A virtual power plant multi-objective time domain coupling feasible region construction method and system

Abstract

The application discloses a kind of virtual power plant multi-objective time domain coupling feasible region construction method and system, the method establishes the vertex search model of virtual power plant neighborhood feasible region, the vertex search model of neighborhood feasible region is solved by vertex search method, obtain the time domain coupling feasible region of t-1 time and t time;The application describes the net flow value of virtual queue as zero under certain time by virtual queue to wide time domain output stability of virtual power plant, Lyapunov function is included in optimization objective function of neighborhood feasible region vertex search model, which represents the congestion degree of virtual queue, by solving the optimization objective, the time domain coupling feasible region considering the stability of whole virtual queue of virtual power plant can be obtained.The application can improve the flexibility of power system, optimize energy structure, energy saving and consumption reduction.

Description

Technical Field

[0001] This invention relates to the field of energy distribution technology, and in particular to a method and system for constructing a feasible domain of multi-objective temporal coupling for virtual power plants. Background Technology

[0002] In recent years, a massive amount of heterogeneous distributed energy resources have been connected to the power grid. By the end of 2022, the cumulative installed capacity of distributed energy sources, mainly distributed photovoltaic, distributed wind power, distributed gas, and small hydropower, in my country had reached 250,004,900 kilowatts, a year-on-year increase of 25.06%. Therefore, the integrated development of power generation, grid, load, and storage is urgent and important. Virtual power plants aggregate various resources scattered across the power grid, such as distributed power sources, energy storage, and loads, for collaborative optimization, operation control, and market trading. This better taps the potential of distributed resources and is an important method to promote organic interaction between power generation, grid, load, and storage, and improve system flexibility.

[0003] Virtual power plants typically participate in various types of electricity markets by submitting external characteristic parameters such as operating curves, ramp rates, and feasible regions. Currently, most virtual power plant external characteristic extraction technologies focus on using vertex search or Minkowski sum methods to extract the active-reactive feasible region of the virtual power plant, and using the full probability criterion and key parameter extraction methods to characterize the probability distribution of the virtual power plant's output. However, in the process of characterizing the feasible region of a virtual power plant, it is necessary to comprehensively consider the impact of the feasible region characterization at a single moment on the output at other times, and existing feasible region construction methods have ignored the study of global stability. Summary of the Invention

[0004] Purpose of the invention: The purpose of this invention is to provide a method and system for constructing a feasible region of a virtual power plant with multi-objective temporal coupling, so as to solve the problem of global output stability in the feasible region search of a virtual power plant.

[0005] Technical solution: The method for constructing a multi-objective temporal coupled feasible domain of a virtual power plant according to the present invention includes the following steps:

[0006] A vertex search model of the neighborhood feasible region of a virtual power plant is established. The vertex search method is used to solve the vertex search model of the neighborhood feasible region to obtain the time-domain coupled feasible region at time t-1 and time t.

[0007] The optimization objective of the neighborhood feasible region vertex search model is: p m,t ∈Ω VPP ,in Let μ be the unit direction vector for the new vertex in the m-th search. P(t),m The unit direction vector of the vertex search base is in Projection in dimension, μ P(t-1),m The unit direction vector of the vertex search base is in Projection in dimensions; Let represent the overall external feasible domain variable of the virtual power plant in the m-th search. The active power output of the virtual power plant at time t, at the connection point with the upper-level power grid. The active power output of the virtual power plant at time t-1, at the connection point with the upper-level power grid, is p. m,t It is the feasible region Ω VPP Points within; ρ is the weight of the global output stability of the virtual power plant; T is the time period, which is 24 in this invention; It is the piecewise linearized Lyapunov function with stability at time t during the m-th search; The calculation methods include:

[0008] Establish as a piecewise linearized Lyapunov function

[0009]

[0010] In the formula, Q t It is the virtual queue at time t, Q t+1 It is the virtual queue at time t+1. It represents the initial operating power of the virtual power plant at time t; Let be the squared linearization variable, representing the piecewise linearized result. The value; This represents the slope of the k-th segment; This represents the intercept of the k-th segment.

