Virtual power plant multi-scene probability configuration optimization method and device

Abstract

The invention relates to the technical field of power systems, in particular to a virtual power plant multi-scene probability configuration optimization method and device, and the method comprises the steps: determining a revenue function of each resource based on a high-dimensional game model, and describing the influence condition of a target resource configuration decision on resource revenue through employing a Nash equilibrium principle, calculating the opportunity cost of each resource according to the influence condition and the revenue function pair; a Markov decision process and Monte Carlo simulation are adopted to carry out probabilistic modeling on a future scene of the virtual power plant so as to calculate a resource income expected value, and a nonlinear function is utilized to represent the matching degree of the resource adjustment characteristic and the demand external characteristic of the virtual power plant; and based on the resource opportunity cost and the matching degree, calculating a comprehensive configuration priority of the virtual power plant, so that the target virtual power plant performs resource configuration according to the comprehensive configuration priority. Therefore, the problems that an existing optimal configuration method is poor in adaptability and robustness of a resource configuration result, high in calculation complexity and the like are solved.

Description

Technical Field

[0001] The present application relates to the technical field of power systems, and in particular to a method and device for optimizing multi-scenario probabilistic configuration of a virtual power plant. Background Art

[0002] With the large-scale grid connection of renewable energy in the new power system, its volatility and intermittent characteristics have led to severe challenges in the real-time supply and demand balance of the power system; at the same time, the rapid growth of new power loads (such as electric vehicles and data centers) has further intensified the flexibility requirements of the system. As an emerging technology, virtual power plants can effectively connect distributed resources with new power systems, reasonably configure the capacity of distributed resources in virtual power plants, effectively improve energy utilization, and achieve flexible regulation of power demand.

[0003] However, traditional optimization configuration methods usually adopt deterministic models, which fail to fully consider the uncertainty of market prices, the interdependence between resources, and the randomness of flexibility requirements, resulting in low adaptability and robustness of resource configuration results. On the other hand, traditional methods are usually based on nonlinear programming or mixed integer programming. As the number of resources and flexibility demand scenarios increase, the scale of the problem grows exponentially, resulting in a sharp increase in computational complexity.

[0004] In summary, the resource allocation results of existing optimization configuration methods have poor adaptability and robustness, and high computational complexity, which needs to be solved urgently. Summary of the invention

[0005] The present application provides a virtual power plant multi-scenario probabilistic configuration optimization method and device to solve the problems of poor adaptability and robustness of resource configuration results of existing optimization configuration methods and high computational complexity.

[0006] The first aspect of the present application provides a method for optimizing the probabilistic configuration of a virtual power plant in multiple scenarios, comprising the following steps: determining the benefit function of each resource in a target virtual power plant based on a preset high-dimensional game model, and using a preset Nash equilibrium principle to describe the impact conditions of the target resource configuration decision on the resource benefit of each resource, so as to perform resource opportunity cost accounting for each resource according to the impact conditions and the benefit function; probabilistically modeling the future scenarios of the target virtual power plant to calculate the expected value of resource benefits, and using a nonlinear function to characterize the matching degree between the resource regulation characteristics and the external demand characteristics of the target virtual power plant; and calculating the comprehensive configuration priority of the target virtual power plant based on the resource opportunity cost and the matching degree, so that the target virtual power plant performs resource configuration according to the comprehensive configuration priority.

[0007] Optionally, in one embodiment of the present application, the profit function of each resource in the target virtual power plant is determined based on a preset high-dimensional game model, and the preset Nash equilibrium principle is used to describe the impact conditions of the target resource allocation decision on the resource benefit of each resource, so as to perform resource opportunity cost accounting for each resource according to the impact conditions and the profit function, including: obtaining the expected output power information and electricity price information of each resource, and constructing an initial profit function corresponding to each resource based on the expected output power information and the electricity price information; describing the impact conditions of the target resource allocation decision on the resource benefit of each resource according to the high-dimensional game model, the Nash equilibrium principle and the initial profit function; calculating the benefit of selecting the current gap configuration decision and the benefit of abandoning the current gap configuration decision corresponding to each resource based on the impact conditions of the resource benefit and the profit function, and calculating the resource opportunity cost corresponding to each resource according to the benefit of selecting the current gap configuration decision and the benefit of abandoning the current gap configuration decision.

[0008] Optionally, in one embodiment of the present application, the future scenarios of the target virtual power plant are probabilistically modeled to calculate the expected value of resource benefits, and a nonlinear function is used to characterize the matching degree between the resource regulation characteristics and the external-demand characteristics of the target virtual power plant, including: determining the probability distribution of the future scenarios, and weighting the future scenarios according to the probability distribution, so as to transform the benefit function through the weighted future scenarios to obtain a game benefit conversion function; based on a preset Markov decision process and Monte Carlo simulation strategy, and in combination with the game benefit conversion function, probabilistic modeling is performed to calculate the expected value of resource benefits; a nonlinear function is established between the resource regulation characteristics and the external-demand characteristics, and the matching degree between the resource regulation characteristics and the external-demand characteristics is quantified through the nonlinear function and the expected value of resource benefits.

