Distributed energy storage aggregation interactive scheduling method and system considering aggregator risk

Abstract

The invention discloses a distributed energy storage aggregation interactive scheduling method and system considering aggregator risks, and the method comprises the steps: determining the incentive expectation of energy storage users in a target region for participating in power grid scheduling according to a constructed energy storage user participation degree probability model; based on a preset scheduling time demand of an energy storage aggregator, performing primary screening on each distributed energy storage main body, and performing secondary screening on each distributed energy storage main body after primary screening according to the excitation expectation to obtain a first energy storage aggregation group conforming to the target excitation expectation; the first energy storage aggregation group is input into a constructed energy storage scheduling model, the schedulable margin of the target area is obtained, and the energy storage scheduling model takes the load allowable change rate, the yield rate and the schedulable margin of each distributed energy storage main body as constraints; and generating a scheduling strategy of the target area based on the schedulable margin, and executing the scheduling strategy to realize scheduling, thereby improving the reliability and stability of power grid scheduling.

Description

Technical Field

[0001] The present invention relates to the field of distributed energy storage aggregation technology, and in particular to a distributed energy storage aggregation interactive scheduling method and system taking into account aggregator risks. Background Art

[0002] With the increase of intermittent renewable energy generation such as solar and wind power, the stability of the power system will be increasingly tested in the future. Due to the technological progress of chemistry, materials science and chemical engineering, energy storage technology has made great progress in recent years. Energy storage has made important contributions to the stability of the power system due to its safe and adjustable characteristics. However, due to the complexity of the main body of distributed energy storage equipment, insufficient single-unit capacity, and wide geographical distribution, it is difficult to effectively control and form market competitiveness, and thus it is difficult to participate in the regulation of the power system through market means.

[0003] The aggregation of multiple energy storage entities to form energy storage aggregators can replace a wide range of small-capacity energy storage equipment to participate in the electricity market, which is an important means to solve the problem of distributed energy storage participating in market activities. However, in the existing dispatching process of the power grid system, only the impact of the energy storage side is considered, and the impact of user-side demand is not considered, resulting in poor dispatching of the entire power grid, affecting the overall dispatching efficiency and operational reliability of the entire power grid system.

[0004] It can be seen that how to optimize the dispatching process of the power grid has become a technical problem that technical personnel in this field need to solve urgently. Summary of the invention

[0005] The present invention provides a distributed energy storage aggregation interactive scheduling method and system taking into account the risks of aggregators, so as to solve how to optimize the distributed energy storage aggregation strategy, reduce the risks of aggregators in participating in peak load regulation, and improve the stability of power grid scheduling.

[0006] In order to solve the above technical problems, an embodiment of the present invention provides a distributed energy storage aggregation interactive scheduling method taking into account the risks of aggregators, which is applied to a power grid system composed of a number of distributed energy storage entities, including:

[0007] According to the constructed energy storage user participation probability model, the incentive expectations of energy storage users in the target area to participate in grid dispatch are determined;

[0008] Based on the preset energy storage aggregator scheduling time requirements, each of the distributed energy storage entities is screened at the first level, and each of the distributed energy storage entities after the first level screening is screened at the second level according to the incentive expectations to obtain a first energy storage aggregation group that meets the target incentive expectations;

[0009] Inputting the first energy storage aggregation group into the constructed energy storage scheduling model to obtain the dispatchable margin of the target area, wherein the energy storage scheduling model is constructed by taking the load allowable change rate, the rate of return and the dispatchable margin of each distributed energy storage entity as constraint conditions;

[0010] A scheduling strategy for the target area is generated based on the schedulable margin, and the scheduling strategy is executed.

[0011] Furthermore, the incentive expectation of energy storage users in the target area to participate in power grid dispatching is determined based on the constructed energy storage user participation probability model, including:

[0012] The energy storage user participation probability model is expressed by the following formula:

[0013]

[0014] In the formula, is the cumulative distribution function of the standard normal distribution, β0, β1...β p is the fitting parameter of the model, X ij is the value of the jth independent variable of the sample, L(Y i =1|X i ) is the probability of energy storage users participating in demand response;

[0015] Determining constant parameters according to the energy storage user participation probability model;

[0016] An incentive expectation formula is constructed according to the constant parameters to calculate the incentive expectation. The incentive expectation formula is expressed as follows:

[0017]

[0018] Where θ(t) is the baseline probability coefficient and β is a constant parameter.

