Frequency control method and device for water-light complementary power generation system

Abstract

The present disclosure relates to a frequency control method and device of a water-light complementary power generation system, which determines a to-be-operated time interval of the water-light complementary power generation system, wherein a starting time point represents a time point at which the system starts to operate. A prediction interval with a preset time length is determined in the to-be-operated time interval, and the prediction interval includes a discrete time point. A prediction error corresponding to each prediction interval is determined according to the preset time length, a preset prediction error initial value, a discrete time step and an error determination function. The error determination function is modeled based on an Ito process and an arbitrary polynomial chaos. An adjustment scheme for adjusting the system frequency in the to-be-operated time interval is generated according to the prediction error of the prediction interval. The present disclosure accurately determines the prediction error through mathematical modeling, reduces the possibility of poor connection and diffusion of photovoltaic output between different prediction intervals, and adjusts the entire system frequency through the prediction error, thereby improving the stability of the system frequency.

Description

Technical Field

[0001] This disclosure relates to the field of power generation, and in particular to a frequency control method and apparatus for a hydro-solar hybrid power generation system. Background Technology

[0002] With the development of clean energy, hydropower, photovoltaic (PV) power generation, and other clean energy sources are widely used in the power generation field. Among them, hydro-solar hybrid power generation systems can utilize the advantages of different energy types to achieve complementary power generation from multiple clean energy sources. However, the output of solar PV is strongly correlated with natural sunlight conditions. Since sunlight conditions constantly change with the weather, the output power of PV also exhibits randomness. Large and frequent random fluctuations in PV output impact the system's active power balance, thereby affecting the system's primary and secondary frequency regulation characteristics and significantly impacting economic dispatch. Therefore, in hydro-solar hybrid power generation systems with a high proportion of PV integration, the randomness of PV output power cannot be ignored. Summary of the Invention

[0003] In view of this, this disclosure proposes a frequency control method and apparatus for a hydro-solar hybrid power generation system, which aims to determine the prediction error of photovoltaic power generation through modeling and adjust the frequency of the entire hydro-solar hybrid power generation system according to the determined prediction error, thereby ensuring the stability of the system.

[0004] According to a first aspect of this disclosure, a frequency control method for a hydro-solar hybrid power generation system is provided, the method comprising:

[0005] Determine the standby time interval of the aforementioned hydro-solar hybrid power generation system;

[0006] Based on the time interval to be worked, at least one prediction interval with a preset duration is determined, and the prediction interval includes at least one discrete time point;

[0007] The prediction error for each prediction interval is determined based on the preset duration, the preset initial value of the prediction error, the discrete time step, and the error determination function. The error determination function is modeled based on the Iton process and arbitrary polynomial chaos. The discrete time step is determined based on the preset duration and the number of discrete time points included in the prediction interval.

[0008] An adjustment scheme is generated based on the prediction error of the at least one prediction interval, and the adjustment scheme is used to adjust the frequency of the hydropower complementary power generation system during the waiting time interval.

[0009] In one possible implementation, determining the prediction error corresponding to each prediction interval based on the preset duration, the preset initial value of the prediction error, the discrete time step, and the error determination function includes:

[0010] In response to the prediction interval being the interval with the first temporal position, the corresponding prediction error is determined based on the preset duration, the initial value of the prediction error, the discrete time step, and the error determination function.

[0011] In response to the prediction interval not being the first interval in the time series, the corresponding prediction error is determined based on the preset duration, the adjacent prediction error corresponding to the previous prediction interval in the time series, the discrete time step, and the error determination function.

[0012] In one possible implementation, the initial value of the prediction error is determined based on the difference between the actual photovoltaic output and the predicted photovoltaic output of the hydro-solar hybrid power generation system at the beginning of the waiting time interval.

[0013] In one possible implementation, the error determination function is determined based on the sum of a prior error term, an error drift term, and an error diffusion term, wherein the prior error term is the initial value of the prediction error or the adjacent prediction error, the error drift term is determined based on the drift term and discrete time step size for each discrete time point in the prediction interval, and the error diffusion term is determined based on the diffusion term, discrete time step size, and truncation parameter term for each discrete time point in the prediction interval.

[0014] In one possible implementation, the error drift term is the sum of the product of the drift term at each discrete time point in the prediction interval and the discrete time step.

[0015] In one possible implementation, the error diffusion term is the sum of the products of the diffusion term, the discrete time step, and the truncation parameter term at each discrete time point in the prediction interval.

[0016] In one possible implementation, the truncation parameter is determined based on a random variable, the preset duration, and discrete time points in the current prediction interval and the preceding prediction interval in the time series.

[0017] In one possible implementation, the truncation parameter term is the sum of the product of the random variable and the discrete parameters of the current prediction interval and the discrete time points within a time interval of the preceding prediction interval in terms of time sequence;

[0018] Wherein, when the prediction interval is the interval with the first time position, the discrete parameter is the square root of the reciprocal of the preset duration; when the prediction interval is not the interval with the first time position, the discrete parameter is determined based on the square root of twice the reciprocal of the preset duration, the preset duration, and discrete time points within the prediction interval.

