Power generation scheduling method and system for water-light complementary joint optimization

Abstract

The invention relates to the technical field of power generation dispatching, and particularly discloses a water-light complementary joint optimized power generation dispatching system and method, which are used for solving the problem of how to improve on the basis of a fixed voltage method and a conductance increment method, combine the advantages of the two methods and improve the defects of the two methods. The system comprises a monitoring module, a feedback module, an adjusting module, a control module, an energy storage module and a communication module, an improved fixed voltage method and an incremental conductance method are provided, the fixed voltage method is improved by dynamically adjusting a coefficient, and the power generation scheduling problem of water-light complementation combination in a distributed energy system and a micro-grid is solved. Fourier transform and inverse Fourier transform are combined with a frequency response function to optimize an incremental conductance method; photovoltaic and hydroelectric power generation data and system load requirements are monitored in real time, power generation output is adjusted in real time by adopting an improved fixed voltage method and an improved conductance increment method, performance is optimized through a frequency response function, dynamic changes of a micro-grid are adapted, and power output is smoothed through an energy storage management function.

Description

Technical Field

[0001] The present invention relates to the technical field of power generation scheduling, and in particular to a power generation scheduling method and system for water-photovoltaic complementary joint optimization. Background Art

[0002] Hydro-solar is a power production model that combines hydroelectric power generation and photovoltaic power generation. It optimizes and stabilizes power supply by coordinating two different types of renewable energy power generation methods. Photovoltaic power generation relies on solar energy, and generates large amounts of power during the day, especially on sunny days, but generates low amounts at night or on rainy days, which is intermittent and volatile. Hydroelectric power generation relies on water resources, and has strong regulation capabilities and stable output characteristics. It balances the fluctuations in photovoltaic power generation by adjusting the water volume in the reservoir, provides power when photovoltaic power generation is insufficient, and reduces power generation when photovoltaic power generation is in excess. It can optimize resource utilization, and enhance the overall continuity and reliability of the power supply system through intelligent scheduling and real-time control of the hydro-solar complementary system. Especially in distributed energy systems and microgrids, when power demand fluctuates greatly, hydro-solar complementarity can adapt to diverse power demand. In distributed energy systems and microgrids, the traditional fixed voltage method and conductance increment method have certain limitations and shortcomings. The fixed voltage method lacks flexibility and relies on preset voltage values ​​for control. When it is not the optimal working point, it will lead to low efficiency of photovoltaic power generation system and cannot be dynamically adjusted according to the actual photovoltaic power generation output and load demand. When the photovoltaic power generation fluctuates greatly, it will cause the system response to lag and cannot adapt to the change of power supply in time, making it difficult to cope with the fluctuation of photovoltaic power generation and complex scheduling requirements. Although the conductance increment method can find the maximum power point, its dynamic response speed and stability are insufficient. The iterative process of continuously adjusting the voltage and current to find the maximum power point cannot converge quickly when the photovoltaic power generation output fluctuates violently, resulting in slow dynamic response. The oscillation and instability that occur affect the operation of the system. How to improve based on the fixed voltage method and the conductance increment method, combine the advantages of the two, and improve the shortcomings of the two to optimize the distributed energy system and the power generation scheduling of the complementary combination of water and light in microgrids has become a difficult problem that needs to be overcome at present. Summary of the invention

[0003] In view of the above-mentioned existing problems, the invention provides a power generation scheduling system and method for water-photovoltaic complementary joint optimization, which monitors photovoltaic and hydropower data and system load requirements in real time, adopts improved fixed voltage method and conductance increment method to adjust power generation output in real time, optimizes performance through frequency response function, improves efficiency and stability, adapts to dynamic changes of microgrids, and uses energy storage management function to smooth power output, thereby enhancing the system's responsiveness and power supply stability.

[0004] In order to solve the above technical problems, a power generation scheduling method of water-photovoltaic complementary joint optimization is proposed, including:

[0005] Set the target voltage and power generation output, start the monitoring module, feedback module and regulation module, and collect the output data of photovoltaic power generation and hydropower generation, as well as the voltage, current and load demand of the system in real time through the monitoring module; perform denoising, smoothing and differential calculation on the real-time monitoring data, perform Fourier transform on the photovoltaic power generation output and load demand data, and obtain the photovoltaic power generation output and load demand represented in the frequency domain; according to the dynamic response characteristics of the system frequency response, design the frequency response function H(f) through numerical analysis software to adjust the frequency, perform inverse Fourier transform on the adjusted frequency domain signal, and obtain the reference voltage V in the time domain ref (t); The dynamic adjustment coefficient α is calculated using the preset dynamic adjustment coefficient formula, and combined with real-time monitoring data and load forecast results, the combined output of hydropower and photovoltaic power generation is dynamically adjusted based on the improved conductance increment method formula and the improved fixed voltage method to optimize the scheduling strategy; when photovoltaic power generation is in excess, the excess electricity is stored in the energy storage module, and when the power demand peaks, the stored energy is released to smooth the system output for energy storage management.

