Method, medium and device for modeling virtual power plant cluster in energy management system

Abstract

The application discloses a modeling method, medium and equipment of virtual power plant clusters in an energy management system, comprising: traversing each virtual power plant cluster, obtaining the virtual power plant type, the first quantity, the first adjustable characteristic, the power grid dispatching instruction and the first power prediction value of each virtual power plant cluster under a plurality of preset prediction scenarios; determining the VPP participation main grid dispatching strategy and the VPP coordinated organization adjustment strategy of each virtual power plant cluster based on the power grid dispatching instruction; determining the first output range and the first equivalent ramp rate of each virtual power plant cluster based on the virtual power plant type, the first quantity, the first adjustable characteristic, the VPP participation main grid dispatching strategy and the VPP coordinated organization adjustment strategy of each virtual power plant cluster; determining the first output interval of each virtual power plant cluster in the future period as the modeling result of the virtual power plant cluster based on the first power prediction value and the first output range with the first equivalent ramp rate as the constraint, so as to realize the energy management system modeling for the virtual power plant application scenario.

Description

Technical Field

[0001] This invention relates to the field of clean energy technology, specifically to a modeling method, medium, and equipment for virtual power plant clusters in an energy management system. Background Technology

[0002] The energy management system is the core technology platform for power system dispatch and operation. It is mainly used in the power grid dispatch center and is an intelligent system that realizes the monitoring, control, dispatch and optimization of the entire process of power generation, transmission, distribution and consumption. Its core objective is to ensure the safe, stable, economical and efficient operation of the power grid, while coordinating the dispatch and management of various power sources and loads.

[0003] Traditional energy management systems are designed only for centralized power sources such as thermal power units. In the application scenario of virtual power plants, energy management systems cannot directly identify and process the complex system inside the virtual power plant, which consists of multiple types of resources such as distributed photovoltaics, wind power, energy storage, micro gas turbines, and controllable loads. They also find it difficult to schedule massive distributed resources individually.

[0004] Therefore, there is an urgent need to propose a modeling method for virtual power plants in energy management systems in order to realize energy management system modeling for virtual power plant application scenarios. Summary of the Invention

[0005] In view of this, the present invention provides a modeling method, medium and equipment for virtual power plant clusters in energy management systems, so as to realize energy management system modeling for virtual power plant application scenarios.

[0006] In a first aspect, the present invention provides a modeling method for virtual power plant clusters in an energy management system. The modeling method includes: traversing each virtual power plant cluster to be modeled, obtaining the virtual power plant type, first quantity, first adjustable characteristic, grid dispatch instructions, and first power prediction values ​​under multiple preset prediction scenarios for each virtual power plant cluster; determining the VPP participation strategy and VPP coordination organization adjustment strategy for each virtual power plant cluster based on the grid dispatch instructions; determining the first output range of each virtual power plant cluster based on the virtual power plant type, first quantity, first adjustable characteristic, and VPP participation strategy; determining the first equivalent ramp rate of each virtual power plant cluster based on the virtual power plant type, first quantity, first adjustable characteristic, and VPP coordination organization adjustment strategy; and using the first equivalent ramp rate as a constraint, determining the first output interval of each virtual power plant cluster in a future time period as the modeling result of each virtual power plant cluster based on the first power prediction value and the first output range.

[0007] As an exemplary embodiment, determining the first output range of each virtual power plant cluster based on the virtual power plant type, first quantity, first adjustable characteristic, and VPP participation in main grid scheduling strategy of each virtual power plant cluster includes: determining the dispatchable capacity of the virtual power plant cluster within multiple preset time periods based on the virtual power plant type, first quantity, first adjustable characteristic, and VPP participation in main grid scheduling strategy of each virtual power plant cluster; determining the first upper output boundary and the first lower output boundary of the first output range based on the maximum and minimum values ​​of the dispatchable capacity within the preset time periods; and determining the first output range based on the first upper output boundary and the first lower output boundary.

[0008] As an exemplary embodiment, determining the first equivalent ramp rate of each virtual power plant cluster based on the virtual power plant type, first quantity, first adjustable characteristic, and VPP coordination organization adjustment strategy of each virtual power plant cluster includes: determining the sub-equivalent ramp rate of each virtual power plant contained in each virtual power plant cluster based on the virtual power plant type, first quantity, first adjustable characteristic, and VPP coordination organization adjustment strategy; and summing the sub-equivalent ramp rates of each virtual power plant contained in the same virtual power plant cluster to obtain the first equivalent ramp rate.

[0009] As an exemplary embodiment, determining the first equivalent ramp rate of each virtual power plant cluster based on the virtual power plant type, first quantity, first adjustable characteristic, and VPP coordination organization adjustment strategy of each virtual power plant cluster further includes: when the virtual power plant type meets the first type, obtaining the installed capacity of the virtual power plant; if the installed capacity meets the first preset installed capacity range, determining the first equivalent ramp rate of the virtual power plant within the first preset time period as the first preset ramp rate, and the first equivalent ramp rate within the second preset time period as the second preset ramp rate; if the installed capacity meets the second preset installed capacity range, determining the first equivalent ramp rate of the virtual power plant within the first preset time period as the third preset ramp rate, and the first equivalent ramp rate within the second preset time period as the fourth preset ramp rate; wherein, the third preset ramp rate and the fourth preset ramp rate are calculated based on the installed capacity; if the installed capacity meets the third preset installed capacity range, determining the first equivalent ramp rate of the virtual power plant within the first preset time period as the fifth preset ramp rate, and the first equivalent ramp rate within the second preset time period as the sixth preset ramp rate.

[0010] As an exemplary embodiment, the modeling method of virtual power plant clusters in an energy management system further includes: traversing each virtual power plant cluster to obtain the type, second quantity, second individual adjustable characteristics, self-organizing adjustment strategy, and second power prediction values ​​under multiple preset prediction scenarios of the distributed resources aggregated within each target virtual power plant contained in each virtual power plant cluster; wherein, the self-organizing adjustment strategy is determined based on the VPP coordinated organization adjustment strategy; determining the second output range, second equivalent ramp rate, and second power generation cost function of each target virtual power plant contained in each virtual power plant cluster based on the type, second quantity, second individual adjustable characteristics, and self-organizing adjustment strategy of the distributed resources; determining the second output interval of the target virtual power plant in each time period based on the second output range, second equivalent ramp rate, and second power generation cost function; and determining the second output interval of each virtual power plant cluster in future time periods as the modeling result of each target virtual power plant contained in each virtual power plant cluster, based on the second equivalent ramp rate and second power generation cost function as constraints and the second power prediction value and second output interval.

[0011] As an exemplary embodiment, determining the second output range of each target virtual power plant in each virtual power plant cluster based on the type of distributed resources, the second quantity, the second individual adjustable characteristics, and the self-organizing adjustment strategy includes: determining the predicted value of the intermittent power output of the target virtual power plant within a preset time period, the expected value of its own load prediction, the maximum output of the virtual power plant's micro gas turbine, and the minimum output of the virtual power plant's micro gas turbine based on the self-organizing adjustment strategy; determining the second upper output boundary of the second output range based on the predicted value, the expected value, and the maximum output; determining the second lower output boundary of the second output range based on the predicted value, the expected value, and the minimum output; and determining the second output range based on the second upper output boundary and the second lower output boundary.