[0011] Furthermore, the constraints on the optimization objective include virtual power plant regulation revenue constraints:

[0012]

[0013] in, This indicates the regulating power compensation of the virtual power plant. This represents the difference between the virtual power plant and its original operating point. This represents the adjustment cost of photovoltaic p in the virtual power plant at time t; This represents the adjustment cost of the micro gas turbine g in the virtual power plant at time t; This represents the adjustment cost of the flexible load l in the virtual power plant at time t; β represents the adjustment cost of energy storage e in the virtual power plant at time t; β is the expected adjustment benefit of the virtual power plant.

[0014] Furthermore, regarding After performing complete equivalent linearization, the constraint on the regulation revenue of the virtual power plant after linearization is:

[0015]

[0016] in, To represent the linearized variable by absolute value; M is a very large number; It is a Boolean variable.

[0017] Furthermore, the regulation cost of photovoltaic power is In the formula: For photovoltaic feed-in tariffs; This represents the output offset of photovoltaic unit p at time t during the boundary characterization of the feasible domain of the virtual power plant;

[0018] Gas turbine regulation cost is In the formula: l u,g The piecewise linearized slope of the quadratic cost function of the gas turbine g represents the slope of the function. This represents the offset of the output power of the gas turbine g during the linearization phase;

[0019] The cost of flexible load adjustment is In the formula: The electricity price for flexible load l at time t; This represents the offset of the flexible load l from the original electricity demand at time t;

[0020] Energy storage regulation cost is In the formula: c ES Unit operating cost of energy storage; This indicates the offset of the energy storage e's charging and discharging power from the original operating point.

[0021] Furthermore, the constraints of the optimization objective also include: distributed photovoltaic operation constraints, distributed gas turbine operation constraints, distributed energy storage operation constraints, flexible load operation constraints, and network power flow constraints.

[0022] Furthermore, the termination condition for the neighborhood feasible region vertex search model is:

[0023]

[0024] In the formula: z 0,t The new vertex generated at time t; z 2,t For the newly generated vertex, the vertex is the vertex on the clockwise side; ||μ m ||2 is the L2 norm of the search direction vector; δ is the set termination condition.

[0025] Furthermore, the method for solving the vertex search model of the feasible neighborhood using the vertex search method includes:

[0026] 7.1) Determine the feasible neighborhood at solution time t and initialize the set of direction vectors. With vertex set

[0027] 7.2) Sequentially calculate the different categories p m,t And store it in the vertex set z. 2,t ;

[0028] 7.3) Calculate the unit outward normal vector between all adjacent vertices, and use it as the direction vector for the next round of search. Update the direction vector set U. 2,t ;

[0029] 7.4) Calculate h for each vertex. If the h of a vertex is less than the set threshold δ, stop the search for the external normal vector determined by that vertex and its neighboring vertices. If it is greater than the set threshold δ, repeat steps 7.2) to 7.4) until the h of all vertices is calculated. j Continue until all termination conditions are met;

[0030] 7.5) The time-domain coupling feasible region at time t-1 and time t is obtained by solving the convex hull of each vertex.

[0031] The virtual power plant multi-objective time-domain coupled feasible domain construction system of the present invention includes:

[0032] The feasible region vertex search modeling unit is used to establish a neighborhood feasible region vertex search model for the virtual power plant. The optimization objective of the neighborhood feasible region vertex search model is: p m,t ∈Ω VPP ,in Let μ be the unit direction vector for the new vertex in the m-th search. P(t),m The unit direction vector of the vertex search base is in Projection in dimension, μ P(t-1),m The unit direction vector of the vertex search base is in Projection in dimensions; Let represent the overall external feasible domain variable of the virtual power plant in the m-th search. The active power output of the virtual power plant at time t, at the connection point with the upper-level power grid. The active power output of the virtual power plant at time t-1, at the connection point with the upper-level power grid, is p. m,t It is the feasible region Ω VPP Points within; ρ is the weight of the global output stability of the virtual power plant; T is the time period, which is 24 in this invention; It is the piecewise linearized Lyapunov function with stability at time t during the m-th search; The calculation methods include:

[0033] Establish as a piecewise linearized Lyapunov function

[0034]

[0035] In the formula, Q t It is the virtual queue at time t, Q t+1 It is the virtual queue at time t+1. It represents the initial operating power of the virtual power plant at time t; Let be the squared linearization variable, representing the piecewise linearized result. The value; This represents the slope of the k-th segment; This represents the intercept of the k-th segment;

[0036] The feasible region solving unit is used to solve the vertex search model of the neighborhood feasible region by vertex search method to obtain the temporal coupled feasible region at time t-1 and time t.

[0037] The electronic device of the present invention includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the computer program is loaded onto the processor, it implements the virtual power plant multi-objective temporal-domain coupled feasible domain construction method.

[0038] The computer-readable storage medium of the present invention stores a computer program, which, when executed by a processor, implements the virtual power plant multi-objective time-domain coupled feasible domain construction method.

[0039] Beneficial Effects: Compared with existing technologies, the advantages of this invention are as follows: This invention considers the process of adjustable capacity application by virtual power plants in the regulatory market. Based on the Lyapunov stability analysis principle, it proposes a method for constructing a multi-objective time-domain coupled feasible domain for virtual power plants that considers global stability. Virtual power plant operators can choose different adjustable capacity application strategies based on their acceptance of global output stability, the intensity of regulatory market compensation, and expected regulatory benefits. This invention has significant implications for improving power system flexibility, optimizing energy structure, and reducing energy consumption. Attached Figure Description

[0040] Figure 1 This is a schematic diagram of the feasible domain for temporal coupling according to the present invention.

[0041] Figure 2 This is a flowchart of the vertex search method of the present invention.

[0042] Figure 3 This is a schematic diagram of a virtual power plant resource aggregation system in an embodiment of the present invention.

[0043] Figure 4 This is a schematic diagram of the photovoltaic output curves of each node in an embodiment of the present invention;

[0044] Figure 5This is a schematic diagram of the time-of-use electricity price in a certain province for a certain month, as shown in an embodiment of the present invention.

[0045] Figure 6 This is a schematic diagram of the virtual power plant output boundary for different ρ values ​​in an embodiment of the present invention;

[0046] Figure 7 This is a schematic diagram illustrating the temporal feasible region characterization results of a virtual power plant under different ρ values ​​in an embodiment of the present invention. Detailed Implementation

[0047] The technical solution of the present invention will be further described below with reference to the accompanying drawings.

[0048] like Figure 1 As shown, the method for constructing a multi-objective temporal-domain coupled feasible domain for a virtual power plant includes the following steps:

[0049] Step 1: Construct a virtual power plant aggregation control model oriented towards grid regulation needs;

[0050] Step 1.1: Determine the aggregated control model of the virtual power plant for grid regulation demand, including distributed photovoltaic operation constraints, distributed gas turbine operation constraints, distributed energy storage operation constraints, flexible load operation constraints, network power flow constraints, and virtual power plant regulation revenue constraints;

[0051] 1) The operational constraints of distributed photovoltaic systems are as follows:

[0052] Because photovoltaic power output is volatile, constraints involving random variables can be expressed as chance constraints, meaning that the constraints are maintained at a specific confidence level. The general form of a chance constraint is:

[0053]

[0054] In the formula: P r {*} represents the probability that event {*} is true; A random variable denoted by *; α * This represents the confidence level corresponding to the opportunity constraint.

[0055] Transforming the above opportunity constraints into deterministic constraints:

[0056]

[0057] In the formula: Q uant (α p |*) represents the quantile; C DF The inverse function of .

