Virtual power plant scheduling method, apparatus and device, and storage medium

Abstract

The invention discloses a virtual power plant scheduling method, device and equipment and a storage medium, and the method comprises the steps: carrying out the scheduling of a virtual power plant based on the output prediction data of a new energy unit in a day-ahead stage, the resource data, carbon transaction data and demand response load data of each unit in the day-ahead stage, and an objective function; determining the operation cost of the day-ahead stage and the output plan of each unit; based on the output prediction data and the load prediction data of the new energy unit in the intra-day stage, the resource data, the carbon transaction data and the demand response load data of each unit in the day-ahead stage, and the target function of the intra-day stage, determining the operation cost of the intra-day stage and the output plan of each unit; and scheduling the virtual power plant based on the operation cost of the day-ahead stage, the output plan of each unit, the operation cost of the intra-day stage and the output plan of each unit. According to the invention, the accuracy of virtual power plant scheduling can be improved.

Description

Technical Field

[0001] The present application relates to the technical field of power dispatching, and in particular to a dispatching method, device, equipment and storage medium for a virtual power plant. Background Art

[0002] With the intensification of the global energy crisis and the increasingly serious environmental pollution problem, renewable energy generation has attracted widespread attention from the world. Among them, wind power and photovoltaic power generation have developed rapidly, but their output is random and volatile. In order to ensure the safe operation of the power grid when new energy is connected to the grid, wind power output and photovoltaic power generation prediction and consumption technology have received widespread attention worldwide. Virtual Power Plant (VPP) can aggregate distributed energy and flexibly dispatch and control to ensure the stable operation of the power system.

[0003] In the existing virtual power plant scheduling method, the output plan of each unit is determined by analyzing and calculating the output and load data of the new energy units within a single time scale. However, the output and load data within a single time scale cannot accurately determine the output plan of each unit, and the accuracy of virtual power plant scheduling is low. Summary of the invention

[0004] In order to solve the above-mentioned problems existing in the prior art, the embodiments of the present application provide a scheduling method, device, equipment and storage medium for a virtual power plant. The operating cost of the virtual power plant in the day-ahead stage and the output plan of each unit in the day-ahead stage are determined through the output forecast data of the new energy units in the day-ahead stage. The operating cost of the virtual power plant in the day-ahead stage and the output plan of each unit in the day-ahead stage are determined through the output forecast data and load forecast data of the new energy units in the intraday stage, thereby improving the accuracy of scheduling of the virtual power plant.

[0005] In a first aspect, an embodiment of the present application provides a scheduling method for a virtual power plant, including:

[0006] Obtain output forecast data of new energy units in virtual power plants in the day-ahead phase;

[0007] Determine the resource data of each unit in the day-ahead stage based on the output forecast data of the new energy unit in the day-ahead stage; determine the carbon trading data and demand response load data of the virtual power plant in the day-ahead stage;

[0008] Determine the operating cost of the virtual power plant in the day-ahead stage and the output plan of each unit in the day-ahead stage based on the output forecast data of the new energy unit in the day-ahead stage, the resource data of each unit in the day-ahead stage, the carbon trading data in the day-ahead stage, the demand response load data and the objective function in the day-ahead stage;

[0009] Obtaining output forecast data and load forecast data of the new energy units in the virtual power plant during the intra-day stage;

[0010] Determine the resource data of each unit during the intra-day period based on the output forecast data of the new energy unit during the intra-day period; determine the carbon trading data and demand response load data of the virtual power plant during the intra-day period;

[0011] Determine the operating cost of the virtual power plant during the intraday stage and the output plan of each unit during the intraday stage based on the output forecast data of the new energy unit during the intraday stage, the load forecast data, the resource data of each unit during the intraday stage, the carbon trading data during the intraday stage, the demand response load data and the objective function during the intraday stage;

[0012] The virtual power plant is dispatched based on the operating cost of the day-ahead stage and the output plan of each unit in the day-ahead stage, the operating cost of the intraday stage and the output plan of each unit in the intraday stage.

[0013] In a second aspect, an embodiment of the present application provides a scheduling device for a virtual power plant, wherein the scheduling device for the virtual power plant includes an acquisition unit and a processing unit;

[0014] The acquisition unit is used to acquire output forecast data of new energy units in the virtual power plant in the day-ahead stage;

[0015] The processing unit is used to determine the resource data of each unit at the day-ahead stage based on the output forecast data of the new energy unit at the day-ahead stage; determine the carbon trading data and demand response load data of the virtual power plant at the day-ahead stage;

[0016] Determine the operating cost of the virtual power plant in the day-ahead stage and the output plan of each unit in the day-ahead stage based on the output forecast data of the new energy unit in the day-ahead stage, the resource data of each unit in the day-ahead stage, the carbon trading data in the day-ahead stage, the demand response load data and the objective function in the day-ahead stage;

[0017] The acquisition unit is used to acquire the output forecast data and load forecast data of the new energy units in the virtual power plant during the intra-day stage;

[0018] The processing unit is used to determine the resource data of each unit during the intraday stage based on the output forecast data of the new energy unit during the intraday stage; determine the carbon trading data and demand response load data of the virtual power plant during the intraday stage;

[0019] Determine the operating cost of the virtual power plant during the intraday stage and the output plan of each unit during the intraday stage based on the output forecast data of the new energy unit during the intraday stage, the load forecast data, the resource data of each unit during the intraday stage, the carbon trading data during the intraday stage, the demand response load data and the objective function during the intraday stage;

[0020] The virtual power plant is dispatched based on the operating cost of the day-ahead stage and the output plan of each unit in the day-ahead stage, the operating cost of the intraday stage and the output plan of each unit in the intraday stage.

[0021] In a third aspect, an embodiment of the present application provides an electronic device, comprising: a processor and a memory, the processor being connected to the memory, the memory being used to store a computer program, and the processor being used to execute the computer program stored in the memory, so that the electronic device performs the method described in the first aspect.

[0022] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and the computer program enables a computer to execute the method described in the first aspect.

[0023] In a fifth aspect, an embodiment of the present application 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 cause a computer to execute the method described in the first aspect.

[0024] Implementing the embodiments of the present application has the following beneficial effects:

[0025] In an embodiment of the present application, first, the output forecast data of the new energy units of the virtual power plant in the day-ahead stage is obtained. Then, based on the output forecast data of the new energy units in the day-ahead stage, the resource data of each unit in the day-ahead stage can be determined. The carbon trading data and demand response load data of the day-ahead stage are determined. Further, with the goal of minimizing the operating cost of the virtual power plant in the day-ahead stage, the objective function of the day-ahead stage is determined, and based on the output forecast data of the new energy units in the day-ahead stage, the resource data of each unit in the day-ahead stage, the carbon trading data of the day-ahead stage, the demand response load data and the objective function of the day-ahead stage, the operating cost of the virtual power plant in the day-ahead stage and the output plan of each unit in the day-ahead stage are determined. Next, the output forecast data and load forecast data of the new energy units of the virtual power plant in the intraday stage are obtained. Then, based on the output forecast data of the new energy units of the virtual power plant in the intraday stage, the resource data of each unit in the virtual power plant in the intraday stage can be determined. Determine the carbon trading data and demand response load data in the intraday stage. Taking the minimization of the operation cost of the virtual power plant in the intraday stage as the goal, the objective function of the intraday stage is determined. Based on the output forecast data, load forecast data, resource data of each unit in the intraday stage, carbon trading data, demand response load data and the objective function of the intraday stage, the operation cost of the virtual power plant in the intraday stage and the output plan of each unit in the intraday stage are determined. Finally, the virtual power plant is dispatched based on the operation cost of the day-ahead stage and the output plan of each unit in the day-ahead stage, the operation cost of the intraday stage and the output plan of each unit in the day-ahead stage. In this way, the day-ahead scheduling and intraday scheduling of the virtual power plant can be realized. Compared with analyzing the output and load data in a single time scale to determine the output plan of each unit, the output plan determined by scheduling in the two stages of day-ahead and intraday is more accurate, which improves the accuracy of virtual power plant scheduling. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0027] Figure 1 A schematic diagram of the structure of a virtual power plant provided in an embodiment of the present application;

[0028] Figure 2 A schematic diagram of a flow chart of a scheduling method for a virtual power plant provided in an embodiment of the present application;

[0029] Figure 3A schematic diagram of a scheduling device for a virtual power plant provided in an embodiment of the present application;

[0030] Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0031] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0032] The terms "first", "second", "third" and "fourth" etc. in the specification and claims of the present application and the 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.

[0033] Reference to "embodiments" herein means that a particular feature, result, or characteristic described in conjunction with the embodiments 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.

[0034] First, see Figure 1 , Figure 1 A schematic diagram of the structure of a virtual power plant provided in an embodiment of the present application. A virtual power plant is a power coordination management system that participates in the power market and power grid operation as a special power plant through information communication technology and software systems, and is applied to processors, such as central processing units (CPUs), microprocessors (MPUs), etc. It is used to achieve the aggregation and coordinated optimization of resources such as distributed power sources, energy storage systems, controllable loads, and electric vehicles.

[0035] like Figure 1As shown, the virtual power plant includes: a carbon capture power plant, a combined heat and power (CHP) unit, a gas boiler, a hydrogen fuel cell (HFC), an electric boiler, and thermal energy storage, electrical energy storage, gas energy storage, etc. Among them, the virtual power plant also includes a wind turbine for wind power generation; a photovoltaic power station for photovoltaic power generation. The virtual power plant also includes a power-to-gas (P2G) device for converting electricity from each unit into hydrogen. P2G and energy storage systems, as flexible scheduling units of VPP, have good time-shift characteristics and can absorb wind power generation and photovoltaic power generation. Optionally, the P2G device can be a two-stage P2G device, which can generate hydrogen by electrolyzing water in the first stage to provide fuel for the hydrogen fuel cell and store the remaining hydrogen in a hydrogen storage tank. In the second stage, hydrogen is reacted with carbon dioxide to generate natural gas.

[0036] In the embodiment of the present application, the electrical load is mainly provided by CHP, carbon capture power plant, wind turbine, photovoltaic power station, hydrogen fuel cell and electric energy storage discharge. The thermal load is mainly provided by CHP, gas boiler, electric boiler, hydrogen fuel cell and thermal energy storage heat release. The gas load is mainly related to the gas purchase volume from the superior gas grid. Optionally, the virtual power plant may also include an electric refrigeration unit and an absorption chiller to provide cooling load.