[0019] Furthermore, the first-level screening of each of the distributed energy storage entities based on the preset energy storage aggregator scheduling time requirements includes:

[0020] Establishing a mathematical model of the power grid system and inputting the set state parameters of each distributed energy storage entity; the state parameters include: the rated capacity, real-time state of charge, charge and discharge power and charging time of the energy storage entity;

[0021] In response to the state parameters, the sustainable charging and discharging time of each of the distributed energy storage entities is calculated;

[0022] The sustainable charging and discharging time is compared and analyzed with the scheduling time requirements of the energy storage aggregator to achieve the first-level screening of the distributed energy storage entities.

[0023] Furthermore, the step of performing secondary screening on each of the distributed energy storage entities after primary screening according to the incentive expectation to obtain a first energy storage aggregation group that meets the target incentive expectation includes:

[0024] Prioritize the incentive expectations corresponding to the distributed energy storage entities that meet the preset target incentive expectation values;

[0025] Under the preset required number of energy storage entities, the secondary screening is performed on each of the distributed energy storage entities according to the set priority order to obtain the first energy storage aggregation group.

[0026] Furthermore, before inputting the first energy storage aggregation group into the constructed energy storage scheduling model, the method further includes:

[0027] Analyzing the dispatchability of the first energy storage aggregation group according to the real-time state of charge, the sustainable charging and discharging time, and the grid status of each of the distributed energy storage entities;

[0028] According to the analysis results, a second energy storage aggregation group that meets the target dispatching capacity is obtained.

[0029] Furthermore, the step of inputting the first energy storage aggregation group into the constructed energy storage scheduling model to obtain the dispatchable margin of the target area includes:

[0030] Based on the energy storage scheduling model, the dispatchable margin of each of the distributed energy storage entities in the second energy storage aggregation group is calculated, and aggregated and accumulated to obtain a total dispatchable margin of the target area.

[0031] Furthermore, the method further comprises:

[0032] When the number of the distributed energy storage entities in the second energy storage aggregation group does not meet the preset energy storage aggregator quantity requirement, the dispatchable margin is supplemented by adding a dispatch compensation strategy.

[0033] Another embodiment of the present invention provides a distributed energy storage aggregation interactive scheduling system taking into account aggregator risks, including:

[0034] The user-side analysis module is used to determine the incentive expectations of energy storage users in the target area to participate in grid dispatching based on the constructed energy storage user participation probability model;

[0035] An aggregation group construction module is used to perform a primary screening of each of the distributed energy storage entities based on a preset energy storage aggregator scheduling time requirement, and perform a secondary screening of each of the distributed energy storage entities after the primary screening according to the incentive expectation to obtain a first energy storage aggregation group that meets the target incentive expectation;

[0036] A scheduling margin analysis module, used for inputting the first energy storage aggregation group into the constructed energy storage scheduling model to obtain the dispatchable margin of the target area, wherein the energy storage scheduling model is constructed by taking the load allowable change rate, the rate of return and the dispatchable margin of each distributed energy storage entity as constraint conditions;

[0037] A scheduling execution module is used to generate a scheduling strategy for the target area based on the schedulable margin and execute the scheduling strategy.

[0038] Another embodiment of the present invention provides a computer device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor implements the distributed energy storage aggregation interactive scheduling method taking into account the risks of aggregators as described above when executing the computer program.

[0039] Yet another embodiment of the present invention provides a computer-readable storage medium storing a computer program, wherein when the device where the computer-readable storage medium is located executes the computer program, the distributed energy storage aggregation interactive scheduling method taking into account the aggregator risks as described above is implemented.

[0040] Compared with the prior art, the embodiments of the present invention have the following advantages:

[0041] 1. The embodiment of the present invention constructs a model framework for aggregators to participate in peak-shaving strategies in peak-shaving scenarios, introduces user participation in scheduling into the model framework to provide incentive expectations, and associates user risks with aggregator peak-shaving, so that aggregators can consider user risks more comprehensively when formulating peak-shaving strategies, thereby ensuring the stability of peak-shaving activities.