[0019] In one possible implementation, the drift term and the diffusion term are determined by a stochastic differential equation obtained by modeling the prediction error using the Iton process.

[0020] According to a second aspect of this disclosure, a frequency control device for a hydro-solar hybrid power generation system is provided, the device comprising:

[0021] The first interval determination module is used to determine the standby time interval of the hydro-solar hybrid power generation system.

[0022] The second interval determination module is used to determine at least one prediction interval with a preset duration based on the time interval to be worked, wherein the prediction interval includes at least one discrete time point;

[0023] The error determination module is used to determine the prediction error corresponding to each prediction interval based on the preset duration, the preset initial value of prediction error, the discrete time step, and the error determination function. The error determination function is obtained by modeling based on the Iton process and arbitrary polynomial chaos. The discrete time step is determined according to the preset duration and the number of discrete time points included in the prediction interval.

[0024] A frequency control module is used to generate an adjustment scheme based on the prediction error of the at least one prediction interval, the adjustment scheme being used to adjust the frequency of the hydropower complementary power generation system during the waiting time interval.

[0025] In one possible implementation, the error determination module includes:

[0026] In response to the prediction interval being the interval with the first temporal position, the corresponding prediction error is determined based on the preset duration, the initial value of the prediction error, the discrete time step, and the error determination function.

[0027] In response to the prediction interval not being the first interval in the time series, the corresponding prediction error is determined based on the preset duration, the adjacent prediction error corresponding to the previous prediction interval in the time series, the discrete time step, and the error determination function.

[0028] In one possible implementation, the initial value of the prediction error is determined based on the difference between the actual photovoltaic output and the predicted photovoltaic output of the hydro-solar hybrid power generation system at the beginning of the waiting time interval.

[0029] In one possible implementation, the error determination function is determined based on the sum of a prior error term, an error drift term, and an error diffusion term, wherein the prior error term is the initial value of the prediction error or the adjacent prediction error, the error drift term is determined based on the drift term and discrete time step size for each discrete time point in the prediction interval, and the error diffusion term is determined based on the diffusion term, discrete time step size, and truncation parameter term for each discrete time point in the prediction interval.

[0030] In one possible implementation, the error drift term is the sum of the product of the drift term at each discrete time point in the prediction interval and the discrete time step.

[0031] In one possible implementation, the error diffusion term is the sum of the products of the diffusion term, the discrete time step, and the truncation parameter term at each discrete time point in the prediction interval.

[0032] In one possible implementation, the truncation parameter is determined based on a random variable, the preset duration, and discrete time points in the current prediction interval and the preceding prediction interval in the time series.

[0033] In one possible implementation, the truncation parameter term is the sum of the product of the random variable and the discrete parameters of the current prediction interval and the discrete time points within a time interval of the preceding prediction interval in terms of time sequence;

[0034] Wherein, when the prediction interval is the interval with the first time position, the discrete parameter is the square root of the reciprocal of the preset duration; when the prediction interval is not the interval with the first time position, the discrete parameter is determined based on the square root of twice the reciprocal of the preset duration, the preset duration, and discrete time points within the prediction interval.

[0035] In one possible implementation, the drift term and the diffusion term are determined by a stochastic differential equation obtained by modeling the prediction error using the Iton process.

[0036] According to a third aspect of this disclosure, an electronic device is provided, comprising: a processor; and a memory for storing processor-executable instructions; wherein the processor is configured to implement the above-described method when executing instructions stored in the memory.

[0037] According to a fourth aspect of this disclosure, a non-volatile computer-readable storage medium is provided that stores computer program instructions thereon, wherein the computer program instructions, when executed by a processor, implement the above-described method.

[0038] According to a fifth aspect of this disclosure, a computer program product is provided, including computer-readable code or a non-volatile computer-readable storage medium carrying the computer-readable code, wherein when the computer-readable code is run in a processor of an electronic device, the processor in the electronic device performs the above-described method.

[0039] In this embodiment, a waiting-to-operate time interval for the hydro-solar hybrid power generation system is determined, where the start time characterizes the moment the system begins operation. At least one prediction interval with a preset duration is determined within the waiting-to-operate time interval, and this prediction interval includes discrete time points. The prediction error corresponding to each prediction interval is determined based on the preset duration, a preset initial value of the prediction error, the discrete time step size, and an error determination function. The error determination function is modeled using Itō processes and arbitrary polynomial chaos. An adjustment scheme for regulating the system frequency within the waiting-to-operate time interval is generated based on the prediction error of the prediction interval. This disclosure accurately determines the prediction error through mathematical modeling, reducing the possibility of poor connection and diffusion of photovoltaic output between different prediction intervals. By adjusting the overall system frequency through the prediction error, the stability of the system frequency is improved.

