Power distribution network-virtual power plant non-iterative collaborative optimization scheduling method and related device

Abstract

The invention discloses a power distribution network-virtual power plant non-iterative collaborative optimization scheduling method and a related device, and relates to the field of power system optimization scheduling calculation, and the method comprises the steps: building a virtual power plant optimal economic scheduling model according to the characteristics of access resources in a virtual power plant; according to the optimal economic dispatching model of the virtual power plant, determining a variation range constraint of the power of the tie line of the virtual power plant based on a rapid approximate projection method of a feasible region of operation of the virtual power plant; constructing a non-iterative collaborative optimization scheduling model of the power distribution network and the virtual power plant based on variation range constraints according to a scheduling decision target of the power distribution network, a scheduling target of the virtual power plant and characteristics of access resources; according to the collaborative optimization scheduling model, solving a power distribution network scheduling result and a virtual power plant tie line power scheduling result; and inputting the tie line power scheduling result into the optimal economic scheduling model of the virtual power plant to obtain an optimal scheduling result of internal resources of the virtual power plant. According to the invention, the decision-making efficiency of collaborative optimization scheduling can be improved.

Description

Technical Field

[0001] The present application relates to the field of power system optimization and scheduling calculation, and in particular to a distribution network-virtual power plant non-iterative collaborative optimization scheduling method and related devices. Background Art

[0002] As small power supply entities connected to the distribution system, virtual power plants (VPPs) can independently control various internal distributed resources, such as gas turbines, electrochemical energy storage, air conditioners, and distributed photovoltaic power sources. They play a vital role in enhancing the power system's ability to absorb renewable energy locally and improve power supply stability. With the rapid growth in the number of VPPs and the continued expansion of peak-shaving demand in new power systems, exploring collaborative and optimized dispatch models will become a key path to addressing the scheduling challenges of active distribution networks and improving the system's flexible regulation capabilities.

[0003] The coordinated scheduling of distribution systems and entities such as virtual power plants faces challenges in terms of decision privacy and computability. Since the distribution system and virtual power plants operate independently and have differentiated optimization scheduling objectives, it is not feasible to merge the two models to form a global optimal coordinated optimization strategy. Among the existing collaborative optimization scheduling methods, non-iterative optimization scheduling methods based on the projection of the feasible domain of virtual power plant operation are commonly used, such as collaborative scheduling methods based on the Fourier-Motzkin elimination method or the progressive vertex enumeration method. This type of method uses the power of the interconnection line connecting the virtual power plant to the distribution network to reflect the characteristics of the feasible domain of operation of the virtual power plant, and uses the feasible domain projection method to project the feasible domain of operation of the virtual power plant to obtain the interconnection line power variation range without any virtual power plant operation decision variables, and incorporates it into the distribution system optimization scheduling model for solution. Only one calculation is required to obtain the optimal coordinated scheduling result. This type of method can obtain a collaborative scheduling strategy without repeated calculations while taking into account the needs of decision-making autonomy and privacy protection. It has high calculation accuracy and simple calculation steps. Its defect is that the projection calculation efficiency is highly correlated with the dimension of the feasible domain of virtual power plant operation. If the virtual power plant contains distributed resources with time coupling characteristics such as energy storage, the projection calculation efficiency of the feasible domain of virtual power plant operation will be greatly reduced, making the above non-iterative calculation process difficult to execute. Summary of the Invention

[0004] The purpose of this application is to provide a distribution network-virtual power plant non-iterative collaborative optimization scheduling method and related devices, which can improve the decision-making efficiency of collaborative optimization scheduling.

[0005] To achieve the above objectives, this application provides the following solutions:

[0006] In a first aspect, the present application provides a distribution network-virtual power plant non-iterative collaborative optimization scheduling method, comprising:

[0007] Based on the characteristics of the access resources in the virtual power plant, an optimal economic dispatch model for the virtual power plant is established, which takes into account the uncertainty of source and load and the time coupling characteristics; the access resources include power supply type distributed resources and energy storage type distributed resources;

[0008] According to the optimal economic dispatch model of the virtual power plant, based on the fast approximate projection method of the feasible domain of the virtual power plant operation, the variation range constraint of the power of the virtual power plant tie line is determined; the fast approximate projection method of the feasible domain of the virtual power plant operation adopts a method of temporal decoupling-parallel projection-equivalent splicing calculation process;

[0009] According to the dispatch decision objectives of the distribution network, the dispatch objectives of the virtual power plant, and the characteristics of the access resources, a non-iterative collaborative optimization dispatch model for the distribution network and the virtual power plant is constructed based on the variation range constraint of the virtual power plant tie line power.

[0010] According to the non-iterative collaborative optimization scheduling model, solving the distribution network scheduling results and the virtual power plant tie line power scheduling results;

[0011] The power dispatch results of the virtual power plant's interconnection lines are input into the virtual power plant's optimal economic dispatch model to obtain the optimal dispatch results of the virtual power plant's internal resources.

[0012] In a second aspect, the present application provides a distribution network-virtual power plant non-iterative collaborative optimization scheduling device, comprising:

[0013] An economic dispatch model building module is used to establish an optimal economic dispatch model for a virtual power plant that takes into account source-load uncertainty and time coupling characteristics based on the characteristics of access resources in the virtual power plant; the access resources include power supply type distributed resources and energy storage type distributed resources;

[0014] A variation range constraint module is used to determine the variation range constraint of the virtual power plant tie line power based on the optimal economic dispatch model of the virtual power plant and a fast approximate projection method of the feasible domain of the virtual power plant operation; the fast approximate projection method of the feasible domain of the virtual power plant operation adopts a method of temporal decoupling-parallel projection-equivalent splicing calculation process;

[0015] The optimization scheduling model construction module is used to build a non-iterative collaborative optimization scheduling model for the distribution network and virtual power plant based on the scheduling decision objectives of the distribution network, the scheduling objectives of the virtual power plant, the characteristics of the access resources, and the variation range constraints of the virtual power plant tie line power;

[0016] A first output module is used to solve the distribution network scheduling result and the virtual power plant tie line power scheduling result according to the non-iterative collaborative optimization scheduling model;

[0017] The second output module is used to input the power scheduling result of the virtual power plant interconnection line into the virtual power plant optimal economic scheduling model to obtain the optimal scheduling result of the internal resources of the virtual power plant.

[0018] In a third aspect, the present application 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 executes the computer program to implement any one of the above-mentioned non-iterative collaborative optimization scheduling methods for a distribution network and a virtual power plant.

