An energy storage and load aggregation method and system based on a comprehensive virtual generator and battery model

Abstract

The application belongs to the technical field of virtual power plants, and relates to an energy storage and load aggregation method and system based on a comprehensive virtual generator and battery model. The method comprises the following steps: according to an aggregated model of a comprehensive virtual generator and a virtual battery, aggregating multiple energy devices in a plurality of distributed source-grid-load-storage energy systems to obtain an aggregate; for undetermined parameters in the aggregated model, solving the parameters according to a constructed parameter robust optimization solving model; the parameter robust optimization solving model is divided into two-stage constraints, and a second-stage constraint condition is established based on a distributed source-grid-load-storage refined mathematical model; performing approximate linear summation on the aggregated model according to a Minkowski approximate linear summation method to solve an adjustable power domain of the aggregate, and realizing resource aggregation of the plurality of distributed source-grid-load-storage energy systems. The aggregation processing and solving method optimizes resource configuration in the plurality of distributed source-grid-load-storage energy systems, and improves the economy and flexibility of the system.

Description

Technical Field

[0001] This invention belongs to the field of virtual power plant technology, specifically relating to a method and system for energy storage and load aggregation based on a comprehensive virtual generator and battery model. Background Technology

[0002] Virtual power plants play a crucial role between the grid management system and distributed energy resources. They require the aggregation of operational characteristics of distributed energy resources, rather than directly reporting detailed physical models of all distributed energy resources to the upper-level grid. This is primarily due to two considerations: First, it simplifies communication and optimization problem solving. Directly reporting detailed physical models of all distributed energy resources to the upper-level grid may result in missing or incorrect model information during communication. Furthermore, the global grid optimization problem is difficult to solve due to the large number of variables. Aggregating the operational characteristics of distributed energy resources significantly simplifies the communication process and the solution of optimization problems, improving system efficiency and stability. Second, it protects trade secrets and maintains a competitive market advantage. Virtual power plants often participate in various electricity market transactions driven by value. To maintain a dominant position in market competition, they are reluctant to disclose detailed internal model information to the grid. Aggregating the operational characteristics of distributed energy resources allows virtual power plants to protect trade secrets while maintaining their competitive advantage. Therefore, aggregating the operational characteristics of distributed energy resources in virtual power plants is an effective management and optimization method that helps simplify system operation and protect trade secrets, thereby better enabling participation in the electricity market and optimizing grid dispatch.

[0003] The technical characteristics of distributed energy resources are typically described in detail using power inequality constraints, mainly including three categories: power constraints, ramping constraints, and capacity constraints. Considering all power constraints of distributed energy resources, the adjustable power domain of a virtual power plant can be mapped to a high-dimensional convex polyhedron in geometric space. To solve for this adjustable power domain of the virtual power plant, a specific high-dimensional convex polyhedron can be used for approximate approximation from the inside or outside.

[0004] Currently, there are two main mathematical models for suitable and widely adopted high-dimensional convex polyhedra: the virtual battery model and the virtual generator model. The virtual battery model has high complexity when facing multi-dimensional uncertainties, and its accuracy is insufficient due to neglecting the dynamic characteristics of the battery. It is suitable for short-term scheduling but difficult to accurately predict long-term behavior. The virtual generator model simplifies system complexity, but lacks an accurate description of the physical processes, is difficult to handle nonlinear behavior, lacks flexibility, and has a large computational cost in large-scale applications. Summary of the Invention

[0005] The purpose of this invention is to provide a method and system for energy storage and load aggregation based on a comprehensive virtual generator and battery model, which solves the problems of insufficient model accuracy, complexity and inaccurate dynamics.

[0006] This invention is achieved through the following technical solution:

[0007] On one hand, this invention discloses an energy storage and load aggregation method based on a comprehensive virtual generator and battery model, comprising the following processes:

[0008] Based on the constructed integrated virtual generator and virtual battery aggregation model, the multi-energy devices in multiple distributed source-grid-load-storage energy systems are aggregated to obtain an aggregate;

[0009] For the undetermined parameters in the aggregation model, the parameters are solved according to the constructed parameter robust optimization solution model. The parameter robust optimization solution model is divided into two-stage constraints. The second-stage constraint conditions are established based on the distributed source-grid-load-storage refined mathematical model. The distributed source-grid-load-storage refined mathematical model includes a distributed power source refined mathematical model, a distributed load refined mathematical model, a distributed energy storage refined mathematical model, and a distributed network refined mathematical model.

[0010] The aggregation model is approximated by linear summation using the Minkowski approximate linear summation method. The adjustable power domain of the aggregation is obtained by solving the problem, thereby realizing resource aggregation of a multi-distributed source-grid-load-storage energy system.

[0011] Based on the above technical solutions, the refined mathematical model of distributed power generation is further divided into a mathematical model of new energy units and a mathematical model of traditional energy units.

[0012] The mathematical model for new energy generating units is as follows:

[0013]

[0014] In the formula: R represents the set of new energy generating units in the distributed generation-grid-load-storage energy system; T represents the set of dispatching time periods in the distributed generation-grid-load-storage energy system; P r,t,max P represents the maximum actual output of the new energy generator unit r during time period t; r,t The active power output of the new energy generator unit r during time period t;

[0015] The mathematical model for traditional energy generating units is:

[0016]

[0017] In the formula: G represents the set of traditional energy generating units in a distributed energy source-grid-load-storage system; P g,t P represents the active power output of a traditional energy generator unit g during time period t;g,max P represents the upper limit of the active power output of a traditional energy generator set g; g,min R is the lower limit of the active power output of a traditional energy generator unit g; g,max R is the upper limit of the output adjustment of the traditional energy generator set g. g,min This is the lower limit for adjusting the output of a traditional energy generator set g.

[0018] Based on the above technical solution, distributed loads are further divided into shiftable loads and reduceable loads according to the type of load response. Shiftable loads correspond to time-based transfers, while reduceable loads correspond to power reductions.

[0019] The refined mathematical model of distributed load is divided into the mathematical model of load that can be shifted and the mathematical model of load that can be reduced.

