Method and device for balancing output of multiple energy storage power stations participating in bidirectional peak regulation of power grid

Abstract

The invention provides an output balancing method and device for multiple energy storage power stations participating in two-way peak regulation of a power grid, and the method comprises the steps: obtaining the load of a power system, power output and the charge states of the energy storage power stations, calculating the peak-valley difference deviation of the load and the peak regulation capacity error of the power grid, and regulating and controlling the discharge of the energy storage power stations in combination with the SOC of the energy storage power stations and a peak regulation capacity state machine of the power grid. And power grid peak regulation and energy storage balance are realized. According to the method, under the constraints of real-time load, peak regulation deviation and energy storage, the peak regulation capacity of the power grid is improved, and stable and safe operation of energy storage balance and new energy and energy storage combined power generation is ensured.

Description

Technical Field

[0001] The present application belongs to the field of power grid peak regulation, and in particular, relates to a method and device for balancing the output of multiple energy storage power stations participating in bidirectional peak regulation of a power grid. Background Art

[0002] In the current power system, as the peak-to-valley difference of load continues to widen and a high proportion of new energy is connected, the problem of insufficient peak-shaving capacity of the power grid is becoming increasingly prominent. Traditional peak-shaving methods mainly rely on thermal power units, but this method has many restrictive factors such as safety and economy, and it is difficult to meet the growing peak-shaving needs. Especially in the context of the rapid development of new energy, the intermittent and volatile characteristics of new energy power generation make the peak-shaving task of the power grid more arduous.

[0003] As an important technology and key basic equipment to support the construction of new power systems, energy storage technology has significant advantages in alleviating the peak load pressure of power grids. Energy storage power stations can achieve the peak load regulation function of the power grid by charging during low load periods and discharging during peak load periods. However, the existing methods of multiple energy storage power stations participating in power grid peak load regulation mostly rely on the energy management strategy of each energy storage power station itself. This method seems to be unable to cope with the complex and changing power grid environment.

[0004] Specifically, the existing coordinated power control and energy management methods of multiple energy storage power stations rarely consider power and energy constraints such as differences in charge and discharge rates and operating time of multiple energy storage batteries. These factors not only limit the full play of the complementary advantages of multiple energy storage, but may also affect the service life of the battery and even interfere with the safe and stable operation of the power system. In addition, since the peak-shaving of the power grid affects a wide area and there are many influencing factors, many of which change in real time. It is difficult to improve the regional peak-shaving capacity simply from the perspective of individual energy storage power stations. Summary of the invention

[0005] The purpose of the present application is to overcome the above-mentioned prior art and provide a method and device for balancing the output of multiple energy storage power stations participating in bidirectional peak regulation of the power grid.

[0006] The present application provides a method for balancing the output of a multi-energy storage power station participating in bidirectional peak regulation of a power grid, comprising:

[0007] Obtain the daily load curve of the power system, the daily output curve of the power source, and the charge status of the energy storage power station;

[0008] Calculate the real-time deviation of the load peak-to-valley difference according to the daily load curve and the daily output curve;

[0009] Calculating a real-time error in the peak-shaving capacity of the power grid according to the real-time deviation;

[0010] Determine the state of the SOC finite state machine of the energy storage power station according to the state of charge;

[0011] Determine the state of a power grid peak load finite state machine according to the real-time deviation and the real-time error;

[0012] The discharge action and discharge amount of the energy storage power station are determined according to the state of the SOC finite state machine of the energy storage power station and the state of the peak-shaving capacity finite state machine of the power grid, and the output balance of multiple energy storage power stations is performed, including: peak-shaving state 1: the energy storage power station does not output power to the outside, and charging or discharging operations are performed according to the state of charge; peak-shaving state 2: all energy storage power stations discharge to the outside, and the discharge power is adjusted according to the SOC state; wind power state 3: all energy storage power stations are charging, and the charging power is also adjusted according to the SOC state.

[0013] Optionally, the state of the energy storage power station SOC finite state machine includes:

[0014] SOC state 1: At time t, the state of charge of the i-th energy storage power station is less than the lower limit of the state of charge;

[0015] SOC state 2: At time t, the state of charge of the i-th energy storage power station is between the upper limit and the lower limit of the state of charge;

[0016] SOC state 3: At time t, the state of charge of the i-th energy storage power station is greater than the upper limit of the state of charge.

