New energy power generation and scheduling optimization method based on biological heuristic

Abstract

The invention discloses a new energy power generation and scheduling optimization method based on biological heuristic, and the method comprises the steps: optimizing path selection through employing a biological heuristic algorithm, initializing an ant colony on a path matrix, generating a path pheromone concentration, and enabling a model to dynamically adjust the priority of path selection on the basis of real-time feedback; if the generating capacity deviation exists, the path pheromone concentration is subjected to self-adaptive adjustment, and it is ensured that the load requirement can be quickly responded; in the global optimization stage, an initial path matrix is further updated in combination with a path scoring mechanism, and an optimal scheduling instruction is generated after convergence, so that the stability and environmental protection of power grid scheduling are improved while the system is ensured to realize the economical efficiency target.

Description

Technical Field

[0001] The present invention relates to the technical field of renewable energy power generation and dispatching, and in particular to a bio-inspired renewable energy power generation and dispatching optimization method. Background Art

[0002] In the field of new energy power generation dispatching, with the increase in the proportion of renewable energy, the dispatching mode of the power grid is gradually shifting to a high-proportion new energy-friendly structure. In the current power generation dispatching technology, power grid dispatching often relies on traditional optimization algorithms and prediction models. These methods are based on load demand and unit output forecasts to balance and dispatch power to ensure the stability of the power grid. However, the volatility and uncertainty of new energy power generation have increased the complexity of dispatching. For example, wind power and photovoltaic power are significantly affected by meteorological conditions and are difficult to accurately predict. To this end, some technical solutions have introduced short-term load adjustment and energy storage technologies, hoping to cope with new energy fluctuations through a diversified power supply combination, but these methods often have problems such as insufficient response speed, low stability and high operating costs in actual applications.

[0003] It is foreseeable that the shortcomings of existing dispatching technologies are mainly reflected in the response to real-time load fluctuations and power generation deviations. Due to the uncertainty of renewable energy generation, the power grid needs a more flexible and intelligent dispatching model to effectively adapt to load changes. However, traditional dispatching methods mostly use static optimization methods in path selection and resource allocation, which cannot continuously and automatically optimize and adjust the path plan, resulting in the dispatching plan being difficult to balance economy and stability. Summary of the invention

[0004] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0005] In view of the above existing problems, the present invention is proposed. Therefore, the present invention provides a new energy generation and scheduling optimization method based on biological inspiration to solve the problems raised in the background technology.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: a new energy power generation and scheduling optimization method based on biological inspiration, comprising:

[0007] Obtain the power generation cost, system stability and carbon emission data of each unit in the power grid, establish a path optimization model, and form an initial path matrix by collecting real-time power grid load and power generation data;

[0008] The path optimization model is initialized by setting dynamic constraints of each unit in the power grid, and the path deviation is calculated based on the initial path matrix and fed back to the path optimization model;

[0009] According to the feedback results, the initial path matrix is ​​globally optimized, and the optimized initial path matrix is ​​used as the optimal path matrix to generate the optimal dispatching instructions for the output of the power grid generator sets.

[0010] As a preferred solution of the bio-inspired renewable energy generation and dispatch optimization method described in the present invention, the power generation cost, system stability and carbon emission data of each unit in the power grid are obtained, a path optimization model is established, and an initial path matrix is ​​formed by collecting real-time power grid load and renewable energy generation data, including:

[0011] Through real-time grid load and power generation data, the power generation cost, system stability and carbon emission data of each unit are considered respectively, and multiple paths are formed by permutation and combination according to the priorities;

[0012] Assign weights to each path, and adjust the weight change gradient of each path according to different grid load and power generation data;

[0013] Create an initial path matrix and correspond each path to a unique output configuration of a generator set combination.

[0014] As a preferred solution of the bio-inspired renewable energy generation and dispatch optimization method described in the present invention, the dynamic constraints of each unit in the power grid are set, and the path optimization model is initialized, including:

[0015] Establishing a power balance constraint, that is, calculating the total output of all units in real time within each dispatching cycle t, so that the total output meets the grid load demand within each dispatching cycle t;

[0016] Establish the output range constraints of the generator sets, that is, set the output range within which each generator set is allowed.