[0019] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any one of the above-mentioned non-iterative collaborative optimization scheduling methods for a distribution network-virtual power plant.

[0020] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0021] The present application provides a distribution network-virtual power plant non-iterative collaborative optimization scheduling method and related devices. First, by constructing the optimal economic scheduling model of the virtual power plant, the uncertainty of the source and load and the time coupling characteristics are fully considered, so that the scheduling decision is more accurate and in line with the actual operation situation. Secondly, a fast approximate projection method based on the feasible domain of the virtual power plant operation is adopted to determine the variation range constraint of the power of the virtual power plant interconnection line, effectively narrowing the search space of the decision variables, making the subsequent solution process of the non-iterative collaborative optimization scheduling model of the distribution network and the virtual power plant more efficient. Furthermore, the construction of the collaborative optimization scheduling model comprehensively considers the scheduling decision objectives of the distribution network, the scheduling objectives of the virtual power plant and the characteristics of the access resources, so that the scheduling decision can take into account the interests of all parties, reduce the decision-making repetitions and adjustments caused by conflicts of interest, and thus improve the decision-making efficiency. Finally, by solving the distribution network scheduling results and the virtual power plant interconnection line power scheduling results in a non-iterative manner, the convergence problems that may occur in the iterative solution process are avoided, the solution time is further shortened, and the decision-making efficiency is improved. At the same time, the power dispatch results of the virtual power plant interconnection lines are input into the optimal economic dispatch model of the virtual power plant to obtain the optimal dispatch results of the internal resources of the virtual power plant, realizing the rapid implementation and execution of dispatch decisions. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0023] Figure 1This is an application environment diagram of a distribution network-virtual power plant non-iterative collaborative optimization scheduling method in one embodiment of the present application;

[0024] Figure 2 A flow chart of a non-iterative collaborative optimization scheduling method for a distribution network and a virtual power plant provided in one embodiment of the present application;

[0025] Figure 3 A calculation flow chart provided for an embodiment of the present application;

[0026] Figure 4 A schematic diagram of a fast approximate projection method for the feasible region of virtual power plant operation provided in one embodiment of the present application;

[0027] Figure 5 An improved IEEE 33-node wiring diagram provided in one embodiment of the present application;

[0028] Figure 6 A schematic diagram of the functional modules of a distribution network-virtual power plant non-iterative collaborative optimization scheduling device provided in one embodiment of the present application;

[0029] Figure 7 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0030] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0031] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0032] The distribution network-virtual power plant non-iterative collaborative optimization scheduling method provided in the embodiment of the present application can be applied to Figure 1In the application environment shown, the terminal 102 communicates with the server 104 via a network. The data storage system can store data that the server 104 needs to process. The data storage system can be set up separately, integrated on the server 104, or placed on the cloud or other servers. Terminal 102 can send the characteristics of the access resources in the virtual power plant to server 104. After receiving the characteristics of the access resources in the virtual power plant, server 104 establishes an optimal economic dispatch model for the virtual power plant that takes into account the uncertainty of source and load and the time coupling characteristics of the access resources in the virtual power plant. Based on the optimal economic dispatch model of the virtual power plant, a fast approximate projection method of the feasible region of the virtual power plant operation is used to determine the variation range constraint of the virtual power plant tie line power. Based on the scheduling decision objectives of the distribution network, the scheduling objectives of the virtual power plant, and the characteristics of the access resources, a non-iterative collaborative optimization scheduling model for the distribution network and the virtual power plant is constructed based on the variation range constraint of the virtual power plant tie line power. Based on the non-iterative collaborative optimization scheduling model, the distribution network scheduling result and the virtual power plant tie line power scheduling result are solved. The virtual power plant tie line power scheduling result is input into the virtual power plant optimal economic dispatch model to obtain the optimal scheduling result of the internal resources of the virtual power plant. Server 104 can feedback the obtained optimal scheduling result of the internal resources of the virtual power plant and the distribution network scheduling result to terminal 102. In addition, in some embodiments, the distribution network-virtual power plant non-iterative collaborative optimization scheduling method can also be implemented independently by the server 104 or the terminal 102. For example, the terminal 102 can directly process the characteristics of the access resources in the virtual power plant, or the server 104 can obtain the characteristics of the access resources in the virtual power plant from the data storage system and process the characteristics of the access resources in the virtual power plant.

[0033] Terminal 102 may include, but is not limited to, various desktop computers, laptops, smartphones, tablet computers, IoT devices, and portable wearable devices. IoT devices may include smart speakers, smart TVs, smart air conditioners, and smart car devices. Portable wearable devices may include smart watches, smart bracelets, and head-mounted devices. Server 104 may be implemented as a standalone server or a server cluster consisting of multiple servers, or may be a cloud server.

[0034] In an exemplary embodiment, Figure 2 As shown, a distribution network-virtual power plant non-iterative collaborative optimization scheduling method is provided. The method is executed by a computer device, specifically a computer device such as a terminal or a server, or a terminal and a server. In the embodiment of the present application, the method is applied to Figure 1 The server 104 in the example is used as an example to illustrate the process, including the following steps 201 to 205.

[0035] Step 201: Establishing an optimal economic dispatch model for a virtual power plant that considers source-load uncertainty and time coupling characteristics based on the characteristics of access resources in the virtual power plant; the access resources include power supply type distributed resources and energy storage type distributed resources;

[0036] Step 202: Determine the variation range constraint of the virtual power plant tie line power based on the optimal economic dispatch model of the virtual power plant and a fast approximate projection method of the virtual power plant operation feasible region; the fast approximate projection method of the virtual power plant operation feasible region adopts a method of temporal decoupling-parallel projection-equivalent splicing calculation process;

[0037] Step 203: Based on the scheduling decision objectives of the distribution network, the scheduling objectives of the virtual power plant, and the characteristics of the access resources, and based on the variation range constraint of the virtual power plant tie line power, a non-iterative collaborative optimization scheduling model for the distribution network and the virtual power plant is constructed;

[0038] Step 204: solving the distribution network scheduling result and the virtual power plant tie line power scheduling result according to the non-iterative collaborative optimization scheduling model;

[0039] Step 205 , inputting the power dispatch result of the virtual power plant tie line into the optimal economic dispatch model of the virtual power plant to obtain the optimal dispatch result of the internal resources of the virtual power plant.

