Evaluation method and related device for interactive regulation capability of virtual power plant

Abstract

The present invention discloses a method for evaluating the interactive regulation capability of a virtual power plant and a related device. The method includes: obtaining load data, cost values, and output power data of a target virtual power plant in multiple historical time periods, obtaining multiple load data, multiple cost values, and multiple output power data, inputting the multiple load data, multiple cost values, and multiple output power data into a preset distributed energy resource regulation characteristic model, obtaining multiple function sets, determining the interactive regulation capability value corresponding to each function set in the multiple function sets, obtaining multiple interactive regulation capability values, determining at least one target interactive regulation capability value from the multiple interactive regulation capability values ​​based on a multi-objective particle swarm optimization algorithm, and determining the target interactive regulation capability value corresponding to the target virtual power plant based on the at least one target interactive regulation capability value. The implementation of the present application improves the accuracy of the evaluation of the interactive regulation capability of the virtual power plant.

Description

Technical Field

[0001] The present invention relates to the technical field of virtual power plant regulation capability evaluation, and in particular to a method for evaluating the interactive regulation capability of a virtual power plant and related devices. Background Art

[0002] Quantitative evaluation of the interactive regulation capability of virtual power plants is an important basis for accurately measuring the external regulation performance of virtual power plants under different time and space conditions and optimizing the operation of virtual power plants. Under the general trend of global energy structure transformation and intelligent power system, virtual power plants have attracted much attention as key hubs connecting distributed resources. Accurate evaluation of the aggregated interactive regulation capability of virtual power plants is a prerequisite for their participation in interactive response, which can guarantee their competitiveness in a dynamic market environment and ensure that they can meet the ever-changing market and network needs.

[0003] Currently, when evaluating the aggregate interactive regulation capability of virtual power plants, only a single factor is considered, and the evaluation of the aggregate interactive regulation capability of virtual power plants is not accurate enough. Therefore, how to accurately evaluate the interactive regulation capability of virtual power plants is an urgent problem to be solved. Summary of the invention

[0004] The embodiments of the present application provide a method and related device for evaluating the interactive regulation capability of a virtual power plant, which improves the accuracy of evaluating the interactive regulation capability of the virtual power plant by determining a target interactive regulation capability value corresponding to the virtual power plant.

[0005] In a first aspect, an embodiment of the present application provides a method for evaluating the interactive regulation capability of a virtual power plant, the method comprising:

[0006] Obtain the load data, cost value, and output power data of the target virtual power plant in multiple historical time periods, and obtain multiple load data, multiple cost values, and multiple output power data; each historical time period corresponds to one load data, one cost value, and one output power data, the load data includes distributed generation load, adjustable load, and energy storage load, and the output power data includes adjustment capacity and ramp rate;

[0007] Input multiple load data, multiple cost values, and multiple output power data into a preset distributed energy resource regulation characteristic model to obtain multiple function sets; each of the multiple function sets is used to characterize the constraint relationship between the distributed generation load, the adjustable load, the energy storage load and the cost value, the constraint relationship between the distributed generation load, the adjustable load, the energy storage load and the regulation capacity, and the constraint relationship between the distributed generation load, the adjustable load, the energy storage load and the ramp rate, and each historical time period corresponds to a function set;

[0008] Determine the interaction adjustment capability value corresponding to each function set in the plurality of function sets, and obtain a plurality of interaction adjustment capability values; the larger the interaction adjustment capability value, the greater the interaction adjustment capability corresponding to the interaction adjustment capability value;

[0009] Determining at least one target interactive adjustment capability value from a plurality of interactive adjustment capability values ​​based on a multi-objective particle swarm optimization algorithm;

[0010] A target interactive regulation capability value corresponding to the target virtual power plant is determined based on at least one target interactive regulation capability value.

[0011] In a second aspect, an embodiment of the present application provides an evaluation device for interactive regulation capability of a virtual power plant, the device comprising: an acquisition unit and a processing unit;

[0012] An acquisition unit is used to acquire load data, cost values, and output power data of a target virtual power plant in multiple historical time periods, and obtain multiple load data, multiple cost values, and multiple output power data; each historical time period corresponds to one load data, one cost value, and one output power data, the load data includes distributed generation load, adjustable load, and energy storage load, and the output power data includes adjustment capacity and ramp rate;

[0013] A processing unit is used to input multiple load data, multiple cost values, and multiple output power data into a preset distributed energy resource regulation characteristic model to obtain multiple function sets; each of the multiple function sets is used to characterize the constraint relationship between the distributed generation load, the adjustable load, the energy storage load and the cost value, the constraint relationship between the distributed generation load, the adjustable load, the energy storage load and the regulation capacity, and the constraint relationship between the distributed generation load, the adjustable load, the energy storage load and the ramp rate, and each historical time period corresponds to a function set;

[0014] Determine the interaction adjustment capability value corresponding to each function set in the plurality of function sets, and obtain a plurality of interaction adjustment capability values; the larger the interaction adjustment capability value, the greater the interaction adjustment capability corresponding to the interaction adjustment capability value;

[0015] Determining at least one target interactive adjustment capability value from a plurality of interactive adjustment capability values ​​based on a multi-objective particle swarm optimization algorithm;

[0016] A target interactive regulation capability value corresponding to the target virtual power plant is determined based on at least one target interactive regulation capability value.

[0017] In a third aspect, an embodiment of the present invention provides an electronic device, comprising: a processor, a memory, a communication interface, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the processor so that the electronic device executes the method of the first aspect.

[0018] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and the computer program is executed by a processor to implement the method of the first aspect.

[0019] In a fifth aspect, an embodiment of the present invention provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, so that a computer executes the method of the first aspect.

[0020] The implementation of the present invention has the following beneficial effects:

[0021] It can be seen that the evaluation method for the interactive regulation capability of a virtual power plant described in the embodiment of the present invention first obtains the load data, cost value, and output power data of the target virtual power plant in multiple historical time periods to obtain multiple load data, multiple cost values, and multiple output power data, and then inputs the multiple load data, multiple cost values, and multiple output power data into a preset distributed energy resource regulation characteristic model to obtain multiple function sets, and each function set in the multiple function sets is used to characterize the constraint relationship between the distributed generation load, the adjustable load, the energy storage load and the cost value, the constraint relationship between the distributed generation load, the adjustable load, the energy storage load and the regulation capacity, and the constraint relationship between the distributed generation load, the adjustable load, the energy storage load and the ramp rate. Each historical time period corresponds to a function set, and then the interactive regulation capability value corresponding to each function set in the multiple function sets is determined to obtain multiple interactive regulation capability values, and at least one target interactive regulation capability value is determined from the multiple interactive regulation capability values ​​based on a multi-objective particle swarm optimization algorithm, and finally the target interactive regulation capability value corresponding to the target virtual power plant is determined based on the at least one target interactive regulation capability value, thereby improving the accuracy of the evaluation of the interactive regulation capability of the virtual power plant. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the implementation methods of the present application or the background technology, the drawings required for use in the implementation methods of the present application or the background technology will be described below.

[0023] Figure 1 It is a flow chart of a method for evaluating the interactive regulation capability of a virtual power plant provided in an embodiment of the present application;

[0024] Figure 2 It is a flow chart for determining an interactive adjustment capability value provided by an embodiment of the present application;

[0025] Figure 3 It is a structural schematic diagram of a device for evaluating the interactive regulation capability of a virtual power plant provided in an embodiment of the present application;

[0026] Figure 4 It is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0027] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the implementation mode of the present application will be clearly and completely described below in conjunction with the drawings in the implementation mode of the present application. Obviously, the described implementation mode is only a part of the implementation mode of the present application, not all the implementation modes. Based on the implementation mode in the present application, all other implementation modes obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0028] The terms "first", "second", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices.

