Virtual power plant multi-objective optimization scheduling method, system and device and storage medium

Abstract

The invention discloses a virtual power plant multi-objective optimization scheduling method, system and device, and a storage medium. The method comprises the following steps: constructing a virtual power plant architecture; the virtual power plant comprises internal resources and distributed power generation equipment; the internal resources comprise a base station standby energy storage system, an internet data center, an interruptible load and a gas turbine; the distributed power generation equipment comprises wind power generation and photovoltaic power generation; internal resources of the virtual power plant are modeled, specifically, base station standby energy storage system modeling, internet data center modeling, interruptible load modeling and gas turbine modeling are included; establishing an objective function of multi-objective optimization of the virtual power plant; establishing constraint conditions of multi-objective optimization of the virtual power plant; solving based on the objective function and the constraint condition, obtaining a scheduling instruction, and executing scheduling; according to the invention, the operation economy and scheduling flexibility of the virtual power plant system are improved.

Description

Technical Field

[0001] The present invention relates to a multi-objective optimization scheduling method, system, device and storage medium, and in particular to a multi-objective optimization scheduling method, system, device and storage medium for a virtual power plant, belonging to the technical field of virtual power plant scheduling. Background Art

[0002] With the advancement of the "carbon peak and carbon neutrality" goals, China is building a new power system with new energy as the main body. As an important means of consuming renewable energy and improving the system's regulation capabilities, virtual power plants can aggregate and fully utilize a large number of demand-side resources. Virtual power plants refer to "source-grid-load-storage" coordination management systems that effectively integrate distributed resources and participate in the power market through advanced information and communication technologies, intelligent metering and optimization control methods.

[0003] In recent years, my country has vigorously promoted the development of information and communication technology infrastructure. A large number of adjustable loads based on information and communication infrastructure have appeared on the demand side, including backup energy storage and data centers, which can be used as an important part of the internal resources of virtual power plants. At present, China's base stations are huge in scale, and each base station is equipped with an energy storage battery with an average capacity of about 24kWh as a backup power source. The total reserve energy storage capacity can reach 24,000 megawatt-hours, and the adjustable potential is considerable. The power consumption of data centers is extremely high, so high cost has become one of the most serious problems of data centers.

[0004] On the one hand, the adjustable capacity of base station backup energy storage and data centers can increase operating income and reduce electricity costs; on the other hand, it provides economical and efficient system regulation capabilities for the power system and improves overall operating efficiency. This is not only technically feasible but also has great commercial value and can become a new profit growth point for power grid companies. At present, there have been studies on the participation of base station backup energy storage in power grid dispatching and demand response. As a commercial operating entity, virtual power plants have multiple operating goals such as economy and energy saving. Therefore, it is of great significance to study the multi-objective optimization dispatching strategy of virtual power plants considering base station backup energy storage.

[0005] However, existing research on virtual power plant optimization scheduling methods has the following shortcomings: 1) High complexity: The optimization scheduling of virtual power plants involves multiple different types of energy resources, such as wind power, solar energy, energy storage, etc., and considers factors such as market conditions and demand response, which makes the optimization model very complex and the computational cost high. 2) Insufficient model assumptions: The optimization models in existing research may be based on some simplified assumptions, such as ignoring the limitations of the transmission network and the uncertainty of weather forecasts, which may lead to inaccuracies in actual operations. 3) Market and policy restrictions: The operation of virtual power plants is subject to multiple market and policy restrictions, such as different market access conditions for different energy types and different policy support. These factors may not be fully considered in the optimization scheduling model. Lack of standardized methods and evaluation: The research on virtual power plant optimization scheduling methods lacks a unified standardized method and evaluation framework, which makes it difficult to compare and replicate different research results, affecting the feasibility and effect evaluation of practical applications. Therefore, it is of great significance to study the multi-objective optimization scheduling strategy of virtual power plants considering base station backup energy storage. Summary of the invention

[0006] Purpose of the invention: The purpose of the present invention is to provide a multi-objective optimization scheduling method, system, equipment and storage medium for a virtual power plant that can improve the operating economy and scheduling flexibility of the virtual power plant.

