Energy storage device control method considering low-carbon emission factor of user

Abstract

The invention provides an energy storage device control method considering a user low-carbon emission factor. An energy storage device comprises an energy storage module and two grid-connected modules. One sides of the two grid-connected modules are connected with the energy storage module, and the other sides of the two grid-connected modules are respectively connected to a power grid and a sensitive load power supply part owned by a corresponding user side; acquiring real-time operation data of the power grid; judging whether the operation state of the power grid is a demand response state or a voltage support demand state according to the obtained data; determining an operation strategy of the energy storage device according to a power grid operation state judgment result; according to the operation strategy, the grid-connected module is controlled to adopt corresponding actions, if a voltage supporting strategy is adopted, power is directly supplied to the sensitive load through the second grid-connected module to achieve the voltage supporting effect on the sensitive load, and if a demand response strategy is adopted, demand response of low-carbon emission and load adjustment is carried out on the load through the first grid-connected module. According to the invention, the multifunctional application support of the energy storage device on the demand response of the power grid load and the voltage sag treatment can be realized.

Description

Technical Field

[0001] The present invention belongs to the field of energy storage technology control and application, and relates to an energy storage control method participating in multi-functional cross-multiplexing on the grid side. Background Art

[0002] Since 2010, energy storage technology has been demonstrated in power systems, with projects such as the Zhangbei Integrated Wind, Solar, Storage, and Transmission Project, the Longyuan Faku Woniu Stone Wind Farm Energy Storage Station Project, the Shenzhen Baoqing Hydropower Station, and the Dongfushan Island Energy Storage Project all attracting significant attention. After more than five years of technical and application validation, the technology's maturity and effectiveness have been widely recognized, and industry awareness of energy storage modules has gradually increased.

[0003] With the development of new power systems, energy storage technology, as a key flexible regulation resource, will play a vital role in strengthening power regulation capabilities, enhancing grid flexibility, and facilitating the integration and integration of centralized and distributed renewable energy. One of the key issues in energy storage applications is how to fully exploit the potential of energy storage in grid applications. Summary of the Invention

[0004] The present invention proposes a method for controlling an energy storage device that takes into account the user's low-carbon emission factors. By designing the structural module and control method of the user-side energy storage device, multifunctional application support for grid load demand response and voltage sag control is achieved.

[0005] The technical solutions provided in this application are:

[0006] A method for controlling an energy storage device that takes into account user low-carbon emission factors. The energy storage device includes an energy storage module, a first grid-connected module, and a second grid-connected module. One side of each of the first and second grid-connected modules is connected to the energy storage module, and the other sides of the first and second grid-connected modules are respectively connected to the power grid and a sensitive load power supply location owned by the corresponding user.

[0007] The method comprises:

[0008] Step 1: Obtain real-time operation data of the power grid;

[0009] Step 2: Determine the grid operation state based on the data obtained in step 1 in combination with a state analysis method; wherein the grid operation state includes a first state and a second state, the first state being a demand response state and the second state being a voltage support demand state;

[0010] Step 3: Determine an operation strategy for the energy storage device based on the grid operation state judgment result obtained in step 2; wherein the operation strategy includes a first strategy and a second strategy, the first strategy being a demand response strategy and the second strategy being a voltage support strategy; when the grid is determined to be in the second state, the second strategy is adopted, and when the grid is determined to be in the first state, the first strategy is adopted;

[0011] Step 4: Based on the operation strategy determined in step 3, the grid-connected module is controlled to take corresponding actions. If the second strategy is adopted, the sensitive load is directly powered by the second grid-connected module to provide voltage support. If the first strategy is adopted, the load is responded to by the first grid-connected module to meet the demand for low-carbon emissions and load regulation. This completes the active and reactive output control process of the energy storage module.

[0012] In one possible implementation, in step 4, the first grid-connected module performs a demand response for low-carbon emissions and load regulation on the load, including: considering the user's low-carbon emission factors and load regulation requirements, combining the state of the energy storage module itself, determining a demand response optimization objective function, solving the demand response optimization objective function, and obtaining an optimized demand response curve for the charging and discharging power of the energy storage module; and controlling the first grid-connected module to take corresponding actions based on the optimized demand response curve.

