Energy flow-carbon flow joint optimization scheduling method for new energy power system

Abstract

The invention discloses an energy flow-carbon flow joint optimization scheduling method for a new energy power system, and relates to the technical field of new energy scheduling, and the method comprises the steps: connecting the new energy power system, and building a peak load period scene, a low-carbon emission period scene, and an energy storage high-efficiency period scene; establishing priority weights of energy flow, carbon flow and energy storage indexes, and configuring an iteration scheme; configuring a fitness function; executing population initialization; executing probability selection of individuals in the population; generating a crossover constraint factor and a mutation constraint factor based on an iteration scheme, reconstructing a crossover rate and a mutation rate, performing mutation operation, and generating a mutation result; and generating a joint optimization scheduling scheme. The technical problems of low scheduling precision and energy storage efficiency and insufficient carbon emission control of the new energy power system in different scenes in the prior art are solved, and the technical effect of improving the scheduling precision, carbon emission control and energy storage efficiency of the new energy power system in different scenes is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of new energy scheduling, and in particular to an energy flow-carbon flow joint optimization scheduling method for a new energy power system. Background Art

[0002] In the new energy power system, due to the volatility and uncontrollability of renewable energy such as wind power and solar energy, the system scheduling optimization faces many challenges. Traditional power scheduling methods often find it difficult to balance the multiple goals of energy flow, carbon emissions and energy storage, resulting in low system operation efficiency, unsatisfactory carbon emission control effects, and insufficient utilization of energy storage equipment. In addition, the complexity and real-time nature of the new energy power system require flexible adjustment of scheduling plans in different scenarios such as peak load periods, low carbon emission periods, and energy storage high efficiency periods, which puts higher demands on existing scheduling algorithms.

[0003] Existing technologies have technical problems such as low scheduling accuracy and energy storage efficiency of new energy power systems in different scenarios, and insufficient carbon emission control. Summary of the invention

[0004] The present application provides an energy flow-carbon flow joint optimization scheduling method for a new energy power system, which is used to solve the technical problems in the prior art of low scheduling accuracy and energy storage efficiency and insufficient carbon emission control of new energy power systems in different scenarios.

[0005] In view of the above problems, the present application provides an energy flow-carbon flow joint optimization scheduling method for a new energy power system, the method comprising: connecting the new energy power system, dividing the system scenario according to the historical working data of the new energy power system, and establishing a peak load period scenario, a low carbon emission period scenario, and an energy storage high efficiency period scenario; establishing priority weights of energy flow, carbon flow, and energy storage indicators based on the system scenario division results, and configuring an iterative scheme mapped to the system scenario division results; configuring a fitness function, the fitness function is constructed according to the priority weights; performing population initialization, the population includes N individuals, each individual represents a candidate solutions; activating the fitness function through the scene attribution of the current task to calculate the individual fitness values ​​in the population, configuring the individual selection probability based on the individual fitness values, and executing the probabilistic selection of individuals in the population; generating a crossover constraint factor and a mutation constraint factor based on the iterative scheme, and reconstructing the crossover rate and the mutation rate according to the iterative stage, performing data crossover of the individuals in the population through the reconstructed crossover rate, and performing mutation operations on the offspring individuals generated by the crossover at the mutation rate to generate mutation results, performing individual fitness elimination on the mutation results and the initialized population, and executing iterative updates; generating a joint optimization scheduling scheme according to the iterative update results.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0007] Connect the new energy power system, establish the peak load period scenario, low carbon emission period scenario, and energy storage high efficiency period scenario; establish the priority weights of energy flow, carbon flow, and energy storage indicators based on the system scenario division results, and configure the iterative scheme mapped with the system scenario division results; configure the fitness function, which is constructed according to the priority weight; perform population initialization; activate the fitness function through the scenario attribution of the current task to calculate the individual fitness value in the population, and perform the probability selection of individuals in the population; generate cross constraint factors and mutation constraint factors based on the iterative scheme, perform data crossover of the population individuals through the reconstructed crossover rate, generate mutation results, eliminate the individual fitness of the mutation results and the initialized population, perform iterative updates; generate a joint optimization scheduling scheme. The technical effect of improving the scheduling accuracy, carbon emission control, and energy storage efficiency of the new energy power system in different scenarios has been achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0009] Figure 1 A schematic diagram of a process flow of an energy flow-carbon flow joint optimization scheduling method for a new energy power system provided in an embodiment of the present application;

[0010] Figure 2 A schematic diagram of a process for reconstructing the crossover rate and the mutation rate in the energy flow-carbon flow joint optimization scheduling method for a new energy power system provided in an embodiment of the present application. DETAILED DESCRIPTION

[0011] The present application provides an energy flow-carbon flow joint optimization scheduling method for a new energy power system, which is used to solve the technical problems in the prior art of low scheduling accuracy and energy storage efficiency and insufficient carbon emission control of new energy power systems in different scenarios.

[0012] The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0013] Examples, such as Figure 1 As shown, the present application provides an energy flow-carbon flow joint optimization scheduling method for a new energy power system, the method comprising:

[0014] Step S100: Connect the new energy power system, divide the system scenarios according to the historical working data of the new energy power system, and establish peak load period scenarios, low carbon emission period scenarios, and energy storage high efficiency period scenarios.

[0015] Specifically, it is first necessary to connect to the new energy power system and establish an effective connection with the new energy power system so as to obtain its relevant data and status information.

[0016] The system scenarios are divided according to the historical working data of the new energy power system. The historical working data includes the operation status of the system in different time periods, such as power load, energy supply, carbon emission level and the use of energy storage equipment. By analyzing these data, different operation modes and characteristics can be identified.

[0017] The establishment of peak load period scenarios is mainly based on the time periods with higher electricity demand in historical data. During these time periods, there is greater power supply pressure, and it is necessary to focus on how to meet the peak load demand while minimizing operating costs and ensuring system stability.

