Wind and light storage active power control method and system based on whale optimization algorithm

Abstract

The invention relates to the technical field of new energy station power control, and discloses a wind and light storage active power control method and system based on a whale optimization algorithm, and the method comprises the steps: obtaining the equipment parameters of all wind and light storage equipment, and generating the dynamic priority of all equipment in combination with the real-time operation state; the initial whale population is generated as a plurality of power distribution schemes, each whale represents a group of power distribution coefficients, and the sum of the distribution coefficients is 1; constructing a fitness function for calculating a fitness value based on a weighted error term determined by a dynamic priority and a device and system dual penalty term; executing a main optimization loop, dynamically adjusting a search strategy through a convergence factor, updating the whale position by combining a bubble attack strategy and a surrounding strategy, generating a distribution scheme meeting a preset constraint condition, normalizing the optimal whale position, and outputting a power distribution coefficient of each device; and according to the distribution coefficient and the total power instruction, generating a specific power instruction value of each device for controlling the active power of each device, so that dynamic adjustment can be performed in real time according to the operation state of the device and the demand of the system, stable operation of the system and effective output of energy are ensured, and economic benefits are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of new energy power station power control, and particularly relates to a wind-solar-storage active power control method and system based on the whale optimization algorithm. Background Technique

[0002] In the current new energy power system, the regulation of the active power of power stations mostly relies on traditional strategies such as proportional allocation and margin allocation. These methods gradually show insurmountable limitations in engineering practice: on the one hand, in new application scenarios such as large new energy bases and integrated source-network-load-storage, the system scale is huge and the operating conditions are complex. The traditional control methods have the problem of slow convergence of dynamic response and are difficult to quickly adapt to situations such as grid frequency fluctuations and load mutations; on the other hand, their regulation robustness is insufficient. In the face of uncertainties such as the intermittency and randomness of wind and light resources and grid disturbances, they cannot effectively maintain the stable operation of the system.

[0003] The above problems seriously restrict the collaborative control efficiency of large-scale power station groups, resulting in the difficulty of realizing efficient and stable power output of new energy clusters. It not only increases the safety risks of grid operation, but also limits the consumption capacity of new energy power generation, directly affecting the economic output efficiency and investment return of clustered new energy assets, and becoming a technical bottleneck that needs to be broken through urgently for the high-quality development of the new energy industry. Summary of the Invention

[0004] In view of this, the present invention provides a wind-solar-storage active power control method and system based on the whale optimization algorithm to overcome the problems of slow response and poor robustness of the power control method of new energy power stations in the prior art, resulting in low economic benefits.

[0005] In the first aspect, the present invention provides a wind-solar-storage active power control method based on the whale optimization algorithm. The method includes: obtaining the device parameters of each device in the wind power, photovoltaic, and energy storage power stations, and generating the dynamic priority of each device in combination with the real-time operating state;

[0006] Generating an initial whale population as multiple power distribution schemes, each whale representing a set of power distribution coefficients, and the sum of the distribution coefficients is 1;

[0007] Constructing a fitness function based on the weighted error term determined by the dynamic priority, the double penalty terms of the device and the system, and using the fitness value calculated by the fitness function to evaluate the optimization effect of each whale;

[0008] Executing the main optimization loop, dynamically adjusting the search strategy through the convergence factor, updating the whale position by combining the bubble attack strategy and the encirclement strategy, generating a distribution scheme that meets the preset constraint conditions, normalizing the optimal whale position, and outputting the power distribution coefficients of each device in the wind power, photovoltaic, and energy storage power stations;

[0009] Generate specific power command values for each device according to the distribution coefficient and the total power command to control the active power of each device.

[0010] The active power control method provided by the embodiments of the present invention generates an initial whale population as multiple power distribution schemes, and uses a fitness function to evaluate the optimization effect of each whale, which can search for the optimal power distribution scheme among a large number of possible schemes. Compared with the traditional fixed distribution method, it can more flexibly adapt to different operating scenarios and constraint conditions, achieve more reasonable power distribution, and improve the energy utilization efficiency; a fitness function is constructed based on the weighted error term determined by dynamic priority, and the dual penalty terms of the device and the system, so that the optimization process not only considers the priority differences between devices, but also takes into account various constraints and potential problems during the operation of the device and the system, and can be dynamically adjusted in real time according to the operating state of the device and the system requirements, ensuring the stable operation of the system and the effective output of energy, and improving economic benefits.

[0011] In an alternative embodiment, the obtaining of the device parameters of each device in the wind power, photovoltaic, and energy storage stations, and the generation of the dynamic priority of each device in combination with the real-time operating state includes:

[0012] Obtain the maximum active power, real-time active power, real-time operating state, and rated power of each device type in the wind power, photovoltaic, and energy storage stations;

[0013] Set the ramp rate limit and the initial allocation priority weight for each device, and generate the dynamic priority in combination with the real-time operating state;

[0014] Verify the data validity of the real-time data and predicted data of the wind power, photovoltaic, and energy storage stations using the rated power, and perform data cleaning to remove row outliers.

[0015] By obtaining parameters such as the maximum active power, real-time active power, real-time operating state, and rated power of each device type in the wind power, photovoltaic, and energy storage stations, the embodiments of the present invention can comprehensively and accurately understand the actual operating conditions and capabilities of the devices, providing a solid data basis for subsequent power distribution and device management; setting the ramp rate limit and the initial allocation priority weight for each device, and generating the dynamic priority in combination with the real-time operating state, can comprehensively consider the performance characteristics of the device and the current operating conditions to determine its priority in power distribution; verifying the data validity of the real-time data and predicted data of the wind power, photovoltaic, and energy storage stations using the rated power, and performing data cleaning to remove outliers, helps to improve the quality and accuracy of the data.