[0013] In one possible implementation, the consideration of the user's low-carbon emission factors and load regulation requirements, combined with the energy storage module's own state, to determine the demand response optimization objective function includes:

[0014] Load classification based on carbon emission demand: corporate users with carbon emission reduction or low-carbon product manufacturing needs, or users who can obtain tangible economic benefits through carbon emission reduction, are classified as Class A loads; users other than Class A loads are classified as Class B loads;

[0015] For loads of different categories, different demand response optimization objective functions are determined; the demand response optimization objective function for Class A loads includes a low-carbon emission target, while the demand response optimization objective function for Class B loads does not include a low-carbon emission target.

[0016] In a possible implementation, the demand response optimization objective function of the Class A load is:

[0017]

[0018]

[0019]

[0020]

[0021] in, Optimize the objective function for demand response, To achieve the goal of reducing peaks and filling valleys, To achieve low carbon emission goals; and are the weights of the peak shaving and valley filling targets and the low-carbon emission targets respectively; For Class A load Real-time load value at the moment, For energy storage device The charge and discharge power values at the moment, where discharge is negative and charge is positive; After the energy storage device is connected, the average value of the integrated power of the energy storage device and the load to the grid; For the The power supply value of the power grid to the energy storage device and load at any moment; For the power grid The carbon emission coefficient of power supply at all times.

[0022] In a possible implementation, the The calculation formula is:

[0023]

[0024] in, Indicates the Moment The load conditions of the power supply; The type of power source in actual operation of the power grid; For the Carbon emission coefficient of this type of power supply.

[0025] In a possible implementation, the demand response optimization objective function of the Class B load is:

[0026] .

[0027] In one possible implementation, the constraint conditions for solving the demand response optimization objective function are:

[0028] Charge and discharge power limit: ;

[0029] Charge and discharge capacity: ;

[0030] = ;

[0031] in, is the lower limit of the discharge power of the energy storage module, The upper limit of the charging power of the energy storage module, For the The remaining power value of the energy storage module at any moment, is the lower limit of the discharge capacity of the energy storage module, The upper limit of the charging capacity of the energy storage module, is the rated capacity of the energy storage module.

[0032] In one possible implementation, = , = .

[0033] The energy storage device control method provided herein, which takes user-side low-carbon emissions into consideration, achieves a combined optimization of voltage sag control and demand response for low-carbon emissions and load regulation by designing the structural units and control methods of the user-side energy storage device, thereby improving the application value and benefits of the energy storage module and promoting its widespread application. Specifically, the device is designed to include one energy storage module and two grid-connected modules, wherein the grid-connected module further comprises an inverter unit and a control unit. Through the dual grid-connected units, the device achieves a comprehensive demand response for low-carbon emissions and load regulation in the power grid and a multifunctional application for voltage sag control. In terms of control, the device monitors load and voltage fluctuations in real time based on the operating conditions of the power grid in which it is installed. When voltage fluctuations occur, the second grid-connected module is immediately activated to power sensitive loads, completing the voltage support function. When the grid voltage recovers, the device is deactivated by controlling the second grid-connected module, and the power supply to sensitive loads continues to be supported by the grid. In the absence of voltage fluctuations, the device achieves a comprehensive demand response effect for low-carbon emissions and load regulation for the grid load through the second grid-connected module. This method not only solves the problem of grid voltage sag, but also improves the economic application of traditional user-side energy storage devices, facilitating the promotion of energy storage in the grid. By improving both the energy storage device and the control method, this method achieves cross-switching between regulating and managing grid voltage sags and participating in user demand response within the user-side energy storage device's operating cycle. This provides flexible peak-shaving and voltage-regulating resources for the large power grid, improving the grid's ability to operate safely and stably. At the same time, it fully coordinates the model of energy storage participating in grid applications, further exploring its application value and providing technical support for the promotion and application of energy storage modules.