[0018] The determination of the low-carbon emission period scenario is based on periods of lower carbon emission levels in historical data. In this scenario, environmental protection requirements are higher, and it is necessary to focus on reducing the total amount of carbon emissions and reducing carbon emission costs. This involves giving priority to the use of clean energy for power generation, improving energy utilization efficiency, and taking effective carbon emission reduction measures.

[0019] The energy storage high efficiency period scenario is established based on the period in historical data when energy storage equipment can play the greatest role. During these periods, the charging and discharging efficiency of energy storage equipment is high, which can effectively balance the supply and demand of electricity and improve the reliability and flexibility of the system. For example, when the power supply is sufficient, excess electricity can be stored and the stored energy can be released during peak load, thereby achieving optimal utilization of energy.

[0020] By analyzing the historical working data and dividing the scenarios of the new energy power system, we can better understand the operating laws and characteristics of the system, and provide a more accurate and targeted basis for subsequent optimization and scheduling.

[0021] Step S200: Establish priority weights of energy flow, carbon flow, and energy storage indicators based on the system scenario division results, and configure an iterative scheme mapped to the system scenario division results.

[0022] Specifically, based on the system scenario results divided earlier, we began to establish the priority weights of energy flow, carbon flow, and energy storage indicators. First of all, the importance of these indicators will vary for different scenarios. In the peak load period scenario, since the demand for electricity is large at this time, it is crucial to ensure a stable supply of energy, so the energy flow indicator will be given a higher priority weight. At the same time, in order to avoid excessive costs and excessive carbon emissions when meeting peak loads, carbon flow and energy storage indicators also need to be given some consideration, but the weights may be slightly lower than energy flow.

[0023] In the low-carbon emission period, reducing carbon emissions becomes the primary goal, and the priority weight of carbon flow indicators will be significantly increased. At this time, the system will pay more attention to selecting clean energy and taking carbon emission reduction measures to reduce the total amount of carbon emissions and costs. Energy flow and energy storage indicators are still important, but they will be optimized under the premise of meeting low carbon emissions.

[0024] In the energy storage high efficiency period scenario, the priority weight of energy storage indicators will increase, making full use of the efficient charging and discharging characteristics of energy storage equipment, balancing power supply and demand, and improving the flexibility and reliability of the system. Energy flow and carbon flow indicators will also be coordinated on the basis of energy storage to achieve the optimized operation of the overall system.

[0025] At the same time, it is also necessary to configure an iteration scheme that is mapped to the system scenario division results. For different scenarios, the iteration scheme will be adjusted according to their characteristics and goals. For example, in the peak load period scenario, the iteration scheme will pay more attention to quickly finding a feasible solution to meet the immediate power demand. A larger step size and a higher mutation rate will be used to increase the breadth and speed of the search. In the low-carbon emission period scenario, the iteration process will be more refined to ensure that the optimal carbon reduction strategy is found, the mutation rate will be reduced, and the frequency of cross-operations will be increased to make full use of the existing excellent solutions for optimization. In the energy storage high-efficiency period scenario, the iteration scheme will focus on exploring the optimal use strategy of energy storage equipment, and will adjust the parameters according to the characteristics of the energy storage equipment to achieve efficient storage and release of energy.

[0026] By establishing priority weights and configuring iteration schemes, the optimization scheduling process can be made more targeted and efficient, and better adapted to the needs of different system scenarios.

[0027] Step S300: configuring a fitness function, wherein the fitness function is constructed according to priority weights.

[0028] Specifically, the configuration of the fitness function is to evaluate the pros and cons of various possible dispatching schemes in the new energy power system. This fitness function is constructed based on the priority weights of energy flow, carbon flow and energy storage indicators in different scenarios. For example, in the peak load period scenario, the energy flow indicator has a higher priority weight, and the fitness function will focus more on evaluating the performance of the scheme in meeting power demand and ensuring system stability. In the low-carbon emission period scenario, the weight of the carbon flow indicator will increase, and the fitness function will focus on the contribution of the scheme to reducing carbon emissions. In the energy storage high-efficiency period scenario, the energy storage indicator becomes the key, and the fitness function will measure the effect of the scheme in improving the utilization efficiency of energy storage equipment. By comprehensively considering the priority weights of these different indicators, the constructed fitness function can accurately reflect the adaptability of the dispatching scheme in a specific scenario, and provide a powerful evaluation tool for finding the optimal joint optimization dispatching scheme.

[0029] Step S400: Execute population initialization, the population includes N individuals, each individual represents a candidate solution.

[0030] Specifically, a population initialization operation is performed. First, the size of the population is determined to be N, which means that a set of N individuals will be created. Each individual represents a candidate solution in the joint optimization scheduling problem of the new energy power system. These candidate solutions are a specific arrangement of energy distribution, equipment operation status, energy storage strategy, etc. For example, an individual represents the output power distribution scheme of each power generation equipment in a specific time period, the charging and discharging strategy of the energy storage facility, and the load distribution of the energy transmission line. In order to initialize the population, a random generation method is used to randomly generate N different individuals in a given problem space. This ensures that the population has a certain diversity in the initial stage, thereby providing a broader search space for the subsequent evolution process. For example, for a new energy power system containing multiple wind turbines, solar panels, energy storage devices and power loads, an individual is a set of specific wind turbine output power values, solar panel tilt angles, energy storage device charging and discharging power, and energy distribution ratios of each power load. By randomly generating N such individuals, an initial population is established, which provides a basis for subsequent iterations of optimization methods such as genetic algorithms. Population initialization is an important step in the joint optimization dispatching process of renewable energy power systems, laying the foundation for finding the optimal solution.

[0031] Step S500: activating the fitness function through the scene attribution of the current task to calculate the individual fitness values ​​in the population, configuring the individual selection probability based on the individual fitness values, and executing the probabilistic selection of individuals in the population.

[0032] Specifically, the fitness function is activated according to the scenario of the current task. If the current scenario is a peak load period, the fitness function will be calculated based on the specific priority weights of energy flow, carbon flow and energy storage indicators in the scenario. If it is a low-carbon emission period scenario or a high-efficiency energy storage period scenario, the fitness function will also be activated according to the weights of the respective scenarios.