[0007] Technical solution: A virtual power plant multi-objective optimization scheduling method according to the present invention comprises:

[0008] S1: Construct a virtual power plant architecture; the virtual power plant includes internal resources and distributed power generation equipment; the internal resources include base station backup energy storage system, Internet data center, interruptible load and gas turbine; the distributed power generation equipment includes wind power generation and photovoltaic power generation;

[0009] S2: Modeling the internal resources of the virtual power plant, including base station backup energy storage system modeling, Internet data center modeling, interruptible load modeling and gas turbine modeling;

[0010] S3: Establish the objective function of multi-objective optimization of virtual power plant;

[0011] S4: Establish constraints for multi-objective optimization of virtual power plants;

[0012] S5: Solve based on the objective function and constraints, obtain scheduling instructions, and execute scheduling.

[0013] Furthermore, the base station backup energy storage system modeling is specifically to establish a base station backup energy storage system model as follows:

[0014]

[0015] Wmin ≤W t ≤W max (4)

[0016] The discharge power constraints of the base station backup energy storage system are as follows:

[0017]

[0018] Among them, W t is the remaining energy of the base station backup energy storage in time period t; η c and η d are charging efficiency and discharging efficiency respectively; Δt is the duration; are the charging power and discharging power of the base station backup energy storage in time period t; W min and W max are the lower and upper limits of the remaining energy of the base station backup energy storage; P cmax and P dmax are the upper limits of the charging power and discharging power of the base station backup energy storage; P i BS,Load is the load power of the base station;

[0019] The backup capacity constraint of the base station backup energy storage system power lower limit is as follows:

[0020]

[0021] Among them, W Backup is the backup capacity requirement of the base station, which is related to the communication load of the base station; η is the discharge capacity coefficient; α is the battery temperature coefficient; t emp is the lowest ambient temperature of the base station battery pack; K is the safety factor; I real-time is the real-time load current; T Backup is the battery backup time.

[0022] Furthermore, the Internet data center modeling is specifically to express the model of the Internet data center as:

[0023]

[0024] Among them, a i represents the minimum power consumption required by the data load of the Internet data center processing unit of the i-th virtual power plant; θ t represents the total amount of data load to be processed by the Internet data center at time period t; i and β i are the upper and lower limits of the power consumption of the Internet data center of the i-th virtual power plant; N is the total number of virtual power plants;

[0025] α i =(μi -1 / D) / k i , ξ i =k i M i +β i ;if Then (7) are equal.

[0026] Further, the interruptible load is modeled,

[0027] The model of interruptible load can be expressed as,

[0028]

[0029] in, is the interruptible load for time period t, is the upper limit of the interruptible load.

[0030] Furthermore, the gas turbine modeling, specifically, the gas turbine model is established as follows:

[0031]

[0032] in, is the power output of the gas turbine in virtual power plant i at time period t; and are the upper and lower limits of the gas turbine power output; as well as The upper and lower limits of gas turbine power ramp.

[0033] Furthermore, the step S3 is specifically:

[0034] Considering minimizing the economic cost, the objective function is established, which is expressed as:

[0035]

[0036] In the formula, and They are the charging and discharging cost of the base station backup energy storage system, the dispatching cost of the Internet data center, the gas turbine power generation cost, and the demand response dispatching cost. and is the income from online wind power and online photovoltaic power generation in regional virtual power plant i at time period t; and Respectively represent the charging and discharging power of the base station backup energy storage system; Represents the power of Internet data centers; P i,GT (t) represents the power of the gas turbine; represents the power of demand response; Indicates the power of wind power; is the power of photovoltaic power generation; K ES , K IDC , K DR , K WT and K PV are the cost factors of base station backup energy storage system, Internet data center, demand response, wind power generation and photovoltaic power generation; a GT , b GT 、c GT It is the cost factor of power generation from gas turbines; and They are the electricity sales, electricity purchase, electricity sales price and electricity purchase price of virtual power plant i in time period t;

[0037] Consider maximizing the wind power and photovoltaic grid-connected capacity of the virtual power plant during the dispatch cycle:

[0038]

[0039] in, It is the grid-connected capacity of wind power and photovoltaic power; is the actual power generation of wind power and photovoltaic power; N WT Indicates the number of fans; N PV represents the number of photovoltaics; T represents the scheduling period;

[0040] The global objective function of the virtual power plant is established, the multi-objective optimization model is normalized by the standardization method and converted into a single-objective problem by the weighted method, and the global optimal solution is obtained:

[0041] Min F=λ1F1+λ2F2 (13)

[0042] Among them, λ1 and λ2 are normalization coefficients.