[0029] Reference to "embodiment" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiment may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0030] First, the relevant terms involved in this application are explained:

[0031] VPP: Virtual Power Plant (VPP) is an energy management model that aggregates distributed energy resources (such as distributed generation, energy storage, adjustable loads, etc.) through advanced information technology and control technology to achieve coordinated optimized operation and participate in electricity market transactions.

[0032] DERs regulation characteristic model: The Distributed Energy Resources (DERs) regulation characteristic model is a tool used to describe and analyze the operation and regulation capabilities of distributed energy in the power system. Distributed energy resources include distributed power generation (such as solar photovoltaic power generation, wind power generation, micro gas turbines, etc.), energy storage systems (such as battery energy storage, supercapacitors, etc.) and adjustable loads (such as demand response resources). These resources are decentralized, diverse and flexible.

[0033] See also Figure 1 , Figure 1 It is a flow chart of a method for evaluating the interactive regulation capability of a virtual power plant provided in an embodiment of the present application, including but not limited to the following steps:

[0034] S101: Obtain load data, cost values, and output power data of a target virtual power plant in multiple historical time periods to obtain multiple load data, multiple cost values, and multiple output power data.

[0035] In this embodiment, each historical time period corresponds to a load data, a cost value and an output power data, the load data includes distributed generation load, adjustable load, energy storage load, and the output power data includes adjustment capacity and climbing rate.

[0036] S102: Input multiple load data, multiple cost values, and multiple output power data into a preset distributed energy resource regulation characteristic model to obtain multiple function sets.

[0037] In this embodiment, the function set is used to characterize the constraint relationship between the distributed generation load, adjustable load, energy storage load and cost value, the constraint relationship between the distributed generation load, adjustable load, energy storage load and adjustment capacity, and the constraint relationship between the distributed generation load, adjustable load, energy storage load and ramp rate. Each load data corresponds to a function set. In this embodiment, the function set can be represented by the following functional relationship:

[0038] ;

[0039] ;

[0040] ;

[0041] in, represents the distributed generation load, Indicates adjustable load, represents the energy storage load, represents the cost value, Indicates the adjustment capacity, Indicates the climbing rate, is the coefficient corresponding to the cost value, To adjust the coefficient corresponding to the capacity, is the coefficient corresponding to the climbing rate, Used to characterize the constraint relationship between distributed generation load, adjustable load, energy storage load and cost value. Used to characterize the constraint relationship between distributed generation load, adjustable load, energy storage load and regulation capacity. Used to characterize the constraint relationship between distributed generation load, adjustable load, energy storage load and ramp rate.

[0042] In this embodiment, a preset distributed energy resource regulation characteristic model is first determined. The preset distributed energy resource regulation characteristic model includes a distributed power supply model, an adjustable load resource model and an energy storage resource model. The distributed power supply model includes a wind turbine output model, a photovoltaic unit output model, and a micro-combustion unit model. The adjustable load resource model includes a curtailable load model, a shiftable load model, and a transferable load model. The energy storage resource model includes a battery model and an electric vehicle model.

[0043] The upper regulation capacity corresponding to the wind turbine output model satisfies the following function: ,in, To adjust the capacity of wind turbines, is the upper limit of wind turbine output, is the current output of the wind turbine.

[0044] The down-regulation capacity corresponding to the wind turbine output model satisfies the following function: ,in, To adjust the capacity of wind turbines, It is the lower limit of wind turbine output.

[0045] The cost function corresponding to the wind turbine output model satisfies the following function: ,in, is the cost value of the wind turbine, is the unit operating cost coefficient of the wind turbine, is the fixed cost of the wind turbine.

[0046] The upward regulation capacity corresponding to the photovoltaic unit output model satisfies the following function: ,in, To adjust the capacity of photovoltaic units, is the upper limit of the photovoltaic unit output, This is the current output of the PV unit.

[0047] The down-regulation capacity corresponding to the photovoltaic unit output model satisfies the following function: ,in, To adjust the capacity of photovoltaic units. It is the lower limit of photovoltaic unit output.

[0048] The cost function corresponding to the photovoltaic unit output model satisfies the following function: ,in, is the cost value of the photovoltaic unit, is the unit operating cost coefficient of the photovoltaic unit, is the fixed cost of the PV system.

[0049] There are output constraints during the operation of the micro-gas unit: ,in, is the lower limit of the output of the micro-turbines, is the upper limit of the output of the micro-turbine unit, It is the current output of the micro-turbines.

[0050] The upward regulation capacity corresponding to the micro-gas turbine model satisfies the following function: ,in, Adjust the capacity of the micro-turbines.

[0051] The down-regulation capacity corresponding to the micro-turbines model satisfies the following function: ,in, Adjust the capacity of the micro-turbines.

[0052] The cost function corresponding to the micro-turbine model satisfies the following function: ,in, is the cost value of the micro-turbines, is the operating cost coefficient, is the start-stop cost coefficient, is the shutdown cost coefficient, It is a binary variable, which can be 1 or 0. When it is 1, it means the micro-turbine unit is in operation. It is a binary variable, which can be 1 or 0. When it is 1, it means that the micro-gas unit is in the start-stop state. It is a binary variable, which can be 1 or 0. When it is 1, it means that the micro-turbine unit is in the off state. is the unit operating cost coefficient of the micro-turbines, is the output of the micro-turbine unit in the corresponding state, Indicates different states.

[0053] The regulation capacity of the curtailable load model satisfies the following function: ,in, is the cost value of load reduction, The rated power corresponding to the load that can be reduced, is the actual power corresponding to the load that can be reduced. At the same time, the load model that can be reduced must meet the power reduction constraint: ,in, for The amount of electricity that has been reduced by the load resources at any time, for The amount of electricity that has been reduced by the load resources at any time, The maximum amount of power that can be reduced for load resources.

[0054] The translatable load model satisfies the following constraints: , ,in, represents whether the translatable load is Always move the load in. for The load at the moment, the value is 0 or 1, This is the time period during which the load can be moved.

[0055] The transferable load can flexibly adjust the power consumption time. The total power consumed after participating in the response adjustment does not change. The adjustment direction is bidirectional. The adjustment capacity of the transferable load model satisfies the following constraints: , ,in, for The maximum response power at time, for The minimum response power at time, is the rated power, is the actual power, while satisfying the transfer power constraint: , for The amount of electricity that has been transferred from load resources at any given moment, for The amount of electricity that has been transferred from the load resource at the moment. The initial amount of electricity transferred is 0. It is the maximum transferable power of load resources.

[0056] The battery model satisfies the following constraints: , , ,in, for Always store battery energy, for Always store battery energy, for The charging and discharging power at all times, with the discharge power being positive. is the charge and discharge efficiency, , is the upper limit of battery energy storage, is the lower limit of battery energy storage, is the upper limit of charge and discharge power, It is the lower limit of charge and discharge power.

[0057] The electric vehicle model satisfies the following constraints:

[0058] ,

[0059] ,in, For electric vehicles The amount of electricity at the moment, For electric vehicles The maximum amount of electricity that can be dispatched at any time, For electric vehicles The minimum amount of electricity that can be dispatched at any time, The moment when electric vehicles are connected to the grid. Off-grid time for electric vehicles, is the amount of electricity when the electric vehicle is connected to the grid, is the amount of electricity consumed by the electric vehicle when it is off-grid. For the VPP operation center, the operating cost of the electric vehicle is the difference between the discharge subsidy and the charging fee. Let the charging price be equal to the price at which the VPP sells electricity to the load resources, and the discharge price be set at 1.15 times the charging price. The operating cost function corresponding to the electric vehicle model satisfies the following formula: ,in, is the cost value of electric vehicles, is the discharge electricity price, The charging price.

