Energy storage collaborative scheduling method and system based on virtual power plant

Abstract

The present application relates to a kind of energy storage collaborative scheduling method and system based on virtual power plant, belong to electric power management technical field, wherein, the method includes: according to the temporary exit authorization frequency of user side energy storage, power authorization proportion change frequency and local load occupancy risk, generate user temporary exit risk correction coefficient;According to time period user temporary exit risk correction coefficient, modify power authorization proportion, obtain control availability prediction coefficient;According to control availability prediction coefficient, calculate the predicted reliable and callable charge / discharge power of user side energy storage, and according to time period reliable and callable charge / discharge power, generate the collaborative scheduling scheme of user side energy storage.The present application fully considers the power authorization proportion of user side energy storage influenced by uncertain factor when generating collaborative scheduling scheme, can make that the last generated scheme better executable.

Description

Technical Field

[0001] This invention relates to the field of power management technology, and in particular to a method and system for coordinated dispatch of energy storage based on virtual power plants. Background Technology

[0002] With the large-scale integration of distributed photovoltaic power, distributed wind power, electric vehicle charging loads, industrial and commercial flexible loads, and user-side energy storage, the power distribution system is gradually exhibiting characteristics of high coupling between power sources, loads, and storage, as well as rapid changes in operating status. Virtual power plants, by aggregating distributed power sources, energy storage resources, and flexible loads, can form observable, predictable, and dispatchable aggregated regulation capabilities without changing the physical ownership of resources, and participate in the electricity market, demand response, ancillary services, and distribution network operation optimization.

[0003] User-side energy storage, as an important flexible adjustment resource, possesses rapid response, bidirectional power regulation, and energy time-shifting capabilities. During periods of surplus renewable energy, user-side energy storage can absorb electricity from distributed photovoltaic and wind power through charging. During peak load periods or periods with high electricity prices, user-side energy storage can support park loads, reduce the power purchased by the point of common coupling, or participate in demand response by discharging. Therefore, user-side energy storage plays a crucial role in reducing peak-valley differences, mitigating deviation penalties, improving renewable energy absorption rates, and enhancing market returns for virtual power plants.

[0004] However, unlike centralized energy storage or virtual power plant-owned energy storage, user-side energy storage is typically invested and constructed by industrial and commercial users, park users, charging station users, or building users, with ownership and partial usage rights belonging to the users. Users usually authorize the virtual power plant to call upon energy storage only for certain time periods, within certain capacity or power ranges, while requiring that the energy storage's state of charge not fall below the user's set safe reserve capacity to ensure their own backup energy needs. Existing technologies often treat user-side energy storage as an internal resource that can be fully dispatched by the virtual power plant, without fully considering the authorization status and response uncertainties of user-side energy storage, resulting in insufficient feasibility of dispatch schemes. Summary of the Invention

[0005] In view of this, it is necessary to provide a method and system for coordinated scheduling of energy storage based on virtual power plants, in order to solve the problem that existing technologies often regard user-side energy storage as an internal resource that can be fully scheduled by virtual power plants, resulting in insufficient executability of scheduling schemes.

[0006] To address the aforementioned problems, in a first aspect, the present invention provides a method for coordinated energy storage scheduling based on a virtual power plant, comprising:

[0007] Based on the frequency of temporary withdrawal authorization for user-side energy storage, the frequency of power authorization ratio changes, and the risk of local load occupancy, a correction coefficient for temporary withdrawal risk is generated.

[0008] The power authorization ratio is adjusted based on the user temporary exit risk correction coefficient to obtain the control availability prediction coefficient.

[0009] The predicted reliable callable charging / discharging power of user-side energy storage is calculated based on the control availability prediction coefficient, and a collaborative scheduling scheme for user-side energy storage is generated based on the reliable callable charging / discharging power.

[0010] Secondly, the present invention also provides an energy storage collaborative scheduling system based on a virtual power plant, comprising:

[0011] The control availability prediction module is used to generate a user temporary exit risk correction coefficient based on the user-side energy storage's temporary exit authorization frequency, power authorization ratio change frequency, and local load occupancy risk; correct the power authorization ratio based on the user temporary exit risk correction coefficient to obtain a control availability prediction coefficient; and calculate the predicted reliable callable charging / discharging power of the user-side energy storage based on the control availability prediction coefficient.

[0012] The collaborative scheduling optimization module is used to generate a collaborative scheduling scheme for user-side energy storage based on the predicted reliable and callable charging / discharging power.

[0013] The beneficial effects of this invention are:

[0014] This invention considers the authorization status of user-side energy storage, such as the power authorization ratio, during the scheduling of user-side energy storage through a virtual power plant. Furthermore, it takes into account that the nominal authorized power of user-side energy storage is not equivalent to the actual callable power. Even if a user submits a power authorization ratio during the day-ahead or intraday period, the actual callable power may still be lower than the nominal authorized power due to factors such as temporary user withdrawal or sudden increases in local load. Therefore, this invention further generates a user temporary withdrawal risk correction coefficient based on the frequency of temporary withdrawal authorization, the frequency of power authorization ratio changes, and the risk of local load occupancy. The power authorization ratio is then corrected based on this correction coefficient to obtain a control availability prediction coefficient. By considering the uncertainty of user response, the control availability prediction coefficient more accurately reflects the actual power authorization situation compared to the nominally submitted power authorization ratio. Furthermore, the predicted reliable callable charging / discharging power of user-side energy storage is calculated based on the control availability prediction coefficient. A collaborative scheduling scheme for user-side energy storage is then generated based on this predicted reliable callable charging / discharging power, making the scheduling scheme more feasible. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 A flowchart illustrating an embodiment of the energy storage collaborative scheduling based on a virtual power plant provided by the present invention;

[0017] Figure 2 A schematic diagram of the structure of a virtual power plant provided by the present invention;

[0018] Figure 3 for Figure 1 A flowchart of a method according to embodiment S102;

[0019] Figure 4 A calculation flowchart for predicting reliable callable charge / discharge power is provided for this invention;

[0020] Figure 5 A control-robust scheduling flowchart is provided for this invention;

[0021] Figure 6 A schematic diagram of the user-side energy storage control right authorization boundary provided by the present invention;

[0022] Figure 7 A flowchart of a collaborative scheduling method provided by the present invention;

[0023] Figure 8 This is a schematic diagram of an embodiment of the energy storage collaborative scheduling system based on a virtual power plant provided by the present invention. Detailed Implementation

[0024] Preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings, which form part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, but are not intended to limit the scope of the present invention.

[0025] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0026] In the description of the embodiments of this invention, unless otherwise stated, "a plurality of" means two or more. The terms "first," "second," etc., used in the embodiments of this invention are used to distinguish similar objects, and are not used to describe a specific order or sequence, nor to indicate or imply their relative importance or implicitly specify the number of indicated technical features. It should be understood that such data can be interchanged where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," etc., are generally of the same class, and the number of objects is not limited; for example, a first object can be one or more.

[0027] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0028] Reference Figure 1 The diagram illustrates a flowchart of an embodiment of the energy storage collaborative scheduling method based on a virtual power plant provided by the present invention. The method includes:

[0029] S101 generates a temporary exit risk correction coefficient for user-side energy storage based on the frequency of temporary exit authorization, the frequency of power authorization ratio change, and the risk of local load occupancy.

[0030] A virtual power plant refers to a coordinated management system that aggregates distributed power sources, energy storage, and controllable loads into a unified whole through information communication and intelligent control technologies, enabling it to participate in the electricity market or grid dispatch. (Refer to...) Figure 2 This diagram illustrates a structural schematic of a virtual power plant provided by the present invention. The virtual power plant includes the following resources: a distributed photovoltaic system; a distributed wind power system; a user-side energy storage system; load shedding; load transfer; a point of common coupling; an upstream distribution network; a virtual power plant energy management system; a user-side energy storage local control terminal; a user-side power conversion system (PCS); a user-side battery management system (BMS); and a user-side energy consumption management terminal.

[0031] Temporary withdrawal of authorization refers to temporarily withdrawing authorization during the authorized period.

[0032] Power license ratio refers to the percentage of the maximum allowed charging / discharging power relative to the rated charging / discharging power.

[0033] The system can collect response data and risk status data from user-side energy storage. Based on this data, it can statistically determine the frequency of temporary power authorization withdrawal, the frequency of power authorization ratio changes, and the risk of local load occupancy for user-side energy storage. Response data can include historical dispatch command power, actual response power, response delay, response deviation, number of execution rejections, number of limit executions, number of temporary withdrawals, and number of authorization ratio changes. Risk status data can include the user's local load occupancy status.

[0034] High frequency of temporary withdrawal from authorization, high frequency of power authorization ratio changes, and high risk of local load occupancy will all lead to a reduction in the actual user-side energy storage power available for virtual power plants. Therefore, the higher the frequency of temporary withdrawal from authorization, the higher the frequency of power authorization ratio changes, or the greater the risk of local load occupancy, the smaller the correction coefficient for user temporary withdrawal risk.

[0035] S102, adjust the power grant ratio according to the user temporary exit risk correction coefficient to obtain the control availability prediction coefficient.

