Method and system for solving dynamic feasible region of virtual power plant based on coupling constraint decoupling

Abstract

The application provides a virtual power plant dynamic feasible region solving method and system based on coupling constraint decoupling, belongs to the technical field of power plant resource allocation management, establishes a virtual power plant model considering distributed energy and flexible load resources, and determines a virtual power plant feasible region solving model based on a vertex search method; through Lyapunov optimization, virtual queues of energy storage, heating ventilation air conditioning and electric vehicles and other flexible loads are respectively established, and the time coupling constraint of the flexible load is decoupled into a virtual queue stability problem; a virtual time queue is constructed to integrate the decoupled optimization problem into the virtual power plant dynamic feasible region solving of day-ahead and day-ahead, so that the day-ahead and day-ahead feasible region solving algorithm is obtained. Based on the Lyapunov optimization theory, the time coupling constraint is decoupled, a weight coefficient is introduced, and a mathematical model taking the vertex search and queue stability as targets is provided, and finally the virtual power plant dynamic feasible region solving of day-ahead and day-ahead is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of power plant resource allocation management, and in particular to a method and system for solving the dynamic feasible region of a virtual power plant based on coupling constraint decoupling. Background Art

[0002] In the process of building a new power system dominated by renewable energy, the variety and quantity of flexible resources at the demand-side terminal are gradually increasing. Distributed resources are characterized by massive and heterogeneous nature. Exploiting and utilizing these massive flexible resources to participate in grid regulation has become a key task in the construction of this new power system. Virtual power plants (VPPs), as an effective form of distributed resource aggregation, provide a centralized service interface between transmission and distribution systems, promoting the integration and application of distributed energy resources (DERs) and flexible loads. The virtual power plant dynamic feasible region (VDFR) is used to characterize the external aggregation characteristics of a VPP. This involves projecting a high-dimensional model with high coupling characteristics into the external power characteristics of the PCC node. The "dynamic" in VDFR refers to the VPP output feasible region with different output characteristics on different timescales. The application of VDFR is similar to the output characteristic curve of a synchronous generator. While characterizing the VPP's dispatchability and internal resource scheduling capabilities, it effectively protects the privacy of VPP users, allowing them to participate in the system's scheduling of the VPP only through the aggregation characteristics of the PCC node.

[0003] One of the reasons for the high coupling characteristics of VDFR is that the existence of flexible loads results in a large number of time coupling constraints in the solution model. When massive distributed flexible resources are incorporated into VPP, this coupling characteristic will become more complicated.

[0004] Currently, vertex search-based approaches for solving multi-period coupled feasible regions primarily include clustering methods and various improved vertex methods. Some of these methods focus on considering adjacent multi-period temporal coupling constraints, clustering the multi-periods into time periods, and decoupling and reducing the dimensionality of the multi-period feasible region. This effectively addresses the impact of coupling constraints on computational efficiency. However, the incomplete representation of constraints at adjacent moments within two adjacent clustered periods, as well as the long-term, wide-area coupling constraints, warrant further investigation. Building on this, another approach incorporates a per-period clustering reconstruction model, effectively addressing the incomplete constraint representation issue caused by a single clustering scheme. However, this approach still faces challenges in addressing wide-area temporal coupling constraints. Some methods focus on coupling constraint models, such as gas turbine ramping constraints, transforming the vertex search problem into a min-max, two-level progressive vertex search problem. These methods significantly improve computational efficiency compared to the original vertex search method. However, the impact of coupling constraints on flexible load resources on the feasible region solution requires further investigation. Another approach equates flexible loads with energy storage resources and uses the boundary contraction method to solve the feasible domain of equivalent energy storage resources with time coupling constraints and calculate related parameters. The proposed method has a good match for the aggregation of various resources, but still faces certain challenges in terms of the coupling characteristics and computational efficiency of the PQ of technical VPPs.

[0005] In summary, the existing methods for solving the dynamic feasible domain of virtual power plants cannot adapt to the coupling constraints generated by massive heterogeneous flexible load resources; the calculation complexity is high and the calculation efficiency is low when a large number of coupling constraints are taken into account, and the calculation accuracy is poor for calculation methods that do not take coupling constraints into account; especially in intraday scenarios, the ability to satisfy the constraints included in the feasible domain is poor. Summary of the Invention

[0006] The purpose of the present invention is to provide a method and system for solving the dynamic feasible region of a virtual power plant based on coupling constraint decoupling, so as to solve at least one technical problem existing in the above-mentioned background technology.

[0007] In order to achieve the above object, the present invention adopts the following technical solutions:

[0008] In one aspect, the present invention provides a method for solving the dynamic feasible region of a virtual power plant based on coupling constraint decoupling, comprising:

[0009] Establish a VPP multi-aggregate flexible load model and a dynamic feasible domain solution model for virtual power plants based on a vertex search algorithm, and transform the vertex search objective function into a full time domain expression;

[0010] For a virtual queue that represents the accumulation of flexible load state variables, the neighborhood time constraint and the wide-area time coupling constraint in the flexible load are relaxed and decoupled through Lyapunov optimization, and the time coupling constraint is transformed into a virtual queue stability problem.

[0011] by setting the weight of the virtual queue stability problem and the original vertex search problem to meet the upper and lower bound constraints in the neighborhood time coupling constraints;

[0012] The virtual time queue is set to integrate the decoupled optimization problem into the dynamic feasible region solving of the virtual power plant in the day-ahead and intra-day scenarios.

[0013] Preferably, in the VPP multi-aggregation flexible load model, the distributed energy includes distributed photovoltaic and distributed wind power, and the flexible load includes heating, ventilation and air conditioning (HVAC), electric vehicles and distributed energy storage; the distributed energy model includes active power constraints, capacity constraints and power factor constraints; the flexible load includes power constraints and acceptable accumulated load waiting for power supply constraints; the DC power flow constraint conditions include line active and reactive power constraints, line capacity constraints, node injected active and reactive power constraints, node voltage and phase angle constraints, and PCC node active and reactive power constraints.

