A power load distribution method and system of a water-light complementary system

Abstract

This invention discloses a power load allocation method and system for a solar-hydro complementary system. The method includes: communicating with a dispatch center via a communication system to obtain current grid load demand and operating mode; monitoring real-time photovoltaic and hydropower generation capacity using an information acquisition system; predicting the photovoltaic system's photovoltaic generation capacity for the next moment based on a Bayesian model, and predicting hydropower generation capacity based on historical data and flow rates; comprehensively considering grid load, installed capacity, climate, and other factors, and calculating the solar-hydro complementary power generation coefficient to dynamically adjust the output of photovoltaic and hydropower. This invention provides a power load allocation method and system for a solar-hydro complementary system that ensures coordinated power distribution by dynamically adjusting the power generation of the photovoltaic and hydropower stations. With the goal of minimizing total energy consumption, this invention optimizes the photovoltaic and hydropower output of the system based on an improved whale algorithm, thereby improving energy utilization efficiency.

Description

Technical Field

[0001] This invention relates to a power load allocation method and system for a hydro-solar hybrid system, belonging to the field of power system optimization. Background Technology

[0002] Under the dual-carbon context, new energy power generation technologies are developing rapidly. With the continuous increase in the proportion of installed capacity of new energy sources, photovoltaic (PV) power generation and hydropower are playing an important role in the power system. PV power generation is widely used due to its abundant resources and wide distribution; however, it is subject to significant fluctuations and instabilities due to environmental factors, making it difficult to achieve stable operation and reliable power supply relying solely on PV. Hydropower, on the other hand, has a stable output and strong regulation capabilities, effectively balancing power supply and demand. However, its power generation is limited by the seasonal and regional variations of water resources. Therefore, combining PV power generation with hydropower, coordinating and optimizing the output of both PV and hydropower, and improving the system's energy utilization efficiency has become an important research direction.

[0003] Most existing methods employ simple power matching algorithms, failing to fully consider the various constraints and system optimization requirements of photovoltaic and hydropower generation, resulting in low overall system efficiency. Furthermore, the traditional whale algorithm is prone to getting trapped in local optima and has weak global search capabilities, leading to slow convergence speed and low computational accuracy. Summary of the Invention

[0004] The present invention aims to at least partially solve one of the technical problems in the related art.

[0005] Therefore, the first objective of this invention is to propose a method for power load distribution in a water-solar hybrid system.

[0006] The second objective of this invention is to propose a power load distribution system for a water-solar hybrid system.

[0007] The third objective of this invention is to provide an electronic device.

[0008] The fourth objective of this invention is to provide a computer-readable storage medium.

[0009] The fifth objective of this invention is to provide a computer program product.

[0010] To achieve the above objectives, a first aspect of the present invention provides a power load distribution method for a water-solar hybrid system, comprising:

[0011] The system communicates with the dispatch center to obtain the current power grid load demand and operating mode. The operating modes are divided into photovoltaic priority mode and hydropower priority mode. The photovoltaic and hydropower production capacity is monitored in real time through the information collection system. Predict the photovoltaic power generation of the photovoltaic system at the next moment based on Bayesian model, and predict the hydropower generation based on historical data and flow rate. Taking into account factors such as grid load, installed capacity, and climate, and with the goal of maximizing energy utilization, an optimization model is established. The hydro-solar hybrid power generation coefficient is optimized by improving the whale algorithm, and the output of photovoltaic and hydropower is dynamically adjusted.

[0012] Optionally, the current power grid load demand and operating mode can be obtained by communicating with the dispatch center through a communication system, including: Maintain contact with the dispatch center through the communication system to obtain the current power grid load curve and receive control information from the dispatch center; When the system operates in photovoltaic priority mode, the hydropower output demand needs to be calculated; when the system operates in hydropower priority mode, the photovoltaic output demand needs to be calculated. The current power generation of photovoltaic power plants and hydropower plants is obtained through the information collection system.