[0017] As a preferred solution of the bio-inspired new energy power generation and scheduling optimization method described in the present invention, it also includes:

[0018] Initialize the ant colony on the path matrix, randomly select a path for each ant in the colony, and generate the initial pheromone concentration for each path.

[0019] As a preferred solution of the bio-inspired new energy generation and scheduling optimization method described in the present invention, the path deviation is calculated and fed back to the path optimization model, including:

[0020] Collect real-time grid load and power generation data and calculate the deviation value of each path;

[0021] Taking the deviation value as a reference, if there is a positive deviation, that is, excess power generation, the output of each unit on the path is reduced to reduce the pheromone concentration; if there is a negative deviation, that is, insufficient power generation, the output of each unit on the path is increased to increase the pheromone concentration.

[0022] As a preferred solution of the bio-inspired new energy power generation and scheduling optimization method described in the present invention, it also includes:

[0023] The probability value of selecting a path is determined based on the pheromone concentration and the deviation value of the path.

[0024] As a preferred solution of the bio-inspired new energy generation and scheduling optimization method described in the present invention, the initial path matrix is ​​globally optimized according to the feedback results, including:

[0025] The global optimization calculates the comprehensive score of each path in the initial path matrix according to the scoring formula, preferentially removes the path with the lowest score, and updates the initial path matrix.

[0026] As a preferred solution of the bio-inspired renewable energy generation and dispatch optimization method described in the present invention, the optimized initial path matrix is ​​used as the optimal path matrix to generate the optimal dispatch instruction of the power grid generator set output, including:

[0027] The path with the highest pheromone concentration is selected in the path matrix to form the output dispatching instructions and unit combination scheme for each unit, and the unit combination scheme is transmitted to the power grid control system. At the same time, the dispatching instructions are sent to each generator unit in the power grid control system.

[0028] As a preferred solution of the bio-inspired new energy power generation and scheduling optimization method described in the present invention, it also includes:

[0029] If the initial path matrix does not reach convergence during the global optimization process, the initial pheromone concentration of each path is regenerated.

[0030] Compared with the prior art, the invention has the following beneficial effects:

[0031] 1. The present invention can flexibly adapt to grid load fluctuations by continuously collecting real-time data and adjusting path selection, ensuring that the output configuration of each unit always meets the actual needs of the grid. Compared with the static scheduling of traditional methods, the real-time responsiveness and adaptability of grid scheduling are significantly improved;

[0032] 2. In the process of path optimization, the power generation cost, system stability and carbon emissions are comprehensively considered, and the power generation combination is prioritized through the path scoring mechanism, realizing the new energy priority dispatch strategy with the lowest power generation cost; in addition, based on the global pheromone feedback mechanism of the bio-inspired algorithm, the path selection can give priority to the most economical path under multi-objective constraints, reduce the use of high-cost units, improve the utilization efficiency of new energy, and significantly reduce the operation cost of the power grid;

[0033] 3. Gradually accumulate paths with excellent performance through the global optimization mechanism, and dynamically adjust the path priority according to the fluctuations of grid load and renewable energy power generation, so that the dispatch plan can adapt to the uncertainty of renewable energy output and ensure the overall stability of the grid in a volatile environment; at the same time, the path scoring mechanism effectively guarantees the dispatch reliability of different power generation combinations, making the dispatch plan more stable in long-term operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing 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. Among them:

[0035] Figure 1 This is an overall flow chart of a bio-inspired new energy generation and scheduling optimization method according to an embodiment of the present invention;

[0036] Figure 2 This is a trend chart showing the changes in power generation cost and carbon emission data over time for a bio-inspired new energy power generation and scheduling optimization method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0037] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0038] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0039] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0040] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0041] At the same time, in the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the directions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0042] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0043] Example 1

[0044] Reference Figure 1 , which is the first embodiment of the present invention, and provides a new energy generation and scheduling optimization method based on biological inspiration, including:

[0045] S1. Obtain the power generation cost, system stability and carbon emission data of each unit in the power grid, establish a path optimization model, and form an initial path matrix by collecting real-time power grid load and power generation data;

[0046] Specifically, a multi-objective function of the path optimization model is established, and the scheduling of renewable energy generation is considered as a dynamic optimization problem;

[0047] Furthermore, through real-time grid load and power generation data, the power generation cost of each unit, system stability and carbon emission data priorities are considered respectively, and multiple paths are formed by permutation and combination according to the priorities;

