Virtual power plant participation demand response method and device based on multi-target stochastic programming, equipment and medium

Abstract

The invention provides a virtual power plant participation demand response method, device and equipment based on multi-target stochastic programming, and a medium, and belongs to the technical field of power markets, and the method comprises the following steps: modeling each resource in a VPP, and constructing a quantitative expression of each resource; determining each main body operated by the VPP, and constructing a multi-objective optimization function; normalizing the multi-objective optimization function to obtain a multi-objective planning function; obtaining distribution characteristic parameters and weights of internal uncertain variables of the VPP, and converting the deterministic multi-objective programming function into a multi-objective programming function containing the uncertain variables to obtain a multi-objective stochastic programming model; and solving the multi-target stochastic programming model by using a commercial solver to obtain scheduling parameters of each resource participating in demand response in the VPP. According to the method, all main body demands of the VPP are integrated through multi-target planning, normalization is simplified into a single target, the scheduling average value is solved through iteration, benefits are balanced, and the solving difficulty is reduced.

Description

Background Art

[0002] The current power grid is facing a major transformation from traditional power generation to emission-free power generation, while ensuring the reliability and efficiency of the power system. With the photovoltaic application of distributed energy (such as solar power stations, electric vehicles, battery energy storage systems, etc.) and the integration of demand-side control and communication technologies, the operation mode of the power grid has undergone tremendous changes. These changes have gradually transformed consumers in the power grid into prosumers, able to participate in the generation, storage and consumption of energy. However, individual consumers have limited influence at the transmission level, and the complex infrastructure and high transaction costs required to participate in the market limit their participation.

[0003] In order to effectively manage and coordinate distributed energy and improve the flexibility and economy of the power grid, VPP came into being. VPP is the abbreviation of Virtual Power Plant. As an aggregator of distributed energy, VPP realizes the effective management and control of distributed energy by integrating multiple resources such as renewable resources, energy storage units, electric vehicles and controllable loads. However, when participating in the demand response process, VPP needs to face the problem that multiple optimization objectives are difficult to meet at the same time, and also needs to face the problem that uncertain parameters are difficult to handle. Traditional methods are difficult to deal with accurately, and new scheduling strategies are needed to optimize the operation of VPP.

[0004] This is the shortcoming of the existing technology. Therefore, in view of the above-mentioned defects in the existing technology, it is very necessary to provide a method, device, equipment and medium for virtual power plant participation in demand response based on multi-objective stochastic programming. Summary of the invention

[0005] In view of the defects that the above-mentioned traditional methods are difficult to accurately deal with the problem that multiple optimization objectives are difficult to meet simultaneously and the problem that uncertain parameters are difficult to handle, a new scheduling strategy is needed to optimize the operation of VPP. The present invention provides a method, device, equipment and medium for virtual power plant participation in demand response based on multi-objective stochastic programming to solve the above-mentioned technical problems.

[0006] In a first aspect, the present invention provides a method for virtual power plant participation in demand response based on multi-objective stochastic programming, comprising the following steps: S1. Model each resource within the VPP and construct a quantitative expression for each resource; S2. Determine the stakeholders of VPP operation, construct objective functions for each stakeholder, and obtain a multi-objective optimization function; S3. Merge the multi-objective optimization function into a normalized single-objective optimization function, and then sum each single-objective optimization function to obtain a multi-objective planning function; S4. Obtain the distribution characteristic parameters of the uncertain variables inside the VPP, and transform the deterministic multi-objective planning function into a multi-objective planning function containing uncertain variables according to the distribution characteristic parameters and their weights to obtain a multi-objective stochastic planning model; S5. Use a commercial solver to solve the multi-objective stochastic programming model and obtain the scheduling parameters of each resource within the VPP participating in demand response. Furthermore, the specific steps of step S1 are as follows: S11. Model the internal resources of a VPP of solar power station type and construct an expression for power generation; S12. Model the internal resources of the VPP of the electric vehicle charging station type and construct an expression for the charging state; S13. Model the internal resources of the VPP of the battery energy storage device type and construct an expression for the battery capacity; S14. Model the VPP internal resources of controllable load type and construct an expression for energy procurement cost.

