A method and system for configuring energy storage resources under renewable energy access

Abstract

The application provides a renewable energy access under energy storage resource configuration method and system, comprising: based on the obtained renewable energy and load output, the source load fluctuation characteristics of the power grid are calculated; based on the source load fluctuation characteristics and the pre-set reliability index, the demand of the renewable energy access power generation system for the energy storage resource is calculated; based on the demand of the power generation system for the energy storage resource and the obtained energy storage classification, the energy storage capacity is configured by using convolution operation and product operation in time domain and frequency domain respectively; the application determines the demand of the energy storage resource, combines the obtained energy storage classification, and configures the energy storage in time domain and frequency domain, so that the fine configuration of the energy storage resource is realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the configuration of energy storage resources, in particular to a method and system for configuring energy storage resources under renewable energy access. BACKGROUND

[0002] The core of power system planning and operation is to achieve real-time balance of power and energy. With the promotion and exploration of high proportion of renewable energy and demand response on the load side, the various uncertainties of source and load bring great challenges to the planning and operation of the power grid. It is increasingly difficult to rely on simple control means to achieve real-time power and energy balance, which is mainly reflected in the lack of flexibility of the power system. The topology change of transmission and distribution network, the increase of flexible generation resources, the multi-energy complementation of comprehensive energy, the demand response of user side, the energy storage technology, etc. are all effective methods to provide flexibility for the power system, and the reasonable configuration of energy storage resources is one of the most effective solutions.

[0003] Energy storage resources have developed rapidly in recent years, and their related technologies involve the conversion and storage of various forms of energy such as electrical energy, chemical energy, thermal energy, mechanical energy, solar energy, and wind energy. At the same time, energy storage technology is developing towards more diverse energy conversion forms, higher maturity, larger storage scale, and higher conversion efficiency. However, in reality, there is no single energy storage technology that can meet all the needs of the power system.

[0004] The current technical requirements for energy storage mainly reflect the differences in power level, action time, response speed, etc. The classification and configuration of energy storage technology in the power system should be combined with the demand of the power system and the technical characteristics of energy storage itself, and it is difficult to find a suitable point. At present, the configuration method of energy storage resources is still mainly focused on long-time scale optimization, such as the optimization configuration of energy storage considering load fluctuation and voltage fluctuation, and there is a lack of configuration method of energy storage resources under the background of high proportion of renewable energy, resulting in the problem of rough and non-targeted optimization configuration of energy storage resources. SUMMARY

[0005] In view of the fact that the existing configuration method of energy storage resources is still mainly focused on long-time scale optimization, such as the optimization configuration of energy storage considering load fluctuation and voltage fluctuation, and there is a lack of configuration method of energy storage resources under the background of high proportion of renewable energy, resulting in the problem of rough and non-targeted optimization configuration of energy storage resources, the present application provides a configuration method of energy storage resources under renewable energy access, which comprises:

[0006] calculating the source-load fluctuation characteristics of the power grid based on the obtained output of renewable energy and load;

[0007] calculating the demand of the power generation system with renewable energy access for energy storage resources based on the source-load fluctuation characteristics and the pre-set reliability index;

[0008] The energy storage capacity is configured by using convolution operation and multiplication operation in time domain and frequency domain respectively based on the demand of the power generation system for the energy storage resource and the obtained energy storage classification.

[0009] Preferably, the source-load fluctuation characteristics of the power grid are calculated based on the output of the obtained renewable energy and load, comprising:

[0010] The output of wind power and photovoltaic in the renewable energy and the output of load are converted into empirical mode functions.

[0011] Hilbert-Huang transform is performed on each empirical mode function to obtain corresponding frequency spectrum characteristics, and a main frequency component is identified, which is taken as the source-load fluctuation characteristics of the power grid.

[0012] Preferably, the demand of the power generation system for the energy storage resource is calculated based on the source-load fluctuation characteristics and the pre-set reliability index, comprising:

[0013] The new energy unit is equivalent to a multi-state unit by using an equivalent function based on the source-load fluctuation characteristics, to obtain equivalent electric quantity.

[0014] The renewable energy curtailment probability and curtailment electric quantity expectation under different renewable energy penetration rates are calculated by using a curtailment probability and curtailment electric quantity expectation calculation formula based on the equivalent electric quantity.

[0015] The initial value of the energy storage capacity under different renewable energy penetration rates is calculated by using an energy storage capacity calculation formula based on the renewable energy curtailment probability and curtailment electric quantity expectation under different renewable energy penetration rates.

