Power adjustable resource modeling method, system, equipment and medium

Abstract

The invention belongs to the technical field of electric power systems, and discloses an electric power adjustable resource modeling method, system, device and medium, the method comprises the following steps: carrying out typical electric power adjustable resource monomer modeling based on time-space transfer characteristics according to typical electric power adjustable resources to obtain a typical electric power adjustable resource monomer model; carrying out scheduling feasible region description on the typical power adjustable resource monomer model by utilizing a scheduling decision period to obtain a convex polyhedron scheduling feasible region; scaling a convex polyhedron scheduling feasible region of the typical power adjustable resource monomer model into a chinonol polyhedron feasible region by taking the highest similarity as a principle; and obtaining the scheduling feasible region of the power adjustable resource cluster through Minkowski summation according to the feasible region of the sino polyhedron. According to the method, the adjustment capability of the power adjustable resource cluster can be depicted, and then the power adjustable resource cluster can be used as a boundary condition to participate in a scheduling model, so that the problem that a superior power scheduling department is difficult to control mass distributed power adjustable resources is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to a method, system, device and medium for modeling adjustable power resources. Background Art

[0002] Driven by the "dual carbon" goal, new energy sources such as wind power and photovoltaics are widely connected to the power system; however, the uncertainty, intermittency and volatility of the output of renewable energy such as wind and solar power increase the peak load burden of the system. The demand response characteristics of flexible adjustable loads and the energy time-shift characteristics of energy storage equipment can provide a large amount of balancing resources for the power grid and support the supply and demand balance of the power grid. Making full use of the spatiotemporal transfer characteristics of power resources can alleviate the randomness and volatility of wind and solar power generation and other renewable resources to a certain extent, and improve power supply reliability and renewable energy utilization. At present, domestic and foreign scholars have carried out a series of studies on the modeling of adjustable power resources considering spatiotemporal characteristics, forming a variety of monomer or aggregate models that can be connected to the control of power dispatching departments, such as dynamic state distribution models, power distribution models, and virtual battery models.

[0003] However, the existing technology still has many defects when modeling the adjustable power resources considering the temporal and spatial characteristics. For example, the temporal and spatial characteristics of the adjustable power resources of the power system are not sufficiently considered during the modeling process, which makes it difficult to fully utilize its adjustment characteristics and maximize its adjustment capacity; the modeling methods for different adjustable power resources vary greatly, and the universality of the established models is poor; in addition, there are few modeling studies on the aggregation of distributed conventional generators represented by self-contained power plants to participate in system regulation, and a suitable modeling method is urgently needed to support the participation of distributed conventional generators in system regulation. Summary of the invention

[0004] The embodiments of the present invention provide a method, system, device and medium for modeling adjustable power resources to solve the above technical problems in the prior art.

[0005] In order to have a basic understanding of some aspects of the disclosed embodiments, a brief summary is given below. This summary is not intended to be a general review, nor is it intended to identify key / important components or to delineate the scope of protection of these embodiments. Its only purpose is to present some concepts in a simple form as a preface to the detailed description that follows.

[0006] According to a first aspect of an embodiment of the present invention, a method for modeling adjustable power resources is provided.

[0007] In one embodiment, the method for modeling adjustable power resources includes:

[0008] Analyze the adjustable power resources and determine the typical adjustable power resources among the adjustable power resources;

[0009] According to the typical adjustable power resources, based on the time-space transfer characteristics, a typical adjustable power resource monomer model is performed to obtain a corresponding typical adjustable power resource monomer model;

[0010] The scheduling feasible domain of the typical power adjustable resource monomer model is characterized by using the scheduling decision cycle, and the convex polyhedron scheduling feasible domain corresponding to the typical power adjustable resource monomer model is obtained; and the convex polyhedron scheduling feasible domain of the typical power adjustable resource monomer model is scaled to the Chino polyhedron feasible domain based on the principle of the highest similarity;

[0011] According to the Chino polyhedron feasible domain, the scheduling feasible domain of the power adjustable resource cluster is obtained through Minkowski summation; and the regulation capability of the power adjustable resource cluster is characterized based on the scheduling feasible domain of the power adjustable resource cluster.

[0012] In one embodiment, the typical adjustable power resources include: distributed conventional power generator sets, non-industrial air conditioning loads, electric vehicle loads and distributed battery energy storage.

[0013] In one embodiment, according to the typical adjustable power resource, based on the time-space transfer characteristics, a typical adjustable power resource monomer model is performed, and the corresponding typical adjustable power resource monomer model is obtained, including:

[0014] According to the output, ramp power and start / stop status of the distributed conventional power generator set, the distributed conventional power generator set is modeled to obtain a single unit model of the distributed conventional power generator set;

[0015] The formula of the single-unit model of the distributed conventional power generator set is:

[0016]

[0017] In the formula, is the output of the distributed conventional power generator set at time t; is the output of the distributed conventional power generation unit at time t-1; is the start / stop status of the distributed conventional power generation unit at time t, 1 indicates that it is in the start state, and 0 indicates that it is in the stop state; are the lower and upper limits of the output of the distributed conventional power generation unit; R u is the maximum ramp-up power of the distributed conventional power generator set; R d It is the maximum ramp-down power of the distributed conventional power generator set.