[0012] As an exemplary embodiment, determining the second output range, second equivalent ramp rate, and second power generation cost function of each target virtual power plant contained in each virtual power plant cluster based on the type of distributed resources, the second quantity, the second individual adjustable characteristics, and the self-organizing adjustment strategy includes: determining the response characteristics and cost model of distributed resources based on the type of distributed resources; modeling the aggregated distributed resources within each target virtual power plant contained in the virtual power plant cluster based on the response characteristics and cost model to obtain modeling results; using the self-organizing adjustment strategy as the driving condition for distributed resources, obtaining the sub-output range, sub-equivalent ramp rate, and sub-power generation cost function of each distributed resource based on the modeling results, the second quantity, and the second individual adjustable characteristics; and determining the second output range, the second equivalent ramp rate, and the second power generation cost function based on the sub-output range, the sub-equivalent ramp rate, and the sub-power generation cost function.

[0013] Secondly, the present invention provides a modeling device for virtual power plant clusters in an energy management system. The modeling device comprises: an acquisition module, used to traverse each virtual power plant cluster to be modeled, acquiring the virtual power plant type, first quantity, first adjustable characteristic, grid dispatch instructions, and first power prediction values ​​under multiple preset prediction scenarios for each virtual power plant cluster; a strategy determination module, used to determine the VPP participation strategy and VPP coordination organization adjustment strategy for each virtual power plant cluster based on the grid dispatch instructions; an output range determination module, used to determine the first output range of each virtual power plant cluster based on the virtual power plant type, first quantity, first adjustable characteristic, and VPP participation strategy; an equivalent ramp rate determination module, used to determine the first equivalent ramp rate of each virtual power plant cluster based on the virtual power plant type, first quantity, first adjustable characteristic, and VPP coordination organization adjustment strategy; and a modeling module, used to determine the first output interval of each virtual power plant cluster in a future time period as the modeling result of each virtual power plant cluster, constrained by the first equivalent ramp rate and based on the first power prediction value and the first output range.

[0014] Thirdly, the present invention provides a computer device, comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the computer instructions to perform the method described in the first aspect or any corresponding embodiment thereof.

[0015] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to perform the method described in the first aspect or any corresponding embodiment thereof.

[0016] This invention provides a modeling method for virtual power plant clusters in an energy management system. The modeling method includes: traversing each virtual power plant cluster to be modeled, obtaining the virtual power plant type, first quantity, first adjustability characteristic, grid dispatch instructions, and first power prediction values ​​under multiple preset prediction scenarios for each virtual power plant cluster; determining the VPP participation strategy and VPP coordination organization adjustment strategy for each virtual power plant cluster based on the grid dispatch instructions; determining the first output range of each virtual power plant cluster based on the virtual power plant type, first quantity, first adjustability characteristic, and VPP participation strategy; determining the first equivalent ramp rate for each virtual power plant cluster based on the virtual power plant type, first quantity, first adjustability characteristic, and VPP coordination organization adjustment strategy; and using the first equivalent ramp rate... To constrain this, the first output range of each virtual power plant cluster in the future time period is determined based on the first power prediction value and the first output range as the modeling result of each virtual power plant cluster. The above method determines the first output range, first equivalent ramp rate and first output range of the virtual power plant cluster through the virtual power plant type, first number, first adjustable characteristics, grid dispatch instructions and the first power prediction value under multiple preset prediction scenarios. Then, the virtual power plant cluster is modeled in EMS according to the first output range, first equivalent ramp rate and first output range. EMS can directly identify and process the complex system inside the virtual power plant composed of multiple types of resources such as distributed photovoltaic, wind power, energy storage, micro gas turbines and controllable loads through the modeling process, so as to realize the modeling of energy management system for virtual power plant application scenarios. Attached Figure Description

[0017] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0018] Figure 1 This is a flowchart illustrating a modeling method for a virtual power plant cluster in an energy management system according to an embodiment of the present invention. Figure 2 This is a structural block diagram of a modeling device for a virtual power plant cluster in an energy management system according to an embodiment of the present invention; Figure 3 This is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] Traditional energy management systems are designed only for centralized power sources such as thermal power units. In the application scenario of virtual power plants, energy management systems cannot directly identify and process the complex system inside the virtual power plant, which consists of multiple types of resources such as distributed photovoltaics, wind power, energy storage, micro gas turbines, and controllable loads. They also find it difficult to schedule massive distributed resources individually.

[0021] To address the aforementioned problems, according to an embodiment of the present invention, a method for modeling a virtual power plant cluster in an energy management system is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0022] This embodiment provides a method for modeling a virtual power plant cluster in an energy management system. Figure 1 This is a flowchart of a modeling method for a virtual power plant cluster in an energy management system according to an embodiment of the present invention, such as... Figure 1 As shown, the process includes the following steps: Step S101: Traverse each virtual power plant cluster to be modeled, and obtain the virtual power plant type, first quantity, first adjustable characteristic, power grid dispatch instructions, and first power prediction value under multiple preset prediction scenarios for each virtual power plant cluster.

[0023] In this embodiment, in order to model multiple virtual power plants participating in grid dispatch in a cluster in the Energy Management System (EMS), the virtual power plant clusters to be modeled are first traversed to obtain the virtual power plant type, first quantity, first adjustable characteristic, grid dispatch instructions, and first power prediction values ​​under multiple preset prediction scenarios for each virtual power plant cluster.

[0024] In one embodiment, the preset prediction scenario may include a preset time scale; for example, the preset time scale may include ultra-short-term, short-term, medium-to-long-term, and long-term.

[0025] In one embodiment, the first power prediction value under multiple preset prediction scenarios can be obtained through a pre-built power prediction model of the wind farm cluster.

[0026] In one embodiment, grid dispatch instructions can be obtained through communication with the dispatch center of the virtual power plant cluster main network.

[0027] Step S102: Determine the VPP participation strategy and VPP coordination and adjustment strategy for each virtual power plant cluster based on the power grid dispatch instructions.

[0028] For example, grid dispatch instructions can be obtained through communication between virtual power plant clusters and higher-level dispatching units. Specifically, the virtual power plant clusters consider constraints such as equipment output range, ramp rate, and safe operation, calculate and report the operating trajectory of the virtual power plant clusters to the higher-level dispatching unit. After receiving the operating trajectory reported by the virtual power plant clusters, the higher-level dispatching unit performs a safety check based on information such as the electricity purchase and sale price and the upper and lower limits of "output" provided by the virtual power plant clusters. Under the premise of meeting safety constraints, it determines a power generation plan with economic efficiency as the objective, generates grid dispatch instructions based on the power generation plan, and issues them to the virtual power plant clusters. Each virtual power plant cluster responds to the first dispatch instruction from the higher-level dispatching unit, determines its own VPP participation strategy and VPP coordination organization adjustment strategy through the VPP participation strategy in the main grid dispatching, and controls the output of its internal distributed resources through the VPP participation strategy in the main grid dispatching and VPP coordination organization adjustment strategy to ensure trajectory tracking. Therefore, in this embodiment, the VPP participation strategy in the main grid dispatching and VPP coordination organization adjustment strategy of each virtual power plant cluster are determined based on the grid dispatch instructions.