[0016] In an alternative embodiment, setting the ramp rate limit and the initial allocation priority weight for each device, and generating the dynamic priority in combination with the real-time operating state includes:

[0017] Set the maximum and minimum ramp rates for each device type, and initialize the priority weights of each device;

[0018] Dynamically correct the real-time weights of each device according to the real-time average ramp rate, maximum ramp rate, and minimum ramp rate of the same type of devices:

[0019]

[0020] Among them, ω i : The current device priority weight, R i : The available ramp rate of the current device, R avg : The average ramp rate of the same type of devices, R max : The maximum ramp rate of the same type of devices, R min : The minimum ramp rate of the same type of devices, η i : The device availability;

[0021] Calculate the total weights of each device, and normalize the weights of all devices to generate the dynamic priorities of each device.

[0022] In the embodiments of the present invention, the real-time weights of each device are dynamically corrected according to the real-time average ramp rate, maximum ramp rate, and minimum ramp rate of the same type of devices, which can make the priority allocation of the devices more reasonable. When allocating power, devices with strong ramp-up capabilities and good current states are preferentially used. This can avoid overloading devices with limited ramp-up capabilities or poor states with excessive power change tasks, and improve the overall operation efficiency of the devices.

[0023] In an optional implementation manner, constructing a fitness function based on the weighted error term determined by the dynamic priority, the device and system double penalty terms includes:

[0024] Calculate the error between the allocated power and the preset expected target, and weight the error according to the dynamic priority to obtain the weighted error term;

[0025] Check whether the calculated allocated power exceeds the single-device power constraint and / or the system power constraint. If there is an out-of-bounds situation, calculate the single-device penalty term and / or the system penalty term;

[0026] Add the weighted error term and the out-of-bounds penalty term to obtain the fitness function.

[0027] The embodiments of the present invention check whether the allocated power exceeds the power constraints of a single device and the system power constraints, and calculate the corresponding penalty terms, which can effectively prevent the device from being damaged due to overload and ensure the safe and stable operation of the system; adding the weighted error term and the out-of-bounds penalty term to obtain the fitness function realizes the comprehensive evaluation and optimization of the power distribution scheme. The fitness function comprehensively considers the accuracy of power distribution and the safety of the device system, so that the generated power distribution scheme not only minimizes the error from the expected target as much as possible, but also avoids the situation of power out-of-bounds. It can screen out the optimal scheme that meets both the power distribution accuracy requirements and ensures the safe operation of the device and the system from multiple feasible schemes, improving the operation efficiency and stability of the entire system; since the fitness function considers the dynamic priority, and the dynamic priority combines the real-time operating status of the device, this function can adjust the evaluation of the power distribution scheme according to the real-time changes of the system, maintain the good operating state of the system, and improve the adaptability and robustness of the system to different working conditions.

[0028] In an alternative embodiment, the calculation formula for the single-device penalty term is:

[0029]

[0030] where Penalty dev : single-device penalty term, Calculate the power of device i, Maximum power of the device, λ: penalty coefficient;

[0031] The calculation formula for the system penalty term is:

[0032] Penalty total =(P alloc -P total ) 2 *λ

[0033] where P alloc : actual total allocated power, P total : target total power;

[0034] The fitness function is:

[0035]

[0036] where, Calculate the active power of the device, Maximum active power of the device, ω i : current priority weight of device i.

[0037] In the embodiment of the present invention, the fitness function combines the weighted error term and the penalty term, comprehensively considering the accuracy of power allocation and the safety of devices and systems. The weighted error term reflects the closeness between the allocated power and the preset expected target, reflecting the precision of power allocation; the penalty term penalizes the device power overstep and the system power imbalance. In this way, the fitness function can comprehensively evaluate the power allocation scheme and screen out the scheme that not only meets the requirements of power allocation precision but also ensures the safe operation of devices and systems; the fitness function includes the current device priority weight, which reflects the importance of different devices in power allocation and improves the overall operation efficiency and reliability of the system.

[0038] In an alternative embodiment, the execution of the main optimization loop dynamically adjusts the search strategy through a convergence factor, updates the whale position by combining the bubble attack strategy and the encirclement strategy, and generates an allocation scheme that meets the preset constraint conditions, including:

[0039] When the number of iterations does not reach the preset maximum value, the following steps are executed:

[0040] Update the convergence factor a, which linearly decreases with the number of iterations. Calculate the shrinking encirclement coefficient A, the shrinking encirclement coefficient C, the spiral shape control parameter l, and the distance D between the current whale position and the optimal position according to the convergence factor a and the random number, which are used to control the whale movement strategy;

[0041] Select the behavior mode according to the comparison result between the spiral shape constant p and the preset value n:

[0042] If p≥n, update the whale position using the bubble net attack strategy and approach the current optimal solution through a spiral path;

[0043] If p < n, execute the encircling prey strategy. When |A| < 1, shrink the encirclement towards the current optimal solution. When |A| ≥ 1, randomly select other whale individuals as references to expand the exploration range;

[0044] Update the whale position, generate a new combination of power allocation coefficients, and perform constraint processing on the new position to ensure that the power allocation coefficients are within the range of [0, 1] and normalize the coefficients so that their sum is 1;

[0045] Calculate the new fitness, update the global optimal solution according to the new fitness, and normalize the optimal solution vector to an allocation ratio with a sum of 1;

[0046] When the number of iterations does not reach the preset maximum value, based on the final result of the main optimization loop.