[0007] Furthermore, the specific steps of step S2 are as follows: S21. Construct a profit function for the VPP operator based on the relationship between the power generated by the solar power station inside the VPP being supplied to the electric vehicle charging station, the power stored in the battery energy storage device, and the power supplied to the external power system, and take the maximum profit of the VPP operator as the first objective function; S22. Construct a cost function for constructing a solar power generation device within the VPP, and take the minimum cost of constructing the solar power generation device as the second objective function; S23. Constructing the electricity cost function of the electric vehicle charging station and controllable load within the VPP, and taking the minimum electricity cost as the third objective function; S24. Construct a cost function for the degradation of user experience caused by the participation of electric vehicles and controllable loads in the VPP in responding to demand, and minimize the cost as the fourth objective function; S25. Construct a cost function for VPP to purchase electricity from an external power grid, and minimize the cost as the fifth objective function; S26. Integrate the five objective functions to obtain the multi-objective optimization problem that the VPP needs to solve as a multi-objective optimization function.

[0008] Furthermore, the specific steps of step S3 are as follows: S31. Use the Utopia tracking method to normalize the multi-objective optimization problem that the VPP needs to solve into a single-objective problem, and obtain the standardized objective functions; S32. Sum the standardized objective functions to obtain a complete expression of the multi-objective optimization function, and set constraints for various types of resources within the VPP.

[0009] Furthermore, the specific steps of step S4 are as follows: S41. Obtain characteristic parameters of uncertain variables of electric vehicle charging stations and controllable loads as energy demanders among demand response participants, and obtain a random distribution function; S42. An iterative function for solving based on the statistical information of the random distribution function; S43. According to the random distribution function and the iteration function, the expression of the multi-objective optimization function is converted into an expression for minimizing the average value of the multi-objective VPP scheduling problem, and a multi-objective random programming model is obtained.

[0010] Furthermore, the specific steps of step S5 are as follows: S51. Setting the initial parameters and related parameter values ​​of the iteration based on the iteration function; S52. The established multi-objective stochastic programming model is solved using the commercial solver Gurobi to obtain the dispatch parameters of the VPP including solar power stations, electric vehicle charging stations, battery energy storage devices and controllable loads to respond to demand.

[0011] Furthermore, the relevant parameter values ​​in step S51 include VPP internal resource parameters, electricity market price parameters, cost parameters, constraint parameters and uncertainty parameters.

[0012] In a second aspect, the present invention provides a virtual power plant participating in demand response device based on multi-objective stochastic programming, comprising: Resource modeling module, used to model various types of resources within VPP and construct quantitative expressions for various types of resources; The multi-objective function construction module is used to determine the stakeholders of the VPP operation, construct the objective function for each stakeholder, and obtain the multi-objective optimization function; A multi-objective function normalization module is used to merge the multi-objective optimization functions into a normalized single-objective optimization function, and then sum up the single-objective optimization functions to obtain a multi-objective planning function; The uncertainty variable introduction module is used to obtain the distribution characteristic parameters of the uncertain variables inside the VPP, and transform the deterministic multi-objective planning function into a multi-objective planning function containing uncertain variables according to the distribution characteristic parameters and their weights to obtain a multi-objective stochastic planning model; The demand response solution module is used to solve the multi-objective stochastic programming model using a commercial solver to obtain the scheduling parameters of each resource participating in the demand response within the VPP.

[0013] In a third aspect, the present invention provides an electronic device, including a processor and a memory; The memory is used to store a computer program, and the processor is used to call and run the computer program from the memory, so that the electronic device executes the method described in the first aspect above.

[0014] In a fifth aspect, the present invention provides a storage medium, The storage medium stores instructions, which, when executed on a computer, enable the computer to execute the method described in the first aspect.

[0015] The beneficial effects of the present invention are: The virtual power plant participation demand response method, device, equipment and medium based on multi-objective stochastic programming provided by the present invention fully considers the demands of virtual power plant operators and different types of internal resource entities and expresses them as different objective functions; a multi-objective planning model is established for problems that are difficult to uniformly meet with different objective functions. At the same time, in order to reduce the difficulty of solving the problem, the Utopia tracking method is introduced to establish a standardized single objective function. Through continuous iteration, the original problem is transformed into a solution to the average value of the VPP scheduling problem, which effectively reduces the difficulty of solving the problem and protects the interests of each entity.

[0016] It can be seen that compared with the prior art, the present invention has outstanding substantive features and significant progress, and the beneficial effects of its implementation are also obvious. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0018] Figure 1 It is a flow chart of the method for virtual power plant to participate in demand response based on multi-objective stochastic programming of the present invention.