[0029] Step S103: Determine the first output range of each virtual power plant cluster based on the virtual power plant type, first number, first adjustable characteristics, and VPP participation in the main grid scheduling strategy.

[0030] In EMS, VPP clusters are analogous to traditional generator models, but unlike traditional generator models, the parameters of the VPP cluster model to be constructed need to be comprehensively considered from the internal resource characteristics and self-organizing mode aggregation of the VPP. From the perspective of the power grid, VPPs participating in the joint dispatch of the power grid in the form of clusters can exhibit two external characteristics: power supply and load. Their output range is a range from negative to positive, and the upper and lower boundaries of the range change according to different time periods. Therefore, in this invention, the first output range of each virtual power plant cluster is determined based on the virtual power plant type, first number, first adjustable characteristic, and VPP participation in the main grid dispatch strategy.

[0031] For example, the first output range is based on the type, quantity, and adjustability of each virtual power plant, as well as the VPP's participation in the main grid scheduling strategy. The first output range can be obtained through aggregation analysis under different prediction scenarios.

[0032] In one embodiment, virtual power plants participating in grid joint dispatch in a cluster can exhibit both power supply and load characteristics. Their output range is a negative to positive interval, and the upper and lower bounds of this interval vary depending on the time period. The dynamic output constraint can be expressed by equation (1): (1) In equation (1), Let t represent the scheduled capacity of the virtual power plant cluster, nVPP represent the set of n VPP clusters, and j represent the j-th VPP cluster. Step S104: Determine the first equivalent ramp rate of each virtual power plant cluster based on the virtual power plant type, first quantity, first adjustable characteristics, and VPP coordination organization adjustment strategy of each virtual power plant cluster.

[0033] In EMS, a VPP cluster is analogous to a traditional generator model; therefore, the parameters of the model to be built for the VPP cluster also need to take into account the ramp rate requirements.

[0034] For example, the first equivalent ramp rate can be obtained by aggregating the equivalent ramp rates of each virtual power plant contained in the virtual power plant cluster.

[0035] The equivalent ramp rate of each virtual power plant within the virtual power plant cluster can be obtained based on the type of virtual power plant.

[0036] For example, the first equivalent gradient can be determined by equation (2): (2) In equation (2), Let be the equivalent ramp rate of the j-th virtual power plant within a virtual power plant cluster containing n virtual power plants.

[0037] Step S105: Using the first equal ramp rate as a constraint, the first output range of each virtual power plant cluster in the future time period is determined based on the first power prediction value and the first output range as the modeling result of each virtual power plant cluster.

[0038] In practical applications, the relationship between the power grid and the VPP cluster is only one of buying and selling, and does not involve the control of the resources aggregated within the VPP cluster. Therefore, the external characteristics of the VPP cluster in the equivalent EMS model can be expressed as follows: the VPP cluster is constrained by the first equivalent ramp rate, and the output range of each VPP cluster is dynamically changed according to the first power prediction value and the first output range.

[0039] This invention provides a modeling method for virtual power plant clusters in an energy management system. The modeling method includes: traversing each virtual power plant cluster to be modeled, obtaining the virtual power plant type, first quantity, first adjustability characteristic, grid dispatch instructions, and first power prediction values ​​under multiple preset prediction scenarios for each virtual power plant cluster; determining the VPP participation strategy and VPP coordination organization adjustment strategy for each virtual power plant cluster based on the grid dispatch instructions; determining the first output range of each virtual power plant cluster based on the virtual power plant type, first quantity, first adjustability characteristic, and VPP participation strategy; determining the first equivalent ramp rate for each virtual power plant cluster based on the virtual power plant type, first quantity, first adjustability characteristic, and VPP coordination organization adjustment strategy; and using the first equivalent ramp rate... To constrain this, the first output range of each virtual power plant cluster in the future time period is determined based on the first power prediction value and the first output range as the modeling result of each virtual power plant cluster. The above method determines the first output range, first equivalent ramp rate and first output range of the virtual power plant cluster through the virtual power plant type, first number, first adjustable characteristics, grid dispatch instructions and the first power prediction value under multiple preset prediction scenarios. Then, the virtual power plant cluster is modeled in EMS according to the first output range, first equivalent ramp rate and first output range. EMS can directly identify and process the complex system inside the virtual power plant composed of multiple types of resources such as distributed photovoltaic, wind power, energy storage, micro gas turbines and controllable loads through the modeling process, so as to realize the modeling of energy management system for virtual power plant application scenarios.

[0040] As an exemplary embodiment, determining the first output range of each virtual power plant cluster based on the virtual power plant type, first quantity, first adjustable characteristic, and VPP participation in main grid scheduling strategy of each virtual power plant cluster includes: determining the dispatchable capacity of the virtual power plant cluster within multiple preset time periods based on the virtual power plant type, first quantity, first adjustable characteristic, and VPP participation in main grid scheduling strategy of each virtual power plant cluster; determining the first upper output boundary and the first lower output boundary of the first output range based on the maximum and minimum values ​​of the dispatchable capacity within the preset time periods; and determining the first output range based on the first upper output boundary and the first lower output boundary.

[0041] As mentioned above, virtual power plants participating in grid joint dispatch in a cluster form can exhibit both power supply and load characteristics. Their output range is a range from negative to positive, and the upper and lower bounds of this range vary depending on the time period. The dynamic output constraint can be expressed as equation (3): (3) in, The virtual power plant cluster is scheduled for time period t.

[0042] As an exemplary embodiment, determining the first equivalent ramp rate of each virtual power plant cluster based on the virtual power plant type, first quantity, first adjustable characteristic, and VPP coordination organization adjustment strategy of each virtual power plant cluster includes: determining the sub-equivalent ramp rate of each virtual power plant contained in each virtual power plant cluster based on the virtual power plant type, first quantity, first adjustable characteristic, and VPP coordination organization adjustment strategy; and summing the sub-equivalent ramp rates of each virtual power plant contained in the same virtual power plant cluster to obtain the first equivalent ramp rate.

[0043] In this embodiment, the VPP cluster equivalent ramp rate is obtained by summing the sub-equivalent ramp rates of each virtual power plant contained in the same virtual power plant cluster, based on the types of virtual power plants, adjustable characteristics, and VPP coordination and organization adjustment strategies that can be identified through parameters.