[0048] Specifically, the permutation and combination method is, for example, if it is detected in real time that the grid load volatility increases, the priority of system stability is increased and the priority of power generation cost of each unit is reduced; if the grid load volatility is stable, the priority of carbon emissions is increased;

[0049] Specifically, the multi-objective function F composed of the power generation cost of each unit, system stability and carbon emission data is expressed as:

[0050] F=C×ω1+S×ω2+E×ω3

[0051] Among them, C represents the power generation cost function of each unit, S represents the system stability, which is obtained from the load fluctuation data, E represents the carbon emission function of each unit, which is calculated by the output and emission factor of the thermal power unit, and the carbon emission of the new energy unit is regarded as 0, ω1, ω2, ω3 represent the weights of the power generation cost, system stability and carbon emission data of each unit respectively;

[0052] Furthermore, a weight is assigned to each path, and the weight change gradient of each path is adjusted according to different grid load and power generation data;

[0053] Specifically, when it is detected that the grid load fluctuation is greater than the original grid load fluctuation (such as 10%), the weight is adjusted in the following manner:

[0054] ω n =ω n +Δω

[0055] Where n is a certain weight of the multi-objective function, Δω represents the offset gradient factor to adapt to the changing conditions of the power grid;

[0056] Furthermore, an initial path matrix is ​​created, and each path corresponds to a unique output configuration of a generator set combination, such as a wind power-thermal power-energy storage combination path;

[0057] S2. Initialize the path optimization model by setting the dynamic constraints of each unit in the power grid, calculate the path deviation based on the initial path matrix and feed it back to the path optimization model;

[0058] Furthermore, a power balance constraint is established, that is, in each dispatching cycle t, the sum of the outputs of all units is calculated in real time so that the sum of the outputs meets the grid load demand in each dispatching cycle t;

[0059] Specifically, the power balance constraint can be expressed as:

[0060]

[0061] Among them, P i,t is the output of the ith generator set in period t, D tis the total load demand of the power grid in period t;

[0062] Furthermore, the output range constraints of the generator sets are established, that is, the output range allowed for each generator set is set; for example, photovoltaic power generation is prioritized during the day when the sunlight is strong, and wind power is prioritized during the period of high wind speed; for thermal power units, considering their long startup and shutdown time, supplementary scheduling is only carried out when new energy is insufficient;

[0063] Specifically, the processing range constraint of the generator set is expressed as:

[0064] P i,min ≤P i,t ≤P i,max

[0065] Among them, P i,min It is expressed as the minimum output range of the i-th generator set, P i,max It is expressed as the maximum output range of the i-th generator set;

[0066] Furthermore, the ant colony is initialized on the path matrix, and a path is randomly selected for each ant in the colony to generate the initial pheromone concentration of each path;

[0067] Specifically, the pheromone concentration represents the possible probability of choosing this path;

[0068] It should be noted that each ant will calculate the performance gap of the current path in meeting the load demand, and thus decide the direction of the next path adjustment; for example, if the actual load demand is higher than the total power generation of the current path, the power generation of the current path will be increased; if it is lower than the demand, the output of the current path will be reduced;

[0069] Furthermore, real-time grid load and power generation data are collected to calculate the deviation value of each path;

[0070] Specifically, the deviation value Δp i The calculation formula is expressed as:

[0071]

[0072] Among them, when Δp i When >0, it means that the total output of the path needs to be reduced; Δp i <0 indicates that the total output of the path needs to be increased; Δp i =0, the total output of the path does not change;

[0073] Furthermore, taking the deviation value as a benchmark, if there is a positive deviation, that is, excess power generation, the output of each unit on the path is reduced to reduce the pheromone concentration; if there is a negative deviation, that is, insufficient power generation, the output of each unit on the path is increased to increase the pheromone concentration;

[0074] Furthermore, the probability value of the selected path is determined according to the pheromone concentration and the deviation value of the path;

[0075] Specifically, when the deviation value is small, the priority of the path is increased, thereby increasing the pheromone concentration of the path (i.e., the selection probability); when the deviation is large, the priority of the increased path is reduced, and the pheromone concentration (i.e., the selection probability) is reduced;

