Collaborative optimization method for power system with multiple virtual power plants based on feasible region equivalent projection theory

Abstract

The application discloses a multi-virtual power plant (VPP) coordinated optimization method based on feasible region equivalent projection theory, which comprises the following steps: establishing a main grid operation model; modeling wind and light uncertainty by using an opportunity constraint optimization method and establishing a virtual power plant operation model; converting the opportunity constraint into a deterministic constraint through quantile transformation; giving a target function of the virtual power plant-main grid coordinated optimization and establishing an integrated scheduling model of the multi-virtual power plant power system; eliminating internal variables of the virtual power plant in the target function by using the upper mirror graph theory; projecting the multi-period power-cost feasible region of the virtual power plant at the public coupling point based on the feasible region equivalent projection theory and the vertex projection convex hull search method; and establishing a VPP-main grid coordinated optimization model based on the feasible region equivalent projection, and solving the model by calling a GUROBI solver in a Matlab2021a environment. The method solves the multi-period virtual power plant-main grid coordinated optimization model in a non-iterative manner, and the solving time is greatly reduced compared with a traditional iterative method, and the method has the same optimality as the traditional method.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of virtual power plant (VPP)-main grid collaborative optimization, and particularly relates to a multi-virtual power plant power system collaborative optimization method based on feasible region equivalent projection theory. BACKGROUND

[0002] Distributed energy resources (DER), represented by wind and solar power, are gaining increasing attention worldwide due to their stability, economic benefits, flexibility, and environmental friendliness. Despite the obvious advantages of distributed energy resources (DER), there are also some problems. Due to the small scale and wide distribution of distributed energy resources (DER), it brings new challenges to the dispatching management of power systems. To cope with this challenge, virtual power plant technology emerges as the times require. Virtual power plant (VPP) integrates various distributed energy resources using advanced communication and measurement technology, and participates in the dispatch of the transmission grid as a unified virtual entity.

[0003] Most existing research on VPP-main grid collaborative optimization is based on the idea of unified optimization. For example, document (Jiang T, Wu C, Huang T, et al. Optimal market participation of VPPs in TSO-DSO coordinated energy and flexibility markets[J]. APPLIED ENERGY, 2024, 360.) proposes an optimal market model for VPP participation in transmission-level market clearing, which uses an adaptive penalty parameter multiplier alternation direction method for solution. The adaptive penalty parameter multiplier alternation direction method used in the document to solve the proposed market model requires multiple iterations, is very dependent on computing power, and the calculation results may have problems such as non-convergence.

[0004] Document (Zhang G, Wang X, Jiang C, et al. Economic analysis of virtual power plant with double-layer optimization scheduling[J]. Power System Technology, 2016, 40(8): 2295-2301.) establishes a double-layer optimization scheduling model for the main grid and VPP, analyzes from the perspective of VPP occupying different capacity proportions of the system, and provides support for the dispatch of the main grid and VPP. Solving the VPP-main grid collaborative optimization problem with the idea of unified optimization requires the use of iterative solution methods, but problems such as non-convergence or oscillation may occur during the solution process.

[0005] To solve the above problems, some studies have proposed non-iterative VPP-main grid collaborative optimization methods. For example, the literature (Ren J, Zeng Y, Qin C, et al. Characterization and application of flexible operation region of virtual power plant[J]. APPLIED ENERGY, 2024, DOI: 10.1016 / j.apenergy.2024.123733.) based on the concept of flexible operation region proposes a VPP scheduling boundary characterization method, which characterizes the flexible operation feasible region of VPP, and finally projects it to the public coupling point to support the power grid scheduling. However, such non-iterative methods still have problems such as high algorithm complexity or heavy computational burden. SUMMARY

[0006] In order to solve the problem that the traditional solution method may not converge or oscillate in the current virtual power plant-main grid collaborative optimization. The present application proposes a multi-virtual power plant power system collaborative optimization method based on the feasible region equivalent projection theory. This method solves the multi-period virtual power plant-main grid collaborative optimization model in a non-iterative way, greatly reducing the solution time compared to traditional iterative methods, and has the same optimality as traditional methods.

[0007] The technical scheme adopted by the present application is:

[0008] The multi-virtual power plant power system collaborative optimization method based on the feasible region equivalent projection theory comprises the following steps:

[0009] Step 1: considering the constraints of the main grid power network and the transmission capacity, a main grid operation model is established; considering the uncertainty of wind power and photovoltaic output, a virtual power plant operation model is established;

[0010] Step 2: convert the wind power and photovoltaic opportunity constraints into deterministic constraints through quantile transformation;

[0011] Step 3: establish the objective function of virtual power plant-main grid collaborative optimization, and establish the integrated scheduling model of multi-virtual power plant power system based on step 1 and step 2;

[0012] Step 4: eliminate the internal variables of the virtual power plant in the objective function using the up-mirror graph theory, and describe the multi-period power-cost feasible region projection of the virtual power plant at the public coupling point based on the feasible region equivalent projection theory and the vertex projection convex hull search method;

[0013] Step 5: establish the VPP-main grid collaborative optimization model based on the feasible region equivalent projection, and solve the model.

[0014] In step 1, the main network operation model includes generator output constraints, generator ramping constraints, exchange power injection constraints, node power balance constraints, main network branch power flow constraints, and line transmission capacity constraints.

[0015] 1) Generator output constraints:

[0016]

[0017] In formula (1), P G b t represents the active power output of the generator at node b in the main network at time t; P G b t represents the active power output of the generator at node b in the main network at time t;

[0018] 2) Generator ramping constraints:

[0019]

[0020] In formula (2), P R b t represents the generator ramping capacity at node b in the main network at time t; P G b t+1 represents the active power output of the generator at node b in the main network at time t+1;

[0021] 3) Exchange power injection constraints:

[0022]

[0023] In formula (3), P i represents the upper limit of the power injected by the i-th VPP into the main network; P i t represents the active power injected by the i-th virtual power plant at time t into the main network public connection point.

[0024] 4) Node power balance constraints:

[0025]

[0026] In formula (4), P G b t represents the active power of the generator at node b in the main network at time t; P L b q t represents the active power of the line between nodes b and q in the main network at time t; P i t represents the active power injected by the i-th virtual power plant at time t into the public connection point; P D b t represents the active load at node b in the main network at time t; b P b represents the set of nodes connected to node b in the main network;e VPP A represents the association matrix of the virtual power plant access nodes and the main network nodes;N VPP N represents the total number of virtual power plants; i represents the virtual power plant index.