[0058] 2) The operating constraints of distributed gas turbines are as follows:

[0059]

[0060] Let g be the active power output of the distributed gas turbine at time t; Let g be the upper limit of the output power of the distributed gas turbine at time t; Let g be the upper limit of the ramp power of the distributed gas turbine at time t.

[0061] 3) The operational constraints of distributed energy storage are as follows:

[0062]

[0063]

[0064] In the formula: and , respectively, represent the charging and discharging power of the stored energy e at time t; Let e ​​represent the charging and discharging state of the stored energy e at time t, where 1 represents discharging and 0 represents charging. This represents the maximum charging and discharging power of the energy storage device e; Let e ​​be the state of charge of the stored energy e at time t; The charging and discharging efficiency of energy storage e; and These represent the maximum and minimum charge states of energy storage e, respectively. This represents the initial state of charge of energy storage e during the scheduling cycle. This represents the state of charge of energy storage e at the end of the scheduling cycle.

[0065] 4) The constraints for flexible load operation are as follows:

[0066]

[0067] In the formula: The actual power of the adjustable load l in the virtual power plant at time t; and These represent the upper and lower limits of the adjustable load power l in the virtual power plant at time t, respectively. in S represents the actual power demand of load l at time t; OL,l,t This represents the cumulative amount of electricity that has not met the load demand at time t.

[0068] 5) Network flow constraints are as follows:

[0069]

[0070] In the formula: Let x be the active power of line ij at time t; ij Let θ be the reactance of line ij; i,t Let be the phase angle of node i at time t; The active power output of the virtual power plant at time t, at the connection point with the upper-level power grid; and L represents the upper and lower limits of the active power flow of line ij; pv L represents the number of photovoltaic units. mt L represents the number of micro gas turbine units. L L represents the number of adjustable loads. E This represents the number of energy storage units.

[0071] 6) The constraints on the regulation revenue of the virtual power plant are as follows:

[0072]

[0073] In the formula, This represents the difference between the virtual power plant and its original operating point, taking into account the nonlinear term in the revenue opportunity constraint of the virtual power plant. This article uses mathematical methods to... After performing complete equivalent linearization, the payoff opportunity constraints are as follows:

[0074]

[0075] In the formula: This indicates the regulating power compensation of the virtual power plant; To represent the linearized variable by absolute value; M is a very large number; It is a Boolean variable; This represents the adjustment cost of photovoltaic p in the virtual power plant at time t; This represents the adjustment cost of the micro gas turbine g in the virtual power plant at time t; This represents the adjustment cost of the flexible load l in the virtual power plant at time t; β represents the adjustment cost of energy storage e in the virtual power plant at time t; β is the expected adjustment benefit of the virtual power plant.

[0076] (1) Photovoltaic regulation costs:

[0077]

[0078] In the formula: For photovoltaic feed-in tariffs; This represents the output offset of photovoltaic unit p at time t during the process of characterizing the feasible domain boundary of the virtual power plant.

[0079] (2) Gas turbine regulation costs:

[0080] This invention linearizes the secondary operating cost function of a micro gas turbine piecewise, as shown below.

[0081]

[0082] In the formula: l u,g The piecewise linearized slope of the quadratic cost function of the gas turbine g represents the slope of the function. This represents the offset of the output power u of the gas turbine g during the linearization phase.

[0083] (3) Flexible load adjustment cost

[0084]

[0085] In the formula: The electricity price for flexible load l at time t; This represents the offset of the flexible load l from the original electricity demand at time t.

[0086] (4) Energy storage regulation costs:

[0087]

[0088] In the formula: c ES Unit operating cost of energy storage; This indicates the offset of the energy storage e's charging and discharging power from the original operating point.

[0089] Step 2: Based on the aggregated control model of the virtual power plant oriented towards grid regulation demand constructed in Step 1, construct the vertex search model of the neighborhood feasible region of the virtual power plant.