[0047] Based on a variety of strategies and an optimized loop method with dynamic adjustment, the embodiments of the present invention can adapt to the complex and changeable operating environments and different power distribution requirements of wind power, photovoltaic, and energy storage power stations. Whether under the condition of large fluctuations in new energy power generation or when the equipment state changes, the algorithm can adjust and optimize according to the real-time situation, generate a suitable power distribution plan, and enhance the adaptability and robustness of the system to different working conditions.

[0048] In a second aspect, the present invention provides a system for controlling the active power of wind-solar-storage based on the whale optimization algorithm, including:

[0049] A dynamic priority acquisition module, configured to acquire the equipment parameters of each device in the wind power, photovoltaic, and energy storage power stations, and generate the dynamic priority of each device in combination with the real-time operating state;

[0050] An initialization module, configured to generate an initial whale population as multiple power distribution plans, where each whale represents a set of power distribution coefficients, and the sum of the distribution coefficients is 1;

[0051] A fitness function definition module, configured to construct a fitness function based on the weighted error term determined by the dynamic priority, and the dual penalty terms of the device and the system. The fitness value calculated by the fitness function is used to evaluate the optimization effect of each whale;

[0052] A main optimization loop module, which dynamically adjusts the search strategy through a convergence factor, updates the whale position by combining the bubble attack strategy and the surrounding strategy, generates a distribution plan that meets the preset constraint conditions, normalizes the optimal whale position, and outputs the power distribution coefficients of each device in the wind power, photovoltaic, and energy storage power stations;

[0053] An active power control module, configured to generate specific power command values for each device according to the distribution coefficients and the total power command to control the active power of each device.

[0054] In a third aspect, the present invention provides a computer device, including: a memory and a processor, which are communicatively connected to each other. The memory stores computer instructions, and the processor executes the computer instructions to execute the method for controlling the active power of wind-solar-storage based on the whale optimization algorithm according to the first aspect or any corresponding embodiment thereof.

[0055] In a fourth aspect, the present invention provides a computer-readable storage medium, on which computer instructions are stored. The computer instructions are used to cause a computer to execute the method for controlling the active power of wind-solar-storage based on the whale optimization algorithm according to the first aspect or any corresponding embodiment thereof.

[0056] Fifth aspect, the present invention provides a computer program product, including computer instructions for causing a computer to execute the wind-solar-storage active power control method based on the whale optimization algorithm according to the first aspect or any corresponding embodiment thereof as described above. Description of the Drawings

[0057] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for use in the description of the specific embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0058] Figure 1 is a schematic flowchart of the wind-solar-storage active power control method based on the whale optimization algorithm according to an embodiment of the present invention;

[0059] Figure 2 is a flowchart of the wind-solar-storage active power control method of a specific example of the whale optimization algorithm according to an embodiment of the present invention;

[0060] Figure 3 is a block diagram of the structure of the wind-solar-storage active power control system based on the whale optimization algorithm according to an embodiment of the present invention;

[0061] Figure 4 is a schematic diagram of the hardware structure of the computer device according to an embodiment of the present invention. Detailed Embodiments

[0062] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0063] In order to overcome the problems in the prior art that the power control method of new energy power stations has slow response and poor robustness, resulting in low economic benefits, a wind-solar-storage active power control method based on the whale optimization algorithm is provided in this embodiment. Figure 1 is a flowchart of the wind-solar-storage active power control method based on the whale optimization algorithm according to an embodiment of the present invention. As Figure 1 shown, the process includes the following steps:

[0064] S11, obtain the device parameters of each device in the wind power, photovoltaic, and energy storage power stations, and generate the dynamic priorities of each device in combination with the real-time operating status.

[0065] Specifically, the embodiments of the present invention obtain the maximum active power, real-time active power, real-time operating status, and rated power of each device type in wind power, photovoltaic, and energy storage power stations; set the ramp rate limit and initial allocation priority weight for each device, and generate a dynamic priority in combination with the real-time operating status; verify the data validity of the real-time data and prediction data of wind power, photovoltaic, and energy storage power stations using the rated power, and perform data cleaning to remove row outliers.

[0066] In a specific embodiment, for example:

[0067] Wind turbine generator (WTG): 60 units, single-unit maximum active power 2.5 MW; Photovoltaic inverter (PV): 50 units, single-unit maximum active power 1.8 MW; Energy storage PCS (ESS): 40 units, single-unit maximum active power 3.0 MW (maximum discharge 3 MW, minimum charge -3 MW).

[0068] Ramp rate limit:

[0069] R WTGmax =3.0, R WTGmin =2.0, R PVmax =5.0, R PVmin =3.0,

[0070] R ESSmax =2.5, R ESSmin =1.0

[0071] max: Maximum ramp rate, min: Minimum ramp rate.

[0072] Set the initial allocation priority weight:

[0073] ω WTG =1.2, ω PV =1.0, ω ESS =1.5

[0074] Dynamic priority adjustment:

[0075] The priority weight needs to be dynamically corrected according to the real-time status of the device:

[0076]

[0077] Among them, ω i : Current device priority weight; R i : Current device available ramp rate; R avg : Average ramp rate of similar devices; R max : Maximum ramp rate of similar devices; R min : Minimum ramp rate of similar devices; η i: Equipment availability (0 - 1, health status).

[0078] For example, the current available ramp rate R of the first wind turbine i = 2.8 MW / min, and the average ramp rate R of similar equipment avg = 2.5 MW / min, then the corrected weight:

[0079]

[0080] Calculate the total weight:

[0081]

[0082] Normalize:

[0083]

[0084] Further new priority weight matrix:

[0085]

[0086] In the embodiment of the present invention, by obtaining the maximum active power of each equipment type, the upper limit of the power generation capacity of the equipment under ideal and rated working conditions can be clarified, which is helpful for the overall power generation planning and dispatching of the power station. By understanding the real-time active power, the current power generation status of the equipment can be grasped in real time, and it can be judged whether it is operating normally and the level of power generation efficiency. Through the real-time operating status, it can be known whether the equipment is in the power generation, standby or fault state, etc., which is convenient for timely discovering problems and taking corresponding measures.

