Virtual power plant multi-time scale optimization configuration method and device

Abstract

The invention relates to the technical field of electric power systems, in particular to a virtual power plant multi-time scale optimal configuration method and device.The method comprises the steps that ultra-short-term evaluation is conducted on the flexibility requirement of a novel electric power system based on a historical energy consumption load deconstruction and standardized modeling method, and on this basis, a virtual power plant multi-time scale optimal configuration method is established; the method comprises the following steps: describing external characteristics of a virtual power plant based on coordinate transformation and a projection method, and constructing a virtual power plant bilevel planning configuration model considering capacity coupling association constraints in combination with external characteristic differences of flexibility requirements and resource availability limitations; and performing multi-time-scale optimization configuration on the target virtual power plant according to the virtual power plant double-layer planning configuration model. Therefore, the problems that an existing optimal configuration method cannot provide a credible planning configuration boundary for the virtual power plant, the configuration result is low in accuracy, and safety and stability of a system are difficult to effectively guarantee are solved.

Description

Technical Field

[0001] This application relates to the technical field of power systems, and particularly to a multi-time scale optimal configuration method and device for a virtual power plant. Background Art

[0002] In recent years, the characteristics of the power grid source-load in the new power system have changed significantly. Although the grid connection ratio of renewable energy has been increasing continuously, which helps to reduce the carbon emissions of traditional units, due to its volatility and intermittency characteristics, the real-time power supply-demand balance of the new power system faces great pressure; in addition, with the continuous development of emerging industries, the natural growth of new power loads impacts the flexibility requirements of the system. As an emerging power system dispatching mode, the virtual power plant can effectively improve the operation stability of the power system by dynamically and accurately configuring various distributed resources.

[0003] However, the traditional optimal configuration method is carried out under a preset flexibility demand scenario, that is, the configuration boundary is known, but it ignores the influence of the dual randomness of the source-load on the flexibility demand of the new power system and cannot provide a credible planning configuration boundary for the virtual power plant; in addition, the traditional optimal configuration method aims to meet the long-term power supply-demand balance from the planning perspective, ignoring the differences in the external characteristics of peak shaving and ramping from the operation perspective and the market operation environment from the social perspective, making the configuration result inaccurate and unable to effectively ensure the safety and stability of the system.

[0004] In summary, the existing optimal configuration methods cannot provide a credible planning configuration boundary for the virtual power plant, and the configuration result has a low accuracy, making it difficult to effectively ensure the safety and stability of the system, which urgently needs to be solved. Summary of the Invention

[0005] This application provides a multi-time scale optimal configuration method and device for a virtual power plant to solve problems such as that the existing optimal configuration methods cannot provide a credible planning configuration boundary for the virtual power plant, the configuration result has a low accuracy, and it is difficult to effectively ensure the safety and stability of the system.

[0006] The first aspect of the embodiments of the present application provides a multi-time scale optimization configuration method for a virtual power plant, including the following steps: obtaining historical energy consumption load data corresponding to a target power system, and deconstructing the historical energy consumption load data to obtain an energy consumption base load that meets preset volatility requirements, and describing the influence mechanism of different resources on the flexibility requirements of the target power system through a preset normalized modeling strategy, so as to obtain a probabilistic flexible demand boundary corresponding to the target power system based on the influence mechanism and the energy consumption base load; determining the external characteristics of the target virtual power plant according to a preset coordinate transformation and projection strategy, and constructing long-time series supply-demand balance constraints and short-time series capacity coupling correlation constraints of the target virtual power plant based on the target virtual power plant, the external characteristic differences of the flexibility requirements, and preset resource availability limitations; obtaining a power market price signal corresponding to the target power market, and constructing a two-layer planning configuration model for the virtual power plant based on the probabilistic flexible demand boundary, the power market price signal, the long-time series supply-demand balance constraints, and the short-time series capacity coupling correlation constraints, so as to perform multi-time scale optimization configuration on the target virtual power plant according to the two-layer planning configuration model for the virtual power plant.

[0007] Optionally, in an embodiment of the present application, the obtaining historical energy consumption load data corresponding to a target power system, and deconstructing the historical energy consumption load data to obtain an energy consumption base load that meets preset volatility requirements, and describing the influence mechanism of different resources on the flexibility requirements of the target power system through a preset normalized modeling strategy, so as to obtain a probabilistic flexible demand boundary corresponding to the target power system based on the influence mechanism and the energy consumption base load, includes: determining the base load change law and random fluctuation model corresponding to the target power system, and jointly modeling the base load change law and the random fluctuation model to obtain a corresponding posterior distribution, and calculating the energy consumption base load through the posterior distribution; dividing the different resources into demand-oriented resources and price-sensitive resources, and normalizing the demand-oriented resources through a preset demand effect function and behavior effect function to obtain a demand-oriented resource mathematical model, and quantifying the peak load change trend corresponding to the target power system according to the demand-oriented resource mathematical model; determining the coupling relationship between the strategic behavior corresponding to the price-sensitive resources and the electricity price change based on a preset Monte Carlo simulation strategy, and generating a random sample corresponding to the coupling relationship, so as to calculate the daily charging amount standard deviation according to the random sample; calculating the occurrence probability of a preset wind-solar alternation effect event and the wind-solar alternation effect power gap based on a pre-constructed wind-solar output fluctuation model and a preset output threshold; determining the probabilistic flexible demand boundary corresponding to the target power system based on the peak load change trend, the daily charging amount standard deviation, the occurrence probability, and the wind-solar alternation effect power gap.

[0008] Optionally, in an embodiment of the present application, determining the external characteristics of the target virtual power plant according to a preset coordinate transformation and projection strategy, and constructing the long-term supply-demand balance constraint and short-term capacity coupling association constraint of the target virtual power plant based on the external characteristic differences between the target virtual power plant and the flexibility requirements and the preset resource availability limit, includes: based on the preset coordinate transformation strategy, eliminating the internal variables of various heterogeneous resource load models preset to transform the preset Minkowski sum problem into a projection problem, and characterizing the external characteristics of the target virtual power plant through the projection problem; establishing the corresponding response delay constraint, maximum regulation duration constraint, maximum cumulative regulation time constraint and regulation interval constraint of the target virtual power plant, and determining the resource availability limit according to the response delay constraint, the maximum regulation duration constraint, the maximum cumulative regulation time constraint and the regulation interval constraint; establishing the long-term supply-demand balance constraint and the short-term capacity coupling association constraint based on the external characteristic differences and the resource availability limit.