[0037] In the embodiment of the present application, the carbon capture power plant includes: a thermal power unit, a carbon capture device and a carbon storage tank. Among them, the thermal power unit is a traditional thermal power unit, part of the electric energy output by the thermal power unit is provided to the carbon capture device, and the other part is used to power other units or devices in the virtual power plant. The thermal power unit is also the main source of carbon emissions. The carbon capture device can capture part of the carbon emissions of the thermal power unit and store it in the carbon storage tank.

[0038] It should be noted that the output of renewable energy is random and volatile. For example, wind power generation and photovoltaic power generation are both affected by the environment, and the output data in each time period are different. Among them, output refers to the power of power generation. Therefore, the output forecast and load forecast of renewable energy are crucial. Based on the output forecast data and load forecast data of renewable energy units, virtual power plants can formulate output plans for each unit to ensure the safe operation of each unit and the power grid. In traditional technical solutions, virtual power plants are usually dispatched using output forecast data and load forecast data within a single time scale, resulting in low accuracy of the formulated output plan.

[0039] To this end, an embodiment of the present application provides a scheduling method for a virtual power plant, wherein the processor obtains output forecast data of new energy units in the virtual power plant in the day-ahead stage; determines the resource data of each unit in the day-ahead stage based on the output forecast data of the new energy units in the day-ahead stage; determines the carbon trading data and demand response load data of the virtual power plant in the day-ahead stage; determines the operating cost of the virtual power plant in the day-ahead stage and the output plan of each unit in the day-ahead stage based on the output forecast data of the new energy units in the day-ahead stage, the resource data of each unit in the day-ahead stage, the carbon trading data, the demand response load data and the objective function of the day-ahead stage; obtains the output forecast data of the new energy units in the virtual power plant in the intraday stage The virtual power plant is dispatched based on the output forecast data of the new energy units in the intraday stage, the resource data of each unit in the intraday stage, the carbon trading data of the virtual power plant in the intraday stage and the demand response load data; the output forecast data of the new energy units in the intraday stage, the load forecast data, the resource data of each unit in the intraday stage, the carbon trading data of the intraday stage, the demand response load data and the objective function of the intraday stage, the operating cost of the virtual power plant in the intraday stage and the output plan of each unit in the intraday stage; the virtual power plant is dispatched based on the operating cost in the day-ahead stage and the output plan of each unit in the day-ahead stage, the operating cost in the intraday stage and the output plan of each unit in the intraday stage.

[0040] It can be seen that the application of the above virtual power plant can realize the dispatch of multiple energy generation to ensure the safe operation of each unit and the power grid. In addition, the output forecast data of the new energy unit in the day-ahead stage can be used to determine the output plan of each unit in the day-ahead stage, and the output forecast data and load forecast data of the new energy unit in the day-ahead stage can be used to determine the output plan of each unit in the day-ahead stage, so as to realize the dispatch of the virtual power plant in the day-ahead and day-ahead stages and improve the accuracy of the dispatch.

[0041] See also Figure 2 , Figure 2 The present invention provides a flow chart of a virtual power plant scheduling method. The method is applied to a scheduling device of a virtual power plant, and the scheduling device of the virtual power plant can be a processor. Figure 2 As shown, the method includes but is not limited to the following steps:

[0042] 201: Obtain output forecast data of new energy units in the virtual power plant in the day-ahead phase.

[0043] In the embodiment of the present application, the output prediction data of the new energy units in the day-ahead stage includes: the output prediction value of the wind turbine units in the day-ahead stage, the output prediction value of the photovoltaic power station in the day-ahead stage, etc. Among them, the processor mainly obtains the output prediction data of the new energy units in the day-ahead stage, so as to formulate the output plan of each unit according to the output prediction data of the new energy in the day-ahead stage.

[0044] It should be noted that before obtaining the output forecast data of the new energy units in the virtual power plant in the day-ahead stage, it is necessary to first build the following Figure 1 The virtual power plant shown in the figure. By obtaining the operation function of the resource data of each unit of the virtual power plant and reading the operation parameters in the external file, the output forecast data of the new energy units in the virtual power plant in the day-ahead stage can be obtained.

[0045] 202: Based on the output forecast data of the new energy units in the day-ahead stage, determine the resource data of each unit in the day-ahead stage; determine the carbon trading data and demand response load data of the virtual power plant in the day-ahead stage.

[0046] In the embodiment of the present application, resource data includes: carbon capture amount, carbon storage amount, power generation, carbon emissions, hydrogen production power, heating power, natural gas power, etc. The specific resource data of each unit is determined by the function of each unit. Carbon trading data includes: carbon emission quota, actual carbon emissions, and carbon emission amount referenced to the carbon emission market. Demand response load data includes: electrical load before demand response and electrical load after demand response.

[0047] Specifically, based on the output forecast data and actual output data of the new energy units in the day-ahead stage, the output constraints of each unit can be established, so as to determine the resource data of each unit in the day-ahead stage based on the output forecast data and actual output data of the new energy units in the day-ahead stage and the output constraints of each unit.

[0048] Exemplarily, based on the output forecast data of the new energy units in the day-ahead stage, the resource data of each unit in the day-ahead stage is determined, including:

[0049] Based on the energy supply of the new energy unit in the day-ahead stage, determine the actual output data of the new energy unit in the day-ahead stage;

[0050] Based on the output forecast data and actual output data of the new energy generating units in the day-ahead phase, the output constraints of the new energy generating units in the day-ahead phase are determined;

[0051] Based on the output constraints of the new energy units in the day-ahead phase, the resource data of each unit in the day-ahead phase is determined.

[0052] In the embodiment of the present application, the energy supply of the new energy unit in the day-ahead stage includes the energy supply of the new energy unit at each moment in the day-ahead stage. The output constraints of the new energy unit include: wind turbine output constraints, photovoltaic power station output constraints and thermal power unit output constraints.

[0053] Specifically, the processor can determine the actual output data of the new energy generator set at each moment in the day-ahead stage based on the energy supply of the new energy generator set in the day-ahead stage, including the energy supply of the new energy generator set at each moment in the day-ahead stage, and determine the output constraint of the new energy generator set in the day-ahead stage based on the output prediction data and actual output data of the new energy generator set at each moment in the day-ahead stage. The actual output data of the wind turbine generator set at each moment in the day-ahead stage can be expressed by formula (1), and the output constraint of the wind turbine generator set in the day-ahead stage can be expressed by formula (2):

[0054]

[0055] In formula (1), It represents the actual output data of the wind turbine at time t in the day-ahead phase. It represents the energy supplied by the wind turbine to the virtual power plant at time t in the day-ahead phase. is the energy supplied by the wind turbine to the P2G device at time t. In formula (2), It represents the output forecast data of the wind turbine at time t in the day-ahead phase. As can be seen from formula (1), the processor can determine the actual output data of the wind turbine at each moment in the day-ahead phase by the energy supplied by the wind turbine to the virtual power plant and the P2G device at each moment in the day-ahead phase. Therefore, based on the actual output data and output forecast data of the wind turbine at each moment in the day-ahead phase, the output constraint of formula (2) is established.

[0056] For example, the actual output data of the photovoltaic power station at each moment in the day-ahead phase can be expressed by formula (3), and the output constraint of the photovoltaic power station in the day-ahead phase can be expressed by formula (4):

[0057]

[0058]

[0059] In formula (3), It represents the actual output data of the photovoltaic power station at time t in the day-ahead phase. It indicates the energy supplied by the photovoltaic power station to the P2G device at time t in the day-ahead phase. represents the energy supplied by the photovoltaic power station to the virtual power plant at time t in the day-ahead phase. In formula (4), It represents the output forecast data of the photovoltaic power station at time t in the day-ahead phase. As can be seen from formula (3), the processor can determine the actual output data of the photovoltaic power station at each moment in the day-ahead phase by the energy supplied by the photovoltaic power station to the virtual power plant and the P2G device at each moment in the day-ahead phase. Therefore, based on the actual output data and output forecast data of the photovoltaic power station at each moment in the day-ahead phase, the output constraint of formula (4) is established.

[0060] Then, based on the output constraints of the new energy units in the day-ahead phase, the resource data of each unit in the day-ahead phase can be determined. Thus, the processor can determine the actual output data of the new energy units in the day-ahead phase through the energy supply of the new energy units in the day-ahead phase, and determine the output constraints of the new energy units in the day-ahead phase based on the actual output data and output forecast data of the new energy units in the day-ahead phase, thereby determining the resource data of each unit in the day-ahead phase, thereby improving the accuracy of the output forecast of the new energy units.

[0061] Specifically, in a carbon capture power plant, resource data includes: energy consumption of the carbon capture device, carbon capture amount, carbon storage amount of the carbon storage tank, etc. The carbon capture power plant consists of a thermal power unit, a carbon capture device, and a carbon storage tank. The power of the carbon capture device is mainly provided by the thermal power unit. Part of the carbon dioxide captured by the carbon capture device is stored in the carbon storage tank, and the other part is provided to the P2G device.

[0062] The processor may determine the mass of carbon dioxide captured by the carbon capture device at each moment of the day-ahead phase based on the mass of carbon dioxide generated by the thermal power unit at each moment of the day-ahead phase. Thus, based on the mass of carbon dioxide captured by the carbon capture device at each moment of the day-ahead phase, the energy consumption of the carbon capture device at each moment of the day-ahead phase is determined. The energy consumption of the carbon capture device at each moment of the day-ahead phase can be expressed by formula (5):

[0063]

[0064] In formula (5), represents the operating energy consumption of the carbon capture device at time t in the day-ahead phase. s It is the energy consumption for capturing unit mass of carbon dioxide. It represents the mass of carbon dioxide captured by the carbon capture device at time t in the day-ahead phase. represents the energy consumption of the carbon capture device at time t in the day-ahead phase. P0 represents the fixed energy consumption of the carbon capture device at time t in the day-ahead phase. The mass of carbon dioxide captured by the carbon capture device at time t in the day-ahead phase can be calculated by the mass of carbon dioxide produced by the thermal power unit at time t in the day-ahead phase, as shown in formula (6):

[0065]

[0066] In formula (6), It represents the mass of carbon dioxide produced by the thermal power unit at time t in the day-ahead phase. η c is the capture efficiency of the carbon capture device. Thus, based on the mass of carbon dioxide generated by the thermal power unit at time t in the day-ahead phase, the mass of carbon dioxide captured by the carbon capture device at time t in the day-ahead phase can be determined. Based on the mass of carbon dioxide generated by the thermal power unit at each moment in the day-ahead phase, the mass of carbon dioxide captured by the carbon capture device at each moment in the day-ahead phase can be determined. Thus, based on the mass of carbon dioxide captured by the carbon capture device at each moment in the day-ahead phase, the energy consumption of the carbon capture device at each moment in the day-ahead phase can be determined.