[0018] In one embodiment, according to the typical adjustable power resource, based on the time-space transfer characteristics, a typical adjustable power resource monomer model is performed, and the corresponding typical adjustable power resource monomer model is obtained, including:

[0019] According to the load state, charge and discharge power, battery capacity, scheduling time, maximum power, standby power, power consumption and ambient temperature of the non-industrial air-conditioning virtual battery, the non-industrial air-conditioning virtual battery is modeled to obtain the non-industrial air-conditioning load monomer model;

[0020] The formula of the non-industrial air conditioning load single model is:

[0021]

[0022] In the formula, is the SOC state of the non-industrial air-conditioning virtual battery at time t; is the SOC state of the non-industrial air-conditioning virtual battery at time t+1; are the charge and discharge power of the virtual battery of the non-industrial air conditioner at time t; E ac is the capacity of the non-industrial air-conditioning virtual battery; Δt is the time step of the non-industrial air-conditioning virtual battery participating in the power system dispatch; They are the maximum power and standby power of non-industrial air conditioner respectively; is the electric power consumed by the non-industrial air conditioner at time t; T max 、T min are the highest and lowest temperatures acceptable to the user; T in,t is the real-time temperature in the room at time t.

[0023] In one embodiment, according to the typical adjustable power resource, based on the time-space transfer characteristics, a typical adjustable power resource monomer model is performed, and the corresponding typical adjustable power resource monomer model is obtained, including:

[0024] According to the capacity state, charge and discharge power, battery capacity and dispatch time of the electric vehicle battery, the electric vehicle battery is modeled to obtain the electric vehicle battery monomer model;

[0025] The formula of the electric vehicle battery monomer model is:

[0026]

[0027] In the formula, is the capacity state of the electric vehicle battery at time t; is the capacity state of the electric vehicle battery at time t+1; They are the charging and discharging efficiency of electric vehicle batteries respectively; are the charging and discharging power of the electric vehicle battery at time t; Δt is the time step for the electric vehicle to participate in the power system dispatch; are the maximum charge and discharge power of electric vehicle batteries respectively; The upper limit of the capacity of electric vehicle batteries; It is the lower limit of electric vehicle battery capacity.

[0028] In one embodiment, according to the typical adjustable power resource, based on the time-space transfer characteristics, a typical adjustable power resource monomer model is performed, and the corresponding typical adjustable power resource monomer model is obtained, including:

[0029] According to the load state, charging and discharging power, battery storage capacity and scheduling time of the distributed energy storage battery, the distributed energy storage battery is modeled to obtain a distributed battery energy storage monomer model;

[0030] The formula of the distributed battery energy storage monomer model is:

[0031]

[0032] In the formula, is the SOC state of the distributed energy storage battery at time t; is the SOC state of the distributed energy storage battery at time t-1; be is the dissipation coefficient; They are the charging and discharging efficiency of distributed energy storage batteries; are the charging and discharging power of the distributed energy storage battery at time t respectively; is the storage capacity of the distributed energy storage battery; Δt is the time step of the battery energy storage participating in the power system dispatch; They are the upper limits of charging and discharging power of distributed energy storage batteries; The lower limit of SOC state of distributed energy storage battery; It is the upper limit of SOC state of distributed energy storage battery.

[0033] In one embodiment, the calculation formula for obtaining the scheduling feasible region of the power adjustable resource cluster by Minkowski summation according to the Chino polyhedron feasible region is:

[0034]

[0035] In the formula, is the feasible region for cluster scheduling; is the scheduling feasible domain of the jth adjustable resource unit in the cluster; They respectively represent the scheduling feasible domain of electric vehicles within the power adjustable resource cluster, the scheduling feasible domain of non-industrial air conditioners, the scheduling feasible domain of distributed energy storage batteries, and the scheduling feasible domain of distributed conventional power generators.

[0036] According to a second aspect of an embodiment of the present invention, a system for modeling adjustable power resources is provided.

[0037] In one embodiment, the power adjustable resource modeling system includes:

[0038] An adjustable power resource analysis module is used to analyze the adjustable power resources and determine typical adjustable power resources among the adjustable power resources;

[0039] A typical adjustable resource modeling module is used to perform typical adjustable power resource monomer modeling based on the typical adjustable power resources and the time-space transfer characteristics to obtain a corresponding typical adjustable power resource monomer model;

[0040] The model scheduling feasible domain characterization module is used to characterize the scheduling feasible domain of the typical power adjustable resource monomer model by using the scheduling decision cycle, and obtain the convex polyhedron scheduling feasible domain corresponding to the typical power adjustable resource monomer model; and based on the principle of the highest similarity, the convex polyhedron scheduling feasible domain of the typical power adjustable resource monomer model is scaled to a Chino polyhedron feasible domain;

[0041] The cluster scheduling feasible domain determination module is used to obtain the scheduling feasible domain of the power adjustable resource cluster through Minkowski summation according to the Chino polyhedron feasible domain; and characterize the regulation capability of the power adjustable resource cluster based on the scheduling feasible domain of the power adjustable resource cluster.

[0042] In one embodiment, the typical adjustable power resources include: distributed conventional power generator sets, non-industrial air conditioning loads, electric vehicle loads and distributed battery energy storage.

[0043] In one embodiment, when the typical adjustable resource modeling module performs typical adjustable power resource monomer modeling based on the typical adjustable power resource and the time-space transfer characteristics to obtain the corresponding typical adjustable power resource monomer model,

[0044] According to the output, ramp power and start / stop status of the distributed conventional power generator set, the distributed conventional power generator set is modeled to obtain a single unit model of the distributed conventional power generator set;

[0045] The formula of the single-unit model of the distributed conventional power generator set is:

[0046]

[0047] In the formula, is the output of the distributed conventional power generator set at time t; is the output of the distributed conventional power generation unit at time t-1; is the start / stop status of the distributed conventional power generator set at time t, 1 indicates that it is in the start state, and 0 indicates that it is in the stop state; are the lower and upper limits of the output of the distributed conventional power generation unit; R u is the maximum ramp-up power of the distributed conventional power generator set; R d It is the maximum ramp-down power of the distributed conventional power generator set.