[0009] Optionally, in an embodiment of the present application, obtaining the electricity market price signal corresponding to the target electricity market, and constructing a two-layer programming configuration model of the virtual power plant based on the electricity market price signal, the long-term supply-demand balance constraint and the short-term capacity coupling association constraint, so as to perform multi-time scale optimal configuration on the target virtual power plant according to the two-layer programming configuration model of the virtual power plant, includes: obtaining the electricity market price signal corresponding to the target electricity market, and constructing a revenue model of the target virtual power plant in the target electricity market based on the electricity market price signal; constructing the two-layer programming configuration model of the virtual power plant based on the long-term supply-demand balance constraint, the short-term capacity coupling association constraint, the revenue model and the probabilistic flexible demand boundary.

[0010] Optionally, in an embodiment of the present application, the mathematical expression of the two-layer programming configuration model of the virtual power plant is:

[0011] Wherein, F cost represents the total cost of the target virtual power plant; Q supply represents the supply capacity of the target virtual power plant to meet the flexibility requirements; C ( x , y ) represents the construction cost of the target virtual power plant; B ( x , y ) represents the revenue of the target virtual power plant; C inv,PSR represents the configuration cost coefficient of PSR; EPSR Represents the configured capacity of the PSR; C inv,DDR Represents the configured cost coefficient of the DDR; E DDR Represents the configured capacity of the DDR; C om Represents the operation and maintenance cost coefficient; Represents t The output of the PSR at a certain moment; Represents t The output of the DDR at a certain moment; Represents t The electricity purchase quantity at a certain moment; Represents the time-of-use electricity price; B peak-shaving Represents the profit of the target virtual power plant providing peak shaving services; B ramp Represents the profit of the target virtual power plant participating in the ramping market; B reserve Represents the profit of the target virtual power plant participating in the reserve market.

[0012] The second aspect of the embodiments of the present application provides a virtual power plant multi-time scale optimal configuration device, including: a deconstruction module, configured to obtain historical energy consumption load data corresponding to a target power system, and deconstruct the historical energy consumption load data to obtain an energy base load that meets preset volatility requirements, and describe the influence mechanism of different resources on the flexibility requirements of the target power system through a preset normalized modeling strategy, so as to obtain a probabilistic flexible demand boundary corresponding to the target power system based on the influence mechanism and the energy base load; a construction module, configured to determine the external characteristics of the target virtual power plant according to a preset coordinate transformation and projection strategy, and construct long-time series supply-demand balance constraints and short-time series capacity coupling association constraints of the target virtual power plant based on the target virtual power plant, the external characteristic differences of the flexibility requirements, and preset resource availability limitations; an optimal configuration module, configured to obtain a power market price signal corresponding to a target power market, and construct a virtual power plant two-layer programming configuration model based on the probabilistic flexible demand boundary, the power market price signal, the long-time series supply-demand balance constraints, and the short-time series capacity coupling association constraints, so as to perform multi-time scale optimal configuration on the target virtual power plant according to the virtual power plant two-layer programming configuration model.

[0013] Optionally, in an embodiment of the present application, the deconstruction module includes: a joint modeling unit, configured to determine the base load change law and the stochastic fluctuation model corresponding to the target power system, and perform joint modeling on the base load change law and the stochastic fluctuation model to obtain the corresponding posterior distribution, and calculate the energy consumption base load through the posterior distribution; a division unit, configured to divide the different resources into demand-oriented resources and price-sensitive resources, and perform standardized modeling on the demand-oriented resources through a preset demand effect function and behavior effect function to obtain a demand-oriented resource mathematical model, and quantify the peak load change trend corresponding to the target power system according to the demand-oriented resource mathematical model; a generation unit, configured to determine the coupling relationship between the strategic behavior and the electricity price change corresponding to the price-sensitive resources based on a preset Monte Carlo simulation strategy, and generate random samples corresponding to the coupling relationship, so as to calculate the daily charging amount standard deviation according to the random samples; a calculation unit, configured to calculate the occurrence probability of a preset wind-solar alternation effect event and the wind-solar alternation effect power gap based on a pre-constructed wind-solar output fluctuation model and a preset output threshold; a determination unit, configured to determine the probabilistic flexible demand boundary corresponding to the target power system based on the peak load change trend, the daily charging amount standard deviation, the occurrence probability, and the wind-solar alternation effect power gap.

[0014] Optionally, in an embodiment of the present application, the construction module includes: a characterization unit, configured to eliminate the internal variables of a preset heterogeneous resource load model based on a preset coordinate transformation strategy, so as to transform a preset Minkowski sum problem into a projection problem, and characterize the external characteristics of the target virtual power plant through the projection problem; a first establishment unit, configured to establish the response delay constraint, the maximum regulation duration constraint, the maximum cumulative regulation time constraint, and the regulation interval constraint corresponding to the target virtual power plant, and determine the resource availability limit according to the response delay constraint, the maximum regulation duration constraint, the maximum cumulative regulation time constraint, and the regulation interval constraint; a second establishment unit, configured to establish the long-term supply-demand balance constraint and the short-term capacity coupling association constraint based on the external characteristic difference and the resource availability limit.

[0015] Optionally, in an embodiment of the present application, the optimization configuration module includes: an acquisition unit, configured to acquire the electricity market price signal corresponding to the target electricity market, and construct a revenue model of the target virtual power plant in the target electricity market based on the electricity market price signal; a third establishment unit, configured to construct a two-layer programming configuration model of the virtual power plant based on the long-term supply-demand balance constraint, the short-term capacity coupling association constraint, the revenue model, and the probabilistic flexible demand boundary.

[0016] Optionally, in an embodiment of the present application, the mathematical expression of the virtual power plant two-layer planning configuration model is as follows:

[0017] Wherein, F cost represents the total cost of the target virtual power plant; Q supply represents the supply capacity of the target virtual power plant to meet the flexibility demand; C ( x , y ) represents the construction cost of the target virtual power plant; B ( x , y ) represents the revenue of the target virtual power plant; C inv,PSR represents the configuration cost coefficient of the PSR; E PSR represents the configuration capacity of the PSR; C inv,DDR represents the configuration cost coefficient of the DDR; E DDR represents the configuration capacity of the DDR; C om represents the operation and maintenance cost coefficient; represents t the output of the PSR at time represents t the output of the DDR at time represents t the electricity purchase quantity at time represents the time-of-use electricity price; B peak-shaving represents the revenue of the target virtual power plant for providing peak shaving services; B ramp represents the revenue of the target virtual power plant participating in the ramping market; B reserve represents the revenue of the target virtual power plant participating in the reserve market.