[0020] The mathematical model for a transferable load is:

[0021]

[0022] In the formula: D represents the set of responsive loads in a distributed energy source-grid-load-storage system; Ω T+ Ω represents the set of time periods during which load is shifted in; T- P represents the set of time periods during which load is removed; shift+,d,t P represents the amount of load that can be moved into the responsive load d within the time period t; shift-,d,t P represents the amount of load that can be removed from the responsive load d within the time period t; shift+,d,max and P shift+,d,min P represents the maximum and minimum values ​​of the load moved into the responsive load d within the time period t, respectively; shift-,d,max and P shift-,d,min These represent the maximum and minimum values ​​of the amount of load removed from the responsive load d within the time period t, respectively.

[0023] The mathematical model for load reduction is as follows:

[0024]

[0025] In the formula: D represents the set of responsive loads in a distributed energy source-grid-load-storage system; Ω Tcut P represents the set of time periods during which load reduction occurs; cut,d,t P represents the amount of load that can be reduced during the time period t, representing the responsive load d. cut,d,max and P cut,d,min These represent the maximum and minimum values ​​of load reduction for the responsive load d within a unit time period, respectively.

[0026] Based on the above technical solution, the refined mathematical model for distributed energy storage is further as follows:

[0027]

[0028] In the formula: ES represents the collection of different distributed energy storage devices in a distributed energy source-grid-load-storage system; E es,t P c,es,t and P dc,es,t This represents the state of charge, charging power, and discharging power of the energy storage device es during time period t; ζ c,E and ζ dc,E These represent the charging efficiency and discharging efficiency of the energy storage device E; es,min E es,max P represents the upper and lower limits of the capacity of the energy storage device es; c,es,max P represents the upper limit of the charging power of the energy storage device es; c,es,min P represents the lower limit of the charging power of the energy storage device es; dc,es,max P represents the upper limit of the discharge power of the energy storage device es; dc,es,min This indicates the lower limit of the discharge power of the energy storage device es.

[0029] Based on the above technical solution, the refined mathematical model of the distributed network only considers power balance constraints when it is established; the power balance constraints of the distributed source-grid-load-storage energy system include the overall power balance constraints of the distributed source-grid-load-storage energy system and the power balance constraints before and after load demand response.

[0030] The overall power balance constraint of a distributed energy source-grid-load-storage system is:

[0031]

[0032] In the formula: P d,t The load demand after demand response for responsive load d; P tie,t For the interaction power of the distributed generation-grid-load-storage energy system and the main power grid via the interconnection line, if P tie,t A positive value indicates that the distributed generation-grid-load-storage energy system purchases electricity from the main power grid. If P tie,t A negative value indicates that the distributed generation-grid-load-storage energy system sells electricity to the main power grid;

[0033] The power balance constraints before and after load demand response are:

[0034]

[0035] In the formula: P d,t The load demand after demand response for responsive load d; P d0,t For responsive load d, load demand is required before demand response; P shift,d,t The load for demand response to be shifted; P cut,d,t To reduce the load on demand response; P shift-,d,t P represents the load that is shifted out during time period t;shift+,d,t The load shifted out during time period t.

[0036] Based on the above technical solution, the integrated model of the virtual generator and the virtual battery is further shown in the following equation:

[0037]

[0038] In the formula: Ω is the feasible region of the interaction power between the aggregate and the large power grid constrained by the aggregation model;

[0039] P Juhe,t Let P be the interaction power between the aggregate and the main grid at time t, corresponding to the interaction power P between the distributed energy source-grid-load-storage system and the main grid via the tie line. tie,t ;

[0040] P represents the power of the aggregate interacting with the main power grid during time period t. Juhe,t,max P represents the upper limit of the power interaction between the aggregate and the main grid during time period t. Juhe,t,min P represents the lower limit of the power of the aggregate interacting with the main grid during time period t. Juhe,t,max and P Juhe,t,min These are time-varying parameters;

[0041] E Juhe,t,max E represents the upper limit of the aggregate's capacity during time period t. Juhe,t,min E represents the lower limit of the aggregate's capacity at time t. Juhe,t,max and E Juhe,t,min These are time-varying parameters;

[0042] R Juhe,max R represents the ramp-up limit of the power exchange between the aggregate and the main power grid at different time periods. Juhe,min R represents the lower limit of the ramp-up power of the aggregate interacting with the main grid at different time periods. Juhe,max and R Juhe,min It is a constant parameter.

[0043] Based on the above technical solution, furthermore, the integrated model of the virtual generator and the virtual battery has many parameters to be determined, specifically including P. Juhe,t,max and P Juhe,t,min E Juhe,t,max and E Juhe,t,min and R Juhe,max and R Juhe,min ;

[0044] The parameter robust optimization solution model is a two-stage robust optimization model, and the above-mentioned parameters are solved through the two-stage robust optimization model;

[0045] The objective function of the two-stage robust optimization model is:

[0046]

[0047] In the formula: x represents the first-stage variable, which is the parameter to be determined in the aggregation model; y represents the second-stage variable, including the output of distributed power sources, the demand response of distributed loads, the capacity and charging / discharging power of distributed energy storage devices, the power interaction of the large power grid, and the newly introduced non-negative relaxation variable of power balance constraints in the distributed source-grid-load-storage energy system; γ P,min γ P,max γ E,min γ E,max γ R,min and γ R,max These are the weighting coefficients for the corresponding parameters; M is a large real number; κ t,+ and κ t,- To introduce nonnegative relaxation variables for power balance constraints;

[0048] First-stage constraints:

[0049]

[0050] In the formula: P max P min R max and R min This represents the initial boundary of the parameters to be determined in the comprehensive aggregation model;

[0051] The second-stage constraint is a refined mathematical model of distributed generation-grid-load-storage energy systems, where the overall power balance constraint of the distributed generation-grid-load-storage energy system is replaced by:

[0052]

[0053] The constraints for the coupling of the first and second stages are the aggregated model of the integrated virtual generator and the virtual battery.

[0054] Based on the above technical solution, further, the process of performing an approximate linear summation on the aggregation model using the Minkowski approximate linear summation method to obtain the adjustable power domain of the aggregation is as follows:

[0055] First, the adjustable power domain of a single distributed source-grid-load-storage energy system is mapped to the geometric space to form a high-dimensional convex polyhedron. Then, the vertex set or half-plane set of the high-dimensional convex polyhedron corresponding to each distributed source-grid-load-storage energy system is solved, and the vertex set or half-plane set is used to characterize the adjustable power domain of each distributed source-grid-load-storage energy system.

