A method and system for evaluating adjustable capacity of virtual power plant

Abstract

The present invention discloses a method and system for evaluating the adjustable capacity of a virtual power plant, relates to the technical field of virtual power plant evaluation, and solves the problem that the existing evaluation method for the adjustable capacity of a virtual power plant does not consider the underlying network and flow constraints, cannot describe the time coupling operation constraints of distributed energy across time, ignores the analysis of reactive power, and causes inaccurate evaluation results. The key points of the technical solution are: by considering the power coupling characteristics and time coupling characteristics in the distributed energy operation constraints, a distributed energy polyhedron constraint feasible set is established, all distributed energy on a single distribution network node are aggregated through the zonotope method to form a distribution network node power operation feasible set, and a virtual power plant adjustable capacity evaluation model is established based on network constraints such as line flow and voltage limit to calculate the multi-time period adjustable capacity.

Description

Technical Field

[0001] The present invention relates to the technical field of virtual power plant evaluation, and more specifically, to a method and system for evaluating the adjustable capacity of a virtual power plant. Background Art

[0002] In recent years, distributed energy has rapidly spread in distribution systems in order to reduce fossil fuel consumption and mitigate global warming. However, the increasing penetration of distributed energy poses a major threat to system stability, and its intermittent and random nature may exacerbate the imbalance between supply and demand. At the same time, the coordinated scheduling of distributed energy has significant potential to provide additional flexibility and improve controllability for power systems. Therefore, in order to fully tap the potential of distributed energy and improve system flexibility and reliability while ensuring operational safety, system-level coordination and management of distributed energy is essential. However, due to the high computational complexity, it is challenging to manage a large number of distributed energy for system-wide real-time optimization and control. The concept of virtual power plant provides a positive solution to the above problems. By aggregating the technical parameters of a large number of small distributed power sources into a single unit, virtual power plants are able to provide energy and ancillary services to the power grid. Therefore, evaluating the adjustable capacity of virtual power plants is crucial for the regulation of power systems. Otherwise, unreasonable virtual power plant scheduling may cause the local system to fail to execute and cause safety issues such as power imbalance, voltage violation, and transformer overload.

[0003] At present, the evaluation methods of the adjustable capacity of virtual power plants are still relatively one-sided. Some studies use polyhedron sets to describe the power adjustable capacity of a single distributed energy source, and then use the Minkowski sum of all polyhedron sets to calculate the overall adjustable capacity. However, these studies do not consider the underlying network and power flow constraints, which may cause inaccurate evaluation results. Some studies use Monte Carlo simulation-based methods to estimate the adjustable capacity of virtual power plants under a large number of sampling scenarios. However, this method can only explain the adjustable capacity at a single time point and may not be able to describe the time-coupled operation constraints of distributed energy sources across time, such as energy storage and energy constraints of electric vehicles. In addition, some studies only analyze the active power adjustable capacity of virtual power plants, ignore the analysis of reactive power, and do not fully consider the importance of the power coupling characteristics of virtual power plants in the adjustable capacity evaluation process.

[0004] Therefore, the present application provides a method and system for evaluating the adjustable capacity of a virtual power plant to solve the above-mentioned problems. Summary of the invention

[0005] The purpose of this application is to provide a method and system for evaluating the adjustable capacity of a virtual power plant, so as to solve the problem that the existing method for evaluating the adjustable capacity of a virtual power plant does not take into account the underlying network and flow constraints, cannot describe the time-coupled operation constraints of distributed energy across time, and ignores the analysis of reactive power, resulting in inaccurate evaluation results.

[0006] The present application first provides a method for evaluating the adjustable capacity of a virtual power plant, including: analyzing the operating constraints of distributed energy, converting the operating constraints of distributed energy into a polyhedral feasible set form, and obtaining a polyhedral feasible set of distributed energy operating constraints, wherein distributed energy includes: energy storage distributed energy, photovoltaic distributed energy, and HVAC system distributed energy; calculating the optimal zonotope approximation of the distributed energy operating constraint polyhedron feasible set, and obtaining the zonotope approximation of the distributed energy operating constraint polyhedron feasible set; aggregating the zonotope approximations of all distributed energy operating constraint polyhedron feasible sets under the same distribution network node, and establishing a distribution network node aggregated power operating feasible set; constructing a distribution network model to form distribution network node aggregated power operating constraints; based on the distribution network model, the distribution network node aggregated power operating constraints, and the distribution network node aggregated power operating feasible set, constructing a virtual power plant adjustable capacity evaluation model, and calculating the multi-period adjustable capacity range of the virtual power plant by a boundary search method.