[0017] Optionally, the state of the power grid peak load finite state machine includes:

[0018] Peak load state 1: the real-time error is less than a preset threshold, and the energy storage power station performs internal charge balancing;

[0019] Peak load state 2: the real-time error is greater than a preset threshold and the real-time deviation is less than 0, and the energy storage power station discharges to compensate for the load;

[0020] Peak shaving state 3: the real-time error is greater than a preset threshold and the real-time deviation is greater than 0, and the energy storage power station is charged to store excess electricity.

[0021] Optionally, the expression of the real-time deviation is as follows:

[0022] Δ p (t) = p source (t)-p load (t)

[0023] Among them, Δ p (t) is the real-time deviation of peak load capacity power at time t, p load (t) is the load power at time t, p source (t) is the power supply power at time t.

[0024] Optionally, the expression of the real-time error is as follows:

[0025]

[0026] Among them, δ p (t) is the real-time error of the peak load capacity of the power grid at time t, p N is the system capacity.

[0027] The present application also provides a multi-energy storage power station output balancing device participating in bidirectional peak regulation of a power grid, comprising:

[0028] Acquisition module, to obtain the daily load curve of the power system, the daily output curve of the power source, and the charge state of the energy storage power station;

[0029] A deviation module, which calculates the real-time deviation of the load peak-to-valley difference according to the daily load curve and the daily output power curve;

[0030] An error module, which calculates a real-time error of the peak-shaving capacity of the power grid according to the real-time deviation;

[0031] A state machine, which determines the state of the energy storage power station SOC finite state machine according to the state of charge; and determines the state of the power grid peak load capacity finite state machine according to the real-time deviation and the real-time error;

[0032] The processing module determines the discharge action and discharge amount of the energy storage power station according to the state of the SOC finite state machine of the energy storage power station and the state of the peak load finite state machine of the power grid, and performs output balancing of multiple energy storage power stations.

[0033] Optionally, the state of the energy storage power station SOC finite state machine includes:

[0034] SOC state 1: At time t, the state of charge of the i-th energy storage power station is less than the lower limit of the state of charge;

[0035] SOC state 2: At time t, the state of charge of the i-th energy storage power station is between the upper limit and the lower limit of the state of charge;

[0036] SOC state 3: At time t, the state of charge of the i-th energy storage power station is greater than the upper limit of the state of charge.

[0037] Optionally, the state of the power grid peak load finite state machine includes:

[0038] Peak load state 1: the real-time error is less than a preset threshold, and the energy storage power station performs internal charge balancing;

[0039] Peak load state 2: the real-time error is greater than a preset threshold and the real-time deviation is less than 0, and the energy storage power station discharges to compensate for the load;

[0040] Peak shaving state 3: the real-time error is greater than a preset threshold and the real-time deviation is greater than 0, and the energy storage power station is charged to store excess electricity.

[0041] Optionally, the expression of the real-time deviation is as follows:

[0042] Δ p (t) = p source (t)-p load (t)

[0043] Among them, Δ p (t) is the real-time deviation of peak load capacity power at time t, p load (t) is the load power at time t, p source (t) is the power supply power at time t.

[0044] Optionally, the expression of the real-time error is as follows:

[0045]

[0046] Among them, δ p (t) is the real-time error of the peak load capacity of the power grid at time t, p N is the system capacity.

[0047] The beneficial effects of this application are:

[0048] The present application provides a method for balancing the output of multiple energy storage power stations participating in bidirectional peak regulation of the power grid, including: obtaining the daily load curve of the power system, the daily output curve of the power source, and the state of charge of the energy storage power station; calculating the real-time deviation of the load peak-to-valley difference according to the daily load curve and the daily output curve; calculating the real-time error of the peak regulation capacity of the power grid according to the real-time deviation; determining the state of the SOC finite state machine of the energy storage power station according to the state of charge; determining the state of the peak regulation capacity finite state machine of the power grid according to the real-time deviation and the real-time error; determining the discharge action and discharge amount of the energy storage power station according to the state of the SOC finite state machine of the energy storage power station and the state of the peak regulation capacity finite state machine of the power grid, and balancing the output of multiple energy storage power stations. Under the premise of considering the real-time load curve, the positive and negative peak regulation deviations, and the state of charge constraints of multiple energy storage power stations, the present application realizes the bidirectional peak regulation of the power grid, while improving the balance of the state of charge of many distributed energy storage power stations, and ensuring the stable, reliable and safe operation of the new energy + energy storage combined power generation. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 A schematic diagram of the output balancing process of multiple energy storage power stations participating in the bidirectional peak regulation of the power grid in this application;

