Virtual power plant peak clipping response scheduling optimization method and device, equipment and storage medium

Abstract

The invention relates to a virtual power plant peak clipping response scheduling optimization method and device, equipment and a storage medium. According to the main technical scheme, the method comprises the steps that the temperature thermal inertia time value, the thermal inertia period virtual power generation power value and the energy-saving period virtual power generation power value of the single comprehensive energy cooling / heating system are obtained; according to the temperature thermal inertia time value, the thermal inertia period virtual power generation power value and the energy-saving period virtual power generation power value, a virtual response power value of the single comprehensive energy cooling / heating system is obtained; obtaining an adjustable output power constraint condition of the distributed energy; according to the virtual response power value and the adjustable output power constraint condition, an optimization target equation is constructed, and the optimization target equation takes the maximum virtual power generation income value responded by the peak clipping demand side as a target; and obtaining an adjustable output power curve according to the optimization target equation so as to realize scheduling optimization. According to the method, the effects of maximizing the output capacity of the virtual power plant in the peak clipping period and ensuring the maximum benefit of the demand side response can be achieved.

Description

Technical Field

[0001] This application relates to the field of virtual energy storage technology, and in particular to a method, apparatus, equipment and storage medium for optimizing peak shaving response scheduling in a virtual power plant. Background Technology

[0002] As is well known, a Virtual Power Plant (VPP) is an energy management model that uses advanced information and communication technologies and software systems to aggregate and coordinate distributed energy resources such as distributed power sources, energy storage systems, controllable loads, and electric vehicles to form a virtual, dispatchable power supply system to participate in the electricity market and grid operation. Moreover, as an important technological means in energy system transformation, the VPP is still in its early stages of development.

[0003] In traditional implementations, the optimal scheduling of virtual power plants primarily focuses on the aggregation of single user-side resources, such as battery storage systems, electric vehicle charging stations, battery swapping stations, and communication base stations. These resources are prioritized for virtual power plant implementations due to their relatively simple control mechanisms and clear settlement mechanisms.

[0004] However, since the above methods are limited to single resources such as battery storage, charging stations, and battery swapping stations, when large-scale units are involved, multiple energy forms (such as electricity, heat, and gas) and load types (such as production equipment, lighting, and air conditioning) may be involved. The control model and settlement mechanism are also more complex, which leads to higher requirements for the scheduling optimization of virtual power plants. At present, there is no better way to achieve scheduling optimization and quantitative calculation of the electricity, heat, and peak-shaving markets when large-scale units are involved, and naturally, the requirement to maximize the revenue of virtual power generation cannot be achieved. Summary of the Invention

[0005] Based on this, this application provides a virtual power plant peak shaving response scheduling optimization method, device, equipment, and storage medium. It forms virtual energy storage through the cooling / heating system of a single integrated energy source to achieve the quantification and controllability of flexible load in the first stage. By constraining the adjustable output power of distributed energy, it achieves the scheduling and optimization of the second stage, thereby completing the construction of the optimization objective equation and achieving the effect of maximizing the output capacity of the virtual power plant during peak shaving periods and ensuring the maximum benefit of demand-side response.

[0006] Firstly, a virtual power plant peak-shaving response scheduling optimization method is provided, the method comprising: Obtain the temperature thermal inertia time value, the virtual power generation value during the thermal inertia period, and the virtual power generation value during the energy-saving period of a single integrated energy cooling / heating system; Based on the temperature thermal inertia time value, the virtual power generation value during the thermal inertia period, and the virtual power generation value during the energy-saving period, the virtual response power value of a single integrated energy cooling / heating system is obtained. Obtain the adjustable output power constraints of distributed energy sources; Based on the virtual response power value and the adjustable output power constraint, an optimization objective equation for the peak-shaving demand-side response is constructed, wherein the optimization objective equation aims to maximize the virtual power generation revenue value of the peak-shaving demand-side response. Based on the optimization objective equation, the adjustable output power curve of the peak-shaving demand-side response and the maximum revenue of virtual generation are obtained to achieve dispatch optimization.

[0007] According to one feasible method in the embodiments of this application, the temperature thermal inertia time value, the virtual power generation value during the thermal inertia period, and the virtual power generation value during the energy-saving period of a single integrated energy cooling / heating system are obtained, including: Obtain the demand response impact parameters of a single integrated energy cooling / heating system; Based on the preset response model and corresponding demand response impact parameters of the single integrated energy cooling / heating system, the corresponding temperature thermal inertia time value is obtained; Obtain the historical baseline value of power consumption and the historical power consumption difference of a single integrated energy cooling / heating system; Based on the historical power consumption baseline value and the historical power consumption difference, the virtual power generation value during the thermal inertia period is obtained; Obtain the energy efficiency value and energy-saving temperature difference value of a single integrated energy cooling / heating system; Based on the historical baseline power consumption, energy efficiency value, and energy-saving temperature difference value, the virtual power generation value for the energy-saving period is obtained.

[0008] According to one achievable method in an embodiment of this application, the adjustable output power constraint of distributed energy includes the adjustable charging power constraint of charging stations. Obtaining the adjustable output power constraint of distributed energy includes: Obtain the baseline value of the charging power of the charging station and the preset minimum adjustable power value; The charging power difference of the charging station is obtained based on the baseline charging power value and the preset minimum adjustable power value. Based on the preset initial charging power value and the difference in charging power, the adjustable charging power constraint conditions of the charging station are obtained.

[0009] According to one achievable method in an embodiment of this application, the adjustable output power constraint of distributed energy includes the adjustable discharge power constraint of energy storage batteries. Obtaining the adjustable output power constraint of distributed energy further includes: Obtain the historical baseline value of discharge power and the preset maximum value of discharge power of the energy storage battery; The difference in discharge power of the energy storage battery is obtained based on the historical baseline discharge power value and the preset maximum discharge power value. Based on the preset initial value of discharge power and the difference in discharge power, the initial conditions for the adjustable discharge power constraint of the energy storage battery are obtained. Obtain the discharge capacity constraints of the energy storage battery; Based on the initial conditions for adjustable discharge power constraints and the discharge capacity constraints, the adjustable discharge power constraints for the energy storage battery are obtained.

[0010] According to one achievable method in an embodiment of this application, the adjustable output power constraint of distributed energy includes the adjustable cooling power constraint of water-based cooling systems. Obtaining the adjustable output power constraint of distributed energy further includes: Obtain the historical baseline value of cooling power released by water storage and the preset maximum value of cooling power; The difference in cooling power for water storage is obtained based on the historical baseline value of cooling power and the preset maximum value of cooling power. Based on the preset initial value of cooling power and the difference in cooling power, the initial conditions for adjustable cooling power constraint are obtained; Obtain the cooling capacity constraints of water-based cooling systems; Based on the initial conditions for adjustable cooling power and the constraints for cooling capacity, the adjustable cooling power constraints for water-based cooling storage are obtained.

