A virtual power plant automatic response tracking system and method based on an energy storage system

Abstract

The application provides a virtual power plant automatic response tracking system and method based on an energy storage system, comprising: a monitoring module, configured to monitor power grid operation state information and energy storage state information of the energy storage system in real time, analyze the power grid operation state information and the energy storage state information, and determine an initial response strategy of the energy storage system; a strategy optimization module, configured to determine a response constraint condition of the energy storage system based on a dispatch management standard, and perform conditional limitation analysis on the initial response strategy based on the response constraint condition to obtain a target response strategy; and an execution and correction module, configured to perform charging and discharging control on the energy storage system based on the target response strategy, and perform iterative optimization on the target response strategy according to an actual response state of the energy storage system based on a control result. The effect of the energy storage system on power balance of the power grid, the performance and the accuracy of response tracking are improved, and the response speed of the energy storage system is also improved.

Description

Technical Field

[0001] This invention relates to the field of equipment control technology, and in particular to an automatic response tracking system and method for a virtual power plant based on an energy storage system. Background Technology

[0002] Currently, with the development of the power system, the access of different types of energy resources has increased the complexity and uncertainty of the power system. Virtual power plants integrate different types of energy resources and energy storage systems to achieve coordinated management and optimized scheduling of multiple energy sources.

[0003] However, traditional virtual power plants often face problems such as slow response speed and low tracking accuracy when responding to grid dispatch commands. As a result, they cannot effectively respond to and track the grid based on the state of the energy storage system itself, which greatly reduces the power balance regulation effect and response performance of the energy storage system.

[0004] Therefore, in order to overcome the above-mentioned defects, the present invention provides a virtual power plant automatic response tracking system and method based on energy storage system. Summary of the Invention

[0005] This invention provides a virtual power plant automatic response tracking system and method based on an energy storage system. It analyzes monitored grid operation status information and energy storage status information to effectively determine the initial response strategy of the energy storage system, facilitating response. Secondly, it determines the response constraints of the energy storage system and limits the initial response strategy based on these constraints, further obtaining the target response strategy and ensuring the reliability of the energy storage system during response tracking. Finally, it controls the charging and discharging of the energy storage system according to the target response strategy and iteratively optimizes the target response strategy based on the actual response status of the energy storage system. This improves the energy storage system's effect on grid power balance, as well as the performance and accuracy of response tracking, and also increases the response speed of the energy storage system.

[0006] This invention provides a virtual power plant automatic response tracking system based on an energy storage system, comprising:

[0007] The monitoring module is used to monitor the grid operation status information and the energy storage status information of the energy storage system in real time, and to analyze the grid operation status information and energy storage status information to determine the initial response strategy of the energy storage system.

[0008] The strategy optimization module is used to determine the response constraints of the energy storage system based on the scheduling management standards, and to perform condition constraint analysis on the initial response strategy based on the response constraints to obtain the target response strategy.

[0009] The execution and correction module is used to control the charging and discharging of the energy storage system based on the target response strategy, and to iteratively optimize the target response strategy based on the actual response state of the energy storage system according to the control results.

[0010] Preferably, a virtual power plant automatic response tracking system based on an energy storage system includes a monitoring module comprising:

[0011] The equipment configuration unit is used to determine the monitoring dimensions of the power grid and energy storage system and the monitoring indicators under each monitoring dimension based on the management terminal, and to determine the corresponding sensors based on the monitoring dimensions. At the same time, it configures the parameters of the corresponding sensors based on the monitoring indicators.

[0012] The data acquisition unit is used for:

[0013] The sensors are grouped based on the parameter configuration results, and a parallel control mechanism for each sensor in each group is constructed based on the grouping results.

[0014] The parallel control mechanism is used to control each sensor in each group in parallel, and the grid operation status information and energy storage status information of the energy storage system are obtained in real time based on the parallel control results.

[0015] Preferably, a virtual power plant automatic response tracking system based on an energy storage system includes a monitoring module comprising:

[0016] The data acquisition unit is used for:

[0017] The obtained power grid operation status information and energy storage status information are retrieved, and data objects are retrieved from the power grid operation status information based on bidirectional business categories;

[0018] The data object retrieval results are read to determine whether the power grid operation status information contains power grid dispatch instructions, and if so, the power grid dispatch instructions are used as the first analysis indicator.

[0019] Meanwhile, when there are no grid dispatch instructions, the grid operation status information is traversed based on the business dimension to obtain the data value fluctuation range under each business dimension, and the grid dispatch demand is predicted based on the data value fluctuation range under each business dimension.

[0020] The prediction results will be used as the second analytical indicator.

[0021] Data analysis unit, used for:

[0022] The energy storage status information is analyzed to determine the current energy storage capacity in the energy storage system. At the same time, the safe energy storage threshold of the energy storage system is determined based on the operating standards of the energy storage system.

[0023] The amount and timing of power dispatch for the energy storage system are determined based on the first or second analysis index combined with the current energy storage capacity and the safe energy storage threshold of the energy storage system.

[0024] The initial response strategy of the energy storage system is obtained based on the power dispatch quantity and the power dispatch time.