[0044] In one embodiment, to meet national standards, the first equivalent ramp-up rate of each virtual power plant within a virtual power plant cluster can be directly determined based on the type of virtual power plants included in the cluster. Specifically, determining the first equivalent ramp-up rate of each virtual power plant cluster based on its virtual power plant type, first number, first adjustable characteristic, and VPP coordination organization adjustment strategy further includes: when the virtual power plant type meets the first type requirement, obtaining the installed capacity of the virtual power plant; if the installed capacity meets the first preset installed capacity range, determining the first equivalent ramp-up rate of the virtual power plant within a first preset time period as the first preset ramp-up rate. The slope rate is determined as follows: the first equivalent ramp rate within the second preset time period is the second preset ramp rate; if the installed capacity meets the second preset installed capacity range, the first equivalent ramp rate of the virtual power plant within the first preset time period is the third preset ramp rate, and the first equivalent ramp rate within the second preset time period is the fourth preset ramp rate; wherein, the third preset ramp rate and the fourth preset ramp rate are calculated based on the installed capacity; if the installed capacity meets the third preset installed capacity range, the first equivalent ramp rate of the virtual power plant within the first preset time period is the fifth preset ramp rate, and the first equivalent ramp rate within the second preset time period is the sixth preset ramp rate.

[0045] The first type can be a wind farm or a photovoltaic power station.

[0046] The first preset installed capacity range can be (0MWh, 10MWh), the second preset installed capacity range can be (30MWh, 150MWh), and the third preset installed capacity range can be (150MWh, ∞).

[0047] The first preset duration can be 10 minutes, the first preset ramp rate can be 10 MW, the second preset duration can be 1 minute, and the second preset ramp rate can be 3 MW; that is, when the installed capacity of wind farms and photovoltaic power stations is less than 10 MW, the maximum limit of active power change in 10 minutes is 10 MW, and the maximum limit of active power change in 1 minute is 3 MW.

[0048] The third and fourth preset ramp rates are calculated based on the installed capacity. Specifically, the third preset ramp rate can be 1 / 3 of the installed capacity, and the fourth preset ramp rate can be 1 / 10 of the installed capacity. That is, for wind farms and photovoltaic power stations with an installed capacity of 30-150 MW, the maximum limit of active power change in 10 minutes is 1 / 3 of the installed capacity of the wind farm or photovoltaic power station, and the maximum limit of active power change in 1 minute is 1 / 10 of the installed capacity of the wind farm or photovoltaic power station.

[0049] Among them, the fifth preset ramp rate can be 50 MW, and the sixth preset ramp rate can be 15 MW; that is, when the installed capacity of wind farms and photovoltaic power stations is greater than 150 MW, the maximum limit of active power change in 10 minutes is 50 MW, and the maximum limit of active power change in 1 minute is 15 MW.

[0050] As an exemplary embodiment, each virtual power plant cluster is traversed to obtain the distributed resource type, second quantity, second individual adjustable characteristics, self-organizing adjustment strategy, and second power prediction value under multiple preset prediction scenarios for each target virtual power plant contained in each virtual power plant cluster. The self-organizing adjustment strategy is determined based on the VPP coordinated organization adjustment strategy. The second output range, second equivalent ramp rate, and second power generation cost function of each target virtual power plant contained in each virtual power plant cluster are determined based on the distributed resource type, second quantity, second individual adjustable characteristics, and self-organizing adjustment strategy. The second output interval of the target virtual power plant in each time period is determined based on the second output range, second equivalent ramp rate, and second power generation cost function. With the second equivalent ramp rate and second power generation cost function as constraints, the second output interval of each virtual power plant cluster in future time periods is determined based on the second power prediction value and the second output interval as the modeling result for each target virtual power plant contained in each virtual power plant cluster.

[0051] In this embodiment, for each target virtual power plant included in the virtual power plant cluster, the second output range, second equivalent ramp rate, and second power generation cost function of each target virtual power plant are determined based on the type of distributed resources aggregated within the target virtual power plant, the second quantity, the second individual adjustable characteristics, the self-organizing adjustment strategy, and the second power prediction value under multiple preset prediction scenarios. Thus, the second output range of the target virtual power plant in the future time period is determined based on the target virtual power plant as the modeling result of each target virtual power plant included in the virtual power plant cluster.

[0052] Specifically, each distributed resource can be modeled in the EMS in advance based on the response characteristics, second quantity, and second individual adjustable characteristics of the distributed resource types aggregated within the power plant. Further, the modeled distributed resources are aggregated into corresponding target virtual power plants according to the inclusion relationship. Based on the response characteristics and cost model of the modeled distributed resources, the second output range, second equivalent ramp rate, and second power generation cost function of each target virtual power plant are determined. Thus, the second output range of the target virtual power plant in the future time period is determined based on the target virtual power plant as the modeling result of each target virtual power plant contained in each virtual power plant cluster.

[0053] Based on this, as an exemplary embodiment, the modeling method of virtual power plant clusters in an energy management system further includes: determining the second output range, second equivalent ramp rate, and second power generation cost function of each target virtual power plant contained in each virtual power plant cluster based on the type of distributed resources, the second quantity, the second individual adjustable characteristics, and the self-organizing adjustment strategy. This includes: determining the response characteristics and cost model of distributed resources based on the type of distributed resources; modeling the aggregated distributed resources within each target virtual power plant contained in the virtual power plant cluster based on the response characteristics and cost model to obtain modeling results; using the self-organizing adjustment strategy as the driving condition for distributed resources, obtaining the sub-output range, sub-equivalent ramp rate, and sub-power generation cost function of each distributed resource based on the modeling results, the second quantity, and the second individual adjustable characteristics; and determining the second output range, the second equivalent ramp rate, and the second power generation cost function based on the sub-output range, the sub-equivalent ramp rate, and the sub-power generation cost function.

[0054] For example, distributed resource types may include distributed power sources, flexible loads, energy storage systems, diesel generators, and small hydropower.

[0055] For example, when determining the second output range, second equivalent ramp rate, and second power generation cost function of each target virtual power plant based on the type of distributed resources aggregated within the target virtual power plant, the second quantity, the second individual adjustable characteristics, the self-organizing adjustment strategy, and the second power prediction values ​​under multiple preset prediction scenarios, the response characteristics and cost model of the aggregated resources can be determined based on the distributed resource type of each specific aggregated resource. This allows for further determination of the second output range, second equivalent ramp rate, and second power generation cost function of each target virtual power plant based on the response characteristics and cost model of the aggregated resources, the second quantity, the second individual adjustable characteristics, the self-organizing adjustment strategy, and the second power predictions under multiple preset prediction scenarios.

[0056] In one embodiment, for aggregated resources of distributed resource type, wind turbine and photovoltaic, the response characteristics of wind turbine can be considered by taking into account the theoretical power of the wind turbine, determining the output power curve of the wind turbine based on the maximum power point tracking control mode of the wind turbine, and simultaneously determining the optimal power-speed characteristic curve of the wind turbine. Finally, the wind turbine obtains wind speed data in the environment through sensors, calculates the current output of the wind turbine through wind speed parameters in the internal database and calculation formulas in the knowledge method base, so as to determine the output range and ramp rate of the aggregated resource type wind turbine.

