Distributed resource power distribution network comprehensive treatment method, system, equipment and medium

Abstract

The invention belongs to the technical field of power grid optimization operation, and discloses a distributed resource power distribution network comprehensive treatment method, system and device and a medium, and the method comprises the steps: constructing an interaction mode of a power distribution network distributed resource and a virtual power plant based on a power distribution network active and reactive cooperative regulation and control mechanism of a virtual power plant technology; aggregating the multi-target distributed resources, constructing a heterogeneous resource aggregation model, and converting the heterogeneous resource aggregation model into standardized feasible region representation and cost function representation by using an equivalent external characteristic approximation algorithm; based on the interaction mode of the power distribution network distributed resources and the virtual power plant and standardized feasible region representation and cost function representation, a cooperative regulation and control model is constructed, the cooperative regulation and control model is utilized to carry out comprehensive regulation and control on the multi-target distributed resources, and an optimal scheduling instruction is output, so that multi-target comprehensive treatment of the power distribution network is realized. According to the invention, the regulation intention of the distributed resource main body is effectively reflected, and the high-reliability implementation of the regulation instruction is realized.

Description

Technical Field

[0001] This invention relates to the field of power grid optimization operation technology, and in particular to a method, system, equipment and medium for comprehensive management of distributed resource distribution networks. Background Technology

[0002] Distributed renewable energy can generate electricity locally, reduce transmission losses, and effectively utilize idle spaces such as rooftops, thereby improving energy efficiency, ensuring power supply reliability, and promoting the consumption and development of renewable energy. Therefore, it has developed rapidly in recent years, with installed capacity growing rapidly, and in some regions it has surpassed the installed capacity of centralized renewable energy. At the same time, distributed resources such as distributed energy storage, micro-turbines, and electric vehicles are also being widely connected to the distribution network, leading to more frequent problems such as voltage exceeding limits, power flow exceeding limits, and equipment overload.

[0003] Traditional control methods are no longer fully adequate for the construction and development needs of new power systems, necessitating the exploration of new control methods within the system. By actively guiding and efficiently managing distributed resources within the distribution network, voltage management can be achieved without adding new physical equipment, thus reducing grid investment costs.

[0004] Current research on the comprehensive management of distribution networks with large-scale distributed resources mainly focuses on centralized or distributed optimal scheduling of distributed resources, modeling and scheduling of distributed resources under uncertain environments, aggregation optimization of distributed resources, and interactive iterative optimization of distributed resources and the distribution network. However, the above studies do not consider the participation willingness of distributed resources, nor the coupling relationship between active and reactive power. They assume that distributed resources are directly dispatchable resources of the distribution network and optimize the scheduling of active and reactive power separately, which has shortcomings such as discrepancies with actual scheduling effects and inability to fully and accurately utilize the regulation capabilities of distributed resources.

[0005] Therefore, how to provide a comprehensive management method, system, equipment, and medium for distributed resource distribution networks is an urgent problem to be solved. Summary of the Invention

[0006] This invention provides a method, system, equipment, and medium for comprehensive management of distributed resource distribution networks to solve the problems mentioned above in the prior art.

[0007] According to a first aspect of the present invention, a method for comprehensive management of distributed resource distribution networks is provided.

[0008] In one embodiment, the distributed resource distribution network integrated management method includes: a distribution network active and reactive power coordinated control mechanism based on virtual power plant technology, constructing an interaction mode between distributed resources and virtual power plants; aggregating multi-objective distributed resources to construct a heterogeneous resource aggregation model, and using an equivalent external characteristic approximation algorithm to transform the heterogeneous resource aggregation model into a standardized feasible region representation and cost function representation; based on the interaction mode between distributed resources and virtual power plants and the standardized feasible region representation and cost function representation, constructing a coordinated control model, and using the coordinated control model to comprehensively control multi-objective distributed resources, outputting optimal scheduling instructions to achieve multi-objective integrated management of the distribution network.

[0009] In one embodiment, the active and reactive power coordinated control mechanism of the distribution network based on virtual power plant technology, which constructs an interaction mode between the distributed resources of the distribution network and the virtual power plant, includes: using the virtual power plant to aggregate distributed resources and establishing a two-way information interaction mechanism with the distribution network operator; using the virtual power plant as a basic unit participating in the operation of the distribution network, responsible for providing regulation capabilities and regulation costs to the distribution network operator, so as to achieve efficient aggregation and control of distributed resources.

[0010] In one embodiment, the virtual power plant, as a basic unit participating in the operation of the distribution network, is responsible for providing regulation capabilities and regulation costs to the distribution network operator to achieve efficient aggregation and control of distributed resources. This includes: in terms of regulation capabilities, the virtual power plant needs to provide four types of core descriptive quantities to the distribution network operator to comprehensively characterize its regulation capabilities; the four types of core descriptive quantities include: upper and lower limits of active and reactive power to characterize the power regulation range that the virtual power plant can provide at the current moment; joint active and reactive power constraints to reflect the coupling relationship between active and reactive output caused by the physical limits of the power electronic converter; upper and lower limits of energy state to characterize the continuous regulation capability of the virtual power plant with energy storage over a longer time scale; and upper and lower limits of the ramp rate of the virtual power plant's power output change capability per unit time. In terms of regulation costs, the regulation cost of the virtual power plant is defined as a bivariate function related to both active and reactive power to accurately characterize the impact of reactive power regulation on converter losses and the economic efficiency of active power output. Through the standardized interaction of regulation capabilities and regulation costs, efficient aggregation and control of distributed resources can be achieved.

[0011] In one embodiment, the aggregation of multi-objective distributed resources based on the interaction mode of distributed resources in the distribution network and virtual power plants to construct a heterogeneous resource aggregation model, and the transformation of the heterogeneous resource aggregation model into a standardized feasible region representation and cost function representation using an equivalent external characteristic approximation algorithm, includes: aggregating distributed photovoltaics, energy storage systems, electric vehicle charging stations, and flexible loads based on the interaction mode of distributed resources in the distribution network and virtual power plants to construct a heterogeneous resource aggregation model; reducing the complexity of the heterogeneous resource aggregation model using an equivalent external characteristic approximation algorithm, and meeting the standardized and uniform representation requirements of the interaction mode for the heterogeneous resource aggregation model based on the mathematical method of the Chino polyhedron; sampling the heterogeneous resource aggregation model within a preset range of active and reactive power, and using a backpropagation neural network to fit the nonlinear relationship between active power, reactive power, and virtual power plant operating costs to obtain a discrete power-cost correspondence, thereby simplifying the model and improving computational efficiency; and transforming the heterogeneous resource model into a standardized feasible region representation and cost function representation based on the standardized representation requirements and the discrete power-cost correspondence using geometric approximation and artificial intelligence techniques.

[0012] In one embodiment, the construction of a collaborative control model based on the interaction mode between distributed resources and virtual power plants in the distribution network, along with standardized feasible region and cost function representations, and the use of this model to comprehensively control multi-objective distributed resources and output optimal scheduling instructions to achieve multi-objective comprehensive governance of the distribution network includes: constructing a collaborative control model guided by the interaction mode between distributed resources and virtual power plants, comprehensively considering multiple optimization objectives, and combining virtual power plant operating constraints with standardized feasible region and cost function representations; solving the collaborative control model using a heuristic algorithm, processing virtual power plant operating constraints, finding global or near-global optimal solutions, and finally outputting optimal scheduling instructions for the active and reactive power of each virtual power plant.

