A multi-energy storage power station output balancing method and device participating in bidirectional peak regulation of a power grid

Abstract

The application provides a multi-energy storage power station output balancing method and device participating in bidirectional peak regulation of a power grid. The method calculates a load peak-valley difference deviation and a power grid peak regulation capacity error by acquiring power system load, power source output and energy storage power station state of charge, combines the energy storage power station SOC and the power grid peak regulation capacity state machine, regulates and controls discharging of the energy storage power station, and realizes balancing of the power grid peak regulation and the energy storage. The method improves the power grid peak regulation capacity under real-time load, peak regulation deviation and energy storage constraints, and ensures stable and safe operation of new energy + energy storage combined power generation.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of power grid peak regulation, and particularly relates to a multi-energy storage power station output balancing method and device participating in bidirectional peak regulation of a power grid. BACKGROUND

[0002] In the current power system, with the continuous widening of load peak-valley difference and high proportion of new energy access, the problem of insufficient power grid peak regulation capacity is increasingly prominent. The traditional peak regulation method mainly relies on thermal power units, but this method has many restrictive factors such as safety and economy, and it is difficult to meet the growing demand for peak regulation. Especially under the background of rapid development of new energy, due to the intermittent and volatile characteristics of new energy generation, the task of power grid peak regulation is more difficult.

[0003] Energy storage technology, as an important technology and key basic equipment supporting the construction of new power systems, has significant advantages in relieving the pressure of power grid peak regulation. Energy storage power stations can charge during load valleys and discharge during load peaks, thereby achieving the function of peak regulation of the power grid. However, the existing methods of multi-energy storage power stations participating in power grid peak regulation mostly rely on the energy management strategies of individual energy storage power stations. This approach is not sufficient when faced with complex and changing power grid environments.

[0004] Specifically, the existing coordinated power control and energy management methods of multi-energy storage power stations rarely consider power and energy constraints such as multi-element energy storage battery charge-discharge rate differences and operation time differences. These factors not only limit the full play of the complementary advantages of multi-element energy storage, but also may affect the service life of the battery and even interfere with the safe and stable operation of the power system. In addition, due to the wide influence area and numerous influencing factors of power grid peak regulation, many influencing factors change in real time, and it is difficult to further improve the regional peak regulation capacity from the perspective of individual energy storage power stations. SUMMARY

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

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

[0007] obtaining a daily load curve of a power system, a daily output curve of a power source, and a state of charge of an energy storage power station;

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

[0009] calculating a real-time error of a power grid peak regulation capacity according to the real-time deviation;

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

[0011] determining a state of a grid peak shaving capacity finite state machine according to the real-time deviation and the real-time error;

[0012] determining a discharging action and a discharging amount of the energy storage power station according to the state of the energy storage power station SOC finite state machine and the state of the grid peak shaving capacity finite state machine, and performing multi-energy storage power station output balancing, including: peak shaving state 1: the energy storage power station does not output power to the outside, and performs charging or discharging operation according to the state of charge; peak shaving state 2: all energy storage power stations discharge to the outside, and adjust the discharging power according to the SOC state; and wind power state 3: all energy storage power stations perform charging operation, and adjust the charging power 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 t moment, the state of charge of the i th energy storage power station is less than the lower limit value of the state of charge;

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

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

[0017] Optionally, the state of the grid peak shaving capacity finite state machine includes:

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

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

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

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

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

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

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

[0025]

[0026] wherein δ p (t) is the real-time error of the grid peak shaving capacity at time t, p N is the system capacity.

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

[0028] an acquisition module, configured to acquire a daily load curve of a power system, a daily output curve of a power source, and a state of charge of an energy storage power station;

[0029] a deviation module, configured to calculate a real-time deviation of a load peak-valley difference according to the daily load curve and the daily output curve;

[0030] an error module, configured to calculate a real-time error of a grid peak shaving capacity according to the real-time deviation;

[0031] a state machine, configured to determine a state of an energy storage power station SOC finite state machine according to the state of charge, and determine a state of a grid peak shaving capacity finite state machine according to the real-time deviation and the real-time error;

[0032] a processing module, configured to determine a discharging action and a discharging amount of the energy storage power station according to the state of the energy storage power station SOC finite state machine and the state of the grid peak shaving capacity finite state machine, and perform multi-energy storage power station output balancing.

