Multi-time Scale Optimization Configuration Method and Device for Virtual Power Plant

Abstract

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. The method includes: performing an ultra-short-term assessment of the flexibility requirements of a new power system through a historical energy consumption load deconstruction and normalization modeling method. On this basis, characterizing the external characteristics of the virtual power plant based on coordinate transformation and projection methods, and combining the external characteristic differences of the flexibility requirements and the resource availability constraints to construct a two-layer planning configuration model of the virtual power plant considering capacity coupling and correlation constraints, so as to perform multi-time scale optimal configuration on the target virtual power plant according to the two-layer planning configuration model of the virtual power plant. Thereby, it solves the problems that the existing optimal configuration methods cannot provide a credible planning configuration boundary for the virtual power plant, and the accuracy of the configuration results is relatively low, making it difficult to effectively ensure the safety and stability of the system, etc.

Description

Technical Field

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

[0002] In recent years, the characteristics of power grid sources and loads in new power systems have changed significantly. Although the proportion of renewable energy grid connection 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 and demand balance of new power systems faces great pressure; in addition, with the continuous development of emerging industries, the natural growth of new power loads has impacted the flexibility requirements of the system. As an emerging power system dispatching mode, a virtual power plant can effectively improve the operation stability of the power system by dynamically and accurately configuring various distributed resources.

[0003] However, traditional optimization configuration methods are carried out under preset flexibility demand scenarios, that is, the configuration boundaries are known, but they ignore the impact of the dual randomness of sources and loads on the flexibility requirements of new power systems and cannot provide credible planning configuration boundaries for virtual power plants; in addition, traditional optimization configuration methods aim to meet the long-term power supply and demand balance from a planning perspective, ignoring the differences in external characteristics requirements for peak shaving and ramping from an operation perspective and the market operation environment from a social perspective, resulting in inaccurate configuration results and being unable to effectively ensure the safety and stability of the system.

[0004] In summary, the existing optimization configuration methods cannot provide credible planning configuration boundaries for virtual power plants, and the accuracy of the configuration results is relatively low, 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 virtual power plant multi-time scale optimization configuration method and device to solve problems such as the existing optimization configuration methods being unable to provide credible planning configuration boundaries for virtual power plants, having relatively low accuracy of configuration results, and being difficult to effectively ensure the safety and stability of the system.

[0006] The first aspect embodiment of the present application provides a multi-time scale optimal 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 a long-time series supply-demand balance constraint and a short-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 a preset resource availability limit; obtaining a power market price signal corresponding to a 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, 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.

[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 and electricity price change corresponding to the price-sensitive resources based on a preset Monte Carlo simulation strategy, and generating a random sample corresponding to the coupling relationship, so as to calculate the standard deviation of daily charging amount according to the random sample; calculating the occurrence probability of a preset wind-solar alternation effect event and the power gap of the wind-solar alternation effect 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 standard deviation of daily charging amount, the occurrence probability, and the power gap of the wind-solar alternation effect.

[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 time-series supply-demand balance constraint and short-term time-series capacity coupling correlation 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: eliminating the internal variables of various heterogeneous resource load models preset according to the preset coordinate transformation strategy, so as 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 time-series supply-demand balance constraint and the short-term time-series capacity coupling correlation 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 virtual power plant two-layer programming configuration model based on the electricity 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 virtual power plant two-layer programming configuration model, 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 virtual power plant two-layer programming configuration model based on the long-term time-series supply-demand balance constraint, the short-term time-series capacity coupling correlation constraint, the revenue model and the probabilistic flexible demand boundary.

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

[0011]

[0012] 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 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,PSRRepresents 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 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.

[0013] 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 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 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 a long-time sequence supply-demand balance constraint and a short-time sequence capacity coupling association constraint of the target virtual power plant based on the target virtual power plant, the external characteristic differences of the flexibility requirements, and a preset resource availability limit; an optimal configuration module, configured to obtain an electricity market price signal corresponding to a 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-time sequence supply-demand balance constraint, and the short-time sequence capacity coupling association 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.