[0016] The demand of the energy storage resource is calculated based on the initial value of the energy storage capacity under different renewable energy penetration rates in combination with the unmet probability of unbalanced energy.

[0017] Preferably, the equivalent function is as follows:

[0018]

[0019] In the formula, i is the number of convolution calculation, C is the rated capacity of the multi-state unit, the reduced capacity of the multi-state unit, x is the maximum load of the system, g is the probability of output corresponding power, d is a differential symbol, y is power, f (i) (x) is the equivalent electric quantity function of the i-th calculation, f (i-1) : the equivalent electric quantity function of the i-1-th calculation.

[0020] Preferably, the configuration of the energy storage capacity based on the demand of the power generation system for energy storage resources and the obtained energy storage classification adopts convolution operation in the time domain, including:

[0021] The energy storage power instruction is determined based on the energy storage power instruction and the autocorrelation function of the energy storage power instruction under the condition of satisfying Gaussian distribution;

[0022] The energy storage state signal is determined based on the energy storage state signal and the autocorrelation function of the energy storage state signal under the condition of satisfying Gaussian distribution;

[0023] The energy storage capacity value satisfying the first energy storage capacity constraint is calculated based on the energy storage power instruction and the energy storage state signal;

[0024] The energy storage capacity is configured by using the energy storage capacity value satisfying the energy storage capacity constraint in the time domain.

[0025] Preferably, the energy storage state signal is determined according to the following formula:

[0026]

[0027] In the formula, f(e ES ) is the equivalent electric quantity of energy storage, e ES (t) is the energy storage state signal, e ES (0) is the energy storage initialization state signal, and R e is the autocorrelation function of the energy storage state signal.

[0028] Preferably, the first energy storage capacity constraint is as follows:

[0029]

[0030] In the formula, E r is the energy storage capacity constraint condition, R e (0) is the autocorrelation function of the energy storage initialization state signal, S1 and S2 are the upper and lower limits of the energy storage SOC respectively, F -1 is the inverse function of the standard Gaussian distribution function, and α and β are the set probability thresholds respectively.

[0031] Preferably, the configuration of the energy storage capacity based on the demand of the power generation system for energy storage resources and the obtained energy storage classification adopts multiplication operation in the frequency domain, including:

[0032] The power spectral density function is calculated based on the Fourier function of the energy storage power instruction by using the power spectral density function calculation formula;

[0033] The energy storage capacity value is calculated based on the power spectral density function by using the maximum charging and discharging power constraint and the second energy storage capacity constraint;

[0034] The energy storage capacity is configured in a frequency domain based on the energy storage capacity value.

[0035] Preferably, the power spectrum density function calculation formula is as follows:

[0036]

[0037] In the formula, E represents expectation, F p represents the Fourier function of a signal, S p (ω) represents the power spectrum density function, and T represents the time length of the signal.

[0038] Preferably, the second energy storage capacity constraint is as follows:

[0039]

[0040] In the formula, E r is an energy storage capacity constraint condition, and S1 and S2 are upper and lower limits of the energy storage SOC respectively.

[0041] In still another aspect, the application further provides a configuration system of energy storage resources under renewable energy access, comprising:

[0042] A first calculation module is configured to calculate source-load fluctuation characteristics of a power grid based on the obtained output of renewable energy and load;

[0043] A second calculation module is configured to calculate the demand of a power generation system of renewable energy access for energy storage resources based on the source-load fluctuation characteristics and a pre-set reliability index;

[0044] A configuration module is configured to configure energy storage capacity in a time domain and a frequency domain by convolution operation and product operation respectively based on the demand of the power generation system of renewable energy access for energy storage resources and the obtained energy storage classification.

[0045] Compared with the prior art, the application has the following beneficial effects:

[0046] 1. The application provides a configuration method of energy storage resources under renewable energy access, comprising: calculating source-load fluctuation characteristics of a power grid based on the obtained output of renewable energy and load; calculating the demand of a power generation system of renewable energy access for energy storage resources based on the source-load fluctuation characteristics and a pre-set reliability index; and configuring energy storage capacity in a time domain and a frequency domain by convolution operation and product operation respectively based on the demand of the power generation system of renewable energy access for energy storage resources and the obtained energy storage classification. The application realizes fine configuration of energy storage resources by determining the demand of energy storage resources and combining the obtained energy storage classification to configure energy storage in a time domain and a frequency domain.