[0040] Other features and aspects of this disclosure will become clear from the following detailed description of exemplary embodiments with reference to the accompanying drawings. Attached Figure Description

[0041] The accompanying drawings, which are included in and form part of this specification, illustrate exemplary embodiments, features, and aspects of this disclosure together with the specification and serve to explain the principles of this disclosure.

[0042] Figure 1 A flowchart is shown illustrating a frequency control method for a hydro-solar hybrid power generation system according to an embodiment of the present disclosure;

[0043] Figure 2 A schematic diagram showing a waiting time interval according to an embodiment of the present disclosure;

[0044] Figure 3 A schematic diagram illustrating a method for determining the prediction error corresponding to a prediction interval according to an embodiment of the present disclosure is shown.

[0045] Figure 4 A schematic diagram of a frequency control device for a hydro-solar hybrid power generation system according to an embodiment of the present disclosure is shown.

[0046] Figure 5 A schematic diagram of an electronic device according to an embodiment of the present disclosure is shown;

[0047] Figure 6 A schematic diagram of another electronic device according to an embodiment of the present disclosure is shown. Detailed Implementation

[0048] Various exemplary embodiments, features, and aspects of this disclosure will now be described in detail with reference to the accompanying drawings. The same reference numerals in the drawings denote elements that have the same or similar functions. Although various aspects of the embodiments are shown in the drawings, they are not necessarily drawn to scale unless specifically indicated otherwise.

[0049] The term “exemplary” as used herein means “serving as an example, embodiment, or illustration.” Any embodiment illustrated herein as “exemplary” is not necessarily to be construed as superior to or better than other embodiments.

[0050] Furthermore, to better illustrate this disclosure, numerous specific details are set forth in the following detailed description. Those skilled in the art will understand that this disclosure can be practiced without certain specific details. In some instances, methods, means, components, and circuits well known to those skilled in the art have not been described in detail in order to highlight the main points of this disclosure.

[0051] The frequency control method for the hydro-solar hybrid power generation system of this disclosure can be executed by electronic devices such as terminal devices or servers. The terminal device can be any fixed or mobile terminal, such as user equipment (UE), mobile device, user terminal, terminal, cellular phone, cordless phone, personal digital assistant (PDA), handheld device, computing device, vehicle-mounted device, or wearable device. The server can be a single server or a server cluster consisting of multiple servers. Any electronic device can implement the frequency control method for the hydro-solar hybrid power generation system of this disclosure by having its processor call computer-readable instructions stored in its memory.

[0052] Figure 1 A flowchart illustrating a frequency control method for a hydro-solar hybrid power generation system according to an embodiment of the present disclosure is shown. Figure 1 As shown, the frequency control method for a hydro-solar hybrid power generation system in this embodiment may include the following steps S10-S40.

[0053] Step S10: Determine the standby time interval of the hydro-solar hybrid power generation system.

[0054] In one possible implementation, the hydropower-photovoltaic hybrid power generation system is a power generation system that combines hydropower generation and photovoltaic power generation. This embodiment of the disclosure uses a method of predicting the prediction error of the photovoltaic power generation to pre-regulate the frequency of the hydropower-photovoltaic hybrid power generation system, ensuring the stability of the entire system's output frequency. Optionally, the waiting-to-work time interval is the time when the hydropower-photovoltaic hybrid power generation system is about to start working, and its length can be preset according to actual needs. That is, the start time of the waiting-to-work time interval can characterize the time when the hydropower-photovoltaic hybrid power generation system starts working. For example, the start time of the waiting-to-work time interval can be the time when the hydropower-photovoltaic hybrid power generation system switches from a non-working state to a working state, or the time when the hydropower-photovoltaic hybrid power generation system, which operates periodically for a preset working duration, changes its working cycle, wherein the length of the working cycle can be the length of the waiting-to-work time interval.

[0055] Step S20: Determine at least one prediction interval with a preset duration based on the time interval to be worked.

[0056] In one possible implementation, the electronic device determines at least one prediction interval within a waiting time interval based on a preset duration, and further determines the prediction error for each prediction interval on a per-interval basis. The length of each prediction interval is the preset duration, and each prediction interval has a temporal sequence. The start time of the first prediction interval within the entire waiting time interval is the start time of the waiting time interval, and the end time of the last prediction interval is the end time of the waiting time interval. In temporally, in every two adjacent prediction intervals, the end time of the previous prediction interval is the start time of the next prediction interval. When the preset duration is the same as the waiting time interval, the waiting time interval includes only one prediction interval.

[0057] Optionally, each prediction interval may also include at least one discrete time point, and each discrete time point has a temporal order. The discrete time step size between any two adjacent discrete time points is equal throughout the entire working time interval, and this step size can be determined based on the length of the prediction interval and the number of discrete time points it includes. That is, when the prediction interval includes only one discrete time point, the distance between the discrete time point and discrete time points in adjacent prediction intervals is the discrete time step size. Optionally, each prediction interval includes the same number of discrete time points, and the discrete time step size can be the quotient of a preset duration and the number of discrete time points included in the prediction interval.

