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

Abstract

The invention provides a virtual power plant scheduling optimization method and device, a storage medium and equipment. The method comprises the following steps: establishing a virtual power plant scheduling optimization model considering economic and environmental factors; wherein a target function of the virtual power plant scheduling optimization model is to minimize economic cost and environmental cost of the virtual power plant during scheduling; and solving the virtual power plant scheduling optimization model based on an SC-PSO particle swarm optimization algorithm to obtain output parameters of various power generation energy sources. The virtual power plant scheduling optimization method based on the SC-PSO algorithm has the advantages of being few in parameter, simple in structure, convenient to implement, high in convergence speed and the like, the problem that population diversity in the later iteration stage of a traditional algorithm is reduced is solved, and the capacity of the algorithm for jumping out of local optimum is improved.

Description

Technical Field

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

[0002] In the past few years, my country's total electricity consumption and the maximum load of the whole society have continuously reached new highs. With the economic recovery, the national electricity load has increased significantly. Among them, the tertiary industry and residential electricity consumption have accelerated, and their cyclical electricity demand has further widened the gap between the peaks and valleys. In addition, the sales of new energy vehicles have surged, and the charging time is unevenly distributed, which has strained the power system. In the process of transitioning to a clean and low-carbon power grid structure, the development of virtual power plants (VPPs) can suppress the random volatility of renewable energy generation, promote the consumption of renewable energy, and achieve the purpose of peak shaving and valley filling more economically. Therefore, VPPs have the advantage of alleviating the imbalance between supply and demand in the power system, which has prompted people to conduct extensive research on VPPs.

[0003] VPP is an integration of traditional energy, new energy, distributed energy DG, energy storage system ESS, and elastic loads that can aggregate, optimize, control and manage multiple DER. However, the generation of renewable energy is highly random and uncertain. Replacing fossil fuel power plants with renewable energy will reduce the flexibility of the energy system, thereby endangering the security of the energy system. Through the complementary generation of renewable energy and traditional energy, the complementary advantages between different types of power generation types can be achieved, which is of great value for reducing the abandonment of renewable energy and establishing a clean and low-carbon VPP structure. With the increase in the share of renewable energy in VPP, the complexity of traditional problems is transformed into a hybrid dynamic environmental economic dispatch problem. The model has the characteristics of multi-objective, large-scale, multi-variable, nonlinear, and robust constraints. Traditionally, techniques such as genetic algorithms are used to solve it. However, these methods rely on rules such as Pareto advantage and non-inferiority. They can obtain a series of Pareto optimal solutions. But these algorithms do not have global optimization to guide the optimization process, nor do they have a memory mechanism for individual updates. Summary of the invention

[0004] To this end, the present invention provides a virtual power plant scheduling optimization method and device, storage medium, and equipment, aiming to solve the technical problem that traditional algorithms rely on local optimization and cannot meet the global optimization requirements to guide the optimization process when scheduling power for virtual power plants.

[0005] To achieve the above objectives, the present invention adopts the following technical solutions:

[0006] According to a first aspect of the present invention, the present invention provides a virtual power plant scheduling optimization method, the method comprising:

[0007] Establishing a virtual power plant scheduling optimization model that takes into account economic and environmental factors; wherein the objective function of the virtual power plant scheduling optimization model is to minimize the economic cost and environmental cost of the virtual power plant during the scheduling period;

[0008] The virtual power plant scheduling optimization model is solved based on the SC-PSO particle swarm optimization algorithm to obtain the output parameters of various power generation energy sources.

[0009] Furthermore, the various types of power generation energy include: at least one of distributed energy, gas energy, wind power energy and photovoltaic energy.

[0010] Furthermore, the objective function of the virtual power plant scheduling optimization model includes:

[0011] minF=F(P dg )+F(P gt )+F(P wp )+F(P pv )+F(P ess )+F(P market )

[0012] Where F is the economic cost of the virtual power plant; F(P dg ) is the economic cost of distributed energy; F(P gt ) is the economic cost of gas energy; F(P wp ) is the economic cost of wind power energy; F(P pv ) is the economic cost of photovoltaic energy; F(P ess ) is the economic cost of the energy storage system; F(P market ) is the revenue cost of the virtual power plant.