[0047] 2, The application calculates the quantitative demand of the high proportion renewable energy power generation system on the energy storage resource by converting the output of wind power, photovoltaic and load into empirical mode function through the classical modal decomposition method, and then obtaining the frequency spectrum characteristics by the marginal spectrum of Hilbert amplitude spectrum spectrum transformation on each empirical mode function, and classifying the energy storage according to the actual region, and calculating the maximum charge and discharge power and the energy storage capacity in the time domain by convolution operation and classifying and configuring. BRIEF DESCRIPTION OF DRAWINGS

[0048] Figure 1 It is a configuration method for energy storage resources suitable for high proportion renewable energy access of the application;

[0049] Figure 2 It is a configuration system diagram of energy storage resources suitable for high proportion renewable energy access of the application;

[0050] Figure 3 It is a main frequency empirical mode component diagram of wind farm, photovoltaic power station and provincial unified load of the application;

[0051] Figure 4 It is an instantaneous frequency spectrum diagram of wind farm, photovoltaic power station and provincial unified load of the application;

[0052] Figure 5 It is a part of empirical mode component and instantaneous frequency spectrum diagram of the application of the grid unified load;

[0053] Figure 6 It is an equivalent continuous load curve diagram under each renewable energy generation penetration rate scenario of the application;

[0054] Figure 7 It is an unbalanced energy continuous curve under different penetration rates and a time sequence configuration diagram of the actual region energy storage classification of the application;

[0055] Figure 8 It is a power spectrum density curve diagram of renewable energy power generation of the application;

[0056] Figure 9 It is a schematic diagram of the comparison method of time domain and frequency configuration of the application. DETAILED DESCRIPTION

[0057] Embodiment 1

[0058] According to the time scale of the role of energy storage, the application of energy storage is divided into time scale application. The efficient application of energy storage resources can not only solve the safety and stability problem of the grid under high proportion renewable energy access in the future, but also bring great economic benefits to the planning and operation of the grid, which has very high value and significance.

[0059] The application provides a configuration method of energy storage resources under renewable energy access, as shown in the formula: Figure 1

[0060] Step 1: calculating the source-load fluctuation characteristics of the power grid based on the obtained output of renewable energy and load;

[0061] Step 2: calculating the demand of the power generation system of renewable energy access for energy storage resources based on the source-load fluctuation characteristics and the pre-set reliability index;

[0062] Step 3: configuring the energy storage capacity based on the demand of the power generation system for energy storage resources and the obtained energy storage classification by using convolution operation and product operation in time domain and frequency domain respectively.

[0063] The application provides a configuration method of energy storage resources under renewable energy access, as shown in the formula: Figure 2 The application provides a configuration method of energy storage resources under renewable energy access, as shown in the formula:

[0064] The specific content of the source-load fluctuation characteristics of the power grid calculated based on the obtained output of renewable energy and load in step 1 is as follows:

[0065] The source-load fluctuation characteristics of the power grid under high-proportion renewable energy access are counted and measured, the output of wind power, photovoltaic and load is converted into an empirical mode function (IMF) by a classical modal decomposition (EMD) method, then the Hilbert-Huang transform (HHT) is performed on each IMF function, and the corresponding frequency spectrum characteristics can be obtained, and the main frequency component is identified. The Hilbert amplitude spectrum is defined as:

[0066]

[0067] In the formula, H is the HHT transform function, ω is the signal angular frequency, t is the signal time, Re is the real part, e is the exponential constant, n is the number of decomposed signals, k is the serial number of the decomposed signal, a k : the serial number of the kth decomposed signal, the phase of the signal, j is the imaginary symbol, ω k (t) is the signal angular frequency function.

[0068] The marginal spectrum of the signal is defined as:

[0069]

[0070] In the formula, h is the marginal spectrum function definition, and T is the time length of the signal.

[0071] The signal sampling frequency satisfies the Shannon sampling theorem.

[0072] ​The step 2 is based on the source load fluctuation characteristics and the pre-set reliability index to calculate the demand of the renewable energy access power generation system for the energy storage resource, which is specifically introduced as follows:

[0073] The demand of the high proportion renewable energy power generation system for the energy storage resource is calculated based on the reliability angle.

[0074] The loss of load probability and system loss of load expectation under different renewable energy penetration rates are calculated by the equivalent electric quantity function method. Considering the fluctuation characteristics of new energy units, they are equivalent to multi-state units, and the convolution form of the equivalent function is as follows:

[0075]

[0076] In the formula, i: the number of convolution calculation, C: the rated capacity of multi-state unit, The reduced capacity of multi-state unit, x: the maximum load of the system, g: the probability of output corresponding power, d: differential symbol, y: power, f (i) (x) is the equivalent electric quantity function of the i-th calculation, f (i-1) : the equivalent electric quantity function of the i-1-th calculation.

