Power market cooperative regulation and evaluation method and system considering new energy volatility

Abstract

The invention provides an electric power market cooperative regulation and evaluation method and system considering new energy volatility, and belongs to the technical field of electric power market operation and new energy grid connection. The method comprises the following steps: establishing a new energy and power market interaction system model according to output characteristics of new energy and a transaction rule with a power market; introducing a new energy marketing influence evaluation model to quantify the dynamic influence of the new energy on the market price, the standby demand and the power supply reliability, and obtaining an influence evaluation result; on the basis of an influence evaluation result, constructing a collaborative regulation and control optimization model, and integrating multi-market rules and resource constraints; and solving the constructed collaborative regulation and control optimization model by adopting a fluctuation self-adaptive improved grey wolf algorithm to generate an optimal regulation and control strategy of the power market. The method can improve the new energy consumption rate, reduces the market fluctuation, meets the real-time regulation and control demands of the power market, and provides support for the stable operation of the power market under the access of high-proportion new energy.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of power market operation and new energy grid connection, and particularly relates to a power market coordinated regulation and evaluation method and system considering new energy fluctuation. BACKGROUND

[0002] The statements in this section merely provide background information related to the present application and do not necessarily constitute prior art.

[0003] With the rapid increase of wind energy, solar energy and other new energy in the power system, and due to the significant volatility, intermittency and low predictability of new energy generation, it brings serious challenges to the stable operation of the power market. The traditional power market model is mainly designed based on controllable power sources (such as thermal power, hydroelectric power), and its core assumption is that the power output can be accurately predicted and scheduled. However, the large-scale grid connection of new energy leads to the following key problems of the power market: (1) Insufficient response to volatility: The traditional power market model assumes stable power output and does not fully consider the random fluctuation characteristics of new energy (such as wind power and photovoltaic power), resulting in dramatic market price fluctuations and affecting market fairness and stability.

[0004] (2) One-sided impact evaluation: Existing research focuses on the single impact of new energy on spot electricity prices, and does not establish a linkage evaluation mechanism between new energy and auxiliary service markets, making it difficult to quantify the transmission effects of the increase of new energy penetration rate on reserve capacity demand and frequency modulation cost.

[0005] (3) Lack of coordinated regulation mechanism: There is a lack of coordinated regulation strategies that take into account new energy consumption and market stability. Existing dispatching models only suppress fluctuations by abandoning wind and light, and do not use flexible resources such as demand response and energy storage to achieve "source-load-storage" coordination, resulting in low new energy consumption rate.

[0006] (4) Poor solution adaptability: In the face of high-dimensional fluctuation scenarios of new energy, traditional optimization algorithms have the problems of long calculation time and weak adaptability to fluctuation scenarios, which cannot meet the real-time clearing needs of the power market. SUMMARY

[0007] To overcome the shortcomings of the prior art, the present application provides a power market coordinated regulation and evaluation method and system considering new energy fluctuation, which builds a double-layer evaluation and regulation model considering new energy fluctuation characteristics, multi-market linkage and "source-load-storage" coordination, and combines an efficient solution strategy based on a fluctuation adaptive improved grey wolf algorithm (FA-IGWO) to realize the coordinated optimization of new energy consumption maximization and power market stability.

[0008] To achieve the above purpose, one or more embodiments of the present application provide the following technical solutions: The first aspect of this invention provides a method for coordinated regulation and evaluation of the power market that takes into account the volatility of new energy sources; A method for coordinated regulation and evaluation of the electricity market that considers the volatility of new energy sources includes: Establish an interactive system model between new energy sources and the electricity market based on the output characteristics of new energy sources and the trading rules of the electricity market; Based on the construction of an interactive system model, a new energy market entry impact assessment model is introduced to quantify the dynamic impact of new energy on market prices, reserve demand, and power supply reliability, and the impact assessment results are obtained. Based on the impact assessment results, a collaborative regulation and optimization model is constructed with the objective functions of maximizing new energy consumption, minimizing market fluctuations, and minimizing operating costs, integrating multiple market rules and resource constraints. An improved gray wolf algorithm based on fluctuation adaptation is used to solve the constructed collaborative regulation optimization model and generate the optimal regulation strategy for the power market.