[0013] Furthermore, the objective function of the virtual power plant scheduling optimization model also includes:

[0014]

[0015] Where E is the environmental cost of the virtual power plant; α m is the cost coefficient for treating polluted gas m; P t dg,y is the output parameter of distributed energy; β y,m is the pollutant gas emission coefficient of distributed energy; P t gt,k is the output parameter of gas energy; β k,m is the pollutant gas emission coefficient of gas energy; P t market is the exchange power between the virtual power plant and the main grid; β market,m is the pollutant gas emission coefficient of the virtual power plant.

[0016] Furthermore, the constraint conditions of the virtual power plant scheduling optimization model include power balance constraints, which are expressed as follows:

[0017]

[0018] Among them, P t dg,y is the output parameter of distributed energy; P t gt,k is the output parameter of the gas turbine; P t wp is the output parameter of wind power energy; P t pv is the output parameter of photovoltaic energy; P t esd is the discharge power of the energy storage system; P t esdg is the charging power of the energy storage system; P t market is the exchange power between the virtual power plant and the main grid; P t ld is the power load demand; P t ls It is the power transmission loss.

[0019] Furthermore, the SC-PSO particle swarm optimization algorithm includes:

[0020]

[0021] Where R1 is the amplitude of the control sine and cosine functions; x ij is the position of the particle in the D-dimensional space; t is the number of iterations; t max is the maximum number of iterations, a is a constant; Gbest ij is the global extreme value of the particle; R2, R3, and R4 are three random numbers that follow uniform distribution.

[0022] Furthermore, the economic cost of the distributed energy F(P dg ) is calculated as follows:

[0023]

[0024] Where Y is the number of distributed energy resources; P t dg,y is the output parameter of distributed energy; a y 、b y 、c y are the power generation cost coefficients of distributed energy; OM dg,y The operation and maintenance costs of distributed energy;

[0025] and / or,

[0026] The economic cost of gas energy F(P gt ) is calculated as follows:

[0027]

[0028] Among them, c gt is the unit cost of natural gas; P t gt,k is the output parameter of the gas turbine; LHV f is the calorific value of natural gas; η e For power generation efficiency;

[0029] and / or,

[0030] The economic cost of wind power F(P wp ) is calculated as follows:

[0031]

[0032] Among them, c wp is the average cost of wind power energy during its economic life; P t wp is the output parameter of wind power energy;

[0033] and / or,

[0034] The economic cost of photovoltaic energy F(P pv ) is calculated as follows:

[0035]

[0036] Among them, c pv is the converted photovoltaic energy power generation cost; P t pv is the output parameter of photovoltaic energy;

[0037] and / or,

[0038] The economic cost of the energy storage system F(P ess ) is calculated as follows:

[0039]

[0040] Among them, P t esd is the discharge power of the energy storage system; P t esdg is the charging power of the energy storage system; α esdg is the charging cost coefficient of the energy storage system; β esd is the discharge cost coefficient of the energy storage system;

[0041] and / or,

[0042] The revenue cost of the virtual power plant F(P market ) is calculated as follows:

[0043]

[0044] Among them, P t market is the exchange power between the virtual power plant and the main grid; μ t market The transaction price.

[0045] According to a second aspect of the present invention, the present invention provides a virtual power plant scheduling optimization device, the device comprising:

[0046] A model building module, used to establish a virtual power plant scheduling optimization model that takes into account economic and environmental factors; wherein the objective function of the virtual power plant scheduling optimization model is to minimize the economic cost and environmental cost of the virtual power plant during the scheduling period;

[0047] The model solving module is used to solve the virtual power plant scheduling optimization model based on the SC-PSO particle swarm optimization algorithm to obtain the output parameters of various power generation energy sources.

[0048] According to the third aspect of the present invention, the present invention provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the processor executes the virtual power plant scheduling optimization method as described in any one of the first aspect of the present invention.

[0049] According to the fourth aspect of the present invention, the present invention provides a computer device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor implements a virtual power plant scheduling optimization method as described in any one of the first aspects of the present invention when executing the computer program.

[0050] The present invention adopts the above technical solution and has at least the following beneficial effects:

[0051] Through the scheme of the present invention, a virtual power plant scheduling optimization model considering economic and environmental factors is established; wherein the objective function of the virtual power plant scheduling optimization model is to minimize the economic cost and environmental cost of the virtual power plant during the scheduling period; the virtual power plant scheduling optimization model is solved based on the SC-PSO particle swarm optimization algorithm to obtain the output parameters of various types of power generation energy. Therefore, a virtual power plant scheduling optimization method based on the SC-PSO algorithm is proposed, which has the advantages of few parameters, simple structure, convenient implementation, fast convergence speed, etc., avoids the problem of decreased population diversity in the later iteration stage of the traditional algorithm, and improves the ability of the algorithm to jump out of the local optimum.