[0043] Furthermore, the constraint conditions in step S4 are as follows:

[0044]

[0045] Based on the same inventive concept, the present invention also provides a virtual power plant multi-objective optimization scheduling system, comprising:

[0046] A power plant construction module is used to construct a virtual power plant architecture; the virtual power plant includes internal resources and distributed power generation equipment; the internal resources include a base station backup energy storage system, an Internet data center, an interruptible load and a gas turbine; the distributed power generation equipment includes wind power generation and photovoltaic power generation;

[0047] Internal model building module, used to model the internal resources of the virtual power plant, including base station backup energy storage system modeling, Internet data center modeling, interruptible load modeling and gas turbine modeling;

[0048] An objective function establishment module is used to establish the objective function of multi-objective optimization of the virtual power plant;

[0049] Constraint establishment module, used to establish the constraints for multi-objective optimization of virtual power plants;

[0050] The scheduling module is used to solve the objective function and constraints, obtain scheduling instructions, and execute scheduling.

[0051] Based on the same inventive concept, the present invention also provides a computing device, comprising: one or more processors, one or more memories and one or more programs, wherein the programs are stored in the memories and configured to be executed by the processors, and when the programs are loaded into the processors, the steps of the multi-dimensional indicator calculation method described in any one of the above items are implemented.

[0052] Based on the same inventive concept, the present invention also provides a computer-readable storage medium, which stores a computer program, and the computer program includes program instructions. When the program instructions are executed by a processor, the processor executes the steps of the multi-dimensional indicator calculation method described in any one of the above items.

[0053] Beneficial effects: Compared with the prior art, the present invention increases the adjustable range of the virtual power plant through a multi-objective optimization scheduling method, smoothes the load curve with time-of-use electricity prices as a guide, and improves the system operation economy and scheduling flexibility. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 is a flow chart of a method according to an embodiment of the present invention;

[0055] Figure 2 A schematic diagram of the structure of a virtual power plant according to an embodiment of the present invention;

[0056] Figure 3 A schematic diagram of actual wind power generation and grid-connected capacity according to an embodiment of the present invention;

[0057] Figure 4 A schematic diagram of actual photovoltaic power generation and grid-connected capacity according to an embodiment of the present invention;

[0058] Figure 5 Schematic diagram of the surplus power storage and backup demand curve of the base station backup energy storage system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0059] The principles and features of the present invention are described below in conjunction with the accompanying drawings. The examples given are only used to explain the present invention and are not used to limit the scope of the present invention. The present invention is described in more detail by way of example with reference to the accompanying drawings in the following paragraphs. The advantages and features of the present invention will become clearer according to the following description and claims. It should be noted that the drawings are all in a very simplified form and are not in precise proportions, and are only used to conveniently and clearly assist in explaining the purpose of the embodiments of the present invention.

[0060] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used in the specification of the present invention herein are only for the purpose of describing specific embodiments and are not intended to limit the present invention.

[0061] As attached Figure 1 As shown, the multi-objective optimization scheduling method of a virtual power plant in this embodiment includes:

[0062] S1: Construct a virtual power plant architecture; the virtual power plant includes internal resources and distributed power generation equipment; the internal resources include base station backup energy storage system, Internet data center, interruptible load and gas turbine; the distributed power generation equipment includes wind power generation and photovoltaic power generation;

[0063] S2: Modeling the internal resources of the virtual power plant, including base station backup energy storage system modeling, Internet data center modeling, interruptible load modeling and gas turbine modeling;

[0064] S3: Establish the objective function of multi-objective optimization of virtual power plant;

[0065] S4: Establish constraints for multi-objective optimization of virtual power plants;

[0066] S5: Solve based on the objective function and constraints, obtain scheduling instructions, and execute scheduling.