[0076] S3. According to the feedback results, the initial path matrix is ​​globally optimized, and the optimized initial path matrix is ​​used as the optimal path matrix to generate the optimal dispatching instruction of the power grid generator set output;

[0077] Furthermore, in global optimization, the comprehensive score of each path in the initial path matrix is ​​calculated according to the scoring formula, the path with the lowest score is removed first, and the initial path matrix is ​​updated;

[0078] Specifically, the comprehensive score formula is expressed as:

[0079]

[0080] Furthermore, the path with the highest pheromone concentration is selected in the path matrix to form the output dispatching instructions and unit combination schemes for each unit, and the unit combination schemes are transmitted to the power grid control system. At the same time, the dispatching instructions are sent to each generator unit in the power grid control system, so that the dispatching scheme can meet the real-time demand and adapt to the volatility of renewable energy power generation.

[0081] Furthermore, if the initial path matrix does not reach a convergence state during the global optimization process, the initial pheromone concentration of each path is regenerated;

[0082] It should be noted that in the global optimization process, if the path matrix has not reached convergence and the optimal solution has not been found, regenerating the pheromone concentration can help the ant colony re-explore the search space; this can effectively prevent the algorithm from falling into local optimality, enhance the global search capability of path optimization, and enable the algorithm to find a better power generation combination path in a larger range.

[0083] Example 2

[0084] Reference Figure 2, which is the second embodiment of the present invention, and this embodiment provides a method for optimizing renewable energy generation and scheduling based on biological inspiration, including: In order to verify the advantages of the method for optimizing renewable energy generation and scheduling based on biological inspiration of the present invention, this experiment uses a regional power grid system as an experimental platform, configures various types of generator sets such as wind power, photovoltaic, thermal power and energy storage, and conducts a real scheduling optimization simulation; the experiment compares the invention scheme with the traditional static scheduling method to comprehensively analyze the actual effectiveness of the present invention;

[0085] Generator set configuration:

[0086] Wind turbines: 4 units, each with a capacity of 30MW and a maximum total output of 120MW;

[0087] Photovoltaic units: 4 units, each with a capacity of 25MW and a maximum total output of 100MW;

[0088] Thermal power units: 2 units, each with a capacity of 50MW and a maximum total output of 100MW, mainly used for peak load regulation and load supplementation;

[0089] Energy storage equipment configuration:

[0090] Energy storage device: 2 groups, each with a capacity of 20MWh, and a maximum charge and discharge power of 10MW, mainly used to balance the fluctuations of new energy;

[0091] Load demand data: Daily load fluctuations simulate peak and valley demand, with a maximum load of 250MW and a minimum load of 120MW, updated every hour to reflect dynamic changes in load demand;

[0092] The experimental data are as follows, recording the performance of the invention scheme and the traditional scheduling method in various key indicators;

[0093] Table 1

[0094]

[0095] It can be clearly seen from the data in Table 1 that the cost of power generation is significantly reduced: the total power generation cost of the traditional scheduling method is 2.3 million yuan, while the power generation cost of the scheme of the present invention is only 1.7 million yuan, which is a cost reduction of about 26%; the scheduling demand of high-cost thermal power units is reduced, thereby effectively reducing the total cost of power generation and significantly optimizing the economy; reduction of carbon emissions: the carbon emissions of the traditional scheme are 160 tons, while the scheme of the present invention gives priority to the low-carbon path combination through dynamic adjustment of pheromones, and the carbon emissions are only 90 tons, which is a reduction of about 44%; it shows that the path scoring mechanism effectively reduces the output demand of thermal power units during the scheduling process and improves the priority of new energy, thereby significantly reducing carbon emissions and meeting environmental protection goals; improvement of new energy utilization rate: in terms of new energy utilization rate, the scheme of the present invention reaches 88%, while the traditional scheme is only 65%; it shows that the new energy units are given priority, and the output priority of new energy is improved under dynamic conditions through the path optimization mechanism, thereby effectively improving the utilization efficiency of new energy and reducing dependence on traditional thermal power units;

[0096] Secondly, refer to Figure 2 By simulating the changing trends of power generation cost and carbon emissions over time within a week, it can be seen that the power generation cost and carbon emissions of the scheme of the present invention are relatively stable over time and are generally lower than those of the traditional scheme, which further illustrates the advantages of the scheme of the present invention in terms of operational stability and carbon emission reduction;

[0097] In summary, with the support of experimental data, the scheme of the present invention has demonstrated its advantages in economy, environmental protection and system stability, and can effectively cope with the uncertainty of grid load fluctuations and renewable energy power generation, thereby achieving more efficient and low-carbon power dispatching.