[0009] Optionally, in one embodiment of the present application, the comprehensive configuration priority of the target virtual power plant is calculated based on the resource opportunity cost and the matching degree, so that the target virtual power plant performs resource configuration according to the comprehensive configuration priority, including: generating an initial resource configuration priority corresponding to the target virtual power plant based on the resource opportunity cost and the matching degree, and dimensionlessly processing the resource opportunity cost and the matching degree, so as to convert the initial resource configuration priority using the dimensionlessly processed resource opportunity cost and matching degree to obtain the comprehensive configuration priority; constructing the flexibility demand constraint corresponding to the target virtual power plant, and performing resource configuration according to the comprehensive configuration priority and the flexibility demand constraint.

[0010] Optionally, in one embodiment of the present application, the mathematical expression of the resource opportunity cost is:

[0011] Among them, Δ F ( t )express t The need for flexibility at all times; Representation Resources R i exist t The expected output power at the time; t 0 represents the initial moment of flexibility demand; t T Indicates the end point of the flexibility requirement; C OC,i Indicates the resource R i the opportunity cost of resources; d i Indicates the resource R i In the proportion of adjustments allocated to flexibility needs; d j Representation Resources R j Adjustment allocation ratio for flexibility needs; express t Electricity price at any time.

[0012] The second aspect of the present application provides a virtual power plant multi-scenario probabilistic configuration optimization device, including: an accounting module, which is used to determine the benefit function of each resource in the target virtual power plant based on a preset high-dimensional game model, and use a preset Nash equilibrium principle to describe the impact conditions of the target resource configuration decision on the resource benefit of each resource, so as to perform resource opportunity cost accounting for each resource according to the impact conditions and the benefit function; a probabilistic modeling module, which is used to perform probabilistic modeling of future scenarios of the target virtual power plant to calculate the expected value of resource benefits, and use a nonlinear function to characterize the matching degree between the resource regulation characteristics and the external demand characteristics of the target virtual power plant; a resource configuration module, which is used to calculate the comprehensive configuration priority of the target virtual power plant based on the resource opportunity cost and the matching degree, so that the target virtual power plant performs resource configuration according to the comprehensive configuration priority.

[0013] Optionally, in one embodiment of the present application, the accounting module includes: a first construction unit, used to obtain the expected output power information and electricity price information of each resource, and construct the initial profit function corresponding to each resource based on the expected output power information and the electricity price information; a description unit, used to describe the impact conditions of the target resource allocation decision on the resource benefit of each resource according to the high-dimensional game model, the Nash equilibrium principle and the initial profit function; a first calculation unit, used to calculate the benefit of selecting the current gap configuration decision and the benefit of abandoning the current gap configuration decision corresponding to each resource based on the impact conditions of the resource benefit and the profit function, and calculate the resource opportunity cost corresponding to each resource based on the benefit of selecting the current gap configuration decision and the benefit of abandoning the current gap configuration decision.

[0014] Optionally, in one embodiment of the present application, the probabilistic modeling module includes: a weighting unit, used to determine the probability distribution of the future scenario, and weight the future scenario according to the probability distribution, so as to transform the profit function through the weighted future scenario to obtain a game profit conversion function; a second calculation unit, used to perform probabilistic modeling based on a preset Markov decision process and Monte Carlo simulation strategy, and in combination with the game profit conversion function, to calculate the expected value of the resource benefit; a quantification unit, used to establish a nonlinear function between the resource regulation characteristics and the external demand characteristics, and quantify the matching degree between the resource regulation characteristics and the external demand characteristics through the nonlinear function and the expected value of the resource benefit.

[0015] Optionally, in one embodiment of the present application, the resource configuration module includes: a dimensionless processing unit, used to generate an initial resource configuration priority corresponding to the target virtual power plant based on the resource opportunity cost and the matching degree, and perform dimensionless processing on the resource opportunity cost and the matching degree, so as to convert the initial resource configuration priority using the dimensionless processed resource opportunity cost and matching degree to obtain the comprehensive configuration priority; a second construction unit, used to construct the flexibility demand constraint corresponding to the target virtual power plant, and perform resource configuration according to the comprehensive configuration priority and the flexibility demand constraint.

[0016] Optionally, in one embodiment of the present application, the mathematical expression of the resource opportunity cost is:

[0017] Among them, Δ F ( t )express t The need for flexibility at all times; Representation Resources R i existt The expected output power at the time; t 0 represents the initial moment of flexibility demand; t T Indicates the end point of the flexibility requirement; C OC,i Indicates the resource R i the opportunity cost of resources; d i Indicates the resource R i In the proportion of adjustments allocated to flexibility needs; d j Representation Resources R j Adjustment allocation ratio for flexibility needs; express t Electricity price at any time.