[0067] Furthermore, step S1 includes the following steps to realize the construction of a virtual power plant architecture considering the base station backup energy storage:

[0068] like Figure 2 As shown, the architecture of the virtual power plant considering base station backup energy storage includes base station backup energy storage (BSES), Internet data center (IDC), distributed generation (WT, PV), gas turbine (GT) and interruptible load;

[0069] By aggregating resources in virtual power plants and interacting with grid energy, users’ electricity consumption behavior is guided by time-of-use electricity prices; Internet data centers can quickly transmit data loads with other Internet data centers in other regions to achieve adjustable capacity;

[0070] Further, step S2 includes the following steps for modeling the internal resources of the virtual power plant considering the base station backup energy storage:

[0071] Modeling the internal resources of the virtual power plant considering the base station backup energy storage includes base station backup energy storage system modeling, Internet data center modeling, interruptible load modeling, and micro gas turbine modeling;

[0072] Further, the following steps are included to model the base station backup energy storage system of the virtual power plant considering the base station backup energy storage:

[0073] Base stations are usually equipped with energy storage batteries as backup power sources to prevent power outages. Some communication base stations are even equipped with diesel generators as backup power sources. When the power system is operating normally, the base station backup energy storage can be in a dispatchable idle / floating charge state.

[0074] Since the backup demand of communication base stations is related to the real-time communication load, the capacity of the base station backup energy storage can be divided into two parts: one part is to meet the power supply reliability requirements of the base station, and the remaining part is the dispatchable capacity; in the process of participating in the virtual power plant, the base station backup energy storage is dispatched in the form of aggregated virtual units;

[0075] The backup capacity demand of the base station is time-varying, and the adjustable capacity model of the base station backup energy storage is:

[0076]

[0077] W min ≤W t ≤W max (4)

[0078] The discharge power constraints of the base station backup energy storage system are as follows:

[0079]

[0080] Among them, W t is the remaining energy of the base station backup energy storage in time period t; η c and η d are charging efficiency and discharging efficiency respectively; Δt is the duration; are the charging power and discharging power of the base station backup energy storage in time period t; W min and W max are the lower and upper limits of the remaining energy of the base station backup energy storage; P cmax and P dmax are the upper limits of the charging power and discharging power of the base station backup energy storage; P i BS,Load is the load power of the base station;

[0081] For the base station backup energy storage system, the power lower limit should also be constrained by its backup capacity.

[0082]

[0083] Among them, W Backup is the backup capacity requirement of the base station, which is related to the communication load of the base station; η is the discharge capacity coefficient, which is generally related to the discharge time; α is the battery temperature coefficient, α=0.008; t emp is the lowest ambient temperature (℃) where the base station battery pack is located, which is considered to be 15℃; if there is no heating equipment, it is considered to be 5℃; K is the safety factor, K=1.25; I real -time is the real-time load current (A), calculated by the base station demand; T Backup is the battery backup time (h).

[0084] Further, the following steps are included to model an Internet data center of a virtual power plant considering base station backup energy storage:

[0085] The total energy consumption of a data center mainly comes from the energy consumption of IT equipment and cooling systems;

[0086] The power consumption model of Internet data centers is expressed as:

[0087]

[0088] Among them, α i , β i ,θ t , i It is the parameter that the Internet data center operator needs to provide; α i represents the minimum variable power consumption required for data center i to process unit data load; θ t represents the total amount of data load to be processed by the Internet data center at time t; ξ i and β i is the upper and lower limits of the power consumption of the Internet data center of the i-th virtual power plant; α i =(μ i -1 / D) / k i , ξ i =k i M i +β i ;if Then (7) are equal.

[0089] Further, the following steps are included to model the interruptible load of the virtual power plant considering the base station backup energy storage:

[0090] The model of interruptible load can be expressed as,

[0091]

[0092] in, is the interruptible load for time period t, is the upper limit of the interruptible load.

