A distributed scheduling method and system for virtual power plants with assisted ramping

Abstract

The present invention provides a distributed scheduling method and system for a virtual power plant that assists in ramping, which belongs to the technical field of power system operation and scheduling. The method includes: obtaining power load information and distribution network topology parameters to construct a hot start linearized power flow model; obtaining distributed resource operation parameters to construct a distributed resource physical operation model; based on the hot start linearized power flow model and the distributed resource physical operation model, with the target optimization target as a constraint, constructing a virtual power plant optimization scheduling model; decomposing the virtual power plant optimization scheduling model to obtain distribution network operation sub-problems and multiple virtual power plant operation sub-problems; solving the distribution network operation sub-problems to obtain the optimal scheduling strategy for the distribution network, and solving multiple virtual power plant operation sub-problems to obtain the optimal scheduling strategy for the virtual power plant. The method provided by the present invention is applied to the power system to reduce the ramping pressure of the main grid power system.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system operation and dispatching, and in particular relates to a distributed dispatching method and system for a virtual power plant with assisted ramping. Background Art

[0002] Distributed energy resources, such as rooftop photovoltaics, micro-gas turbines, and energy storage, have seen significant development. These distributed energy resources are diverse, small-scale, large in number, and dispersed. Independently participating in power dispatching and operation poses significant challenges to the power system, including overvoltages and extreme ramping. Virtual power plants, as new power market players, can effectively aggregate and control the operation of distributed resources, fully leveraging their flexibility. These virtual power plants are crucial for improving grid reliability and providing ramping assistance services.

[0003] In new power systems, extreme ramp events occur frequently, posing challenges to the safety, stability and normal operation of the power systems. Summary of the Invention

[0004] In view of the above problems, an embodiment of the present application provides a distributed scheduling method and system for a virtual power plant with assisted ramping, so as to overcome the above problems or at least partially solve the above problems.

[0005] In a first aspect, an embodiment of the present application provides a distributed scheduling method for a virtual power plant with assisted ramping, the method comprising:

[0006] Obtain power load information and distribution network topology parameters, and build a hot start linear power flow model;

[0007] Obtaining distributed resource operating parameters and building a distributed resource physical operating model; wherein the distributed resource operating parameters include: photovoltaics, micro gas turbines, and energy storage within the virtual power plant;

[0008] Based on the hot start linearized power flow model and the distributed resource physical operation model, a virtual power plant optimization scheduling model is constructed with target optimization objectives as constraints; wherein the target optimization objectives are minimizing the ramp cost, minimizing the main grid power purchase cost, minimizing the distribution network line loss cost, and minimizing the distributed energy operation cost;

[0009] Decomposing the virtual power plant optimization scheduling model to obtain a distribution network operation sub-problem and multiple virtual power plant operation sub-problems;

[0010] The distribution network operation sub-problem is solved to obtain the optimal dispatching strategy of the distribution network, and the multiple virtual power plant operation sub-problems are solved to obtain the optimal dispatching strategy of the virtual power plants.

[0011] Furthermore, based on the hot start linearized power flow model and the distributed resource physical operation model, a virtual power plant optimization scheduling model is constructed with the target optimization goal as a constraint, including:

[0012] Determining an objective function based on the physical operation model of the distributed resources and taking the target optimization goal as a constraint;

[0013] Obtaining auxiliary constraints for peak ramping and coupling constraints between the distribution network and the virtual power plant;

[0014] Based on the auxiliary constraints, the coupling constraints and the objective function, the virtual power plant optimization scheduling model is constructed.

[0015] Furthermore, the virtual power plant optimization scheduling model is decomposed to obtain a distribution network operation sub-problem and multiple virtual power plant operation sub-problems, including:

[0016] Constructing a Lagrangian function of the virtual power plant optimization scheduling model; wherein the Lagrangian function includes an unbounded term;

[0017] Obtaining a stability condition, and eliminating the unbounded term based on the stability condition to obtain an optimal multiplier corresponding to the auxiliary constraint;

[0018] Based on the optimal multiplier, the Lagrangian function is updated to obtain a distribution network operation subproblem and multiple virtual power plant operation subproblems.

[0019] Furthermore, the Lagrangian function of constructing the virtual power plant optimization scheduling model includes:

[0020] Relaxing the auxiliary constraint and the coupling constraint respectively to obtain a first multiplier and a second multiplier corresponding to the auxiliary constraint, and a third multiplier corresponding to the coupling constraint;

[0021] The Lagrangian function is constructed based on the first multiplier, the second multiplier, and the third multiplier.

[0022] Furthermore, the updating of the Lagrangian function based on the optimal multiplier to obtain a distribution network operation subproblem and multiple virtual power plant operation subproblems includes:

[0023] Based on the optimal multiplier, the Lagrangian function is updated to obtain an updated target Lagrangian function;

[0024] Decomposing the target Lagrangian function based on the exchange power vectors between the distribution network and the main grid and the virtual power plant, respectively, to obtain the distribution network operation sub-problem; and

[0025] Based on the power injected by the virtual power plant into the distribution network and the internal resource scheduling output vector, the target Lagrangian function is decomposed to obtain the multiple virtual power plant operation sub-problems.

[0026] Furthermore, solving the distribution network operation sub-problem to obtain the optimal dispatching strategy of the distribution network includes:

[0027] Setting a target optimal multiplier to obtain the target demand power of the distribution network and the target injection power of the virtual power plant;

[0028] Based on the target optimal multiplier, updating the first multiplier and the second multiplier respectively to obtain an updated first multiplier and an updated second multiplier;

[0029] and updating the third multiplier based on the target optimal multiplier, the target required power, and the target injected power to obtain an updated third multiplier;

[0030] Based on the target demand power, the target injection power, the updated first multiplier, the updated second multiplier and the updated third multiplier, the distribution network operation subproblem is iteratively solved by the alternating direction multiplier method to obtain the optimal scheduling strategy of the distribution network.

[0031] Furthermore, solving the multiple virtual power plant operation sub-problems to obtain the optimal scheduling strategy of the virtual power plant includes:

[0032] Setting a target optimal multiplier to obtain the target demand power of the distribution network and the target injection power of the virtual power plant;

[0033] Based on the target optimal multiplier, updating the first multiplier and the second multiplier respectively to obtain an updated first multiplier and an updated second multiplier;

[0034] Based on the target demand power, the target injection power, the updated first multiplier, the updated second multiplier and the updated third multiplier, the multiple virtual power plant operation sub-problems are iteratively solved by the alternating direction multiplier method to obtain the optimal scheduling strategy of the virtual power plant.