[0034] Compared with the prior art, the present invention has the following beneficial effects:

[0035] The present invention proposes a method for controlling an energy storage device that takes into account the user's low-carbon emission factors, and combines the voltage support role of energy storage in the power grid with the demand response role of low-carbon emissions and load regulation according to time periods, thereby improving the application value of energy storage. Through data detection and discrimination, a targeted control mode is adopted to achieve the cross-application of energy storage's participation in the voltage support role and demand response role of the power grid, while ensuring the effectiveness of energy storage in both applications, and improving the technical and economic efficiency of energy storage applications in the power grid. This method provides flexible adjustment resources for large power grids and improves the safe and stable operation capabilities of the power grid by adjusting the cross-switching of the voltage support role and demand response role modes of energy storage in the power grid within the research cycle. At the same time, it also fully coordinates the mode of energy storage's participation in the power grid application, further explores its application value, and provides technical support for the promotion and application of energy storage modules. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 This is a flow chart of the control method in an embodiment of the present application. DETAILED DESCRIPTION

[0037] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0038] This application provides a method for controlling an energy storage device that takes into account user low-carbon emission factors. By designing a control method for a user-side energy storage device, it achieves multi-application support for grid load demand response and voltage sag control. Specifically, in terms of structure, the energy storage device is designed to include an energy storage module and two grid-connected modules. Each grid-connected module includes an inverter unit and a control unit. Through the dual grid-connected modules, the energy storage device can achieve a multi-functional composite application of demand response and voltage sag control in the grid. In terms of control method, based on the low-carbon emission requirements of the configured energy storage user and the differences in carbon emission coefficients of power supply in different time periods of the grid, combined with the user's requirements for voltage reliability, and considering the actual operation of the grid, load and voltage fluctuations are detected in real time. When voltage fluctuations occur, the second grid-connected module is immediately mobilized and used to power sensitive loads to complete the voltage support function. When the grid voltage recovers, the device can be shut down by controlling the second grid-connected module, and the power supply of sensitive loads continues to be supported by the grid. In the absence of voltage fluctuations, the device achieves the effect of differentiated demand response on the user side through the first grid-connected module. As a result, energy storage can be used to manage voltage sags, respond to low-carbon emissions, and regulate loads for users. This method solves the problem of grid voltage sags by improving two aspects of the control method of energy storage devices. On the one hand, it solves the problem of grid voltage sags. On the other hand, it improves the application economy of traditional user-side energy storage devices and improves the level of grid operation carbon emission control through demand response, providing flexible peak-shaving and voltage-regulating resources for large power grids. It realizes cross-switching of the action modes of regulating and managing grid sags and participating in user demand response within the operation cycle of the user-side energy storage device, ensuring the effectiveness of energy storage in various applications. At the same time, it improves the application technology economy and benefits of energy storage in the grid, contributing to the promotion and application of energy storage in the grid and the development of low-carbon businesses.

[0039] The following is a detailed description of the embodiments of the present application.

[0040] This embodiment proposes a method for controlling an energy storage device that takes into account the user's low-carbon emission factors. The process is as follows: Figure 1 The energy storage device includes an energy storage module, a first grid-connected module, and a second grid-connected module; one side of the first grid-connected module and the second grid-connected module are both connected to the energy storage module, and the other sides of the first grid-connected module and the second grid-connected module are respectively connected to the power grid and the sensitive load power supply owned by the corresponding user side;

[0041] The method comprises:

[0042] Step 1: Obtain real-time operation data of the power grid;

[0043] Among them, real-time operation data can include ultra-short-term load forecast data and supply voltage data for sensitive loads;

[0044] Step 2: Based on the data obtained in step 1, combined with a state analysis method, determine the grid operation state (the state of the grid); wherein the grid operation state includes a first state and a second state, the first state is a demand response state, and the second state is a voltage support demand state;

[0045] Step 3: Determine an operation strategy for the energy storage device based on the grid operation state judgment result obtained in step 2; wherein the operation strategy includes a first strategy and a second strategy, the first strategy being a demand response strategy and the second strategy being a voltage support strategy; when the grid is determined to be in the second state, the second strategy is adopted, and when the grid is determined to be in the first state, the first strategy is adopted;

[0046] Step 4: Based on the operation strategy determined in step 3, the grid-connected module is controlled to take corresponding actions. If the second strategy is adopted, the second grid-connected module directly supplies power to the sensitive load to provide voltage support. If the first strategy is adopted, the first grid-connected module responds to the demand for low-carbon emissions and load regulation. This completes the active and reactive output control process of the energy storage module.