[0018] The third aspect of the present application provides an electronic device, 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 multi-scenario probabilistic configuration optimization method for a virtual power plant as described in the above embodiment.

[0019] The fourth aspect embodiment of the present application provides a computer-readable storage medium, which stores a computer program, and when the program is executed by a processor, it implements the above-mentioned virtual power plant multi-scenario probability configuration optimization method.

[0020] 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-scenario probabilistic configuration optimization method.

[0021] Therefore, the embodiments of the present application have the following beneficial effects: The embodiment of the present application can determine the revenue function of each resource in the target virtual power plant based on a preset high-dimensional game model, and use the preset Nash equilibrium principle to describe the impact conditions of the target resource allocation decision on the resource revenue of each resource, so as to calculate the resource opportunity cost of each resource according to the impact conditions and the revenue function; perform probabilistic modeling on the future scenario of the target virtual power plant to calculate the expected value of resource revenue, and use nonlinear functions to characterize the matching degree of the resource regulation characteristics and the external characteristics of the target virtual power plant; based on the resource opportunity cost and matching degree, calculate the comprehensive configuration priority of the target virtual power plant, so that the target virtual power plant can perform resource configuration according to the comprehensive configuration priority. The present application can calculate the opportunity cost of resources based on a high-dimensional game model, quantify the matching degree of resource regulation characteristics and external characteristics of demand through nonlinear functions, and dynamically generate the comprehensive configuration priority of resources, thereby effectively improving the adaptability and robustness of the resource configuration results. As a result, the problems of poor adaptability and robustness of the resource configuration results of the existing optimization configuration method and high computational complexity are solved.

[0022] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned 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 apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which: Figure 1 A flowchart of a multi-scenario probability configuration optimization method for a virtual power plant provided according to an embodiment of the present application; Figure 2 This is an example diagram of a virtual power plant multi-scenario probability configuration optimization device according to an embodiment of the present application; Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application.

[0024] Among them, 10-virtual power plant multi-scenario probabilistic configuration optimization device; 100-accounting module, 200-probabilistic modeling module, 300-resource configuration module; 301-memory, 302-processor, 303-communication interface. DETAILED DESCRIPTION

[0025] Embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0026] The following describes the virtual power plant multi-scenario probability configuration optimization method and device of the embodiment of the present application with reference to the accompanying drawings. In response to the problems mentioned in the above background technology, the present application provides a virtual power plant multi-scenario probability configuration optimization method, in which the revenue function of each resource in the target virtual power plant is determined based on a preset high-dimensional game model, and the preset Nash equilibrium principle is used to describe the influence conditions of the target resource configuration decision on the resource revenue of each resource, so as to calculate the resource opportunity cost of each resource according to the influence conditions and the revenue function; the future scenario of the target virtual power plant is probabilistically modeled to calculate the expected value of resource revenue, and the resource regulation characteristics of the target virtual power plant and the matching degree of the external demand characteristics are characterized by a nonlinear function; based on the resource opportunity cost and matching degree, the comprehensive configuration priority of the target virtual power plant is calculated, so that the target virtual power plant performs resource configuration according to the comprehensive configuration priority. The present application can calculate the opportunity cost of resources based on a high-dimensional game model, and quantify the matching degree of resource regulation characteristics and external demand characteristics through a nonlinear function, and dynamically generate the comprehensive configuration priority of resources, thereby effectively improving the adaptability and robustness of the resource configuration results. Thus, the problems of poor adaptability and robustness of resource allocation results of existing optimization configuration methods and high computational complexity are solved.

[0027] Specifically, Figure 1 A flowchart of a multi-scenario probability configuration optimization method for a virtual power plant provided in an embodiment of the present application.

[0028] like Figure 1 As shown, the virtual power plant multi-scenario probability configuration optimization method includes the following steps: In step S101, based on the preset high-dimensional game model, the profit function of each resource in the target virtual power plant is determined, and the preset Nash equilibrium principle is used to describe the impact conditions of the target resource allocation decision on the resource benefit of each resource, so as to calculate the resource opportunity cost of each resource according to the impact conditions and the profit function.

[0029] The embodiments of the present application can first calculate the opportunity cost of resources based on a high-dimensional game model, comprehensively consider the uncertainty of market prices and the interdependence between resources, and use the Nash equilibrium principle to optimize resource allocation decisions, thereby ensuring the economy and rationality of resource allocation.