[0040] The purpose of this embodiment is to provide a fast approximate projection method for the feasible domain of virtual power plant operation and its non-iterative collaborative optimization scheduling method applicable to distributed resources such as electrochemical energy storage, gas turbines, temperature control loads, and distributed photovoltaic power sources. The collaborative optimization scheduling process is as follows: Figure 3 shown.

[0041] In some embodiments, when executing step 201, the specific steps may be as follows:

[0042] Step 1.1: Based on the physical characteristics of the resources (distributed resources) connected to the virtual power plant, they are divided into power-type distributed resources, such as distributed photovoltaic power sources and gas turbines, and energy storage-type distributed resources, such as electrochemical energy storage and temperature control loads. A virtual power model and a virtual energy storage model are used to construct models describing the physical characteristics of these two types of resources, respectively. They are as follows:

[0043] The virtual power model includes: operating power constraints for each period: Power supply ramp constraints:

[0044] The virtual energy storage model includes: charging and discharging power constraints in each period: Energy storage state of charge constraints:

[0045] Where: They represent the output power of virtual power resources and virtual energy storage resources in time period t. Since energy storage resources have the ability to regulate charging and discharging, if If it is greater than 0, it means the energy storage is charging, and if it is less than 0, it means the energy storage is discharging; They represent the upper and lower limits of the power generation of the virtual power resource in period t respectively; They represent the upper limit of discharge power and charging power of virtual energy storage resources in period t, respectively. is a negative value; Indicates the ramp power limit of the virtual power resource in continuous time periods; Indicates the range of change in the energy storage charge state; Δt represents the total duration of the unit time period, and T is the scheduling time period set.

[0046] This example measures a virtual power plant system whose distributed resources include three small wind turbines, three distributed photovoltaic power sources, three gas turbines, two electrochemical energy storage systems, and one internal transformer. The wind and solar output and load demand characteristics of the virtual power plant are adjusted based on publicly available data from the U.S. National Energy Laboratory's online database and public datasets from the European Union and its neighboring regions. The source and load characteristics of the virtual power plant are described by superimposing the predicted deterministic scenarios and errors. The errors follow a Gaussian probability distribution function with mean 0 and variance 0.5.

[0047] Step 1.2: Substitute the physical characteristics of resources such as the gas turbine, electrochemical energy storage, and temperature control load into the unified model of virtual power and virtual energy storage constructed in step 1.1 to establish the physical characteristic models of the gas turbine, electrochemical energy storage, and temperature control load. The details are as follows:

[0048] Objective function:

[0049] Set of decision variables:

[0050] Gas turbine operating power constraints:

[0051] Gas turbine power ramp constraints:

[0052] Electrochemical energy storage charging power constraints:

[0053] Electrochemical energy storage discharge power constraints:

[0054] Constraints for calculating the state of charge of electrochemical energy storage during period t+1:

[0055] Electrochemical energy storage state of charge constraints:

[0056] Temperature control load operating power constraints:

[0057] Constraints for calculating the state of charge of the temperature-controlled load during period t+1:

[0058] Temperature control load state of charge constraints:

[0059] Temperature-controlled load charging power upper limit constraint:

[0060] Temperature-controlled load discharge power upper limit constraint:

[0061] Where: They represent the output power of the gas turbine in period t, the charging power of the electrochemical energy storage in period t, the discharging power of the electrochemical energy storage in period t, the state of charge of the electrochemical energy storage in period t, the output power of the temperature control load in period t, and the state of charge of the temperature control load in period t respectively; Indicates the upper and lower limits of the gas turbine output power in period t; represents the ramp power limit of the gas turbine during period t; Indicates the upper and lower limits of the charging power of electrochemical energy storage in period t; Represents the upper and lower limits of the discharge power of electrochemical energy storage in period t; η c ,η d Indicates the charge and discharge efficiency of electrochemical energy storage; Q EES Indicates the installed capacity of electrochemical energy storage; Indicates the range of change in the state of charge of electrochemical energy storage; They represent the upper limit of charging power and the upper limit of discharging power of the temperature-controlled load in period t respectively; ξ AC ,γ AC Represents the thermal effect coefficient of the temperature control load; Γ AC Indicates the upper limit of the state of charge of the temperature control load; Indicates the upper and lower limits of the power load of the temperature control load. Indicates the baseline power demand of the temperature-controlled load.

[0062] Step 1.3: Use a method that integrates deterministic scenarios and random probability distribution functions to characterize the uncertain characteristics of distributed photovoltaic output characteristics and load power demand, and construct the internal supply and demand balance constraints of the virtual power plant, as follows:

[0063] Load power demand constraints:

[0064] Distributed photovoltaic output characteristic constraints:

[0065] Virtual power plant supply and demand balance constraints:

[0066] Where: They represent the expected values of load power demand and distributed photovoltaic output in period t, They represent the predicted values of load power demand and distributed photovoltaic output in period t respectively; They represent the prediction errors of load power demand and distributed photovoltaic output in period t, respectively, and both obey Gaussian distribution with mean 0, that is, φ R =N(0,δ R ),φ L =N(0,δ L ), where δ R ,δ L They represent variance respectively.

[0067] Step 1.4: Using gas turbines as backup power sources, a chance constraint is used to describe the backup power constraints that take into account the uncertainty of distributed photovoltaic output and load power demand, as follows:

[0068] Backup power supply opportunity constraints:

[0069] Where: Pr(·) represents the chance probability constraint calculator, β represents the probability value of the above chance constraint being true, Indicates the increased output power of the gas turbine as a backup power source.

[0070] According to the probability operation rules, the above chance constraints can be equivalently transformed into the following deterministic constraints:

[0071] Backup power deterministic constraints:

[0072] In some embodiments, when executing step 202, the specific steps may be as follows:

[0073] Based on all the constraints in the optimal economic dispatch model of the virtual power plant, the set of feasible domains of the virtual power plant is obtained by characterizing them in a set manner.

