Virtual power plant scheduling method and device considering various demand responses, and electronic equipment

Abstract

The invention provides a virtual power plant scheduling method and device considering various demand responses, and electronic equipment, and relates to the technical field of power optimization scheduling. The method comprises the following steps: constructing a target function according to the operation cost, the carbon dioxide emission and the total electric quantity purchased from a power transmission network of the virtualized power plant in a scheduling period; determining a plurality of constraint conditions of the operation of the virtualized power plant according to a plurality of demands of the demand side of the virtualized power plant; and solving the target function by taking the plurality of constraint conditions as boundaries, and determining a scheduling strategy of the virtualized power plant.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power optimization scheduling, and more particularly to a virtual power plant scheduling method considering multiple demand responses, a device and an electronic equipment. BACKGROUND

[0002] With the rapid development of distributed renewable energy and the transformation and upgrading of energy structure, the power system gradually evolves towards distribution, low carbonization and intelligence. As a new energy management and scheduling mode, virtual power plant aggregates and uniformly schedules distributed photovoltaic power generation, electric vehicles, air conditioners and other adjustable loads and other distributed energy through information and communication technology, so that it behaves as a controllable and tradable whole unit in the power market and power grid operation, and plays an important role in improving the utilization rate of renewable energy, enhancing the flexibility and economy of the power grid.

[0003] The existing virtual power plant optimization scheduling research usually focuses on the coordination of the power generation side and energy storage devices, and the participation of demand side resources is relatively limited. With the large-scale access of electric vehicles and the extensive use of air conditioners and other flexible loads, demand side resources provide new scheduling means for peak-valley regulation and flexible operation of the system. Among them, electric vehicles are both power loads and can realize energy interaction through charging and discharging process; air conditioning load has certain peak load shifting ability; photovoltaic power generation is green and clean, but its output is volatile and uncontrollable, which needs other resources for compensation and coordination. SUMMARY

[0004] Therefore, the present application provides a virtual power plant scheduling method considering multiple demand responses, a device and an electronic equipment.

[0005] One aspect of the present application provides a virtual power plant scheduling method considering multiple demand responses, comprising: constructing a target function according to the operation cost, carbon dioxide emission and total power purchased from the power grid of the virtual power plant in a scheduling period; determining multiple constraint conditions of the operation of the virtual power plant according to multiple demands of the demand side of the virtual power plant; solving the target function with the multiple constraint conditions as boundaries to determine the scheduling strategy of the virtual power plant.

[0006] According to the embodiments of the present application, the target function is expressed as: ; wherein, minF(x) is the target function; F cost (x) is the operation cost of the virtual power plant in a scheduling period; F carbon (x) is the carbon dioxide emission of the virtual power plant in a scheduling period; F import (x) is the total power purchased from the power grid of the virtual power plant in a scheduling period.

[0007] According to the embodiments of the present application, the operation cost of the virtual power plant in a scheduling period includes at least one of a demand response load cost, a gas turbine cost, a power purchase and sale cost, and an electric vehicle related cost; the demand response load cost is expressed as: ; wherein, F1 is the demand response load cost; pil(1, t) is the interruption amount of the first level interruptible load at time t, pil(2, t) is the interruption amount of the second level interruptible load at time t, pil(3, t) is the interruption amount of the third level interruptible load at time t, t is a variable ranging from 1 to 24 after dividing 24 hours of a day into 24 time points, kil1 represents the unit compensation cost of the first level interruptible load; kil2 represents the unit compensation cost of the second level interruptible load; kil3 represents the unit compensation cost of the third level interruptible load; the gas turbine cost is expressed as: ; wherein, F2 is the gas turbine cost; k represents a fixed cost generated when the gas turbine is working, K mt is a linearization cost coefficient, K s is a start-stop cost coefficient; X conv (t) is a working state variable; Y conv (t) is a start-stop state variable; P mt (t) is the output power of the gas turbine; the power purchase and sale cost is expressed as: ; wherein, F3 is the power purchase and sale cost; P mb (t) is the power purchase amount; X b (t) is the power purchase price at time t; P ms (t) is the power sale amount; X s (t) is the power sale price at time t; the electric vehicle related cost is expressed as: ; wherein, F4 is the electric vehicle related cost; P rva (t) is the discharging power; η cra is the battery discharging efficiency; P cva (t) is the charging power; η cca is the battery charging efficiency; C eqa is the equivalent electricity price when the vehicle is charging; C cpua is the unit mileage power consumption coefficient; D va (t) is the driving distance at time t; C dega is the battery depreciation loss of the electric vehicle.

[0008] According to the embodiments of the present application, the carbon dioxide emission amount of the virtual power plant in a scheduling period is expressed as: ; wherein, F carbon is the total carbon dioxide emission amount in the scheduling period; γ mt is the carbon dioxide emission factor of the unit power generation of the gas turbine; P mt(t) is the output power of the gas turbine; γ grid is the average carbon dioxide emission factor of the regional power grid; P mb (t) is the electricity purchase amount.

[0009] According to the embodiments of the present application, the total electricity amount purchased from the power transmission grid by the virtual power plant in the dispatching period is represented as: ; wherein F import is the total electricity amount purchased from the power transmission grid in the dispatching period; P mb (t) is the electricity purchase amount.

[0010] According to the embodiments of the present application, the plurality of constraint conditions include a purchase and sale electricity model constraint; the purchase and sale electricity model constraint is represented as: ; wherein u b (t) is the electricity purchase operation state variable; u s (t) is the electricity sale operation state variable; P mb (t) is the electricity purchase amount; P ms (t) is the electricity sale amount; is the maximum transaction amount.

[0011] According to the embodiments of the present application, the plurality of constraint conditions further include a power balance model constraint; the power balance model constraint is represented as: ; wherein P load (t) is the original load at time period t; pil(m, t) is the interruption amount of the interruptible load of the mth level at time t; P ess (t) is the charging power of the energy storage system; P ms (t) is the electricity sale amount; P cold (t) is the electrical power of the air conditioning load model; P cva (t) is the charging power; P pv (t) is the photovoltaic power generation amount; P mt (t) is the output power; P esr (t) is the discharging power; P mb (t) is the electricity purchase amount; P rva (t) is the discharging power.

[0012] According to the embodiments of the present application, the solving of the target function with the plurality of constraint conditions as boundaries to determine the scheduling strategy of the virtual power plant includes: calculating a first target function value of the target function of the virtual power plant under a plurality of initial scheduling schemes; performing non-dominated sorting on the plurality of initial scheduling schemes according to the first target function value to obtain a plurality of initial scheduling scheme sets; the non-dominated sorting levels of each initial scheduling scheme set are different; the non-dominated sorting levels of each initial scheduling scheme in the same initial scheduling scheme set are the same; applying genetic operations to update the plurality of initial scheduling schemes according to the non-dominated sorting levels of the initial scheduling schemes to obtain a plurality of updated scheduling schemes; calculating a second target function value corresponding to each scheduling scheme in a joint scheduling scheme group composed of the plurality of updated scheduling schemes and the plurality of initial scheduling schemes; performing non-dominated sorting on each scheduling scheme in the joint scheduling scheme group according to the second target function value to obtain a plurality of joint scheduling scheme sets; determining a plurality of target scheduling schemes from the plurality of joint scheduling scheme sets according to a structured reference point set and the non-dominated levels of the scheduling schemes in the joint scheduling scheme sets; taking the plurality of target scheduling schemes as the plurality of initial scheduling schemes, iteratively performing the operations of updating the plurality of initial scheduling schemes, calculating the second target function value, non-dominated sorting, and determining the plurality of target scheduling schemes until a preset iteration condition is reached; and determining the scheduling strategy of the virtual power plant according to the result of the non-dominated sorting of the plurality of target scheduling schemes obtained when the preset iteration condition is reached.