[0093] Further, the following steps are included for modeling a micro gas turbine of a virtual power plant considering the base station backup energy storage:

[0094] The model of the micro gas turbine can be expressed as:

[0095]

[0096] in, is the power output of the gas turbine in virtual power plant i at time period t; and are the upper and lower limits of the gas turbine power output; as well as The upper and lower limits of gas turbine power ramp.

[0097] Further, the following steps are included to establish an objective function for multi-objective optimization of a virtual power plant considering base station backup energy storage:

[0098] The objective function of multi-objective optimization of the virtual power plant considering the backup energy storage of the base station includes minimizing the economic cost of the virtual power plant while maximizing the grid-connected capacity of wind power generation and photovoltaic power generation;

[0099] The first goal of the virtual power plant is to minimize the economic costs, including the charging and discharging costs of the base station backup energy storage, the Internet data center and demand response dispatch costs, the power generation costs of the gas turbine, and the profits of the power market and grid-connected wind power and photovoltaic power, which can be expressed as:

[0100]

[0101] In the formula, and They are the charging and discharging cost of the base station backup energy storage system, the dispatching cost of the Internet data center, the gas turbine power generation cost, and the demand response dispatching cost. and is the income from online wind power and online photovoltaic power generation in regional virtual power plant i at time period t; and Respectively represent the charging and discharging power of the base station backup energy storage system; Represents the power of Internet data centers; P i,GT (t) represents the power of the gas turbine; represents the power of demand response; Indicates the power of wind power; is the power of photovoltaic power generation; K ES , K IDC , K DR , K WT and K PV are the cost factors of base station backup energy storage system, Internet data center, demand response, wind power generation and photovoltaic power generation; a GT 、b GT 、c GT It is the cost factor of power generation from gas turbines; and They are respectively the electricity sales amount, electricity purchase amount, electricity sales price and electricity purchase price of virtual power plant i in time period t.

[0102] The second goal of the virtual power plant is to maximize the wind power and photovoltaic grid-connected capacity of the virtual power plant during the dispatch cycle:

[0103]

[0104] in,, It is the grid-connected capacity of wind power and photovoltaic power; is the actual power generation of wind power and photovoltaic power; N WT Indicates the number of fans; N PV represents the number of photovoltaics; T represents the scheduling period.

[0105] The global goal of the virtual power plant is to minimize operating costs and maximize the grid capacity for renewable energy generation; the multi-objective optimization model is normalized using a standardized method and converted into a single-objective problem using a weighted method to obtain a global optimal solution:

[0106] Min F=λ1F1+λ2F2 (13)

[0107] Among them, λ1 and λ2 are normalization coefficients.

[0108] Further, the following steps are included to establish the constraints of multi-objective optimization of the virtual power plant considering the base station backup energy storage:

[0109] The constraints include power balance constraints and operation constraints of each distributed resource, see (1-9 and 14),

[0110]

[0111] Taking a single virtual power plant as an example, the virtual power plant includes a 500kW micro gas turbine, wind power, photovoltaic, load, Internet data center and base station backup energy storage. With lh as the optimization time interval and 24h as the optimization cycle, the scheduling target is the minimum operating cost and maximum grid-connected capacity of renewable energy power generation. According to the previous objective function and constraints, the single-objective and multi-objective optimization problems are solved to compare the optimization results under different scenarios, as shown in Table 1.

[0112] Table 1 Results of different optimization objects

[0113]

[0114] It can be seen from Table 1 that: compared with a single objective, under multi-objective optimization, the virtual power plant control strategy can achieve the dual optimization goals of economy and green power generation, its operating cost can reach 1982.07 yuan, and the grid-connected capacity can reach 19379 kW.

[0115] At the same time, the virtual units aggregated by the base station backup energy storage are discharged during periods of high electricity prices and charged during periods of low electricity prices, enabling the virtual power plant to obtain higher profits. At the same time, it can achieve peak shaving and valley filling to a certain extent, alleviating electricity demand during peak periods.