[0014] 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, perform normalized modeling on the demand-oriented resources through a preset demand effect function and a 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 corresponding to the price-sensitive resources and the electricity price change based on a preset Monte Carlo simulation strategy, and generate random samples corresponding to the coupling relationship to calculate the standard deviation of the daily charging amount according to the random samples; a calculation unit, configured to calculate the occurrence probability of a preset wind-solar alternation effect event and the power gap of the wind-solar alternation effect 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 standard deviation of the daily charging amount, the occurrence probability, and the power gap of the wind-solar alternation effect.

[0015] 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, convert 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 adjustment duration constraint, the maximum cumulative adjustment time constraint, and the adjustment interval constraint corresponding to the target virtual power plant, and determine the resource availability limit according to the response delay constraint, the maximum adjustment duration constraint, the maximum cumulative adjustment time constraint, and the adjustment 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.

[0016] 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.

[0017] Optionally, in an embodiment of the present application, the mathematical expression of the virtual power plant two-layer planning configuration model is as follows:

[0018]

[0019] 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 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.

[0020] 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, and the processor executes the program to implement the virtual power plant multi-time scale optimization configuration method as described in the above embodiment.

[0021] An embodiment of the fourth aspect of the present application provides a computer-readable storage medium, and the computer-readable storage medium stores a computer program, and when the program is executed by a processor, the virtual power plant multi-time scale optimization configuration method as described above is implemented.

[0022] An 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 virtual power plant multi-time scale optimal configuration method.

[0023] Therefore, the embodiments of the present application have the following beneficial effects:

[0024] The embodiments of the present application can obtain the historical energy consumption load data corresponding to the target power system, and 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 association constraint of the target virtual power plant based on the external characteristics difference of the target virtual power plant, flexible demand 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 association 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 it is difficult to effectively ensure the safety and stability of the system are solved.

[0025] 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. Description of the Drawings

[0026] 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:

[0027] Figure 1 is a flowchart of a virtual power plant multi-time scale optimal configuration method provided according to an embodiment of the present application;

[0028] Figure 2 is an example diagram of a virtual power plant multi-time scale optimal configuration device according to an embodiment of the present application;

[0029] Figure 3 is a schematic structural diagram of an electronic device provided according to an embodiment of the present application.

[0030] Among them, 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

[0031] The embodiments of the present application will be described in detail below. The 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 from beginning to end. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to explain the present application, but should not be construed as limiting the present application.

[0032] The virtual power plant multi - time - scale optimization 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 - mentioned background technology, the present application provides a virtual power plant multi - time - scale optimization 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 to obtain the energy base load that meets the preset volatility requirements, and by using a preset normalized modeling strategy to describe the influence mechanism of different resources on the flexibility requirements of the target power system, a probabilistic flexible demand boundary corresponding to the target power system is obtained based on the influence mechanism and the energy base load; 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 flexible demand, and the preset resource availability limit, 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; the power market price signal corresponding to the target power market is obtained, and 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, a virtual power plant two - layer planning configuration model is constructed 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. Thus, the problems that the existing optimization 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.

[0033] Specifically, Figure 1 is a flowchart of a virtual power plant multi - time - scale optimization configuration method provided by an embodiment of the present application.

[0034] As Figure 1 shown, the virtual power plant multi - time - scale optimization configuration method includes the following steps:

[0035] 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 consumption 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 normalization 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 consumption base load.

[0036] The embodiments of the present application can first perform a very short-term assessment of the flexibility requirements of the 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, predict the energy consumption base load with relatively small volatility. On the other hand, describe the influence mechanism of the dual randomness of the source and load on the system flexibility requirements through a normalization modeling method, and add it to the energy consumption base load to obtain the probabilistic flexible demand boundary of the new power system.

[0037] Optionally, in an embodiment of the present application, obtaining the historical energy consumption load data corresponding to the target power system, and deconstructing the historical energy consumption load data to obtain an energy consumption 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 normalization 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 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 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 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; based on a preset Monte Carlo simulation strategy, determine the coupling relationship between the strategic behavior corresponding to the price-sensitive resources and the electricity price change, and generate random samples corresponding to the coupling relationship to calculate the daily charging amount standard deviation according to the random samples; based on the pre-constructed wind and light output fluctuation model and the preset output threshold, calculate the occurrence probability of the preset wind-light alternation effect event and the wind-light alternation effect power gap; based on the peak load change trend, the daily charging amount standard deviation, the occurrence probability, and the wind-light alternation effect power gap, determine the probabilistic flexible demand boundary corresponding to the target power system.