[0009] As a further technical solution, a new energy and electricity market interaction system model is established based on the output characteristics of new energy sources and the trading rules of the electricity market, including: Modeling based on the characteristics of new energy output includes constructing a wind power prediction model and a photovoltaic power fluctuation model; Multi-market interaction modeling based on trading rules of the new energy and electricity markets; By introducing system operation constraints, the model of the interaction between new energy and the electricity market is provided with operational boundaries and feasibility basis, ensuring that the model truly reflects the actual operating rules of the power system.

[0010] As a further technical solution, the wind power prediction model is as follows:

[0011] in, Let t be the actual output power of the wind power. This refers to the rated power of the wind power. Let t be the actual wind speed at time t; , , These are the cut-in wind speed, rated wind speed, and cut-out wind speed, respectively.

[0012] As a further technical solution, the photovoltaic power fluctuation model is as follows:

[0013] in, Let t be the actual output power of the photovoltaic system. Let t be the predicted photovoltaic power. Let be the fluctuation coefficient at time t.

[0014] As a further technical solution, the process of the new energy market entry influence evaluation model quantifying the dynamic influence of new energy on market price, reserve demand and power supply reliability is: The dynamic price fluctuation index is used to describe the influence of new energy on spot electricity price; The safety factor and the maximum and minimum output value of new energy are introduced to quantify the additional demand of reserve capacity caused by the volatility of new energy; The power shortage probability is used to evaluate the influence of new energy volatility on power supply reliability.

[0015] As a further technical solution, the objective function established by maximizing new energy consumption, minimizing market fluctuation and minimizing operation cost is:

[0016]

[0017] In the formula, is the comprehensive benefit; is the new energy consumption rate; is the market price fluctuation index; is the total operation cost; , , is the weight coefficient.

[0018] As a further technical solution, the process of solving the collaborative regulation optimization model constructed by using the adaptive improved grey wolf algorithm based on fluctuation is: An initialization strategy driven by new energy fluctuation scenarios is used to generate initial solutions covering high, medium and low fluctuations; The exploration and utilization capabilities of the adaptive inertia weight and learning factor dynamic adjustment algorithm are used to adapt to the intensity of new energy fluctuation; The fluctuation compensation factor is introduced to correct the individual position, and the projection method is used for constraint processing.

[0019] The second aspect of the present application provides a power market collaborative regulation and evaluation system considering new energy fluctuation.

[0020] The power market collaborative regulation and evaluation system considering new energy fluctuation comprises: An interactive system model establishment module is configured to establish a new energy and power market interactive system model according to the output characteristics of new energy and the transaction rules of the power market; An evaluation result acquisition module is configured to introduce a new energy market entry influence evaluation model to quantify the dynamic influence of new energy on market price, reserve demand and power supply reliability on the basis of constructing the interactive system model, and obtain the influence evaluation result; The synergistic regulation optimization model construction module is configured to construct a synergistic regulation optimization model with new energy consumption maximization, market fluctuation minimization and operation cost minimization as objective functions based on the influence evaluation result, and integrate multi-market rules and resource constraints; The optimal regulation strategy generation module is configured to solve the constructed synergistic regulation optimization model by using a fluctuation adaptive improved grey wolf algorithm (FA-IGWO) to generate an optimal regulation strategy of the power market.

[0021] The third aspect of the present application provides a computer readable storage medium having a program stored thereon, which, when executed by a processor, implements the steps in the synergistic regulation and evaluation method of the power market considering new energy fluctuation as described in the first aspect of the present application.

[0022] The fourth aspect of the present application provides an electronic device comprising a memory, a processor and a program stored on the memory and executable on the processor, wherein the processor implements the steps in the synergistic regulation and evaluation method of the power market considering new energy fluctuation as described in the first aspect of the present application when executing the program.

[0023] The above one or more technical solutions have the following beneficial effects: (1) The present application solves the problem of insufficient consideration of random fluctuation characteristics of new energy in traditional deterministic models by establishing a dynamic correlation model between new energy output fluctuation and the power market, quantifying the influence of uncertainty through air density correction term, fluctuation coefficient and compensation factor, improving the wind power prediction accuracy in the low wind speed section, and better fitting the actual output characteristics of new energy.