[0025] Preferably, a virtual power plant automatic response tracking system based on an energy storage system includes a data acquisition unit comprising:

[0026] The historical data acquisition subunit is used to acquire historical operating data of the power grid at multiple time scales and to classify the historical operating data at each time scale into multiple categories.

[0027] The pattern determines the sub-unit, used for:

[0028] Based on the classification results of multiple components, the relative change trend between each component category and the grid energy dispatch demand is determined, and the parameters of each component category and the grid energy dispatch demand are quantified based on the relative change trend.

[0029] The relationship function between each category of components and the grid energy dispatch demand is determined based on the quantified values ​​of the parameters. At the same time, the role weight of each category of components is determined based on management standards.

[0030] The prediction subunit is used to analyze the power grid operation status information based on the relation function and the role weight of each category component, and predict the power grid dispatch demand for the next stage based on the analysis results.

[0031] Preferably, a virtual power plant automatic response tracking system based on an energy storage system includes a monitoring module comprising:

[0032] Policy caching unit, used for:

[0033] Obtain the initial response strategy and temporarily cache it in the database;

[0034] Based on the temporary cache results, the initial response strategy is subject to effective permission restrictions and conditions for lifting the permission restrictions. Based on the effective permission restriction results and the conditions for lifting the permission restrictions, the database access interface is opened.

[0035] The policy processing unit is used to configure the access objects and access methods for the access interface, and after the configuration is completed, it connects the access interface with the initial response policy.

[0036] Preferably, a virtual power plant automatic response tracking system based on an energy storage system includes a strategy optimization module, comprising:

[0037] The condition determination unit is used to obtain the scheduling management standard based on the management terminal, and to parse the scheduling management standard to determine the response constraints of the energy storage system. The response constraints include charging and discharging power limits, response time limits, and the error between actual scheduling and demand scheduling.

[0038] Condition-bound analysis unit, used for:

[0039] A global traversal of the energy storage system is performed to determine the system's own equipment conditions, and based on these conditions, the parameter limit range of the energy storage system under different response constraints is determined.

[0040] At the same time, the equipment structure of the energy storage system is determined, and the target energy storage capacity of each energy storage component is determined based on the equipment structure.

[0041] Based on the determination of the parameter limit range under the target energy storage and different response constraints, an adaptive fluctuation limiting mechanism is established for each energy storage component in the energy storage system during charging and discharging.

[0042] The charging and discharging power sequence of the energy storage system at different times is determined based on an adaptive fluctuation limiting mechanism.

[0043] The target response strategy determination unit is used for:

[0044] The known optimization objective is obtained, and the charge and discharge power sequence is used as the optimization factor for multiple fine-tuning adjustments with fixed values.

[0045] Each fine-tuning result is linearly fitted to the optimization objective, and the optimal charge and discharge power sequence is obtained when the linear fitting result satisfies the optimization objective.

[0046] The target response strategy is derived based on the optimal charge and discharge power sequence.

[0047] Preferably, an automatic response tracking system for a virtual power plant based on an energy storage system includes an execution and correction module, comprising:

[0048] Execution unit, used for:

[0049] The target response strategy is obtained and interpreted to obtain the control quantity of the power converter in the energy storage system at different times.

[0050] The power converter is charged and discharged based on the control quantity, and the real-time response status information of the energy storage system under the charge and discharge control is collected in real time.

[0051] The response evaluation unit is used to compare the real-time response status information with the demand response information under the target response strategy, and determine the response effect of the energy storage system based on the comparison results.

[0052] Iterative optimization unit, used for:

[0053] When the response effect does not meet the preset requirements, the optimization node and optimization parameters of the target response strategy are determined based on the actual response state of the energy storage system.

[0054] The target response strategy is iteratively optimized using negative feedback based on the optimization nodes and their optimization parameters.

[0055] Preferably, an automatic response tracking system for a virtual power plant based on an energy storage system includes an iterative optimization unit comprising:

[0056] The device tracking subunit is used for:

[0057] The real-time status of each device component in the energy storage system is tracked to obtain the real-time status of each device component, and a status self-check is performed on each device component based on the real-time status.

[0058] Based on the state self-check, the state decay of each device component is determined, and the state decay is used as an optimization index to iteratively optimize the target response strategy in a synchronous manner.

[0059] The report generation subunit is used to generate a device alarm notification when the state decay amount is greater than a preset threshold, and to synchronously record the device alarm notification and the iterative optimization results of the target response strategy in the record report to obtain a response record report.

[0060] This invention provides an automatic response tracking method for virtual power plants based on energy storage systems, comprising:

[0061] Step 1: Monitor the grid operation status information and the energy storage status information of the energy storage system in real time, analyze the grid operation status information and the energy storage status information, and determine the initial response strategy of the energy storage system;

[0062] Step 2: Determine the response constraints of the energy storage system based on the scheduling and management standards, and perform condition constraint analysis on the initial response strategy based on the response constraints to obtain the target response strategy;

[0063] Step 3: Perform charge and discharge control on the energy storage system based on the target response strategy, and iteratively optimize the target response strategy based on the actual response state of the energy storage system according to the control results.