[0052] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] 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, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0054] Figure 1 A schematic diagram of a flow chart of a virtual power plant scheduling optimization method provided by an embodiment of the present invention is shown;

[0055] Figure 2 A schematic diagram of the structure of a virtual power plant scheduling optimization device provided by an embodiment of the present invention is shown;

[0056] Figure 3 A schematic diagram of the physical structure of a computer device provided by an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0057] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0058] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the statement "include..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0059] The embodiment of the present invention provides a virtual power plant scheduling optimization method, such as Figure 1 As shown, at least the following steps S101 to S102 may be included:

[0060] Step S101, establishing a virtual power plant scheduling optimization model that takes economic and environmental factors into consideration.

[0061] The virtual power plant scheduling optimization model established in the embodiment of the present invention is used to optimize and adjust the output parameters of various types of power generation energy, including distributed energy DG, gas energy, wind power energy, and photovoltaic energy, so as to minimize the economic cost and environmental cost of the virtual power plant VPP during the scheduling period. Therefore, the embodiment of the present invention takes minimizing the economic cost and environmental cost of the virtual power plant as the objective function of the virtual power plant scheduling optimization model, and the formulas are as follows (1) to (2):

[0062] minF=F(P dg )+F(P gt )+F(P wp )+F(P pv )+F(P ess )+F(P market ) (1)

[0063] Where F is the economic cost of the virtual power plant; F(P dg ) is the economic cost of distributed energy; F(P gt ) is the economic cost of gas energy; F(P wp ) is the economic cost of wind power energy; F(P pv ) is the economic cost of photovoltaic energy; F(P ess ) is the economic cost of the energy storage system; F(P market ) is the revenue cost of the virtual power plant.

[0064] It should be noted that environmental costs mainly refer to the costs of dealing with emissions that affect the environment during the operation of the virtual power plant VPP. Renewable energy does not emit pollutants. Polluting gases mainly come from distributed energy DG, gas energy, and electricity purchased from the main grid. Therefore, the environmental cost calculation formula for the virtual power plant VPP is expressed as follows:

[0065]

[0066] Where E is the environmental cost of the virtual power plant; α m is the cost coefficient for treating polluted gas m; P t dg,y is the output parameter of distributed energy; β y,m is the pollutant gas emission coefficient of distributed energy; P t gt,k is the output parameter of gas energy; β k,m is the pollutant gas emission coefficient of gas energy; P t market is the exchange power between the virtual power plant and the main grid; β market,m is the pollutant gas emission coefficient of the virtual power plant.

[0067] The following is a detailed explanation of how to calculate each economic cost incurred during the operation and maintenance of a virtual power plant:

[0068] The economic cost of distributed energy DG (P dg ), the calculation formula is as follows (3):

[0069]

[0070] Where Y is the number of distributed energy resources; P t dg,y is the output parameter of distributed energy; a y , b y 、c y are the power generation cost coefficients of distributed energy; OM dg,y The operation and maintenance cost of distributed energy.

[0071] The economic cost of gas energy F(P gt ), the calculation formula is as follows (4):

[0072]

[0073] Among them, c gt is the unit cost of natural gas; P t gt,k is the output parameter of the gas turbine; LHV f is the calorific value of natural gas; η eFor power generation efficiency, the calculation formula can be (5):

[0074]

[0075] The economic cost of wind power F(P wp ), including construction investment cost and operation and maintenance cost, the embodiment of the present invention is evaluated based on the average power generation cost of wind power energy, and the calculation formula is as follows (6):

[0076]

[0077] Among them, c wp is the average cost of wind power during its economic life; P t wp It is the output parameter of wind power energy.

[0078] The economic cost of photovoltaic energy F(P pv ), which can be calculated using a standardized power generation cost model. The embodiment of the present invention takes into account factors such as operation and maintenance costs, internal rate of return, financial costs, and depreciation of the total cost of building investment, and the calculation formula is as follows (7):

[0079]

[0080] Among them, C pv is the converted photovoltaic energy power generation cost; P t pv It is the actual output parameter of photovoltaic energy.