[0090] Step 2.1: Determine the optimization objective of the vertex search model;

[0091] The objective function for determining the feasible region vertex search is:

[0092]

[0093] Let be the unit direction vector for the m-th search of the new vertex; Let Ω represent the overall external feasible region variable of the virtual power plant in the m-th search. VPP points within.

[0094] Step 2.2: Determine the termination condition for the vertex search model;

[0095] The polygon represented by the boundary of the feasible neighborhood is a set of vertices and edges. Among the different characteristics of the new vertex and the original vertex, relative displacement is the most intuitive and effective. Therefore, the termination condition is set as the relative displacement from the new vertex to its corresponding original line being less than a certain specific value δ. Let this relative displacement be h. j h, that is:

[0096]

[0097] In the formula: z 0,tThe new vertex generated at time t; z 2,t For the newly generated vertex, the vertex is the vertex on the clockwise side; ||μ m ||2 is the L2 norm of the search direction vector; δ is the set termination condition.

[0098] Step 2.3: Determine the algorithm flow of the vertex search model; such as Figure 2 As shown, the process for solving the feasible neighborhood region based on the vertex search method is as follows:

[0099] 1) Determine the feasible neighborhood at solution time t and initialize the set of direction vectors. With vertex set

[0100] 2) Sequentially calculate the different categories p m,t And store it in the vertex set z. 2,t ;

[0101] 3) Calculate the unit outward normal vector between all adjacent vertices, and use it as the direction vector for the next round of search. Update the direction vector set U. 2,t ;

[0102] 4) Calculate h for each vertex. If the h of a vertex is less than the set threshold δ, stop the search for the external normal vector determined by that vertex and its neighboring vertices; if it is greater than the set threshold δ, repeat steps 2)-4) until the h of all vertices is calculated. j The termination conditions will be met until all conditions are met.

[0103] 5) The time-domain coupling feasible region at time t-1 and time t is obtained by solving the convex hull of each vertex.

[0104] Step 3: Based on the Lyapunov stability analysis principle, construct a Lyapunov function symbolizing the global stability of the virtual power plant. Combined with the vertex search model constructed in Step 2, obtain a virtual power plant multi-objective time-domain coupled feasible domain construction model that considers global stability.

[0105] Step 3.1: Construct a Lyapunov function to characterize the global output stability of the virtual power plant, and introduce the Lyapunov function into the optimization objective of the vertex search model proposed in Step 2.1, transforming it into a vertex characterization problem that considers the overall virtual queue stability of the virtual power plant.

[0106] Step 3.1.1: Construct a virtual queue;

[0107] This invention constructs a virtual queue for the output of a virtual power plant based on the Lyapunov stability analysis principle to analyze the stability of the virtual power plant. To this end, a virtual queue is constructed for the global output of the virtual power plant. First, a virtual queue Q is constructed to reflect the accumulated offset of the virtual power plant relative to the original operating point during the feasible region boundary characterization process. t ,Right now:

[0108]

[0109] The concept of a virtual queue allows us to describe the stable output of a virtual power plant over a wide time domain as a situation where the net flow value of the virtual queue is zero over a certain period of time, which is equivalent to the stability problem of the virtual queue. These constraints are then added to the virtual power plant aggregation control model constructed in step 1.1.

[0110] To address the aforementioned virtual queue stability problem, this invention models the problem by constructing a Lyapunov function that characterizes the crowding level of the virtual queue, thereby enabling the construction and solution of a virtual queue stability model.

[0111] Step 3.1.2: Construct a Lyapunov function to characterize the global output stability of the virtual power plant;

[0112] To address the aforementioned virtual queue stability problem, this invention models the problem by constructing a Lyapunov function that characterizes the crowding level of the virtual queue, thereby enabling the construction and solution of a virtual queue stability model.

[0113] Constructed Lyapunov function H t for:

[0114]

[0115] It can be seen that when H t When the density is small, all virtual queues are less crowded, and the virtual queue stability is good; conversely, at least one virtual queue is more crowded, and the virtual queue stability is poor. Therefore, the output stability problem of the virtual power plant can be relaxed to finding H... t The smallest problem.