[0057] For example, the response characteristics of photovoltaics can be determined by the current-voltage relationship of solar cells; and by testing the output characteristics of photovoltaic cells, their voltage / current external characteristic curves can be obtained; finally, photovoltaics obtains the ambient light irradiance S and the actual ambient temperature T through sensors, calculates the current photovoltaic power output, and updates the data.

[0058] In one embodiment, for the cost model of distributed power sources, the quadratic polynomial of the distributed power source with respect to output power, the start-up cost, and the shutdown cost can be predetermined, thereby using the quadratic polynomial of the distributed power source with respect to output power, the start-up cost, and the shutdown cost as the cost model of the distributed power source.

[0059] In one embodiment, aggregated resources of the distributed resource type flexible load may include HVAC loads and electric vehicles. For the response characteristics of HVAC loads, energy parameters and power parameters can be predefined. The energy parameters can be the energy that the energy storage system can supply back to the grid and the energy that the grid can charge the energy storage system. The power parameter can be defined as the current charging power of the cold and heat source energy storage system. During modeling, the response characteristics of the HVAC load can be defined as follows: temperature setpoint T, power P, current temperature Tr, upper temperature limit T+, and lower temperature limit T-. The frequency offset is Δf. During the control process, the maximum value of the upper temperature limit is T+max, and the minimum value is T+min; the maximum value of the lower temperature limit is T-max, and the minimum value is T-min; the on / off state of the device is s, where s takes a value of 0 to indicate off and a value of 1 to indicate on, for modeling in the EMS.

[0060] In one embodiment, when modeling HVAC loads, the modeling object is set as n temperature-controlled flexible load devices, and the power parameters and energy parameters of the temperature-controlled flexible load cluster are tuned.

[0061] For modeling the adjustable characteristics of the second unit of HVAC load, during the control process, the HVAC equipment is dynamically divided into controllable and uncontrollable equipment according to the current temperature Tr of the equipment, so as to be modeled in EMS.

[0062] For the setting of the power parameters of the HVAC type load, assume that at time t, there are mt controllable devices. Considering the jth controllable device, if sj = 0, then Pvir_j = 0; if sj = 1, then Pvir_j = P. The total virtual charging power of the HVAC type load during modeling can be expressed by Equation (4): (4) The dynamic upper boundary of the total virtual charging power of the system can be expressed by Equation (5) (5) The lower boundary is 0, that is, all controllable devices are in the off state.

[0063] For the setting of the energy parameters of the HVAC type load, for the ith device, first consider its switch state si; in the present invention, changing the state of a device from on to off is equivalent to supplying power back to the system. Therefore, if si is 0, then; next, consider the current temperature of this device; if T > T - max, then ; If T < T - max, then ; where, to is the time taken for the temperature to rise from To to T + max when the device is turned on.

[0064] If it is 1, similarly ; consider the current temperature T; if T > T + min, then Evir_i = 0; if T < T + min, then Evir_i = ; where is the time taken for the temperature to drop from To to T - min when the device is turned off.

[0065] Based on this, the total energy parameters of the load cluster of the HVAC type load can be determined by Equation (6): (6) where represents the total energy parameters of the load cluster of the HVAC type load, i represents the ith HVAC type load in the load cluster of HVAC type loads composed of n HVAC type loads, represents the energy parameters of the ith HVAC type load.

[0066] For the response characteristics of electric vehicles, energy parameters and power parameters can be predefined. Among them, as a mobile energy storage device, the effective energy storage and effective energy storage capacity of an electric vehicle are real-time and dynamically changing; in the present invention, in order to evaluate the energy state of the electric vehicle load group, exemplarily, Table 1 gives the effective energy storage capacity and effective energy storage value of a single electric vehicle. The effective energy storage represents the electric energy that an electric vehicle can supply back to the power grid, and the difference between the effective energy storage capacity and the effective energy storage represents the electric energy that can be absorbed from the power grid.

[0067]

[0068] For example, the effective energy storage capacity of the electric vehicle load group and the effective energy storage calculation method are shown in Equation (7).

[0069] (7) In Equation (7), Ei(t) is the rated capacity of the i-th controllable electric vehicle; SOTi(t) is the real-time state of charge of the i-th electric vehicle; E(t) is the effective energy storage of the electric vehicle load group; Er(t) is the effective energy storage capacity of the electric vehicle load group; and N is the number of controllable electric vehicles.

[0070] For the power parameters of electric vehicles, according to the classification of electric vehicle load, the boundary values ​​of charging and discharging power of a single electric vehicle are shown in Table 2:

[0071] The calculation method for the charging and discharging power of the electric vehicle load group and its upper and lower boundaries is shown in Equation (8): (8) In equation (8), Pi(t), Pmax_i(t), and Pmin_i(t) are the real-time charging and discharging power and their upper and lower boundaries of the i-th controllable electric vehicle, respectively; p(t), Pup(t), and Pdown(t) are the real-time charging and discharging power and their upper and lower boundaries of the electric vehicle load group, respectively; and N is the number of controllable electric vehicles.

[0072] For the cost model of flexible loads, the participation of electric vehicle users in V2G response requires the establishment of a corresponding price compensation mechanism. The price incentive participation degree (ρp) increases with the increase of compensation intensity. In a single electricity sales scenario, the participation degree of electric vehicle users in the V2G market is a concave function of the electricity purchase price of the power company. Finally, as the compensation intensity continues to increase, ρp gradually approaches 1.0.

[0073] For example, when the V2G capability of the electric vehicle is used to improve the reactive power response capability of the system during charging, the power factor of the charging will be reduced. The compensation for reactive power will be calculated based on the lost active power. Taking the compensation price as Pp (yuan / kWh) and the power factor as cosφ decreasing from 1.0 (the apparent power during operation is the rated value S0) as an example, Equation (9) gives the price compensation Pq (yuan / kWh) per unit of reactive power.

[0074] (9) Finally, based on the cost model and response characteristics of flexible loads, the distributed resources of flexible load type aggregated in the target virtual power plant are modeled, and the modeling results are obtained.

[0075] The response characteristics of an energy storage system can be represented by the energy state value (SSOC); specifically, SSOC can be represented by equation (10): (10) In equation (10), EN is the rated capacity of the battery; SSOCt and SSOCt-1 are the battery energy states at times t and t-1, respectively; ∆SSOC is the change in energy state within a time step; PC is the charging / discharging power of the battery, a positive value indicates that the battery energy storage system is charging, and a negative value indicates that it is discharging; PN is the rated charging / discharging power of the battery; and Vb is the terminal voltage of the battery when it is working.

[0076] For the cost model of energy storage system, after multiple cycles of charging and discharging, lithium batteries and lead-acid batteries will lose some capacity. When the battery capacity drops to a certain level, the battery must be replaced. Therefore, the operation and maintenance cost of ESS mainly refers to the consumption caused by the aging process of battery charging and discharging. In this invention, SOC is used to represent the DoD of the battery, as shown in formula (11).