[0035] Furthermore, the updating of the first multiplier and the second multiplier based on the target optimal multiplier to obtain the updated first multiplier and the updated second multiplier includes:

[0036] Get the fixed projection set;

[0037] Determining projections of the first multiplier and the second multiplier in the fixed projection set based on the target optimal multiplier;

[0038] The projection is solved by using a Euclidean distance projection problem to obtain an updated first multiplier and an updated second multiplier.

[0039] Furthermore, the obtaining of distributed resource operation parameters and the construction of a distributed resource physical operation model include:

[0040] Obtaining the predicted output power and actual output power of the photovoltaic at a preset time, and constructing a photovoltaic physical model;

[0041] Obtaining the maximum output power and actual output power of the micro gas turbine at a preset time, and constructing a physical model of the micro gas turbine;

[0042] Obtain the output power, charging power, and discharging power of the energy storage at a preset time, and construct an energy storage physical model;

[0043] The distributed resource physical operation model is constructed based on the photovoltaic physical model, the micro gas turbine physical model and the energy storage physical model.

[0044] In a second aspect of an embodiment of the present application, a distributed scheduling system for a virtual power plant with assisted ramping is provided, the system comprising:

[0045] The first construction module is used to obtain power load information and distribution network topology parameters and build a hot start linear power flow model;

[0046] The second construction module is used to obtain distributed resource operating parameters and construct a distributed resource physical operation model; wherein the distributed resource operating parameters include: photovoltaic, micro gas turbine and energy storage within the virtual power plant;

[0047] A third construction module is configured to construct a virtual power plant optimization scheduling model based on the hot start linearized power flow model and the distributed resource physical operation model, with target optimization objectives as constraints; wherein the target optimization objectives are minimizing the ramp cost, minimizing the main grid power purchase cost, minimizing the distribution network line loss cost, and minimizing the distributed energy operation cost;

[0048] A first acquisition module is used to decompose the virtual power plant optimization scheduling model to obtain a distribution network operation sub-problem and multiple virtual power plant operation sub-problems;

[0049] The second acquisition module is used to solve the distribution network operation sub-problem to obtain the optimal scheduling strategy of the distribution network, and to solve the multiple virtual power plant operation sub-problems to obtain the optimal scheduling strategy of the virtual power plants.

[0050] Furthermore, the third building block includes:

[0051] A fourth construction module is configured to determine an objective function based on the distributed resource physical operation model and with the target optimization objective as a constraint;

[0052] Obtaining auxiliary constraints for peak ramping and coupling constraints between the distribution network and the virtual power plant;

[0053] Based on the auxiliary constraints, the coupling constraints and the objective function, the virtual power plant optimization scheduling model is constructed.

[0054] Furthermore, the first acquisition module includes:

[0055] A third acquisition module is used to construct a Lagrangian function of the virtual power plant optimization scheduling model; wherein the Lagrangian function includes an unbounded term;

[0056] Obtaining a stability condition, and eliminating the unbounded term based on the stability condition to obtain an optimal multiplier corresponding to the auxiliary constraint;

[0057] Based on the optimal multiplier, the Lagrangian function is updated to obtain a distribution network operation subproblem and multiple virtual power plant operation subproblems.

[0058] Furthermore, the third acquisition module includes:

[0059] a fourth acquisition module, configured to relax the auxiliary constraint and the coupling constraint respectively, and acquire a first multiplier and a second multiplier corresponding to the auxiliary constraint, and a third multiplier corresponding to the coupling constraint;

[0060] The Lagrangian function is constructed based on the first multiplier, the second multiplier, and the third multiplier.

[0061] Furthermore, the third acquisition module includes:

[0062] a fifth acquisition module, configured to update the Lagrangian function based on the optimal multiplier to obtain an updated target Lagrangian function;

[0063] Decomposing the target Lagrangian function based on the exchange power vectors between the distribution network and the main grid and the virtual power plant, respectively, to obtain the distribution network operation sub-problem; and

[0064] Based on the power injected by the virtual power plant into the distribution network and the internal resource scheduling output vector, the target Lagrangian function is decomposed to obtain the multiple virtual power plant operation sub-problems.

[0065] Furthermore, the second acquisition module includes:

[0066] a sixth acquisition module, configured to set a target optimal multiplier to obtain a target demand power of the distribution network and a target injection power of the virtual power plant;

[0067] Based on the target optimal multiplier, updating the first multiplier and the second multiplier respectively to obtain an updated first multiplier and an updated second multiplier;

[0068] and updating the third multiplier based on the target optimal multiplier, the target required power, and the target injected power to obtain an updated third multiplier;

[0069] Based on the target demand power, the target injection power, the updated first multiplier, the updated second multiplier and the updated third multiplier, the distribution network operation subproblem is iteratively solved by the alternating direction multiplier method to obtain the optimal scheduling strategy of the distribution network.

[0070] Furthermore, the second acquisition module includes:

[0071] a seventh acquisition module, configured to set a target optimal multiplier to obtain a target demand power of the distribution network and a target injection power of the virtual power plant;

[0072] Based on the target optimal multiplier, updating the first multiplier and the second multiplier respectively to obtain an updated first multiplier and an updated second multiplier;

[0073] Based on the target demand power, the target injection power, the updated first multiplier, the updated second multiplier and the updated third multiplier, the multiple virtual power plant operation sub-problems are iteratively solved by the alternating direction multiplier method to obtain the optimal scheduling strategy of the virtual power plant.

[0074] Furthermore, the seventh acquisition module includes:

[0075] An eighth acquisition module, configured to acquire a fixed projection set;

[0076] Determining projections of the first multiplier and the second multiplier in the fixed projection set based on the target optimal multiplier;

[0077] The projection is solved by using a Euclidean distance projection problem to obtain an updated first multiplier and an updated second multiplier.

[0078] Furthermore, the second building block includes:

[0079] A fifth construction module is used to obtain the predicted output power and actual output power of the photovoltaic at a preset time and construct a photovoltaic physical model;

[0080] Obtaining the maximum output power and actual output power of the micro gas turbine at a preset time, and constructing a physical model of the micro gas turbine;

[0081] Obtain the output power, charging power, and discharging power of the energy storage at a preset time, and construct an energy storage physical model;

[0082] The distributed resource physical operation model is constructed based on the photovoltaic physical model, the micro gas turbine physical model and the energy storage physical model.