[0058] Figure 2 A schematic diagram illustrating a waiting time interval according to an embodiment of the present disclosure is shown. Figure 2 As shown, the electronic device can predetermine the operating time interval NT, which includes multiple prediction intervals (K-1)T-KT with time order and equal length. Each prediction interval may also include at least one discrete time point.

[0059] Step S30: Determine the prediction error corresponding to each prediction interval based on the preset duration, the preset initial value of prediction error, the discrete time step, and the error determination function.

[0060] In one possible implementation, after determining the working time interval, the included prediction intervals, and the discrete time points within each prediction interval, the electronic device determines the prediction error corresponding to each prediction interval based on the prediction duration of the prediction interval, a preset initial value of the prediction error, the discrete time step between two discrete time points, and an error determination function. The error determination function can be modeled using Itō processes and arbitrary polynomial chaos. The initial value of the prediction error represents the prediction error at the beginning of the working time interval and can be determined based on the difference between the actual photovoltaic output and the predicted photovoltaic output of the hydro-solar hybrid power generation system at the beginning of the working time interval. Optionally, the prediction error corresponding to each prediction interval can be a vector, where each element of the vector represents the error in predicting the photovoltaic output at a discrete time point within that prediction interval.

[0061] Optionally, the prediction error corresponding to each prediction interval needs to be determined based on the prediction error at the start time of the current prediction interval. However, the initial value of the prediction error can only represent the prediction error at the start time of the time interval to be worked, that is, the prediction error at the start time of the first prediction interval. Therefore, for prediction intervals at different positions, the input of the error determination function represents different prediction error parameters at the start time of the prediction interval. For example, in response to the prediction interval being the first interval in the time series, the corresponding prediction error can be determined based on the preset duration, the initial value of the prediction error, the discrete time step, and the error determination function. Furthermore, in response to the prediction interval not being the first interval in the time series, the corresponding prediction error can be determined based on the preset duration, the adjacent prediction error corresponding to the previous prediction interval in the time series, the discrete time step, and the error determination function.

[0062] Furthermore, the error determination function obtained by modeling based on the Iton process and arbitrary polynomial chaos can be determined by the sum of the prior error term, the error drift term, and the error diffusion term. Here, the prior error term is the initial value of the prediction error or adjacent prediction errors; the error drift term is determined based on the drift term and discrete time step size at each discrete time point in the prediction interval; and the error diffusion term is determined based on the diffusion term, discrete time step size, and truncation parameter term at each discrete time point in the prediction interval. Optionally, the error determination function can be as follows: (1).

[0063]

[0064] In formula (1), k represents the position of the current prediction interval within the waiting time interval. Let ξ be the prediction error for the current prediction interval. k-1 For the prior error term, For the error drift term, This is the error diffusion term. When k = 1, the prior error term represents the initial prediction error. When k > 1, the online error term represents the prediction error at the end of the previous prediction period. n is the number of discrete time points included in the prediction interval, and h is the discrete time step. ζ j Let be a random variable that follows a Gaussian distribution.

[0065] Optionally, according to Equation (1), the error drift term can be the sum of the product of the drift term and the discrete time step at each discrete time point in the prediction interval. The error diffusion term can be the sum of the product of the diffusion term, the discrete time step, and the truncation parameter term at each discrete time point in the prediction interval. The truncation parameter term is determined based on the random variable, the preset duration, and the discrete time points in the current prediction interval and the preceding prediction interval in terms of time sequence. It can be the sum of the product of the random variable and the discrete parameters of the discrete time points in a time interval in the current prediction interval and the preceding prediction interval in terms of time sequence. The drift term and the diffusion term are determined by the stochastic differential equation obtained by modeling the prediction error using the Iton process. This stochastic differential equation can be shown in Equation (2).

[0066] dξ t =μ(ξ t )dt+σ(ξ t )dW t (2)

[0067] Where, μ(ξ) t ) represents the drift term, σ(ξ) t Let be the diffusion term. Furthermore, based on the Karhunen-Loève theorem in Itō processes, the infinitesimal Wiener process dW can be... t Orthogonally decomposed into a series of independent Gaussian random variables In practical applications, the KL expansion is truncated, where K represents the truncation order, and the following formula (3) can be obtained to characterize the truncation parameter term in the error diffusion term.

[0068]

[0069] Where, m j (t) represents the discrete parameter in the truncation parameter term. It is a function defined on the interval [0,T] and can be expressed by the following formula (4).

[0070]

[0071] As shown in formula (4), when the prediction interval is the interval with the first time position, the discrete parameter is the square root of the reciprocal of the preset duration. When the prediction interval is not the interval with the first time position, the discrete parameter is determined based on the square root of twice the reciprocal of the preset duration, the preset duration, and the discrete time points within the prediction interval.