[0067] Exemplarily, the carbon storage tank is a carbon storage device in the embodiment of the present application, which is used to achieve the decoupling of the carbon capture power plant and the P2G device. The carbon storage amount at time t in the day-ahead phase can be expressed by formula (7):

[0068]

[0069] In formula (7), Represents the carbon storage capacity of the carbon storage tank at time t in the day-ahead phase. CS Indicates the carbon storage loss rate of the carbon storage tank. η CSC , η CSD They respectively represent the carbon filling efficiency and carbon release efficiency of the carbon storage tank. They respectively represent the carbon filling power and carbon releasing power of the carbon storage tank at time t in the day-ahead stage. Respectively represent the state variables of carbon filling and carbon release of the carbon storage tank, with a value of 0 or 1, 0 represents carbon filling, and 1 represents carbon release. CS,min , S CS,max are the minimum and maximum values ​​of the carbon storage tank capacity respectively; P CSC,max , P CSD,max They are the upper limits of carbon filling and carbon discharge in the carbon storage tank respectively.

[0070] The carbon storage capacity of the carbon storage tank at each moment in the day-ahead phase is the sum of the remaining carbon storage capacity lost at the previous moment and the mass of carbon dioxide generated at each moment, minus the mass of carbon dioxide released. Thus, the carbon storage capacity of the carbon capture power plant at each moment in the day-ahead phase can be determined.

[0071] For example, thermal power units are the main source of carbon emissions, and thermal power units also serve as the energy source for carbon capture devices. Part of the electricity generated by the thermal power units is supplied to the carbon capture device, and the other part is used to supply power to other units or devices in the virtual power plant. The power generation power of the thermal power units can be expressed by formula (8):

[0072]

[0073] In formula (8), It represents the power generation of the thermal power unit at time t in the day-ahead phase. represents the electricity provided by the carbon capture power plant to the virtual power plant at time t in the day-ahead phase. g Represents the actual carbon emissions generated by the unit power supply of the thermal power unit. Therefore, the energy consumption of the carbon capture device at time t in the day-ahead phase and the electric energy provided by the carbon capture power plant to the virtual power plant at time t in the day-ahead phase can determine the power generation of the thermal power unit at time t in the day-ahead phase. Based on the electric energy provided by the carbon capture power plant to the virtual power plant at time t in the day-ahead phase, the mass of carbon dioxide generated by the thermal power unit at time t in the day-ahead phase is determined to obtain the resource data of the thermal power unit at each moment in the day-ahead phase.

[0074] For example, the output constraint of a thermal power unit can be expressed by formula (9):

[0075]

[0076] in, and They respectively represent the maximum and minimum values ​​of the power output of the thermal power unit. and They respectively represent the maximum and minimum values ​​of the climbing output of the thermal power unit.

[0077] For example, the energy utilization efficiency in the actual power-to-gas process in the P2G device is low, so the present invention divides P2G into two stages, namely, power-to-hydrogen and methanation. The power-to-hydrogen (P2H) and methanation stages can be expressed by formula (10):

[0078]

[0079] In formula (10), It represents the electric energy consumed by the P2G device to produce hydrogen at time t in the day-ahead phase. It represents the power of hydrogen generated by the P2G device at time t in the day-ahead phase. η P2H Indicates the conversion efficiency of electricity to hydrogen; It represents the power consumed by the P2G device for methanogenesis at time t during the day-ahead phase. It represents the gas power generated by methanogenesis of the P2G device at time t in the day-ahead phase. H2G Indicates the efficiency of methanogenesis. They respectively represent the hydrogen energy provided by electro-hydrogen conversion and the hydrogen energy provided by the hydrogen storage device at time t in the day-ahead stage. Indicates the hydrogen energy provided by the P2G device, Indicates the efficiency of the hydrogen storage tank in storing the input hydrogen.

[0080] During the methanogenesis process, the volume of carbon dioxide consumed by the P2G device is equal to the volume of carbon dioxide generated. Therefore, the equilibrium constraint of the methanogenesis stage can be obtained, as shown in formula (11):

[0081]

[0082] In formula (11), represents the mass of carbon dioxide consumed by methanogenesis at time t in the day-ahead phase, is the density of carbon dioxide, represents the heat of methane, q e Indicates the heat converted per unit of electrical energy. represents the mass of carbon dioxide stored in the carbon storage tank by the P2G device at time t in the day-ahead phase. Therefore, under the balance constraint of formula (10), the processor can determine the electric energy consumed by the P2G device to produce hydrogen at each moment in the day-ahead phase, the power of the P2G device to produce hydrogen at each moment in the day-ahead phase, the power of the P2G device to methanogenize hydrogen at each moment in the day-ahead phase, the gas power generated by the P2G device at each moment in the day-ahead phase, etc., through the electric-to-hydrogen and methanogenization processes shown in formula (9), that is, determine the resource data of the P2G device in the day-ahead phase.

[0083] For example, hydrogen fuel cells (HFC) can generate electricity and heat by consuming hydrogen generated by P2G, generating power generation and heating. In addition, the heat and electricity conversion efficiency of HFC is adjustable and has strong flexibility. Combining with P2G devices can reduce operating costs and achieve a low-carbon economy. Among them, the power generation and heating of HFC can be expressed by formula (12):

[0084]

[0085] In formula (12), They represent the power generation and heating of HFC at time t in the day-ahead phase. HFC,e , η HFC,h They represent the power generation efficiency and heat generation efficiency of HFC respectively; It represents the power of hydrogen input into HFC at time t in the day-ahead phase; They represent the maximum and minimum power of HFC input hydrogen respectively. They represent the maximum and minimum values ​​of the hydrogen energy ramping power input into HFC respectively. HFC,max , k HFC,min They represent the maximum and minimum values ​​of the HFC power-to-heat ratio respectively. They respectively represent the hydrogen energy provided by the hydrogen storage tank and the hydrogen energy provided by the P2G device.

[0086] Thus, the processor determines the power of hydrogen input to the HFC at time t in the day-ahead stage based on the hydrogen energy provided by the hydrogen storage device and the hydrogen energy provided by the P2G device. Then, the power generation power of the HFC is determined based on the power of hydrogen input to the HFC at time t in the day-ahead stage and the power generation efficiency of the HFC. The heat generation power of the HFC is determined based on the power of hydrogen input to the HFC at time t in the day-ahead stage and the heat generation efficiency of the HFC.

[0087] In the embodiment of the present application, there may be hydrogen loss in the hydrogen storage tube. Therefore, when determining the hydrogen stored in the hydrogen storage tank, the hydrogen loss needs to be considered, as shown in formula (13):

[0088]

[0089] In formula (13), It represents the mass of hydrogen stored in the hydrogen storage tank at time t in the day-ahead phase. Indicates the loss rate of hydrogen stored in the hydrogen storage tank. They respectively represent the efficiency of the hydrogen storage tank in storing input hydrogen and producing hydrogen. They respectively represent the power of hydrogen input and hydrogen generation by the gas storage at time t in the day-ahead stage. They represent the state variables of hydrogen input and hydrogen production respectively, and their values ​​are 0 or 1, 0 represents hydrogen input, and 1 represents hydrogen production. They respectively represent the minimum and maximum capacity of the hydrogen storage tank. They represent the maximum value of hydrogen input and the maximum value of hydrogen produced by the hydrogen storage tank, respectively. It can be seen that the mass of hydrogen stored in the hydrogen storage tank at time t in the previous stage is equal to the sum of the mass of hydrogen remaining in the previous moment and the mass of hydrogen input to the hydrogen storage tank at time t in the previous stage, minus the mass of hydrogen produced.

[0090] For example, the CHP unit mainly includes a gas turbine, and the resource data of the CHP is the power generation power and heating power of the CHP unit at each moment in the day-ahead phase. The power generation power and heating power of the CHP unit at each moment in the day-ahead phase can be expressed by formula (14):

[0091]

[0092] In formula (14), They represent the power generation and heating output of the CHP unit at time t in the day-ahead phase. e , η h They represent the power generation efficiency and heating efficiency of the CHP unit respectively. It represents the power of natural gas input to the CHP unit at time t in the day-ahead phase. They represent the maximum and minimum power generation capacity of the CHP unit respectively. They respectively represent the maximum and minimum values ​​of the heating power of the CHP unit. They respectively represent the upper and lower limits of the ramp of the natural gas input to the CHP unit.

[0093] Thus, the processing can determine the power of natural gas input to the CHP unit at time t in the day-ahead phase, and determine the power output of the CHP unit at time t in the day-ahead phase based on the power of natural gas input to the CHP unit at time t in the day-ahead phase and the power generation efficiency of the CHP unit. Based on the power of natural gas input to the CHP unit at time t in the day-ahead phase and the heating efficiency of the CHP unit, determine the heating power output of the CHP unit at time t in the day-ahead phase. Thus, the resource data of the CHP unit is determined.

[0094] In the embodiment of the present application, the gas boiler is used to provide thermal power for the virtual power plant. The resource data of the gas boiler is the thermal power output by the gas boiler at each moment in the day-ahead phase, as shown in formula (15):

[0095]

[0096] In formula (15), It represents the thermal power output of the gas boiler at time t in the day-ahead phase. It represents the power of natural gas input to the gas boiler at time t in the day-ahead phase. η GB.h It is the conversion efficiency of thermal power of gas boiler. They respectively represent the maximum and minimum values ​​of the thermal power output of the gas boiler. They respectively represent the upper and lower limits of the natural gas input into the gas boiler.