[0074] According to the feasible domain set of virtual power plant operation, the fast approximate projection method of the feasible domain of virtual power plant operation is used to determine the feasible domain set of virtual power plant operation

[0075] Performing temporal decoupling on the high-dimensional operation feasible domain of the virtual power plant to obtain several low-dimensional operation feasible domains of the virtual power plant;

[0076] For each low-dimensional operation feasible domain, a vertex search algorithm is used in parallel to solve the problem, and a low-dimensional projection space of each low-dimensional operation feasible domain is obtained; the low-dimensional operation feasible domain is used to determine the variation range constraint of the virtual power plant interconnection line power; the variation range constraint of the virtual power plant interconnection line power includes the gas turbine operation power constraint, the gas turbine power supply ramping constraint, the electrochemical energy storage charging power constraint, the electrochemical energy storage discharge power constraint, the electrochemical energy storage t+1 period state of charge calculation constraint, the electrochemical energy storage state of charge constraint, the temperature control load operation power constraint, the temperature control load t+1 period state of charge calculation constraint, the temperature control load state of charge constraint, the temperature control load charging power upper limit constraint, the temperature control load discharge power upper limit constraint, the gas turbine decoupling ramping constraint, the electrochemical energy storage state of charge consistency constraint, and the temperature control load state of charge consistency constraint;

[0077] Perform projection calculation and splicing on the low-dimensional projection space of each low-dimensional operation feasible domain to generate a projection space;

[0078] Among them, Ax m ≤b represents all the constraints of the feasible region within the virtual power plant. express and x m The constraint relationship between m and β m Indicates the elimination of the virtual power plant internal variable x m After that, the low-dimensional operation feasible domain of the virtual power plant is Ω 2,m The coefficient matrix of .

[0079] Specifically, step 2.1: All constraints of the virtual power plant optimal economic dispatch model, i.e., the constraints in step 201, are represented in a set manner. The set of feasible operating regions of the mth virtual power plant is defined as Ω 1,m , where the variable vector includes the output x of the distributed resources in the virtual power plant and the power of the tie line between the virtual power plant and the distribution system Since there are many distributed resources in the virtual power plant, the vector dimension of the decision variables of the virtual power plant is high. In order to reduce the number of decision variables in the virtual power plant, this embodiment defines a feasible domain set Ω of the virtual power plant operation that only includes the tie line power. 2,m , compared to Ω 1,m ,Ω 2,m The dimension of the virtual power plant will be greatly reduced. In this embodiment, the feasible domain projection is defined as the virtual power plant operation feasible domain from the original high-dimensional operation feasible domain Ω 1,m Run to the lower-dimensional feasible region Ω 2,m The process of performing equivalent calculations. 1,m and Ω 2,m It can be expressed as:

[0080] Feasible domain of high-dimensional operation of virtual power plant:

[0081] Feasible domain of low-dimensional operation of virtual power plant:

[0082] Where: Ax m ≤b represents all the constraints of the feasible region of the virtual power plant operation listed in step 201. express and x m The constraint relationship between m and β m Indicates the elimination of the virtual power plant internal variable x m After that, the low-dimensional operation feasible domain of the virtual power plant is Ω 2,m The purpose of feasible region projection is to eliminate the internal variables x m Then, solve the coefficient matrix α m and β m The value of .

[0083] Step 2.2: Since Ω 1,m The constraints in are all linear constraints, so the feasible domain of high-dimensional operation is Ω 1,m The spatial structure of is a high-dimensional convex polyhedron. According to the convex set computing theory, the convex polyhedron Ω 1,m Constructed low-dimensional feasible region Ω 2,m It will also be a convex polyhedron. Therefore, this embodiment will use the vertex search method to solve the projection coefficient matrix α m and β m The value of .

[0084] In order to establish the coupling relationship between the virtual power plant dispatching cost and the low-dimensional operation feasible domain decision variables (i.e., tie line power), the operating cost of the distributed resources in the virtual power plant is converted into As a one-dimensional projection variable, it is included in the vertex search structure for projection calculation. Therefore, the vertex search problem of the feasible region of virtual power plant operation can be described as solving the following optimization problem OP1:

[0085] OP1 objective function:

[0086] OP1 constraints:

[0087] Where: δ T Represents the search direction. In the vertex search algorithm, the search direction is the direction of the outer normal vector of the hyperplane. The vertex search problem is essentially to solve the optimization problem and traverse all the vertices in the direction of the outer normal vector. After all the vertices are obtained, the convex hull can be constructed to achieve Ω. 2,m and Equivalent representation of .

[0088] Step 2.3: Since the duration of each period Δt in the day-ahead scheduling problem is usually 1 hour, the low-dimensional operation feasible domain Ω 2,m The variable dimension is typically 24. However, due to the computational complexity of vertex enumeration, the vertex search method cannot perform projection calculations on high-dimensional feasible domains with dimensions exceeding 6 in polynomial time. Therefore, it is difficult to directly apply the projection calculations to the feasible domain of a virtual power plant. Because a virtual power plant incorporates several energy storage units and gas turbines, their ramping constraints and state-of-charge constraints are temporally coupled, resulting in a temporally coupled feasible domain for the virtual power plant, making it difficult to directly separate and solve them.

[0089] Therefore, this embodiment adopts the method of "time decoupling-parallel projection-equivalent splicing" to decouple the original operating feasible domain of the virtual power plant with time coupling characteristics into several low-dimensional operating feasible domains for projection solution.

[0090] The temporal decoupling process of the high-dimensional operation feasible domain of the virtual power plant adopted in this embodiment is as follows: Figure 4 shown.

[0091] Step 2.4: In the timing decoupling link, the virtual power plant pre-optimization scheduling will be performed to perform timing decoupling on the high-dimensional operation feasible domain of the virtual power plant.

[0092] The virtual power plant pre-dispatch optimization decision model OP2 is constructed. The model is guided by the priority of internal autonomy of the virtual power plant and aims to minimize the operation and dispatch cost of the virtual power plant. Among them, the cost coefficient of the transmission power of the virtual power plant tie line is It is set to a value much larger than the internal distributed resource cost coefficient. Specifically:

[0093] OP2 objective function:

[0094] OP2 constraints:

[0095] After solving OP2, we can get Ω 1,m Reference values of various time-coupled constraint variables, including P GT Reference value SOC ES Reference value SOC AC Reference value

[0096] Then, the decoupling section is set based on the principle of less than 6 dimensions in the 24-hour scheduling cycle. The decoupling section set is K = {s k |s1,s2,…,s K}. When s k =t kWhen the adjacent decoupling sections enclose the operating period is This constitutes the corresponding k+1th low-dimensional operation feasible domain.

