An online balancing method for state of charge power scheduling of energy storage battery cluster

Abstract

The present invention belongs to the field of energy storage batteries, and discloses an online balancing method for power dispatching of the state of charge of an energy storage battery cluster. The present invention determines the charge and discharge state of the energy storage battery cluster according to the power dispatching instruction, estimates the online balancing time in real time through the state of charge, charge and discharge limit power and power dispatching instruction value of each energy storage battery cluster, calculates the charging power or discharge power of each energy storage battery cluster in real time through the online balancing time, and sends the power dispatching instruction of each energy storage battery cluster to the power converter of each energy storage battery cluster, and each power converter controls the charging or discharging of each energy storage battery cluster. The present invention takes into account the charge and discharge limit power of the battery cluster, that is, the influence of the charge and discharge rate, so that the state of charge of the energy storage battery cluster can realize the online balancing function in the power dispatching process.

Description

Technical Field

[0001] The present invention belongs to the technical field of energy storage batteries, and in particular relates to an online balancing method for state of charge power scheduling of an energy storage battery cluster. Background Art

[0002] At present, in the fields of electric vehicles, more electric aircraft and energy storage, energy storage technology is the most important link in new energy technology. Among them, high energy density lithium-ion batteries are attracting more and more attention. Since the single voltage and current of lithium batteries are low and often cannot meet the requirements, lithium-ion battery cells can be designed into lithium battery packs through series and parallel connection. According to the target requirements, lithium battery packs are then connected in series and parallel to form battery clusters, and the battery clusters are connected to power converters to obtain ideal driving voltage and power.

[0003] However, as the battery parameters such as capacity and internal resistance vary during the manufacturing, assembly, transportation, storage and use of battery cells, the voltage of the cells will be unbalanced, and the inconsistency problem after the batteries are grouped and clustered will become more prominent. Generally, battery packs and battery stacks will undergo consistency testing after leaving the factory, and the capacity of each part of the battery will be balanced. However, during the use of battery packs and battery clusters, the imbalance problem will still slowly emerge due to various reasons such as converter efficiency and internal resistance. If not managed, the inconsistency of battery packs and battery clusters will affect the charge and discharge capacity of the battery pack, making it easier to over-discharge batteries with a lower state of charge and overcharge batteries with a higher state of charge, which will further aggravate the imbalance within the battery pack, resulting in reduced safety of energy storage batteries, reduced cycle life, and even safety problems. Therefore, effective balancing technology can extend the service life of batteries and improve battery safety.

[0004] The current balancing technology classification includes passive balancing and active balancing. Passive balancing is to supplement the battery in the group through energy dissipation or external power devices, and active balancing is to achieve balancing through energy transfer of batteries in the battery group. Passive balancing methods include resistance dissipation balancing and single-cell supplementary balancing. Active balancing methods can be divided into capacitive, inductive and transformer balancing.

[0005] The passive balancing method has a simple structure, is easy to implement and has low cost. However, the unbalanced electric energy is consumed in the resistor, resulting in energy waste, which reduces the energy utilization rate of the system. In addition, there are problems such as small balancing current, low efficiency and long balancing time, which makes it difficult to meet the needs of efficient and fast balancing. Active balancing is gradually gaining more and more attention from academia and industry due to its advantages such as less heat generation and high efficiency. However, current research on energy storage battery balancing is mostly between single cells and between series-parallel modules, and there is less research on balancing between battery clusters containing power converters. In addition, large power stations or aviation energy storage with high consistency requirements will affect the execution of dispatch instructions when imbalance occurs, so this aspect needs to be studied more.

[0006] At present, there are many academic papers analyzing and proposing solutions for the online balancing technology of the state of charge of energy storage battery clusters, such as:

[0007] 1. Titled (“Research on the State of Charge and Active Balancing Technology of Lithium-ion Batteries”, Mao Xiaoyi, May 2020) The energy storage characteristics of inductors are used to achieve power balancing between batteries. The switch tube is controlled by pulse width modulation (PWM). The on-off time of the switch tube is controlled based on the voltage difference between the batteries. A hierarchical balancing circuit for lithium-ion battery groups based on distributed energy storage is designed, which not only achieves battery power balancing, but also shortens the balancing time. However, the algorithm requires additional hardware circuits. In addition, this strategy can only achieve balancing between battery groups and cannot be applied to dynamic balancing of battery clusters.

[0008] 2. The article entitled ("Active Balancing Management and Efficiency Optimization of Lithium Batteries", Gui Handong, March 2016) proposed a hierarchical balancing architecture, which regroups the series lithium battery packs. The bottom layer adopts the traditional A-C2C structure, and the top layer is connected to each battery pack. A multi-directional multi-port converter is used to form the top-level balancing circuit. The proposed architecture can achieve energy decoupling of different battery packs, avoid the problem of repeated charging and discharging during the balancing process, and is beneficial to extend battery life and improve battery health status (SOH). In addition, the proposed architecture can reduce the rated current required by the balancing circuit in the system, thereby reducing costs and improving system efficiency. However, the article has the following shortcomings: the article only considers the voltage balancing between single-series battery packs, and does not consider the impact of multiple series-parallel structures on balancing. Moreover, accurate balancing requires additional complex hardware circuits to achieve.