[0097] Therefore, the processor can determine the thermal power output by the gas boiler at time t in the day-ahead stage through the power of the natural gas input to the gas boiler at time t in the day-ahead stage and the conversion efficiency of the thermal power of the gas boiler, thereby determining the thermal power provided by the gas boiler to the virtual power plant in the day-ahead stage, that is, the resource data of the gas boiler.

[0098] For example, the electric boiler can also output thermal power to provide thermal power for the virtual power plant. The thermal power output of the electric boiler in the day-ahead phase can be expressed by formula (16):

[0099]

[0100] In formula (16), It represents the thermal power output by the electric boiler at time t in the day-ahead phase. It represents the electric power input to the electric boiler at time t in the day-ahead phase. η EB.h Indicates the conversion efficiency of thermal power of electric boiler. They respectively represent the maximum and minimum values ​​of the thermal power output of the electric boiler. They respectively represent the upper and lower limits of the natural gas ramp input to the electric boiler.

[0101] Therefore, the processor can determine the thermal power output by the electric boiler at time t in the day-ahead phase, that is, the resource data of the electric boiler, based on the electric power input by the electric boiler at time t in the day-ahead phase and the conversion efficiency of the thermal power of the electric boiler.

[0102] For example, the electrical energy storage, thermal energy storage and gas energy storage in the embodiments of the present application can all be expressed by formula (17):

[0103]

[0104] In formula (17), It represents the storage capacity of electric energy storage, thermal energy storage or gas energy storage at time t in the day-ahead phase. n Indicates the self-loss rate of electrical energy storage, thermal energy storage or gas energy storage. η nc , η nd Respectively represent the charging and discharging efficiency of electrical energy storage, thermal energy storage or gas energy storage. They respectively represent the charging and discharging power of electric energy storage, thermal energy storage or gas energy storage at time t in the day-ahead stage. Respectively represent the state variables of charging and discharging of electric energy storage, thermal energy storage or gas energy storage at time t in the day-ahead phase, with a value of 0 or 1, 0 for charging and 1 for discharging. n.min , S n.max Represents the minimum and maximum values ​​of electrical, thermal or gas storage capacity, respectively. They represent the maximum value of the charge and discharge of electric energy storage, thermal energy storage or gas energy storage respectively. It can be seen that the storage capacity of electric energy storage, thermal energy storage or gas energy storage at each moment in the day-ahead stage is equal to the sum of the remaining storage capacity at the previous moment and the charge capacity of the energy storage, minus the release capacity of the energy storage. Therefore, the storage capacity of electric energy storage, thermal energy storage or gas energy storage at each moment in the day-ahead stage can be determined.

[0105] Based on this, the processor can determine the resource data of each unit in the day-ahead phase. Then, the processor will determine the carbon trading data and demand response data of the virtual power plant in the day-ahead phase.

[0106] Exemplarily, determining carbon trading data of a virtual power plant during the intraday period may include:

[0107] Determine the carbon emission quota for the day-ahead phase based on the power generation and heating power of the cogeneration unit, the power generation of the thermal power unit, and the heating power of the gas boiler.

[0108] Determine the actual carbon emissions during the day-ahead period based on the power generation and heating power of the cogeneration unit during the day-ahead period, the heating power of the gas boiler during the day-ahead period, the carbon emissions of the carbon capture power plant during the day-ahead period, and the amount of electricity purchased;

[0109] The carbon trading data for the day-ahead period is determined based on the carbon emission quota for the day-ahead period and the actual carbon emissions for the day-ahead period.

[0110] In the embodiment of the present application, the carbon emission quota of the virtual power plant in the day-ahead stage mainly includes the electricity purchased from the power grid by CHP, gas boiler and carbon capture power plant. The carbon emission quota can be expressed by formula (18):

[0111]

[0112] In formula (18), E' VPP.t It represents the carbon emission quota of the virtual power plant at time t in the day-ahead phase. Indicates the carbon emission quota obtained by CHP per unit of power supply. Indicates the carbon emission quota obtained by CHP per unit of heating. It represents the carbon emission quota obtained by the thermal power units in the carbon capture power plant per unit of power supply. Indicates the carbon emissions of a gas boiler per unit of heating.

[0113] Specifically, the processor determines the power generation and heating power of the CHP at time t in the day-ahead stage, and based on the carbon emission quota obtained by the CHP in the unit power supply and the power generation of the CHP at time t in the day-ahead stage, the carbon emission quota obtained by the power supply of the CHP at time t in the day-ahead stage can be determined. Based on the carbon emission quota obtained by the CHP in the unit power supply and the heating power of the CHP at time t in the day-ahead stage, the carbon emission quota obtained by the heat supply of the CHP at time t in the day-ahead stage can be determined. Then, the power generation of the thermal power unit at time t in the day-ahead stage is obtained, and based on the power generation of the thermal power unit at time t in the day-ahead stage and the carbon emission quota obtained by the thermal power unit in the unit power supply, the carbon emission quota obtained by the power supply of the thermal power unit at time t in the day-ahead stage is determined. Next, the thermal power outputted by the gas boiler at time t in the day-ahead stage is obtained, and based on the carbon emission quota obtained by the gas boiler in the unit heat supply and the thermal power outputted by the gas boiler at time t in the day-ahead stage, the carbon emission quota obtained by the gas boiler in the heat supply at time t in the day-ahead stage is determined. Therefore, the sum of the carbon emission quotas obtained from power supply and heat supply of each unit will be used as the carbon emission quota of the virtual power plant.

[0114] It should be noted that the carbon capture device in the carbon capture power plant absorbs part of the carbon dioxide produced by the thermal power unit. Therefore, the actual carbon emissions of the virtual power plant in the day-ahead phase are not equal to the carbon emission quota. The actual carbon emissions of the virtual power plant at each moment in the day-ahead phase can be expressed by formula (19):

[0115]

[0116] In formula (19), E VPP.t It represents the actual carbon emissions of the virtual power plant at time t in the day-ahead phase. Indicates the actual carbon emissions per unit of electricity generated by CHP. Indicates the actual carbon emissions per unit of heat generated by CHP. Represents the actual carbon emissions from the carbon capture plant. Indicates the actual carbon emissions per unit of heating provided by a gas boiler. Represents the carbon emissions per unit of electricity purchased from the grid. It represents the electric power purchased by the virtual power plant from the grid at time t in the day-ahead phase.

[0117] Specifically, the processor may determine the actual carbon emissions of the CHP power generation at time t in the day-ahead phase based on the power generation of the CHP at time t in the day-ahead phase and the actual carbon emissions per unit of power generation of the CHP. Based on the heat generation power of the CHP at time t in the day-ahead phase and the actual carbon emissions per unit of heat generation of the CHP, the actual carbon emissions of the heat generation of the CHP at time t in the day-ahead phase are determined. Based on the thermal power output of the gas boiler at time t in the day-ahead phase and the actual carbon emissions per unit of heat supply of the gas boiler, the actual carbon emissions of the heat supply of the gas boiler at time t in the day-ahead phase are determined. Based on the carbon emissions per unit of electric energy purchased from the power grid and the electric power purchased by the virtual power plant from the power grid at time t in the day-ahead phase, the actual carbon emissions purchased at time t in the day-ahead phase are determined. Finally, the actual carbon emissions of the CHP power generation at time t in the day-ahead phase, the actual carbon emissions of the heat generation, the actual carbon emissions of the gas boiler for heat supply at time t in the day-ahead phase, the actual carbon emissions purchased at time t in the day-ahead phase, and the actual carbon emissions of the carbon capture power plant can be used to determine the actual carbon emissions at time t in the day-ahead phase.

[0118] Finally, based on the carbon emission quota in the day-ahead stage and the actual carbon emissions in the day-ahead stage, the carbon trading data in the day-ahead stage can be determined, that is, the carbon emission amount participating in the electricity trading market and the carbon emission amount participating in the electricity trading market. It can be expressed by formula (20):

[0119]

[0120] In the embodiment of the present application, the carbon trading data also includes the carbon trading cost. The carbon trading cost at each moment in the day-ahead phase is determined by the carbon emissions of the participating power trading market at that moment. The carbon trading cost at each moment in the day-ahead phase can be expressed by formula (21):

[0121]

[0122] In formula (21), represents the carbon trading cost at time t in the day-ahead phase. A carbon trading cost greater than 0 indicates the purchase of carbon emission allowances, and a carbon trading cost less than 0 indicates the sale of carbon emission allowances. β represents the carbon trading base price. γ represents the carbon trading growth price. h represents the length of the carbon emission interval. ω represents the carbon trading compensation price. It can be seen from formula (21) that the growth of carbon trading costs is distributed in a step-like manner. The more carbon emission allowances purchased, the higher the carbon trading cost expenditure per unit of carbon emission. The more carbon emission allowances sold, the higher the carbon trading cost. In this way, electricity users can be encouraged to reduce carbon emissions.

[0123] It should be noted that in the process of interaction with the electricity and gas grids, interaction constraints with the electricity and gas grids need to be established, as shown in formula (22):

[0124]

[0125] In formula (22), It represents the electric power purchased by the virtual power plant from the large power grid at time t in the day-ahead phase. Indicates the maximum value of electric power purchased by the virtual power plant from the large power grid. It represents the gas power purchased by the virtual power plant from the gas grid at time t in the day-ahead phase. Indicates the maximum value of gas power purchased by the virtual power plant from the gas grid.

[0126] It can be seen that the processor can determine the carbon emission quota for the day-ahead stage based on the power generation and heating power of the CHP in the day-ahead stage, the power generation and heating power of the thermal power unit in the day-ahead stage, and the heating power of the gas boiler in the day-ahead stage. Then, the actual carbon emissions for the day-ahead stage are determined based on the power generation and heating power of the CHP in the day-ahead stage, the heating power of the gas boiler in the day-ahead stage, the carbon emissions of the carbon capture power plant in the day-ahead stage, and the amount of electricity purchased. Finally, based on the carbon emission quota and actual carbon emissions in the day-ahead stage, the carbon trading data for the day-ahead stage can be determined, so that the operating cost of the virtual power plant in the day-ahead stage can be determined through the carbon trading data, so as to determine the output plan of each unit in the day-ahead stage, thereby improving the accuracy of the scheduling of the virtual power plant.