[0013] Another aspect of the present application provides a virtual power plant scheduling device considering a plurality of demand responses, which includes: a target function construction module configured to construct a target function according to the operation cost, the carbon dioxide emission, and the total power purchased from a power grid of a virtual power plant in a scheduling period; a constraint condition determination module configured to determine a plurality of constraint conditions of the operation of the virtual power plant according to a plurality of demands of a demand side of the virtual power plant; and a scheduling strategy determination module configured to solve the target function with the plurality of constraint conditions as boundaries to determine a scheduling strategy of the virtual power plant.

[0014] Another aspect of the present application provides an electronic device, which includes: one or more processors; a memory configured to store one or more programs, wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the method described above.

[0015] According to the embodiments of the present application, in view of the deficiencies of the existing virtual power plant multi-objective scheduling method in solution set quality, boundary coverage ability and decision flexibility, a three-objective optimization model including minimum total operation cost, minimum total carbon dioxide emission and minimum total purchased power is constructed, without presetting target weight, a distributed and comprehensive Pareto optimal scheduling scheme set can be obtained, and various resources such as electric vehicles, air conditioning loads, gas turbines and photovoltaic power generation can be effectively coordinated under the conditions of conventional information and calculation, and the collaborative optimization ability of the virtual power plant in economy, low carbon and operation autonomy can be significantly improved, and optional high-quality day-ahead scheduling strategies are provided for different operation preferences, such as cost priority, carbon emission priority or less dependence on the main network. BRIEF DESCRIPTION OF DRAWINGS

[0016] The above and other objects, features and advantages of the present application will become more apparent from the following description of the embodiments of the present application taken with reference to the accompanying drawings, in which:

[0017] Figure 1 A flowchart of a virtual power plant scheduling method considering multiple demand responses according to an embodiment of the present application is schematically shown;

[0018] Figure 2 A flowchart of a virtual power plant scheduling solution process considering multiple demand responses according to an embodiment of the present application is schematically shown;

[0019] Figure 3 A block diagram of a virtual power plant scheduling device considering multiple demand responses according to an embodiment of the present application is schematically shown;

[0020] Figure 4 A block diagram of an electronic device suitable for implementing a virtual power plant scheduling method considering multiple demand responses according to an embodiment of the present application is schematically shown. DETAILED DESCRIPTION

[0021] Hereinafter, embodiments of the present application will be described with reference to the accompanying drawings. It is to be understood, however, that the description is merely exemplary and is not intended to limit the scope of the present application. In the following detailed description of the embodiments of the present application, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the present application. However, it would be apparent to those skilled in the art that the embodiments of the present application can be practiced without these specific details. In other instances, well-known structures and techniques have been not described in detail in order to avoid obscuring aspects of the present application.

[0022] The terms used herein are merely used to describe specific embodiments and are not intended to limit the present application. The terms "include", "comprise" and the like used herein indicate the presence of the described features, steps, operations and / or components, but do not exclude the presence or addition of one or more other features, steps, operations or components.

[0023] All terms used herein, including technical and scientific terms, have the meanings commonly understood by one of ordinary skill in the art, unless otherwise defined. It should be noted that the use of any terms herein should not be interpreted to imply any limitation on the scope of the disclosure unless otherwise defined. The term "consisting of" is intended to mean the inclusion of the listed items and the exclusion of any other item, whether or not the other item is similar to those items specifically listed. The term "comprising" is intended to mean inclusion of the listed items and items analogous to those listed, that is, items of the same or similar kind. The term "including" is intended to mean inclusion of the listed items and other non-specified items of the same or similar kind. The term "associated with" as used herein is intended to mean associated with, connected with, in communication with, or some other relationship between one item and another item.

[0024] In the case of using expressions similar to "at least one of A, B, and C, etc.", it should generally be interpreted that the meaning of the expression is at least one of A, B, and C, etc. (for example, "a system having at least one of A, B, and C" should include but not be limited to a system having A alone, a system having B alone, a system having C alone, a system having both A and B, a system having both A and C, a system having both B and C, and / or a system having A, B, and C, etc.).

[0025] In the embodiments of the present application, the collection, updating, analysis, processing, use, transmission, provision, disclosure, storage, etc. of the data involved (for example, including but not limited to user personal information) comply with the relevant legal regulations, are used for legal purposes, and do not violate public order and good customs. In particular, necessary measures have been taken to prevent illegal access to user personal information data, and to maintain user personal information security, network security and national security.

[0026] In the embodiments of the present application, the authorization or consent of the user is obtained before the user's personal information is acquired or collected.

[0027] In the related art, the optimal scheduling of virtual power plants has gradually evolved from a single target to a multi-target paradigm. Some studies use intelligent optimization methods such as genetic algorithms to handle multiple objectives such as operating costs, carbon emissions, and renewable energy consumption. However, existing methods still face significant challenges: on the one hand, if a weighted sum strategy is used, it relies on subjective weight setting and is difficult to adapt to different operating preferences; on the other hand, even if a multi-objective evolutionary algorithm (such as NSGA-II) is introduced, it is limited by the diversity preservation mechanism based on crowding distance when dealing with three or more conflicting objectives, often leading to uneven distribution of the Pareto frontier and easy omission of extreme solutions (such as the lowest cost, minimum power purchase, or near-zero carbon emission scheme). In addition, most models do not fully coordinate the response characteristics of demand-side resources (such as electric vehicle and air conditioning loads), and often rely on complex stochastic or robust optimization frameworks to deal with uncertainties, resulting in high computational complexity and difficulty in applying to day-ahead scheduling and other scenarios with high time efficiency requirements.

[0028] Figure 1 A flowchart of a virtual power plant scheduling method considering multiple demand responses according to an embodiment of the present application is schematically shown.

[0029] As shown in Figure 1 the method includes operations S101-S103.

[0030] In operation S101, a target function is constructed according to the operation cost, the carbon dioxide emission and the total electricity purchased from the power grid of the virtual power plant in a dispatch cycle.

[0031] In operation S102, a plurality of constraint conditions of the virtual power plant operation are determined according to a plurality of demands of the demand side of the virtual power plant.

[0032] In operation S103, the target function is solved with the plurality of constraint conditions as boundaries to determine the dispatch strategy of the virtual power plant.