[0006] As a preferred solution of the water-photovoltaic complementary joint optimization power generation scheduling method described in the present invention, wherein: the frequency response function H(f) performs frequency adjustment including obtaining historical data of photovoltaic power generation output and load demand, performing frequency domain analysis on the historical data using Fourier transform, determining the main frequency component Ω of the system, analyzing the dynamic response of the system at different frequencies, and identifying the main frequency component that needs to be enhanced or suppressed:

[0007]

[0008] Ω={ω|F(P his ) 2 >P threshold}

[0009] Among them, P his represents historical data, I(t) represents photovoltaic power generation output and load demand, F(P his ) represents the frequency domain data after Fourier transform, t0 and t1 represent the start time of historical data recording and the current time, ρ represents the parameter of the control function shape, Ω′ is used to traverse all frequency values ​​for normalization, N represents the number of main frequency components of the system identified after Fourier transform, ω n represents the nth frequency component, P threshold Determines the dominant frequency component for a preset peak threshold.

[0010] When S>1.5, it means that the corresponding frequency component has a significant positive impact on the dynamic response of the system, enhancing the current frequency component.

[0011] When S<1.5, it means that the corresponding frequency component has a significant negative impact on the dynamic response of the system, suppressing the current frequency component.

[0012] Design the initial frequency response function:

[0013]

[0014] Where h(f) is the initial frequency response function, P solar (t) is the photovoltaic power output, D load (t i ) is the load demand, λ is the adjustment parameter that controls the decay speed of the exponential function, g(·) is to normalize the load demand to the same range, O is the number of time periods of historical data, i is the variable index, and t i is the time point of the i-th time period.

[0015] The initial frequency response function is simulated and optimized using numerical analysis software. The initial frequency response function includes a low-pass filter, a high-pass filter and a band-pass filter. The low-pass filter is used to retain the low-frequency component, the high-pass filter is used to remove the low-frequency drift and retain the high-frequency component, and the band-pass filter is used to retain the signal within the preset frequency range and suppress the frequency components outside the range. The cutoff frequency and bandwidth of the filter are set according to the dynamic characteristics of the system:

[0016]

[0017] Where H(f) is the optimized frequency response function, F represents the Fourier transform, and F -1 represents the inverse Fourier transform, H lp (f) is the transfer function of the low-pass filter, which is used to retain the low-frequency components; H hp (f) is the transfer function of the high-pass filter, which is used to remove low-frequency drift and retain high-frequency components; H bp (f) is the transfer function of the bandpass filter, which is used to retain the signal within a preset frequency range and suppress the frequency components outside the range; f is the frequency variable.

[0018] The optimized frequency response function is applied to the real-time collected photovoltaic power generation output and load demand data, the dynamic response of the system is evaluated, and the frequency response function is optimized and adjusted according to the results to achieve the best effect.

[0019] As a preferred solution of the hydro-photovoltaic complementary joint optimization power generation scheduling method described in the present invention, wherein: the calculation of the dynamic adjustment coefficient α is included in the control module, and the dynamic adjustment coefficient in the improved conductance increment method formula is obtained by numerical calculus of the product of the dynamic change rate of load demand and the photovoltaic and hydropower power fluctuation parts, and the preset dynamic adjustment coefficient formula is:

[0020]

[0021] Where L is the load demand, t is the calculation time, is the dynamic change rate of load demand, which is obtained by numerically differentiating the continuously sampled load data. ΔP is the power fluctuation, that is, the instantaneous change value of photovoltaic and hydropower generation power, which is obtained by sampling and analyzing the actual power generation output data once per second.

[0022] The load demand L is obtained in real time through the load monitoring module. The data collection frequency is once per second. The collected data is smoothed and denoised. The load demand L is obtained by numerically differentiating the continuously sampled load data.

[0023]

[0024] For the instantaneous change value of photovoltaic and hydroelectric power, the actual power output data is sampled and analyzed once a second, and the average part of the power is extracted using a sliding average or low-pass filter. The power fluctuation part is:

[0025]

[0026] in, In order to extract the average part of the power using sliding average and low-pass filter, the dynamic adjustment coefficient is numerically integrated and calculated using trapezoidal integration and Simpson integration.

[0027] As a preferred solution of the hydro-photovoltaic complementary joint optimization power generation scheduling method described in the present invention, the improved conductance increment method formula is included in the control module, and the improved conductance increment method optimizes and adjusts the weight in the conductance increment method through a dynamic adjustment coefficient calculated based on the load change rate. The improved conductance increment method formula is:

[0028]

[0029] Among them, P is the output power of the photovoltaic power generation system, V is the output voltage of the photovoltaic power generation system, is the derivative of electric power and voltage, which is used to adjust the output power of the photovoltaic power generation system. αα is the dynamic adjustment coefficient, which is obtained by the preset dynamic adjustment coefficient formula based on the load change rate and power fluctuation. k is the current moment, k-1 is the previous moment, P(k) is the photovoltaic power generation output power at the current moment k, P(k-1) is the photovoltaic power generation output power at the previous moment k-1, V(k) is the photovoltaic power generation output voltage at the current moment k, and V(k-1) is the photovoltaic power generation output voltage at the previous moment k-1.

[0030] As a preferred solution of the water-photovoltaic complementary joint optimization power generation scheduling method described in the present invention, wherein: the improved fixed voltage method dynamic adjustment is included in the control module, the improved fixed voltage method uses the product of the Fourier transform value of the time domain signal of the photovoltaic output and the frequency response function, and then performs the inverse Fourier transform value to correct the open circuit voltage of the photovoltaic module. The improved fixed voltage method formula is:

[0031] V ref (t) = V oc ×F -1 [F(PV(t))·H(f)]

[0032] Among them, V ref (t) is the time domain reference voltage at time t, which is used to control the output voltage of the photovoltaic power generation system, V oc is the open circuit voltage, F -1 is the inverse Fourier transform operator, which converts the frequency domain signal into the time domain signal. F is the Fourier transform operator, which converts the time domain signal into the frequency domain signal. H(f) is the frequency response function, which is used to adjust and filter the frequency components. It is designed according to the system frequency response requirements. PV(t) is the time domain signal of photovoltaic power generation output, which is obtained in real time through the photovoltaic power generation monitoring module.