[0048] In one embodiment, when the typical adjustable resource modeling module performs typical adjustable power resource monomer modeling based on the typical adjustable power resource and the time-space transfer characteristics to obtain the corresponding typical adjustable power resource monomer model,

[0049] According to the load state, charge and discharge power, battery capacity, scheduling time, maximum power, standby power, power consumption and ambient temperature of the non-industrial air-conditioning virtual battery, the non-industrial air-conditioning virtual battery is modeled to obtain the non-industrial air-conditioning load monomer model;

[0050] The formula of the non-industrial air conditioning load single model is:

[0051]

[0052] In the formula, is the SOC state of the non-industrial air-conditioning virtual battery at time t; is the SOC state of the non-industrial air-conditioning virtual battery at time t+1; are the charge and discharge power of the virtual battery of the non-industrial air conditioner at time t; E ac is the capacity of the non-industrial air-conditioning virtual battery; Δt is the time step of the non-industrial air-conditioning virtual battery participating in the power system dispatch; They are the maximum power and standby power of non-industrial air conditioner respectively; is the electric power consumed by the non-industrial air conditioner at time t; T max , T min are the highest and lowest temperatures acceptable to the user; T in,t is the real-time temperature in the room at time t.

[0053] In one embodiment, when the typical adjustable resource modeling module performs typical adjustable power resource monomer modeling based on the typical adjustable power resource and the time-space transfer characteristics to obtain the corresponding typical adjustable power resource monomer model,

[0054] According to the capacity state, charge and discharge power, battery capacity and dispatch time of the electric vehicle battery, the electric vehicle battery is modeled to obtain the electric vehicle battery monomer model;

[0055] Among them, the formula of the electric vehicle battery monomer model is:

[0056]

[0057] In the formula, is the capacity state of the electric vehicle battery at time t; is the capacity state of the electric vehicle battery at time t+1; They are the charging and discharging efficiency of electric vehicle batteries respectively; are the charging and discharging power of the electric vehicle battery at time t; Δt is the time step for the electric vehicle to participate in the power system dispatch; are the maximum charge and discharge power of electric vehicle batteries respectively; The upper limit of the capacity of electric vehicle batteries; It is the lower limit of electric vehicle battery capacity.

[0058] In one embodiment, when the typical adjustable resource modeling module performs typical adjustable power resource monomer modeling based on the typical adjustable power resource and the time-space transfer characteristics to obtain the corresponding typical adjustable power resource monomer model,

[0059] According to the load state, charging and discharging power, battery storage capacity and scheduling time of the distributed energy storage battery, the distributed energy storage battery is modeled to obtain a distributed battery energy storage monomer model;

[0060] The formula of the distributed battery energy storage monomer model is:

[0061]

[0062] In the formula, is the SOC state of the distributed energy storage battery at time t; is the SOC state of the distributed energy storage battery at time t-1; be is the dissipation coefficient; They are the charging and discharging efficiency of distributed energy storage batteries; are the charging and discharging power of the distributed energy storage battery at time t respectively; is the storage capacity of the distributed energy storage battery; Δt is the time step of the battery energy storage participating in the power system dispatch; They are the upper limits of charging and discharging power of distributed energy storage batteries; The lower limit of SOC state of distributed energy storage battery; It is the upper limit of SOC state of distributed energy storage battery.

[0063] In one embodiment, the cluster scheduling feasible region determination module calculates the scheduling feasible region of the power adjustable resource cluster by Minkowski summation based on the Chino polyhedron feasible region as follows:

[0064]

[0065] In the formula, is the feasible region for cluster scheduling; is the scheduling feasible domain of the jth adjustable resource unit in the cluster; They respectively represent the scheduling feasible domain of electric vehicles within the power adjustable resource cluster, the scheduling feasible domain of non-industrial air conditioners, the scheduling feasible domain of distributed energy storage batteries, and the scheduling feasible domain of distributed conventional power generators.

[0066] According to a third aspect of an embodiment of the present invention, a computer device is provided.

[0067] In one embodiment, the computer device includes a memory and a processor, the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0068] According to a fourth aspect of embodiments of the present invention, a computer-readable storage medium is provided.

[0069] In one embodiment, the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0070] The technical solution provided by the embodiment of the present invention may have the following beneficial effects:

[0071] The present invention firstly establishes a monomer operation model for typical distributed adjustable power resources based on their spatiotemporal characteristics, and then characterizes the convex polyhedron scheduling feasible domain of each adjustable power resource monomer. In view of the problem that the boundary complexity of the convex polyhedron scheduling feasible domain increases exponentially with the increase of the number of monomers participating in the aggregation during aggregation, the convex polyhedron scheduling feasible domain is converted into a Chino polyhedron feasible domain through optimization solution, and then the scheduling feasible domain of the adjustable power resource cluster is obtained through Minkowski summation, so as to realize the characterization of the regulating capacity of the adjustable power resource cluster in a certain area, and then it can be used as a boundary condition to participate in the scheduling model, solving the problem that the superior power dispatching department has difficulty in controlling massive distributed adjustable power resources; by characterizing the adjustable power resource scheduling feasible domain by Chino polyhedron, the problem of high aggregation complexity of the scheduling feasible domain of a large number of distributed adjustable power resources is alleviated, the calculation scale is reduced, and the computer solution speed is accelerated.