[0060] After determining the preset distributed energy resource regulation characteristic model, multiple load data, multiple cost values ​​and multiple output power data are input into the preset distributed energy resource regulation characteristic model to determine multiple function sets. The multiple function sets are the results obtained by inputting load data, cost values ​​and output power data into the preset distributed energy resource regulation characteristic model. These multiple function sets are used to characterize various relationships and constraints of distributed energy resources under different conditions. The VPP interactive regulation capability evaluation model is based on these function sets and evaluates the interactive regulation capability of the virtual power plant with the goal of minimizing total cost, maximizing up and down regulation capacity and up and down ramping rate.

[0061] It can be seen that the preset distributed energy resource regulation characteristic model covers a variety of models of distributed power sources, adjustable load resources and energy storage resources, which can comprehensively and accurately reflect the complex characteristics and operating laws of distributed energy systems, thereby providing a more reliable basis for subsequent evaluations. By subdividing different types of models, such as wind turbines and photovoltaic units in distributed power sources, as well as a variety of specific models in adjustable load resources, it is possible to conduct refined analysis and processing of various energy sources and loads, improve the accuracy and pertinence of the evaluation, and construct a VPP interactive regulation capability evaluation model based on multiple function sets, which can fully consider the relationship and constraints of distributed energy resources under various conditions, thereby more accurately evaluating the interactive regulation capabilities of virtual power plants in terms of cost, regulation capacity and ramp rate, etc., which is helpful to formulate more reasonable energy management strategies and scheduling plans to achieve the goal of lowest total cost and optimal regulation performance, and provide strong decision-making support for the optimized operation of virtual power plants.

[0062] S103: Determine the interaction adjustment capability value corresponding to each function set in the multiple function sets to obtain multiple interaction adjustment capability values; the larger the interaction adjustment capability value is, the greater the interaction adjustment capability corresponding to the interaction adjustment capability value is.

[0063] In this implementation, in view of the uncertainty of the coordinated participation of distributed energy resources in the regulation process, a VPP interactive regulation capability evaluation model is constructed with the goal of minimizing total cost, maximizing up and down regulation capacity, and maximizing up and down ramping rate.

[0064] The cost function of the VPP interactive regulation capability evaluation model satisfies the following constraints: , are different moments corresponding to multiple historical time periods. for The operating cost function of VPP at time instant, Covers the total set of all devices in the VPP except fixed loads (loads that are relatively stable and remain basically unchanged over a specific period of time). Each device has a unique device number. The above equipment includes but is not limited to wind turbines, micro gas turbines, air conditioners and other adjustable resource equipment. For the Resource devices in The power output at any time (the adjustable load output power can take a negative value), is the total power of fixed non-adjustable loads (loads that cannot be adjusted by human intervention or system control) in the VPP. for The power purchased and sold by VPP at the moment, with the purchased power as positive, Resource devices in The cost function at time instant is defined as At the same time, VPP The electricity price at the time is also included in the calculation.

[0065] When optimizing the scheduling of VPP, in addition to considering the equipment constraints of DERs, it is also necessary to ensure the balance of electric power, that is, ,in, is the output power of VPP, for The power purchase and sales of VPP at that moment.

[0066] The upper limit of the adjustment capacity of the VPP interactive adjustment ability evaluation model is , the lower limit of the regulation capacity is In addition, the maximum and minimum power constraints of electricity purchase and sale must be considered: , To purchase and sell the minimum power, The maximum power for purchasing and selling electricity.

[0067] The climbing rate upper limit function of the VPP interactive regulation capability evaluation model is: , the lower limit function of the climbing rate is: , for The output power of VPP at the moment, for The output power of VPP at the moment.

[0068] After determining the VPP interactive adjustment capability evaluation model, determine the interactive adjustment capability value corresponding to each function set in multiple function sets according to the VPP interactive adjustment capability evaluation model to obtain multiple interactive adjustment capability values. Figure 2 , Figure 2 A flowchart for determining an interactive adjustment capability value provided by an embodiment of the present application includes but is not limited to the following steps:

[0069] S201: Determine a first cost value, a first adjustment capacity, and a first climbing rate corresponding to a first function set.

[0070] In this embodiment, the first function set is any one of the multiple function sets. By substituting the load data, cost value, and output power data in the first time period corresponding to the first function set into the first function set, the first cost value, first regulation capacity, and first climbing rate corresponding to the first function set can be obtained.

[0071] S202: Determine a first regulating capability value corresponding to the first cost value, a second regulating capability value corresponding to the first regulating capacity, and a third regulating capability value corresponding to the first climbing rate.

[0072] In this embodiment, the first adjustment capability value corresponding to the first cost value, the second adjustment capability value corresponding to the first adjustment capacity, and the third adjustment capability value corresponding to the first climbing rate are determined by the VPP interactive adjustment capability evaluation model.

[0073] It needs to be explained that, since the relationship between the cost value and the first adjustment capability value is determined in the VPP interactive adjustment capability evaluation model: F1 (first cost value) = first adjustment capability value; the relationship between the adjustment capacity and the second adjustment capability value is: F2 (first adjustment capacity) = second adjustment capability value; the relationship between the climbing rate and the third adjustment capability value is: F3 (first climbing rate) = third adjustment capability value, the first cost value is substituted into F1, the first adjustment capacity is substituted into F2, and the first climbing rate is substituted into F3. Through the calculation rules and algorithms in the VPP interactive adjustment capability evaluation model, the corresponding first adjustment capability value, second adjustment capability value and third adjustment capability value can be obtained respectively.

[0074] It can be seen that by establishing a clear functional relationship between the cost value, regulation capacity and ramp rate and their respective regulation capacity values ​​in the VPP interactive regulation capacity evaluation model, the evaluation process has a clear quantitative logic and avoids subjective judgment and ambiguity.

[0075] S203: Determine the interactive adjustment capability value corresponding to the first function set based on the first adjustment capability value, the second adjustment capability value, and the third adjustment capability value.

[0076] In this embodiment, exemplarily, the first weight corresponding to the first adjustment capacity value and the second weight corresponding to the second adjustment capacity value are determined. The entropy method can be used to determine the first weight corresponding to the first adjustment capacity value and the second weight corresponding to the second adjustment capacity value. First, the data such as the first cost value, the first adjustment capacity and the first climbing rate are standardized to eliminate the influence of the dimension so that the data of different indicators are comparable. For each indicator (first cost value, first adjustment capacity, first climbing rate), its proportion in all samples is calculated, and the entropy value of each indicator is calculated according to the proportion. The entropy value reflects the degree of discreteness of the indicator. The difference coefficient is obtained by subtracting the entropy value from 1. The larger the difference coefficient, the greater the amount of information of the indicator and the greater the impact on the comprehensive evaluation. The weight of each indicator is calculated according to the difference coefficient to obtain the first weight corresponding to the first adjustment capacity value and the second weight corresponding to the second adjustment capacity value.

[0077] Exemplarily, since a higher electricity purchase price will directly increase the electricity purchase cost of the virtual power plant, this means that with the same electricity consumption, more fees need to be paid, which will lead to an increase in the total cost. Conversely, a lower electricity purchase price can reduce this part of the cost, so the electricity purchase and sale price in the historical time period corresponding to the first function set is obtained, and the first optimization factor corresponding to the electricity purchase and sale price is determined. The first weight is optimized according to the first optimization factor to obtain the first target weight. Specifically, a mapping relationship between a preset electricity purchase and sale price and the optimization factor can be pre-stored, and the first optimization factor corresponding to the electricity purchase and sale price is determined based on the mapping relationship. Then, the first weight is optimized according to the first optimization factor to obtain the first target weight. The specific calculation formula is as follows: first target weight = first weight × (1 + first optimization factor); The first target weight can be obtained according to the above formula.