[0083] A distributed scheduling method for a virtual power plant with assisted ramping provided in this embodiment first constructs a hot start linearized power flow model by acquiring power load information and distribution network topology parameters; the hot start linearized power flow model solves the bidirectional power flow problem caused by large-scale distributed energy access.

[0084] Then, the distributed resource operating parameters are obtained and a distributed resource physical operation model is constructed. The distributed resource operating parameters include: photovoltaics, micro gas turbines and energy storage within the virtual power plant. Based on the hot start linearized power flow model and the distributed resource physical operation model, a virtual power plant optimization scheduling model is constructed with the target optimization goal as a constraint. The target optimization goal is to minimize the ramping cost, minimize the main grid power purchase cost, minimize the distribution network line loss cost and minimize the distributed energy operation cost. The virtual power plant optimization scheduling model constructed with the target optimization goal as a constraint can give full play to the flexibility of the distributed resource physical model and reduce the ramping pressure of the main grid power system.

[0085] Then the virtual power plant optimization scheduling model is decomposed to obtain the distribution network operation sub-problem and multiple virtual power plant operation sub-problems; finally, the distribution network operation sub-problem is solved to obtain the optimal scheduling strategy of the distribution network, and the multiple virtual power plant operation sub-problems are solved to obtain the optimal scheduling strategy of the virtual power plant. By decomposing the virtual power plant optimization scheduling model, the original large-scale problem is decomposed into distribution network operation sub-problems and multiple virtual power plant operation sub-problems, and they are solved separately, which reduces the complexity of problem solving and can improve scheduling efficiency to ensure the safe and stable operation of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0086] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments of the present application. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0087] Figure 1 This is a flowchart of the steps of a distributed scheduling method for a virtual power plant with assisted ramping provided by an embodiment of the present application;

[0088] Figure 2 This is a schematic diagram of a power distribution network topology provided in an embodiment of the present application;

[0089] Figure 3 This is a schematic diagram of optimization results of a virtual power plant participating in distribution network scheduling provided by an embodiment of the present application;

[0090] Figure 4 This is a schematic diagram of the iterative convergence process of a distributed solution algorithm provided in an embodiment of the present application;

[0091] Figure 5 Schematic diagram of the Lagrange dual multiplier iterative convergence process provided by an embodiment of the present application;

[0092] Figure 6 This is a schematic diagram of a distributed scheduling system for a virtual power plant with assisted ramping provided in an embodiment of the present application. DETAILED DESCRIPTION

[0093] The exemplary embodiments of the present application will be described in more detail below in conjunction with the accompanying drawings in the embodiments of the present application. Although the accompanying drawings show exemplary embodiments of the present application, it should be understood that the present application can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present application and to fully convey the scope of the present application to those skilled in the art.

[0094] In recent years, some research has explored leveraging demand-side resource flexibility to support system ramping capabilities. As virtual power plants become a new type of distributed energy operator, they can optimize and control the output of internal resources, exchange power with the grid, and enhance power system flexibility.

[0095] However, the participation of virtual power plants in power system ramping regulation faces the following challenges. First, the integration of large-scale distributed resources into virtual power plants increases the computational complexity of power system optimization scheduling. Second, in the process of exchanging electricity between the power system and the virtual power plant, in order to maximize the interests of both parties, the power system and the virtual power plant need to exchange information on the operating status of each system, which may lead to privacy leaks for each entity. Finally, compared with traditional distribution networks, the integration of distributed energy resources causes bidirectional power flow in new distribution networks, which makes it difficult to calculate network losses in low-voltage distribution networks. Existing research has not fully considered these challenges, which has a significant impact on the accuracy of virtual power plant scheduling.

[0096] Therefore, in order to overcome the above-mentioned shortcomings, the present application provides a distributed scheduling method for virtual power plants with assisted ramping, constructs a virtual power plant optimization scheduling model considering ramping requirements, adopts a distribution network hot start linearized power flow model considering network losses, and utilizes dual decomposition and KKT (Karush-Kuhn-Tucker) conditions to decompose the original problem into multiple smaller sub-problems that can be solved independently, reducing the complexity of the virtual power plant optimization scheduling model, while protecting the distributed energy privacy within the virtual power plant, and achieving the minimization of peak ramping requirements.

[0097] Reference Figure 1 , Figure 1 This is a flowchart of the steps of a distributed scheduling method for a virtual power plant with assisted ramping provided by an embodiment of the present application. Figure 1 The steps in the process include:

[0098] Step S101: Obtain power load information and distribution network topology parameters, and construct a hot start linearized power flow model.

[0099] In this embodiment, the power load information refers to the load required on the load side. The load side may be an electrical device that requires power supply from the distribution network. The distribution network topology parameters refer to the topology parameters at each node of the transmission line between the distribution network, the main network and the virtual power plant. The topology parameters may be the active power flow, reactive power flow, conductance and susceptance, etc. on each node line. Then, a hot start linearized power flow model is constructed based on the power load information and the distribution network topology parameters.

[0100] Among them, the hot start linear power flow model is:

[0101] (1)

[0102] (2)

[0103] (3)

[0104] (4)

[0105] (5)

[0106] (6)

[0107] (7)

[0108] in, and They are respectively the active and reactive power flows on the line (i, j) with the starting point at node i and the end point at node j at time t; and are the conductance and susceptance of line (i, j) respectively; and are the phase angle difference between node i and node j at time t and the initial value of the phase angle difference hot start; and are the voltage of node i at time t and the initial value of voltage hot start; and are the active and reactive injected powers of node i at time t respectively; and are the active and reactive power injected into the distribution network by the virtual power plant k at time t; and are the active and reactive loads of node i at time t respectively; and are the set of virtual power plants and the set of lines connected to node i respectively.

[0109] Step S102: Obtain distributed resource operating parameters and construct a distributed resource physical operating model; wherein the distributed resource operating parameters include: photovoltaics, micro gas turbines and energy storage within the virtual power plant.