[0027] 5) Main network branch power flow constraints:

[0028]

[0029] in formula (5), Pbiq and Qbiq are the active and reactive power of the line between nodes b and q in the main grid t period, respectively; b bq bq Gbiq and Cbiq are the susceptance and conductance values of the line between nodes b and q in the main grid t period, respectively; Vb and Vq are the voltage amplitude of nodes b and q in the main grid t period, respectively; θb and θq are the voltage phase angle of nodes b and q in the main grid t period, respectively; Z is the number of segments of the piecewise linearization; z is the index number of the segment; Vbmax and Vbmin are the upper and lower limit values of the main grid voltage amplitude, respectively; θbmax and θbmin are the upper and lower limit values of the main grid voltage phase angle, respectively; Cbiq represents the capacity of the line between nodes b and q in the main grid t period.

[0030] 6) Line transmission capacity constraint:

[0031]

[0032] in formula (6), Pbiq and Qbiq are the active and reactive power of the line between nodes b and q in the main grid t period, respectively; b is the power transmission allocation factor of the main grid node b to branch l; is the power transmission allocation factor of the i-th virtual power plant to the main grid branch l; is the capacity of the main grid branch l; N Main represents the number of main grid nodes; t represents the scheduling time index; T represents the total scheduling time.

[0033] In step 1, the virtual power plant operation model includes power network constraints, gas turbine output constraints, photovoltaic output constraints, wind power output constraints, node power balance constraints, and exchange power constraints;

[0034] 1) Power network constraints:

[0035] The power network constraints include linearized virtual power plant flow constraints and power system operation constraints. Formulas (7)-(8) are linearized virtual power plant flow constraints; formula (9) is a linearized form of the virtual power plant branch flow constraint, and formulas (10)-(11) are power system operation constraints.

[0036]

[0037] In the above formula, i is the index number of the virtual power plant; Pbiq and Qbiq are the active and reactive power of the line between nodes b and q in the main grid t period, respectively; b i,mn i,mn ​​​​​Bim and Bin are the susceptance and conductance values of the line between the ith virtual power plant nodes m and n, respectively; and Vim and Vnm are the voltage magnitude of the ith virtual power plant t-period node m and n, respectively; and θim and θnm are the voltage phase angle of the ith virtual power plant t-period node m and n, respectively; G is the number of piecewise linearization segments; g is the index number of the segment; V max ,V min Vmax and Vmin are the upper and lower limits of the voltage magnitude, respectively; θ max ,θ min θmax and θmin are the upper and lower limits of the voltage phase angle, respectively; Cim and Cin represent the capacity of the line between the ith virtual power plant nodes m and n.

[0038] 2) Gas turbine power output constraints:

[0039]

[0040] In the above equation, P max ,P min Pmax and Pmin are the upper and lower limits of the gas turbine active power output, respectively; Q max ,Q min Qmax and Qmin are the upper and lower limits of the gas turbine reactive power output, respectively; Pgi is the ith virtual power plant t-period node m gas turbine active power output; Qgi is the ith virtual power plant t-period node m gas turbine reactive power output.

[0041] 3) Photovoltaic power generation constraints:

[0042] Photovoltaic power generation uses chance constraints to model its uncertainty, as shown in equation (14),

[0043]

[0044] In equation (14), P denotes the probability of the event ; Pgi is the ith virtual power plant t-period node m photovoltaic active power output; Pgi is the ith virtual power plant t-period node m photovoltaic active power output prediction; η PV is the confidence level.

[0045] 4) Wind power generation constraints:

[0046] The wind power generation constraint is the same as the photovoltaic power generation constraint, and the wind power generation constraint is as follows:

[0047]

[0048] In equation (15), probability of event ; is the wind power injection at node m of the ith virtual power plant at time period t; is the wind power injection at node m of the ith virtual power plant at time period t; W is the confidence level.

[0049] 5) Node power balance constraint:

[0050]

[0051] where, is the active load at node m of the ith virtual power plant at time period t; is the reactive load at node m of the ith virtual power plant at time period t; m is the set of nodes connected to node m; n represents the index of the terminal node of a branch in each virtual power plant.

[0052] 6) Exchange power constraint:

[0053]

[0054] In equation (18), is the active power injection at the point of common coupling (PCC) of the ith virtual power plant at time period t; is the active power of the branch connected to the PCC in the ith virtual power plant; pcc is the set of nodes connected to the PCC. m represents the index of the starting node of a branch in each virtual power plant.

[0055] In step 2, taking photovoltaic power generation as an example, the process of converting the quantile into a deterministic constraint is as follows:

[0056]

[0057]

[0058] In the above equation, probability of event ; represents the average value of the photovoltaic predicted power at node m of the ith virtual power plant at time period t; Φ(·) represents the distribution cumulative function; represents the inverse distribution cumulative function of the standard normal distribution N(0, 1); is the standard deviation of the photovoltaic predicted power at node m of the ith virtual power plant at time period t; PV represents the confidence level of the photovoltaic.

[0059] After the above conversion, the photovoltaic power opportunity constraint equation (14) is converted into the deterministic constraint equation (22).

[0060] The step of constraining the wind power generation to the deterministic constraint is the same as the photovoltaic power generation, as shown in equation (23);

[0061]

[0062] In equation (23), represents the average value of the predicted wind power output of the ith virtual power plant at node m at time t; is the standard deviation of the predicted wind power output of the ith virtual power plant at node m at time t; η W represents the confidence of the wind power.

[0063] In step 3, the sum of the main grid operation cost and the operation cost of each virtual power plant is minimized as the optimization objective, and the investment cost of each energy equipment is ignored. The main grid only considers the generation cost of conventional units, and each VPP only considers the generation cost of gas turbines and the operation and maintenance cost of new energy equipment. As shown in equation (24):

[0064]

[0065] In equation (24), min Cost represents the minimization of the total operation cost of the system; are the cost functions of the gas turbine, photovoltaic, and wind power of the ith virtual power plant at time t, respectively; are the active power outputs of the gas turbine, photovoltaic, and wind power of the ith virtual power plant at node m at time t, respectively; C b,t is the generator cost function at node b of the main grid at time t; is the active power output of the generator at node b of the main grid at time t; i is the virtual power plant index; N VPP is the total number of virtual power plants; m is the internal node index of the virtual power plant; is the internal node number of each virtual power plant; b is the main grid node index; N main is the total number of main grid nodes; t is the time period index; T is the total number of time periods.

[0066] The established integrated dispatching model of the multi-virtual power plant power system is as follows:

[0067] Objective function:

[0068] The objective function is equation (24);

[0069] Constraint conditions:

[0070] Main grid operation constraints: equations (1)-(26);

[0071] VPP operation constraints: equations (7)-(13), equations (16)-(18), equation (22), and equation (23).