[0042] 2. The embodiment of the present invention determines the user's participation expectation for the distributed energy storage unit aggregation group to calculate the dispatchable capacity of the aggregator under different dispatch compensations, so as to monitor and evaluate user risks in real time, flexibly adjust the aggregator's dispatch strategy according to the risk assessment results, and improve the stability of the power grid operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 It is a schematic flow chart of a distributed energy storage aggregation interactive scheduling method taking into account the aggregator risk in one embodiment of the present invention;

[0044] Figure 2 It is a schematic diagram of the polymer group screening process in one embodiment of the present invention;

[0045] Figure 3It is a schematic diagram of the structure of a distributed energy storage aggregation interactive scheduling system taking into account the risks of aggregators in one embodiment of the present invention;

[0046] Figure 4 It is a structural block diagram of a computer device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0047] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. The purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0048] In the description of this application, the terms "first", "second", "third", etc. are used for descriptive purposes only and are not to be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first", "second", "third", etc. may explicitly or implicitly include one or more of the feature. In the description of this application, unless otherwise specified, "plurality" means two or more.

[0049] In the description of the present application, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be a connection between the two elements. The terms "vertical", "horizontal", "left", "right", "upper", "lower" and similar expressions used herein are only for illustrative purposes, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention. The term "and / or" used herein includes any and all combinations of one or more related listed items. For those of ordinary skill in the art, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0050] In the description of this application, it should be noted that, unless otherwise defined, all technical and scientific terms used in the present invention have the same meaning as those commonly understood by those skilled in the art. The terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. For those of ordinary skill in the art, the specific meanings of the above terms in this application can be understood by specific circumstances.

[0051] The existing distributed energy storage aggregation control methods do not consider the impact of the participation of each user-side energy storage unit on the energy storage aggregator. For example, users show randomness and uncertainty in the process of electricity consumption and participation in peak load regulation, which leads to potential risks in aggregation operations and implementation of peak load regulation. Therefore, research on how to improve user participation and reduce the operating risks of aggregators is of great significance to maintaining the stability and reliability of the power grid.

[0052] Based on this, an embodiment of the present invention provides a distributed energy storage aggregation interactive scheduling method taking into account the risk of the aggregator, which is applied to a power grid system composed of a number of distributed energy storage entities. For details, please refer to Figure 1 , Figure 1 The figure shows a flow chart of a distributed energy storage aggregation interactive scheduling method taking into account the aggregator risk in one embodiment of the present invention, which includes the following steps:

[0053] S1. Determine the incentive expectations of energy storage users in the target area to participate in grid dispatch based on the constructed energy storage user participation probability model.

[0054] In the actual electricity trading process, the grid-side institutions may issue dispatch instructions to distributed energy storage users through aggregators, and users have the right to choose to accept or reject these instructions. However, this mechanism may lead to inefficient execution of dispatch instructions and even affect the stable operation of the entire power system.

[0055] Based on this, this embodiment will introduce a means of giving users incentive responses, calculate the incentive expectations of energy storage users, and quantitatively convert them into the willingness of energy storage users to participate in the response, encourage users to participate in peak load regulation, thereby increasing the dispatching income of aggregators and reducing the risks of user participation. Specifically,

[0056] A probability fitting model of the participation of energy storage users in demand response is established, that is, an energy storage user participation probability model. In some implementations of the present invention, the following formula is used to represent the participation probability of energy storage users:

[0057]

[0058] In the formula, is the cumulative distribution function of the standard normal distribution, β0, β1...β p is the fitting parameter of the model, X ij is the value of the jth independent variable of the sample, L(Y i =1|X i ) is the probability of energy storage users participating in demand response.

[0059] The fitting parameters in the above model are calculated by maximum likelihood estimation:

[0060]

[0061] And use Solve the maximum likelihood estimation of the above formula to obtain the parameter estimate of the model To determine the constant parameter value β.

[0062] An incentive expectation formula is constructed according to the constant parameters to calculate the incentive expectation. The incentive expectation formula is expressed as follows:

[0063]

[0064] Where θ(t) is the baseline probability coefficient and β is a constant parameter.

[0065] It is worth noting that after obtaining the estimated value of parameter β, the energy storage user participation probability model can be used to predict the decision of new sample participation, such as: for the new sample X new , the probability of participating in scheduling is:

[0066]

[0067] If P(Y new =1|X new ) is greater than the preset probability threshold, it can be predicted that the user of the distributed energy storage will participate in demand response.