[0011] According to one achievable method in the embodiments of this application, the optimization objective equation includes the electricity sales revenue value and the generation cost value. Based on the virtual response power value and the adjustable output power constraint, the optimization objective equation for the peak-shaving demand-side response is constructed, including: Obtain the coefficient of performance (COP) value of a single integrated energy cooling / heating system, the interruptible load value of a virtual power plant, and the difference in photovoltaic power generation. Obtain the response value of the peak shaving demand side, and based on the response value, performance coefficient value, interruptible load value, power generation difference, virtual response power value, and adjustable output power constraint, obtain the electricity sales revenue value of the peak shaving demand side response. Obtain the discharge cost value of distributed energy resources, and based on the performance coefficient value, discharge cost value, and adjustable output power constraints, obtain the generation cost value of peak-shaving demand-side response. Based on the revenue from selling electricity and the cost of generating electricity, an optimization objective equation for peak-shaving demand-side response is constructed.

[0012] According to one achievable method in the embodiments of this application, the response value on the peak-shaving demand side includes a response price value and a response time value. Based on the response value, performance coefficient value, interruptible load value, generation power difference, virtual response power value, and adjustable output power constraints, the electricity sales revenue value of the peak-shaving demand side response is obtained, including: Based on the interruptible load value, power generation difference, performance coefficient value, virtual response power value, and adjustable output power constraint, the adjustable optimized capacity value on the peak shaving demand side is obtained. Based on the response price, response time, and adjustable optimization capacity, the revenue from selling electricity in response to peak shaving demand is obtained.

[0013] Secondly, a virtual power plant peak shaving response scheduling optimization device is provided, the device comprising: The first acquisition unit is used to acquire the temperature thermal inertia time value, the virtual power generation value during the thermal inertia period, and the virtual power generation value during the energy-saving period of a single integrated energy cooling / heating system. The first calculation unit is used to obtain the virtual response power value of a single integrated energy cooling / heating system based on the temperature thermal inertia time value, the virtual power generation value during the thermal inertia period, and the virtual power generation value during the energy-saving period. The second acquisition unit is used to acquire the adjustable output power constraints of the distributed energy source. The second calculation unit is used to construct the optimization objective equation of the peak-shaving demand-side response based on the virtual response power value and the adjustable output power constraint. The optimization objective equation aims to maximize the virtual power generation revenue value of the peak-shaving demand-side response. The scheduling optimization unit is used to obtain the adjustable output power curve of peak-shaving demand-side response and the maximum revenue of virtual power generation according to the optimization objective equation, so as to achieve scheduling optimization.

[0014] Thirdly, a computer device is provided, comprising: At least one processor; and A memory that is communicatively connected to at least one processor; wherein, The memory stores computer instructions that can be executed by at least one processor to enable the at least one processor to perform the methods involved in the first aspect above.

[0015] Fourthly, a computer-readable storage medium is provided, having stored thereon computer instructions, characterized in that the computer instructions are used to cause a computer to perform the methods involved in the first aspect above.

[0016] According to the technical content provided in the embodiments of this application, the embodiments of this application obtain the temperature thermal inertia time value, the virtual power generation value during the thermal inertia period, and the virtual power generation value during the energy-saving period of a single integrated energy cooling / heating system; based on the temperature thermal inertia time value, the virtual power generation value during the thermal inertia period, and the virtual power generation value during the energy-saving period, obtain the virtual response power value of the single integrated energy cooling / heating system; obtain the adjustable output power constraint conditions of distributed energy; based on the virtual response power value and the adjustable output power constraint conditions, construct the optimization objective equation for peak-shaving demand-side response, wherein the optimization objective equation aims to maximize the virtual power generation revenue value of peak-shaving demand-side response; based on the optimization objective equation, obtain the adjustable output power curve of peak-shaving demand-side response and the maximum revenue value of virtual power generation, so as to achieve scheduling optimization. The above operations form virtual energy storage through the cooling / heating system of a single integrated energy source, realizing the quantification and controllability of the first-stage flexible load; by constraining the adjustable output power of distributed energy, the dispatchability and optimizability of the second stage are realized, thereby completing the construction of the optimization objective equation, achieving the effect of maximizing the output capacity of the virtual power plant during peak shaving periods and ensuring the maximum benefit of demand-side response. Attached Figure Description

[0017] Figure 1 This is a system architecture diagram of a virtual power plant peak shaving response scheduling optimization method in one embodiment; Figure 2 This is a flowchart illustrating a peak-shaving response scheduling optimization method for a virtual power plant in one embodiment; Figure 3 This is a schematic diagram illustrating the scheduling optimization principle in a virtual power plant peak shaving response scheduling optimization method in one embodiment. Figure 4 This is a schematic diagram illustrating the charging power of a charging station under different conditions in a virtual power plant peak shaving response scheduling optimization method in one embodiment. Figure 5 This is a schematic diagram of the discharge power of an energy storage battery in a virtual power plant peak shaving response scheduling optimization method in one embodiment; Figure 6 This is a schematic diagram illustrating the cooling power release of water storage in a virtual power plant peak shaving response scheduling optimization method in one embodiment. Figure 7 This is a structural block diagram of a scheduling optimization device for peak shaving response of a virtual power plant in one embodiment; Figure 8 This is a schematic structural diagram of a computer device in one embodiment. Detailed Implementation

[0018] The present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only for explaining the present application and are not intended to limit the present application.

[0019] For ease of understanding, the system to which this application applies will first be described. The virtual power plant peak-shaving response scheduling optimization method provided in this application can be applied to, for example... Figure 1 The system architecture shown includes: server 101, integrated energy unit 103, and distributed energy unit 105. Server 101 is communicatively connected to both integrated energy unit 103 and distributed energy unit 105. Specifically, server 101 acquires the temperature thermal inertia time value, the virtual power generation value during the thermal inertia period, and the virtual power generation value during the energy saving period of the integrated energy unit 103's cooling / heating system; based on these values, it obtains the virtual response power value of the integrated energy unit's cooling / heating system; server 101 acquires the adjustable output power constraints of distributed energy unit 105; based on the virtual response power value and the adjustable output power constraints, it constructs an optimization objective equation for peak-shaving demand-side response, where the objective equation aims to maximize the virtual power generation revenue of peak-shaving demand-side response; based on the optimization objective equation, it obtains the adjustable output power curve of peak-shaving demand-side response and the maximum virtual power generation revenue to achieve scheduling optimization.

[0020] Figure 2 A flowchart of a virtual power plant peak shaving response scheduling optimization method provided in this application embodiment is shown. This method can be implemented by, for example... Figure 1 Server 101 in the system shown is executing. Figure 3 This is a schematic diagram illustrating the scheduling optimization principle in a virtual power plant peak shaving response scheduling optimization method provided in an embodiment of this application, combined with... Figure 2 , Figure 3 As shown, this application may include the following steps: Step S201: Obtain the temperature thermal inertia time value, the virtual power generation value during the thermal inertia period, and the virtual power generation value during the energy-saving period of the single integrated energy cooling / heating system.

[0021] The integrated energy system for individual entities includes campuses, factories, and industrial parks. This application uses campus integrated energy systems as an example for further explanation, whereby campus integrated energy systems include cooling and heating systems.

[0022] In one feasible approach, the campus integrated energy cooling system, during flexible load peak shaving, can be divided into two time series: the temperature thermal inertia period and the energy-saving period.

