Virtual power plant network access detection method and system

Abstract

The invention relates to the technical field of virtual power plant network access detection, in particular to a virtual power plant network access detection method and system. Systematically verifying the aggregate resource list of the virtual power plant; the resource aggregation capability of the virtual power plant is analyzed by considering the operation cost of the aggregation resources; virtual power plant regulation response performance testing is carried out; and introducing a breadth-first traversal algorithm, and constructing a comprehensive risk assessment system. Through the network access detection technology, the performance and state of each component in the virtual power plant can be accurately evaluated, it is ensured that resources aggregated by the virtual power plant meet the stability and reliability requirements of a power grid, a more reasonable resource allocation scheme is provided for the power grid, the energy utilization efficiency is improved, and the energy supply safety is guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of virtual power plant network access detection, and in particular to a virtual power plant network access detection method and system. Background Art

[0002] As the electricity market is gradually transforming from a traditional unilateral power generation market to a more flexible bilateral market. In this context, Virtual Power Plant (VPP) as an innovative operation mode, by integrating distributed power generation resources, demand response and other diversified resources in the power grid, participates in the unified dispatch of the power grid in an integrated form. VPP can give full play to the synergy between distributed resources, optimize resource allocation, and improve the operation efficiency and service level of the power grid. The traditional safety and stability guarantee strategy dominated by power supply side regulation is no longer sufficient to meet the development needs of modern power systems. In view of the diversity and flexibility advantages of demand-side resources, it is of great significance to promote the transformation of the power system from the "source follows load" mode to the "source-load interaction" mode and fully tap the potential of demand-side resources in the modern power system to ensure the safe and stable operation of the power grid. Grid companies urgently need to take effective measures to promote the participation and utilization of demand-side resources to adapt to the new development trend of the power market.

[0003] At present, the network access detection technology and methods of virtual power plants are still under exploration. Since virtual power plants involve a variety of distributed energy sources and controllable loads, there is currently a lack of unified network access detection technology standards and specifications, which has led to uneven technical levels of virtual power plants built by different regions and operators. In addition, virtual power plants need to collect and process a large amount of distributed resource data information in real time, but there are many types of users, and it is difficult to collect and detect information on underlying equipment. The information acquisition is not sound, which reduces the accuracy of adjustable potential assessment. The operational risks, economic risks, and safety risks that cannot be ignored in the network access detection process also interfere with the safe network access of virtual power plants, and risk assessment and management issues are highlighted. In this context, the exploration of network access detection methods for virtual power plants is particularly important.

[0004] In view of the above problems, the present invention provides a method for detecting the access of a virtual power plant to the grid. First, the aggregated resource list of the virtual power plant is systematically verified to ensure the dispatchability and reliability of the resources. Secondly, the operating cost of the aggregated resources is considered, and a game pricing model is constructed to quantitatively evaluate the resource aggregation and optimal configuration capabilities of the virtual power plant. Thirdly, a demand-side response model with microgrid indicators as the core is established, and the scheduling strategy is optimized through electrical signals to achieve precise control and optimization of the regulation capabilities of the virtual power plant. Finally, in order to comprehensively evaluate the operating risks of the virtual power plant, the breadth-first traversal algorithm (BFS) is introduced to construct a comprehensive risk assessment system, which provides scientific decision-making support for the stable operation and risk management of the virtual power plant. Summary of the invention

[0005] In view of the above problems existing in the prior art, the present invention is proposed.

[0006] The invention of this patent aims to provide a virtual power plant access detection method and system. First, the aggregated resource list of the virtual power plant is verified; secondly, the operating cost of the aggregated resources is divided into distributed photovoltaic operating cost, distributed energy storage operating cost, and demand response cost, and a cooperative game pricing model is constructed to reflect the resource aggregation capability of the virtual power plant; thirdly, microgrid indicators are established for the regulation and response performance of the virtual power plant, and a demand-side response model is constructed based on the microgrid indicators to perform electrical signal optimization scheduling; finally, a breadth-first traversal algorithm (BFS) is introduced to comprehensively evaluate the operating risks of the virtual power plant.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions: a method for detecting network access of a virtual power plant, comprising systematically verifying the aggregated resource list of the virtual power plant;

[0008] Considering the operating cost of aggregated resources, analyze the resource aggregation capacity of virtual power plants;

[0009] Conduct virtual power plant regulation response performance test;

[0010] Introduce the breadth-first traversal algorithm to build a comprehensive risk assessment system.