[0050] Figure 2 This is a schematic diagram of multiple energy storage power stations jointly participating in power grid peak regulation in this application;

[0051] Figure 3 This is a schematic diagram of the control process of the finite state machine control module in this application;

[0052] Figure 4 It is a schematic diagram of an output balancing device of a multi-energy storage power station participating in bidirectional peak regulation of a power grid in this application. DETAILED DESCRIPTION

[0053] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0054] Please refer to Figure 1 As shown, the present application provides a method for balancing the output of multiple energy storage power stations participating in bidirectional peak regulation of a power grid, the steps of which include:

[0055] S101. Obtain a daily load curve of the power system, a daily output curve of the power source, and a charge state of the energy storage power station.

[0056] In this application, the load data at each time point of the day is obtained through the power system monitoring system to form a daily load curve. The daily output data of various power sources (including traditional power sources and new energy power sources) in the power system are collected and sorted to form a daily output curve of the power source.

[0057] like Figure 3 As shown in the figure, the multi-storage power station is a system that integrates independent and dispersed energy storage resources on the power supply side, grid side, and user side, and coordinates and dispatches them uniformly through the grid. The daily output curve of the power cluster is one of the important input data for calculating the peak-shaving demand of the grid. The overall output of the power cluster directly affects the peak-to-valley difference of the load of the grid and the demand for peak-shaving capacity.

[0058] The entire system consists of multiple energy storage power stations, power grids and various power supply clusters.

[0059] Real-time monitoring of the state of charge (SOC) of each distributed energy storage power station, including parameters such as the current power of each energy storage power station, the upper and lower limits of the state of charge, etc.

[0060] S102: Calculate the real-time deviation of the load peak-to-valley difference according to the daily load curve and the daily output power curve.

[0061] The calculation process of real-time deviation of power grid peak load capacity is as follows:

[0062] The difference between the power supply at time t and the load power at time t, that is:

[0063] Δ p (t) = psource (t)-p load (t)

[0064] Among them, Δ p (t) is the real-time deviation of peak load capacity power at time t, p load (t) is the load power at time t, p source (t) is the power supply power at time t.

[0065] S103: Calculate the real-time error of the peak-shaving capacity of the power grid according to the real-time deviation.

[0066] The real-time error calculation process of the power grid peak load capacity is as follows:

[0067] The ratio of the real-time deviation of the power grid peak load regulation capacity to the system capacity at time t is:

[0068]

[0069] Among them, δ p (t) is the real-time error of the peak load capacity of the power grid at time t, p N is the system capacity.

[0070] S104: Determine the state of the SOC finite state machine of the energy storage power station according to the state of charge.

[0071] The SOC finite state machine of each energy storage power station is set to the following three states:

[0072] SOC state 1: At time t, the state of charge of the i-th energy storage station is less than the lower limit of the state of charge, that is, soc i (t)<soc min .

[0073] SOC state 2: At time t, the state of charge of the i-th energy storage station is between the upper and lower limits of the state of charge, that is, SOC min <soc i (t)<soc max .

[0074] SOC state 3: At time t, the state of charge of the i-th energy storage station is greater than the upper limit of the state of charge, that is, soc i (t)>soc max .

[0075] Among them, soc i (t) is the state of charge of the i-th energy storage station at time t, soc max is the upper limit of the state of charge of the energy storage station, soc min It is the lower limit of the charge state of the energy storage station.

[0076] S105. Determine the state of a power grid peak-shaving capacity finite state machine according to the real-time deviation and the real-time error.

[0077] like Figure 2 As shown, the power grid peak load finite state machine is set to the following three states.

[0078] Peak load state 1: At time t, the real-time error of the peak load capacity of the power grid is less than the preset threshold, i.e., δ p (t)<ε.