[0072] In the given prediction interval [0,T], the discrete time step is h, and the total number of discrete time points is n=T / h. Therefore, the prediction error corresponding to each discrete time point and the prediction error of the previous discrete time point are determined by the discretization simulation method of formula (2) as shown in formula (5), and can be further expanded by KL to obtain formula (6).

[0073]

[0074]

[0075] According to formula (6), the drift term and diffusion term of each discrete time point in the error drift term and error diffusion term in formula (1) can be represented by the drift term and diffusion term of each discrete time point in the previous prediction interval. Since there are no other prediction intervals before the first prediction interval, formula (1) can be transformed into formula (7) to calculate the prediction error corresponding to the first prediction interval when k=1, and the polynomial approximation expression (8) of formula (7) can be obtained further by the probability collocation method.

[0076]

[0077]

[0078] Furthermore, the calculation formulas (9) for each order moment can be obtained by randomly sampling the polynomial approximation expression (8) using random sampling methods. For example, the first moment μ1 can represent the mean, and the second moment μ2 can represent the variance. Based on the chaos theory of arbitrary polynomials, the value of the prediction error can be regarded as a new random variable, and the orthogonal polynomial basis functions (10) of each order that satisfy the distribution of the random variable can be determined.

[0079]

[0080]

[0081] Among them, the polynomial coefficients in the basis functions of each order of orthogonal polynomials (10) It can be obtained by solving the following system of linear equations (11).

[0082]

[0083] In one possible implementation, when k is greater than 1, similar to formula (7), formula (1) can also be used to obtain the polynomial approximation expression (12) of the corresponding prediction error by the probability collocation method.

[0084]

[0085] Optionally, the electronic device can obtain the prediction error of each prediction interval in the time interval to be worked through the above formulas (1)-(12). Based on the prediction value and prediction error that characterize the photovoltaic output, the electronic device can obtain the corrected prediction value corresponding to each prediction interval through formula (13) to further adjust the entire system.

[0086]

[0087] in, This is the corrected predicted value. These are the original predicted values.

[0088] Figure 3 This diagram illustrates a method for determining the prediction error corresponding to a prediction interval according to an embodiment of the present disclosure. Figure 3 As shown, when determining the prediction error corresponding to each prediction interval in the time interval to be worked, the electronic device first determines the Kth prediction interval in sequence according to the time order. Further, it is determined whether K is less than or equal to N. If it is less than or equal to N, it is then determined whether K is equal to 1. If it is equal to 1, the initial value of the prediction error is determined, and the corresponding prediction error is determined according to the initial value of the prediction error by the above formula (7). If it is not equal to 1, the adjacent prediction error corresponding to the earlier prediction interval in time is determined, and the prediction error is determined according to the adjacent prediction error by the above formula (1). After determining the error, K is reassigned to K+1 in sequence. If the newly determined K still satisfies the condition K≤N, the corresponding prediction error is determined. If it does not satisfy the condition, the prediction error corresponding to each prediction interval is output in sequence to determine the prediction error sequence. Among them, each prediction error in the prediction error sequence corresponds to a prediction interval, and each prediction error can be a vector. Each element in the vector can correspond to a discrete time point in the prediction interval.

[0089] Step S40: Generate an adjustment scheme based on the prediction error of the at least one prediction interval.

[0090] In one possible implementation, after determining the prediction error for each prediction interval within the expected working time interval, the electronic device can generate a corresponding adjustment scheme based on the prediction error. This adjustment scheme is used to regulate the frequency of the hydropower-solar hybrid power generation system within the expected working time interval. For example, when the prediction error is positive, the hydropower output of the hydropower-solar hybrid power generation system is reduced, and when the prediction error is negative, the hydropower output is increased, to ensure the stability of the output frequency of the hydropower-solar hybrid power generation system.

[0091] Optionally, after generating the regulation scheme, the electronic equipment can also regulate the hydro-solar hybrid power generation system through the regulation scheme to achieve frequency control of the hydro-solar hybrid power generation system.

[0092] Based on the above, this disclosure establishes a prediction error stochasticity model based on the Itō process, which can be used to describe the probability distribution and temporal correlation of prediction errors, and provides a numerical simulation method. Furthermore, based on the stochasticity model, a simulation method for photovoltaic stochasticity diffusion is proposed using Arbitrary Polynomial Chaos (aPC), solving the problem of connection and diffusion of photovoltaic power output stochasticity across different prediction intervals. An error prediction function that accurately describes the prediction error is obtained to determine the stochasticity of photovoltaic power output, reducing the possibility of poor connection and diffusion of photovoltaic power output across different prediction intervals. By adjusting the overall system frequency through prediction errors, the stability of the system frequency is improved.