[0056] Then, according to the Minkowski approximate linear summation method, the vertices or half-planes of the high-dimensional convex polyhedra corresponding to the adjustable power domains of all distributed source-grid-load-storage energy systems are arranged sequentially and added together to obtain a new set of vertices or half-planes.

[0057] A new set of vertices or half-planes is used to represent the adjustable power domain of multiple aggregates.

[0058] Based on the above technical solution, the high-dimensional convex polyhedron is further characterized using a half-plane form, and the compact form of the representation is as follows:

[0059]

[0060] Where: Ω (j) The adjustable power domain of polymer j described by the half-plane method; The power P is adjusted by aggregate j in each time period within the scheduling period T. Juhe,t The column vectors formed; E, A (j) Let J be the power constraint coefficient matrix corresponding to aggregate j; Let be the power constraint parameter vector corresponding to aggregate j.

[0061] On the other hand, the present invention also discloses an energy storage and load aggregation system based on a comprehensive virtual generator and battery model, comprising:

[0062] The aggregation module is used to aggregate multiple energy devices in multiple distributed source-grid-load-storage energy systems based on the constructed aggregation model of integrated virtual generators and virtual batteries, to obtain an aggregate;

[0063] The solution module is used to solve for the undetermined parameters in the aggregation model based on the constructed parameter robust optimization solution model. The parameter robust optimization solution model is divided into two-stage constraints. The second-stage constraints are established based on the refined mathematical model of distributed source-grid-load-storage. The refined mathematical model of distributed source-grid-load-storage includes a refined mathematical model of distributed power sources, a refined mathematical model of distributed loads, a refined mathematical model of distributed energy storage, and a refined mathematical model of distributed networks.

[0064] The adjustable power domain calculation module is used to perform approximate linear summation on the aggregation model according to the Minkowski approximate linear summation method, and solve for the adjustable power domain of the aggregation, so as to realize the resource aggregation of multi-distributed source-grid-load-storage energy system.

[0065] Compared with the prior art, the present invention has the following beneficial technical effects:

[0066] This invention discloses a method for energy storage and load aggregation based on a comprehensive virtual generator and battery model. By establishing a refined mathematical model of distributed power sources, distributed loads, distributed energy storage, and distributed networks, this invention provides an accurate representation of the collaborative interaction of distributed generation, grid, load, and storage (distributed generation, grid, load, and energy storage) in distributed generation-grid-load-storage energy systems. Simultaneously, the aggregation model of the comprehensive virtual generator and virtual battery, established by comprehensively considering power constraints, ramping constraints, and capacity constraints, ensures the efficiency and reliability of the optimized operation of distributed generation-grid-load-storage energy systems. This method can comprehensively reflect the complexity and dynamics of actual system operation, providing strong support for the optimized operation of distributed generation-grid-load-storage energy systems.

[0067] The key innovations of this invention lie in its effective aggregation processing of the operating characteristics of multiple energy devices in a system using a comprehensive virtual generator and virtual battery aggregation model, and the proposed two-stage robust optimization parameter solution method for the comprehensive aggregation model. This aggregation processing and solution method not only optimizes resource allocation in a multi-distributed source-grid-load-storage energy system, improving its economy and flexibility, but also achieves effective processing of high-dimensional convex polyhedra through half-plane representation and the application of the Minkowski sum principle. This optimizes the operating characteristics of multiple energy devices at the system-wide level and enables the system to adapt to parameter uncertainties and changes in the external environment, thus enhancing the robustness of the distributed source-grid-load-storage energy system. Attached Figure Description

[0068] Figure 1 This is a flowchart illustrating the design process of the energy storage and load aggregation method based on a comprehensive virtual generator and battery model in Embodiment 1 of the present invention.

[0069] Figure 2 This is a schematic diagram of the Minkowski approximate linear summation in Embodiment 1 of the present invention;

[0070] Figure 3 This is a flowchart of the energy storage and load aggregation method based on a comprehensive virtual generator and battery model in Embodiment 2 of the present invention;

[0071] Figure 4 This is a schematic diagram of an energy storage and load aggregation system based on a comprehensive virtual generator and battery model according to the present invention. Detailed Implementation

[0072] To make the objectives, technical solutions, and advantages of the present invention clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention; that is, the described embodiments are only a part of the embodiments of the present invention, and not all of them.

[0073] The detailed description of the embodiments of the present invention provided in the following figures is not intended to limit the scope of the claimed invention, but merely to illustrate one selected embodiment of the invention. All other embodiments obtained by those skilled in the art based on the figures and embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0074] The features and performance of the present invention will be further described in detail below with reference to embodiments.

[0075] Example 1

[0076] From a design perspective, this invention describes an energy storage and load aggregation method based on a comprehensive virtual generator and battery model, such as... Figure 1 As shown, it includes the following parts:

[0077] (I) Establishing a refined mathematical model for distributed source-grid-load-storage

[0078] (1.1) Refined Mathematical Model of Distributed Power Sources

[0079] Distributed power sources refer to various power generation devices deployed in a decentralized manner within a power system, capable of meeting local or specific power demands. These distributed power sources can be either new energy sources or traditional energy sources, mainly including solar photovoltaic power generation systems, wind power generation systems, small hydropower systems, biomass power generation systems, geothermal power generation systems, and micro nuclear power generation systems.

[0080] In terms of mathematical models, new energy sources typically only constrain the unit's output without considering the impact of unit ramping, while traditional energy sources must constrain both the unit's output and the limitations of unit ramping.

[0081] New energy generating units can be modeled as follows:

[0082]

[0083] In the formula: R represents the set of new energy generating units in the distributed generation-grid-load-storage energy system; T represents the set of dispatching time periods in the distributed generation-grid-load-storage energy system; P r,t,max P represents the maximum actual output of the new energy generator unit r during time period t; r,t This refers to the active power output of the new energy generator unit r during the time period t.