[0018] An embodiment of the third aspect of the present application provides an electronic device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor executes the program to implement the virtual power plant multi-time scale optimization configuration method as described in the above embodiment.

[0019] An embodiment of the fourth aspect of the present application provides a computer-readable storage medium, where the computer-readable storage medium stores a computer program, and when the program is executed by a processor, it implements the virtual power plant multi-time scale optimization configuration method as described above.

[0020] The embodiment of the fifth aspect of the present application provides a computer program product, including a computer program, which is executed to implement the above-mentioned virtual power plant multi-time scale optimization configuration method.

[0021] Therefore, the embodiments of the present application have the following beneficial effects: The embodiments of the present application can obtain the historical energy consumption load data corresponding to the target power system, deconstruct the historical energy consumption load data to obtain the energy base load that meets the preset volatility requirements, and describe the influence mechanism of different resources on the flexibility requirements of the target power system through a preset normalized modeling strategy, so as to obtain the probabilistic flexible demand boundary corresponding to the target power system based on the influence mechanism and the energy base load; determine the external characteristics of the target virtual power plant according to the preset coordinate transformation and projection strategy, and construct the long-time series supply-demand balance constraint and short-time series capacity coupling correlation constraint of the target virtual power plant based on the external characteristics difference of the target virtual power plant, flexibility requirements and the preset resource availability limit; obtain the power market price signal corresponding to the target power market, and construct a virtual power plant two-layer planning configuration model based on the probabilistic flexible demand boundary, power market price signal, long-time series supply-demand balance constraint and short-time series capacity coupling correlation constraint, so as to perform multi-time scale optimization configuration on the target virtual power plant according to the virtual power plant two-layer planning configuration model, thereby improving the accuracy of the virtual power plant configuration result and effectively ensuring the safety and stability of the system. Therefore, the problems that the existing optimization configuration methods cannot provide credible planning configuration boundaries for virtual power plants, the configuration results have low accuracy, and it is difficult to effectively ensure the safety and stability of the system are solved.

[0022] The additional aspects and advantages of the present application will be partially given in the following description, partially become obvious from the following description, or be understood through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The above and / or additional aspects and advantages of the present application will become obvious and easy to understand from the following description of the embodiments in conjunction with the drawings, where: Figure 1 FIG. is a flowchart of a virtual power plant multi-time scale optimization configuration method according to an embodiment of the present application; Figure 2 FIG. is an example diagram of a virtual power plant multi-time scale optimization configuration device according to an embodiment of the present application; Figure 3 FIG. is a schematic structural diagram of an electronic device provided by an embodiment of the present application.

[0024] Wherein, 10 - virtual power plant multi-time scale optimization configuration device; 100 - deconstruction module, 200 - construction module, 300 - optimization configuration module; 301 - memory, 302 - processor, 303 - communication interface. Detailed implementation manners

[0025] The embodiments of the present application will be described in detail below. Examples of the embodiments are shown in the accompanying drawings, where the same or similar reference numerals denote the same or similar elements or elements with the same or similar functions throughout. The embodiments described below by referring to the accompanying drawings are exemplary and are intended to explain the present application, and should not be construed as a limitation to the present application.

[0026] The virtual power plant multi-time scale optimal configuration method and device according to the embodiments of the present application will be described below with reference to the accompanying drawings. In view of the problems mentioned in the above background art, the present application provides a virtual power plant multi-time scale optimal configuration method. In this method, by obtaining the historical energy consumption load data corresponding to the target power system and deconstructing the historical energy consumption load data, the energy base load that meets the preset volatility requirements is obtained, and the influence mechanism of different resources on the flexibility requirements of the target power system is described through a preset normalized modeling strategy, so as to obtain the probabilistic flexible demand boundary corresponding to the target power system based on the influence mechanism and the energy base load; determine the external characteristics of the target virtual power plant according to the preset coordinate transformation and projection strategy, and construct the long-time scale supply-demand balance constraint and short-time scale capacity coupling correlation constraint of the target virtual power plant based on the external characteristics difference of the target virtual power plant, flexibility requirements and the preset resource availability limit; obtain the power market price signal corresponding to the target power market, and construct a virtual power plant two-layer planning configuration model based on the probabilistic flexible demand boundary, power market price signal, long-time scale supply-demand balance constraint and short-time scale capacity coupling correlation constraint, so as to perform multi-time scale optimal configuration on the target virtual power plant according to the virtual power plant two-layer planning configuration model, thereby improving the accuracy of the virtual power plant configuration result and effectively ensuring the safety and stability of the system. Thus, the problems that the existing optimal configuration methods cannot provide credible planning configuration boundaries for virtual power plants, and the configuration results have low accuracy and are difficult to effectively ensure the safety and stability of the system are solved.

[0027] Specifically, Figure 1 is a flowchart of a virtual power plant multi-time scale optimal configuration method provided by an embodiment of the present application.

[0028] As Figure 1 shown, the virtual power plant multi-time scale optimal configuration method includes the following steps: In step S101, obtain the historical energy consumption load data corresponding to the target power system, and deconstruct the historical energy consumption load data to obtain an energy base load that meets the preset volatility requirements, and describe the influence mechanism of different resources on the flexibility requirements of the target power system through a preset normalized modeling strategy, so as to obtain the probabilistic flexible demand boundary corresponding to the target power system based on the influence mechanism and the energy base load.

[0029] Embodiments of the present application can first perform a very short-term assessment of the flexibility requirements of a new power system, that is, deconstruct the traditional power load and the new power load in the historical energy consumption data. On the one hand, based on the traditional power load, a relatively stable energy consumption base load is predicted. On the other hand, a standardized modeling method is used to describe the influence mechanism of the dual randomness of the source-load on the system flexibility requirements, and add it to the energy consumption base load to obtain the probabilistic flexible demand boundary of the new power system.

