Virtual power plant scheduling method and system based on risk avoidance and demand response, medium and processor

Abstract

The invention provides a virtual power plant scheduling method based on risk avoidance and demand response. The method comprises the following steps: S1, respectively constructing a power market price model, a power grid operation model, a power demand model of a virtual power plant and an available resource model; s2, considering market fluctuation and equipment fault risk factors, and establishing a risk avoidance model of the virtual power plant; s3, based on the risk avoidance model, designing an optimization scheduling strategy for minimizing the risk avoidance index; s4, considering interaction between the virtual power plant and the power market and the power grid, and designing an interaction response mechanism; and S5, performing virtual power plant scheduling simulation evaluation by integrating the models, the scheduling strategies, the constraint conditions and the interactive response mechanism, and adjusting model parameters and strategies according to an evaluation result to optimize performance. According to the scheme, the technical problems in virtual power plant scheduling are solved more comprehensively and flexibly, and compared with a traditional method, the method has obvious superiority in performance.

Description

Technical Field

[0001] The present invention relates to the field of virtual power plant dispatching control technology, and in particular to a virtual power plant dispatching method, system, medium and processor based on risk avoidance and demand response. Background Art

[0002] A virtual power plant is a power coordination management system that uses advanced information and communication technologies and software systems to achieve the integration and coordinated optimization of distributed resources such as distributed power sources, energy storage systems, controllable loads, and electric vehicles. It is designed to participate in the power market and power grid operation as a unique power plant. The application of the interactive response technology of virtual power plants can effectively reduce the demand for electricity during peak hours, improve the operational flexibility of the power grid, and significantly reduce equipment investment and improve resource utilization efficiency. Despite this, the interactive response technology of virtual power plants in existing related technologies is still at an immature stage, and the design of the interactive mechanism is unreasonable, resulting in unsatisfactory implementation of the interactive response of virtual power plants.

[0003] In view of this, a virtual power plant scheduling method, system, medium and processor based on risk avoidance and demand response are needed. Summary of the invention

[0004] In view of the problem in the prior art that the interactive response implementation effect of virtual power plants is not ideal, the present invention provides a virtual power plant scheduling method, system, medium and processor based on risk avoidance and demand response, which can solve the problem that the interactive response implementation effect is not ideal. The specific technical solution is as follows:

[0005] A virtual power plant dispatching method based on risk avoidance and demand response includes the following steps:

[0006] S1: Construct the power market price model, power grid operation model, power demand model and available resource model of the virtual power plant respectively;

[0007] S2: Considering market fluctuations and equipment failure risk factors, a risk avoidance model for virtual power plants is established;

[0008] S3: Based on the risk aversion model, an optimized scheduling strategy is designed to minimize the risk aversion index;

[0009] S4: Consider the interaction between virtual power plants and the electricity market and power grid, and design an interactive response mechanism;

[0010] S5: Comprehensively integrate the above models, scheduling strategies, constraints and interactive response mechanisms to conduct virtual power plant scheduling simulation evaluation, and adjust model parameters and strategies according to the evaluation results to optimize performance.

[0011] Furthermore, the construction of the power market price model, the power grid operation model, the power demand model of the virtual power plant and the available resource model respectively comprises the following steps:

[0012] The electricity market price model is constructed as follows:

[0013] P market,t =P market,t-1 +r t ;

[0014] The grid operation model is constructed as follows:

[0015] V i =V j -Z ij I ij ;

[0016] The power demand model for building a virtual power plant is as follows:

[0017]

[0018] The available resource model includes a distributed power supply model, an energy storage system model and a controllable load model; the energy storage system model is constructed as follows:

[0019] R t =R t-1 +P charge,t -P discharge,t ;

[0020] Among them, R t is the available energy storage at time t, P charge,t and P discharge,t are the charging and discharging powers at time t, respectively; P market,t is the market price at time t; P market,t-1 is the price at the previous moment; r t is a random disturbance; V i and V j are the voltages at nodes i and j respectively; Z ij is the impedance of line ij; I ij is the current of line ij; D t is the electricity demand at time t; is a constant term; and θ j are the model parameter coefficients; p and q represent the order of autoregression and moving average respectively; μ t represents the white noise error.