[0064] Preferably, in a virtual power plant automatic response tracking method based on an energy storage system, step 1 involves real-time monitoring of grid operation status information and energy storage status information of the energy storage system, including:

[0065] Based on the management terminal, the monitoring dimensions for the power grid and energy storage system and the monitoring indicators for each monitoring dimension are determined respectively. The corresponding sensors are determined based on the monitoring dimensions, and the parameters of the corresponding sensors are configured based on the monitoring indicators.

[0066] The sensors are grouped based on the parameter configuration results, and a parallel control mechanism for each sensor in each group is constructed based on the grouping results.

[0067] The parallel control mechanism is used to control each sensor in each group in parallel, and the grid operation status information and energy storage status information of the energy storage system are obtained in real time based on the parallel control results.

[0068] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0069] By analyzing the monitored grid operation status information and energy storage status information of the energy storage system, the initial response strategy of the energy storage system can be effectively determined, facilitating the response. Secondly, the response constraints of the energy storage system are determined, and the initial response strategy is limited by the response constraints to further obtain the target response strategy, ensuring the reliability of the energy storage system during response tracking. Finally, the charging and discharging control of the energy storage system is performed according to the target response strategy, and the target response strategy is iteratively optimized according to the actual response status of the energy storage system. This improves the effect of the energy storage system on grid power balance, as well as the performance and accuracy of response tracking, and also improves the response speed of the energy storage system.

[0070] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in this application.

[0071] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0072] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0073] Figure 1 This is a structural diagram of an automatic response tracking system for a virtual power plant based on an energy storage system, as described in an embodiment of the present invention.

[0074] Figure 2 This is a structural diagram of the monitoring module in an automatic response tracking system for a virtual power plant based on an energy storage system, according to Embodiment 1 of the present invention.

[0075] Figure 3 This is a flowchart of an automatic response tracking method for a virtual power plant based on an energy storage system, as described in an embodiment of the present invention. Detailed Implementation

[0076] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0077] Example 1:

[0078] This embodiment provides a virtual power plant automatic response tracking system based on an energy storage system, such as... Figure 1 As shown, it includes:

[0079] The monitoring module is used to monitor the grid operation status information and the energy storage status information of the energy storage system in real time, and to analyze the grid operation status information and energy storage status information to determine the initial response strategy of the energy storage system.

[0080] The strategy optimization module is used to determine the response constraints of the energy storage system based on the scheduling management standards, and to perform condition constraint analysis on the initial response strategy based on the response constraints to obtain the target response strategy.

[0081] The execution and correction module is used to control the charging and discharging of the energy storage system based on the target response strategy, and to iteratively optimize the target response strategy based on the actual response state of the energy storage system according to the control results.

[0082] In this embodiment, the power grid operation status information refers to information such as the load in the power grid, the energy dispatch demand in the power grid, and the energy supply status.

[0083] In this embodiment, the energy storage system is pre-configured to store energy through equipment components. When the power grid needs energy, it serves as an energy output source to assist the power grid. When the power grid has abundant energy, it can store excess energy.

[0084] In this embodiment, energy storage status information refers to the energy stored in the energy storage system and the conditions that need to be met during operation.

[0085] In this embodiment, the initial response strategy refers to the electrical energy that needs to be scheduled through the energy storage system and the scheduling method during scheduling, obtained after parsing the grid operation status information and energy storage status information. It is a preliminary scheduling scheme.

[0086] In this embodiment, the scheduling management standard is known in advance and is used to characterize the limiting conditions that need to be met when scheduling, such as the limitation of charging and discharging power, the limitation of response time, and the limitation of error during the scheduling process.

[0087] In this embodiment, the response constraints refer to the standards or rules that need to be followed when performing energy dispatch, including charging and discharging power, etc.

[0088] In this embodiment, condition constraint analysis refers to optimizing the initial response strategy and adjusting its parameters through response constraints to ensure that the final target response strategy can meet the scheduling requirements.

[0089] The beneficial effects of the above technical solution are as follows: By analyzing the monitored grid operation status information and energy storage status information of the energy storage system, the initial response strategy of the energy storage system can be effectively determined, which facilitates the response. Secondly, the response constraints of the energy storage system are determined, and the initial response strategy is limited by the response constraints to further obtain the target response strategy, ensuring the reliability of the energy storage system when tracking the response. Finally, the charging and discharging control of the energy storage system is performed according to the target response strategy, and the target response strategy is iteratively optimized according to the actual response status of the energy storage system, which improves the effect of the energy storage system on grid power balance and the performance and accuracy of response tracking. At the same time, it also improves the response speed of the energy storage system.

[0090] Example 2:

[0091] Based on Example 1, this example provides a virtual power plant automatic response tracking system based on an energy storage system, such as... Figure 2 As shown, the monitoring module includes:

[0092] The equipment configuration unit is used to determine the monitoring dimensions of the power grid and energy storage system and the monitoring indicators under each monitoring dimension based on the management terminal, and to determine the corresponding sensors based on the monitoring dimensions. At the same time, it configures the parameters of the corresponding sensors based on the monitoring indicators.