[0033] Next, the fitness value of each individual in the population is calculated using the activated fitness function. This fitness value reflects the pros and cons of the individual as a scheduling scheme for the new energy power system in the current scenario. For example, during the peak load period, if an individual can effectively meet the high load demand while maintaining low costs and reasonable carbon emissions, then its fitness value will be relatively high. Based on the calculated individual fitness value, the individual selection probability is configured. The higher the fitness value, the greater the probability of being selected. This is because individuals with high fitness are more likely to represent better scheduling schemes, and should have a greater chance of being retained and further optimized in the subsequent evolution process. Finally, the probabilistic selection of individuals in the population is performed. Through random selection, the probability of selection is determined according to the selection probability of the individual. This ensures that individuals with high fitness are more likely to be selected, and individuals with low fitness also have a certain chance of being retained to maintain the diversity of the population. Through such steps, more potential individuals can be selected according to the scenario of the current task, providing a better foundation for subsequent evolutionary operations, and gradually approaching the optimal joint optimization scheduling scheme.

[0034] Step S600: Generate a crossover constraint factor and a mutation constraint factor based on the iterative scheme, reconstruct the crossover rate and the mutation rate according to the iterative stage, perform data crossover of individuals in the population through the reconstructed crossover rate, and perform mutation operation on the offspring individuals generated by the crossover at the mutation rate to generate mutation results, perform individual fitness elimination on the mutation results and the initialized population, and perform iterative update.

[0035] Specifically, the generation of crossover constraint factors and mutation constraint factors is to ensure that during the crossover and mutation operations of the genetic algorithm, the newly generated individuals still meet the various constraints of the new energy power system. For example, in the new energy power system, there are output power limits for power generation equipment, capacity limits for energy storage equipment, load limits for energy transmission lines, etc. The crossover constraint factor adjusts the new individuals that exceed the constraint range during the crossover operation. For example, if after two parent individuals are crossed, the output power of a power generation device in the offspring individual exceeds its maximum allowed value, the crossover constraint factor can adjust the power value to the maximum value. Similarly, the mutation constraint factor plays a similar role in the mutation operation, ensuring that the mutated individuals do not violate the system constraints.

[0036] As the genetic algorithm iterates, the crossover rate and mutation rate need to be dynamically adjusted according to the iteration stage. In the early stages of the genetic algorithm, in order to increase the diversity of the population, a higher mutation rate and an appropriate crossover rate are usually set. This allows the genetic algorithm to explore more extensively in the search space. As the iterations progress, the mutation rate is gradually reduced and the crossover rate is increased. Because the population already has a certain degree of diversity at this time, more local search and optimization of excellent individuals are needed.

[0037] Crossover is an important way to generate new individuals in genetic algorithms. Common crossover methods include single-point crossover, two-point crossover, uniform crossover, etc. Taking single-point crossover as an example, two individuals are randomly selected from the population as parent individuals, and then a crossover point is randomly selected to exchange the parts of the two parent individuals after the crossover point to generate two child individuals. The reconstructed crossover rate is used to determine whether to perform a crossover operation. Generate a random number between 0 and 1. If this random number is less than the crossover, a crossover operation is performed; otherwise, no crossover is performed.

[0038] After the offspring individuals are generated by crossover, mutation operations are performed on these offspring individuals at the mutation rate. Mutation operations can introduce new gene combinations and increase the diversity of the population. For example, a gene value in the offspring individual is randomly perturbed, or a gene position is randomly selected for change. Similarly, a random number between 0 and 1 is generated. If this random number is less than the mutation rate, the offspring individual is mutated; otherwise, no mutation is performed.

[0039] The mutation results generated by the mutation operation are merged with the initialized population, and then the fitness value of each individual is calculated according to the fitness function. The fitness function is usually constructed according to the optimization objectives of the new energy power system, such as minimizing energy costs, minimizing carbon emissions, maximizing energy supply reliability, etc. Elimination operations are performed according to the individual fitness values. A part of the individuals with higher fitness values ​​can be selected to be retained to form a new population and enter the next round of iterations, which can ensure that the population continues to evolve in a better direction. By repeating the above steps, the genetic algorithm gradually searches for a better solution in the joint optimization scheduling problem of the new energy power system. As the iteration proceeds, the individuals in the population are more and more adapted to the requirements of the problem, and it is expected that a joint optimization scheduling solution that meets various constraints and has the best optimization objectives will be found.

[0040] Step S700: Generate a joint optimization scheduling solution according to the iterative update result.

[0041] Specifically, a joint optimization scheduling scheme is generated based on the results of iterative updates. After a series of previous steps, the population gradually evolves in the continuous iteration process, and the candidate solutions represented by each individual are also continuously optimized.

[0042] First, analyze the population after iterative update. The individuals in this population have undergone operations such as crossover, mutation, and fitness elimination, and have greatly improved their fitness. At this time, select the individual with the highest fitness or multiple individuals with high fitness from the population for further analysis. The candidate solutions represented by these individuals with high fitness contain a combination of various parameters in the new energy power system, such as the power generation ratio of different energy sources, the charging and discharging strategy of energy storage equipment, and the operating parameters of the power grid. According to these parameters, a specific joint optimization scheduling scheme can be constructed. For example, if an individual has a high fitness in the peak load period scenario, then the corresponding scheduling scheme is to give priority to calling efficient power generation resources to meet the load demand, and at the same time reasonably arrange the energy storage equipment to discharge at the peak load. In the low-carbon emission period scenario, the scheme represented by individuals with high fitness is to give priority to the use of clean energy for power generation and reduce the total carbon emissions and costs. In the energy storage high-efficiency period scenario, the scheduling scheme focuses on making full use of the efficient charging and discharging characteristics of energy storage equipment to balance the supply and demand of electricity.

[0043] The optimization results under different scenarios are comprehensively considered and integrated to form a comprehensive joint optimization dispatching scheme. This scheme can achieve the goals of efficient operation of new energy power systems, reduce carbon emissions, and improve the utilization efficiency of energy storage equipment under different scenarios, thereby providing a scientific and reasonable decision-making basis for the optimization dispatching of new energy power systems.