[0011] As a preferred scheme of the virtual power plant network access detection method described in the present invention, the systematic verification of the aggregated resource list of the virtual power plant includes performing precise verification of the aggregated resource list of the virtual power plant, combined with detailed reconciliation of actual load equipment in the area, to ensure the consistency of the resource list with the actual load configuration.

[0012] As a preferred solution of a virtual power plant network access detection method described in the present invention, wherein: the virtual power plant resource aggregation capability analysis includes:

[0013] Analyze the cost of distributed photovoltaic power generation when it operates alone, and analyze the cost of distributed energy storage when it operates alone;

[0014] The costs of industrial and residential users performing demand response and interacting with the market individually;

[0015] Taking into account that the virtual power plant aggregates distributed photovoltaics, distributed energy storage and demand response to participate in market transactions in a cooperative mode, the total operating cost within the virtual power plant is calculated, and the difference in cost with the separate operation mode of each type of resource is calculated.

[0016] As a preferred solution of a virtual power plant network access detection method described in the present invention, wherein: the virtual power plant regulation response performance test includes:

[0017] Microgrid indicators serve as the basis for establishing the demand-side response model. The calculation results will directly affect the system host's ability to dispatch and process the virtual power plant's power transmission signals.

[0018] The demand-side response model assists the host components to accurately identify the transmission power signal;

[0019] Based on the known demand-side response model function, the transmission mapping relationship between adjacent electrical quantity signal data is determined.

[0020] As a preferred solution of the virtual power plant network access detection method described in the present invention, the virtual power plant BFS risk assessment includes designing the virtual power plant operation safety system in the form of an undirected graph as the specific protection object of the virtual power plant, evaluating the security level of the access location during the operation of the virtual power plant, and then completing the mark identification.

[0021] As a preferred solution of the virtual power plant network access detection method described in the present invention, the breadth-first traversal algorithm is introduced to set a function system for different states of the power plant, calculate the risk value of the virtual power plant operation node, and evaluate the risk of the virtual power plant.

[0022] As a preferred scheme of the virtual power plant network access detection method described in the present invention, the construction of a comprehensive risk assessment system includes designing the virtual power plant operation safety system in the form of an undirected graph, evaluating the security level of the access location during the operation of the virtual power plant based on the specific protection objects of the virtual power plant, and completing the mark identification.

[0023] As a preferred solution of a virtual power plant network access detection system described in the present invention, it includes:

[0024] A verification module that systematically verifies the aggregated resource inventory of the virtual power plant;

[0025] The analysis module considers the operating cost of aggregated resources and analyzes the resource aggregation capacity of the virtual power plant;

[0026] Test module, to test the regulation response performance of virtual power plant;

[0027] The assessment module introduces the breadth-first traversal algorithm to build a comprehensive risk assessment system.

[0028] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and wherein the processor implements the steps of any one of the methods for detecting network access to a virtual power plant when executing the computer program.

[0029] A computer-readable storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the steps of any one of the methods for detecting network access to a virtual power plant are implemented.

[0030] Beneficial effects of the present invention: This method studies the network access detection method and system of virtual power plants. By verifying the aggregated resource list of the virtual power plant, it is helpful for the virtual power plant to provide necessary auxiliary services for the power grid, ensuring that the aggregated resources (such as distributed power generation, energy storage equipment, controllable loads, etc.) can work together efficiently; by constructing a cooperative game pricing model, effective pricing of resources can be achieved, and various distributed resources in the virtual power plant can be reasonably allocated to obtain more gains brought by cost reduction, and achieve optimal resource configuration; based on microgrid indicators, a demand response model is established to test the regulation response performance of the virtual power plant, evaluate the demand response benefits of different users in different scenarios, thereby optimizing the scheduling strategy, helping power grid operators to better balance the supply and demand of electricity, and reduce dependence on traditional power generation resources through demand-side management; virtual power plant BFS risk assessment can be performed to improve the information security system, reduce risks, and ensure the smooth progress of business. Through the network access detection technology, the performance and status of each component in the virtual power plant can be accurately evaluated to ensure that the resources aggregated by the virtual power plant meet the stability and reliability requirements of the power grid, provide a more reasonable resource allocation plan for the power grid, improve energy utilization efficiency, and ensure energy supply security. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0032] Figure 1 A flowchart of a virtual power plant network access detection method provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0033] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0034] Example 1