[0014] Preferably, the VFDR solving process based on the vertex search algorithm in the full time domain is as follows:

[0015] At time t, the search direction vector set and the vertex set U are initialized j,t = U O,t , Z j = Z O ;

[0016] The axial unit vector is selected as the first round solving direction vector U 1,t = [(1, 0), (0, -1), (-1, 0), (0, 1)]. The optimization problem M0 under the initial search direction vector is sequentially solved, and the corresponding vertex set

[0017] The outer unit normal vector between adjacent vertices is sequentially calculated, and the search direction vector matrix U is updated m,t , which is sequentially substituted into the optimization problem M0 to obtain the vertex corresponding to the search direction, and the vertex search solution set Z is updated m,t ;

[0018] The vertex relative displacement h m corresponding to each vertex is calculated, and if the h m corresponding to a certain vertex is less than a set threshold ε, the outer normal vector search link determined by the vertex and the adjacent vertex is stopped;

[0019] If it is greater than the set threshold ε, the calculation of the h m corresponding to each vertex is repeatedly executed until the h m calculated by all vertices meets the termination condition requirement, and the VFDR at time t is obtained.

[0020] Preferably, for a virtual queue that is set to characterize the accumulation of flexible load state quantities, the neighborhood time constraint and the wide-area time coupling constraint in the flexible load are relaxed and decoupled through Lyapunov optimization, and the time coupling constraint is converted into a virtual queue stability problem, including: constructing a virtual queue for energy storage i to reflect the accumulation of energy storage power, describing the wide-area time coupling constraint as a net flow value of the virtual queue under a certain time period of zero, which can be equivalent to a virtual queue stability problem, and finally performing a matrix representation of the VPP multi-aggregate flexible load model.

[0021] Preferably, the neighborhood time coupling constraint and the cross-period coupling constraint in the original vertex search problem are relaxed to the following: t , for electric vehicle virtual array N t and L t Stability problem; construct Lyapunov function H t Used to represent the virtual queue Q t 、N t and for the energy storage virtual queue L t The degree of congestion; when H t When it is smaller, all virtual queues are less crowded and the virtual queues are more stable. On the contrary, at least one virtual queue is more crowded and the virtual queue is less stable. Define the LD function ΔH t Used to represent the difference in congestion levels between time t and time t+1.

[0022] Preferably, a virtual time queue is set to integrate the decoupled optimization problem into the dynamic feasible domain solution of the virtual power plant in the day-ahead and intraday scenarios, including: designing a virtual time queue in the day-ahead scenario to meet the VDFR solution at any time in the day-ahead stage; the virtual time queue is: based on the VDFR solution problem at time t the day before, a virtual time queue of τ∈[0,t] is established, and the optimization problem at time τ is iteratively solved in sequence to update the flexible load virtual queue, and finally the VDFR at time t is obtained.

[0023] In a second aspect, the present invention provides a virtual power plant dynamic feasible region solution system based on coupling constraint decoupling, comprising:

[0024] A construction module is used to establish a VPP multi-aggregate flexible load model and a dynamic feasible domain solution model for virtual power plants based on a vertex search algorithm, and to convert the vertex search objective function into a full time domain expression;

[0025] The decoupling module is used to relax and decouple the neighborhood time constraints and the wide-area time coupling constraints in the flexible load through Lyapunov optimization for the virtual queue that represents the accumulation of flexible load state quantities, thus converting the time coupling constraints into the virtual queue stability problem.

[0026] A setting module, used for satisfying the upper and lower bound constraints in the neighborhood time coupling constraint by setting the weights of the virtual queue stability problem and the original vertex search problem;

[0027] The solution module is used to set up virtual time queues to integrate the decoupled optimization problem into the dynamic feasible domain solution of the virtual power plant in day-ahead and intraday scenarios.

[0028] In a third aspect, the present invention provides a non-transitory computer-readable storage medium, which is used to store computer instructions. When the computer instructions are executed by a processor, the method for solving the dynamic feasible domain of a virtual power plant based on coupling constraint decoupling as described above is implemented.

[0029] In a fourth aspect, the present invention provides a computer program product, comprising a computer program, which, when running on one or more processors, is used to implement the method for solving the dynamic feasible region of a virtual power plant based on coupling constraint decoupling as described above.

[0030] In a fifth aspect, the present invention provides an electronic device comprising: a processor, a memory and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory so that the electronic device executes instructions for implementing the method for solving the dynamic feasible domain of a virtual power plant based on coupling constraint decoupling as described above.

[0031] The beneficial effects of the present invention are as follows: by relaxing and decoupling the neighborhood time coupling constraints and the wide-area time coupling constraints, the coupling constraints are converted into a virtual queue stability problem, and the massive heterogeneous characteristics of flexible load resources are comprehensively considered, thereby effectively reducing the computational complexity of the optimization problem in the vertex search algorithm; effectively improving the computational efficiency and constraint satisfaction of the VDFR solution, verifying its applicability in the scenario of massive heterogeneous distributed resources, and realizing the efficient utilization of distributed resources; being simple and efficient, the method is conducive to the integrated aggregation of distributed resources, and has certain engineering promotion and application value.

[0032] Additional advantages of the present invention will be more clearly given in the following description or learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0034] Figure 1A flow chart of a virtual power plant dynamic feasible region solving method based on coupling constraint decoupling according to an embodiment of the present application.

[0035] Figure 2 A virtual power plant dynamic feasible region scene schematic diagram according to an embodiment of the present application.

[0036] Figure 3 A VSLO-DA algorithm flow chart according to an embodiment of the present application.

[0037] Figure 4 A VSLO-RT algorithm flow chart according to an embodiment of the present application.