[0007] By adopting the above technical scheme, by considering the power coupling characteristics and time coupling characteristics in the distributed energy operation constraints, a polyhedral feasible set of distributed energy operation constraints is established, and all distributed energy on a single distribution network node is aggregated through the zonotope method to form a distribution network node aggregated power operation feasible set. Based on network constraints such as line flow and voltage limitation, a virtual power plant adjustable capacity evaluation model is established, and the multi-time period adjustable capacity range of the virtual power plant is calculated.

[0008] In a possible implementation, the operation constraints of energy storage distributed energy include: the active power output of the energy storage is placed between the upper and lower limits of the output active power, the reactive power output of the energy storage is placed between the upper and lower limits of the reactive power, the capacity of the energy storage does not exceed the rated capacity of the energy storage device, the ramp rate of the energy storage output is placed between the upper and lower limits of the ramp rate, and the energy state of the energy storage is placed between the energy limits of the energy storage device.

[0009] In a possible implementation, the operation constraints of photovoltaic distributed energy include: the active power of photovoltaic output is set between 0 and the upper limit of active power, the reactive power of photovoltaic output is set between the upper and lower limits of reactive power, and the capacity of photovoltaic does not exceed the maximum capacity of the photovoltaic unit.

[0010] In one possible implementation, the operating constraints of the distributed energy of the HVAC system include: the active power of the HVAC is set between 0 and the active power limit, the reactive power of the HVAC is set between the upper and lower limits of the reactive power, the capacity of the HVAC does not exceed the rated capacity of the HVAC, and the indoor and outdoor temperatures of the HVAC are set between the upper and lower limits of the allowable temperature.

[0011] In one possible implementation, the operating constraints of distributed energy are analyzed, and the operating constraints of distributed energy are converted into a polyhedral feasible set form to obtain a polyhedral feasible set of distributed energy operating constraints, including: converting the operating constraints of distributed energy into a universal linearized approximate representation; converting the distributed energy operating constraints of the universal linearized approximate representation into a linear inequality equation group representation; defining feasible power solutions that satisfy the linear inequality equation group to form a polyhedral feasible set of distributed energy operating constraints.

[0012] In a possible implementation, the optimal zonotope approximation of the distributed energy operation constraint polyhedron feasible set is calculated to obtain the zonotope approximation of the distributed energy operation constraint polyhedron feasible set, including: defining a zonotope approximation, where the zonotope approximation is a set of direction vectors, geometric centers, and scaling factors; calculating all possible zonotope approximations of the distributed energy operation constraint polyhedron feasible set; and calculating the distances between all possible zonotope approximations. The extension distance of the feasible set of distributed energy operation constraints polyhedron in all possible zonotope approximate normal directions ;by and The closest solution is the optimal zonotope approximation for the objective, as the zonotope approximation of the feasible set of the distributed energy operation constraint polyhedron.

[0013] In one possible implementation, the zonotope approximation of all distributed energy operation constraint polyhedron feasible sets under the same distribution network node is aggregated to establish the distribution network node aggregate power operation feasible set, including: performing Minkowski and aggregation calculation on the zonotope approximation of all distributed energy operation constraint polyhedron feasible sets under the same distribution network node to obtain the distribution network node aggregate power operation feasible set.

[0014] In one possible implementation, a distribution network model is constructed to form aggregated power operation constraints for distribution network nodes, including: based on fixed point linearization theory, deriving a linear multiphase power flow model of the distribution network as a distribution network model; and forming aggregated power operation constraints for distribution network nodes based on the distribution network model, including: node voltage constraints and line capacity constraints.

[0015] In one possible implementation, based on the distribution network model, the distribution network node aggregate power operation constraints and the distribution network node aggregate power operation feasible set, a virtual power plant adjustable capacity evaluation model is constructed, and the multi-time period adjustable capacity range of the virtual power plant is calculated by the boundary search method, including: taking the distribution network node aggregate power operation constraints and the distribution network node aggregate power operation feasible set as constraints and solving the maximum virtual power plant aggregate power as the goal, constructing a virtual power plant adjustable capacity evaluation model; by successively solving the maximum virtual power plant aggregate power that meets the constraints in different time periods, the multi-time period adjustable capacity range of the virtual power plant is obtained.