[0116] like Figure 3 and Figure 4 The actual power generation and grid-connected capacity of wind power and photovoltaic power are shown. The phenomenon of wind and photovoltaic abandonment is almost non-existent, which will not cause waste of resources and meet the requirements of multi-objective optimization;

[0117] like Figure 5 As shown in the figure, the remaining energy curve of the virtual unit aggregated by the base station backup energy storage is given. In order to meet the backup demand of the base station, the scheduling of the virtual unit aggregated by the base station backup energy storage should meet the requirement that the remaining storage power is still greater than the backup capacity demand of the base station, so as to ensure the reliability of the base station backup as much as possible and obtain greater benefits.

[0118] Based on the same inventive concept, this embodiment also provides a virtual power plant multi-objective optimization scheduling system, including:

[0119] A power plant construction module is used to construct a virtual power plant architecture; the virtual power plant includes internal resources and distributed power generation equipment; the internal resources include a base station backup energy storage system, an Internet data center, an interruptible load and a gas turbine; the distributed power generation equipment includes wind power generation and photovoltaic power generation;

[0120] Internal model building module, used to model the internal resources of the virtual power plant, including base station backup energy storage system modeling, Internet data center modeling, interruptible load modeling and gas turbine modeling;

[0121] An objective function establishment module is used to establish the objective function of multi-objective optimization of the virtual power plant;

[0122] Constraint establishment module, used to establish the constraints for multi-objective optimization of virtual power plants;

[0123] The scheduling module is used to solve the optimization model, obtain scheduling instructions, and execute scheduling.

[0124] Based on the same inventive concept, this embodiment also provides a computing device, including: one or more processors, one or more memories and one or more programs, wherein the programs are stored in the memories and configured to be executed by the processors, and when the programs are loaded into the processors, the steps of the multi-dimensional indicator calculation method described in any one of the above items are implemented.

[0125] Based on the same inventive concept, this embodiment also provides a computer-readable storage medium, which stores a computer program. The computer program includes program instructions, and when the program instructions are executed by a processor, the processor executes the steps of the multi-dimensional indicator calculation method described in any one of the above items.

[0126] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Any ordinary technician in the industry can smoothly implement the present invention as shown in the drawings and described above. However, any equivalent changes, modifications and evolutions made by technicians familiar with the profession without departing from the scope of the technical solution of the present invention using the technical content disclosed above are all equivalent embodiments of the present invention. At the same time, any equivalent changes, modifications and evolutions made to the above embodiments based on the essential technology of the present invention are still within the protection scope of the technical solution of the present invention.

Claims

1. A multi-objective optimization scheduling method for a virtual power plant, characterized in that: include: S1: Construct a virtual power plant architecture; the virtual power plant includes internal resources and distributed power generation equipment; the internal resources include base station backup energy storage system, Internet data center, interruptible load and gas turbine; the distributed power generation equipment includes wind power generation and photovoltaic power generation; S2: Modeling the internal resources of the virtual power plant, including base station backup energy storage system modeling, Internet data center modeling, interruptible load modeling and gas turbine modeling; S3: Establish the objective function of multi-objective optimization of virtual power plant; S4: Establish constraints for multi-objective optimization of virtual power plants; S5: Solve based on the objective function and constraints, obtain scheduling instructions, and execute scheduling.

2. The multi-objective optimization scheduling method for a virtual power plant according to claim 1 is characterized in that: The base station backup energy storage system modeling is specifically to establish a base station backup energy storage system model as follows: IN min ≤In t ≤In max (4) The discharge power constraints of the base station backup energy storage system are as follows: Among them, W t is the remaining energy of the base station backup energy storage in time period t; η c and η d are charging efficiency and discharging efficiency respectively; Δt is the duration; are the charging power and discharging power of the base station backup energy storage in time period t; W min and W max are the lower and upper limits of the remaining energy of the base station backup energy storage; P cmax and P dmax are the upper limits of the charging power and discharging power of the base station backup energy storage; P i BS,Load is the load power of the base station; The backup capacity constraint of the base station backup energy storage system power lower limit is as follows: Among them, W Backup is the backup capacity requirement of the base station, which is related to the communication load of the base station; η is the discharge capacity coefficient; α is the battery temperature coefficient; t emp is the lowest ambient temperature of the base station battery pack; K is the safety factor; I real-time is the real-time load current; T Backup is the battery backup time.