[0038] Figure 5 A comparison chart of average number of decision variables per vertex search according to an embodiment of the present application.

[0039] Figure 6 A comparison chart of average number of constraints per vertex search according to an embodiment of the present application.

[0040] Figure 7 An impact of different virtual queue initial values on VDFR schematic diagram according to an embodiment of the present application. DETAILED DESCRIPTION

[0041] Embodiments of the present application are described in detail below with reference to the attached drawing figures, wherein the same or like reference numerals are used throughout the drawings to refer to the same or like elements or elements having the same or similar functionality. The embodiments described below are illustrative only and are not intended to be limiting on the present application.

[0042] As those skilled in the art will appreciate, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0043] It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting.

[0044] As those skilled in the art will appreciate, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0045] In the description of this specification, reference to the terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples" means that the specific features, structures, materials, or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. Moreover, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. In addition, those skilled in the art may combine and integrate different embodiments or examples described in this specification, as well as features of different embodiments or examples, unless otherwise inconsistent.

[0046] To facilitate understanding of the present invention, the present invention is further explained below with reference to specific embodiments in conjunction with the accompanying drawings. However, the specific embodiments do not constitute a limitation on the embodiments of the present invention.

[0047] Those skilled in the art should understand that the drawings are merely schematic diagrams of embodiments, and the components in the drawings are not necessarily necessary for implementing the present invention.

[0048] Example 1

[0049] In this embodiment 1, a virtual power plant dynamic feasible region solution system based on coupling constraint decoupling is first provided, including:

[0050] A construction module is used to establish a VPP multi-aggregate flexible load model and a dynamic feasible domain solution model for virtual power plants based on a vertex search algorithm, and to convert the vertex search objective function into a full time domain expression;

[0051] The decoupling module is used to relax and decouple the neighborhood time constraints and the wide-area time coupling constraints in the flexible load through Lyapunov optimization for the virtual queue that represents the accumulation of flexible load state quantities, thus converting the time coupling constraints into the virtual queue stability problem.

[0052] A setting module, used for satisfying the upper and lower bound constraints in the neighborhood time coupling constraint by setting the weights of the virtual queue stability problem and the original vertex search problem;

[0053] The solution module is used to set up virtual time queues to integrate the decoupled optimization problem into the dynamic feasible domain solution of the virtual power plant in day-ahead and intraday scenarios.

[0054] In this embodiment 1, the above-mentioned system is used to implement a method for solving the dynamic feasible region of a virtual power plant based on coupling constraint decoupling, including:

[0055] Establish a VPP multi-aggregate flexible load model and a dynamic feasible domain solution model for virtual power plants based on a vertex search algorithm, and transform the vertex search objective function into a full time domain expression;

[0056] For a virtual queue that represents the accumulation of flexible load state variables, the neighborhood time constraint and the wide-area time coupling constraint in the flexible load are relaxed and decoupled through Lyapunov optimization, and the time coupling constraint is transformed into a virtual queue stability problem.

[0057] By setting the weights of the virtual queue stability problem and the original vertex search problem, the upper and lower bounds in the neighborhood time coupling constraint are met;

[0058] A virtual time queue is set to integrate the decoupled optimization problem into the dynamic feasible region solution of the virtual power plant in the day-ahead and intraday scenarios.

[0059] In the VPP multi-aggregation flexible load model, distributed energy includes distributed photovoltaic and distributed wind power, and flexible loads include heating, ventilation and air conditioning (HVAC), electric vehicles, and distributed energy storage. The distributed energy model includes active power constraints, capacity constraints, and power factor constraints. Flexible loads include power constraints and constraints on the cumulative amount of acceptable waiting power loads. DC power flow constraints include line active and reactive power constraints, line capacity constraints, node injected active and reactive power constraints, node voltage and phase angle constraints, and PCC node active and reactive power constraints.

[0060] The VFDR solution process based on the vertex search algorithm in the full time domain is as follows:

[0061] Determine time t, initialize the search direction vector set and vertex set U j,t =U O,t , Z j =Z O ;

[0062] Select the axial unit vector and set it as the first round of solution direction vector U 1,t =[(1,0),(0,-1),(-1,0),(0,1)]. Sequentially solve the optimization problem M0 under the initial search direction vector and obtain the corresponding vertex set

[0063] Calculate the external unit normal vectors between adjacent vertices in sequence and update the search direction vector matrix U m,t , substitute into the optimization problem M0 in turn, get the vertex corresponding to the search direction, and update the vertex search solution set Z m,t ;

[0064] Calculate the relative displacement h corresponding to each vertex m , if the h corresponding to a vertex m If it is less than the set threshold ε, the search for the external normal vector determined by the vertex and the adjacent vertices is stopped;

[0065] If it is greater than the set threshold ε, the calculation of h corresponding to each vertex is repeatedm , until all vertices calculate h m Until all the termination conditions are met, the VFDR at time t is obtained.

[0066] For a virtual queue that represents the accumulation of flexible load state quantities, the neighborhood time constraints and wide-area time coupling constraints in the flexible load are relaxed and decoupled through Lyapunov optimization, and the time coupling constraints are transformed into a virtual queue stability problem, including: constructing a virtual queue for energy storage i to reflect the accumulation of energy storage power, describing the wide-area time coupling constraint as a net flow value of zero in the virtual queue under a certain time, which is equivalent to the stability problem of the virtual queue. Finally, the VPP multi-aggregate flexible load model is represented by a matrix.

[0067] The neighborhood time coupling constraint and cross-period coupling constraint in the original vertex search problem are relaxed to the following: t , for electric vehicle virtual array N t and L t Stability problem; construct Lyapunov function H t Used to represent the virtual queue Q t 、N t and for the energy storage virtual queue L t The degree of congestion; when H t When it is smaller, all virtual queues are less crowded and the virtual queues are more stable. On the contrary, at least one virtual queue is more crowded and the virtual queue is less stable. Define the LD function ΔH t Used to represent the difference in congestion levels between time t and time t+1.