[0081] The economic cost of energy storage system F(P ess ), the calculation formula is as follows (8):

[0082]

[0083] Among them, P t esd is the discharge power of the energy storage system; P t esdg is the charging power of the energy storage system; α esdg is the charging cost coefficient of the energy storage system; β esd is the discharge cost coefficient of the energy storage system.

[0084] The revenue cost of virtual power plants F(P market ), the calculation formula is as follows (9):

[0085]

[0086] Among them, P t market is the exchange power between the virtual power plant and the main grid; μt market The transaction price.

[0087] Furthermore, the embodiment of the present invention also sets constraints on the virtual power plant scheduling optimization model, including power balance constraints, which are expressed as follows (10):

[0088]

[0089] Among them, P t dg,y is the output parameter of distributed energy; P t gt,k is the output parameter of the gas turbine; P t wp is the output parameter of wind power energy; P t pv is the output parameter of photovoltaic energy; P t esd is the discharge power of the energy storage system; P t esdg is the charging power of the energy storage system; P t market is the exchange power between the virtual power plant and the main grid; P t ld is the power load demand; P t ls It is the power transmission loss.

[0090] Step S102, solving the virtual power plant scheduling optimization model based on the SC-PSO particle swarm optimization algorithm to obtain the output parameters of various types of power generation energy.

[0091] Specifically, the embodiment of the present invention is based on the SC-PSO particle swarm optimization algorithm, and uses the periodicity and oscillation of the sine function to solve the global optimization of the virtual power plant scheduling optimization model. The SC-PSO algorithm proposed in the present invention is an improvement on the traditional PSO algorithm.

[0092] The basic idea of ​​the traditional PSO algorithm is to use the information of each particle to represent the optimal solution to the optimization problem. The principle is: Assuming the population size is N, the position and velocity of the particle in the D-dimensional space are represented by x i =(x i1 , x i2 , ..., x ij , ..., x iD ) and v i =(v i1 , v i2 , ..., v ij , ..., v iD). Particle i updates its own speed and position information through the individual extreme value Pbest and the global extreme value Gpest, and the formulas are as follows (11)~(14):

[0093] v ij (t+1)=ω×v ij (t)+c1×r1×(Pbest ij (t)-x ij (t))+c2×r2×(Gbest ij (t)-x ij (t)) (11)

[0094] x ij (t+1)=x ij (t)+v ij (t+1) (12)

[0095] v ij =v max ,v ij >v max (13)

[0096] v ij =-v max ,v ij <v max (14)

[0097] Where ω is the inertia weight that controls the algorithm’s exploration and convergence capabilities; when ω is large, the algorithm has better global search capabilities; when ω is small, it is more conducive to local search; c1 is the learning coefficient; c2 is the social coefficient; c1 and c2 are designed to control the algorithm to find the optimal solution; r1 and r 2分别 is a normally distributed random number in the interval [0,1].

[0098] However, the traditional PSO algorithm often suffers from the problem of decreased population diversity in the later iteration stage. Therefore, the present invention develops the SC-PSO algorithm to improve it. The formulas are as follows (15) to (16):

[0099]

[0100] Where R1 is the amplitude of the control sine and cosine functions; x ij is the position of the particle in the D-dimensional space; t is the number of iterations; t max is the maximum number of iterations, a is a constant; Gbest ij is the global extreme value of the particle; R2, R3, and R4 are three random numbers that follow uniform distribution.

[0101] The embodiment of the present invention uses the SC-PSO algorithm shown above to solve the virtual power plant scheduling optimization model, and obtains the output optimization adjustment results of various types of power generation energy. It should be noted that in the global optimization of adaptive parameters, the SC-PSO algorithm uses the periodicity and oscillation of the sine function to solve the global optimization. It has better exploration capabilities than algorithms such as PSO, and can avoid falling into a local optimal state. The algorithm has the advantages of fewer parameters, simple structure, convenient implementation, and fast convergence speed. It improves the communication ability between each particle and the optimal particle, effectively guides a single particle to converge to the optimal global solution, reduces the blindness of traditional algorithms in optimization and the possibility of falling into a local optimal state, thereby improving the accuracy and convergence speed of the algorithm.