[0084] Traditional energy units can be modeled as follows:

[0085]

[0086] In the formula: G represents the set of traditional energy generating units in a distributed energy source-grid-load-storage system; P g,tP represents the active power output of a traditional energy generator unit g during time period t; g,max P represents the upper limit of the active power output of a traditional energy generator set g; g,min R is the lower limit of the active power output of a traditional energy generator unit g; g,max R is the upper limit of the output adjustment of the traditional energy generator set g. g,min This is the lower limit for adjusting the output of a traditional energy generator set g.

[0087] (1.2) Refined Mathematical Model of Distributed Load

[0088] Distributed loads refer to various electrical devices and loads distributed throughout the power system. These are typically located in different places and at different users, including household and commercial loads such as lighting, air conditioning, and appliances; industrial loads such as electric motors, electric boilers, and electric refrigeration; agricultural loads such as irrigation, ventilation, heating, and agricultural machinery; transportation loads such as electric vehicles, electric bicycles, and electric buses; and communication equipment loads such as communication base stations and data centers. These different loads can flexibly adjust their electricity consumption behavior according to their own needs and economic conditions, guided by electricity prices or incentive measures, thereby saving electricity costs—this is known as load demand response.

[0089] Loads can be categorized into shiftable loads and reduceable loads based on their response type, corresponding to shifts in time and reductions in electricity consumption, respectively.

[0090] Specifically, movable loads can be moved over time while keeping the total demand constant, i.e., adjusting working hours while ensuring the completion of the total workload.

[0091] The load can be reduced and cut off within a limited range over a specific period of time.

[0092] The transferable load can be modeled as:

[0093]

[0094] In the formula: D represents the set of responsive loads in a distributed energy source-grid-load-storage system; Ω T+ Ω represents the set of time periods during which load is shifted in; T- P represents the set of time periods during which load is removed; shift+,d,t P represents the amount of load that can be moved into the responsive load d within the time period t; shift-,d,t P represents the amount of load that can be removed from the responsive load d within the time period t; shift+,d,max and P shift+,d,min P represents the maximum and minimum values ​​of the load moved into the responsive load d within the time period t, respectively; shift-,d,max and P shift-,d,min These represent the maximum and minimum values ​​of the load removed from the responsive load d within the time period t, respectively.

[0095] The load reduction can be modeled as follows:

[0096]

[0097] In the formula: D represents the set of responsive loads in a distributed energy source-grid-load-storage system; Ω Tcut P represents the set of time periods during which load reduction occurs; cut,d,t P represents the amount of load that can be reduced during the time period t, representing the responsive load d. cut,d,max and P cut,d,min These represent the maximum and minimum values ​​of load reduction for the responsive load d within a unit time period, respectively.

[0098] (1.3) Refined Mathematical Model of Distributed Energy Storage

[0099] Distributed energy storage refers to various energy storage devices distributed throughout the power system, used to store electrical energy and release it when needed, in order to balance the supply and demand of the power system and improve the system's stability.

[0100] Common distributed energy storage devices include lithium-ion battery energy storage systems, supercapacitor energy storage systems, compressed air energy storage systems, and electric vehicle battery energy storage systems.

[0101] Distributed energy storage devices can be uniformly modeled as follows:

[0102]

[0103] In the formula: ES represents the collection of different distributed energy storage devices in a distributed energy source-grid-load-storage system; E es,t P c,es,t and P dc,es,t This represents the state of charge, charging power, and discharging power of the energy storage device es during time period t; ζ c,E and ζ dc,E These represent the charging efficiency and discharging efficiency of the energy storage device E; es,min E es,max P represents the upper and lower limits of the capacity of the energy storage device es; c,es,max P represents the upper limit of the charging power of the energy storage device es; c,es,min P represents the lower limit of the charging power of the energy storage device es; dc,es,max P represents the upper limit of the discharge power of the energy storage device es; dc,es,min This indicates the lower limit of the discharge power of the energy storage device es.

[0104] (1.4) Refined Mathematical Model of Distributed Networks

[0105] For microgrids and integrated energy systems in industrial parks and commercial complexes with relatively concentrated distributed resources, their scale is smaller and their service range is more limited compared to traditional power transmission and distribution networks. Therefore, in terms of network design, only power balance constraints are typically considered to simplify the modeling and operation management of microgrids and integrated energy systems in industrial parks with relatively concentrated distributed resources, thereby reducing system complexity.

[0106] The power balance constraints of a distributed generation-grid-load-storage energy system consist of two parts: the overall power balance constraints of the distributed generation-grid-load-storage energy system and the power balance constraints before and after load demand response.

[0107] The overall power balance constraint of a distributed energy source-grid-load-storage system is:

[0108]

[0109] In the formula: P d,t The load demand after demand response for responsive load d; P tie,t For the interaction power of the distributed generation-grid-load-storage energy system and the main power grid via the interconnection line, if P tie,t A positive value indicates that the distributed generation-grid-load-storage energy system purchases electricity from the main power grid. If P tie,t A negative value indicates that the distributed generation-grid-load-storage energy system sells electricity to the main power grid.

[0110] The power balance constraints before and after load demand response are:

[0111]

[0112] In the formula: P d,t The load demand after demand response for responsive load d; P d0,t For responsive load d, load demand is required before demand response; P shift,d,t The load for demand response to be shifted; P cut,d,t To reduce the load on demand response; P shift-,d,t P represents the load that is shifted out during time period t; shift+,d,t The load shifted out during time period t.

[0113] (ii) Establishing an integrated model of virtual generators and virtual batteries

[0114] Virtual power plants play a crucial role between the grid management system and distributed energy resources. They require the aggregation of operational characteristics of distributed energy resources, rather than directly reporting detailed physical models of all distributed energy resources to the upper-level grid. This is primarily due to two considerations: First, it simplifies communication and optimization problem solving. Directly reporting detailed physical models of all distributed energy resources to the upper-level grid may result in missing or incorrect model information during communication. Furthermore, the global grid optimization problem is difficult to solve due to the large number of variables. Aggregating the operational characteristics of distributed energy resources significantly simplifies the communication process and the solution of optimization problems, improving system efficiency and stability. Second, it protects trade secrets and maintains a competitive market advantage. Virtual power plants often participate in various electricity market transactions driven by value. To maintain a dominant position in market competition, they are reluctant to disclose detailed internal model information to the grid. Aggregating the operational characteristics of distributed energy resources allows virtual power plants to protect trade secrets while maintaining their competitive advantage. Therefore, aggregating the operational characteristics of distributed energy resources in virtual power plants is an effective management and optimization method that helps simplify system operation and protect trade secrets, thereby better enabling participation in the electricity market and optimizing grid dispatch.