[0019] Figure 2 It is a schematic diagram of a virtual power plant participating in a demand response device based on multi-objective stochastic programming of the present invention. DETAILED DESCRIPTION

[0020] The present invention takes into account the system fluctuations and uncertainties caused by the introduction of a large number of renewable energy sources in the current power system, and takes the power consumption of electric vehicles and controllable loads as examples to process their uncertain parameters. First, the probability density function is assumed to be a Gaussian distribution function, and then the mean, standard deviation, probability density function and other statistical information are used to design the iterative function and its corresponding parameters, and the multi-scenario representation of the random parameters is replaced by the equivalent of the average value of the objective function.

[0021] The method for virtual power plant participation in demand response based on multi-objective stochastic programming can be applied to one or more electronic devices, wherein the electronic device is an electronic device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to microprocessors, application specific integrated circuits (ASIC), programmable gate arrays (FPGA), digital processors (DSP), embedded devices, etc.

[0022] An electronic device may be any electronic product that can interact with a user, such as a personal computer, a tablet computer, a smart phone, a personal digital assistant (PDA), an interactive network television (IPTV), etc. The network in which the electronic device is located includes but is not limited to the Internet, a wide area network, a metropolitan area network, a local area network, a virtual private network (VPN), etc.

[0023] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0024] See also Figure 1 The figure shows a method for virtual power plant to participate in demand response based on multi-objective stochastic programming in a specific implementation, which includes the following steps: S1. Model each resource within the VPP and construct a quantitative expression for each resource; S2. Determine the stakeholders of VPP operation, construct objective functions for each stakeholder, and obtain a multi-objective optimization function; S3. Merge the multi-objective optimization function into a normalized single-objective optimization function, and then sum each single-objective optimization function to obtain a multi-objective planning function; S4. Obtain the distribution characteristic parameters of the uncertain variables inside the VPP, and transform the deterministic multi-objective planning function into a multi-objective planning function containing uncertain variables according to the distribution characteristic parameters and their weights to obtain a multi-objective stochastic planning model; S5. Use a commercial solver to solve the multi-objective stochastic programming model and obtain the scheduling parameters of each resource within the VPP participating in demand response.

[0025] The following example uses an IEEE33 node in the power topology system with a voltage level of 12.66kV. The VPP gathers resources such as solar power stations, electric vehicle charging stations, battery energy storage devices, and controllable loads. Each electric vehicle terminal station is equipped with 10 different types of electric vehicle chargers. The media can accommodate 100 electric vehicles charging at the same time. At the same time, each battery energy storage device is also equipped with 10 batteries that match the specifications of electric vehicle batteries.