[0030] Optionally, in one embodiment of the present application, based on a preset high-dimensional game model, the profit function of each resource in the target virtual power plant is determined, and the preset Nash equilibrium principle is used to describe the impact conditions of the target resource allocation decision on the resource profit of each resource, so as to perform resource opportunity cost accounting for each resource according to the impact conditions and the profit function, including: obtaining the expected output power information and electricity price information of each resource, and constructing the initial profit function corresponding to each resource based on the expected output power information and electricity price information; describing the impact conditions of the target resource allocation decision on the resource profit of each resource according to the high-dimensional game model, the Nash equilibrium principle and the initial profit function; based on the impact conditions and profit function of resource profit, calculating the profit of selecting the current gap configuration decision and the profit of abandoning the current gap configuration decision corresponding to each resource, and calculating the resource opportunity cost corresponding to each resource based on the profit of selecting the current gap configuration decision and the profit of abandoning the current gap configuration decision.

[0031] Specifically, in the embodiment of the present application, the resource set of the virtual power plant is , the virtual power plant makes resource allocation decisions based on the flexibility gap. In order to calculate the opportunity cost of each resource, the embodiment of the present application may define a revenue function for each resource, as shown in the following formula:

[0032] in, For resources Flexibility oriented The benefits obtained during configuration; express t The electricity price at the time; Representation Resources exist t The expected output power at the time.

[0033] Those skilled in the art should understand that resource benefits depend not only on the gap of the current choice, but also on the decision-making of other resources. Therefore, resource allocation is a game decision problem, and the selection decisions of multiple resources affect each other. Assume that the resource allocation decision To form a decision vector, Representation Resources The configuration decision of each resource will affect its benefit through the following game model to obtain the corresponding benefit function, as shown in the following formula:

[0034] in, resource In the adjustment allocation ratio for flexibility needs.

[0035] Secondly, according to the definition of Nash equilibrium, each resource in the embodiment of the present application can make an optimal decision to maximize its own benefits under the condition that the configuration of other resources is known, that is, when all other resource configurations are fixed, the resource The configuration decision no longer changes, and the Nash equilibrium condition can be expressed by the following equation:

[0036] in, Representation Resources In the configuration decision vector The following income; Indicates that except resources In addition, the configuration decision vectors of other resources.

[0037] Optionally, in one embodiment of the present application, the mathematical expression of resource opportunity cost is:

[0038] Among them, Δ F ( t )express t The need for flexibility at all times; Representation Resources R i exist t The expected output power at the time; t 0 represents the initial moment of flexibility demand; t T Indicates the end point of the flexibility requirement; C OC,i Representation Resources R i the opportunity cost of resources; d i Representation Resources R i In the proportion of adjustments allocated to flexibility needs; d j Representation Resources R j Adjustment allocation ratio for flexibility needs; express t Electricity price at any time.

[0039] It should be noted that under this Nash equilibrium condition, resources Opportunity cost The difference between the benefit brought by choosing the current gap configuration decision and the maximum benefit that may be brought by giving up the current gap configuration and choosing other gap configurations is calculated. This difference is the resource opportunity cost, as shown in the following formula:

[0040] Among them, Δ F ( t )express t The need for flexibility at all times; Representation Resources R i exist t The expected output power at the time; t 0 represents the initial moment of flexibility demand; t T Indicates the end point of the flexibility requirement; C OC,i Representation Resources R i the opportunity cost of resources; d i Representation Resources R i In the proportion of adjustments allocated to flexibility needs; d j Representation Resources R j Adjustment allocation ratio for flexibility needs; express t Electricity price at any time.

[0041] Therefore, the embodiments of the present application calculate the opportunity cost of resources based on a high-dimensional game model, thereby providing reliable data support for the subsequent probabilistic modeling and resource allocation.

[0042] In step S102, a probabilistic modeling is performed on the future scenarios of the target virtual power plant to calculate the expected value of resource benefits, and a nonlinear function is used to characterize the matching degree between the resource regulation characteristics and the external demand characteristics of the target virtual power plant.

[0043] Furthermore, the embodiments of the present application also need to use Markov decision process and Monte Carlo simulation to probabilistically model future scenarios in response to the randomness of flexibility requirements, and calculate the expected value of resource benefits; thereafter, the embodiments of the present application can quantify the matching degree between resource regulation characteristics (such as regulation rate, capacity) and external demand characteristics (such as climbing rate requirements) through nonlinear functions to dynamically generate resource configuration priorities, thereby ensuring that resources with high matching degrees are preferentially configured.

[0044] Optionally, in one embodiment of the present application, future scenarios of the target virtual power plant are probabilistically modeled to calculate expected values ​​of resource benefits, and a nonlinear function is used to characterize the degree of matching between resource regulation characteristics and external-demand characteristics of the target virtual power plant, including: determining the probability distribution of future scenarios, and weighting the future scenarios according to the probability distribution, so as to transform the benefit function through the weighted future scenarios to obtain a game benefit conversion function; based on a preset Markov decision process and Monte Carlo simulation strategy, and in combination with the game benefit conversion function, probabilistic modeling is performed to calculate the expected value of resource benefits; a nonlinear function between resource regulation characteristics and external-demand characteristics is established, and the degree of matching between resource regulation characteristics and external-demand characteristics is quantified through the nonlinear function and the expected value of resource benefits.