[0021] Furthermore, the risk avoidance model of a virtual power plant is established by considering market fluctuations and equipment failure risk factors, including the following steps:

[0022] Considering the impact of market fluctuations on the operation of virtual power plants, a volatility model E is established. b as follows:

[0023]

[0024] E b =σ t ·Z t ;

[0025] Considering the impact of equipment failure on the operation of the virtual power plant, the reliability model of the entire virtual power plant system is established as follows:

[0026] R(t)=e -λt ;

[0027] Combining the impact of market fluctuations and equipment failures, the risk aversion model of virtual power plants is established as follows:

[0028] E t =σ t ·Z t +(1-R(t))·γ;

[0029] In the above formula, E t is the risk aversion index; γ is the additional penalty when the system is unavailable; is the rate of change of market price at time t; ω is a constant term; α is the influence coefficient of the historical residual square term on the current volatility; β is the influence coefficient of the previous conditional variance on the current conditional variance; is the residual square of the previous moment; Z t is a standard normal random variable; R(t) is the reliability of the system at time t, and λ is the failure rate.

[0030] Furthermore, the design optimization scheduling strategy includes the following steps:

[0031] Constructed to minimize the risk aversion index E t The objective function for the goal is as follows:

[0032]

[0033] To ensure the rationality and feasibility of the dispatching scheme, the following constraints are added to the output of the virtual power plant, the charging power and the discharging power of the energy storage system:

[0034]

[0035] P charge,min ≤P charge,t ≤P charge,max ;

[0036] P discharge,min ≤P discharge,t≤P discharge,max ;

[0037] In the above formula, P t is the output of the virtual power plant; P charge,t is the charging power of the energy storage system; P discharge,t is the discharge power of the energy storage system; P charge,min , P charge,max are the minimum and maximum charging power of the energy storage system respectively; P discharge,min , P discharge,max are the minimum and maximum discharge power of the energy storage system respectively; D t is the electricity demand at time t; T is the scheduling time range.

[0038] Furthermore, the interactive response mechanism formula is as follows:

[0039] P t =min(P base +η·E t , G t , M t );

[0040] In the above formula, P t is the output of the virtual power plant; P base is the basic output of the virtual power plant; η is the interactive response coefficient; E t is the risk aversion index at time t; M t Contribute to market demand; t Contribute to the real-time status requirements of the power grid.

[0041] Furthermore, the virtual power plant scheduling simulation evaluation includes economic, stability and reliability evaluation, and the specific steps are as follows:

[0042] The economic feasibility of the system is evaluated using the following formula:

[0043]

[0044] To evaluate the stability of the system, the stability can be evaluated by focusing on the average risk aversion index of the system under different scenarios. To evaluate, the calculation formula is as follows:

[0045]

[0046] The reliability of the system can be evaluated by examining the overall average participation of virtual power plants in the power market under different scenarios. The calculation formula is as follows:

[0047]

[0048] Among them C totalis the total cost; P market,t is the market price at time t; C maintenance,t is the equipment maintenance cost at time t; P t For the output of virtual power plants; is the average risk aversion index; E t is the risk aversion index; is the average participation rate.

[0049] Furthermore, adjusting the model parameters and strategies according to the evaluation results includes using a genetic algorithm or a simulated annealing algorithm to adjust the parameters of the risk aversion model and the coefficients of the interactive response mechanism.

[0050] A virtual power plant dispatching system based on risk avoidance and demand response, applied to the virtual power plant dispatching method based on risk avoidance and demand response described above, comprising:

[0051] A first construction module is used to respectively construct an electricity market price model, a power grid operation model, an electricity demand model of a virtual power plant, and an available resource model;

[0052] The second building block is used to consider market fluctuations and equipment failure risk factors to establish a risk avoidance model for virtual power plants;

[0053] The strategy module is used to design an optimized scheduling strategy based on the risk aversion model to minimize the risk aversion index;

[0054] The interactive mechanism module is used to consider the interaction between the virtual power plant and the power market and power grid, and to design an interactive response mechanism;

[0055] The simulation module is used to integrate the above models, scheduling strategies, constraints and interactive response mechanisms to conduct virtual power plant scheduling simulation evaluation, and adjust model parameters and strategies according to the evaluation results to optimize performance.

[0056] A computer-readable storage medium, comprising a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute the virtual power plant scheduling method based on risk avoidance and demand response as described above.

[0057] A processor is used to run a program, wherein the program, when running, executes the virtual power plant scheduling method based on risk avoidance and demand response as described above.