[0072] In step 4, first, the objective function is converted based on the theory of epigraph. The epigraph of a function describes the region above the graph of a function within a given domain. For a general function f(x), its epigraph is the set consisting of the graph of the function and all points above it. Formally, the epigraph of f(x) is defined as:

[0073] epi(f(x)) = {(x, o) e R n x R | f(x) < epi(f(x)) < N} (25);

[0074] In equation (25), epi(f(x)) represents the epigraph of function f(x); (x, o) represents the input of function f(x); R n x represents the dimension of x; R represents the dimension of o; N is a real number, which can be any number greater than f(x). The epigraph essentially represents the region between the function f(x) and N.

[0075] Based on equation (25), the operating cost part of the virtual power plant in equation (24) is converted, and the converted objective function is as follows:

[0076]

[0077] In the above equation, is an auxiliary variable introduced to represent the cost function of the ith virtual power plant at time t;

[0078] respectively represent the gas turbine, photovoltaic, and wind power operating costs of the mth node of the ith virtual power plant at time t; respectively represent the quadratic, linear, and constant coefficients of the main grid cost function. For The specific form of its epigraph is:

[0079]

[0080] In the above equation, represents the gas turbine operating cost coefficient of the mth node of the ith virtual power plant at time t;

[0081] represents the photovoltaic power operating cost coefficient of the mth node of the ith virtual power plant at time t; represents the wind power operating cost coefficient of the mth node of the ith virtual power plant at time t;

[0082] is a constant greater than

[0083] ​So far, the internal variables of each virtual power plant in the original objective function are eliminated, the objective function is replaced by formula (26), and formula (29) and formula (30) are used as the virtual power plant operation constraints to participate in system operation.

[0084] In step 4, the operation feasible region of the virtual power plant is an irregular polyhedron, and the first step of projection is to determine the plane of projection;

[0085] The variable constituting the projection plane is referred to as the projection variable. For the ith virtual power plant, the projection variable is taken as : The dimension reduction model is the equivalent projection of the virtual power plant operation feasible region to the plane , and the internal variable is . The feasible region solving method based on vertex projection convex hull search simultaneously depicts the equivalent projection of the multi-virtual power plant multi-period feasible region. According to the geometric principle, each vertex of the projection polygon is an extreme point, and the feasible region of the projection variable can be obtained by solving the optimization problem;

[0086]

[0087] s.t. formula (7)-formula (13), formula (16)-formula (18), formula (23), formula (24), formula (29), formula (30)

[0088] In formula (31), μ T is a vertex search direction; Vertex represents the objective function of vertex search; wherein, the new vertex corresponding to the distance between the two points of the new search direction :

[0089]

[0090] In formula (32), x is the new vertex horizontal coordinate of the ith virtual power plant t period obtained in the λth new vertex search; is the new vertex vertical coordinate of the ith virtual power plant t period obtained in the λth new vertex search; respectively represent the linear equation coefficient of the connecting line of the adjacent two points in the convex hull corresponding to the search direction in the λth new vertex search of the ith virtual power plant t period.

[0091] Based on the depiction of the equivalent projection of the virtual power plant feasible region in step 4, the internal variables are eliminated from the operation model of each virtual power plant, and the equivalent dimension reduction of the VPP model is realized.

[0092] In step 5, the mathematical model of the equivalent projection of the VPP operation feasible region is as follows:

[0093]

[0094] In formula (33), Respectively, the first i virtual power plant t period public connection point exchange power The lower limit value and the upper limit value of the equivalent projection region; Respectively, the first i virtual power plant t period cost The lower limit value and the upper limit value of the equivalent projection region; Respectively, the first i virtual power plant t period projection polygon boundary each straight line equation coefficient.

[0095] The multi-VPP-main grid collaborative optimization model based on the feasible region equivalent projection is as follows:

[0096]

[0097] The Gurobi solver in Matlab is called to solve the problem formula (34) first, and the Optimal value Indicate the optimal value of the active power injected into the public connection point of the i th virtual power plant t period; Indicate the optimal value of the operation cost of the i th virtual power plant t period.

[0098] The main grid issues it to the virtual power plant VPP; secondly, each virtual power plant VPP is optimized and dispatched by solving the problem formula (35) with As the boundary condition.

[0099] The present application is a kind of based on the feasible region equivalent projection theory's containing multi virtual power plant power system collaborative optimization method, with the following beneficial effects:

[0100] 1), the step 1 of the present application considers the main grid power network constraint and transmission capacity constraint to establish the main grid operation model, and considers the uncertainty of wind power and photovoltaic output to establish the virtual power plant operation model, improves the reliability and stability of power system, and provides accurate system state and parameter for subsequent optimization, lays a solid foundation for realizing more efficient resource allocation and scheduling.

[0101] 2), the step 2 of the present application converts the chance constraint of wind power and photovoltaic into deterministic constraint through quantile transformation, simplifies the complexity of optimization problem, so that the uncertainty factor originally difficult to handle can be quantified and determined, not only improves the solvability of model, but also makes the optimization result more robust.

[0102] 3), the step 3 of the method establishes the integrated scheduling model of the power system containing multiple virtual power plants based on the step 1 and the step 2, realizes the coordination and optimization between the virtual power plant and the main network, can more effectively utilize the renewable energy, improves the economy and the environmental friendliness of the system, and ensures the stability and economy of the power supply.

[0103] 4), the step 4 of the method converts the target function and projects the exchange power feasible region at the point of common coupling. After conversion, the target function eliminates the internal variables of the virtual power plant, so that the complexity of the integrated scheduling model is reduced, the projection of the feasible region is described, the dimension of the virtual power plant operation model is reduced, and detailed data of the exchange power at the point of common coupling are given, which is of great significance for the collaborative optimization of the virtual power plant-main network two-level system.

[0104] 5), the step 5 of the method establishes the VPP-main network collaborative optimization model based on the equivalent projection of the feasible region, realizes the non-iterative solution of the virtual power plant-main network collaborative optimization, greatly reduces the model solving time, is beneficial to fully utilize various distributed energy, improves the distributed energy scheduling management effect, and protects the VPP data privacy. BRIEF DESCRIPTION OF DRAWINGS

[0105] The application will be further described below in combination with the drawings and embodiments:

[0106] Figure 1 It is an example system structure diagram.

[0107] Figure 2 It is a wind and light active power output prediction value curve diagram.

[0108] Figure 3 It is a VPP multi-period feasible region projection slice diagram.