[0115] The technical characteristics of distributed energy resources are typically described in detail using power inequality constraints, mainly including three categories: power constraints, ramping constraints, and capacity constraints. Considering all power constraints of distributed energy resources, the adjustable power domain of a virtual power plant can be mapped to a high-dimensional convex polyhedron in geometric space. To solve for this adjustable power domain of the virtual power plant, a specific high-dimensional convex polyhedron can be used for approximation from the inside or outside. Currently, there are two main mathematical models for suitable and widely adopted high-dimensional convex polyhedra: the virtual battery model and the virtual generator model.

[0116] However, to more comprehensively consider the power constraints, ramping constraints, and capacity constraints of distributed energy resources, this invention adopts a comprehensive model, namely, an aggregated model that integrates virtual generators and virtual batteries. This comprehensive model can more accurately reflect the actual operating status of virtual power plants and provide a more reliable basis for energy management decisions.

[0117] The combined model of the virtual generator and the virtual battery is shown in the following equation:

[0118]

[0119] In the formula: Ω represents the feasible region of the interaction power between the aggregate and the large power grid constrained by the aggregation model; P Juhe,t Let P be the interaction power between the aggregate and the main grid at time t, corresponding to the interaction power P between the distributed energy source-grid-load-storage system and the main grid via the tie line. tie,t ; P represents the power of the aggregate interacting with the main power grid during time period t. Juhe,t,max and P Juhe,t,min Here, E represents the upper and lower limits of the power interaction between the aggregate and the main grid at time t, and is a time-varying parameter. Juhe,t,max and E Juhe,t,min R represents the upper and lower limits of the aggregate's capacity at time t, and is a time-varying parameter. Juhe,max and R Juhe,min This represents the upper and lower limits of the ramp-up power of the aggregate interacting with the main power grid at different time periods, and is a constant parameter.

[0120] The first formula in equation (16) represents the power constraint, the second formula represents the capacity constraint, and the third formula represents the ramp constraint.

[0121] (III) Establishing a robust optimization solution model for aggregate model parameters

[0122] The aggregation model combining virtual generators and virtual batteries has many parameters to be determined, including the upper and lower power limits P of the aggregation's interaction with the main grid during time period t. Juhe,t,max and P Juhe,t,min The upper and lower capacity limits E of the polymer in time period t Juhe,t,max and E Juhe,t,min And the ramp-up limits R of the power interaction between the aggregate and the main grid at different time periods. Juhe,max and R Juhe,min .

[0123] The aforementioned parameters can be solved by constructing a two-stage robust optimization model. In the first stage, variable x represents the parameters to be solved in the integrated aggregation model. In the second stage, variable y represents the output of distributed power sources, the demand response of distributed loads, the capacity and charging / discharging power of distributed energy storage devices, the power interaction of the large power grid, and the newly introduced non-negative relaxation variable κ of the power balance constraint in the distributed source-grid-load-storage energy system. t,+ and κ t,- The two-stage robust optimization model aims to minimize the maximum power balance deviation using the integrated aggregation model, i.e., to find an integrated aggregation model that fits as closely as possible to the actual power feasible region of the distributed source-grid-load-storage energy system.

[0124] The objective function of the two-stage robust optimization model is:

[0125]

[0126] In the formula: x represents the first-stage variable, which is the parameter to be determined in the integrated aggregation model; y represents the second-stage variable, including the output of distributed power sources, the demand response of distributed loads, the capacity and charging / discharging power of distributed energy storage devices, the power interaction of the large power grid, and the newly introduced non-negative relaxation variable of power balance constraints in the distributed source-grid-load-storage energy system; γ P,min γ P,maxγ E,min γ E,max γ R,min and γ R,max These are the weighting coefficients for the corresponding parameters; M is a large real number; κ t,+ and κ t,- To introduce non-negative relaxation variables for power balance constraints.

[0127] First-stage constraints:

[0128]

[0129] In the formula: P max P min R max and R min This represents the initial boundary of the parameters to be determined in the comprehensive aggregation model.

[0130] The second-stage constraint is a refined mathematical model of distributed generation-grid-load-storage energy system, in which the overall power balance constraint of the distributed generation-grid-load-storage energy system should be changed to:

[0131]

[0132] The constraints for the coupling of the first and second stages are the aggregated model of the integrated virtual generator and the virtual battery.

[0133] Two-stage robust optimization models can decompose the original problem into a main problem and subproblems through the Column & Constraint Generation (C&CG) algorithm or the Benders decomposition algorithm, and solve them alternately. There are already many studies explaining this, so it will not be elaborated here.

[0134] (iv) Minkowski approximate linear summation method for integrated aggregation model

[0135] The aggregate model is approximated linearly summed using the Minkowski approximate linear summation method to obtain the adjustable power domain of the aggregate. The specific process is as follows:

[0136] First, the adjustable power domain of a single distributed source-grid-load-storage energy system is mapped to the geometric space to form a high-dimensional convex polyhedron. Then, the vertex set or half-plane set of the high-dimensional convex polyhedron corresponding to each distributed source-grid-load-storage energy system is solved, and the vertex set or half-plane set is used to characterize the adjustable power domain of each distributed source-grid-load-storage energy system.

[0137] Then, according to the Minkowski approximate linear summation method, the vertices or half-planes of the high-dimensional convex polyhedra corresponding to the adjustable power domains of all distributed source-grid-load-storage energy systems are arranged sequentially and added together to obtain a new set of vertices or half-planes.

[0138] A new set of vertices or half-planes is used to represent the adjustable power domain of multiple aggregates.