[0045] It should be noted that the above opportunity cost calculation only considers the relationship between flexibility gap and resource allocation. However, flexibility demand is not a deterministic variable. Its occurrence and size are random, and different scenarios have different probability distributions. Therefore, opportunity cost accounting needs to take into account different demand scenarios that may appear in the future and weight these scenarios through probability distribution. The converted resource benefit function is shown in the following formula:

[0046] in, is the joint probability distribution of flexibility demand and electricity price.

[0047] Considering that there may be long-term dependence between the flexibility gap and the adjustment capacity of resources, the embodiment of the present application may use the state transition matrix in the Markov decision process to describe the state evolution process, as shown in the following formula:

[0048] in, Indicates from the state Start expecting the benefits you will gain; represents the state transition probability; Represents the weight of future opportunities. In order to improve the calculation accuracy, the embodiment of the present application introduces Monte Carlo simulation to generate multiple scenarios of flexibility gap demand and market electricity price, and calculates the expected benefits through the simulation results:

[0049] in, N is the number of simulations; The probability of scenarios where flexibility needs to occur.

[0050] Furthermore, it can be understood that the configuration priority of virtual power plant resources to fill flexibility needs should not only be determined based on opportunity cost (resources with lower opportunity cost are configured first), but also consider the matching degree between resource regulation characteristics (such as regulation rate, capacity) and external demand characteristics (such as ramp rate, power requirements), and dynamically generate resource priorities to ensure priority configuration with higher matching degree.

[0051] In order to make the quantification of the matching degree more realistic and precise, the embodiment of the present application introduces a nonlinear function to describe the relationship between these variables:

[0052] in, and They represent the nonlinear relationship function between the regulation rate and regulation capacity and the external characteristics of demand respectively; and is the adjustment coefficient, which is used to balance the effects of adjustment rate and adjustment capacity on matching degree; It is a resource exist t Ability to adjust at any time; is the response time; is a weighted function, which indicates the influence of different moments on the matching degree; Indicates the maximum flexibility requirement; Indicates the impact of changes in flexibility requirements on weights.

[0053] Therefore, the embodiments of the present application dynamically generate resource priorities through high-dimensional uncertainty modeling and calculation of the matching degree of adjustment characteristics, so as to effectively ensure priority configuration with high matching degree, and provide data guidance and basis for the multi-scenario probabilistic configuration optimization of virtual power plants.

[0054] In step S103, based on the resource opportunity cost and the matching degree, the comprehensive configuration priority of the target virtual power plant is calculated, so that the target virtual power plant performs resource configuration according to the comprehensive configuration priority.

[0055] Furthermore, the embodiments of the present application also need to calculate the comprehensive configuration priority of the virtual power plant based on the opportunity cost of the resources and the matching degree of the regulation characteristics, so that the virtual power plant configures resources from high to low according to the comprehensive configuration priority to ensure that the total resource configuration meets the flexibility requirements.

[0056] Optionally, in one embodiment of the present application, the comprehensive configuration priority of the target virtual power plant is calculated based on the resource opportunity cost and the matching degree, so that the target virtual power plant performs resource configuration according to the comprehensive configuration priority, including: generating an initial resource configuration priority corresponding to the target virtual power plant based on the resource opportunity cost and the matching degree, and dimensionlessly processing the resource opportunity cost and the matching degree, so as to convert the initial resource configuration priority using the dimensionlessly processed resource opportunity cost and the matching degree to obtain a comprehensive configuration priority; constructing a flexibility demand constraint corresponding to the target virtual power plant, and performing resource configuration according to the comprehensive configuration priority and the flexibility demand constraint.

[0057] In the actual implementation process, the configuration priority of the virtual power plant resources in the embodiment of the present application will be calculated in combination with the opportunity cost and matching degree:

[0058] in, Representation Resources The configuration priority of the resource (i.e. the initial resource configuration priority); and Represents the weight coefficient, which is used to balance the importance of opportunity cost and matching degree in priority calculation; M i Representation Resources degree of matching.

[0059] It should be noted that the opportunity cost in the above formula affects the priority through an inverse relationship. The resource with a smaller opportunity cost has a higher priority, and the matching degree is directly weighted into the priority. Resources with a high matching degree will get a higher priority. However, the dimensions of the two are inconsistent, resulting in an inaccurate configuration priority for individual resources. Therefore, dimensionless processing is required, as shown in the following formula:

[0060] in, X is the original data; X min and X max are the minimum and maximum values ​​of the data respectively.