[0127] Then, the processor needs to determine the demand response load data of the virtual power plant in the day-ahead phase. Exemplarily, determining the demand response load data of the virtual power plant in the day-ahead phase may include:

[0128] Based on the electricity load of the virtual power plant in the day-ahead stage, determine the electricity price elasticity coefficient at each moment in the day-ahead stage;

[0129] The elasticity coefficients at each moment in the day-ahead phase are concatenated to obtain the electricity price elasticity coefficient matrix;

[0130] Based on the electric load and electricity price elasticity coefficient matrix of the virtual power plant in the day-ahead stage, the demand response load data of the day-ahead stage is determined.

[0131] In an embodiment of the present application, the demand response may be an electricity price-based demand response. For example, by adjusting the electricity price at each moment, electricity users adjust their electricity load, thereby adjusting the electricity load of the power grid to improve the safety and stability of power grid use.

[0132] Specifically, the processor can determine the electricity price elasticity coefficient at each moment in the day-ahead phase through the electricity load at each moment in the day-ahead phase and the change in the electricity load after the demand response. The electricity price elasticity coefficient is expressed by formula (23):

[0133]

[0134] In formula (23), It represents the elasticity coefficient of the virtual power plant's electric load at time t and the electricity price at time i in the day-ahead stage, and is located in the tth row and ith column of the electricity price elasticity coefficient matrix E. It represents the change in electric load after and before the demand response at time t in the day-ahead phase. t represents the electric load of the virtual power plant before the demand response at time t in the day-ahead phase. Δp i Indicates the change in electricity price at time t in the day-ahead phase. i represents the original electricity price at time t in the day-ahead period.

[0135] Thus, the processor can determine the price elasticity coefficient at each moment of the day-ahead stage based on the change in the electric load of the virtual power plant after and before the demand response at each moment of the day-ahead stage, the electric load before the demand response, the change in the electricity price, and the original electricity price. Then, the price elasticity coefficients at each moment are concatenated to obtain the price elasticity coefficient matrix E. Finally, based on the price elasticity coefficient matrix and the electric load of the virtual power plant at the day-ahead stage, the demand response load data of the day-ahead stage can be determined. Among them, the demand response load data can be the electric load of the virtual power plant after the demand response, and the demand response load data at each moment of the day-ahead stage can be expressed by formula (24):

[0136]

[0137] In formula (24), Represents the electric load of the virtual power plant after the demand response at time t in the day-ahead phase. The processor can determine the change in electric load after the demand response at each moment based on the original electricity price at each moment in the day-ahead phase, the change in electricity price at each moment, the electric load of the virtual power plant before the demand response at each moment, and the electricity price elasticity coefficient matrix. Then, the difference between the electric load of the virtual power plant before the demand response at each moment and the change in electric load after the demand response at each moment is taken as the electric load of the virtual power plant after the demand response at that moment.

[0138] It can be seen that in the embodiment of the present application, the processor can determine the electricity price elasticity coefficient at each moment in the day-ahead stage based on the electricity load of the virtual power plant in the day-ahead stage. Then, the elasticity coefficients at each moment in the day-ahead stage are spliced ​​to obtain the electricity price elasticity coefficient matrix. Finally, based on the electricity load of the virtual power plant in the day-ahead stage and the electricity price elasticity coefficient matrix, the demand response load data for the day-ahead stage is determined. Formulating the output plan of each unit in the day-ahead stage based on the demand response load data can improve the accuracy of the output plan of each unit.

[0139] 203: Based on the output forecast data of the new energy units in the day-ahead stage, the resource data of each unit in the day-ahead stage, the carbon trading data in the day-ahead stage, the demand response load data and the objective function in the day-ahead stage, determine the operating cost of the virtual power plant in the day-ahead stage and the output plan of each unit in the day-ahead stage.

[0140] In the embodiment of the present application, the day-ahead phase is mainly based on a time interval of 1 hour. The objective function of the day-ahead phase is established with the operating cost of the virtual power plant as the target.

[0141] Exemplarily, based on the output forecast data of the new energy units in the day-ahead stage, the resource data of each unit in the day-ahead stage, the carbon trading data in the day-ahead stage, the demand response load data and the objective function in the day-ahead stage, the operating cost of the virtual power plant in the day-ahead stage and the output plan of each unit in the day-ahead stage are determined, for example, including:

[0142] Based on the resource data of each unit in the day-ahead phase and the demand response data in the day-ahead phase, the balance constraint conditions in the day-ahead phase are established;

[0143] Determine the predicted operating cost of the day-ahead phase based on the carbon trading data of the day-ahead phase, the resource data of each unit in the day-ahead phase, and the objective function of the day-ahead phase;

[0144] Based on the output forecast data of the new energy units in the day-ahead stage, the output deviation coefficient of each unit and the predicted operating cost, the operating cost of the day-ahead stage and the output plan of each unit in the day-ahead stage are obtained.

[0145] In the embodiment of the present application, the balance constraints in the day-ahead stage include: electric power balance constraints, gas power balance constraints, and thermal power balance constraints. The output deviation coefficient of each unit can be pre-set based on the actual output plan. Specifically, based on the resource data of each unit in the day-ahead stage and the demand response data of the day-ahead stage, the electric power balance constraints in the day-ahead stage established can be expressed by formula (25), the thermal power balance constraints can be expressed by formula (26), and the gas power balance constraints can be expressed by formula (27):

[0146]

[0147] Among them, H t It represents the thermal power demand of the virtual power plant at time t in the day-ahead phase. It represents the gas power demand of the virtual power plant at time t in the day-ahead phase. represents the power demand of the virtual power plant at time t in the day-ahead phase. Therefore, based on the resource data of each unit in the day-ahead phase and the demand response data in the day-ahead phase, the above-mentioned balance constraint conditions in the day-ahead phase can be established. Then, based on the pre-established objective function, the predicted operating cost in the day-ahead phase can be determined.

[0148] In the embodiment of the present application, the objective function of the day-ahead stage can be expressed by formula (28):

[0149]

[0150] Among them, f RQ represents the operating cost of the virtual power plant in the day-ahead phase. e.buy 、f g.buy They represent the electricity purchase cost and gas purchase cost of the virtual power plant in the day-ahead stage. G represents the fuel cost of the carbon capture power plant in the day-ahead period. represents the carbon trading cost of the virtual power plant in the day-ahead stage. For example, the gas purchase cost, electricity purchase cost, fuel cost of the carbon capture power plant and carbon trading cost in the day-ahead stage are respectively expressed by formula (29), formula (30), formula (31) and formula (32):

[0151]

[0152] in, It represents the gas purchase price of the virtual power plant at time t in the day-ahead phase. represents the electricity purchase price of the virtual power plant at time t in the day-ahead phase. a, b, c are cost fuel coefficients, which can be pre-set based on the actual fuel cost. Therefore, the processor takes the minimum operating cost in the day-ahead phase as the goal, and determines the predicted operating cost in the day-ahead phase by determining the carbon trading cost in the day-ahead phase, the fuel cost of the carbon capture power plant, the electricity purchase cost in the day-ahead phase, and the gas purchase cost.

[0153] Furthermore, in the embodiment of the present application, the objective function of the day-ahead stage can also be optimized by the Information Gap Decision Theory (IGDT) model. The IGDT model can be divided into a robustness model (RM) and an opportunity model (OM). Since RM can avoid the adverse effects of uncertainty on the virtual power plant VPP, the present invention uses RM to optimize the objective function of the VPP day-ahead stage to analyze the uncertainty of renewable energy generation and reduce the impact of the volatility of renewable energy generation on the power system. The optimization model of VPP can be expressed by formula (33):

[0154]

[0155] In formula (33), f(X, k) represents the objective function of VPP in the day-ahead stage. h(X, k) represents the balance constraint in the day-ahead stage. g(X, k) represents the imbalance constraint in the day-ahead stage. X is a decision variable. k represents an uncertain variable. Specifically, the optimization of the objective function by RM can be expressed by formula (34):

[0156]

[0157] in, represents the fluctuation space of the uncertain variable k. σ represents the deviation factor of the objective function. ψ represents the deviation coefficient of the uncertain variable k. represents the predicted value of the uncertain variable. f0 represents the initial value of the objective function, which can be pre-set based on the actual operating cost. c It indicates the maximum acceptable operating cost under the condition of uncertain variables.

[0158] Therefore, the processor predicts the uncertain variables and continuously adjusts the uncertain variable k in the fluctuation space based on the predicted value of the uncertain variables to determine the predicted operating cost of the day-ahead stage. It should be noted that since the IGDT model is a two-layer model and the fluctuations of the wind turbine output and the photovoltaic power station output are within the information gap interval, the less the wind turbine output and the photovoltaic power station output, the stronger the robustness of the IGDT model. Therefore, in order to improve the computing efficiency of the processor, formula (34) can be simplified to formula (35):

[0159]

[0160] in, and They represent the output forecast data of wind turbines and photovoltaic power stations at time t in the day-ahead stage. w Indicates the output deviation coefficient of the wind turbine. ψ v It indicates the output deviation coefficient of the photovoltaic power station. It can be seen from formula (35) that when the output deviation coefficient of each unit is the maximum value, the processor can predict the power output of each unit at each moment in the day-ahead stage based on the output prediction data and output deviation coefficient of each unit at each moment in the day-ahead stage, thereby continuously adjusting the balance constraint conditions and unbalance constraint conditions in the day-ahead stage to optimize the objective function of the day-ahead stage.

[0161] It should be noted that since the IGDT model can only solve a single uncertain variable, it is necessary to simplify multiple uncertain variables. Specifically, multiple uncertain variables are first normalized, and then the normalized uncertain variables are assigned weight coefficients for simplex processing. The normalization and simplex methods for multiple uncertain variables are respectively expressed by formula (36) and formula (37):

[0162]

[0163]

[0164] Among them, ψ w,wor Represents the robust deviation coefficient of IGDT solution when the wind turbine is an uncertain variable. ψ w,opt It represents the chance deviation coefficient of IGDT solution when the wind turbine is an uncertain variable. v,wor It represents the robust deviation coefficient of IGDT solution when the output of photovoltaic power station is an uncertain variable. v,opt It represents the chance deviation coefficient of IGDT solution when the output of photovoltaic power station is an uncertain variable. ξ w , v express and The corresponding weight coefficient.