[0109] Figure 4 It is a VPP scheduling result diagram.

[0110] Figure 5 It is a VPP injection main network exchange power diagram.

[0111] Figure 6 It is a main network scheduling result. DETAILED DESCRIPTION

[0112] The method for collaborative optimization of the power system containing multiple virtual power plants based on the feasible region equivalent projection theory comprises the following steps:

[0113] Step S1: considering the main network power network constraint and the transmission capacity constraint, a main network operation model is established; considering the uncertainty of wind power and photovoltaic output, a virtual power plant operation model is established.

[0114] The main network operation model and the virtual power plant operation model comprise:

[0115] The main grid operation model includes generator output constraints, generator ramping constraints, exchange power injection constraints, node power balance constraints, main grid branch power flow constraints, and line transmission capacity constraints.

[0116] 1) Generator output constraints:

[0117]

[0118] wherein, Pb(t) represents the active power output of the generator at the node b in the main grid at time period t; Pbmin and Pbmax represent the upper and lower limits of the active power output of the main grid generator at node b, respectively.

[0119] 2) Generator ramping constraints:

[0120]

[0121] wherein, Rb(t) represents the ramping capacity of the generator at node b in the main grid at time period t.

[0122] 3) Exchange power injection constraints:

[0123]

[0124] wherein, Pimax(i) represents the upper limit of the power injected into the main grid by the i-th VPP; Pb(i, t) represents the active power injected into the point of common coupling by the i-th virtual power plant at time period t.

[0125] 4) Node power balance constraints:

[0126]

[0127] wherein, Pb(t) represents the active power of the generator at node b in the main grid at time period t; Pbq(t) represents the active power of the line between nodes b and q in the main grid at time period t; Pb(i, t) represents the active power injected into the point of common coupling by the i-th virtual power plant at time period t; Pb(t) represents the active load at node b in the main grid at time period t. b Nb represents the set of nodes connected to node b in the main grid. VPP A represents the association matrix of the virtual power plant access nodes and the main grid nodes. VPP N represents the total number of virtual power plants.

[0128] 5) Main grid branch power flow constraints:

[0129]

[0130] wherein, respectively, are the active and reactive power of the line between nodes b and q in the main grid t period;b bq ,g bq respectively, are the susceptance and conductance values of the line between nodes b and q in the main grid t period; and respectively, are the voltage amplitude of nodes b and q in the main grid t period; and respectively, are the voltage phase angle of nodes b and q in the main grid t period; Z is the number of segments of piecewise linearization; z is the index number of the segment; respectively, are the upper and lower limit values of the voltage amplitude of the main grid; respectively, are the upper and lower limit values of the voltage phase angle of the main grid.

[0131] 6) Line transmission capacity constraint:

[0132]

[0133] In the formula, is the power transmission allocation factor of node b to branch l in the main grid; is the power transmission allocation factor of the i th virtual power plant to the main grid branch l; is the capacity of the main grid branch l.

[0134] The VPP operation model includes power grid constraints, gas turbine output constraints, photovoltaic output constraints, wind power output constraints, node power balance constraints, and exchange power constraints.

[0135] 1) Power grid constraints:

[0136] The power grid constraints include linearized virtual power plant flow constraints and power system operation constraints. Formulas (7)-(8) are linearized virtual power plant flow constraints, formula (9) is a linearized form of the virtual power plant branch flow constraint, and formulas (10)-(11) are power system operation constraints.

[0137]

[0138] In the formula, i is the index number of the virtual power plant; respectively, are the active and reactive power of the line between nodes m and n in the i th virtual power plant t period;b i,mn ,g i,mn respectively, are the susceptance and conductance values of the line between nodes m and n in the i th virtual power plant; and respectively, are the voltage amplitude of nodes m and n in the i th virtual power plant t period; and respectively, are the voltage phase angle of nodes m and n in the i th virtual power plant t period; G is the number of segments of piecewise linearization; g is the index number of the segment; Vmax V min are upper and lower limits of voltage amplitude, respectively; θ max , θ min are upper and lower limits of voltage phase angle, respectively.

[0139] 2) Gas turbine power output constraint:

[0140]

[0141] where P max , P min are upper and lower limits of gas turbine active power output, respectively; Q max , Q min are upper and lower limits of gas turbine reactive power output, respectively; is the active power output of the i-th virtual power plant at node m in time period t; is the reactive power output of the i-th virtual power plant at node m in time period t.

[0142] 3) Photovoltaic power generation constraint

[0143] The photovoltaic power generation uses a chance constraint to model its uncertainty, as shown in equation (14),

[0144]

[0145] where, denotes the probability of event ; and is the active power output of the photovoltaic power generation at node m in time period t of the i-th virtual power plant; is the predicted value of the active power output of the photovoltaic power generation at node m in time period t of the i-th virtual power plant; and η PV is the confidence level.

[0146] 4) Wind power generation constraint

[0147] The wind power generation constraint is the same as the photovoltaic power generation constraint, and the wind power generation constraint is as follows:

[0148]

[0149] where, denotes the probability of event ; and is the active power output of the wind power generation at node m in time period t of the i-th virtual power plant; is the predicted value of the active power output of the wind power generation at node m in time period t of the i-th virtual power plant; and η W is the confidence level.

[0150] 5) Node power balance constraint

[0151]

[0152] where, is the active load at node m in the ith virtual power plant at time period t; is the reactive load at node m in the ith virtual power plant at time period t; χ m is the set of nodes connected to node m.

[0153] 6) Exchange power constraints

[0154]

[0155] where, is the active power injected into the point of common coupling by the ith virtual power plant at time period t; is the active power of the branch connected to the point of common coupling in the ith virtual power plant; χ pcc is the set of nodes connected to the point of common coupling.

[0156] Step S2: Transform the wind power and photovoltaic chance constraints into deterministic constraints through quantile transformation.

[0157] The process of transforming the chance constraints into deterministic constraints through quantile transformation includes:

[0158] Taking photovoltaic power generation as an example, the process of transforming the chance constraints into deterministic constraints through quantile transformation is as follows:

[0159]

[0160] where, denotes the probability of event ; denotes the average value of the predicted output of photovoltaic power at node m in the ith virtual power plant at time period t; Φ(·) denotes the distribution cumulative function; denotes the inverse distribution cumulative function of the standard normal distribution N(0, 1); is the standard deviation of the predicted output of photovoltaic power at node m in the ith virtual power plant at time period t; η PV denotes the confidence of photovoltaic power generation.