[0035] Reference Figure 1 , which is the first embodiment of the present invention, and provides a method for detecting network access of a virtual power plant, comprising:

[0036] like Figure 1 As shown, the present invention proposes a method for detecting the access of virtual power plants to the network, which detects the access of virtual power plants to the network through multiple dimensions such as verification of the aggregated resource list, quantitative analysis of resource aggregation capabilities, and evaluation of the regulation response performance. Based on the resource operating cost, a cooperative game pricing model is constructed to evaluate the resource aggregation efficiency of the virtual power plant; based on the total-fractional supply circuit architecture, combined with microgrid indicators, a demand-side response model is constructed, and the automatic optimization scheduling of the virtual power plant is realized through the electrical signal optimization algorithm; in order to comprehensively evaluate the operating risks of the virtual power plant, the breadth-first traversal algorithm (BFS) is introduced, a state setting function system is established, and the risk value of the virtual power plant operating node is quantitatively calculated, providing a scientific evaluation method for the risk management of the virtual power plant.

[0037] S1, Virtual Power Plant Aggregate Resource Inventory Verification

[0038] Perform accurate verification of the virtual power plant aggregate resource list, combined with detailed reconciliation of actual load equipment in the region to ensure consistency between the resource list and the actual load configuration.

[0039] S2, Virtual Power Plant Resource Aggregation Capability Analysis

[0040] Aggregate resource operating costs

[0041] Distributed photovoltaic operation cost: The cost of distributed photovoltaic power generation when it is operated independently mainly includes operation and maintenance costs and penalty costs for deviation in electricity consumption.

[0042] 2) Distributed energy storage operating costs. The cost of distributed energy storage when operating independently mainly includes distributed energy storage operating costs and electricity purchase costs.

[0043] 3) Demand response costs. The costs of industrial and residential users performing demand response and interacting with the market independently mainly include energy costs and the cost of reduced utility caused by demand response.

[0044] Cooperative Game Pricing Model

[0045] Taking into account that the virtual power plant aggregates distributed photovoltaics, distributed energy storage and demand response to participate in market transactions in a cooperative mode, the total operating cost within the virtual power plant is calculated, and the difference in cost with the separate operation mode of each type of resource is calculated.

[0046] As the operating costs of various resources are reduced, the reduction in operating costs is allocated from the two aspects of resource response plan completion rate and marginal contribution rate. Based on the plan completion rate and marginal contribution value of various resources, the cost allocation method is used to further determine the pricing model of various resources.

[0047] S3, virtual power plant regulation response performance test

[0048] Microgrid indicators

[0049] In the virtual power plant automatic optimization dispatching system, the selection of microgrid indicators is a necessary execution link for the establishment of the demand-side response model. Under the premise of knowing the characteristic values ​​of the scale of each level of power signal, the numerical mapping relationship between the selected power data can be calculated. As the basis for the establishment of the demand-side response model, the calculation results of the microgrid indicators will directly affect the system host's dispatching and processing capabilities for the virtual power plant power transmission signal.

[0050] Demand-side response model

[0051] For the virtual power plant automatic optimization dispatching system, the demand side response model can assist the host components to accurately distinguish the transmission power signal. Generally speaking, the larger the physical value range interval related to the model discrimination index, the stronger the host component's ability to accurately distinguish the transmission power signal; conversely, if the physical value range interval related to the model discrimination index is relatively small, it means that the host component has a weaker ability to accurately distinguish the transmission power signal. Based on physical quantities such as response frequency, rated current output value, and electrical signal transmission frequency cycle, a demand side response model for the virtual power plant automatic optimization dispatching system is constructed. Under the action of the demand side response model, as the electrical signal transmission capacity continues to increase, the virtual power plant microgrid organization's ability to present power data is also gradually increasing.

[0052] Electric signal optimization dispatch conditions

[0053] The establishment of optimized dispatching conditions for electric signals is the final processing step in the design of the automatic optimized dispatching system for virtual power plants. It can determine the transmission mapping relationship between adjacent electric quantity signal data based on the known capabilities of the demand-side response model. On the one hand, it ensures the application stability of the microgrid structure, and on the other hand, it fully stimulates the sharing and dispatching value of the selected microgrid indicators.