[0030] Optionally, in an embodiment of the present application, historical energy consumption load data corresponding to the target power system is obtained, and the historical energy consumption load data is deconstructed to obtain an energy consumption base load that meets the preset volatility requirements. And a preset standardized modeling strategy is used to describe the influence mechanism of different resources on the flexibility requirements of the target power system, so as to obtain the probabilistic flexible demand boundary corresponding to the target power system based on the influence mechanism and the energy consumption base load, including: determining the base load change law and random fluctuation model corresponding to the target power system, and jointly modeling the base load change law and the random fluctuation model to obtain the corresponding posterior distribution, and calculating the energy consumption base load through the posterior distribution; dividing different resources into demand-oriented resources and price-sensitive resources, and standardizing and modeling the demand-oriented resources through preset demand effect functions and behavioral effect functions to obtain a mathematical model of demand-oriented resources, and quantifying the peak load change trend corresponding to the target power system according to the mathematical model of demand-oriented resources; based on a preset Monte Carlo simulation strategy, determining the coupling relationship between the strategic behavior corresponding to the price-sensitive resources and the electricity price change, and generating random samples corresponding to the coupling relationship to calculate the standard deviation of the daily charging amount according to the random samples; based on a pre-constructed wind-solar power output fluctuation model and a preset output threshold, calculating the occurrence probability of a preset wind-solar alternation effect event and the power gap of the wind-solar alternation effect; determining the probabilistic flexible demand boundary corresponding to the target power system based on the peak load change trend, the daily charging amount standard deviation, the occurrence probability, and the power gap of the wind-solar alternation effect.

[0031] In the actual implementation process, the embodiments of the present application need to probabilistically characterize the flexibility demand boundary of the new power system, as described below: 1. Load deconstruction and base load generation of historical energy consumption data: In the embodiments of the present application, the base load of the power system is affected by multiple factors such as the external environment and shows a certain non-linear change, as shown in the following formula:

[0032] Among them, represents the change law of the base load, which depends on the base load at the previous moment and external environmental factors .

[0033] Random fluctuation part It can be modeled using Gaussian processes, as shown in the following equation:

[0034] where is the covariance function, which can be represented using a radial basis function kernel; controls the amplitude of the fluctuations; controls the smoothness of the fluctuations.

[0035] In summary, the embodiments of the present application can jointly model the base load and random fluctuations, and through Bayes' formula, obtain the posterior distribution of the base load and random fluctuations, thereby obtaining an estimated value of the base load:

[0036] where is the prior distribution, which can be described using a Dirichlet distribution for multiple dependent probability distributions; is the normalization constant. 2. Generation of dynamic demand curves: The embodiments of the present application can classify various resources according to the influencing factors of their load characteristics into two categories: demand-oriented resources and price-sensitive resources, and systematically analyze the boundary effects of the two types of resources on the flexibility demand in a new power system through a standardized modeling method.

[0037] (1) Demand-oriented resources (such as air conditioners, ordinary charging piles for electric vehicles, etc.) are mainly driven by changes in user demand, and can be standardized modeled through a demand effect function and a behavior effect function to quantify the change trend of peak load:

[0038] where is the demand effect function, which is used to describe the non-linear impact of external environment changes on the load of demand-oriented resources; is the user behavior effect function, which is used to simulate the behavioral characteristics of users adjusting the air conditioner load when the environmental temperature changes; is an s-shaped response function, representing a smooth non-linear relationship; is the user sensitivity coefficient, representing the sensitivity of users to temperature changes; is the random disturbance term, reflecting the randomness and uncertainty in user behavior.

[0039] (2) Price-sensitive resources mainly adjust their energy consumption strategies according to price fluctuations in the electricity market. This strategic convergence behavior describes the coupling relationship between the strategic behavior of resources and electricity price changes through the Monte Carlo simulation method, as shown in the following equation:

[0040] Among them, is the average daily service times; is the single charge amount; is the charging efficiency. All three parameters are uncertain, and the total daily charging amount is affected by the fluctuations of these three random variables; is the standard deviation of each parameter. Based on the above model, random samples are generated and the standard deviation of the daily charging amount is calculated:

[0041] (3) The determination condition for the wind-solar alternation effect event is that the photovoltaic output is lower than the threshold value and the wind power output fails to reach the threshold value at any moment. The specific calculation process is shown in the following table: Table 1

[0042] Thus, the embodiments of the present application perform a very short-term evaluation of the flexibility requirements of the new power system through the historical energy consumption load deconstruction and normalization modeling method, thereby providing reliable data support for the realization of the multi-time scale optimal configuration of the virtual power plant.

[0043] In step S102, the external characteristics of the target virtual power plant are determined according to the preset coordinate transformation and projection strategy, and based on the target virtual power plant, the external characteristic differences of the flexibility requirements, and the preset resource availability limitations, the long-time series supply-demand balance constraint and the short-time series capacity coupling correlation constraint of the target virtual power plant are constructed.

[0044] Furthermore, the embodiments of the present application also need to characterize the external characteristics of the virtual power plant based on the coordinate transformation and projection method, and combine the external characteristic differences of the peak shaving, climbing and other requirements and the resource availability limitations to construct the long-time series supply-demand balance constraint and the short-time series capacity coupling correlation constraint of the virtual power plant.

[0045] Optionally, in an embodiment of the present application, the external characteristics of the target virtual power plant are determined according to a preset coordinate transformation and projection strategy, and based on the external characteristics difference between the target virtual power plant and the flexibility requirements and the preset resource availability limit, long-term supply-demand balance constraints and short-term capacity coupling correlation constraints of the target virtual power plant are constructed, including: based on the preset coordinate transformation strategy, internal variables of various heterogeneous resource load models are eliminated to transform the preset Minkowski sum problem into a projection problem, and the external characteristics of the target virtual power plant are characterized by the projection problem; response delay constraints, maximum regulation duration constraints, maximum cumulative regulation time constraints, and regulation interval constraints corresponding to the target virtual power plant are established, and resource availability limits are determined according to the response delay constraints, maximum regulation duration constraints, maximum cumulative regulation time constraints, and regulation interval constraints; based on the external characteristics difference and resource availability limits, long-term supply-demand balance constraints and short-term capacity coupling correlation constraints are established.

[0046] It should be noted that the embodiments of the present application can use the coordinate transformation method to eliminate the internal variables of various heterogeneous resource load models, transform the Minkowski sum problem into a projection problem, and thus characterize the external characteristics of the virtual power plant, as shown in the following formula:

[0047] Among them, using and represent two polyhedra with linear inequality constraints; represents and Minkowski sum of; x and y represent the internal vectors after transformation and translation.

[0048] Those skilled in the art should understand that the optimal configuration of the virtual power plant should not only meet the long-term power supply-demand balance of the power system, but also meet the flexibility requirements such as short-term peak shaving and climbing, as shown in the following formula: Therefore, the embodiments of the present application fully consider the external characteristics difference between the regulation resources and the regulation requirements and the coupling of time series, and construct the capacity coupling correlation constraints of the virtual power plant based on constraints such as response delay, regulation duration, and interval time, as specifically described below: 1. Response delay constraint: There is a certain time response delay for demand-oriented resources:

[0049] Among them, is the command issuance time; is the regulation start time.