[0033] In the embodiments of the present application, the dispatch cycle can be selected as 24 hours a day. The target function includes the operation cost target function of the virtual power plant in the dispatch cycle, the carbon dioxide target function of the virtual power plant in the dispatch cycle and the total electricity purchased from the power grid target function of the virtual power plant in the dispatch cycle. Therefore, the target function can be expressed as:

[0034] ;

[0035] Wherein, minF(x) is the target function; F cost (x) is the operation cost of the virtual power plant in a dispatch cycle; F carbon (x) is the carbon dioxide emission of the virtual power plant in a dispatch cycle; F import (x) is the total electricity purchased from the power grid of the virtual power plant in a dispatch cycle.

[0036] The operation cost of the virtual power plant in the dispatch cycle includes at least one of the demand response load cost, the gas turbine cost, the electricity purchase and sale cost and the electric vehicle related cost.

[0037] The demand response load cost is expressed as:

[0038] ;

[0039] Wherein, F1 is the demand response load cost; pil(1,t) is the interruption amount of the first level interruptible load at t time, pil(2,t) is the interruption amount of the second level interruptible load at t time, pil(3,t) is the interruption amount of the third level interruptible load at t time, t is a variable with a value range of 1 to 24 after dividing 24 hours a day into 24 time points, kil1 represents the unit compensation cost of the first level interruptible load; kil2 represents the unit compensation cost of the second level interruptible load; kil3 represents the unit compensation cost of the third level interruptible load.

[0040] The gas turbine cost is expressed as:

[0041] ;

[0042] Wherein, F2 is the cost of gas turbine; k represents the fixed cost generated when the gas turbine is working, K mt is the linear cost coefficient, which is related to the cost of gas turbine power generation and power generation; K s is the start-stop cost coefficient, which represents the cost when the gas turbine has start-stop action; X conv (t) is the working state variable; Y conv (t) is the start-stop state variable; P mt (t) is the output power of the gas turbine.

[0043] The electricity purchase and sale price is represented as:

[0044] ;

[0045] Wherein, F3 is the electricity purchase and sale price; P mb (t) is the electricity purchase amount; X b (t) is the electricity purchase price at time t; P ms (t) is the electricity sale amount; X s (t) is the electricity sale price at time t.

[0046] The cost related to electric vehicles is represented as:

[0047] ;

[0048] Wherein, F4 is the cost related to electric vehicles; P rva (t) is the discharge power; η cra is the battery discharge efficiency; P cva (t) is the charging power; η cca is the battery charging efficiency; C eqa is the equivalent electricity price when charging the car; C cpua is the unit mileage power consumption coefficient; D va (t) is the driving distance at time t; C dega is the battery depreciation loss of electric vehicles.

[0049] The virtual power plant considers the local gas turbine emission and the corresponding regional power grid emission factor of purchased power; the carbon dioxide emission of the virtual power plant in the dispatching period is represented as:

[0050] ;

[0051] Wherein, F carbon is the total carbon dioxide emission in the dispatching period; γ mt is the carbon dioxide emission factor per unit power generation of the gas turbine; P mt (t) is the output power of the gas turbine; γ gridis the average carbon dioxide emission factor of the regional power grid; P mb (t) is the purchased power.

[0052] By taking the total power purchased from the main grid as one of the objective functions, the source-load-storage collaborative autonomy and market independence of the virtual power plant can be improved. Therefore, the total power purchased from the power grid by the virtual power plant in the dispatching period is represented as:

[0053] ;

[0054] wherein F import is the total power purchased from the power grid in the dispatching period; P mb (t) is the purchased power.

[0055] In an embodiment of the present application, the cost-related part parameters in the objective function can be as shown in Table 1.

[0056] Table 1 Cost-related part parameters in the objective function

[0057]

[0058] In an embodiment of the present application, the plurality of constraint conditions includes an interruptible load model constraint, a gas turbine output model constraint, an air conditioner load model constraint, an energy storage model constraint, a power purchase and sale model constraint, an electric vehicle battery model constraint, a photovoltaic power generation model constraint, and a power balance model constraint.

[0059] The photovoltaic power generation power of the photovoltaic power generation model constraint adopts a day-ahead prediction value and is given in advance. For example, the photovoltaic power generation capacity of 24 time points can be given according to the actual situation, or can be artificially given. In the power balance model constraint, at any time, the power demand on the load side and the power supply of the grid distributed power source should be equal, and the multi-dimensional interaction of photovoltaic power generation, interruptible load, energy storage system charging and discharging, grid power purchase and sale, air conditioner power consumption, and electric vehicles is comprehensively considered.

[0060] The interruptible load model constraint can be represented as:

[0061] ;

[0062] In an embodiment of the present application, the interruptible load is divided into three levels, one day (24 hours) is taken as a dispatching period T, 24 hours of a day are divided into 24 time points, a variable t with a value range of 1 to 24 is used to represent, the interruption amount of the interruptible load of the mth level at the tth time point is represented by pil(m, t), and the maximum interruption load ratio of the mth level is represented by c il (m). load (t) is the original load of the time period t.

[0063] The first row of constraints indicates that the interruption load in the mth stage should not be higher than the corresponding maximum interruption ratio; the second row of constraints indicates that the cumulative interruption load of adjacent time periods should not exceed a certain proportion of the current load, in the embodiment of the application, the proportion is 20%, in addition, c il (1) can be 30%, c il (2) can be 15%, c il (3) can be 5%, P load (t) can be set according to actual conditions or artificially given.

[0064] The gas turbine output model constraints can be represented as:

[0065]

[0066] The working state variable is represented by X conv (t), which is a 0-1 variable, 0 represents shutdown, and 1 represents running, the output power is represented by P mt (t), the ramping constraint limit power is represented by r limit (t), the start-stop state variable is represented by Y conv (t), which is a 0-1 variable, 0 represents no start-stop action, and 1 represents start-stop action. The first row of constraints indicates that the output of the gas turbine should be constrained in the upper and lower limit ranges; the second and third rows of constraints indicate that the output of the gas turbine should not change by more than the ramping rate limit in adjacent time periods; the third and fourth rows of constraints associate the working state variable of the gas turbine with the start-stop action state variable.

[0067] In the embodiment of the application, some parameters of the gas turbine output model constraints can be as shown in Table 2.

[0068] Table 2 Some parameters of the gas turbine output model constraints

[0069]

[0070] The air conditioning load model constraints can be represented as:

[0071]

[0072] The indoor temperature is represented by T in (t), the heat dissipation function is represented by H, the outdoor temperature is represented by T out (t), the total cooling capacity is represented by Q cold (t), the heat transfer coefficient is represented by b, the refrigerating capacity is represented by Q ch (t), the cold storage capacity is represented by Q es (t), the maximum cold storage capacity is represented by (t), the maximum cold release capacity is represented by er (t), the maximum cold release capacity is represented by ​​X (t) represents the charging state variable of the energy storage system es X (t) represents the discharging state variable of the energy storage system er X (t) represents the charging capacity of the energy storage system c X (t) represents the maximum charging capacity of the energy storage system X (t) represents the charging efficiency of the energy storage system s X (t) represents the discharging efficiency of the energy storage system r X (t) represents the energy efficiency ratio of the refrigeration machine ch X (t) represents the energy efficiency ratio of the energy storage system in charging es X (t) represents the energy efficiency ratio of the energy storage system in discharging er X (t) represents the energy efficiency ratio of the energy storage system

[0073] The first row of constraints establishes the relationship between indoor temperature and outdoor temperature, and total cooling capacity; the second row of constraints establishes the relationship between total cooling capacity and air conditioning refrigeration capacity, energy storage tank charging capacity, and energy storage tank discharging capacity; the third to fifth rows respectively constrain the value range of air conditioning refrigeration capacity, energy storage tank charging capacity, and energy storage tank discharging capacity; the sixth row of constraints limits that the charging and discharging operations of the energy storage tank cannot be performed simultaneously, wherein X es X (t) represents the charging state of the energy storage tank when t=1, and X er X (t) represents the discharging state of the energy storage tank when t=1; the seventh row of constraints establishes the functional relationship of the dynamic change of the energy storage tank; the eighth row of constraints limits the capacity range of the energy storage tank; the ninth row of constraints establishes the functional relationship between the power consumption of the air conditioner and the cooling capacity.