[0036] The control availability prediction coefficient reflects the proportion of power that can actually be called upon by user-side energy storage.

[0037] S103, calculate the predicted reliable callable charging / discharging power of user-side energy storage based on the control availability prediction coefficient, and generate a collaborative scheduling scheme for user-side energy storage based on the reliable callable charging / discharging power.

[0038] Predicted reliable callable charging / discharging power can serve as a constraint, namely, that the power of user-side energy storage called in the collaborative scheduling scheme shall not exceed the predicted reliable callable charging / discharging power.

[0039] The energy storage collaborative scheduling method based on virtual power plants provided in this embodiment can be applied to virtual power plants, and more specifically, to the energy management system within a virtual power plant. The energy management system can be a software system running on a terminal device. The terminal device can be a tablet computer, in-vehicle device, augmented reality (AR) / virtual reality (VR) device, laptop computer, ultra-mobile personal computer (UMPC), etc. This embodiment does not impose any restrictions on the specific type of terminal device.

[0040] In summary, this embodiment combines user response uncertainties with the nominal power grant ratio on the user side to generate a control availability prediction coefficient, which can more accurately reflect the actual power grant situation. Furthermore, based on the control availability prediction coefficient, the predicted reliable callable charging / discharging power of user-side energy storage is calculated, and a collaborative scheduling scheme for user-side energy storage is generated based on the predicted reliable callable charging / discharging power, making the scheduling scheme more feasible to execute.

[0041] In some embodiments of the present invention, S101 includes:

[0042] The user temporary exit risk correction coefficient is calculated using the following formula:

[0043]

[0044] Where i is the user-side energy storage number, and t is the time period. The temporary exit risk correction coefficient for user-side energy storage i in time period t; To temporarily withdraw from authorized frequencies, To change the frequency for the power license ratio, To mitigate the risk of local load occupancy, These are the weighting coefficients, and .

[0045] Specifically, t represents the current time period in the virtual power plant dispatch cycle. The frequency of temporary exit authorization, the frequency of power authorization ratio change, and the local load occupancy risk corresponding to the current time period can be obtained by statistics or prediction based on the response data and risk status data of historical time periods.

[0046] In some embodiments of the present invention, local load occupancy risk is used to represent the degree to which a user's own load demand occupies the adjustable capacity of energy storage, and can be calculated based on the user's local load forecast value and its typical load range normalized:

[0047]

[0048] in, For users During the period Local load forecast power, This is the user's baseline load. This represents the user's historical peak load. To prevent extremely small positive numbers with a denominator of zero.

[0049] In some embodiments of the present invention, such as Figure 3 As shown, the control availability prediction coefficient includes a first control availability prediction coefficient in the discharge direction and a second control availability prediction coefficient in the charging direction; S102 includes:

[0050] S301, based on the difference between the State of Charge (SOC) of the user-side energy storage and the safe value of the State of Charge, determine the charge availability correction coefficient in the discharge direction.

[0051] The State of Charge (SOC) safety value is a type of user-side authorization restriction that prevents virtual power plants from lowering the SOC of user-side energy storage below this safety value when calling upon user-side energy storage.

[0052] The greater the difference between the state of charge of user-side energy storage and the safe state of charge, i.e. the greater the available power of user-side energy storage, the greater the charge availability correction coefficient in the discharge direction.

[0053] In one example, the charge state availability correction factor for the discharge direction can be calculated using the following formula:

[0054]

[0055] in, The charge state availability correction coefficient for user-side energy storage i in time period t and discharge direction. ; The charge state of user-side energy storage i during time period t; The safe state of charge of user-side energy storage i during time period t; This represents the maximum charge state of user-side energy storage i.

[0056] S302, determine the charge state availability correction coefficient in the charging direction based on the difference between the charge state of the user-side energy storage and the maximum charge state value.

[0057] The greater the difference between the state of charge of user-side energy storage and its maximum value, i.e., the greater the available power of user-side energy storage, the greater the charge availability correction coefficient in the charging direction.

[0058] In one example, the charge state availability correction factor in the charging direction can be calculated using the following formula:

[0059]

[0060] in, The charge state availability correction coefficient for user-side energy storage i in time period t and charging direction. ; This represents the minimum charge state of user-side energy storage i.

[0061] S303 generates equipment health status correction coefficient, communication status correction coefficient, and historical response correction coefficient based on the equipment health status, communication status, and response deviation of the user-side energy storage device.

[0062] Specifically, the historical response bias is defined as:

[0063]

[0064] in, For virtual power plants in historical time periods The power command issued to the user-side energy storage device. For user-side energy storage i in historical time periods The actual response power, The maximum power of user-side energy storage i. To prevent extremely small positive numbers with a denominator of zero.

[0065] In length of Within the historical window, calculate the weighted average response deviation:

[0066]

[0067] in, For coefficients:

[0068]

[0069] The historical response correction factor is defined as:

[0070]

[0071] And perform amplitude limiting:

[0072]

[0073] The communication status correction factor is defined as:

[0074]

[0075] in, The communication is online. Due to communication delay, This is the upper limit of communication latency. For communication packet loss rate, This represents the upper limit of the packet loss rate in communication. Let be the weighting coefficient, and satisfy: .

[0076] The equipment health status correction factor is defined as:

[0077]

[0078] in, PCS running status The BMS is in a safe state. For battery health, Temperature status, Let be the weighting coefficient, and satisfy: .

[0079] S304. The power grant ratio is corrected based on the charge state availability correction coefficient in the discharge direction, the equipment health state correction coefficient, the communication state correction coefficient, the response correction coefficient, and the user temporary exit risk correction coefficient to obtain the first control right availability prediction coefficient.

[0080] The first control availability prediction coefficient is defined as:

[0081]

[0082] in, For user i, the authorization status during time period t. ,when When, it indicates that the user allows the virtual power plant to access its energy storage during time period t; when This indicates that the user is not allowed to use the virtual power plant's energy storage during that time period; The power grant ratio for user i during time period t. ,when When, it indicates that the user does not grant control over the energy storage; when When, it means that the user has granted full callable control rights during that time period; when When this is the case, it indicates that the user has only granted partial power control rights.

[0083] ,when When, it indicates that the user-side energy storage has a high degree of control availability during that time period; when When this occurs, it indicates that the user-side energy storage cannot be accessed by the virtual power plant during that time period; when This indicates that although the user-side energy storage is authorized, the actual available capacity needs to be reduced.

[0084] S305, based on the charge state availability correction coefficient in the charging direction, the device health state correction coefficient, the communication state correction coefficient, the response correction coefficient, and the user temporary exit risk correction coefficient, the power grant ratio is corrected to obtain the second control availability prediction coefficient.

[0085] The second control availability prediction coefficient is defined as:

[0086]

[0087] in, The physical meaning is as described above.

[0088] This embodiment further enriches the understanding of uncertainties in user response. In addition to generating a correction coefficient for the risk of temporary user withdrawal, it also considers the charge state, equipment health state, communication state, and response deviation of the user-side energy storage. It generates correction coefficients for equipment health state, communication state, and response, respectively. By combining various uncertainties, the power authorization ratio is corrected so that the control right availability prediction coefficient accurately reflects the actual available power control right of the user-side energy storage.

[0089] Reference Figure 4 This paper illustrates a calculation flowchart for predicting reliable and callable charging / discharging power provided by the present invention. First, it acquires user-side basic energy storage operation data, control authorization information, historical response data, and risk status data. The basic operation data includes: rated energy storage capacity, maximum charging power, maximum discharging power, current energy storage capacity, current SOC, upper SOC limit, lower SOC limit, charging efficiency, discharging efficiency, energy storage health status, PCS operating status, BMS alarm status, and battery temperature status. The control authorization information includes: user authorization status, authorization period, authorization ratio, maximum authorized power, maximum authorized capacity, safe reserved capacity, safe reserved SOC, and user priority. Historical response data includes historical scheduling command power, actual response power, response delay, response deviation, number of execution rejections, number of limited executions, number of temporary exits, and number of authorization ratio changes. Risk status data includes communication online status, communication delay, packet loss rate, device health status, and user local load occupancy status.

[0090] Then, multi-dimensional state feature extraction is performed on the collected data, such as extracting features needed for subsequent calculations, including the number of temporary exits and communication delays. Historical response deviations are calculated based on these multi-dimensional state features to assess communication status, device health status, and exit risks. Correction coefficients are then calculated based on the assessment results, and prediction coefficients for the availability of charge / discharge direction control are calculated based on these correction coefficients. Finally, the predicted reliable and callable charge / discharge power for the charge / discharge direction is calculated based on the prediction coefficients for the availability of charge / discharge direction control.

[0091] In some embodiments of the present invention, the step of calculating the predicted reliable callable charging / discharging power of user-side energy storage based on the control availability prediction coefficient includes:

[0092] Calculate the following formula: User-side energy storage during time period The predicted reliable callable charging power:

[0093]

[0094] in, The maximum charging power (rated charging power) for the energy storage of the i-th user side. This represents the maximum permissible capacity for energy storage. Let be the energy storage capacity of the i-th user-side energy storage during time period t; For charging efficiency; The length of the scheduling period.