[0097] Gas turbine ramping constraints with time coupling characteristics in step 1.2 The OP2 model is introduced to solve the gas turbine output results of each decoupling period. Construct the gas turbine ramping constraint to release the coupling relationship at the decoupling section, as follows:

[0098] Gas turbine decoupling ramping constraints:

[0099] Based on the state of charge constraints of the electrochemical energy storage and temperature control load constructed in step 1.2, the state of charge consistency constraints are added as follows:

[0100] Consistency constraints on electrochemical energy storage state of charge:

[0101] Consistency constraints on the state of charge of temperature-controlled loads:

[0102] Step 2.5: In the parallel projection phase, for each low-dimensional operation feasible domain, the vertex search algorithm is used in parallel to solve. For example, the constraints covered are as follows:

[0103] Gas turbine operating power constraints:

[0104] Gas turbine power ramp constraints:

[0105] Electrochemical energy storage charging power constraints:

[0106] Electrochemical energy storage discharge power constraints:

[0107] Constraints for calculating the state of charge of electrochemical energy storage during period t+1:

[0108] Electrochemical energy storage state of charge constraints:

[0109] Temperature control load operating power constraints:

[0110] Constraints for calculating the state of charge of the temperature-controlled load during period t+1:

[0111] Temperature control load state of charge constraints:

[0112] Temperature-controlled load charging power upper limit constraint:

[0113] Temperature-controlled load discharge power upper limit constraint:

[0114] Gas turbine decoupling ramping constraints:

[0115] Consistency constraints on electrochemical energy storage state of charge:

[0116] Consistency constraints on the state of charge of temperature-controlled loads:

[0117] Where: T k+1 ={s k ,t k+1 ,t k+2 ,…,s k+1}.

[0118] In the process of using vertex search method to obtain the feasible regions of low-dimensional operation of virtual power plants, After the projection vertex, the corresponding low-dimensional projection space can be obtained

[0119] Step 2.6: In the equivalent splicing link, since the high-dimensional operational feasible domain of the virtual power plant is a convex polyhedron, according to the convex set calculation rule, the projection spaces of its various low-dimensional operational feasible domains are all connected, and equivalent splicing calculations can be performed. It should be noted that since this embodiment uses a temporal decoupling method to first determine the decoupling section results and then perform the projection calculation and splicing of the low-dimensional operational feasible domain, the constructed splicing projection space is actually an inscribed polyhedron of the projection space formed by the original high-dimensional operational feasible domain. The equivalent projection space Ω formed 3,m It can be described as:

[0120] Equivalent splicing projection space:

[0121] Step 2.7: Use the MPT3 toolbox in MATLAB to transform the equivalent splicing projection space represented by vertices to obtain the equivalent splicing projection space represented by hyperplanes, as follows:

[0122] Feasible domain of low-dimensional operation of virtual power plant:

[0123] In some embodiments, when executing step 203, the specific steps may be as follows:

[0124] The non-iterative collaborative scheduling method proposed in this embodiment is verified by using an improved IEEE 33-node example system. The topology of the improved IEEE 33-node system is as follows: Figure 5 As shown. The distribution system is connected to 4 distributed photovoltaic power sources (PV), 4 wind turbines (WG), 4 gas turbines (GT), 4 static VAR compensators (SVC), 5 energy storage (ES) and 1 virtual power plant. The access locations of the above distributed resources refer to Figure 5 , the virtual power plant is connected to the 13th node.

[0125] Step 3.1: Since both the distribution system and the virtual power plant aim to minimize operating costs and seek the optimal scheduling strategy within their respective feasible regions, a non-iterative collaborative optimization scheduling model that takes into account the operating costs of the virtual power plant is constructed, combining the power variation range constraints of the virtual power plant tie lines formed in step 202. The specific details are as follows:

[0126] Objective function:

[0127] Distribution system internal constraint set: h DS (x [M] ,y)≤0.

[0128] Internal constraints set of virtual power plant:

[0129] Virtual power plant operating cost constraint set:

[0130] Where: x, y represent the optimization variable sets of the virtual power plant and the distribution system respectively; π r represents the operating cost of the mth virtual power plant, m∈[M] represents the set of all virtual power plants, where [M]={1,…,M}; C DS (·) and C MG (·) represents the cost function of the distribution system and the virtual power plant respectively, and its value is only related to the internal variables of the two; is the upper limit of the operating cost of the mth virtual power plant.

[0131] Step 3.2: Formula in step 3.1 The distribution system internal constraint set represented will include distribution network topology security constraints, backup power constraints, distribution network internal distributed resources and other constraints. Among them, the distribution network topology security constraints are constructed based on the second-order cone AC power flow model, as follows:

[0132] Feeder voltage drop constraint:

[0133] Node active power balance constraints:

[0134] Node reactive power balance constraints:

[0135] Feeder power flow constraints:

[0136] Feeder active power constraint: P ij,t ≤P ij,max .

[0137] Feeder current operating constraints: l ij,t ≤l ij,max .

[0138] Node voltage constraint: V i,min ≤V i,t ≤V i,max .

[0139] Where: U j,t represents the voltage amplitude of node j in time period t, l ij,t represents the current amplitude of feeder branch ij in time period t; P ij,t ,Q ij,t They represent the active power and reactive power of feeder branch ij in time period t respectively; r ij ,x ij They represent the resistance and reactance of the feeder branch ij respectively; p j,t ,q j,t They represent the injected active power and injected reactive power of node j in time period t respectively; P ij,max ,l ij,max Respectively represent the upper limit of active power and current amplitude of feeder branch ij; V i,min ,V i,max They represent the upper and lower limits of the voltage amplitude at node i respectively.

[0140] Step 3.3: Use the virtual power plant operation feasible domain equivalent projection space model constructed in step 2.6 Replace the formula in step 3.1 The internal constraint set of the virtual power plant represented by is thus formed into a collaborative optimization scheduling model that considers the cost of the virtual power plant and its feasible domain projection, as follows:

[0141] Objective function:

[0142] Internal constraints set of virtual power plant:

[0143] Virtual power plant operating cost constraint set:

[0144] Feeder voltage drop constraint:

[0145] Node active power balance constraints:

[0146] Node reactive power balance constraints:

[0147] Feeder power flow constraints:

[0148] Feeder active power constraint: P ij,t ≤P ij,max .

[0149] Feeder current operating constraints: l ij,t ≤l ij,max .

[0150] Node voltage constraint: V i,min ≤V i,t ≤V i,max .

[0151] Step 3.4: Solve the collaborative optimization scheduling model described in step 3.3 to obtain the power of each virtual power plant tie line, the optimal scheduling strategy of the distribution system, and the scheduling cost of the virtual power plant

[0152] Specifically, the computational conditions of this example are as follows: all algorithms were developed using MATLAB R2023a and the commercial optimization solver GUROBI. All simulations were performed on a PC platform equipped with 16GB of memory and an Intel Core i7-12700H CPU (2.70GHz).