[0161] After the above transformation, the photovoltaic power chance constraint formula (14) is transformed into the deterministic constraint formula (22), and the step of transforming the wind power chance constraint into a deterministic constraint is the same as that of the photovoltaic power generation, as shown in formula (13), and thus will not be described again.

[0162]

[0163] where, denotes the average value of the predicted output of wind power at node m in the ith virtual power plant at time period t; denotes the inverse distribution cumulative function of the standard normal distribution N(0, 1); is the standard deviation of the predicted output of the wind power at the ith virtual power plant at node m at time period t; η W represents the confidence of the wind power.

[0164] Step S3: Establish a target function of virtual power plant-main grid collaborative optimization, and establish an integrated scheduling model of a power system containing multiple virtual power plants based on steps S1 and S2.

[0165] The collaborative optimization target function and the integrated scheduling model include:

[0166] The present application takes the minimum sum of the main grid operation cost and the operation cost of each virtual power plant as the optimization target, ignores the investment cost of each energy equipment, and only considers the generation cost of conventional units in the main grid and the generation cost of gas turbines and the operation and maintenance cost of new energy equipment in each VPP. As shown in the following formula:

[0167]

[0168] In the formula, are the cost functions of the gas turbine, photovoltaic and wind power at the ith virtual power plant at time period t, respectively; are the active power outputs of the gas turbine, photovoltaic and wind power at the ith virtual power plant at time period t at node m, respectively; C b,t (·) is the generator cost function at node b of the main grid at time period t; is the active power output of the generator at node b of the main grid at time period t; i is the virtual power plant index; N VPP is the total number of virtual power plants; m is the internal node index of the virtual power plant; is the number of internal nodes of each virtual power plant; b is the main grid node index; N main is the total number of main grid nodes; t is the time period index; T is the total number of time periods.

[0169] Based on the above steps, an integrated scheduling model of a power system containing multiple virtual power plants is established, as shown below:

[0170] Target function:

[0171] The target function is formula (24);

[0172] Constraint condition:

[0173] Main grid operation constraint: formula (1)-formula (6);

[0174] VPP operation constraint: formula (7)-formula (14) formula (16)-formula (18), formula (22), formula (23).

[0175] Step S4: Eliminate the internal variables of the virtual power plant in the target function by using the upper mirror graph theory, and project the multi-time period power-cost feasible region of the virtual power plant at the public coupling point based on the feasible region equivalent projection theory and the vertex projection convex hull search method.

[0176] Eliminating the VPP internal variables and projecting the VPP feasible region from the objective function include:

[0177] Firstly, the objective function is converted based on the upper contour theory. The upper contour of a function describes the region above the function's graph within a given domain. For a general function f(x), its upper contour is the set consisting of the graph of the function and all points above it. Formally, the upper contour of f(x) is defined as:

[0178] epi(f(x)) = {(x, t) e R n x R | f(x) < t < N} (25).

[0179] where (x, t) represents the input of the function f(x); R n x represents the dimension of x; R represents the dimension of o; N is a real number which can be any number greater than f(x), and the upper contour essentially represents the region between the function f(x) and N.

[0180] Based on equation (25), the operation cost part of the VPP in equation (24) is converted, and the converted objective function is as follows:

[0181]

[0182] where is the introduced auxiliary variable, representing the cost function of the ith VPP at time t;

[0183] represent the gas turbine, photovoltaic, and wind power operation cost of the mth node of the ith VPP at time t, respectively; represent the quadratic, linear, and constant coefficients of the main grid cost function, respectively.

[0184] For its upper contour is specifically:

[0185]

[0186] where represents the gas turbine operation cost coefficient of the mth node of the ith VPP at time t; represents the photovoltaic operation cost coefficient of the mth node of the ith VPP at time t; represents the wind power operation cost coefficient of the mth node of the ith VPP at time t; is any constant greater than .

[0187] So far, the internal variables of each virtual power plant in the original objective function are eliminated, the objective function is replaced by formula (26), formula (29) and formula (30) are taken as the virtual power plant operation constraints to participate in the system operation.

[0188] The operation feasible region of the virtual power plant is an irregular polyhedron, the first step of projection is to determine the plane of projection, the variable constituting the projection plane is called the projection variable in the application. For the ith virtual power plant, the projection variable is taken as : The dimension reduction model is the equivalent projection of the virtual power plant operation feasible region to the plane , and the internal variable is

[0189] The application is based on the feasible region solving method of vertex projection convex hull search, simultaneously describes the equivalent projection of the multi-virtual power plant multi-period feasible region, according to the geometric principle, each vertex of the projection polygon is an extreme point, and the feasible region of the projection variable can be obtained by solving the optimization problem:

[0190]

[0191] s.t. formula (7)-formula (13), formula (16)-formula (18), formula (23), formula (24), formula (29), formula (30)

[0192] In the formula, μ T is the vertex search direction.

[0193] Wherein, the new vertex The distance between the connecting line of the two points corresponding to the new search direction is:

[0194]

[0195] In the formula, is the new vertex horizontal coordinate of the ith virtual power plant t period obtained in the λth new vertex search; is the new vertex vertical coordinate of the ith virtual power plant t period obtained in the λth new vertex search; Respectively represent the linear equation coefficient of the connecting line of the two adjacent points in the convex hull corresponding to the search direction in the λth new vertex search of the ith virtual power plant t period.

[0196] Step S5: On the basis of steps S1, S2, S3 and S4, a VPP-main grid collaborative optimization model based on the equivalent projection of the feasible region is established, and a GUROBI solver is called in the Matlab2021a environment to solve.

[0197] The VPP-main grid collaborative optimization model based on the equivalent projection of the feasible region includes:

[0198] Based on the description of the equivalent projection of the virtual power plant feasible region in step S4, the internal variables are removed from the operation model of each virtual power plant, and the equivalent dimension reduction of the VPP model is realized. The mathematical model of the equivalent projection of the VPP operation feasible region is as follows:

[0199]

[0200] wherein, are the public connection point exchange power of the ith virtual power plant at time period t, respectively. are the lower limit value and the upper limit value in the equivalent projection region, respectively. are the cost of the ith virtual power plant at time period t, respectively. are the lower limit value and the upper limit value in the equivalent projection region, respectively. are the coefficients of the straight line equation of the boundary of the projection polygon of the ith virtual power plant at time period t, respectively.

[0201] The multi-VPP-main grid collaborative optimization model based on the feasible region equivalent projection is as shown in formula (34) and formula (35):

[0202]

[0203] The Gurobi solver is called in Matlab to first solve the problem of formula (34), and the optimal value of formula (34) is obtained. The optimal value The main grid issues it to the VPP; secondly, each VPP optimizes the scheduling by solving the problem of formula (35) with as the boundary condition.