[0044] In a possible manner, step S300 further includes:

[0045] The objective function of the fitness function includes an operation cost function, an energy utilization efficiency function, a total carbon emission function, a carbon emission cost function, and an energy storage device utilization efficiency function. The fitness function is as follows:

[0046] Among them, ω1, ω2, ω3, ω4, and ω5 are the weight coefficients of operating cost, energy utilization efficiency, total carbon emissions, carbon emission cost, and energy storage equipment utilization efficiency, respectively, and ω i =λ i ·W i ,ω i is the i-th weight coefficient, λ i Represents the priority weight in the current scenario, W i is the basic weight of the corresponding target, C run (x) is the operating cost function, η energy (x) is the energy efficiency function, C carbon (x) is the total carbon emission function, is the carbon emission cost function, η storage (x) is the efficiency function of energy storage equipment.

[0047] Specifically, the fitness function of the present invention is mainly used to measure the pros and cons of the dispatching scheme of the new energy power system, and is calculated by combining multiple key factors. The fitness function consists of the following five objective functions: 1. Operation cost function C run (x): Operation cost function C run (x) is used to represent the total cost of the system during operation, including power generation cost, transmission and distribution cost, and maintenance cost. The specific form is: Among them, x i represents the operating status of the i-th device in the system, C gen (x i ) is the power generation cost, C trans (x i ) is the transmission and distribution cost, C maint (x i ) is the maintenance cost. 2. Energy efficiency function η energy (x): Energy efficiency function η energy (x) is used to indicate the utilization efficiency of system energy, which is usually related to the system's power generation efficiency and transmission efficiency. Its calculation method is: Among them, E use (x i ) is the actual power consumption of the ith device, E gen (x i ) is the power generation of the ith device. 3. Total carbon emission function C carbon (x): Total carbon emission function C carbon (x) is used to measure the total carbon emissions of the system, mainly considering the product of the carbon emission factor of each power source and the output power. The formula is: Among them, α i represents the carbon emission factor of the i-th power generation equipment, P gen (x i ) is its output power. 4. Carbon emission cost function C curboncost (x): Carbon emission cost function C curboncost (x) is used to represent the cost of the system within and outside the carbon emission quota, and its formula is: Among them, C permit is the cost within the carbon emission quota, C exceed is the penalty cost for exceeding the quota, P quota is the carbon emission quota. 5. Energy storage equipment efficiency function η storage (x): Energy storage equipment utilization efficiency function η storage (x) is used to indicate the efficiency of energy storage equipment, which is usually related to energy storage charging and discharging efficiency, loss rate, etc. The calculation formula is: Among them, E charge (x i ) and E discharge (x i ) represent the charge and discharge capacity of the energy storage device respectively.

[0048] Each objective function has a corresponding weight coefficient ω i , these weight coefficients are obtained by the scene priority weight λ i and the base weight W i Jointly determined, the formula is: ω i =λ i ·W i , where λ i Reflects the priority weight in the current scenario, W i The adjustment of the weight coefficient enables the system to flexibly adjust the focus of the optimization target according to different scenarios (such as peak load period, low carbon emission period, etc.), so as to achieve the optimal scheduling plan.

[0049] Through this fitness function, the system can balance operating costs, energy utilization efficiency, total carbon emissions, carbon emission costs and energy storage equipment utilization efficiency, thereby achieving joint optimal scheduling of energy flow and carbon flow.

[0050] In one possible approach, Figure 2 As shown, step S600 also includes:

[0051] Step S610: Divide the iteration process into stages, and configure an early iteration stage, an intermediate iteration stage, and a late iteration stage.

[0052] Step S620: Establishing an initial crossover rate and an initial mutation rate corresponding to the stage division result.

[0053] Step S630: completing the crossover rate reconstruction through the crossover constraint factor and the initial crossover rate, and completing the mutation rate reconstruction according to the mutation constraint factor and the initial mutation rate.

[0054] Specifically, it is a very important step to divide the iteration process into stages, which clearly divides the iteration process into the early iteration stage, the middle iteration stage and the late iteration stage. Such a division helps to adopt appropriate strategies to optimize the performance of the genetic algorithm according to the characteristics and requirements of different stages.

[0055] Establish the initial crossover rate and initial mutation rate corresponding to the stage division results. The settings of the initial crossover rate and initial mutation rate will affect the search ability and diversity of the genetic algorithm at different stages. In the early stage of iteration, a higher initial crossover rate and mutation rate are required to promote the diversity of the population and a wide search of the solution space. In the later stage of iteration, a lower initial crossover rate and mutation rate are required to more finely search the area near the optimal solution.

[0056] The reconstruction of the crossover rate is completed through the crossover constraint factor and the initial crossover rate. The crossover constraint factor adjusts the intensity and method of the crossover operation according to the specific problem and the requirements of the genetic algorithm. Combined with the initial crossover rate, the crossover rate can be adapted to the search requirements of the genetic algorithm at different stages. At the same time, the reconstruction of the mutation rate is completed according to the mutation constraint factor and the initial mutation rate. The mutation constraint factor can control the degree and direction of the mutation operation, and works together with the initial mutation rate to ensure that the mutation rate can effectively introduce new genes at different stages and avoid the genetic algorithm from falling into the local optimal solution.

[0057] Through such steps, the crossover rate and mutation rate can be reasonably reconstructed according to the stage division of the iterative process, thereby improving the search efficiency of the genetic algorithm at different stages and its ability to find the optimal solution.

[0058] In a possible manner, step S630 further includes:

[0059] Step S631: Establish an adaptive update network for self-update. When reconstructing the crossover rate and mutation rate, activate the adaptive update network to perform update self-check. The update self-check includes:

[0060] Step S632: read the current iteration stage, perform end approximation analysis of the current stage according to the current iteration stage, and generate a stage approximation self-check result.