[0016] The present application also provides a virtual power plant adjustable capacity evaluation system for implementing the above-mentioned virtual power plant adjustable capacity evaluation method, including: an energy operation constraint analysis module, which is used to analyze the operation constraints of distributed energy, convert the operation constraints of distributed energy into a polyhedron feasible set form, and obtain a polyhedron feasible set of distributed energy operation constraints, where the distributed energy includes: energy storage distributed energy, photovoltaic distributed energy, and HVAC system distributed energy; a zonotope approximation solution module, which is used to calculate the optimal zonotope approximation of the distributed energy operation constraint polyhedron feasible set, and obtain the distributed energy operation constraint polyhedron feasible set. Zonotope approximation of polyhedral feasible sets; distribution network node aggregation module, used to aggregate the zonotope approximations of all distributed energy operation constraint polyhedral feasible sets under the same distribution network node, and establish the distribution network node aggregate power operation feasible set; distribution network model construction module, used to construct the distribution network model and form the distribution network node aggregate power operation constraints; adjustable capacity calculation module, used to construct the virtual power plant adjustable capacity evaluation model based on the distribution network model, distribution network node aggregate power operation constraints and distribution network node aggregate power operation feasible set, and calculate the multi-period adjustable capacity range of the virtual power plant through the boundary search method.

[0017] Compared with the prior art, the present application has the following beneficial effects: it fully considers the time coupling characteristics in the operation constraints of distributed energy, can realize the multi-period adjustable capacity evaluation of virtual power plants, and provides more comprehensive and accurate evaluation results; establishes a feasible set of distribution network node aggregated power operation through the zonotope method, and then considers the restrictions of the distribution network structure and flow constraints on the power aggregation of virtual power plants, and establishes a virtual power plant adjustable capacity evaluation model based on optimal flow, which improves the evaluation accuracy while reducing the calculation complexity. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The drawings described herein are used to provide a further understanding of the embodiments of the present invention, constitute a part of this application, and do not constitute a limitation of the embodiments of the present invention. In the drawings:

[0019] Figure 1A flowchart of a method for evaluating the adjustable capacity of a virtual power plant is shown;

[0020] Figure 2 Schematic diagram of the virtual power plant adjustable capacity evaluation system

[0021] Figure 3 The structural diagram of the 15-bus distribution system used in the case of the present invention;

[0022] Figure 4 It is a diagram of the adjustable capability evaluation results of the Monte Carlo method and the method proposed by the present invention;

[0023] Figure 5 A schematic diagram of the adjustable capacity range of the virtual power plant at different time points;

[0024] Figure 6 It is a trend chart of the change of the adjustable capacity range of the virtual power plant at different time points;

[0025] Figure 7 This is the evaluation result diagram of the upper and lower limits of the virtual power plant's adjustable capacity. DETAILED DESCRIPTION

[0026] In order to make the objectives, technical solutions and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with examples and drawings. The illustrative implementation scheme of the present application and its description are only used to explain the present application and are not intended to limit the present application.

[0027] See also Figure 1 As shown, Figure 1 The flowchart of the method for evaluating the adjustable capacity of a virtual power plant is shown in FIG1. ​​The method includes: S1. Analyzing the operating constraints of distributed energy, converting the operating constraints of distributed energy into a polyhedral feasible set, and obtaining a polyhedral feasible set of distributed energy operating constraints. Distributed energy includes: energy storage distributed energy, photovoltaic distributed energy, and HVAC system distributed energy; S2. Calculating the optimal zonotope approximation of the polyhedral feasible set of distributed energy operating constraints, and obtaining the zonotope approximation of the polyhedral feasible set of distributed energy operating constraints; S3. Aggregating the zonotope approximations of the polyhedral feasible sets of all distributed energy operating constraints under the same distribution network node, and establishing a distribution network node aggregate power operating feasible set; S4. Constructing a distribution network model to form distribution network node aggregate power operating constraints; S5. Based on the distribution network model, the distribution network node aggregate power operating constraints, and the distribution network node aggregate power operating feasible set, construct a virtual power plant adjustable capacity evaluation model, and calculate the multi-period adjustable capacity range of the virtual power plant by the boundary search method.

[0028] Specifically, this scheme first expresses the operating constraints of distributed energy as a polyhedral feasible set; then uses zonotope as an approximation of the polyhedral feasible set and aggregates it to form an aggregated power operation feasible set of distribution network nodes; finally, establishes a virtual power plant adjustable capacity evaluation model to calculate the multi-period adjustable capacity range of the virtual power plant.