[0013] In one embodiment, the multiple optimization objectives include: improving system economy by minimizing the overall operating cost of all connected virtual power plants; improving energy utilization efficiency by minimizing the total network loss of the system; ensuring power supply quality and safe and stable operation of the system by minimizing the sum of voltage deviations of all nodes; and coordinating and optimizing each sub-objective through configurable weight coefficients to adapt to the scheduling needs under different operating scenarios, forming a complete distribution network comprehensive governance objective system.

[0014] In one embodiment, the virtual power plant operation constraints include: adopting precise network constraints based on AC power flow to fully describe the balance relationship between active and reactive power in the distribution network, the safe range of node voltage, and the line current transmission limit, so as to ensure that the optimization scheme conforms to the physical laws and safety standards of the power grid; introducing virtual power plant standardization representation constraints based on the Kino polyhedron algorithm, and aggregating the feasible domains of multiple virtual power plants into a unified operating boundary through geometric operations.

[0015] According to a second aspect of the present invention, a distributed resource distribution network integrated management system is provided.

[0016] In one embodiment, a distributed resource distribution network integrated governance system includes: an interactive management module for constructing an interactive mode between distributed resources and virtual power plants based on a virtual power plant technology-based active and reactive power coordinated control mechanism; an aggregation and standardization module for aggregating multi-objective distributed resources, constructing a heterogeneous resource aggregation model, and using an equivalent external characteristic approximation algorithm to transform the heterogeneous resource aggregation model into a standardized feasible region representation and cost function representation; and a resource control module for constructing a coordinated control model based on the interactive mode between distributed resources and virtual power plants and the standardized feasible region representation and cost function representation, and using the coordinated control model to comprehensively control multi-objective distributed resources and output optimal scheduling instructions to achieve multi-objective integrated governance of the distribution network.

[0017] In one embodiment, the active and reactive power coordinated control mechanism of the distribution network based on virtual power plant technology, which constructs an interaction mode between the distributed resources of the distribution network and the virtual power plant, includes: using the virtual power plant to aggregate distributed resources and establishing a two-way information interaction mechanism with the distribution network operator; using the virtual power plant as a basic unit participating in the operation of the distribution network, responsible for providing regulation capabilities and regulation costs to the distribution network operator, so as to achieve efficient aggregation and control of distributed resources.

[0018] In one embodiment, the virtual power plant, as a basic unit participating in the operation of the distribution network, is responsible for providing regulation capabilities and regulation costs to the distribution network operator to achieve efficient aggregation and control of distributed resources. This includes: in terms of regulation capabilities, the virtual power plant needs to provide four types of core descriptive quantities to the distribution network operator to comprehensively characterize its regulation capabilities; the four types of core descriptive quantities include: upper and lower limits of active and reactive power to characterize the power regulation range that the virtual power plant can provide at the current moment; joint active and reactive power constraints to reflect the coupling relationship between active and reactive output caused by the physical limits of the power electronic converter; upper and lower limits of energy state to characterize the continuous regulation capability of the virtual power plant with energy storage over a longer time scale; and upper and lower limits of the ramp rate of the virtual power plant's power output change capability per unit time. In terms of regulation costs, the regulation cost of the virtual power plant is defined as a bivariate function related to both active and reactive power to accurately characterize the impact of reactive power regulation on converter losses and the economic efficiency of active power output. Through the standardized interaction of regulation capabilities and regulation costs, efficient aggregation and control of distributed resources can be achieved.

[0019] In one embodiment, the aggregation of multi-objective distributed resources based on the interaction mode of distributed resources in the distribution network and virtual power plants to construct a heterogeneous resource aggregation model, and the transformation of the heterogeneous resource aggregation model into a standardized feasible region representation and cost function representation using an equivalent external characteristic approximation algorithm, includes: aggregating distributed photovoltaics, energy storage systems, electric vehicle charging stations, and flexible loads based on the interaction mode of distributed resources in the distribution network and virtual power plants to construct a heterogeneous resource aggregation model; reducing the complexity of the heterogeneous resource aggregation model using an equivalent external characteristic approximation algorithm, and meeting the standardized and uniform representation requirements of the interaction mode for the heterogeneous resource aggregation model based on the mathematical method of the Chino polyhedron; sampling the heterogeneous resource aggregation model within a preset range of active and reactive power, and using a backpropagation neural network to fit the nonlinear relationship between active power, reactive power, and virtual power plant operating costs to obtain a discrete power-cost correspondence, thereby simplifying the model and improving computational efficiency; and transforming the heterogeneous resource model into a standardized feasible region representation and cost function representation based on the standardized representation requirements and the discrete power-cost correspondence using geometric approximation and artificial intelligence techniques.

[0020] In one embodiment, the construction of a collaborative control model based on the interaction mode between distributed resources and virtual power plants in the distribution network, along with standardized feasible region and cost function representations, and the use of this model to comprehensively control multi-objective distributed resources and output optimal scheduling instructions to achieve multi-objective comprehensive governance of the distribution network includes: constructing a collaborative control model guided by the interaction mode between distributed resources and virtual power plants, comprehensively considering multiple optimization objectives, and combining virtual power plant operating constraints with standardized feasible region and cost function representations; solving the collaborative control model using a heuristic algorithm, processing virtual power plant operating constraints, finding global or near-global optimal solutions, and finally outputting optimal scheduling instructions for the active and reactive power of each virtual power plant.

[0021] In one embodiment, the multiple optimization objectives include: improving system economy by minimizing the overall operating cost of all connected virtual power plants; improving energy utilization efficiency by minimizing the total network loss of the system; ensuring power supply quality and safe and stable operation of the system by minimizing the sum of voltage deviations of all nodes; and coordinating and optimizing each sub-objective through configurable weight coefficients to adapt to the scheduling needs under different operating scenarios, forming a complete distribution network comprehensive governance objective system.

[0022] In one embodiment, the virtual power plant operation constraints include: adopting precise network constraints based on AC power flow to fully describe the balance relationship between active and reactive power in the distribution network, the safe range of node voltage, and the line current transmission limit, so as to ensure that the optimization scheme conforms to the physical laws and safety standards of the power grid; introducing virtual power plant standardization representation constraints based on the Kino polyhedron algorithm, and aggregating the feasible domains of multiple virtual power plants into a unified operating boundary through geometric operations.

[0023] According to a third aspect of the present invention, a computer device is provided.

[0024] In some embodiments, the computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps of the method described above.

[0025] According to a fourth aspect of the present invention, a computer-readable storage medium is provided.

[0026] In one embodiment, a computer program is stored on the computer-readable storage medium, which, when executed by a processor, implements the steps of the above method.

[0027] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects:

[0028] 1) This invention establishes a coordinated control mechanism for active and reactive power in distribution networks based on virtual power plant technology, which can effectively reflect the adjustment intentions of distributed resource subjects and achieve highly reliable implementation of control commands.

[0029] 2) This invention approximates the equivalent external characteristics of the virtual power plant, thereby simplifying the external characteristic representation of the virtual power plant, reducing model complexity, and improving solution speed.

[0030] 3) This invention establishes a distribution network active and reactive power coordinated control model adapted to multiple virtual power plants, taking into account the coupling control cost and control effect of active and reactive power, and reducing the multi-objective governance cost of the distribution network.

[0031] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit the invention. Attached Figure Description

[0032] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0033] Figure 1 This is a flowchart illustrating a comprehensive management method for distributed resource distribution networks according to an exemplary embodiment;

[0034] Figure 2 This is a schematic diagram illustrating the principle of a distributed resource distribution network integrated management system according to an exemplary embodiment;

[0035] Figure 3 This is a schematic diagram of the structure of a computer device according to an exemplary embodiment;

[0036] Figure 4 This is a flowchart of the integrated management process for power distribution networks based on virtual power plant technology.