[0061] Step S633: Acquire the search status of the previous iteration, perform a status self-check according to the search status, and establish a status self-check result.

[0062] Step S634: Perform adaptation analysis on the phase approximation self-check result and the state self-check result, complete the self-check update with the adaptation analysis result, and adjust the reconstructed crossover rate and mutation rate according to the self-check update result.

[0063] Specifically, an adaptive update network for self-update is established. When the crossover rate and mutation rate need to be reconstructed, the network will be activated to perform update self-checking, which includes multiple aspects of checking.

[0064] The system will first read the specific information of the current iteration stage, including the number of iterations that have been performed, the current time point, and the position in the entire iteration process. This information is crucial for judging the progress of the current stage. Then, the system will perform the end approximation analysis of the current stage based on the information of the current iteration stage read. This analysis process aims to evaluate the closeness between the current stage and the preset end condition. The system will check the diversity of the current population, the distribution of individual fitness, and the degree of optimization of the objective function. When performing the end approximation analysis, the system calculates the mean and standard deviation of the individual fitness in the population to evaluate the diversity of the population; compares the current objective function value with the historical optimal value to judge the progress of the optimization degree. Based on these analyses and calculations, the system will generate a stage approximation self-check result. This result is a specific value that indicates the closeness of the current stage to the end condition. For example, if the diversity of the population has reached a high level, the individual fitness is generally good, and the degree of optimization of the objective function is close to the expected goal, then the stage approximation self-check result will be displayed as "close to the end". On the contrary, if the diversity of the population is low, the individual fitness varies greatly, or the optimization of the objective function progresses slowly, the result may be displayed as "further iterations are still needed". By reading the current iteration stage and performing the end approximation analysis, the system is provided with important feedback on the iteration process, which helps the system make reasonable decisions, such as whether to adjust parameters such as the crossover rate and mutation rate to ensure that the iteration can proceed smoothly towards the optimal solution.

[0065] Get the search status information of the previous iteration. This search status information includes the distribution of the population, the fitness change trend of individuals, the effects of crossover and mutation operations, etc. in the previous round or previous iterations. By obtaining this information, the system can fully understand the previous search process and results. The system will perform a state self-check based on these search states. During the self-check process, the system will analyze whether the distribution of the population is reasonable and whether there is a trap of local optimal solution; whether the fitness change trend of individuals is stable, whether there is obvious improvement or stagnation; whether the crossover and mutation operations effectively promote the evolution of the population, and whether the relevant parameters need to be adjusted. Through a comprehensive analysis of these factors, the system can establish a state self-check result. This result is an evaluation of the current search state, judging whether the search is in the right direction and whether it needs to be adjusted or improved. By obtaining the search state of the previous iteration and performing a state self-check, it can provide the system with important feedback on the search process, helping the system to adjust the strategy in time, improve the search efficiency and the possibility of finding the optimal solution.

[0066] A comprehensive adaptation analysis is performed on the stage approximation self-check results and the state self-check results. This analysis process aims to deeply understand the progress and potential problems of the current iteration process and optimize the subsequent iteration strategy based on this information. The system will carefully compare the stage approximation self-check results and the state self-check results to find the correlation and mutual influence between them. For example, if the stage approximation self-check results show that the current stage is approaching the end, but the state self-check results show that the population may be trapped in a local optimal solution, then the system needs to consider how to avoid being trapped in the local optimal solution while approaching the end. Based on the results of the adaptation analysis, the system will complete the self-check update. This includes adjusting the parameters of the adaptive update network, updating the search strategy, or re-evaluating the priority. The purpose of the self-check update is to enable the system to better adapt to the current iteration state and provide more effective guidance for subsequent iterations. According to the results of the self-check update, the system will make corresponding adjustments to the reconstructed crossover rate and mutation rate. If the self-check update results show that the diversity of the population needs to be increased, the system will increase the mutation rate to introduce more new individuals and new features. On the contrary, if the system believes that the current population is diverse enough and needs to focus more on optimizing the existing solution, it will reduce the mutation rate and appropriately adjust the crossover rate to promote gene exchange and combination between excellent individuals. For example, if the results of the adaptation analysis show that the diversity of the current population is insufficient, and the results of the stage approximation self-check show that further iteration is needed, the system will greatly increase the mutation rate and moderately increase the crossover rate to encourage more exploration and innovation. If the results of the stage approximation self-check show that it is close to the end, and the results of the state self-check show that the population is relatively stable, the system will fine-tune the crossover rate and mutation rate to ensure that while converging to the optimal solution, the progress that has been made will not be destroyed due to excessive mutation. Through the adaptation analysis and corresponding adjustments of the stage approximation self-check results and the state self-check results, the crossover rate and mutation rate can be more in line with the needs of the current iteration, thereby improving the efficiency and accuracy of the genetic algorithm and making it more likely to find the optimal joint optimization scheduling solution.

[0067] In a possible manner, step S600 further includes:

[0068] Step S640: Establish a taboo retention set, which is a set of individuals whose taboo values ​​meet a preset threshold, and the taboo value is calculated by the individual fitness value and the individual offspring fitness value.

[0069] Step S650: performing sequential sorting and screening of taboo values ​​on the individuals in the taboo retention set, updating the sequential sorting results into the population, and marking them as single-round iterative retention data.

[0070] Step S660: Continue iterative updating according to the updated population.