[0024] (2) By constructing a comprehensive evaluation system covering price fluctuation, reserve demand and power supply reliability, the limitation of existing research focusing only on single market influence is broken. The conduction effect of the increase of new energy penetration rate on reserve capacity demand, frequency modulation cost, etc. is quantified, providing data support for power market rule optimization. With "new energy consumption maximization + market fluctuation minimization + operation cost minimization" as the multi-objective, integrating "source-load-storage" flexible resources, replacing the traditional regulation mode of wind and light abandonment, effectively improving the new energy consumption rate, and at the same time, through the constraint control of price fluctuation, realizing the stable operation of the market under high proportion of new energy access.

[0025] (3) By using the fluctuation adaptive improved grey wolf algorithm (FA-IGWO), through the initialization driven by fluctuation scenario, dynamic parameter adjustment and fluctuation compensation factor, the adaptability to high-dimensional fluctuation scenario of new energy is improved, the solving efficiency is improved, the time for completing single period optimization is reduced, and the real-time clearing requirement of the power market is met.

[0026] The advantages of the additional aspects of the present application will be partially given in the following description, partially will become obvious from the following description, or will be learned by the practice of the present application. Attached Figure Description

[0027] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0028] Figure 1 This is a flowchart of the method in the first embodiment.

[0029] Figure 2 This is a system structure diagram of the second embodiment. Detailed Implementation

[0030] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0031] It should be noted that the terminology used herein is for the purpose of describing particular implementations only and is not intended to limit the exemplary implementations of the present invention.

[0032] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0033] Example 1 This embodiment discloses a method for coordinated regulation and evaluation of the power market that takes into account the volatility of new energy sources; like Figure 1 As shown, the methods for coordinated regulation and evaluation of the power market considering the volatility of new energy sources include: Step S1: Establish an interaction system model between new energy sources and the electricity market based on the output characteristics of new energy sources and the trading rules of the electricity market; Step S2: Based on the construction of the interactive system model, a new energy market entry impact assessment model is introduced to quantify the dynamic impact of new energy on market prices, reserve demand, and power supply reliability, and the impact assessment results are obtained. Step S3: Based on the impact assessment results, construct a collaborative regulation and optimization model with the objective functions of maximizing new energy consumption, minimizing market fluctuations, and minimizing operating costs, integrating multiple market rules and resource constraints; Step S4: The constructed collaborative regulation optimization model is solved using the improved gray wolf algorithm based on fluctuation adaptation to generate the optimal regulation strategy for the power market.

[0034] Specifically, it also includes the following: In step S1, a new energy and electricity market interaction system model is established to describe the output characteristics of new energy, the trading rules of the electricity market, and the core constraints of system operation, providing a data framework for subsequent impact assessment and regulation models.

[0035] Specifically, the construction of the interaction system model between new energy and the electricity market includes: Step S11, New Energy Output Modeling. This step clearly describes the interaction between energy resources (distributed power generation, energy storage) within the virtual power plant and multiple external markets (spot market, frequency regulation, demand response, carbon market), and clarifies the core constraints of system operation, providing a data framework for subsequent analysis of market coupling characteristics and the construction of optimization models. The new energy output modeling includes a wind power prediction model and a photovoltaic power fluctuation model.

[0036] (1) Since the traditional model only considers the wind speed range, an air density correction term is added to this model, which can be multiplied by in actual calculations. , This is the actual air density. Using standard air density, it can effectively improve the prediction accuracy in low wind speed ranges.

[0037] In this embodiment, considering the influence of wind speed and air density on power output, the formula for the constructed wind power prediction model is as follows:

[0038] in, for Real-time wind power output; This refers to the rated power of the wind power. for Real-time wind speed; , , These are the cut-in wind speed, rated wind speed, and cut-out wind speed, respectively.

[0039] (2) Introduce a fluctuation coefficient into the basic power prediction and construct a photovoltaic power fluctuation model, the formula of which is:

[0040] in, Let t be the actual output power of the photovoltaic system. Let t be the predicted photovoltaic power. Let be the fluctuation coefficient at time t.

[0041] Step S12: Define market transaction variables for new energy participation through multi-market interaction modeling.