[0061] Therefore, the above configuration priority (i.e. initial resource configuration priority) It can be converted into the following formula to obtain the corresponding comprehensive configuration priority:

[0062] In summary, the virtual power plant will be based on comprehensive configuration priorities Allocate resources from high to low. The configuration process is as follows:

[0063] in, Indicates the configuration capacity; Indicates the maximum configuration capacity of the resource.

[0064] In addition, the configuration target of the virtual power plant needs to meet the flexibility requirements, so the virtual power plant configuration must also meet the following constraints:

[0065] To summarize, the embodiments of the present application calculate the opportunity cost of resources based on a high-dimensional game model, and quantify the matching degree between resource regulation characteristics and external demand characteristics through nonlinear functions, so as to dynamically generate a comprehensive configuration priority of resources, so that the virtual power plant completes resource configuration from high to low according to the configuration priority, and ensures that the total amount of resource configuration meets the flexibility requirements.

[0066] According to the virtual power plant multi-scenario probabilistic configuration optimization method proposed in the embodiment of the present application, based on the preset high-dimensional game model, the revenue function of each resource in the target virtual power plant is determined, and the preset Nash equilibrium principle is used to describe the impact conditions of the target resource allocation decision on the resource revenue of each resource, so as to calculate the resource opportunity cost of each resource according to the impact conditions and the revenue function; the future scenarios of the target virtual power plant are probabilistically modeled to calculate the expected value of resource revenue, and the resource regulation characteristics of the target virtual power plant and the matching degree of the external characteristics of demand are characterized by nonlinear functions; based on the resource opportunity cost and matching degree, the comprehensive configuration priority of the target virtual power plant is calculated, so that the target virtual power plant performs resource configuration according to the comprehensive configuration priority. The present application can calculate the opportunity cost of resources based on a high-dimensional game model, quantify the matching degree of resource regulation characteristics and external characteristics of demand through nonlinear functions, and dynamically generate the comprehensive configuration priority of resources, thereby effectively improving the adaptability and robustness of the resource configuration results.

[0067] Secondly, the multi-scenario probability configuration optimization device of a virtual power plant proposed according to an embodiment of the present application is described with reference to the accompanying drawings.

[0068] Figure 2 It is a block diagram of a multi-scenario probability configuration optimization device for a virtual power plant according to an embodiment of the present application.

[0069] like Figure 2 As shown, the virtual power plant multi-scenario probabilistic configuration optimization device 10 includes: a calculation module 100, a probabilistic modeling module 200 and a resource configuration module 300.

[0070] Among them, the accounting module 100 is used to determine the profit function of each resource in the target virtual power plant based on a preset high-dimensional game model, and use the preset Nash equilibrium principle to describe the impact conditions of the target resource allocation decision on the resource benefit of each resource, so as to perform resource opportunity cost accounting for each resource according to the impact conditions and the profit function.

[0071] The probabilistic modeling module 200 is used to perform probabilistic modeling on the future scenarios of the target virtual power plant to calculate the expected value of resource benefits and to characterize the matching degree between the resource regulation characteristics and the external demand characteristics of the target virtual power plant using a nonlinear function.

[0072] The resource configuration module 300 is used to calculate the comprehensive configuration priority of the target virtual power plant based on the resource opportunity cost and the matching degree, so that the target virtual power plant performs resource configuration according to the comprehensive configuration priority.

[0073] Optionally, in one embodiment of the present application, the accounting module 100 includes: a first construction unit, a description unit and a first calculation unit.

[0074] The first construction unit is used to obtain the expected output power information and electricity price information of each resource, and to construct an initial revenue function corresponding to each resource based on the expected output power information and electricity price information.

[0075] The description unit is used to describe the influence conditions of the target resource allocation decision on the resource benefit of each resource according to the high-dimensional game model, the Nash equilibrium principle and the initial benefit function.

[0076] The first calculation unit is used to calculate the benefit of selecting the current gap configuration decision and the benefit of abandoning the current gap configuration decision corresponding to each resource based on the influencing conditions and benefit functions of the resource benefits, and calculate the resource opportunity cost corresponding to each resource based on the benefit of selecting the current gap configuration decision and the benefit of abandoning the current gap configuration decision.

[0077] Optionally, in one embodiment of the present application, the probabilistic modeling module 200 includes: a weighting unit, a second calculation unit and a quantization unit.

[0078] Among them, the weighting unit is used to determine the probability distribution of future scenarios and weight the future scenarios according to the probability distribution, so as to transform the profit function through the weighted future scenarios to obtain the game profit conversion function.

[0079] The second calculation unit is used to perform probabilistic modeling based on a preset Markov decision process and Monte Carlo simulation strategy and in combination with a game payoff conversion function to calculate the expected value of resource payoff.

[0080] The quantification unit is used to establish a nonlinear function between the resource regulation characteristics and the external demand characteristics, and quantify the matching degree between the resource regulation characteristics and the external demand characteristics through the nonlinear function and the expected value of resource benefits.