[0116] However, H t Including the squares of variables transforms the original optimization problem into a MIQCP problem, which significantly increases the computational burden on the solver. To simplify the computation, this invention, based on the principle of piecewise linearization, modifies equation H... t The process of converting it into a segmented lower bound is shown below.

[0117]

[0118] In the formula, Let be the squared linearization variable, representing the piecewise linearized result. The value; This represents the slope of the k-th segment; This represents the intercept of the k-th segment; This is the piecewise linearized Lyapunov function. The transformation process is as follows: add the above linearization formula to the virtual power plant aggregation control model constructed in step 1.1. Through the above transformation, the original problem is solved by finding... Minimum is transformed into taking The lower boundary is determined, transforming the original MIQCP problem into a MILP problem, which greatly improves the efficiency of problem solving.

[0119] Step 3.1.3: Construct a vertex characterization problem that considers the overall stability of the virtual queue of the virtual power plant;

[0120] To ensure the stability of the virtual queue throughout the entire time period, and considering the vertex search model of the feasible neighborhood of the virtual power plant, the original problem is transformed into the following vertex characterization problem considering the overall stability of the virtual queue of the virtual power plant:

[0121]

[0122] The problem transformation process is as follows: replace the objective function of the feasible region vertex search described in step 2.1 with the above optimization objective.

[0123] Based on the virtual power plant multi-objective time-domain coupled feasible domain construction model considering global stability constructed in step 3, and combined with the virtual power plant aggregated control model for grid regulation demand constructed in step 1, the full-time output boundary and neighborhood feasible domain of the virtual power plant are solved to obtain the virtual power plant multi-objective time-domain coupled feasible domain considering global stability.

[0124] Step 4: Based on the virtual power plant multi-objective time-domain coupled feasible region construction model considering global stability built in Steps 2 and 3, and combined with the virtual power plant aggregated control model for grid regulation demand built in Step 1, solve the full-time output boundary and neighborhood feasible region of the virtual power plant to obtain the virtual power plant multi-objective time-domain coupled feasible region considering global stability, that is, the time-domain coupled feasible region at time t-1 and time t.

[0125] The method described in this invention will be verified through specific examples below.

[0126] The virtual power plant constructed in this example is composed of distributed photovoltaics, distributed gas turbines, distributed energy storage, and flexible loads aggregated from nodes {6, 26-33} in the IEEE 33 standard test system. The virtual power plant aggregates resources as follows: Figure 3 The photovoltaic output curves for each node are shown below. Figure 4The electricity price for flexible loads in this article is referenced from the time-of-use electricity price of a certain province in a certain month, as detailed below. Figure 5 The relevant parameters of distributed energy storage and gas turbines are shown in Table 1. The electricity provided by virtual power plants for regulation services in the regulation market is subsidized at RMB 2,000 / MWh.

[0127] Table 1. Relevant parameters of distributed energy storage and gas turbine

[0128]

[0129] This example solves for the output boundary and feasible neighborhood of the virtual power plant, yielding the output boundary of the virtual power plant for different values ​​of ρ, as shown below. Figure 6 As shown, the temporal feasible region characterization results of the virtual power plant under different ρ values ​​are as follows: Figure 7 As shown.