[0077] DoD = 1 - SOC (11) The mathematical expression between battery cycle life and DoD is expressed by equation (12). (12) In formula (12), CL represents the battery life; DR represents the battery's rated DoD. =320, =1.703, =-3.59.

[0078] The operating cost of the battery can then be expressed by equation (13): (13) In equation (13), This indicates the replacement cost of a single battery. This indicates the aging loss caused by each discharge.

[0079] Finally, based on the cost model and response characteristics of the energy storage system, the distributed resources aggregated in the target virtual power plant, which are flexible loads, are modeled, and the modeling results are obtained.

[0080] Regarding the response characteristics of diesel generators, diesel generators and gas turbines are controllable micro-sources, and their output is limited by the rated power of the unit itself, which can be expressed by equation (14): (14) In equation (14), Pout is the actual output of the diesel generator or gas turbine, Prate is the rated power of the diesel generator or gas turbine, and Plow is the minimum output of the diesel generator or gas turbine.

[0081] The output power of a diesel generator can be stabilized by controlling the fuel input. The fuel consumption of a diesel generator is related to its output power and can be calculated according to formula (15): (15) In the formula, and These are the generated power and rated power, respectively, in units of , and A and B are the correlation coefficients of the consumption curves.

[0082] For the modeling of the cost of diesel generators, the power generation cost of diesel generator sets mainly consists of two parts: fixed cost and variable cost. Among them, the fixed cost is reflected in the depreciation cost, while the variable cost consists of fuel cost, operation and maintenance cost, and pollutant treatment cost. The depreciation cost is the ratio of the average annual investment cost to the total annual power generation (estimated value). Among the variable costs, the fuel cost is inversely proportional to the real-time output power of the unit, while the operation and maintenance cost is directly proportional to the real-time output power of the unit. The pollutant treatment cost is determined by the environmental cost coefficient of the types of pollutants emitted (NOx, SO2, CO2, CO and dust) and the fuel cost. The power generation cost of diesel generator sets is shown in Equation (16): (16) In formula (16) These represent the output power of the diesel generator set. The costs include depreciation, operation and maintenance, fuel, and pollution treatment. This indicates the average annual investment cost of the diesel generator set; This represents the predicted annual power generation of diesel generator sets based on historical data from typical years. This represents the unit's operation and maintenance cost coefficient; Indicates the rated power of the diesel generator set; Indicates the price of diesel fuel; This represents the environmental cost coefficient of the k-th type of pollutant (k = 1, 2, 3, 4, 5 represent NOx, SO2, CO2, CO and dust, respectively).

[0083] Finally, based on the cost model and response characteristics of diesel generators, the distributed resources of flexible load type aggregated in the target virtual power plant are modeled, and the modeling results are obtained.

[0084] Regarding the response characteristics of small-scale hydropower units, hydropower generates electricity using the potential energy of water. The power output is directly related to the flow rate and is also affected by the head. Therefore, the hydropower output characteristic curve is a typical nonlinear curve. In addition, there is a type of cascade hydropower in hydropower units, that is, there is a water flow connection between upstream and downstream hydropower stations. In this invention, it is represented by equation (17): (17) In equation (17), η is the power generation efficiency of the hydropower station; Q is the water flow rate through the turbine. The velocity of the water flow at the cross section; γ ρ is the specific gravity of water; g is the acceleration due to gravity; h represents the pressure difference between the reservoir water level and the power generation head.

[0085] Hydropower units generally refer to the collective entity including water diversion pipelines, turbines, and generators. In the economic dispatching of hydropower plants, the main parameters used are the unit's water consumption characteristic curve and its incremental characteristic curve.

[0086] The water consumption characteristic of a generator unit is the relationship curve between the flow rate used for power generation and the output of the generator unit at a certain operating head. It represents the additional water consumption required to increase the unit's output per unit.

[0087] The water consumption characteristics of a hydropower unit refer to the relationship between the amount of water consumed and the power generated per unit time. When the turbine diameter and speed in a hydropower unit are constant, its water consumption characteristics vary with the head conditions, which can be obtained from the head-output-flow table (i.e., the H-N-Q table). However, in actual operation, the daily head variation of a hydropower station is not significant and mostly stays near the rated head. Therefore, it can be approximated that the hydropower unit operates at the rated head.

[0088] Water consumption rate ρ is an important parameter characterizing the efficiency of hydropower units. It is defined as the amount of water consumed per unit kilowatt of output, i.e., ρ = Q / P. Among them, the relative value of water consumption rate ρ is the ratio of the actual water consumption rate to the minimum water consumption rate.

[0089] The power generation cost of small hydropower enterprises includes two parts: one-time construction costs and operation and maintenance costs. Small hydropower generally has a small installed capacity, so it is mainly privately and collectively owned, and the one-time construction costs account for a large proportion of the cost; due to its flexible operation mode and easy maintenance, it has lower operation and maintenance costs compared with traditional coal-fired power sources; the primary energy of small hydropower is renewable water energy, so its fuel cost is zero. Therefore, small hydropower generally has the characteristics of high investment, low operation and maintenance costs, and zero fuel costs. The power generation cost of small hydropower can be expressed as the power generation cost per unit of electricity. If we assume that the social benefits of small hydropower are greater than its social costs, then the power generation cost per unit of electricity of small hydropower can be expressed by equation (18): (18) In equation (18), r is the fixed annual interest rate, n is the investment repayment period; Maz is the installation / investment cost of distributed power generation; f is the capacity factor, f = annual power generation / 8760; Myw is the operation and maintenance cost of small hydropower.

[0090] Finally, based on the cost model and response characteristics of small-scale hydropower units, the distributed resources aggregated within the target virtual power plant, which are of the flexible load type, are modeled, and the modeling results are obtained.

[0091] Following the above implementation method, modeling of the response characteristics and cost model of distributed resource types aggregated within the power plant is realized. Furthermore, a self-organizing adjustment strategy can be used as the driving condition for distributed resources. Based on the modeling results, the second quantity, and the second individual adjustable characteristics, the sub-output range, sub-equivalent ramp rate, and sub-generation cost function of each distributed resource are obtained. Based on the sub-output range, sub-equivalent ramp rate, and sub-generation cost function, the second output range, second equivalent ramp rate, and second generation cost function are determined.

[0092] As an exemplary embodiment, determining the second output range of each target virtual power plant in each virtual power plant cluster based on the type of distributed resources, the second quantity, the second individual adjustable characteristics, and the self-organizing adjustment strategy includes: determining the predicted value of the intermittent power output of the target virtual power plant within a preset time period, the expected value of its own load prediction, the maximum output of the virtual power plant's micro gas turbine, and the minimum output of the virtual power plant's micro gas turbine based on the self-organizing adjustment strategy; determining the second upper output boundary of the second output range based on the predicted value, the expected value, and the maximum output; determining the second lower output boundary of the second output range based on the predicted value, the expected value, and the minimum output; and determining the second output range based on the second upper output boundary and the second lower output boundary.