[0042] in, for The time in the market The transaction variable.

[0043] The model explicitly couples the transaction volume of new energy in the spot market with the capacity of the reserve market , and achieves coordinated resource allocation through constraints.

[0044] Step S13, by introducing system operation constraints, the new energy and power market interaction system model provides operation boundaries and feasibility basis, ensuring that the model truly reflects the actual operation rules of the power system.

[0045] System operation constraints are the "feasibility link" of new energy output modeling and multi-market interaction modeling: on the one hand, the wind power and photovoltaic output range , obtained by new energy output modeling is directly used as the core parameter of new energy output constraint, ensuring that the new energy output does not deviate from the actual physical characteristics; on the other hand, the transaction variable defined in multi-market interaction modeling needs to be included in the power balance constraint, and the capacity demand of the reserve market needs to match , in the reserve constraint, to avoid market chaos caused by conflicts between transaction rules and system safety. For example, when the new energy output is reduced due to fluctuations , the amount of spot transaction needs to be reduced and the amount of reserve calling needs to be increased in multi-market interaction, and this adjustment needs to meet the power balance constraint to ensure real-time balance between supply and demand.

[0046] Specifically, the system operation constraints include: (1) Power balance constraint, which represents the real-time balance of new energy, traditional power sources, energy storage and load, as shown in the following formula:

[0047] where, is the new energy set (wind power, photovoltaic), is the output of new energy i; is the traditional power source set, is the output of traditional power source ; is the energy storage set, , are the charging and discharging power of energy storage, respectively; is the load set, is the load demand.

[0048] (2) New energy output constraint, which is used to consider the upper and lower limits of fluctuation.

[0049]

[0050] wherein, is a fluctuation tolerance coefficient, which can be set according to the prediction accuracy.

[0051] In step S2, a new energy market entry influence evaluation model is introduced to quantize the dynamic influence of new energy on market price, reserve demand and power supply reliability, and an influence evaluation result is obtained.

[0052] Specifically, the new energy market entry influence evaluation model quantizes the dynamic influence of new energy on market price, reserve demand and power supply reliability to provide a quantitative basis for the design of regulation strategies, and the specific process includes: Step S21, the influence of new energy on spot electricity price is described by using a dynamic price fluctuation index:

[0053] wherein, is a price fluctuation index at time t; is a spot electricity price at time t; is an average electricity price; is a new energy penetration rate (new energy output / total load) at time t, and the higher the penetration rate, the greater the weight.

[0054] Step S22, a safety factor and maximum and minimum output values of new energy are introduced to quantize the additional demand of reserve capacity caused by new energy volatility. As shown in the following formula:

[0055] wherein, is an incremental reserve at time t; is a safety factor; , are maximum and minimum outputs of new energy, respectively.

[0056] Step S23, the influence of new energy fluctuation on power supply reliability is evaluated by using a loss of load probability (LOLP), as shown in the following formula:

[0057] wherein, is a probability function; , , are new energy output, traditional power source output and load demand, respectively.

[0058] In step S3, based on the influence evaluation result, an optimization model is set to maximize new energy consumption, minimize market fluctuation and minimize operation cost, and multi-market rules and resource constraints are integrated.

[0059] Step S31, the objective function established with the goals of maximizing new energy consumption, minimizing market fluctuations and minimizing operating costs is:

[0060]

[0061] In the formula, is the comprehensive benefit; is the new energy consumption rate; is the market price fluctuation index; is the total operating cost; , , is the weight coefficient.

[0062] Subsequently, the multi-market rules and resource constraints are integrated, including: Step S32, the cost function is constructed, including the traditional power generation cost and the energy storage scheduling cost.

[0063] The traditional power generation cost is:

[0064] In the formula, , , is the cost coefficient.

[0065] The energy storage scheduling cost is:

[0066] In the formula, , are the charging and discharging unit costs respectively.

[0067] Step S33, the regulation constraints are performed, including the new energy consumption constraint, the price fluctuation constraint and the reserve constraint.

[0068] The new energy consumption constraint is:

[0069] In the formula, is the minimum consumption rate, such as 90%.

[0070] The price fluctuation constraint is:

[0071] In the formula, is the maximum allowed fluctuation, such as 20% of the average electricity price.