[0058] Compared with the prior art, the present invention has the following beneficial effects:

[0059] 1. This solution achieves comprehensive optimization of virtual power plants by introducing risk avoidance models, optimized scheduling strategies, interactive response mechanisms and other links, and more comprehensively considers factors such as market fluctuations and equipment failures.

[0060] 2. This solution adopts an iterative process of simulation and adjustment to achieve dynamic adjustment of model parameters and strategies, enabling the virtual power plant to adapt to changing market and grid conditions and improving the adaptability and flexibility of the system.

[0061] 3. This solution achieves a comprehensive evaluation of the performance of the virtual power plant by introducing multiple performance indicators such as economy, stability, and reliability, so that the system can achieve a balance in different aspects.

[0062] 4. This solution introduces logical associations to ensure consistency in the simulation and adjustment process. At the same time, through the real-time feedback mechanism, the system can adjust the strategy in time to better adapt to the actual operation situation.

[0063] 5. This scheme uses an optimization algorithm to adjust parameters, realizes the adaptive optimization of the virtual power plant scheduling method, and continuously learns and optimizes to cope with dynamic market and grid conditions.

[0064] 6. This solution solves the technical problems in virtual power plant scheduling in a more comprehensive and flexible manner, and has obvious performance advantages over traditional methods. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following is a brief introduction to the drawings required for the specific embodiments or the description of the prior art. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn according to the actual scale.

[0066] Figure 1 A flowchart of a virtual power plant dispatching method based on risk avoidance and demand response is shown;

[0067] Figure 2 A structural diagram of a virtual power plant dispatching system based on risk avoidance and demand response. DETAILED DESCRIPTION

[0068] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0069] It should be understood that when used in this specification and the appended claims, the terms "include" and "comprises" indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0070] It should also be understood that the terms used in the present specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0071] It should be further understood that the term "and / or" used in the present description and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0072] Embodiment 1

[0073] like Figure 1 The figure shows a flow chart of a virtual power plant dispatching method based on risk avoidance and demand response, the method comprising the following steps:

[0074] S1: Evaluate the demand and available resources of virtual power plants, establish a comprehensive simulation model, and construct the electricity market price model, grid operation model, virtual power plant power demand model and available resource model respectively. These models need to reflect the dynamic characteristics and uncertainties of the actual system.

[0075] Furthermore, considering the uncertainty and dynamic changes of the market, a random process model is used, in which the market price is subject to random disturbances at every moment. The electricity market price model is as follows:

[0076] P market,t =P market,t-1 +r t ;

[0077] Among them, P market,t is the market price at time t; P market,t-1 is the price at the previous moment; r t is a random disturbance.

[0078] Furthermore, the operation model of the power grid needs to consider the impact of load changes and equipment failures, and use the load flow model to simulate the operation status of the power grid. The power grid operation model is as follows:

[0079] V i =V j -Z ij I ij ;

[0080] Among them, V i and V j is the voltage between node i and node j; Z ij is the impedance of line ij; I ij is the current in line ij.

[0081] Furthermore, the power demand model of the virtual power plant is as follows:

[0082]

[0083] Among them, D t is the electricity demand at time t; is a constant term; and θ j are the model parameter coefficients; p and q represent the order of autoregression and moving average respectively; μ t represents the white noise error.

[0084] Furthermore, the model of the virtual power plant needs to include the characteristics of various dispatchable resources. The available resource model includes a distributed power supply model, an energy storage system model and a controllable load model; the energy storage system model is as follows:

[0085] R t =R t-1 +P charge,t -P discharge,t ;

[0086] Among them, R t is the available energy storage at time t, P charge,t and P discharge,t are the charging and discharging powers at time t respectively.

[0087] In all models, random variables are introduced to simulate uncertainty. Demand and resource assessment is the basis of virtual power plant scheduling. Accurate estimation of power demand directly affects the formulation of subsequent scheduling strategies, while reasonable assessment of available resources provides virtual power plants with practical and operational energy. The connection between the two is that through dynamic assessment of demand and resources, the operation of virtual power plants can be better planned and flexible response to power markets and power grids can be achieved. Such an evaluation process can be carried out periodically to adapt to the dynamic changes of the power system. At the same time, the continuous optimization and updating of the evaluation model is also an important link to ensure the accuracy and practicality of the virtual power plant scheduling strategy.