[0071] Specifically, it is an important operation to establish a taboo retention set. The taboo retention set is a special set that contains individuals whose taboo values ​​meet the preset threshold. These individuals are considered to have certain specialness or importance and therefore need to be specially retained. The calculation of the taboo value is based on the individual fitness value and the individual offspring fitness value. The individual fitness value reflects the survival ability and advantage of the individual in the current environment, while the individual offspring fitness value takes into account the genetic potential of the individual and the impact on the offspring. By comprehensively considering these two factors, the value of the individual can be evaluated more comprehensively. The weighted summation method is used to calculate the taboo value and establish the taboo retention set, and the weights of the individual fitness value and the individual offspring fitness value are determined. These weights are set according to the needs and experience of the specific problem, and are used to represent the relative importance of the individual fitness value and the individual offspring fitness value in calculating the taboo value. Then, for each individual, its taboo value is calculated. The taboo value is obtained by multiplying the individual fitness value by its corresponding weight, and then adding the individual offspring fitness value multiplied by its corresponding weight, and finally summing them. Next, a preset threshold is set, which is used to determine whether an individual should be included in the taboo retention set. Finally, individuals whose taboo values ​​meet the preset threshold are selected into the taboo retention set. These individuals are considered to have high value or specialness and need to be specially treated or retained in subsequent iterations. In this way, the weighted sum method can comprehensively consider the individual fitness value and the individual offspring fitness value to determine the individual taboo value, thereby establishing the taboo retention set.

[0072] The individuals in the taboo retention set are further processed. First, these individuals are sorted and screened in order according to the size of the taboo value, which means that the individuals in the taboo retention set will be sorted from large to small according to their taboo values. After the sorting is completed, the results of the sequential sorting will be updated to the population. Specifically, these screened and sorted individuals will be replaced or added to the original population to update the composition of the population. At the same time, these updated individuals will be identified as single-round iteration retention data. The purpose of this identification is to clearly know that these individuals are retained in the current round of iterations in the subsequent iteration process, which is helpful for tracking and analyzing the operation and results of the genetic algorithm. Through such steps, the population can be continuously optimized, individuals with higher potential can be retained, and the possibility of the genetic algorithm finding the optimal solution can be increased.

[0073] The iterative update process is continued based on the updated population. The system uses the updated population as the basis to re-execute the previous iterative steps, including calculating individual fitness values, performing probability selection, generating crossover constraint factors and mutation constraint factors, performing data crossover and mutation operations, etc. In this process, the individuals in the updated population have different characteristics and fitness values, which will affect the subsequent iteration results. The system will evaluate the pros and cons of the individuals based on their fitness values, and select individuals with higher fitness for reproduction and crossover to produce new offspring individuals. At the same time, a certain degree of randomness is introduced through mutation operations to increase the diversity of the population and prevent the genetic algorithm from falling into a local optimal solution. As the iteration proceeds, the population will continue to evolve and optimize, gradually approaching the optimal solution. The system will continue to monitor the changes in the population and determine whether to end the iteration based on the preset termination conditions. If the termination conditions have not been met, the system will continue to repeat the above steps and continuously update the population until the optimal solution that meets the requirements is found or the maximum number of iterations is reached. By continuously iteratively updating the population, the system can gradually increase the possibility of finding the optimal solution and realize the joint optimization scheduling of the energy flow and carbon flow of the new energy power system.

[0074] In a possible manner, step S700 further includes:

[0075] Step S710: Acquire forecast task data of the new energy power system, perform scene transition analysis based on the forecast task data and the current task, and establish a scene transition interval.

[0076] Step S720: updating the priority weights linearly based on the scene transition interval.

[0077] Step S730: reconstructing the fitness function using the updated priority weights, and establishing a buffer strategy mapped to the scene transition interval using the reconstructed fitness function.

[0078] Step S740: performing joint optimization scheduling management according to the buffer strategy.

[0079] Specifically, first obtain the forecast task data of the new energy power system. These forecast task data include the forecast of energy production in the future, such as the expected power generation of renewable energy such as solar energy and wind energy, and the forecast of energy demand, such as the electricity demand in various regions in different time periods. At the same time, it also involves the forecast of equipment operation status, such as the failure probability of certain key equipment. After obtaining these forecast task data, compare and analyze them with the current ongoing tasks. Consider the progress of the current task, the completed part and the unfinished part, and combine the future trend presented by the forecast task data to conduct scenario transition analysis. For example, if the energy supply in the current task is relatively stable, but the forecast data shows that energy demand may increase significantly in the future, or the output of renewable energy may decrease due to weather changes, then it is necessary to conduct an in-depth analysis of the transition from the current relatively stable scenario to the future scenario with an imbalance between supply and demand. Through a comprehensive analysis of the forecast task data and the current task, a scenario transition interval is determined, which covers the time period of transition from the current state to the future forecast state, as well as various intermediate states that may appear during this time period. It can help the system better understand and grasp the process of scenario change, and provide an important basis for subsequent optimization and scheduling decisions.

[0080] Understanding the scenario transition interval means recognizing the process of transitioning from the current mission scenario to the future predicted mission scenario. During this process, the importance of different factors and goals will change, and the priority weights determine the relative importance of each goal in the joint optimization scheduling. As the scenario gradually transitions from the current state to the future state, some goals may be more important at the beginning, and their importance gradually decreases as the transition progresses; conversely, other goals may be less critical in the initial stage, but become critical as the future scenario approaches. To adapt to this change, the priority weights are updated in a linear change manner. This is like smoothly adjusting the weights of each goal on a timeline according to the progress of the scenario transition. Set a starting weight and a final weight, and then gradually adjust the weight values ​​in a linear proportion according to the progress of the scenario transition interval. If the stability of energy supply is the primary goal at the beginning of the scenario transition interval, the corresponding priority weight is high. But as the transition progresses, the goals of energy cost control or environmental sustainability become more important, and the priority weights of these goals will gradually increase, while the weight of energy supply stability will decrease linearly accordingly. By updating the priority weights through linear changes, the new energy power system can respond more flexibly to changes in different objectives during the scenario transition process and achieve more efficient and reasonable joint optimization scheduling management.