[0047] It should be understood that in the above steps, if no voltage sag occurs or the voltage sag disappears (i.e. the grid supply voltage returns to normal), the grid connection module does not operate or exits the operation, and the sensitive load is still powered by the grid line;

[0048] In this way, the energy storage device can be used to respond to grid demand or provide voltage support, thereby improving grid operation stability.

[0049] The energy storage device of this application utilizes dual grid-connected modules to implement its targeted functions at different grid-connected points. The first grid-connected module is identical to the grid-connected device in the power conversion system (PCS) of conventional user-side energy storage devices, connecting directly to the grid at the transformer end. The second grid-connected module is deployed at the sensitive load supply location on the user side. The dual grid-connected modules and grid-connected points provide the hardware foundation for the device's multifunctional applications in voltage sag control, low-carbon emissions, and demand response for load regulation. The detailed structure is shown in the structural diagram.

[0050] The method of the present application obtains the grid operation data in real time, detects and judges the state of the grid, and provides a basis for judging the corresponding strategy. First, according to the function of the method and device, the grid operation state is divided into two states, namely the first state and the second state. Among them, the second state is regarded as the voltage sag demand state after the corresponding data collection and indicator judgment meet the conditions, that is, the device at this time needs to operate according to the strategy of voltage sag control priority. The first state is the demand response state, which is a routine action performed when the grid state does not need voltage support based on data collection and judgment, and participates in demand response. Therefore, according to the frequency and urgency of voltage sag, the second state is a state with a lower trigger frequency than the first state, but a higher priority. According to the definition of voltage sag, the root mean square value U(t) of the supply voltage of sensitive loads under power frequency conditions is reduced to 0.1-0.9 times the rated voltage U N If the voltage drops between 0 and 1, it is regarded as a voltage sag.

[0051] The step 2 is to judge the operation status of the power grid based on the data obtained in step 1 and the state analysis method, including: recording the voltage drop to 0.1U N ~0.9U N If △T>T, where T is the duration threshold, it is determined that a voltage sag occurs in the power grid at this time, and the power grid operation state is the second state.

[0052] In some embodiments, T may be set to 10 ms.

[0053] The specific judgment is as follows:

[0054] 1) When the RMS value of the supply voltage U(t) of the sensitive load is detected to drop to 0.1U for the first time N ~0.9U N When , record the time at this moment as t1 and the voltage as U(t1);

[0055] 2) At the next moment t2=t1+1, if the voltage detection value U(t2)∈[0.1U N ~0.9U N ], then record t2, and record duration △T=t2-t1;

[0056] 3) Similarly, continue recording t3, △T, t4, △T, ..., tn, △T until △T = tn - t1, △T > T, then it is determined that the grid voltage sag occurs at this time and the grid enters the second state;

[0057] 4) If U(t2)>0.9U N , then all recorded moments are cleared and the power grid is in the first state.

[0058] It should be pointed out that according to the above-mentioned conditions and methods for determining the state, it can be seen that the device is in the first state in most cases to perform the demand response application function.

[0059] Furthermore, in step 4, the first grid-connected module performs a demand response for low-carbon emission and load regulation on the load, including:

[0060] Taking into account the user's low-carbon emission factors and load regulation requirements, combined with the state of the energy storage module itself, the demand response optimization objective function is determined, and the demand response optimization objective function is solved to obtain the optimized demand response curve of the energy storage module's charging and discharging power; according to the optimized demand response curve, the first grid-connected module is controlled to take corresponding actions.

[0061] The optimized demand response curve is used as an execution plan to control the first grid-connected module to take corresponding actions, thereby completing the active and reactive power output control process of the energy storage module.