[0079] Peak load state 2: At time t, the real-time error of the peak load capacity of the power grid is greater than the preset threshold, and the real-time deviation of the peak load capacity of the power grid is less than 0, that is, δ p (t)>εandΔ p (t)<0.

[0080] Peak-shaving state 3: At time t, the real-time error of the peak-shaving capacity of the power grid is greater than the preset threshold, and the real-time deviation of the peak-shaving capacity of the power grid is greater than 0, that is, δ p (t)>εandΔ p (t)>0.

[0081] S106. Determine the discharge action and discharge amount of the energy storage power station according to the state of the SOC finite state machine of the energy storage power station and the state of the peak load finite state machine of the power grid, and perform output balancing of multiple energy storage power stations.

[0082] When the grid peak-shaving capacity finite state machine is in "peak-shaving state 1", each energy storage power station does not output power to the outside, and the multi-energy storage power station balances the charge state of each energy storage power station. The steps are as follows:

[0083] When the charge state of the i-th energy storage station at time t is in "SOC state 1", the i-th energy storage station performs charging action, and the charging power is

[0084] When the state of charge of the i-th energy storage power station at time t is in "SOC state 2", the i-th energy storage power station does not operate.

[0085] When the charge state of the i-th energy storage station is in "SOC state 3" at time t, the i-th energy storage station performs a discharge action, and the discharge power is

[0086] in, is the average state of charge of the energy storage power station in "SOC state 1" and "SOC state 3"; is the rated capacity of the i-th energy storage power station.

[0087] The calculation process is as follows:

[0088] Among them, J is the total number of energy storage power stations in "SOC state 1" and "SOC state 3" under "peak shaving state 1".

[0089] When the grid peak-shaving capacity finite state machine is in "peak-shaving state 2", each energy storage power station discharges externally, and the multi-energy storage power station balances the charge state of each energy storage power station. The steps are as follows:

[0090] When the state of charge of the i-th energy storage station at time t is in "SOC state 1", the i-th energy storage station does not operate.

[0091] When the charge state of the i-th energy storage station is in "SOC state 2" at time t, the i-th energy storage station performs a discharge action, and the discharge power is

[0092] When the charge state of the i-th energy storage station is in "SOC state 3" at time t, the i-th energy storage station performs a discharge action, and the discharge power is

[0093] in, is the maximum discharge power of the energy storage power station; α i (t) is the discharge weight of the i-th energy storage power station at time t.

[0094] α i (t) The calculation process is as follows:

[0095] Among them, N is the total number of energy storage power stations in "SOC state 2" under "peak shaving state 2".

[0096] When the grid peak load capacity finite state machine is in "wind power state 3", each energy storage power station is charged, and the charge state of each energy storage power station is balanced inside the multi-energy storage power station. The steps are as follows:

[0097] When the charge state of the i-th energy storage station is in "SOC state 1" at time t, the i-th energy storage station performs charging action, and the charging power is

[0098] When the charge state of the i-th energy storage station is in "SOC state 2" at time t, the i-th energy storage station performs charging action, and the charging power is

[0099] When the state of charge of the i-th energy storage power station at time t is in "SOC state 3", the i-th energy storage power station does not operate.

[0100] in, is the maximum charging power of the energy storage station; β i (t) is the charging weight of the i-th energy storage station at time t.

[0101] β i (t) The calculation process is as follows:

[0102] Among them, M is the total number of energy storage power stations in "SOC state 2" under "peak shaving state 3".

[0103] Please refer to Figure 4 As shown, the present application also provides a multi-energy storage power station output balancing device participating in bidirectional peak regulation of a power grid, comprising:

[0104] Acquisition module 201, acquires the daily load curve of the power system, the daily output curve of the power source, and the charge state of the energy storage power station;

[0105] Deviation module 202, for calculating the real-time deviation of the load peak-to-valley difference according to the daily load curve and the daily output power curve;

[0106] An error module 203 calculates a real-time error of the peak-shaving capacity of the power grid according to the real-time deviation;

[0107] The state machine 204 determines the state of the SOC finite state machine of the energy storage power station according to the state of charge; determines the state of the peak load capacity finite state machine of the power grid according to the real-time deviation and the real-time error;

[0108] The processing module 205 determines the discharge action and discharge amount of the energy storage power station according to the state of the SOC finite state machine of the energy storage power station and the state of the peak load finite state machine of the power grid, and performs output balancing of multiple energy storage power stations.