[0013] Optionally, the photovoltaic power generation of the photovoltaic system at the next moment can be predicted based on a Bayesian model, including: Collect the current total grid load L(t) and photovoltaic output PPV(t), and predict the photovoltaic output at the next time step based on a Bayesian model:

[0014] Where PPV(t+Δt) represents the photovoltaic output at the next moment, λ is the photovoltaic power generation attenuation coefficient, and t is the current moment. For the predicted time.

[0015] The difference between the predicted and current photovoltaic power output is calculated as follows:

[0016] The dispatchable power of the photovoltaic system is: .

[0017] Optionally, when adopting the photovoltaic-first control mode, the system first maximizes the power generation of the photovoltaic power station. At the same time, in order to ensure the stable operation of the power system, it is necessary to calculate the power generation demand of the hydropower station and calculate the load demand of the hydropower plant based on the total grid load. ; When the system control mode prioritizes hydropower generation, the required power generation of the photovoltaic power station is calculated by collecting the real-time power generation data Pw(t) of the hydropower station at the current moment and the total load demand L(t) of the power system. Taking into account water flow and historical power generation data, the hydropower generation at future moments is predicted, expressed as:

[0018] In the above formula, For water flow rate, For the power generation efficiency of hydropower stations; The difference between the predicted and current hydropower output values ​​is calculated as follows:

[0019] The total dispatchable output of the hydropower plant at the current moment is calculated as follows:

[0020] The photovoltaic power output demand is calculated as follows: .

[0021] Optionally, considering the total installed capacity of photovoltaic power plants and hydropower plants, and aiming at maximizing energy utilization, a coordinated optimization model is established. This model calculates the photoelectric coefficient of the photovoltaic-hydropower complementary system using an improved whale algorithm, and coordinates the output of photovoltaic and hydropower, including: Let the objective function be:

[0022] In the formula: PPV.adj(t) is the adjusted photovoltaic output, Pw.adj(t) is the adjusted hydropower output, Ph(t) is the dispatchable hydropower output, and Pz(t) is the dispatchable photovoltaic output. Based on the traditional whale algorithm, an improved whale algorithm is obtained by introducing a nonlinear time-varying factor, an adaptive weighting strategy, a random learning strategy, and a Cauchy mutation strategy. Position updates are performed based on the introduced nonlinear time-varying factor, adaptive weighting strategy, stochastic learning strategy, and Cauchy mutation strategy. The specific formula is as follows:

[0023]

[0024]

[0025]

[0026] In the formula: x(i+1) is the updated position of the whale, xrand(i) is any position of the whale, ω(i) is the adaptive weight, and A is an important parameter for adjusting the global survey and local optimization of the algorithm; Drand=|c xrand(i) - xnew1(i)|, c = 2r, r is a random number between 0 and 1, x(i) is the current position of the whale, xnew1(i) is the optimal individual after the random learning strategy; p is the random probability; xnew(i) is the new value of the current optimal individual after Cauchy mutation; D1 = |c x (t)- x(i)|,x (i) represents the current optimal individual, D2=|x (t)- x(i)|, b=1 is a constant coefficient, l is a random number between -1 and 1; a represents a nonlinear time-varying factor.

[0027] The nonlinear time-varying factor is:

[0028] In the formula: a represents the nonlinear time-varying factor, i is the current iteration number, and I is the maximum iteration number; The adaptive weighting strategy is as follows:

[0029] In the formula: This represents the adaptive weights; i is the current iteration number, and I is the maximum iteration number; The deviation between the adjustable output of photovoltaic power plants and hydropower plants and the load demand is calculated and expressed as:

[0030] In the formula: ΔPPV(t) is the deviation of photovoltaic power generation, PPV.n(t) is the photovoltaic power generation demand at the current moment, ΔPw(t) is the deviation of hydropower power generation, and Pw.n(t) is the hydropower generation demand at the current moment; The adjusted photovoltaic and hydropower outputs are calculated using the following formulas:

[0031]

[0032] In the formula, PPV.adj(t) is the adjusted photovoltaic output, Pw.adj(t) is the adjusted hydropower output, y1 is the photovoltaic power generation coefficient, and y2 is the hydropower generation coefficient.