[0110] The distributed physical operation model leverages the flexibility of distributed resources, integrating them into the main grid power system. This model reduces the ramping pressure on the main grid power system by outputting power or supplying electricity to the distribution network. The distributed resource physical operation model is used to output power externally, that is, to the distribution network. A virtual power plant refers to equipment that is distributed over a large area and can be used for power generation. Specifically, these equipment may include photovoltaic power generation equipment, micro gas turbine power generation equipment, and energy storage equipment. Distributed resource operating parameters refer to the actual operating parameters of each power generation device.

[0111] In one embodiment, when obtaining the distributed resource operating parameters and constructing the distributed resource physical operation model, the predicted output power and actual output power of the photovoltaic at a preset time can be obtained to construct a photovoltaic physical model; then the maximum output power and actual output power of the micro gas turbine at the preset time are obtained to construct a micro gas turbine physical model; then the output power, charging power and discharging power of the energy storage at the preset time are obtained to construct an energy storage physical model; finally, based on the photovoltaic physical model, the micro gas turbine physical model and the energy storage physical model, the distributed resource physical operation model is constructed.

[0112] Assume that photovoltaic is m, micro gas turbine is g, and energy storage is e.

[0113] Then, the photovoltaic physical model is constructed based on the photovoltaic operating parameters. for:

[0114] (8)

[0115] in, and are the predicted value and actual output value of photovoltaic m at time t, i.e., the operating parameters of photovoltaic; It is a binary variable, representing the aggregation status of photovoltaic m participating in the virtual power plant at time t. When it is 1, it participates in the aggregation, and when it is 0, it does not participate in the aggregation.

[0116] Construct a physical model of the micro gas turbine based on the operating parameters of the micro gas turbine for:

[0117] (9)

[0118] in, and are the output value and maximum output value of the micro gas turbine g at time t, that is, the operating parameters of the micro gas turbine.

[0119] Construct energy storage physical model through energy storage operating parameters for:

[0120] (10)

[0121] (11)

[0122] (12)

[0123] (13)

[0124] (14)

[0125] (15)

[0126] in, , and are the net output power, charging power and discharging power of the energy storage e at time t, i.e., the operating parameters of the energy storage; and are the maximum charging power and discharging power of energy storage e, respectively; and are binary variables, representing the charging and discharging states of the energy storage e at time t; and They are energy storage charging and discharging efficiency; , , , and They are the minimum, maximum, initial, T and t energy values ​​of the stored energy e.

[0127] Then according to the photovoltaic physical model , Micro gas turbine physical model and energy storage physical models , build a distributed resource physical operation model, the distributed resource physical operation model is:

[0128] (16)

[0129] in, is the output power of virtual power plant k to the distribution network at time t; and They represent the photovoltaic, micro gas turbine and energy storage sets belonging to the virtual power plant k respectively.

[0130] Step S103: Based on the hot start linearized power flow model and the distributed resource physical operation model, a virtual power plant optimization scheduling model is constructed with the target optimization goal as a constraint; wherein the target optimization goal is to minimize the ramp cost, minimize the main grid power purchase cost, minimize the distribution network line loss cost and minimize the distributed energy operation cost.

[0131] Minimizing ramp costs refers to the cost of increasing the main grid's generators from low-load operation to rated load. Minimizing the main grid's electricity purchase costs refers to the cost of purchasing electricity from external sources. Minimizing distribution network line loss costs refers to the network loss costs of power transmission along the distribution network. Minimizing distributed energy operating costs refers to the cost of outputting power from the photovoltaic, micro-gas turbine, and energy storage systems within the virtual power plant. The virtual power plant's optimal scheduling model aims to minimize the main grid's ramping pressure at the lowest cost.

[0132] In one embodiment, the objective function can be determined based on the physical operation model of the distributed resources and with the target optimization objective as a constraint; then, the auxiliary constraints of peak climbing and the coupling constraints between the distribution network and the virtual power plant are obtained; finally, based on the auxiliary constraints, the coupling constraints and the objective function, the virtual power plant optimization scheduling model is constructed.

[0133] In this embodiment, based on the distributed resource physical operation model and with the target optimization goal as a constraint, the process of determining the objective function is as follows:

[0134] Based on the distributed resource physical operation model of formula (16), the external output power of the virtual power plant is obtained .

[0135] Then, the objective function is determined based on the constraints of minimizing the ramp cost, minimizing the main grid power purchase cost, minimizing the distribution network line loss cost and minimizing the distributed energy operation cost.

[0136] The objective function is:

[0137] (17)

[0138] in, and represent the photovoltaic aggregation vector and the distribution grid and virtual power plant power vectors respectively; and They represent the coefficients of minimizing ramp cost, minimizing main grid power purchase cost, minimizing distribution network line loss cost, and minimizing distributed energy operation cost respectively; represents the node marginal electricity price, that is, the electricity price purchased from the main grid at time t; and are the unit power generation cost coefficients of photovoltaic and micro gas turbine respectively; and They are time, node and virtual power plant set.

[0139] Then, the auxiliary constraints of peak ramping and the coupling constraints between the distribution network and the virtual power plant are obtained.

[0140] Among them, the auxiliary constraints are:

[0141] (18)

[0142] (19)

[0143] in, is the peak climbing limit; Indicates the system ramp demand during period t.

[0144] The coupling constraints are:

[0145] (20)

[0146] in, At time t, the amount of power supplied to the distribution network by the kth virtual power plant is equal to the demand.

[0147] Finally, based on auxiliary constraints, coupling constraints and objective function, the virtual power plant optimization scheduling model is constructed as follows:

[0148] (twenty one)

[0149] Among them, formula (1)-formula (16) and formula (18)-formula (20) are the constraints of the virtual power plant optimization scheduling model.

[0150] Step S104: Decompose the virtual power plant optimization scheduling model to obtain a distribution network operation sub-problem and multiple virtual power plant operation sub-problems.

[0151] In order to facilitate the solution of the virtual power plant optimization scheduling model, reduce the solution complexity of the virtual power plant optimization scheduling model, and also to protect the privacy of the virtual power plant and the main power grid, the virtual power plant optimization scheduling model is decomposed to obtain the distribution network operation sub-problem and multiple virtual power plant operation sub-problems.

[0152] In one embodiment, the virtual power plant optimization scheduling model is decomposed to obtain a distribution network operation subproblem and multiple virtual power plant operation subproblems, which can be achieved by constructing a Lagrangian function of the virtual power plant optimization scheduling model; wherein the Lagrangian function includes unbounded terms; then a stability condition is obtained, and based on the stability condition, the unbounded terms are eliminated to obtain the optimal multiplier corresponding to the auxiliary constraint; finally, based on the optimal multiplier, the Lagrangian function is updated to obtain the distribution network operation subproblem and multiple virtual power plant operation subproblems.