[0109] Furthermore, the state of the SOC finite state machine of the energy storage power station includes:

[0110] SOC state 1: At time t, the state of charge of the i-th energy storage power station is less than the lower limit of the state of charge;

[0111] SOC state 2: At time t, the state of charge of the i-th energy storage power station is between the upper limit and the lower limit of the state of charge;

[0112] SOC state 3: At time t, the state of charge of the i-th energy storage power station is greater than the upper limit of the state of charge.

[0113] Furthermore, the state of the power grid peak load capacity finite state machine includes:

[0114] Peak load state 1: the real-time error is less than a preset threshold, and the energy storage power station performs internal charge balancing;

[0115] Peak load state 2: the real-time error is greater than a preset threshold and the real-time deviation is less than 0, and the energy storage power station discharges to compensate for the load;

[0116] Peak shaving state 3: the real-time error is greater than a preset threshold and the real-time deviation is greater than 0, and the energy storage power station is charged to store excess electricity.

[0117] Furthermore, the expression of the real-time deviation is as follows:

[0118] Δ p (t) = p source (t)-p load (t)

[0119] Among them, Δ p (t) is the real-time deviation of peak load capacity power at time t, p load (t) is the load power at time t, p source (t) is the power supply power at time t.

[0120] Furthermore, the expression of the real-time error is as follows:

[0121]

[0122] Among them, δ p (t) is the real-time error of the peak load capacity of the power grid at time t, p N is the system capacity.

[0123] This application integrates the scattered energy storage resources on the power supply side, the grid side, and the user side, and transfers them to the grid for unified coordination and dispatch, which significantly improves the peak-shaving capacity of the regional power grid. Compared with the traditional independent peak-shaving of a single energy storage power station, the joint peak-shaving of multiple energy storage power stations can better cope with large-scale load changes and reduce the peak-shaving valley difference.

[0124] This application constructs a finite state machine control module, which achieves real-time compensation of peak-to-valley differences while maintaining charge balance in the energy storage power station by real-time regulation of the output of each energy storage power station. This dynamic regulation mechanism ensures the healthy operation of the energy storage power station, extends the battery life, and improves the overall economic benefits of the energy storage system. By comprehensively considering the real-time load curve, positive and negative peak-shaving deviations, and the charge state constraints of multiple energy storage power stations, the method of the present invention can provide stable peak-shaving capabilities and ensure the safe and stable operation of the power grid when new energy power generation is unstable. At the same time, it also provides strong support for the large-scale access of new energy.

[0125] The technical solution of the present application can flexibly adjust the output state of the energy storage power station according to the actual needs of the power grid to cope with different load change scenarios. This flexibility not only improves the response speed of the power grid, but also enhances the power grid's ability to respond to emergencies. By uniformly coordinating and dispatching the resources of multiple energy storage power stations, the present invention avoids waste of resources and duplication of construction. At the same time, since the charge state of the energy storage power station is evenly managed, the performance degradation and maintenance costs caused by overcharging or over-discharging of the battery are reduced.

Claims

1. A method for balancing the output of multiple energy storage power stations participating in bidirectional peak regulation of a power grid, characterized in that: include: Obtain the daily load curve of the power system, the daily output curve of the power source, and the charge status of the energy storage power station; Calculate the real-time deviation of the load peak-to-valley difference according to the daily load curve and the daily output curve; Calculating a real-time error in the peak-shaving capacity of the power grid according to the real-time deviation; Determine the state of the SOC finite state machine of the energy storage power station according to the state of charge; Determine the state of a power grid peak load finite state machine according to the real-time deviation and the real-time error; The discharge action and discharge amount of the energy storage power station are determined according to the state of the SOC finite state machine of the energy storage power station and the state of the peak-shaving capacity finite state machine of the power grid, and the output balance of multiple energy storage power stations is performed, including: peak-shaving state 1: the energy storage power station does not output power to the outside, and charging or discharging operations are performed according to the state of charge; peak-shaving state 2: all energy storage power stations discharge to the outside, and the discharge power is adjusted according to the SOC state; wind power state 3: all energy storage power stations are charging, and the charging power is also adjusted according to the SOC state.