[0093] Figure 4 A schematic diagram of a frequency control device for a hydro-solar hybrid power generation system according to an embodiment of the present disclosure is shown. Figure 4 As shown, the frequency control device for the hydro-solar hybrid power generation system of this disclosure includes:

[0094] The first interval determination module 40 is used to determine the standby time interval of the hydro-solar hybrid power generation system.

[0095] The second interval determination module 41 is used to determine at least one prediction interval with a preset duration based on the time interval to be worked, wherein the prediction interval includes at least one discrete time point.

[0096] Error determination module 42 is used to determine the prediction error corresponding to each prediction interval based on the preset duration, the preset initial value of prediction error, the discrete time step and the error determination function. The error determination function is obtained by modeling based on the Iton process and arbitrary polynomial chaos. The discrete time step is determined according to the preset duration and the number of discrete time points included in the prediction interval.

[0097] The frequency control module 43 is used to generate an adjustment scheme based on the prediction error of the at least one prediction interval, the adjustment scheme being used to adjust the frequency of the hydropower complementary power generation system during the waiting time interval.

[0098] In one possible implementation, the error determination module 42 includes:

[0099] In response to the prediction interval being the interval with the first temporal position, the corresponding prediction error is determined based on the preset duration, the initial value of the prediction error, the discrete time step, and the error determination function.

[0100] In response to the prediction interval not being the first interval in the time series, the corresponding prediction error is determined based on the preset duration, the adjacent prediction error corresponding to the previous prediction interval in the time series, the discrete time step, and the error determination function.

[0101] In one possible implementation, the initial value of the prediction error is determined based on the difference between the actual photovoltaic output and the predicted photovoltaic output of the hydro-solar hybrid power generation system at the beginning of the waiting time interval.

[0102] In one possible implementation, the error determination function is determined based on the sum of a prior error term, an error drift term, and an error diffusion term, wherein the prior error term is the initial value of the prediction error or the adjacent prediction error, the error drift term is determined based on the drift term and discrete time step size for each discrete time point in the prediction interval, and the error diffusion term is determined based on the diffusion term, discrete time step size, and truncation parameter term for each discrete time point in the prediction interval.

[0103] In one possible implementation, the error drift term is the sum of the product of the drift term at each discrete time point in the prediction interval and the discrete time step.

[0104] In one possible implementation, the error diffusion term is the sum of the products of the diffusion term, the discrete time step, and the truncation parameter term at each discrete time point in the prediction interval.

[0105] In one possible implementation, the truncation parameter is determined based on a random variable, the preset duration, and discrete time points in the current prediction interval and the preceding prediction interval in the time series.

[0106] In one possible implementation, the truncation parameter term is the sum of the product of the random variable and the discrete parameters of the current prediction interval and the discrete time points within a time interval of the preceding prediction interval in terms of time sequence;

[0107] Wherein, when the prediction interval is the interval with the first time position, the discrete parameter is the square root of the reciprocal of the preset duration; when the prediction interval is not the interval with the first time position, the discrete parameter is determined based on the square root of twice the reciprocal of the preset duration, the preset duration, and discrete time points within the prediction interval.

[0108] In one possible implementation, the drift term and the diffusion term are determined by a stochastic differential equation obtained by modeling the prediction error using the Iton process.

[0109] In some embodiments, the functions or modules of the apparatus provided in this disclosure can be used to perform the methods described in the above method embodiments. The specific implementation can be referred to the description of the above method embodiments, and for the sake of brevity, it will not be repeated here.

[0110] This disclosure also proposes a computer-readable storage medium storing computer program instructions that, when executed by a processor, implement the above-described method. The computer-readable storage medium can be volatile or non-volatile.

[0111] This disclosure also proposes an electronic device, including: a processor; and a memory for storing processor-executable instructions; wherein the processor is configured to implement the above method when executing the instructions stored in the memory.

[0112] This disclosure also provides a computer program product, including computer-readable code, or a non-volatile computer-readable storage medium carrying computer-readable code, wherein when the computer-readable code is run in a processor of an electronic device, the processor in the electronic device performs the above-described method.

[0113] Figure 5 A schematic diagram of an electronic device 800 according to an embodiment of the present disclosure is shown. For example, the electronic device 800 may be a mobile phone, computer, digital broadcasting terminal, messaging device, game console, tablet device, medical device, fitness equipment, personal digital assistant, etc.

[0114] Reference Figure 5 The electronic device 800 may include one or more of the following components: processing component 802, memory 804, power supply component 806, multimedia component 808, audio component 810, input / output (I / O) interface 812, sensor component 814, and communication component 816.

[0115] Processing component 802 typically controls the overall operation of electronic device 800, such as operations associated with display, telephone calls, data communication, camera operation, and recording operations. Processing component 802 may include one or more processors 820 to execute instructions to complete all or part of the steps of the methods described above. Furthermore, processing component 802 may include one or more modules to facilitate interaction between processing component 802 and other components. For example, processing component 802 may include a multimedia module to facilitate interaction between multimedia component 808 and processing component 802.