3. The multi-objective optimization scheduling method for virtual power plants according to claim 2 is characterized in that: The Internet data center modeling is specifically to express the model of the Internet data center as: Among them, α i represents the minimum power consumption required by the data load of the Internet data center processing unit of the i-th virtual power plant; θ t represents the total amount of data load to be processed by the Internet data center at time period t; i and β i are the upper and lower limits of the power consumption of the Internet data center of the i-th virtual power plant; N is the total number of virtual power plants.

4. The multi-objective optimization scheduling method for virtual power plants according to claim 3 is characterized in that: The interruptible load is modeled, The model of interruptible load can be expressed as, in, is the interruptible load for time period t, is the upper limit of the interruptible load.

5. The multi-objective optimization scheduling method for virtual power plants according to claim 4 is characterized in that: The gas turbine modeling, specifically, the gas turbine model is established as follows: in, is the power output of the gas turbine in virtual power plant i at time period t; and are the upper and lower limits of the gas turbine power output; as well as The upper and lower limits of gas turbine power ramp.

6. The multi-objective optimization scheduling method for virtual power plants according to claim 5 is characterized in that: The step S3 is specifically as follows: Considering minimizing the economic cost, the objective function is established, which is expressed as: In the formula, and They are the charging and discharging cost of the base station backup energy storage system, the dispatching cost of the Internet data center, the gas turbine power generation cost, and the demand response dispatching cost. and is the income from online wind power and online photovoltaic power generation in regional virtual power plant i at time period t; and Respectively represent the charging and discharging power of the base station backup energy storage system; represents the power of Internet data centers; represents the power of the gas turbine; represents the power of demand response; Indicates the power of wind power; is the power of photovoltaic power generation; K ES , K IDC , K DR , K WT and K PV are the cost factors of base station backup energy storage system, Internet data center, demand response, wind power generation and photovoltaic power generation; a GT , b GT 、c GT It is the cost factor of power generation from gas turbines; and They are the electricity sales, electricity purchase, electricity sales price and electricity purchase price of virtual power plant i in time period t; Consider maximizing the wind power and photovoltaic grid-connected capacity of the virtual power plant during the dispatch cycle: in, It is the grid-connected capacity of wind power and photovoltaic power; is the actual power generation of wind power and photovoltaic power; N WT Indicates the number of fans; N PV represents the number of photovoltaics; T represents the scheduling period; The global objective function of the virtual power plant is established, the multi-objective optimization model is normalized by the standardization method and converted into a single-objective problem by the weighted method, and the global optimal solution is obtained: Min F=λ1F1+λ2F2 (13) Among them, λ1 and λ2 are normalization coefficients.

7. The multi-objective optimization scheduling method for a virtual power plant according to claim 6 is characterized in that: The constraints described in step S4 are as follows:

8. A virtual power plant multi-objective optimization scheduling system, characterized in that: include: Power plant building module, used to build virtual power plant architecture; The virtual power plant includes internal resources and distributed power generation equipment; the internal resources include base station backup energy storage system, Internet data center, interruptible load and gas turbine; the distributed power generation equipment includes wind power generation and photovoltaic power generation; Internal model building module, used to model the internal resources of the virtual power plant, including base station backup energy storage system modeling, Internet data center modeling, interruptible load modeling and gas turbine modeling; An objective function establishment module is used to establish the objective function of multi-objective optimization of the virtual power plant; Constraint establishment module, used to establish the constraints for multi-objective optimization of virtual power plants; The scheduling module solves the objective function and constraints, obtains scheduling instructions, and executes scheduling.

9. A computing device, characterized in that include: One or more processors, one or more memories and one or more programs, wherein the programs are stored in the memories and configured to be executed by the processors, and when the programs are loaded into the processors, the steps of the multi-objective optimization scheduling method for a virtual power plant according to any one of claims 1 to 7 are implemented.

10. A storage medium, characterized in that: The storage medium stores a computer program, which includes program instructions. When the program instructions are executed by a processor, the processor executes the steps of the multi-objective optimization scheduling method for a virtual power plant according to any one of claims 1 to 7.

Images