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

[0034] SOC state 1: at time t, the state of charge of the i-th energy storage power station is less than a lower limit value 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 an upper limit value and the lower limit value 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 value of the state of charge.

[0037] Optionally, the state of the grid peak shaving capacity finite state machine comprises:

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

[0039] peak shaving state 2: the real-time error is greater than the preset threshold value and the real-time deviation is less than 0, and the energy storage power station discharges externally 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 power.

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

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

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

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

[0045]

[0046] Wherein, δ p (t) is the real-time error of the grid peak shaving capacity at t, p N is the system capacity.

[0047] The beneficial effects of the present application are:

[0048] The present application provides a multi-energy storage power station output balancing method for participating in grid bidirectional peak shaving, comprising: 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-valley difference according to the daily load curve and the daily output curve; calculating the real-time error of the grid peak shaving capacity according to the real-time deviation; determining the state of the energy storage power station SOC finite state machine according to the state of charge; determining the state of the grid peak shaving capacity finite state machine according to the real-time deviation and the real-time error; determining the discharging action and the discharging amount of the energy storage power station according to the state of the energy storage power station SOC finite state machine and the state of the grid peak shaving capacity finite state machine, and balancing the output of the multi-energy storage power station. The present application realizes the bidirectional peak shaving of the grid under the premise of considering the real-time load curve, the forward and reverse peak shaving deviation, and the state of charge constraint of the multi-energy storage power station, improves the balance of the state of charge of the numerous distributed energy storage power stations, and ensures the stable, reliable and safe operation of the new energy + energy storage combined power generation. BRIEF DESCRIPTION OF DRAWINGS

[0049] Figure 1 It is a multi-energy storage power station output balancing process schematic diagram participating in grid bidirectional peak shaving in the present application;

[0050] Figure 2 It is a multi-energy storage power station joint participation in grid peak shaving schematic diagram in the present 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 This is a schematic diagram of the multi-energy storage power station output balancing device participating in bidirectional peak shaving of the power grid in this application. Detailed Implementation

[0053] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

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

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

[0056] In this application, load data at various points in time throughout the day is acquired through a power system monitoring system to form a daily load curve. Daily power generation data for various power sources (including traditional and renewable energy sources) within the power system are collected and organized to form daily power generation curves.

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

[0058] The entire system consists of multiple energy storage power stations, a power grid, 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 capacity, upper and lower limits of the SOC of each energy storage power station.

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

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

[0062] The difference between the power supply at time t and the load power at time t, i.e.:

[0063] Δ p (t)=psource (t)-p load (t)

[0064] wherein, Δ p (t) is the real-time deviation of the peak regulation capacity 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, calculating the real-time error of the grid peak regulation capacity according to the real-time deviation.

[0066] The process of calculating the real-time error of the grid peak regulation capacity is as follows:

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

[0068]

[0069] wherein, δ p (t) is the real-time error of the grid peak regulation capacity at time t, p N is the system capacity.

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

[0071] Each energy storage power station SOC finite state machine is set as follows:

[0072] SOC state 1: at time t, the state of charge of the i-th energy storage power station is less than the lower limit value 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 power station is between the upper limit value and the lower limit value 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 power station is greater than the upper limit value of the state of charge, that is, soc i (t) > soc max .

[0075] wherein, soc i (t) is the state of charge of the i-th energy storage power station at time t, soc max is the upper limit value of the state of charge of the energy storage power station, and soc min is the lower limit value of the state of charge of the energy storage power station.

[0076] S105, determining a state of the grid peak shaving capacity limited state machine according to the real-time deviation and the real-time error.

[0077] As shown in the figure, the grid peak shaving capacity limited state machine is set as follows three states. Figure 2

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

[0079] Peak state 2: at time t, the real-time error of the grid peak shaving capacity is greater than the preset threshold, and the real-time deviation of the grid peak shaving capacity is less than 0, i.e. δ p (t) > ε and Δ p (t) < 0.