[0116] Memory 804 is configured to store various types of data to support the operation of electronic device 800. Examples of this data include instructions for any application or method operating on electronic device 800, contact data, phonebook data, messages, pictures, videos, etc. Memory 804 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0117] Power supply component 806 provides power to various components of electronic device 800. Power supply component 806 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to electronic device 800.

[0118] Multimedia component 808 includes a screen that provides an output interface between the electronic device 800 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touchscreen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensors may sense not only the boundaries of the touch or swipe action but also the duration and pressure associated with the touch or swipe operation. In some embodiments, multimedia component 808 includes a front-facing camera and / or a rear-facing camera. When the electronic device 800 is in an operating mode, such as a shooting mode or a video mode, the front-facing camera and / or the rear-facing camera may receive external multimedia data. Each front-facing camera and rear-facing camera may be a fixed optical lens system or have focal length and optical zoom capabilities.

[0119] Audio component 810 is configured to output and / or input audio signals. For example, audio component 810 includes a microphone (MIC) configured to receive external audio signals when electronic device 800 is in an operating mode, such as call mode, recording mode, and voice recognition mode. The received audio signals may be further stored in memory 804 or transmitted via communication component 816. In some embodiments, audio component 810 also includes a speaker for outputting audio signals.

[0120] I / O interface 812 provides an interface between processing component 802 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to, home buttons, volume buttons, power buttons, and lock buttons.

[0121] Sensor assembly 814 includes one or more sensors for providing state assessments of various aspects of electronic device 800. For example, sensor assembly 814 can detect the on / off state of electronic device 800, the relative positioning of components such as the display and keypad of electronic device 800, changes in position of electronic device 800 or a component of electronic device 800, the presence or absence of user contact with electronic device 800, orientation or acceleration / deceleration of electronic device 800, and temperature changes of electronic device 800. Sensor assembly 814 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. Sensor assembly 814 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, sensor assembly 814 may also include an accelerometer, gyroscope, magnetometer, pressure sensor, or temperature sensor.

[0122] Communication component 816 is configured to facilitate wired or wireless communication between electronic device 800 and other devices. Electronic device 800 can access wireless networks based on communication standards, such as WiFi, 2G, or 3G, or combinations thereof. In one exemplary embodiment, communication component 816 receives broadcast signals or broadcast-related information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, communication component 816 also includes a near-field communication (NFC) module to facilitate short-range communication. For example, the NFC module may be implemented based on radio frequency identification (RFID) technology, Infrared Data Association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

[0123] In an exemplary embodiment, the electronic device 800 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the methods described above.

[0124] In an exemplary embodiment, a non-volatile computer-readable storage medium is also provided, such as a memory 804 including computer program instructions that can be executed by a processor 820 of an electronic device 800 to perform the above-described method.

[0125] Figure 6 A schematic diagram of another electronic device 1900 according to an embodiment of the present disclosure is shown. For example, the electronic device 1900 may be provided as a server or a terminal device. (Refer to...) Figure 6The electronic device 1900 includes a processing component 1922, which further includes one or more processors, and memory resources represented by memory 1932 for storing instructions, such as application programs, that can be executed by the processing component 1922. The application programs stored in memory 1932 may include one or more modules, each corresponding to a set of instructions. Furthermore, the processing component 1922 is configured to execute instructions to perform the methods described above.

[0126] Electronic device 1900 may also include a power supply component 1926 configured to perform power management of electronic device 1900, a wired or wireless network interface 1950 configured to connect electronic device 1900 to a network, and an input / output (I / O) interface 1958. Electronic device 1900 can operate on an operating system stored in memory 1932, such as Windows Server™, Mac OS X™, Unix™, Linux™, FreeBSD™, or similar.

[0127] In an exemplary embodiment, a non-volatile computer-readable storage medium is also provided, such as a memory 1932 including computer program instructions that can be executed by a processing component 1922 of an electronic device 1900 to perform the above-described method.

[0128] This disclosure can be a system, method, and / or computer program product. A computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for causing a processor to implement various aspects of this disclosure.

[0129] Computer-readable storage media can be tangible devices capable of holding and storing instructions for use by an instruction execution device. Computer-readable storage media can be, for example—but not limited to—electrical storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory sticks, floppy disks, mechanical encoding devices, such as punch cards or recessed protrusions storing instructions thereon, and any suitable combination thereof. The computer-readable storage media used herein are not to be construed as transient signals themselves, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses through fiber optic cables), or electrical signals transmitted through wires.

[0130] The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to the computer-readable storage media in the respective computing / processing device.

[0131] Computer program instructions used to perform the operations of this disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, status setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing the status information of the computer-readable program instructions to implement various aspects of this disclosure.

[0132] Various aspects of this disclosure are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It should 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-readable program instructions.