[0088] In all models, it is critical to consider uncertainty. Random variables, probability distributions, or Monte Carlo simulations are introduced to simulate uncertainty. For example, in the electricity market price model, random disturbance terms can be introduced; in the power grid operation model, random load fluctuations can be considered. There are logical connections between the various sub-models. Changes in market prices will affect the economic operation of the virtual power plant, and changes in the power grid operation status will affect the real-time adjustment of the virtual power plant. By establishing logical connections in the entire system, it is ensured that the simulation model can fully and accurately reflect the dynamic characteristics of the virtual power plant in actual operation.

[0089] S2: Considering the risk factors of market fluctuations and equipment failure, a risk avoidance model for virtual power plants is established.

[0090] Furthermore, the risk avoidance model of a virtual power plant is established by considering market fluctuations and equipment failure risk factors, including the following steps:

[0091] Considering the impact of market fluctuations on the operation of virtual power plants, we can use historical market price data to establish a volatility model. b as follows:

[0092]

[0093] E b =σ t ·Z t ;

[0094] in, is the rate of change of market price at time t; ω is a constant term; α is the influence coefficient of the historical residual square term on the current volatility; β is the influence coefficient of the previous conditional variance on the current conditional variance; is the residual square of the previous moment; Z t is a standard normal random variable.

[0095] Considering the impact of equipment failure on the operation of the virtual power plant, an equipment reliability model can be established. Using several important parameters in reliability theory, such as failure rate λ and maintenance rate μ, the reliability model of the entire virtual power plant system is established using the reliability block diagram method as follows:

[0096] R(t)=e -λt ;

[0097] In the above formula, R(t) is the reliability of the system at time t, and λ is the failure rate.

[0098] Combining the impact of market fluctuations and equipment failures, we define the risk aversion index E. t , represents the overall risk at time t, and the risk aversion model of the virtual power plant is established as follows:

[0099] E t =σ t ·Z t +(1-R(t))·γ;

[0100] In the above formula, E t is the risk aversion index; γ is the additional penalty when the system is unavailable.

[0101] The establishment of the risk aversion model provides a comprehensive risk assessment for virtual power plant scheduling by considering both market fluctuations and equipment failure risk factors. In the next step of optimizing the scheduling strategy, this index will be used to formulate a more robust scheduling plan to minimize the overall risk of the system.

[0102] S3: Based on the risk aversion model, an optimized scheduling strategy is designed to minimize the risk aversion index.

[0103] Furthermore, the optimization goal is to minimize the risk aversion index E t , the design optimization scheduling strategy includes the following steps:

[0104] Constructed to minimize the risk aversion index E t The objective function for the goal is as follows:

[0105]

[0106] Where T is the scheduling time range.

[0107] In order to minimize the objective function, a set of optimization variables are introduced, such as the output of the virtual power plant, the charging and discharging power of the energy storage system, etc. These variables can be expressed as:

[0108] P t ,P charge,t ,P discharge,t ;

[0109] To ensure the rationality and feasibility of the dispatching scheme, the following constraints are added to the output of the virtual power plant, the charging power and the discharging power of the energy storage system:

[0110] The total output of the virtual power plant should meet the needs of the power market and the power grid:

[0111] The charging and discharging power of the energy storage system should be subject to technical limitations: charge,min ≤P charge,t ≤P charge,max ,

[0112] P discharge,min ≤P discharge,t ≤P discharge,max ;

[0113] In the above formula, P t is the output of the virtual power plant; P charge,t is the charging power of the energy storage system; P discharge,t is the discharge power of the energy storage system; P charge,min , P charge,max are the minimum and maximum charging power of the energy storage system respectively; P discharge,min , P discharge,max are the minimum and maximum discharge power of the energy storage system respectively; D t It represents the electricity demand that needs to be met by the electricity market or power grid at a specific time point t.

[0114] Introducing the risk aversion index as a constraint to ensure that the system takes risk factors into account during optimization:

[0115] E t =σ1·Z t +(1-R(t))·γ;

[0116] This constraint ensures that the system not only focuses on the needs of the electricity market and the grid during optimization, but also fully considers the impact of market fluctuations and equipment failures to minimize overall risk.

[0117] To minimize the risk aversion index E t The objective function is combined with the optimization scheduling strategy of the constraints, so that the system not only focuses on the needs of the electricity market and the power grid during optimization, but also fully considers the impact of market fluctuations and equipment failures. A more robust and secure virtual power plant scheduling plan can be formulated, which can be continuously optimized in real-time operation to adapt to the dynamic changes of the market and the system.

[0118] S4: Consider the interaction between virtual power plants and the electricity market and power grid, and design an interactive response mechanism.