[0037] Figure 5 This is a flowchart of the cost representation process for a virtual power plant based on neural networks;

[0038] Figure 6 This is a schematic diagram of the internal structure of a neural network. Detailed Implementation

[0039] The following description and accompanying drawings fully illustrate specific embodiments described herein to enable those skilled in the art to practice them. Some portions and features of certain embodiments may be included in or replace portions and features of other embodiments. The scope of the embodiments herein includes the entire scope of the claims and all available equivalents thereof. The various embodiments described herein are presented in a progressive manner, with each embodiment focusing on its differences from other embodiments; similar or identical parts between embodiments can be referred to interchangeably.

[0040] The modules in the apparatus or system of this application can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0041] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0042] Figure 1 An embodiment of a distributed resource distribution network integrated management method of the present invention is shown.

[0043] In this optional embodiment, the method for comprehensive management of distributed resource distribution networks includes:

[0044] Step S101: Based on the virtual power plant technology, a coordinated control mechanism for active and reactive power in the distribution network is established to construct an interaction mode between distributed resources in the distribution network and the virtual power plant.

[0045] Step S102: Aggregate multi-objective distributed resources, construct a heterogeneous resource aggregation model, and use the equivalent external characteristic approximation algorithm to transform the heterogeneous resource aggregation model into a standardized feasible region representation and cost function representation.

[0046] Step S103: Based on the interaction mode of distributed resources and virtual power plants in the distribution network and the standardized feasible domain representation and cost function representation, construct a collaborative control model, and use the collaborative control model to comprehensively control multi-objective distributed resources, output the optimal scheduling command, so as to realize the multi-objective comprehensive governance of the distribution network.

[0047] Specifically, such as Figure 4 As shown, a collaborative control mechanism for active and reactive power in distribution networks based on virtual power plant technology is proposed, establishing the interaction mode between distributed resources and the distribution network. Based on virtual power plant technology, multiple distributed resources are aggregated, and an approximation method for the equivalent external characteristics of virtual power plants is proposed. Finally, a collaborative control model for active and reactive power in distribution networks adapted to multiple virtual power plants is proposed, enabling efficient control of distributed resources and achieving multi-objective comprehensive management of the distribution network.

[0048] In this optional embodiment, the active and reactive power coordinated control mechanism of the distribution network based on virtual power plant technology, which constructs the interaction mode between the distributed resources of the distribution network and the virtual power plant, includes: using the virtual power plant to aggregate distributed resources and establishing a two-way information interaction mechanism with the distribution network operator; using the virtual power plant as the basic unit participating in the operation of the distribution network, responsible for providing regulation capabilities and regulation costs to the distribution network operator, so as to achieve efficient aggregation and control of distributed resources.

[0049] Specifically, a standardized interaction mode between distributed resources and the distribution network is constructed. In this mechanism, the virtual power plant, as the basic unit participating in the operation of the distribution network, is responsible for providing the distribution network operator with information on its regulation capacity and regulation costs to support system-level optimization scheduling and control decisions. In the distribution network active and reactive power coordinated control system based on the virtual power plant, the virtual power plant, as an integrated unit aggregating distributed resources, establishes a two-way information interaction mechanism with the distribution network operator. To ensure the efficiency and reliability of system operation, this patent establishes a standardized and regulated information interaction framework. Specifically, the virtual power plant needs to provide the operator with two types of key information: regulation capacity information and regulation cost information. All information is associated with specific moments and grid nodes to ensure its spatiotemporal accuracy. A distribution network active and reactive power coordinated control mechanism based on virtual power plant technology is proposed, establishing an interaction mode between distributed resources and the distribution network. The virtual power plant, as the smallest unit participating in the operation of the distribution network, provides the distribution network operator with its regulation capacity and regulation costs.

[0050] In the active and reactive power coordinated control system of distribution networks based on virtual power plants, the virtual power plant, as an aggregator of distributed resources, has frequent and complex two-way information exchanges with the distribution network operator. Establishing a standardized and regulated information exchange mode is the foundation and prerequisite for the efficient and reliable operation of this patent mechanism. Therefore, this invention explicitly stipulates that the virtual power plant must provide the distribution network operator with two types of standardized information: regulation capacity and regulation cost, and all information is based on a specific time and a specific grid node to ensure the spatiotemporal accuracy of the information.

[0051] In this optional embodiment, the virtual power plant, as a basic unit participating in the operation of the distribution network, is responsible for providing regulation capabilities and regulation costs to the distribution network operator to achieve efficient aggregation and control of distributed resources. This includes: in terms of regulation capabilities, the virtual power plant needs to provide four types of core descriptive quantities to the distribution network operator to comprehensively characterize its regulation capabilities; the four types of core descriptive quantities include: upper and lower limits of active and reactive power to characterize the power regulation range that the virtual power plant can provide at the current moment; active and reactive power joint power constraints to reflect the coupling relationship between active and reactive output caused by the physical limits of the power electronic converter; upper and lower limits of energy state to characterize the continuous regulation capability of the virtual power plant with energy storage over a longer time scale; and upper and lower limits of the ramp rate of the virtual power plant's power output change capability per unit time. In terms of regulation costs, the regulation cost of the virtual power plant is defined as a bivariate function related to both active and reactive power to accurately characterize the impact of reactive power regulation on converter losses and the economic efficiency of active power output. Through the standardized interaction of regulation capabilities and regulation costs, efficient aggregation and control of distributed resources can be achieved.

[0052] Specifically, the regulation capability information is used to describe the physical power boundary of a virtual power plant that can be dispatched and controlled at a specific access point at a given time. This patent selects four core descriptive quantities to comprehensively characterize its regulation capability: First, the upper and lower limits of active and reactive power, representing the power regulation range that the virtual power plant can provide at the current time. Active power directly affects system frequency stability and power balance, while reactive power plays a crucial role in node voltage levels. The upper and lower limits of the virtual power plant's power are the algebraic superposition of the adjustable ranges of all aggregated distributed resources at that time. Second, the joint active and reactive power constraint, reflecting the coupling relationship between active and reactive power outputs caused by the physical limits of power electronic converters. Ignoring this constraint will lead to dispatch commands potentially exceeding the actual operating capacity of the equipment, resulting in command execution failure. Therefore, this constraint is crucial to ensuring dispatch feasibility and safe operation of the equipment. Third, the upper and lower limits of energy state, mainly used to characterize the continuous regulation capability of a virtual power plant containing energy storage over a longer time scale. Energy state information reflects its remaining charging and discharging capacity, which is crucial for assessing the continuity of power support during multi-period dispatching processes and avoiding operational risks caused by energy depletion. Fourth, the upper and lower limits of the ramp rate describe the limits of the virtual power plant's ability to change power output per unit time. This constraint ensures the smoothness and controllability of the power regulation process, avoids the impact of sudden power changes on the power grid, and provides a time-scale constraint for dynamic optimization control. Regarding regulation costs, the regulation cost of the virtual power plant is defined as a bivariate function related to both active and reactive power. Since reactive power regulation often leads to increased converter losses and affects the economics of active power output, traditional linear cost superposition models cannot accurately characterize its coupled economic characteristics. Therefore, this patent stipulates that the regulation cost must be expressed as a bivariate function to more realistically reflect the economic behavior in actual operation.

[0053] Through the aforementioned mechanism, this patent constructs a transparent, accurate, and feasible active and reactive power coordinated control system, providing a technical foundation for the safe, stable, and economical operation of distribution networks with a high proportion of distributed energy access. Regulation capability defines the range of physical power that a virtual power plant can provide to the distribution network operator for dispatch control at a given time and at a given access point. This patent selects upper and lower limits of active / reactive power, joint active and reactive power constraints, upper and lower limits of energy state, and upper and lower limits of ramp rate as core descriptive quantities.