[0095] Calculate the following formula: User-side energy storage during time period The predicted reliable discharge power is:

[0096]

[0097] in, This is the maximum discharge power (rated discharge power). To preserve power for safety; This refers to the discharge efficiency.

[0098] Therefore, in the collaborative scheduling scheme generated by the virtual power plant, the user-side energy storage charging power and discharge power The following constraints must be met:

[0099]

[0100]

[0101] In some embodiments of the present invention, the energy storage collaborative scheduling method based on virtual power plants further includes:

[0102] The robust effective power grant ratio is calculated using the following formula:

[0103]

[0104]

[0105]

[0106] Where i is the user-side energy storage number, and t is the time period. The robust effective power authorization ratio for user-side energy storage i in time period t; The power authorization ratio submitted by the user, This represents the maximum possible decrease in the power licensing ratio. The uncertainty variable is the reduction in the authorized capacity; N is the amount of energy storage on the user side. A budget parameter for uncertainty of control, used to limit the size of users or the degree of uncertainty that may lead to a decrease in authorization within the same time period;

[0107] The robust and valid authorization status is calculated using the following formula:

[0108]

[0109]

[0110] in, The authorization status is robust and valid. The authorization status submitted by the user. Variables for temporary user exit; Time period The maximum number of users allowed to temporarily log out simultaneously;

[0111] Based on the robust effective power grant ratio and robust effective grant status, the robust effective callable charging / discharging power of user-side energy storage is calculated, and a collaborative scheduling scheme for user-side energy storage is generated based on the robust effective callable charging / discharging power.

[0112] In some embodiments of the present invention The system is dynamically adjusted based on the user equipment health status, user communication status, user authorization exit frequency, power authorization ratio change frequency, and response deviation.

[0113] Specifically, define the user group control risk index:

[0114]

[0115] in, This refers to the frequency of users logging out recently. The frequency of recent changes in the user's authorization ratio; For user historical response deviation; This is a communication status correction coefficient; This is a correction factor for the equipment's health status. Let be the weighting coefficient, and satisfy:

[0116] The robust budget parameters (i.e., control uncertainty budget parameters) are dynamically set based on the risk index:

[0117]

[0118] in, The minimum value of the robust budget parameter represents the lowest robust conservative level adopted by the system when the user-side energy storage response is relatively stable and the risk of withdrawal is low. The maximum value of the robust budget parameter represents the highest level of robust conservatism that the system is allowed to adopt when a user temporarily leaves, the authorization ratio decreases, or the risk of communication equipment malfunctions is high.

[0119] When a user group experiences frequent logouts, significant response deviations, or poor device communication, Increase, the system will automatically increase Enhance scheduling robustness; when the user group's response is stable, the system reduces... This reduces unnecessary backup costs.

[0120] This embodiment uses dynamic adjustment This can avoid the cooperative scheduling scheme from being too conservative or too risky due to fixed robust budget parameters.

[0121] In some embodiments of the present invention, the step of calculating the robust effective callable charge / discharge power of user-side energy storage based on the robust effective power grant ratio and the robust effective grant status includes:

[0122] Constructing a set of control uncertainty:

[0123]

[0124] Simultaneously, construct a set of user exit uncertainties:

[0125]

[0126] The overall control effectiveness coefficient can be expressed as:

[0127]

[0128] To facilitate linear optimization, a conservative linear form can be used:

[0129]

[0130] Calculate the following formula: User-side energy storage during time period Robust and efficient callable charging power:

[0131]

[0132] Calculate the following formula: User-side energy storage during time period Robust and efficient callable discharge power:

[0133]

[0134] Therefore, in the collaborative dispatch scheme generated by the virtual power plant, the user-side energy storage power should meet the following constraints:

[0135]

[0136]

[0137] In some embodiments of the present invention, a collaborative scheduling scheme for generating user-side energy storage based on robust and efficient callable charging / discharging power includes:

[0138] Set an upward adjustment constraint:

[0139]

[0140] in, To increase the power margin of the virtual power plant, The maximum discharge power for user-side energy storage;

[0141] Set a lower standby constraint:

[0142]

[0143] in, To reduce the power margin of the virtual power plant, Maximum charging power for user-side energy storage;

[0144] Based on the coordinated scheduling scheme of increasing and decreasing reserve constraints, and generating user-side energy storage using robust and effective callable charging / discharging power.

[0145] Specifically, and This indicates the robust reserve capacity or robust reserve power.

[0146] Reference Figure 5 This paper illustrates a robust control scheduling flowchart provided by the present invention. First, user-side energy storage data is acquired, and uncertainties regarding the decrease in the authorized ratio and temporary user withdrawal are determined based on the acquired data. Then, robust budget parameters are set, and a comprehensive control power effectiveness coefficient is calculated based on the robust budget parameters, the uncertainties regarding the decrease in the authorized ratio, and the uncertainties regarding temporary user withdrawal. Robust effective callable charging and discharging power is then calculated based on the comprehensive control power effectiveness coefficient. Finally, models are built for increasing and decreasing reserve capacity, outputting robust effective callable charging and discharging power, increased reserve capacity, and decreased reserve capacity, etc.

[0147] In summary, when the user-side energy storage discharge control rights decrease, the virtual power plant may experience a power supply gap; when the user-side energy storage charging control rights decrease, the virtual power plant may not be able to fully absorb the surplus power of new energy. This embodiment, by setting upward and downward reserve constraints, can maintain system power balance and new energy absorption capacity when the actual callable capacity of user-side energy storage decreases.

[0148] In some embodiments of the present invention, the first User-side energy storage during time period The power up-adjustment capability is:

[0149]

[0150] in, The nominal effective callable discharge power of the i-th user-side energy storage during time period t.

[0151]

[0152] No. User-side energy storage during time period The power reduction capability is:

[0153]

[0154] in, The nominal effective callable charging power of the i-th user-side energy storage during time period t.

[0155]

[0156] The "upward adjustment capability" refers to the ability of a virtual power plant to increase discharge or reduce charging power support through user-side energy storage when the system power is insufficient; the "downward adjustment capability" refers to the ability of a virtual power plant to increase charging or reduce discharging power absorption through user-side energy storage when there is a surplus of renewable energy or when the load is low.

[0157] Virtual power plants in time period The total nominal power up-adjustment capability is:

[0158]

[0159] Virtual power plants in time period The total nominal power reduction capability is:

[0160]

[0161] in, and These refer to the power up-adjustment capability and power down-adjustment capability that flexible loads can provide.

[0162] In some embodiments of the present invention, the step of generating a collaborative scheduling scheme for user-side energy storage based on robust and efficient callable charging / discharging power includes:

[0163] Based on robust and effective callable charge / discharge power, establish power balance constraints for the virtual power plant;

[0164] Based on robust and effective callable charging / discharging power and power balance constraints, a collaborative scheduling scheme for user-side energy storage is generated.

[0165] In one example, such as Figure 2 The virtual power plant shown has the following power balance constraints:

[0166]

[0167] in, This represents the actual power utilized by photovoltaic systems. This represents the actual utilized power of wind power. In order to purchase power from the main grid, To sell electricity to the main grid, For the load demand power, In order to reduce load power, Adjusting power for transferable loads, This refers to the power of wind and solar power that has been curtailed.

[0168] In robust scheduling, the power balance constraint is further required to be:

[0169]

[0170] This constraint is designed to ensure that the virtual power plant still has sufficient power supply capacity when the user-side energy storage discharge control is reduced.

[0171] For scenarios with surplus new energy, the requirements are:

[0172]

[0173] This constraint is designed to ensure that the virtual power plant still has a certain capacity to absorb new energy sources when the user-side energy storage charging control is reduced.

[0174] In some embodiments of the present invention, the step of generating a collaborative scheduling scheme for user-side energy storage based on predicted reliable and callable charging / discharging power includes:

[0175] The objective function for coordinated scheduling is constructed to maximize the overall benefits of virtual power plants. The objective function includes a penalty cost term for user-side energy storage call when control availability is low, as well as penalty cost terms for output power down-adjustment margin and output power up-adjustment margin of virtual power plants.

[0176] By using the predicted reliable and callable charging / discharging power as a constraint, the cooperative scheduling objective function is solved to obtain the cooperative scheduling parameters, and a cooperative scheduling scheme is generated based on the cooperative scheduling parameters.

[0177] Continue with Figure 2 Taking the virtual power plant shown as an example, the cooperative scheduling objective function can be expressed as:

[0178]

[0179] in, For revenue from electricity trading, For demand response revenue, For ancillary service revenue, Main grid electricity purchase cost, To compensate for the cost of user-side energy storage control rights Costs related to energy storage lifespan depletion. To compensate for costs with flexible loads, The cost of curtailing wind and solar power As a cost of penalty for deviation, Penalty costs for user-side energy storage calls with low control availability. The penalty cost for lowering the output power margin and raising the output power margin of a high virtual power plant, also known as the robust reserve cost.