[0133] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that, when executed by the processor of the computer or other programmable data processing apparatus, they create means for implementing the functions / actions specified in one or more blocks of the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium that causes a computer, programmable data processing apparatus, and / or other device to operate in a particular manner; thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing aspects of the functions / actions specified in one or more blocks of the flowchart and / or block diagram.

[0134] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to perform the functions / actions specified in one or more boxes of a flowchart and / or block diagram.

[0135] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of an instruction containing one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than those shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0136] The various embodiments of this disclosure have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen to best explain the principles, practical application, or technical improvements to the embodiments in the market, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A frequency control method of a water-light complementary power generation system, characterized by, The method comprises: determining a to-be-operated time interval of the water-light complementary power generation system; determining at least one prediction interval with a preset time length according to the to-be-operated time interval, the prediction interval comprising at least one discrete time point; determining a prediction error corresponding to each prediction interval according to the preset time length, a preset prediction error initial value, a discrete time step, and an error determination function, the error determination function being modeled based on an Ito process and an arbitrary polynomial chaos; generating an adjustment scheme according to the prediction errors of the at least one prediction interval, the adjustment scheme being used to adjust the frequency of the water-light complementary power generation system within the to-be-operated time interval; the prediction error initial value being determined according to a difference between an actual photovoltaic output and a predicted photovoltaic output of the water-light complementary power generation system at a starting time of the to-be-operated time interval; the error determination function being determined according to a sum of a previous error term, an error drift term, and an error diffusion term, wherein the previous error term is the prediction error initial value or a neighboring prediction error, the error drift term being determined according to a drift term of each discrete time point in the prediction interval and the discrete time step, and the error diffusion term being determined according to a diffusion term of each discrete time point in the prediction interval, the discrete time step, and a truncation parameter term.

2. The method of claim 1, wherein, the determination of the prediction error corresponding to each prediction interval according to the preset time length, the preset prediction error initial value, the discrete time step, and the error determination function comprises: in response to the prediction interval being a first interval in time sequence, determining the corresponding prediction error according to the preset time length, the prediction error initial value, the discrete time step, and the error determination function; in response to the prediction interval not being the first interval in time sequence, determining the corresponding prediction error according to the preset time length, a neighboring prediction error corresponding to a previous prediction interval in time sequence, the discrete time step, and the error determination function.

3. The method of claim 1, wherein, the error drift term is a sum of products of the drift term of each discrete time point in the prediction interval and the discrete time step.

4. The method of claim 1, wherein, the error diffusion term is a sum of products of the diffusion term of each discrete time point in the prediction interval, the discrete time step, and a truncation parameter term.

5. The method of claim 4, wherein, the truncation parameter term is determined according to a random variable, the preset time length, and the discrete time points in the current prediction interval and a previous prediction interval in time sequence.

6. The method of claim 5, wherein, the truncation parameter term is a product sum of the random variable and the discrete parameters of the discrete time points in one time interval of the current prediction interval and the previous prediction interval in time sequence; wherein, in the case that the prediction interval is the first interval in time sequence, the discrete parameter is a square root of a reciprocal of the preset time length, and in the case that the prediction interval is not the first interval in time sequence, the discrete parameter is determined according to a square root of twice the reciprocal of the preset time length, the preset time length, and the discrete time points in the prediction interval.

7. The method according to any one of claims 3-6, characterized in that, the drift term and the diffusion term are determined by a stochastic differential equation obtained by Ito process modeling of the prediction error.

8. A frequency control device of a water-light complementary power generation system, characterized by, the device comprises: A first interval determining module is configured to determine a working time interval of the water-light complementary power generation system; A second interval determining module is configured to determine at least one prediction interval with a preset time length according to the working time interval, the prediction interval including at least one discrete time point; An error determining module is configured to determine a prediction error corresponding to each prediction interval according to the preset time length, a preset prediction error initial value, a discrete time step and an error determining function, the error determining function being modeled based on an Ito process and an arbitrary polynomial chaos, and the discrete time step being determined according to the preset time length and the number of discrete time points included in the prediction interval; A frequency control module is configured to generate an adjustment scheme according to the prediction error of the at least one prediction interval, the adjustment scheme being used to adjust the frequency of the water-light complementary power generation system within the working time interval; The prediction error initial value is determined according to the difference between the actual photovoltaic output and the predicted photovoltaic output of the water-light complementary power generation system at the starting time of the working time interval; The error determining function is determined according to the sum of a previous error term, an error drift term and an error diffusion term, wherein the previous error term is the prediction error initial value or a neighboring prediction error, the error drift term is determined according to the drift term of each discrete time point in the prediction interval and the discrete time step, and the error diffusion term is determined according to the diffusion term of each discrete time point in the prediction interval, the discrete time step and a truncation parameter term.

9. An electronic device, comprising: comprise: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to implement the method of any one of claims 1 to 7 when executing the instructions stored in the memory.

10. A non-transitory computer readable storage medium having stored thereon computer program instructions, wherein, The computer program instructions, when executed by the processor, implement the method of any one of claims 1 to 7.

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