[0080] Peak state 3: at time t, the real-time error of the grid peak shaving capacity is greater than the preset threshold, and the real-time deviation of the grid peak shaving capacity is greater than 0, i.e. δ p (t) > ε and Δ p (t) > 0.

[0081] S106, determining the discharging action and the discharging capacity of the energy storage power station according to the state of the energy storage power station SOC limited state machine and the state of the grid peak shaving capacity limited state machine, and performing multi-energy storage power station output balancing.

[0082] When the grid peak shaving capacity limited state machine is in "peak state 1", each energy storage power station does not output power, and the state of charge of each energy storage power station is balanced within the multi-energy storage power station, and the steps are as follows:

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

[0085] 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 performs a discharging action, and the discharging capacity is

[0086] wherein, 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] wherein J is the total number of energy storage power stations in "SOC state 1" and "SOC state 3" under "peak regulation state 1".

[0089] When the grid peak regulation capacity limited state machine is in "peak regulation state 2", each energy storage power station discharges, and multiple energy storage power stations balance the state of charge of each energy storage power station, the steps are as follows:

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

[0091] 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 performs a discharging action, and the discharging power is

[0092] 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 performs a discharging action, and the discharging power is

[0093] wherein, is the maximum discharging power of the energy storage power station; and i (t) is the discharging weight of the i-th energy storage power station at time t.

[0094] i (t) is calculated as follows:

[0095] wherein N is the total number of energy storage power stations in "SOC state 2" under "peak regulation state 2".

[0096] When the grid peak regulation capacity limited state machine is in "wind power state 3", each energy storage power station charges, and multiple energy storage power stations balance the state of charge of each energy storage power station, the steps are as follows:

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

[0098] 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 performs a 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 act.

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

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

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

[0103] Please refer to Figure 4 The application further provides a multi-energy storage power station output balancing device participating in two-way peak regulation of a power grid, comprising:

[0104] An acquisition module 201 acquires a daily load curve of a power system, a daily output curve of a power source, and a state of charge of an energy storage power station.

[0105] A deviation module 202 calculates a real-time deviation of a load peak-valley difference according to the daily load curve and the daily output curve.

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

[0107] A state machine 204 determines a state of an energy storage power station SOC finite state machine according to the state of charge, and determines a state of a power grid peak regulation capacity finite state machine according to the real-time deviation and the real-time error.

[0108] A processing module 205 determines a discharging action and a discharging amount of the energy storage power station according to the state of the energy storage power station SOC finite state machine and the state of the power grid peak regulation capacity finite state machine, and performs multi-energy storage power station output balancing.

[0109] Further, the state of the energy storage power station SOC finite state machine comprises:

[0110] SOC state 1: At time t, the state of charge of the i-th energy storage power station is less than a lower limit value 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 an upper limit value and a lower limit value 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 value of the state of charge.

[0113] Further, the state of the power grid peak regulation capacity finite state machine comprises:

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

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

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

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

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

[0119] Wherein, Δ p (t) is the real-time deviation of the peak shaving capacity 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] Further, the expression of the real-time error is as follows:

[0121]

[0122] Wherein, δ p (t) is the real-time error of the grid peak shaving capacity at time t, p N is the system capacity.

[0123] The present application integrates dispersed power supply side, grid side and user side energy storage resources, and submits them to the grid for unified coordination and scheduling, which significantly improves the peak shaving capacity of the regional grid. Compared with the traditional single energy storage power station independent peak shaving, the multi-energy storage power station joint peak shaving can better cope with large-scale load changes and reduce the peak-valley difference.

[0124] The present application constructs a finite state machine control module, which realizes real-time compensation of peak-valley difference while maintaining the charge balance of 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, prolongs the service life of the battery, and improves the overall economic benefit of the energy storage system. By comprehensively considering the real-time load curve, positive and negative peak shaving deviation and multi-energy storage power station state of charge constraints, the method of the present application can provide stable peak shaving capacity under unstable new energy generation, ensuring the safe and stable operation of the grid. At the same time, it also provides strong support for 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 demand of the grid to cope with different load change scenarios. This flexibility not only improves the response speed of the grid, but also enhances the response ability of the grid to emergencies. By unified coordination and scheduling of multi-energy storage power station resources, the present application avoids waste of resources and repeated construction. At the same time, since the state of charge of the energy storage power station is balanced, the performance degradation and maintenance cost caused by overcharging or overdischarging of the battery are reduced.