[0072] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0074] Figure 1 is a flow chart of a method for modeling adjustable power resources according to an exemplary embodiment;

[0075] Figure 2 is a structural block diagram of a power adjustable resource modeling system according to an exemplary embodiment;

[0076] Figure 3 is a schematic diagram of the diameters of a convex polyhedron and an inscribed Chino polyhedron in the direction of a normal vector according to an exemplary embodiment;

[0077] Figure 4 is a schematic diagram of a Minkowski summation method according to an exemplary embodiment;

[0078] Figure 5 The figure is a schematic diagram showing the structure of a computer device according to an exemplary embodiment. DETAILED DESCRIPTION

[0079] The following description and accompanying drawings fully illustrate the specific embodiments of this article so that those skilled in the art can practice them. Parts and features of some embodiments may be included in or replace parts and features of other embodiments. The scope of the embodiments of this article includes the entire scope of the claims, as well as all available equivalents of the claims. Herein, the terms "first", "second", etc. are only used to distinguish one element from another, without requiring or implying any actual relationship or order between these elements. In fact, the first element can also be called the second element, and vice versa. Moreover, the terms "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, so that the structure, device or equipment including a series of elements includes not only those elements, but also other elements that are not explicitly listed, or also include elements inherent to such structure, device or equipment. In the absence of more restrictions, the elements defined by the sentence "including one..." do not exclude the existence of other identical elements in the structure, device or equipment including the elements. Each embodiment is described in a progressive manner herein, and each embodiment focuses on the differences from other embodiments, and the same and similar parts between the embodiments can be referred to each other.

[0080] The terms "longitudinal", "lateral", "upper", "lower", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc. in this document indicate the orientation or position relationship based on the orientation or position relationship shown in the drawings, and are only for the convenience of describing this document and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operate in a specific orientation, and therefore cannot be understood as a limitation on the present invention. In the description of this document, unless otherwise specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a mechanical connection or an electrical connection, it can also be the internal communication of two elements, it can be a direct connection, or it can be an indirect connection through an intermediate medium. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to specific circumstances.

[0081] As used herein, the term "plurality" means two or more than two, unless otherwise specified.

[0082] In this document, the character " / " indicates that the preceding and following objects are in an "or" relationship. For example, A / B means: A or B.

[0083] In this article, the term "and / or" is a description of the association relationship between objects, indicating that three relationships may exist. For example, A and / or B means: A or B, or, A and B.

[0084] It should be understood that, although the various steps in the flow chart are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear description in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the figure may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these sub-steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

[0085] Each module in the device or system of the present application can be implemented in whole or in part by software, hardware, or a combination thereof. The above modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to the above modules.

[0086] In the absence of conflict, the embodiments of the present invention and the features of the embodiments may be combined with each other.

[0087] Figure 1An embodiment of a method for modeling adjustable power resources of the present invention is shown.

[0088] In this optional embodiment, the method for modeling adjustable power resources includes:

[0089] Step S101, analyzing the adjustable power resources to determine typical adjustable power resources among the adjustable power resources;

[0090] Step S103, according to the typical adjustable power resource, based on the time-space transfer characteristics, a typical adjustable power resource monomer model is performed to obtain a corresponding typical adjustable power resource monomer model;

[0091] Step S105, using the scheduling decision cycle, characterize the scheduling feasible domain of the typical power adjustable resource monomer model, and obtain the convex polyhedron scheduling feasible domain corresponding to the typical power adjustable resource monomer model; and based on the principle of the highest similarity, scale the convex polyhedron scheduling feasible domain of the typical power adjustable resource monomer model into a Chino polyhedron feasible domain;

[0092] Step S107, according to the Chino polyhedron feasible region, the scheduling feasible region of the power adjustable resource cluster is obtained by Minkowski summation; and the regulation capability of the power adjustable resource cluster is characterized based on the scheduling feasible region of the power adjustable resource cluster.

[0093] Figure 2 An embodiment of a power adjustable resource modeling system of the present invention is shown.

[0094] In this optional embodiment, the power adjustable resource modeling system includes:

[0095] The power adjustable resource analysis module 201 is used to analyze the power adjustable resources and determine the typical power adjustable resources among the power adjustable resources;

[0096] A typical adjustable resource modeling module 203 is used to perform typical adjustable power resource monomer modeling according to the typical adjustable power resource and based on the time-space transfer characteristics to obtain a corresponding typical adjustable power resource monomer model;

[0097] The model scheduling feasible domain characterization module 205 is used to characterize the scheduling feasible domain of the typical power adjustable resource monomer model by using the scheduling decision cycle, and obtain the convex polyhedron scheduling feasible domain corresponding to the typical power adjustable resource monomer model; and based on the principle of the highest similarity, the convex polyhedron scheduling feasible domain of the typical power adjustable resource monomer model is scaled to a Chino polyhedron feasible domain;

[0098] The cluster scheduling feasible domain determination module 207 is used to obtain the scheduling feasible domain of the power adjustable resource cluster through Minkowski summation according to the Chino polyhedron feasible domain; and characterize the regulation capability of the power adjustable resource cluster based on the scheduling feasible domain of the power adjustable resource cluster.