[0087] By setting the equipment ramp rate limit, the rapid change of the equipment power can be prevented, the safe and stable operation of the equipment can be protected, and at the same time, the power distribution can be made more stable. The initial distribution priority weight combined with the real-time operating status generates a dynamic priority, which can dynamically adjust the importance of the equipment in the power distribution according to the current actual situation of the equipment, and give priority to ensuring the efficient and stable operation of the equipment, improving the overall power generation efficiency and system stability; after normalization processing, the sum of the weights of all equipment is 1, ensuring that in the whole system, the priorities of each equipment are relatively fair and reasonable, avoiding the situation of unreasonable resource allocation caused by too high or too low weights of some equipment, so as to realize the optimal allocation of resources.

[0088] Using the rated power to verify the effectiveness and clean the real-time data and prediction data can remove outliers, ensure the accuracy and reliability of the data, and high-quality data helps to provide a reliable basis for more accurate equipment status evaluation and power distribution decision-making.

[0089] S12. Generate an initial whale population as multiple power distribution schemes, and each whale represents a set of power distribution coefficients, and the sum of the distribution coefficients is 1.

[0090] Specifically, a group of "whales" is initialized, and each whale represents a potential power allocation scheme (i.e., a set of power coefficients allocated to various types of devices). For example, referring to the operation history data of the reference power station over a period of time, analyze the actual power allocation of various types of devices under different operating conditions, and set the initial coefficients based on this. For example, during the day with sufficient sunlight, photovoltaic devices usually have a high power output, and the initial coefficients can be set according to the average power ratio of photovoltaic devices during the day in the historical data. If there are many types of devices in the wind power, photovoltaic, and energy storage power stations, the operating conditions are complex, and there are many constraints on power allocation, a larger population size needs to be set to search the solution space more comprehensively. For example, when there are multiple different models of wind power and photovoltaic devices, and each device has different performance parameters and constraints, a larger number of whales can increase the possibility of finding the optimal solution.

[0091] The size of the population (the number of whales) is an algorithm parameter that needs to be preset, and these initial coefficients are preset settings. For example, if there are many types of devices in the wind power, photovoltaic, and energy storage power stations, the operating conditions are complex, and there are many constraints on power allocation, a larger population size needs to be set to search the solution space more comprehensively. For example, when there are multiple different models of wind power and photovoltaic devices, and each device has different performance parameters and constraints, a larger number of whales can increase the possibility of finding the optimal solution. If there are many types of devices in the wind power, photovoltaic, and energy storage power stations, the operating conditions are complex, and there are many constraints on power allocation, a larger population size needs to be set to search the solution space more comprehensively. For example, when there are multiple different models of wind power and photovoltaic devices, and each device has different performance parameters and constraints, a larger number of whales can increase the possibility of finding the optimal solution.

[0092] In addition to setting the initial coefficients based on the above basis, the initial power allocation coefficients can also be randomly generated within a certain range to increase the diversity of the initial population. This can enable the algorithm to explore the solution space more widely during the search process and avoid falling into a local optimal solution prematurely. For example, under the condition that the sum of the power allocation coefficients is 1, randomly assign values to the power allocation coefficients of various types of devices, or stratify according to the different characteristics or operating states of the devices, and then sample and generate the initial scheme in each layer. For example, stratify the wind power devices according to the different power levels of the wind turbines, and generate the initial power allocation coefficients in each layer, which can ensure that different types and performance devices have a certain representativeness in the initial population.

[0093] S13. Construct a fitness function based on the weighted error term determined by the dynamic priority and the dual penalty terms of the device and the system. The fitness value calculated by the fitness function is used to evaluate the optimization effect of each whale.

[0094] Specifically, the dynamic priority in the embodiments of the present invention can be integrated into the fitness function according to the real-time operating state of the device, making the optimization process more in line with the actual operating conditions, giving priority to those devices that have a greater impact on the overall performance and stability of the system, and ensuring the efficient operation of the system under various constraint conditions; the setting of the dual penalty terms for the device and the system not only pays attention to the operating conditions of a single device, such as whether the device power exceeds its maximum power limit, but also considers the performance indicators of the entire system, such as the deviation between the actual total allocated power and the target total power. This dual penalty mechanism can achieve a balance between the individual device and the overall system during the optimization process, avoiding overloading or damage to individual devices caused by excessive pursuit of system goals, and also preventing the optimization that only focuses on the device itself while ignoring the overall system performance.

[0095] The weighted error term in the fitness function can assign different weights according to the importance of different devices or the degree of influence on the system performance. In this way, during the optimization process, the algorithm will pay more attention to those factors that have a greater impact on the system performance, guiding the whale individuals to search in a more optimal direction, thereby improving the optimization effect and accuracy, and finding the optimal power allocation scheme that meets the system requirements more quickly. By constructing a fitness function that includes multiple factors, the algorithm can comprehensively consider the changes of various factors when facing a complex and changeable actual operating environment, rather than relying solely on a single goal or constraint. When some parameters in the system change or some unexpected situations occur, such as equipment failures, uncertainties in new energy output, etc., the optimization algorithm based on this fitness function can still adjust the power allocation scheme to keep the system operating as stably as possible, showing strong robustness and adaptability.