[0054] (1) The formulas for the upper and lower limits of active power and reactive power are as follows:

[0055]

[0056]

[0057] In the formula, and These represent the active and reactive power outputs of the virtual power plant at node i during time period t, respectively. and These represent the upper and lower limits of the active and reactive power output of the virtual power plant at node i during that time period.

[0058] (2) The formula for the combined active and reactive power constraint is:

[0059]

[0060] In the formula, and R represents the active and reactive power output of the virtual power plant at node i during time period t; 2 Represents a two-dimensional vector space; Ω i,t This represents the coupling constraint relationship satisfied by the active and reactive power output of the virtual power plant; this constraint originates from the physical limits of the power electronic converter. Whether it's a photovoltaic inverter or an energy storage converter, its maximum apparent power output capability is limited by its power devices and heat dissipation conditions. This means that there is a coupling relationship between its active and reactive power output, making it impossible to simultaneously reach the theoretical maximum values ​​of both active and reactive power.

[0061] (3) The formulas for the upper and lower limits of energy are:

[0062]

[0063] In the formula, This indicates the remaining charge status of the aggregated energy storage units; and These represent the upper and lower limits of energy. They represent the continuous regulation capability of a virtual power plant over a long timescale. For example, if the SOC of an energy storage system is close to the lower limit, its ability to continuously generate active power is about to be exhausted; conversely, if the SOC is close to the upper limit, its ability to absorb active power is limited.

[0064] (4) The formulas for the upper and lower limits of the climbing slope are:

[0065]

[0066] In the formula, This represents the ramp-up capability of the virtual power plant at node i during time period t; and These represent the upper and lower limits of its ramp-up capability. This constraint indicates the virtual power plant's ability to adjust its output power per unit time, characterizing the limit of the rate of change of the virtual power plant's output power. The cost of the virtual power plant is the result of the combined effects of active and reactive power regulation. For example, generating reactive power will lead to increased converter losses, thus affecting its ability and cost to generate active power. A simple linear summation function C(P) + C(Q) cannot characterize this coupled economy. Here, it is stipulated that the regulation cost of the virtual power plant is a bivariate function related to its active and reactive power in that time period. The standard form of the regulation cost formula is as follows:

[0067]

[0068] In the formula, C VPP The regulation cost of the virtual power plant is represented by f(·,·); f(·,·) represents a bivariate function of the independent variable within the parentheses. This represents the ramp-up capability of the virtual power plant at node i during time period t; This represents the reactive power output of the virtual power plant at node i during time period t.

[0069] In this optional embodiment, the aggregation of multi-objective distributed resources based on the interaction mode of distributed network resources and virtual power plants to construct a heterogeneous resource aggregation model, and the transformation of the heterogeneous resource aggregation model into a standardized feasible region representation and cost function representation using an equivalent external characteristic approximation algorithm, includes: aggregating distributed photovoltaics, energy storage systems, electric vehicle charging stations, and flexible loads based on the interaction mode of distributed network resources and virtual power plants to construct a heterogeneous resource aggregation model; reducing the complexity of the heterogeneous resource aggregation model using an equivalent external characteristic approximation algorithm, and meeting the standardized and unified representation requirements of the interaction mode for the heterogeneous resource aggregation model based on the mathematical method of Zonotope; sampling the heterogeneous resource aggregation model within a preset range of active and reactive power, and using backpropagation (Back) The Propagation neural network fits the nonlinear relationship between active power, reactive power and virtual power plant operating costs to obtain a discretized power-cost correspondence, thereby simplifying the model and improving computational efficiency. Through geometric approximation and artificial intelligence technology, the heterogeneous resource model is transformed into a standardized feasible domain representation and cost function representation based on the standardized representation requirements and the discretized power-cost correspondence.

[0070] Specifically, this invention proposes an approximate characterization method for the operating characteristics of a virtual power plant, aiming to reduce the complexity of its internal heterogeneous resource aggregation model and meet the unified and standardized characterization requirements of the distribution network interaction mechanism for the virtual power plant model. A virtual power plant typically contains various types of distributed resources, such as distributed photovoltaic systems, energy storage systems, electric vehicle charging stations, and flexible loads, each with unique operating constraints and cost characteristics.

[0071] For distributed photovoltaic (PV) systems, their operational characteristics mainly include the predicted and actual values ​​of active power output, the actual and upper limits of reactive power output, and the active-reactive power coupling relationship constrained by the apparent power limit of the equipment. Operating costs comprehensively consider both power generation revenue and curtailment penalties. Energy storage systems require consideration of independent upper and lower limits for charging and discharging power, reactive power support capacity, apparent power constraints, the state of stored electricity, and energy losses during charging and discharging. Their cost models are typically linked to the grid's electricity purchase and sale price. Electric vehicle charging station operations involve the adjustment range of the original load and actual power consumption, reactive power output capacity, active power interaction with the grid and its limitations, and costs encompass electricity purchase costs and reactive power compensation revenue. The characteristics of flexible loads mainly include the deviation between the original load and the adjusted actual load, active and reactive power adjustment capacity and their upper limits, and costs are related to the magnitude of load adjustment.

[0072] To integrate the aforementioned heterogeneous resources, this patent constructs a heterogeneous resource aggregation model for a virtual power plant. This model aggregates all distributed resources connected to the same distribution network node, and its total cost is the sum of the costs of all types of resources, while the total output is the total active and reactive power injected into the distribution network node.

[0073] To simplify and standardize the interaction of complex internal models, this patent employs a mathematical method based on Zonotopes to characterize the feasible operating domain of the virtual power plant. This method represents the feasible domain as a geometric structure extending along multiple directional vectors with a center point as the reference, its extension length constrained by preset maximum values. The design of these directional vectors encompasses key operational limitations such as single-time-period power upper and lower limits, active and reactive power coupling constraints, and power summation and difference constraints across multiple time periods. By summing multiple directional vectors, the overall regulation capability of the virtual power plant across multiple time periods is comprehensively described.

[0074] like Figure 5 and Figure 6As shown, in terms of cost characterization, this patent obtains a discrete power-cost correspondence by systematically sampling within a preset range of active and reactive power. Then, a backpropagation neural network is used to construct a nonlinear mapping model with active and reactive power as input and operating cost as output. This network structure has two neurons in the input layer and one neuron in the output layer, and preferably a three-layer hidden layer structure. The number of neurons is optimized using combinatorial mathematics to ensure high-precision fitting of complex cost characteristics with limited training samples.

[0075] This method uses geometric approximation and artificial intelligence to transform the complex heterogeneous resource model inside the virtual power plant into a standardized feasible region and cost function representation, which significantly reduces the model complexity and computational burden. At the same time, it ensures the accuracy and reliability of the information exchanged with the distribution network operator, providing key technical support for the participation of large-scale distributed resources in the coordinated regulation of the power grid.

[0076] The internal resources of the virtual power plant include distributed photovoltaics, energy storage, electric vehicles, and flexible loads, and their respective physical models are shown below:

[0077] 1) The calculation formula for distributed photovoltaic power is:

[0078]

[0079] In the formula, npv represents the distributed photovoltaic device number; This represents the predicted value of the active power output of distributed photovoltaic systems. This represents the actual active power output of distributed photovoltaic systems. and These represent the actual value and upper limit value of the reactive power output of distributed photovoltaic systems, respectively. This indicates the upper limit of the apparent power of distributed photovoltaic power. This represents the overall operating cost of distributed photovoltaic power generation. This represents the unit operating cost of distributed photovoltaic systems. This indicates the penalty for curtailment of distributed photovoltaic units; This indicates the amount of curtailed solar power generated by distributed photovoltaic (PV) systems.