[0068] A virtual time queue is set up to integrate the decoupled optimization problem into the dynamic feasible domain solution of the virtual power plant in the day-ahead and intraday scenarios, including: designing a virtual time queue in the day-ahead scenario to meet the VDFR solution at any time in the day-ahead stage; the virtual time queue is: based on the VDFR solution problem at time t the day before, a virtual time queue of τ∈[0,t] is established, and the optimization problem at time τ is iteratively solved in sequence to update the flexible load virtual queue, and finally the VDFR at time t is obtained.

[0069] Example 2

[0070] In this embodiment 2, a method for solving the dynamic feasible domain of a virtual power plant based on coupling constraint decoupling is provided, which belongs to the field of virtual power plant resource aggregation technology. A virtual power plant model considering distributed energy and flexible load resources is established, and a feasible domain solution model for a virtual power plant based on the vertex search method is determined; virtual queues are established for flexible loads such as energy storage, HVAC and electric vehicles respectively through Lyapunov optimization, and the time coupling constraints of flexible loads are decoupled into a virtual queue stability problem; a virtual time queue is constructed to integrate the decoupling optimization problem into the dynamic feasible domain solution of the virtual power plant on the day before and within the day, thereby obtaining a feasible domain solution algorithm on the day before and within the day. Based on the Lyapunov optimization theory, the time coupling constraints are decoupled, a weight coefficient is introduced, and a mathematical model with vertex search and queue stability as the goal is proposed, which ultimately realizes the dynamic feasible domain solution of the virtual power plant on the day before and within the day.

[0071] like Figure 1 As shown, a virtual power plant model with multiple types of distributed resources is first determined, and a dynamic feasible domain solution model for the virtual power plant is established based on a vertex search algorithm. Then, based on Lyapunov optimization, the time coupling constraints of HVAC, electric vehicles, and distributed energy storage are relaxed and decoupled into a virtual queue stability problem. A mathematical model with vertex search and virtual queue stability as objective functions is determined, and weight coefficients are set to satisfy the coupling constraints. This transforms the original vertex search optimization problem into a single-period uncoupled constraint problem. Finally, by setting a virtual time queue, the decoupled optimization problem is integrated into the vertex search algorithm, and feasible domain solution algorithms are designed for both day-ahead and intraday scenarios.

[0072] The method for solving the dynamic feasible region of a virtual power plant based on coupling constraint decoupling includes the following steps:

[0073] Establishing a VPP model including distributed energy and flexible load resources;

[0074] Distributed energy includes distributed photovoltaic and distributed wind power, and flexible loads include heating, ventilation and air conditioning (HVAC), electric vehicles, and distributed energy storage. The distributed energy model includes active power constraints, capacity constraints, and power factor constraints. The formulas are:

[0075]

[0076]

[0077]

[0078] Where, and P g,i DERi The maximum and minimum active output; DER at time t i The output active power; DER at time t i Output reactive power; DER i capacity; and f g, i DER i The maximum and minimum power factor angles.

[0079] Load aggregators and smart community service providers respond to dispatch instructions and appropriately interrupt HVAC supply without affecting user comfort, thus demonstrating HVAC adjustable capabilities;

[0080] According to the operating characteristics of HVAC, its energy consumption in each operating time interval is determined, and the operating time is determined by the service provider's energy management center;

[0081] Set the HVAC model as:

[0082]

[0083]

[0084]

[0085]

[0086]

[0087]

[0088] Where: A i,t is the power demand of the i-th HVAC at time t; is the power demand of the i-th HVAC at time t; Δt is the time interval; is the maximum power demand of the i-th HVAC; B i,t is the actual power consumption of the i-th HVAC at time t; is the switch state of the i-th HVAC at time t, Indicates that HVAC is on. Indicates that HVAC is in the off state; is the actual power consumption of the i-th HVAC unit at time t; is the cumulative unmet power demand of the i-th HVAC at time t; is the maximum acceptable unmet energy demand accumulation of the i-th HVAC unit.

[0089] Electric vehicle load is supplied by time delay to respond to grid dispatch instructions, thus embodying the adjustable capacity of electric vehicle load; the electric vehicle load model is:

[0090]

[0091]

[0092]

[0093]

[0094]

[0095]

[0096] In the formula: C i,t is the electric energy demand of the ith electric vehicle at time t; is the maximum electric energy demand of the ith electric vehicle; D i,t is the actual electric energy consumed by the ith electric vehicle at time t; is the charging power of the ith electric vehicle at time t; is the charging and discharging efficiency of the ith electric vehicle; is the maximum load of the ith electric vehicle; is the delay supply load accumulation of the ith electric vehicle at time t; is the maximum acceptable delay supply load accumulation of the ith electric vehicle.

[0097] The distributed energy storage adopts electrochemical energy storage, including power constraints and SOC operation constraints;

[0098] The model is:

[0099]

[0100]

[0101]

[0102]

[0103]

[0104] In the formula: and are the discharging and charging power of the energy storage i at time t; is the charging and discharging state of the energy storage i at time t, indicates that the energy storage is in discharging state, indicates that the energy storage is in charging state; is the maximum charge-discharge power of the energy storage i; is the state of charge of the energy storage i at time t; η i is the charge-discharge efficiency of the energy storage i; and are the minimum and maximum state of charge of the energy storage i, respectively; is the initial state of charge of the energy storage i in the dispatch cycle; is the final state of charge of the energy storage i in the dispatch cycle;

[0105] The constraint conditions of the direct current flow model include: line active power and reactive power constraints, line capacity constraints, node injected active power and reactive power constraints, node voltage and phase angle constraints, PCC node active power and reactive power constraints; the model is:

[0106]

[0107]

[0108]

[0109]

[0110]

[0111]

[0112]

[0113]

[0114] U O,t = U O , θ O,t = θ O (29)

[0115]

[0116]

[0117] In the formula: and are the active power and reactive power of the line ij at time t, respectively; g ij and b ij are the conductance and susceptance of the line ij, respectively; U i,t is the square value of the voltage of the node i at time t; θ i,t is the phase angle of the node i at time t; U O is the fixed voltage of the PCC node; θ O is the fixed phase of the PCC node; is the maximum carrying power of line ij; and are the active and reactive power of node i respectively; P t O and Q t O are the output active and reactive power of the PCC node respectively.