[0072] The reserve constraint is:

[0073] wherein, , is a traditional power supply, energy storage provides backup, is the basic backup.

[0074] Step S4, for the high dimension and strong randomness of new energy fluctuation scenarios, the model solving method based on fluctuation adaptive improved grey wolf algorithm is used to complete single period optimization.

[0075] Step S41, through particle coding and fluctuation initialization, the initial solution covering high, medium and low new energy fluctuation scenarios is generated, which avoids the initial solution concentrated in stable scenarios and provides a comprehensive basis for subsequent optimization.

[0076] (1) Based on the particle coding rule, the core variables in collaborative regulation, i.e. new energy output , market transaction variables , energy storage charging and discharging power , are coded into different dimensions of particles. For example, the first dimension of the particle corresponds to the wind power output , the second dimension corresponds to the spot transaction electricity , the third dimension corresponds to the energy storage discharging power , forming a vector , wherein is the particle number.

[0077] (2) The fluctuation driven initialization formula is constructed as follows:

[0078] wherein, is the initial position of the d-th particle in the d-th dimension; , is the minimum value and maximum value of the d-th variable; is a random number in the range [0, 1]; is the fluctuation coefficient, which is dynamically adjusted according to the new energy fluctuation rate: in high fluctuation, the value is 0.2-0.3, in medium fluctuation, the value is 0.1-0.2, in low fluctuation the value is 0.05-0.1, which ensures that the initial solution covers the actual fluctuation scenario.

[0079] Step S42, use the improved grey wolf algorithm based on fluctuation adaptation to select leader wolves and calculate fitness.

[0080] The initial solution is evaluated by the fitness function, and the leader wolves with "global optimal" "suboptimal" "third optimal" regulation effect are selected and defined as​ Wolf, Wolf and Wolf, provide direction for subsequent position updates.

[0081] Wherein, the fitness function takes the collaborative regulation objective function as the core, and considers the constraint compliance, and the formula is:

[0082] Wherein, is the fitness value, and the higher the value is, the better the regulation scheme is; the constraints include that the new energy consumption rate is greater than or equal to (such as 90%), the price fluctuation is less than or equal to (such as 20% of the average electricity price), and the reserve capacity is greater than or equal to When the constraints are violated, the coefficient is punished to force the screening of compliant solutions.

[0083] Secondly, the leader wolf screening is performed. By calculating the of all particles, the highest particle is set as Wolf (global optimal solution), the second highest is set as Wolf (local optimal solution), the third highest is set as Wolf (candidate optimal solution), and the remaining particles are ordinary wolves, and the position updates of subsequent ordinary wolves will follow the direction of , , Wolf.

[0084] Step S43, by dynamically adjusting the convergence factor, the ability to avoid falling into local optimum and accelerating convergence is avoided, and the new energy fluctuation intensity is adapted.

[0085] Specifically, by using adaptive inertia weight and learning factor, the exploration and utilization ability of the algorithm is dynamically adjusted to adapt to the new energy fluctuation intensity:

[0086] Wherein, is the convergence factor; is the fluctuation intensity coefficient at time, which is represented as the deviation ratio of the actual output of the new energy to the predicted output.

[0087] Step S44, the new energy output fluctuation corrects the particle position, so that the solution update directly responds to the real-time fluctuation of the new energy, and avoids the disconnection between the optimization result and the actual operation scene.

[0088] Wherein, the core formula of position update is:

[0089]

[0090]

[0091] wherein, is the current optimal position, i.e. the wolf position; is the current position of the th particle; is a [0,1] random number, used to simulate the randomness of the wolf pack hunting; D is the distance of the individual from the optimal position; is a compensation coefficient; is the new energy output fluctuation.

[0092] Step S45, the out-of-limit variable is corrected by the projection method, to ensure that all solutions meet the system safety and market rule constraints, and an iteration termination condition is set, and the final optimal regulation scheme is output.

[0093] wherein, the constraint processing rule adopts the projection method, and the variable that violates the constraint after the position is updated is forced to project to the feasible domain boundary, for example: if the traditional power output (exceeds the maximum output), it is forced to be set to ; if the energy storage discharging power (exceeds the maximum discharging power), it is forced to be set to ; if the new energy consumption rate , the spot transaction volume is adjusted until .