[0054] When the level of electrical signal transmission remains stable, the optimized scheduling condition expression can directly affect the actual application capability of the system host. Generally speaking, due to the existence of the demand-side response model, any electricity data will not affect the operating capability of related hardware application equipment. This is also the main reason why the new virtual power plant automatic optimization scheduling system has strong feasibility value.

[0055] S4, Virtual Power Plant BFS Risk Assessment

[0056] The principle of the breadth-first search algorithm (BFS) is to design the virtual power plant operation safety system in the form of an undirected graph, which is used as the specific protection object of the virtual power plant to evaluate the security level of the access location during the operation of the virtual power plant, and then complete the tag identification. The risk preference of the marked node is established through the risk avoidance of the corresponding node, and the avoidance virtual power plant operation function is established. Finally, the risk avoidance method is evaluated by BFS, and the risk assessment data is output by BFS, thereby evaluating a more accurate risk value to ensure the safety of the power plant operation environment.

[0057] Example 2

[0058] The second embodiment of the present invention is different from the previous embodiment in that:

[0059] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0060] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0061] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0062] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0063] Embodiment 3 is the third embodiment of the present invention, which provides a virtual power plant network access detection method. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through experiments.

[0064] In this experiment, the present invention designs a method and system for detecting the access of a virtual power plant to the grid, which meets the requirements for access to the grid from various aspects such as the verification of the aggregated resource list of the virtual power plant, the analysis of the aggregation capacity, and the adjustment response performance test. The characteristics of the aggregated resources of the virtual power plant are analyzed, the operating costs of the aggregated resources are considered, and a cooperative game pricing model is constructed to detect the aggregation capacity of the virtual power plant resources. On the basis of the total-divided supply circuit, the execution environment of the virtual power plant automatic optimization and dispatching system is built, and a complete demand-side response model is established using microgrid indicators. Then, the execution environment of the system software for optimizing the dispatching conditions using electrical signals is built, thereby completing the design of the virtual power plant automatic optimization and dispatching system to detect the response capacity of the virtual power plant. The breadth-first traversal algorithm (BFS) is used to set the function system for different states of the power plant, calculate the risk value of the virtual power plant operation node, and evaluate the risk of the virtual power plant. The specific implementation steps of this method are as follows:

[0065] Virtual Power Plant Aggregate Resource Inventory Verification

[0066] Perform accurate verification of the virtual power plant aggregate resource list, combined with detailed reconciliation of actual load equipment in the region to ensure consistency between the resource list and the actual load configuration.

[0067] Analysis of Virtual Power Plant Resource Aggregation Capabilities

[0068] Aggregate resource operating costs

[0069] Distributed photovoltaic operation cost. The cost of distributed photovoltaic power generation when it is operated independently It mainly includes operation and maintenance costs and penalty costs for deviation in electricity consumption.

[0070]

[0071] Where: j is the set of distributed photovoltaic companies; They are the operation and maintenance cost and deviation electricity cost of the jth distributed photovoltaic power generator at time t respectively; are the actual power generation, predicted power generation and deviation power generation of the jth distributed photovoltaic power generator respectively; is the deviation penalty rate of the jth distributed photovoltaic power generator; They are unit operation and maintenance cost and deviation electricity cost respectively.

[0072] 2) Distributed energy storage operating costs. The cost of distributed energy storage when operating alone It mainly includes distributed energy storage operating costs and electricity purchasing costs.

[0073]

[0074] Where: s is the set of distributed energy storage providers; They are the operating cost and electricity purchase cost of the sth distributed energy storage provider at time t; They are the unit charging cost and discharging cost of the sth distributed energy storage provider at time t. Usually, the unit charging cost is consistent with the discharging cost. is the unit electricity purchase cost of distributed energy storage at time t; is the charge and discharge amount of the battery at time t.

[0075] 3) Demand response cost. The cost of industrial and residential users to respond to demand and interact with the market separately Mainly includes energy cost and cost of utility reduction caused by demand response

[0076]

[0077] Where: are the energy cost and the cost caused by utility decline at time t respectively; are the initial load demands of industrial users and residential users respectively; They are the first and second types of demand response scales respectively; The average unit energy cost for industrial and residential users; The change rate of user energy satisfaction; are the coefficients of the quadratic term, linear term and constant term of user utility cost respectively.