[0077] Meanwhile, considering that the renewable energy generation exists zero output state, the probability density of this point cannot be obtained, so the zero state needs to be handled separately as follows:

[0078]

[0079] Then, the power shortage probability LOLP and the power shortage expectation EENS are calculated according to LOLP=P(x>x0)=f (i) (x0) and (x0) is the equivalent electric quantity function of the i-th calculation for the load x0; P(x>x0) represents the probability that the maximum load x of the system is greater than the load x0; x0 represents the load, f (i) (x0): the equivalent electric quantity function of the i-th calculation for the load x0; P(x>x0) represents the probability that the maximum load x of the system is greater than the load x0; x0 represents the load, f (i) (x): the equivalent electric quantity function of the i-th calculation, f (i-1) : the equivalent electric quantity function of the i-1-th calculation.

[0080] Meanwhile, considering the curtailment problem of renewable energy, the renewable energy curtailment probability and curtailment electric quantity expectation under different renewable energy penetration rates are defined and calculated as follows:

[0081] COREP=f RE (0 + )-f (i) (C)+P(y=0)=1-f (i) (C)

[0082]

[0083] In the formula, f RE (0 + ) represents the probability that the power output of renewable energy is greater than 0.

[0084] Based on the equivalent charge function, through Preliminary calculations were performed on the energy storage capacity under different renewable energy penetration rates, where C ES For energy storage capacity under different renewable energy penetration rates, C represents the inverse function of the equivalent energy function calculated in the i-th time, and LOLP0 represents the initial calculated probability of insufficient power; RE Indicates the capacity of renewable energy; C T This represents the capacity of the multi-state unit when the signal duration is T. Simultaneously, the energy demand for energy storage is calculated by defining the unmet energy dissipation probability UBEP = P(x > x0) = u(x0), where u is used as the probability function, and UBEP is the unmet energy dissipation probability.

[0085] In step 3, based on the demand for energy storage resources from the power generation system and the classification of acquired energy storage, convolution and multiplication operations are used in the time and frequency domains respectively to configure the energy storage capacity. The details are as follows:

[0086] The statistics cover all types of energy storage in the region, and classify them in detail according to energy storage time, application type, application scenario, and operating characteristics to serve subsequent classification and configuration.

[0087] Based on quantitative requirements from a reliability perspective and the actual regional energy storage classification, the timing configuration of energy storage is achieved. First, the power command p of the energy storage is determined. ref (t), which follows a Gaussian distribution:

[0088]

[0089] In the formula, R p : Power command p for energy storage ref The autocorrelation function of (t).

[0090] pass In the formula, F -1 P is the inverse function of the standard Gaussian distribution function. r For maximum charge / discharge power, R p (0) represents the power command p for energy storage. ref The autocorrelation function of (t), where α is a set probability threshold. The maximum charge / discharge power P is configured. r Then determine the energy storage state signal e, which also follows a Gaussian distribution. ES (t), which satisfies:

[0091]

[0092] In the formula, e ES (t) represents the energy storage state signal, which is obtained by solving... The calculated energy storage capacity is obtained. Here, S1 and S2 are the upper and lower limits of the energy storage SOC, respectively, α and β are the set probability thresholds, and R... e Energy storage state signal e ES The autocorrelation function of (t).

[0093] Configuring energy storage capacity in the time domain requires convolution operations. In frequency domain analysis, this can be transformed into a simple multiplication operation. First, the power spectral density function is calculated:

[0094]

[0095] In the formula, E represents the expectation, and F p S represents the Fourier function of the signal. p (ω) represents the power spectral density function, and T is the duration of the signal.

[0096] pass Configured maximum charge / discharge power P r In the formula, S p This represents the power spectral density function. (Through...) The calculated value of the energy storage capacity is obtained, where E r This indicates the energy storage capacity constraints.

[0097] Frequency domain analysis is performed on the unbalanced power. Based on the frequency configuration method proposed above, the power and capacity of energy storage are determined according to different time periods and energy storage technology types, and the quantitative demand is formulated by classification.