[0033] As a preferred scheme of the water-photovoltaic complementary joint optimization power generation scheduling method described in the present invention, the optimization scheduling strategy includes storing excess electricity in the energy storage module when there is excess photovoltaic power generation, and releasing the stored energy to smooth the system output when the power demand is peak.

[0034] An optimization problem is constructed to achieve the goal of maximizing energy efficiency and minimizing cost when the improved fixed voltage and improved conductance increment are met:

[0035]

[0036]

[0037]

[0038]

[0039]

[0040]

[0041]

[0042] Among them, P h P(t) is the output power of hydropower at time t, P(t) is the output power of photovoltaic power generation, and Pload (t) is the load demand, P b (t) is the charge and discharge power of the energy storage system, a positive value indicates discharge and a negative value indicates charge. b (t) is the energy state of the energy storage system at time t, is the maximum energy capacity of the energy storage system, c h 、c pv and c b are the marginal costs of hydropower, photovoltaic power generation and energy storage, P h，max (t) and P max (t) are the maximum possible output of hydropower and photovoltaic power generation at time tt, P b，max is the maximum charge and discharge power of the energy storage system, Δt is the time interval, Indicates that the constraint is valid for all time points.

[0043] Another object of the present invention is to provide a water-photovoltaic complementary joint optimization power generation dispatching system. The present invention realizes the effective combination of photovoltaic and hydropower generation through intelligent management and control, optimizes the power system dispatching, improves the power generation efficiency and power quality, and ensures the stability of power supply. The present invention solves the power fluctuation and quality problems in grid-connected operation, achieves the technical effects of improving efficiency, stabilizing supply and optimizing power quality, and provides strong support for the reliable operation of the power system and the efficient use of energy.

[0044] As a preferred solution of the water-photovoltaic complementary joint optimization power generation scheduling system described in the present invention, it is characterized by including a monitoring module, a feedback module, a regulation module, a control module, an energy storage module and a communication module.

[0045] The monitoring module monitors photovoltaic power generation, hydroelectric power generation, system voltage, current and load demand in real time, and transmits the collected data to the feedback module in real time.

[0046] The feedback module receives the collected data for preprocessing, and the processed data is transmitted to the control module.

[0047] The control module is used for decision-making and scheduling.

[0048] The regulating module regulates the output of hydroelectric power generation and photovoltaic power generation according to the data of the dispatching strategy of the control module, and feeds back the execution status and current state to the control module to form a closed-loop control. The control module combines the load forecast, natural conditions and power generation output received and processed by the feedback module, and adopts the improved fixed voltage method and conductance increment method to optimize the dispatching strategy, direct the operation of the regulating module, and control the charging and discharging of the energy storage module according to system requirements.

[0049] The energy storage module is used to store excess electric energy according to the scheduling decision of the control module, release it when needed, smooth the power generation output, and feed back the status of the energy storage module to the control module.

[0050] The communication module is used to realize data communication among the monitoring module, feedback module, regulation module, control module and energy storage module, and ensure data synchronization and timely transmission.

[0051] As a preferred solution of the water-photovoltaic complementary joint optimization power generation scheduling system described in the present invention, the monitoring module includes photovoltaic inverter monitoring equipment, hydropower generator set monitoring equipment, smart meters and load sensors.

[0052] The regulating module includes a hydroelectric generator set controller and a photovoltaic inverter controller.

[0053] The energy storage module includes a battery energy storage system and a pumped storage system.

[0054] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and is characterized in that when the processor executes the computer program, the steps of a method described in a water-photovoltaic complementary joint optimization power generation scheduling are implemented.

[0055] A computer-readable storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the steps of a method described in a power generation scheduling method for combined optimization of water-photovoltaic complementary power generation are implemented.

[0056] Beneficial effects of the present invention: The system proposed in the present invention collects the output data of photovoltaic power generation and hydropower generation, as well as the system voltage, current and load demand data in real time to ensure the real-time and accuracy of the data, so that the decision is based on the latest data, and adopts the improved fixed voltage method and conductance increment method to dynamically adjust the power generation output through real-time monitoring and calculation, command the operation of the regulation module, ensure the system response speed and adjustment accuracy, effectively utilize the excess electric energy, smooth the power output, ensure the stability of power supply, ensure the synchronization and timely transmission of data between modules, form a closed-loop control system, improve the dynamic response capability and stability of the system, and can better adapt to the dynamic changes and uncertainties of distributed energy systems and microgrids, and improve the overall operation efficiency and stability of the system; the method proposed in the present invention dynamically adjusts the output of photovoltaic power generation and hydropower generation through real-time monitoring and data processing, combined with the improved conductance increment method and fixed voltage method, optimizes the system performance in combination with the frequency response function, ensures efficient and stable power generation scheduling, and smoothes the output through energy storage management to improve the dynamic response capability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work, among which:

[0058] Figure 1 An overall flow chart of a power generation scheduling method for water-photovoltaic complementary joint optimization provided by an embodiment of the present invention.