[0062] The demand response for low-carbon emissions and load regulation includes: considering the low-carbon emission requirements of the load corresponding to the configured energy storage device and the differences in carbon emission coefficients of power supply in different periods of the power grid, and optimizing its demand response curve using a dynamic programming method.

[0063] Dynamic programming is used to optimize the demand response curve, taking into account the low-carbon emission requirements of the corresponding loads of the configured energy storage devices to form differentiated and targeted optimization targets. Optimization targets vary depending on the low-carbon emission requirements. For corporate users with carbon emission reduction or low-carbon product manufacturing needs, or users who can obtain tangible economic benefits through carbon emission reduction, they can be classified as Class A loads (with low-carbon needs), and their optimization target is low-carbon emissions. In addition to Class A loads, users with no special requirements for reducing carbon emissions are uniformly classified as Class B loads, and their demand response optimization target is peak shaving and valley filling to maintain a stable load curve.

[0064] Load classification is carried out based on carbon emission requirements, and based on load classification and combined with low-carbon emission targets, corresponding control and configuration of energy storage devices are carried out to meet the needs of different users.

[0065] The demand response optimization objective function of Class A load is as follows:

[0066]

[0067]

[0068]

[0069]

[0070] in, Optimize the objective function for demand response, To achieve the goal of reducing peaks and filling valleys, To achieve low carbon emission goals; and are the weights of the peak shaving and valley filling targets and the low-carbon emission targets respectively; For Class A load The real-time load value at the moment can be taken as Ultra-short-term load forecast data at the moment ; For energy storage device The charge and discharge power values at the moment, where discharge is negative and charge is positive. After the energy storage device is connected, the average value of the integrated power of the energy storage device and the load to the grid; For the The power supply value of the power grid to the energy storage device and load at any moment; For the power grid The carbon emission coefficient of power supply at all times.

[0071]

[0072] in, Indicates the Moment The load conditions of the power supply; Refers to the type of power source, such as thermal power, hydropower, nuclear power, wind power, photovoltaic power, etc., and the value is an integer ( =1,2,3,…, ),For example, =1, Indicates the The load situation of thermal power source at all times, =2, Indicates the The load situation of hydropower at all times, and so on. is the type of power source in actual operation of the power grid, The value of can be determined according to the type of power supply in actual operation of the power grid. For the The carbon emission coefficients of different types of power sources vary. It should be noted that the average parameters calculated or equivalently converted using existing conventional technologies are primarily used here, without considering differences in energy efficiency or operating losses between units of the same type of power source, thereby limiting the feasibility and versatility of this method.

[0073] The demand response optimization objective function for Class B load is as follows:

[0074]

[0075] For different types of loads (Class A / Class B loads), the carbon emission coefficient of power supply at different times of the grid , combined with the real-time load curve (ultra-short-term load forecast curve) P FC Taking the charge and discharge power and charge and discharge capacity of the energy storage module as constraints, the dynamic programming method is used for optimization planning to obtain the corresponding optimized demand response curve.

[0076] Charge and discharge power limit:

[0077] Charge and discharge capacity:

[0078] =

[0079] in, is the lower limit of the discharge power of the energy storage module, The upper limit of the charging power of the energy storage module, For the The remaining power value of the energy storage module at any moment, is the lower limit of the discharge capacity of the energy storage module, The upper limit of the charging capacity of the energy storage module, is the rated capacity of the energy storage module.

[0080] Further, = , = .

[0081] In the optimized demand response curve obtained according to the above method, the control output instruction of the energy storage module controls the energy storage module and the grid-connected module to complete the output process of the energy storage module.

[0082] This method analyzes the differences in low-carbon emission demands on the user side by studying improvements in the structure and control methods of energy storage devices. It achieves the reuse and switching of the action mode of user-side energy storage devices to address voltage sag problems and participate in user demand response for low-carbon emissions and load regulation during the operation cycle, providing flexible and elastic regulation resources for the large power grid, improving the safe and stable operation capabilities of the power grid, and at the same time, fully coordinating the mode of energy storage participating in power grid applications, further exploring its application value, and providing technical support for the promotion and application of energy storage modules.