[0050] 2. Maximum regulation duration constraint:

[0051] Among them, is the adjustment state at time t.

[0052] 3. Maximum cumulative adjustment time constraint:

[0053] 4. Adjustment interval constraint: Ensure that after one response adjustment ends, there will be no secondary response adjustment within a certain period of time, as shown in the following formula:

[0054] Boundary condition adjustment:

[0055] The following constraints are obtained by logically equivalent conditions to ensure that there is no response adjustment within the time interval:

[0056] There are a large number of timing couplings in the above constraints. The adjustment indication variable is introduced to create and y associated relationship.

[0057] Associated constraint between adjustment start and adjustment state

[0058] Inheritance relationship of adjustment state:

[0059] Coverage relationship of adjustment state to adjustment start:

[0060] Thus, the embodiments of the present application effectively ensure the construction of a multi-objective optimal configuration model for a virtual power plant (i.e., a two-layer planning configuration model for a virtual power plant) that takes into account capacity coupling and associated constraints by considering the external characteristic differences of short-term peak shaving and ramping requirements.

[0061] In step S103, obtain the electricity market price signal corresponding to the target electricity market, and construct a two-layer planning configuration model for the virtual power plant based on the probabilistic flexible demand boundary, the electricity market price signal, the long-term supply-demand balance constraint, and the short-term capacity coupling and associated constraints, so as to perform multi-time scale optimal configuration on the target virtual power plant according to the two-layer planning configuration model for the virtual power plant.

[0062] Furthermore, the embodiments of the present application also need to consider the incentive drive of the electricity market price signal for the flexibility release of the virtual power plant, so as to construct a two-layer planning and configuration model of the virtual power plant for the physical-social coupling, and then optimize the configuration of the virtual power plant on multiple time scales according to the two-layer planning and configuration model of the virtual power plant.

[0063] Optionally, in an embodiment of the present application, the electricity market price signal corresponding to the target electricity market is obtained, and a two-layer planning and configuration model of the virtual power plant is constructed based on the electricity market price signal, the long-term supply-demand balance constraint, and the short-term capacity coupling correlation constraint, so as to optimize the configuration of the target virtual power plant on multiple time scales according to the two-layer planning and configuration model of the virtual power plant, including: obtaining the electricity market price signal corresponding to the target electricity market, and constructing a revenue model of the target virtual power plant in the target electricity market based on the electricity market price signal; constructing a two-layer planning and configuration model of the virtual power plant based on the long-term supply-demand balance constraint, the short-term capacity coupling correlation constraint, the revenue model, and the probabilistic flexible demand boundary.

[0064] In the specific implementation process, the embodiment of the present application constructs a revenue model of the target virtual power plant in the target electricity market by obtaining the electricity market price signal corresponding to the electricity market, and constructs a two-layer planning and configuration model of the virtual power plant based on the long-term supply-demand balance constraint, the short-term capacity coupling correlation constraint, the revenue model, and the probabilistic flexible demand boundary.

[0065] Optionally, in an embodiment of the present application, the mathematical expression of the two-layer planning and configuration model of the virtual power plant is:

[0066] where F cost represents the total cost of the target virtual power plant; Q supply represents the supply capacity of the target virtual power plant to meet the flexibility demand; C ( x , y ) represents the construction cost of the target virtual power plant; B ( x , y ) represents the revenue of the target virtual power plant; C inv,PSR represents the configuration cost coefficient of the PSR; E PSR represents the configuration capacity of the PSR; C inv,DDR represents the configuration cost coefficient of the DDR; E DDR represents the configuration capacity of the DDR; C om represents the operation and maintenance cost coefficient; denote t the output of the PSR at time denote t the output of the DDR at time denote t the electricity purchase quantity at time denote the time-of-use electricity price; B peak-shaving denote the revenue of the target virtual power plant providing peak shaving services; B ramp denote the revenue of the target virtual power plant participating in the ramping market; B reserve denote the revenue of the target virtual power plant participating in the reserve market.

[0067] It should be noted that, on the basis of the planning model from the perspective of planning and operation, the embodiments of the present application further consider the market perspective, that is, while ensuring that the supply capacity of the virtual power plant in the upper layer is as large as possible greater than the flexibility requirements of the new power system, a revenue model obtained by the virtual power plant strategically participating in different trading varieties is introduced in the lower layer, so as to construct a corresponding two-layer planning and configuration model of the virtual power plant. The mathematical expression of this two-layer planning and configuration model of the virtual power plant is:

[0068] Among them, F cost denote the total cost of the target virtual power plant; Q supply denote the supply capacity of the target virtual power plant to meet the flexibility requirements; C ( x , y ) denote the construction cost of the target virtual power plant; B ( x , y ) denote the revenue of the target virtual power plant; C inv,PSR denote the configuration cost coefficient of the PSR; E PSR denote the configuration capacity of the PSR; C inv,DDR denote the configuration cost coefficient of the DDR; E DDR denote the configuration capacity of the DDR; C om denote the operation and maintenance cost coefficient; denote t the output of the PSR at time denote t the output of the DDR at time denote t the electricity purchase quantity at time denote the time-of-use electricity price;B peak-shaving It represents the revenue of the target virtual power plant providing peak shaving services; B ramp It represents the revenue of the target virtual power plant participating in the ramping market; B reserve It represents the revenue of the target virtual power plant participating in the reserve market.

[0069] Thus, the embodiments of the present application construct a two - layer programming configuration model of the virtual power plant through the revenue model obtained by introducing the strategic participation of the virtual power plant in different trading varieties for optimal configuration, effectively ensuring the security and stability of the power system.