[0093] The data acquisition unit is used for:

[0094] The sensors are grouped based on the parameter configuration results, and a parallel control mechanism for each sensor in each group is constructed based on the grouping results.

[0095] The parallel control mechanism is used to control each sensor in each group in parallel, and the grid operation status information and energy storage status information of the energy storage system are obtained in real time based on the parallel control results.

[0096] In this embodiment, the monitoring dimension refers to the monitoring items or monitoring services that monitor the power grid and energy storage system.

[0097] In this embodiment, the monitoring index refers to the specific data type that needs to be monitored under each monitoring dimension, such as the real-time load in the power grid or the stored energy of the energy storage system.

[0098] In this embodiment, the parallel control mechanism refers to a strategy or method for parallel control of each sensor in each group.

[0099] In this embodiment, the power grid operation status information refers to the operation status of the power grid at any given moment, including the energy dispatch demand and real-time load in the power grid.

[0100] In this embodiment, energy storage status information refers to information such as the energy stored in each energy storage component in the energy storage system.

[0101] The beneficial effects of the above technical solution are: by determining the monitoring dimensions and indicators of the power grid and energy storage system, the sensors can be effectively configured, and the power grid operation status information and energy storage status information of the energy storage system can be effectively obtained based on the parameter configuration results, providing reliable data support for the response tracking of the energy storage system.

[0102] Example 3:

[0103] Based on Example 1, this example provides a virtual power plant automatic response tracking system based on an energy storage system, including a monitoring module comprising:

[0104] The data acquisition unit is used for:

[0105] The obtained power grid operation status information and energy storage status information are retrieved, and data objects are retrieved from the power grid operation status information based on bidirectional business categories;

[0106] The data object retrieval results are read to determine whether the power grid operation status information contains power grid dispatch instructions, and if so, the power grid dispatch instructions are used as the first analysis indicator.

[0107] Meanwhile, when there are no grid dispatch instructions, the grid operation status information is traversed based on the business dimension to obtain the data value fluctuation range under each business dimension, and the grid dispatch demand is predicted based on the data value fluctuation range under each business dimension.

[0108] The prediction results will be used as the second analytical indicator.

[0109] Data analysis unit, used for:

[0110] The energy storage status information is analyzed to determine the current energy storage capacity in the energy storage system. At the same time, the safe energy storage threshold of the energy storage system is determined based on the operating standards of the energy storage system.

[0111] The amount and timing of power dispatch for the energy storage system are determined based on the first or second analysis index combined with the current energy storage capacity and the safe energy storage threshold of the energy storage system.

[0112] The initial response strategy of the energy storage system is obtained based on the power dispatch quantity and the power dispatch time.

[0113] In this embodiment, the bidirectional business category refers to the grid dispatch instructions and grid operation status information that may exist in the grid operation status information. The grid operation status can be comprehensively and effectively determined through the bidirectional business category, thereby determining the corresponding initial response strategy according to the needs.

[0114] In this embodiment, the power grid dispatch instruction is a power dispatch request sent directly from the management terminal to the energy storage system, including the power dispatch time and the power dispatch amount.

[0115] In this embodiment, the first analytical indicator refers to the power grid dispatching instructions existing in the power grid.

[0116] In this embodiment, the business dimension refers to the data type and business type when traversing the power grid operation status information, such as load information in the power grid.

[0117] In this embodiment, the second analytical indicator refers to the grid dispatch demand predicted after data analysis of grid operation status information.

[0118] In this embodiment, the operating standard refers to the parameter requirements of the energy storage system during operation, including the power output, power input, and the minimum amount of electricity that needs to be retained in the energy storage system.

[0119] In this embodiment, the safe energy storage threshold refers to the minimum electrical energy that needs to be retained in the energy storage system.

[0120] The beneficial effects of the above technical solution are as follows: by analyzing the grid operation status information and energy storage status information, the grid dispatching instructions and grid dispatching needs in the grid can be accurately and effectively determined. At the same time, the energy storage capacity and safe energy storage threshold in the energy storage system can be determined. Finally, through comprehensive analysis, the power dispatching quantity and power dispatching time of the energy storage system can be determined, thereby enabling the reliable formulation of the initial response strategy and providing a basis for the response tracking of the energy storage system.

[0121] Example 4:

[0122] Based on Example 3, this example provides a virtual power plant automatic response tracking system based on an energy storage system, including a data acquisition unit comprising:

[0123] The historical data acquisition subunit is used to acquire historical operating data of the power grid at multiple time scales and to classify the historical operating data at each time scale into multiple categories.

[0124] The pattern determines the sub-unit, used for:

[0125] Based on the classification results of multiple components, the relative change trend between each component category and the grid energy dispatch demand is determined, and the parameters of each component category and the grid energy dispatch demand are quantified based on the relative change trend.

[0126] The relationship function between each category of components and the grid energy dispatch demand is determined based on the quantified values ​​of the parameters. At the same time, the role weight of each category of components is determined based on management standards.