[0023] During the thermal inertia period, the adjustable flexible load output resource serves as pre-cooling temperature difference energy storage for the chilled water network and building spaces. Specifically, before the peak demand-side response begins, the output of the cooling system can be increased in advance to lower the temperature of the chilled water network and building spaces. After the peak demand-side response begins, considering energy conservation standards and comfort, the upper limit of indoor cooling temperature can be increased, and the chilled water system units can be shut down, causing the circulating pumps of the units to operate at low frequency. At this time, the entire campus cooling is provided by virtual energy storage, thereby allowing the acquisition of the campus cooling system's temperature thermal inertia time value and the virtual power generation value during the thermal inertia period, assuming T respectively. c and This indicates that, in particular, the temperature thermal inertia time value is the time value at which the virtual energy storage can support the cooling demand of the entire campus during peak demand-side response, assuming the cooling system is shut down.

[0024] During the energy-saving period, some units of the cooling system can be turned on to maintain the upper limit of indoor cooling temperature, thereby obtaining the virtual power generation value during the energy-saving period. Let's assume we use... express.

[0025] In another feasible approach, the campus integrated energy heating system, when undergoing flexible load peak shaving, can also be divided into two time series: the temperature thermal inertia period and the energy-saving period.

[0026] During the thermal inertia period, the adjustable flexible load output resources serve as preheating temperature difference energy storage for pipelines and building spaces. Before the peak-shaving demand-side response begins, the heating system output can be increased in advance to raise the temperature of the heating hot water network and building spaces. Considering campus comfort and design specifications, the heating temperature can be appropriately reduced to create a demand-response energy-saving temperature difference, and the heating system units can be shut down, causing the unit circulation pumps to operate at low frequency. At this time, the entire campus heating is provided by virtual energy storage, thereby obtaining the temperature thermal inertia time value and the virtual power generation value during the thermal inertia period of the campus heating system, assuming they are represented by T respectively. h and This indicates that the temperature thermal inertia time value is the time value used to determine whether the virtual energy storage can support the heating demand of the entire campus during peak demand-side response, assuming the heating system is shut down.

[0027] During the energy-saving period, some units of the heating system can be turned on to maintain the indoor cooling temperature at its lower limit, thereby obtaining the virtual power generation value during the energy-saving period. (Assuming...) express.

[0028] Step S203: Based on the temperature thermal inertia time value, the virtual power generation value during the thermal inertia period, and the virtual power generation value during the energy-saving period, obtain the virtual response power value of the single integrated energy cooling / heating system.

[0029] Among them, the virtual response power value of the campus integrated energy cooling system can be used This indicates that the virtual response power value of the campus integrated energy heating system can be used... express.

[0030] Specifically, since the demand-side response time is determined every 15 minutes, the preset response time is assumed to be represented by t, which can generally be set to 15 minutes. Therefore, the overall expression for the virtual response power value of the campus integrated energy peak-shaving flexible load cooling system can be expressed as follows: ; The overall expression for the virtual response power value of the campus integrated energy peak-shaving flexible load heating system can be represented as follows: ; Step S205: Obtain the adjustable output power constraints of the distributed energy source.

[0031] Distributed energy sources include, but are not limited to, charging stations, energy storage batteries, water-cooled storage, and distributed photovoltaics. Charging stations are facilities or locations specifically designed to provide charging services for electronic devices, such as electric vehicles. Energy storage batteries are devices that convert electrical energy into chemical energy for storage and then convert that chemical energy back into electrical energy when needed. Their working principle is based on electrochemical reactions, using ion migration between the positive and negative electrodes within the battery to achieve the charging and discharging process. During periods of low electricity demand, excess power is used to charge the energy storage batteries, storing the electrical energy. During periods of high electricity demand, the energy storage batteries discharge, supplementing the grid and balancing power supply and demand, thereby improving grid stability and reducing power generation costs. Water-based cooling is an energy-saving air conditioning technology that utilizes the sensible heat of water to store cooling capacity. By operating the chiller unit during off-peak electricity hours at night, the water is cooled to 4°C-7°C and stored in a water tank. During peak electricity hours in the daytime, the stored chilled water is released to supply the air conditioning system, achieving peak shaving and valley filling of the power load, balancing the pressure on the power grid, and reducing the user's electricity costs. Distributed photovoltaic refers to small-scale photovoltaic power generation systems built near the user (such as rooftops, industrial parks, agricultural greenhouses, etc.).

[0032] Specifically, during peak-shaving periods, distributed energy resources are treated as adjustable decision variables for optimal scheduling, while distributed photovoltaic power within these resources is considered as an uncontrollable source for calculation. Therefore, adjustable power output constraints for distributed energy resources can be obtained, facilitating further optimized scheduling.

[0033] Step S207: Based on the virtual response power value and the adjustable output power constraint, construct the optimization objective equation for the peak-shaving demand-side response.

[0034] The optimization objective equation includes the revenue from selling electricity and the cost of generating electricity. The objective equation aims to maximize the virtual revenue from peak-shaving demand-side response.

[0035] Specifically, the objective equation for optimization can be expressed as follows: ; in, This represents the virtual power generation revenue value; This represents the revenue from selling electricity; This represents the cost of electricity generation.

[0036] Here, based on the scheduling optimization objective equation, a heat-determined power scheduling optimization strategy can be realized; that is, the virtual response power value is obtained through adjustable flexible cold load, and the adjustable output power constraint is obtained through adjustable distributed system. Then, based on the virtual response power value and the adjustable output power constraint, the electricity sales revenue value and generation cost value of peak shaving demand-side response can be obtained, and the optimization objective equation is constructed based on the electricity sales revenue value and generation cost value.

[0037] Step S209: Based on the optimization objective equation, obtain the adjustable output power curve of the peak-shaving demand-side response and the maximum revenue of virtual power generation to achieve dispatch optimization.

[0038] Specifically, since the objective equation for optimization aims to maximize the virtual power generation revenue of the peak-shaving demand-side response, after obtaining the objective equation, the adjustable output power curve of the peak-shaving demand-side response and the maximum revenue of virtual power generation can be automatically obtained based on the optimization objective of the objective equation, thereby realizing the scheduling optimization of the peak-shaving demand-side response.

[0039] As can be seen, the embodiments of this application obtain the temperature thermal inertia time value, the virtual power generation value during the thermal inertia period, and the virtual power generation value during the energy-saving period of a single integrated energy cooling / heating system; based on the temperature thermal inertia time value, the virtual power generation value during the thermal inertia period, and the virtual power generation value during the energy-saving period, the virtual response power value of the single integrated energy cooling / heating system is obtained; the adjustable output power constraint of the distributed energy is obtained; based on the virtual response power value and the adjustable output power constraint, an optimization objective equation for peak-shaving demand-side response is constructed, wherein the optimization objective equation aims to maximize the virtual power generation revenue value of peak-shaving demand-side response; based on the optimization objective equation, the adjustable output power curve of peak-shaving demand-side response and the maximum revenue value of virtual power generation are obtained to achieve scheduling optimization. The above operations form virtual energy storage through the cooling / heating system of a single integrated energy source, realizing the quantification and controllability of the first-stage flexible load; by constraining the adjustable output power of distributed energy, the dispatchability and optimizability of the second stage are realized, thereby completing the construction of the optimization objective equation, achieving the effect of maximizing the output capacity of the virtual power plant during peak shaving periods and ensuring the maximum benefit of demand-side response.

[0040] The following reference Figure 3 The different steps in the above method flow are described in detail. First, step 201, namely "obtaining the temperature thermal inertia time value, the virtual power generation value during the thermal inertia period, and the virtual power generation value during the energy-saving period of the single integrated energy cooling / heating system", is described in detail with reference to the embodiment.