[0098] Example 2

[0099] The present invention provides a method for configuring energy storage resources applicable to high-proportion renewable energy access. The following detailed implementation examples further illustrate this solution:

[0100] This study statistically analyzes and measures the source-load fluctuation characteristics of the power grid under a high proportion of renewable energy integration. Using the classical mode decomposition (EMD) method, the output of wind power, photovoltaic power, and load is transformed into empirical mode functions (IMFs). Figure 5 Then, by performing a Hilbert-Huang transform (HHT) on each IMF function, the corresponding spectral characteristics can be obtained, and its dominant frequency component can be identified. The number of sampling points is... Where N is the number of sampling points, f s Here, Δf is the sampling frequency, and Δf is the frequency resolution. Figure 3 andFigure 4 The output of wind power, photovoltaic and load is converted into empirical mode function (IMF), and then the corresponding frequency spectrum characteristics can be obtained by performing Hilbert-Huang transform (HHT) on each IMF function. It is found through calculation that the intra-day and intra-month fluctuation components account for the main components of wind power fluctuation, the intra-day and seasonal fluctuation components account for the main components of the photovoltaic power station rate fluctuation, and the monthly and seasonal fluctuation components account for the main components of load power fluctuation. The vertical coordinate frequency in the figure represents frequency, and Time represents time.

[0101] The demand of the energy storage resource of the power generation system connected with renewable energy is calculated based on the source-load fluctuation characteristics and the preset reliability index, and specifically includes:

[0102] The new energy unit is equivalent to a multi-state unit based on the source-load fluctuation characteristics, and equivalent power is obtained;

[0103] The renewable energy curtailment probability and curtailment power expectation under different renewable energy penetration rates are calculated based on the equivalent power using the curtailment probability and curtailment power expectation calculation formula;

[0104] The initial value of the energy storage capacity under different renewable energy penetration rates is calculated based on the renewable energy curtailment probability and curtailment power expectation under different renewable energy penetration rates using the energy storage capacity calculation formula;

[0105] The demand of the energy storage resource is calculated based on the initial value of the energy storage capacity under different renewable energy penetration rates combined with the unbalanced energy unfulfillment probability.

[0106] The loss of load probability and system loss of load expectation value under different renewable energy penetration rates are calculated by the equivalent power function method, combined with Table 1.

[0107] Table 1 Renewable energy penetration scenario setting

[0108] Penetration Scenario 1 Scenario 2 Scenario 3 Scenario 4 Wind power installed capacity 0.5 1.0 1.0 1.5 Photovoltaic installed capacity 0.0 0.0 0.5 1.0 Conventional unit installed capacity 0.8 0.8 0.6 0.4

[0109] Considering the fluctuation characteristics of the new energy unit, it is equivalent to a multi-state unit, and the convolution form of its equivalent function is as follows:

[0110]

[0111] In the formula, i: the number of convolution calculations, C: the rated capacity of the multi-state unit, The reduced capacity of the multi-state unit, x: the maximum load of the system, g: the probability of output corresponding power, d: differential symbol, y: power, f (i) (x) is the equivalent power function of the i-th calculation of the maximum load of the system.

[0112] At the same time, considering the zero output state of renewable energy generation, the point probability density cannot be obtained, so the zero state needs to be handled separately as follows:

[0113]

[0114] Then the power shortage probability LOLP is calculated according to LOLP = P(x > x0) = f (i) (x0) and the expected energy shortage EENS is calculated according to EENS = ∫x0∞f(x)dx. Combining the above, the equivalent continuous load curve under different penetration rates is shown in Fig. 2. Figure 6

[0115] Combining Table 2, considering the abandonment of renewable energy, the renewable energy abandonment probability and the expected abandonment energy under different renewable energy penetration rates are defined and calculated as follows:

[0116] COREP = f RE (0 + )-f (i) (C)+P(y = 0) = 1-f (i) (C)

[0117]

[0118] Table 2 Reliability indicators under renewable energy penetration scenarios

[0119]

[0120] Among them, under the condition of 150% wind power, 100% photovoltaic, 40% available traditional units, the LOLP of the system is 0.42, and the EENS is 0.01. Compared with the scenario of 100% wind power, 50% photovoltaic, 60% available traditional units, the LOLP is reduced by 0.0438, that is, the high installed capacity of renewable energy generation cannot guarantee the reliability level of the system, and needs to be supported by traditional units as a hot standby. At the same time, the renewable energy generation has exceeded the system's consumption capacity, the COREP is 0.3254, and the ERENU is 0.1525. The proportion of renewable energy generation is unreasonable, and under Scenario 4, the generation capacity of traditional units is 0.2827, and the generation hours are 5164h, which has basically reached the economic utilization hour level of thermal power units. At this time, in order to guarantee the system reliability, it is necessary to increase the installed capacity of traditional units, but this will reduce the utilization hours of traditional units. It shows that the system needs an economic and efficient flexible regulation resource to meet the system's reliability requirements.