[0098] Those skilled in the art will appreciate that the embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the application can adopt the form of complete hardware embodiments, complete software embodiments, or embodiments in combination with software and hardware. Moreover, the application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program codes. The scheme in the embodiments of the present application can be implemented in various computer languages, for example, object-oriented programming language Java and literal scripting language JavaScript, etc.

[0099] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0100] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0101] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0102] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0103] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A bio-inspired new energy generation and scheduling optimization method, characterized in that: include: Obtain the power generation cost, system stability and carbon emission data of each unit in the power grid, establish a path optimization model, and form an initial path matrix by collecting real-time power grid load and power generation data; The path optimization model is initialized by setting dynamic constraints of each unit in the power grid, and the path deviation is calculated based on the initial path matrix and fed back to the path optimization model; According to the feedback results, the initial path matrix is ​​globally optimized, and the optimized initial path matrix is ​​used as the optimal path matrix to generate the optimal dispatching instructions for the output of the power grid generator sets.

2. The bio-inspired new energy generation and scheduling optimization method according to claim 1, characterized in that: Obtain the power generation cost, system stability and carbon emission data of each unit in the power grid, establish a path optimization model, and form an initial path matrix by collecting real-time power grid load and new energy power generation data, including: Through real-time grid load and power generation data, the power generation cost, system stability and carbon emission data of each unit are considered respectively, and multiple paths are formed by permutation and combination according to the priorities; Assign weights to each path, and adjust the weight change gradient of each path according to different grid load and power generation data; Create an initial path matrix and correspond each path to a unique output configuration of a generator set combination.

3. The bio-inspired new energy generation and dispatch optimization method according to claim 2, characterized in that: The dynamic constraints of each unit in the power grid are set, and the path optimization model is initialized, including: Establishing a power balance constraint, that is, calculating the total output of all units in real time within each dispatching cycle t, so that the total output meets the grid load demand within each dispatching cycle t; Establish the output range constraints of the generator sets, that is, set the output range within which each generator set is allowed.

4. The bio-inspired new energy generation and dispatch optimization method according to claim 3, characterized in that: Also includes: Initialize the ant colony on the path matrix, randomly select a path for each ant in the colony, and generate the initial pheromone concentration for each path.

5. The bio-inspired new energy generation and dispatch optimization method according to claim 4, characterized in that: Calculate path deviations and feed them back into the path optimization model, including: Collect real-time grid load and power generation data and calculate the deviation value of each path; Taking the deviation value as a reference, if there is a positive deviation, that is, excess power generation, the output of each unit on the path is reduced to reduce the pheromone concentration; if there is a negative deviation, that is, insufficient power generation, the output of each unit on the path is increased to increase the pheromone concentration.

6. The bio-inspired new energy generation and dispatch optimization method according to claim 5, characterized in that: Also includes: The probability value of selecting a path is determined based on the pheromone concentration and the deviation value of the path.

7. The bio-inspired new energy generation and scheduling optimization method according to claim 3 or 5, characterized in that: According to the feedback results, the initial path matrix is ​​globally optimized, including: The global optimization calculates the comprehensive score of each path in the initial path matrix according to the scoring formula, preferentially removes the path with the lowest score, and updates the initial path matrix.

8. The bio-inspired new energy generation and dispatch optimization method according to claim 7, characterized in that: The optimized initial path matrix is ​​used as the optimal path matrix to generate the optimal dispatching instructions for the output of the power grid generator sets, including: The path with the highest pheromone concentration is selected in the path matrix to form the output dispatching instructions and unit combination scheme for each unit, and the unit combination scheme is transmitted to the power grid control system. At the same time, the dispatching instructions are sent to each generator unit in the power grid control system.

9. The bio-inspired new energy generation and dispatch optimization method according to claim 7, characterized in that: Also includes: If the initial path matrix does not reach convergence during the global optimization process, the initial pheromone concentration of each path is regenerated.

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