[0127] The prediction subunit is used to analyze the power grid operation status information based on the relation function and the role weight of each category component, and predict the power grid dispatch demand for the next stage based on the analysis results.

[0128] In this embodiment, multiple time scales refer to different time periods, such as the morning and afternoon of each day, and different numbers of days in each month.

[0129] In this embodiment, multi-category components refer to different categories of data contained in historical operating data, such as power data and electrical energy data.

[0130] In this embodiment, the relative change trend refers to the relative value change relationship between each category of component and the grid energy dispatch demand.

[0131] In this embodiment, parameter quantization refers to determining the specific variable values ​​for each category of component and the grid energy dispatch demand, that is, the amount by which the grid energy dispatch demand changes when the unit value of each category of component changes.

[0132] In this embodiment, the relational function refers to a function that can characterize the relative change relationship between each category of components and the grid energy dispatch demand.

[0133] In this embodiment, the effect weight refers to the severity of the impact of each category of components on the grid dispatch demand.

[0134] The beneficial effects of the above technical solution are: by analyzing historical operating data, the relationship between different influencing factors and grid energy dispatching needs can be determined, thereby enabling accurate and effective prediction of grid dispatching needs in the next stage at different times, further ensuring the response speed and accuracy of the energy storage system.

[0135] Example 5:

[0136] Based on Example 1, this example provides a virtual power plant automatic response tracking system based on an energy storage system, including a monitoring module comprising:

[0137] Policy caching unit, used for:

[0138] Obtain the initial response strategy and temporarily cache it in the database;

[0139] Based on the temporary cache results, the initial response strategy is subject to effective permission restrictions and conditions for lifting the permission restrictions. Based on the effective permission restriction results and the conditions for lifting the permission restrictions, the database access interface is opened.

[0140] The policy processing unit is used to configure the access objects and access methods for the access interface, and after the configuration is completed, it connects the access interface with the initial response policy.

[0141] In this embodiment, the effective permission restriction refers to restricting the operation of the initial response policy, with the aim of further optimizing the initial response policy.

[0142] In this embodiment, the permission restriction removal condition refers to the conditions and requirements for restricting access to the initial response policy.

[0143] The beneficial effects of the above technical solution are: by caching the obtained initial response strategy and configuring the access interface for the cached initial response strategy with permission restrictions, it is convenient to retrieve and optimize the initial response strategy in the future, and it also avoids the arbitrary use of the initial response strategy, thus ensuring the response effect of the energy storage system.

[0144] Example 6:

[0145] Based on Example 1, this example provides a virtual power plant automatic response tracking system based on an energy storage system, including a strategy optimization module:

[0146] The condition determination unit is used to obtain the scheduling management standard based on the management terminal, and to parse the scheduling management standard to determine the response constraints of the energy storage system. The response constraints include charging and discharging power limits, response time limits, and the error between actual scheduling and demand scheduling.

[0147] Condition-bound analysis unit, used for:

[0148] A global traversal of the energy storage system is performed to determine the system's own equipment conditions, and based on these conditions, the parameter limit range of the energy storage system under different response constraints is determined.

[0149] At the same time, the equipment structure of the energy storage system is determined, and the target energy storage capacity of each energy storage component is determined based on the equipment structure.

[0150] Based on the determination of the parameter limit range under the target energy storage and different response constraints, an adaptive fluctuation limiting mechanism is established for each energy storage component in the energy storage system during charging and discharging.

[0151] The charging and discharging power sequence of the energy storage system at different times is determined based on an adaptive fluctuation limiting mechanism.

[0152] The target response strategy determination unit is used for:

[0153] The known optimization objective is obtained, and the charge and discharge power sequence is used as the optimization factor for multiple fine-tuning adjustments with fixed values.

[0154] Each fine-tuning result is linearly fitted to the optimization objective, and the optimal charge and discharge power sequence is obtained when the linear fitting result satisfies the optimization objective.

[0155] The target response strategy is derived based on the optimal charge and discharge power sequence.

[0156] In this embodiment, the scheduling management standard refers to the response constraints of the energy storage system obtained from the management terminal.

[0157] In this embodiment, the self-equipment condition refers to the performance parameters of each device included in the energy storage system.

[0158] In this embodiment, the parameter limit range refers to the range within which each operating parameter can be adjusted when the energy storage system performs power dispatch under different response constraints.

[0159] In this embodiment, the target energy storage refers to the electrical energy stored in each energy storage component of the energy storage system.

[0160] In this embodiment, the adaptive fluctuation limiting mechanism refers to a scheme for controlling the charging and discharging power and remaining power of each energy storage component when the energy storage system is performing charging and discharging operations.

[0161] In this embodiment, the optimization objective refers to the purpose of optimizing the charging and discharging power and the parameters to be optimized.

[0162] In this embodiment, the fixed adjustment amount means that the adjustment amount is a fixed value each time.