[0118] Since both Equation (2) and Equation (23) are second-order constraints, they are piecewise linearized, Equation (2) is transformed into Equation (32), and Equation (23) is transformed into (33)

[0119]

[0120]

[0121] Where N1 and N2 are the secant numbers that approximate the maximum branch capacity;

[0122] Since all constraints in the VPP model are linear constraints, the faces of the polyhedron in the high-dimensional space of the VPP model are all planes, so their projections are polygons with a finite number of sides. The vertex search method can be used to solve each vertex and then obtain the feasible domain polygon;

[0123] The objective function of the feasible region vertex search is determined as

[0124]

[0125] Where: is the search direction vector of VDFR at time t; z m,t is time t The vertex obtained by solving the direction;

[0126] Sure The optimization problem for solving the vertex in the direction is the optimization problem M0;

[0127] Objective function: Equation (34);

[0128] Constraints: Equations (1), (3)-(22), (24)-(33);

[0129] The above constraints are a high-dimensional representation of VDFR, where the decision variables can be divided into coordination variables for VDFR’s interaction with the external environment and internal variables that characterize the power allocation of internal resources, namely:

[0130] Coordination variable U 1,t =[(1,0),(0,-1),(-1,0),(0,1)]

[0131] Internal variables

[0132] The termination condition is determined to be that the relative displacement of the new vertex to the corresponding original plane is less than a certain value, and the displacement is set to h j ,Right now

[0133]

[0134] z 2,t The vertex on the clockwise side of the new vertex generated at time t-1; z 0,t is the new vertex generated at time t; ||μ m ||2 is the binary norm of the search direction vector; h δ The termination condition is set;

[0135] The VFDR solution process based on the vertex search algorithm in the full time domain is as follows:

[0136] Determine the time t to solve VDFR, initialize the search direction vector set and vertex set U j,t =U O,t , Z j =Z O ;

[0137] Select the axial unit vector and set it as the first round of solution direction vector U 1,t =[(1,0),(0,-1),(-1,0),(0,1)], solve the optimization problem M0 under the initial search direction vector in turn, and get the corresponding vertex set

[0138] Calculate the external unit normal vectors between adjacent vertices in sequence and update the search direction vector matrix U m,t , substitute into the optimization problem M0 in turn, get the vertex corresponding to the search direction, and update the vertex search solution set Z m,t ;

[0139] Calculate the relative displacement h corresponding to each vertex m , if the h corresponding to a vertex m If the h is less than the set threshold ε, the search for the external normal vector determined by the vertex and the adjacent vertices is stopped; if it is greater than the set threshold ε, steps S32 and S33 are repeated until the h calculated for all vertices is m Until all the termination conditions are met, it becomes VFDR at time t;

[0140] The objective function (34) of the original optimization problem M0 is a vertex search problem in a single direction at a single moment, while the virtual queue stability involves all time periods in the scheduling cycle, so the objective function (34) needs to be transformed;

[0141] Set time t The vertex search objective function under the direction is:

[0142]

[0143] Where: T is the total number of scheduling period time periods;

[0144] For solving the vertex search objective function at time t, the weighted summation expression of the whole cycle can be understood as that in the time 0-T, the objective function only works when solving the VDFR at time t;

[0145] Define the optimization problem M1, M1:

[0146] Objective function: Equation (36);

[0147] Constraints: Equations (1), (3)-(22), (24)-(33).

[0148] The vertex value obtained by solving the optimization problem M1 set by the above scheme at a specific time t is consistent with the original vertex search problem M0;

[0149] For the i-th aggregate HVAC, a virtual queue is constructed to reflect the accumulation of unmet HVAC energy demand. The same method is applied to the electric vehicle virtual queue. For the energy storage i, a virtual queue is constructed to reflect the accumulation of energy storage power, that is,

[0150] Q i,t+1 =Q i,t -B i,t +A i,t (37)

[0151] N j,t =N j,t -D j,t +C j,t (38)

[0152]

[0153]

[0154]

[0155] Where, ξ ES is a positive number, and its value can be adjusted to meet the upper and lower limits of the neighborhood time coupling constraint; μ P,m is the horizontal coordinate representing the search direction vector;

[0156] The concept of virtual queue can be used to describe the wide-area time coupling constraint as the net flow value of the virtual queue is zero under a certain time, which is equivalent to the stability problem of the virtual queue.