[0078] For example, when the capacity of the transmission line is large, the virtual power plant can be allowed to transmit or obtain larger amounts of electricity to the power grid, thereby providing the virtual power plant with a larger regulation capacity space. This means that when responding to changes in power supply and demand, the virtual power plant can more flexibly adjust the output power, increase or decrease power supply, to meet the needs of the power grid. If the capacity of the transmission line is small, the regulation capacity of the virtual power plant will be limited. Even if the virtual power plant itself has a strong regulation capability, it cannot fully transmit the regulated electricity or obtain the required electricity from the power grid in a timely manner due to the bottleneck of the transmission line, resulting in the actual achievable regulation capacity being far lower than its potential. In terms of capacity, the capacity of the transmission line between the target virtual power plant and the power grid is obtained to obtain the target capacity, the second optimization factor corresponding to the target capacity is determined, the second weight is optimized according to the second optimization factor, and the second target weight is obtained. Specifically, a mapping relationship between the capacity of the transmission line between the target virtual power plant and the power grid and the optimization factor is pre-stored, and the second optimization factor corresponding to the target capacity is determined based on the mapping relationship. Then, the second weight is optimized according to the second optimization factor to obtain the second target weight. The specific calculation formula is as follows: second target weight = second weight × (1 + second optimization factor); The second target weight can be obtained according to the above formula.

[0079] Exemplarily, the third target weight corresponding to the third adjustment capability value is determined based on the first target weight and the second target weight. Specifically, since the sum of the first target weight, the second target weight and the third target weight is 1, after determining the first target weight and the second target weight, the third target weight corresponding to the third adjustment capability value can be determined.

[0080] Exemplarily, based on the first adjustment capability value, the second adjustment capability value, the third adjustment capability value, the first target weight, the second target weight, and the third target weight, the calculation is performed to obtain the interactive adjustment capability value corresponding to the first function set. Specifically, according to the first adjustment capability value, the second adjustment capability value, the third adjustment capability value, the first target weight, the second target weight, and the third target weight, the calculation is performed to obtain the interactive adjustment capability value corresponding to the first function set. Specifically, the interactive adjustment capability value corresponding to the first function set is calculated according to the following formula:

[0081]

[0082] Among them, F is the interactive adjustment capability value corresponding to the first function set, is the first adjustment capability value, is the second adjustment capability value, is the second adjustment capability value, is the first target weight, is the second target weight, is the third target weight.

[0083] It needs to be explained that the method for determining the interactive adjustment capability value corresponding to each function set in multiple function sets is similar. Since the first function set is any function set in the multiple function sets, the interactive adjustment capability value corresponding to each function set in the multiple function sets can be determined according to the method for determining the interactive adjustment capability value corresponding to the first function set, so as to obtain multiple interactive adjustment capability values.

[0084] It can be seen that by determining the weights of different regulation capacity values ​​and optimizing them in combination with factors such as electricity purchase and sales prices and transmission line capacity, it is possible to comprehensively consider multiple key factors affecting the interactive regulation capacity of virtual power plants, making the evaluation results more comprehensive and accurate. By determining the interactive regulation capacity value, the accuracy and scientificity of the evaluation are improved, providing a reliable basis for the optimization decision-making of virtual power plants.

[0085] S104: Determine at least one target interactive adjustment capability value from multiple interactive adjustment capability values ​​based on a multi-objective particle swarm optimization algorithm.

[0086] In this embodiment, a multi-objective particle swarm optimization algorithm is used to determine at least one target interactive adjustment capability value from multiple interactive adjustment capability values. Specifically, a group of particles are first randomly generated, each particle represents a possible solution, that is, a selection of a group of interactive adjustment capability values, and then a fitness function is determined. The fitness function can be calculated based on a preset interactive adjustment capability value and the interactive adjustment capability value represented by the particle. If the interactive adjustment capability value represented by the particle is greater than the preset value, a higher fitness is given, otherwise, the fitness is lower. Then, according to the particle's own historical optimal position (individual optimal solution), the optimal position of the entire particle swarm (global optimal solution) and certain random factors, the particle's speed and position are updated, that is, the selection of the interactive adjustment capability value represented by them is updated, and the fitness function is continuously determined and the particles are updated until the stop condition is met, such as reaching a predetermined number of iterations or the result of the fitness function is good enough. In the final particle swarm, particles representing interactive adjustment capabilities greater than the preset values ​​are screened out, and the interactive adjustment capability values ​​corresponding to these particles are the target interactive adjustment capability values, and finally at least one target interactive adjustment capability value is obtained. The function sets corresponding to each target interactive regulation capability value in at least one target interactive regulation capability value are helpful to conduct in-depth and accurate analysis of specific situations with good interactive regulation capabilities, and understand the specific relationship between distributed generation load, adjustable load and energy storage load and cost value, regulation capacity, climbing rate, etc. in these situations. By studying these function sets, it can be clarified which combination of factors can achieve better interactive regulation capabilities, so as to make better decisions in resource allocation, operation strategy formulation, etc.

[0087] It can be seen that the multi-objective particle swarm optimization algorithm can efficiently search among many possible combinations of interactive adjustment capability values, quickly find the best solution that meets the conditions, and save a lot of computing time and resources. By considering the individual optimal solution and the global optimal solution of the particles, the algorithm can avoid falling into the local optimum during the search process, and is more likely to find the global optimal or close to the global optimal target interactive adjustment capability value, thereby improving the quality of optimization. The fitness function and algorithm parameters can be flexibly adjusted according to different preset conditions and optimization objectives to adapt to various complex virtual power plant operation scenarios and needs.

[0088] It should be noted that it is also necessary to obtain at least one function set corresponding to at least one target interactive adjustment capability value, and then obtain the expected cost, expected adjustment capacity and expected ramp rate corresponding to the target virtual power plant, and obtain the load data at the current moment to obtain the original load data, which includes the original distributed generation load, the original adjustable load and the original energy storage load, and then determine the membership corresponding to each function set in at least one function set based on the original distributed generation load, the original adjustable load and the original energy storage load. Specifically, first obtain the maximum value of the distributed generation load, the minimum value of the distributed generation load, the maximum value of the adjustable load, the minimum value of the adjustable load, the maximum value of the energy storage load and the minimum value of the energy storage load in the second function set. Since the second function set is used to characterize the constraint relationship between the distributed generation load, the adjustable load, the energy storage load and the cost value, the constraint relationship between the distributed generation load, the adjustable load, the energy storage load and the adjustment capacity, and the constraint relationship between the distributed generation load, the adjustable load, the energy storage load and the ramp rate, the maximum value of the distributed generation load, the minimum value of the distributed generation load, the maximum value of the adjustable load, the minimum value of the adjustable load, the maximum value of the energy storage load and the minimum value of the energy storage load in the second function set can be determined according to the constraint relationship. Exemplarily, the distributed generation load membership corresponding to the original distributed generation load is determined based on the maximum value of the distributed generation load, the minimum value of the distributed generation load, and the original distributed generation load. Specifically, the reference distributed generation load membership is first determined based on the maximum value of the distributed generation load, the minimum value of the distributed generation load, and the original distributed generation load, and is specifically calculated according to the following formula:

[0089]

[0090] in, is the reference distributed generation load membership, is the maximum value of distributed generation load, is the minimum value of distributed generation load, is the original distributed generation load.

[0091] For distributed generation technologies that rely on ambient temperature, such as solar photovoltaic power generation, temperature will directly affect power generation efficiency. Higher temperatures may cause the power generation output of photovoltaic panels to decrease. When calculating the distributed generation load membership, if the original distributed generation load decreases due to the increase in temperature, the calculated reference distributed generation load membership will become smaller accordingly. By introducing a correction coefficient related to the average temperature, this change can be adjusted so that the final distributed generation load membership more accurately reflects the actual power generation situation. Therefore, the average temperature in the historical time period corresponding to the second function set is obtained, and the target correction coefficient corresponding to the average temperature is determined. The reference distributed generation load membership is corrected according to the target correction coefficient to obtain the distributed generation load membership corresponding to the original distributed generation load. Specifically, a mapping relationship between a preset temperature and a correction coefficient can be pre-stored, and the target correction coefficient corresponding to the average temperature is determined based on the mapping relationship. Then, the reference distributed generation load membership is corrected according to the target correction coefficient. The specific calculation formula is as follows: Distributed generation load membership corresponding to the original distributed generation load = reference distributed generation load membership × (1 + target correction coefficient); According to the above formula, the distributed generation load membership corresponding to the original distributed generation load can be obtained.