Claims

1. A method for balancing the output of multiple energy storage power stations participating in bidirectional peak shaving of the power grid, characterized in that, include: Obtain the daily load curve of the power system, the daily power output curve of the power source, and the state of charge of the energy storage power station; The real-time deviation of the load peak-valley difference is calculated based on the daily load curve and the daily power output curve. The expression for the real-time deviation is as follows: ； in, The real-time deviation of peak-shaving capacity power at time t. Let be the load power at time t. Let t be the power supply power at time t; The real-time error of the power grid peak-shaving capacity is calculated based on the real-time deviation, and the expression for the real-time error is as follows: ； in, The real-time deviation of peak-shaving capacity power at time t. Let be the real-time error of the power grid peak-shaving capacity at time t. For system capacity; The state of the SOC finite state machine of the energy storage power station is determined based on the state of charge. 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. The state of the finite state machine for grid peak-shaving capacity is determined based on the real-time deviation and the real-time error. The state of the finite state machine for grid peak-shaving capacity includes: Peak-shaving state 1: The real-time error is less than a preset threshold, and the energy storage station performs internal load balancing; Peak-shaving state 2: The real-time error is greater than the preset threshold and the real-time deviation is less than 0, and the energy storage station discharges externally to compensate for the load; Peak-shaving state 3: The real-time error is greater than the preset threshold and the real-time deviation is greater than 0, and the energy storage station charges to store excess power. The discharge action and discharge amount of the energy storage power station are determined based on the state of the SOC finite state machine of the energy storage power station and the state of the grid peak-shaving capacity finite state machine, 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 externally, and performs charging or discharging operations according to the state of charge; Peak-shaving state 2: All energy storage power stations discharge externally, and the discharge power is adjusted according to the SOC state; Wind power state 3: All energy storage power stations perform charging operations, and the charging power is also adjusted according to the SOC state.

2. A power balancing device for a multi-energy storage power station participating in bidirectional peak shaving of the power grid, characterized in that, include: The acquisition module acquires the daily load curve of the power system, the daily power output curve of the power source, and the state of charge of the energy storage station. The deviation module calculates the real-time deviation of the load peak-valley difference based on the daily load curve and the daily power output curve. The expression for the real-time deviation is as follows: ； in, The real-time deviation of peak-shaving capacity power at time t. Let be the load power at time t. Let t be the power supply power at time t; The error module calculates the real-time error of the power grid peak-shaving capacity based on the real-time deviation. The expression for the real-time error is as follows: ； in, The real-time deviation of peak-shaving capacity power at time t. Let be the load power at time t. Let be the power supply power at time t. Let be the real-time error of the power grid peak-shaving capacity at time t. For system capacity; The state machine determines the state of the SOC finite state machine of the energy storage power station based on the state of charge. 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; The state of the grid peak-shaving capacity finite state machine is determined based on the real-time deviation and the real-time error. The state of the grid peak-shaving capacity finite state machine includes: Peak-shaving state 1: the real-time error is less than a preset threshold, and the energy storage power station performs internal charge balancing; Peak-shaving state 2: the real-time error is greater than the preset threshold and the real-time deviation is less than 0, and the energy storage power station discharges externally to compensate for the load; Peak-shaving state 3: the real-time error is greater than the preset threshold and the real-time deviation is greater than 0, and the energy storage power station charges to store excess power. The processing module determines the discharge action and discharge amount of the energy storage station based on the state of the SOC finite state machine of the energy storage station and the state of the peak-shaving capacity finite state machine of the power grid, and performs power output balancing of multiple energy storage stations, including: Peak-shaving state 1: The energy storage station does not output power externally and performs charging or discharging operations according to the state of charge; Peak-shaving state 2: All energy storage stations discharge externally and adjust the discharge power according to the SOC state; Wind power state 3: All energy storage stations perform charging operations and adjust the charging power according to the SOC state.

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