[0029] The improvement of this scheme lies in that, by considering the power coupling characteristics and time coupling characteristics in the distributed energy operation constraints, a polyhedral feasible set of distributed energy operation constraints is established, all distributed energy on a single distribution network node is aggregated through the zonotope method to form a distribution network node aggregated power operation feasible set, and a virtual power plant adjustable capacity evaluation model is established based on network constraints such as line flow and voltage limitation, and the adjustable capacity range of the virtual power plant in multiple time periods is calculated, which reduces the calculation complexity and improves the evaluation accuracy.

[0030] Step S1 is to construct a polyhedral feasible set of distributed energy operation constraints. Consider three typical distributed energy sources: energy storage distributed energy, photovoltaic distributed energy, and HVAC system distributed energy. Assume that the discrete time range is The operating constraints of various types of distributed energy are as follows:

[0031] 1) Energy storage distributed energy, whose operating constraints include: the output active power of energy storage is placed between the upper and lower limits of the output active power, the output reactive power of energy storage is placed between the upper and lower limits of the reactive power, the capacity of energy storage does not exceed the rated capacity of the energy storage device, the ramp rate of energy storage output is placed between the upper and lower limits of the ramp rate, and the energy state of energy storage is placed between the energy limits of the energy storage device.

[0032] Specifically, assuming that the amount of energy storage is Indicates that for the k of The energy storage on the phase, its output power is constrained as:

[0033] (1)

[0034] (2)

[0035] (3)

[0036] (4)

[0037] (5)

[0038] (6)

[0039] (7)

[0040] Where: ; Output active power for energy storage, Output reactive power for energy storage. , They are the upper and lower limits of the output active power respectively; , are the upper and lower limits of reactive power; is the rated capacity of the energy storage device; is the fixed power factor of the energy storage device; , are the upper and lower limits of the climbing rate; is the self-discharge coefficient of energy storage; is the power conversion efficiency; is the time interval; For energy storage at all times t energy state; , is the energy limit of the energy storage device.

[0041] 2) Photovoltaic distributed energy, its operating constraints include: the active power of photovoltaic output is set between 0 and the upper limit of active power, the reactive power of photovoltaic output is set between the upper and lower limits of reactive power, and the capacity of photovoltaic does not exceed the maximum capacity of the photovoltaic unit.

[0042] Specifically, assuming that the number of photovoltaic Indicates that for the k of The output power of the photovoltaic power plant on the phase is constrained as follows:

[0043] (8)

[0044] (9)

[0045] (10)

[0046] (11)

[0047] Where: ; , is the active power and reactive power output by photovoltaic; is the upper limit of active power; is the photovoltaic power factor; , Reactive power upper and lower limits; It is the maximum capacity of the photovoltaic unit.

[0048] 3) Distributed energy of HVAC system, whose operation constraints include: the active power of HVAC is set between 0 and the active power limit, the reactive power of HVAC is set between the upper and lower limits of reactive power, the capacity of HVAC does not exceed the rated capacity of HVAC, and the indoor and outdoor temperatures of HVAC are set between the upper and lower limits of allowable temperatures.

[0049] Specifically, it is assumed that the number of photovoltaic Indicates that for the k of The output power of the photovoltaic power plant on the phase is constrained as follows:

[0050] (12)

[0051] (13)

[0052] (14)

[0053] (15)

[0054] (17)

[0055] (18)

[0056] Where: ; is the active power of HVAC; is the power limit; is the reactive power of HVAC; , Reactive power upper and lower limits; Rated capacity for HVAC; is the power factor of HVAC; , For at the moment t Indoor and outdoor temperatures; , are the upper and lower limits of the allowed temperature. Formula (17) is the indoor temperature dynamics, where and are the thermal characteristic parameters of the building and the environment, For the time interval.

[0057] Considering the above distributed energy operation constraints, the distributed energy operation constraints are converted into a polyhedral feasible set form, and the polyhedral feasible set of distributed energy operation constraints is obtained, including: S11, converting the distributed energy operation constraints into a general linear approximate representation; S12, converting the distributed energy operation constraints of the general linear approximate representation into a linear inequality equation group representation; S13, defining feasible power solutions that satisfy the linear inequality equation group to form a polyhedral feasible set of distributed energy operation constraints.

[0058] Specifically, the operating constraints of distributed energy can be expressed by the following general linear approximation:

[0059] (1) Power constraints:

[0060] (19)

[0061] (20)

[0062] (2) Climbing constraints:

[0063] (twenty one)

[0064] (3) State constraints:

[0065] (twenty two)

[0066] In addition, all distributed energy sources have internal states driven by system power consumption or generation, such as the energy state of energy storage and the temperature of HVAC. The capacity constraints of distributed resources can be linearly approximated as follows:

[0067] (twenty three)

[0068] Using linear inequalities The system of equations is used to represent the operating constraints of distributed energy resources, thereby defining all feasible power trajectories with respect to the operating constraints. Convex polyhedron :

[0069] (twenty four).