[0081] Optionally, in one embodiment of the present application, the resource configuration module 300 includes: a dimensionless processing unit and a second construction unit.

[0082] Among them, the dimensionless processing unit is used to generate the initial resource configuration priority corresponding to the target virtual power plant based on the resource opportunity cost and matching degree, and to perform dimensionless processing on the resource opportunity cost and matching degree, so as to convert the initial resource configuration priority using the dimensionless processed resource opportunity cost and matching degree to obtain a comprehensive configuration priority.

[0083] The second construction unit is used to construct the flexibility demand constraints corresponding to the target virtual power plant and perform resource allocation according to the comprehensive configuration priority and flexibility demand constraints.

[0084] Optionally, in one embodiment of the present application, the mathematical expression of resource opportunity cost is:

[0085] Among them, Δ F ( t )express t The need for flexibility at all times; Representation Resources R i exist t The expected output power at the time; t 0 represents the initial moment of flexibility demand; t T Indicates the end point of the flexibility requirement; C OC,i Representation Resources R i the opportunity cost of resources; d i Representation Resources R i In the proportion of adjustments allocated to flexibility needs; d j Representation Resources R j Adjustment allocation ratio for flexibility needs; express t Electricity price at any time.

[0086] It should be noted that the aforementioned explanation of the embodiment of the virtual power plant multi-scenario probability configuration optimization method is also applicable to the virtual power plant multi-scenario probability configuration optimization device of this embodiment, and will not be repeated here.

[0087] The multi-scenario probabilistic configuration optimization device for a virtual power plant proposed in an embodiment of the present application includes an accounting module 100, which is used to determine the benefit function of each resource in the target virtual power plant based on a preset high-dimensional game model, and use a preset Nash equilibrium principle to describe the impact conditions of the target resource allocation decision on the resource benefit of each resource, so as to perform resource opportunity cost accounting for each resource according to the impact conditions and the benefit function; a probabilistic modeling module 200, which is used to perform probabilistic modeling of future scenarios of the target virtual power plant to calculate the expected value of resource benefits, and use a nonlinear function to characterize the matching degree between the resource regulation characteristics and the external demand characteristics of the target virtual power plant; a resource allocation module 300, which is used to calculate the comprehensive configuration priority of the target virtual power plant based on the resource opportunity cost and the matching degree, so that the target virtual power plant performs resource allocation according to the comprehensive configuration priority, thereby effectively improving the adaptability and robustness of the resource allocation results.

[0088] Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device may include: A memory 301 , a processor 302 , and a computer program stored in the memory 301 and executable on the processor 302 .

[0089] When the processor 302 executes the program, the virtual power plant multi-scenario probability configuration optimization method provided in the above embodiment is implemented.

[0090] Furthermore, the electronic device further comprises: The communication interface 303 is used for communication between the memory 301 and the processor 302 .

[0091] The memory 301 is used to store computer programs that can be run on the processor 302 .

[0092] The memory 301 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0093] 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 connected to each other through a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 3 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0094] 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 can communicate with each other through an internal interface.

[0095] 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.

[0096] An embodiment of the present application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned virtual power plant multi-scenario probability configuration optimization method.

[0097] An embodiment of the present application also provides a computer program product, including a computer program, which, when executed, is used to implement the above-mentioned virtual power plant multi-scenario probabilistic configuration optimization method.

[0098] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction 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 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, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0099] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0100] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or N executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0101] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples (non-exhaustive list) of computer-readable media include the following: an electrical connection with one or N wirings (electronic device), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways as necessary and then storing it in a computer memory.

[0102] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above embodiment, 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 by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0103] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0104] In addition, each functional unit in each embodiment of the present application may be integrated into a processing module, or each unit may exist physically separately, 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. If 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.

[0105] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, 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 cannot be understood as limiting the present application. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A virtual power plant multi-scenario probability configuration optimization method, characterized in that: The following steps are involved: Based on a preset high-dimensional game model, determine the revenue function of each resource in the target virtual power plant, and use the preset Nash equilibrium principle to describe the impact conditions of the target resource allocation decision on the resource revenue of each resource, so as to calculate the resource opportunity cost of each resource according to the impact conditions and the revenue function; Probabilistically modeling the future scenarios of the target virtual power plant to calculate the expected value of resource benefits, and using a nonlinear function to characterize the matching degree between the resource regulation characteristics and the external demand characteristics of the target virtual power plant; Based on the resource opportunity cost and the matching degree, the comprehensive configuration priority of the target virtual power plant is calculated so that the target virtual power plant performs resource configuration according to the comprehensive configuration priority.