[0163] The beneficial effects of the above technical solution are as follows: by determining the response constraints and equipment conditions of the energy storage system, the charging and discharging power sequence of the energy storage system at different times can be determined based on the response constraints and equipment conditions. This provides convenience for the energy storage system to perform charging and discharging control at different times and under different conditions. Finally, the charging and discharging power sequence is fine-tuned and optimized according to the optimization objective, ensuring the reliability of the final charging and discharging power sequence, thereby ensuring the response tracking effect and accuracy of the energy storage system.

[0164] Example 7:

[0165] Based on Example 1, this example provides a virtual power plant automatic response tracking system based on an energy storage system, including an execution and correction module, comprising:

[0166] Execution unit, used for:

[0167] The target response strategy is obtained and interpreted to obtain the control quantity of the power converter in the energy storage system at different times.

[0168] The power converter is charged and discharged based on the control quantity, and the real-time response status information of the energy storage system under the charge and discharge control is collected in real time.

[0169] The response evaluation unit is used to compare the real-time response status information with the demand response information under the target response strategy, and determine the response effect of the energy storage system based on the comparison results.

[0170] Iterative optimization unit, used for:

[0171] When the response effect does not meet the preset requirements, the optimization node and optimization parameters of the target response strategy are determined based on the actual response state of the energy storage system.

[0172] The target response strategy is iteratively optimized using negative feedback based on the optimization nodes and their optimization parameters.

[0173] In this embodiment, the power converter is pre-configured and used to adjust and control the charging and discharging power of the energy storage system.

[0174] In this embodiment, the preset requirements are set in advance.

[0175] In this embodiment, the optimization node refers to the specific parameter type that needs to be optimized for the target response strategy.

[0176] In this embodiment, the optimization parameter refers to the specific value when optimizing the optimization node.

[0177] The beneficial effects of the above technical solution are: by controlling the charging and discharging of the energy storage system according to the target response strategy, effective response tracking control of the energy storage system can be achieved. At the same time, the response effect of the energy storage system is monitored in real time during the response process, and when the response effect does not meet the requirements, the target response strategy is effectively iterated and optimized, thus ensuring the response reliability and response effect of the energy storage system.

[0178] Example 8:

[0179] Based on Example 7, this example provides a virtual power plant automatic response tracking system based on an energy storage system, including an iterative optimization unit comprising:

[0180] The device tracking subunit is used for:

[0181] The real-time status of each device component in the energy storage system is tracked to obtain the real-time status of each device component, and a status self-check is performed on each device component based on the real-time status.

[0182] Based on the state self-check, the state decay of each device component is determined, and the state decay is used as an optimization index to iteratively optimize the target response strategy in a synchronous manner.

[0183] The report generation subunit is used to generate a device alarm notification when the state decay amount is greater than a preset threshold, and to synchronously record the device alarm notification and the iterative optimization results of the target response strategy in the record report to obtain a response record report.

[0184] In this embodiment, the state decay refers to the performance degradation that occurs in each device component during operation.

[0185] In this embodiment, the preset threshold is set in advance and can be adjusted as needed.

[0186] The beneficial effects of the above technical solution are: by tracking the status of each device component in the energy storage system, the target response strategy can be optimized synchronously in a timely manner when the device status is abnormal or decays, and an alarm notification can be generated when the state decay of the device component is abnormal. Finally, the response record report can be generated, which makes it convenient for users to understand the response tracking status of the energy storage system in a timely manner.

[0187] Example 9:

[0188] This embodiment provides a virtual power plant automatic response tracking method based on an energy storage system, such as... Figure 3 As shown, it includes:

[0189] Step 1: Monitor the grid operation status information and the energy storage status information of the energy storage system in real time, analyze the grid operation status information and the energy storage status information, and determine the initial response strategy of the energy storage system;

[0190] Step 2: Determine the response constraints of the energy storage system based on the scheduling and management standards, and perform condition constraint analysis on the initial response strategy based on the response constraints to obtain the target response strategy;

[0191] Step 3: Perform charge and discharge control on the energy storage system based on the target response strategy, and iteratively optimize the target response strategy based on the actual response state of the energy storage system according to the control results.

[0192] The beneficial effects of the above technical solution are as follows: By analyzing the monitored grid operation status information and energy storage status information of the energy storage system, the initial response strategy of the energy storage system can be effectively determined, which facilitates the response. Secondly, the response constraints of the energy storage system are determined, and the initial response strategy is limited by the response constraints to further obtain the target response strategy, ensuring the reliability of the energy storage system when tracking the response. Finally, the charging and discharging control of the energy storage system is performed according to the target response strategy, and the target response strategy is iteratively optimized according to the actual response status of the energy storage system, which improves the effect of the energy storage system on grid power balance and the performance and accuracy of response tracking. At the same time, it also improves the response speed of the energy storage system.

[0193] Example 10:

[0194] Based on Example 9, this example provides a virtual power plant automatic response tracking method based on an energy storage system. Step 1 involves real-time monitoring of grid operation status information and energy storage system status information, including:

[0195] Based on the management terminal, the monitoring dimensions for the power grid and energy storage system and the monitoring indicators for each monitoring dimension are determined respectively. The corresponding sensors are determined based on the monitoring dimensions, and the parameters of the corresponding sensors are configured based on the monitoring indicators.