[0139] Compared to vertex set representation, half-plane representation is more intuitive and widely applicable for representing the feasible region of distributed source-grid-load-storage energy systems. Therefore, a half-plane form is used to represent high-dimensional convex polyhedra, and its compact form is as follows:

[0140]

[0141] Where: Ω (j) The adjustable power domain of aggregate j of a distributed source-grid-load-storage energy system described by the half-plane method; The power P is adjusted by the distributed energy source-grid-load-storage system aggregate j during each time period within the scheduling cycle T. Juhe,t The column vectors formed; E, A (j) This is the power constraint coefficient matrix corresponding to aggregate j of the distributed source-grid-load-storage energy system; Let be the power constraint parameter vector corresponding to the distributed source-grid-load-storage energy system aggregate j.

[0142] The following section uses two distributed energy source-grid-load-storage system aggregates as an example to introduce the Minkowski approximate linear summation method. Figure 2 As shown, the adjustable power domain of the two distributed source-grid-load-storage energy system aggregates, obtained through the Minkowski approximate linear summation, can be expressed as:

[0143]

[0144] In the formula: The Minkowski summation symbol; The adjustable power domain of a composite of two distributed source-grid-load-storage energy systems described by the half-plane method; This is a column vector representing the adjusted power of two distributed source-grid-load-storage energy systems at various times within the scheduling period T.

[0145] Example 2

[0146] like Figure 3 As shown, from an application perspective, this invention discloses an energy storage and load aggregation method based on a comprehensive virtual generator and battery model, comprising the following processes:

[0147] Based on the constructed integrated virtual generator and virtual battery aggregation model, the multi-energy devices in multiple distributed source-grid-load-storage energy systems are aggregated to obtain an aggregate;

[0148] For the undetermined parameters in the aggregation model, the parameters are solved according to the constructed parameter robust optimization solution model. The parameter robust optimization solution model is divided into two-stage constraints. The second-stage constraint conditions are established based on the distributed source-grid-load-storage refined mathematical model. The distributed source-grid-load-storage refined mathematical model includes a distributed power source refined mathematical model, a distributed load refined mathematical model, a distributed energy storage refined mathematical model, and a distributed network refined mathematical model.

[0149] The aggregation model is approximated by linear summation using the Minkowski approximate linear summation method. The adjustable power domain of the aggregation is obtained by solving the problem. Based on the adjustable power domain, a control strategy is specified to control the distributed source-grid-load-storage energy system, thereby realizing resource aggregation of multiple distributed source-grid-load-storage energy systems.

[0150] Specifically, based on the analysis of the adjustable power domain and the actual needs of the system, corresponding control strategies should be formulated. For example, if the current grid load is large and it is necessary to increase the power supply, this can be achieved by increasing the output power of distributed power sources, releasing the energy of distributed energy storage devices, or adjusting the power consumption of adjustable loads. The control strategy should comprehensively consider factors such as energy costs, equipment lifespan, and power supply reliability.

[0151] Based on the established control strategy, energy equipment is precisely controlled through devices such as electronic controllers. For distributed power sources, their output power setpoints can be adjusted; for distributed energy storage devices, their charging and discharging power can be controlled; and for distributed loads, their power consumption can be adjusted through intelligent control devices. During the implementation of control actions, the system's response must be monitored in real time to ensure that the control effect meets expectations.

[0152] Example 3

[0153] like Figure 4 As shown, this invention discloses an energy storage and load aggregation system based on a comprehensive virtual generator and battery model, comprising:

[0154] The aggregation module is used to aggregate multiple energy devices in multiple distributed source-grid-load-storage energy systems based on the constructed aggregation model of integrated virtual generators and virtual batteries, to obtain an aggregate;

[0155] The solution module is used to solve for the undetermined parameters in the aggregation model based on the constructed parameter robust optimization solution model. The parameter robust optimization solution model is divided into two-stage constraints. The second-stage constraints are established based on the refined mathematical model of distributed source-grid-load-storage. The refined mathematical model of distributed source-grid-load-storage includes a refined mathematical model of distributed power sources, a refined mathematical model of distributed loads, a refined mathematical model of distributed energy storage, and a refined mathematical model of distributed networks.

[0156] The adjustable power domain calculation module is used to perform approximate linear summation on the aggregation model according to the Minkowski approximate linear summation method, and solve for the adjustable power domain of the aggregation. Based on the adjustable power domain, a control strategy is formulated to control the distributed source-grid-load-storage energy system, thereby realizing the resource aggregation of multiple distributed source-grid-load-storage energy systems.

[0157] The operations in the flowchart of this invention can be implemented out of order, or in reverse order, or simultaneously. Furthermore, one or more additional operations can be added to the flowchart. One or more operations can be removed from the flowchart.

[0158] Through the above description of the embodiments, those skilled in the art can clearly understand that this disclosure can be implemented using software plus necessary general-purpose hardware, or it can be implemented using dedicated hardware including dedicated integrated circuits, dedicated CPUs, dedicated memory, dedicated components, etc. Generally, any function performed by a computer program can be easily implemented using corresponding hardware, and the specific hardware structure used to implement the same function can be diverse, such as analog circuits, digital circuits, or dedicated circuits. However, for this disclosure, software implementation is more often a preferred implementation method.

[0159] In summary, this invention addresses the collaborative interaction characteristics of distributed energy source-grid-load-storage systems by establishing refined mathematical models for distributed power sources, distributed loads, distributed energy storage, and distributed networks. To comprehensively consider the power constraints, ramp-up constraints, and capacity constraints of distributed energy source-grid-load-storage systems, an aggregation model combining virtual generators and virtual batteries is used to aggregate the operating characteristics of multiple energy devices in the system. For the numerous undetermined parameters in the aggregate model, a two-stage robust optimization parameter solution method is proposed. Based on the Minkowski sum principle, different aggregate models are approximated linearly summed to obtain the adjustable power domain of the aggregate. A control strategy is then formulated based on the adjustable power domain to control the distributed energy source-grid-load-storage system, thereby achieving resource aggregation of multiple distributed energy source-grid-load-storage systems.

[0160] This invention primarily targets the application scenario of virtual power plants, a promising and highly potential technology in the current energy industry. The core objective of a virtual power plant is to integrate and optimize the management and scheduling of distributed energy resources (such as photovoltaic, wind, energy storage devices, and loads). Based on these needs, this invention proposes a method for energy storage and load aggregation based on a comprehensive virtual generator and battery model, effectively addressing these practical industrial problems. Specifically, the method establishes a comprehensive aggregation model and a robust optimization solution model, enabling resource aggregation and optimization of multiple distributed energy systems while considering uncertainties.