[0094] The iteration termination condition is set: repeat steps S42-S44, and when the iteration number reaches or the optimal fitness value does not improve for 10 consecutive iterations, the iteration is terminated; after the iteration is terminated, the particle corresponding to the wolf in each dimension is the optimal regulation variable, which can be directly converted into an executable regulation strategy.

[0095] Embodiment Two The embodiment discloses a power market coordinated regulation and evaluation system considering new energy fluctuation; As shown in Figure 2 , the power market coordinated regulation and evaluation system considering new energy fluctuation comprises: An interactive system model establishment module is configured to establish a new energy and power market interactive system model according to the output characteristics of the new energy and the transaction rules of the power market; The evaluation result acquisition module is configured to: on the basis of constructing the interactive system model, introducing a new energy market entry influence evaluation model to quantize dynamic influences of the new energy on market prices, standby demand, and power supply reliability, and obtaining influence evaluation results. The collaborative regulation optimization model construction module is configured to: based on the influence evaluation results, constructing a collaborative regulation optimization model with new energy consumption maximization, market fluctuation minimization, and operation cost minimization as objective functions, and integrating multiple market rules and resource constraints. The optimal regulation strategy generation module is configured to: solving the constructed collaborative regulation optimization model by using a fluctuation adaptive improved grey wolf algorithm, and generating an optimal regulation strategy for the power market. Embodiment three An objective of this embodiment is to provide a computer-readable storage medium.

[0096] A computer-readable storage medium has a computer program stored thereon, and the program, when executed by a processor, implements the steps in the power market collaborative regulation and evaluation method considering new energy fluctuation as described in Embodiment 1.

[0097] Embodiment four An objective of this embodiment is to provide an electronic device.

[0098] An electronic device includes a memory, a processor, and a program stored in the memory and executable on the processor, and the processor, when executing the program, implements the steps in the power market collaborative regulation and evaluation method considering new energy fluctuation as described in Embodiment 1.

[0099] The steps and methods in the above embodiments two, three, and four correspond to Embodiment 1, and the specific implementation can be referred to the relevant description in Embodiment 1. The term “computer-readable storage medium” should be understood as including a single medium or multiple media of one or more instruction sets; and should also be understood as including any medium capable of storing, encoding, or carrying instruction sets for execution by a processor and causing the processor to perform any method in the present application.

[0100] Those skilled in the art should understand that each module or step of the present application described above can be implemented by a general computer device, and alternatively, they can be implemented by program codes executable by a computing device, so that they can be stored in a storage device for execution by a computing device, or they can be respectively manufactured into individual integrated circuit modules, or multiple modules or steps among them can be manufactured into a single integrated circuit module. The present application is not limited to any specific combination of hardware and software.

[0101] The above describes the specific embodiments of the present application in combination with the drawings, but is not a limitation on the protection scope of the present application. Those skilled in the art should understand that various modifications or variations made by those skilled in the art on the basis of the technical solutions of the present application without creative labor are still within the protection scope of the present application.

Claims

1. A power market coordinated regulation and evaluation method considering new energy volatility, characterized in that, include: Establish an interactive system model between new energy sources and the electricity market based on the output characteristics of new energy sources and the trading rules of the electricity market; Based on the construction of an interactive system model, a new energy market entry impact assessment model is introduced to quantify the dynamic impact of new energy on market prices, reserve demand, and power supply reliability, and the impact assessment results are obtained. Based on the impact assessment results, a collaborative regulation and optimization model is constructed with the objective functions of maximizing new energy consumption, minimizing market fluctuations, and minimizing operating costs, integrating multiple market rules and resource constraints. An improved gray wolf algorithm based on fluctuation adaptation is used to solve the constructed collaborative regulation optimization model and generate the optimal regulation strategy for the power market.