[0092] Then, the adjustable load membership corresponding to the original adjustable load is determined based on the maximum value of the adjustable load, the minimum value of the adjustable load, and the original adjustable load, and the energy storage load membership corresponding to the original energy storage load is determined based on the maximum value of the energy storage load, the minimum value of the energy storage load, and the energy storage load. Since the method for determining the adjustable load membership and the energy storage load membership is the same as the method for determining the distributed generation load membership, they will not be repeated here.

[0093] Exemplarily, the membership corresponding to the second function set is determined based on the distributed generation load membership, the adjustable load membership, and the energy storage load membership. Specifically, certain weights can be assigned to the distributed generation load membership, the adjustable load membership, and the energy storage load membership, respectively, and then they are weighted and summed to determine the membership corresponding to the second function set. The weights can be set based on factors such as the importance of each load in the virtual power plant and the degree of influence on the overall performance. The membership corresponding to the second function set can also be calculated by calculating the geometric mean of the three memberships.

[0094] It needs to be explained that since the second function set is any function set in at least one function set, the membership corresponding to each function set in at least one function set can be determined according to the method of determining the membership corresponding to the second function set, and at least one membership can be obtained.

[0095] Exemplarily, the maximum membership among at least one membership is determined, and then the function set corresponding to the maximum membership is determined to obtain the target function set, and the target distributed generation load, target adjustable load, and target energy storage load of the target virtual power plant are determined based on the target function set, expected cost, expected regulation capacity, and expected ramp rate. Specifically, a comparison and screening are performed among the multiple memberships calculated previously to find the membership with the largest value. This means that in the comparison of the degree of association with each function set, the original load data represented by the maximum membership has the closest relationship or the most consistent relationship with the corresponding function set. Since the membership of the original load data and each function set has been calculated previously, now the maximum membership is found. The corresponding specific function set can be clearly identified. This determined function set is the target function set we need. It best matches the current original load data among all function sets. By utilizing the relationship and constraints between load and cost, regulation capacity, ramp rate, etc. contained in the selected target function set, and combining the target virtual power plant's pre-set expected cost, expected regulation capacity, expected ramp rate and other specific requirements, through a series of calculations, analysis and optimization processing, we can finally determine the target distributed generation load, target adjustable load and target energy storage load that the target virtual power plant should achieve. These values ​​are designed to enable the virtual power plant to achieve the optimal operation and regulation effects on the basis of meeting the expected conditions.

[0096] It can be seen that by determining the maximum membership degree and its corresponding function set, the relationship model that best matches the current original load data can be accurately found, so that more accurate calculations and optimizations can be performed based on this, ensuring that the target load value obtained best meets the actual situation and expected requirements, avoiding complex and comprehensive considerations of all function sets, focusing on the most relevant function sets, reducing the amount of calculation and uncertainty, and improving the efficiency and accuracy of determining the target load. It fully combines the key indicators of the virtual power plant, such as the expected cost, regulation capacity and ramp rate, so that the final target load can actually meet the preset expectations, ensure the efficient operation and economic performance of the virtual power plant, and help to reasonably allocate distributed power generation, adjustable load and energy storage load, achieve optimal utilization of resources, and improve the overall benefits and energy utilization efficiency of the virtual power plant.

[0097] S105: Determine a target interactive regulation capability value corresponding to the target virtual power plant based on at least one target interactive regulation capability value.

[0098] In this embodiment, the average interactive regulation capability value corresponding to at least one target interactive regulation capability value is first determined, and then at least one historical time period corresponding to at least one target interactive regulation capability value is determined. Fitting is performed based on at least one target interactive regulation capability value and at least one historical time period to obtain a target interactive regulation capability straight line. Specifically, fitting can be performed using a linear regression method or a polynomial fitting method. After fitting is completed, the target interactive regulation capability straight line can be obtained. The target interactive regulation capability straight line obtained by the fitting method can simply and intuitively highlight the overall change direction of the interactive regulation capability, which is convenient for determining the changing trend of the interactive regulation capability.

[0099] Exemplarily, the target slope corresponding to the target interactive adjustment capability line is determined, the adjustment coefficient corresponding to the target slope is determined, and the average interactive adjustment capability value is adjusted based on the adjustment coefficient to obtain the target interactive adjustment capability value corresponding to the target virtual power plant. Specifically, the slope corresponding to the target interactive adjustment capability line can be calculated according to the coordinates corresponding to two points on the target interactive adjustment capability line to obtain the target slope. The mapping relationship between the preset slope and the interactive adjustment capability value can be pre-stored, and the target interactive adjustment capability value corresponding to the target slope is determined based on the mapping relationship. Then, the average interactive adjustment capability value is adjusted based on the adjustment coefficient to obtain the target interactive adjustment capability value corresponding to the target virtual power plant. The specific calculation formula is as follows:

[0100] Target interaction adjustment ability value = average interaction adjustment ability value × (1 + adjustment coefficient);

[0101] According to the above formula, the target interactive adjustment capability value can be obtained.

[0102] It can be seen that the target interactive regulation capability straight line obtained by fitting can concisely and intuitively show the overall change direction of the interactive regulation capability. The clear change trend is helpful to predict the future interactive regulation capability, thereby providing a strong basis for the resource planning and operation strategy formulation of the virtual power plant. By determining the target slope and adjustment coefficient to adjust the average interactive regulation capability value, the quantitative adjustment of the interactive regulation capability is achieved, making the evaluation results more accurate and precise.

[0103] In summary, the implementation of the present invention has the following beneficial effects:

[0104] It can be seen that the evaluation method for the interactive regulation capability of a virtual power plant described in the embodiment of the present invention first obtains the load data, cost value, and output power data of the target virtual power plant in multiple historical time periods to obtain multiple load data, multiple cost values, and multiple output power data, and then inputs the multiple load data, multiple cost values, and multiple output power data into a preset distributed energy resource regulation characteristic model to obtain multiple function sets, and each function set in the multiple function sets is used to characterize the constraint relationship between the distributed generation load, the adjustable load, the energy storage load and the cost value, the constraint relationship between the distributed generation load, the adjustable load, the energy storage load and the regulation capacity, and the constraint relationship between the distributed generation load, the adjustable load, the energy storage load and the ramp rate. Each historical time period corresponds to a function set, and then the interactive regulation capability value corresponding to each function set in the multiple function sets is determined to obtain multiple interactive regulation capability values, and at least one target interactive regulation capability value is determined from the multiple interactive regulation capability values ​​based on a multi-objective particle swarm optimization algorithm, and finally the target interactive regulation capability value corresponding to the target virtual power plant is determined based on the at least one target interactive regulation capability value, thereby improving the accuracy of the evaluation of the interactive regulation capability of the virtual power plant.