[0038] In the actual execution process, the embodiments of the present application need to probabilistically characterize the flexibility demand boundary of the new power system, which is specifically described as follows:

[0039] 1. Load deconstruction and base load generation of historical energy consumption data:

[0040] In the embodiments of the present application, the base load of the power system is affected by multiple factors such as the external environment, showing a certain non-linear change, as shown in the following formula:

[0041]

[0042] Wherein, represents the change law of the base load, which depends on the base load at the previous moment and external environmental factors .

[0043] Random fluctuation part can be modeled using a Gaussian process, as shown in the following formula:

[0044]

[0045] Wherein, is the covariance function and can be represented by a radial basis function kernel; controls the fluctuation amplitude; controls the fluctuation smoothness.

[0046] In summary, the embodiments of the present application can jointly model the base load and random fluctuations, and obtain the posterior distribution of the base load and random fluctuations through the Bayesian formula, so as to obtain the estimated value of the base load:

[0047]

[0048] Wherein, is the prior distribution and can be described by a Dirichlet distribution for multiple dependent probability distributions; is the normalization constant.

[0049] 2. Generation of dynamic demand curve:

[0050] In the embodiments of the present application, according to the influencing factors of the load characteristics of various resources, they can be divided into two categories: demand-oriented resources and price-sensitive resources, and the boundary effects of the two types of resources on the flexibility demand in the new power system can be systematically analyzed through a standardized modeling method.

[0051] (1) Demand-oriented resources (such as air conditioners, ordinary charging piles for electric vehicles, etc.) are mainly driven by the demand changes of users, and can be standardized modeled through a demand effect function and a behavior effect function to quantify the change trend of peak load:

[0052]

[0053] Wherein, is the demand effect function, which is used to describe the non-linear influence of external environmental 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-conditioning 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.

[0054] (2) Price-sensitive resources mainly adjust their energy consumption strategies according to the price fluctuations in the electricity market. The 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 formula:

[0055]

[0056] 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. Random samples are generated based on the above model, and the standard deviation of the daily charging amount is calculated:

[0057]

[0058] (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:

[0059] Table 1

[0060]

[0061] 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 standardized modeling method, thereby providing reliable data support for the realization of the multi-time scale optimal configuration of the virtual power plant.

[0062] 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 external characteristics differences between the target virtual power plant and the flexibility requirements and the preset resource availability limit, 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.

[0063] 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 characteristics differences of the peak shaving, climbing and other requirements and the resource availability limit to construct the long-time series supply-demand balance constraint and the short-time series capacity coupling correlation constraint of the virtual power plant.

[0064] 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 long-term power supply-demand balance constraints and short-term capacity coupling correlation constraints of the target virtual power plant are constructed based on the external characteristic differences between the target virtual power plant and the flexibility requirements and the preset resource availability limitations, 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 limitations are determined according to the response delay constraints, maximum regulation duration constraints, maximum cumulative regulation time constraints, and regulation interval constraints; long-term power supply-demand balance constraints and short-term capacity coupling correlation constraints are established based on the external characteristic differences and resource availability limitations.

[0065] 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:

[0066]

[0067] Among them, and represent two polyhedrons with linear inequality constraints; represents and the Minkowski sum of; x and y represent the internal vectors after transformation and translation.

[0068] 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 ramp-up, as shown in the following formula:

[0069] Therefore, the embodiments of the present application fully consider the external characteristic differences 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:

[0070] 1. Response delay constraint: There is a certain time response delay for demand-oriented resources:

[0071]

[0072] Among them, is the instruction issuing time; is the adjustment start time.

[0073] 2. Maximum adjustment duration constraint:

[0074]

[0075] Among them, is the adjustment state at time t.

[0076] 3. Maximum cumulative adjustment time constraint:

[0077]

[0078] 4. Adjustment interval constraint:

[0079] Ensure that after one response adjustment ends, there will be no second response adjustment within a certain period of time, as shown in the following formula:

[0080]

[0081] Boundary condition adjustment:

[0082]

[0083] The following constraints are obtained by equivalent logical conditions to ensure that there is no response adjustment within the time interval:

[0084]

[0085] There are a large number of timing couplings in the above constraints. The adjustment indication variable is introduced and is created to y establish the association relationship with

[0086] Association constraint between adjustment start and adjustment state

[0087]

[0088] Inheritance relationship of adjustment state:

[0089]

[0090] Coverage relationship of adjustment state to adjustment start:

[0091]

[0092] Thus, the embodiments of the present application effectively ensure the construction of a virtual power plant multi-objective optimization configuration model (i.e., the virtual power plant two-layer planning configuration model) considering the external characteristic differences of short-time sequence peak shaving and ramp-up requirements by taking into account the capacity coupling association constraints.