[0153] To verify the effectiveness of the method described in this embodiment, four comparative examples, M1-M4, are set for analysis. The details of the method settings for each comparative example are as follows:

[0154] M1: Instead of using the virtual power plant fast approximate projection method described in step 202 of this embodiment, the alternating direction multiplier method (ADMM) is used to iteratively optimize and solve the virtual power plant economic dispatch model and the distribution network economic dispatch model constructed in steps 201 and 203;

[0155] M2: the method described in this embodiment;

[0156] M3: Decouple the temporal coupling feasible region of the virtual power plant in step 201 using the virtual power plant pre-optimization scheduling model described in this embodiment, then substitute the decoupled virtual power plant model into the collaborative optimization scheduling model constructed in step 203, and solve it using centralized optimization that removes the privacy protection requirement;

[0157] M4: The virtual power plant economic dispatch model constructed in step 201 is completely substituted into the collaborative optimization dispatch model constructed in step 203, and a centralized optimization method that removes the privacy protection requirement is used for solving the problem.

[0158] To apply the M1 method, 24 hours were decomposed into 12 subperiods, each containing 2 hours, and the temporal coupling constraints between subperiods were ignored. In the M2 algorithm, the ADMM iteration step size was set to 10, and the convergence criterion was 10^-4. The M4 algorithm was used to compare the accuracy of the M3 projection results. Furthermore, the system confidence level was set to 0.9.

[0159] The time decoupling method used in this embodiment reduces the size of the operating domain by adding constraints. The choice of the number of decoupling periods will affect the degree of reduction of the operating domain. In theory, the fewer the number of decoupling periods, the larger the operating domain, and the closer the result is to the global optimal solution. However, as the number of decoupling periods decreases, the more hours each sub-period contains, and the difficulty of solving the operating domain increases. Therefore, in order to balance global optimality and computational complexity, this section first analyzes the changes in the number of decoupling periods, economy, and computational time. The computational time T off To solve the time taken by the running domain in each sub-period, the calculation results are shown in Table 1.

[0160] Table 1. Cost and computation time as a function of the number of decoupling periods

[0161] Number of decoupling periods <![CDATA[Total cost (10 5 Renminbi)]]> <![CDATA[T off (s)]]> 1(Traditional centralized optimization) 2.2937 - 4 2.2947 >1200 8 2.2974 457.999 12 2.3007 215.328 24 2.3036 12.476

[0162] The table shows that the total cost increases with the number of decoupling periods, but the change in total cost is minimal. It can be seen that when the number of decoupling periods is 12, the total cost differs from the global optimal solution by only 0.305%. However, the computation time increases exponentially with fewer decoupling periods. Therefore, we choose 12 decoupling periods for analysis, as shown in Table 2.

[0163] Table 2 Distribution network and microgrid allocation costs for each algorithm

[0164] M1 M2 M3 M4 <![CDATA[T on (s)]]> 1145.991 14.710 14.990 16.054 <![CDATA[Distribution network cost (10 5 Renminbi)]]> 2.1175 2.1096 2.1096 2.1221 <![CDATA[Microgrid cost (10 4 RMB)]]> 1.8994 1.9110 1.9110 1.7162 <![CDATA[Total cost (10 5 Renminbi)]]> 2.3074 2.3007 2.3007 2.2937

[0165] Table 2 shows the online computing time T of the four methods. on Compared with the iterative algorithm M2, the online computing time T of the non-iterative algorithm M3 and the centralized algorithms M3 and M4 is onare all relatively low. The calculation results obtained by the M2 and M3 algorithms are completely consistent, indicating that the projection method adopted in this application is completely equivalent to the original operating domain. The total cost of the M1 distributed algorithm is the largest, which is due to the influence of the iteration step size and the failure to converge to the optimal solution. In summary, the non-iterative distribution network microgrid collaborative scheduling method proposed in this application can significantly improve the computing efficiency while ensuring user privacy and a certain degree of economy.

[0166] In some embodiments, when executing steps 204-205, the specific steps may be as follows:

[0167] The power results of each virtual power plant tie line and corresponding costs Substitute into the virtual power plant economic dispatch model constructed in step 201, and solve and calculate the optimal dispatch result of the internal resources of the virtual power plant The details are as follows:

[0168] The formula expression of the objective function of the optimal scheduling result of the internal resources of the virtual power plant is:

[0169] The formula for tie line power constraint is:

[0170] The formula for the operating cost constraint is:

[0171] The formula for the gas turbine operating power constraint is:

[0172] The formula for the gas turbine power ramp constraint is:

[0173] The formula for the electrochemical energy storage charging power constraint is:

[0174] The formula for the electrochemical energy storage discharge power constraint is:

[0175] The formula for calculating the state of charge constraint of electrochemical energy storage during the t+1 period is:

[0176] The formula for the state of charge constraint of electrochemical energy storage is:

[0177] The formula for temperature-controlled load operating power constraint is:

[0178] The formula for calculating the state of charge constraint for the temperature control load in period t+1 is:

[0179] The formula for the state of charge constraint of the temperature control load is:

[0180] The formula for the upper limit constraint of the temperature-controlled load charging power is:

[0181] The upper limit constraint formula of the temperature-controlled load discharge power is:

[0182] In an exemplary embodiment, Figure 6 As shown, a distribution network-virtual power plant non-iterative collaborative optimization scheduling device is provided, including:

[0183] An economic dispatch model building module 601 is used to establish an optimal economic dispatch model for a virtual power plant that takes into account source-load uncertainty and time coupling characteristics based on the characteristics of access resources in the virtual power plant; the access resources include power supply type distributed resources and energy storage type distributed resources;

[0184] The variation range constraint module 602 is configured to determine the variation range constraint of the virtual power plant tie line power according to the optimal economic dispatch model of the virtual power plant and based on a fast approximate projection method of the feasible domain of the virtual power plant operation; the fast approximate projection method of the feasible domain of the virtual power plant operation adopts a method of temporal decoupling-parallel projection-equivalent splicing calculation process;

[0185] The optimization scheduling model construction module 603 is used to construct a non-iterative collaborative optimization scheduling model for the distribution network and the virtual power plant based on the scheduling decision objectives of the distribution network, the scheduling objectives of the virtual power plant, and the characteristics of the access resources, and based on the variation range constraint of the virtual power plant tie line power;

[0186] A first output module 604 is configured to solve the distribution network scheduling result and the virtual power plant tie line power scheduling result according to the non-iterative collaborative optimization scheduling model;

[0187] The second output module 605 is used to input the power scheduling result of the virtual power plant tie line into the virtual power plant optimal economic scheduling model to obtain the optimal scheduling result of the internal resources of the virtual power plant.