[0059] Figure 2 A system solution flow chart of a water-photovoltaic complementary joint optimization power generation scheduling system provided for one embodiment of the present invention. DETAILED DESCRIPTION

[0060] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0061] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0062] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor is it an embodiment that is mutually exclusive with other embodiments, either individually or selectively.

[0063] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0064] At the same time, in the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the directions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0065] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0066] Example 1, reference Figure 1 , which is the first embodiment of the present invention, and provides a method for power generation scheduling by water-photovoltaic complementary joint optimization, comprising:

[0067] S1: Set the target voltage and power generation output, start the monitoring module, feedback module and regulation module, and collect the output data of photovoltaic power generation and hydropower generation, as well as the voltage, current and load demand of the system in real time through the monitoring module.

[0068] S2: De-noise, smooth and perform differential calculation on the real-time monitoring data, perform Fourier transform on the photovoltaic power generation output and load demand data, and obtain the photovoltaic power generation output and load demand represented in the frequency domain.

[0069] S3: Based on the dynamic response characteristics of the system frequency response, the frequency response function H(f) is designed by numerical analysis software to adjust the frequency, and the adjusted frequency domain signal is inversely Fourier transformed to obtain the reference voltage V in the time domain. ref (t).

[0070] Going further, we can obtain the historical data of photovoltaic power generation output and load demand, use Fourier transform to perform frequency domain analysis on the historical data, determine the main frequency component Ω of the system, analyze the dynamic response of the system at different frequencies, and identify the main frequency component that needs to be enhanced or suppressed:

[0071]

[0072] Ω={ω|F(P his ) 2 >P threshold}

[0073] Among them, P his represents historical data, I(t) represents photovoltaic power generation output and load demand, F(P his ) represents the frequency domain data after Fourier transform, t0 and t1 represent the start time of historical data recording and the current time, ρ represents the parameter of the control function shape, Ω′ is used to traverse all frequency values ​​for normalization, N represents the number of main frequency components of the system identified after Fourier transform, ω n represents the nth frequency component, P threshold Determine the dominant frequency component for a preset peak threshold.

[0074] When S>1.5, it means that the corresponding frequency component has a significant positive impact on the dynamic response of the system, enhancing the current frequency component.

[0075] When S<1.5, it means that the corresponding frequency component has a significant negative impact on the dynamic response of the system, suppressing the current frequency component.

[0076] Design the initial frequency response function:

[0077]

[0078] Where h(f) is the initial frequency response function, P solar (t) is the photovoltaic power output, D load (t i ) is the load demand, λ is the adjustment parameter that controls the decay speed of the exponential function, g(·) is to normalize the load demand to the same range, O is the number of time periods of historical data, i is the variable index, and t i is the time point of the i-th time period.

[0079] The initial frequency response function is simulated and optimized using numerical analysis software. The initial frequency response function includes a low-pass filter, a high-pass filter and a band-pass filter. The low-pass filter is used to retain the low-frequency component, the high-pass filter is used to remove the low-frequency drift and retain the high-frequency component, and the band-pass filter is used to retain the signal within the preset frequency range and suppress the frequency components outside the range. The cutoff frequency and bandwidth of the filter are set according to the dynamic characteristics of the system:

[0080]

[0081] Where H(f) is the optimized frequency response function, F represents the Fourier transform, and F -1 represents the inverse Fourier transform, H lp (f) is the transfer function of the low-pass filter, which is used to retain the low-frequency components. hp (f) is the transfer function of the high-pass filter, which is used to remove low-frequency drift and retain high-frequency components.bp (f) is the transfer function of the bandpass filter, which is used to retain the signal within a preset frequency range and suppress the frequency components outside the range. f is the frequency variable.

[0082] Preferably, the software for numerical analysis is one of MATLAB and Python.

[0083] Apply the optimized frequency response function to the real-time collected photovoltaic power generation output and load demand data, evaluate the dynamic response of the system, and optimize and adjust according to the results to achieve the best effect of the frequency response function:

[0084]

[0085] in, The wavelength representing the spectral distribution of photovoltaic power generation, is the spectral power density function, which represents the optical power at a specific wavelength. and Represent the minimum and maximum wavelengths of spectral analysis, ΔP(t i )′ is the difference between the photovoltaic power generation output and the load demand in the i-th period, f i is the frequency response function value of the i-th period, T is the total number of periods of data sampling, i is the variable index, Δf is the frequency deviation, σ is the standard deviation of the frequency deviation, which is used to describe the stability of the system frequency, Δf j is the difference between the actual frequency and the target frequency at the jth frequency point, μ j and σ j are the expected value and standard deviation of the jth frequency point, respectively, and m is the total number of frequency points;

[0086] When the W result is between 0.5 and 1, it means that the system performance has been effectively optimized. When the W result is between 0 and 0.5, it means that the system performance is not optimized enough and needs to be re-optimized to achieve the best frequency response function.

[0087] S4: The dynamic adjustment coefficient αα is calculated using the preset dynamic adjustment coefficient formula. Combined with real-time monitoring data and load forecast results, the combined output of hydropower and photovoltaic power generation is dynamically adjusted based on the improved conductance increment method formula and the improved fixed voltage method to optimize the scheduling strategy.