[0026] In this embodiment, the specific steps of step S1 are as follows: S11. Model the internal resources of a VPP of solar power station type and construct an expression for power generation; The power generation of a solar power station in each operating interval mainly depends on the solar irradiance level and the on-site ambient temperature, and its expression is as follows: (1) (2) (3) (3) (4) in For the The hourly electricity generation of a solar power station, For the The rated power of a solar power station is is the rated power of the solar cell module, is the solar radiation interval, and are the voltage and current of the solar cell module at the maximum power point, and are the open circuit voltage and short circuit current of the solar cell module, and are the voltage and current temperature coefficients, and are the ambient temperature and the nominal operating battery temperature respectively; S12. Model the internal resources of the VPP of the electric vehicle charging station type and construct an expression for the charging state; When an electric vehicle is connected to the grid for charging, it can be modeled as a battery cell, represented by its state of charge SoC: (5) in, Represents an electric vehicle battery cell At the moment of electricity, and are the charging efficiency and discharging efficiency of electric vehicles, is a binary variable, when it is 1, it means the electric vehicle is charging, when it is 0, it means the electric vehicle is discharging; Battery cells for electric vehicles control strategy, is the hourly electricity consumption of the electric vehicle; Indicates time difference; , and are index collections of electric vehicles, electric vehicle charging piles, and time respectively; S13. Model the internal resources of the VPP of the battery energy storage device type and construct an expression for the battery capacity; In the VPP, a battery energy storage device is additionally set up to store the power generated by the solar power station during the day for use at night. The current battery capacity expression is as follows: (6) in, Battery energy storage device At the moment The battery capacity, and are the charging efficiency and discharging efficiency of the battery energy storage device, is a binary variable, when it is 1, it means the battery energy storage device is charging, when it is 0, it means it is discharging; Battery energy storage device control strategy, is the hourly charge and discharge capacity of the battery energy storage device, Indicates the time difference, An index set for a battery energy storage device; An index collection of electric vehicle charging stations, is the index collection of time; S14. Model the VPP internal resources of controllable load type and construct an expression for energy procurement cost; When load-side consumers choose to voluntarily participate in demand response, the goal is to obtain benefits such as reduced electricity bills or incentives. Based on this, the cost of purchasing energy from the public grid for these consumers can be analyzed and expressed as: (7) in, is the energy procurement cost of the nth controllable load, is the original energy consumption of the nth controllable load, is the nth controllable load reduction, a positive value indicates load reduction, and a negative value indicates load increase; is the index set of the corresponding controllable load; Indicates the time difference, is the index collection of time; The specific steps of step S2 are as follows: S21. Construct a profit function for the VPP operator based on the relationship between the power generated by the solar power station inside the VPP being supplied to the electric vehicle charging station, the power stored in the battery energy storage device, and the power supplied to the external power system, and take the maximum profit of the VPP operator as the first objective function; The goal of the VPP operator is to optimize profitability by meeting the needs of electric vehicle charging stations using electricity generated by solar power plants. Any excess solar energy can be stored in battery storage devices and then supplied to the external power system at a reduced electricity price; the profit function of the VPP operator is mathematically modeled as: (8) (9) in, and Respectively represent the profit coefficients under the discharging and charging states of electric vehicles; is a binary variable, when it is 1, it means the electric vehicle is charging, when it is 0, it means the electric vehicle is discharging; , and are index collections of electric vehicles, electric vehicle charging piles, and time respectively; An index collection for battery energy storage devices; is the hourly electricity consumption of the electric vehicle; The hourly charge and discharge capacity of the battery energy storage device; The power generation per hour of the solar power station; Indicates time difference; S22. Construct a cost function for constructing a solar power generation device within the VPP, and take the minimum cost of constructing the solar power generation device as the second objective function; The installation of solar power generation equipment needs to consider daily investment costs, capital recovery and maintenance costs, and its objective function is as follows: (10) in, and are the unit variable cost and fixed cost of investment, is the variable cost correlation coefficient; is the number of solar power generation devices; S23. Constructing the electricity cost function of the electric vehicle charging station and controllable load within the VPP, and taking the minimum electricity cost as the third objective function; The electric vehicle charging stations and controllable loads participating in demand response obtain the maximum arbitrage benefits by reasonably adjusting the charging and discharging arrangements according to the changes in electricity prices. In other words, they minimize their electricity costs. The objective function is as follows: (11) in, and are the electricity costs of electric vehicle charging stations and controllable loads, respectively; is an index collection of electric vehicle charging piles. is the index set of the corresponding controllable load; S24. Construct a cost function for the degradation of user experience caused by the participation of electric vehicles and controllable loads in the VPP in responding to demand, and minimize the cost as the fourth objective function; In the implementation of demand response, the interaction of electric vehicles with the grid may lead to unforeseen costs related to the degradation of electric vehicle batteries, and consumers of controllable loads may be affected by load shifting, so the negative impact caused by demand response needs to be evaluated as follows: (12) in, and The cost of reduced user experience caused by EVs and controllable loads participating in demand response is expressed separately; is the index set of the corresponding controllable load; is an index collection of electric vehicles; S25. Construct a cost function for VPP to purchase electricity from an external power grid, and minimize the cost as the fifth objective function; VPP operators hope to reduce their dependence on external power grids through the interaction of internal resources. This cost can be expressed by the cost of purchasing electricity from external power grids, namely: (13) in, For the moment The electricity purchase price, For the moment Purchase electricity