[0070] According to the virtual power plant multi - time - scale optimal configuration method proposed by the embodiments of the present application, by obtaining the historical energy consumption load data corresponding to the target power system and deconstructing the historical energy consumption load data to obtain the energy base load that meets the preset volatility requirements, and describing the influence mechanism of different resources on the flexibility requirements of the target power system through a preset normalized modeling strategy, to obtain the probabilistic flexible demand boundary corresponding to the target power system based on the influence mechanism and the energy base load; determining the external characteristics of the target virtual power plant according to the preset coordinate transformation and projection strategy, and constructing the long - time - series supply - demand balance constraint and short - time - series capacity coupling correlation constraint of the target virtual power plant based on the external characteristics difference of the target virtual power plant and the flexibility demand and the preset resource availability limit; obtaining the power market price signal corresponding to the target power market, and constructing a two - layer programming configuration model of the virtual power plant based on the probabilistic flexible demand boundary, the power market price signal, the long - time - series supply - demand balance constraint and the short - time - series capacity coupling correlation constraint, to perform multi - time - scale optimal configuration on the target virtual power plant according to the two - layer programming configuration model of the virtual power plant, thereby improving the accuracy of the virtual power plant configuration result and effectively ensuring the security and stability of the system.

[0071] Secondly, a virtual power plant multi - time - scale optimal configuration device proposed according to the embodiments of the present application is described with reference to the accompanying drawings.

[0072] Figure 2 It is a block diagram of the virtual power plant multi - time - scale optimal configuration device of the embodiments of the present application.

[0073] As Figure 2 shown, the virtual power plant multi - time - scale optimal configuration device 10 includes: a deconstruction module 100, a construction module 200, and an optimal configuration module 300.

[0074] Among them, the deconstruction module 100 is used to obtain the historical energy consumption load data corresponding to the target power system, deconstruct the historical energy consumption load data to obtain the energy base load that meets the preset volatility requirements, and describe the influence mechanism of different resources on the flexibility requirements of the target power system through a preset normalized modeling strategy, so as to obtain the probabilistic flexible demand boundary corresponding to the target power system based on the influence mechanism and the energy base load.

[0075] The construction module 200 is used to determine the external characteristics of the target virtual power plant according to the preset coordinate transformation and projection strategy, and construct the long-term time-series supply-demand balance constraint and short-term time-series capacity coupling correlation constraint of the target virtual power plant based on the target virtual power plant, the external characteristic difference of the flexibility demand, and the preset resource availability limit.

[0076] The optimization configuration module 300 is used to obtain the power market price signal corresponding to the target power market, and construct a two-layer programming configuration model of the virtual power plant based on the probabilistic flexible demand boundary, the power market price signal, the long-term time-series supply-demand balance constraint, and the short-term time-series capacity coupling correlation constraint, so as to perform multi-time-scale optimization configuration on the target virtual power plant according to the two-layer programming configuration model of the virtual power plant.

[0077] Optionally, in an embodiment of the present application, the deconstruction module 100 includes: a joint modeling unit, a division unit, a generation unit, a calculation unit, and a determination unit.

[0078] Among them, the joint modeling unit is used to determine the base load change law and random fluctuation model corresponding to the target power system, jointly model the base load change law and random fluctuation model to obtain the corresponding posterior distribution, and calculate the energy base load through the posterior distribution.

[0079] The division unit is used to divide different resources into demand-oriented resources and price-sensitive resources, normalize and model the demand-oriented resources through a preset demand effect function and behavior effect function to obtain a mathematical model of the demand-oriented resources, and quantify the peak load change trend corresponding to the target power system according to the mathematical model of the demand-oriented resources.

[0080] The generation unit is used to determine the coupling relationship between the strategic behavior corresponding to the price-sensitive resources and the electricity price change based on the preset Monte Carlo simulation strategy, generate random samples corresponding to the coupling relationship, and calculate the standard deviation of the daily charging amount according to the random samples.

[0081] The calculation unit is used to calculate the occurrence probability of a preset wind-solar alternation effect event and the wind-solar alternation effect power gap based on the pre-constructed wind-solar power output fluctuation model and the preset output threshold.

[0082] A determination unit, configured to determine a probabilistic flexible demand boundary corresponding to a target power system based on a peak load change trend, a standard deviation of daily charging amount, a probability of occurrence, and a power gap of an alternating effect of wind and light.

[0083] Optionally, in an embodiment of the present application, the construction module 200 includes: a first acquisition unit, a first establishment unit, and a second establishment unit.

[0084] Among them, a characterization unit, configured to eliminate internal variables of various preset heterogeneous resource load models based on a preset coordinate transformation strategy, so as to transform a preset Minkowski sum problem into a projection problem, and characterize the external characteristics of a target virtual power plant through the projection problem.

[0085] The first establishment unit is configured to establish response delay constraints, maximum regulation duration constraints, maximum cumulative regulation time constraints, and regulation interval constraints corresponding to the target virtual power plant, and determine resource availability limitations according to the response delay constraints, maximum regulation duration constraints, maximum cumulative regulation time constraints, and regulation interval constraints.

[0086] The second establishment unit is configured to establish long-term supply-demand balance constraints and short-term capacity coupling correlation constraints based on external characteristic differences and resource availability limitations.

[0087] Optionally, in an embodiment of the present application, the optimization configuration module 300 includes: an acquisition unit and a third establishment unit.

[0088] Among them, the acquisition unit is configured to acquire a power market price signal corresponding to a target power market, and construct a revenue model of the target virtual power plant in the target power market based on the power market price signal.

[0089] The third establishment unit is configured to construct a two-layer programming configuration model of the virtual power plant based on the long-term supply-demand balance constraints, short-term capacity coupling correlation constraints, revenue model, and probabilistic flexible demand boundary.

[0090] Optionally, in an embodiment of the present application, the mathematical expression of the two-layer programming configuration model of the virtual power plant is:

[0091] Among them, F cost represents the total cost of the target virtual power plant; Q supply represents the supply capacity of the target virtual power plant to meet flexibility requirements; C ( x , y ) represents the construction cost of the target virtual power plant; B ( x , yrepresents the revenue of the target virtual power plant; C inv,PSR represents the configuration cost coefficient of PSR; E PSR represents the configured capacity of PSR; C inv,DDR represents the configuration cost coefficient of DDR; E DDR represents the configured capacity of DDR; C om represents the operation and maintenance cost coefficient; represents t the output of PSR at time represents t the output of DDR at time represents t the electricity purchase quantity at time represents the time-of-use electricity price; B peak-shaving represents the revenue of the target virtual power plant providing peak shaving services; B ramp represents the revenue of the target virtual power plant participating in the ramping market; B reserve represents the revenue of the target virtual power plant participating in the reserve market.

[0092] It should be noted that the foregoing explanation of the embodiments of the virtual power plant multi-time scale optimization configuration method also applies to the virtual power plant multi-time scale optimization configuration device of this embodiment, and will not be elaborated here.