[0196] The sensors are grouped based on the parameter configuration results, and a parallel control mechanism for each sensor in each group is constructed based on the grouping results.

[0197] The parallel control mechanism is used to control each sensor in each group in parallel, and the grid operation status information and energy storage status information of the energy storage system are obtained in real time based on the parallel control results.

[0198] The beneficial effects of the above technical solution are: by determining the monitoring dimensions and indicators of the power grid and energy storage system, the sensors can be effectively configured, and the power grid operation status information and energy storage status information of the energy storage system can be effectively obtained based on the parameter configuration results, providing reliable data support for the response tracking of the energy storage system.

[0199] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A virtual power plant automatic response tracking system based on an energy storage system, characterized in that, include: The monitoring module is used to monitor the grid operation status information and the energy storage status information of the energy storage system in real time, and to analyze the grid operation status information and energy storage status information to determine the initial response strategy of the energy storage system. The strategy optimization module is used to determine the response constraints of the energy storage system based on the scheduling management standards, and to perform condition constraint analysis on the initial response strategy based on the response constraints to obtain the target response strategy. The execution and correction module is used to control the charging and discharging of the energy storage system based on the target response strategy, and to iteratively optimize the target response strategy based on the actual response state of the energy storage system according to the control results. The strategy optimization module includes: The condition determination unit is used to obtain the scheduling management standard based on the management terminal, and to parse the scheduling management standard to determine the response constraints of the energy storage system. The response constraints include charging and discharging power limits, response time limits, and the error between actual scheduling and demand scheduling. Condition-bound analysis unit, used for: A global traversal of the energy storage system is performed to determine the system's own equipment conditions, and based on these conditions, the parameter limit range of the energy storage system under different response constraints is determined. At the same time, the equipment structure of the energy storage system is determined, and the target energy storage capacity of each energy storage component is determined based on the equipment structure. Based on the determination of the parameter limit range under the target energy storage and different response constraints, an adaptive fluctuation limiting mechanism is established for each energy storage component in the energy storage system during charging and discharging. The charging and discharging power sequence of the energy storage system at different times is determined based on an adaptive fluctuation limiting mechanism. The target response strategy determination unit is used for: The known optimization objective is obtained, and the charge and discharge power sequence is used as the optimization factor for multiple fine-tuning adjustments with fixed values. Each fine-tuning result is linearly fitted to the optimization objective, and the optimal charge and discharge power sequence is obtained when the linear fitting result satisfies the optimization objective. The target response strategy is derived based on the optimal charge and discharge power sequence; The execution and correction module includes: Execution unit, used for: The target response strategy is obtained and interpreted to obtain the control quantity of the power converter in the energy storage system at different times. The power converter is charged and discharged based on the control quantity, and the real-time response status information of the energy storage system under the charge and discharge control is collected in real time. The response evaluation unit is used to compare the real-time response status information with the demand response information under the target response strategy, and determine the response effect of the energy storage system based on the comparison results. Iterative optimization unit, used for: When the response effect does not meet the preset requirements, the optimization node and optimization parameters of the target response strategy are determined based on the actual response state of the energy storage system. The target response strategy is iteratively optimized using negative feedback based on the optimization nodes and their optimization parameters.

2. The virtual power plant automatic response tracking system based on an energy storage system according to claim 1, characterized in that, The monitoring module includes: The equipment configuration unit is used to determine the monitoring dimensions of the power grid and energy storage system and the monitoring indicators under each monitoring dimension based on the management terminal, and to determine the corresponding sensors based on the monitoring dimensions. At the same time, it configures the parameters of the corresponding sensors based on the monitoring indicators. The data acquisition unit is used for: The sensors are grouped based on the parameter configuration results, and a parallel control mechanism for each sensor in each group is constructed based on the grouping results. The parallel control mechanism is used to control each sensor in each group in parallel, and the grid operation status information and energy storage status information of the energy storage system are obtained in real time based on the parallel control results.

3. The virtual power plant automatic response tracking system based on an energy storage system according to claim 1, characterized in that, The monitoring module includes: The data acquisition unit is used for: The obtained power grid operation status information and energy storage status information are retrieved, and data objects are retrieved from the power grid operation status information based on bidirectional business categories; The data object retrieval results are read to determine whether the power grid operation status information contains power grid dispatch instructions, and if so, the power grid dispatch instructions are used as the first analysis indicator. Meanwhile, when there are no grid dispatch instructions, the grid operation status information is traversed based on the business dimension to obtain the data value fluctuation range under each business dimension, and the grid dispatch demand is predicted based on the data value fluctuation range under each business dimension. The prediction results will be used as the second analytical indicator. Data analysis unit, used for: The energy storage status information is analyzed to determine the current energy storage capacity in the energy storage system. At the same time, the safe energy storage threshold of the energy storage system is determined based on the operating standards of the energy storage system. The amount and timing of power dispatch for the energy storage system are determined based on the first or second analysis index combined with the current energy storage capacity and the safe energy storage threshold of the energy storage system. The initial response strategy of the energy storage system is obtained based on the power dispatch quantity and the power dispatch time.