[0102] The embodiment of the present invention provides a virtual power plant scheduling optimization method, by establishing a virtual power plant scheduling optimization model that considers economic and environmental factors; wherein the objective function of the virtual power plant scheduling optimization model is to minimize the economic cost and environmental cost of the virtual power plant during the scheduling period; the virtual power plant scheduling optimization model is solved based on the SC-PSO particle swarm optimization algorithm to obtain the output parameters of various types of power generation energy. Due to the adoption of the above technical scheme, the present invention has the following advantages: with the increase in the share of renewable energy in the virtual power plant VPP, the complexity of the traditional problem is transformed into a hybrid dynamic environmental economic scheduling problem, which has the characteristics of multi-objective, large-scale, multi-variable, nonlinear, and robust constraints. Traditionally, genetic algorithms and other technologies are used to solve it, but these algorithms do not have global optimization to guide the optimization process, nor do they have a separate updated memory mechanism. The present invention proposes an SC-PSO algorithm that uses the periodicity and oscillation of the sine function to solve the global optimization, which has the advantages of few parameters, simple structure, convenient implementation, and fast convergence speed, and can be widely used in the field of virtual power plant scheduling optimization.

[0103] Further, as Figure 1 The specific implementation of the present invention provides a virtual power plant scheduling optimization device, such as Figure 2 As shown, the device may include: a model building module 210 and a model solving module 220 .

[0104] A model building module 210 is used to establish a virtual power plant scheduling optimization model that takes into account economic and environmental factors; wherein the objective function of the virtual power plant scheduling optimization model is to minimize the economic cost and environmental cost of the virtual power plant during the scheduling period;

[0105] The model solving module 220 is used to solve the virtual power plant scheduling optimization model based on the SC-PSO particle swarm optimization algorithm to obtain the output parameters of various power generation energy sources.

[0106] It should be noted that for other corresponding descriptions of the functional modules involved in the virtual power plant scheduling optimization device provided in the embodiment of the present invention, reference can be made to Figure 1 The corresponding description of the method shown will not be repeated here.

[0107] Based on the above Figure 1 The method shown, accordingly, an embodiment of the present invention also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the virtual power plant scheduling optimization method described in any of the above embodiments are implemented.

[0108] Based on the above Figure 1 The method shown and Figure 2 The embodiment of the device shown in the figure, the embodiment of the present invention also provides a physical structure diagram of a computer device, such as Figure 3 As shown, the computer device may include a communication bus, a processor, a memory and a communication interface, and may also include an input / output interface and a display device, wherein each functional unit may communicate with each other through the bus. The memory stores a computer program, and the processor is used to execute the program stored in the memory and execute the steps of the virtual power plant scheduling optimization method described in the above embodiment.

[0109] Those skilled in the art can clearly understand that the specific working processes of the systems, devices, modules and units described above can refer to the corresponding processes in the aforementioned method embodiments, and for the sake of brevity, they are not further described here.

[0110] In addition, the functional units in various embodiments of the present invention may be physically independent of each other, or two or more functional units may be integrated together, or all functional units may be integrated into one processing unit. The above integrated functional units may be implemented in the form of hardware, or in the form of software or firmware.

[0111] Those skilled in the art can understand that if the integrated functional unit is implemented in the form of software and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention can essentially or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, which includes a number of instructions to enable a computing device (such as a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention when running the instructions. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), magnetic disk or optical disk and other media that can store program codes.

[0112] Alternatively, all or part of the steps of implementing the aforementioned method embodiments may be accomplished by hardware associated with program instructions (such as a computing device such as a personal computer, a server, or a network device), and the program instructions may be stored in a computer-readable storage medium. When the program instructions are executed by a processor of a computing device, the computing device executes all or part of the steps of the methods described in the embodiments of the present invention.

[0113] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that within the spirit and principles of the present invention, the technical solutions described in the aforementioned embodiments can still be modified, or some or all of the technical features therein can be replaced by equivalents. However, these modifications or replacements do not deviate from the protection scope of the present invention.

Claims

1. A virtual power plant scheduling optimization method, It is characterized in that The method comprises: Establishing a virtual power plant scheduling optimization model that takes into account economic and environmental factors; wherein the objective function of the virtual power plant scheduling optimization model is to minimize the economic cost and environmental cost of the virtual power plant during the scheduling period; The virtual power plant scheduling optimization model is solved based on the SC-PSO particle swarm optimization algorithm to obtain the output parameters of various power generation energy sources.