[0180] The penalty cost for user-side energy storage call-up with low control availability is:

[0181]

[0182] Robust backup cost is:

[0183]

[0184] Where T represents the number of time periods. The penalty coefficient for low availability calls. To increase the reserve cost coefficient, To lower the reserve cost coefficient.

[0185] In summary, this embodiment designs a collaborative scheduling scheme using the above objective function, which can achieve coordinated optimization among economic benefits, user compensation, scheduling reliability, and control robustness.

[0186] In some embodiments of the present invention, capacity authorization compensation is as follows:

[0187]

[0188] The power compensation function is activated as follows:

[0189]

[0190] Lifespan loss compensation is:

[0191]

[0192] in, This is the capacity authorization compensation coefficient; To apply the power compensation factor; This is the lifespan loss compensation coefficient;

[0193] The total cost of regaining control is:

[0194]

[0195] In some embodiments of the present invention, the constraints for solving the cooperative scheduling objective function may include predictable reliable callable charging / discharging power constraints, robust and effective callable charging / discharging power constraints, increase reserve constraints, decrease reserve constraints, power balance constraints, and the following constraints:

[0196] 1. Nominal effective callable power constraint for user-side energy storage.

[0197] The actual charging and discharging power of energy storage should meet the following requirements:

[0198]

[0199]

[0200] 2. User-side energy storage safety power constraints.

[0201] To ensure users' own backup energy needs, the user-side energy storage capacity must not be lower than the user's safe reserve capacity:

[0202]

[0203] At the same time, the energy storage capacity should meet the physical operating boundaries:

[0204]

[0205] The state transition equation for energy storage capacity is:

[0206]

[0207] To prevent virtual power plants from excessively consuming user-side energy storage at the end of the dispatch cycle, this invention can further set end-cycle energy recovery constraints:

[0208]

[0209] in, For the first User-side energy storage at the end of the scheduling cycle The energy storage capacity; For the first The energy storage capacity of each user-side energy storage at the beginning of the scheduling cycle; For the first The allowable power deviation threshold for user-side energy storage.

[0210] 3. User-side energy storage charging and discharging mutual exclusion constraints.

[0211] To prevent the same user-side energy storage from charging and discharging simultaneously within the same time period, a charging state variable is introduced. and discharge state variables ,satisfy:

[0212]

[0213]

[0214] The energy storage charging and discharging power constraint is further expressed as:

[0215]

[0216]

[0217] This constraint ensures that energy storage dispatch commands simultaneously meet physical power boundaries, user authorization status, control rights ratio limits, and charge / discharge mutual exclusion requirements.

[0218] 4. Constraints on new energy output.

[0219] Actual Utilization Power of Photovoltaics And actual wind power utilization Not exceeding its available output:

[0220]

[0221]

[0222] The amount of wind and solar power curtailed is:

[0223]

[0224] in, For the predicted available output of photovoltaic power, Forecast available wind power output.

[0225] 5. Flexible load constraints.

[0226] Reduce load satisfy:

[0227]

[0228] Transferable load satisfy:

[0229]

[0230] And it satisfies energy conservation within the scheduling cycle:

[0231]

[0232] 6. Mainnet interaction constraints.

[0233] The power supply capacity of the point of connection meets the following requirements:

[0234]

[0235]

[0236] in, This refers to the upper limit of the power purchase capacity. This is the upper limit of the electricity sales capacity.

[0237] To avoid simultaneous electricity purchases and sales within the same time period, a power purchase state variable is introduced. ,satisfy:

[0238]

[0239]

[0240]

[0241] in, It is a sufficiently large constant.

[0242] 7. Constraints on user participation in revenue.

[0243] To ensure users have an incentive to continue participating in virtual power plant aggregation, the user's returns after participation will not be lower than their independent operating benchmark returns:

[0244]

[0245] in, The overall benefits for users after participating in virtual power plants. The benchmark return for users operating energy storage independently.

[0246] The overall benefits for users participating in the virtual power plant can be expressed as follows:

[0247]

[0248] in, Compensation for the control gained by the user. The incremental revenue allocated to users by the virtual power plant. Revenue reserved for users' personal use For energy storage loss costs, The inconvenience and costs incurred by relinquishing control to users.

[0249] This constraint ensures that when a virtual power plant calls upon user-side energy storage, it not only meets power system control constraints but also user participation incentive constraints.

[0250] In some embodiments of the present invention, after generating a collaborative scheduling scheme, the virtual power plant can generate scheduling instructions and issue them to the corresponding objects. These scheduling instructions include: user-side energy storage charging power instructions, user-side energy storage discharging power instructions, load reduction control instructions, load transfer adjustment instructions, grid power purchase plan, grid power sales plan, reserve adjustment plan, and reserve reduction plan.

[0251] Among them, for the first For a single user-side energy storage system, the scheduling instruction can be expressed as:

[0252]

[0253] Among them, those with " The variable "" represents the optimal or feasible scheduling instruction obtained by solving the model.

[0254] In some embodiments of the present invention, the energy storage collaborative scheduling method based on virtual power plants further includes:

[0255] Obtain security verification feedback information; wherein, the security verification feedback information is the information generated and sent by the user-side energy storage local control terminal when the collaborative scheduling scheme is verified for security, and when the verification fails; the security verification includes at least one of authorized boundary verification, charge state security constraint verification, charge and discharge power constraint verification, equipment operating status constraint verification, and charge and discharge mutual exclusion constraint verification.

[0256] Adjust the collaborative scheduling scheme based on the security verification feedback information.

[0257] Specifically, the virtual power plant generates dispatch instructions based on the collaborative dispatch scheme and sends these instructions to the user-side energy storage local control terminal. The user-side energy storage local control terminal then performs a security check on the dispatch instructions, verifying whether the following conditions are met:

[0258]

[0259]

[0260]

[0261]

[0262]

[0263]

[0264]

[0265] When any condition is not met, the user-side energy storage local control terminal refuses to execute or limits the execution of the dispatch command and sends safety verification feedback information to the virtual power plant energy management system. The safety verification feedback information includes: unexecutable status, limited execution power, or the latest available capacity.

[0266] This embodiment not only introduces control boundary constraints in the energy management system side of the virtual power plant, but also embeds them into the user-side local execution terminal to achieve two-layer anti-unauthorization control.

[0267] Reference Figure 6 This diagram illustrates a user-side energy storage control right authorization boundary provided by the present invention. The authorization boundary includes the authorization period, authorization ratio, safe state of charge (SOC), rated charge / discharge power, etc.

[0268] In some embodiments of the present invention, after the user-side energy storage executes the scheduling command, it feeds back the following data to the virtual power plant energy management system: actual charging power, actual discharging power, actual SOC, user authorization status, user authorization ratio change, PCS operating status, BMS alarm status, communication status, and control command execution status.

[0269] Virtual power plant energy management system calculates response deviation:

[0270]

[0271] When the response deviation exceeds the set threshold If the user-side energy storage response is insufficient, its historical response correction coefficient will be updated.

[0272]

[0273] in, This is the smoothing coefficient.

[0274] Meanwhile, the percentage of users granted authorization may decrease as a result of updates:

[0275]

[0276] in, Update the risk coefficient for authorization reduction.

[0277] The virtual power plant recalculates the user-side energy storage capacity based on the revised control availability prediction coefficient, authorization decline rate, and robust budget parameters, and then enters the next rolling optimization scheduling cycle.

[0278] This embodiment, through the execution feedback and rolling correction mechanism, can update the callable capabilities and robust scheduling parameters when users temporarily withdraw, energy storage response is insufficient, communication is abnormal, or equipment alarms occur, thereby improving the continuous executability of the virtual power plant scheduling plan.

[0279] Reference Figure 7 The diagram illustrates the overall flowchart of a collaborative scheduling method provided by this invention. The process includes: data acquisition, authorization information acquisition, nominal callable capability calculation, control availability prediction, robust control scheduling, scheduling instruction issuance, local security verification, and execution feedback rolling correction.

[0280] The present invention will be further described below with reference to a specific embodiment.

[0281] I. Description of the scenario for collaborative scheduling of user-side energy storage in a park-level virtual power plant.

[0282] This embodiment uses a virtual power plant in an industrial and commercial park as an example. The park is connected to the upper-level distribution network through a common connection point and is equipped with distributed photovoltaic power, small-scale distributed wind power, user-side energy storage, load shedding, and load transfer capabilities. User-side energy storage is distributed across different user entities such as commercial complexes, industrial plants, office buildings, and charging stations. The energy storage assets belong to the users, and the virtual power plant operator can only access their energy storage resources within the scope authorized by the users.

[0283] The virtual power plant in the park is operated and coordinated by a virtual power plant energy management system. The virtual power plant energy management system communicates with the user-side energy storage local control terminal, energy storage converter PCS, battery management system BMS, and user-side energy consumption management terminal to obtain the user-side energy storage operating status, user authorization information, equipment health status, and actual response data, and issues energy storage charging and discharging control commands to the user-side energy storage local control terminal.