[0119] Furthermore, the interactive response mechanism is designed by comprehensively considering the power market demand and the real-time status of the power grid. In order to ensure that the output of the virtual power plant can meet the requirements of the market and the power grid, it can also be adjusted according to the risk aversion index. This not only takes into account the economic efficiency of the market, but also ensures the stability of the power grid. The interactive response mechanism between the virtual power plant and the power market and the power grid is designed as follows:

[0120] P t =min(P base +η·E t , G t , M t );

[0121] In the above formula, P t is the output of the virtual power plant, P base is the basic output of the virtual power plant; η is the interactive response coefficient; Et is the risk aversion index at time t; M t Contribute to market demand; t Contribute to the real-time status requirements of the power grid.

[0122] This interactive response mechanism is dynamic and can flexibly adjust the output of the virtual power plant according to real-time demand and system status, thereby achieving rapid response to the electricity market and power grid. Through continuous monitoring and adjustment, the system can maintain efficient operation under changing market and power grid conditions.

[0123] S5: Comprehensively evaluate the above models, dispatch strategies, constraints and interactive response mechanisms to conduct virtual power plant dispatch simulation, and adjust model parameters and strategies according to the evaluation results to optimize performance. The following steps are included:

[0124] According to the actual data and system characteristics, set the simulation parameters. The parameters include the parameters in the risk aversion model, the rate of change of the market price, and the reliability parameters of the equipment to ensure the authenticity and accuracy of the simulation. Risk aversion model parameter setting, consider the risk aversion model, the parameters of which include the adjustment coefficient of market fluctuations, the influence coefficient of equipment failure, etc. These parameters can be determined through historical data analysis, expert opinions or field tests. For example, let a represent the adjustment coefficient of market fluctuations, b represent the influence coefficient of equipment failure, then: a = 0.1, b = 0.5. Market price change rate setting, the rate of change of market prices can reflect the volatility of the market. This parameter can be analyzed through historical market data or set according to the law of the market. For example, let c represent the rate of change of market prices, then: c = 0.02. Equipment reliability parameter setting, the reliability parameters of the equipment include failure rate λ, maintenance rate μ, etc. These parameters can be determined through the technical specifications of the equipment, data provided by the manufacturer or actual operation data. For example, let λ = 0.0001 (indicating the failure rate per hour), μ = 0.0002 (indicating the maintenance rate per hour). Other related parameter settings, according to the specific sub-models and characteristics in the simulation model, set other related parameters, which may include the capacity and response speed of various resources in the virtual power plant. For example, assuming the maximum charging power of the energy storage system is P charge,max =100MW. Ensure that the simulation parameters are consistent with the actual operating characteristics of the system to ensure the authenticity and accuracy of the simulation. The key to this step is to fully understand the system and reasonably set the parameters based on actual data and experience to obtain an accurate simulation of the system behavior.

[0125] Run the simulation model to simulate the operation of the virtual power plant under different scenarios and record key performance indicators. For example, risk aversion index, electricity market participation, flexibility of grid dispatch, etc. The following steps are included: timing simulation, which simulates the operation of the virtual power plant step by step according to the predetermined time step. It can be a timing simulation in hours to ensure that the dynamic characteristics of the system are considered at every moment. The timing simulation can use an iterative method to gradually update the system status. Record key performance indicators. During the simulation process, record key performance indicators for subsequent performance evaluation. These indicators include risk aversion index, electricity market participation, flexibility of grid dispatch, etc. Take the risk aversion index as an example:

[0126] E t =σ t ·Z t +(1-R(t))·γ

[0127] Among them, σ t is the rate of change of market volatility, Z t is a standard normal random variable, R(t) is the reliability of the equipment, and γ is the additional penalty when the system is unavailable.

[0128] Consider different scenarios. During the simulation, consider different scenarios, such as market price fluctuations, changes in grid load, equipment failures, etc. By introducing these changes in the simulation, evaluate the operating performance of the virtual power plant under different scenarios. Ensure the logical relevance of the simulation operation, that is, the state of the system at each moment is based on the state of the previous moment and the setting of simulation parameters, which helps to maintain the consistency and authenticity of the simulation.

[0129] The performance of the virtual power plant scheduling method is evaluated through simulation results, focusing on whether the optimization goal of minimizing the risk aversion index is achieved; based on the performance evaluation results, the model parameters and strategies are adjusted.