[0130] Figure 7 The feasible time neighborhood regions of the virtual power plant for 1:00-2:00 and 10:00-11:00 were characterized under different index weights ρ. To more clearly demonstrate the impact of ρ on the feasible time neighborhood regions of the virtual power plant, Figure 7 The process involves magnifying certain regions. The feasible region of the time neighborhood can be viewed as the relationship between each adjacent time slice in the virtual power plant, representing the relationship between the output at the previous moment and the output at the next moment. Once the specific output at the previous moment is determined, the upper and lower limits of the output of the virtual power plant at the current moment can be determined, i.e., the ramping parameters. Figure 7 In the middle (a), the feasible region of the time neighborhood from 1:00 to 2:00 is shown. It can be seen that when ρ is greater than 10, the virtual power plant can no longer obtain more regulation capacity from other times, and the feasible region is compressed to only a straight line. The virtual power plant no longer has the ability to regulate in multiple time periods. That is, when the output of the virtual power plant in a single time period is determined, the output of its adjacent time periods is also determined and cannot be freely adjusted. Figure 7 (b) represents the feasible region of the time neighborhood from 10:00 to 11:00. It can be seen that when ρ is less than or equal to 1, ρ has a relatively small impact on the feasible region of the virtual power plant. Figure 7 As shown in the enlarged section of (b), the reduction only occurs at the vertices of {Pt=10=-28.58MW,Pt=11=10.74MW} and {Pt=10=10.57MW,Pt=11=-30.09MW}, respectively. When ρ is greater than 10, the temporal coupling feasible region of the virtual power plant is also compressed to a straight line, and its regulation capability is greatly limited.

Claims

1. A method for constructing a feasible domain of a virtual power plant with multi-objective temporal coupling, characterized in that, Includes the following steps: A vertex search model of the neighborhood feasible region of a virtual power plant is established. The vertex search method is used to solve the vertex search model of the neighborhood feasible region to obtain the time-domain coupled feasible region at time t-1 and time t. The optimization objective of the neighborhood feasible region vertex search model is: p m,t ∈Ω VPP ,in Let μ be the unit direction vector for the new vertex in the m-th search. P(t),m The unit direction vector of the vertex search base in P t PCC Projection in dimension, μ P(t-1),m The unit direction vector of the vertex search base is in Projection in dimensions; Let P represent the overall external feasible domain variable of the virtual power plant in the m-th search. t PCC The active power output of the virtual power plant at time t, at the connection point with the upper-level power grid. The active power output of the virtual power plant at time t-1, at the connection point with the upper-level power grid, is p. m,t It is the feasible region Ω VPP points within; ρ is the weight for the global output stability of the virtual power plant; T is the time period; It is the piecewise linearized Lyapunov function with stability at time t during the m-th search; The calculation methods include: Establish as a piecewise linearized Lyapunov function In the formula, Q t It is the virtual queue at time t, Q t+1 It is the virtual queue at time t+1. It represents the initial operating power of the virtual power plant at time t; Let be the squared linearization variable, representing the piecewise linearized result. The value; This represents the slope of the k-th segment; This represents the intercept of the k-th segment.

2. The method for constructing a feasible domain for multi-objective temporal coupling in a virtual power plant according to claim 1, characterized in that, The constraints on the optimization objective include virtual power plant regulation revenue constraints: in, Represents the regulating power compensation of the virtual power plant, |ΔP t PPC | represents the difference between the virtual power plant and the original operating point. This represents the adjustment cost of photovoltaic p in the virtual power plant at time t; This represents the adjustment cost of the micro gas turbine g in the virtual power plant at time t; This represents the adjustment cost of the flexible load l in the virtual power plant at time t; β represents the adjustment cost of energy storage e in the virtual power plant at time t; β is the expected adjustment benefit of the virtual power plant.

3. The method for constructing a feasible domain for multi-objective temporal coupling in a virtual power plant according to claim 2, characterized in that, For |ΔP t PPC After performing complete equivalent linearization, the constraint on the regulation revenue of the virtual power plant after linearization is: in, To represent the linearized variable by absolute value; M is a very large number; It is a Boolean variable.