[0284] In this embodiment, the virtual power plant scheduling cycle is 24 hours, and the scheduling time interval is 1 hour. The distributed photovoltaic installed capacity in the park is 1.0MW, the small distributed wind power capacity is 0.5MW, the total user-side energy storage capacity is 2.0MWh, the maximum aggregated charge and discharge power of user-side energy storage is 1.0MW, the energy storage charge and discharge efficiency is 95%, the allowable SOC operating range of user-side energy storage is 20%-90%, and the user's safe reserved SOC is set to 30%-40% according to different user needs.

[0285] The basic parameters of a park-level virtual power plant are shown in Table 1 below.

[0286] Table 1 Basic Parameters

[0287]

[0288] During the current phase, the virtual power plant energy management system collects forecasted photovoltaic (PV) output, wind power output, park load forecast, time-of-use pricing, point-of-combination (POC) power cap, and user-side energy storage authorization information for the next 24 hours. User-side energy storage authorization information includes authorization status, authorization ratio, authorization period, maximum authorized power, maximum authorized capacity, and safe reserved state of charge (SOC).

[0289] During the daytime phase, the virtual power plant energy management system makes rolling corrections to the dispatch plan based on actual renewable energy output, real-time load changes, changes in user-side energy storage SOC, equipment communication status, and changes in user authorization.

[0290] II. User-side energy storage control authorization information settings.

[0291] In this embodiment, user-side energy storage is not open to virtual power plants for use throughout the entire time period, capacity, and power range. Instead, different control authorization ratios are set according to different time periods, different electricity price levels, and the user's own energy needs.

[0292] For the Individual user-side energy storage, during a certain period of time The control authorization status is The proportion of control granted is .when At that time, it indicates that the user has allowed the virtual power plant to operate within a certain time period. Call upon its stored energy; when This indicates that the user does not allow the virtual power plant to access its energy storage.

[0293] The proportion of control authorization satisfies: .

[0294] when When, it means the user does not relinquish any control over the energy storage; when When, it means that the user has granted full callable control rights during that time period; when When this is the case, it means that the user only has partial control over the capacity or power.

[0295] As an example, during peak electricity price periods, user-side energy storage has higher discharge revenue, and users are willing to relinquish a higher proportion of discharge control. During off-peak electricity price periods or periods with surplus renewable energy, user-side energy storage can absorb low-priced electricity or renewable energy through charging. During normal flat periods, user-side energy storage retains more autonomy.

[0296] Example authorization policies are shown in Table 2 below:

[0297] Table 2 Authorization Strategy

[0298]

[0299] For users with a high safety-reserved SOC, such as hospitals, data centers, critical production lines, or charging stations, the virtual power plant should prioritize ensuring the safety-reserved capacity of these users when calculating the available discharge power. For ordinary commercial users or office building users, their safety-reserved SOC can be relatively lower, and the virtual power plant can allocate a higher proportion of energy storage control within its authorized scope.

[0300] III. Implementation method for calculating nominally valid callable capabilities.

[0301] After obtaining the authorization information for user-side energy storage control, the virtual power plant energy management system first calculates the nominal effective callable capacity of user-side energy storage.

[0302] For the Individual user-side energy storage, during a certain period of time The nominal effective available charging power is:

[0303]

[0304] The nominal effective available discharge power is:

[0305]

[0306] in, Maximum charging power for user-side energy storage For the maximum discharge power of user-side energy storage, To store the maximum allowable amount of electricity, This represents the current energy storage capacity. To ensure user safety, and These are charging efficiency and discharging efficiency, respectively. This is the scheduling time interval.

[0307] For example, a user-side energy storage system has a rated capacity of 100kWh, a maximum charge / discharge power of 50kW, a current SOC of 70%, a safe reserved SOC of 35%, and an authorized discharge ratio of 0.60 during peak evening hours. Without considering other limitations, its maximum available discharge power based on the authorized discharge ratio is:

[0308]

[0309] Meanwhile, the amount of electricity that can currently be discharged from this energy storage is:

[0310]

[0311] Within a 1-hour dispatch interval, the maximum discharge power of this user-side energy storage, constrained by the safe reserve capacity, is approximately 35kW. Since the authorized power limit is 30kW, the nominal effective discharge power available during this period is 30kW.

[0312] Through this calculation process, the virtual power plant will not only call up user-side energy storage according to the rated power of the energy storage, but will also comprehensively consider the user's authorized ratio, the current SOC, and the safe reserved SOC to determine the available capacity.

[0313] IV. Implementation of Control Availability Prediction

[0314] After obtaining the nominal effective callable capacity, this invention further calculates the availability prediction coefficient for user-side energy storage control rights. This prediction coefficient is used to characterize the reliability of user-side energy storage actually executing virtual power plant dispatch instructions during future dispatch periods.

[0315] For the Individual user-side energy storage, during a certain period of time The control availability prediction coefficient can be expressed as:

[0316]

[0317] in, This is a correction factor for SOC availability, and it is divided into two types based on the charging and discharging direction. This is a historical response correction factor. This is a communication status correction factor. This is a correction factor for the equipment's health status. Adjustment factor for the risk of temporary user logout.

[0318] In this embodiment, the virtual power plant energy management system reads the dispatch command power and actual response power of each user-side energy storage for the most recent few dispatch cycles from the historical database and calculates the historical response deviation. If a user can consistently respond stably according to the virtual power plant commands over a long period, its historical response correction coefficient is high; if a user repeatedly experiences insufficient response, refusal to execute, or limited execution, its historical response correction coefficient is low.

[0319] For example, if User A has an average response deviation of 5% in the last 5 scheduling operations, normal communication status, normal equipment health status, and no temporary outages, then its historical response correction coefficient and risk correction coefficient are relatively high. User B, on the other hand, has repeatedly experienced insufficient response in the last 5 scheduling operations, with an average response deviation of 25%, and suffers from high communication latency; therefore, its control availability prediction coefficient is lower than User A's. In subsequent scheduling operations, the virtual power plant will prioritize User A, reducing its dependence on User B.

[0320] For the charging direction and the discharging direction, the present invention calculates the control availability prediction coefficients respectively:

[0321]

[0322]

[0323] For example, if a user-side energy storage has a high current state of charge (SOC), its availability in the discharge direction is high, but its availability in the charging direction is low; conversely, if a user-side energy storage has a low current SOC, its availability in the charging direction is high, but its availability in the discharge direction is low. By calculating the availability prediction coefficients for both charging and discharging directions separately, the virtual power plant can more precisely determine the actual availability of user-side energy storage in different regulation directions.

[0324] V. Implementation method for predicting trustworthy callability capability calculation.

[0325] After calculating the control availability prediction coefficient, the virtual power plant further calculates the predicted reliable callability of user-side energy storage.

[0326] For the Individual user-side energy storage, during a certain period of time The predicted reliable callable charging power is:

[0327]

[0328] The predicted reliable available discharge power is:

[0329]

[0330] For example, a user-side energy storage system has a maximum discharge power of 50kW and a user-granted ratio of 0.60. Based on the nominal authorized capacity, its maximum callable discharge power is 30kW. However, due to the user's recent large response deviation and generally poor communication status, its discharge direction control availability prediction coefficient is 0.42. Therefore, its predicted reliable callable discharge power is:

[0331]

[0332] In the scheduling model, the virtual power plant no longer treats the user as a 30kW stable callable resource, but optimizes it according to a predicted reliable callable power of 21kW, thereby reducing the deviation between the scheduling plan and the actual execution.

[0333] For example, if another user-side energy storage system has a maximum discharge power of 80kW, a user-authorized ratio of 0.75, stable historical response, normal equipment status, and a discharge direction control availability prediction coefficient of 0.70, then its predicted reliable callable discharge power is:

[0334]

[0335] Virtual power plants can prioritize the use of this type of highly reliable user-side energy storage resources during peak shaving periods in the evening.

[0336] Through the above method, the present invention transforms the nominal authorization capability of user-side energy storage into predictable, reliable, and callable capability, enabling the virtual power plant to identify low-reliability and high-reliability users before generating a scheduling plan, thereby improving the executability of the scheduling plan.

[0337] VI. Implementation of Robust Scheduling for Control Rights.

[0338] In actual operation, user-side energy storage may temporarily withdraw or have its authorized ratio reduced due to increased local energy demand, equipment alarms, communication anomalies, or user-initiated adjustments to strategies. To prevent the dispatch plan from failing under these circumstances, this invention introduces a robust control dispatch mechanism into the virtual power plant dispatch model.

[0339] For the Individual user-side energy storage, during a certain period of time The nominal authorization ratio is The maximum possible decrease is The robust effective authorization ratio is:

[0340]

[0341] in, To authorize the reduction of uncertain variables, the following condition must be met:

[0342]

[0343] The uncertainty of the decline in all user-side energy storage authorizations within the same time period satisfies:

[0344]

[0345] in, Budget parameters for uncertainty of control.

[0346] In this embodiment, the virtual power plant is dynamically determined based on the user group's control risk index. If user responses have been stable recently, communication has been good, and device alarms have been few, then... Choose the smaller value to reduce backup costs and improve economic efficiency; if there are frequent temporary user logouts, frequent changes in authorization ratios, or poor communication equipment status recently, then... Take a larger value to improve the scheduling plan's resilience to disturbances.