[0041] Obtain the demand response impact parameters of a single integrated energy cooling / heating system; based on the preset response model and corresponding demand response impact parameters of the single integrated energy cooling / heating system, obtain the corresponding temperature thermal inertia time value; obtain the historical power consumption baseline value and historical power consumption difference value of the single integrated energy cooling / heating system; based on the historical power consumption baseline value and historical power consumption difference value, obtain the virtual power generation value during the thermal inertia period; obtain the energy-saving efficiency value and energy-saving temperature difference value of the single integrated energy cooling / heating system; based on the historical power consumption baseline value, energy-saving efficiency value, and energy-saving temperature difference value, obtain the virtual power generation value during the energy-saving period.

[0042] Among them, the preset response model of a single integrated energy cooling / heating system can adopt Neural network model.

[0043] In one feasible approach, the demand response impact parameters of a single integrated energy cooling system are obtained. Specifically, during the temperature thermal inertia period, since the adjustable flexible load output resources are the pre-cooling temperature difference energy storage of pipelines and building spaces, the cooling system output can be increased in advance before the peak-shaving demand-side response begins. This lowers the temperature of the chilled water network and building spaces until the building space temperature slowly drops to 22°C, forming a temperature difference energy storage of Δt=4°C with the campus air conditioning energy-saving standard design temperature of 26°C. Subsequently, considering the impact of energy-saving standards and comfort, the upper limit of indoor temperature is set at 28°C, resulting in an energy-saving temperature difference of Δt=2°C. Before the start of the entire temperature thermal inertia period, the virtual energy storage temperature difference between the chilled water network and building spaces can reach Δt=6°C.

[0044] During the period of thermal inertia, the entire campus cooling is provided by virtual energy storage. The virtual energy storage at Δt=6℃ can support the entire campus's cooling demand for the duration specified in T during peak demand-side response. c This time is closely related to the environmental weather parameters of the campus, the indoor traffic flow on campus, and the demand-side response time period. This application obtains the demand response impact parameters of the campus integrated energy cooling system, and based on the preset response model of the individual integrated energy cooling system and the corresponding demand response impact parameters, obtains the corresponding temperature thermal inertia time value. The specific expression can be shown as follows: ; in, This indicates the weather parameters, indoor pedestrian traffic on campus, and demand-side response time every 15 minutes during the peak-shaving demand-side response period; among which, the weather parameters include, but are not limited to, outdoor temperature, wind speed, wind direction, humidity, and solar irradiance.

[0045] Obtain the historical baseline and historical power consumption difference of a single integrated energy cooling system. Specifically, since the cooling system includes chiller units and variable frequency pump units, the historical baseline power consumption of the chiller units and the historical power consumption difference of the variable frequency pump units can be obtained, respectively, using... and This means that, based on the historical baseline power consumption and the historical power consumption difference, the virtual power generation value during the thermal inertia period is obtained. The specific expression can be represented as follows: ; in, This represents the average power consumption of the chiller units over a historical number of days, which the server can obtain in real time; the historical power difference of the variable frequency pump units. This refers to the difference between the historical power output baseline value and the low-frequency operating power of the variable frequency pump unit. The historical power output baseline value of the variable frequency pump unit is the average power output of the variable frequency pump unit within a historical number of days. The low-frequency operating power of the variable frequency pump unit is its own attribute information, which can be obtained by the server in real time.

[0046] It should be noted that, since the historical power output baseline value of the variable frequency pump unit includes the historical power output baseline value of the variable frequency pump unit in the chilled water network system and the historical power output baseline value of the variable frequency pump unit in the cooling water network system, the historical power difference of the variable frequency pump unit refers to the sum of the difference between the historical power output baseline value and the low-frequency operating power of the variable frequency pump unit in the chilled water network system and the difference between the historical power output baseline value and the low-frequency operating power of the variable frequency pump unit in the cooling water network system.

[0047] During the energy-saving period, the energy-saving temperature difference can be set to Δt = 2℃. Based on experience, during the summer cooling season, for every 1℃ increase in indoor temperature, the energy efficiency of the entire cooling system is typically between 6% and 10%. Therefore, the energy efficiency and energy-saving temperature difference of a single integrated energy cooling system can be obtained, which can be represented by α and Δt, respectively. Based on the historical baseline power consumption, energy efficiency, and energy-saving temperature difference, the virtual power generation value during the energy-saving period can be obtained, and the specific expression is as follows: ; Among them, α and Δt can be specifically set based on experience and needs.

[0048] In another feasible approach, the demand response impact parameters of a single integrated energy heating system are obtained. Specifically, during the temperature inertia period, since the adjustable flexible load output resources are the preheating temperature difference energy storage of pipes and building spaces, the heating system output can be increased in advance before the peak-shaving demand-side response begins, raising the temperature of the heating hot water network and building spaces until the building space temperature slowly rises to 26°C. Then, considering the comfort of the single integrated energy system, i.e., the campus, the heating design temperature is set to 20°C to form a temperature difference energy storage of Δt=6°C. Combined with energy-saving standards, the lower limit of indoor temperature is set to 18°C ​​to achieve an energy-saving temperature difference of Δt=2°C. Before the start of the entire temperature inertia period, the virtual energy storage temperature difference between the heating hot water network and building spaces can reach Δt=8°C.

[0049] During the period of thermal inertia, the entire campus heating is provided by virtual energy storage. The virtual energy storage at Δt=8℃ can support the entire campus's heating demand for the duration specified in T during peak demand-side response. wThis time is closely related to the environmental weather parameters of the campus, the indoor traffic flow on campus, and the demand-side response time period. This application obtains the demand response impact parameters of the campus integrated energy heating system, and based on the preset response model of the individual integrated energy heating system and the corresponding demand response impact parameters, obtains the corresponding temperature thermal inertia time value. The specific expression can be as follows: ; in, This indicates the weather parameters, indoor pedestrian traffic on campus, and demand-side response time every 15 minutes during the peak-shaving demand-side response period; among which, the weather parameters include, but are not limited to, outdoor temperature, wind speed, wind direction, humidity, and solar irradiance.

[0050] Obtain the historical baseline and historical power consumption difference of a single integrated energy heating system. Specifically, since the heating system includes heating units and variable frequency pump units, the historical baseline and historical power consumption difference of the heating units can be obtained respectively using... and This means that, based on the historical baseline power consumption and the historical power consumption difference, the virtual power generation value during the thermal inertia period is obtained. The specific expression can be represented as follows: ; in, This represents the average power consumption of the chiller units over a historical number of days, which the server can obtain in real time; the historical power difference of the variable frequency pump units. This refers to the difference between the historical power output baseline value and the low-frequency operating power of the variable frequency pump unit in the heating network system. The historical power output baseline value is the average power output of the variable frequency pump unit within a historical number of days. The low-frequency operating power of the variable frequency pump unit is its own attribute information, which can be obtained by the server in real time.

[0051] During the energy-saving period, the energy-saving temperature difference can be set to Δt = 2℃. Based on experience, during the winter heating season, when the indoor temperature is lowered by 1℃, the energy efficiency of the entire heating system is usually between 3% and 10%. Therefore, the energy efficiency value and energy-saving temperature difference value of a single integrated energy heating system can be obtained, which can be respectively used... Δt represents the virtual power generation value during the energy-saving period, obtained based on the historical baseline power consumption, energy efficiency value, and energy-saving temperature difference value. The specific expression can be given as follows: ; in, And △t can be specifically set based on experience and needs.