[0033] Optional, also includes: If the optimal fitness value of an individual remains the same after multiple iterations, then Cauchy mutation is performed on that individual. The Cauchy mutation strategy updates the position of the current best individual using the following formula:

[0034] In the formula: xnew(i) is the new value of the current best individual after Cauchy mutation, x (i) represents the current best individual, and cauchy(0,1) is the Cauchy operator.

[0035] Optional, also includes: The random learning strategy selects a better individual by comparing the fitness values of two individuals. For the current individual x(i), a different individual x(i1) is randomly selected from the population to generate a new individual:

[0036] In the formula: xnew1(i) is the optimal individual after the random learning strategy, f(x(i)) and f(x(i1)) are the fitness values of individuals x(i) and x(i1) respectively; if f(xnew1(i)) < f(x(i)), then the individual xnew1(i) replaces the individual x(i), otherwise xnew1(i) is directly the individual x(i).

[0037] To achieve the above object, an embodiment of the second aspect of the present invention proposes a power load distribution system based on a water-light complementary system, including: A communication module, used to communicate with the dispatching center through a communication system to obtain the current grid load demand and operation mode. The operation mode is divided into a photovoltaic priority mode and a hydropower priority mode; An information collection module, used to real-time monitor the current photovoltaic power generation capacity and hydropower generation capacity through an information collection system; A prediction module, used to predict the next moment's photovoltaic system photovoltaic power generation capacity based on a Bayesian model and predict the hydropower generation capacity based on historical data and flow; An optimization module, used to comprehensively consider factors such as grid load, installed capacity, and climate, establish an optimization model with the goal of the highest energy utilization rate, optimize the water-light complementary power generation coefficient through an improved whale algorithm, and dynamically adjust the photovoltaic and hydropower output.

[0038] To achieve the above object, an embodiment of the third aspect of the present invention proposes an electronic device, including: a processor, and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the method described in any one of the first aspect.

[0039] To achieve the above object, an embodiment of the fourth aspect of the present invention proposes a computer-readable storage medium, in which computer-executable instructions are stored, and when the computer-executable instructions are executed by a processor, they are used to implement the method described in any one of the first aspect.

[0040] To achieve the above object, an embodiment of the fifth aspect of the present invention proposes a computer program product, which when executed by a processor implements the method described in any one of the first aspect.

[0041] The technical solutions provided by the embodiments of the present invention bring at least the following beneficial effects: by establishing an optimization model for a water-solar complementary system, introducing nonlinear time-varying factors, random learning strategies and Cauchy mutation strategies to improve the convergence speed and calculation accuracy of the whale algorithm, rationally coordinating the output of photovoltaic units and hydropower units, improving energy utilization, and enhancing the economic benefits of system operation.

[0042] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0043] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a flowchart of the method provided in an embodiment of the present invention; Figure 2 A flowchart for improving the whale algorithm; Figure 3 This is a load curve diagram; Figure 4 Output curves for hydropower and photovoltaic power; Figure 5 This is a structural diagram of the power load distribution system provided in an embodiment of the present invention. Detailed Implementation

[0044] Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0045] This invention provides a method for power load distribution in a water-solar hybrid system, such as... Figure 1 As shown, it includes: Step 101: Contact the dispatch center through the communication system to obtain the current power grid load demand and operating mode. The operating modes are divided into photovoltaic priority mode and hydropower priority mode. Step 102: Monitor the current photovoltaic power generation and hydropower generation in real time through the information collection system; Step 103: Predict the photovoltaic power generation of the photovoltaic system at the next moment based on the Bayesian model, and predict the hydropower generation based on historical data and flow rate. Step 104: Taking into account factors such as grid load, installed capacity, and climate, and with the goal of maximizing energy utilization, establish an optimization model, optimize the hydro-solar hybrid power generation coefficient by improving the whale algorithm, and dynamically adjust the output of photovoltaic and hydropower.

[0046] In one embodiment of the present invention, the current total grid load L(t) and photovoltaic output PPV(T) are collected, and the photovoltaic output at the next moment is predicted based on a Bayesian model:

[0047] Where PPV(t+Δt) represents the photovoltaic output at the next moment, λ is the photovoltaic power generation attenuation coefficient, and t is the current moment. For the predicted time.