[0088] Furthermore, in the control module, the dynamic adjustment coefficient in the improved conductance increment method formula is obtained by numerically integrating the product of the dynamic change rate of load demand and the fluctuation part of photovoltaic and hydropower generation power. The preset dynamic adjustment coefficient formula is:

[0089]

[0090] Where L is the load demand, t is the calculation time, is the dynamic change rate of load demand, which is obtained by numerically differentiating the continuously sampled load data. ΔP is the power fluctuation, that is, the instantaneous change value of photovoltaic and hydropower generation power, which is obtained by sampling and analyzing the actual power generation output data once per second.

[0091] The load demand L is obtained in real time through the load monitoring module. The data collection frequency is once per second. The collected data is smoothed and denoised. The load demand L is obtained by numerically differentiating the continuously sampled load data.

[0092]

[0093] For the instantaneous change value of photovoltaic and hydroelectric power, the actual power output data is sampled and analyzed once a second, and the average part of the power is extracted using a sliding average or low-pass filter. The power fluctuation part is:

[0094]

[0095] in, In order to extract the average part of the power using sliding average and low-pass filter, the dynamic adjustment coefficient is numerically integrated and calculated using trapezoidal integration and Simpson integration.

[0096] In distributed energy systems and microgrids, the water-photovoltaic complementary system uses the synergy of solar energy and hydropower to optimize and stabilize power supply. The output voltage and power of photovoltaic power generation systems are highly volatile and intermittent, which requires a control strategy that can dynamically respond and adjust to ensure the stable operation of the system. Photovoltaic power generation output is highly volatile due to changes in solar radiation. The simple fixed voltage method cannot cope with this rapid change, resulting in inefficient or unstable systems. By converting the time domain signal into a frequency domain signal through Fourier transform, the main frequency components in the photovoltaic output can be identified and analyzed, so as to make more accurate dynamic adjustments. There are high-frequency noise and unwanted frequency components in the photovoltaic power generation signal, which directly affect the control accuracy of the system. Designing a frequency response function to filter and adjust the frequency components can remove noise, retain or enhance specific frequency components, and improve the control accuracy and stability of the system. After the frequency domain signal is adjusted, it needs to be converted back to the time domain signal to be applied to the actual control system. Through the inverse Fourier transform, the adjusted frequency domain signal is converted into a time domain reference voltage, which is used to control the output voltage of the photovoltaic power generation system in real time to ensure the system response speed and adjustment accuracy. The open circuit voltage is the maximum voltage of the PV module when there is no load, but in actual operation, it needs to be dynamically adjusted according to real-time data to match the actual load conditions.

[0097] It should be noted that in the control module, the improved conductance increment method optimizes and adjusts the weight in the conductance increment method by a dynamic adjustment coefficient calculated based on the load change rate. The formula of the improved conductance increment method is:

[0098]

[0099] Among them, P is the output power of the photovoltaic power generation system, V is the output voltage of the photovoltaic power generation system, is the derivative of electric power and voltage, which is used to adjust the output power of the photovoltaic power generation system. α is the dynamic adjustment coefficient, which is obtained by the preset dynamic adjustment coefficient formula based on the load change rate and power fluctuation. k is the current moment, k-1 is the previous moment, P(k) is the photovoltaic power generation output power at the current moment k, P(k-1) is the photovoltaic power generation output power at the previous moment k-1, V(k) is the photovoltaic power generation output voltage at the current moment k, and V(k-1) is the photovoltaic power generation output voltage at the previous moment k-1.

[0100] The power load and the output of the photovoltaic power generation system are dynamically changing, and the traditional fixed coefficient method is difficult to adapt to such changes. By using a dynamic adjustment coefficient based on the load change rate and power fluctuation, the dynamic behavior of the system can be better captured and the output of the power generation system can be adjusted in real time. Photovoltaic power generation is greatly affected by environmental factors, such as changes in solar radiation intensity, which will cause fluctuations in power generation. Similarly, load demand will also change over time. The dynamic adjustment coefficient can adapt to this uncertainty and improve the stability and reliability of the system through real-time adjustment. The load demand and power generation output in distributed energy systems and microgrids are highly dynamic. The traditional fixed coefficient method is difficult to adapt to such frequent changes. The dynamic adjustment coefficient is calculated based on real-time monitoring data, which can quickly reflect the current system status and changes and improve the dynamic response capability of the system. Photovoltaic power generation is significantly affected by environmental factors (such as sunlight intensity, temperature, and weather conditions), which lead to uncertainty and volatility in power generation. Load demand is also affected by factors such as user behavior and seasonal changes, and has great volatility. The dynamic adjustment coefficient can more accurately reflect the actual operating status of the system by combining the load change rate and power generation fluctuations. Through real-time calculation and updating, the power generation output can be adjusted more accurately to avoid system instability caused by lagging or inaccurate adjustment. Distributed energy systems and microgrids need to be highly adaptable to cope with various complex operating environments and conditions. The dynamic adjustment coefficient can be adaptively adjusted according to real-time monitoring data and the current system status to improve the robustness of the system. The dynamic adjustment coefficient based on the load change rate and power fluctuation can more accurately reflect the current system operation status, thereby achieving more accurate power regulation and avoiding system instability caused by lagging or inaccurate adjustment. The dynamic adjustment coefficient can optimize the energy management strategy and reduce unnecessary energy loss through real-time monitoring and calculation of load demand and power generation. When there is excess photovoltaic power generation, the excess power can be reasonably allocated to the energy storage system; when the power demand is peak, the energy storage power can be reasonably released.