[0157] Since there are multiple aggregated flexible load models in the VPP, for ease of expression, assume that the VPP contains a total of I aggregated HVAC, a total of J aggregated electric vehicle loads, and a total of K energy storage. All queues can be expressed as:

[0158] Q t =[Q 1,t ,…,Q i,t ,…,Q I,t ] (42)

[0159] N t =[N 1,t ,…,N j,t ,…,N J,t ] (43)

[0160] L t =[L 1,t ,…,L k,t ,…,L K,t ] (44)

[0161] At this point, the neighborhood time coupling constraints (7), (13) and (18) and the cross-period coupling constraints (9), (15) and (20) in the original vertex search problem are relaxed to form a virtual queue Q t 、N t and L t stability issues;

[0162] Construct Lyapunov function H t Used to represent the virtual queue Q t 、N t and L t The degree of congestion is expressed as:

[0163]

[0164] When H t When it is small, all virtual queues have low congestion and good virtual queue stability; conversely, at least one virtual queue has high congestion and poor virtual queue stability;

[0165] Further define the LD (Lyapunov-Drift, LD) function ΔH t Used to represent the difference in congestion levels between time t and time t+1;

[0166] ΔH t =H t+1 -H t (46)

[0167] From the above formula, we can see that if we want to ensure H t The stability problem of the virtual queue represented by ΔH tShould be as small as possible. So far, the original virtual queue stability problem is transformed into minimizing ΔH t Problems;

[0168] To ensure the stability of the virtual queue, while considering the vertex search objective function represented by the optimization problem M0, the L-DPP (Lyapunov-Drift-Plus-Penalty, L-DPP) function is set to replace the objective function of the optimization problem M1 with the minimum value in each time period. This transformation is the same as the problem studied by the original objective function of the optimization problem M0, which is the objective function at a single moment:

[0169]

[0170]

[0171] Where: AC ,ξ EV ,ξ ES are non-negative weight coefficients, which are used to express the balance between the relative distance from the vertex to the centroid after relaxation and the stability of the virtual queue. By properly selecting the values ​​of the three coefficients, the upper and lower bounds of the neighborhood time coupling constraint can be satisfied;

[0172] There is an upper bound, which can be expressed as:

[0173]

[0174] Where M L for:

[0175]

[0176] The objective function is:

[0177]

[0178] The original optimization problem M0 with time coupling constraints is eventually transformed into a decoupled single-period optimization problem M2;

[0179] Objective function: Equation (51);

[0180] Constraints: Equations (1), (3), (16), (17), (21), (22), (24)-(33);

[0181] Assume that the solution of the optimization problem M2 is The final vertex coordinates are

[0182]

[0183] In the formula for:

[0184]

[0185] For HVAC virtual array Q t ,exist

[0186]

[0187] For electric vehicle virtual array N t ,exist

[0188]

[0189] For the energy storage virtual queue L t ,exist

[0190]

[0191]

[0192] In the day-ahead dispatch phase, the VDFR for each hour of the next 24 hours is determined based on the forecast information of distributed generation output and flexible load on the day before. In the intraday operation phase, the VDFR for the next 15 minutes is determined based on the current operating status.

[0193] In solving optimization problem M2, each change in vertex search direction requires changing the objective function and reinitializing the virtual queue. Furthermore, the correlation between VDFR solutions at different times is not considered. This makes optimization problem M2 unsuitable for direct application to vertex search algorithms in day-ahead scenarios.

[0194] Based on the above analysis, in this embodiment 2, a virtual time queue is designed for the day-ahead scenario to satisfy the VDFR solution at any time in the day-ahead phase;

[0195] The definition of the virtual time queue is as follows: Based on the VDFR solution problem at time t the day before, a virtual time queue τ∈[0,t] is established. The optimization problem M2 at time τ is iteratively solved to update the flexible load virtual queue, and finally the VDFR at time t is obtained.

[0196] Aiming at the day-ahead and intraday scenarios respectively, we propose the Vertex Search based on Lyapunov Optimization-Day Ahead (VSLO-DA) algorithm and the Vertex Search based on Lyapunov Optimization-Real Time (VSLO-RT) algorithm. Figure 3 , Figure 4 As shown:

[0197] The calculation method for determining the relative error of VDFR is:

[0198]

[0199] Where: S v,t is the area of ​​VDFR at time t under the test algorithm; S m,t is the area of ​​VDFR at time t under the vertex search method.

[0200] Considering the resource allocation ratio ρ in the IEEE-33 node system F ∈[10%,20%,...,90%],ρ D ∈[10%,30%,...,90%], a total of 45 scenarios. Δt is set to 1 hour before the day and 15 minutes within the day.

[0201] The set examples include: Example N1 (experimental group): applying the VSLO algorithm designed in this patent to calculate the VDFR with time coupling constraints (N1-DA: applying VSLO-DA to solve the day-ahead scenario; N2-RT: applying VSLO-RT to solve the intraday scenario); Example N2 (control group): applying the vertex search algorithm to calculate the VDFR with time coupling constraints (N2-DA: day-ahead scenario; N2-RT: intraday scenario); Example N3 (experimental group): applying the vertex search method to calculate the VDFR without time coupling constraints (N3-DA: day-ahead scenario; N3-RT: intraday scenario).

[0202] Combining the calculation process and the VDFR solution results, it can be seen that the case N3-DA without considering the time coupling constraint is equivalent to solving the VDFR problem of 24 time periods separately. Its computational complexity and time consumption are very low, but the VDFR obtained by it exceeds the original VDFR obtained by the case N3-DA to a large extent, and there is a large deviation from the actual situation. Table 1 gives different ρ F and ρ D The following example shows the maximum relative error data of N1-DA and N3-DA;

[0203] Table 1

[0204]

[0205]

[0206] Figure 5 and Figure 6The constraint conditions and the number of decision variables required for solving each average vertex search optimization problem contained in the original optimization problem M0, the VSLO-RT algorithm (decoupled optimization problem M2) and the VSLO-DA algorithm are given. It can be found through comparison that the calculation complexity of the decoupled optimization problem is significantly reduced; the algorithm provided in the patent can be effectively applied to the scene of high proportion of new energy and flexible load, and the VDFR solving time of the maximum new energy and flexible load proportion in the day-ahead 24 hours can be controlled within 15 minutes;

[0207] In summary, in the day-ahead VDFR solving, the VSLO-DA algorithm can effectively reduce the calculation complexity and improve the calculation efficiency within a certain error range.