[0188] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 7As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O) and a communication interface. The processor, memory and input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store processed data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a distribution network-virtual power plant non-iterative collaborative optimization scheduling method is implemented.

[0189] Those skilled in the art will understand that Figure 7 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0190] In an exemplary embodiment, a computer device is further provided, including a memory and a processor. The memory stores a computer program, and the processor implements the steps in the above method embodiments when executing the computer program.

[0191] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0192] In summary, this application has the following technical effects:

[0193] 1) This application projects the operational boundary of the virtual power plant's interconnection line power by calculating its operational feasible domain. Substituting this operational boundary into a non-iterative collaborative optimization scheduling model, this method achieves the optimal distribution system-microgrid collaborative scheduling strategy with a single calculation. While preserving the virtual power plant's original operational feasible domain characteristics, the non-iterative collaborative scheduling process effectively eliminates the virtual power plant's internal decision variables, ensuring the virtual power plant's decision privacy while overcoming the convergence issues of iterative algorithms, significantly improving the computational efficiency of collaborative scheduling.

[0194] 2) This application adopts the method of "time decoupling-parallel projection-equivalent splicing" to decouple the high-dimensional operation feasible domain of the virtual power plant by executing the pre-dispatching decision of the virtual power plant, and then perform parallel projection calculations on the several low-dimensional operation feasible domains formed after decoupling. Finally, the equivalent union calculation of the several low-dimensional projection spaces is performed to obtain the projection space of the original operation feasible domain. The above decoupling-projection-splicing process decomposes the projection calculation of the high-dimensional operation feasible domain that cannot be directly solved into a parallel projection calculation process of multiple low-dimensional operation feasible domains, significantly reducing the difficulty of the projection calculation of the high-dimensional operation feasible domain with time coupling characteristics, thereby effectively reducing the projection calculation time.

[0195] The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processors involved in the various embodiments provided herein may include, but are not limited to, general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic units, data processing logic units based on quantum computing, and the like.

[0196] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0197] This document uses specific examples to illustrate the principles and implementation methods of this application. The description of the above examples is only intended to help understand the method and core concept of this application. At the same time, for those skilled in the art, based on the concept of this application, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A non-iterative collaborative optimization scheduling method for distribution network-virtual power plant, characterized in that: include: Based on the characteristics of the access resources in the virtual power plant, an optimal economic dispatch model for the virtual power plant is established, which takes into account the uncertainty of source and load and the time coupling characteristics; the access resources include power supply type distributed resources and energy storage type distributed resources; According to the optimal economic dispatch model of the virtual power plant, based on the fast approximate projection method of the feasible domain of the virtual power plant operation, the variation range constraint of the power of the virtual power plant tie line is determined; the fast approximate projection method of the feasible domain of the virtual power plant operation adopts a method of temporal decoupling-parallel projection-equivalent splicing calculation process; According to the dispatch decision objectives of the distribution network, the dispatch objectives of the virtual power plant, and the characteristics of the access resources, a non-iterative collaborative optimization dispatch model for the distribution network and the virtual power plant is constructed based on the variation range constraint of the virtual power plant tie line power. According to the non-iterative collaborative optimization scheduling model, solving the distribution network scheduling results and the virtual power plant tie line power scheduling results; The power dispatch results of the virtual power plant's interconnection lines are input into the virtual power plant's optimal economic dispatch model to obtain the optimal dispatch results of the virtual power plant's internal resources.

2. A distribution network-virtual power plant non-iterative collaborative optimization scheduling method according to claim 1, characterized in that: The optimal economic dispatch model of the virtual power plant is composed of operating power constraints in each time period, power supply ramping constraints, charging and discharging power constraints in each time period, energy storage state of charge constraints, gas turbine operating power constraints, gas turbine power supply ramping constraints, electrochemical energy storage charging power constraints, electrochemical energy storage discharging power constraints, electrochemical energy storage state of charge calculation constraints in the t+1 period, electrochemical energy storage state of charge constraints, temperature-controlled load operating power constraints, temperature-controlled load state of charge calculation constraints in the t+1 period, temperature-controlled load state of charge constraints, temperature-controlled load charging power upper limit constraints, temperature-controlled load discharge power upper limit constraints, load power demand constraints, distributed photovoltaic output characteristic constraints, virtual power plant supply and demand balance constraints and backup power supply deterministic constraints.

3. A distribution network-virtual power plant non-iterative collaborative optimization scheduling method according to claim 2, characterized in that: According to the optimal economic dispatch model of the virtual power plant and the fast approximate projection method of the feasible region of the virtual power plant operation, the variation range constraints of the virtual power plant tie line power are determined, including: Based on all the constraints in the optimal economic dispatch model of the virtual power plant, the set of feasible domains of the virtual power plant is obtained by characterizing them in a set manner. According to the feasible domain set of virtual power plant operation, the fast approximate projection method of the feasible domain of virtual power plant operation is used to determine the feasible domain set of virtual power plant operation Performing temporal decoupling on the high-dimensional operation feasible domain of the virtual power plant to obtain several low-dimensional operation feasible domains of the virtual power plant; For each low-dimensional operation feasible domain, a vertex search algorithm is used in parallel to solve the problem, and a low-dimensional projection space of each low-dimensional operation feasible domain is obtained; the low-dimensional operation feasible domain is used to determine the variation range constraint of the virtual power plant interconnection line power; the variation range constraint of the virtual power plant interconnection line power includes the gas turbine operation power constraint, the gas turbine power supply ramping constraint, the electrochemical energy storage charging power constraint, the electrochemical energy storage discharge power constraint, the electrochemical energy storage t+1 period state of charge calculation constraint, the electrochemical energy storage state of charge constraint, the temperature control load operation power constraint, the temperature control load t+1 period state of charge calculation constraint, the temperature control load state of charge constraint, the temperature control load charging power upper limit constraint, the temperature control load discharge power upper limit constraint, the gas turbine decoupling ramping constraint, the electrochemical energy storage state of charge consistency constraint, and the temperature control load state of charge consistency constraint; Perform projection calculation and splicing on the low-dimensional projection space of each low-dimensional operation feasible domain to generate a projection space; Among them, Ax m ≤b represents all the constraints of the feasible region within the virtual power plant. express and x m The constraint relationship between m and β m Indicates the elimination of the virtual power plant internal variable x m After that, the low-dimensional operation feasible domain of the virtual power plant is Ω 2,m The coefficient matrix of .