[0096] Specifically, whether the allocated power exceeds the limit:

[0097] The allocated power of a single device:

[0098] α i : The power allocation coefficient of a single device

[0099] For example: The total power instruction is 350 MW, and the allocation coefficient of one of the wind turbines is 0.008, then the calculated instruction allocated to this wind turbine is 2.8 MW, which exceeds its maximum power generation capacity of 2.5 MW;

[0100] The quadratic penalty function is used to calculate the penalty term of a single device:

[0101]

[0102] Among them, Penalty dev : The penalty term, Calculate the active power of the device, Maximum active power of the device, λ: Penalty coefficient, preset to 10 6 。

[0103] For example: If the above wind turbine exceeds the limit, Penalty term = (2.8 - 2.5) 2 * 10 6

[0104] Calculate the system penalty term:

[0105] Penalty total =(P alloc - P total ) 2 * λ

[0106] Where: P alloc : Actual total allocated power, P total : Target total power.

[0107] For example: If only the above single wind turbine exceeds the limit, then the system penalty term = (350 - 2.5 - 350) 2 * 10 6 。

[0108] Comprehensive fitness: Combine the weighted error term and the out - of - bounds penalty term (usually by addition) to obtain the final comprehensive fitness value. The smaller this value is, the better the allocation scheme.

[0109] The fitness function is:

[0110]

[0111] Where, Calculate the active power of the device, Maximum active power of the device, ω i : Priority weight of the current device i.

[0112] In one embodiment, for example: If a single wind turbine exceeds the limit, then

[0113] Weighted error: 1.2 * (2.8 - 2.5) = 0.36;

[0114] Total penalty term: (2.8 - 2.5) 2 * 10 6 +(350 - 2.5 - 350) 2 * 10 6 =7,250,000.09;

[0115] Comprehensive fitness: 0.36 + 7,250,000.09≈7,250,000.

[0116] S14. Execute the main optimization loop, dynamically adjust the search strategy through the convergence factor, update the whale position by combining the bubble attack strategy and the encirclement strategy, and generate an allocation plan that meets the preset constraint conditions.

[0117] Specifically, as Figure 2 shown, if the number of iterations has not reached the maximum value:

[0118] A1. Update the convergence factor a, which usually decreases linearly with the increase of the number of iterations:

[0119] a = 2.0 → global exploration, a ≈ 1.0 → balanced search, a ≈ 0 → local optimization;

[0120] A2. Update the convergence factor a to decrease it linearly with the number of iterations. Calculate the shrinking encirclement coefficient A, the shrinking encirclement coefficient C, the spiral shape control parameter l, and the distance D between the current whale position and the optimal position according to the convergence factor a and a random number, which are used to control the whale movement strategy. Specifically:

[0121] A: The coefficient that controls the movement direction and step size, determining the aggressiveness or exploratory nature of the encirclement behavior. A = 2a * r - a, where r is a random number in [0, 1].

[0122] C: The coefficient that amplifies randomness, used to enhance the exploration ability of the solution space. C = 2 * r, where r is a random number in [0, 1].

[0123] A3. Select the behavior pattern and decide which main predation behavior pattern the current whale adopts according to the value of the parameter p (for example, compare with 0.5).

[0124] A4. Bubble attack strategy (p ≥ 0.5), use the bubble net attack strategy to update the whale position. (X new = D * e bl * cos(2πl) + X best , where the distance D = X best - X current , and l is a random number in [-1, 1]). This strategy can quickly lock in the new optimal area when the device power demand changes suddenly, guide the whale individuals to approach the current optimal solution through a spiral path, and balance local development and global exploration.

[0125] Among them, X new : The new position of the whale individual updated through the spiral path in this iteration; D: The distance between the current whale position and the optimal position, determining the initial radius of the spiral. The larger D is, the farther the current individual is from the optimal solution, and the wider the spiral search range; b: The spiral shape constant, controlling the tightness of the spiral, preset to 1; l: The random parameter, determining the phase and direction of the spiral path, to prevent the algorithm from falling into local optimum; X best: The position of the current optimal solution, i.e., the position of the target prey, which guides the whales to gather in the currently known optimal area; X current : The position of the current whale individual.

[0126] A5, Encircle the prey (p < 0.5), when |A| < 1, shrink the encirclement towards the current optimal solution, X new = X best - A * |C * X best - X current |, when |A| ≥ 1, randomly select other whale individuals as references to expand the exploration range, X new = X rand - A * |C * X rand - X current |, expand the search range to achieve a balance between global exploration and local exploitation through random selection and parameter adjustment. Among them, X rand : The position of the randomly selected whale individual, which is used as a reference point to guide the movement of the current individual, any point within the solution space.

[0127] A6, Update the position, update the new position of each whale, i.e., the new combination of power distribution coefficients.

[0128] A7, Constraint handling, check whether the newly generated whale positions (distribution coefficients) are all within the interval [0, 1], and correct the solutions that violate the constraints.

[0129] A8, Normalization coefficient, ensure that the sum of the coefficients is 1.

[0130] A9, Calculate the new fitness.

[0131] A10, Calculate the comprehensive fitness: Combine the weighted error sum and the out-of-bounds penalty term to obtain the final comprehensive fitness value. The smaller this value is, the better the allocation scheme.

[0132] A11, Update the optimal solution, normalize the optimal solution vector to the allocation ratio with a sum of 1:

[0133] Calculate the sum of the coefficients as X i For calculating the allocation coefficient of the i-th device;

[0134] Normalization process:

[0135] S15, Generate the specific power command values for each device according to the allocation coefficient and the total power command to control the active power of each device.