[0208] Figure 7 The influence of different virtual queue initial values on VDFR is given. The virtual queue of the MAX group of HVAC and electric vehicles is seriously accumulated, the virtual queue value of energy storage is small, the overall resource scheduling space is small, and the next moment VDFR is left; the virtual queue of the MIN group of HVAC and electric vehicles is less accumulated, the virtual queue value of energy storage is larger, the overall resource scheduling space is larger, and the next moment VDFR is right;

[0209] In this embodiment, the ρ F and the ρ D The calculation efficiency of the scene is tested by an example, which verifies that the calculation efficiency of the VSLO-RT algorithm provided in the patent is significantly improved compared with the calculation efficiency of the traditional algorithm. The VSLO-RT algorithm can better adapt to the day-ahead VDFR solving in the scene of high proportion of flexible load and new energy, and the VDFR solving time of all time in all example tests of the patent is less than 25s;

[0210] Through the above example test comparison, it can be known that using the VSLO-RT algorithm in the day-ahead scene can improve the calculation efficiency while well meeting the neighborhood and wide area time coupling constraints, and further improving the scheduling ability of flexible load resources through constraint satisfaction.

[0211] Embodiment 3

[0212] Embodiment 3 of the present application provides an electronic device comprising a memory and a processor, the processor and the memory communicate with each other, the memory stores program instructions executable by the processor, and the processor calls the program instructions to execute a virtual power plant dynamic feasible region solving method based on decoupling of coupling constraints, which comprises the following flow steps:

[0213] A VPP multi-aggregated flexible load model and a virtual power plant dynamic feasible region solving model based on a vertex search algorithm are established, and a vertex search objective function is converted into a full-time domain expression;

[0214] For a virtual queue that represents the accumulation of flexible load state variables, the neighborhood time constraint and the wide-area time coupling constraint in the flexible load are relaxed and decoupled through Lyapunov optimization, and the time coupling constraint is transformed into a virtual queue stability problem.

[0215] By setting the weights of the virtual queue stability problem and the original vertex search problem, the upper and lower bounds in the neighborhood time coupling constraint are met;

[0216] A virtual time queue is set to integrate the decoupled optimization problem into the dynamic feasible region solution of the virtual power plant in the day-ahead and intraday scenarios.

[0217] Example 4

[0218] Embodiment 4 of the present invention provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, a method for solving a dynamic feasible region of a virtual power plant based on coupling constraint decoupling is implemented. The method includes the following steps:

[0219] Establish a VPP multi-aggregate flexible load model and a dynamic feasible domain solution model for virtual power plants based on a vertex search algorithm, and transform the vertex search objective function into a full time domain expression;

[0220] For a virtual queue that represents the accumulation of flexible load state variables, the neighborhood time constraint and the wide-area time coupling constraint in the flexible load are relaxed and decoupled through Lyapunov optimization, and the time coupling constraint is transformed into a virtual queue stability problem.

[0221] By setting the weights of the virtual queue stability problem and the original vertex search problem, the upper and lower bounds in the neighborhood time coupling constraint are met;

[0222] A virtual time queue is set to integrate the decoupled optimization problem into the dynamic feasible region solution of the virtual power plant in the day-ahead and intraday scenarios.

[0223] Example 5

[0224] Embodiment 5 of the present invention provides a computer device, including a memory and a processor, wherein the processor and the memory communicate with each other, the memory stores program instructions executable by the processor, and the processor calls the program instructions to execute a method for solving a dynamic feasible region of a virtual power plant based on coupling constraint decoupling, the method comprising the following steps:

[0225] Establish a VPP multi-aggregate flexible load model and a dynamic feasible domain solution model for virtual power plants based on a vertex search algorithm, and transform the vertex search objective function into a full time domain expression;

[0226] For a virtual queue that represents the accumulation of flexible load state variables, the neighborhood time constraint and the wide-area time coupling constraint in the flexible load are relaxed and decoupled through Lyapunov optimization, and the time coupling constraint is transformed into a virtual queue stability problem.

[0227] By setting the weights of the virtual queue stability problem and the original vertex search problem, the upper and lower bounds in the neighborhood time coupling constraint are met;

[0228] A virtual time queue is set to integrate the decoupled optimization problem into the dynamic feasible region solution of the virtual power plant in the day-ahead and intraday scenarios.

[0229] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0230] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0231] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0232] These computer program instructions can also be loaded onto a computer or other programmable data processing device, and a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable device provide the functions for implementing the process. Figure 1a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0233] Although the above describes the specific embodiments of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art on the basis of the technical solutions disclosed in the present invention without the need for creative work should be included in the scope of protection of the present invention.

Claims

1. A method for solving the dynamic feasible region of a virtual power plant based on coupling constraint decoupling, characterized in that: include: A VPP multi-aggregate flexible load model and a dynamic feasible domain solution model for virtual power plants based on a vertex search algorithm are established, and the vertex search objective function is converted into a full-time domain expression. In the VPP multi-aggregate flexible load model, distributed energy includes distributed photovoltaics and distributed wind power, and flexible loads include HVAC, electric vehicles, and distributed energy storage. The distributed energy model includes active power constraints, capacity constraints, and power factor constraints. Flexible loads include power constraints and constraints on the cumulative amount of acceptable waiting loads. DC power flow constraints include line active and reactive power constraints, line capacity constraints, node injected active and reactive power constraints, node voltage and phase angle constraints, and PCC node active and reactive power constraints. For a virtual queue that represents the accumulation of flexible load state variables, the neighborhood time constraint and the wide-area time coupling constraint in the flexible load are relaxed and decoupled through Lyapunov optimization, and the time coupling constraint is transformed into a virtual queue stability problem. By setting the weights of the virtual queue stability problem and the original vertex search problem, the upper and lower bounds in the neighborhood time coupling constraint are satisfied; A virtual time queue is set to integrate the decoupled optimization problem into the dynamic feasible domain solution of the virtual power plant in the day-ahead and intraday scenarios. The VFDR solution process based on the vertex search algorithm in the full time domain is as follows: Determine time t and initialize the search direction vector set and vertex set; Select the axial unit vector as the first round of solution direction vector; solve the optimization problem M0 under the initial search direction vector in turn to obtain the corresponding vertex set; Calculate the external unit normal vectors between adjacent vertices in sequence and update the search direction vector matrix U m,t , substitute into the optimization problem M0 in turn, get the vertex corresponding to the search direction, and update the vertex search solution set Z m,t ; Calculate the relative displacement h corresponding to each vertex m , if the h corresponding to a vertex m If it is less than the set threshold ε, the search for the external normal vector determined by the vertex and the adjacent vertices is stopped; If it is greater than the set threshold, the calculation of h corresponding to each vertex is repeated m , until all vertices calculate h m Until all the termination conditions are met, the VFDR at time t is obtained.