[0048] The difference between the predicted and current photovoltaic power output is calculated as follows:

[0049] The dispatchable power of the photovoltaic system is:

[0050] When adopting the photovoltaic priority control mode, the system first maximizes the power generation of the photovoltaic power station. At the same time, in order to ensure the stable operation of the power system, the required power generation of the hydropower station needs to be calculated. Calculate the hydropower plant's load demand based on the total grid load:

[0051] When the system control mode prioritizes hydropower generation, the required power generation of the photovoltaic power station is calculated by collecting the real-time power generation data Pw(t) of the hydropower station at the current moment and the total load demand L(t) of the power system. Taking into account water flow and historical power generation data, the hydropower generation at future moments is predicted, expressed as:

[0052] In the above formula, For water flow rate, This refers to the power generation efficiency of the hydropower station.

[0053] The difference between the predicted and current hydropower output values ​​is calculated as follows:

[0054] The total dispatchable output of the hydropower plant at the current moment is calculated as follows:

[0055] The photovoltaic power output demand is calculated as follows:

[0056] The deviation between the adjustable output of photovoltaic power plants and hydropower plants and the load demand is calculated and expressed as:

[0057] In the formula: ΔPPV(t) is the deviation of photovoltaic power generation, PPV.n(t) is the photovoltaic power generation demand at the current moment, ΔPw(t) is the deviation of hydropower generation, and Pw.n(t) is the hydropower generation demand at the current moment.

[0058] In one embodiment of the present invention, considering the total installed capacity of photovoltaic power plants and hydropower plants, and with the goal of maximizing energy utilization, a coordinated optimization model for a hydro-photovoltaic complementary system is established, with the objective function being:

[0059] In the formula: PPV.adj(t) is the adjusted photovoltaic output, Pw.adj(t) is the adjusted hydropower output, Ph(t) is the dispatchable hydropower output, and Pz(t) is the dispatchable photovoltaic output.

[0060] Furthermore, based on the traditional whale algorithm, an improved whale algorithm is obtained by introducing a nonlinear time-varying factor, an adaptive weighting strategy, a random learning strategy, and a Cauchy mutation strategy.

[0061] like Figure 2 As shown, the steps of the improved whale algorithm are described as follows: S1. Set the relevant parameters for the algorithm; S2, whale population initialization; S3. Calculate the individual fitness value, find the best individual, determine whether the individual has mutated, and if so, perform Cauchy mutation on the individual. S4. Update the algorithm parameters a, A, c, p, ω(t); S5. When p < 0.5 and |A| > 1, perform random learning to optimize the individual's position and proceed to step S6; when |A| ≤ 1, proceed to step 7; when p ≥ 0.5, proceed to step S8. S6. Perform a global search of the whale population and optimize individuals with poor positions based on a stochastic learning strategy to further update the whale positions; S7. Surround the prey and update the whale's position according to the corresponding formula; S8. Engage in predation and update the whale's position according to the corresponding formula; S9. Determine if the algorithm termination condition is met. If not, proceed to step 3 to continue iterating; otherwise, output the result.

[0062] Furthermore, in this embodiment of the invention, the position is updated based on the introduced nonlinear time-varying factor, adaptive weighting strategy, random learning strategy, and Cauchy mutation strategy, as specifically defined by the following formula:

[0063]

[0064]

[0065]

[0066] In the formula: x(i+1) is the updated position of the whale, xrand(i) is any position of the whale, ω(i) is the adaptive weight, and A is an important parameter for adjusting the global survey and local optimization of the algorithm; Drand=|c xrand(i) - xnew1(i)|, c = 2r, r is a random number between 0 and 1, x(i) is the current position of the whale, xnew1(i) is the optimal individual after the random learning strategy; p is the random probability; xnew(i) is the new value of the current optimal individual after Cauchy mutation; D1 = |c x (t)- x(i)|,x (i) represents the current optimal individual, D2=|x (t)- x(i)|, b=1 is a constant coefficient, l is a random number between -1 and 1; a represents a nonlinear time-varying factor.

[0067] The nonlinear time-varying factor is:

[0068] In the formula: a represents the nonlinear time-varying factor, i is the current iteration number, and I is the maximum iteration number.