[0074] In the embodiments of the present application, part of the parameters of a single air conditioning unit in the air conditioning load model constraint can be as shown in Table 3.

[0075] Table 3 Part of the parameters of the air conditioning load constraint model

[0076]

[0077] The energy storage model constraint conditions are as follows:

[0078] ;

[0079] The charging power of the energy storage system is represented by P ess X (t) represents the maximum charging power The discharging power of the energy storage system is represented by P esr X (t) represents the maximum discharging power The storage capacity of the battery is represented by S es X (t) represents the maximum storage capacity The charging efficiency of the energy storage system is represented by η ess The discharging efficiency of the energy storage system is represented by η esr S es (1) represents the storage capacity of the battery when t=1. P esr(1) represents the discharging power when t = 1; P ess (1) represents the charging power of the energy storage system when t = 1.

[0080] The first to third rows respectively constrain the charging, discharging and battery capacity range of the energy storage system; the fourth row constrains the initial state of the battery; and the fifth row establishes a dynamic change function of the battery capacity.

[0081] In the embodiment of the present application, part of the parameters of the energy storage model constraint can be as shown in Table 4.

[0082] Table 4 Part of the parameters of the energy storage constraint model

[0083]

[0084] The power purchase and sale model constraint is represented as:

[0085] ;

[0086] Wherein, u b (t) is a power purchase working state variable; u s (t) is a power sale working state variable; P mb (t) is a power purchase amount; P ms (t) is a power sale amount; is a maximum transaction amount. In the embodiment of the present application, the maximum transaction amount may be 20 MWh.

[0087] The first row constrains that the power purchase state and the power sale state do not occur at the same time, u b (t) is a 0-1 variable, which is 1 when representing the power purchase state, u s (t) is a 0-1 variable, which is 1 when representing the power sale state; the second to third rows constrain that the power purchase amount and the power sale amount are both not more than the maximum transaction amount.

[0088] The electric vehicle battery model constraint condition is as follows:

[0089] ;

[0090] For the electric vehicle, the battery capacity is represented by S va (t), the rated battery capacity is represented by S am , the charging power is represented by P cva (t), the discharging power is represented by P rva (t), the charging working state variable is represented by u cva (t), the discharging working state variable is represented by u rva (t), the battery charging efficiency is represented by η cca , and the battery discharging efficiency is represented by η craThis indicates that the distance traveled at time t is determined by D. va (t) represents the power consumption coefficient per unit distance, determined by C. cpua express.

[0091] The first line constrains that the battery capacity of the electric vehicle should be maintained within a healthy range, which is set to 15% to 95% of the rated battery capacity in the embodiments of this application; the second line constrains that the charging and discharging states of the battery cannot occur simultaneously. cva (t) is a 0-1 variable; a value of 1 indicates that the battery is in a charging state. Similarly, u rva (t) When it is 1, it means that the battery is working in the discharge state; the third and fourth lines constrain the charging and discharging power range of the electric vehicle battery. In the embodiments of this application, the upper limit of this range is set to 20% of the rated battery capacity; the fifth line constrains the establishment of the dynamic change function of the electric vehicle battery capacity.

[0092] In the embodiments of this application, some parameters of the constraints for a single electric vehicle model can be as shown in Table 5.

[0093] Table 5. Partial parameters of the constraint model for a single electric vehicle

[0094]

[0095] The power balance model constraints are expressed as follows:

[0096] ;

[0097] Among them, P load (t) represents the original load at time t; pil(m,t) represents the interruption amount of the interruptible load of level m at time t; P ess (t) represents the charging power of the energy storage system; P ms (t) represents the electricity sold; P cold (t) represents the electrical power of the air conditioning load model; P cva (t) represents the charging power; P pv (t) represents photovoltaic power generation; P mt (t) represents the work done; P esr (t) represents the discharge power; P mb (t) represents the amount of electricity purchased; P rva (t) represents the discharge power.

[0098] Figure 2 The flowchart illustrating the virtual power plant scheduling solution process considering multiple demand responses according to an embodiment of this application is shown.

[0099] like Figure 2 As shown, the virtual power plant scheduling solution process considering multiple demand responses includes operations S201 to S208.

[0100] In operation S201, a first objective function value of a target function of the virtual power plant under a plurality of initial scheduling schemes is calculated.

[0101] In the embodiments of the present application, a population is initialized, an initial parent population is generated, and three objective function values corresponding to each individual, i.e., total operation cost, total carbon dioxide emission, and total purchased power, are calculated. Each individual in the initial parent population corresponds to a scheduling strategy of the virtual power plant.

[0102] Each initial scheduling scheme corresponds to a first objective function value of the target function; since the target function includes a target function of the operation cost of the virtual power plant in the scheduling period, a target function of the carbon dioxide of the virtual power plant in the scheduling period, and a target function of the total power purchased from the power grid by the virtual power plant in the scheduling period, and are sequentially referred to as the first target function, the second target function, and the third target function, the first objective function value includes three values, i.e., the first objective function value of the first target function, the second objective function value of the second target function, and the third objective function value of the third target function; each initial scheduling scheme corresponds to three first target functions, second target functions, and third target functions.

[0103] For example, the population size N is set to 90, each individual in the population represents a complete virtual power plant day-ahead 24-hour scheduling scheme, and the decision variable adopts a real number coding method. In addition, each individual is generated by uniformly random sampling in the physical feasible region of each corresponding variable, ensuring that all individuals meet the basic constraint conditions, and constraint repair work is performed on each individual to ensure the feasibility of the scheduling scheme. Three objective function values of each individual are calculated. After the initial values are generated, each individual will be used as the input of the subsequent target function evaluation to calculate the corresponding three objective values, i.e., the total operation cost, the total carbon dioxide emission, and the total purchased power.

[0104] In operation S202, the plurality of initial scheduling schemes are non-dominantly sorted according to the first objective function value, and a plurality of initial scheduling scheme sets are obtained; the non-dominantly sorted levels of the initial scheduling scheme sets are different; the non-dominantly sorted levels of the initial scheduling schemes in the initial scheduling scheme set of the same group are the same.