[0093] In step S103, obtain the electricity market price signal corresponding to the target electricity market, and construct a two-layer programming 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 correlation 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.

[0094] Furthermore, the embodiments of the present application also need to consider the incentive drive of the electricity market price signal on the flexibility release of the virtual power plant, so as to construct a two-layer programming configuration model for the virtual power plant facing physical-social coupling, and thus perform multi-time scale optimal configuration on the virtual power plant according to the two-layer programming configuration model of the virtual power plant.

[0095] Optionally, in an embodiment of the present application, obtain the electricity market price signal corresponding to the target electricity market, and construct a two-layer programming configuration model for the virtual power plant 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 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, 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 programming configuration model for 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.

[0096] In the specific implementation process, the embodiments of the present application construct 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 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, thereby constructing a two-layer programming configuration model for the virtual power plant.

[0097] Optionally, in an embodiment of the present application, the mathematical expression of the two-layer programming configuration model for the virtual power plant is:

[0098]

[0099] 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 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,PSRRepresents 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 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.

[0100] It should be noted that based on the planning model from the perspectives of planning and operation, the embodiments of this 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 to meet the flexibility requirements of the new power system, a revenue model for the virtual power plant's strategic participation in different trading varieties is introduced in the lower layer, thereby constructing a corresponding two-layer planning and configuration model for the virtual power plant. The mathematical expression of this two-layer planning and configuration model for the virtual power plant is:

[0101]

[0102] 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 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 the PSR; E PSR Represents the configuration capacity of the PSR; C inv,DDR Represents the configuration cost coefficient of the DDR; E DDRRepresents the configured capacity of DDR; C om Represents the operation and maintenance cost coefficient; Represents t The output of PSR at a certain moment; Represents t The output of 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 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.

[0103] Thus, the embodiments of the present application construct a two-layer programming configuration model of the virtual power plant by introducing the revenue model obtained from the strategic participation of the virtual power plant in different trading varieties for optimal configuration, effectively ensuring the safety and stability of the power system.

[0104] 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 safety and stability of the system.

[0105] Secondly, describe the virtual power plant multi-time scale optimal configuration device proposed by the embodiments of the present application with reference to the accompanying drawings.

[0106] Figure 2 Is the block diagram of the virtual power plant multi-time scale optimal configuration device of the embodiments of the present application.

[0107] As Figure 2As shown in the figure, 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.

[0108] 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 an energy consumption 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 consumption base load.

[0109] 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-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.

[0110] The optimal configuration module 300 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, 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 optimal configuration on the target virtual power plant according to the virtual power plant two-layer programming configuration model.

[0111] 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.

[0112] 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 consumption base load through the posterior distribution.

[0113] The division unit is used to divide different resources into demand-oriented resources and price-sensitive resources, perform normalized 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.

[0114] The generation unit is used to determine the coupling relationship between the strategic behavior and electricity price change corresponding to the price-sensitive resources based on the preset Monte Carlo simulation strategy, generate random samples corresponding to the coupling relationship, and calculate the daily charging amount standard deviation according to the random samples.

[0115] A calculation unit, configured to calculate the occurrence probability of a preset wind-solar alternation effect event and the power gap of the wind-solar alternation effect based on a pre-constructed wind-solar output fluctuation model and a preset output threshold.

[0116] A determination unit, configured to determine a probabilistic flexible demand boundary corresponding to a target power system based on the peak load change trend, the standard deviation of the daily charging amount, the occurrence probability, and the power gap of the wind-solar alternation effect.

[0117] 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.

[0118] Among them, a characterization unit, configured to eliminate 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 a target virtual power plant through the projection problem.

[0119] 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.

[0120] The second establishment unit is configured to establish long-term supply-demand balance constraints and short-term capacity coupling correlation constraints based on the external characteristic differences and resource availability limitations.