from external power grids; Indicates time difference; is the index collection of time; S26. The five objective functions are integrated to obtain the multi-objective optimization problem that the VPP needs to solve as a multi-objective optimization function, as shown below: (14) The specific steps of step S3 are as follows: S31. Use the Utopia tracking method to solve the multi-objective optimization problem that VPP needs to solve Normalization is converted into a single objective problem, and the standardized objective functions are as follows: (15); S32. Sum the standardized objective functions to obtain the expression of the complete multi-objective optimization function, and set the constraints of various types of resources within the VPP; The complete deterministic representation of the multi-objective optimization function can be obtained: (16) is the weight of each single objective function; The constraints are as follows: (17) in, Indicates the amount of electricity generated by a solar power generation device. and Represents the minimum and maximum power generation per hour (18) in, is the hourly power consumption of the electric vehicle, and They represent the minimum and maximum hourly power consumption of electric vehicles respectively; (19) in, Indicates the charge and discharge capacity of the energy storage device. and Respectively represent the minimum and maximum charging and discharging power of the energy storage device; (20) in, and Respectively represent the profit coefficients under the discharging and charging states of electric vehicles; and It is the inherent coefficient of electric vehicle charging and discharging, which characterizes the correlation between the benefit coefficient under the charging and discharging state, and can generally be 0.1 and 1; (twenty one) in, is the nth controllable load reduction, a positive value indicates load reduction, and a negative value indicates load increase; and They represent the minimum and maximum values ​​of controllable load reduction respectively; (twenty two) in, Represents an electric vehicle battery cell At the moment The amount of electricity, and They represent the minimum and maximum values ​​of the battery cell power of an electric vehicle respectively; (twenty three) in, represents the charge of the battery energy storage device at time t, and Respectively represent the minimum and maximum values ​​of the capacity of the battery energy storage device; (twenty four) in, Indicates that the battery unit n of the electric vehicle has the maximum charge when the charging is completed and leaves, indicating that the charging is completed; (25) in, It means that the battery energy storage device n has the maximum power when the electric vehicle arrives for charging to better meet the charging needs; (26) in, is the nth controllable load reduction; (27) in, represents the absolute value operator of the controllable load reduction, when When it is positive, it takes the value 1, otherwise it takes the value -1; represents the original energy consumption of the nth controllable load, is the nth controllable load reduction, A collection of indices representing time; (28) in, For the Controllable load reduction, For the Controllable load reduction, For the moment The electricity purchase price, A collection of indices representing time; Indicates controllable load The cost of decreased user experience caused by participating in the response requirements indicates that Indicates controllable load The cost of participating in the degraded user experience caused by the response requirements; (29) in, is the hourly power consumption of the electric vehicle, is the hourly power consumption of the electric vehicle, is the power generated by the solar power station per hour, represents an index collection of electric vehicles, An index set for a battery energy storage device; (30) in, is a binary variable. When it is 1, it means the electric vehicle is charging, and when it is 0, it means the electric vehicle is discharging. and are index collections of electric vehicle charging piles and time respectively; Indicates the profit coefficient of the electric vehicle in the discharge state; Indicates time difference; , is the hourly electricity consumption of the electric vehicle; The hourly charge and discharge capacity of the battery energy storage device; The power generation per hour of the solar power station; represents an index collection of electric vehicles, An index set for a battery energy storage device; The specific steps of step S4 are as follows: S41. Obtain characteristic parameters of uncertain variables of electric vehicle charging stations and controllable loads as energy demanders among demand response participants, and obtain a random distribution function; In the random scenario, the uncertain input variables are characterized by probabilistic information derived from their mean, standard deviation, and probability density function. To represent the uncertainty of energy demand of demand response participants, i.e., EVs and controllable loads, their probability density function is considered as a Gaussian distribution function, expressed as (32) in, represents the probability density function, is an uncertain parameter value. is the uncertain parameter mean, is the standard deviation of the uncertain parameter; S42. An iterative function for solving based on the statistical information of the random distribution function; The statistical information in step S41 is used to set the iterative function for solving the problem. The goal is to use the mean and variance of the independent variable to derive the mean and variance of the target function as follows: (33) (34) in: (35) (36) (37) (38) in, is the uncertain parameter mean of the independent variable, is the standard deviation of the uncertainty parameter of the independent variable, represents the derived objective function concentration point, It represents the probability of a point in the target function concentration. represents the set of independent variables, u represents the uncertain parameter, k represents the column index of the independent variable vector, and They represent the third and fourth order moments of the independent variable respectively; S43. According to the random distribution function and the iteration function, the expression of the multi-objective optimization function is converted into an expression for minimizing the average value of the multi-objective VPP scheduling problem, and a multi-objective stochastic programming model is obtained; Combining steps 4.1 and 4.2, the multi-objective programming with uncertainty can be expressed as multiple iterations of deterministic multi-objective programming, and its objective function becomes to minimize the average value of the multi-objective VPP scheduling problem, that is: (39) in, ; The specific steps of step S5 are as follows: S51. Setting the initial parameters and related parameter values ​​of the iteration based on the iteration function; S52. The established multi-objective stochastic programming model is solved using the commercial solver Gurobi to obtain the dispatch parameters of the VPP including solar power stations, electric vehicle charging stations, battery energy storage devices and controllable loads to respond to demand; the relevant parameter values ​​include VPP internal resource parameters, electricity market price parameters, cost parameters, constraint parameters and uncertainty parameters.