[0130] Furthermore, the performance evaluation includes economic, stability and reliability evaluation, and the specific steps are as follows:

[0131] Considering the economic feasibility of the virtual power plant, the total cost of the simulation operation can be calculated. This includes the benefits of participating in the electricity market, the cost of equipment operation and maintenance, etc., and the economic feasibility of the system is evaluated. The formula is as follows:

[0132]

[0133] Among them C total is the total cost; P market,t is the market price at time t; C maintenance,t is the equipment maintenance cost at time t.

[0134] A lower risk aversion index indicates that the system has better stability in the face of market fluctuations and equipment failures. To evaluate the stability of the system, the stability can be evaluated by focusing on the average risk aversion index of the system under different scenarios. To evaluate, the calculation formula is as follows:

[0135]

[0136] The reliability of the system can be evaluated by examining the overall average participation of virtual power plants in the power market under different scenarios. The calculation formula is as follows:

[0137]

[0138] Among them C total is the total cost; P market,t is the market price at time t; C maintenance,t is the equipment maintenance cost at time t; P t For the output of virtual power plants; is the average risk aversion index; E t is the risk aversion index; is the average participation rate.

[0139] Ensure that the performance evaluation indicators are consistent with the scheduling objectives and design principles of the virtual power plant. The balance between economy, stability and reliability is the key to the evaluation, and the logical correlation ensures the comprehensiveness of the evaluation results. According to the performance evaluation results, adjust the model parameters and strategies, including adjusting the parameters of the risk aversion model, optimizing the weights of the scheduling strategy, and adjusting the coefficients of the interactive response mechanism. This process can be carried out using optimization algorithms, such as genetic algorithms and simulated annealing. Through the iterative process of simulation and adjustment, the virtual power plant scheduling method is gradually optimized to make it more suitable for operation under different market and power grid conditions. Ensure that the scheduling method can respond flexibly to actual changes and remain efficient and stable.

[0140] This embodiment has the following beneficial effects:

[0141] 1. This solution achieves comprehensive optimization of virtual power plants by introducing risk avoidance models, optimized scheduling strategies, interactive response mechanisms and other links, and more comprehensively considers factors such as market fluctuations and equipment failures.

[0142] 2. This solution adopts an iterative process of simulation and adjustment to achieve dynamic adjustment of model parameters and strategies, enabling the virtual power plant to adapt to changing market and grid conditions and improving the adaptability and flexibility of the system.

[0143] 3. This solution achieves a comprehensive evaluation of the performance of the virtual power plant by introducing multiple performance indicators such as economy, stability, and reliability, so that the system can achieve a balance in different aspects.

[0144] 4. This solution introduces logical associations to ensure consistency in the simulation and adjustment process. At the same time, through the real-time feedback mechanism, the system can adjust the strategy in time to better adapt to the actual operation situation.

[0145] 5. This scheme uses an optimization algorithm to adjust parameters, realizes the adaptive optimization of the virtual power plant scheduling method, and continuously learns and optimizes to cope with dynamic market and grid conditions.

[0146] 6. This solution solves the technical problems in virtual power plant scheduling in a more comprehensive and flexible manner, and has obvious performance advantages over traditional methods.

[0147] Embodiment 2

[0148] like Figure 2 The figure shows a schematic diagram of the structure of a virtual power plant dispatching system based on risk avoidance and demand response, which is applied to the virtual power plant dispatching method based on risk avoidance and demand response described above, including:

[0149] A first construction module is used to respectively construct an electricity market price model, a power grid operation model, an electricity demand model of a virtual power plant, and an available resource model;

[0150] The second building block is used to consider market fluctuations and equipment failure risk factors to establish a risk avoidance model for virtual power plants;

[0151] The strategy module is used to design an optimized scheduling strategy based on the risk aversion model to minimize the risk aversion index;

[0152] The interactive mechanism module is used to consider the interaction between the virtual power plant and the power market and power grid, and to design an interactive response mechanism;

[0153] The simulation module is used to integrate the above models, scheduling strategies, constraints and interactive response mechanisms to conduct virtual power plant scheduling simulation evaluation, and adjust model parameters and strategies according to the evaluation results to optimize performance.

[0154] Embodiment 3

[0155] A computer-readable storage medium, comprising a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute the virtual power plant scheduling method based on risk avoidance and demand response as described above.

[0156] Embodiment 4

[0157] A processor is used to run a program, wherein the program, when running, executes the virtual power plant scheduling method based on risk avoidance and demand response as described above.