[0121] Based on the equivalent energy function, it is assumed that the availability of energy storage is 100%, then under the required LOLP level of the system, through ​The capacity of energy storage under different renewable energy penetration rates is calculated initially, and the energy demand of energy storage is calculated by defining the unfulfilled probability of unbalanced energy UBEP = P(x > x0) = u(x0). The quantitative demand of energy storage under renewable energy penetration scenarios is shown in Tables 3 and 4:

[0122] Table 3 Quantitative demand of energy storage under renewable energy penetration scenarios (LOLP = 0.1)

[0123] Scenario Scenario 1 Scenario 2 Scenario 3 Scenario 4 C ES ]]> 0.05 0.03 0.20 0.39

[0124] Table 4 Quantitative demand of energy storage under renewable energy penetration scenarios (COREP = 0.1)

[0125] Scenario Scenario 1 Scenario 2 Scenario 3 Scenario 4 C ES ]]> -- -- 0.05 0.51

[0126] All types of energy storage involved in the statistical area are classified in detail in terms of energy storage time, application type, application scenario, and operation characteristics to serve subsequent classification and configuration. The classification and selection of energy storage technology in the power system should be combined with the demand of the power system and the technical characteristics of energy storage itself to find the fitting point. According to the time scale of the role of energy storage, the application of energy storage is divided into time scales, as shown in Table 5.

[0127] Table 5 Types of energy storage and application types

[0128]

[0129] In combination with Figure 7 First, the power instruction p ref (t) of energy storage is determined, which satisfies the Gaussian distribution By configuring the maximum charge and discharge power P r , the e ES (t) that also satisfies the Gaussian distribution is determined, which satisfies By solving , the calculated value of the capacity of energy storage is obtained. S1 and S2 are the upper and lower limits of the SOC of energy storage, respectively, and a and β are the set probability thresholds.

[0130] Convolution operation is required when configuring the capacity of energy storage in the time domain. In frequency domain analysis, it can be transformed into simple multiplication operation. First, the power spectral density function is calculated in combination with Figure 8 , the maximum charge and discharge power P r is configured by , and the calculated value of the capacity of energy storage is obtained by . In combination with Figure 9 , the time domain and frequency configuration methods are compared, and it can be found that the frequency domain method can more effectively calculate the capacity demand of energy storage, and the physical meaning is more explicit.

[0131] The unbalanced power is analyzed in the frequency domain, and the power and capacity of the energy storage are determined according to different time periods, energy storage technology types, etc., to make classified and quantitative demands, and the results are combined with Tables 6, 7, 8 and 9.

[0132] Table 6: Classified and quantitative demands of energy storage technology (Scenario 1)

[0133]

[0134] Table 7: Classified and quantitative demands of energy storage technology (Scenario 2)

[0135]

[0136] Table 8: Classified and quantitative demands of energy storage technology (Scenario 3)

[0137]

[0138]

[0139] Table 9: Classified and quantitative demands of energy storage technology (Scenario 4)

[0140]

[0141] Example 3

[0142] Based on the same inventive concept, the present application provides a configuration system for energy storage resources under renewable energy access, comprising:

[0143] A first calculation module for calculating the source-load fluctuation characteristics of the power grid based on the output of the obtained renewable energy and load;

[0144] A second calculation module for calculating the demand of the energy storage resources of the power generation system under renewable energy access based on the source-load fluctuation characteristics and the pre-set reliability index;

[0145] A configuration module for configuring the capacity of the energy storage based on the demand of the energy storage resources of the power generation system and the obtained energy storage classification using convolution operation and multiplication operation in the time domain and frequency domain, respectively.

[0146] The first calculation module comprises:

[0147] A conversion submodule for converting the output of wind power and photovoltaic in renewable energy and the output of load into empirical mode functions;

[0148] The characteristic recognition sub-module is configured to perform Hilbert-Huang transformation on each empirical mode function to obtain a corresponding frequency spectrum characteristic, and recognize a main frequency component of the corresponding frequency spectrum characteristic as a source-load fluctuation characteristic of the power grid.

[0149] The second calculation module comprises:

[0150] The equivalent electric quantity calculation sub-module is configured to equivalently convert the new energy unit into a multi-state unit based on the source-load fluctuation characteristic, to obtain an equivalent electric quantity;

[0151] The probability expectation calculation sub-module is configured to calculate renewable energy curtailment probability and curtailment electric quantity expectation under different renewable energy penetrations based on the equivalent electric quantity and a curtailment probability and curtailment electric quantity expectation calculation formula.

[0152] The capacity calculation sub-module is configured to calculate an initial value of energy storage capacity under the different renewable energy penetrations based on the renewable energy curtailment probability and curtailment electric quantity expectation under the different renewable energy penetrations and an energy storage capacity calculation formula.