[0070] Step S2 is to calculate the optimal zonotope approximation of the feasible set of the distributed energy operation constraint polyhedron. It includes: S21, defining the zonotope approximation, the zonotope approximation is a set of direction vectors, geometric centers and scaling factors; S22, calculating all possible zonotope approximations of the feasible set of the distributed energy operation constraint polyhedron; S23, calculating the distances between all possible zonotope approximations The extension distance of the feasible set of distributed energy operation constraints polyhedron in all possible zonotope approximate normal directions ; S24, with and The closest solution is the optimal zonotope approximation for the objective, as the zonotope approximation of the feasible set of the distributed energy operation constraint polyhedron.

[0071] Specifically, the polyhedron feasible set of distributed energy operation constraints in the zonotope approximation step S1 is constructed, and the zonotope can be expressed as follows:

[0072] (25)

[0073] Where: is the direction vector, is the geometric center, is the scaling factor. The vector defines the direction in which the zonotope can be extended, and Determines the scale of the zonotope along the direction. A vector consisting of active power and reactive power is injected into each time node.

[0074] Compute all possible zonotope approximations of the feasible set of distributed energy operation constraint polyhedra such that: , that is:

[0075] (26)

[0076] The distance between all possible zonotope approximations of the feasible set of distributed energy operation constraint polyhedron is calculated as :

[0077] (27)

[0078] in is a matrix containing the normal vectors of all possible zonotope faces.

[0079] Similarly, the extension distance of the feasible set of distributed energy operation constraints polyhedron in all possible zonotope approximate normal directions is expressed as .

[0080] when When , zonotope can perfectly approximate the polyhedron. Therefore, the optimal zonotope approximation of the feasible set of distributed energy operation constraint polyhedron can be obtained by solving the following optimization problem:

[0081] (28).

[0082] Step S3 is to aggregate the distributed energy resources of a single distribution network node and establish a feasible set of aggregated power operation of the distribution network node, including: performing Minkowski and aggregation calculations on the zonotope approximation of the polyhedron feasible set of operation constraints of all distributed energy resources under the same distribution network node to obtain the feasible set of aggregated power operation of the distribution network node.

[0083] Specifically, based on the zonotope approximation of the polyhedron feasible set of distributed energy operation constraints obtained in the above steps, the following steps are aggregated to establish the distribution network node power operation feasible set:

[0084] use represents the aggregate adjustable power domain, which is given by the Minkowski sum of the feasible sets of all distributed energy resources:

[0085] (29)

[0086] For the zonotope approximation obtained in step S2 and , whose Minkowski sum can be efficiently calculated as:

[0087] (30)

[0088] Therefore, the zonotope approximation aggregation of distributed resources is calculated as:

[0089] (31)

[0090] The feasible set of distribution network node aggregate power operation is calculated as:

[0091] (32)

[0092] Step S4 is to construct a distribution network model and form the aggregated power operation constraints of the distribution network nodes. It includes: S41, based on the fixed point linearization theory, deriving the linear multiphase power flow model of the distribution network as the distribution network model; S42, based on the distribution network model, forming the aggregated power operation constraints of the distribution network nodes, including: node voltage constraints and line capacity constraints.

[0093] Specifically, based on the fixed point linearization theory, the linear multiphase power flow model of the distribution network is derived to form the distribution network node aggregation power operation constraints:

[0094] (33)

[0095] (34)

[0096] (35)

[0097] (36)

[0098] In the formula, , is the injected power at the coupling point between the distribution network and the transmission network; the matrix , ,vector , , , , scalar , These are system parameters. is the vector composed of active power and reactive power of each node, is the bus voltage vector, is the line current vector.

[0099] Therefore, the distribution network node aggregate power operation constraints include the following node voltage constraints and line capacity constraints:

[0100] (37)

[0101] (38).

[0102] Step S5 is to construct a virtual power plant adjustable capacity evaluation model, and calculate the multi-period adjustable capacity range of the virtual power plant by the boundary search method. It includes: S51, taking the distribution network node aggregate power operation constraints and the distribution network node aggregate power operation feasible set as constraints, and solving the maximum virtual power plant aggregate power as the goal, constructing a virtual power plant adjustable capacity evaluation model; S52, by successively solving the maximum virtual power plant aggregate power that meets the constraints in different time periods, the multi-period adjustable capacity range of the virtual power plant is obtained.