[0136] In the embodiments of the present invention, by taking into account the volatility of new energy power generation (wind power and photovoltaic power) and the changes in the charge and discharge states of energy storage devices, the power command generation needs to have the ability to dynamically adjust, ensuring that the output of each device conforms to the overall optimized distribution plan and achieving power balance at the system level. Through precise power command control, the potential of each device can be fully exploited, reducing the phenomena of wind curtailment and light curtailment, and improving the new energy consumption capacity. For example, when the light is sufficient but the grid load is low, the charging power of the energy storage device is reasonably allocated to store the excess photovoltaic power and release it during the peak power consumption period, realizing the time translation and efficient utilization of energy. At the same time, it helps to maintain the stability of the grid frequency and voltage, and reduce the impact of new energy access on the grid. The energy storage device can quickly respond to grid fluctuations according to the power command, perform active power regulation and reactive power compensation, improve the dynamic stability and power quality of the grid, optimize the charge and discharge periods of the energy storage device, reduce the electricity cost, and improve the economic benefits.

[0137] The active power control method for wind-solar-storage based on the whale optimization algorithm provided by the embodiments of the present invention continuously optimizes and iterates the distribution coefficients according to the real-time states of each wind-solar-storage power station, dynamically distributes the target power of each power station, and improves the wind-solar power generation efficiency. At the same time, as the robustness of the control of the power station group increases over time, the new energy discovery benefit of the source-network-load-storage integrated power station group can be maximized, the economic benefits of the power station can be improved, and the constraint conditions and control parameters can be increased or decreased according to the actual engineering application, enhancing the generality of the control strategy.

[0138] This embodiment provides a system for an active power control method for wind-solar-storage based on the whale optimization algorithm, as Figure 3 shown, including:

[0139] A dynamic priority acquisition module 301, configured to acquire the device parameters of each device in the wind power, photovoltaic, and energy storage power stations, and generate the dynamic priority of each device in combination with the real-time operating state;

[0140] An initialization module 302, configured to generate an initial whale population as multiple power distribution schemes, each whale representing a set of power distribution coefficients, and the sum of the distribution coefficients is 1;

[0141] A fitness function definition module 303, configured to construct a fitness function based on the weighted error term determined by the dynamic priority, the dual penalty terms of the device and the system, and use the fitness value calculated by the fitness function to evaluate the optimization effect of each whale;

[0142] A main optimization loop module 304, dynamically adjusts the search strategy through a convergence factor, updates the whale position in combination with the bubble attack strategy and the encirclement strategy, generates a distribution scheme that meets the preset constraint conditions, normalizes the optimal whale position, and outputs the power distribution coefficients of each device in the wind power, photovoltaic, and energy storage power stations;

[0143] The active power control module 305 is used to generate specific power command values for each device according to the distribution coefficient and the total power command to control the active power of each device.

[0144] In some optional embodiments, the dynamic priority acquisition module 301 includes:

[0145] The initialization parameter acquisition unit is used to acquire the maximum active power, real-time active power, real-time operating status, and rated power of each device type in the wind power, photovoltaic, and energy storage power stations;

[0146] The dynamic priority generation unit is used to set the ramp rate limit and the initial allocation priority weight for each device, and generate the dynamic priority in combination with the real-time operating status;

[0147] The data cleaning unit is used to verify the data validity of the real-time data and predicted data of the wind power, photovoltaic, and energy storage power stations using the rated power, and perform data cleaning to remove row outliers.

[0148] In some optional embodiments, the dynamic priority generation unit includes:

[0149] The initial value setting subunit is used to set the maximum ramp rate, minimum ramp rate of each device type, and initialize the priority weight of each device;

[0150] The real-time weight correction subunit is used to dynamically correct the real-time weight of each device according to the real-time average ramp rate, maximum ramp rate, and minimum ramp rate of the same type of device:

[0151]

[0152] where, ω i : The current device priority weight, R i : The available ramp rate of the current device, R avg : The average ramp rate of the same type of device, R max : The maximum ramp rate of the same type of device, R min : The minimum ramp rate of the same type of device, η i : The device availability;

[0153] The weight normalization subunit is used to calculate the total weight of each device, and normalize the weights of all devices to generate the dynamic priority of each device.

[0154] In some optional embodiments, the fitness function definition module 303 includes:

[0155] The weighted error term calculation unit is used to calculate the error between the allocated power and the preset expected target, and weight the error according to the dynamic priority to obtain the weighted error term;

[0156] The penalty term calculation unit is used to check whether the calculated allocated power exceeds the single-device power constraint and / or the system power constraint, and calculate the single-device penalty term and / or the system penalty term if there is an over-limit situation;

[0157] The fitness function unit is used to add the weighted error term and the over-limit penalty term to obtain the fitness function.

[0158] In some optional embodiments, the allocated power of a single device:

[0159] Among them, α i : the power allocation coefficient of a single device, P total : the target total power;

[0160] The calculation formula for the single-device penalty term is:

[0161]

[0162] Among them, Penalty dev : the single-device penalty term, Calculate the power of device i, The maximum power of the device, λ: the penalty coefficient;

[0163] The calculation formula for the system penalty term is:

[0164] Penalty total =(P alloc -P total ) 2 *λ

[0165] Among them, P alloc : the actual allocated total power, P total : the target total power;

[0166] The fitness function is:

[0167]

[0168] Among them, Calculate the active power of the device, The maximum active power of the device, ω i : the priority weight of the current device i

[0169] In some optional embodiments, the main optimization loop module 304 includes: when the number of iterations does not reach the preset maximum value, execute the following steps:

[0170] Update the convergence factor a to decrease it linearly with the number of iterations. Calculate the contraction and enclosure coefficients A, the contraction and enclosure coefficient C, the spiral shape control parameter l, and the distance D between the current whale position and the optimal position according to the convergence factor a and a random number, which are used to control the whale movement strategy;