[0027] It should be understood that the order of execution of the steps in the above embodiment does not mean that the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0028] The following is an embodiment of a device for participating in demand response of a virtual power plant based on multi-objective random programming provided in an embodiment of the present disclosure. The device and the method for participating in demand response of a virtual power plant based on multi-objective random programming in the above-mentioned embodiments belong to the same inventive concept. For details not fully described in the embodiment of the device for participating in demand response of a virtual power plant based on multi-objective random programming, please refer to the embodiment of the method for participating in demand response of a virtual power plant based on multi-objective random programming mentioned above.

[0029] like Figure 2 As shown, the device comprises: Resource modeling module, used to model various types of resources within VPP and construct quantitative expressions for various types of resources; The multi-objective function construction module is used to determine the stakeholders of the VPP operation, construct the objective function for each stakeholder, and obtain the multi-objective optimization function; A multi-objective function normalization module is used to merge the multi-objective optimization functions into a normalized single-objective optimization function, and then sum up the single-objective optimization functions to obtain a multi-objective planning function; The uncertainty variable introduction module is used to obtain the distribution characteristic parameters of the uncertain variables inside the VPP, and transform the deterministic multi-objective planning function into a multi-objective planning function containing uncertain variables according to the distribution characteristic parameters and their weights to obtain a multi-objective stochastic planning model; The demand response solution module is used to solve the multi-objective stochastic programming model using a commercial solver to obtain the scheduling parameters of each resource participating in the demand response within the VPP.

[0030] The method for virtual power plant participation in demand response based on multi-objective stochastic programming provided by the present invention is a combination of the modules and algorithm steps of each example described in the embodiments disclosed herein, and can be implemented by electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0031] Those skilled in the art will appreciate that the various aspects of the method for virtual power plant participation in demand response based on multi-objective stochastic programming provided by the present invention can be implemented as a system, method or program product. Therefore, various aspects of the present disclosure can be specifically implemented in the following forms, namely: a complete hardware implementation, a complete software implementation (including firmware, microcode, etc.), or an implementation combining hardware and software, which can be collectively referred to as "circuit", "module" or "system" here.

[0032] In the non-transitory computer-readable storage medium storing the method for virtual power plant participation in demand response based on multi-objective stochastic programming, it can be but not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or devices, or any combination of the above. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0033] Although the present invention has been described in detail with reference to the accompanying drawings and in combination with the preferred embodiments, the present invention is not limited thereto. Without departing from the spirit and essence of the present invention, a person of ordinary skill in the art may make various equivalent modifications or substitutions to the embodiments of the present invention, and these modifications or substitutions shall be within the scope of the present invention. Any person of ordinary skill in the art may easily think of changes or substitutions within the technical scope disclosed by the present invention, and these shall be within the scope of protection of the present invention.

Claims

1. A method for virtual power plant participation in demand response based on multi-objective stochastic programming, characterized in that: The steps include: S1. Model each resource within the VPP and construct a quantitative expression for each resource; S2. Determine the stakeholders of VPP operation, construct objective functions for each stakeholder, and obtain a multi-objective optimization function; S3. Merge the multi-objective optimization function into a normalized single-objective optimization function, and then sum each single-objective optimization function to obtain a multi-objective planning function; S4. Obtain the distribution characteristic parameters of the uncertain variables inside the VPP, and transform the deterministic multi-objective planning function into a multi-objective planning function containing uncertain variables according to the distribution characteristic parameters and their weights to obtain a multi-objective stochastic planning model; S5. Use a commercial solver to solve the multi-objective stochastic programming model and obtain the scheduling parameters of each resource within the VPP participating in demand response.