[0081] The fitness function is reconstructed using the updated priority weights. The fitness function plays a key role in the joint optimization dispatch of the renewable energy power system. It is used to evaluate the pros and cons of different dispatch schemes. The updated priority weights reflect the changes in the importance of different goals in the scene transition interval. Incorporating these new weights into the fitness function enables the fitness function to more accurately measure the adaptability of the dispatch scheme in different scenarios. For example, if the reliability weight of energy supply increases in the scene transition interval, the reconstructed fitness function will pay more attention to the performance of the dispatch scheme in ensuring a stable energy supply. Then, based on the reconstructed fitness function, a buffer strategy mapped to the scene transition interval is established. The purpose of this buffer strategy is to provide a smooth transition mechanism for the system during the scene transition process. It takes into account that the scene changes do not occur instantly, but gradually evolve within a certain time interval. The buffer strategy includes multiple aspects. For example, in the early stage of the scene transition, a more conservative dispatch scheme is adopted to ensure the stability of the system. As the transition proceeds, more innovative schemes are gradually introduced to adapt to the needs of new scenarios. At the same time, the buffer strategy can also dynamically adjust the dispatch scheme according to the evaluation results of the fitness function to achieve the optimal joint optimization dispatch.

[0082] The buffer strategy provides clear guidance for the system during the scenario transition. It takes into account various factors and changing trends in the process of transitioning from the current mission scenario to the future predicted mission scenario. In the joint optimization scheduling management, the system will comprehensively consider multiple factors, including energy production, transmission, distribution and storage. According to the buffer strategy, the system will dynamically adjust the operation of each link to achieve the optimal resource allocation and efficiency improvement. For example, in terms of energy production, the system will adjust the operating parameters of renewable energy power generation equipment according to the buffer strategy to adapt to the changes in energy demand in different scenarios. If it is predicted that future energy demand will increase, the system will increase the blade angle of the wind turbine or the tilt angle of the solar panel in advance to increase energy production. In the energy transmission and distribution link, the buffer strategy can guide the system to optimize the operation mode of the power grid to ensure that energy can be efficiently delivered to the demand location. This may include adjusting the voltage level of the transformer, optimizing the load distribution of the line, etc. At the same time, in terms of energy storage, the buffer strategy can determine when to store excess energy and when to release the stored energy to balance the supply and demand relationship. For example, when the energy supply is sufficient, the excess energy is stored for use during the peak of energy demand in the future. By following the buffer strategy for joint optimization and dispatch management, the new energy power system can more smoothly cope with the challenges brought by scenario transition, improve the reliability, stability and efficiency of the system, and achieve rational use of energy and sustainable development.

[0083] In a possible manner, step S740 further includes:

[0084] Step S741: Perform joint optimization scheduling with the joint optimization scheduling scheme, perform consistency monitoring of scheduling responses, and establish monitoring and early warning.

[0085] Step S742: Utilize the monitoring early warning to generate optimization feedback, dynamically adjust the fitness function based on the optimization feedback, and regenerate a joint optimization scheduling solution with the dynamically adjusted fitness function.

[0086] Specifically, the system will actually carry out the joint optimization dispatching of the new energy power system according to the previously determined joint optimization dispatching scheme, aiming to achieve efficient production, transmission, distribution and storage of energy to meet the energy needs in different scenarios. Once the joint optimization dispatching is started, the system will simultaneously start the consistency monitoring of the dispatching response and collect data from various links, including the actual output of energy production equipment, the load of transmission lines, the energy flow direction of distribution nodes, and the status of storage facilities. By analyzing these actual operation data, the system can determine whether the actual dispatching is consistent with the expected plan. If it is found that there is a large deviation between the actual dispatching and the expected plan, the system will immediately establish a monitoring and early warning, which can be presented in various forms, such as sounding an alarm, displaying a warning message on the display screen, or sending a notification to relevant personnel. The purpose of the early warning is to promptly remind system managers and operators to pay attention to potential problems so that they can quickly take appropriate measures to make adjustments. After receiving the early warning, the relevant personnel can quickly troubleshoot, adjust the equipment operating parameters, or take other remedial measures to ensure that the joint optimization dispatching can return to normal as soon as possible. By performing consistency monitoring of dispatching responses and establishing monitoring and early warning, an effective guarantee mechanism is provided for the joint optimization dispatching of the new energy power system to ensure that the system can operate stably and efficiently.

[0087] Monitoring and early warning play an important role and provide key optimization feedback for the system. When the monitoring and early warning is triggered, it will report detailed information about the difference between the current scheduling situation and the expected situation. These feedbacks include the deviation between the actual energy output and the expected output, the abnormal loss during the energy transmission process, the imbalance of energy distribution and other problems. Based on these optimization feedbacks, the system begins to dynamically adjust the fitness function. The fitness function plays a core role in joint optimization scheduling. It is used to evaluate the advantages and disadvantages of different scheduling schemes. According to the feedback information, the system will adjust the various parameters and weights in the fitness function to better reflect the actual needs and problems. For example, if the energy demand in a certain area is monitored to increase suddenly, and the current scheduling scheme cannot meet it, then the weight of the energy supply in the fitness function will be increased to encourage the new scheduling scheme to pay more attention to the energy distribution in the area. Once the fitness function is dynamically adjusted, the system will regenerate the joint optimization scheduling scheme based on this adjusted fitness function. This new solution will solve current problems in a more targeted manner, while also taking into account possible changes in the future. By continuously receiving feedback from monitoring and early warning and dynamically adjusting the fitness function, the system can continuously optimize the joint optimization scheduling plan to ensure that the new energy power system always operates in an efficient and stable manner.

[0088] In a possible manner, step S740 further includes:

[0089] Step S743: Establish a digital twin model of the new energy power system, and perform a visual presentation of the joint scheduling based on the digital twin model and the joint optimization scheduling scheme.

[0090] Specifically, it is of great significance to establish a digital twin model of the new energy power system. The digital twin model is a virtual mapping of the actual new energy power system, which can accurately simulate the various operating states and parameters of the system. By establishing a digital twin model, data in the actual system can be obtained in real time, and these data can be transferred to the digital twin model for synchronous update. The digital twin model can accurately reflect the current state of the actual system. Based on the digital twin model and the joint optimization scheduling scheme, the joint scheduling can be visualized. This visualization can be presented in various forms, such as through a three-dimensional graphical interface, a data dashboard or virtual reality technology. In the visualization, you can intuitively see the various components of the new energy power system, such as power generation equipment, transmission lines, distribution nodes and storage facilities. At the same time, you can also display the flow path of energy, the operating parameters of each link and the execution of the joint optimization scheduling scheme. For example, through visualization, you can clearly see the real-time power generation of different power generation equipment, the load of the transmission line and the degree of energy demand satisfaction in each region. If the joint optimization scheduling scheme involves adjusting the output power of a certain power generation equipment, the visualization interface can dynamically display the power change of the equipment and the impact on the entire system. This visualization not only helps system managers better understand and monitor the process of joint optimization scheduling, but also provides an intuitive basis for decision-making. By observing the visualization interface, managers can promptly identify problems, adjust scheduling strategies, and optimize future scheduling plans.

