle, so the maximum, minimum and average output values, dynamic characteristics and positive peak and negative peak days within a week are calculated.

[0144] Step 2.2: Construct new energy dynamic and static characteristic indicators, which are divided into longitudinal error and transverse error. The longitudinal error describes the difference between the predicted results and the actual results in a certain period of time in the vertical direction, and the transverse error describes the difference between the predicted results and the actual results on the horizontal time axis.

[0145] 1) Longitudinal error: Longitudinal error indicators include average relative error, average absolute error, root mean square error, and error frequency distribution:

[0146] a) Average relative error e MRE The errors are normalized by dividing them by their true values ​​to make them comparable:

[0147]

[0148] Where:

[0149] t is a typical day divided into 24 identical and independent T periods, T = 24;

[0150] e t is the absolute error at time t;

[0151] S is the rated capacity of the equipment.

[0152] b) Mean absolute error e MAE Evaluate the average magnitude of the prediction error:

[0153]

[0154] Where:

[0155] t is a typical day divided into 24 identical and independent T periods, T = 24;

[0156] e t is the absolute error at time t;

[0157] S is the rated capacity of the equipment.

[0158] c) Root mean square error e RMSE A measure of the dispersion of the errors:

[0159]

[0160] Where:

[0161] t is a typical day divided into 24 identical and independent T periods, T = 24;

[0162] e t is the absolute error at time t;

[0163] S is the rated capacity of the equipment.

[0164] 2) The lateral error is described by the correlation coefficient of the correlation between the data, the correlation coefficient cc:

[0165]

[0166] Where:

[0167] Y 1 is the measured power;

[0168] Y 2 To predict power;

[0169] D is the variance;

[0170] cov(Y 1 ,Y 2 ) is Y 1 and Y 2The covariance of .

[0171] Step 2.3: Construct new energy output power and electricity indicators:

[0172] 1) Construct a typical daily output curve of new energy to indicate the most likely output of new energy power stations on a typical day, reflect the output characteristics of new energy power stations, and provide information and guidance for the construction and operation of the power system.

[0173] 2) Construct a new energy power abandonment rate α indicator, which represents the ratio of the new energy output to the maximum transmission power of the power system and the power consumption of the load.

[0174]

[0175] Where:

[0176] P G Output value for new energy;

[0177] P T The maximum amount of electricity transmitted by the power system;

[0178] P L To consume electricity for the load.

[0179] 3) Construct the new energy output distribution characteristic index β to describe the distribution law of new energy output and measure the balance of new energy output:

[0180]

[0181] Where:

[0182] t is a typical day divided into 24 identical and independent T periods, T = 24;

[0183] X i is the state variable;

[0184] P t Provides new energy for the period t;

[0185] P max It is the maximum output power of new energy.

[0186] 4) Constructing the new energy output fluctuation rate ρ t , quantitative analysis of new energy volatility:

[0187]

[0188] Where:

[0189] t is a typical day divided into 24 identical and independent T periods, T = 24;

[0190] P tContribute to the new energy at time t;

[0191] S is the rated capacity of new energy installed.

[0192] Step 3: On the basis of the evaluation index system that only includes new energy, according to the supporting energy storage policy of new energy power generation bases, add relevant indicators to describe the changes in the external output characteristics of new energy after the investment and construction of supporting energy storage.

[0193] Step 3.1: The volatility of renewable energy output will cause grid frequency fluctuations when it is connected to the grid. Energy storage, as an intermediate material for power buffering, can smooth out the output power fluctuations of renewable energy. In order to characterize the ability of supporting energy storage to smooth out the output fluctuations of renewable energy, combined with the output fluctuation rate index of renewable energy, the smoothing fluctuation rate ξ is proposed:

[0194]

[0195] Where:

[0196] t is a typical day divided into 24 identical and independent T periods, T = 24;

[0197] ρ t is the actual new energy output fluctuation rate at time t;

[0198] It is the fluctuation rate of the equivalent output after adding energy storage at time t.