[0105] In the embodiments of the present application, the initial parent population is quickly non-dominated sorted based on the first objective function, the second objective function and the third objective function. Specifically, for any two individuals x and y, if x is not worse than y in the first objective function, the second objective function and the third objective function, and strictly better than y in at least one of the first objective function, the second objective function and the third objective function, then x is said to dominate y. By traversing and comparing, the multiple initial scheduling schemes are divided into several non-intersecting non-dominated frontiers. The first frontier G1 includes all solutions that are not dominated by any other individual, i.e., the current Pareto optimal candidate set; the second frontier G2 includes solutions that are only dominated by individuals in G1, but not dominated by each other, and so on.

[0106] In operation S203, according to the non-dominated sorting level of each initial scheduling scheme, genetic operations are applied to update the multiple initial scheduling schemes to obtain multiple updated scheduling schemes.

[0107] In the embodiments of the present application, the current parent population is selected and operated, and the offspring population is generated through crossover and mutation operations. The current parent population is the multiple initial scheduling schemes. The genetic operation can select the mutation operation and the crossover operation.

[0108] For example, the selection operation: a binary tournament selection is adopted to select parent individuals from the current parent population. Specifically, two individuals are randomly selected, and their non-dominated sorting levels (ranks) are compared, and the individual with a lower rank is preferentially selected; if the two individuals belong to the same non-dominated frontier (i.e., the ranks are the same), one of them is randomly selected. This strategy is only used to construct a mating pool, and does not affect the diversity of the final solution set, which is guaranteed by the subsequent reference point guiding mechanism.

[0109] Crossover operation: considering that there are both continuous decision variables and 0-1 discrete decision variables in the virtual power plant scheduling model, a hybrid coding strategy is adopted in the embodiments, and different types of variables are subjected to adaptive crossover operators. Specifically as follows:

[0110] For continuous variables, simulated binary crossover (SBX) is used to generate offspring. SBX is an efficient recombination operator for real number coding, which can maintain the excellent characteristics of the parent while introducing local search capability. In the embodiments, the crossover probability P c =0.9, and the distribution index η c =20. For any pair of continuous variable components x1,x2∈[x min ,x max ] in the parent individuals, two offspring components and , as shown below:

[0111] ;

[0112] where β is based on η c and a random number generated scaling factor, ensuring that the offspring are distributed within the parent neighborhood with high probability.

[0113] For 0-1 discrete variables, uniform crossover is adopted. In this embodiment, the crossover probability P c = 0.9, for the value of the i-th binary bit of the parent A and B, a random number is independently generated, and the corresponding bit of the offspring is generated according to the following rules:

[0114] ;

[0115] where and represent the value of the i-th binary bit of the parent A and B; and represent the value of the i-th binary bit of the offspring 1 and offspring 2. This operation ensures that the offspring is still a legal 0-1 sequence, and can effectively explore the combination space.

[0116] Mutation operation: to enhance the diversity of the population and avoid premature convergence, the offspring individuals after crossover are further subjected to mutation operation, which is also processed according to the variable type.

[0117] For continuous variables, polynomial mutation is performed with mutation probability P mutation = 1 / n cont In this example, n cont = 400, the distribution index η m = 20, and the new value after mutation is generated according to the polynomial probability density function in the interval [x min , x max ]. If it exceeds the boundary, it is truncated to the nearest boundary value.

[0118] For 0-1 discrete variables, bit flip operation is performed with mutation probability P mutation-bin = 1 / n bin In this example, n bin = 168. Specifically, if a bit is selected for mutation, its value is changed from 0 to 1 or from 1 to 0.

[0119] Constraint repair processing: to ensure that all candidate scheduling schemes meet the physical and operating constraints of the internal devices of the virtual power plant, after completing the crossover and mutation operations, before calculating the objective function, constraint repair operation is performed on each offspring individual, thereby obtaining multiple updated scheduling schemes.

[0120] In operation S204, a second objective function value corresponding to each scheduling scheme in a joint scheduling scheme group composed of multiple update scheduling schemes and multiple initial scheduling schemes is calculated.

[0121] In an embodiment of the present application, a parent population and an offspring population are combined to form a joint population, and a target function value of a newly generated individual is evaluated; the parent population is multiple initial scheduling schemes; the offspring population is multiple update scheduling schemes. The joint population is a joint scheduling scheme group; the joint scheduling scheme group includes multiple scheduling schemes. The second target function value includes three function values, which are a second target function value of the first target function, a second target function value of the second target function, and a second target function value of the third target function. That is, each scheduling scheme corresponds to three target functions, and correspondingly, three second target function values.

[0122] In operation S205, according to the second target function value, non-dominated sorting is performed on each scheduling scheme in the joint scheduling scheme group, and multiple joint scheduling scheme sets are obtained.

[0123] In an embodiment of the present application, the joint population is subjected to non-dominated sorting to obtain a hierarchical non-dominated front set; each target function value is subjected to normalization processing to eliminate dimensional differences. Each joint scheduling scheme set includes multiple normalized scheduling schemes. The non-dominated sorting levels of the joint scheduling scheme sets in different groups are different; the non-dominated sorting levels of the scheduling schemes in the same group are the same.

[0124] After the joint population is subjected to non-dominated sorting to obtain a hierarchical non-dominated front set, normalization processing is further needed. The normalization processing is to eliminate the dimensional and order-of-magnitude differences of the three optimization targets (total cost, carbon emission, and external power purchase), perform normalization operation on the individuals in the first non-dominated front G1, and obtain multiple joint scheduling scheme sets. The normalization operation is specifically as follows:

[0125] An ideal point is calculated, and the minimum values of each target in G1 are taken to form an ideal point , wherein, . Wherein, represents an ideal point; represents three components of the ideal point , the numerical value of is equal to the minimum value obtained by calculating all individuals corresponding to the three target functions (the first target function, the second target function, and the third target function) in the G1 front; Let represent the i-th objective function; calculate the extreme point, and for each objective i, construct a special weight vector. The design principle is: assign a larger weight to the i-th objective direction, and assign smaller and equal weights to the other two objective directions, so as to guide the search to extend along the i-th objective direction. In this embodiment, ε=10. -6 The weights of each objective are: ; ; For each weight vector, in G1, the weighted Chebyshev distance for each individual is calculated: .in, A candidate solution (the set of all variables in a scheduling scheme); Let i be the j-th component of the i-th weight vector. In the extreme point search, i can be regarded as i=j, but a complete representation requires both subscripts and superscripts. Let x represent the difference between individual x and the ideal point on the j-th target; in G1, select the individual that minimizes the weighted Chebyshev distance as a candidate for the extreme point in the i-th target direction, then the intercept of the normalized hyperplane on the i-th target axis is defined as: . Let represent the original function value of the candidate individual at the i-th extreme point on the i-th objective; using the obtained ideal point and intercept, normalize the original objective vector of each individual in the joint population to obtain the normalized objective vector: . This represents the original function value of each individual on the i-th objective.

[0126] In operation S206, multiple target scheduling schemes are determined from multiple sets of joint scheduling schemes based on the structured reference point set and the non-dominated hierarchy of the scheduling schemes in each set of joint scheduling schemes.

[0127] In the embodiments of this application, constructing a reference point set involves building a structured and uniformly distributed set of reference points in a three-dimensional target space to guide the distribution of the population on the Pareto front.