[0078] Cooperative Game Pricing Model

[0079] The virtual power plant aggregates distributed photovoltaics, distributed energy storage and demand response to participate in market transactions in a cooperative mode. At this time, the total operating cost within the virtual power plant is as follows:

[0080]

[0081] Where: is the total operating cost within the virtual power plant; They are the costs of distributed photovoltaic, battery and demand response in the virtual power plant when operating in cooperative mode at time t.

[0082] Compared with the model where each resource runs separately, the cost difference is as follows:

[0083]

[0084] Where: It is the difference between the cost of cooperative game operation mode and the cost of individual operation mode.

[0085] Due to the reduction in operating costs caused by the cooperation of various resources, the present invention allocates the reduction in operating costs from two aspects: the resource response plan completion rate and the marginal contribution rate. The plan completion rate and marginal contribution value are as follows:

[0086]

[0087] Where: v is the resource type; MC v are the response plan completion rate and marginal contribution rate of resource v respectively; are the actual call scale and planned call scale of resource v respectively; R vpp (N), R vpp (N-{v}) are the benefits of the virtual power plant when all resources are aggregated and the benefits after removing resource v.

[0088] The cost allocation method based on the planned completion rate and marginal contribution value of each type of resources is as follows. Resources with higher planned completion rates and higher marginal contribution values ​​should obtain more gains from cost reduction.

[0089]

[0090] Where: Cost reduction gain allocated to resource v; is the allocation coefficient of resource v; V is the set of resource types, V∈{pv,ba,DR}, where pv,ba,DR represent photovoltaic power generation, battery discharge and demand response, respectively.

[0091] Further determine the pricing model for each resource:

[0092]

[0093] Where: They are respectively the pricing of distributed photovoltaic, distributed energy storage and demand response under the cooperative model; are the pricing of distributed photovoltaic, distributed energy storage and demand response in separate modes; ΔE pv , They are the deviations of photovoltaic power generation and battery discharge respectively.

[0094] Virtual power plant regulation response performance test

[0095] Microgrid indicators

[0096] In the virtual power plant automatic optimization dispatching system, the selection of microgrid indicators is a necessary execution link for the establishment of the demand-side response model. Under the premise of knowing the characteristic values ​​of the scale of each level of power signal, the numerical mapping relationship between the selected power data can be calculated. Let ξ represent the electrical signal transmission coefficient in the virtual power plant microgrid loop, x1, x2, … x n Indicates n different power marking coefficients, v1, v2, ... v n represents n different electrical signal induction vectors, and n represents the number of cyclic transmissions of the electrical signal in the microgrid loop. Combining the above physical quantities, the electrical signal can be scaled to eigenvalues ​​c1, c2, … c n Respectively expressed as:

[0097]

[0098] The microgrid index selection expression of the virtual power plant automatic optimization dispatching system is defined as:

[0099]

[0100] In the formula, refers to the microgrid index; σ, υ represent the expected values ​​of two different electrical signal demands, ψ σ σ represents the electrical signal response coefficient under expected conditions, ψ v υ represents the electrical signal response coefficient under expected conditions, b σ represents the electrical signal response characteristics related to the expected condition of σ, b v represents the electrical signal response characteristics related to the desired condition υ, ω max Represents the maximum power scheduling authority value, ω min Indicates the minimum power scheduling authority value.

[0101] Microgrid indicators serve as the basis for establishing the demand-side response model, and their calculation results will directly affect the system host's scheduling and processing capabilities for virtual power plant power transmission signals.

[0102] Demand-side response model

[0103] For the virtual power plant automatic optimization dispatching system, the demand side response model can assist the host component to accurately identify the transmission power signal. Generally speaking, the larger the physical value range interval related to the model discrimination index, the stronger the host component's ability to accurately identify the transmission power signal; conversely, if the physical value range interval related to the model discrimination index is relatively small, it means that the host component's ability to accurately identify the transmission power signal is weak. min Indicates the minimum value of the electric quantity signal discrimination coefficient, k max Indicates the maximum value of the electric quantity signal discrimination coefficient. In the virtual power plant automatic optimization dispatching system, k minIndicator, k max The definition and judgment conditions of the indicator are always met:

[0104]

[0105] In the formula, s represents the load capacity coefficient of the electric quantity signal. In the actual calculation process, the minimum value of the s index can only be equal to the natural number "1". s represents the optimized characteristic value of the electrical signal under the s load capacity factor, It represents the mean result of the electrical signal optimization characteristics, and ΔD represents the value of the electrical signal output that the virtual power plant host can bear per unit time.