4. The method for constructing a feasible domain for multi-objective temporal coupling in a virtual power plant according to claim 2 or 3, characterized in that, Photovoltaic regulation cost is In the formula: For photovoltaic feed-in tariffs; This represents the output offset of photovoltaic unit p at time t during the boundary characterization of the feasible domain of the virtual power plant; Gas turbine regulation cost is In the formula: l u,g The piecewise linearized slope of the quadratic cost function of the gas turbine g represents the slope of the function. This represents the offset of the output power of the gas turbine g during the linearization phase; The cost of flexible load adjustment is In the formula: The electricity price for flexible load l at time t; This represents the offset of the flexible load l from the original electricity demand at time t; Energy storage regulation cost is In the formula: c ES Unit operating cost of energy storage; This indicates the offset of the energy storage e's charging and discharging power from the original operating point.

5. The method for constructing a feasible domain for multi-objective temporal coupling in a virtual power plant according to claim 2, characterized in that, The constraints on the optimization objectives also include: distributed photovoltaic operation constraints, distributed gas turbine operation constraints, distributed energy storage operation constraints, flexible load operation constraints, and network power flow constraints.

6. The method for constructing a feasible domain for multi-objective temporal coupling in a virtual power plant according to claim 1, characterized in that, The termination condition for the neighborhood feasible region vertex search model is: In the formula: z 0,t The new vertex generated at time t; z 2,t For the newly generated vertex, the vertex is the vertex on the clockwise side; ||μ m ||2 is the L2 norm of the search direction vector; δ is the set termination condition.

7. The method for constructing a feasible domain for multi-objective temporal coupling in a virtual power plant according to claim 1, characterized in that, The method for solving the vertex search model of the feasible neighborhood region using the vertex search method includes: 7.1) Determine the feasible neighborhood at solution time t and initialize the set of direction vectors. With vertex set 7.2) Sequentially calculate the different categories p m,t And store it in the vertex set z. 2,t ; 7.3) Calculate the unit outward normal vector between all adjacent vertices, and use it as the direction vector for the next round of search. Update the direction vector set U. 2,t ; 7.4) Calculate h for each vertex. If the h of a vertex is less than the set threshold δ, stop the search for the external normal vector determined by that vertex and its neighboring vertices. If it is greater than the set threshold δ, repeat steps 7.2) to 7.4) until the h of all vertices is calculated. j Until the termination conditions are met; 7.5) The time-domain coupling feasible region at time t-1 and time t is obtained by solving the convex hull of each vertex.

8. A system for constructing a multi-objective temporal-domain coupled feasible domain for a virtual power plant, characterized in that, include: The feasible region vertex search modeling unit is used to establish a neighborhood feasible region vertex search model for the virtual power plant. The optimization objective of the neighborhood feasible region vertex search model is: p m,t ∈Ω VPP ,in Let μ be the unit direction vector for the new vertex in the m-th search. P(t),m The unit direction vector of the vertex search base in P t PCC Projection in dimension, μ P(t-1),m The unit direction vector of the vertex search base is in Projection in dimensions; Let P represent the overall external feasible domain variable of the virtual power plant in the m-th search. t PCC The active power output of the virtual power plant at time t, at the connection point with the upper-level power grid. The active power output of the virtual power plant at time t-1, at the connection point with the upper-level power grid, is p. m,t It is the feasible region Ω VPP points within; ρ is the weight for the global output stability of the virtual power plant; T is the time period; It is the piecewise linearized Lyapunov function with stability at time t during the m-th search; The calculation methods include: Establish as a piecewise linearized Lyapunov function In the formula, Q t It is the virtual queue at time t, Q t+1 It is the virtual queue at time t+1. It represents the initial operating power of the virtual power plant at time t; Let be the squared linearization variable, representing the piecewise linearized result. The value; This represents the slope of the k-th segment; This represents the intercept of the k-th segment; The feasible region solving unit is used to solve the vertex search model of the neighborhood feasible region by vertex search method to obtain the temporal coupled feasible region at time t-1 and time t.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the computer program is loaded into the processor, it implements the virtual power plant multi-objective temporal-domain coupled feasible domain construction method according to any one of claims 1-7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the virtual power plant multi-objective temporal-domain coupled feasible domain construction method according to any one of claims 1-7.

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