[0153] In this embodiment, with respect to constructing the Lagrangian function of the virtual power plant optimization scheduling model, the auxiliary constraint and the coupling constraint can be relaxed respectively to obtain the first multiplier and the second multiplier corresponding to the auxiliary constraint, and the third multiplier corresponding to the coupling constraint; finally, the Lagrangian function is constructed based on the first multiplier, the second multiplier and the third multiplier.

[0154] The Lagrangian function is constructed as follows:

[0155] First, we use Lagrangian relaxation to relax the constraints (18) and (20), and we can get the Lagrangian function constructed for formula (21):

[0156] (twenty two)

[0157] in, and are the Lagrange multipliers corresponding to formula (18), namely the first multiplier and the second multiplier; are the Lagrange multipliers corresponding to formula (20), i.e., the third multiplier; is the penalty coefficient of the augmentation term; The relevant terms are unbounded.

[0158] Then we obtain the stability condition, which is .

[0159] Then, using the stability condition, the unbounded term in formula (22) is eliminated to obtain the optimal multiplier corresponding to the auxiliary constraint.

[0160] (twenty three)

[0161] in, and is the optimal multiplier of the auxiliary constraint.

[0162] In this embodiment, regarding updating the Lagrangian function based on the optimal multiplier to obtain the distribution network operation sub-problem and multiple virtual power plant operation sub-problems, the Lagrangian function can be updated based on the optimal multiplier to obtain the updated target Lagrangian function; finally, based on the exchange power vectors between the distribution network and the main grid and the virtual power plant respectively, the target Lagrangian function is decomposed to obtain the distribution network operation sub-problem; and based on the power injected by the virtual power plant into the distribution network and the internal resource scheduling output vector, the target Lagrangian function is decomposed to obtain the multiple virtual power plant operation sub-problems.

[0163] According to the optimal multiplier and , update formula (22) and obtain the updated target Lagrangian function:

[0164] (twenty four)

[0165] Then, the power vector is exchanged with the main grid and the virtual power plant according to the distribution network. , decompose the target Lagrangian function and obtain the distribution network operation sub-problem as follows:

[0166] (25)

[0167] in, is the multiplier vector; The exchange power vector between the distribution network, the main grid and the virtual power plant; Determine the optimal solution for the k-th virtual power plant dispatch subproblem.

[0168] According to the power injected into the distribution network by the virtual power plant and the output vector of the internal resource scheduling , decompose the target Lagrangian function and obtain multiple virtual power plant operation sub-problems as follows:

[0169] (26)

[0170] in, is the photovoltaic aggregation variable vector; Inject power into the distribution network for the virtual power plant and dispatch output vectors of internal resources; The optimal solution for the distribution network to dispatch the k-th virtual power plant output demand.

[0171] Step S105: Solve the distribution network operation sub-problem to obtain the optimal dispatching strategy of the distribution network, and solve the multiple virtual power plant operation sub-problems to obtain the optimal dispatching strategy of the virtual power plants.

[0172] In this embodiment, the distribution network operation subproblem and multiple virtual power plant operation subproblems have been obtained from step S104. Therefore, the original problem has been reduced to a process of subproblem minimization and Lagrange multiplier iterative update. It is only necessary to solve the distribution network operation subproblem to obtain the optimal dispatching strategy for the distribution network. Furthermore, it is necessary to solve the multiple virtual power plant operation subproblems to obtain the optimal dispatching strategy for the virtual power plants. By dispatching the distribution network and virtual power plants using their respective optimal dispatching strategies, the main grid power system can be reduced in ramping pressure while ensuring normal and safe operation, and the dispatching efficiency of the distribution network and virtual power plants can be improved.

[0173] In one embodiment, with respect to solving the distribution network operation sub-problem and obtaining the optimal dispatching strategy of the distribution network, the target demand power of the distribution network and the target injection power of the virtual power plant can be obtained by setting a target optimal multiplier; then, based on the target optimal multiplier, the first multiplier and the second multiplier are updated respectively to obtain the updated first multiplier and the updated second multiplier; and based on the target optimal multiplier, the target demand power and the target injection power, the third multiplier is updated to obtain the updated third multiplier; finally, based on the target demand power, the target injection power, the updated first multiplier, the updated second multiplier and the updated third multiplier, the distribution network operation sub-problem is iteratively solved by the alternating direction multiplier method to obtain the optimal dispatching strategy of the distribution network.

[0174] In this embodiment, the target optimal multiplier is set , obtain the target required power of the distribution network and the target injection power of the virtual power plant .

[0175] According to the target optimal multiplier , target required power and target injected power, update the third multiplier corresponding to formula (20) , the specific update method is as follows:

[0176] (27)

[0177] in, The value of the multiplier corresponding to the It-th iteration, that is, the third multiplier; Update step size for the multiplier.

[0178] According to the target optimal multiplier , the target required power and the target injection power, the first multiplier corresponding to formula (18) and the second multiplier Update, get the updated first multiplier and the updated second multiplier.

[0179] In one embodiment, regarding the updating of the first multiplier and the second multiplier based on the target optimal multiplier, respectively, to obtain the updated first multiplier and the updated second multiplier, a fixed projection set can be obtained; then, based on the target optimal multiplier, the projections of the first multiplier and the second multiplier in the fixed projection set are determined; finally, the projections are solved by a Euclidean distance projection problem to obtain the updated first multiplier and the updated second multiplier.

[0180] In this embodiment, a fixed projection set is obtained, and the fixed projection set is:

[0181]

[0182] Then determine the projection of the first multiplier and the second multiplier on the projection set as follows:

[0183] Given a projection set , the multiplier is updated as follows:

[0184] (28)

[0185] (29)

[0186] in, is the updated first multiplier, is the updated second multiplier.

[0187] The specific multiplier update can be obtained according to the Euclidean distance projection problem as follows:

[0188] (30)

[0189] in, yes The only solution of .

[0190] Finally, the optimization subproblem is iteratively solved using ADMM (Alternating Direction Method of Multipliers) until the algorithm converges and the optimal dispatching strategy for the distribution network is obtained.