[0199] Step 3.2: Utilize the supporting energy storage to absorb energy when the output of new energy is large and release energy when the output of new energy is small or no, to build the peak-to-valley difference reduction rate η of new energy output:

[0200]

[0201] Where:

[0202] t is a typical day divided into 24 identical and independent T periods, T = 24;

[0203] Ω is a set of 24 identical and independent time periods, t∈Ω={1,...,T};

[0204] P t The actual new energy output at time t;

[0205] P t ES is the equivalent output after adding energy storage at time t;

[0206] S is the installed capacity of new energy.

[0207] Step 3.3: Characterize the ability of supporting energy storage to reduce the peak-to-valley difference of the system, and combine the peak-to-valley difference changes caused by the intervention of new energy to construct the system peak-to-valley difference reduction rate ΔP:

[0208]

[0209] Where:

[0210] The peak-to-valley difference is increased due to the introduction of new energy;

[0211] ΔP ES The peak-to-valley difference is caused by the simultaneous intervention of new energy and supporting energy storage.

[0212] Embodiment 2

[0213] In order to further demonstrate the benefits of the present invention, the new energy wind power and new energy photovoltaic data of a certain region are used, combined with a new energy multi-dimensional evaluation method with supporting energy storage proposed in the present invention, to evaluate the new energy output characteristics of the region.

[0214] First, draw the typical weekly output curves of new energy wind power and new energy photovoltaic power, such as Figure 1 , 2 As shown, based on the statistical analysis of the data, the peak-to-valley difference, maximum value, minimum value and average value of the new energy output in a typical week are obtained, as shown in Tables 1 and 2.

[0215] Table 1 Peak-to-valley difference, maximum value, minimum value and average value of new energy wind power output in a typical week

[0216]

[0217] Table 2 Maximum and average output of new energy photovoltaic power in a typical week

[0218]

[0219] Secondly, select some wind farms to compare their forecast and actual output, and conduct dynamic and static evaluation of new energy. Select the new energy wind power output curve and forecast curve on a certain day for evaluation. Figure 3 shown.

[0220] After calculation, the dynamic error of the day is 1.93 hours, and the static error is 31.33%. The predicted output curve after correction is different from the actual output curve as shown in the figure below. Figure 4 shown.

[0221] Select the new energy photovoltaic output curve and forecast curve for a certain day for evaluation. The forecast output and actual output on that day are as follows: Figure 5 shown.

[0222] After calculation, the dynamic error of the day was -0.23 hours, the static error was 9.14%, and the predicted output curve after correction was consistent with the actual output curve. Figure 6 shown.

[0223] Next, the annual output of a photovoltaic power station A and B is analyzed according to the calculation method of the new energy output distribution characteristic index β proposed in the present invention, and the new energy output distribution curves of the photovoltaic power stations A and B are calculated;

[0224] like Figure 7 The output distribution of photovoltaic power station B, which has a smaller output distribution characteristic index β, is more uneven and has a larger skewness in output distribution. When building supporting energy storage for station scheduling, the supporting energy storage required for the maximum output of photovoltaic power station B should be more than that of photovoltaic power station A.

[0225] The probability distribution of renewable energy wind power output in the assessment area on an hourly scale is obtained through data statistics.

[0226] like Figure 8 As shown in the figure, the volatility of wind power output is generally high in the middle and low on both sides, that is, the greater the volatility, the lower the possibility of occurrence. The volatility of wind power output in summer and autumn is basically the same, followed by winter, and the volatility of wind power output in spring is the smallest.