[0347] For example, during normal peak hours, user response is stable. A smaller value can be chosen; during the evening peak hours, due to strong local energy demand from users, the control of energy storage discharge may change. It can be appropriately increased; during periods when communication anomalies or equipment alarms occur in concentrated periods, It can be further improved.

[0348] Additionally, this invention can also introduce a user temporary exit variable. This is used to describe situations where a user temporarily exits before the schedule is executed:

[0349]

[0350] in, In nominal authorization status, This indicates that the user has temporarily logged out. This indicates the user has not logged out. The number of users who temporarily logged out meets the following requirement:

[0351]

[0352] Through the robust modeling approach described above, the virtual power plant scheduling model no longer assumes that all user-authorized capabilities can be fully realized, but instead considers the risks of decreased user authorization ratios and temporary user withdrawals within a certain range.

[0353] VII. Robust Backup and Power Balance Implementation Methods.

[0354] In robust control scheduling, virtual power plants need to reserve certain backup capacity for user-side energy storage authorization reductions or temporary withdrawals.

[0355] When the user-side energy storage discharge control decreases, the virtual power plant may experience a power supply gap, therefore, the reserve capacity should be increased. Increased reserve capacity can be provided by a combination of highly reliable user-side energy storage, load shedding, power purchase adjustments from the main grid, or other adjustable resources.

[0356] When user-side energy storage charging control decreases, virtual power plants may not be able to fully absorb surplus output from solar and wind power, thus requiring a reduction in reserve capacity. The reduction in reserve capacity can be provided by other rechargeable energy storage, transferable load, adjustment of main grid power sales, or control of wind and solar curtailment.

[0357] During evening peak hours, if some users temporarily reduce their discharge authorization ratio, the virtual power plant maintains power balance in the following ways: 1. Increase the discharge power of high-reliability user-side energy storage; 2. Call up loads that can be reduced; 3. Increase the power purchased from the main grid; 4. Use reserved standby capacity; 5. Re-optimize energy storage and flexible load plans in the next rolling dispatch cycle.

[0358] In scenarios with surplus photovoltaic power, if some users temporarily reduce their charging authorization ratio, virtual power plants can reduce the risk of curtailment by: 1. Increasing the charging power of energy storage on other users' sides; 2. Arranging for transferable loads to be transferred; 3. Increasing the power sold to the main grid; 4. Using reserved backup capacity; 5. Minimizing the curtailment power.

[0359] Therefore, this invention can maintain the power balance of the virtual power plant when the user-side energy storage control changes, reducing the risk of deviation assessment caused by temporary user withdrawal.

[0360] VIII. Implementation methods for solving the collaborative scheduling problem of virtual power plants.

[0361] After completing the calculation of the nominal effective callable capacity of user-side energy storage, the prediction of control availability, and the robust modeling of control, the virtual power plant establishes a source-load-storage coordinated scheduling model.

[0362] The decision variables of the model include: 1. User-side energy storage charging power; 2. User-side energy storage discharging power; 3. User-side energy storage SOC; 4. Load power that can be reduced; 5. Load power that can be transferred; 6. Power purchased by the main grid; 7. Power sold by the main grid; 8. Power curtailed wind and solar power; 9. Increase in reserve capacity; 10. Decrease in reserve capacity.

[0363] The model constraints include: 1. User-side energy storage nominal authorization boundary constraints; 2. User-side energy storage predictive reliability and callability constraints; 3. User-side energy storage robust and effective callability constraints; 4. Energy storage SOC upper and lower limits constraints; 5. User safe reserve capacity constraints; 6. Charge and discharge mutual exclusion constraints; 7. New energy output constraints; 8. Flexible load adjustment constraints; 9. Main grid interaction power constraints; 10. Virtual power plant power balance constraints; 11. Upward and downward reserve adjustment constraints; 12. User participation revenue constraints.

[0364] The objective function of the model is to maximize the comprehensive revenue of the virtual power plant, taking into account the revenue from electricity trading, demand response revenue, ancillary service revenue, main grid electricity purchase cost, user-side energy storage control right compensation cost, energy storage lifetime loss cost, flexible load compensation cost, wind and solar curtailment loss cost, deviation assessment cost, low availability call penalty cost of control right, and robust standby cost.

[0365] In one implementation, the model can be transformed into a mixed-integer linear programming model for solution; in another implementation, when the set of control uncertainty is complex, robust dual transformation, column constraint generation, or rolling optimization methods can be used for solution. After obtaining the user-side energy storage charging and discharging power, flexible load adjustment, main grid interaction power, and reserve capacity for each scheduling time period, the virtual power plant generates corresponding control commands.

[0366] IX. Implementation method of local secondary security verification on the user side.

[0367] After obtaining the scheduling plan from the virtual power plant solution, the first... User-side energy storage during time period The control command is sent to the user-side energy storage local control terminal. This control command includes at least the target charging power, target discharging power, command execution time period, user authorization status, user authorization ratio, and safe reserved capacity.

[0368] Before executing the control command, the user-side energy storage local control terminal performs a secondary security check. The checks include: 1. Verifying whether the user is still in an authorized state during the specified time period; 2. Verifying whether the dispatch command is within the maximum power range corresponding to the user's authorized ratio; 3. Verifying whether the current State of Charge (SOC) is higher than the user's safe reserved SOC; 4. Verifying whether the energy storage capacity is within the allowable operating range; 5. Verifying whether the PCS is in normal operating condition; 6. Verifying whether the BMS has any serious alarms; 7. Verifying whether the communication timestamp and the command time period are consistent; 8. Verifying whether the user has temporarily withdrawn authorization on the local terminal.

[0369] When all verification conditions are met, the user-side energy storage local control terminal executes the control command. If any condition is not met, the local control terminal refuses to execute or limits the execution of the control command, and reports the reason for non-execution and the current maximum executable power to the virtual power plant energy management system.

[0370] For example, a virtual power plant issues a 30kW discharge command to a user-side energy storage system. However, before executing the command, the user lowers the authorized ratio from 0.60 to 0.30, and the maximum discharge power of the energy storage system is 50kW. In this case, the local control terminal determines that the maximum discharge power corresponding to the current authorized boundary is 15kW. The local control terminal can either reject the 30kW discharge command or limit its execution to 15kW and feed the result of the limited execution back to the virtual power plant's energy management system.

[0371] Through the aforementioned secondary security verification mechanism, this invention can prevent virtual power plants from unauthorizedly accessing user-side energy storage due to delays in scheduling plans, changes in user authorization, or changes in equipment status.

[0372] 10. Implementation of Feedback and Rolling Correction Methods.

[0373] After the user-side energy storage executes the dispatch command, the user-side energy storage local control terminal feeds back the actual execution results to the virtual power plant energy management system. The feedback includes actual charging power, actual discharging power, actual SOC, execution success status, limited execution status, execution rejection status, user authorization change status, PCS operation status, BMS alarm status, and communication status.

[0374] The virtual power plant energy management system calculates the response deviation based on the dispatch command power and the actual response power:

[0375]

[0376] When the response deviation is less than the set threshold, it indicates that the user-side energy storage is performing well, and its historical response correction coefficient remains unchanged or is appropriately increased. When the response deviation exceeds the set threshold, it indicates that the user-side energy storage has problems such as insufficient response, limited execution, refusal to execute, or communication anomalies. The virtual power plant energy management system reduces the user's control availability prediction coefficient in subsequent dispatch cycles.

[0377] For example, if a user's energy storage dispatch command is for a 40kW discharge, but the actual response is only 25kW, then the response deviation is significant. The virtual power plant energy management system will lower the user's historical response correction factor and increase the estimated decrease in its authorized ratio. In the next rolling scheduling cycle, both the predicted reliable callable power and the robust effective callable power of this user-side energy storage will decrease accordingly.

[0378] If a user responds stably for several consecutive scheduling cycles, the system gradually increases its historical response correction coefficient and may increase its call priority in the scheduling order. If a user refuses to execute multiple times or experiences communication failures, the system lowers its call priority and, if necessary, temporarily suspends the use of that user's energy storage in subsequent cycles.

[0379] Through the above feedback correction process, the present invention forms a closed-loop control mechanism of "scheduling plan generation - control command execution - actual response feedback - control right availability correction - robust parameter update - next cycle rolling optimization".

[0380] 11. Example of the running effect in the scenario of temporary user logout.

[0381] To further illustrate the role of this invention in scenarios where users temporarily withdraw from the grid, suppose that during the evening peak period from 18:00 to 20:00, the virtual power plant originally planned to use some user-side energy storage for discharge to reduce the power purchased by the point of common coupling. However, before actual execution, several users temporarily reduced the authorized proportion of energy storage control due to increased production load or reserve requirements, with some users even completely withdrawing from the scheduling during this period.

[0382] Without adopting the robust scheduling method of this invention, if the virtual power plant still calls the aforementioned user-side energy storage according to the original plan, it may result in insufficient actual discharge power, and the power purchased by the point of common coupling may exceed the day-ahead planned value, thereby generating deviation assessment costs.