[0121] Optionally, in an embodiment of the present application, the optimization configuration module 300 includes: an acquisition unit and a third establishment unit.

[0122] 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.

[0123] The third establishment unit is configured to construct a virtual power plant two-layer programming configuration model based on the long-term supply-demand balance constraints, short-term capacity coupling correlation constraints, revenue model, and probabilistic flexible demand boundary.

[0124] Optionally, in an embodiment of the present application, the mathematical expression of the virtual power plant two-layer programming configuration model is:

[0125]

[0126] Among them, F cost represents the total cost of the target virtual power plant; Q supplyIndicates 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 the PSR; E PSR Represents the configured capacity of the PSR; C inv,DDR Represents the configuration 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 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 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.

[0127] It should be noted that the foregoing explanation of the embodiments of the virtual power plant multi-time scale optimal configuration method also applies to the virtual power plant multi-time scale optimal configuration device of this embodiment, and will not be elaborated here.

[0128] The virtual power plant multi-time scale optimal configuration device proposed according to the embodiments of the present application 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 an energy consumption 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 consumption 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 series supply-demand balance constraint and short-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; an optimal 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 planning configuration model 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, 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.

[0129] Figure 3 The structural schematic diagram of the electronic device provided by the embodiments of the present application. The electronic device may include:

[0130] A memory 301, a processor 302, and a computer program stored on the memory 301 and executable on the processor 302.

[0131] When the processor 302 executes the program, it implements the virtual power plant multi-time scale optimal configuration method provided in the above embodiments.

[0132] Further, the electronic device further includes:

[0133] A communication interface 303 for communication between the memory 301 and the processor 302.

[0134] The memory 301 is used to store a computer program executable on the processor 302.

[0135] 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.

[0136] 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 can be interconnected via a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 3 only a thick line is used in Figure 3 , but it does not mean that there is only one bus or one type of bus.

[0137] Optionally, in a specific implementation, if the memory 301, the processor 302, and the communication interface 303 are integrated on a single chip, the memory 301, the processor 302, and the communication interface 303 can communicate with each other through an internal interface.

[0138] 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.

[0139] The embodiments of the present application also provide a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the above virtual power plant multi-time scale optimization configuration method is implemented.

[0140] The embodiments of the present application also provide a computer program product, including a computer program, and when the computer program is executed, it is used to implement the above virtual power plant multi-time scale optimization configuration method.

[0141] In the description of this specification, the descriptions referring to terms such as "one embodiment", "some embodiments", "examples", "specific examples", 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 this application. In this specification, the schematic representations of the above terms do not necessarily refer 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 can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0142] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be construed 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 these features. In the description of this application, the meaning of "N" is at least two, such as two, three, etc., unless otherwise specifically defined.

[0143] Any process or method description shown in the flowchart or described in other ways herein can be understood as representing a module, segment, or part of code including one or N executable instructions for implementing a customized logical function or process, and the scope of the preferred embodiments of this 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 to which the embodiments of this application belong.

[0144] 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 part (electronic device) having one or N wirings, a portable computer disk cartridge (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, because the program can be obtained electronically by optically scanning the paper or other media, followed by editing, interpretation, or other suitable processing as necessary, and then stored in a computer memory.

[0145] 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.

[0146] 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 instructing relevant hardware through a program, 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.

[0147] 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.

[0148] 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 optimal configuration method for a virtual power plant, characterized in that The method includes the following steps: Obtain the historical energy consumption load data corresponding to the target power system, and decompose the historical energy consumption load data to obtain an energy consumption base load that meets the preset volatility requirements. Describe the influence mechanism of different resources on the flexibility requirements of the target power system through a preset normalized modeling strategy, and based on the influence mechanism and the energy consumption base load, obtain the probabilistic flexible demand boundary corresponding to the target power system; Determine the external characteristics of the target virtual power plant 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 constraints, construct the long-term time series supply-demand balance constraint and the short-term time series capacity coupling correlation constraint of the target virtual power plant; Obtain the power market price signal corresponding to the target power market, and 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, construct a virtual power plant two-layer planning configuration model to perform multi-time scale optimal configuration of the target virtual power plant according to the virtual power plant two-layer planning configuration model; Among them, the step of obtaining the historical energy consumption load data corresponding to the target power system, decomposing the historical energy consumption load data to obtain an energy consumption 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, and based on the influence mechanism and the energy consumption base load, obtaining the probabilistic flexible demand boundary corresponding to the target power system, includes: Determine the base load change law and the stochastic fluctuation model corresponding to the target power system, and jointly model 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; Divide the different resources into demand-oriented resources and price-sensitive resources, and perform normalized 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; Based on the preset Monte Carlo simulation strategy, determine the coupling relationship between the strategic behavior corresponding to the price-sensitive resource and the electricity price change, and generate a random sample corresponding to the coupling relationship, and calculate the standard deviation of the daily charging amount according to the random sample; Based on the pre-constructed wind-solar power output fluctuation model and the preset power output threshold, calculate the occurrence probability of the preset wind-solar alternation effect event and the wind-solar alternation effect power gap; Based on the peak load change trend, the standard deviation of the daily charging amount, the occurrence probability, and the wind-solar alternation effect power gap, determine the probabilistic flexible demand boundary corresponding to the target power system.