[0091] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0092] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

[0093] This specification and the drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. An energy flow-carbon flow joint optimization scheduling method for a new energy power system, characterized in that: The method comprises: Connect the new energy power system, divide the system scenarios according to the historical working data of the new energy power system, and establish peak load period scenarios, low carbon emission period scenarios, and energy storage high efficiency period scenarios; Based on the system scenario division results, priority weights of energy flow, carbon flow, and energy storage indicators are established, and an iterative solution mapped to the system scenario division results is configured; configuring a fitness function, wherein the fitness function is constructed according to priority weights; Perform population initialization, wherein the population includes N individuals, each individual represents a candidate solution; Activating the fitness function through the scene attribution of the current task to calculate the fitness values ​​of individuals in the population, configuring the individual selection probability based on the individual fitness values, and performing the probabilistic selection of individuals in the population; Generate a crossover constraint factor and a mutation constraint factor based on the iterative scheme, reconstruct the crossover rate and the mutation rate according to the iterative stage, perform data crossover of individuals in the population through the reconstructed crossover rate, perform mutation operation on the offspring individuals generated by the crossover at the mutation rate, generate mutation results, perform individual fitness elimination on the mutation results and the initialized population, and perform iterative update; Generate a joint optimization scheduling plan based on the iterative update results.

2. The energy flow-carbon flow joint optimization scheduling method for a new energy power system according to claim 1, characterized in that: The objective function of the fitness function includes an operation cost function, an energy utilization efficiency function, a total carbon emission function, a carbon emission cost function, and an energy storage device utilization efficiency function. The fitness function is as follows: Among them, ω1, ω2, ω3, ω4, and ω5 are the weight coefficients of operating cost, energy utilization efficiency, total carbon emissions, carbon emission cost, and energy storage equipment utilization efficiency, respectively, and ω i =λ i ·W i ,ω i is the wth weight coefficient, λ i Represents the priority weight in the current scenario, W i is the basic weight of the corresponding target, C run (x) is the operating cost function, η energy (x) is the energy efficiency function, C carbon (x) is the total carbon emission function, is the carbon emission cost function, η storage (X) is the efficiency function of energy storage equipment.

3. The energy flow-carbon flow joint optimization scheduling method for a new energy power system according to claim 1, characterized in that: Reconstruct the crossover rate and mutation rate according to the iteration stage, including: Divide the iteration process into stages, including the early iteration stage, the middle iteration stage, and the late iteration stage; Establishing the initial crossover rate and initial mutation rate corresponding to the stage division results; The crossover rate reconstruction is completed by the crossover constraint factor and the initial crossover rate, and the mutation rate reconstruction is completed according to the mutation constraint factor and the initial mutation rate.

4. The energy flow-carbon flow joint optimization scheduling method for a new energy power system according to claim 3, characterized in that: The crossover rate reconstruction is completed by the crossover constraint factor and the initial crossover rate, and the mutation rate reconstruction is completed according to the mutation constraint factor and the initial mutation rate, and further includes: An adaptive update network for self-update is established. When the crossover rate and the mutation rate are reconstructed, the adaptive update network is activated to perform update self-checking. The update self-checking includes: Read the current iteration stage, perform end approximation analysis of the current stage according to the current iteration stage, and generate the stage approximation self-check result; Obtaining a search status of a previous iteration, performing a state self-check according to the search status, and establishing a state self-check result; Adaptation analysis is performed on the stage approximation self-inspection results and the state self-inspection results, and the self-inspection update is completed based on the adaptation analysis results. The reconstructed crossover rate and mutation rate are adjusted according to the self-inspection update results.

5. The energy flow-carbon flow joint optimization scheduling method for a new energy power system according to claim 1, characterized in that: Eliminate the individual fitness of the mutation results and the initialized population, and also include: Establishing a taboo reservation set, wherein the taboo reservation set is a set of individuals whose taboo values ​​meet a preset threshold, and the taboo value is calculated by the individual fitness value and the individual offspring fitness value; Performing taboo value order sorting and screening on the individuals in the taboo retention set, updating the order sorting results into the population, and marking them as single-round iterative retention data; Continue iterating and updating based on the updated population.

6. The energy flow-carbon flow joint optimization scheduling method for a new energy power system according to claim 1, characterized in that: The method further comprises: Acquire forecast task data of the new energy power system, perform scenario transition analysis based on the forecast task data and the current task, and establish a scenario transition interval; Performing a linear change update on the priority weight based on the scene transition interval; Reconstruct the fitness function using the updated priority weights, and establish a buffer strategy mapped with the scene transition interval using the reconstructed fitness function; Joint optimization scheduling management is performed according to the buffer strategy.

7. The energy flow-carbon flow joint optimization scheduling method for a new energy power system according to claim 1, characterized in that: The method further comprises: Perform joint optimization scheduling with the joint optimization scheduling scheme, perform consistency monitoring of scheduling responses, and establish monitoring and early warning; The monitoring early warning alarm is used to generate optimization feedback, the fitness function is dynamically adjusted based on the optimization feedback, and the joint optimization scheduling solution is regenerated with the dynamically adjusted fitness function.

8. The energy flow-carbon flow joint optimization scheduling method for a new energy power system according to claim 1, characterized in that: The method further comprises: A digital twin model of the new energy power system is established, and a visualization presentation of the joint scheduling is performed based on the digital twin model and the joint optimization scheduling scheme.

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