[0068] S2. Based on the preset scheduling time requirements of the energy storage aggregator, each of the distributed energy storage entities is subjected to primary screening, and according to the incentive expectations, each of the distributed energy storage entities after the primary screening is subjected to secondary screening to obtain a first energy storage aggregation group that meets the target incentive expectations.

[0069] In this embodiment, the distributed energy storage entities / units are screened in consideration of the incentive expectations of the user side in order to select energy storage power stations (energy storage entities) that meet the aggregation conditions of participating energy storage aggregators.

[0070] In this step, it is necessary to first establish a mathematical model of the power grid system and input the set state parameters of each distributed energy storage entity into the model. These state parameters include: the rated capacity of the energy storage entity, the real-time state of charge, the charge and discharge power, and the charging time.

[0071] In response to the state parameter, the sustainable charge and discharge time of each distributed energy storage entity is calculated. As an example, in response to the state parameter of the i-th energy storage entity in the power grid system, its corresponding sustainable charge and discharge time can be calculated.

[0072] The sustainable charging and discharging time is compared and analyzed with the scheduling time requirements of the energy storage aggregator to achieve the first-level screening of the distributed energy storage entities. Specifically,

[0073] When the shortest sustainable charging and discharging time of the distributed energy storage unit is T min , which is less than the energy storage dispatching time T required by the energy storage aggregator d , the energy storage unit does not meet the response conditions and is not included in the aggregation group.

[0074] When T min Greater than T d When the user's incentive expectation for participating in the scheduling is calculated, it is necessary to determine whether it meets the preset conditions in order to perform secondary screening of the distributed energy storage units.

[0075] Specifically, this embodiment prioritizes the incentive expectations corresponding to the distributed energy storage entities that meet the preset target incentive expectation values, and performs the secondary screening on each of the distributed energy storage entities according to the set priority order under the preset number of energy storage entity requirements to obtain the first energy storage aggregation group.

[0076] Specifically, please refer to Figure 2 , Figure 2 FIG. 1 is a schematic diagram of a cluster screening process in one embodiment of the present invention. It can be seen that in this embodiment, as an example, the preset target incentive expectation value is set to E(B i ), if the user's incentive expectation E(C i )>E(B i ), all eligible distributed energy storage units are sorted according to the priority of the expected incentive value from small to large, and according to the required energy storage demand number N, a secondary screening is performed in order starting from the distributed energy storage units with lower user expectations for scheduling participation incentives, to obtain an eligible energy storage aggregation group, namely the first energy storage aggregation group.

[0077] S3. Input the first energy storage aggregation group into the constructed energy storage scheduling model to obtain the dispatchable margin of the target area, wherein the energy storage scheduling model is constructed by taking the load allowable change rate, rate of return and the dispatchable margin of each distributed energy storage entity as constraints.

[0078] It is understandable that energy storage aggregators must perform charging and discharging according to the day-ahead bid quantity during deep peak load regulation and peak shaving periods. In the actual operation and dispatching of the power grid, when there is a forecast deviation in the actual controllable capacity of aggregated energy storage, the execution result of the day-ahead dispatch by the aggregator will be less than the bid quantity. At this time, compensation needs to be collected from the aggregator to reduce the dispatching cost of the power grid.

[0079] Based on this, in this embodiment, it is considered that in addition to participating in deep peak shaving and peak shaving to obtain dispatch compensation, energy storage aggregators can also contribute in other time periods, utilize price differences in the energy market to obtain revenue, and reduce the risks caused by forecast deviations. In addition, considering battery loss, energy storage aggregators need to make dispatch compensation to distributed energy storage users. Based on this, this step uses the rate of return and dispatchable margin of each distributed energy storage entity as constraints to construct a calculation framework for maximizing the aggregator's revenue in the peak shaving scenario, that is, the energy storage dispatch model described in the embodiment of the present invention. Specifically, the energy storage dispatch model is represented by the following objective function:

[0080]

[0081] In the formula, p t is the price of energy market during period t; C V , C P are the actual compensation costs of deep peak regulation and peak shaving, and the actual compensation cost of peak shaving C P The calculation function and depth peak modulation C V consistent; λ is the unit electricity cost of the shortage; δ is the battery loss rate converted to unit electricity of charge and discharge; p MV (t) is the marginal cost on the demand side of the deep peak-shaving market during period t; P is the mth winning bid of DESAP in the deep peak load regulation market during period t; c (x, t) is the charging power of distributed energy storage x in the local power grid during period t; P dis (x, t) is the discharge power of distributed energy storage x in the local power grid during period t; P c (x,t)Δt is the dispatchable capacity.