[0158] The present application provides a virtual power plant scheduling method based on risk aversion and demand response, comprising the following steps: S1: constructing a power market price model, a power grid operation model, a power demand model of a virtual power plant, and an available resource model respectively; S2: considering market fluctuations and equipment failure risk factors, establishing a risk aversion model for a virtual power plant; S3: designing an optimized scheduling strategy based on the risk aversion model to minimize the risk aversion index; S4: considering the interaction between the virtual power plant and the power market and the power grid, designing an interactive response mechanism; S5: conducting a virtual power plant scheduling simulation evaluation based on the above models, scheduling strategies, constraints, and interactive response mechanisms, and adjusting model parameters and strategies according to the evaluation results to optimize performance. This scheme achieves comprehensive optimization of virtual power plants by introducing multiple links such as risk aversion models, optimized scheduling strategies, and interactive response mechanisms, and more comprehensively considers factors such as market fluctuations and equipment failures. The iterative process of simulation and adjustment is adopted to achieve dynamic adjustment of model parameters and strategies, so that virtual power plants can adapt to changing market and power grid conditions, and improve the adaptability and flexibility of the system. By introducing multiple performance indicators such as economy, stability, and reliability, a comprehensive evaluation of the performance of the virtual power plant is achieved, so that the system can achieve a balance in different aspects. The introduction of logical associations ensures consistency in the simulation and adjustment process. At the same time, through the real-time feedback mechanism, the system can adjust the strategy in time to better adapt to the actual operation situation. The optimization algorithm is used for parameter adjustment to achieve adaptive optimization of the virtual power plant scheduling method, and continuous learning and optimization are carried out to cope with dynamic market and power grid conditions. It solves the technical problems in virtual power plant scheduling in a more comprehensive and flexible way, and has obvious advantages in performance compared with traditional methods.

[0159] Those of ordinary skill in the art will appreciate that the units of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition of each example has been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0160] In the embodiments provided by the present invention, it should be understood that the division of units is only a logical function division, and there may be other division methods in actual implementation, for example, multiple units can be combined into one unit, one unit can be split into multiple units, or some features can be ignored, etc.

[0161] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0162] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-0nlyMemory), random access memory (RAM, RandomAccessMemory), mobile hard disk, magnetic disk or optical disk, etc., which can store program code.

[0163] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. These modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and specification of the present invention.

Claims

1. A virtual power plant scheduling method based on risk avoidance and demand response, characterized in that: The following steps are involved: S1: Construct the power market price model, power grid operation model, power demand model and available resource model of the virtual power plant respectively; S2: Considering market fluctuations and equipment failure risk factors, a risk avoidance model for virtual power plants is established; S3: Based on the risk aversion model, an optimized scheduling strategy is designed to minimize the risk aversion index; S4: Consider the interaction between virtual power plants and the electricity market and power grid, and design an interactive response mechanism; S5: Comprehensively integrate the above models, scheduling strategies, constraints and interactive response mechanisms to conduct virtual power plant scheduling simulation evaluation, and adjust model parameters and strategies according to the evaluation results to optimize performance.

2. The virtual power plant scheduling method based on risk avoidance and demand response according to claim 1 is characterized in that: The method of respectively constructing the power market price model, the power grid operation model, the power demand model and the available resource model of the virtual power plant comprises the following steps: The electricity market price model is constructed as follows: P market,t =P market,t-1 +r t ; The grid operation model is constructed as follows: V i =V j -Z ij ·I ij ; The power demand model for building a virtual power plant is as follows: The available resource model includes a distributed power supply model, an energy storage system model and a controllable load model; the energy storage system model is constructed as follows: R t =R t-1 +P charge,t -P discharge,t ; Among them, R t is the available energy storage at time t, P charge,t and P discharge,t are the charging and discharging powers at time t, respectively; P market,t is the market price at time t; P market,t-1 is the price at the previous moment; r t is a random disturbance; V i and V j are the voltages at nodes i and j respectively; Z ij is the impedance of line ij; I ij is the current of line ij; D t is the electricity demand at time t; is a constant term; and θ j are the model parameter coefficients; p and q represent the order of autoregression and moving average respectively; μ t represents the white noise error.