[0161] In practical industrial operations, power grid dispatching systems need to optimize and schedule multiple distributed energy devices. The integrated virtual generator and virtual battery model proposed in this invention, through aggregation processing, transforms the operational characteristics of numerous distributed energy sources into a simpler and more controllable dispatching model, greatly simplifying the complexity of dispatching. For example, through refined mathematical models of new energy and traditional energy generators, the power grid dispatching center can monitor and control the output power of these devices in real time, thereby ensuring the stability of power supply. Furthermore, the energy storage device model in this invention helps the dispatching center flexibly arrange the charging and discharging strategies of energy storage devices based on the real-time load conditions of the power grid, balancing power supply and demand and avoiding grid instability. Refined modeling of distributed loads, especially models for loads that can be shifted or reduced, allows the power dispatching system to reduce grid load pressure and improve system stability during peak power demand periods by reducing or delaying the electricity consumption time of specific loads.

[0162] In actual industrial operations of virtual power plants, the operating environment and external conditions (such as weather, load demand, and market electricity prices) of the equipment are highly uncertain. The two-stage robust optimization solution model of this invention effectively addresses these common fluctuation problems in actual industrial operations by introducing uncertain variables: the first-stage constraints impose specific power constraints on the output power of distributed energy devices and the charging and discharging power of energy storage devices. These constraints are dynamically adjusted according to the actual needs of the power grid. The second-stage constraints are established based on a practical mathematical model of distributed generation, grid, load, and storage, capable of handling complex power trading and dispatching problems under different electricity market environments. Through the two-stage optimization solution method, the power grid dispatching system can ensure the overall stability and economy of the power grid in actual industrial scenarios by adjusting the power output, charging and discharging strategies, and load response behavior of various distributed energy devices.

[0163] Addressing the uncertainties of external environmental fluctuations and equipment failures in practical industrial applications, the robust design proposed in this invention possesses strong industrial applicability. Through the model and algorithm of this invention, the virtual power plant can maintain stable system operation even when facing these practical problems, ensuring the normal operation of various distributed energy systems under different environments. This has significant practical implications for the large-scale grid connection of current new energy sources and for improving the intelligent management level of the power grid.

[0164] In summary, this invention is closely related to actual industrial activities, and has broad application prospects, particularly in practical industrial applications such as virtual power plant technology, optimized management of distributed energy systems, and power dispatch. By integrating virtual generator and battery models, and employing robust optimization solutions and the Minkowski approximate linear summation method, this invention solves many technical challenges in distributed energy dispatch and optimization in practical industrial operations, providing effective technical support for achieving more efficient energy management.