[0191] In one embodiment, the multiple virtual power plant operation sub-problems are solved to obtain the optimal scheduling strategy of the virtual power plant. The target demand power of the distribution network and the target injection power of the virtual power plant can also be obtained by setting a target optimal multiplier; then, based on the target optimal multiplier, the first multiplier and the second multiplier are updated respectively to obtain the updated first multiplier and the updated second multiplier; finally, based on the target demand power, the target injection power, the updated first multiplier, the updated second multiplier and the updated third multiplier, the multiple virtual power plant operation sub-problems are iteratively solved by the alternating direction multiplier method to obtain the optimal scheduling strategy of the virtual power plant.

[0192] In this embodiment, the target optimal multiplier is set , obtain the target required power of the distribution network and the target injection power of the virtual power plant Then according to the target optimal multiplier Refer to formula (22) and formula (28) for the first multiplier and the second multiplier Update, get the updated first multiplier and the updated second multiplier .

[0193] Referring to formula (20), formula (22), and formula (27), update the third multiplier , get the updated third multiplier .

[0194] Finally, referring to Formula (26)-Formula (30), the target demand power, the target injection power, the updated first multiplier, the updated second multiplier and the updated third multiplier are used to iteratively solve multiple virtual power plant operation sub-problems through the alternating direction multiplier method to obtain the optimal scheduling strategy of the virtual power plant.

[0195] A distributed scheduling method for a virtual power plant with assisted ramping provided in this embodiment first constructs a hot start linearized power flow model by acquiring power load information and distribution network topology parameters; the hot start linearized power flow model solves the bidirectional power flow problem caused by large-scale distributed energy access.

[0196] Then, the distributed resource operating parameters are obtained and a distributed resource physical operation model is constructed. The distributed resource operating parameters include: photovoltaics, micro gas turbines and energy storage within the virtual power plant. Based on the hot start linearized power flow model and the distributed resource physical operation model, a virtual power plant optimization scheduling model is constructed with the target optimization goal as a constraint. The target optimization goal is to minimize the ramping cost, minimize the main grid power purchase cost, minimize the distribution network line loss cost and minimize the distributed energy operation cost. The virtual power plant optimization scheduling model constructed with the target optimization goal as a constraint can give full play to the flexibility of the distributed resource physical model and reduce the ramping pressure of the main grid power system.

[0197] Then the virtual power plant optimization scheduling model is decomposed to obtain the distribution network operation sub-problem and multiple virtual power plant operation sub-problems; finally, the distribution network operation sub-problem is solved to obtain the optimal scheduling strategy of the distribution network, and the multiple virtual power plant operation sub-problems are solved to obtain the optimal scheduling strategy of the virtual power plant. By decomposing the virtual power plant optimization scheduling model, the original large-scale problem is decomposed into distribution network operation sub-problems and multiple virtual power plant operation sub-problems, and they are solved separately, which reduces the complexity of problem solving and can improve scheduling efficiency to ensure the safe and stable operation of the power system.

[0198] For example, we will use Figure 2-Figure 5 The results of applying the virtual power plant distributed scheduling method provided in this embodiment to the distributed scheduling of virtual power plants oriented to ramping demand are demonstrated.

[0199] Figure 2 This is a schematic diagram of a power distribution network topology provided in an embodiment of the present application. Figure 2 It can be seen that three virtual power plants are used to participate in the 24-hour energy exchange process of the distribution network, and the IEEE-69 node distribution network system and three virtual power plants connected to nodes 25, 44 and 63 are used to simulate and verify the effectiveness of the model and algorithm.

[0200] The virtual power plants connected to nodes 25, 44, and 63 include 8 photovoltaic units, 13 micro-gas turbines, and 1 energy storage unit, 13 photovoltaic units, 9 micro-gas turbines, and 1 energy storage unit, and 9 photovoltaic units, 8 micro-gas turbines, and 1 energy storage unit, respectively. The maximum and minimum energy storage capacity is 40 kWh and 20 kWh, with a maximum charge and discharge power of 10 kW and a charge and discharge efficiency of 90%. The capacity of a single photovoltaic unit is 4.8 kW, and the capacity of a single micro-gas turbine is 6 kW. Other parameter values ​​are: a1=0.4, a2=0.25, a3=0.2, and a4=0.15. , The original load and marginal electricity price values ​​are shown in Table 1. Table 1 is the original load and marginal electricity price values ​​provided in this embodiment.

[0201] Table 1

[0202]

[0203] Figure 3 This is a schematic diagram of the optimization results of a virtual power plant participating in distribution network scheduling provided in an embodiment of the present application. Figure 3 The optimization results of a virtual power plant participating in distribution network scheduling were presented. The peak load ramping occurred at 0.57 MW / h and 0.61 MW / h in period 8, respectively, before and after optimization. After optimization, the peak load ramping was reduced by up to 30.5%. This demonstrates that during periods of high net load ramping demand, the virtual power plant leverages its flexibility to smooth the net load and mitigate peak ramping demand in the power system. Figure 4 This is a schematic diagram of the iterative convergence process of a distributed solution algorithm provided in an embodiment of the present application. After 50 iterations, the function values ​​of the original objective and the dual objective are basically consistent, and the algorithm converges. Figure 5 This is a schematic diagram of the iterative convergence process of the Lagrange dual multiplier provided in an embodiment of the present application. After 50 iterations, the multiplier basically no longer changes, and the algorithm converges. Figure 4 and Figure 5 The iterative convergence of the distributed solution algorithm is demonstrated, proving the effectiveness of the algorithm provided in this embodiment.

[0204] Based on the same inventive concept, Figure 6 , Figure 6 Schematic diagram of a distributed scheduling system for a virtual power plant with assisted ramping provided by an embodiment of the present application. Figure 6 The system includes: a first construction module 601, a second construction module 602, a third construction module 603, a first acquisition module 604, and a second acquisition module 605.

[0205] The first building module 601 is used to obtain power load information and distribution network topology parameters and build a hot start linear power flow model.

[0206] The second construction module 602 is used to obtain distributed resource operating parameters and construct a distributed resource physical operation model; wherein the distributed resource operating parameters include: photovoltaics, micro gas turbines and energy storage within the virtual power plant.

[0207] The third construction module 603 is used to construct a virtual power plant optimization scheduling model based on the hot start linearized power flow model and the distributed resource physical operation model, with the target optimization goal as a constraint; wherein the target optimization goal is to minimize the climbing cost, minimize the main grid power purchase cost, minimize the distribution network line loss cost and minimize the distributed energy operation cost.