[0106] β represents the response frequency of the electric quantity signal in the unit transmission time, I represents the rated current output value of the virtual power plant, represents the average resistance of the connected resistors, f represents the frequency period of the electrical signal transmission, is the characteristic value of power dispatch in the virtual power plant microgrid environment. Combining the above physical quantities, the demand side response model of the virtual power plant automatic optimization dispatch system can be expressed as

[0107]

[0108] Under the influence of the demand-side response model, as the transmission capacity of electrical signals continues to increase, the ability of virtual power plant microgrid organizations to present electricity data is also gradually increasing.

[0109] Electric signal optimization dispatching conditions

[0110] The establishment of optimized dispatching conditions for electric signals is the final processing step in the design of the automatic optimized dispatching system for virtual power plants. It can determine the transmission mapping relationship between adjacent electric quantity signal data based on the known demand-side response model capability. On the one hand, it ensures the application stability of the microgrid structure, and on the other hand, it fully stimulates the sharing and dispatching value of the selected microgrid indicators. Without considering other interference conditions, the establishment of optimized dispatching conditions for electric signals is directly affected by the power data transmission authority. The specific calculation expression is as follows:

[0111]

[0112] In the formula, q represents the electric signal load, F represents the demand-side response value of the virtual power plant automatic optimization dispatching system; P represents the standard electric power calculation value, represents the mean value of the electric field strength in the virtual power plant microgrid environment, t1 and t2 represent two randomly selected electric signal scheduling marking coefficients, Indicates the power optimization scale value that matches the t1 coefficient, Indicates the power optimization scale value that matches the t2 coefficient.

[0113] Define L as the optimal scheduling value of the electrical signal, g min Indicates the minimum value of the power debugging indicator, g max represents the maximum value of the power debugging index, and β represents the frequency condition of the electric signal response in the virtual power plant microgrid environment. With the support of the above physical quantities, the electric signal optimization scheduling condition can be expressed as:

[0114]

[0115] When the level of electrical signal transmission remains stable, the optimized scheduling condition expression can directly affect the actual application capability of the system host. Generally speaking, due to the existence of the demand-side response model, any electricity data will not affect the operating capability of related hardware application equipment. This is also the main reason why the new virtual power plant automatic optimization scheduling system has strong feasibility value.

[0116] Virtual Power Plant BFS Risk Assessment

[0117] The principle of the breadth-first traversal algorithm (BFS) is to design the virtual power plant operation safety system in the form of an undirected graph, which is used as the specific protection object of the virtual power plant to evaluate the security level of the access location during the operation of the virtual power plant, and then complete the tag identification. The risk preference of this node is recorded as:

[0118] Date=(v1,v2,v3)

[0119] Where Date represents the risk preference of a particular node in the virtual power plant, and (v1, v2, v3) represents the coordinates of any three nodes in the operation of the virtual power plant.

[0120] Through the risk avoidance method of the corresponding node, the avoided virtual power plant operation function is expressed as

[0121] State=(S v1 ,S v2 ,S v3 )

[0122] Where State represents the avoidance function adopted by the power plant risk preference node, (S v1 ,S v2 ,S v3 ) represents the node risk coordinates to be avoided.

[0123] In the virtual power plant, node selection and avoidance function establishment are performed respectively, and finally the risk avoidance method is evaluated by BFS. The evaluation function is recorded as

[0124] Sr=(Node atc ,Ar)

[0125] In the formula, Sr represents the BFS evaluation avoidance method data function, Node atc Represents the node selection point of the virtual power plant, and Ar represents the avoidance function adopted at the selection point.

[0126] In the BFS evaluation, since the setting environment is a virtual power plant, the input data values ​​often have certain deviations, which leads to uncertainty in the evaluation results during the algorithm calculation process. Therefore, such data is excluded, namely:

[0127]

[0128] Where r(t) represents the uncertainty data exclusion of BFS risk assessment, represents the mean of the virtual deviations of the input, y s (t) represents the deviation of the virtual power plant input, and c(t) represents the output deviation risk function.