[0093] According to the virtual power plant multi-time scale optimization configuration device proposed in the embodiments of the present application, it includes a deconstruction module 100, which is used to obtain the historical energy consumption load data corresponding to the target power system, deconstruct the historical energy consumption load data to obtain the energy base load that meets the preset volatility requirements, and describe the influence mechanism of different resources on the flexibility requirements of the target power system through a preset normalized modeling strategy, so as to obtain the probabilistic flexible demand boundary corresponding to the target power system based on the influence mechanism and the energy base load; a construction module 200, which is used to determine the external characteristics of the target virtual power plant according to the preset coordinate transformation and projection strategy, and construct the long-time sequence supply-demand balance constraint and short-time sequence capacity coupling correlation constraint of the target virtual power plant based on the target virtual power plant, the external characteristic difference of the flexibility demand, and the preset resource availability limit; an optimization configuration module 300, which is used to obtain the power market price signal corresponding to the target power market, and construct a virtual power plant two-layer programming configuration model based on the probabilistic flexible demand boundary, the power market price signal, the long-time sequence supply-demand balance constraint, and the short-time sequence capacity coupling correlation constraint, so as to perform multi-time scale optimization configuration on the target virtual power plant according to the virtual power plant two-layer programming configuration model, thereby improving the accuracy of the virtual power plant configuration result and effectively ensuring the safety and stability of the system.

[0094] Figure 3 This is a schematic structural diagram of the electronic device provided by the embodiment of the present application. The electronic device may include: A memory 301, a processor 302, and a computer program stored on the memory 301 and executable on the processor 302.

[0095] When the processor 302 executes the program, it implements the virtual power plant multi-time scale optimization configuration method provided in the above embodiment.

[0096] Furthermore, the electronic device further includes: A communication interface 303 for communication between the memory 301 and the processor 302.

[0097] The memory 301 is used to store a computer program executable on the processor 302.

[0098] The memory 301 may include a high-speed RAM memory, and may also include a non-volatile memory, such as at least one disk memory.

[0099] If the memory 301, the processor 302, and the communication interface 303 are implemented independently, the communication interface 303, the memory 301, and the processor 302 may be interconnected through a bus and complete communication with each other. The bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. For the sake of representation, Figure 3 only a thick line is used to represent it in the figure, but it does not mean that there is only one bus or one type of bus.

[0100] Optionally, in a specific implementation, if the memory 301, the processor 302, and the communication interface 303 are integrated on a chip, the memory 301, the processor 302, and the communication interface 303 may complete communication with each other through an internal interface.

[0101] The processor 302 may be a Central Processing Unit (CPU), or an Application Specific Integrated Circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

[0102] The embodiments of the present application also provide a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, the virtual power plant multi-time scale optimization configuration method as described above is implemented.

[0103] The embodiments of the present application also provide a computer program product, including a computer program. When the computer program is executed, it is used to implement the virtual power plant multi-time scale optimization configuration method as described above.

[0104] In the description of this specification, the descriptions with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples", etc. mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms are not necessarily directed to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any one or N embodiments or examples in a suitable manner. In addition, without contradiction, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0105] In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include at least one of the features. In the description of the present application, the meaning of "N" is at least two, such as two, three, etc., unless otherwise specifically defined.

[0106] Any process or method description in the flowchart or described in other ways herein may be understood to represent a module, segment, or part of code including one or N executable instructions for implementing a customized logic function or process. The scope of the preferred embodiments of the present application includes additional implementations, where the functions may be executed in a substantially simultaneous manner or in a reverse order according to the involved functions, rather than in the order shown or discussed, which should be understood by those skilled in the art of the embodiments of the present application.

[0107] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a definite sequence list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device), or in conjunction with these instruction execution systems, apparatus, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device. More specific examples (non-exhaustive list) of computer-readable media include the following: an electrical connection portion (electronic device) having one or N wirings, a portable computer diskette (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can even be paper or other suitable media on which the program can be printed, since the program can be obtained electronically by optically scanning the paper or other media, followed by editing, interpretation, or otherwise processing as appropriate, and then stored in a computer memory.

[0108] It should be understood that various parts of the present application can be implemented by hardware, software, firmware, or a combination thereof. In the above-described embodiments, the N steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application-specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc.

[0109] Those of ordinary skill in the art of this technology can understand that all or part of the steps carried by the method of implementing the above embodiments can be completed by a program instructing relevant hardware, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiments.

[0110] In addition, each functional unit in various embodiments of the present application may be integrated into a processing module, or each unit may exist physically alone, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. When the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0111] The above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disc, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present application. Those of ordinary skill in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present application.

Claims

1. A multi-time scale optimization configuration method for a virtual power plant, characterized in that: The following steps are involved: Obtaining historical energy load data corresponding to the target power system, and deconstructing the historical energy load data to obtain an energy base load that meets the preset volatility requirements, and describing the impact mechanism of different resources on the flexibility demand of the target power system through a preset normalized modeling strategy, so as to obtain the probabilistic flexible demand boundary corresponding to the target power system based on the impact mechanism and the energy base load; Determine the external characteristics of the target virtual power plant according to a preset coordinate transformation and projection strategy, and construct a long-term supply-demand balance constraint and a short-term capacity coupling association constraint of the target virtual power plant based on the external characteristic differences of the target virtual power plant, the flexibility demand, and the preset resource availability constraints; The electricity market price signal corresponding to the target electricity market is obtained, and based on the probabilistic flexible demand boundary, the electricity market price signal, the long-term supply and demand balance constraint and the short-term capacity coupling association constraint, a two-layer planning configuration model for a virtual power plant is constructed to perform multi-time scale optimization configuration of the target virtual power plant according to the two-layer planning configuration model of the virtual power plant.

2. The multi-time scale optimization configuration method of a virtual power plant according to claim 1, characterized in that: The acquiring of historical energy load data corresponding to the target power system and deconstructing the historical energy load data to obtain an energy base load that meets a preset volatility requirement, and describing the influence mechanism of different resources on the flexibility demand of the target power system through a preset normalized modeling strategy, so as to obtain a probabilistic flexible demand boundary corresponding to the target power system based on the influence mechanism and the energy base load, including: Determine the base load variation law and the random fluctuation model corresponding to the target power system, and jointly model the base load variation law and the random fluctuation model to obtain the corresponding posterior distribution, and calculate the energy base load through the posterior distribution; The different resources are divided into demand-oriented resources and price-sensitive resources, and the demand-oriented resources are standardizedly modeled by a preset demand effect function and a behavior effect function to obtain a demand-oriented resource mathematical model, and the peak load change trend corresponding to the target power system is quantified according to the demand-oriented resource mathematical model; Based on a preset Monte Carlo simulation strategy, determining a coupling relationship between the strategic behavior corresponding to the price-sensitive resource and the change in electricity price, and generating a random sample corresponding to the coupling relationship, so as to calculate a daily charging capacity standard deviation according to the random sample; Based on the pre-built wind-solar output fluctuation model and the preset output threshold, the occurrence probability of the preset wind-solar alternating effect event and the wind-solar alternating effect power gap are calculated; Based on the peak load change trend, the daily charging amount standard deviation, the occurrence probability and the power gap of the wind-solar alternation effect, the probabilistic flexible demand boundary corresponding to the target power system is determined.