[0052] The above operations, by setting adjustable flexible cooling loads, enable controllable element networks and building spaces to pre-cool / preheat inertial regulation, allowing individual integrated energy cooling / heating systems to build energy-saving temperature difference output regulation within the allowable range of regulations, establish basic virtual power generation output, and thus achieve the effect of quantifiable and controllable flexible loads.

[0053] Secondly, combining Figure 3 The embodiments provide a detailed description of step S205, namely, "obtaining the adjustable output power constraints of distributed energy sources".

[0054] In one feasible approach, refer to Figure 4 As shown, the baseline value of the charging power of the charging station and the preset minimum adjustable power value are obtained; the charging power difference of the charging station is obtained based on the baseline value of the charging power and the preset minimum adjustable power value; and the adjustable charging power constraint conditions of the charging station are obtained based on the preset starting value of the charging power and the charging power difference.

[0055] Among them, the adjustable output power constraint of distributed energy includes the adjustable charging power constraint of charging stations.

[0056] Here, when charging stations participate in peak-shaving demand-side response, the charging power of the charging stations is assumed to be... This indicates that the specific expression used will vary depending on the circumstances, as detailed below: when When, it can be represented as follows: ; when ,and When, it can be represented as follows: ; when ,and When, it can be represented as follows: ; in, This represents the baseline value of the charging power of the charging station, which can be obtained from the average value of historical charging power. This indicates that the predicted charging power value of the charging station can be predicted by a specific system and obtained directly from the server. This represents the adjustable output power value of the charging station. This value needs to be determined by setting corresponding constraints first, and then obtained during the optimization scheduling process. It should be noted that, based on the theory of physical relative kinematics, the above expressions can all use the baseline value of the charging station's charging power. Using this as a reference point, and considering it as the upper limit of the charging station's peak-shaving scheduling capacity, we optimize the peak-shaving scheduling strategy to obtain the adjustable output power value of the charging station. The reason for not using... As a reference point, because it is used When using a reference point, the above expressions need to be combined to obtain the charging power of the charging station. And with For reference, in Once determined, the values ​​can be directly substituted into the corresponding expressions that meet the conditions to obtain the charging power of the charging station.

[0057] Specifically, the server can obtain the charging power baseline value and the preset minimum adjustable power value of the charging station, which can be used respectively... and This indicates that the charging power difference is obtained based on the baseline charging power value and the preset minimum adjustable power value, and can be used... The specific expression can be represented as follows: ; in, This is an inherent property of the charging station itself. When the charging station supplies power to the reverse distribution network, its value can be negative.

[0058] Based on the preset initial charging power value and the difference in charging power, the adjustable output power constraint condition of the charging station is obtained. The preset initial charging power value can generally be set to 0, and the specific expression can be represented as follows: ; In another possible approach, refer to Figure 5 As shown, obtaining the adjustable output power constraint conditions for distributed energy sources further includes: obtaining the historical discharge power baseline value and the preset maximum discharge power value of the energy storage battery; obtaining the discharge power difference of the energy storage battery based on the historical discharge power baseline value and the preset maximum discharge power value; obtaining the initial condition for adjustable discharge power constraint of the energy storage battery based on the preset initial discharge power value and the discharge power difference; obtaining the discharge capacity constraint conditions of the energy storage battery; and obtaining the adjustable discharge power constraint conditions of the energy storage battery based on the initial condition for adjustable discharge power constraint and the discharge capacity constraint conditions.

[0059] Among them, the adjustable output power constraints of distributed energy sources include the adjustable discharge power constraints of energy storage batteries.

[0060] Here, the discharge power of the energy storage battery can be used as... The specific expression can be as follows: ; in, The decision variable representing the energy storage battery's participation in the peak-shaving response discharge regulation strategy is the adjustable discharge power value. This value needs to be determined by setting constraints to participate in the optimal scheduling. This represents the historical baseline value of the discharge power of the energy storage battery, which can be obtained from the average value of the historical discharge power of the energy storage battery.

[0061] Specifically, the historical baseline value of the energy storage battery's discharge power and the preset maximum value of the discharge power are obtained. It can be defined that energy storage discharge is positive and charging is negative, based on the historical baseline value of the energy storage battery's discharge power. To use as a guideline, the distance from the preset maximum discharge power can be... The distance represented is used as the upper limit of the adjustment capability, and is calculated in time intervals according to the 15-minute statistical unit.

[0062] Based on the historical baseline discharge power value and the preset maximum discharge power value, the discharge power difference of the energy storage battery is obtained, which can be used... The specific expression can be represented as follows: ; Based on the preset initial discharge power value and the discharge power difference, the initial conditions for the adjustable discharge power constraint of the energy storage battery are obtained. The preset initial discharge power value can generally be set to 0, and its specific expression is as follows: ; Obtain the discharge capacity constraints of the energy storage battery, where the discharge capacity constraint value can be obtained using... This means that the energy storage battery can reserve sufficient discharge capacity, which can be expressed as follows: ; in, This indicates the lower limit of the discharge capacity of the energy storage battery. This indicates the upper limit of the discharge capacity of the energy storage battery.

[0063] Based on the initial conditions for adjustable discharge power constraints and the discharge capacity constraints, the adjustable discharge power constraints for the energy storage battery are obtained, and their specific expressions are as follows: ; in, This indicates the historical baseline value of the discharge power of the energy storage battery; Indicates the adjustable discharge power value; The statistical measurement unit for peak-shaving demand-side response is time, and it is generally set to 15 minutes. and Both represent the number of time units, i.e., the number of 15-minute intervals.

[0064] In another possible approach, refer to Figure 6As shown, the adjustable output power constraint conditions for distributed energy include the adjustable cooling power constraint conditions for water-based cooling. Obtaining the adjustable output power constraint conditions for distributed energy also includes: obtaining the historical baseline value of cooling power and the preset maximum value of cooling power for water-based cooling; obtaining the cooling power difference of water-based cooling based on the historical baseline value and the preset maximum value of cooling power; obtaining the initial condition for adjustable cooling power constraint based on the preset initial value of cooling power and the cooling power difference; obtaining the cooling capacity constraint condition for water-based cooling; and obtaining the adjustable cooling power constraint conditions for water-based cooling based on the initial condition for adjustable cooling power constraint and the cooling capacity constraint condition.

[0065] Among them, the adjustable output power constraint of distributed energy includes the adjustable cooling power constraint of water storage cooling.

[0066] Here, the cooling capacity of water-based cooling can be used as... The specific expression can be represented as follows: ; in, The decision variable representing the water-based cooling system's participation in the peak-shaving response cooling regulation strategy is the adjustable cooling power value. This value needs to be determined by setting constraints to participate in scheduling optimization. The historical baseline value of the cooling power output of water-based cooling systems can be obtained from the average value of the historical cooling power output of water-based cooling systems.

[0067] Specifically, the historical baseline value of cooling power released by water storage and the preset maximum value of cooling power are obtained. It can be defined that energy storage cooling is positive and cold storage is negative, with the historical baseline value of cooling power for water storage being used. To use as a guideline, the distance from the preset maximum cooling power can be... The distance represented is used as the upper limit of the adjustment capability, and is calculated in time intervals according to the 15-minute statistical unit.