[0165] Therefore, through the output deviation coefficient of each unit and the output forecast data of the new energy units in the day-ahead stage, the predicted operating cost of the day-ahead stage can be optimized, so as to obtain the operating cost of the day-ahead stage, and formulate the output plan of each unit in the day-ahead stage, which improves the accuracy of the output plan formulation in the day-ahead stage and the accuracy of the scheduling of the virtual power plant.

[0166] 204: Obtain output forecast data and load forecast data of new energy units in the virtual power plant during the intraday stage.

[0167] In an embodiment of the present application, based on the output plan of the day-ahead stage, the time scale of the intraday stage is transformed from 1 hour to 15 minutes to formulate the output plan of the intraday stage. The load forecast data includes: electric load forecast value, gas load forecast value, thermal load forecast value, and so on. Among them, the method of obtaining the output forecast data of the new energy units in the virtual power plant in the intraday stage is similar to the method of obtaining the output forecast data of the new energy units in the virtual power plant in the day-ahead stage, which will not be repeated here.

[0168] 205: Based on the output forecast data of the new energy units in the daily stage, determine the resource data of each unit in the daily stage; determine the carbon trading data and demand response load data of the virtual power plant in the daily stage.

[0169] In an embodiment of the present application, a method for determining resource data of each unit in the intraday stage based on output forecast data of the new energy unit in the intraday stage is similar to a method for determining resource data of each unit in the day-ahead stage based on output forecast data of the new energy unit in the day-ahead stage, and will not be repeated here.

[0170] In an embodiment of the present application, the method of determining the carbon trading data and demand response load data of the virtual power plant in the intraday stage is similar to the method of determining the carbon trading data and demand response load data of the virtual power plant in the day-ahead stage, and will not be repeated here.

[0171] 206: Based on the output forecast data of the new energy units in the daily stage, the load forecast data, the resource data of each unit in the daily stage, the carbon trading data in the daily stage, the demand response load data and the objective function in the daily stage, determine the operating cost of the virtual power plant in the daily stage and the output plan of each unit in the daily stage.

[0172] In the embodiment of the present application, it is necessary to construct the objective function of the intraday stage in the intraday stage, and perform rolling optimization on the output of each unit at a time interval of 15 minutes. Exemplarily, based on the output forecast data of the new energy unit in the intraday stage, the load forecast data, the resource data of each unit in the intraday stage, the carbon trading data in the intraday stage, the demand response load data and the objective function in the intraday stage, the operating cost of the virtual power plant in the intraday stage and the output plan of each unit in the intraday stage are determined, for example, it may include:

[0173] Based on the resource data of each unit in the intra-day stage, the load forecast data of the new energy unit in the intra-day stage and the demand response data in the intra-day stage, the balance constraint conditions in the intra-day stage are established;

[0174] Obtain the deviation penalty cost between the intraday stage and the day-ahead stage;

[0175] Based on the carbon trading data of the virtual power plant in the intraday stage, the output forecast data of the new energy units in the intraday stage, the resource data of each unit in the intraday stage, the deviation penalty cost in the day-ahead stage and the objective function of the day-ahead stage, the operating cost of the intraday stage and the output plan of each unit in the intraday stage are determined.

[0176] Specifically, the intra-day balance constraints include: intra-day electric power balance constraints, thermal power balance constraints, and gas power balance constraints. The processor can establish the intra-day electric power balance constraints based on the power generation and power consumption of each unit in the virtual power plant during the intra-day phase, as shown in formula (38):

[0177]

[0178] in, represents the power generation of the carbon capture power plant at time t during the day. It represents the power generation of the wind turbine at time t during the day. It represents the power generation of the photovoltaic power station at time t during the day. It represents the power generated by the hydrogen fuel cell at time t during the day. It represents the power generation of CHP at time t during the intraday phase. Represents the electric power interacting between the virtual power plant and the grid at time t during the intra-day period. It represents the electric power consumed by the electric boiler at time t during the day. It represents the predicted value of the electric load of the new energy unit at time t during the day. They represent the discharge power and charging power of the energy storage at time t in the intraday stage. Therefore, by establishing the power balance constraint conditions in the intraday stage, the processor can determine the power load and power purchase cost that need to be purchased from the power grid in the intraday stage based on the power load forecast value of the new energy unit at each moment in the intraday stage.

[0179] Then, the processor can establish the thermal power balance constraint condition in the intra-day stage based on the heating power and charging power of the new energy units in the virtual power plant in the intra-day stage, as shown in formula (39):

[0180]

[0181] in, It represents the heat generation power of the hydrogen fuel cell at time t during the day. It represents the heat generation power of CHP at time t during the intraday phase. It represents the heating power of the electric boiler at time t during the day. It represents the heating power of the gas boiler at time t during the day. They represent the heat release power and heat charging power of the thermal energy storage at time t in the intraday stage. t.nr It represents the predicted heat load value of the virtual power plant at time t in the intra-day phase. Therefore, the processor can determine the fuel cost of the carbon capture power plant in the intra-day phase based on the predicted heat load value of the new energy unit at each moment in the intra-day phase by establishing the thermal power balance constraint condition in the intra-day phase.

[0182] Furthermore, the processor may establish a gas power balance constraint condition in the intra-day stage based on the gas power and charging power outputted by the new energy units in the virtual power plant in the intra-day stage, as shown in formula (40):

[0183]

[0184] in, Represents the interaction power between the virtual power plant and the gas grid at time t during the intraday phase. It represents the gas power output by the power-to-gas device at time t during the day. They respectively represent the gas power and charging power output at time t during the intraday stage of the gas energy storage day. It represents the gas load forecast value of the virtual power plant at time t during the intraday stage. It represents the gas power consumed by CHP at time t during the day. Indicates the gas power consumed by the gas boiler at time t in the intra-day stage. Therefore, by establishing the gas power balance constraint condition in the intra-day stage, the processor can determine the gas purchase cost required to purchase gas from the gas grid in the intra-day stage based on the gas load forecast value of the new energy unit at each moment in the intra-day stage.

[0185] Then, the processor needs to obtain the deviation penalty cost between the intraday stage and the day-ahead stage. The deviation penalty cost is used to reduce the deviation between the output forecast data and the actual output data, and can be determined based on the output of each unit in the intraday stage and the day-ahead stage. Exemplarily, obtaining the deviation penalty cost between the intraday stage and the day-ahead stage may include:

[0186] Obtain the penalty coefficients of each unit in terms of multiple energy output and the penalty coefficients of interactive energy deviation with multiple energy networks;

[0187] The deviation penalty cost is determined based on the output of each unit in the day-ahead phase, the output in the intraday phase, the penalty coefficients of multiple energy outputs, and the penalty coefficients of interactive energy deviations with multiple energy networks.

[0188] In the embodiment of the present application, the deviation penalty cost can be expressed by formula (41):

[0189]

[0190] Among them, f' CF represents the deviation penalty cost between the intraday stage and the day-ahead stage. J can represent any one of electric energy, gas energy and thermal energy. i,j It represents the penalty coefficient of the jth output deviation of the i-th unit. e Represents the energy deviation penalty coefficient for interaction with the power grid. μ g Represents the deviation penalty coefficient of the interaction energy with the gas network. i t It represents the output of the i-th unit in the day-ahead phase. Represents the output of the i-th unit in the intraday stage. Specifically, the processor can obtain the penalty coefficients of each unit for the output of multiple energy sources in the day-ahead stage and the intraday stage, as well as the penalty coefficients of the interactive energy deviation with multiple energy networks. Based on the output of each unit in the day-ahead stage and the intraday stage, the penalty coefficients of the output deviation of each unit in the day-ahead stage and the intraday stage for multiple energy outputs, the electricity purchase cost and gas purchase cost of the virtual power plant in the day-ahead stage and the intraday stage, and the penalty coefficients of the interactive energy deviation with multiple energy networks, the deviation penalty cost between the intraday stage and the day-ahead stage can be determined.

[0191] In this way, the deviation penalty cost between the intraday stage and the day-ahead stage can be determined, and the deviation between the output forecast data and the actual output data of the new energy unit can be reduced through the deviation penalty cost, thereby improving the accuracy of the output plan and the accuracy of scheduling the virtual power plant.

[0192] Furthermore, the processor obtains the objective function of the intraday stage. The objective function of the intraday stage aims to minimize the operating cost of the virtual power plant in the intraday stage, and establishes a functional relationship between the operating cost of the virtual power plant in the intraday stage and the power purchase cost, gas purchase cost, fuel cost of the carbon capture power plant, carbon trading cost and deviation penalty cost of the virtual power plant in the intraday stage. Specifically, the objective function of the intraday stage can be expressed by formula (42):

[0193]

[0194] In formula (42), f RN represents the operating cost of the virtual power plant during the intraday stage. f′ e.buy , f′ g.buy Respectively represent the cost of electricity and gas purchased during the day. G represents the fuel cost of the carbon capture plant during the intra-day period. represents the carbon trading cost during the intraday period, f' CFrepresents the deviation penalty cost between the intraday stage and the day-ahead stage. Among them, the gas purchase cost, electricity purchase cost, fuel cost of the carbon capture power plant, and carbon trading cost in the intraday stage are expressed by formula (43), formula (44), formula (45), and formula (46), respectively:

[0195]

[0196] in, It represents the gas purchase price at time t during the intraday period. It represents the energy purchased at time t during the intraday period. It represents the electricity purchase price at time t during the intraday period. represents the electricity purchased at time t during the intraday phase. a, b, c represent the cost fuel coefficients. It represents the output power of the thermal power unit in the carbon capture power plant at time t during the intraday stage. Represents the carbon trading cost at time t during the intraday period.