[0083] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be included in the scope of the claims of the present invention.

Claims

1. A method for controlling an energy storage device taking into account user low-carbon emission factors, characterized in that: The energy storage device includes an energy storage module, a first grid-connected module, and a second grid-connected module; one side of the first grid-connected module and the second grid-connected module are both connected to the energy storage module, and the other sides of the first grid-connected module and the second grid-connected module are respectively connected to the power grid and the sensitive load power supply owned by the corresponding user side; The method comprises: Step 1: Obtain real-time operation data of the power grid; Step 2: Determine the grid operation state based on the data obtained in step 1 in combination with a state analysis method; wherein the grid operation state includes a first state and a second state, the first state being a demand response state and the second state being a voltage support demand state; Step 3: Determine an operation strategy for the energy storage device based on the grid operation state judgment result obtained in step 2; wherein the operation strategy includes a first strategy and a second strategy, the first strategy being a demand response strategy and the second strategy being a voltage support strategy; when the grid is determined to be in the second state, the second strategy is adopted, and when the grid is determined to be in the first state, the first strategy is adopted; Step 4: Based on the operation strategy determined in step 3, the grid-connected module is controlled to take corresponding actions. If the second strategy is adopted, the sensitive load is directly powered by the second grid-connected module to provide voltage support. If the first strategy is adopted, the load is responded to by the first grid-connected module to meet the demand for low-carbon emissions and load regulation. This completes the active and reactive output control process of the energy storage module.

2. The method according to claim 1, characterized in that In step 4, the first grid-connected module performs a demand response for low-carbon emissions and load regulation on the load, including: considering the user's low-carbon emission factors and load regulation requirements, combining the state of the energy storage module itself, determining the demand response optimization objective function, solving the demand response optimization objective function, and obtaining an optimized demand response curve for the charging and discharging power of the energy storage module; and controlling the first grid-connected module to take corresponding actions according to the optimized demand response curve.

3. The method according to claim 2, characterized in that The demand response optimization objective function is determined by considering the user's low-carbon emission factors and load regulation requirements and combining the energy storage module's own state, including: Load classification based on carbon emission demand: corporate users with carbon emission reduction or low-carbon product manufacturing needs, or users who can obtain tangible economic benefits through carbon emission reduction, are classified as Class A loads; users other than Class A loads are classified as Class B loads; For loads of different categories, different demand response optimization objective functions are determined; the demand response optimization objective function for Class A loads includes a low-carbon emission target, while the demand response optimization objective function for Class B loads does not include a low-carbon emission target.

4. The method according to claim 3, characterized in that The demand response optimization objective function of Class A load is: ； ； ； ； in, Optimize the objective function for demand response, To achieve the goal of reducing peaks and filling valleys, To achieve low carbon emission goals; and are the weights of the peak shaving and valley filling targets and the low-carbon emission targets respectively; For Class A load Real-time load value at the moment, For energy storage device The charge and discharge power values at the moment, where discharge is negative and charge is positive; After the energy storage device is connected, the average value of the integrated power of the energy storage device and the load to the grid; For the The power supply value of the power grid to the energy storage device and load at any moment; For the power grid The carbon emission coefficient of power supply at all times.

5. The method according to claim 4, characterized in that described The calculation formula is: ； in, Indicates the Moment The load conditions of the power supply; The type of power source in actual operation of the power grid; For the Carbon emission coefficient of this type of power supply.

6. The method according to claim 4, characterized in that The demand response optimization objective function of class B load is: 。 7. The method according to any one of claims 1 to 6, characterized in that The constraints for solving the demand response optimization objective function are: Charge and discharge power limit: ; Charge and discharge capacity: ; ； in, is the lower limit of the discharge power of the energy storage module, The upper limit of the charging power of the energy storage module, For the The remaining power value of the energy storage module at any moment, is the lower limit of the discharge capacity of the energy storage module, The upper limit of the charging capacity of the energy storage module, is the rated capacity of the energy storage module.

8. The method according to claim 7, characterized in that = ， = 。

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