2. The method according to claim 1, It is characterized in that The various types of power generation energy include: at least one of distributed energy, gas energy, wind power energy and photovoltaic energy.

3. The method according to claim 1, It is characterized in that The objective function of the virtual power plant scheduling optimization model includes: minF=F(P dg )+F(P gt )+F(P wp )+F(P pv )+F(P ess )+F(P market ) Where F is the economic cost of the virtual power plant; F(P dg ) is the economic cost of distributed energy; F(P gt ) is the economic cost of gas energy; F(P wp ) is the economic cost of wind power energy; F(P pv ) is the economic cost of photovoltaic energy; F(P ess ) is the economic cost of the energy storage system; F(P market ) is the revenue cost of the virtual power plant.

4. The method according to claim 1, It is characterized in that The objective function of the virtual power plant scheduling optimization model also includes: Where E is the environmental cost of the virtual power plant; α m is the cost coefficient for treating polluted gas m; P t dg,y is the output parameter of distributed energy; β y,m is the pollutant gas emission coefficient of distributed energy; P t gt,k is the output parameter of gas energy; β k,m is the pollutant gas emission coefficient of gas energy; P t market is the exchange power between the virtual power plant and the main grid; β market,m is the pollutant gas emission coefficient of the virtual power plant.

5. The method according to claim 1, It is characterized in that The constraints of the virtual power plant scheduling optimization model include power balance constraints, which are expressed as follows: Among them, P t dg,y is the output parameter of distributed energy; P t gt,k is the output parameter of the gas turbine; P t wp is the output parameter of wind power energy; P t pv is the output parameter of photovoltaic energy; P t esd is the discharge power of the energy storage system; P t esdg is the charging power of the energy storage system; P t market is the exchange power between the virtual power plant and the main grid; P t ld is the power load demand; P t ls It is the power transmission loss.

6. The method according to claim 1, It is characterized in that The SC-PSO particle swarm optimization algorithm includes: Among them, R 1 To control the amplitude of the sine and cosine functions; x ij is the position of the particle in the D-dimensional space; t is the number of iterations; t max is the maximum number of iterations, a is a constant; Gbest ij is the global extreme value of the particle; R 2 , R 3 , R 4 are three random numbers following uniform distribution.

7. The method according to claim 3, Features: The economic cost of distributed energy F(P dg ) is calculated as follows: Where Y is the number of distributed energy resources; P t dg,y is the output parameter of distributed energy; a y , b y 、c y are the power generation cost coefficients of distributed energy; OM dg,y The operation and maintenance costs of distributed energy; and / or, The economic cost of gas energy F(P gt ) is calculated as follows: Among them, c gt is the unit cost of natural gas; P t gt,k is the output parameter of the gas turbine; LHV f is the calorific value of natural gas; η e For power generation efficiency; and / or, The economic cost of wind power energy F(P wp ) is calculated as follows: Among them, c wp is the average cost of wind power energy during its economic life; P t wp is the output parameter of wind power energy; and / or, The economic cost of photovoltaic energy F(P pv ) is calculated as follows: Among them, c pv is the converted photovoltaic energy power generation cost; P t pv is the output parameter of photovoltaic energy; and / or, The economic cost of the energy storage system F(P ess ) is calculated as follows: Among them, P t esd is the discharge power of the energy storage system; P t esdg is the charging power of the energy storage system; α esdg is the charging cost coefficient of the energy storage system; β esd is the discharge cost coefficient of the energy storage system; and / or, The revenue cost of the virtual power plant F(P market ) is calculated as follows: Among them, P t market is the exchange power between the virtual power plant and the main grid; μ t market The transaction price.

8. A virtual power plant dispatch optimization device, It is characterized in that The device comprises: A model building module, used to establish a virtual power plant scheduling optimization model that takes into account economic and environmental factors; wherein the objective function of the virtual power plant scheduling optimization model is to minimize the economic cost and environmental cost of the virtual power plant during the scheduling period; The model solving module is used to solve the virtual power plant scheduling optimization model based on the SC-PSO particle swarm optimization algorithm to obtain the output parameters of various power generation energy sources.

9. A computer-readable storage medium, It is characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the processor executes the virtual power plant scheduling optimization method according to any one of claims 1 to 7.

10. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, It is characterized in that When the processor executes the computer program, the virtual power plant scheduling optimization method according to any one of claims 1 to 7 is implemented.

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