[0208] The first acquisition module 604 is used to decompose the virtual power plant optimization scheduling model to obtain a distribution network operation sub-problem and multiple virtual power plant operation sub-problems.

[0209] The second acquisition module 605 is used to solve the distribution network operation sub-problem to obtain the optimal scheduling strategy of the distribution network, and to solve the multiple virtual power plant operation sub-problems to obtain the optimal scheduling strategy of the virtual power plants.

[0210] In this embodiment, the third building block 603 includes:

[0211] The fourth construction module is used to determine the objective function based on the physical operation model of the distributed resources and with the target optimization objective as a constraint; obtain the auxiliary constraints of peak climbing and the coupling constraints between the distribution network and the virtual power plant; and construct the virtual power plant optimization scheduling model based on the auxiliary constraints, the coupling constraints and the objective function.

[0212] In this embodiment, the first acquisition module 604 includes:

[0213] The third acquisition module is used to construct the Lagrangian function of the virtual power plant optimization scheduling model; wherein, the Lagrangian function includes unbounded terms; obtain stability conditions, and eliminate the unbounded terms based on the stability conditions to obtain the optimal multiplier corresponding to the auxiliary constraint; based on the optimal multiplier, update the Lagrangian function to obtain the distribution network operation sub-problem and multiple virtual power plant operation sub-problems.

[0214] In this embodiment, the third acquisition module includes: a fourth acquisition module, which is used to relax the auxiliary constraint and the coupling constraint respectively, obtain the first multiplier and the second multiplier corresponding to the auxiliary constraint, and the third multiplier corresponding to the coupling constraint; based on the first multiplier, the second multiplier and the third multiplier, construct the Lagrangian function.

[0215] In this embodiment, the third acquisition module includes: a fifth acquisition module, which is used to update the Lagrangian function based on the optimal multiplier to obtain an updated target Lagrangian function; decompose the target Lagrangian function based on the exchange power vectors between the distribution network and the main grid and the virtual power plant respectively to obtain the distribution network operation sub-problem; and decompose the target Lagrangian function based on the power injected by the virtual power plant into the distribution network and the internal resource scheduling output vector to obtain the multiple virtual power plant operation sub-problems.

[0216] In this embodiment, the second acquisition module 605 includes: a sixth acquisition module, which is used to set a target optimal multiplier to obtain the target demand power of the distribution network and the target injection power of the virtual power plant; based on the target optimal multiplier, the first multiplier and the second multiplier are updated respectively to obtain the updated first multiplier and the updated second multiplier, and based on the target optimal multiplier, the target demand power and the target injection power, the third multiplier is updated to obtain an updated third multiplier; based on the target demand power, the target injection power, the updated first multiplier, the updated second multiplier and the updated third multiplier, the distribution network operation subproblem is iteratively solved by the alternating direction multiplier method to obtain the optimal scheduling strategy of the distribution network.

[0217] In this embodiment, the second acquisition module 605 includes: a seventh acquisition module for setting a target optimal multiplier to obtain the target demand power of the distribution network and the target injection power of the virtual power plant; based on the target optimal multiplier, updating the first multiplier and the second multiplier respectively to obtain the updated first multiplier and the updated second multiplier; based on the target demand power, the target injection power, the updated first multiplier, the updated second multiplier and the updated third multiplier, iteratively solving the multiple virtual power plant operation sub-problems through the alternating direction multiplier method to obtain the optimal scheduling strategy of the virtual power plant

[0218] In this embodiment, the seventh acquisition module includes: an eighth acquisition module, used to obtain a fixed projection set; based on the target optimal multiplier, determine the projection of the first multiplier and the second multiplier in the fixed projection set; solve the projection through the Euclidean distance projection problem to obtain the updated first multiplier and the updated second multiplier.

[0219] In this embodiment, the second building module 602 includes:

[0220] The fifth construction module is used to obtain the predicted output power and actual output power of the photovoltaic at a preset time to construct a photovoltaic physical model; obtain the maximum output power and actual output power of the micro gas turbine at a preset time to construct a micro gas turbine physical model; obtain the output power, charging power and discharging power of the energy storage at a preset time to construct an energy storage physical model; and construct the distributed resource physical operation model based on the photovoltaic physical model, the micro gas turbine physical model and the energy storage physical model.

[0221] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0222] The embodiments of the present invention are described with reference to the flowcharts and / or block diagrams of the methods and apparatuses according to the 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 the combination 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 formula processing machine, or other programmable data processing terminal device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing terminal 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.

[0223] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing terminal device to operate in a specific manner, so that the instructions stored in the computer readable memory produce a manufactured product including 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.

[0224] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal device so that a series of operating steps are executed on the computer or other programmable terminal device to produce a computer-implemented process, thereby providing instructions for executing on the computer or other programmable terminal device to implement the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0225] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they become aware of the basic creative concepts. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the embodiments of the present invention.

[0226] Finally, it should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "includes," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that includes a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or terminal device. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of additional identical elements in the process, method, article, or terminal device that includes the element.

[0227] The above is a detailed introduction to the distributed scheduling method and system of a virtual power plant with assisted ramping provided by the present invention. Specific examples are used in this article to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea; at the same time, for general technical personnel in this field, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. A distributed scheduling method for virtual power plants with assisted ramping, characterized in that: The method comprises: Obtain power load information and distribution network topology parameters, and build a hot start linear power flow model; Obtaining distributed resource operating parameters and building a distributed resource physical operating model; wherein the distributed resource operating parameters include: photovoltaics, micro gas turbines, and energy storage within the virtual power plant; Based on the hot start linearized power flow model and the distributed resource physical operation model, a virtual power plant optimization scheduling model is constructed with target optimization objectives as constraints; wherein the target optimization objectives are minimizing the ramp cost, minimizing the main grid power purchase cost, minimizing the distribution network line loss cost, and minimizing the distributed energy operation cost; Decomposing the virtual power plant optimization scheduling model to obtain a distribution network operation sub-problem and multiple virtual power plant operation sub-problems; Solving the distribution network operation sub-problem to obtain the optimal dispatching strategy for the distribution network, and solving the multiple virtual power plant operation sub-problems to obtain the optimal dispatching strategy for the virtual power plants; Wherein, based on the hot start linearized power flow model and the distributed resource physical operation model, a virtual power plant optimization scheduling model is constructed with the target optimization goal as a constraint, including: Determining an objective function based on the physical operation model of the distributed resources and taking the target optimization goal as a constraint; Obtaining auxiliary constraints for peak ramping and coupling constraints between the distribution network and the virtual power plant; Based on the auxiliary constraints, the coupling constraints and the objective function, constructing the virtual power plant optimization scheduling model; The decomposition of the virtual power plant optimization scheduling model to obtain a distribution network operation sub-problem and multiple virtual power plant operation sub-problems includes: The auxiliary constraint and the coupling constraint are relaxed respectively to obtain a first multiplier and a second multiplier corresponding to the auxiliary constraint, and a third multiplier corresponding to the coupling constraint; based on the first multiplier, the second multiplier, and the third multiplier, a Lagrangian function of the virtual power plant optimization scheduling model is constructed; wherein the Lagrangian function includes an unbounded term; Obtaining a stability condition, and eliminating the unbounded term based on the stability condition to obtain an optimal multiplier corresponding to the auxiliary constraint; Based on the optimal multiplier, the Lagrangian function is updated to obtain a distribution network operation subproblem and multiple virtual power plant operation subproblems.