[0082] In order to prevent the energy storage aggregator from over-responding to the price signal of the energy market, this embodiment needs to set a load allowable change rate constraint on its output, which is expressed as follows:

[0083]

[0084] Where P l 0 (t), P l (t) are the initial load and the load after taking into account the output of the energy storage aggregator; ε is the allowable rate of change of load in adjacent time periods.

[0085] It is worth noting that in some embodiments of the present invention, after obtaining a first energy storage aggregation group that meets the expected incentive conditions, the dispatchable capacity of the first energy storage aggregation group can be analyzed according to the real-time charge state, sustainable charging and discharging time and grid status of each of the distributed energy storage entities, and based on the analysis results, a second energy storage aggregation group that meets the target dispatchable capacity can be obtained.

[0086] Then, based on the above energy storage scheduling model, the dispatchable margin of each of the distributed energy storage entities in the second energy storage aggregation group is calculated, and the results are aggregated and accumulated to obtain the total dispatchable margin of the target area.

[0087] It is worth noting that in some embodiments of the present invention, energy storage units managed by energy storage aggregators can be excluded according to corresponding constraints to meet the flexibility of charging and discharging and two-way interaction with the aggregator, that is, to further screen the energy storage aggregation group used to carry out dispatchable margin calculation.

[0088] It can be understood that if the dispatchable capacity of the first energy storage aggregation group is analyzed and it is found that it can be dispatched, the dispatchable margin of the energy storage unit is calculated and the dispatchable margin is added to the total aggregate capacity.

[0089] The total dispatchable margin of the target area can be expressed by the following formula:

[0090]

[0091] When the number of the distributed energy storage entities in the second energy storage aggregation group does not meet the preset number requirement of energy storage aggregators, a secondary invitation is carried out to supplement the dispatchable margin by adding a dispatch compensation strategy.

[0092] S4. Generate a scheduling strategy for the target area based on the schedulable margin, and execute the scheduling strategy.

[0093] According to the dispatchable margin and grid demand, a dispatch strategy that reflects the interaction of energy storage aggregation is formulated. For example, during peak load periods, energy storage entities are given priority to discharge to relieve grid pressure; during low load periods, energy storage entities are encouraged to charge to store energy.

[0094] In some real-time modes of the present invention, power grid emergencies may also be considered. In this case, this embodiment will quickly evaluate the dispatchable margin of each distributed energy storage entity, and consider factors such as the response time, charge and discharge rate of the energy storage entity to determine the energy storage capacity that can be used for emergency dispatch.

[0095] The corresponding dispatching strategy is issued to the distributed energy storage entity, and its execution is monitored in real time. The strategy is adjusted in time according to the execution status to ensure the stable operation of the power grid system.

[0096] In summary, the embodiments of the present invention introduce an incentive expectation for users to participate in scheduling in the process of calculating the dispatchable capacity of distributed energy storage entities, so that energy storage aggregators can more comprehensively consider user risks when formulating peak-shaving strategies and enhance the safety of scheduling; and by screening qualified energy storage entities to build aggregation groups, it can more effectively utilize distributed energy storage resources and improve the scheduling efficiency of energy storage aggregators; and by dynamically adjusting the composition of the energy storage aggregation group and the target dispatchable capacity of energy storage to adapt to different scheduling requirements and scenario changes, the stability of power grid operation is enhanced.