3. The multi-time scale optimization configuration method of a virtual power plant according to claim 1, characterized in that: The method of determining the external characteristics of the target virtual power plant according to the preset coordinate transformation and projection strategy, and constructing the long-term supply and demand balance constraint and the short-term capacity coupling association constraint of the target virtual power plant based on the external characteristic difference of the target virtual power plant, the flexibility demand and the preset resource availability restriction, includes: Based on a preset coordinate transformation strategy, internal variables of various preset heterogeneous resource load models are eliminated to transform the preset Minkowski sum problem into a projection problem, and the external characteristics of the target virtual power plant are characterized by the projection problem; Establishing a response delay constraint, a maximum regulation duration constraint, a maximum cumulative regulation time constraint, and a regulation interval constraint corresponding to the target virtual power plant, and determining the resource availability constraint according to the response delay constraint, the maximum regulation duration constraint, the maximum cumulative regulation time constraint, and the regulation interval constraint; Based on the external characteristic difference and the resource availability restriction, the long-term supply-demand balance constraint and the short-term capacity coupling association constraint are established.

4. The multi-time scale optimization configuration method of a virtual power plant according to claim 3 is characterized in that: The step of obtaining a power market price signal corresponding to the target power market, and constructing a virtual power plant two-layer planning configuration model based on the power market price signal, the long-time supply and demand balance constraint, and the short-time capacity coupling association constraint, so as to perform multi-time scale optimization configuration on the target virtual power plant according to the virtual power plant two-layer planning configuration model, includes: Acquire an electricity market price signal corresponding to the target electricity market, and construct a revenue model of the target virtual power plant in the target electricity market based on the electricity market price signal; Based on the long-term supply-demand balance constraints, the short-term capacity coupling association constraints, the profit model and the probabilistic flexible demand boundary, a two-layer planning configuration model for the virtual power plant is constructed.

5. The multi-time scale optimization configuration method of a virtual power plant according to claim 4, characterized in that: The mathematical expression of the two-level planning configuration model of the virtual power plant is: in, F cost represents the total cost of the target virtual power plant; Q supply represents the supply capacity of the target virtual power plant to meet the flexibility demand; C ( x , y ) represents the target virtual power plant construction cost; B ( x , y ) represents the target virtual power plant revenue; C inv,PSR represents the configuration cost coefficient of PSR; E PSR Indicates the configuration capacity of PSR; C inv,DDR It represents the configuration cost coefficient of DDR; E DDR Indicates the configuration capacity of DDR; C om represents the operation and maintenance cost coefficient; express t The output of PSR at all times; express t The output of DDR at all times; express t The amount of electricity purchased at any given moment; Indicates time-of-use electricity price; B peak-shaving represents the revenue of the target virtual power plant providing peak shaving services; B ramp represents the benefits of the target virtual power plant participating in the ramping market; B reserve Represents the revenue of the target virtual power plant participating in the reserve market.

6. A multi-time scale optimization configuration device for a virtual power plant, characterized in that: include: A deconstruction module, used to obtain historical energy load data corresponding to the target power system, and deconstruct the historical energy load data to obtain an energy base load that meets the preset volatility requirements, and describe the influence mechanism of different resources on the flexibility demand of the target power system through a preset normalized modeling strategy, so as to obtain a probabilistic flexible demand boundary corresponding to the target power system based on the influence mechanism and the energy base load; A construction module, used to determine the external characteristics of the target virtual power plant according to a preset coordinate transformation and projection strategy, and to construct a long-term supply-demand balance constraint and a short-term capacity coupling association constraint of the target virtual power plant based on the external characteristic differences of the target virtual power plant, the flexibility demand and the preset resource availability constraints; The optimization configuration module is used to obtain the electricity market price signal corresponding to the target electricity market, and construct a virtual power plant two-layer planning configuration model based on the probabilistic flexible demand boundary, the electricity market price signal, the long-term supply and demand balance constraint and the short-term capacity coupling association constraint, so as to perform multi-time scale optimization configuration of the target virtual power plant according to the virtual power plant two-layer planning configuration model.

7. The multi-time scale optimization configuration device for a virtual power plant according to claim 6, characterized in that: The deconstruction module includes: A joint modeling unit, used for determining a base load variation law and a random fluctuation model corresponding to the target power system, and jointly modeling the base load variation law and the random fluctuation model to obtain a corresponding posterior distribution, and calculating the energy base load through the posterior distribution; a division unit, used for dividing the different resources into demand-oriented resources and price-sensitive resources, performing standardized modeling on the demand-oriented resources through preset demand effect functions and behavior effect functions to obtain a demand-oriented resource mathematical model, and quantifying a peak load change trend corresponding to the target power system according to the demand-oriented resource mathematical model; A generating unit, configured to determine, based on a preset Monte Carlo simulation strategy, a coupling relationship between the strategic behavior corresponding to the price-sensitive resource and the change in electricity price, and generate a random sample corresponding to the coupling relationship, so as to calculate a daily charging capacity standard deviation according to the random sample; A calculation unit, used to calculate the probability of occurrence of a preset wind-solar alternation effect event and the wind-solar alternation effect power gap based on a pre-built wind-solar output fluctuation model and a preset output threshold; A determination unit is used to determine the probabilistic flexible demand boundary corresponding to the target power system based on the peak load change trend, the daily charging amount standard deviation, the occurrence probability and the power gap of the wind-solar alternation effect.

8. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the multi-time scale optimization configuration method for a virtual power plant as described in any one of claims 1 to 5.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement the multi-time scale optimization configuration method of a virtual power plant as described in any one of claims 1-5.

10. A computer program product, comprising a computer program, characterized in that The program is executed to implement the multi-time scale optimization configuration method of a virtual power plant as described in any one of claims 1-5.

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