[0171] Select the behavior mode according to the comparison result between the spiral shape constant p and the preset value n:

[0172] If p ≥ n, update the whale position using the bubble net attack strategy and approach the current optimal solution along a spiral path;

[0173] If p < n, execute the prey enclosure strategy. When |A| < 1, contract the enclosure towards the current optimal solution. When |A| ≥, randomly select other whale individuals as references to expand the exploration range;

[0174] Update the whale position, generate a new combination of power distribution coefficients, and perform constraint processing on the new position to ensure that the power distribution coefficients are within the range of [0, 1] and normalize the coefficients so that their sum is 1;

[0175] Calculate the new fitness, update the global optimal solution according to the new fitness, and normalize the optimal solution vector to an allocation ratio with a sum of 1;

[0176] When the number of iterations does not reach the preset maximum value, based on the final result of the main optimization loop.

[0177] The further function descriptions of the above-mentioned various modules and units are the same as those in the corresponding embodiments above, and will not be elaborated here.

[0178] The system of the active power control method for wind-solar-storage based on the whale optimization algorithm in this embodiment is presented in the form of functional units. Here, the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and a memory that execute one or more software or fixed programs, and / or other devices that can provide the above functions.

[0179] This embodiment of the present invention also provides a computer device having the above Figure 3 shown system of the active power control method for wind-solar-storage based on the whale optimization algorithm.

[0180] Please refer to Figure 4 , Figure 4 which is the structural schematic diagram of the computer device provided by an alternative embodiment of the present invention. As Figure 4As shown, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting the components, including a high-speed interface and a low-speed interface. Each component communicates with each other using different buses and can be installed on a common motherboard or installed in other ways as needed. The processor can process instructions executed within the computer device, including instructions stored in the memory or on the memory to display graphical information of the GUI on an external input / output device (such as a display device coupled to the interface). In some alternative embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (for example, as a server array, a set of blade servers, or a multi-processor system). Figure 4 In [the figure], a processor 10 is taken as an example.

[0181] The processor 10 can be a central processing unit, a network processor, or a combination thereof. Among them, the processor 10 can further include a hardware chip. The above-mentioned hardware chip can be an application-specific integrated circuit, a programmable logic device, or a combination thereof. The above-mentioned programmable logic device can be a complex programmable logic device, a field-programmable gate array, a generic array logic, or any combination thereof.

[0182] Among them, the memory 20 stores instructions executable by at least one processor 10, so that at least one processor 10 executes the method shown in the above embodiments.

[0183] The memory 20 can include a program storage area and a data storage area. Among them, the program storage area can store an operating system and application programs required for at least one function; the data storage area can store data created according to the use of the computer device, etc. In addition, the memory 20 can include a high-speed random access memory, and can also include a non-transitory memory, such as at least one disk storage device, a flash memory device, or other non-transitory solid-state storage devices. In some alternative embodiments, the memory 20 can optionally include a memory remotely set relative to the processor 10, and these remote memories can be connected to the computer device through a network. Examples of the above-mentioned network include but are not limited to the Internet, an enterprise intranet, a local area network, a mobile communication network, and combinations thereof.

[0184] The memory 20 can include a volatile memory, such as a random access memory; the memory can also include a non-volatile memory, such as a flash memory, a hard disk, or a solid-state drive; the memory 20 can also include a combination of the above types of memories.

[0185] The computer device further includes a communication interface 30 for the computer device to communicate with other devices or communication networks.

[0186] Embodiments of the present invention also provide a computer-readable storage medium. The method according to the embodiments of the present invention can be implemented in hardware, firmware, or can be implemented as computer code recorded on a storage medium, or can be implemented as computer code originally stored in a remote storage medium or a non-transitory machine-readable storage medium and to be downloaded through a network and stored in a local storage medium, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only memory, a random access memory, a flash memory, a hard disk, or a solid-state drive, etc.; further, the storage medium can also include a combination of the above types of memories. It can be understood that a computer, a processor, a microprocessor central control system, or programmable hardware includes a storage component that can store or receive software or computer code, and when the software or computer code is accessed and executed by the computer, the processor, or the hardware, the method shown in the above embodiments is implemented.

[0187] A part of the present invention can be applied as a computer program product, for example, computer program instructions, when executed by a computer, can call or provide the method and / or technical solution according to the present invention through the operation of the computer. Those skilled in the art should be able to understand that the forms of existence of computer program instructions in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways in which computer program instructions are executed by a computer include, but are not limited to: the computer directly executes the instruction, or the computer compiles the instruction and then executes the corresponding compiled program, or the computer reads and executes the instruction, or the computer reads and installs the instruction and then executes the corresponding installed program. Herein, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible by the computer.

[0188] Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A method for controlling the active power of wind-solar-storage based on the whale optimization algorithm, characterized in that, Including: Obtain the device parameters of each device in wind power, photovoltaic, and energy storage power stations, and generate the dynamic priority of each device in combination with the real-time operating status; Generate an initial whale population as multiple power distribution schemes, where each whale represents a set of power distribution coefficients, and the sum of the distribution coefficients is 1; Construct a fitness function based on the weighted error term determined by the dynamic priority, and the double penalty terms of the device and the system. The fitness value calculated by the fitness function is used to evaluate the optimization effect of each whale; Execute the main optimization loop, dynamically adjust the search strategy through the convergence factor, update the whale position by combining the bubble attack strategy and the encirclement strategy, generate a distribution scheme that meets the preset constraint conditions, normalize the optimal whale position, and output the power distribution coefficients of each device in the wind power, photovoltaic, and energy storage power stations; Generate specific power command values for each device according to the distribution coefficient and the total power command to control the active power of each device.