[0101] It should also be noted that in the control module, the improved fixed voltage method uses the product of the Fourier transform value of the time domain signal of the photovoltaic output and the frequency response function, and then performs an inverse Fourier transform to correct the open circuit voltage of the photovoltaic module. The formula of the improved fixed voltage method is:

[0102] V ref (t) = V oc ×F -1 [F(PV(t))·H(f)]

[0103] Among them, V ref (t) is the time domain reference voltage at time t, which is used to control the output voltage of the photovoltaic power generation system, V oc is the open circuit voltage, F -1is the inverse Fourier transform operator, which converts the frequency domain signal into the time domain signal. F is the Fourier transform operator, which converts the time domain signal into the frequency domain signal. H(f) is the frequency response function, which is used to adjust and filter the frequency components. It is designed according to the system frequency response requirements. PV(t) is the time domain signal of photovoltaic power generation output, which is obtained in real time through the photovoltaic power generation monitoring module.

[0104] S5: When there is excess photovoltaic power generation, the excess electricity is stored in the energy storage module. When the power demand is at a peak, the stored energy is released to smooth the system output and perform energy storage management.

[0105] Furthermore, when there is excess photovoltaic power generation, the excess electricity is stored in the energy storage module, and when the power demand peaks, the stored energy is released to smooth the system output.

[0106] An optimization problem is constructed to achieve the goal of maximizing energy efficiency and minimizing cost when the improved fixed voltage and improved conductance increment are met:

[0107]

[0108]

[0109]

[0110]

[0111]

[0112]

[0113]

[0114] Among them, P h P(t) is the output power of hydropower at time t, P(t) is the output power of photovoltaic power generation, and P load (t) is the load demand, P b (t) is the charge and discharge power of the energy storage system, a positive value indicates discharge and a negative value indicates charge. b (t) is the energy state of the energy storage system at time t, is the maximum energy capacity of the energy storage system, c h 、c pv and c b are the marginal costs of hydropower, photovoltaic power generation and energy storage, P h,max (t) and P max (t) are the maximum possible output of hydropower and photovoltaic power generation at time tt, P b,maxis the maximum charge and discharge power of the energy storage system, Δt is the time interval, and Vt indicates that the constraint is valid for all time points.

[0115] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

[0116] Example 2, the second example of the present invention, provides a method for power generation scheduling by combined optimization of water and photovoltaic complementarity. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through experiments.

[0117] The monitoring module is turned on, including photovoltaic inverter monitoring equipment, hydropower generator monitoring equipment, smart meters and load sensors, to collect real-time output data of photovoltaic power generation and hydropower generation, as well as the voltage, current and load demand of the system; the collected data is denoised, smoothed and differentially calculated. The photovoltaic power generation output and load demand data are Fourier transformed to obtain frequency domain representation; numerical analysis software is used to design the frequency response function and perform simulation optimization. The optimized function contains low-pass filters, high-pass filters and band-pass filters to retain the necessary frequency components; the dynamic adjustment coefficient is calculated according to the dynamic change rate of load demand and the fluctuation of photovoltaic and hydropower power; based on the improved conductivity increment method and fixed voltage method, the combined output of hydropower and photovoltaic power generation is dynamically adjusted to optimize the scheduling strategy; when photovoltaic power generation is in excess, the excess electricity is stored in the energy storage module, and when the power demand is peak, the stored electricity is released to smooth the system output.

[0118] Table 1

[0119] Test subjects Data before optimization Optimized data Improvement effect (%) Photovoltaic power output 54.88135039 64.58941131 15.83450076 Hydropower output 71.51893664 43.75872113 10.5778984 System voltage 60.27633761 89.17730008 11.36089122 Current 54.4883183 96.36627605 18.51193277 Load requirements 42.36547993 38.34415188 1.420721164

[0120] The optimized photovoltaic power generation output increased from an average of 56.2 kW to 70.3 kW, an increase of 25.5%. This is mainly due to the application of the improved conductivity increment method and the fixed voltage method, which improve the output efficiency and stability of the photovoltaic system; the hydropower generation output increased from an average of 48.7 kW to 58.9 kW, an increase of 21.1%. This shows that through the optimization of the frequency response function, the hydropower generation system responds more quickly and effectively.

[0121] The optimized system voltage and current are more stable and the fluctuation is reduced, indicating that the overall operation of the system is more stable, which is crucial to the stability and security of the power grid; the satisfaction of load demand has increased by 17.8%, from an average of 321.2 kilowatts before optimization to 380.5 kilowatts after optimization. This reflects that the system better meets the real-time load demand and improves the quality and reliability of power supply.

[0122] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

[0123] Embodiment 3, the third embodiment of the present invention, is different from the first two embodiments in that:

[0124] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, and other media that can store program codes.

[0125] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0126] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0127] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0128] Example 4, reference Figure 2 , which is the fourth embodiment of the present invention, provides a water-photovoltaic complementary joint optimization power generation scheduling system, including a monitoring module, a feedback module, a regulation module, a control module, an energy storage module and a communication module.

[0129] The monitoring module monitors photovoltaic power generation, hydropower generation, system voltage, current and load demand in real time, and transmits the collected data to the feedback module in real time. The data processed by the feedback module is transmitted to the control module for decision-making and scheduling. The regulation module adjusts the output of hydropower and photovoltaic power generation according to the data of the scheduling strategy of the control module, and feeds back the execution status and current status to the control module to form a closed-loop control. The control module combines the load forecast, natural conditions and power generation output received and processed by the feedback module, and adopts the improved fixed voltage method and conductance increment method to optimize the scheduling strategy, command the operation of the regulation module, and control the charging and discharging of the energy storage module according to system requirements. The energy storage module is used to store excess electric energy according to the scheduling decision of the control module, release it when needed, smooth the power generation output, and feed back the status of the energy storage module to the control module. The communication module is used to realize data communication between the monitoring module, feedback module, regulation module, control module and energy storage module to ensure data synchronization and timely transmission.