[0099] In this embodiment, the typical adjustable power resources include: distributed conventional power generator sets, non-industrial air-conditioning loads, electric vehicle loads and distributed battery energy storage.

[0100] For the above four typical adjustable power resources, based on the time-space transfer characteristics, the typical adjustable power resource monomer modeling is as follows:

[0101] 1. Model the flexible adjustment capability of distributed conventional power generation units represented by self-provided power plants:

[0102]

[0103] In the formula, is the output of the distributed conventional power generator set at time t; is the output of the distributed conventional power generation unit at time t-1; is the start / stop status of the distributed conventional power generation unit at time t, 1 indicates that it is in the start state, and 0 indicates that it is in the stop state; are the lower and upper limits of the output of the distributed conventional power generation unit; R u is the maximum ramp-up power of the distributed conventional power generator set; R d It is the maximum ramp-down power of the distributed conventional power generator set.

[0104] 2. Based on the flexible adjustment capability of non-industrial air conditioning, a single load model of non-industrial air conditioning considering time transfer characteristics is established:

[0105]

[0106] In the formula, is the SOC state of the non-industrial air-conditioning virtual battery at time t; is the SOC state of the non-industrial air-conditioning virtual battery at time t+1; are the charge and discharge power of the virtual battery of the non-industrial air conditioner at time t; E ac is the capacity of the non-industrial air-conditioning virtual battery; Δt is the time step of the non-industrial air-conditioning virtual battery participating in the power system dispatch; They are the maximum power and standby power of non-industrial air conditioner respectively; is the electric power consumed by the non-industrial air conditioner at time t; T max 、T min are the highest and lowest temperatures acceptable to the user; Tin,t is the real-time temperature in the room at time t.

[0107] Among them, the capacity of the non-industrial air conditioning virtual battery is E ac Determined by the following formula:

[0108]

[0109] Where η ac is the energy efficiency ratio of non-industrial air conditioners; They are the maximum upward and downward heat in the room respectively;

[0110] The maximum upward and downward heat adjustment in the room is determined by the following formula:

[0111]

[0112] Where, T set Set the temperature for the user; c room is the heat capacity of the room;

[0113] The relationship between the heating / cooling capacity of non-industrial air conditioners and the electrical power consumption is determined by the following formula:

[0114]

[0115] In the formula, is the electric power consumed by the non-industrial air conditioner at time t; is the heating / cooling capacity of the non-industrial air conditioner at time t; M ac is the indicator variable of the working state of the non-industrial air conditioner, 1 means it is in the heating state, and -1 means it is in the cooling state; η ac The efficiency of energy conversion from electrical energy to heating or cooling.

[0116] 3. Based on the "mobile energy storage" capability of electric vehicles, establish an electric vehicle load monomer model that takes into account the temporal and spatial mobility characteristics:

[0117]

[0118] In the formula, is the capacity state of the electric vehicle battery at time t; is the capacity state of the electric vehicle battery at time t+1; They are the charging and discharging efficiency of electric vehicle batteries respectively; are the charging and discharging power of the electric vehicle battery at time t; Δt is the time step for the electric vehicle to participate in the power system dispatch; are the maximum charge and discharge power of electric vehicle batteries respectively; The upper limit of the capacity of electric vehicle batteries; This is the lower capacity limit of electric vehicle batteries.

[0119] In addition, it should be ensured that the electric vehicle's power reaches the specified range when charging is completed:

[0120]

[0121] In the formula, The amount of electricity when charging the electric vehicle is complete; The expected power level when charging is completed, set by the user.

[0122] 4. Based on the power storage capacity of distributed energy storage battery equipment, a distributed battery energy storage monomer model considering the energy time shift characteristics is established:

[0123]

[0124] In the formula, is the SOC state of the distributed energy storage battery at time t; is the SOC state of the distributed energy storage battery at time t-1; be is the dissipation coefficient; They are the charging and discharging efficiency of distributed energy storage batteries; are the charging and discharging power of the distributed energy storage battery at time t respectively; is the storage capacity of the distributed energy storage battery; Δt is the time step of the battery energy storage participating in the power system dispatch; They are the upper limits of charging and discharging power of distributed energy storage batteries; The lower limit of SOC state of distributed energy storage battery; It is the upper limit of SOC state of distributed energy storage battery.

[0125] In addition, in the above-mentioned embodiments, for the established distributed conventional power generation unit model, the non-industrial air conditioning load unit model considering the time transfer characteristics, the electric vehicle load unit model considering the time and space mobility characteristics, and the distributed battery energy storage unit model considering the energy time shift characteristics, their scheduling feasible domains can all be represented by a convex polyhedron represented by the following set of inequality constraints:

[0126]

[0127] In the formula, represents the convex polyhedron scheduling feasible domain of the power adjustable resource monomer; P represents the power feasible point of the power adjustable resource monomer within the N time period scheduling cycle; A and b represent the coefficients of the inequality constraints in the power adjustable resource monomer model when they are expressed in matrix form; is the N-dimensional real number field.

[0128] In order to reduce the complexity of feasible domain aggregation, it is necessary to convert the obtained convex polyhedron scheduling feasible domain of the typical power adjustable resource monomer into its inscribed approximate Chino polyhedron to represent the scheduling feasible domain of the power adjustable resource monomer, that is, based on the principle of the highest similarity, the convex polyhedron scheduling feasible domain of the typical power adjustable resource monomer model is scaled into the Chino polyhedron feasible domain;

[0129] A Chino polyhedron can be characterized by a center point and multiple generators:

[0130]

[0131] In the formula, is the feasible domain of Chino polyhedron scheduling; P represents the power feasible point of the power adjustable resource monomer in the N time period scheduling cycle; c is the center point of the Chino polyhedron; G is the generator matrix, which represents the extension direction of each section of the Chino polyhedron; β is the generator scaling factor, which represents the extension distance of each section of the Chino polyhedron; is the N-dimensional real number field.