[0103] Specifically, the distribution network model established in the above steps S1-S4 and the distribution network node aggregate power operation constraints are combined to construct a virtual power plant adjustable capacity evaluation model. This problem is expressed as an optimization problem to solve the virtual power plant aggregate power limit under various operation constraints. Based on the distribution network model, the following optimal power flow model is established:

[0104] (39)

[0105] (40)

[0106] (41)

[0107] (42)

[0108] (43)

[0109] (44)

[0110] (45)

[0111] (46)

[0112] Where: ; Formula (46) represents the feasible set constraints for the aggregated power operation of distribution network nodes.

[0113] The basic idea of ​​evaluating the adjustability of virtual power plants in this scheme is to find the adjustable capacity boundary of aggregated power by changing. By changing from 0 to , a series of optimal power flow calculations of equations (40)-(46) are performed to obtain a set of results, forming an aggregated power range that changes with , which represents the adjustable capacity range of virtual power plants in multiple time periods.

[0114] See also Figure 2 As shown, Figure 2 This is a structural diagram of the virtual power plant adjustable capacity evaluation system. The system is used to implement the above-mentioned virtual power plant adjustable capacity evaluation method, including: an energy operation constraint analysis module, which is used to analyze the operation constraints of distributed energy, convert the operation constraints of distributed energy into a polyhedral feasible set form, and obtain a polyhedral feasible set of distributed energy operation constraints. Distributed energy includes: energy storage distributed energy, photovoltaic distributed energy and HVAC system distributed energy; a zonotope approximation solution module, which is used to calculate the optimal zonotope approximation of the distributed energy operation constraint polyhedron feasible set, and obtain the zonotope approximation of the distributed energy operation constraint polyhedron feasible set; a distribution network node aggregation module, which is used to aggregate the zonotope approximations of all distributed energy operation constraint polyhedron feasible sets under the same distribution network node, and establish a distribution network node aggregated power operation feasible set; a distribution network model construction module, which is used to construct a distribution network model and form a distribution network node aggregated power operation constraint; an adjustable capacity calculation module, which is used to construct a virtual power plant adjustable capacity evaluation model based on the distribution network model, the distribution network node aggregated power operation constraints and the distribution network node aggregated power operation feasible set, and calculate the multi-period adjustable capacity range of the virtual power plant through a boundary search method.

[0115] It should be noted that the virtual power plant adjustable capacity assessment system is used to implement the above-mentioned virtual power plant adjustable capacity assessment method, corresponds one-to-one to the method, and has an effect that is compatible with the method, so it will not be elaborated on here.

[0116] See also Figure 3-7 In order to verify the effectiveness of the designed method, the following Figure 3The 15-bus radial distribution system shown in the figure is used for example testing. Energy storage, photovoltaics and HVAC are used as distributed energy sources aggregated by the virtual power plant, and their distribution locations in the distribution network are shown in Table 1. The probability distribution functions of various distributed energy parameters are shown in Tables 2, 3 and 4, and their parameters are randomly drawn from the corresponding distribution functions. The self-consumption coefficient of energy storage is 0.99, and the power conversion efficiency of energy storage is 95%.

[0117] Table 1 Distribution location of distributed energy

[0118] type Access Node Photovoltaic 2,3,5,11,12,14 Energy Storage 3,5,5,7,8,10,15 HVAC 3,7,8,10,12,15

[0119] Table 2 Probability distribution of photovoltaic parameters

[0120] parameter Mean Standard Deviation Minimum Maximum Rated power(MW) 0.2 0.5 0.1 0.4 Rated capacity(MVA) 0.4 0.5 0.2 0.6

[0121] Table 3 Probability distribution of energy storage parameters

[0122] parameter Mean Standard Deviation Minimum Maximum Rated power(MW) 0.2 0.8 0.12 0.3 Rated capacity(MVA) 0.25 0.5 0.2 0.45 Maximum energy (MWh) 1 5 0.7 1.7 Minimum energy (MWh) 0.2 1 0.1 0.3 Initial energy (MWh) 0.5 5 0.2 1.2

[0123] Table 4 Probability distribution of HVAC parameters

[0124] parameter Mean Standard Deviation Minimum Maximum Rated power(MW) 0.2 1 0.16 0.4 Rated capacity(MVA) 0.3 0.5 0.22 0.56 Maximum temperature(℃) 28 0 0 0 Minimum temperature (℃) 22 0 0 0 Initial temperature(℃) 24 1 23 27