[0227] Finally, the output characteristics of renewable energy with supporting energy storage are evaluated. The output curve of renewable energy wind power and supporting energy storage on a typical day is as follows: Fig. 9 As shown, the combined output curve of new energy photovoltaic and supporting energy storage is as follows Fig.10 As shown;

[0228] Energy storage, as a power buffer, can smooth out the output power fluctuations of renewable energy through flexible and rapid power throughput. Energy storage acts as a power filter, filtering out the high-frequency active components in the original renewable energy output, and using the relatively smoothly changing synthetic output as the grid-connected component to improve the stability of renewable energy grid connection. Taking wind farm A as an example, the simulated volatility probability distribution is as follows: Fig.11 As shown:

[0229] Through calculation, it is found that the original volatility of wind farm A is 0.0449, the supporting energy storage volatility is 0.0192, and the smoothing volatility is 0.0257.

[0230] A supporting energy storage simulation was conducted on wind farm A. The original peak-to-valley difference was 176.52MW, and the peak-to-valley difference after supporting energy storage was 128.92MW, a reduction of 47.60MW, and a reduction rate of 26.97%. The supporting energy storage absorbs energy when the output of new energy is large, and releases energy when the output of new energy is small or even no output, so as to reduce the peak-to-valley difference of new energy output and reduce the volatility and randomness of new energy output.

[0231] In summary, the present invention is used to guide the evaluation of the output of renewable energy with supporting energy storage in the planning and operation of the power system. Considering the impact of the random fluctuation characteristics of the output of renewable energy after a large proportion of renewable energy is connected to the grid on the planning evaluation, a multi-dimensional evaluation index system for renewable energy with supporting energy storage is constructed to reflect the impact of a high proportion of renewable energy access on the power system and the role of supporting energy storage therein, so as to provide guidance for power system planning.

[0232] The above shows and describes the basic principles, main features and advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A multi-dimensional evaluation method for output characteristics of a new energy power generation base with supporting energy storage, characterized by: The specific steps include: Step 1: Combined with the impact of random fluctuation characteristics of the output of renewable energy after large-scale grid connection on planning and evaluation, the new energy evaluation index system is studied to build the foundation of the conventional new energy evaluation index system; Step 2: On the basis of the conventional evaluation index system, add new energy output power and electricity indicators, dynamic and static characteristic indicators, and multi-time scale evaluation indicators to build a multi-dimensional evaluation system for new energy; Step 3: On the basis of the evaluation index system in step 2, additional indicators are added to describe the changes in the external output characteristics of new energy after the investment in supporting energy storage, so as to achieve a multi-dimensional evaluation of the output characteristics of the new energy power generation base; among them, the additional indicators include the volatility smoothing rate, the reduction rate of the peak-to-valley difference of new energy output, and the reduction rate of the peak-to-valley difference of the system.

2. According to claim 1, a multi-dimensional evaluation method for output characteristics of a new energy power generation base with supporting energy storage is characterized by: In step 1, the foundation of the conventional evaluation index system for new energy is constructed, specifically: S1: Construct a conventional indicator system for new energy wind power output; S2: Construct conventional evaluation indicators for new energy photovoltaic output.

3. The multi-dimensional evaluation method for output characteristics of a new energy power generation base with supporting energy storage according to claim 2 is characterized in that: S2 constructs conventional evaluation indicators for new energy photovoltaic output, specifically: 1) Seasonal attributes of new energy photovoltaic output; 2) Obtain the maximum daily output of new energy photovoltaics: By formula Get the maximum daily output of new energy photovoltaic Where t is a typical day divided into 24 identical and independent T periods, T = 24; P t S is the renewable energy photovoltaic output at time t; Ω is a set of 24 identical and independent time periods, t∈Ω={1,...,T}; 3) Obtain the average output of new energy photovoltaics: By formula Get the average output of new energy photovoltaic Among them, P t S is the new energy photovoltaic output at time t.