[0165] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for energy storage and load aggregation based on integrated virtual generator and battery model, characterized in that, The method comprises the following processes: According to the constructed aggregate model of the comprehensive virtual generator and the virtual battery, a plurality of energy devices in a plurality of distributed source-grid-load-storage energy systems are aggregated to obtain an aggregate; For the undetermined parameters in the aggregate model, the parameters are solved according to a constructed parameter robust optimization solving model; the parameter robust optimization solving model is divided into two-stage constraints, and a second-stage constraint condition is established based on a distributed source-grid-load-storage refined mathematical model; the distributed source-grid-load-storage refined mathematical model comprises a distributed power source refined mathematical model, a distributed load refined mathematical model, a distributed storage refined mathematical model and a distributed network refined mathematical model; According to a Minkowski approximate linear summation method, the aggregate model is approximately linearly summed to obtain an adjustable power domain of the aggregate, and a control strategy is formulated based on the adjustable power domain to control the distributed source-grid-load-storage energy system, so that resource aggregation of the plurality of distributed source-grid-load-storage energy systems is realized; The aggregate model of the comprehensive virtual generator and the virtual battery is as follows: In the formula: The feasible region of the interaction power of the aggregation body with the large power grid constrained by the aggregation model; for a period of time t interaction power with the large power grid, the distributed source grid load storage energy system interacts with the large power grid through the tie line ; a time period for the aggregate t power to interact with the large grid; a time period for the aggregate t an upper limit on power to interact with the large grid, a time period for the aggregate t a lower limit on power to interact with the large grid, and is a time-varying parameter; an upper limit on the capacity of the aggregate over the time period t an upper limit on the capacity of the aggregate over the time period a lower limit on the capacity of the aggregate over the time period t a lower limit on the capacity of the aggregate over the time period and is a time-varying parameter; an upper ramping limit for the power exchanged between the aggregation and the large grid for each time interval, a lower ramping limit for the power exchanged between the aggregation and the large grid for each time interval, and are constant parameters; There are several parameters to be solved in the aggregated model of virtual generator and virtual battery, including and , and as well as and ; The parameter robust optimization solving model is a two-stage robust optimization model, and the above-mentioned to-be-solved parameters are solved through the two-stage robust optimization model; The objective function of the two-stage robust optimization model is as follows: In the formula: represents the first stage variable, which is the to-be-solved parameter of the aggregation model; represents the second stage variable, including the output of the distributed power source, the demand response amount of the distributed load, the capacity and charging / discharging power of the distributed energy storage device, the interactive power of the large power grid, and the newly introduced non-negative relaxation variable of the power balance constraint in the distributed source-grid-load-energy storage system; and are weight coefficients of corresponding parameters, respectively; M is a positive number; and are non-negative relaxation variables introduced for the power balance constraint.​​​​ 2.The energy storage and load aggregation method based on integrated virtual generator and battery model according to claim 1, wherein, The distributed power source refined mathematical model is divided into a new energy unit mathematical model and a traditional energy unit mathematical model; The new energy unit mathematical model is as follows: In the formula: denotes a set of new energy generating units in a distributed source-grid-load-storage energy system; denotes a set of dispatching time periods in the distributed source-grid-load-storage energy system; is the active power output of the new energy generating unit r in the t time period; is the active power output of the new energy generating unit r in the t time period; The traditional energy unit mathematical model is as follows: In the formula: denotes a set of conventional energy generating units in the distributed source-grid-load-storage energy system; denotes the active power of the conventional energy generating unit g In t the time period; denotes the upper limit of the active power of the conventional energy generating unit g ; denotes the lower limit of the active power of the conventional energy generating unit g ; denotes the upper limit of the power adjustment of the conventional energy generating unit g ; denotes the lower limit of the power adjustment of the conventional energy generating unit g . 3.The energy storage and load aggregation method based on integrated virtual generator and battery model of claim 1, wherein, The distributed load is divided into a translatable load and a reducible load according to the type of load response, the translatable load corresponds to a transfer in time, and the reducible load corresponds to a reduction in power; The distributed load refined mathematical model is divided into a translatable load mathematical model and a reducible load mathematical model; The translatable load mathematical model is as follows: In the formula: represents a set of responsive loads in the distributed source-net-load-storage energy system; represents a set of time periods in which loads are moved in; represents a set of time periods in which loads are moved out; represents a responsive load d In t the amount of load moved in within the time period; represents a responsive load d In t the amount of load moved out within the time period; and respectively represent a responsive load d the maximum and minimum values of the amount of load moved in within the time period t; and respectively represent a responsive load d the maximum and minimum values of the amount of load moved out within the time period t; The reducible load mathematical model is as follows: In the formula: represents a set of responsive loads in a distributed source-grid-load-storage energy system; represents a set of time periods for load curtailment; represents a responsive load d In t the amount of load curtailed in a time period; and respectively represent a responsive load d the maximum and minimum values of load curtailment in a unit time period. 4.The energy storage and load aggregation method based on integrated virtual generator and battery model of claim 1, wherein, The distributed storage refined mathematical model is as follows: In the formula: denotes the set of different distributed energy storage devices in the distributed source-grid-load-storage energy system; , and denote the state of charge, charging power and discharging power of the energy storage device es at t time period; and are the charging efficiency and discharging efficiency of the energy storage device es , respectively; , denote the upper and lower limits of the capacity of the energy storage device es ; denotes the upper limit of the charging power of the energy storage device es ; denotes the lower limit of the charging power of the energy storage device es ; denotes the upper limit of the discharging power of the energy storage device es ; denotes the lower limit of the discharging power of the energy storage device es . 5.The energy storage and load aggregation method based on integrated virtual generator and battery model of claim 1, wherein, The distributed network refined mathematical model only considers a power balance constraint when being established; the power balance constraint of the distributed source-grid-load-storage energy system comprises a power balance constraint of the overall distributed source-grid-load-storage energy system and a power balance constraint before and after load demand response; The power balance constraint of the overall distributed source-grid-load-storage energy system is as follows: In the formula: is the load demand that can be responded to d is the load demand after demand response is performed; is the interactive power of the tie line between the distributed source grid load storage energy system and the large power grid, if is positive, indicating that the distributed source grid load storage energy system purchases electric energy from the large power grid, if is negative, indicating that the distributed source grid load storage energy system sells electric energy to the large power grid; The power balance constraint before and after load demand response is as follows: In the formula: is a load that can respond to a load d demand after demand response is performed; is a load that can respond to a load d demand before demand response is performed; is a load amount that performs a shift demand response; is a load amount that performs a reduction demand response; is a load shifted out at a t period; is a load shifted out at a t period. 6.The energy storage and load aggregation method based on integrated virtual generator and battery model of claim 1, wherein, The first-stage constraint condition is as follows: wherein: , , and denote initial bounds for the parameters of the integrated polymerization model to be solved. The second-stage constraint condition is the distributed source-grid-load-storage refined mathematical model, wherein the power balance constraint of the overall distributed source-grid-load-storage energy system is replaced by: The constraint condition coupled between the first stage and the second stage is the aggregate model of the comprehensive virtual generator and the virtual battery.

7. The energy storage and load aggregation method based on integrated virtual generator and battery model according to claim 1, wherein, According to the Minkowski approximate linear summation method, the aggregate model is approximately linearly summed to obtain the adjustable power domain of the aggregate, and the specific process is as follows: Firstly, the adjustable power domain of a single distributed source-grid-load-storage energy system is mapped to a geometric space to form a high-dimensional convex polyhedron; a vertex set or a half-plane set corresponding to the high-dimensional convex polyhedron of each distributed source-grid-load-storage energy system is solved, and the vertex set or the half-plane set is used to represent the adjustable power domain of each distributed source-grid-load-storage energy system; Then, according to the Minkowski approximate linear summation method, the adjustable power domains of all the distributed source-grid-load-storage energy systems are sequentially arranged and added to corresponding vertices or half-planes of a high-dimensional convex polyhedron, so as to obtain a new vertex set or half-plane set; The new vertex set or half-plane set is used to represent the adjustable power domains of multiple aggregates. 8.The energy storage and load aggregation method based on integrated virtual generator and battery model of claim 7, wherein, The high-dimensional convex polyhedron is characterized in the form of a half-plane, and the compact form of the characterization is: In the formula: Polymers described by the half-plane method j Adjustable power domain; For polymer j During the scheduling period T Power adjustment in different time periods The column vector formed; , As an aggregate j The corresponding power constraint coefficient matrix; , As an aggregate j The corresponding power constraint parameter vector.

9. A storage and load aggregation system based on the integrated virtual generator and battery model according to any one of claims 1-8, characterized in that, It comprises: An aggregation module is configured to aggregate multiple energy devices in the multiple distributed source-grid-load-storage energy systems according to the constructed aggregation model of the comprehensive virtual generator and the virtual battery, so as to obtain an aggregate; A solving module is configured to solve the undetermined parameters in the aggregation model according to a constructed parameter robust optimization solving model; the parameter robust optimization solving model is divided into two-stage constraints, and the second-stage constraint condition is established based on a distributed source-grid-load-storage refined mathematical model; the distributed source-grid-load-storage refined mathematical model comprises a distributed power source refined mathematical model, a distributed load refined mathematical model, a distributed storage refined mathematical model, and a distributed network refined mathematical model; An adjustable power domain calculation module is configured to perform approximate linear summation on the aggregation model according to the Minkowski approximate linear summation method, so as to obtain the adjustable power domain of the aggregate; A control strategy is formulated based on the adjustable power domain, so as to control the distributed source-grid-load-storage energy system and realize resource aggregation of the multiple distributed source-grid-load-storage energy systems.

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