[0093] In this embodiment, the virtual power plant participating in the joint grid dispatch can exhibit two external characteristics: power supply and load. Its output range is a range from negative to positive, and the upper and lower boundaries of the range change according to different time periods. The output dynamic constraint can be expressed as equation (19): (19) In equation (19), Indicates the upper output boundary of the output range; The lower output boundary indicates the range of output force.

[0094] Among them, the upper limit of the output of the virtual power plant It can be expressed as equation (20): (20) In equation (20), This represents the predicted value for intermittent power output. This represents the expected value of its own load forecast; nMT is the collection of micro gas turbines in the virtual power plant; This represents the maximum output of the micro gas turbine in the virtual power plant.

[0095] Among them, the lower bound of the virtual power plant's output. It can be expressed as equation (21): (twenty one) In equation (21), This represents the minimum output of the micro gas turbine in the virtual power plant.

[0096] because and It is dynamic and therefore It can be positive or negative.

[0097] Since the power grid and VPP only have a buying and selling relationship and do not care about the control of each aggregated resource within the VPP, the external characteristics of the VPP in the equivalent EMS model can be expressed as: the dynamically changing output range calculated by the VPP based on the predicted output values ​​of each aggregated unit.

[0098] This embodiment provides a modeling device for a virtual power plant cluster in an energy management system, such as... Figure 2 As shown, it includes: The acquisition module 501 is used to traverse each virtual power plant cluster to be modeled, and acquire the virtual power plant type, first quantity, first adjustable characteristic, power grid dispatch instructions and first power prediction values ​​under multiple preset prediction scenarios for each virtual power plant cluster. The strategy determination module 502 is used to determine the VPP participation strategy and VPP coordination and organization adjustment strategy of each virtual power plant cluster based on the power grid dispatch instructions. The output range determination module 503 is used to determine the first output range of each virtual power plant cluster based on the virtual power plant type, first number, first adjustable characteristics and VPP participation in the main grid scheduling strategy of each virtual power plant cluster. The equivalent ramp rate determination module 504 is used to determine the first equivalent ramp rate of each virtual power plant cluster based on the virtual power plant type, first quantity, first adjustable characteristics and VPP coordination organization adjustment strategy of each virtual power plant cluster. Modeling module 505 is used to determine the first output range of each virtual power plant cluster in the future time period based on the first power prediction value and the first output range, constrained by the first equal ramp rate, as the modeling result of each virtual power plant cluster.

[0099] It should be noted that the examples and application scenarios implemented by the above modules and corresponding steps are the same, but are not limited to the content disclosed in the above embodiments.

[0100] It should be noted that the above modules, as part of the device, can be implemented in software or hardware, with the hardware environment including the network environment.

[0101] This invention also provides a computer device, including a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; the memory is used to store computer programs; and the processor is used to execute the methods described in any of the above embodiments by running the computer programs stored in the memory.

[0102] Figure 3 This is a structural block diagram of an optional computer device according to an embodiment of this application, such as... Figure 3 As shown, it includes a processor 10, a communication interface 20, a memory 30, and a communication bus 40. The processor 10, communication interface 20, and memory 30 communicate with each other via the communication bus 40. Memory 30 is used to store computer programs; When the processor 10 executes a computer program stored in the memory 30, it implements the method as described in any of the above embodiments.

[0103] Optionally, in this embodiment, the communication bus can be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. This communication bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 3 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0104] The communication interface is used for communication between the aforementioned computer equipment and other devices.

[0105] The memory may include RAM, or non-volatile memory, such as at least one disk storage device. Optionally, the memory may also be at least one storage device located remotely from the aforementioned processor.

[0106] The processor mentioned above can be a general-purpose processor, including but not limited to: CPU (Central Processing Unit), NP (Network Processor), etc.; it can also be DSP (Digital Signal Processor), ASIC (Application Specific Integrated Circuit), FPGA (Field-Programmable Gate Array) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0107] Optionally, specific examples in this embodiment can refer to the examples described in the above embodiments, and will not be repeated here.

[0108] Those skilled in the art will understand that Figure 3 The structure shown is for illustrative purposes only. The device that implements any of the methods in the above embodiments can be a terminal device, such as a smartphone (e.g., an Android phone, an iOS phone), a tablet computer, a PDA, a mobile Internet device (MID), a PAD, or other terminal devices. Figure 3 This does not limit the structure of the aforementioned electronic device. For example, the terminal device may also include components that are more... Figure 3 The more or fewer components shown (such as network interfaces, display devices, etc.), or having the same Figure 3 The different configurations shown.

[0109] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing the hardware related to the terminal device. The program can be stored in a computer-readable storage medium, which may include: flash drive, ROM, RAM, disk or optical disk, etc.

[0110] As an exemplary embodiment, this application also provides a computer-readable storage medium storing a computer program, wherein the computer program is configured to execute the method steps of any one of the embodiments in this application at runtime.

[0111] Optionally, in this embodiment, the storage medium described above can be used to execute program code for the method steps of the embodiments of this application.

[0112] Optionally, in this embodiment, the storage medium may be located on at least one of the network devices in the network shown in the above embodiment.

[0113] Optionally, in this embodiment, the storage medium is configured to store methods for performing the above embodiments.

[0114] Optionally, specific examples in this embodiment can refer to the examples described in the above embodiments, and will not be repeated in this embodiment.

[0115] Optionally, in this embodiment, the storage medium may include, but is not limited to, various media capable of storing program code, such as USB flash drives, ROMs, RAMs, portable hard drives, magnetic disks, or optical disks.

[0116] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0117] If the integrated units in the above embodiments are implemented as software functional units and sold or used as independent products, they can be stored in the aforementioned computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause one or more computer devices (which may be personal computers, servers, or network devices, etc.) to execute all or part of the steps of the methods in the above embodiments.

[0118] In the several embodiments provided in this application, it should be understood that the disclosed client can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, or the indirect coupling or communication connection of units or modules may be electrical or other forms.

[0119] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of the solution provided in this embodiment, depending on actual needs.

[0120] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0121] In the above embodiments of this application, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0122] The above are merely preferred embodiments of this application. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A modeling method for virtual power plant clusters in an energy management system, characterized in that, The modeling method includes: Traverse each of the virtual power plant clusters to be modeled, and obtain the virtual power plant type, first quantity, first adjustable characteristic, power grid dispatching instructions, and first power prediction values ​​under multiple preset prediction scenarios for each virtual power plant cluster. Based on the power grid dispatch instructions, determine the VPP participation strategy and VPP coordination and adjustment strategy for each of the virtual power plant clusters in the main grid dispatching process. The first output range of each virtual power plant cluster is determined based on the virtual power plant type, the first quantity, the first adjustable characteristic, and the VPP participation in the main grid scheduling strategy. The first equal ramp rate of each virtual power plant cluster is determined based on the virtual power plant type, the first quantity, the first adjustable characteristic, and the VPP coordination organization adjustment strategy of each virtual power plant cluster. Using the first equivalent ramp rate as a constraint, the first output range of each virtual power plant cluster in the future time period is determined based on the first power prediction value and the first output range as the modeling result of each virtual power plant cluster.