4. The multi-dimensional evaluation method for output characteristics of a new energy power generation base with supporting energy storage according to claim 1 is characterized in that: In step 2, a multi-dimensional evaluation system for new energy is constructed, specifically: S21: Constructing a new energy assessment system with multiple time scales: S22: Construct dynamic and static characteristic indicators of new energy, which are divided into longitudinal error and transverse error. The longitudinal error describes the difference between the predicted result and the actual result in the vertical direction of a certain period of time, and the transverse error describes the difference between the predicted result and the actual result on the horizontal time axis; S23: Establish indicators for the output power and electricity of new energy.

5. The multi-dimensional evaluation method for output characteristics of a new energy power generation base with supporting energy storage according to claim 4 is characterized in that: S21 builds a new energy assessment system with multiple time scales, specifically: 1) Build an evaluation system with time attributes to obtain the minimum output during peak hours, the maximum output during valley hours, and the daily peak-valley difference; 2) Construct an evaluation system with seasonal attributes. New energy wind power and new energy photovoltaic power will change with weather conditions including temperature difference and sunrise and sunset time factors; 3) Construct an intraday anti-peak regulation characteristic evaluation index, that is, the peak-to-valley difference change value of new energy intervention: By formula The peak-to-valley difference change value of new energy access is obtained, ΔP, where P t D is the load time series; P t W It is the time series of new energy wind power output; 4) Construct weekly output characteristics.

6. The multi-dimensional evaluation method for output characteristics of a new energy power generation base with supporting energy storage according to claim 4 is characterized by: S22 constructs new energy dynamic and static characteristic indicators, specifically: 1) Obtaining longitudinal error, where longitudinal error indicators include mean relative error, mean absolute error, root mean square error, and error frequency distribution; 2) Obtain the lateral error using the correlation coefficient between the data to describe the correlation degree, through the formula The correlation coefficient cc is obtained; among them, Y1 is the measured power; Y2 is the predicted power; D is the variance; cov(Y1,Y2) is the covariance of Y1 and Y2.

7. The multi-dimensional evaluation method for output characteristics of a new energy power generation base with supporting energy storage according to claim 4 is characterized by: Construct new energy output power and electricity indicators, specifically: 1) Construct a typical daily output curve of new energy to indicate the most likely output of new energy power stations on a typical day, reflect the output characteristics of new energy power stations, and provide information and guidance for the construction and operation of the power system; 2) Construct a new energy curtailment rate indicator to indicate the ratio of the output of new energy to the maximum transmission capacity of the power system and the power consumption of the load; 3) Construct a new energy output distribution characteristic index to describe the distribution law of new energy output and measure the balance of new energy output; 4) Construct the volatility of new energy output and quantitatively analyze the volatility of new energy.

8. The multi-dimensional evaluation method for output characteristics of a new energy power generation base with supporting energy storage according to claim 1 is characterized by: The method for obtaining the smoothed volatility in step 3 is: Combined with the output volatility index of new energy, through the formula Get the smoothed volatility ξ; Among them, ρ t is the actual new energy output fluctuation rate at time t; It is the fluctuation rate of the equivalent output after adding energy storage at time t.

9. The multi-dimensional evaluation method for output characteristics of a new energy power generation base with supporting energy storage according to claim 1 is characterized by: The method for obtaining the peak-to-valley difference reduction rate of new energy output in step 3 is: By utilizing the characteristics of supporting energy storage, it absorbs energy when the output of new energy is large, and releases energy when the output of new energy is small or no output. The peak-to-valley difference reduction rate η of new energy output is obtained; where P t is the actual new energy output at time t; P t ES is the equivalent output after adding energy storage at time t; S is the installed capacity of new energy.

10. A multi-dimensional evaluation method for output characteristics of a new energy power generation base with supporting energy storage according to claim 1, characterized in that: The method for obtaining the system peak-to-valley difference reduction rate in step 3 is: combining the peak-to-valley difference change of new energy intervention, through the formula The system peak-to-valley difference reduction rate ΔP is obtained; where, is the peak-to-valley difference due to the introduction of new energy; ΔP ES The peak-to-valley difference is caused by the simultaneous intervention of new energy and supporting energy storage.

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