[0080] 2) The formula for calculating energy storage is:

[0081]

[0082]

[0083] In the formula, nes represents the energy storage system number; and These represent the charging power and discharging power of the energy storage system, respectively. and These represent the corresponding upper limits: It is the reactive power of energy storage; This indicates the upper limit of the apparent power of energy storage; ξ represents the total amount of electricity stored in the energy storage system; Loss η ch and η dis Both represent the loss coefficients during the energy storage process; This indicates the operating cost of the energy storage system; and These represent the prices for purchasing electricity from the grid and selling electricity to the grid, respectively.

[0084] 3) The calculation formula for electric vehicles is:

[0085]

[0086] In the formula, ncs represents the electric vehicle charging station number; This indicates the original active power load of the electric vehicle charging station during a given time period. This indicates the actual active power consumed by the electric vehicle charging station during a given time period; and These represent its upper and lower limits, respectively. This represents the reactive power generated by the electric vehicle charging station during time period t; The angle representing the maximum power factor of an electric vehicle charging station; This indicates the active power supplied by the electric vehicle charging station to the power grid during the specified time period. Indicates its upper limit; This represents the additional active power absorbed by electric vehicle charging stations from the grid during a given time period. Indicates its upper limit; This indicates the charging cost at electric vehicle charging stations; This represents the unit cost of electricity purchased from the grid by electric vehicle charging stations. This indicates the unit revenue obtained by an electric vehicle charging station due to reactive power compensation.

[0087] 4) The calculation formula for flexible load is:

[0088]

[0089] In the formula, nd represents the number of the flexible load; This represents the initial active power load during time period t; This represents the actual active load after adjustment; and These represent the regulation capacity of active and reactive power provided by conventional flexible loads, respectively. This indicates the upper limit of the active power regulation capacity; The angle representing the power factor of the load; This indicates the operating cost of flexible loads; This indicates the actual reactive load after adjustment;

[0090] This represents the initial reactive power load during time period t; This indicates the price at which electricity is purchased from the power grid.

[0091] The resulting heterogeneous resource aggregation model for the virtual power plant is shown below:

[0092]

[0093]

[0094] In the formula, This represents the overall operating cost of distributed photovoltaic power generation. This indicates the operating cost of the energy storage system; This indicates the charging cost at electric vehicle charging stations; This indicates the operating cost of flexible loads; This represents the actual value of the reactive power output of distributed photovoltaic systems. This indicates the reactive power of energy storage; This represents the reactive power generated by the electric vehicle charging station during time period t; Indicates the actual reactive load after adjustment; i represents the distribution network node; Ω i This represents the set of distributed resources accessing node i; This represents the cost of the virtual power plant at node i. These represent the active and reactive power injected into the distribution network by the virtual power plant, respectively.

[0095] Based on the requirements of the Kino polyhedron and interaction mechanism, the feasible domain of the virtual power plant is characterized in a standardized manner, as follows:

[0096] The feasible region representation method for a virtual power plant based on the Kino polyhedron is as follows:

[0097]

[0098] P G =[P G(1) ,P G(2) ,…,P G(M) ]∈R 2T×M (1.35)

[0099] ||P G(m) ||2=1 m=1,2,…,M (1.36)

[0100] λ=[λ1,λ2,…,λ M ]T (1.37)

[0101] Where F represents the feasible region of active / reactive power of the distributed resource over T time periods, T represents the number of time periods, and P represents a 2T-dimensional vector; P c P represents the center point of the feasible region of a distributed resource. G The direction matrix represents the feasible region extending from the center point in all directions, consisting of M vectors P. G(m) The direction vector is constructed such that its magnitude is kept at 1, and λ represents the extension length of the feasible region of the distributed resource in each direction. This indicates the maximum extension length.

[0102] For the upper and lower limits of active / reactive power constraints in a single time period, the direction vector is selected as follows:

[0103]

[0104] For the coupling upper and lower bound constraints of active and reactive power, the direction vector is selected as follows:

[0105]

[0106] For the upper and lower limits of the sum of power across multiple time periods, the direction vector is selected as follows:

[0107]

[0108] For the upper and lower bound constraints of power difference between multiple time periods, the direction vector is selected as follows:

[0109]

[0110] Equation (1.38) contains 2T generators, representing the constraints on active or reactive power in each time period; Equation (1.39) contains 2T generators, representing the coupling constraint relationship between active and reactive power in each time period; Equation (1.40) contains 2T-2 generators, representing the constraint on the sum of power in adjacent time periods; Equation (1.41) contains 2T-2 generators, representing the constraint on the difference in power between adjacent time periods. The total number of generators is 8T-4.

[0111] Based on the feasible region construction and active and reactive power sampling, the active power P s From 0 to P max The interval is ΔP, and the reactive power is Q. s From 0 to Q max The corresponding cost C is obtained. s The cost representation of the discretized virtual power plant is obtained as follows:

[0112] s={P s,n Q s,nC s,n} (1.42)

[0113] Based on a back propagation (BP) neural network, using sampled power and cost as training data, a nonlinear relationship is constructed between the active and reactive power of a virtual power plant and its operating cost. The parameter settings are as follows:

[0114] Input = [P] s,n Q s,n (1.43)

[0115] Output = [C s,n (1.44)

[0116] If the input is the active and reactive power of the virtual power plant, then the number of neurons in the input layer is 2; if the output is the cost of the virtual power plant, then the number of neurons in the output layer is 1. This invention selects three hidden layers, and the size of the hidden layers is selected using the following formula:

[0117] d = max[d1, d2, d3] (1.45)

[0118]

[0119] d3 = log2(d in (1.48)

[0120] In the formula, d represents the number of neurons in the hidden layer; d represents the number of combinations of selecting i elements from t elements; k represents the number of training samples; d in d out These represent the number of neurons in the input layer and the output layer, respectively, which are 2 and 1 here; ε represents a positive integer parameter, which is set to 2 here.

[0121] In this optional embodiment, the construction of a collaborative control model based on the interaction mode between distributed resources and virtual power plants in the distribution network, along with standardized feasible region and cost function representations, and the use of this collaborative control model to comprehensively control multi-objective distributed resources and output optimal scheduling instructions to achieve multi-objective comprehensive governance of the distribution network, includes: taking the interaction mode between distributed resources and virtual power plants in the distribution network as a guide, comprehensively considering multiple optimization objectives, and combining virtual power plant operating constraints with standardized feasible region and cost function representations to construct a collaborative control model; using heuristic algorithms to solve the collaborative control model, processing virtual power plant operating constraints and finding global or near-global optimal solutions, and finally outputting optimal scheduling instructions for the active and reactive power of each virtual power plant.

[0122] Specifically, this model addresses the system-level optimization and comprehensive management of large-scale distributed resources. Guided by the global operational goals of the distribution network, it comprehensively considers multiple optimization objectives, including economy, safety, and power quality, and integrates precise physical operational constraints with a standardized virtual power plant aggregation model. For this high-dimensional, non-convex, and nonlinear mixed-integer optimization model, this patent employs an advanced heuristic algorithm for solution. This solution method effectively handles complex constraints and finds global or near-global optimal solutions, ultimately outputting optimal active and reactive power scheduling commands for each virtual power plant.