[0153] The energy demand calculation sub-module is configured to calculate energy demand of the energy storage based on the initial value of the energy storage capacity under the different renewable energy penetrations and an imbalance energy unfulfillment probability.

[0154] The equivalent function in the equivalent electric quantity calculation sub-module is as follows:

[0155]

[0156] In the formula, i represents a convolution calculation number, C represents a rated capacity of the multi-state unit, x represents a maximum load of the system, g represents a probability of outputting corresponding power generation, d represents a differential symbol, y represents power generation, and f represents a function. In the formula, i represents a convolution calculation number, C represents a rated capacity of the multi-state unit, x represents a maximum load of the system, g represents a probability of outputting corresponding power generation, d represents a differential symbol, y represents power generation, and f represents a function. (i) (x) is an equivalent electric quantity function of the i-th calculation of the maximum load x of the system.

[0157] The configuration module comprises a time domain configuration sub-module and a frequency domain configuration sub-module.

[0158] The time domain configuration sub-module is specifically configured to:

[0159] The energy storage power instruction is determined based on the energy storage power instruction and an autocorrelation function of the energy storage power instruction under a condition of satisfying a Gaussian distribution.

[0160] The energy storage state signal is determined based on the energy storage state signal and an autocorrelation function of the energy storage state signal under a condition of satisfying a Gaussian distribution.

[0161] The energy storage capacity value satisfying the first energy storage capacity constraint is calculated based on the energy storage power instruction and the energy storage state signal.

[0162] The energy storage capacity is configured based on the energy storage capacity value satisfying the energy storage capacity constraint in the time domain.

[0163] The energy storage state signal is determined according to the following formula:

[0164]

[0165] In the formula, f(e ES ) is the equivalent electric quantity of the energy storage, e ES (t) is the energy storage state signal, e ES (0) is the energy storage initialization state signal, and R e is the autocorrelation function of the energy storage state signal.

[0166] The first energy storage capacity constraint is shown in the following formula:

[0167]

[0168] In the formula, E r is the energy storage capacity constraint condition, R e (0) is the autocorrelation function of the energy storage initialization state signal, S1 and S2 are the upper and lower limits of the energy storage SOC respectively, F -1 is the inverse function of the standard Gaussian distribution function, and α and β are the set probability thresholds respectively.

[0169] The frequency domain configuration submodule is used to:

[0170] The Fourier function based on the energy storage power instruction is used to calculate the power spectral density function according to the power spectral density function calculation formula;

[0171] The energy storage capacity value is calculated based on the power spectral density function through the maximum charge-discharge power constraint and the second energy storage capacity constraint;

[0172] The energy storage capacity is configured in the frequency domain based on the energy storage capacity value.

[0173] The power spectral density function calculation formula is shown in the following formula:

[0174]

[0175] In the formula, E represents expectation, F p represents the Fourier function of the signal, S p (ω) represents the power spectral density function, and T represents the time length of the signal.

[0176] The second energy storage capacity constraint is shown in the following formula:

[0177]

[0178] In the formula, Er S1 and S2 are upper and lower limits of the energy storage SOC, respectively, for the energy storage capacity constraint.

[0179] Those skilled in the art will appreciate that embodiments of the application can be readily used as software, hardware, and / or firmware. Furthermore, embodiments of the application can take the form of a program product on one or more computer-readable storage media (e.g., magnetic, optical or semiconductor storage media).

[0180] The present application is described in reference to the flowchart illustrations and / or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general purpose computer, special purpose computer, embedded processing system or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart illustrations and / or block diagrams block or blocks. Figure 1 one or more functions specified in the flowchart illustrations and / or block diagrams block or blocks. Figure 1 means for carrying out each of the

[0181] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instructions which implement the flowchart illustrations and / or block diagrams block or blocks. Figure 1 one or more functions specified in the flowchart illustrations and / or block diagrams block or blocks. Figure 1 means for carrying out each of the

[0182] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the flowchart illustrations and / or block diagrams block or blocks. Figure 1 one or more functions specified in the flowchart illustrations and / or block diagrams block or blocks. Figure 1 means for carrying out each of the

[0183] The above merely provides an embodiment of the application, but is not intended to limit the application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the application shall fall within the scope of the application.