[0097] An embodiment of the present invention provides a distributed energy storage aggregation interactive scheduling system taking into account the risks of aggregators. For details, please refer to Figure 3 , Figure 3 The structure diagram of a distributed energy storage aggregation interactive scheduling system taking into account the aggregator risk in one embodiment of the present invention is shown, including:

[0098] The user-side analysis module M1 is used to determine the incentive expectations of energy storage users in the target area to participate in grid dispatching based on the constructed energy storage user participation probability model;

[0099] Aggregation group construction module M2, used for performing primary screening of each of the distributed energy storage entities based on the preset energy storage aggregator scheduling time requirements, and performing secondary screening of each of the distributed energy storage entities after the primary screening according to the incentive expectations, to obtain a first energy storage aggregation group that meets the target incentive expectations;

[0100] A scheduling margin analysis module M3 is used to input the first energy storage aggregation group into the constructed energy storage scheduling model to obtain the dispatchable margin of the target area, wherein the energy storage scheduling model is constructed by taking the load allowable change rate, the rate of return and the dispatchable margin of each distributed energy storage entity as constraint conditions;

[0101] The scheduling execution module M4 is used to generate a scheduling strategy for the target area based on the schedulable margin and execute the scheduling strategy.

[0102] like Figure 4 As shown, the embodiment of the present invention further provides a computer device, Figure 4 A structural block diagram of a preferred embodiment of a computer device provided by the present invention, wherein the computer device includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and the processor implements the distributed energy storage aggregation interactive scheduling method taking into account aggregator risks as described above when executing the computer program.

[0103] Preferably, the computer program can be divided into one or more modules / units (such as computer program 1, computer program 2, ...), and the one or more modules / units are stored in the memory and executed by the processor to complete the present invention. The one or more modules / units can be a series of computer program instruction segments that can complete specific functions, and the instruction segments are used to describe the execution process of the computer program in the computer device.

[0104] The processor may be a central processing unit (CPU), or other general-purpose processors, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor, or the processor may be any conventional processor. The processor is the control center of the terminal device, and various parts of the terminal device are connected using various interfaces and lines.

[0105] The memory mainly includes a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function, etc., and the data storage area can store related data, etc. In addition, the memory can be a high-speed random access memory, or a non-volatile memory, such as a plug-in hard disk, a smart memory card (SmartMedia Card, SMC), a secure digital (Secure Digital, SD) card, and a flash card (Flash Card), etc., or the memory can also be other volatile solid-state storage devices.

[0106] It should be noted that the above terminal device may include, but is not limited to, a processor and a memory. Those skilled in the art will understand that Figure 4The structural block diagram is only an example of a terminal device and does not constitute a limitation on the terminal device. It may include more or fewer components than shown in the figure, or combine certain components, or different components. A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program. The program can be stored in a computer-readable storage medium. When the program is executed, it may include the processes of the embodiments of the above-mentioned methods. Among them, the storage medium may be a disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM), etc.

[0107] Accordingly, an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the steps in the distributed energy storage aggregation interactive scheduling method taking into account the aggregator risk in the above embodiment, for example Figure 1 Steps S1 to S4 described in .

[0108] The technical features and technical effects of the distributed energy storage aggregation interactive scheduling system taking into account the aggregator risk proposed in the embodiment of the present invention are the same as the technical features and technical effects of the distributed energy storage aggregation interactive scheduling method taking into account the aggregator risk proposed in the embodiment of the present invention, and will not be repeated here.

[0109] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. A distributed energy storage aggregation interactive scheduling method taking into account the risks of aggregators, characterized in that: Applied to the power grid system composed of a number of distributed energy storage entities, including: According to the constructed energy storage user participation probability model, the incentive expectations of energy storage users in the target area to participate in grid dispatch are determined; Based on the preset energy storage aggregator scheduling time requirements, each of the distributed energy storage entities is screened at the first level, and each of the distributed energy storage entities after the first level screening is screened at the second level according to the incentive expectations to obtain a first energy storage aggregation group that meets the target incentive expectations; Inputting the first energy storage aggregation group into the constructed energy storage scheduling model to obtain the dispatchable margin of the target area, wherein the energy storage scheduling model is constructed by taking the load allowable change rate, the rate of return and the dispatchable margin of each distributed energy storage entity as constraint conditions; A scheduling strategy for the target area is generated based on the schedulable margin, and the scheduling strategy is executed.