[0069] The adaptive weighting strategy is as follows:

[0070] In the formula: This represents the adaptive weights; i is the current iteration number, and I is the maximum iteration number.

[0071] If the optimal fitness value of an individual remains the same after multiple iterations, then Cauchy mutation is performed on that individual. The Cauchy mutation strategy updates the position of the current best individual using the following formula:

[0072] In the formula: xnew(i) is the new value of the current best individual after Cauchy mutation, x (i) represents the current best individual, and cauchy(0,1) is the Cauchy operator.

[0073] The stochastic learning strategy selects the better individual by comparing the fitness values ​​of two individuals. For the current individual x(i), a new individual x(i1) is randomly selected from the population.

[0074] Where: xnew1(i) is the optimal individual after the stochastic learning strategy, and f(x(i)) and f(x(i1)) are the fitness values of individuals x(i) and x(i1), respectively; if f(xnew1(i)) < f(x(i)), then individual xnew1(i) replaces individual x(i), otherwise xnew1(i) is directly individual x(i).

[0075] Further, the embodiment of the present invention obtains the optimal power generation coefficients of photovoltaic and hydropower through improved whale optimization, and dynamically adjusts the output of photovoltaic and hydropower. The formula is as follows:

[0076]

[0077] In the formula, PPV.adj(t) is the adjusted photovoltaic output, Pw.adj(t) is the adjusted hydropower output, y1 is the photovoltaic power generation coefficient, and y2 is the hydropower generation coefficient.

[0078] Combined with experimental data, the present invention gives the following optional specific implementation manners: Based on a water-light complementary system composed of a hydropower plant with an installed capacity of 2500 MW and a photovoltaic power plant with an installed capacity of 600 MW, the effectiveness of the proposed method is verified. The population size of the improved whale algorithm is set to 50, the spatial dimension is 3, the searchable space of the whale population is [-50 50], and the number of iterations is 100 times.

[0079] Taking the given load of the power grid on a certain day as an example, assuming sunny weather, the system is optimized in the photovoltaic priority operation mode to coordinate the output of the hydropower unit and the photovoltaic power plant. The load curve, hydropower output, and photovoltaic output are as Figure 3 、 4 shown.

[0080] To implement the above embodiments, the present invention also proposes a power load distribution system based on a water-light complementary system. Figure 5 This is a schematic structural diagram of a power load distribution system based on a water-light complementary system provided by an embodiment of the present invention. As Figure 5 shown, the device includes: A communication module for contacting the dispatching center through a communication system to obtain the current power grid load demand and operation mode. The operation mode is divided into a photovoltaic priority mode and a hydropower priority mode; An information acquisition module for real-time monitoring of the current photovoltaic power generation capacity and hydropower generation capacity through an information acquisition system; A prediction module for predicting the next moment's photovoltaic system photovoltaic power generation capacity based on a Bayesian model and predicting the hydropower generation capacity based on historical data and flow; The optimization module is used to comprehensively consider factors such as grid load, installed capacity, and climate, and to establish an optimization model with the goal of maximizing energy utilization. It optimizes the hydro-solar hybrid power generation coefficient by improving the whale algorithm and dynamically adjusts the output of photovoltaic and hydropower.

[0081] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0082] To implement the above embodiments, the present invention also proposes an electronic device, comprising: a processor, and a memory communicatively connected to the processor; the memory stores computer execution instructions; the processor executes the computer execution instructions stored in the memory to implement the method provided in the foregoing embodiments.

[0083] To implement the above embodiments, the present invention also proposes a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the methods provided in the foregoing embodiments.

[0084] To implement the above embodiments, the present invention also proposes a computer program product, including a computer program that, when executed by a processor, implements the methods provided in the foregoing embodiments.

[0085] The collection, storage, use, processing, transmission, provision, and disclosure of user personal information involved in this invention all comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0086] It should be noted that personal information collected from users should be used for legitimate and reasonable purposes and should not be shared or sold outside of these legitimate uses. Furthermore, such collection / sharing should only be conducted after receiving the user's informed consent, including but not limited to notifying the user to read the user agreement / user notice and sign an agreement / authorization that includes authorization of relevant user information before the user uses the function. In addition, any necessary steps must be taken to protect and safeguard access to such personal information data and ensure that others with access to personal information data comply with their privacy policies and procedures.