Claims

1. A method for configuration of energy storage resources under renewable energy integration, characterized in that, The method comprises the following steps: calculating the source-load fluctuation characteristics of the power grid based on the output of the obtained renewable energy and load; calculating the demand of the power generation system with renewable energy access for energy storage resources based on the source-load fluctuation characteristics and the pre-set reliability index; configuring the energy storage capacity in the time domain and frequency domain by convolution operation and product operation respectively based on the demand of the power generation system for energy storage resources and the obtained energy storage classification, comparing the configuration methods in the time domain and frequency domain, and determining the power and capacity of the energy storage according to different time periods and energy storage technology types according to the frequency domain configuration method; configuring the energy storage capacity in the time domain by convolution operation based on the demand of the power generation system for energy storage resources and the obtained energy storage classification, comprising: determining the energy storage power instruction based on the energy storage power instruction and the autocorrelation function of the energy storage power instruction under the condition of satisfying the Gaussian distribution; determining the energy storage state signal based on the energy storage state signal and the autocorrelation function of the energy storage state signal under the condition of satisfying the Gaussian distribution; calculating the energy storage capacity value satisfying the first energy storage capacity constraint based on the energy storage power instruction and the energy storage state signal; configuring the energy storage capacity in the time domain by using the energy storage capacity value satisfying the first energy storage capacity constraint; configuring the energy storage capacity in the frequency domain by product operation based on the demand of the power generation system for energy storage resources and the obtained energy storage classification, comprising: calculating the power spectral density function based on the Fourier function of the energy storage power instruction by using the power spectral density function calculation formula; calculating the energy storage capacity value based on the power spectral density function by using the maximum charge-discharge power constraint and the second energy storage capacity constraint; configuring the energy storage capacity in the frequency domain based on the energy storage capacity value calculated based on the second energy storage capacity constraint.

2. The method of claim 1, wherein, The method comprises the following steps: transforming the output of wind power and photovoltaic power in the renewable energy and the output of the load into empirical mode functions based on the output of the renewable energy and the load; performing Hilbert-Huang transformation on each empirical mode function to obtain corresponding frequency spectrum characteristics, and identifying the main frequency component as the source-load fluctuation characteristics of the power grid.

3. The method of claim 1, wherein, The method comprises the following steps: equivalent function is used to equivalent the new energy unit into multi-state unit based on the source-load fluctuation characteristics, and equivalent power is obtained; the renewable energy curtailment probability and the expected curtailment power under different renewable energy penetrations are calculated based on the equivalent power by using the curtailment probability and the expected curtailment power calculation formula; the initial value of the energy storage capacity under different renewable energy penetrations is calculated based on the renewable energy curtailment probability and the expected curtailment power under different renewable energy penetrations by using the energy storage capacity calculation formula; the demand of the energy storage resource is calculated based on the initial value of the energy storage capacity under different renewable energy penetrations and the imbalance energy dissatisfaction probability.

4. The method of claim 3, wherein, The equivalent function is as follows: In the formula, : number of convolution calculations, : rated capacity of the multi-state machine group, : reduced capacity of the multi-state machine group, : maximum load of the system, : probability of outputting the corresponding power generation, : differential symbol, : power generation, is the equivalent electric quantity function of the i-th calculation, : equivalent electric quantity function of the i-1-th calculation.

5. The method of claim 1, wherein, The energy storage state signal is determined as follows: wherein is the energy storage equivalent electric quantity, is the energy storage state signal, is the energy storage initialization state signal, is the autocorrelation function of the energy storage initial state signal, is the autocorrelation function of the energy storage state signal at signal time t, is the signal time.

6. The method of claim 1, wherein, The first energy storage capacity constraint is as follows: wherein, is the energy storage capacity constraint, is the autocorrelation function of the energy storage initialization state signal, and are the upper and lower limits of the energy storage SOC, respectively, is the inverse function of the standard Gaussian distribution function, and are the set probability thresholds, respectively.

7. The method of claim 1, wherein, The power spectral density function calculation formula is as follows: wherein denotes the expectation, denotes the Fourier function of the signal, denotes the power spectral density function, : the time length of the signal.

8. The method of claim 7, wherein, The second energy storage capacity constraint is shown in the following formula: wherein, is the energy storage capacity constraint, and are the upper and lower limits of the energy storage SOC, respectively.

9. A system for implementing the method of configuring energy storage resources under renewable energy access according to any one of claims 1-8, characterized in that, Comprise: A first calculation module configured to calculate source-load fluctuation characteristics of a power grid based on the obtained renewable energy and load output; A second calculation module configured to calculate the demand of energy storage resources of a power generation system connected with renewable energy based on the source-load fluctuation characteristics and pre-set reliability indexes; A configuration module configured to configure energy storage capacity by convolution operation and product operation in time domain and frequency domain respectively based on the demand of energy storage resources of the power generation system and the obtained energy storage classification.

Images