[0132] The larger the volume of the inscribed Chino polyhedron, the higher the similarity with the original convex polyhedron scheduling feasible domain, and the more fully the description of the regulation potential of power adjustable resources; therefore, a random search algorithm is used to randomly generate S normal vectors α s (s=1,…S), and the diameters of the feasible regions of the inscribed Chino polyhedron and the convex polyhedron in the direction of each normal vector are determined by optimization, such as Figure 3 As shown, the similarity between the feasible domain of the inscribed Chino polyhedron scheduling and the feasible domain of the convex polyhedron scheduling in this direction is expressed by the following formula:

[0133]

[0134] In the formula, Λ s Kinos polyhedron With convex polyhedron similarity; are the diameters of the two polyhedron regions in the same direction.

[0135] The feasible region of convex polyhedron scheduling is α s The diameter in the direction is calculated as:

[0136]

[0137] In the formula, The feasible region for scheduling a convex polyhedron is in α s The diameter in the direction; |α s ||2 is the normal vector α s 2-norm of; x is a convex polyhedron points inside; ε takes a very large constant.

[0138] The feasible region of Chino polyhedron scheduling is α s The diameter in the direction is calculated as:

[0139]

[0140] In the formula, The feasible region for scheduling the Chino polyhedron is α s The diameter in the direction; the generator matrix G is the same setting quantity for all power adjustable resource monomers, β s is the generator scaling factor to be determined.

[0141] The parameters c and β of the feasible region of Chino polyhedron scheduling are obtained by solving the following optimization problem:

[0142] (1) Objective function:

[0143]

[0144] (2) Constraints:

[0145] The Chino polyhedron must be inscribed in the scheduling feasible region of the convex polyhedron:

[0146]

[0147] That is, the following constraints apply:

[0148] Ac+|AG|β≤b

[0149] By solving the optimization problem, the parameters c and β of the feasible domain of Chino polyhedron scheduling can be obtained. Combined with the generator matrix G, the feasible domain of Chino polyhedron scheduling of the power adjustable resource unit can be determined.

[0150] In addition, in the above optional embodiment, for the obtained Chino polyhedron scheduling feasible domain of the typical power adjustable resource monomer, the scheduling feasible domain of the power adjustable resource cluster is obtained by Minkowski summation, thereby completing the characterization of the adjustment capability of the power adjustable resource cluster in a certain area. And according to the Chino polyhedron feasible domain, the calculation formula for obtaining the scheduling feasible domain of the power adjustable resource cluster by Minkowski summation is:

[0151]

[0152] In the formula, is the feasible region for cluster scheduling; is the scheduling feasible domain of the jth adjustable resource unit in the cluster; They respectively represent the scheduling feasible domain of electric vehicles within the power adjustable resource cluster, the scheduling feasible domain of non-industrial air conditioners, the scheduling feasible domain of distributed energy storage batteries, and the scheduling feasible domain of distributed conventional power generators.

[0153] Figure 4 The Minkowski summation method of the feasible domain of Chino polyhedron scheduling of two adjustable power resource monomers is demonstrated using two-dimensional space as an example, that is, the area included in the feasible domain of adjustable power resource monomer 2 swept along the envelope of the feasible domain of adjustable power resource monomer 1.

[0154] Figure 5 An embodiment of a computer device of the present invention is shown. The computer device may be a server, and the computer device includes a processor, a memory, and a network interface connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store static information and dynamic information data. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, the steps in the above method embodiment are implemented.

[0155] Those skilled in the art will understand that Figure 5 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present invention, and does not constitute a limitation on the computer device to which the solution of the present invention is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0156] In addition, the present invention also provides a computer device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiment when executing the computer program.

[0157] In addition, the present invention further provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps in the above method embodiment are implemented.

[0158] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided by the present invention can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory or optical memory, etc. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0159] The present invention is not limited to the structures which have been described above and shown in the drawings, and various modifications and changes may be made without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.

Claims

1. A method for modeling adjustable power resources, characterized in that: include: Analyze the adjustable power resources and determine the typical adjustable power resources among the adjustable power resources; According to the typical adjustable power resources, based on the time-space transfer characteristics, a typical adjustable power resource monomer model is performed to obtain a corresponding typical adjustable power resource monomer model; The scheduling feasible domain of the typical power adjustable resource monomer model is characterized by using the scheduling decision cycle, and the convex polyhedron scheduling feasible domain corresponding to the typical power adjustable resource monomer model is obtained; and the convex polyhedron scheduling feasible domain of the typical power adjustable resource monomer model is scaled to the Chino polyhedron feasible domain based on the principle of the highest similarity; According to the Chino polyhedron feasible domain, the scheduling feasible domain of the power adjustable resource cluster is obtained by Minkowski summation; And based on the scheduling feasible domain of the power adjustable resource cluster, the regulation capability of the power adjustable resource cluster is characterized.

2. The method for modeling adjustable power resources according to claim 1, characterized in that: The typical adjustable power resources include: distributed conventional power generators, non-industrial air-conditioning loads, electric vehicle loads and distributed battery energy storage.