2. The virtual power plant multi-time scale optimization configuration method according to claim 1, wherein Determine the external characteristics of the target virtual power plant according to the preset coordinate transformation and projection strategy, and construct the long-term supply-demand balance constraint and short-term capacity coupling correlation constraint of the target virtual power plant based on the external characteristic differences between the target virtual power plant and the flexibility demand and the preset resource availability limit, including: Based on the preset coordinate transformation strategy, eliminate the internal variables of various heterogeneous resource load models preset, so as to transform the preset Minkowski sum problem into a projection problem, and characterize the external characteristics of the target virtual power plant through the projection problem; Establish the response delay constraint, maximum adjustment duration constraint, maximum cumulative adjustment time constraint and adjustment interval constraint corresponding to the target virtual power plant, and determine the resource availability limit according to the response delay constraint, the maximum adjustment duration constraint, the maximum cumulative adjustment time constraint and the adjustment interval constraint; Based on the external characteristic differences and the resource availability limit, establish the long-term supply-demand balance constraint and the short-term capacity coupling correlation constraint.

3. The virtual power plant multi-time scale optimization configuration method according to claim 2, wherein Obtain the electricity market price signal corresponding to the target electricity market, and construct 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 correlation 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, including: Obtain 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; 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, construct the two-layer programming configuration model of the virtual power plant.

4. The virtual power plant multi-time scale optimal configuration method according to claim 3, characterized in that The mathematical expression of the two-layer programming configuration model of the virtual power plant is: 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 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 configured capacity of the PSR; C inv,DDR represents the configuration 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 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.

5. A virtual power plant multi-time scale optimization configuration device, characterized in that, Including: A deconstruction module for obtaining the historical energy consumption load data corresponding to the target power system, deconstructing the historical energy consumption load data to obtain an energy base load that meets the preset volatility requirements, 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 the probabilistic flexible demand boundary corresponding to the target power system based on the influence mechanism and the energy base load; A construction module for 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-demand balance constraint and short-term capacity coupling correlation constraint of the target virtual power plant based on the external characteristic differences between the target virtual power plant and the flexibility demand and the preset resource availability limit; An optimal configuration module for 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 probabilistic flexible demand boundary, the electricity market price signal, the long-term supply-demand balance constraint and the short-term capacity coupling correlation 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; Among them, the deconstruction module includes: A joint modeling unit, configured to determine the base load change pattern and the stochastic fluctuation model corresponding to the target power system, and perform joint modeling on the base load change pattern and the stochastic fluctuation model to obtain the corresponding posterior distribution, and calculate the energy consumption base load through the posterior distribution; A partitioning unit, configured to divide the different resources into demand-oriented resources and price-sensitive resources, 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 generating unit, configured to determine 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 generate random samples corresponding to the coupling relationship to calculate the daily charging volume standard deviation according to the random samples; A calculating 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 determining 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 volume standard deviation, the occurrence probability, and the wind-solar alternation effect power gap; 6. An electronic device, characterized in that, Comprising: A memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor executes the program to implement the virtual power plant multi-time scale optimization configuration method according to any one of claims 1-4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to be used for implementing the virtual power plant multi-time scale optimization configuration method according to any one of claims 1-4.

8. A computer program product, comprising a computer program, characterized in that, The program is executed to be used for implementing the virtual power plant multi-time scale optimization configuration method according to any one of claims 1-4.

Images