2. The method for balancing the output of multiple energy storage power stations participating in bidirectional peak regulation of a power grid according to claim 1, characterized in that: The state of the SOC finite state machine of the energy storage power station includes: SOC state 1: At time t, the state of charge of the i-th energy storage power station is less than the lower limit of the state of charge; SOC state 2: At time t, the state of charge of the i-th energy storage power station is between the upper limit and the lower limit of the state of charge; SOC state 3: At time t, the state of charge of the i-th energy storage power station is greater than the upper limit of the state of charge.

3. The method for balancing the output of multiple energy storage power stations participating in bidirectional peak regulation of a power grid according to claim 1, characterized in that: The states of the power grid peak load finite state machine include: Peak load state 1: the real-time error is less than a preset threshold, and the energy storage power station performs internal charge balancing; Peak load state 2: the real-time error is greater than a preset threshold and the real-time deviation is less than 0, and the energy storage power station discharges to compensate for the load; Peak shaving state 3: the real-time error is greater than a preset threshold and the real-time deviation is greater than 0, and the energy storage power station is charged to store excess electricity.

4. The method for balancing the output of multiple energy storage power stations participating in bidirectional peak regulation of a power grid according to claim 1, characterized in that: The expression of the real-time deviation is as follows: Δ p (t)=p source (t)-p load (t) Among them, Δ p (t) is the real-time deviation of peak load capacity power at time t, p load (t) is the load power at time t, p source (t) is the power supply power at time t.

5. The method for balancing the output of multiple energy storage power stations participating in bidirectional peak regulation of a power grid according to claim 4, characterized in that: The expression of the real-time error is as follows: Among them, δ p (t) is the real-time error of the peak load capacity of the power grid at time t, p N is the system capacity.

6. A multi-energy storage power station output balancing device participating in bidirectional peak regulation of a power grid, characterized in that: include: Acquisition module, to obtain the daily load curve of the power system, the daily output curve of the power source, and the charge state of the energy storage power station; A deviation module, which calculates the real-time deviation of the load peak-to-valley difference according to the daily load curve and the daily output power curve; An error module, which calculates a real-time error of the peak-shaving capacity of the power grid according to the real-time deviation; A state machine, which determines the state of the energy storage power station SOC finite state machine according to the state of charge; and determines the state of the power grid peak load capacity finite state machine according to the real-time deviation and the real-time error; The processing module determines the discharge action and discharge amount of the energy storage power station according to the state of the SOC finite state machine of the energy storage power station and the state of the peak load finite state machine of the power grid, and performs output balancing of multiple energy storage power stations.

7. The output balancing device of a multi-energy storage power station participating in bidirectional peak regulation of a power grid according to claim 6, characterized in that: The state of the SOC finite state machine of the energy storage power station includes: SOC state 1: At time t, the state of charge of the i-th energy storage power station is less than the lower limit of the state of charge; SOC state 2: At time t, the state of charge of the i-th energy storage power station is between the upper limit and the lower limit of the state of charge; SOC state 3: At time t, the state of charge of the i-th energy storage power station is greater than the upper limit of the state of charge.

8. The output balancing device of a multi-energy storage power station participating in bidirectional peak regulation of a power grid according to claim 6, characterized in that: The states of the power grid peak load finite state machine include: Peak load state 1: the real-time error is less than a preset threshold, and the energy storage power station performs internal charge balancing; Peak load state 2: the real-time error is greater than a preset threshold and the real-time deviation is less than 0, and the energy storage power station discharges to compensate for the load; Peak shaving state 3: the real-time error is greater than a preset threshold and the real-time deviation is greater than 0, and the energy storage power station is charged to store excess electricity.

9. The output balancing device of a multi-energy storage power station participating in bidirectional peak regulation of a power grid according to claim 6, characterized in that: The expression of the real-time deviation is as follows: Δ p (t)=p source (t)-p load (t) Among them, Δ p (t) is the real-time deviation of peak load capacity power at time t, p load (t) is the load power at time t, p source (t) is the power supply power at time t.

10. The output balancing device of a multi-energy storage power station participating in bidirectional peak regulation of a power grid according to claim 9, characterized in that: The expression of the real-time error is as follows: Among them, δ p (t) is the real-time error of the peak load capacity of the power grid at time t, p N is the system capacity.

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