2. The distributed energy storage aggregation interactive scheduling method taking into account the aggregator risk as claimed in claim 1 is characterized in that: Determining the incentive expectation of energy storage users in the target area to participate in power grid dispatching based on the constructed energy storage user participation probability model includes: The energy storage user participation probability model is expressed by the following formula: In the formula, is the cumulative distribution function of the standard normal distribution, β0, β1...β p is the fitting parameter of the model, X ij is the value of the jth independent variable of the sample, L(Y i =1|X i ) is the probability of energy storage users participating in demand response; Determining constant parameters according to the energy storage user participation probability model; An incentive expectation formula is constructed according to the constant parameters to calculate the incentive expectation. The incentive expectation formula is expressed as follows: Where θ(t) is the baseline probability coefficient and β is a constant parameter.

3. The distributed energy storage aggregation interactive scheduling method taking into account the aggregator risk as claimed in claim 1 is characterized in that: The first-level screening of each of the distributed energy storage entities based on the preset energy storage aggregator scheduling time requirements includes: Establishing a mathematical model of the power grid system and inputting the set state parameters of each distributed energy storage entity; the state parameters include: the rated capacity, real-time state of charge, charge and discharge power and charging time of the energy storage entity; In response to the state parameters, the sustainable charging and discharging time of each of the distributed energy storage entities is calculated; The sustainable charging and discharging time is compared and analyzed with the scheduling time requirements of the energy storage aggregator to achieve the first-level screening of the distributed energy storage entities.

4. The distributed energy storage aggregation interactive scheduling method taking into account the aggregator risk as claimed in claim 1, characterized in that: The step of performing secondary screening on each of the distributed energy storage entities after primary screening according to the incentive expectation to obtain a first energy storage aggregation group that meets the target incentive expectation includes: Prioritize the incentive expectations corresponding to the distributed energy storage entities that meet the preset target incentive expectation values; Under the preset required number of energy storage entities, the secondary screening is performed on each of the distributed energy storage entities according to the set priority order to obtain the first energy storage aggregation group.

5. The distributed energy storage aggregation interactive scheduling method taking into account the aggregator risk as claimed in claim 3 is characterized in that: Before inputting the first energy storage aggregation group into the constructed energy storage scheduling model, the method further includes: Analyzing the dispatchability of the first energy storage aggregation group according to the real-time state of charge, the sustainable charging and discharging time, and the grid status of each of the distributed energy storage entities; According to the analysis results, a second energy storage aggregation group that meets the target dispatching capacity is obtained.

6. The distributed energy storage aggregation interactive scheduling method taking into account the aggregator risk as claimed in claim 5, characterized in that: The step of inputting the first energy storage aggregation group into the constructed energy storage scheduling model to obtain the dispatchable margin of the target area includes: Based on the energy storage scheduling model, the dispatchable margin of each of the distributed energy storage entities in the second energy storage aggregation group is calculated, and aggregated and accumulated to obtain a total dispatchable margin of the target area.

7. The distributed energy storage aggregation interactive scheduling method taking into account the aggregator risk as claimed in claim 5, characterized in that: The method further comprises: When the number of the distributed energy storage entities in the second energy storage aggregation group does not meet the preset energy storage aggregator quantity requirement, the dispatchable margin is supplemented by adding a dispatch compensation strategy.

8. A distributed energy storage aggregation interactive scheduling system taking into account the risks of aggregators, characterized in that: include: The user-side analysis module is used to determine the incentive expectations of energy storage users in the target area to participate in grid dispatching based on the constructed energy storage user participation probability model; An aggregation group construction module is used to perform a primary screening of each of the distributed energy storage entities based on a preset energy storage aggregator scheduling time requirement, and perform a secondary screening of each of the distributed energy storage entities after the primary screening according to the incentive expectation to obtain a first energy storage aggregation group that meets the target incentive expectation; A scheduling margin analysis module, used for inputting the first energy storage aggregation group into the constructed energy storage scheduling model to obtain the dispatchable margin of the target area, wherein the energy storage scheduling model is constructed by taking the load allowable change rate, the rate of return and the dispatchable margin of each distributed energy storage entity as constraint conditions; A scheduling execution module is used to generate a scheduling strategy for the target area based on the schedulable margin and execute the scheduling strategy.

9. A computer device, characterized in that: It includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and when the processor executes the computer program, it implements the distributed energy storage aggregation interactive scheduling method taking into account the risks of aggregators as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, wherein when the device where the computer-readable storage medium is located executes the computer program, the distributed energy storage aggregation interactive scheduling method taking into account the aggregator risks as described in any one of claims 1 to 7 is implemented.

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