[0383] By adopting this invention, the virtual power plant's dispatch model pre-considers the risks of declining user authorization ratios and temporary withdrawals, and sets control uncertainty budget parameters and reserve adjustment constraints. Therefore, when the actual discharge capacity of some user-side energy storage decreases, the virtual power plant can compensate in the following ways: 1. Calling upon other user-side energy storage with higher control availability prediction coefficients; 2. Using reserved reserve capacity; 3. Adjusting loads that can be reduced; 4. Increasing the power purchased from the main grid; 5. Re-optimizing the dispatch plan in the next rolling cycle.

[0384] This implementation method can reduce the impact of temporary user withdrawal on the power balance and market fulfillment capabilities of virtual power plants, and improve the resilience of dispatch plans.

[0385] XII. Examples of operational performance in scenarios with surplus new energy sources.

[0386] During the peak photovoltaic output period from 12:00 to 14:00, the industrial park may experience surplus renewable energy power. Virtual power plants typically need to utilize user-side energy storage for charging to improve the local absorption rate of renewable energy.

[0387] If some user-side energy storage temporarily reduces its charging authorization ratio during this period, traditional deterministic scheduling methods may not be able to absorb the surplus photovoltaic power in time, leading to an increase in curtailed power.

[0388] By adopting this invention, when calculating the user-side energy storage charging capacity, the virtual power plant considers not only the nominal authorized ratio but also the charging direction control right availability prediction coefficient and robust effective callable charging power. When the charging capacity of some user-side energy storage decreases, the system prioritizes calling other user-side energy storage with higher charging availability by lowering the reserve constraint and using a rolling optimization mechanism, or arranges for transferable loads to be transferred into that time period, thereby improving the renewable energy absorption capacity.

[0389] For example, if the surplus photovoltaic power is 300kW during a certain period, the virtual power plant originally planned to call upon several user-side energy storage devices to charge a total of 250kW and arrange 50kW of transferable load. If, in actual execution, the authorized power of some energy storage users decreases, resulting in a reduction of 60kW in the rechargeable power, the system can call upon reserved reserve capacity to increase the charging power of other users' energy storage devices or increase the amount of transferable load, thereby reducing the curtailed photovoltaic power.

[0390] Thirteen, examples of scheduling effect comparison.

[0391] Before and after using the method of this invention, the operating effects of the above-mentioned park-level virtual power plant can be compared according to the indicators shown in Table 3.

[0392] Table 3 Indicator Comparison

[0393]

[0394] As shown in Table 3, after adopting this invention, the maximum net load of the park decreased, the minimum net load increased, the peak-valley difference significantly decreased, the renewable energy absorption rate improved, and the amount of wind and solar power curtailed decreased. Simultaneously, due to the introduction of control availability prediction and robust control scheduling mechanisms in this invention, the average response deviation of user-side energy storage decreased, and the deviation in power caused by temporary user withdrawal decreased.

[0395] The above effects demonstrate that the present invention can not only achieve coordinated scheduling of energy storage within the user-authorized boundary, but also improve the adaptability of virtual power plants to uncertain behavior of user-side energy storage.

[0396] XIV. Example of economic effect measurement.

[0397] After adopting this invention, the economic benefits of virtual power plants mainly include peak-valley price difference benefits, renewable energy consumption benefits, demand response benefits, and reduced deviation assessment costs; the costs mainly include user-side energy storage capacity authorization compensation costs, dispatched electricity compensation costs, energy storage life loss compensation costs, flexible load compensation costs, low availability dispatch penalty costs, and robust standby costs.

[0398] The virtual power plant draws 0.86 MWh of electricity from user-side energy storage for charging during low-price periods and 0.78 MWh for discharging during high-price periods. With a peak electricity price of 1.12 yuan / kWh and an off-peak price of 0.32 yuan / kWh, the virtual power plant can profit from the peak-valley price difference after considering energy storage efficiency.

[0399] Meanwhile, after adopting this invention, the daily curtailment of wind and solar power decreased from 0.72 MWh to 0.26 MWh, increasing the amount of renewable energy consumed by approximately 0.46 MWh. If calculated based on an average value of 0.72 yuan / kWh for renewable energy replacing purchased electricity in the industrial park, the incremental revenue from renewable energy consumption would be approximately 331.2 yuan.

[0400] Regarding deviation assessment, if the daily deviation assessment electricity consumption of the virtual power plant was 0.84 MWh before optimization, it is reduced to 0.39 MWh after adopting this invention, resulting in a reduction of 0.45 MWh. If the deviation penalty price is calculated at 0.80 yuan / kWh, the deviation assessment cost is reduced by approximately 360.0 yuan.

[0401] In terms of demand response, if the effective response electricity volume on a given day is 0.52 MWh, and the demand response compensation price is calculated at RMB 0.60 / kWh, then the virtual power plant can obtain a demand response revenue of approximately RMB 312.0.

[0402] Regarding user-side energy storage compensation, the total compensation for capacity authorization, dispatched electricity, and lifetime loss is set at 337.6 yuan. After considering the low availability dispatch penalty cost and robust standby cost, the virtual power plant can still obtain positive daily comprehensive incremental revenue.

[0403] The above economic calculations show that, under the premise of ensuring the user-side energy storage control boundary and safe power retention, the present invention can improve the overall operating income of virtual power plants by increasing new energy consumption, utilizing peak-valley price differences, reducing deviation penalties, and increasing demand response revenue.

[0404] 15. Example of user-side revenue.

[0405] For user-side energy storage entities, participating in virtual power plant dispatch can provide capacity authorization compensation, dispatched electricity compensation, and energy storage lifespan loss compensation.

[0406] Taking a 100kWh commercial user-side energy storage as an example, assuming its average daily authorization ratio is 0.45, the daily cumulative called charging and discharging power is 72kWh, the capacity authorization compensation price is 0.12 yuan / kWh, the called power compensation price is 0.08 yuan / kWh, and the lifetime loss compensation price is 0.06 yuan / kWh.

[0407] This user can receive capacity authorization compensation, electricity usage compensation, and lifetime depreciation compensation. Since the user has not transferred ownership of the energy storage assets, but only relinquished partial control within the authorized boundaries, they can obtain additional benefits while ensuring their own backup energy needs.

[0408] For industrial users with a capacity of 300kWh, the daily compensation amount will increase accordingly if their average authorization ratio and call frequency are higher. This compensation method can enhance users' enthusiasm for continuous participation in virtual power plant dispatch.

[0409] Meanwhile, this invention ensures that the user's SOC is not breached through local secondary security verification on the user side, thereby preventing the virtual power plant from excessively utilizing energy storage resources and reducing user concerns about participation.

[0410] This invention also provides an energy storage collaborative scheduling system based on a virtual power plant, such as... Figure 8 As shown, system 800 includes:

[0411] The control availability prediction module 801 is used to generate a user temporary exit risk correction coefficient based on the user-side energy storage's temporary exit authorization frequency, power authorization ratio change frequency, and local load occupancy risk; correct the power authorization ratio based on the user temporary exit risk correction coefficient to obtain the control availability prediction coefficient; and calculate the predicted reliable callable charging / discharging power of the user-side energy storage based on the control availability prediction coefficient.

[0412] The collaborative scheduling optimization module 802 is used to generate a collaborative scheduling scheme for user-side energy storage based on the predicted reliable and callable charging / discharging power.

[0413] In some embodiments of the present invention, system 800 may further include:

[0414] The data acquisition module is used to collect virtual power plant operation data, which includes: new energy predicted output, load predicted power, time-of-use electricity price, common coupling point constraints, energy storage SOC, energy storage power, energy storage capacity, equipment health status, communication status, and market transaction information.

[0415] The user control authorization management module is used to record and update the user-side energy storage control authorization status. The authorization status includes authorization period, authorization ratio, authorization capacity, authorization power, safe reserved power, safe reserved SOC, and user priority, etc.

[0416] The nominal callable capacity calculation module is used to calculate the nominal effective callable charging power, nominal effective callable discharging power, nominal up-adjustment capacity and nominal down-adjustment capacity of each user-side energy storage in each scheduling period, based on the user-side energy storage control authorization boundary and the current operating status of the energy storage.

[0417] The robust scheduling module for control is used to construct a set of control uncertainty based on the uncertainty of the user-side energy storage authorization ratio, the risk of temporary user withdrawal, and historical response deviation, and to generate a robust scheduling plan that meets the constraints of the user-side energy storage control uncertainty.

[0418] The safety verification module is used to verify whether the scheduling instructions meet the user-side energy storage authorization boundary, SOC safety constraints, charging and discharging power constraints, equipment operating status constraints, and charging and discharging mutual exclusion constraints.

[0419] The instruction issuing module is used to send energy storage charging and discharging control instructions that have passed safety verification to the user-side energy storage converter, energy storage energy management terminal or local controller.

[0420] The execution feedback module is used to collect the actual response power, actual SOC, execution status, user authorization change status, equipment alarm status and communication status of user-side energy storage, and to feed the actual response results back to the virtual power plant energy management system.

[0421] The rolling correction module is used to recalculate the user-side energy storage control right availability prediction coefficient, authorization decline magnitude, robust budget parameters and callable capacity, and update the virtual power plant dispatch plan when there are deviations in user-side energy storage response, changes in user authorization status, equipment unavailability or communication anomalies.