[0197] Specifically, the processor can determine the gas purchase cost at each moment based on the gas purchase price and the purchased gas energy at each moment in the intraday stage. The electricity purchase cost at each moment can be determined based on the electricity purchase price and the purchased electricity energy at each moment in the intraday stage. The fuel cost of the carbon capture power plant in the intraday stage can be determined based on the output power of the thermal power units in the carbon capture power plant at each moment in the intraday stage. The carbon trading cost of the intraday stage can be determined based on the carbon trading cost at each moment in the intraday stage. Thus, based on the gas purchase cost, electricity purchase cost, fuel cost of the carbon capture power plant in the intraday stage, and carbon trading cost at each moment in the intraday stage, the operating cost of the intraday stage is determined. When the number of scrolling times in the intraday stage is greater than the scrolling threshold, the operating cost of the intraday stage and the output plan of the intraday stage are output.

[0198] It can be seen that in an embodiment of the present application, the processor can establish a balance constraint condition for the intraday stage based on the resource data, load forecast data and demand response data of each unit in the intraday stage. Then, the deviation penalty cost between the intraday stage and the day-ahead stage is obtained. Finally, based on the carbon trading data of the virtual power plant in the intraday stage, the resource data of each unit in the intraday stage, the deviation penalty cost of the day-ahead stage and the objective function of the day-ahead stage, the operating cost of the intraday stage and the output plan of each unit in the intraday stage are determined. Thus, by analyzing the deviation between the intraday stage and the day-ahead stage, a more accurate operating cost and output plan can be determined, thereby improving the accuracy of the virtual power plant scheduling.

[0199] 207: The virtual power plant is dispatched based on the operating cost of the day-ahead phase and the output plan of each unit in the day-ahead phase, the operating cost of the intraday phase and the output plan of each unit in the intraday phase.

[0200] It can be understood that when scheduling a virtual power plant, it can be scheduled based on the operating cost of the day-ahead stage and the output plan of each unit in the day-ahead stage, or it can be scheduled based on the operating cost of the intraday stage and the output plan of each unit in the day-ahead stage. The virtual power plant can also be scheduled based on the operating cost of the day-ahead stage and the output plan of each unit in the day-ahead stage, the operating cost of the intraday stage and the output plan of each unit in the day-ahead stage, and the present application does not limit this.

[0201] In summary, in the embodiment of the present application, first, the output forecast data of the new energy units of the virtual power plant in the day-ahead stage is obtained. Then, based on the output forecast data of the new energy units in the day-ahead stage, the resource data of each unit in the day-ahead stage can be determined. Determine the carbon trading data and demand response load data of the day-ahead stage. Further, with the goal of minimizing the operating cost of the virtual power plant in the day-ahead stage, determine the objective function of the day-ahead stage, and determine the operating cost of the virtual power plant in the day-ahead stage and the output plan of each unit in the day-ahead stage based on the output forecast data of the new energy units in the day-ahead stage, the resource data of each unit in the day-ahead stage, the carbon trading data in the day-ahead stage, the demand response load data and the objective function of the day-ahead stage. Next, obtain the output forecast data and load forecast data of the new energy units of the virtual power plant in the intraday stage. Then, based on the output forecast data of the new energy units of the virtual power plant in the intraday stage, the resource data of each unit in the virtual power plant in the intraday stage can be determined. Determine the carbon trading data and demand response load data in the intraday stage. Taking the minimization of the operation cost of the virtual power plant in the intraday stage as the goal, the objective function of the intraday stage is determined. Based on the output forecast data, load forecast data, resource data of each unit in the intraday stage, carbon trading data, demand response load data and the objective function of the intraday stage, the operation cost of the virtual power plant in the intraday stage and the output plan of each unit in the intraday stage are determined. Finally, the virtual power plant is dispatched based on the operation cost of the day-ahead stage and the output plan of each unit in the day-ahead stage, the operation cost of the intraday stage and the output plan of each unit in the day-ahead stage. In this way, the day-ahead scheduling and intraday scheduling of the virtual power plant can be realized. Compared with analyzing the output and load data in a single time scale to determine the output plan of each unit, the output plan determined by scheduling in the two stages of day-ahead and intraday is more accurate, which improves the accuracy of virtual power plant scheduling.

[0202] See also Figure 3 , Figure 3 A schematic diagram of a virtual power plant scheduling device provided in an embodiment of the present application. The virtual power plant scheduling device 300 includes an acquisition unit 301 and a processing unit 302. The virtual power plant scheduling device 300 may be a processor.

[0203] An acquisition unit 301 is used to acquire output forecast data of new energy units in a virtual power plant in the day-ahead phase;

[0204] The processing unit 302 is used to determine the resource data of each unit in the day-ahead stage based on the output forecast data of the new energy unit in the day-ahead stage; determine the carbon trading data and demand response load data of the virtual power plant in the day-ahead stage;

[0205] Based on the output forecast data of new energy units in the day-ahead phase, the resource data of each unit in the day-ahead phase, the carbon trading data in the day-ahead phase, the demand response load data and the objective function in the day-ahead phase, the operating cost of the virtual power plant in the day-ahead phase and the output plan of each unit in the day-ahead phase are determined;

[0206] The acquisition unit 301 is used to acquire the output forecast data and load forecast data of the new energy units in the virtual power plant during the intra-day stage;

[0207] The processing unit 302 is used to determine the resource data of the new energy unit in the intraday stage based on the output forecast data of the new energy unit in the intraday stage; determine the carbon trading data and demand response load data of the virtual power plant in the intraday stage;

[0208] Based on the output forecast data of the new energy units during the daily stage, the load forecast data, the resource data of each unit during the daily stage, the carbon trading data during the daily stage, the demand response load data and the objective function during the daily stage, the operating cost of the virtual power plant during the daily stage and the output plan of each unit during the daily stage are determined;

[0209] The virtual power plant is dispatched based on the operating cost of the day-ahead stage and the output plan of each unit in the day-ahead stage, the operating cost of the intraday stage and the output plan of each unit in the intraday stage.

[0210] In a feasible embodiment, in determining the resource data of each unit in the day-ahead stage based on the output prediction data of the new energy unit in the day-ahead stage, the processing unit 302 is specifically used to:

[0211] Based on the energy supply of the new energy unit in the day-ahead stage, determine the actual output data of the new energy unit in the day-ahead stage;

[0212] Based on the output forecast data and actual output data of the new energy generating units in the day-ahead phase, the output constraints of the new energy generating units in the day-ahead phase are determined;

[0213] Based on the output constraints of the new energy units in the day-ahead phase, the resource data of each unit in the day-ahead phase is determined.

[0214] In a feasible embodiment, the virtual power plant includes: a combined heat and power unit, a thermal power unit, a gas boiler, and a carbon capture power plant;

[0215] In determining the carbon trading data of the virtual power plant during the intraday period, the processing unit 302 is specifically configured to:

[0216] Determine the carbon emission quota for the day-ahead phase based on the power generation and heating power of the cogeneration unit, the power generation of the thermal power unit, and the heating power of the gas boiler.

[0217] Determine the actual carbon emissions during the day-ahead period based on the power generation and heating power of the cogeneration unit during the day-ahead period, the heating power of the gas boiler during the day-ahead period, the carbon emissions of the carbon capture power plant during the day-ahead period, and the amount of electricity purchased;

[0218] The carbon trading data for the day-ahead period is determined based on the carbon emission quota for the day-ahead period and the actual carbon emissions for the day-ahead period.

[0219] In a feasible embodiment, in determining the demand response load data of the virtual power plant in the day-ahead phase, the processing unit 302 is specifically configured to:

[0220] Based on the electricity load of the virtual power plant in the day-ahead stage, determine the electricity price elasticity coefficient at each moment in the day-ahead stage;

[0221] The elasticity coefficients at each moment in the day-ahead phase are concatenated to obtain the electricity price elasticity coefficient matrix;

[0222] Based on the electric load and electricity price elasticity coefficient matrix of the virtual power plant in the day-ahead stage, the demand response load data of the day-ahead stage is determined.

[0223] In a feasible embodiment, in determining the operating cost of the virtual power plant in the day-ahead stage and the output plan of each unit in the day-ahead stage based on the output forecast data of the new energy unit in the day-ahead stage, the resource data of each unit in the day-ahead stage, the carbon trading data in the day-ahead stage, the demand response load data and the objective function in the day-ahead stage, the processing unit 302 is specifically used to:

[0224] Based on the resource data of each unit in the day-ahead phase and the demand response data in the day-ahead phase, the balance constraint conditions in the day-ahead phase are established;

[0225] Determine the predicted operating cost of the day-ahead phase based on the carbon trading data of the day-ahead phase, the resource data of each unit in the day-ahead phase, and the objective function of the day-ahead phase;

[0226] Based on the output forecast data of the new energy units in the day-ahead stage, the output deviation coefficient of each unit and the predicted operating cost, the operating cost of the day-ahead stage and the output plan of each unit in the day-ahead stage are obtained.

[0227] In a feasible embodiment, in determining the operation cost of the virtual power plant in the intraday stage and the output plan of each unit in the intraday stage based on the output forecast data of the new energy unit in the intraday stage, the load forecast data, the resource data of each unit in the intraday stage, the carbon trading data in the intraday stage, the demand response load data and the objective function in the intraday stage, the processing unit 302 is specifically used to:

[0228] Based on the resource data of the new energy units in the intra-day stage, the load forecast data of the new energy units in the intra-day stage and the demand response data in the intra-day stage, the balance constraint conditions in the intra-day stage are established;

[0229] Obtain the deviation penalty cost between the intraday stage and the day-ahead stage;

[0230] Based on the carbon trading data of the virtual power plant in the intraday stage, the output forecast data of the new energy units in the intraday stage, the resource data of each unit in the intraday stage, the deviation penalty cost in the day-ahead stage and the objective function of the day-ahead stage, the operating cost of the intraday stage and the output plan of the new energy units in the intraday stage are determined.

[0231] In a feasible embodiment, in terms of obtaining the deviation penalty cost between the intraday stage and the day-ahead stage, the processing unit 302 is specifically configured to:

[0232] Obtain the penalty coefficients of each unit in terms of multiple energy output and the penalty coefficients of interactive energy deviation with multiple energy networks;

[0233] The deviation penalty cost is determined based on the output of each unit in the day-ahead phase, the output in the intraday phase, the penalty coefficients of multiple energy outputs, and the penalty coefficients of interactive energy deviations with multiple energy networks.

[0234] See also Figure 4 , Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. 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 can transmit the data stored in the memory 403 to the processor 402. The electronic device 400 can be a scheduling device 300 of a virtual power plant. The transceiver 401 can be an acquisition unit 301, and the processor 402 can be a processing unit 302.