[0068] Based on the historical baseline cooling power and the preset maximum cooling power, the difference in cooling power for water storage is obtained, which can be used... The specific expression can be represented as follows: ; Based on the preset initial value of cooling power and the difference in cooling power, the initial conditions for adjustable cooling power constraints are obtained. The preset initial value of cooling power can generally be set to 0, and its specific expression is as follows: ; Obtain the cooling capacity constraints for water-based cooling storage, where the cooling capacity constraint value can be obtained using... This means that sufficient cooling capacity can be reserved for water storage, which can be expressed as follows: ; in, This indicates the lower limit of the cooling capacity of water storage. This indicates the upper limit of the cooling capacity of the water storage system.

[0069] Based on the initial conditions for adjustable cooling power and the constraints for cooling capacity, the adjustable cooling power constraints for water-based cooling storage are obtained, and their specific expressions are as follows: ; in, This represents the historical baseline value of cooling power. This indicates the adjustable cooling power value; The statistical measurement unit for peak-shaving demand-side response is time, and it is generally set to 15 minutes. and Both represent the number of time units, i.e., the number of 15-minute intervals.

[0070] The above operations, based on vector and relative motion theory, construct adjustable power constraints and corresponding power models for charging stations, energy storage batteries, and water-cooled systems, so as to participate in the final scheduling optimization and provide further assurance for maximizing the output capacity of virtual power plants during peak shaving periods in the later stage.

[0071] Finally, combining Figure 3 The embodiments provide a detailed description of step S207, namely, "constructing an optimization objective equation for the peak-shaving demand-side response based on the virtual response power value and the adjustable output power constraint."

[0072] Obtain the coefficient of performance (COP) values ​​of a single integrated energy cooling / heating system, the interruptible load value of a virtual power plant, and the difference in photovoltaic power generation. Obtain the response value of the peak-shaving demand side. Based on the response value, COP value, interruptible load value, power generation difference, virtual response power value, and adjustable output power constraints, obtain the electricity sales revenue value of the peak-shaving demand side response. Obtain the discharge cost value of distributed energy. Based on the COP value, discharge cost value, and adjustable output power constraints, obtain the power generation cost value of the peak-shaving demand side response. Based on the electricity sales revenue value and power generation cost value, construct the optimization objective equation for the peak-shaving demand side response.

[0073] Among them, the discharge cost of distributed energy includes the discharge cost of energy storage batteries, which can be represented by a; the discharge cost of water-based cold storage, which can be represented by b; and the orderly discharge cost of electric vehicles, which can be represented by c.

[0074] Specifically, the performance coefficient value of a single integrated energy cooling or heating system obtained by the server can be represented by COP; the interruptible load value of a virtual power plant can be represented by COP. The difference in power generation between photovoltaics and solar power can be represented by... express, Specifically, it refers to the difference between the photovoltaic power generation forecast and the photovoltaic power generation baseline, which can be obtained in real time by the aforementioned variable servers.

[0075] Obtain the response value of the peak shaving demand side. Based on the response value, performance coefficient value, interruptible load value, power generation difference, virtual response power value, and adjustable output power constraint, obtain the electricity sales revenue value of the peak shaving demand side response.

[0076] In one feasible approach, the adjustable optimized capacity value for peak shaving demand side is obtained based on the interruptible load value, the power generation difference, the performance coefficient value, the virtual response power value, and the adjustable output power constraint; and the electricity sales revenue value for the peak shaving demand side response is obtained based on the response price value, the response time value, and the adjustable optimized capacity value.

[0077] Among them, the response value on the demand side for peak shaving includes the response price value and the response time value, which can be respectively used as... and express.

[0078] Specifically, based on the interruptible load value, power generation difference, performance coefficient value, virtual response power value, and adjustable output power constraints, the adjustable optimized capacity value on the peak-shaving demand side is obtained, which can be used... The specific expression can be represented as follows: ; in, … All of these represent the adjustable charging power values ​​of the charging station. … All of these represent the adjustable discharge power value of the energy storage battery; All of these represent the adjustable cooling power values ​​of water-based cooling systems; Indicates interruptible load; This represents the difference between the photovoltaic power generation forecast and the photovoltaic power generation baseline. When the current campus's integrated energy system is a cooling system, When the current campus's integrated energy system is the heating system, take ; The coefficient represents the performance factor; 1, 2, ..., n are the segmented statistics, i.e., the number of 15-minute intervals.

[0079] Based on the response price, response time, and adjustable optimization capacity, the revenue from electricity sales in response to peak shaving demand can be obtained, which can then be used... The specific expression can be represented as follows: = ; in, Indicates the response price value; This represents the response time value, and It can be obtained by using the piecewise statistics n and the statistic time, and the specific expression can be expressed as follows: ; Furthermore, the revenue from selling electricity in response to peak demand shaving The overall expression can be represented as: ; The meanings of the variables have been described above and will not be repeated here.

[0080] Obtain the discharge cost value of distributed energy resources, and based on the performance coefficient value, discharge cost value, and adjustable output power constraints, obtain the generation cost value of peak-shaving demand-side response.

[0081] Here, the cost value of energy storage battery discharge (a), the cost value of water-based cold energy release (b), and the cost value of orderly discharge of electric vehicle (c) are obtained, based on a, b, c, and To obtain the generation cost value of peak-shaving demand-side response, it can be used... The specific expression can be represented as follows: ; in, This indicates the adjustable discharge power value of the energy storage battery; This indicates the adjustable cooling power value of water-based cooling systems. This indicates the adjustable charging power value of the charging station.

[0082] Based on the revenue from selling electricity and the cost of generating electricity, an optimization objective equation for peak-shaving demand-side response is constructed.

[0083] Here, after obtaining the revenue from electricity sales and the cost of electricity generation, the objective equation for optimization can be constructed. It should be noted that after the scheduling optimization is completed, since the adjustable output power curve of the peak-shaving demand-side response can be obtained, this adjustable output power curve can be distributed. Specifically, the adjustable charging power value of the corresponding charging station can be substituted into the charging station's charging power expression to obtain the corresponding charging power. Similarly, by substituting the adjustable discharge power value of the corresponding energy storage battery into the expression for the energy storage battery's discharge power, we obtain the energy storage battery's discharge power value. Substitute the corresponding adjustable cooling power value of the water storage system into the expression for the cooling power of the water storage system to obtain the cooling power value of the water storage system. .

[0084] The above operations establish a basic virtual power output by integrating interruptible loads and adjustable flexible cooling loads; at the same time, they are coordinated and optimized with charging stations, distributed photovoltaics, energy storage batteries and water-based cooling to construct an optimization objective equation, thereby maximizing the virtual power generation revenue during peak demand response periods and achieving the scheduling optimization effect of peak demand response.

[0085] It should be understood that, although Figure 2 The steps in the flowchart are shown sequentially as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated in this application, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Furthermore, Figure 2 At least some of the steps in the process may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least some of the sub-steps or stages of other steps.