4. The virtual power plant automatic response tracking system based on an energy storage system according to claim 3, characterized in that, The data acquisition unit includes: The historical data acquisition subunit is used to acquire historical operating data of the power grid at multiple time scales and to classify the historical operating data at each time scale into multiple categories. The pattern determines the sub-unit, used for: Based on the classification results of multiple components, the relative change trend between each component category and the grid energy dispatch demand is determined, and the parameters of each component category and the grid energy dispatch demand are quantified based on the relative change trend. The relationship function between each category of components and the grid energy dispatch demand is determined based on the quantified values ​​of the parameters. At the same time, the role weight of each category of components is determined based on management standards. The prediction subunit is used to analyze the power grid operation status information based on the relation function and the role weight of each category component, and predict the power grid dispatch demand for the next stage based on the analysis results.

5. The virtual power plant automatic response tracking system based on an energy storage system according to claim 1, characterized in that, The monitoring module includes: Policy caching unit, used for: Obtain the initial response strategy and temporarily cache it in the database; Based on the temporary cache results, the initial response strategy is subject to effective permission restrictions and conditions for lifting the permission restrictions. Based on the effective permission restriction results and the conditions for lifting the permission restrictions, the database access interface is opened. The policy processing unit is used to configure the access objects and access methods for the access interface, and after the configuration is completed, it connects the access interface with the initial response policy.

6. The virtual power plant automatic response tracking system based on an energy storage system according to claim 1, characterized in that, Iterative optimization units include: The device tracking subunit is used for: The real-time status of each device component in the energy storage system is tracked to obtain the real-time status of each device component, and a status self-check is performed on each device component based on the real-time status. Based on the state self-check, the state decay of each device component is determined, and the state decay is used as an optimization index to iteratively optimize the target response strategy in a synchronous manner. The report generation subunit is used to generate a device alarm notification when the state decay amount is greater than a preset threshold, and to synchronously record the device alarm notification and the iterative optimization results of the target response strategy in the record report to obtain a response record report.

7. A virtual power plant automatic response tracking method based on an energy storage system, characterized in that, include: Step 1: Monitor the grid operation status information and the energy storage status information of the energy storage system in real time, analyze the grid operation status information and the energy storage status information, and determine the initial response strategy of the energy storage system; Step 2: Determine the response constraints of the energy storage system based on the scheduling and management standards, and perform condition constraint analysis on the initial response strategy based on the response constraints to obtain the target response strategy; Step 3: Perform charge and discharge control on the energy storage system based on the target response strategy, and iteratively optimize the target response strategy based on the actual response state of the energy storage system according to the control results; Step 2 includes: The scheduling management standard is obtained based on the management terminal, and the scheduling management standard is analyzed to determine the response constraints of the energy storage system. The response constraints include charging and discharging power limits, response time limits, and the error between actual scheduling and demand scheduling. A global traversal of the energy storage system is performed to determine the system's own equipment conditions, and based on these conditions, the parameter limit range of the energy storage system under different response constraints is determined. At the same time, the equipment structure of the energy storage system is determined, and the target energy storage capacity of each energy storage component is determined based on the equipment structure. Based on the determination of the parameter limit range under the target energy storage and different response constraints, an adaptive fluctuation limiting mechanism is established for each energy storage component in the energy storage system during charging and discharging. The charging and discharging power sequence of the energy storage system at different times is determined based on an adaptive fluctuation limiting mechanism. The known optimization objective is obtained, and the charge and discharge power sequence is used as the optimization factor for multiple fine-tuning adjustments with fixed values. Each fine-tuning result is linearly fitted to the optimization objective, and the optimal charge and discharge power sequence is obtained when the linear fitting result satisfies the optimization objective. The target response strategy is derived based on the optimal charge and discharge power sequence; Step 3 includes: The target response strategy is obtained and interpreted to obtain the control quantity of the power converter in the energy storage system at different times. The power converter is charged and discharged based on the control quantity, and the real-time response status information of the energy storage system under the charge and discharge control is collected in real time. The real-time response status information and the demand response information under the target response strategy are compared, and the response effect of the energy storage system is determined based on the comparison results. When the response effect does not meet the preset requirements, the optimization node and optimization parameters of the target response strategy are determined based on the actual response state of the energy storage system. The target response strategy is iteratively optimized using negative feedback based on the optimization nodes and their optimization parameters.

8. The automatic response tracking method for a virtual power plant based on an energy storage system according to claim 7, characterized in that, In step 1, real-time monitoring of grid operation status information and energy storage system status information includes: Based on the management terminal, the monitoring dimensions for the power grid and energy storage system and the monitoring indicators for each monitoring dimension are determined respectively. The corresponding sensors are determined based on the monitoring dimensions, and the parameters of the corresponding sensors are configured based on the monitoring indicators. The sensors are grouped based on the parameter configuration results, and a parallel control mechanism for each sensor in each group is constructed based on the grouping results. The parallel control mechanism is used to control each sensor in each group in parallel, and the grid operation status information and energy storage status information of the energy storage system are obtained in real time based on the parallel control results.

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