2. The modeling method for virtual power plant clusters in an energy management system as described in claim 1, characterized in that, The determination of the first output range of each virtual power plant cluster based on the virtual power plant type, the first quantity, the first adjustable characteristic, and the VPP participation in the main network scheduling strategy includes: The scheduled capacity of each virtual power plant cluster is determined within multiple preset time periods based on the virtual power plant type, the first quantity, the first adjustable characteristic, and the VPP participation in the main grid scheduling strategy. The first upper output boundary and the first lower output boundary of the first output range are determined based on the maximum and minimum values ​​of the scheduled capacity within a preset time period. The first output range is determined based on the first upper output boundary and the first lower output boundary.

3. The modeling method for virtual power plant clusters in an energy management system as described in claim 1, characterized in that, The determination of the first equivalent ramp rate for each virtual power plant cluster based on the virtual power plant type, the first quantity, the first adjustable characteristic, and the VPP coordination organization adjustment strategy includes: The sub-equivalent ramp rate of each virtual power plant contained in each virtual power plant cluster is determined based on the virtual power plant type, the first quantity, the first adjustable characteristic, and the VPP coordination organization adjustment strategy. The first equivalent ramp rate is obtained by summing the sub-equivalent ramp rates of each virtual power plant contained in the same virtual power plant cluster.

4. The modeling method for virtual power plant clusters in an energy management system as described in any one of claims 1 or 3, characterized in that, The determination of the first equivalent ramp rate for each virtual power plant cluster based on the virtual power plant type, the first quantity, the first adjustable characteristic, and the VPP coordination organization adjustment strategy further includes: When the virtual power plant type meets the first type, obtain the installed capacity of the virtual power plant; If the installed capacity meets the first preset installed capacity range, the first equivalent ramp rate of the virtual power plant within the first preset time period is determined to be the first preset ramp rate, and the first equivalent ramp rate within the second preset time period is determined to be the second preset ramp rate. If the installed capacity meets the second preset installed capacity range, the first equivalent ramp rate of the virtual power plant within the first preset time period is determined to be the third preset ramp rate, and the first equivalent ramp rate within the second preset time period is determined to be the fourth preset ramp rate; wherein, the third preset ramp rate and the fourth preset ramp rate are calculated based on the installed capacity; If the installed capacity meets the third preset installed capacity range, the first equivalent ramp rate of the virtual power plant within the first preset time period is determined to be the fifth preset ramp rate, and the first equivalent ramp rate within the second preset time period is determined to be the sixth preset ramp rate.

5. The modeling method for virtual power plant clusters in an energy management system as described in claim 1, characterized in that, The modeling method for the virtual power plant cluster in the energy management system also includes: Traverse each of the virtual power plant clusters to obtain the aggregated distributed resource types, second quantity, second individual adjustable characteristics, self-organizing adjustment strategy, and second power prediction values ​​under multiple preset prediction scenarios within each target virtual power plant contained in each virtual power plant cluster; wherein, the self-organizing adjustment strategy is determined based on the VPP coordinated organization adjustment strategy; Based on the type of the distributed resources, the second quantity, the second individual adjustable characteristics, and the self-organizing adjustment strategy, determine the second output range, the second equivalent ramp rate, and the second power generation cost function of each target virtual power plant contained in each virtual power plant cluster; The second output range of the target virtual power plant in each time period is determined based on the second output range, the second equivalent ramp rate, and the second power generation cost function. Using the second equivalent ramp rate and the second power generation cost function as constraints, the second power output range of each virtual power plant cluster in the future time period is determined based on the second power prediction value and the second output range as the modeling result of each target virtual power plant contained in each virtual power plant cluster.

6. The modeling method for virtual power plant clusters in an energy management system as described in claim 5, characterized in that, The determination of the second output range of each target virtual power plant contained in each virtual power plant cluster based on the type, second quantity, second individual adjustable characteristics of the distributed resources and the self-organizing adjustment strategy includes: Based on the self-organizing adjustment strategy, the predicted value of the intermittent power output of the target virtual power plant, the expected value of its own load prediction, the maximum output of the virtual power plant's micro gas turbine, and the minimum output of the virtual power plant's micro gas turbine are determined within a preset time period. The second upper output boundary of the second output range is determined based on the predicted value, the expected value, and the maximum output. The second lower output boundary of the second output range is determined based on the predicted value, the expected value, and the minimum output. The second output range is determined based on the second upper output boundary and the second lower output boundary.

7. The modeling method for virtual power plant clusters in an energy management system as described in claim 5, characterized in that, The determination of the second output range, second equivalent ramp rate, and second power generation cost function of each target virtual power plant contained in each virtual power plant cluster based on the type, second quantity, second individual adjustable characteristics of the distributed resources, and the self-organizing adjustment strategy includes: The response characteristics and cost model of the distributed resources are determined based on the type of the distributed resources. Based on the response characteristics and the cost model, the distributed resources aggregated in each target virtual power plant contained in the virtual power plant cluster are modeled respectively, and the modeling results are obtained. Using the self-organizing adjustment strategy as the driving condition for the distributed resources, the sub-output range, sub-equivalent ramp rate and sub-generation cost function of each distributed resource are obtained based on the modeling results, the second quantity and the second individual adjustable characteristics. The second output range, the second equivalent ramp rate, and the second power generation cost function are determined based on the aforementioned sub-output range, sub-equivalent ramp rate, and sub-power generation cost function.

8. A modeling device for a virtual power plant cluster in an energy management system, characterized in that, The modeling device includes: The acquisition module is used to traverse each of the virtual power plant clusters to be modeled, and acquire the virtual power plant type, first quantity, first adjustable characteristic, power grid dispatching instructions, and first power prediction values ​​under multiple preset prediction scenarios for each virtual power plant cluster. The strategy determination module is used to determine the VPP participation strategy and VPP coordination and adjustment strategy of each virtual power plant cluster based on the power grid dispatching instructions. The output range determination module is used to determine the first output range of each virtual power plant cluster based on the virtual power plant type, the first quantity, the first adjustable characteristic, and the VPP participation in the main grid scheduling strategy of each virtual power plant cluster. The equivalent ramp rate determination module is used to determine the first equivalent ramp rate of each virtual power plant cluster based on the virtual power plant type, the first quantity, the first adjustable characteristic, and the VPP coordination organization adjustment strategy of each virtual power plant cluster. The modeling module is used to determine the first output range of each virtual power plant cluster in a future time period based on the first power prediction value and the first output range, constrained by the first equivalent ramp rate, as the modeling result of each virtual power plant cluster.

9. A computer device, characterized in that, include: A memory and a processor are interconnected, the memory storing computer instructions, and the processor executing the computer instructions to perform the modeling method of the virtual power plant cluster in the energy management system according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to execute the modeling method of the virtual power plant cluster in the energy management system as described in any one of claims 1 to 7.

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