2. The method according to claim 1, wherein The obtaining of the device parameters of each device in the wind power, photovoltaic, and energy storage power stations and generating the dynamic priority of each device in combination with the real-time operating status includes: Obtain the maximum active power, real-time active power, real-time operating status, and rated power of each device type in the wind power, photovoltaic, and energy storage power stations; Set the ramp rate limit and the initial distribution priority weight for each device, and generate the dynamic priority in combination with the real-time operating status; Perform data validity verification on the real-time data and predicted data of the wind power, photovoltaic, and energy storage power stations using the rated power, and perform data cleaning to remove row outliers.

3. The method according to claim 2, wherein The setting of the ramp rate limit and the initial distribution priority weight for each device and generating the dynamic priority in combination with the real-time operating status includes: Set the maximum ramp rate value, the minimum ramp rate value of each device type, and initialize the priority weight of each device; Dynamically correct the real-time weight of each device according to the real-time average ramp rate, the maximum ramp rate value, and the minimum ramp rate value of the same type of device; Among them, ω i : The priority weight of the current device, R i : The available ramp rate of the current device, R avg : The average ramp rate of similar devices, R max : The maximum ramp rate of similar devices, R min : The minimum ramp rate of similar devices, η i : The availability of the device; Calculate the total weight of each device, and normalize the weights of all devices to generate the dynamic priority of each device.

4. The method according to claim 1, characterized in that The constructing of the fitness function based on the weighted error term determined by the dynamic priority and the double penalty terms of the device and the system includes: Calculate the error between the allocated power and the preset expected target, and weight the error according to the dynamic priority to obtain the weighted error term; Check whether the calculated allocated power exceeds the single-device power constraint and / or the system power constraint. If there is an out-of-bounds situation, calculate the single-device penalty term and / or the system penalty term; Add the weighted error term and the out-of-bounds penalty term to obtain the fitness function.

5. According to the method described in claim 4, wherein Allocated power of a single device: Among them, α i : Power distribution coefficient of a single device, P total : Total power; The calculation formula of the single-device penalty term is: Among them, Penalty dev : Penalty term for a single device Calculate the power of device i Maximum power of the device, λ: Penalty coefficient The calculation formula of the system penalty term is: Penalty total = (P alloc - P total ) 2 * λ Among them, P alloc : actual total allocated power, P total : target total power; The fitness function is: Among them, Calculate the active power of the device, The maximum active power of the device, ω i : The priority weight of the current device i.

6. The method according to claim 1 or 4, characterized in that, The execution of the main optimization loop, dynamically adjusting the search strategy through the convergence factor, and updating the whale position by combining the bubble attack strategy and the encirclement strategy to generate a distribution scheme that meets the preset constraint conditions includes: When the number of iterations does not reach the preset maximum value, execute the following steps: Update the convergence factor a to decrease it linearly with the number of iterations. Calculate the shrinking bounding coefficient A, the shrinking bounding coefficient C, the spiral shape control parameter l, and the distance D between the current whale position and the optimal position according to the convergence factor a and the random number, which are used to control the whale movement strategy; Select the behavior mode according to the comparison result between the spiral shape constant p and the preset value n: If p≥n, update the whale position using the bubble net attack strategy and approach the current optimal solution along a spiral path; If p < n, execute the strategy of surrounding the prey. When |A| < 1, shrink the surrounding circle towards the current optimal solution. When |A|≥1, randomly select other whale individuals as references to expand the exploration range; Update the whale position, generate a new combination of power distribution coefficients, and perform constraint processing on the new position to ensure that the power distribution coefficients are within the range of [0,1] and normalize the coefficients so that their sum is 1; Calculate the new fitness, update the global optimal solution according to the new fitness, and normalize the optimal solution vector to an allocation ratio with a sum of 1; When the number of iterations does not reach the preset maximum value, based on the final result of the main optimization loop.

7. A wind-solar-storage active power control system based on the whale optimization algorithm, characterized in that, It includes: A dynamic priority acquisition module for acquiring the device parameters of each device in the wind power, photovoltaic, and energy storage power stations and generating the dynamic priority of each device in combination with the real-time operating state; An initialization module for generating an initial whale population as multiple power distribution schemes, where each whale represents a set of power distribution coefficients and the sum of the distribution coefficients is 1; A fitness function definition module for constructing a fitness function based on the weighted error term determined by the dynamic priority and the double penalty terms of the device and the system. The fitness value calculated by the fitness function is used to evaluate the optimization effect of each whale; A main optimization loop module for dynamically adjusting the search strategy through the convergence factor, updating the whale position by combining the bubble attack strategy and the surrounding strategy, generating an allocation scheme that meets the preset constraint conditions, normalizing the optimal whale position, and outputting the power distribution coefficients of each device in the wind power, photovoltaic, and energy storage power stations; An active power control module for generating specific power command values for each device according to the distribution coefficients and the total power command to control the active power of each device.

8. A computer device, characterized in that, It includes: A memory and a processor, which are communicatively connected to each other. The memory stores computer instructions, and the processor executes the computer instructions to execute the active power control method for wind-solar-storage based on the whale optimization algorithm according to any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, Computer instructions are stored on the computer-readable storage medium, and the computer instructions are used to cause the computer to execute the active power control method for wind-solar-storage based on the whale optimization algorithm according to any one of claims 1-6.

10. A computer program product, characterized in that, It includes computer instructions, and the computer instructions are used to cause the computer to execute the active power control method for wind-solar-storage based on the whale optimization algorithm according to any one of claims 1-6.

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