2. The method for virtual power plant participation in demand response based on multi-objective stochastic programming according to claim 1 is characterized in that: The specific steps of step S1 are as follows: S11. Model the internal resources of a VPP of solar power station type and construct an expression for power generation; S12. Model the internal resources of the VPP of the electric vehicle charging station type and construct an expression for the charging state; S13. Model the internal resources of the VPP of the battery energy storage device type and construct an expression for the battery capacity; S14. Model the VPP internal resources of controllable load type and construct an expression for energy procurement cost.

3. The method for virtual power plant participation in demand response based on multi-objective stochastic programming according to claim 2 is characterized in that: The specific steps of step S2 are as follows: S21. Construct a profit function for the VPP operator based on the relationship between the power generated by the solar power station inside the VPP being supplied to the electric vehicle charging station, the power stored in the battery energy storage device, and the power supplied to the external power system, and take the maximum profit of the VPP operator as the first objective function; S22. Construct a cost function for constructing solar power generation devices within the VPP, and take the minimum cost of constructing solar power generation devices as the second objective function; S23. Constructing the electricity cost function of the electric vehicle charging station and controllable load within the VPP, and taking the minimum electricity cost as the third objective function; S24. Construct a cost function for the degradation of user experience caused by the participation of electric vehicles and controllable loads in the VPP in responding to demand, and minimize the cost as the fourth objective function; S25. Construct a cost function for VPP to purchase electricity from an external power grid, and minimize the cost as the fifth objective function; S26. Integrate the five objective functions to obtain the multi-objective optimization problem that the VPP needs to solve as a multi-objective optimization function.

4. The method for virtual power plant participation in demand response based on multi-objective stochastic programming according to claim 3 is characterized in that: The specific steps of step S3 are as follows: S31. Use the Utopia tracking method to normalize the multi-objective optimization problem that the VPP needs to solve into a single-objective problem, and obtain the standardized objective functions; S32. Sum the standardized objective functions to obtain a complete expression of the multi-objective optimization function, and set constraints for various types of resources within the VPP.

5. The method for virtual power plant participation in demand response based on multi-objective stochastic programming according to claim 4 is characterized in that: The specific steps of step S4 are as follows: S41. Obtain characteristic parameters of uncertain variables of electric vehicle charging stations and controllable loads as energy demanders among demand response participants, and obtain a random distribution function; S42. An iterative function for solving based on the statistical information of the random distribution function; S43. According to the random distribution function and the iteration function, the expression of the multi-objective optimization function is converted into an expression for minimizing the average value of the multi-objective VPP scheduling problem, and a multi-objective random programming model is obtained.

6. The method for virtual power plant participation in demand response based on multi-objective stochastic programming according to claim 5 is characterized in that: The specific steps of step S5 are as follows: S51. Setting the initial parameters and related parameter values ​​of the iteration based on the iteration function; S52. The established multi-objective stochastic programming model is solved using the commercial solver Gurobi to obtain the dispatch parameters of the VPP including solar power stations, electric vehicle charging stations, battery energy storage devices and controllable loads to respond to demand.

7. The method for virtual power plant participation in demand response based on multi-objective stochastic programming according to claim 6 is characterized in that: The relevant parameter values ​​in step S51 include VPP internal resource parameters, power market price parameters, cost parameters, constraint parameters and uncertainty parameters.

8. A virtual power plant participating in demand response device based on multi-objective stochastic programming, characterized in that: include: Resource modeling module, used to model various types of resources within VPP and construct quantitative expressions for various types of resources; The multi-objective function construction module is used to determine the stakeholders of the VPP operation, construct the objective function for each stakeholder, and obtain the multi-objective optimization function; A multi-objective function normalization module is used to merge the multi-objective optimization functions into a normalized single-objective optimization function, and then sum up the single-objective optimization functions to obtain a multi-objective planning function; The uncertainty variable introduction module is used to obtain the distribution characteristic parameters of the uncertain variables inside the VPP, and transform the deterministic multi-objective planning function into a multi-objective planning function containing uncertain variables according to the distribution characteristic parameters and their weights to obtain a multi-objective stochastic planning model; The demand response solution module is used to solve the multi-objective stochastic programming model using a commercial solver to obtain the scheduling parameters of each resource participating in the demand response within the VPP.

9. An electronic device, characterized in that: including a processor and a memory; The memory is used to store a computer program, and the processor is used to call and run the computer program from the memory, so that the electronic device executes the method described in any one of claims 1 to 7.

10. A storage medium, characterized in that: The storage medium stores instructions, which, when executed on a computer, enable the computer to execute the method described in any one of claims 1 to 7.

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