2. The distributed scheduling method for virtual power plants according to claim 1, characterized in that: The updating of the Lagrangian function based on the optimal multiplier to obtain a distribution network operation subproblem and a plurality of virtual power plant operation subproblems includes: Based on the optimal multiplier, the Lagrangian function is updated to obtain an updated target Lagrangian function; Decomposing the target Lagrangian function based on the exchange power vectors between the distribution network and the main grid and the virtual power plant, respectively, to obtain the distribution network operation sub-problem; and Based on the power injected by the virtual power plant into the distribution network and the internal resource scheduling output vector, the target Lagrangian function is decomposed to obtain the multiple virtual power plant operation sub-problems.

3. The distributed scheduling method for virtual power plants according to claim 1, characterized in that: Solving the distribution network operation sub-problem to obtain the optimal dispatching strategy for the distribution network includes: Setting a target optimal multiplier to obtain the target demand power of the distribution network and the target injection power of the virtual power plant; Based on the target optimal multiplier, updating the first multiplier and the second multiplier respectively to obtain an updated first multiplier and an updated second multiplier; and updating the third multiplier based on the target optimal multiplier, the target required power, and the target injected power to obtain an updated third multiplier; Based on the target demand power, the target injection power, the updated first multiplier, the updated second multiplier and the updated third multiplier, the distribution network operation subproblem is iteratively solved by the alternating direction multiplier method to obtain the optimal scheduling strategy of the distribution network.

4. The distributed scheduling method for virtual power plants according to claim 1, characterized in that: Solving the multiple virtual power plant operation sub-problems to obtain the optimal scheduling strategy of the virtual power plant includes: Setting a target optimal multiplier to obtain the target demand power of the distribution network and the target injection power of the virtual power plant; Based on the target optimal multiplier, updating the first multiplier and the second multiplier respectively to obtain an updated first multiplier and an updated second multiplier; Based on the target demand power, the target injection power, the updated first multiplier, the updated second multiplier and the updated third multiplier, the multiple virtual power plant operation sub-problems are iteratively solved by the alternating direction multiplier method to obtain the optimal scheduling strategy of the virtual power plant.

5. The distributed scheduling method for virtual power plants according to claim 4, characterized in that: The updating of the first multiplier and the second multiplier based on the target optimal multiplier to obtain the updated first multiplier and the updated second multiplier includes: Get the fixed projection set; Determining projections of the first multiplier and the second multiplier in the fixed projection set based on the target optimal multiplier; The projection is solved by using a Euclidean distance projection problem to obtain an updated first multiplier and an updated second multiplier.

6. The distributed scheduling method for virtual power plants according to claim 1, characterized in that: The step of obtaining the distributed resource operation parameters and constructing the distributed resource physical operation model includes: Obtaining the predicted output power and actual output power of the photovoltaic at a preset time, and constructing a photovoltaic physical model; Obtaining the maximum output power and actual output power of the micro gas turbine at a preset time, and constructing a physical model of the micro gas turbine; Obtain the output power, charging power, and discharging power of the energy storage at a preset time, and construct an energy storage physical model; The distributed resource physical operation model is constructed based on the photovoltaic physical model, the micro gas turbine physical model and the energy storage physical model.

7. A distributed scheduling system for virtual power plants with assisted ramping, characterized in that: The system comprises: The first construction module is used to obtain power load information and distribution network topology parameters and build a hot start linear power flow model; The second construction module is used to obtain distributed resource operating parameters and construct a distributed resource physical operation model; wherein the distributed resource operating parameters include: photovoltaic, micro gas turbine and energy storage within the virtual power plant; The third construction module is used to construct a virtual power plant optimization scheduling model based on the hot start linearized power flow model and the distributed resource physical operation model, with the target optimization target as a constraint; wherein the target optimization target is to minimize the climbing cost, minimize the main network power purchase cost, minimize the distribution network line loss cost and minimize the distributed energy operation cost; wherein, based on the hot start linearized power flow model and the distributed resource physical operation model, with the target optimization target as a constraint, construct the virtual power plant optimization scheduling model, including: based on the distributed resource physical operation model, with the target optimization target as a constraint, determining the objective function; obtaining the auxiliary constraints of peak climbing, and the coupling constraints between the distribution network and the virtual power plant; constructing the virtual power plant optimization scheduling model based on the auxiliary constraints, the coupling constraints and the objective function; A first acquisition module is configured to relax the auxiliary constraint and the coupling constraint respectively to obtain a first multiplier and a second multiplier corresponding to the auxiliary constraint, and a third multiplier corresponding to the coupling constraint; construct a Lagrangian function of the virtual power plant optimization scheduling model based on the first multiplier, the second multiplier, and the third multiplier; wherein the Lagrangian function includes an unbounded term; and obtain a stability condition, and eliminate the unbounded term based on the stability condition to obtain an optimal multiplier corresponding to the auxiliary constraint; and update the Lagrangian function based on the optimal multiplier to obtain a distribution network operation subproblem and multiple virtual power plant operation subproblems; The second acquisition module is used to solve the distribution network operation sub-problem to obtain the optimal scheduling strategy of the distribution network, and to solve the multiple virtual power plant operation sub-problems to obtain the optimal scheduling strategy of the virtual power plants.

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