[0123] A collaborative active and reactive power control model for distribution networks adapted to multiple virtual power plants is proposed. The operational objectives of the distribution network include cost, network loss, and voltage deviation, as shown in the following formulas:

[0124] minF VPP =γ1F co +γ2F loss +γ3F vol (1.49)

[0125]

[0126] In the formula, F VPP The objective function is defined as follows: N represents the total number of nodes connected to the virtual power plant; F represents the objective function. co F represents the operating cost of a virtual power plant. loss Indicates the network loss of the system; F vol Indicates the voltage deviation of the system; I ij,t U represents the square of the current flowing through line ij; i,t V represents the square of the voltage at node i; i ref V represents the square of the node reference voltage; i,t γ represents the squared value of the node voltage; γ1, γ2, and γ3 represent the weights of the sub-objective function, respectively; r ij This indicates the resistance of line ij.

[0127] The constraints consist of two parts: the AC power flow model and the virtual power plant representation model.

[0128] The power flow constraint formula for a distribution network is as follows:

[0129]

[0130] In the formula, α(i) represents the set of end nodes of the branch with i as the first end node; β(i) represents the set of first nodes of the branch with i as the first end node; P represents the active and reactive loads at node i, respectively; ki,t Q ki,t P represents the active and reactive power injected into node i, respectively; ij,t Qij,t These represent the active and reactive power injected by node i into other branches, respectively; r ij x ij Represent the resistance and reactance of line ij, respectively; I ij,t , These represent the upper and lower limits of the square of the current flowing through line ij, respectively. These represent the upper and lower limits of the squared value of the voltage at node i, respectively.

[0131] The constraint of virtual power plant resources, i.e., the formula for the feasible region of the Chino polyhedron after aggregation of distributed resources, is as follows:

[0132]

[0133] In the formula, F VPP This represents the feasible region of the total Chino polyhedron after aggregation; Represents the XOR operation: N c P represents the number of feasible regions for aggregation, i.e., the number of aggregated resources; cVPP Represents the coordinates of the center point of the feasible region of the aggregated Chino polyhedron; Indicates the coordinates of the s-th center point; λ represents the total extension length along the m-th generator direction of the feasible region of the aggregated chino polyhedron; m,s It represents the extension length of the feasible region of the s-th chino polyhedron before aggregation in the direction of the m-th generator.

[0134] The above model is solved using a heuristic algorithm, enabling comprehensive governance of large-scale distributed resource distribution networks based on virtual power plant technology.

[0135] In this optional embodiment, the multiple optimization objectives include: improving system economy by minimizing the overall operating cost of all connected virtual power plants; improving energy utilization efficiency by minimizing the total network loss of the system; ensuring power supply quality and safe and stable operation of the system by minimizing the sum of voltage deviations of all nodes; and coordinating and optimizing each sub-objective through configurable weight coefficients to adapt to the scheduling needs under different operating scenarios, forming a complete distribution network comprehensive governance objective system.

[0136] Specifically, the optimization objective function of the collaborative control model encompasses the following core elements: first, minimizing the overall operating cost of all connected virtual power plants to improve the economic efficiency of distribution network operation; second, minimizing the total network loss of the system to improve energy utilization efficiency; and third, minimizing the sum of voltage deviations of all nodes to ensure power supply quality and the safe and stable operation of the system. These sub-objectives are coordinated through configurable weighting coefficients to adapt to scheduling needs in different scenarios.

[0137] In this optional embodiment, the virtual power plant operation constraints include: adopting precise network constraints based on AC power flow to fully describe the balance relationship between active and reactive power in the distribution network, the safe range of node voltage, and the line current transmission limit, so as to ensure that the optimization scheme conforms to the physical laws and safety standards of the power grid; introducing virtual power plant standardization representation constraints based on the Kino polyhedron algorithm, and aggregating the feasible domains of multiple virtual power plants into a unified operating boundary through geometric operations.

[0138] Specifically, regarding constraints, this model strictly adheres to the physical operating laws of the distribution network. Its core comprises two parts: First, precise network constraints based on AC power flow. This part comprehensively describes the balance between active and reactive power in the distribution network, the upper and lower limits of node voltage, and the safe operating range of line current, ensuring that the optimization results conform to the physical characteristics and safety standards of the actual power grid. Second, standardized representation model constraints for virtual power plants, namely, the aggregated feasible region constructed based on the Chino polyhedron method. This feasible region aggregates the individual feasible regions of multiple virtual power plants through geometric operations. The total feasible region after aggregation is determined by the coordinates of the center point of each unit's feasible region and its extension length in each direction, thus accurately representing the overall power regulation capability and operating boundary when multiple virtual power plants operate jointly.

[0139] Figure 2 An embodiment of a distributed resource distribution network integrated management system according to the present invention is shown.

[0140] In this optional embodiment, a distributed resource distribution network integrated management system includes:

[0141] Interactive management module 201 is used for the active and reactive power coordinated control mechanism of distribution network based on virtual power plant technology, and to build the interaction mode between distributed resources of distribution network and virtual power plant.

[0142] The aggregation and standardization module 202 is used to aggregate multi-objective distributed resources, construct a heterogeneous resource aggregation model, and use the equivalent external characteristic approximation algorithm to transform the heterogeneous resource aggregation model into a standardized feasible region representation and cost function representation.

[0143] Resource regulation module 203 is used to construct a collaborative regulation model based on the interaction mode of distributed resources and virtual power plants in the distribution network and the standardized feasible domain representation and cost function representation. The collaborative regulation model is used to comprehensively regulate multi-objective distributed resources and output the optimal scheduling command to achieve multi-objective comprehensive governance of the distribution network.

[0144] In this optional embodiment, the active and reactive power coordinated control mechanism of the distribution network based on virtual power plant technology, which constructs the interaction mode between the distributed resources of the distribution network and the virtual power plant, includes: using the virtual power plant to aggregate distributed resources and establishing a two-way information interaction mechanism with the distribution network operator; using the virtual power plant as the basic unit participating in the operation of the distribution network, responsible for providing regulation capabilities and regulation costs to the distribution network operator, so as to achieve efficient aggregation and control of distributed resources.

[0145] In this optional embodiment, the virtual power plant, as a basic unit participating in the operation of the distribution network, is responsible for providing regulation capabilities and regulation costs to the distribution network operator to achieve efficient aggregation and control of distributed resources. This includes: in terms of regulation capabilities, the virtual power plant needs to provide four types of core descriptive quantities to the distribution network operator to comprehensively characterize its regulation capabilities; the four types of core descriptive quantities include: upper and lower limits of active and reactive power to characterize the power regulation range that the virtual power plant can provide at the current moment; active and reactive power joint power constraints to reflect the coupling relationship between active and reactive output caused by the physical limits of the power electronic converter; upper and lower limits of energy state to characterize the continuous regulation capability of the virtual power plant with energy storage over a longer time scale; and upper and lower limits of the ramp rate of the virtual power plant's power output change capability per unit time. In terms of regulation costs, the regulation cost of the virtual power plant is defined as a bivariate function related to both active and reactive power to accurately characterize the impact of reactive power regulation on converter losses and the economic efficiency of active power output. Through the standardized interaction of regulation capabilities and regulation costs, efficient aggregation and control of distributed resources can be achieved.