[0087] This invention is intended to provide implementation schemes for users to selectively prevent the use or access to personal information data. That is, this disclosure is intended to provide hardware and / or software to prevent or block access to such personal information data. Once personal information data is no longer needed, risks can be minimized by restricting data collection and deleting data. Furthermore, where applicable, such personal information can be de-identified to protect user privacy.

[0088] In the foregoing descriptions of the embodiments, the terms "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0089] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0090] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing custom logic functions or processes, and the scope of preferred embodiments of the invention includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of the invention pertain.

[0091] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0092] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware as in another embodiment, it can be implemented using any of the following techniques known in the art, or a combination thereof: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0093] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0094] Furthermore, the functional units in the various embodiments of the present invention can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0095] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present invention.

[0096] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0097] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for power load distribution in a hydro-solar hybrid system, characterized in that, include: The system communicates with the dispatch center to obtain the current power grid load demand and operating mode. The operating modes are divided into photovoltaic priority mode and hydropower priority mode. The photovoltaic and hydropower production capacity is monitored in real time through the information collection system. Predict the photovoltaic power generation of the photovoltaic system at the next moment based on Bayesian model, and predict the hydropower generation based on historical data and flow rate. Taking into account factors such as grid load, installed capacity, and climate, and with the goal of maximizing energy utilization, an optimization model is established. The hydro-solar hybrid power generation coefficient is optimized by improving the whale algorithm, and the output of photovoltaic and hydropower is dynamically adjusted.

2. The power load distribution method for a water-solar hybrid system as described in claim 1, characterized in that, By communicating with the dispatch center through the communication system, the current power grid load demand and operating mode can be obtained, including: Maintain contact with the dispatch center through the communication system to obtain the current power grid load curve and receive control information from the dispatch center; When the system operates in photovoltaic priority mode, the hydropower output demand needs to be calculated; when the system operates in hydropower priority mode, the photovoltaic output demand needs to be calculated. The current power generation of photovoltaic power plants and hydropower plants is obtained through the information collection system.