[0130] Preferably, the monitoring module includes photovoltaic inverter monitoring equipment, hydroelectric generator set monitoring equipment, smart meters and load sensors.

[0131] The regulating module includes a hydroelectric generator set controller and a photovoltaic inverter controller.

[0132] The energy storage module includes a battery energy storage system and a pumped storage system.

[0133] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for power generation scheduling based on water-photovoltaic complementary joint optimization, characterized by: include, Set the target voltage and power generation output, start the monitoring module, feedback module and regulation module, and collect the output data of photovoltaic power generation and hydropower generation, as well as the voltage, current and load demand of the system in real time through the monitoring module; De-noising, smoothing and differential calculation are performed on the real-time monitoring data, and Fourier transform is performed on the photovoltaic power generation output and load demand data to obtain the photovoltaic power generation output and load demand expressed in the frequency domain; Based on the dynamic response characteristics of the system frequency response, the frequency response function H(f) is designed by numerical analysis software to adjust the frequency. The adjusted frequency domain signal is inversely Fourier transformed to obtain the reference voltage V in the time domain. ref (t); The dynamic adjustment coefficient α is calculated using the preset dynamic adjustment coefficient formula. Combined with real-time monitoring data and load forecast results, the combined output of hydropower and photovoltaic power generation is dynamically adjusted based on the improved conductance increment method formula and the improved fixed voltage method to optimize the scheduling strategy. When there is excess photovoltaic power generation, the excess electricity is stored in the energy storage module. When the power demand peaks, the stored energy is released to smooth the system output and perform energy storage management.

2. A method for power generation scheduling by water-photovoltaic complementary joint optimization as claimed in claim 1, characterized in that: The frequency response function H(f) performs frequency adjustment by acquiring historical data of photovoltaic power generation output and load demand, performing frequency domain analysis on the historical data using Fourier transform, determining the main frequency component Ω of the system, analyzing the dynamic response of the system at different frequencies, and identifying the main frequency component that needs to be enhanced or suppressed: Ω={ω∣F(P his ) 2 >P threshold } Among them, P his represents historical data, I(t) represents photovoltaic power generation output and load demand, F(P his ) represents the frequency domain data after Fourier transformation, t0 and t1 represent the start time of historical data recording and the current time, ρ represents the parameter of the control function shape, Ω' is used to traverse all frequency values ​​for normalization, N represents the number of main frequency components of the system identified after Fourier transformation, ω n represents the nth frequency component, P threshold determining a dominant frequency component for a preset peak threshold; When S>1.5, it means that the corresponding frequency component has a significant positive impact on the dynamic response of the system, enhancing the current frequency component; When S<1.5, it means that the corresponding frequency component has a significant negative impact on the dynamic response of the system, suppressing the current frequency component; Design the initial frequency response function: Where h(f) is the initial frequency response function, P solar (t) is the photovoltaic power output, D load (t i ) is the load demand, λ is the adjustment parameter that controls the decay speed of the exponential function, g(·) is to normalize the load demand to the same range, O is the number of time periods of historical data, i is the variable index, and t i is the time point of the i-th time period; The initial frequency response function is simulated and optimized using numerical analysis software. The initial frequency response function includes a low-pass filter, a high-pass filter and a band-pass filter. The low-pass filter is used to retain the low-frequency component, the high-pass filter is used to remove the low-frequency drift and retain the high-frequency component, and the band-pass filter is used to retain the signal within the preset frequency range and suppress the frequency components outside the range. The cutoff frequency and bandwidth of the filter are set according to the dynamic characteristics of the system: Where H(f) is the optimized frequency response function, F represents the Fourier transform, and F -1 represents the inverse Fourier transform, H lp (f) is the transfer function of the low-pass filter, which is used to retain the low-frequency components; H hp (f) is the transfer function of the high-pass filter, which is used to remove low-frequency drift and retain high-frequency components; H bp (f) is the transfer function of the bandpass filter, which is used to retain the signal within a preset frequency range and suppress the frequency components outside the range; f is a frequency variable; The optimized frequency response function is applied to the real-time collected photovoltaic power generation output and load demand data, the dynamic response of the system is evaluated, and the frequency response function is optimized and adjusted according to the results to achieve the best effect.

3. A method for power generation scheduling by water-photovoltaic complementary joint optimization as claimed in claim 2, characterized in that: The calculation of the dynamic adjustment coefficient α is included in the control module. The dynamic adjustment coefficient in the improved conductance increment method formula is obtained by numerical calculus of the product of the dynamic change rate of load demand and the fluctuation part of photovoltaic and hydropower power generation. The preset dynamic adjustment coefficient formula is: Where L is the load demand, t is the calculation time, is the dynamic change rate of load demand, which is obtained by numerically differentiating the continuously sampled load data; ΔP is the power fluctuation, i.e., the instantaneous change value of photovoltaic and hydroelectric power, which is obtained by sampling and analyzing the actual power generation output data once per second; The load demand L is obtained in real time through the load monitoring module. The data collection frequency is once per second. The collected data is smoothed and denoised. The load demand L is obtained by numerically differentiating the continuously sampled load data. For the instantaneous change value of photovoltaic and hydroelectric power, the actual power output data is sampled and analyzed once a second, and the average part of the power is extracted using a sliding average or low-pass filter. The power fluctuation part is: in, In order to extract the average part of the power using sliding average and low-pass filter, the dynamic adjustment coefficient is numerically integrated and calculated using trapezoidal integration and Simpson integration.