2. The method for solving the dynamic feasible region of a virtual power plant based on coupling constraint decoupling according to claim 1 is characterized in that: For a virtual queue that represents the accumulation of flexible load state quantities, the neighborhood time constraints and wide-area time coupling constraints in the flexible load are relaxed and decoupled through Lyapunov optimization, and the time coupling constraints are transformed into a virtual queue stability problem, including: constructing a virtual queue for energy storage i to reflect the accumulation of energy storage power, describing the wide-area time coupling constraint as a net flow value of zero in the virtual queue under a certain time, which is equivalent to the stability problem of the virtual queue. Finally, the VPP multi-aggregate flexible load model is represented by a matrix.

3. The method for solving the dynamic feasible region of a virtual power plant based on coupling constraint decoupling according to claim 2 is characterized in that: The neighborhood time coupling constraint and cross-period coupling constraint in the original vertex search problem are relaxed to the following: t , for electric vehicle virtual array N t and L t Stability problem; construct Lyapunov function H t Used to represent the virtual queue Q t 、N t and for the energy storage virtual queue L t The degree of congestion; when H t When it is small, all virtual queues have low congestion and good virtual queue stability; conversely, at least one virtual queue has high congestion and poor virtual queue stability; Define the LD function ΔH t Used to represent the difference in congestion levels between time t and time t+1.

4. The method for solving the dynamic feasible region of a virtual power plant based on coupling constraint decoupling according to claim 3 is characterized in that: A virtual time queue is set up to integrate the decoupled optimization problem into the dynamic feasible domain solution of the virtual power plant in the day-ahead and intraday scenarios, including: designing a virtual time queue in the day-ahead scenario to meet the VDFR solution at any time in the day-ahead stage; the virtual time queue is: based on the VDFR solution problem at time t the day before, a virtual time queue of τ∈[0,t] is established, and the optimization problem at time τ is iteratively solved in sequence to update the flexible load virtual queue, and finally the VDFR at time t is obtained.

5. A virtual power plant dynamic feasible region solution system based on coupling constraint decoupling, characterized in that: include: A construction module is used to establish a VPP multi-aggregate flexible load model and a dynamic feasible domain solution model for virtual power plants based on a vertex search algorithm, and to convert the vertex search objective function into a full-time domain expression. In the VPP multi-aggregate flexible load model, distributed energy includes distributed photovoltaics and distributed wind power, and flexible loads include HVAC, electric vehicles, and distributed energy storage. The distributed energy model includes active power constraints, capacity constraints, and power factor constraints. Flexible loads include power constraints and constraints on the cumulative amount of acceptable waiting loads. DC power flow constraints include line active and reactive power constraints, line capacity constraints, node injected active and reactive power constraints, node voltage and phase angle constraints, and PCC node active and reactive power constraints. The decoupling module is used to relax and decouple the neighborhood time constraints and the wide-area time coupling constraints in the flexible load through Lyapunov optimization for the virtual queue that represents the accumulation of flexible load state quantities, thus converting the time coupling constraints into the virtual queue stability problem. A setting module, used for satisfying the upper and lower bound constraints in the neighborhood time coupling constraint by setting the weights of the virtual queue stability problem and the original vertex search problem; The solution module is used to set up virtual time queues to integrate the decoupled optimization problem into the dynamic feasible domain solution of the virtual power plant in the day-ahead and intraday scenarios. The VFDR solution process based on the vertex search algorithm in the full time domain is as follows: Determine time t and initialize the search direction vector set and vertex set; Select the axial unit vector as the first round of solution direction vector; solve the optimization problem M0 under the initial search direction vector in turn to obtain the corresponding vertex set; Calculate the external unit normal vectors between adjacent vertices in sequence and update the search direction vector matrix U m,t , substitute into the optimization problem M0 in turn, get the vertex corresponding to the search direction, and update the vertex search solution set Z m,t ; Calculate the relative displacement h corresponding to each vertex m , if the h corresponding to a vertex m If it is less than the set threshold ε, the search for the external normal vector determined by the vertex and the adjacent vertices is stopped; If it is greater than the set threshold, the calculation of h corresponding to each vertex is repeated m , until all vertices calculate h m Until all the termination conditions are met, the VFDR at time t is obtained.

6. A non-transitory computer-readable storage medium, characterized in that The non-transitory computer-readable storage medium is used to store computer instructions. When the computer instructions are executed by the processor, the method for solving the dynamic feasible region of a virtual power plant based on coupling constraint decoupling as described in any one of claims 1 to 4 is implemented.

7. A computer program product, characterized in that It comprises a computer program, which, when running on one or more processors, is used to implement the method for solving the dynamic feasible region of a virtual power plant based on coupling constraint decoupling as described in any one of claims 1 to 4.

8. An electronic device, characterized in that: include: A processor, a memory and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory to enable the electronic device to execute instructions for implementing the method for solving the dynamic feasible domain of a virtual power plant based on coupling constraint decoupling as described in any one of claims 1 to 4.

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