[0422] The user revenue settlement module is used to calculate the compensation revenue for users participating in virtual power plant dispatch based on the user's authorized capacity, actual electricity dispatched, energy storage life loss, and user response reliability.

[0423] In some embodiments of the present invention, the control availability prediction module may specifically include: a historical response deviation calculation unit; a SOC availability assessment unit; a communication status assessment unit; a device health status assessment unit; a user exit risk assessment unit; a control availability prediction unit; and a predicted trusted callable power output unit.

[0424] In some embodiments of the present invention, the robust control scheduling module may specifically include: an authorization decline magnitude determination unit; a user temporary exit risk modeling unit; a control uncertainty set construction unit; a control risk index calculation unit; a robust budget parameter setting unit; a robust callable power calculation unit; a robust reserve capacity calculation unit; and a robust scheduling solution unit.

[0425] In some embodiments of the present invention, the collaborative scheduling optimization module is also used to establish and solve the virtual power plant source-load-storage collaborative scheduling model, and output the user-side energy storage charging and discharging power, flexible load adjustment amount, main grid interactive power plan, upward adjustment of reserve capacity and downward adjustment of reserve capacity.

[0426] It should be noted that the implementation principles or processes of the above modules can be referred to the aforementioned implementation examples of the energy storage collaborative scheduling method based on virtual power plants, and will not be elaborated here.

[0427] Those skilled in the art will understand that all or part of the processes of the methods described in the above embodiments can be implemented by a computer program instructing related hardware, and the program can be stored in a computer-readable storage medium. The computer-readable storage medium may be a disk, optical disk, read-only memory, or random access memory, etc.

[0428] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for coordinated energy storage scheduling based on virtual power plants, characterized in that, include: Based on the frequency of temporary withdrawal authorization for user-side energy storage, the frequency of power authorization ratio changes, and the risk of local load occupancy, a correction coefficient for user temporary withdrawal risk is generated; where the frequency of temporary withdrawal authorization is the frequency at which a user withdraws from the user-side energy storage usage authorization before scheduling execution; and the power authorization ratio is the percentage of the maximum allowed charging / discharging power to the rated charging / discharging power. The power authorization ratio is adjusted based on the user temporary exit risk correction coefficient to obtain the control availability prediction coefficient. The predicted reliable callable charging / discharging power of user-side energy storage is calculated based on the control availability prediction coefficient, and a collaborative scheduling scheme for user-side energy storage is generated based on the reliable callable charging / discharging power. The process of generating a temporary user outage risk correction coefficient based on the frequency of temporary outage authorization for user-side energy storage, the frequency of power authorization ratio changes, and local load occupancy risk includes: The user temporary exit risk correction coefficient is calculated using the following formula: ； Where i is the user-side energy storage number, and t is the time period. The temporary exit risk correction coefficient for user-side energy storage i in time period t; To temporarily withdraw from authorized frequencies, To change the frequency for the power license ratio, To mitigate the risk of local load occupancy, These are the weighting coefficients.

2. The energy storage collaborative scheduling method based on virtual power plants according to claim 1, characterized in that, The control availability prediction coefficients include a first control availability prediction coefficient in the discharge direction and a second control availability prediction coefficient in the charging direction. The step of adjusting the power grant ratio based on the user temporary exit risk correction coefficient to obtain the control availability prediction coefficient includes: The charge state availability correction coefficient for the discharge direction is determined based on the difference between the charge state of the user-side energy storage and the charge state safety value. The charge state availability correction coefficient in the charging direction is determined based on the difference between the charge state of the user-side energy storage and the maximum charge state. Based on the device health status, communication status, and response deviation to historical power dispatch commands of the user-side energy storage, device health status correction coefficient, communication status correction coefficient, and historical response correction coefficient are generated respectively. The power grant ratio is corrected based on the charge state availability correction coefficient, equipment health state correction coefficient, communication state correction coefficient, historical response correction coefficient, and user temporary exit risk correction coefficient in the discharge direction to obtain the first control availability prediction coefficient. The power grant ratio is corrected based on the charge state availability correction coefficient, device health state correction coefficient, communication state correction coefficient, historical response correction coefficient, and user temporary exit risk correction coefficient in the charging direction to obtain the second control availability prediction coefficient.

3. The energy storage collaborative scheduling method based on virtual power plants according to claim 1, characterized in that, The method further includes: The robust effective power grant ratio is calculated using the following formula: ； ； ； Where i is the user-side energy storage number, and t is the time period. The robust effective power authorization ratio for user-side energy storage i in time period t; The power authorization ratio submitted by the user, This represents the maximum possible decrease in the power licensing ratio. The uncertainty variable for the authorization decreases; N is the amount of user-side energy storage; Budget parameters for uncertainty of control; The robust and valid authorization status is calculated using the following formula: ； ； in, The authorization status is robust and valid. The authorization status submitted by the user. Variables for temporary user exit; Time period The maximum number of users allowed to temporarily log out simultaneously; Based on the robust effective power grant ratio and the robust effective grant status, the robust effective callable charging / discharging power of user-side energy storage is calculated, and a collaborative scheduling scheme for user-side energy storage is generated based on the robust effective callable charging / discharging power.

4. The energy storage collaborative scheduling method based on virtual power plants according to claim 3, characterized in that, The system is dynamically adjusted based on the user equipment health status, user communication status, user authorization exit frequency, power authorization ratio change frequency, and response deviation.

5. The energy storage collaborative scheduling method based on virtual power plants according to claim 3, characterized in that, The collaborative scheduling scheme for generating user-side energy storage based on the robust and efficient callable charging / discharging power includes: Set an upward adjustment constraint: ； in, To increase the power margin of the virtual power plant, The maximum discharge power for energy storage on the user side; Set a lower standby constraint: ； in, To reduce the power margin of the virtual power plant, Maximum charging power for user-side energy storage; Based on the aforementioned upward adjustment of reserve constraints, the aforementioned downward adjustment of reserve constraints, and the aforementioned robust and effective callable charging / discharging power to generate user-side energy storage collaborative scheduling scheme.

6. The energy storage collaborative scheduling method based on virtual power plants according to claim 3, characterized in that, The collaborative scheduling scheme for generating user-side energy storage based on the robust and efficient callable charging / discharging power includes: Based on the robust and effective callable charging / discharging power, establish the power balance constraints of the virtual power plant; Based on the robust and effective callable charging / discharging power and the power balance constraints, a collaborative scheduling scheme for user-side energy storage is generated.

7. The energy storage collaborative scheduling method based on virtual power plants according to claim 1, characterized in that, The collaborative scheduling scheme for generating user-side energy storage based on the predicted reliable and callable charging / discharging power includes: A collaborative scheduling objective function is constructed to maximize the overall benefits of virtual power plants. The objective function includes a penalty cost term for calling user-side energy storage when control availability is low, as well as penalty cost terms for output power downscaling margin and output power upscaling margin of virtual power plants. Using the predicted reliable and callable charging / discharging power as a constraint, the cooperative scheduling objective function is solved to obtain the cooperative scheduling parameters, and a cooperative scheduling scheme is generated based on the cooperative scheduling parameters.

8. The energy storage collaborative scheduling method based on virtual power plants according to claim 1, characterized in that, The method further includes: Obtain security verification feedback information; wherein, the security verification feedback information is information generated and sent by the user-side energy storage local control terminal when it performs security verification on the collaborative scheduling scheme and fails the verification; the security verification includes at least one of authorized boundary verification, charge state security constraint verification, charge and discharge power constraint verification, equipment operating state constraint verification, and charge and discharge mutual exclusion constraint verification; The collaborative scheduling scheme is adjusted based on the security verification feedback information.

9. A collaborative dispatch system for energy storage based on a virtual power plant, characterized in that, include: The control availability prediction module is used to generate a user temporary exit risk correction coefficient based on the frequency of temporary exit authorization for user-side energy storage, the frequency of power authorization ratio change, and local load occupancy risk. The power authorization ratio is adjusted based on the user temporary exit risk correction coefficient to obtain the control availability prediction coefficient. The predicted reliable callable charging / discharging power of user-side energy storage is calculated based on the control right availability prediction coefficient; where, the temporary exit authorization frequency is the frequency at which a user exits the user-side energy storage usage authorization before scheduling execution; and the power authorization ratio is the percentage of the maximum allowed charging / discharging power to the rated charging / discharging power. The collaborative scheduling optimization module is used to generate a collaborative scheduling scheme for user-side energy storage based on the predicted reliable and callable charging / discharging power. The process of generating a temporary user outage risk correction coefficient based on the frequency of temporary outage authorization for user-side energy storage, the frequency of power authorization ratio changes, and local load occupancy risk includes: The user temporary exit risk correction coefficient is calculated using the following formula: ； Where i is the user-side energy storage number, and t is the time period. The temporary exit risk correction coefficient for user-side energy storage i in time period t; To temporarily withdraw from authorized frequencies, To change the frequency for the power license ratio, To mitigate the risk of local load occupancy, These are the weighting coefficients.

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