[0204] Embodiment:

[0205] This case builds a multi-virtual power plant power system containing three VPPs, all models and algorithms are programmed and implemented on MATLAB 2021a, and Gurobi 10.0.2 is called for solving. The test environment CPU is the ninth generation Intel Core i5 2.40 GHz, 16 GB memory. The first virtual power plant and the second virtual power plant use the improved IEEE 9-node test system, the third virtual power plant uses the improved IEEE 33-node test system, and the main grid uses the IEEE 30-node test system. The system structure is as shown in Figure 1 .

[0206] The main grid has 6 regular generators, each with a maximum active power output of 30 MW; the first virtual power plant has 2 gas turbines, each with a maximum active power output of 8 MW, 2 photovoltaic power stations with a capacity of 7 MW, and 2 wind power stations with a capacity of 3 MW; the second virtual power plant has the same configuration as the first virtual power plant; the third virtual power plant has 7 gas turbines, each with a maximum output of 9 MW, 7 photovoltaic power stations with a capacity of 4 MW, and 7 wind power stations with a capacity of 3.5 MW. The dispatch time scale is 1 hour, the time span is 24 hours, and the wind and light prediction error is 10% of the corresponding active power output prediction value.

[0207] According to step S1, the main grid operation model and the VPP operation model are established, and the relevant main grid part data are shown in Tables 1-2:

[0208] Table 1 Main grid generator parameters

[0209] Generator node Active power upper limit / MW Active power lower limit / MW 1 30 0 2 30 0 22 30 0 27 30 0 23 30 0 13 30 0

[0210] Table 2 Main grid generator cost coefficient (unit: US dollars)

[0211] Generator node Quadratic term coefficient Linear term coefficient Constant term coefficient 1 0.5 50 0 2 0.4375 43.75 0 22 1.5625 25 0 27 0.2085 81.25 0 23 0.625 75 0 13 0.625 75 0

[0212] The first and second VPPs have basically the same data, and the first VPP part data are shown in Tables 3-4:

[0213] Table 3 First VPP gas turbine parameters

[0214] Gas turbine node Active power upper limit / MW Active power lower limit / MW 1 8 0 2 8 0 8 8 0 9 9 0

[0215] Table 4 First VPP gas turbine cost coefficient (unit: US dollars)

[0216] Gas turbine node Linear term coefficient Constant term coefficient 1 60 30 2 52.5 125 8 57.5 30 9 55 125

[0217] The third VPP part data are shown in Tables 5-6:

[0218] Table 5 Third VPP gas turbine parameters

[0219] Gas turbine node Active power upper limit / MW Active power lower limit / MW 1 9 0 14 9 0 18 9 0 22 10 0 25 9 0 30 10 0 32 9 0

[0220] Table 6 Third VPP gas turbine cost coefficient (unit: US dollars)

[0221] Gas turbine node Linear term coefficient Constant term coefficient 1 52.5 125 14 55 30 18 55.625 25 22 52.5 125 25 55 30 30 52.5 30 32 55 32.5

[0222] The branch and node parameters of the main grid operation model and the VPP operation model can be obtained in matpower7.0.

[0223] According to step S2, the wind power and photovoltaic opportunity constraints are converted into deterministic constraints through quantile transformation, and the wind power and photovoltaic output prediction average values of each virtual power plant are as follows:Figure 2 as shown.

[0224] Based on steps S1, S2, an integrated scheduling model of a power system containing multiple virtual power plants is established.

[0225] According to step S4, the objective function is converted, and the multi-period feasible region projection of the virtual power plant at the public coupling point is depicted, and the related process and parameters are as shown below.

[0226]

[0227] According to step S5, on the basis of steps S1, S2, S3 and S4, a VPP-main grid collaborative optimization model based on feasible region equivalent projection is established, and the GUROBI solver is called in the Matlab2021a environment to solve.

[0228] Collaborative optimization results:

[0229] (1) Feasible region equivalent projection depiction results:

[0230] Figure 3 Fig. 1 is a multi-period feasible region equivalent projection slice diagram of the first VPP, and the selected time periods are 1:00, 5:00, 9:00, 13:00, 17:00 and 21:00, containing valley and peak load periods in a natural day. The pink area is the multi-period feasible region equivalent projection of the first VPP, and from the figure, it can be seen that the feasible region equivalent projections of each period are quite different. In terms of the area of the feasible region projection, the area of the feasible region projection is gradually decreasing with the increase of the load. This is because the feasible region projection selected by the present application is a plane related to the exchange power and the VPP operation cost, and the horizontal axis is the exchange power and the vertical axis is the VPP operation cost. Therefore, when the load increases, the VPP operation cost increases, the bottom edge of the feasible region projection rises, and the projection area decreases.

[0231] The blue solid line at the bottom of the coordinate axis in the figure is the exchange power range of the VPP injected into the main grid in each period, and when the power load level is low, the maximum active power injected into the main grid by the VPP is at a high level, but the maximum active power injected into the VPP by the main grid is at a low level. When the power load level is high, it is the opposite.

[0232] (2) VPP scheduling results:

[0233] For convenience of display, the first VPP is taken as an example, Figure 4 and Figure 5 are the scheduling results of the first VPP and the exchange power thereof, respectively. From Figure 4It can be seen that from 1:00 to 5:00, the load gradually decreases to its lowest point, and the active power output of the gas turbine decreases accordingly. From 7:00 to 13:00, the load begins to increase, and the output of the photovoltaic unit gradually rises with the increase in sunlight intensity. From 19:00 to 20:00, the output of the photovoltaic unit is zero, and the load gradually rises from a lower level to a second peak. Therefore, the output of the gas turbine begins to increase significantly. Figure 5 As can be seen, the trend of power exchange between VPP 1 and the main grid is opposite to that of the VPP power load.

[0234] (3) Main network scheduling results:

[0235] The main network scheduling results are as follows Figure 6 As shown. From Figure 6 It can be observed that the active power of the main grid generators shows the same trend as the main grid load increases or decreases. However, the power injected into the main grid by the VPPs varies. This is because the trend of power load change is the same in a typical intraday system; when the main grid is at its valley or peak load, each VPP is also at its valley or peak load simultaneously.

[0236] (4) Comparison with traditional methods:

[0237] Table 7 compares the computation time of different solution methods for the integrated dispatch model of power systems with multiple virtual power plants proposed in this invention. It can be seen that the method used in this invention significantly improves the solution time compared to traditional coordination optimization methods such as Benders decomposition and multi-parameter programming, reducing computation time by approximately 51%-61%.