[0125] In order to illustrate the effectiveness of the present invention, the method proposed in the present invention is used to evaluate the adjustable capacity range of the virtual power plant at a single time point, and the Monte Carlo simulation method is used to sample the adjustable capacity of the virtual power plant, which is compared with the present invention. The evaluation results of the adjustable capacity range of the virtual power plant are shown in Figure 2. Figure 4 As shown. The gray point area sampled by the Monte Carlo method is regarded as the actual adjustable capacity range of the virtual power plant. It can be found that the adjustable capacity range evaluated by the present invention is almost consistent with the actual adjustable range and is within the actual adjustable range, which can avoid the overestimation of the adjustable capacity and the situation where the scheduling instruction cannot be executed, verifying the effectiveness of the present invention.

[0126] At the same time, compared with the calculation time of 10704.67 seconds for using a Monte Carlo simulation method to perform a large number of sampling to obtain the adjustable capacity range, the calculation time required by the present invention is only 33.25 seconds, which has advantages in calculation efficiency and practical application prospects.

[0127] In order to study the effect of the present invention on analyzing the adjustable capacity evaluation of the virtual power plant within a duration, a simulation analysis of the adjustable capacity evaluation in multiple time periods was performed. The simulation time range was 24 hours and the granularity was 1 hour. Figure 5 The schematic diagram of the adjustable capacity range of the virtual power plant in multiple time periods calculated by the present invention is shown. The results show that the present invention can effectively handle the power coupling and time coupling characteristics of distributed energy and evaluate the adjustable capacity range at different time points. Figure 5 It can be seen from the figure that over time, the size and shape of the adjustable capacity range of the virtual power plant have changed significantly. Further, the trend of the adjustable capacity range is shown in Figure 6 The adjustable capacity of the virtual power plant has the following characteristics: during the day (7:00-19:00), photovoltaics can generate electricity. As the photovoltaic output continues to increase, the virtual power plant can aggregate more power, and its adjustable capacity is significantly improved. On the contrary, at night (20:00-6:00), because photovoltaics cannot output active power, the only distributed energy that the virtual power plant can aggregate is energy storage and HVAC, and the adjustable capacity is at a low level. Furthermore, during Figure 7 The changes in the parameters related to the active power adjustable capability of the virtual power plant are analyzed in this paper, including the active power constraints and the upper and lower limits of the ramp rate at each time point. It can be observed that the active power adjustable capability of the virtual power plant fluctuates over time. During daytime photovoltaic power generation, the upper and lower limits of the active power and the ramp rate increase. However, since photovoltaics cannot absorb active power from the grid, the change trend of the lower limit of active power is not closely related to the photovoltaic output.

[0128] In summary, the present invention can effectively handle the problem of estimating the adjustable capacity of virtual power plants in multiple time periods and has high reliability.