[0105] See also Figure 3 , Figure 3 It is a structural schematic diagram of a virtual power plant interactive regulation capability evaluation device provided in an embodiment of the present application. The virtual power plant interactive regulation capability evaluation device 300 includes: an acquisition unit 301 and a processing unit 302;

[0106] The acquisition unit 301 is used to acquire the load data, cost value, and output power data of the target virtual power plant in multiple historical time periods, and obtain multiple load data, multiple cost values, and multiple output power data; each historical time period corresponds to one load data, one cost value, and one output power data, the load data includes distributed generation load, adjustable load, and energy storage load, and the output power data includes adjustment capacity and ramp rate;

[0107] The processing unit 302 is used to input multiple load data, multiple cost values, and multiple output power data into a preset distributed energy resource regulation characteristic model to obtain multiple function sets; each of the multiple function sets is used to characterize the constraint relationship between the distributed generation load, the adjustable load, the energy storage load and the cost value, the constraint relationship between the distributed generation load, the adjustable load, the energy storage load and the regulation capacity, and the constraint relationship between the distributed generation load, the adjustable load, the energy storage load and the ramp rate, and each historical time period corresponds to one function set;

[0108] Determine the interaction adjustment capability value corresponding to each function set in the plurality of function sets, and obtain a plurality of interaction adjustment capability values; the larger the interaction adjustment capability value, the greater the interaction adjustment capability corresponding to the interaction adjustment capability value;

[0109] Determining at least one target interactive adjustment capability value from a plurality of interactive adjustment capability values ​​based on a multi-objective particle swarm optimization algorithm;

[0110] A target interactive regulation capability value corresponding to the target virtual power plant is determined based on at least one target interactive regulation capability value.

[0111] In some possible implementations, in determining the interaction adjustment capability value corresponding to each function set in the plurality of function sets to obtain the plurality of interaction adjustment capability values, the processing unit 302 is specifically configured to:

[0112] Determine a first cost value, a first adjustment capacity, and a first climbing rate corresponding to a first function set; the first function set is any one of the multiple function sets;

[0113] Determine a first regulating capability value corresponding to the first cost value, a second regulating capability value corresponding to the first regulating capacity, and a third regulating capability value corresponding to the first climbing rate;

[0114] The interactive adjustment capability value corresponding to the first function set is determined based on the first adjustment capability value, the second adjustment capability value, and the third adjustment capability value.

[0115] In some possible implementations, in determining the interactive adjustment capability value corresponding to the first function set based on the first adjustment capability value, the second adjustment capability value, and the third adjustment capability value, the processing unit 302 is specifically configured to:

[0116] Determine a first weight corresponding to the first adjustment capability value and a second weight corresponding to the second adjustment capability value;

[0117] Obtaining the purchase and sale electricity prices in the historical time period corresponding to the first function set;

[0118] Determine a first optimization factor corresponding to the purchase and sale price of electricity;

[0119] Optimize the first weight according to the first optimization factor to obtain a first target weight;

[0120] Obtain the capacity of the transmission line between the target virtual power plant and the power grid to obtain the target capacity;

[0121] Determining a second optimization factor corresponding to the target capacity;

[0122] Optimize the second weight according to the second optimization factor to obtain a second target weight;

[0123] Determine a third target weight corresponding to the third adjustment capability value based on the first target weight and the second target weight;

[0124] The interactive adjustment capability value corresponding to the first function set is obtained by performing calculation based on the first adjustment capability value, the second adjustment capability value, the third adjustment capability value, the first target weight, the second target weight, and the third target weight.

[0125] In some possible implementations, the processing unit 302 is further specifically configured to:

[0126] Obtaining at least one function set corresponding to at least one target interaction adjustment capability value;

[0127] Obtain the expected cost, expected regulation capacity and expected ramp rate corresponding to the target virtual power plant;

[0128] Obtain the load data at the current moment to obtain the original load data; the original load data includes the original distributed generation load, the original adjustable load, and the original energy storage load;

[0129] Determine the degree of membership corresponding to each function set in at least one function set based on the original distributed generation load, the original adjustable load, and the original energy storage load;

[0130] determining a maximum degree of membership among at least one degree of membership;

[0131] Determine the function set corresponding to the maximum membership degree and obtain the target function set;

[0132] The target distributed generation load, target adjustable load and target energy storage load of the target virtual power plant are determined based on the objective function set, expected cost, expected regulation capacity and expected ramp rate.

[0133] In some possible implementations, in determining the degree of membership corresponding to each function set in at least one function set based on the original distributed generation load, the original adjustable load, and the original energy storage load, the processing unit 302 is specifically configured to:

[0134] Obtaining a maximum value of a distributed generation load, a minimum value of a distributed generation load, a maximum value of an adjustable load, a minimum value of an adjustable load, a maximum value of an energy storage load, and a minimum value of an energy storage load in a second function set; the second function set is any one of the at least one function set;

[0135] Determine the distributed generation load membership corresponding to the original distributed generation load based on the maximum distributed generation load, the minimum distributed generation load, and the original distributed generation load;

[0136] Determine the adjustable load membership corresponding to the original adjustable load based on the maximum value of the adjustable load, the minimum value of the adjustable load, and the original adjustable load;

[0137] Determine the energy storage load membership corresponding to the original energy storage load based on the maximum energy storage load, the minimum energy storage load, and the energy storage load;

[0138] The membership corresponding to the second function set is determined based on the distributed generation load membership, the adjustable load membership, and the energy storage load membership.

[0139] In some possible implementations, in determining the distributed generation load membership corresponding to the original distributed generation load based on the maximum distributed generation load, the minimum distributed generation load, and the original distributed generation load, the processing unit 302 is specifically configured to:

[0140] The reference distributed generation load membership is determined based on the maximum distributed generation load, the minimum distributed generation load, and the original distributed generation load, and is calculated specifically according to the following formula:

[0141]

[0142] in, is the reference distributed generation load membership, is the maximum value of distributed generation load, is the minimum value of distributed generation load, is the original distributed generation load;

[0143] Obtain the average temperature in the historical time period corresponding to the second function set;

[0144] Determine the target correction factor corresponding to the average temperature;

[0145] The reference distributed generation load membership is corrected according to the target correction coefficient to obtain the distributed generation load membership corresponding to the original distributed generation load.

[0146] In some possible implementations, in determining the target interactive regulation capability value corresponding to the target virtual power plant based on at least one target interactive regulation capability value, the processing unit 302 is specifically configured to:

[0147] Determining an average interaction regulation capability value corresponding to at least one target interaction regulation capability value;

[0148] Determine at least one historical time period corresponding to at least one target interaction adjustment capability value;

[0149] Based on fitting at least one target interaction adjustment capability value and at least one historical time period, a target interaction adjustment capability line is obtained;

[0150] Determine the target slope corresponding to the target interactive regulatory ability line;

[0151] determining an adjustment factor corresponding to a target slope;

[0152] The average interactive regulation capability value is adjusted based on the adjustment coefficient to obtain the target interactive regulation capability value corresponding to the target virtual power plant.

[0153] See also Figure 4 , Figure 4 Schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. Figure 4 As shown, the electronic device 400 includes a transceiver 401, a processor 402 and a memory 403. They are connected via a bus 404. The memory 403 is used to store computer programs and data, and the transceiver 401 can transmit the data stored in the memory 403 to the processor 402. The above program includes instructions for executing the following steps:

[0154] Obtain the load data, cost value, and output power data of the target virtual power plant in multiple historical time periods, and obtain multiple load data, multiple cost values, and multiple output power data; each historical time period corresponds to one load data, one cost value, and one output power data, the load data includes distributed generation load, adjustable load, and energy storage load, and the output power data includes adjustment capacity and ramp rate;

[0155] Input multiple load data, multiple cost values, and multiple output power data into a preset distributed energy resource regulation characteristic model to obtain multiple function sets; each of the multiple function sets is used to characterize the constraint relationship between the distributed generation load, the adjustable load, the energy storage load and the cost value, the constraint relationship between the distributed generation load, the adjustable load, the energy storage load and the regulation capacity, and the constraint relationship between the distributed generation load, the adjustable load, the energy storage load and the ramp rate, and each historical time period corresponds to a function set;

[0156] Determine the interaction adjustment capability value corresponding to each function set in the plurality of function sets, and obtain a plurality of interaction adjustment capability values; the larger the interaction adjustment capability value, the greater the interaction adjustment capability corresponding to the interaction adjustment capability value;

[0157] Determining at least one target interactive adjustment capability value from a plurality of interactive adjustment capability values ​​based on a multi-objective particle swarm optimization algorithm;

[0158] A target interactive regulation capability value corresponding to the target virtual power plant is determined based on at least one target interactive regulation capability value.