[0128] To guide the evolutionary direction of the population in the three-dimensional target space and maintain the diversity of the solution set, a systematic method proposed by Das and Dennis is used to construct a structured reference point set. Specifically, the reference point layer number parameter p=12 is set, and the set is constructed within a three-dimensional unit simplex (i.e., satisfying...). Generate all satisfying the condition on the plane. weight vector As a reference point, when P=12, a total of There are 1 reference point. Among them, i, j, and k have no actual physical meaning and are used to calculate the weight vector; m refers to the number of objective functions.

[0129] Considering the population size N = 90 set in this embodiment, which is slightly less than the total number of reference points, the algorithm will give priority to retaining individuals associated with the reference point with the lowest usage frequency in the subsequent environmental selection stage, thereby ensuring the coverage integrity of the Pareto front. The reference point set is distributed in a uniform triangular grid in the target space, which can effectively cover various typical scheduling preference directions including "purely economic type" (w = (1, 0, 0)), "purely low-carbon type" (w = (0, 1, 0)), and "highly autonomous type" (w = (0, 0, 1)).

[0130] The individuals in the joint population are associated with the nearest reference point, and the number of individuals associated with each reference point is counted. Starting from the first non-dominated front, individuals are selected layer by layer. When a front cannot be included completely, individuals associated with the reference point with the lowest usage frequency are given priority, and the new population size is restored to the initial set value.

[0131] For each reference point, a ray is drawn in the direction of the reference point from the origin of the normalized target space. For all individuals in the joint population, the perpendicular distance of their normalized target vector to each reference direction ray is calculated:

[0132] ;

[0133] wherein, represents the normalized target vector, and represents the plan of the three objective functions, and the normalized values of the three objective functions respectively constitute three component vectors of .

[0134] Subsequently, the individual x is associated with the reference direction The number of individuals associated with each reference point is counted. Among them, argmin represents the input that minimizes the function, and represents the formula that finally obtains the nearest reference point; H is the total number of reference points, and in this embodiment, the value of H is 91.

[0135] The next generation of parent population is selected from the joint population. This embodiment adopts a two-stage selection strategy based on non-dominated sorting and reference point association, and the specific steps are as follows:

[0136] (1) Select layer by layer according to the non-dominated level: according to the non-dominated sorting result, consider each front in turn. Initialize an empty set S, and set the current number of selected individuals nsel = N. If the total number of individuals does not exceed N after adding the entire front Gl to S, all of them are included in S, otherwise, stop selecting layer by layer, and enter the next stage, only select part of the individuals from the current front Gl to fill the remaining vacancies.

[0137] (2) Perform diversity selection on the critical front: if the above process stops at Gl, select individuals in Gl according to the association results of the reference points. First, count the number of individuals in S associated with each reference point, and then preferentially select individuals associated with the reference point that is "currently unoccupied" or "has the fewest occupants". If multiple individuals are associated with the same reference point, select the individual with the smallest perpendicular distance to the reference direction. Repeat the above process until the number of individuals in S is N, forming a new generation of parent population.

[0138] In operation S207, the plurality of target scheduling schemes are taken as a plurality of initial scheduling schemes, and operations for updating the plurality of initial scheduling schemes, calculating a second target function value, non-dominated sorting, and determining the plurality of target scheduling schemes are iteratively performed until a preset iteration condition is reached.

[0139] In operation S208, a scheduling strategy of the virtual power plant is determined according to a result of non-dominated sorting of the plurality of target scheduling schemes obtained when the preset iteration condition is reached.

[0140] In an embodiment of the present application, the maximum iteration number is used as the termination criterion. When initialized, the maximum iteration number Tmax is set to 500, and after each main loop, the current iteration number t is incremented by 1. If t ≥ Tmax, the iteration is terminated, otherwise the loop process returns to operation S203 for continuous execution. The population formed by the plurality of target scheduling schemes obtained when the preset iteration condition is reached is subjected to fast non-dominated sorting, and all individuals in the first front Gl are taken as the final Pareto optimal scheduling scheme set.

[0141] Table 6 shows the optimization results of the partial solution set on the three target functions of operating cost, carbon emission, and total electricity purchase under the embodiment of the present application.

[0142] Table 6 Partial solution results

[0143]

[0144] According to an embodiment of the present application, a third generation non-dominated sorting genetic algorithm (NSGA-III) can also be used to solve the target function with multiple constraint conditions as boundaries to determine the scheduling strategy of the virtual power plant. Specifically, by constructing a reference point guiding mechanism, the Pareto optimal solution set is searched in the three target spaces of the operating cost of the virtual power plant in the scheduling period, the total carbon dioxide of the virtual power plant in the scheduling period, and the total electricity purchased from the power grid by the virtual power plant in the scheduling period, and a non-dominated scheduling scheme set that meets the daily scheduling requirements of the virtual power plant is output.

[0145] Embodiments of the present application are applicable to virtual power plant operation scenarios containing multiple types of resources such as electric vehicles, air conditioning loads, and distributed photovoltaic power generation. Embodiments of the present application focus on simplicity and practicality in model design. By establishing a simplified physical model that can reflect key factors such as electric vehicle charging and discharging characteristics, air conditioning load power regulation and user comfort constraints, and photovoltaic power generation forecast output, the operating characteristics and constraint conditions of various resources are accurately described under the premise of ensuring computational efficiency. On this basis, a three-objective optimization scheduling model is constructed, including minimizing total operating cost, minimizing total carbon dioxide emissions, and minimizing total electricity purchase. The third generation of non-dominated sorting genetic algorithm (NSGA-III) is used for efficient solution. This algorithm can output a comprehensive Pareto optimal scheduling scheme set without pre-setting target weights, effectively supporting day-ahead decision-making for different operation preferences. Through the above technical solutions, embodiments of the present application significantly improve the coordinated utilization level of electric vehicles, air conditioning loads, and photovoltaic power generation and other resources under lower modeling and computational complexity, and enhance the comprehensive optimization capability of virtual power plants in economy, low carbon, and operation autonomy.

[0146] Embodiments of the present application aim to synergistically improve economy, low carbon, and new energy consumption capacity. Based on the source-load-storage coordination architecture of virtual power plants, a multi-type resource scheduling model is constructed, including gas turbines, interruptible loads, air conditioning loads, energy storage systems, and electric vehicles, and constraint conditions such as power balance, equipment operating limits, and state dynamics are introduced.

[0147] To address the problem that traditional single-objective optimization cannot balance multiple operation demands, a three-objective optimization framework is adopted to simultaneously minimize total operating cost, total carbon emissions, and external electricity purchase, and an improved multi-objective evolutionary algorithm based on NSGA-III is designed for solution. Through a hybrid coding strategy, continuous and discrete decision variables are processed, and a constraint repair mechanism is used to ensure the feasibility of solutions. Normalized reference points are used to maintain the convergence and uniformity of the Pareto front, and an environmental selection strategy is used to generate a set of diversified non-dominated scheduling schemes that meet various physical and operating constraints, providing day-ahead optimization decision support for virtual power plants that balances economic efficiency, low-carbon operation, and efficient new energy consumption.

[0148] Figure 3 A block diagram of a virtual power plant scheduling device considering multiple demand responses according to an embodiment of the present application is schematically shown.