[0146] In this optional embodiment, the aggregation of multi-objective distributed resources based on the interaction mode of distributed resources in the distribution network and virtual power plants to construct a heterogeneous resource aggregation model, and the transformation of the heterogeneous resource aggregation model into a standardized feasible region representation and cost function representation using an equivalent external characteristic approximation algorithm, includes: aggregating distributed photovoltaics, energy storage systems, electric vehicle charging stations, and flexible loads based on the interaction mode of distributed resources in the distribution network and virtual power plants to construct a heterogeneous resource aggregation model; reducing the complexity of the heterogeneous resource aggregation model using an equivalent external characteristic approximation algorithm, and meeting the standardized and uniform representation requirements of the interaction mode for the heterogeneous resource aggregation model based on the mathematical method of the Chino polyhedron; sampling the heterogeneous resource aggregation model within a preset range of active and reactive power, and using a backpropagation neural network to fit the nonlinear relationship between active power, reactive power, and virtual power plant operating costs to obtain a discrete power-cost correspondence, thereby simplifying the model and improving computational efficiency; and transforming the heterogeneous resource model into a standardized feasible region representation and cost function representation based on the standardized representation requirements and the discrete power-cost correspondence using geometric approximation and artificial intelligence technology.

[0147] In this optional embodiment, the construction of a collaborative control model based on the interaction mode between distributed resources and virtual power plants in the distribution network, along with standardized feasible region and cost function representations, and the use of this collaborative control model to comprehensively control multi-objective distributed resources and output optimal scheduling instructions to achieve multi-objective comprehensive governance of the distribution network, includes: taking the interaction mode between distributed resources and virtual power plants in the distribution network as a guide, comprehensively considering multiple optimization objectives, and combining virtual power plant operating constraints with standardized feasible region and cost function representations to construct a collaborative control model; using heuristic algorithms to solve the collaborative control model, processing virtual power plant operating constraints and finding global or near-global optimal solutions, and finally outputting optimal scheduling instructions for the active and reactive power of each virtual power plant.

[0148] In this optional embodiment, the multiple optimization objectives include: improving system economy by minimizing the overall operating cost of all connected virtual power plants; improving energy utilization efficiency by minimizing the total network loss of the system; ensuring power supply quality and safe and stable operation of the system by minimizing the sum of voltage deviations of all nodes; and coordinating and optimizing each sub-objective through configurable weight coefficients to adapt to the scheduling needs under different operating scenarios, forming a complete distribution network comprehensive governance objective system.

[0149] In this optional embodiment, the virtual power plant operation constraints include: adopting precise network constraints based on AC power flow to fully describe the balance relationship between active and reactive power in the distribution network, the safe range of node voltage, and the line current transmission limit, so as to ensure that the optimization scheme conforms to the physical laws and safety standards of the power grid; introducing virtual power plant standardization representation constraints based on the Kino polyhedron algorithm, and aggregating the feasible domains of multiple virtual power plants into a unified operating boundary through geometric operations.

[0150] To facilitate understanding of the above technical solutions of the present invention, the following further describes the above technical solutions of the present invention from the perspectives of architecture and principle, as follows:

[0151] First, a coordinated active and reactive power control mechanism for distribution networks based on virtual power plant technology is proposed, establishing the interaction mode between distributed resources and the distribution network. Second, based on virtual power plant technology, multiple distributed resources are aggregated, and an approximation method for the equivalent external characteristics of virtual power plants is proposed. Finally, a coordinated active and reactive power control model for distribution networks adapted to multiple virtual power plants is proposed, enabling efficient control of distributed resources and achieving multi-objective comprehensive management of the distribution network.

[0152] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 3As shown, the computer device includes a processor, memory, and a network interface connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database stores static and dynamic information data. The network interface communicates with external terminals via a network connection. When the computer program is executed by the processor, it implements the steps in the above method embodiments.

[0153] Those skilled in the art will understand that Figure 3 The structure shown is merely a block diagram of a portion of the structure related to the present invention and does not constitute a limitation on the computer device to which the present invention is applied. A specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0154] In addition, the present invention also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above method embodiments.

[0155] In addition, the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the above method embodiments.

[0156] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the methods described above. Any references to memory, storage, databases, or other media used in the embodiments provided by this 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 storage, etc. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0157] This invention is not limited to the structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this invention is limited only by the appended claims.

Claims

1. A comprehensive management method for distributed resource distribution networks, characterized in that, include: A distribution network active and reactive power coordinated control mechanism based on virtual power plant technology is established to construct an interaction mode between distributed resources of the distribution network and the virtual power plant. Multi-objective distributed resources are aggregated to construct a heterogeneous resource aggregation model. Then, using the equivalent external characteristic approximation algorithm, the heterogeneous resource aggregation model is transformed into a standardized feasible region representation and cost function representation. Based on the interaction mode of distributed resources and virtual power plants in the distribution network, and the standardized feasible region and cost function representation, a collaborative control model is constructed. The collaborative control model is then used to comprehensively control multi-objective distributed resources and output the optimal dispatch command to achieve multi-objective comprehensive governance of the distribution network.

2. The method for comprehensive management of distributed resource distribution networks according to claim 1, characterized in that, The active and reactive power coordinated control mechanism for distribution networks based on virtual power plant technology, which constructs an interaction mode between distributed resources of the distribution network and the virtual power plant, includes: Virtual power plants are used to aggregate distributed resources and establish a two-way information exchange mechanism with distribution network operators. Virtual power plants are used as basic units to participate in the operation of the distribution network, and are responsible for providing regulation capabilities and regulation costs to the distribution network operators in order to achieve efficient aggregation and control of distributed resources.

3. The method for comprehensive management of distributed resource distribution networks according to claim 2, characterized in that, The use of virtual power plants as basic units participating in the operation of the distribution network, responsible for providing regulation capabilities and costs to the distribution network operator, to achieve efficient aggregation and control of distributed resources includes: In terms of regulation capability, virtual power plants need to provide four types of core descriptive quantities to distribution network operators to comprehensively characterize their regulation capability. The four types of core descriptive quantities include: upper and lower limits of active and reactive power to characterize the range of power regulation that the virtual power plant can provide at the current moment; active and reactive power constraints to reflect the coupling relationship between active and reactive output caused by the physical limits of power electronic converters; upper and lower limits of energy state to characterize the continuous regulation capability of virtual power plants with energy storage over a longer time scale; and upper and lower limits of ramp rate to describe the limit of power output change capability of virtual power plants per unit time. Regarding regulation costs, the regulation costs of virtual power plants are defined as a bivariate function that is related to both active and reactive power, so as to accurately characterize the impact of reactive power regulation on converter losses and the economic efficiency of active power output. By standardizing the interaction between adjustment capabilities and adjustment costs, efficient aggregation and control of distributed resources can be achieved.

4. The method for comprehensive management of distributed resource distribution networks according to claim 1, characterized in that, The interaction mode between distributed resources in the distribution network and virtual power plants involves aggregating multi-objective distributed resources to construct a heterogeneous resource aggregation model. Then, using an equivalent external characteristic approximation algorithm, the heterogeneous resource aggregation model is transformed into a standardized feasible region representation and cost function representation. Based on the interaction mode between distributed resources and virtual power plants in the distribution network, distributed photovoltaics, energy storage systems, electric vehicle charging stations and flexible loads are aggregated to construct a heterogeneous resource aggregation model. By utilizing the equivalent external characteristic approximation algorithm, the complexity of the heterogeneous resource aggregation model is reduced, and based on the mathematical method of the Kino polyhedron, the unified and standardized representation requirements of the interaction mode for the heterogeneous resource aggregation model are met. By sampling the heterogeneous resource aggregation model within a preset range of active and reactive power, and using a backpropagation neural network to fit the nonlinear relationship between active power, reactive power and virtual power plant operating costs, a discretized power-cost correspondence is obtained, which simplifies the model and improves computational efficiency. By using geometric approximation and artificial intelligence techniques, and based on the standardized representation requirements and the correspondence between discretized power and cost, heterogeneous resource models are transformed into standardized feasible domain representations and cost function representations.