[0159] In some possible implementations, in determining the interaction adjustment capability value corresponding to each function set in the plurality of function sets to obtain the plurality of interaction adjustment capability values, the program includes instructions for executing the following steps:

[0160] Determine a first cost value, a first adjustment capacity, and a first climbing rate corresponding to a first function set; the first function set is any one of the multiple function sets;

[0161] Determine a first regulating capability value corresponding to the first cost value, a second regulating capability value corresponding to the first regulating capacity, and a third regulating capability value corresponding to the first climbing rate;

[0162] The interactive adjustment capability value corresponding to the first function set is determined based on the first adjustment capability value, the second adjustment capability value, and the third adjustment capability value.

[0163] In some possible implementations, in terms of determining the interactive adjustment capability value corresponding to the first function set based on the first adjustment capability value, the second adjustment capability value, and the third adjustment capability value, the program includes instructions for executing the following steps:

[0164] Determine a first weight corresponding to the first adjustment capability value and a second weight corresponding to the second adjustment capability value;

[0165] Obtaining the purchase and sale electricity prices in the historical time period corresponding to the first function set;

[0166] Determine a first optimization factor corresponding to the purchase and sale price of electricity;

[0167] Optimize the first weight according to the first optimization factor to obtain a first target weight;

[0168] Obtain the capacity of the transmission line between the target virtual power plant and the power grid to obtain the target capacity;

[0169] Determining a second optimization factor corresponding to the target capacity;

[0170] Optimize the second weight according to the second optimization factor to obtain a second target weight;

[0171] Determine a third target weight corresponding to the third adjustment capability value based on the first target weight and the second target weight;

[0172] The interactive adjustment capability value corresponding to the first function set is obtained by performing calculation based on the first adjustment capability value, the second adjustment capability value, the third adjustment capability value, the first target weight, the second target weight, and the third target weight.

[0173] In some possible implementations, the above program includes instructions for performing the following steps:

[0174] Obtaining at least one function set corresponding to at least one target interaction adjustment capability value;

[0175] Obtain the expected cost, expected regulation capacity and expected ramp rate corresponding to the target virtual power plant;

[0176] Obtain the load data at the current moment to obtain the original load data; the original load data includes the original distributed generation load, the original adjustable load, and the original energy storage load;

[0177] Determine the degree of membership corresponding to each function set in at least one function set based on the original distributed generation load, the original adjustable load, and the original energy storage load;

[0178] determining a maximum degree of membership among at least one degree of membership;

[0179] Determine the function set corresponding to the maximum membership degree and obtain the target function set;

[0180] The target distributed generation load, target adjustable load and target energy storage load of the target virtual power plant are determined based on the objective function set, expected cost, expected regulation capacity and expected ramp rate.

[0181] In some possible implementations, in terms of determining the degree of membership corresponding to each function set in at least one function set based on the original distributed generation load, the original adjustable load, and the original energy storage load, the above program includes instructions for performing the following steps:

[0182] Obtaining a maximum value of a distributed generation load, a minimum value of a distributed generation load, a maximum value of an adjustable load, a minimum value of an adjustable load, a maximum value of an energy storage load, and a minimum value of an energy storage load in a second function set; the second function set is any one of the at least one function set;

[0183] Determine the distributed generation load membership corresponding to the original distributed generation load based on the maximum distributed generation load, the minimum distributed generation load, and the original distributed generation load;

[0184] Determine the adjustable load membership corresponding to the original adjustable load based on the maximum value of the adjustable load, the minimum value of the adjustable load, and the original adjustable load;

[0185] Determine the energy storage load membership corresponding to the original energy storage load based on the maximum energy storage load, the minimum energy storage load, and the energy storage load;

[0186] The membership corresponding to the second function set is determined based on the distributed generation load membership, the adjustable load membership, and the energy storage load membership.

[0187] In some possible implementations, in terms of determining the distributed generation load membership corresponding to the original distributed generation load based on the maximum distributed generation load, the minimum distributed generation load, and the original distributed generation load, the above program includes instructions for performing the following steps:

[0188] The reference distributed generation load membership is determined based on the maximum distributed generation load, the minimum distributed generation load, and the original distributed generation load, and is calculated specifically according to the following formula:

[0189]

[0190] in, is the reference distributed generation load membership, is the maximum value of distributed generation load, is the minimum value of distributed generation load, is the original distributed generation load;

[0191] Obtain the average temperature in the historical time period corresponding to the second function set;

[0192] Determine the target correction factor corresponding to the average temperature;

[0193] The reference distributed generation load membership is corrected according to the target correction coefficient to obtain the distributed generation load membership corresponding to the original distributed generation load.

[0194] In some possible implementations, in terms of determining a target interactive regulation capability value corresponding to a target virtual power plant based on at least one target interactive regulation capability value, the program includes instructions for executing the following steps:

[0195] Determining an average interaction regulation capability value corresponding to at least one target interaction regulation capability value;

[0196] Determine at least one historical time period corresponding to at least one target interaction adjustment capability value;

[0197] Based on fitting at least one target interaction adjustment capability value and at least one historical time period, a target interaction adjustment capability line is obtained;

[0198] Determine the target slope corresponding to the target interactive regulatory ability line;

[0199] determining an adjustment factor corresponding to a target slope;

[0200] The average interactive regulation capability value is adjusted based on the adjustment coefficient to obtain the target interactive regulation capability value corresponding to the target virtual power plant.

[0201] It should be understood that the electronic devices in this application may include smart phones (such as Android phones, iOS phones, Windows Phone phones, etc.), tablet computers, PDAs, laptops, mobile Internet devices MID (Mobile Internet Devices, referred to as: MID) or wearable devices or servers, edge computing nodes, etc. The above electronic devices are only examples, not exhaustive, and include but are not limited to the above electronic devices.

[0202] An embodiment of the present application also provides a computer-readable storage medium, which stores a computer program. The computer program is executed by a processor to implement part or all of the steps of any method for evaluating the interactive regulation capability of a virtual power plant as described in the above method implementation.

[0203] An embodiment of the present application also provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to enable a computer to execute part or all of the steps of any method for evaluating the interactive regulation capability of a virtual power plant as recorded in the above method implementation.

[0204] It should be noted that, for the above-mentioned various method implementations, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that this application is not limited by the order of the actions described, because according to this application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the implementations described in the specification are all optional implementations, and the actions and modules involved are not necessarily required by this application.

[0205] In the above-mentioned embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0206] In the several embodiments provided in this application, it should be understood that the disclosed device can be implemented in other ways. For example, the device implementation described above is only schematic, such as the division of units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0207] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0208] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above integrated unit may be implemented in the form of hardware or in the form of a software program module.

[0209] If the integrated unit is implemented in the form of a software program module and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a memory, including a number of instructions to enable a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of each implementation method of the present application. The aforementioned memory includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, disk or optical disk and other media that can store program codes.