[0129] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for evaluating the adjustable capacity of a virtual power plant, characterized in that: include: Analyze the operation constraints of distributed energy, convert the operation constraints of distributed energy into a polyhedral feasible set form, and obtain a polyhedral feasible set of distributed energy operation constraints, wherein the distributed energy includes: energy storage distributed energy, photovoltaic distributed energy, and HVAC system distributed energy; Calculate the optimal zonotope approximation of the polyhedron feasible set of distributed energy operation constraints, and obtain the zonotope approximation of the polyhedron feasible set of distributed energy operation constraints; The zonotope approximation of all the distributed energy operation constraint polyhedron feasible sets under the same distribution network node is aggregated to establish the distribution network node aggregate power operation feasible set; Construct a distribution network model to form the aggregate power operation constraints of distribution network nodes; Based on the distribution network model, the distribution network node aggregate power operation constraints and the distribution network node aggregate power operation feasible set, a virtual power plant adjustable capacity evaluation model is constructed, and the multi-period adjustable capacity range of the virtual power plant is calculated by the boundary search method; The operation constraints of distributed energy are analyzed, and the operation constraints of distributed energy are converted into a polyhedral feasible set form to obtain a polyhedral feasible set of distributed energy operation constraints, including: converting the operation constraints of the distributed energy into a universal linear approximate representation; converting the distributed energy operation constraints of the universal linear approximate representation into a linear inequality equation group representation; defining feasible power solutions that satisfy the linear inequality equation group to form a polyhedral feasible set of distributed energy operation constraints; The optimal zonotope approximation of the distributed energy operation constraint polyhedron feasible set is calculated, and the zonotope approximation of the distributed energy operation constraint polyhedron feasible set is obtained, including: defining the zonotope approximation, the zonotope approximation is a set of direction vectors, geometric centers and scaling coefficients; calculating all possible zonotope approximations of the distributed energy operation constraint polyhedron feasible set; calculating the distances between all possible zonotope approximations. The extension distance of the feasible set of distributed energy operation constraints polyhedron in all possible zonotope approximate normal directions ;by and The closest to solving the optimal zonotope approximation for the objective is the zonotope approximation of the feasible set of the polyhedron of distributed energy operation constraints; The zonotope approximation of all the distributed energy operation constraint polyhedron feasible sets under the same distribution network node is aggregated to establish the distribution network node aggregate power operation feasible set, including: performing Minkowski and aggregation calculation on the zonotope approximation of all the distributed energy operation constraint polyhedron feasible sets under the same distribution network node to obtain the distribution network node aggregate power operation feasible set; Construct a distribution network model and form the aggregate power operation constraints of the distribution network nodes, including: deriving a linear multiphase power flow model of the distribution network as the distribution network model based on the fixed point linearization theory; forming the aggregate power operation constraints of the distribution network nodes based on the distribution network model, including: node voltage constraints and line capacity constraints; Based on the distribution network model, the distribution network node aggregate power operation constraints and the distribution network node aggregate power operation feasible set, a virtual power plant adjustable capacity evaluation model is constructed, and the multi-time period adjustable capacity range of the virtual power plant is calculated by the frontier search method, including: taking the distribution network node aggregate power operation constraints and the distribution network node aggregate power operation feasible set as constraints and solving the maximum virtual power plant aggregate power as the goal, constructing a virtual power plant adjustable capacity evaluation model; by successively solving the maximum virtual power plant aggregate power that meets the constraints in different time periods, the multi-time period adjustable capacity range of the virtual power plant is obtained.

2. A method for evaluating the adjustable capacity of a virtual power plant according to claim 1, characterized in that: The operation constraints of the energy storage distributed energy include: the active power output of the energy storage is placed between the upper and lower limits of the output active power, the reactive power output of the energy storage is placed between the upper and lower limits of the reactive power, the capacity of the energy storage does not exceed the rated capacity of the energy storage device, the ramp rate of the energy storage output is placed between the upper and lower limits of the ramp rate, and the energy state of the energy storage is placed between the energy limits of the energy storage device.

3. The method for evaluating the adjustable capacity of a virtual power plant according to claim 1, characterized in that: The operation constraints of the photovoltaic distributed energy include: the active power of the photovoltaic output is set between 0 and the upper limit of the active power, the reactive power of the photovoltaic output is set between the upper and lower limits of the reactive power, and the photovoltaic capacity does not exceed the maximum capacity of the photovoltaic unit.

4. The method for evaluating the adjustable capacity of a virtual power plant according to claim 1, characterized in that: The operation constraints of the distributed energy of the HVAC system include: the active power of the HVAC is set between 0 and the active power limit, the reactive power of the HVAC is set between the upper and lower limits of the reactive power, the capacity of the HVAC does not exceed the rated capacity of the HVAC, and the indoor and outdoor temperatures of the HVAC are set between the upper and lower limits of the allowable temperature.

5. A virtual power plant adjustable capacity evaluation system, characterized in that: A method for evaluating the adjustable capacity of a virtual power plant according to any one of claims 1 to 4, comprising: An energy operation constraint analysis module is used to analyze the operation constraints of distributed energy, convert the operation constraints of distributed energy into a polyhedral feasible set form, and obtain a polyhedral feasible set of distributed energy operation constraints. The distributed energy includes: energy storage distributed energy, photovoltaic distributed energy, and HVAC system distributed energy; The zonotope approximation solution module is used to calculate the optimal zonotope approximation of the distributed energy operation constraint polyhedron feasible set, and obtain the zonotope approximation of the distributed energy operation constraint polyhedron feasible set; The distribution network node aggregation module is used to aggregate the zonotope approximation of all the distributed energy operation constraint polyhedron feasible sets under the same distribution network node, and establish the distribution network node aggregate power operation feasible set; The distribution network model building module is used to build the distribution network model and form the distribution network node aggregation power operation constraints; The adjustable capacity calculation module is used to construct a virtual power plant adjustable capacity evaluation model based on the distribution network model, the distribution network node aggregate power operation constraints and the distribution network node aggregate power operation feasible set, and calculate the multi-period adjustable capacity range of the virtual power plant through the boundary search method.

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