2. The virtual power plant multi-scenario probability configuration optimization method according to claim 1 is characterized in that: The method of determining the revenue function of each resource in the target virtual power plant based on the preset high-dimensional game model and using the preset Nash equilibrium principle to describe the impact conditions of the target resource allocation decision on the resource revenue of each resource, so as to calculate the resource opportunity cost of each resource according to the impact conditions and the revenue function, includes: Acquire expected output power information and electricity price information of each resource, and construct an initial revenue function corresponding to each resource based on the expected output power information and the electricity price information; Describing the influence conditions of the target resource allocation decision on the resource benefits of each resource according to the high-dimensional game model, the Nash equilibrium principle and the initial benefit function; Based on the influencing conditions of the resource benefits and the benefit function, the benefits of selecting the current gap configuration decision and the benefits of abandoning the current gap configuration decision corresponding to each resource are calculated, and the resource opportunity cost corresponding to each resource is calculated based on the benefits of selecting the current gap configuration decision and the benefits of abandoning the current gap configuration decision.

3. The virtual power plant multi-scenario probability configuration optimization method according to claim 2 is characterized in that: The probabilistic modeling of the future scenario of the target virtual power plant to calculate the expected value of resource benefits and characterize the matching degree of the resource regulation characteristics and the external demand characteristics of the target virtual power plant by using a nonlinear function includes: Determine the probability distribution of the future scenario, and weight the future scenario according to the probability distribution, so as to convert the payoff function through the weighted future scenario to obtain a game payoff conversion function; Based on the preset Markov decision process and Monte Carlo simulation strategy, and in combination with the game benefit conversion function, probabilistic modeling is performed to calculate the expected value of the resource benefit; A nonlinear function is established between the resource regulation characteristic and the external demand characteristic, and the matching degree between the resource regulation characteristic and the external demand characteristic is quantified by the nonlinear function and the expected value of resource benefit.

4. The virtual power plant multi-scenario probability configuration optimization method according to claim 3 is characterized in that: The calculating, based on the resource opportunity cost and the matching degree, the comprehensive configuration priority of the target virtual power plant so that the target virtual power plant performs resource configuration according to the comprehensive configuration priority includes: Based on the resource opportunity cost and the matching degree, an initial resource configuration priority corresponding to the target virtual power plant is generated, and the resource opportunity cost and the matching degree are dimensionlessly processed, so as to convert the initial resource configuration priority using the dimensionlessly processed resource opportunity cost and matching degree to obtain the comprehensive configuration priority; Construct flexibility demand constraints corresponding to the target virtual power plant, and perform resource allocation according to the comprehensive configuration priority and the flexibility demand constraints.

5. The virtual power plant multi-scenario probability configuration optimization method according to claim 2 is characterized in that: The mathematical expression of the resource opportunity cost is: Among them, Δ F ( t )express t The need for flexibility at all times; Representation Resources R i exist t The expected output power at the time; t 0 represents the initial moment of flexibility demand; t T Indicates the end point of the flexibility requirement; C OC,i Indicates the resource R i the opportunity cost of resources; d i Indicates the resource R i In the proportion of adjustments allocated to flexibility needs; d j Representation Resources R j Adjustment allocation ratio for flexibility needs; express t Electricity price at any time.

6. A virtual power plant multi-scenario probability configuration optimization device, characterized in that: include: An accounting module, for determining the revenue function of each resource in the target virtual power plant based on a preset high-dimensional game model, and using a preset Nash equilibrium principle to describe the impact conditions of the target resource allocation decision on the resource revenue of each resource, so as to perform resource opportunity cost accounting for each resource according to the impact conditions and the revenue function; A probabilistic modeling module, used to perform probabilistic modeling on future scenarios of the target virtual power plant to calculate expected resource benefits and characterize the matching degree of resource regulation characteristics and external demand characteristics of the target virtual power plant using a nonlinear function; A resource configuration module is used to calculate the comprehensive configuration priority of the target virtual power plant based on the resource opportunity cost and the matching degree, so that the target virtual power plant performs resource configuration according to the comprehensive configuration priority.

7. The virtual power plant multi-scenario probability configuration optimization device according to claim 6 is characterized in that: The accounting module includes: A first construction unit, configured to obtain expected output power information and electricity price information of each resource, and construct an initial revenue function corresponding to each resource based on the expected output power information and the electricity price information; A description unit, used for describing the influence conditions of the target resource allocation decision on the resource benefit of each resource according to the high-dimensional game model, the Nash equilibrium principle and the initial benefit function; The first calculation unit is used to calculate the benefit of selecting the current gap configuration decision and the benefit of abandoning the current gap configuration decision corresponding to each resource based on the influencing conditions of the resource benefits and the benefit function, and calculate the resource opportunity cost corresponding to each resource according to the benefit of selecting the current gap configuration decision and the benefit of abandoning the current gap configuration decision.

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-scenario probabilistic configuration optimization 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-scenario probabilistic configuration optimization method for 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 virtual power plant multi-scenario probabilistic configuration optimization method as described in any one of claims 1-5.

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