[0086] Figure 7 This is a schematic diagram of a virtual power plant peak shaving response scheduling optimization device provided in an embodiment of this application. This device can be installed in... Figure 1 The server in the system shown is used to perform, for example... Figure 2 The method flow is shown below. Figure 7 As shown, the device may include: a first acquisition unit 701, a first calculation unit 703, a second acquisition unit 705, a second calculation unit 707, and a scheduling optimization unit 709. The main functions of each component module are as follows: The first acquisition unit 701 is used to acquire the temperature thermal inertia time value, the virtual power generation value during the thermal inertia period, and the virtual power generation value during the energy-saving period of a single integrated energy cooling / heating system. The first calculation unit 703 is used to obtain the virtual response power value of a single integrated energy cooling / heating system based on the temperature thermal inertia time value, the virtual power generation value during the thermal inertia period, and the virtual power generation value during the energy-saving period. The second acquisition unit 705 is used to acquire the adjustable output power constraint conditions of the distributed energy source. The second calculation unit 707 is used to construct an optimization objective equation for the peak-shaving demand-side response based on the virtual response power value and the adjustable output power constraint, wherein the optimization objective equation aims to maximize the virtual power generation revenue value of the peak-shaving demand-side response. The scheduling optimization unit 709 is used to obtain the adjustable output power curve of the peak-shaving demand-side response and the maximum revenue of virtual power generation according to the optimization objective equation, so as to achieve scheduling optimization.

[0087] In one embodiment, the first acquisition unit 701 is further configured to: Obtain the demand response impact parameters of a single integrated energy cooling / heating system; Based on the preset response model and corresponding demand response impact parameters of the single integrated energy cooling / heating system, the corresponding temperature thermal inertia time value is obtained; Obtain the historical baseline value of power consumption and the historical power consumption difference of a single integrated energy cooling / heating system; Based on the historical power consumption baseline value and the historical power consumption difference, the virtual power generation value during the thermal inertia period is obtained; Obtain the energy efficiency value and energy-saving temperature difference value of a single integrated energy cooling / heating system; Based on the historical baseline power consumption, energy efficiency value, and energy-saving temperature difference value, the virtual power generation value for the energy-saving period is obtained.

[0088] In one embodiment, the adjustable output power constraint of the distributed energy source includes the adjustable charging power constraint of the charging station, and the second acquisition unit 705 is further configured to: Obtain the baseline value of the charging power of the charging station and the preset minimum adjustable power value; The charging power difference of the charging station is obtained based on the baseline charging power value and the preset minimum adjustable power value. Based on the preset initial charging power value and the difference in charging power, the adjustable charging power constraint conditions of the charging station are obtained.

[0089] In one embodiment, the adjustable output power constraint of the distributed energy source includes the adjustable discharge power constraint of the energy storage battery, and the second acquisition unit 705 is further configured to: Obtain the historical baseline value of discharge power and the preset maximum value of discharge power of the energy storage battery; The difference in discharge power of the energy storage battery is obtained based on the historical baseline discharge power value and the preset maximum discharge power value. Based on the preset initial value of discharge power and the difference in discharge power, the initial conditions for the adjustable discharge power constraint of the energy storage battery are obtained. Obtain the discharge capacity constraints of the energy storage battery; Based on the initial conditions for adjustable discharge power constraints and the discharge capacity constraints, the adjustable discharge power constraints for the energy storage battery are obtained.

[0090] In one embodiment, the adjustable output power constraint of the distributed energy source includes the adjustable cooling power constraint of water-based cooling storage, and the second acquisition unit 705 is further configured to: Obtain the historical baseline value of cooling power released by water storage and the preset maximum value of cooling power; The difference in cooling power for water storage is obtained based on the historical baseline value of cooling power and the preset maximum value of cooling power. Based on the preset initial value of cooling power and the difference in cooling power, the initial conditions for adjustable cooling power constraint are obtained; Obtain the cooling capacity constraints of water-based cooling systems; Based on the initial conditions for adjustable cooling power and the constraints for cooling capacity, the adjustable cooling power constraints for water-based cooling storage are obtained.

[0091] In one embodiment, the objective equation for optimization includes the revenue from electricity sales and the cost of electricity generation. The second calculation unit 707 is further used for: Obtain the coefficient of performance (COP) value of a single integrated energy cooling / heating system, the interruptible load value of a virtual power plant, and the difference in photovoltaic power generation. Obtain the response value of the peak shaving demand side, and based on the response value, performance coefficient value, interruptible load value, power generation difference, virtual response power value, and adjustable output power constraint, obtain the electricity sales revenue value of the peak shaving demand side response. Obtain the discharge cost value of distributed energy resources, and based on the performance coefficient value, discharge cost value, and adjustable output power constraints, obtain the generation cost value of peak-shaving demand-side response. Based on the revenue from selling electricity and the cost of generating electricity, an optimization objective equation for peak-shaving demand-side response is constructed.

[0092] In one embodiment, the response value on the peak-shaving demand side includes a response price value and a response time value. The second calculation unit 707 is further used for: Based on the interruptible load value, power generation difference, performance coefficient value, virtual response power value, and adjustable output power constraint, the adjustable optimized capacity value of the peak shaving demand side is obtained; based on the response price value, response time value, and adjustable optimized capacity value, the electricity sales revenue value of the peak shaving demand side response is obtained.

[0093] The same or similar parts among the above embodiments can be referred to interchangeably. Each embodiment focuses on describing the differences from other embodiments. In particular, the device embodiments are basically similar to the method embodiments, so the description is relatively simple, and the relevant parts can be referred to the description of the method embodiments.

[0094] It should be noted that the embodiments of this application may involve the use of user data. In practical applications, user-specific personal data may be used in the scheme described herein within the scope permitted by applicable laws and regulations, provided that it complies with the applicable laws and regulations of the country (e.g., explicit consent from the user, actual notification to the user, explicit authorization from the user, etc.).

[0095] According to embodiments of this application, this application also provides a computer device and a computer-readable storage medium.

[0096] like Figure 8 The diagram shown is a block diagram of a computer device according to an embodiment of this application. The term "computer device" is intended to represent various forms of digital computers or mobile devices. The digital computer may include a desktop computer, a portable computer, a workbench, a personal digital assistant, a server, a mainframe computer, and other suitable computers. The mobile device may include a tablet computer, a smartphone, a wearable device, etc.

[0097] like Figure 8 As shown, device 800 includes a computing unit 801, a ROM 802, a RAM 803, a bus 804, and an input / output (I / O) interface 805. The computing unit 801, ROM 802, and RAM 803 are interconnected via the bus 804. The input / output (I / O) interface 805 is also connected to the bus 804.

[0098] The computing unit 801 can execute various processes in the method embodiments of this application according to computer instructions stored in read-only memory (ROM) 802 or computer instructions loaded from storage unit 808 into random access memory (RAM) 803. The computing unit 801 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. The computing unit 801 can include, but is not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. In some embodiments, the methods provided in the embodiments of this application can be implemented as computer software programs, which are tangibly contained in a computer-readable storage medium, such as storage unit 808.

[0099] RAM 803 can also store various programs and data required for the operation of device 800. Part or all of the computer program can be loaded and / or installed on device 800 via ROM 802 and / or communication unit 809.

[0100] The input unit 806, output unit 807, storage unit 808, and communication unit 809 in device 800 can be connected to I / O interface 805. The input unit 806 can be, for example, a keyboard, mouse, touchscreen, or microphone; the output unit 807 can be, for example, a display, speaker, or indicator light. Device 800 can exchange information and data with other devices through the communication unit 809.