[0129] After the uncertain results are eliminated, the algorithm output results are more accurate. Therefore, for the risk data generated by the operation of the established virtual power plant, after being processed by a systematic avoidance method, the risk assessment data output by BFS is

[0130]

[0131] Where, L 1,2 represents the risk assessment of the virtual line operation between node 1 and node 2, L 2,3 represents the risk assessment of the virtual line operation between node 2 and node 3, S v1 represents the risk aversion function of node 1 input, S v2 represents the risk aversion function of node 2 input, S v3 represents the risk aversion function inputted by node 3, and ☉ represents the BFS XOR operation between the aversion functions. Thus, a more accurate risk value can be evaluated to ensure the safety of the power plant operating environment.

[0132] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

[0133] Embodiment 4, the fourth embodiment of the present invention, provides a virtual power plant network access detection system, characterized in that: it includes:

[0134] A verification module that systematically verifies the aggregated resource inventory of the virtual power plant;

[0135] The analysis module considers the operating cost of aggregated resources and analyzes the resource aggregation capacity of the virtual power plant;

[0136] Test module, to test the regulation response performance of virtual power plant;

[0137] The assessment module introduces the breadth-first traversal algorithm to build a comprehensive risk assessment system.

[0138] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for detecting network access of a virtual power plant, characterized in that: include, Systematic verification of the aggregated resource inventory of virtual power plants; Considering the operating cost of aggregated resources, analyze the resource aggregation capacity of virtual power plants; Conduct virtual power plant regulation response performance test; Introduce the breadth-first traversal algorithm to build a comprehensive risk assessment system.

2. A virtual power plant network access detection method according to claim 1, characterized in that: The systematic verification of the aggregated resource list of the virtual power plant includes performing accurate verification of the aggregated resource list of the virtual power plant, combined with detailed reconciliation of actual load equipment in the area, to ensure consistency between the resource list and the actual load configuration.

3. A virtual power plant network access detection method as claimed in claim 2, characterized in that: The virtual power plant resource aggregation capability analysis includes: Analyze the cost of distributed photovoltaic power generation when it operates alone, and analyze the cost of distributed energy storage when it operates alone; The costs of industrial and residential users performing demand response and interacting with the market individually; Taking into account that the virtual power plant aggregates distributed photovoltaics, distributed energy storage and demand response to participate in market transactions in a cooperative mode, the total operating cost within the virtual power plant is calculated, and the difference in cost with the separate operation mode of each type of resource is calculated.

4. A method for detecting network access of a virtual power plant according to claim 3, characterized in that: The virtual power plant regulation response performance test includes: Microgrid indicators serve as the basis for establishing the demand-side response model. The calculation results will directly affect the system host's ability to dispatch and process the virtual power plant's power transmission signals. The demand-side response model assists the host components to accurately identify the transmission power signal; Based on the known demand-side response model function, the transmission mapping relationship between adjacent electrical quantity signal data is determined.

5. A virtual power plant network access detection method as claimed in claim 4, characterized in that: The virtual power plant BFS risk assessment includes designing the virtual power plant operation safety system in the form of an undirected graph as the specific protection object of the virtual power plant, evaluating the security level of the access location during the operation of the virtual power plant, and then completing the tag identification.

6. A method for detecting network access of a virtual power plant according to claim 5, characterized in that: The breadth-first traversal algorithm is introduced to set a function system for different states of the power plant, calculate the risk value of the virtual power plant operation node, and evaluate the risk of the virtual power plant.

7. A method for detecting network access of a virtual power plant according to claim 6, characterized in that: The construction of a comprehensive risk assessment system includes designing the virtual power plant operation safety system in the form of an undirected graph, evaluating the security level of access locations during the operation of the virtual power plant based on the specific protection objects of the virtual power plant, and completing mark identification.

8. A system based on a virtual power plant network access detection method according to any one of claims 1 to 7, characterized in that: include, A verification module that systematically verifies the aggregated resource inventory of the virtual power plant; The analysis module considers the operating cost of aggregated resources and analyzes the resource aggregation capacity of the virtual power plant; Test module, to test the regulation response performance of virtual power plant; The assessment module introduces the breadth-first traversal algorithm to build a comprehensive risk assessment system.

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

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

Images