[0210] A person skilled in the art may understand that all or part of the steps in the various methods of the above-mentioned embodiments may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable memory, and the memory may include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0211] The above is a detailed introduction to the implementation methods of the present application. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above implementation methods is only used to help understand the method and core idea of ​​the present application. At the same time, for general technical personnel in this field, according to the idea of ​​the present application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A method for evaluating the interactive regulation capability of a virtual power plant, characterized in that: The method comprises: Obtain load data, cost values, and output power data of a target virtual power plant in multiple historical time periods, and obtain multiple load data, multiple cost values, and multiple output power data; each historical time period corresponds to one load data, one cost value, and one output power data, the load data includes distributed generation load, adjustable load, and energy storage load, and the output power data includes adjustment capacity and ramp rate; Input the multiple load data, the multiple cost values, and the multiple output power data into a preset distributed energy resource regulation characteristic model to obtain multiple function sets; each of the multiple function sets is used to characterize the constraint relationship between the distributed generation load, the adjustable load, the energy storage load and the cost value, the constraint relationship between the distributed generation load, the adjustable load, the energy storage load and the regulation capacity, and the constraint relationship between the distributed generation load, the adjustable load, the energy storage load and the ramp rate, and each historical time period corresponds to one function set; Determine the interaction adjustment capability value corresponding to each function set in the plurality of function sets to obtain a plurality of interaction adjustment capability values; the larger the interaction adjustment capability value, the greater the interaction adjustment capability corresponding to the interaction adjustment capability value; Determining at least one target interactive adjustment capability value from the plurality of interactive adjustment capability values ​​based on a multi-objective particle swarm optimization algorithm; The target interactive regulation capability value corresponding to the target virtual power plant is determined based on the at least one target interactive regulation capability value.

2. The method according to claim 1, characterized in that The step of determining the interaction adjustment capability value corresponding to each function set in the plurality of function sets to obtain a plurality of interaction adjustment capability values ​​comprises: Determine a first cost value, a first adjustment capacity, and a first climbing rate corresponding to a first function set; the first function set is any one of the multiple function sets; Determine a first regulating capability value corresponding to the first cost value, a second regulating capability value corresponding to the first regulating capacity, and a third regulating capability value corresponding to the first climbing rate; An interactive adjustment capability value corresponding to the first function set is determined based on the first adjustment capability value, the second adjustment capability value, and the third adjustment capability value.

3. The method according to claim 2, characterized in that The determining the interactive adjustment capability value corresponding to the first function set based on the first adjustment capability value, the second adjustment capability value, and the third adjustment capability value includes: Determine a first weight corresponding to the first adjustment capability value and a second weight corresponding to the second adjustment capability value; Obtaining the purchase and sale prices of electricity in the historical time period corresponding to the first function set; Determining a first optimization factor corresponding to the electricity purchase and sale price; Optimize the first weight according to the first optimization factor to obtain a first target weight; Acquire the capacity of the transmission line between the target virtual power plant and the power grid to obtain a target capacity; Determining a second optimization factor corresponding to the target capacity; Optimize the second weight according to the second optimization factor to obtain a second target weight; Determining a third target weight corresponding to the third adjustment capability value based on the first target weight and the second target weight; The interactive adjustment capability value corresponding to the first function set is obtained by performing calculation based on the first adjustment capability value, the second adjustment capability value, the third adjustment capability value, the first target weight, the second target weight, and the third target weight.

4. The method according to claim 2 or 3, characterized in that The method further comprises: Obtaining at least one function set corresponding to the at least one target interaction adjustment capability value; Obtaining the expected cost, expected regulation capacity and expected ramp rate corresponding to the target virtual power plant; Obtaining the load data at the current moment to obtain original load data; the original load data includes original distributed generation load, original adjustable load, and original energy storage load; Determine the membership degree corresponding to each function set in the at least one function set based on the original distributed generation load, the original adjustable load, and the original energy storage load, and obtain at least one membership degree; determining a maximum degree of membership among the at least one degree of membership; Determine a function set corresponding to the maximum membership degree to obtain a target function set; The target distributed generation load, target adjustable load and target energy storage load of the target virtual power plant are determined based on the objective function set, the expected cost, the expected regulation capacity and the expected ramp rate.

5. The method according to claim 4, characterized in that The determining the degree of membership corresponding to each function set in the at least one function set based on the original distributed generation load, the original adjustable load, and the original energy storage load to obtain at least one degree of membership includes: Obtaining a maximum value of a distributed generation load, a minimum value of a distributed generation load, a maximum value of an adjustable load, a minimum value of an adjustable load, a maximum value of an energy storage load, and a minimum value of an energy storage load in a second function set; the second function set is any one of the at least one function set; Determine the distributed generation load membership corresponding to the original distributed generation load based on the maximum value of the distributed generation load, the minimum value of the distributed generation load, and the original distributed generation load; Determine the adjustable load membership corresponding to the original adjustable load based on the maximum value of the adjustable load, the minimum value of the adjustable load, and the original adjustable load; Determine the energy storage load membership corresponding to the original energy storage load based on the maximum energy storage load, the minimum energy storage load, and the energy storage load; The membership corresponding to the second function set is determined based on the distributed generation load membership, the adjustable load membership, and the energy storage load membership.

6. The method according to claim 5, characterized in that The determining the distributed generation load membership corresponding to the original distributed generation load based on the maximum value of the distributed generation load, the minimum value of the distributed generation load, and the original distributed generation load includes: The reference distributed generation load membership is determined based on the maximum value of the distributed generation load, the minimum value of the distributed generation load, and the original distributed generation load, and is specifically calculated according to the following formula: in, is the reference distributed generation load membership, is the maximum value of the distributed generation load, is the minimum value of the distributed generation load, is the original distributed generation load; Obtaining the average temperature in the historical time period corresponding to the second function set; Determining a target correction coefficient corresponding to the average temperature; The reference distributed generation load membership is corrected according to the target correction coefficient to obtain the distributed generation load membership corresponding to the original distributed generation load.

7. The method according to claim 1, characterized in that The determining, based on the at least one target interactive adjustment capability value, a target interactive adjustment capability value corresponding to the target virtual power plant includes: Determining an average interaction regulation capability value corresponding to the at least one target interaction regulation capability value; Determine at least one of the historical time periods corresponding to the at least one target interaction adjustment capability value; Based on the at least one target interaction adjustment capability value, fitting is performed with the at least one historical time period to obtain a target interaction adjustment capability straight line; Determining a target slope corresponding to the target interactive regulation ability straight line; determining an adjustment coefficient corresponding to the target slope; The average interactive regulation capability value is adjusted based on the adjustment coefficient to obtain the target interactive regulation capability value corresponding to the target virtual power plant.

8. A device for evaluating the interactive regulation capability of a virtual power plant, characterized in that: The device comprises: an acquisition unit and a processing unit; The acquisition unit is used to acquire the load data, cost value, and output power data of the target virtual power plant in multiple historical time periods, and obtain multiple load data, multiple cost values, and multiple output power data; each historical time period corresponds to one load data, one cost value, and one output power data, the load data includes distributed generation load, adjustable load, and energy storage load, and the output power data includes adjustment capacity and climbing rate; The processing unit is used to input the multiple load data, the multiple cost values, and the multiple output power data into a preset distributed energy resource regulation characteristic model to obtain multiple function sets; each of the multiple function sets is used to characterize the constraint relationship between the distributed generation load, the adjustable load, the energy storage load and the cost value, the constraint relationship between the distributed generation load, the adjustable load, the energy storage load and the regulation capacity, and the constraint relationship between the distributed generation load, the adjustable load, the energy storage load and the ramp rate, and each historical time period corresponds to one function set; Determine the interaction adjustment capability value corresponding to each function set in the plurality of function sets to obtain a plurality of interaction adjustment capability values; the larger the interaction adjustment capability value, the greater the interaction adjustment capability corresponding to the interaction adjustment capability value; Determining at least one target interactive adjustment capability value from the plurality of interactive adjustment capability values ​​based on a multi-objective particle swarm optimization algorithm; The target interactive regulation capability value corresponding to the target virtual power plant is determined based on the at least one target interactive regulation capability value.

9. An electronic device, characterized in that: The method comprises a processor, a memory, a communication interface and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the processor, and the one or more programs include instructions for executing the steps in the method described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and the computer program is executed by a processor to implement the method according to any one of claims 1 to 7.

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