[0238] Table 7 Comparison of Calculation Time (seconds) for Different Solution Methods

[0239]

[0240] Table 8 shows a comparison of the costs of each VPP and the total system cost under different solution methods. It can be seen that the costs of each VPP and the total system cost are almost the same when different solution methods are used. This proves that the method used in this invention still has the same optimality as the traditional solution method when the original running model is reduced in dimensionality. It also significantly reduces the solution time while protecting the privacy of each VPP data.

[0241] Table 8. Comparison of Costs (USD) of Different Solution Methods

[0242]

Claims

1. A method for collaborative optimization of power systems containing multiple virtual power plants based on the theory of feasible region equivalent projection, characterized in that The method comprises the following steps: Step 1: considering the constraints of the main grid power network and the transmission capacity, a main grid operation model is established; considering the uncertainty of wind power and photovoltaic output, a virtual power plant operation model is established; Step 2: the wind power and photovoltaic opportunity constraints are converted into deterministic constraints through quantile transformation; Step 3: a target function of virtual power plant-main grid collaborative optimization is established, and a multi-virtual power plant integrated power system scheduling model is established based on step 1 and step 2; Step 4: the virtual power plant internal variables in the target function are eliminated by using the mirror chart, and the multi-period power-cost feasible region projection of the virtual power plant at the public coupling point is described based on the feasible region equivalent projection theory and the vertex projection convex hull search method; Step 5: a VPP-main grid collaborative optimization model based on the feasible region equivalent projection is established, and the model is solved; The multi-VPP-main grid collaborative optimization model based on the feasible region equivalent projection is as follows: （34）； wherein, auxiliary variables introduced for the on-mirror graph; denotes the total number of virtual power plants; t denotes the dispatch time index; T denotes the total dispatch time; denotes the number of main grid nodes; b denotes the main grid node index; denotes the main grid t denotes the period node b at which the generator cost function; denotes the main grid t denotes the period node b at which the generator active power; wherein, represents the i virtual power plant t active power injected into the main grid public point of connection; representing the i first virtual power plant t optimal value of the active power injected into the point of common coupling for the time period; The Gurobi solver in Matlab first solves the problem in equation (34) by constraint conditions to obtain optimal value ; representing the first i virtual power plant t time period; and The main network issues it to the virtual power plant VPP; secondly, each virtual power plant VPP solves the optimization scheduling through the constraint condition of the boundary condition of the problem formula (35). The main network issues it to the virtual power plant VPP; secondly, each virtual power plant VPP solves the optimization scheduling through the constraint condition of the boundary condition of the problem formula (35).

2. The method of claim 1, wherein the method is characterized in that: In the step 1, the main grid operation model comprises generator output constraints, generator ramping constraints, exchange power injection constraints, node power balance constraints, main grid branch flow constraints and line transmission capacity constraints; 1) generator output constraints: （1）； In equation (1), Mainnet t Time period nodes b The generator has active power output; They are nodes b The upper and lower limits of the active power output of generators in the main grid; 2) generator ramping constraints: （2）； In formula (2), is the main grid t is the period node b is the generator ramping capacity at the period node; is the main grid is the period node b is the generator active power at the period node; 3) exchange power injection constraints: （3）； In equation (3), Indicates the first i The maximum power limit for each VPP to inject into the main network; representing the i first virtual power plant t active power injected into the main grid public point of connection; 4) node power balance constraints: （4）； In equation (4), Mainnet t Time period nodes b The active power of the generator at the location; Mainnet t Time period nodes b and q The active power of the lines between them; For the first i A virtual power plant t Active power injected into the common connection point during the time period; Mainnet t Time period nodes b Active load; Indicates nodes in the main network b A set of connected nodes; This is the association matrix between virtual power plant access nodes and main network nodes; Indicates the total number of virtual power plants; i Indicates the virtual power plant number index; 5) main grid branch flow constraints: （5）； In formula (5), are the main network t are the time period nodes b and q are the active and reactive power of the line between are the main network t are the time period nodes b and q are the susceptance and conductance values of the line between are the main network are the time period nodes t and b are the voltage amplitudes of the main network q are the time period nodes are the main network are the time period nodes t and b are the voltage phase angles of the main network q are the time period nodes Z number of segments for piecewise linearization; z index number of a segment; upper and lower limits of the main grid voltage amplitude, respectively; upper and lower limits of the main grid voltage phase angle, respectively; denotes the main grid t time period node b and q capacity of the line between 6) line transmission capacity constraints: （6）； In formula (6), is the main grid node b to the branch l power transmission allocation factor; is the power transmission allocation factor of the i virtual power plant to the main grid branch l ; is the capacity of the main grid branch l ; denotes the number of main grid nodes; t denotes the dispatch time index; T denotes the total dispatch time.

3. The method of claim 2, wherein the method is characterized in that: The virtual power plant operation model comprises power network constraints, gas turbine output constraints, photovoltaic output constraints, wind power output constraints, node power balance constraints and exchange power constraints; 1) power network constraints: The power network constraints comprise linearized virtual power plant flow constraints, power system operation constraints; formula (7) to formula (8) are linearized virtual power plant flow constraints; formula (9) is a linearized form of virtual power plant branch flow constraints, and formula (10) to formula (11) are power system operation constraints; （7）； （8）； （9）； （10）； （11）； In the above formula, i Number the index for the virtual power plant; The first i A virtual power plant t Time period nodes m and n The active and reactive power of the lines between them; The first i Virtual power plant nodes m and n The susceptance and conductance values ​​of the lines between them; and The first i A virtual power plant t Time period nodes m and n The voltage amplitude; and The first i A virtual power plant t Time period nodes m and n The voltage phase angle; G number of segments for piecewise linearization; g index number of a segment; upper and lower limit values of voltage amplitude, respectively; upper and lower limit values of voltage phase angle, respectively; denotes the capacity of the line between the i virtual power plant nodes m and n ​ 2) gas turbine output constraints: （12）； （13）； in the above formula, are respectively upper and lower limits of the active power output of the gas turbine; are respectively upper and lower limits of the reactive power output of the gas turbine; is the first i virtual power plant t is the time period node m is the active power output of the gas turbine at the time period node; is the first i virtual power plant t is the time period node m is the reactive power output of the gas turbine at the time period node; 3) photovoltaic power generation constraints: The photovoltaic power generation adopts an opportunity constraint to model the uncertainty, as shown in formula (14), （14）； In formula (14), representing an event probability; is the i virtual power plant t photovoltaic active power at the time period node m ; is the i virtual power plant t photovoltaic active power prediction at the time period node m ; is the confidence level; 4) wind power generation constraints: The wind power generation constraint is the same as the photovoltaic power generation constraint, and the wind power generation constraint is as follows: （15）； In formula (15), representing an event probability; is the wind power active power at the i virtual power plant t time period node m ; is the wind power active power at the i virtual power plant t time period node m ; is the confidence level; 5) node power balance constraints: （16）； （17）； In the formula, is the first virtual power plant i is the first virtual power plant t is the first virtual power plant m is the first virtual power plant is the first virtual power plant i is the first virtual power plant t is the first virtual power plant m is the first virtual power plant is the first virtual power plant m is the first virtual power plant n is the first virtual power plant 6) exchange power constraints: （18）； In formula (18), is the i virtual power plant t active power injected into the point of common coupling at the time period t; is the i active power of a branch connected to the point of common coupling in the virtual power plant is the set of nodes connected to the point of common coupling; m denotes the index of the starting node of a branch in the virtual power plant.