2. The method according to claim 1, wherein the method further comprises: determining the new energy fluctuation; and determining the new energy fluctuation according to the new energy fluctuation and the new energy fluctuation range. Based on the output characteristics of new energy sources and the trading rules of the electricity market, a model for the interaction between new energy sources and the electricity market is established, including: Modeling based on the characteristics of new energy output includes constructing a wind power prediction model and a photovoltaic power fluctuation model; Multi-market interaction modeling based on trading rules of the new energy and electricity markets; By introducing system operation constraints, the model of the interaction between new energy and the electricity market is provided with operational boundaries and feasibility basis, ensuring that the model truly reflects the actual operating rules of the power system. 3.The method of claim 2, wherein the method further comprises: determining the new energy fluctuation; and determining the new energy fluctuation according to the new energy fluctuation and the new energy fluctuation range. The wind power prediction model is as follows: wherein, is the actual output power of the wind power at the moment; is the rated power of the wind power; is is the actual wind speed at the moment; , , are respectively the cut-in wind speed, the rated wind speed and the cut-out wind speed. 4.The method of claim 2, wherein the method further comprises: determining the new energy fluctuation; and determining the new energy fluctuation according to the new energy fluctuation and the new energy fluctuation range. The photovoltaic power fluctuation model is as follows: wherein, is the actual output power of the photovoltaic at the time instant t; is the predicted output power of the photovoltaic at the time instant t; is the fluctuation coefficient at the time instant t.

5. The method according to claim 1, wherein the method further comprises: determining the new energy fluctuation; and determining the new energy fluctuation according to the new energy fluctuation and the new energy fluctuation range. The process of introducing a new energy market entry impact assessment model to quantify the dynamic impact of new energy on market prices, reserve demand, and power supply reliability is as follows: The impact of new energy sources on spot electricity prices is characterized by a dynamic price volatility index. Introducing a safety factor and the maximum and minimum output values ​​of new energy sources to quantify the additional reserve capacity demand caused by the volatility of new energy sources; The impact of power shortage probability on the reliability of power supply is assessed by evaluating the impact of renewable energy fluctuations. 6.The method of claim 1, wherein the method further comprises: determining a new energy fluctuation of the new energy; and determining a new energy fluctuation range of the new energy based on the new energy fluctuation. The objective function established to maximize renewable energy consumption, minimize market volatility, and minimize operating costs is as follows: In the formula, is the comprehensive benefit; is the new energy consumption rate; is the market price fluctuation index; is the total operation cost; , , is the weight coefficient.

7. The method according to claim 1, wherein the method further comprises: determining the new energy fluctuation; and determining the new energy fluctuation according to the new energy fluctuation and the new energy fluctuation range. The process of solving the constructed collaborative regulation optimization model using the fluctuation-adaptive improved Grey Wolf algorithm to generate the optimal regulation strategy for the power market is as follows: An initialization strategy driven by new energy fluctuation scenarios is adopted to generate initial solutions covering high, medium, and low fluctuations; The ability to explore and utilize adaptive inertia weights and learning factors in a dynamic adjustment algorithm is adopted to adapt to the intensity of new energy fluctuations. A fluctuation compensation factor is introduced to correct the individual position, and a constraint is applied using the projection method.

8. A power market coordinated regulation and evaluation system considering new energy volatility, characterized in that: include: The interactive system model building module is configured to: build an interactive system model between new energy sources and the electricity market based on the output characteristics of new energy sources and the trading rules of the electricity market; The assessment results acquisition module is configured to: based on the construction of the interactive system model, introduce the new energy market entry impact assessment model to quantify the dynamic impact of new energy on market prices, reserve demand, and power supply reliability, and obtain the impact assessment results; The collaborative regulation and optimization model construction module is configured to: based on the impact assessment results, construct a collaborative regulation and optimization model with the objective functions of maximizing new energy consumption, minimizing market fluctuations, and minimizing operating costs, and integrate multiple market rules and resource constraints; The optimal control strategy generation module is configured to use the improved Grey Wolf algorithm based on fluctuation adaptation to solve the constructed collaborative control optimization model and generate the optimal control strategy for the power market.

9. A computer-readable storage medium having stored thereon a program, characterized in that, The program is executed by the processor to implement the steps in the power market coordinated regulation and evaluation method considering new energy fluctuation according to any one of claims 1-7.

10. An electronic device comprising a memory, a processor, and a program stored on the memory and executable on the processor, characterized in that, The processor executes the program to implement the steps in the power market coordinated regulation and evaluation method considering new energy fluctuation according to any one of claims 1-7.

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