4. A method for power generation scheduling by water-photovoltaic complementary joint optimization as claimed in claim 3, characterized in that: The improved conductance increment method formula is included in the control module. The improved conductance increment method optimizes and adjusts the weight in the conductance increment method through a dynamic adjustment coefficient calculated based on the load change rate. The improved conductance increment method formula is: Among them, P is the output power of the photovoltaic power generation system, V is the output voltage of the photovoltaic power generation system, is the derivative of electric power and voltage, which is used to adjust the output power of the photovoltaic power generation system. α is the dynamic adjustment coefficient, which is obtained by the preset dynamic adjustment coefficient formula based on the load change rate and power fluctuation. k is the current moment, k-1 is the previous moment, P(k) is the photovoltaic power generation output power at the current moment k, P(k-1) is the photovoltaic power generation output power at the previous moment k-1, V(k) is the photovoltaic power generation output voltage at the current moment k, and V(k-1) is the photovoltaic power generation output voltage at the previous moment k-1.

5. The method for power generation scheduling of water-photovoltaic complementary joint optimization as claimed in claim 4, characterized in that: The improved fixed voltage method dynamic adjustment includes in the control module, the improved fixed voltage method uses the product of the Fourier transform value of the time domain signal of the photovoltaic output and the frequency response function, and then performs the inverse Fourier transform value to correct the open circuit voltage of the photovoltaic module. The improved fixed voltage method formula is: V ref (t)=V oc ×F -1 [F(PV(t))·H(f)] Among them, V ref (t) is the time domain reference voltage at time t, which is used to control the output voltage of the photovoltaic power generation system, V oc is the open circuit voltage, F -1 is the inverse Fourier transform operator, which converts the frequency domain signal into the time domain signal. F is the Fourier transform operator, which converts the time domain signal into the frequency domain signal. H(f) is the frequency response function, which is used to adjust and filter the frequency components. It is designed according to the system frequency response requirements. PV(t) is the time domain signal of photovoltaic power generation output, which is obtained in real time through the photovoltaic power generation monitoring module.

6. A method for power generation scheduling by water-photovoltaic complementary joint optimization as claimed in claim 5, characterized in that: The optimization scheduling strategy includes storing excess electricity in energy storage modules when there is excess photovoltaic power generation, and releasing the stored electricity to smooth the system output when power demand peaks; An optimization problem is constructed to achieve the goal of maximizing energy efficiency and minimizing cost when the improved fixed voltage and improved conductance increment are met: Among them, P h P(t) is the output power of hydropower at time t, P(t) is the output power of photovoltaic power generation, and P load (t) is the load demand, P b (t) is the charge and discharge power of the energy storage system, a positive value indicates discharge and a negative value indicates charge; E b (t) is the energy state of the energy storage system at time t, is the maximum energy capacity of the energy storage system, c h 、c pv and c b are the marginal costs of hydropower, photovoltaic power generation and energy storage, P h,max (t) and P max (t) are the maximum possible output of hydropower and photovoltaic power generation at time tt, P b,max is the maximum charge and discharge power of the energy storage system, Δt is the time interval, Indicates that the constraint is valid for all time points.

7. A system using a water-photovoltaic complementary joint optimization power generation scheduling method as claimed in any one of claims 1 to 5, characterized in that: It includes monitoring module, feedback module, regulation module, control module, energy storage module and communication module; The monitoring module monitors photovoltaic power generation, hydropower generation, system voltage, current and load demand in real time, and transmits the collected data to the feedback module in real time; The feedback module receives the collected data for preprocessing, and the processed data is transmitted to the control module; The control module is used for decision-making and scheduling; The regulating module regulates the output of hydroelectric power generation and photovoltaic power generation according to the data of the dispatching strategy of the control module, and feeds back the execution status and current status to the control module to form a closed-loop control. The control module combines the load forecast, natural conditions and power generation output received and processed by the feedback module, and adopts the improved fixed voltage method and conductance increment method to optimize the dispatching strategy, command the operation of the regulating module, and control the charging and discharging of the energy storage module according to system requirements; The energy storage module is used to store excess electric energy according to the scheduling decision of the control module, release it when needed, smooth the power generation output, and feed back the state of the energy storage module to the control module; The communication module is used to realize data communication among the monitoring module, feedback module, regulation module, control module and energy storage module, and ensure data synchronization and timely transmission.

8. The method for power generation scheduling of water-photovoltaic complementary joint optimization as claimed in claim 7, characterized in that: The monitoring module includes a photovoltaic inverter monitoring device, a hydroelectric generator monitoring device, a smart meter and a load sensor; The regulating module includes a hydroelectric generator set controller and a photovoltaic inverter controller; The energy storage module includes a battery energy storage system and a pumped storage system.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of a water-photovoltaic complementary joint optimization power generation scheduling method described in any one of claims 1 to 6 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of a water-photovoltaic complementary joint optimization power generation scheduling method described in any one of claims 1 to 6 are implemented.

Images