[0149] As shown in Figure 3 , the virtual power plant scheduling device 300 considering multiple demand responses includes a target function construction module 310, a constraint condition determination module 320, and a scheduling strategy determination module 330.

[0150] The objective function construction module 310 is configured to construct an objective function according to the operation cost of the virtual power plant in the dispatching period, the carbon dioxide emission, and the total power purchased from the power grid.

[0151] The constraint condition determination module 320 is configured to determine a plurality of constraint conditions of the operation of the virtual power plant according to a plurality of demands of the demand side of the virtual power plant.

[0152] The dispatching strategy determination module 330 is configured to solve the objective function with the plurality of constraint conditions as boundaries, and determine a dispatching strategy of the virtual power plant.

[0153] Any one or more of the modules, sub-modules, units, sub-units according to the embodiments of the present application, or at least part of the functions of any one or more of the modules, sub-modules, units, sub-units, can be implemented in one module. Any one or more of the modules, sub-modules, units, sub-units according to the embodiments of the present application can be split into a plurality of modules for implementation. Any one or more of the modules, sub-modules, units, sub-units according to the embodiments of the present application can be implemented at least in part as a hardware circuit, for example, a field programmable gate array (FPGA), a programmable logic array (PLA), a system on chip, a system on board, a system on package, an application specific integrated circuit (ASIC), or any other reasonable manner of hardware or firmware through integration or packaging of the circuit, or in any one of software, hardware, and firmware, or in an appropriate combination of any one or more of the three. Alternatively, one or more of the modules, sub-modules, units, sub-units according to the embodiments of the present application can be implemented at least in part as a computer program module, which can perform the corresponding functions when the computer program module is run.

[0154] For example, any plurality of the objective function construction module 310, constraint determination module 320, and scheduling strategy determination module 330 can be combined into one module / unit / subunit, or any one of these modules / units / subunits can be split into multiple modules / units / subunits. Alternatively, at least part of the functionality of one or more of these modules / units / subunits can be combined with at least part of the functionality of other modules / units / subunits and implemented in one module / unit / subunit. According to embodiments of this application, at least one of the objective function construction module 310, constraint determination module 320, and scheduling strategy determination module 330 can be at least partially implemented as hardware circuitry, such as a field-programmable gate array (FPGA), a programmable logic array (PLA), a system-on-a-chip, a system-on-a-substrate, a system-on-package, an application-specific integrated circuit (ASIC), or any other reasonable means of integrating or packaging circuitry, or implemented in software, hardware, or firmware, or in any one of the three implementation methods or a suitable combination of any of them. Alternatively, at least one of the objective function construction module 310, constraint determination module 320, and scheduling strategy determination module 330 may be implemented at least partially as a computer program module, which can perform the corresponding function when the computer program module is run.

[0155] It should be noted that the data processing system part in the embodiments of this application corresponds to the data processing method part in the embodiments of this application. The specific description of the data processing system part is referred to in the data processing method part, and will not be repeated here.

[0156] Figure 4 A block diagram of an electronic device suitable for implementing a virtual power plant scheduling method that takes into account multiple demand responses, according to an embodiment of this application, is illustrated schematically. Figure 4 The electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0157] like Figure 4 As shown, an electronic device 400 according to an embodiment of this application includes a processor 401, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 402 or a program loaded from a storage portion 408 into a random access memory (RAM) 403. The processor 401 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or an associated chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 401 may also include onboard memory for caching purposes. The processor 401 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of this application.

[0158] In the RAM 403, various programs and data required for the operation of the electronic device 400 are stored. The processor 401, the ROM 402, and the RAM 403 are connected to each other via the bus 404. The processor 401 performs various operations of the method flow according to the embodiments of the present application by executing the programs in the ROM 402 and / or the RAM 403. It is to be noted that the programs can also be stored in one or more memories other than the ROM 402 and the RAM 403. The processor 401 can also perform various operations of the method flow according to the embodiments of the present application by executing the programs stored in the one or more memories.

[0159] According to the embodiments of the present application, the electronic device 400 can further include an input / output (I / O) interface 405, which is also connected to the bus 404. The electronic device 400 can further include one or more of the following components connected to the input / output (I / O) interface 405: an input part 406 including a keyboard, a mouse, etc.; an output part 407 including a display such as a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage part 408 including a hard disk, etc.; and a communication part 409 including a network interface card such as a LAN card, a modem, etc. The communication part 409 performs communication processing via a network such as the Internet. A drive 410 is also connected to the input / output (I / O) interface 405 as necessary. A removable medium 411 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc. is attached to the drive 410 as necessary, so that a computer program read out therefrom is installed in the storage part 408 as necessary.

[0160] According to the embodiments of the present application, the method flow according to the embodiments of the present application can be implemented as a computer software program. For example, the embodiments of the present application include a computer program product including a computer program carried on a computer-readable storage medium, the computer program containing program codes for executing the method shown in the flowchart. In such embodiments, the computer program can be downloaded and installed from a network by the communication part 409, and / or installed from the removable medium 411. When the computer program is executed by the processor 401, the above-described functions defined in the system of the embodiments of the present application are performed. According to the embodiments of the present application, the system, the device, the apparatus, the module, the unit, etc. described above can be implemented by computer program modules.

[0161] The application further provides a computer readable storage medium, which can be included in the device / apparatus / system described in the above embodiments, or can exist independently without being assembled into the device / apparatus / system. The computer readable storage medium carries one or more programs, which, when executed, implement the method according to the embodiments of the application.

[0162] According to the embodiments of the application, the computer readable storage medium can be a non-volatile computer readable storage medium. For example, it can include, but is not limited to, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In this application, a computer readable storage medium can be any tangible medium that contains or stores a program, which can be used by or in connection with an instruction execution system, apparatus, or device.

[0163] For example, according to the embodiments of the application, the computer readable storage medium can include one or more memories other than the ROM 402 and / or the RAM 403 described above.

[0164] The embodiments of the application also include a computer program product, which includes a computer program containing program codes for executing the method provided by the embodiments of the application, and when the computer program product is run on an electronic device, the program codes are used to make the electronic device implement the virtual power plant scheduling method considering multiple demand responses provided by the embodiments of the application.

[0165] When the computer program is executed by the processor 401, the above functions defined in the system / apparatus of the embodiments of the application are executed. According to the embodiments of the application, the system, apparatus, module, unit, etc. described above can be implemented by computer program modules.

[0166] In one embodiment, the computer program can rely on tangible storage media such as optical storage media, magnetic storage media, etc. In another embodiment, the computer program can also be transmitted, distributed, downloaded and installed in the form of signals on a network medium, and be downloaded and installed through the communication part 409 and / or installed from the detachable medium 411. The program codes contained in the computer program can be transmitted by any appropriate network medium, including but not limited to wireless, wired, etc., or any suitable combination of the foregoing.

[0167] According to an embodiment of the present application, program code for implementing the computer programs provided by the embodiments of the present application can be written in any combination of one or more programming languages, and specifically, can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. The programming languages include, but are not limited to, such as Java, C++, python, "C" language, or similar programming languages. The program code can be executed entirely on a user computing device, partially on a user device, partially on a remote computing device, or entirely on a remote computing device or server. In the case involving a remote computing device, the remote computing device can be connected to the user computing device through any kind of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (for example, connected to the Internet through an Internet service provider).