5. The method for comprehensive management of distributed resource distribution networks according to claim 1, characterized in that, The aforementioned collaborative control model, based on the interaction mode of distributed resources and virtual power plants in the distribution network and standardized feasible region and cost function representations, is used to construct a collaborative control model. This model is then used to comprehensively control multi-objective distributed resources and output optimal dispatch commands to achieve multi-objective comprehensive governance of the distribution network. Guided by the interaction mode between distributed resources in the distribution network and virtual power plants, and taking into account multiple optimization objectives, a collaborative control model is constructed by combining virtual power plant operation constraints and standardized feasible region and cost function representations. Heuristic algorithms are used to solve the coordinated control model, handle the operating constraints of virtual power plants, and find the global or near-global optimal solution. Finally, the optimal scheduling instructions for the active and reactive power of each virtual power plant are output.

6. The method for comprehensive management of distributed resource distribution networks according to claim 5, characterized in that, The multiple optimization objectives include: Improve system economics by minimizing the overall operating costs of all connected virtual power plants; Energy efficiency is improved by minimizing the total network loss of the system. By minimizing the sum of voltage deviations at all nodes, the power supply quality and the safe and stable operation of the system can be guaranteed. Each sub-objective is coordinated and optimized through configurable weight coefficients to adapt to the scheduling needs under different operating scenarios, forming a complete distribution network comprehensive governance objective system.

7. A method for comprehensive management of distributed resource distribution networks according to claim 5, characterized in that, The virtual power plant operating constraints include: Using precise network constraints based on AC power flow, the balance between active and reactive power in the distribution network, the safe range of node voltage, and the limit of line current transmission are fully described to ensure that the optimization scheme conforms to the physical laws of the power grid and safety standards. The virtual power plant standardization representation constraint is introduced by the Kino polyhedron algorithm, and the feasible domains of multiple virtual power plants are aggregated into a unified operating boundary through geometric operations.

8. A distributed resource distribution network integrated management system, characterized in that, The system includes: The interactive management module is used for the active and reactive power coordinated control mechanism of the distribution network based on virtual power plant technology, and to build an interactive mode between the distributed resources of the distribution network and the virtual power plant. The aggregation and standardization module is used to aggregate multi-objective distributed resources, construct a heterogeneous resource aggregation model, and use the equivalent external characteristic approximation algorithm to transform the heterogeneous resource aggregation model into a standardized feasible region representation and cost function representation. The resource regulation module is used to construct a collaborative regulation model based on the interaction mode of distributed resources and virtual power plants in the distribution network, as well as the standardized feasible domain representation and cost function representation. The collaborative regulation model is then used to comprehensively regulate multi-objective distributed resources and output optimal scheduling instructions to achieve multi-objective comprehensive governance of the distribution network.

9. A distributed resource distribution network integrated management system according to claim 8, characterized in that, The active and reactive power coordinated control mechanism for distribution networks based on virtual power plant technology, which constructs an interaction mode between distributed resources of the distribution network and the virtual power plant, includes: Virtual power plants are used to aggregate distributed resources and establish a two-way information exchange mechanism with distribution network operators. Virtual power plants are used as basic units to participate in the operation of the distribution network, and are responsible for providing regulation capabilities and regulation costs to the distribution network operators in order to achieve efficient aggregation and control of distributed resources.

10. A distributed resource distribution network integrated management system according to claim 9, characterized in that, The use of virtual power plants as basic units participating in the operation of the distribution network, responsible for providing regulation capabilities and costs to the distribution network operator, to achieve efficient aggregation and control of distributed resources includes: In terms of regulation capability, virtual power plants need to provide four types of core descriptive quantities to distribution network operators to comprehensively characterize their regulation capability. The four types of core descriptive quantities include: upper and lower limits of active and reactive power to characterize the range of power regulation that the virtual power plant can provide at the current moment; active and reactive power constraints to reflect the coupling relationship between active and reactive output caused by the physical limits of power electronic converters; upper and lower limits of energy state to characterize the continuous regulation capability of virtual power plants with energy storage over a longer time scale; and upper and lower limits of ramp rate to describe the limit of power output change capability of virtual power plants per unit time. Regarding regulation costs, the regulation costs of virtual power plants are defined as a bivariate function that is related to both active and reactive power, so as to accurately characterize the impact of reactive power regulation on converter losses and the economic efficiency of active power output. By standardizing the interaction between adjustment capabilities and adjustment costs, efficient aggregation and control of distributed resources can be achieved.

11. A distributed resource distribution network integrated management system according to claim 8, characterized in that, The interaction mode between distributed resources in the distribution network and virtual power plants involves aggregating multi-objective distributed resources to construct a heterogeneous resource aggregation model. Then, using an equivalent external characteristic approximation algorithm, the heterogeneous resource aggregation model is transformed into a standardized feasible region representation and cost function representation. Based on the interaction mode between distributed resources and virtual power plants in the distribution network, distributed photovoltaics, energy storage systems, electric vehicle charging stations and flexible loads are aggregated to construct a heterogeneous resource aggregation model. By utilizing the equivalent external characteristic approximation algorithm, the complexity of the heterogeneous resource aggregation model is reduced, and based on the mathematical method of the Kino polyhedron, the unified and standardized representation requirements of the interaction mode for the heterogeneous resource aggregation model are met. By sampling the heterogeneous resource aggregation model within a preset range of active and reactive power, and using a backpropagation neural network to fit the nonlinear relationship between active power, reactive power and virtual power plant operating costs, a discretized power-cost correspondence is obtained, which simplifies the model and improves computational efficiency. By using geometric approximation and artificial intelligence techniques, and based on the standardized representation requirements and the correspondence between discretized power and cost, heterogeneous resource models are transformed into standardized feasible domain representations and cost function representations.

12. A distributed resource distribution network integrated management system according to claim 8, characterized in that, The aforementioned collaborative control model, based on the interaction mode of distributed resources and virtual power plants in the distribution network and standardized feasible region and cost function representations, is used to construct a collaborative control model. This model is then used to comprehensively control multi-objective distributed resources and output optimal dispatch commands to achieve multi-objective comprehensive governance of the distribution network. Guided by the interaction mode between distributed resources in the distribution network and virtual power plants, and taking into account multiple optimization objectives, a collaborative control model is constructed by combining virtual power plant operation constraints and standardized feasible region and cost function representations. Heuristic algorithms are used to solve the coordinated control model, handle the operating constraints of virtual power plants, and find the global or near-global optimal solution. Finally, the optimal scheduling instructions for the active and reactive power of each virtual power plant are output.

13. A distributed resource distribution network integrated management system according to claim 12, characterized in that, The multiple optimization objectives include: Improve system economics by minimizing the overall operating costs of all connected virtual power plants; Energy efficiency is improved by minimizing the total network loss of the system. By minimizing the sum of voltage deviations at all nodes, the power supply quality and the safe and stable operation of the system can be guaranteed. Each sub-objective is coordinated and optimized through configurable weight coefficients to adapt to the scheduling needs under different operating scenarios, forming a complete distribution network comprehensive governance objective system.

14. A distributed resource distribution network integrated management system according to claim 12, characterized in that, The virtual power plant operating constraints include: Using precise network constraints based on AC power flow, the balance between active and reactive power in the distribution network, the safe range of node voltage, and the limit of line current transmission are fully described to ensure that the optimization scheme conforms to the physical laws of the power grid and safety standards. The virtual power plant standardization representation constraint is introduced by the Kino polyhedron algorithm, and the feasible domains of multiple virtual power plants are aggregated into a unified operating boundary through geometric operations.

15. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

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

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