[0009] 3. The article entitled (“Research on Distributed Online Active Balancing Control System for Power Battery Packs”, Ge Gaorui, June 2020) proposed an active balancing control method in charge / discharge mode. According to the deviation between the single cell SOC and the average SOC, the bus voltage adjustment coefficient and the balancing acceleration coefficient are adjusted in the discharge mode. Under the premise of ensuring the stability of the bus voltage, the discharge rate of the power battery pack can be adjusted; in the charging mode, the charging balancing acceleration coefficient is adjusted to achieve the adjustment of the charging rate. In order to solve the problem of slow balancing rate of power battery packs, a balancing acceleration coefficient adaptive adjustment balancing method is proposed. In the charge / discharge mode, the balancing acceleration coefficient is adjusted between the single cell groups according to their own SOC differences to achieve adaptive adjustment of the charge / discharge current and complete the efficient balancing management of the power battery pack. However, this method still requires additional hardware circuits, and the algorithm calculation is also large. Summary of the invention

[0010] Purpose of the invention: For battery clusters containing power converters such as large energy storage power stations or aviation energy storage with relatively high consistency requirements, there is little research on online balancing in the upper-level scheduling instruction process, which affects power scheduling and battery life and safety. A method for online balancing of the state of charge power scheduling of energy storage battery clusters is provided. This method determines the charge and discharge state of the energy storage battery cluster according to the power scheduling instruction, calculates the charging power or discharging power of each energy storage battery cluster in real time online through the charge state, charge and discharge limit power and power scheduling instruction value of each group of energy storage battery clusters, and estimates the online balancing time in real time. This method takes into account the charge and discharge limit power of the battery cluster, that is, the influence of the charge and discharge rate, and realizes the online balancing function of the state of charge power scheduling of the energy storage battery cluster.

[0011] The technical solution of the present invention is:

[0012] An online balancing method for charging state power scheduling of an energy storage battery cluster, the specific steps are as follows:

[0013] Step S1: Obtain the capacity S of the energy storage battery cluster in the energy storage power station rate , the number of energy storage battery clusters n, the power dispatch instruction P of the energy storage power station ref ;

[0014] Get the state of charge (SOC) of each energy storage battery cluster in the xth group respectively x , Charging limit power P limc_x , discharge limit power P limd_x ;

[0015] Among them, P limc_x ≥0, P limd_x ≥0, soc x∈[0,1], n≥2 and n is a positive integer, x is a positive integer and x∈[1,n]; Step S2: according to the energy storage power station power dispatch instruction P obtained in step S1 ref , determine the status flag of the energy storage power station state ;

[0016] The status flag bit judgment method is as follows:

[0017]

[0018] Among them, flag state =1 means the energy storage battery is in discharge state. If flag state =1, then execute steps S3-S5; flag state =0 means the energy storage battery is in charging state. If flag state =0, then execute steps S6-S8;

[0019] Step S3: According to the power dispatch instruction P of the energy storage power station ref The discharge limit power of each of the n energy storage battery clusters is used to calculate the discharge state power scheduling instruction

[0020] Step S4: Calculate the energy storage battery cluster discharge state balance time T BalancedDischarge ;

[0021] Step S5: Calculate the power dispatch instruction P of the x-th group of energy storage battery cluster in the discharge state discharge_refx , execute step S9;

[0022] Step S6: According to the power dispatch instruction P of the energy storage power station ref and the charging limit power calculation of each of the n energy storage battery clusters to calculate the charging state power scheduling instruction

[0023] Step S7: Calculate the energy storage battery cluster charging state equalization time T BalancedCharge ;

[0024] Step S8: Calculate the charging state power dispatch instruction P of the x-th group of energy storage battery clusters charge_refx , execute step S9;

[0025] Step S9: according to the discharge state power dispatch instruction P of the xth group of energy storage battery clusters discharge_refx and / or the xth group of energy storage battery cluster charging state power scheduling instruction P charge_refx Get the power dispatch instruction P of the xth group of energy storage battery clusters refx ;

[0026] Power balancing time T according to discharge state BalancedDischarge and charging state power balancing time TBalancedCharge Get the charge state equalization time T of the energy storage battery cluster BalancedSOC ;

[0027] Step S10: Send the xth group of energy storage battery cluster power scheduling instruction P refx The power converter of the x-th energy storage battery cluster controls the charging or discharging of the x-th energy storage battery cluster.