3. The method for modeling adjustable power resources according to claim 2, characterized in that: According to the typical adjustable power resources, based on the time-space transfer characteristics, a typical adjustable power resource monomer model is performed, and the corresponding typical adjustable power resource monomer model includes: According to the output, ramp power and start / stop status of the distributed conventional power generator set, the distributed conventional power generator set is modeled to obtain a single unit model of the distributed conventional power generator set; The formula of the single-unit model of the distributed conventional power generator set is: Where P t g is the output of the distributed conventional power generator set at time t; is the output of the distributed conventional power generation unit at time t-1; is the start / stop status of the distributed conventional power generation unit at time t, 1 indicates that it is in the start state, and 0 indicates that it is in the stop state; are the lower and upper limits of the output of the distributed conventional power generation unit; R u is the maximum ramp-up power of the distributed conventional power generator set; R d It is the maximum ramp-down power of the distributed conventional power generator set.

4. The method for modeling adjustable power resources according to claim 2, characterized in that: According to the typical adjustable power resources, based on the time-space transfer characteristics, a typical adjustable power resource monomer model is performed, and the corresponding typical adjustable power resource monomer model includes: According to the load state, charge and discharge power, battery capacity, scheduling time, maximum power, standby power, power consumption and ambient temperature of the non-industrial air-conditioning virtual battery, the non-industrial air-conditioning virtual battery is modeled to obtain the non-industrial air-conditioning load monomer model; The formula of the non-industrial air conditioning load single model is: In the formula, is the SOC state of the non-industrial air-conditioning virtual battery at time t; is the SOC state of the non-industrial air-conditioning virtual battery at time t+1; are the charge and discharge power of the virtual battery of the non-industrial air conditioner at time t; E ac is the capacity of the non-industrial air-conditioning virtual battery; Δt is the time step of the non-industrial air-conditioning virtual battery participating in the power system dispatch; are the maximum power and standby power of non-industrial air conditioner respectively; P t ac is the electric power consumed by the non-industrial air conditioner at time t; T max 、T min are the highest and lowest temperatures acceptable to the user; T in,t is the real-time temperature in the room at time t.

5. The method for modeling adjustable power resources according to claim 2, characterized in that: According to the typical adjustable power resources, based on the time-space transfer characteristics, a typical adjustable power resource monomer model is performed, and the corresponding typical adjustable power resource monomer model includes: According to the capacity state, charge and discharge power, battery capacity and dispatch time of the electric vehicle battery, the electric vehicle battery is modeled to obtain the electric vehicle battery monomer model; Among them, the formula of the electric vehicle battery monomer model is: In the formula, is the capacity state of the electric vehicle battery at time t; is the capacity state of the electric vehicle battery at time t+1; They are the charging and discharging efficiency of electric vehicle batteries respectively; are the charging and discharging power of the electric vehicle battery at time t; Δt is the time step for the electric vehicle to participate in the power system dispatch; are the maximum charge and discharge power of electric vehicle batteries respectively; The upper limit of the capacity of electric vehicle batteries; It is the lower limit of electric vehicle battery capacity.

6. The method for modeling adjustable power resources according to claim 2, characterized in that: According to the typical adjustable power resources, based on the time-space transfer characteristics, a typical adjustable power resource monomer model is performed, and the corresponding typical adjustable power resource monomer model includes: According to the load state, charging and discharging power, battery storage capacity and scheduling time of the distributed energy storage battery, the distributed energy storage battery is modeled to obtain a distributed battery energy storage monomer model; The formula of the distributed battery energy storage monomer model is: In the formula, is the SOC state of the distributed energy storage battery at time t; is the SOC state of the distributed energy storage battery at time t-1; be is the dissipation coefficient; They are the charging and discharging efficiency of distributed energy storage batteries; are the charging and discharging power of the distributed energy storage battery at time t respectively; is the storage capacity of the distributed energy storage battery; Δt is the time step of the battery energy storage participating in the power system dispatch; They are the upper limits of charging and discharging power of distributed energy storage batteries; The lower limit of SOC state of distributed energy storage battery; It is the upper limit of SOC state of distributed energy storage battery.

7. The method for modeling adjustable power resources according to claim 1, characterized in that: According to the Chino polyhedron feasible region, the calculation formula for obtaining the scheduling feasible region of the power adjustable resource cluster through Minkowski summation is: In the formula, is the feasible region for cluster scheduling; is the scheduling feasible domain of the jth adjustable resource unit in the cluster; They respectively represent the scheduling feasible domain of electric vehicles within the power adjustable resource cluster, the scheduling feasible domain of non-industrial air conditioners, the scheduling feasible domain of distributed energy storage batteries, and the scheduling feasible domain of distributed conventional power generators.

8. A power adjustable resource modeling system, characterized in that: include: An adjustable power resource analysis module is used to analyze the adjustable power resources and determine typical adjustable power resources among the adjustable power resources; A typical adjustable resource modeling module is used to perform typical adjustable power resource monomer modeling based on the typical adjustable power resources and the time-space transfer characteristics to obtain a corresponding typical adjustable power resource monomer model; The model scheduling feasible domain characterization module is used to characterize the scheduling feasible domain of the typical power adjustable resource monomer model by using the scheduling decision cycle, and obtain the convex polyhedron scheduling feasible domain corresponding to the typical power adjustable resource monomer model; and based on the principle of the highest similarity, the convex polyhedron scheduling feasible domain of the typical power adjustable resource monomer model is scaled to a Chino polyhedron feasible domain; A cluster scheduling feasible domain determination module is used to obtain the scheduling feasible domain of the power adjustable resource cluster through Minkowski summation according to the Chino polyhedron feasible domain; And based on the scheduling feasible domain of the power adjustable resource cluster, the regulation capability of the power adjustable resource cluster is characterized.