[0168] The flow diagrams and the block diagrams in the drawings are illustrations of possible architectures, functions, and operations for systems, methods, and computer program products according to various embodiments of the present application. In this regard, each block in the flow diagrams or block diagrams can represent a module, a segment, or a portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that in some alternative implementations, the functions noted in the blocks can occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently or the blocks may

[0169] The embodiments of the present application are described above. However, these embodiments are merely for the purpose of illustration, and are not intended to limit the scope of the present application. Although each embodiment is described above separately, this does not mean that the measures in each embodiment cannot be used advantageously in combination. Without departing from the scope of the present application, those skilled in the art can make various substitutions and modifications, and these substitutions and modifications should all fall within the scope of the present application.

Claims

1. A virtual power plant scheduling method considering multiple demand responses, characterized in that, The method comprises: building a target function according to the operation cost, carbon dioxide emission and total power purchased from the power grid of the virtual power plant in a scheduling period; determining a plurality of constraint conditions of the virtual power plant operation according to a plurality of demands of the demand side of the virtual power plant; solving the target function with the plurality of constraint conditions as boundaries to determine the scheduling strategy of the virtual power plant. 2.The method of claim 1, wherein, The target function is expressed as: ； wherein minF(x) is the objective function; F cost (x) is the operation cost of the virtual power plant in a dispatch cycle; F carbon (x) is the carbon dioxide emission of the virtual power plant in a dispatch cycle; F import (x) is the total electricity purchased from the power grid by the virtual power plant in a dispatch cycle. 3.The method of claim 2, wherein, The operation cost of the virtual power plant in the scheduling period comprises at least one of demand response load cost, gas turbine cost, power purchase and sale cost and electric vehicle related cost; The demand response load cost is expressed as: ； wherein, F1 is the demand response load cost; pil(1,t) is the interruption amount of the first level interruptible load at time t, pil(2,t) is the interruption amount of the second level interruptible load at time t, pil(3,t) is the interruption amount of the third level interruptible load at time t, t is a variable with a value range of 1 to 24 after dividing 24 hours of a day into 24 time points, kil1 represents the unit compensation cost of the first level interruptible load; kil2 represents the unit compensation cost of the second level interruptible load; kil3 represents the unit compensation cost of the third level interruptible load; The gas turbine cost is expressed as: ； where F2 is the gas turbine cost; k represents the fixed cost generated when the gas turbine is working, K mt is the linearized cost coefficient, K s is the start-stop cost coefficient; X conv (t) is the working state variable; Y conv (t) is the start-stop state variable; P mt (t) is the gas turbine output power; The power purchase and sale cost is expressed as: ； Wherein, F3 is the electricity purchase and sale fee; P mb (t) is the electricity purchase amount; X b (t) is the electricity purchase price at time t; P ms (t) is the electricity sale amount; X s (t) is the electricity sale price at time t; The electric vehicle related cost is expressed as: ； Wherein, F4 is the cost related to electric vehicle; P rva (t) is the discharge power; η cra is the battery discharge efficiency; P cva (t) is the charging power; η cca is the battery charging efficiency; C eqa is the equivalent electricity price when charging the car; C cpua is the unit mileage power consumption coefficient; D va (t) is the driving distance at time t; C dega is the battery depreciation loss of electric vehicle. 4.The method of claim 1, wherein, The carbon dioxide emission of the virtual power plant in the scheduling period is expressed as: ； Wherein, F carbon is the total amount of carbon dioxide emissions within the dispatch cycle; γ mt is the carbon dioxide emission factor of the gas turbine per unit of power generation; P mt (t) is the output power of the gas turbine; γ grid is the average carbon dioxide emission factor of the regional power grid; P mb (t) is the power purchase amount. 5.The method of claim 1, wherein, The total power purchased from the power grid of the virtual power plant in the scheduling period is expressed as: ； where F import is the total electricity purchased from the grid during the dispatch period; P mb (t) is the electricity purchase. 6.The method of claim 1, wherein, The plurality of constraint conditions comprises a power purchase and sale model constraint; The power purchase and sale model constraint is expressed as: ； wherein, u b (t) is the purchase operation state variable; u s (t) is the sale operation state variable; P mb (t) is the purchase amount; P ms (t) is the sale amount; is the maximum transaction amount.

7. The method of claim 6, wherein, The plurality of constraint conditions further comprises a power balance model constraint; The power balance model constraint is expressed as: ； wherein P load (t) is the original load at time period t; pil(m, t) is the interruption amount of the interruptible load of the mth level at time t; P ess (t) is the charging power of the energy storage system; P ms (t) is the amount of electricity sold; P cold (t) is the electrical power of the air conditioning load model; P cva (t) is the charging power; P pv (t) is the photovoltaic power generation; P mt (t) is the output power; P esr (t) is the discharging power; P mb (t) is the amount of electricity purchased; P rva (t) is the discharging power. 8.The method of claim 1, wherein, The solving of the target function with the plurality of constraint conditions as boundaries to determine the scheduling strategy of the virtual power plant comprises: calculating a first target function value of the target function of the virtual power plant under a plurality of initial scheduling schemes; performing non-dominated sorting on the plurality of initial scheduling schemes according to the first target function value to obtain a plurality of initial scheduling scheme sets; the non-dominated sorting levels of each initial scheduling scheme set are different; the non-dominated sorting levels of each initial scheduling scheme in the same initial scheduling scheme set are the same; updating the plurality of initial scheduling schemes by applying genetic operations according to the non-dominated sorting levels of each initial scheduling scheme to obtain a plurality of updated scheduling schemes; calculating a second target function value corresponding to each scheduling scheme in a joint scheduling scheme group composed of the plurality of updated scheduling schemes and the plurality of initial scheduling schemes; performing non-dominated sorting on each scheduling scheme in the joint scheduling scheme group according to the second target function value to obtain a plurality of joint scheduling scheme sets; determining a plurality of target scheduling schemes from the plurality of joint scheduling scheme sets according to a structured reference point set and the non-dominated levels of the scheduling schemes in each joint scheduling scheme set; The operation of updating the plurality of initial scheduling schemes, calculating the second objective function value, non-dominant sorting, and determining the plurality of target scheduling schemes is iteratively performed until a preset iteration condition is reached, with the plurality of target scheduling schemes as the plurality of initial scheduling schemes. A scheduling strategy of the virtual power plant is determined according to a result of non-dominant sorting of the plurality of target scheduling schemes obtained when the preset iteration condition is reached. 9.A virtual power plant scheduling device considering multiple demand responses, characterized in that, The apparatus comprises: An objective function construction module configured to construct an objective function according to an operation cost, a carbon dioxide emission, and a total power amount purchased from a power grid of the virtual power plant in a scheduling period; A constraint condition determination module configured to determine a plurality of constraint conditions of operation of the virtual power plant according to a plurality of demands of a demand side of the virtual power plant; A scheduling strategy determination module configured to solve the objective function with the plurality of constraint conditions as boundaries, and determine a scheduling strategy of the virtual power plant.

10. An electronic device, comprising: Comprise: One or more processors; A memory for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors implement the method of any one of claims 1 to 8.

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