[0235] The processor 402 is used to read the computer program in the memory 403 and perform the following operations:

[0236] Obtain output forecast data of new energy units in virtual power plants in the day-ahead phase;

[0237] Based on the output forecast data of new energy units in the day-ahead phase, determine the resource data of each unit in the day-ahead phase; determine the carbon trading data and demand response load data of the virtual power plant in the day-ahead phase;

[0238] Based on the output forecast data of new energy units in the day-ahead phase, the resource data of each unit in the day-ahead phase, the carbon trading data in the day-ahead phase, the demand response load data and the objective function in the day-ahead phase, the operating cost of the virtual power plant in the day-ahead phase and the output plan of each unit in the day-ahead phase are determined;

[0239] Obtain the output forecast data and load forecast data of the new energy units in the virtual power plant during the daily stage;

[0240] Based on the output forecast data of new energy units in the daily stage, determine the resource data of each unit in the daily stage; determine the carbon trading data and demand response load data of the virtual power plant in the daily stage;

[0241] Based on the output forecast data of the new energy units during the daily stage, the load forecast data, the resource data of each unit during the daily stage, the carbon trading data during the daily stage, the demand response load data and the objective function during the daily stage, the operating cost of the virtual power plant during the daily stage and the output plan of each unit during the daily stage are determined;

[0242] The virtual power plant is dispatched based on the operating cost of the day-ahead stage and the output plan of each unit in the day-ahead stage, the operating cost of the intraday stage and the output plan of each unit in the intraday stage.

[0243] The above mainly introduces the scheme of the embodiment of the present application from the perspective of the execution process of the method side. It is understandable that the electronic device 400 includes a hardware structure and / or software module corresponding to each function in order to realize the above functions. Those skilled in the art should easily realize that, in combination with the units and algorithm steps of each example described in the embodiment provided herein, the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present application.

[0244] An embodiment of the present application also provides a computer-readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement part or all of the steps of any virtual power plant scheduling method recorded in the above method embodiments.

[0245] 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 cause a computer to execute part or all of the steps of any virtual power plant scheduling method recorded in the above method embodiments.

[0246] It should be noted that, for the aforementioned method embodiments, 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 the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily required by the present application.

[0247] In the above 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.

[0248] In the several embodiments provided in the present application, it should be understood that the disclosed device can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of the 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 the device or unit can be electrical or other forms.

[0249] 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 solution of this embodiment.

[0250] 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.

[0251] 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 is essentially 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, and 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 perform all or part of the steps of the method described in each embodiment 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 CD-ROM and other media that can store program codes.

[0252] A person skilled in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable memory, and the memory can include: a flash drive, a read-only memory (English: Read-Only Memory, abbreviated as: ROM), a random access memory (English: Random Access Memory, abbreviated as: RAM), a magnetic disk or an optical disk, etc.

[0253] The embodiments of the present application are introduced in detail above. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above embodiments 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 method and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A scheduling method for a virtual power plant, characterized in that: include: Obtain output forecast data of new energy units in virtual power plants in the day-ahead phase; Determine the resource data of each unit in the day-ahead stage based on the output forecast data of the new energy unit in the day-ahead stage; determine the carbon trading data and demand response load data of the virtual power plant in the day-ahead stage; Determine the operating cost of the virtual power plant in the day-ahead stage and the output plan of each unit in the day-ahead stage based on the output forecast data of the new energy unit in the day-ahead stage, the resource data of each unit in the day-ahead stage, the carbon trading data in the day-ahead stage, the demand response load data and the objective function in the day-ahead stage; Obtaining output forecast data and load forecast data of the new energy units in the virtual power plant during the intra-day stage; Determine the resource data of each unit during the intra-day period based on the output forecast data of the new energy unit during the intra-day period; determine the carbon trading data and demand response load data of the virtual power plant during the intra-day period; Determine the operating cost of the virtual power plant during the intraday stage and the output plan of each unit during the intraday stage based on the output forecast data of the new energy unit during the intraday stage, the load forecast data, the resource data of each unit during the intraday stage, the carbon trading data during the intraday stage, the demand response load data and the objective function during the intraday stage; The virtual power plant is dispatched based on the operating cost of the day-ahead stage and the output plan of each unit in the day-ahead stage, the operating cost of the intraday stage and the output plan of each unit in the intraday stage.

2. The method according to claim 1, characterized in that The determining of resource data of each unit in the day-ahead stage based on the output forecast data of the new energy unit in the day-ahead stage includes: Based on the energy supply of the new energy unit in the day-ahead phase, determining actual output data of the new energy unit in the day-ahead phase; Determine the output constraint of the new energy generating unit in the day-ahead stage based on the output forecast data and actual output data of the new energy generating unit in the day-ahead stage; Based on the output constraints of the new energy units in the day-ahead phase, resource data of each unit in the day-ahead phase is determined.

3. The method according to claim 1, characterized in that The virtual power plant includes: cogeneration units, thermal power units, gas boilers, and carbon capture power plants; The determining of the carbon trading data of the virtual power plant during the intraday period includes: Determine the carbon emission quota for the day-ahead period based on the power generation and heating power of the cogeneration unit in the day-ahead period, the power generation power of the thermal power unit in the day-ahead period, and the heating power of the gas boiler in the day-ahead period; Determine the actual carbon emissions in the day-ahead period based on the power generation and heating power of the cogeneration unit in the day-ahead period, the heating power of the gas boiler in the day-ahead period, the carbon emissions of the carbon capture power plant in the day-ahead period, and the amount of electricity purchased; The carbon trading data for the day-ahead period is determined based on the carbon emission quota for the day-ahead period and the actual carbon emissions for the day-ahead period.

4. The method according to claim 1, characterized in that The determining of the demand response load data of the virtual power plant in the day-ahead phase includes: Determine the electricity price elasticity coefficient at each moment in the day-ahead stage based on the electricity load of the virtual power plant in the day-ahead stage; The elasticity coefficients at each moment in the day-ahead stage are concatenated to obtain an electricity price elasticity coefficient matrix; Based on the electric load of the virtual power plant in the day-ahead stage and the electricity price elasticity coefficient matrix, the demand response load data of the day-ahead stage is determined.

5. The method according to any one of claims 1 to 4, characterized in that: The step of determining the operating cost of the virtual power plant in the day-ahead stage and the output plan of each unit in the day-ahead stage based on the output forecast data of the new energy unit in the day-ahead stage, the resource data of each unit in the day-ahead stage, the carbon trading data in the day-ahead stage, the demand response load data and the objective function in the day-ahead stage includes: Establishing a balance constraint condition for the day-ahead stage based on the resource data of each unit in the day-ahead stage and the demand response data of the day-ahead stage; Determine the predicted operating cost of the day-ahead stage based on the carbon trading data of the day-ahead stage, the resource data of each unit in the day-ahead stage, and the objective function of the day-ahead stage; Based on the output forecast data of the new energy units in the day-ahead stage, the output deviation coefficient of each unit and the predicted operating cost, the operating cost of the day-ahead stage and the output plan of each unit in the day-ahead stage are obtained.

6. The method according to any one of claims 1 to 4, characterized in that: The method of determining the operating cost of the virtual power plant during the intraday stage and the output plan of each unit during the intraday stage based on the output forecast data of the new energy unit during the intraday stage, the load forecast data, the resource data of each unit during the intraday stage, the carbon trading data during the intraday stage, the demand response load data and the objective function during the intraday stage includes: Establishing a balance constraint condition for the intra-day stage based on resource data of each unit in the intra-day stage, load forecast data of the new energy unit in the intra-day stage, and demand response data in the intra-day stage; Obtaining a deviation penalty cost between the intraday stage and the day-ahead stage; Based on the carbon trading data of the virtual power plant during the intraday stage, the output forecast data of the new energy units during the intraday stage, the resource data of each unit during the intraday stage, the deviation penalty cost of the day-ahead stage and the objective function of the day-ahead stage, the operating cost of the intraday stage and the output plan of each unit during the intraday stage are determined.

7. The method according to claim 6, characterized in that The obtaining of the deviation penalty cost between the intraday stage and the day-ahead stage includes: Obtain the penalty coefficients of each unit in terms of multiple energy output and the penalty coefficients of interactive energy deviation with multiple energy networks; The deviation penalty cost is determined based on the output of each unit in the day-ahead phase, the output in the intra-day phase, the penalty coefficients of the multiple energy outputs, and the interactive energy deviation penalty coefficients with the multiple energy networks.

8. A virtual power plant dispatching device, characterized in that: The scheduling device of the virtual power plant includes an acquisition unit and a processing unit; The acquisition unit is used to acquire output forecast data of new energy units in the virtual power plant in the day-ahead stage; The processing unit is used to determine the resource data of each unit in the day-ahead stage based on the output forecast data of the new energy unit in the day-ahead stage; determine the carbon trading data and demand response load data of the virtual power plant in the day-ahead stage; Determine the operating cost of the virtual power plant in the day-ahead stage and the output plan of each unit in the day-ahead stage based on the output forecast data of the new energy unit in the day-ahead stage, the resource data of each unit in the day-ahead stage, the carbon trading data in the day-ahead stage, the demand response load data and the objective function in the day-ahead stage; The acquisition unit is used to acquire the output forecast data and load forecast data of the new energy units in the virtual power plant during the intra-day stage; The processing unit is used to determine the resource data of each unit during the intraday stage based on the output forecast data of the new energy unit during the intraday stage; determine the carbon trading data and demand response load data of the virtual power plant during the intraday stage; Determine the operating cost of the virtual power plant during the intraday stage and the output plan of each unit during the intraday stage based on the output forecast data of the new energy unit during the intraday stage, the load forecast data, the resource data of each unit during the intraday stage, the carbon trading data during the intraday stage, the demand response load data and the objective function during the intraday stage; The virtual power plant is dispatched based on the operating cost of the day-ahead stage and the output plan of each unit in the day-ahead stage, the operating cost of the intraday stage and the output plan of each unit in the intraday stage.

9. An electronic device, characterized in that: include: A processor and a memory, wherein the processor is connected to the memory, the memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory, so that the electronic device executes the method as 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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