[0101] It should be noted that the device may also include other components necessary for normal operation. It may also include only the components necessary for implementing the solution of this application, without necessarily including all the components shown in the figures.

[0102] Various implementations of the systems and techniques described herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SOCs), payload programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof.

[0103] The computer instructions used to implement the methods of this application may be written in any combination of one or more programming languages. These computer instructions may be provided to the computing unit 801 such that when executed by the computing unit 801, such as a processor, the computer instructions cause the execution of the steps involved in the embodiments of the methods of this application.

[0104] The computer-readable storage medium provided in this application can be a tangible medium that can contain or store computer instructions for performing the steps involved in the method embodiments of this application. The computer-readable storage medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, and other forms of storage media.

[0105] The specific embodiments described above do not constitute a limitation on the scope of protection of this application. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A virtual power plant peak shaving response scheduling optimization method, characterized in that, The method includes: Obtain the temperature thermal inertia time value, the virtual power generation value during the thermal inertia period, and the virtual power generation value during the energy-saving period of a single integrated energy cooling / heating system; Based on the temperature thermal inertia time value, the virtual power generation value during the thermal inertia period, and the virtual power generation value during the energy-saving period, the virtual response power value of a single integrated energy cooling / heating system is obtained. Obtain the adjustable output power constraints of distributed energy sources; Based on the virtual response power value and the adjustable output power constraint, an optimization objective equation for the peak-shaving demand-side response is constructed, wherein the optimization objective equation aims to maximize the virtual power generation revenue value of the peak-shaving demand-side response. Based on the aforementioned optimization objective equation, the adjustable output power curve of the peak-shaving demand-side response and the maximum revenue of virtual generation are obtained to achieve dispatch optimization.

2. The method of claim 1, wherein, The acquisition of the temperature thermal inertia time value, the virtual power generation value during the thermal inertia period, and the virtual power generation value during the energy-saving period of a single integrated energy cooling / heating system includes: Obtain the demand response impact parameters of a single integrated energy cooling / heating system; Based on the preset response model of the single integrated energy cooling / heating system and the corresponding demand response influence parameters, the corresponding temperature thermal inertia time value is obtained; Obtain the historical baseline value of power consumption and the historical power consumption difference of a single integrated energy cooling / heating system; Based on the historical power consumption baseline value and the historical power consumption difference, the virtual power generation value during the thermal inertia period is obtained; Obtain the energy efficiency value and energy-saving temperature difference value of a single integrated energy cooling / heating system; The virtual power generation value for the energy-saving period is obtained based on the historical power consumption baseline value, the energy-saving efficiency value, and the energy-saving temperature difference value.

3. The method of claim 2, wherein, The adjustable output power constraint of the distributed energy source includes the adjustable charging power constraint of the charging station. Obtaining the adjustable output power constraint of the distributed energy source includes: Obtain the baseline value of the charging power of the charging station and the preset minimum adjustable power value; The charging power difference of the charging station is obtained based on the baseline charging power value and the preset minimum adjustable power value. The adjustable charging power constraint conditions of the charging station are obtained based on the preset initial charging power value and the charging power difference.

4. The method of claim 2, wherein, The adjustable output power constraint of the distributed energy source includes the adjustable discharge power constraint of the energy storage battery. Obtaining the adjustable output power constraint of the distributed energy source further includes: Obtain the historical baseline value of discharge power and the preset maximum value of discharge power of the energy storage battery; The discharge power difference of the energy storage battery is obtained based on the historical discharge power baseline value and the preset discharge power maximum value. Based on the preset initial value of discharge power and the difference in discharge power, the initial conditions for the adjustable discharge power constraint of the energy storage battery are obtained. Obtain the discharge capacity constraints of the energy storage battery; Based on the initial conditions for adjustable discharge power constraints and the discharge capacity constraints, the adjustable discharge power constraints of the energy storage battery are obtained.

5. The method of claim 2, wherein, The adjustable output power constraint condition of the distributed energy includes an adjustable cold release power constraint condition of the water storage, and the obtaining of the adjustable output power constraint condition of the distributed energy further includes: obtaining a historical cold release power baseline value and a preset maximum cold release power value of the water storage; obtaining a cold release power difference value of the water storage according to the historical cold release power baseline value and the preset maximum cold release power value; obtaining an adjustable cold release power constraint initial condition according to a preset cold release power initial value and the cold release power difference value; obtaining a cold release capacity constraint condition of the water storage; obtaining the adjustable cold release power constraint condition of the water storage according to the adjustable cold release power constraint initial condition and the cold release capacity constraint condition.

6. The method according to any one of claims 1 to 5, characterized in that, The optimization target equation includes a power selling revenue value and a power generation cost value, and the constructing of the optimization target equation of the peak shaving demand side response according to the virtual response power value and the adjustable output power constraint condition includes: obtaining a performance coefficient value of a single comprehensive energy cooling / heating system, an interruptible load value of a virtual power plant and a power generation power difference value of photovoltaic; obtaining a response value of the peak shaving demand side, and obtaining a power selling revenue value of the peak shaving demand side response according to the response value, the performance coefficient value, the interruptible load value, the power generation power difference value, the virtual response power value and the adjustable output power constraint condition; obtaining a power generation cost value of the peak shaving demand side response according to the performance coefficient value, the power generation cost value and the adjustable output power constraint condition; constructing the optimization target equation of the peak shaving demand side response according to the power selling revenue value and the power generation cost value.

7. The method of claim 6, wherein, The response value of the peak shaving demand side includes a response price value and a response time value, and the obtaining of the power selling revenue value of the peak shaving demand side response according to the response value, the performance coefficient value, the interruptible load value, the power generation power difference value, the virtual response power value and the adjustable output power constraint condition includes: obtaining an adjustable optimization capacity value of the peak shaving demand side according to the interruptible load value, the power generation power difference value, the performance coefficient value, the virtual response power value and the adjustable output power constraint condition; obtaining the power selling revenue value of the peak shaving demand side response according to the response price value, the response time value and the adjustable optimization capacity value.

8. A virtual power plant peak shaving response scheduling optimization apparatus, characterized in that, The device includes: a first obtaining unit configured to obtain a temperature thermal inertia time value of a single comprehensive energy cooling / heating system, a thermal inertia period virtual power value and an energy saving period virtual power value; a first calculating unit configured to obtain a virtual response power value of the single comprehensive energy cooling / heating system according to the temperature thermal inertia time value, the thermal inertia period virtual power value and the energy saving period virtual power value; a second obtaining unit configured to obtain an adjustable output power constraint condition of the distributed energy; a second calculating unit configured to construct an optimization target equation of the peak shaving demand side response according to the virtual response power value and the adjustable output power constraint condition, wherein the optimization target equation takes maximizing a virtual power generation revenue value of the peak shaving demand side response as a target. The scheduling optimization unit is configured to obtain the adjustable output power curve of the peak shaving demand side response and the maximum benefit of the virtual power generation according to the optimization target equation, so as to realize scheduling optimization.

9. A computer device comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores computer instructions executable by the at least one processor, and the computer instructions are executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-7.

10. A computer readable storage medium having stored thereon computer instructions, wherein, The computer instructions are used to enable the computer to perform the method of any one of claims 1 to 7.

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