4. The method of claim 3, wherein the method is characterized in that: In the step 2, for photovoltaic power generation, the process of converting the opportunity constraint into a deterministic constraint through quantile transformation is as follows: （19）； （20）； （21）； （22）； in the above formula, the probability of an event ; the average value of the photovoltaic predicted power at the i th time interval node in the t th virtual power plant; m the average value of the photovoltaic predicted power at the th time interval node in the th virtual power plant; the inverse distribution cumulative function of the standard normal distribution ; i the standard deviation of the photovoltaic predicted power at the t th time interval node in the m th virtual power plant; the confidence of the photovoltaic; After the above conversion, the photovoltaic power generation opportunity constraint formula (14) is converted into the deterministic constraint formula (22).

5. The method of claim 4, wherein the method is characterized in that: The wind power generation opportunity constraint is converted into a deterministic constraint, as shown in formula (23); （23）； In formula (23), represents the i th virtual power plant t at the time period node m average value of the wind power prediction output; is the i th virtual power plant t at the time period node m standard deviation of the wind power prediction output; represents the confidence of the wind power.

6. The method of claim 5, wherein the method is characterized in that: In the step 3, the sum of the main grid operation cost and the virtual power plant operation cost is taken as the optimization target, the investment cost of each energy equipment is ignored, the main grid only considers the generation cost of conventional units, and each VPP only considers the generation cost of gas turbines and the operation and maintenance cost of new energy equipment; as shown in formula (24): （24）； In equation (24), This represents minimizing the total operating cost of the system; The first i A virtual power plant t Cost functions for gas turbines, photovoltaics, and wind power during specific time periods; The first i A virtual power plant t Time period nodes m The active power output of gas turbines, photovoltaics, and wind power; Mainnet t Time period nodes b Generator cost function; Mainnet t Time period nodes b The generator has active power output; i For indexing virtual power plants; This represents the total number of virtual power plants. m For the internal node index of the virtual power plant; This represents the number of nodes within each virtual power plant. b Main network node index; The total number of mainnet nodes; t Indexed by time period; T This represents the total number of time periods.

7. The method of claim 6, wherein the method is characterized in that: The established multi-virtual power plant integrated power system scheduling model is as follows: Target function: The target function is formula (24); Constraint condition: Main grid operation constraints: formula (1) to formula (6); VPP operation constraints: formula (7) to formula (13), formula (16) to formula (18), formula (22), formula (23).

8. The method of claim 7, wherein the method is characterized in that: In step 4, first, the objective function is converted based on the upper contour map, which describes the upper region of the value domain of a function within a given definition domain. For a general function The upper contour map of a function refers to a set consisting of the image of the function and all points above it; formally, The upper contour map of a function is defined as: （25）； In formula (25), represents the upper mirror of the function ; represents the input of the function ; represents the dimension of x ; R represents the dimension of o ; N is a real number whose value is any number greater than ; the upper mirror essentially represents the area between the functions and N ; Based on the formula (25) to the virtual power plant in formula (24) operation cost part of the conversion, the conversion of the objective function as follows: （26）； （27）； （28）； In the above formula, is an introduced auxiliary variable, representing the cost function corresponding to the i th virtual power plant t time period; respectively represent the gas turbine, photovoltaic and wind power generation operation costs of the i th virtual power plant m node t in the time period; respectively represent the quadratic term coefficient, the linear term coefficient and the constant term coefficient of the main grid cost function; For on which the mirror image is specifically represented as: （29）； （30）； In the above formula, represents the first i virtual power plant t time period node m gas turbine operation cost coefficient at the node; represents the first i virtual power plant t time period node m photovoltaic power generation operation cost coefficient at the node; represents the first i virtual power plant t time period node m wind power generation operation cost coefficient at the node; for any constant greater than 0. So far, the original objective function of each virtual power plant internal variables are eliminated, the objective function is replaced by formula (26), formula (29), formula (30) as virtual power plant operation constraints involved in the system operation.

9. The method of claim 8, wherein the method is characterized in that: The feasible region of virtual power plant operation is irregular polyhedron, the first step of projection to determine the plane of projection; Let the variables constituting the projection plane be called the projection variables; for the first i virtual power plant, take the projection variables to be: , the reduced dimension model will be the equivalent projection of the virtual power plant operating feasible region to the plane with internal variables ; A feasible region solving method based on vertex projection convex hull search, simultaneously depicting the equivalent projection of multi-virtual power plant multi-period feasible region, according to the geometric principle, each vertex of the projection polygon is an extreme point, solving the optimization problem can obtain the feasible region of the projection variable . represents the i th virtual power plant t period public connection point exchange power; （31）； In formula (31), is a vertex search direction; is an objective function for vertex search; new vertex distance of two-point connection line corresponding to new search direction is: （32）； In equation (32), For the first i A virtual power plant t The time period is in The x-coordinate of the new vertex obtained in the next vertex search; For the first i A virtual power plant t The time period is in The new vertex coordinates obtained in the next vertex search; They represent the first i A virtual power plant t The time period is in In the search for a new vertex, the equation coefficients of the line connecting two adjacent points in the convex hull corresponding to the search direction.

10. The method of claim 9, wherein the method is characterized in that: The step 5, VPP operation feasible region equivalent projection mathematical model is shown as follows: （33）； In formula (33), the first i virtual power plant t period public connection point exchange power the lower limit value and the upper limit value of the equivalent projection area; the first i virtual power plant t period cost the lower limit value and the upper limit value of the equivalent projection area; the first i virtual power plant t period projection polygon boundary each straight line equation coefficient.

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