3. The power load distribution method for a water-solar hybrid system as described in claim 2, characterized in that, Predicting the photovoltaic power generation of the photovoltaic system in the next moment based on a Bayesian model includes: Collect the current total grid load L(t) and photovoltaic output PPV(t), and predict the photovoltaic output at the next time step based on a Bayesian model: Where PPV(t+Δt) represents the photovoltaic output at the next moment, λ is the photovoltaic power generation attenuation coefficient, and t is the current moment. For the predicted time. The difference between the predicted and current photovoltaic power output is calculated as follows: The dispatchable power of the photovoltaic system is: 。 4. The power load distribution method for a water-solar hybrid system as described in claim 3, characterized in that, Also includes: When adopting a photovoltaic-first control mode, the system first maximizes the power generation of the photovoltaic power station. Simultaneously, to ensure the stable operation of the power system, the required power generation of the hydropower station needs to be calculated, and the load demand of the hydropower plant is calculated based on the total grid load. ； When the system control mode prioritizes hydropower generation, the required power generation of the photovoltaic power station is calculated by collecting the real-time power generation data Pw(t) of the hydropower station at the current moment and the total load demand L(t) of the power system. Taking into account water flow and historical power generation data, the hydropower generation at future moments is predicted, expressed as: In the above formula, For water flow rate, For the power generation efficiency of hydropower stations; The difference between the predicted and current hydropower output values ​​is calculated as follows: The total dispatchable output of the hydropower plant at the current moment is calculated as follows: The photovoltaic power output demand is calculated as follows: 。 5. The power load allocation method for a hydro-solar hybrid system as described in claim 4, based on the total installed capacity of the photovoltaic power plant and the hydropower plant, establishes a coordinated optimization model with the goal of maximizing energy utilization, calculates the photoelectric coefficient of the hydro-solar hybrid system through an improved whale algorithm, and coordinates and controls the output of photovoltaic and hydropower, including: Let the objective function be: In the formula: PPV.adj(t) is the adjusted photovoltaic output, Pw.adj(t) is the adjusted hydropower output, Ph(t) is the dispatchable hydropower output, and Pz(t) is the dispatchable photovoltaic output. Based on the traditional whale algorithm, an improved whale algorithm is obtained by introducing a nonlinear time-varying factor, an adaptive weighting strategy, a random learning strategy, and a Cauchy mutation strategy. Position updates are performed based on the introduced nonlinear time-varying factor, adaptive weighting strategy, stochastic learning strategy, and Cauchy mutation strategy. The specific formula is as follows: In the formula: x(i+1) is the updated position of the whale, xrand(i) is any position of the whale, ω(i) is the adaptive weight, and A is an important parameter for adjusting the global survey and local optimization of the algorithm; Drand=|c xrand(i) - xnew1(i)|, c = 2r, r is a random number between 0 and 1, x(i) is the current position of the whale, xnew1(i) is the optimal individual after the random learning strategy; p is the random probability; xnew(i) is the new value of the current optimal individual after Cauchy mutation; D1 = |c x (t)- x(i)|,x (i) represents the current optimal individual, D2=|x (t)- x(i)|, b=1 is a constant coefficient, l is a random number between -1 and 1; a represents a nonlinear time-varying factor. The nonlinear time-varying factor is: In the formula: a represents the nonlinear time-varying factor, i is the current iteration number, and I is the maximum iteration number; The adaptive weighting strategy is as follows: In the formula: This represents the adaptive weights; i is the current iteration number, and I is the maximum iteration number; Calculate the deviation between the adjustable output of the PV power station and the hydropower station and the load demand, which is expressed as: Where: ΔPPV(t) is the deviation of PV power generation, PPV.n(t) is the PV power generation demand at the current moment, ΔPw(t) is the deviation of hydropower generation, and Pw.n(t) is the hydropower generation demand at the current moment; Calculate the adjusted PV output and hydropower output through the following formula: Where, PPV.adj(t) is the adjusted PV output, Pw.adj(t) is the adjusted hydropower output, y1 is the PV generation coefficient, and y2 is the hydropower generation coefficient.

6. The power load distribution method for a water-solar hybrid system according to claim 5, characterized in that, It also includes: If the optimal fitness values of an individual are the same after multiple iterations, perform Cauchy mutation on the individual. The formula for updating the position of the current optimal individual by the Cauchy mutation strategy is as follows: In the formula: xnew(i) is the new value of the current best individual after Cauchy mutation, x (i) represents the current best individual, and cauchy(0,1) is the Cauchy operator.

7. The power load distribution method for a water-solar hybrid system according to claim 6, characterized in that, It also includes: The stochastic learning strategy selects a better individual by comparing the fitness values of two individuals. For the current individual x(i), randomly select a different individual x(i1) from the population to generate a new individual: Where: xnew1(i) is the optimal individual after the stochastic learning strategy, f(x(i)) and f(x(i1)) are the fitness values of individuals x(i) and x(i1) respectively; if f(xnew1(i)) < f(x(i)), then the individual xnew1(i) replaces the individual x(i), otherwise xnew1(i) is directly the individual x(i).

8. A power load distribution system based on a water-solar interconnection system employing the method described in any one of claims 1-7, characterized in that, It includes: A communication module for contacting the dispatching center through a communication system to obtain the current grid load demand and operation mode. The operation mode is divided into a PV priority mode and a hydropower priority mode; An information acquisition module for real-time monitoring of the current PV power generation capacity and hydropower generation capacity through an information acquisition system; A prediction module for predicting the next moment's PV system PV power generation capacity based on the Bayesian model and predicting the hydropower generation capacity based on historical data and flow; An optimization module for comprehensively considering factors such as grid load, installed capacity, and climate, aiming at the highest energy utilization rate, establishing an optimization model, and optimizing the PV-hydro complementary generation coefficient through an improved whale algorithm to dynamically adjust the PV and hydropower output.

9. An electronic device, characterized in that, It includes: A processor and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are executed by the processor, they are used to implement the method according to any one of claims 1-7.

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