4. A distribution network-virtual power plant non-iterative collaborative optimization scheduling method according to claim 1, characterized in that: The non-iterative collaborative optimization scheduling model consists of an objective function, a set of internal constraints of a virtual power plant, a set of operating cost constraints of a virtual power plant, feeder voltage drop constraints, node active power balance constraints, node reactive power balance constraints, feeder flow constraints, feeder active power constraints, feeder current operation constraints and node voltage constraints.

5. A distribution network-virtual power plant non-iterative collaborative optimization scheduling method according to claim 4, characterized in that: The objective function is expressed as follows: The formula expression of the internal constraint set of the virtual power plant is: The formula expression of the virtual power plant operation cost constraint set is: The feeder voltage drop constraint formula is: The formula expression of the node active power balance constraint is: The formula expression of the node reactive power balance constraint is: The formula for the feeder power flow constraint is: The formula for the feeder active power constraint is: P ij,t ≤P ij,max ; The feeder current operation constraint formula is: ij,t ≤l ij,max ; The node voltage constraint formula is: V i,min ≤V i,t ≤V i,max ; in: U j,t represents the voltage amplitude of node j in time period t, l ij,t represents the current amplitude of feeder branch ij in time period t; P ij,t ,Q ij,t They represent the active power and reactive power of feeder branch ij in time period t respectively; r ij ,x ij They represent the resistance and reactance of the feeder branch ij respectively; p j,t ,q j,t They represent the injected active power and injected reactive power of node j in time period t respectively; P ij,max ,l ij,max Respectively represent the upper limit of active power and current amplitude of feeder branch ij; V i,min ,V i,max They represent the upper and lower limits of the voltage amplitude of node i, C DS (·) and C MG (·) represent the cost functions of the distribution system and virtual power plant, respectively, is the upper limit of the operating cost of the mth virtual power plant, and y represents the set of optimized variables of the distribution system.

6. A distribution network-virtual power plant non-iterative collaborative optimization scheduling method according to claim 1, characterized in that: The optimal scheduling result of the internal resources of the virtual power plant is composed of the objective function of the optimal scheduling result of the internal resources of the virtual power plant, the interconnection line power constraint, the operating cost constraint, the gas turbine operating power constraint, the gas turbine power supply ramp constraint, the electrochemical energy storage charging power constraint, the electrochemical energy storage discharging power constraint, the electrochemical energy storage t+1 period state of charge calculation constraint, the electrochemical energy storage state of charge constraint, the temperature-controlled load operating power constraint, the temperature-controlled load t+1 period state of charge calculation constraint, the temperature-controlled load state of charge constraint, the temperature-controlled load charging power upper limit constraint and the temperature-controlled load discharge power upper limit constraint.

7. A distribution network-virtual power plant non-iterative collaborative optimization scheduling method according to claim 1, characterized in that: The formula expression of the objective function of the optimal scheduling result of the internal resources of the virtual power plant is: The formula for tie line power constraint is: The formula for the operating cost constraint is: The formula for the gas turbine operating power constraint is: The formula for the gas turbine power ramp constraint is: The formula for the electrochemical energy storage charging power constraint is: The formula for the electrochemical energy storage discharge power constraint is: The formula for calculating the state of charge constraint of electrochemical energy storage during the t+1 period is: The formula for the state of charge constraint of electrochemical energy storage is: The formula for temperature-controlled load operating power constraint is: The formula for calculating the state of charge constraint for the temperature control load in period t+1 is: The formula for the state of charge constraint of the temperature control load is: The formula for the upper limit constraint of the temperature-controlled load charging power is: The upper limit constraint formula of the temperature-controlled load discharge power is: in, They represent the output power of the gas turbine in period t, the charging power of the electrochemical energy storage in period t, the discharging power of the electrochemical energy storage in period t, the state of charge of the electrochemical energy storage in period t, the output power of the temperature control load in period t, and the state of charge of the temperature control load in period t respectively; Indicates the upper and lower limits of the gas turbine output power in period t; represents the ramp power limit of the gas turbine during period t; Indicates the upper and lower limits of the charging power of electrochemical energy storage in period t; Represents the upper and lower limits of the discharge power of electrochemical energy storage in period t; η c ,η d Indicates the charge and discharge efficiency of electrochemical energy storage; Q EES Indicates the installed capacity of electrochemical energy storage; Indicates the range of change in the state of charge of electrochemical energy storage; They represent the upper limit of charging power and the upper limit of discharging power of the temperature-controlled load in period t respectively; ξ AC ,γ AC Represents the thermal effect coefficient of the temperature control load; Γ AC Indicates the upper limit of the state of charge of the temperature control load; Respectively represent the upper and lower limits of the power load of the temperature control load, Indicates the baseline power demand of the temperature-controlled load.

8. A distribution network-virtual power plant non-iterative collaborative optimization scheduling device, characterized in that: include: An economic dispatch model building module is used to establish an optimal economic dispatch model for a virtual power plant that takes into account source-load uncertainty and time coupling characteristics based on the characteristics of access resources in the virtual power plant; the access resources include power supply type distributed resources and energy storage type distributed resources; A variation range constraint module is used to determine the variation range constraint of the virtual power plant tie line power based on the optimal economic dispatch model of the virtual power plant and a fast approximate projection method of the feasible domain of the virtual power plant operation; the fast approximate projection method of the feasible domain of the virtual power plant operation adopts a method of temporal decoupling-parallel projection-equivalent splicing calculation process; The optimization scheduling model construction module is used to build a non-iterative collaborative optimization scheduling model for the distribution network and virtual power plant based on the scheduling decision objectives of the distribution network, the scheduling objectives of the virtual power plant, the characteristics of the access resources, and the variation range constraints of the virtual power plant tie line power; A first output module is used to solve the distribution network scheduling result and the virtual power plant tie line power scheduling result according to the non-iterative collaborative optimization scheduling model; The second output module is used to input the power scheduling result of the virtual power plant interconnection line into the virtual power plant optimal economic scheduling model to obtain the optimal scheduling result of the internal resources of the virtual power plant.

9. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement a distribution network-virtual power plant non-iterative collaborative optimization scheduling method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, it implements a distribution network-virtual power plant non-iterative collaborative optimization scheduling method according to any one of claims 1 to 7.

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