9. The power adjustable resource modeling system according to claim 8, characterized in that: The typical adjustable power resources include: distributed conventional power generators, non-industrial air-conditioning loads, electric vehicle loads and distributed battery energy storage.

10. The power adjustable resource modeling system according to claim 9, characterized in that: When the typical adjustable resource modeling module performs typical adjustable power resource monomer modeling according to the typical adjustable power resource and based on the time-space transfer characteristics to obtain a corresponding typical adjustable power resource monomer model, According to the output, ramp power and start / stop status of the distributed conventional power generator set, the distributed conventional power generator set is modeled to obtain a single unit model of the distributed conventional power generator set; The formula of the single-unit model of the distributed conventional power generator set is: Where P t g is the output of the distributed conventional power generator set at time t; is the output of the distributed conventional power generation unit at time t-1; is the start / stop status of the distributed conventional power generation unit at time t, 1 indicates that it is in the start state, and 0 indicates that it is in the stop state; are the lower and upper limits of the output of the distributed conventional power generation unit; R u is the maximum ramp-up power of the distributed conventional power generator set; R d It is the maximum ramp-down power of the distributed conventional power generator set.

11. The power adjustable resource modeling system according to claim 9, characterized in that: When the typical adjustable resource modeling module performs typical adjustable power resource monomer modeling according to the typical adjustable power resource and based on the time-space transfer characteristics to obtain a corresponding typical adjustable power resource monomer model, According to the load state, charge and discharge power, battery capacity, scheduling time, maximum power, standby power, power consumption and ambient temperature of the non-industrial air-conditioning virtual battery, the non-industrial air-conditioning virtual battery is modeled to obtain the non-industrial air-conditioning load monomer model; The formula of the non-industrial air conditioning load single model is: In the formula, is the SOC state of the non-industrial air-conditioning virtual battery at time t; is the SOC state of the non-industrial air-conditioning virtual battery at time t+1; are the charge and discharge power of the virtual battery of the non-industrial air conditioner at time t; E ac is the capacity of the non-industrial air-conditioning virtual battery; Δt is the time step of the non-industrial air-conditioning virtual battery participating in the power system dispatch; are the maximum power and standby power of non-industrial air conditioner respectively; P t ac is the electric power consumed by the non-industrial air conditioner at time t; T max 、T min are the highest and lowest temperatures acceptable to the user; T in,t is the real-time temperature in the room at time t.

12. The power adjustable resource modeling system according to claim 9, characterized in that: When the typical adjustable resource modeling module performs typical adjustable power resource monomer modeling according to the typical adjustable power resource and based on the time-space transfer characteristics to obtain a corresponding typical adjustable power resource monomer model, According to the capacity state, charge and discharge power, battery capacity and dispatch time of the electric vehicle battery, the electric vehicle battery is modeled to obtain the electric vehicle battery monomer model; Among them, the formula of the electric vehicle battery monomer model is: In the formula, is the capacity state of the electric vehicle battery at time t; is the capacity state of the electric vehicle battery at time t+1; They are the charging and discharging efficiency of electric vehicle batteries respectively; are the charging and discharging power of the electric vehicle battery at time t; Δt is the time step for the electric vehicle to participate in the power system dispatch; are the maximum charge and discharge power of electric vehicle batteries respectively; The upper limit of the capacity of electric vehicle batteries; It is the lower limit of electric vehicle battery capacity.

13. The power adjustable resource modeling system according to claim 9, characterized in that: When the typical adjustable resource modeling module performs typical adjustable power resource monomer modeling according to the typical adjustable power resource and based on the time-space transfer characteristics to obtain a corresponding typical adjustable power resource monomer model, According to the load state, charging and discharging power, battery storage capacity and scheduling time of the distributed energy storage battery, the distributed energy storage battery is modeled to obtain a distributed battery energy storage monomer model; The formula of the distributed battery energy storage monomer model is: In the formula, is the SOC state of the distributed energy storage battery at time t; is the SOC state of the distributed energy storage battery at time t-1; be is the dissipation coefficient; They are the charging and discharging efficiency of distributed energy storage batteries; are the charging and discharging power of the distributed energy storage battery at time t respectively; is the storage capacity of the distributed energy storage battery; Δt is the time step of the battery energy storage participating in the power system dispatch; They are the upper limits of charging and discharging power of distributed energy storage batteries; The lower limit of SOC state of distributed energy storage battery; It is the upper limit of SOC state of distributed energy storage battery.

14. The power adjustable resource modeling system according to claim 8, characterized in that: The calculation formula of the cluster scheduling feasible region determination module when obtaining the scheduling feasible region of the power adjustable resource cluster through Minkowski summation based on the Chino polyhedron feasible region is: In the formula, is the feasible region for cluster scheduling; is the scheduling feasible domain of the jth adjustable resource unit in the cluster; They respectively represent the scheduling feasible domain of electric vehicles within the power adjustable resource cluster, the scheduling feasible domain of non-industrial air conditioners, the scheduling feasible domain of distributed energy storage batteries, and the scheduling feasible domain of distributed conventional power generators.

15. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

16. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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