3. The virtual power plant scheduling method based on risk avoidance and demand response according to claim 1 is characterized in that: The risk avoidance model of a virtual power plant is established by taking into account market fluctuations and equipment failure risk factors, including the following steps: Considering the impact of market fluctuations on the operation of virtual power plants, a volatility model E is established. b as follows: E b =σ t ·WITH t ; Considering the impact of equipment failure on the operation of the virtual power plant, the reliability model of the entire virtual power plant system is established as follows: Combining the impact of market fluctuations and equipment failures, the risk aversion model of virtual power plants is established as follows: E t =s t ·Z t +(1-R(t))·γ; In the above formula, E t is the risk aversion index; γ is the additional penalty when the system is unavailable; is the rate of change of market price at time t; ω is a constant term; α is the coefficient of influence of the historical residual square term on the current volatility; β is the coefficient of influence of the previous conditional variance on the current conditional variance; is the residual square of the previous moment; Z t is a standard normal random variable; R(t) is the reliability of the system at time t, and λ is the failure rate.

4. The virtual power plant scheduling method based on risk avoidance and demand response according to claim 1 is characterized in that: The design optimization scheduling strategy includes the following steps: Constructed to minimize the risk aversion index E t The objective function for the goal is as follows: To ensure the rationality and feasibility of the dispatching scheme, the following constraints are added to the output of the virtual power plant, the charging power and the discharging power of the energy storage system: P charge,min ≤P charge,t ≤P charge,max ; P discharge,min ≤P discharge,t ≤P discharge,max ; In the above formula, P t is the output of the virtual power plant; P charge,t is the charging power of the energy storage system; P discharge,t is the discharge power of the energy storage system; P charge,min , P charge,max are the minimum and maximum charging power of the energy storage system respectively; P discharge,min , P discharge,max are the minimum and maximum discharge power of the energy storage system respectively; D t is the electricity demand at time t; T is the scheduling time range.

5. The virtual power plant scheduling method based on risk avoidance and demand response according to claim 1 is characterized in that: The interactive response mechanism formula is as follows: P t =min(P base +η·E t ,G t ,M t ); In the above formula, P t is the output of the virtual power plant; P base It is the basic output of the virtual power plant; η is the interaction response coefficient; E t is the risk aversion index at time t; M t Contribute to market demand; t Contribute to the real-time status needs of the power grid.

6. The virtual power plant scheduling method based on risk avoidance and demand response according to claim 1 is characterized in that: The virtual power plant scheduling simulation evaluation includes economic, stability and reliability evaluation, and the specific steps are as follows: The economic feasibility of the system is evaluated using the following formula: To evaluate the stability of the system, the stability can be evaluated by focusing on the average risk aversion index of the system under different scenarios. To evaluate, the calculation formula is as follows: The reliability of the system can be evaluated by examining the overall average participation of virtual power plants in the power market under different scenarios. The calculation formula is as follows: Among them C total is the total cost; P market,t is the market price at time t; C maintenance,t is the equipment maintenance cost at time t; P t is the output of the virtual power plant; E is the average risk aversion index; E t is the risk aversion index; is the average participation rate.

7. The virtual power plant scheduling method based on risk avoidance and demand response according to claim 6 is characterized in that: The adjusting of model parameters and strategies according to the evaluation results includes adjusting the parameters of the risk aversion model and the coefficients of the interactive response mechanism using a genetic algorithm or a simulated annealing algorithm.

8. A virtual power plant dispatching system based on risk avoidance and demand response, characterized in that: The virtual power plant scheduling method based on risk avoidance and demand response applied to any one of claims 1 to 7 comprises: A first construction module is used to respectively construct an electricity market price model, a power grid operation model, an electricity demand model of a virtual power plant, and an available resource model; The second building block is used to consider market fluctuations and equipment failure risk factors to establish a risk avoidance model for virtual power plants; The strategy module is used to design an optimized scheduling strategy based on the risk aversion model to minimize the risk aversion index; The interactive mechanism module is used to consider the interaction between the virtual power plant and the power market and power grid, and to design an interactive response mechanism; The simulation module is used to integrate the above models, scheduling strategies, constraints and interactive response mechanisms to conduct virtual power plant scheduling simulation evaluation, and adjust model parameters and strategies according to the evaluation results to optimize performance.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute the virtual power plant scheduling method based on risk avoidance and demand response as described in any one of claims 1 to 7.

10. A processor, characterized in that: The processor is used to run a program, wherein the program, when running, executes the virtual power plant scheduling method based on risk avoidance and demand response as described in any one of claims 1 to 7.

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