[0028] Preferably, in step S3, the discharge state power scheduling instruction The calculation steps are as follows:

[0029] Step S31, calculating the total discharge limit power P limd ,

[0030] Step S32, calculating the discharge state power scheduling instruction

[0031]

[0032] Preferably, in step S4, the energy storage battery cluster discharge state balancing time T BalancedDischarge The calculation steps are as follows:

[0033] Step S41, respectively calculating the minimum balancing time T of the discharge state of the x-th group of energy storage battery clusters minDisc_x :

[0034]

[0035] Wherein, m is a positive integer and m∈[1,n], ΔP is a bias power to prevent division by zero, generally, ΔP∈(-∞,1];

[0036] Step S42, calculating the minimum balance time T of the discharge state minBalancedDischarge :

[0037] T minBalancedDischarge =max(0,T minDisc_1 ,…,T minDisc_n );

[0038] Step S43, respectively calculating the maximum balancing time T of the x-th group of energy storage battery clusters in the discharge state maxDisc_x ,

[0039]

[0040] Among them, T maxD is a constant, here the value is T minDisc_x The maximum value of the selected data type;

[0041] Step S44, calculating the maximum balance time T of the discharge state maxBalancedDischarge :

[0042] T maxBalancedDischarge =min(T maxDisc_1 ,…,T maxDisc_n )

[0043] Step S45, discharge state active equalization flag flag DisBalance :

[0044]

[0045] Step S46, calculating the discharge state equilibrium time T BalancedDischarge :

[0046]

[0047] Among them, ΔT is the charge and discharge state balance time offset value. Generally, ΔT∈(-∞,1], T maxD is a constant, here the value is T BalancedDischarge The maximum value for the selected data type.

[0048] Preferably, in step S5, the discharge state power scheduling instruction P of the xth group of energy storage battery clusters discharge_refx The calculation steps are as follows:

[0049] Step S51, calculating the power dispatch instruction reference value of the xth group of energy storage battery cluster in the discharge state

[0050]

[0051] Step S52, calculating the discharge state power dispatch instruction P of the xth group of energy storage battery clusters discharge_refx :

[0052]

[0053] Preferably, in step S6, the charging state power scheduling instruction The calculation steps are as follows:

[0054] Step S61, calculating the total charging limit power P limc :

[0055]

[0056] Step S62: Calculate the power dispatch instruction of the energy storage battery cluster charging state

[0057]

[0058] Preferably, in step S7, the energy storage battery cluster charging state equalization time T BalancedCharge The calculation steps are as follows:

[0059] Step S71: Calculate the minimum balancing time T of the x-th group of energy storage battery clusters minC_x :

[0060]

[0061] Step S72, calculate the minimum equalization time T of the charging state minBalancedCharge :

[0062] T minBalancedCharge =max(0,T minC_1 ,…,T minC_n );

[0063] Step S73, respectively calculate the maximum balancing time T of the x-th group of energy storage battery clusters maxC_x :

[0064]

[0065] Among them, T maxC is a constant, here the value is T minC_x The maximum value of the selected data type;

[0066] Step S74, calculating the maximum equalization time T of the charging state maxBalancedDischarge :

[0067] T maxBalancedDischarge =min(T maxDisc_1 ,…,T maxDisc_n );

[0068] Step S75, calculate the active equalization flag of the charging state CBalance :

[0069]

[0070] Step S76, calculate the charging state equalization time T BalancedCharge :

[0071]

[0072] Among them, ΔT is the charge and discharge state balance time offset value, T maxC is a constant, here the value is T BalancedCharge The maximum value for the selected data type.

[0073] Preferably, in step S8, the charging state power scheduling instruction P of the xth group of energy storage battery clusters charge_refx The calculation steps are as follows:

[0074] Step S81, calculating the charging state power dispatch instruction reference value of the x-th group of energy storage battery clusters

[0075]

[0076] Step S82, calculating the charging state power scheduling instruction P of the x-th group of energy storage battery clusters charge_refx :

[0077]

[0078] Preferably, in step S9, the power scheduling instruction P of the xth group of energy storage battery clusters refx The calculation formula is as follows:

[0079]

[0080] Preferably, in step S9, the energy storage battery cluster charge state equalization time T BalancedSOC :

[0081]

[0082] Beneficial effects: Compared with the existing online balancing method for the state of charge of energy storage batteries, the online balancing method for the state of charge of energy storage batteries disclosed in the present invention has the following beneficial effects:

[0083] The existing current energy storage battery balancing research is mostly between single cells and between series-parallel modules, and there is less research on battery clusters containing power converters. In addition, due to the large power station or aviation energy storage with relatively high consistency requirements, when imbalance occurs, it will affect the execution of the dispatching instruction. This method first determines the charging state and discharging state according to the power dispatching instruction of the energy storage station, and calculates the current shortest allowed charging and discharging state balancing time according to the charging and discharging state, the charging and discharging limit power and the current state of charge of each energy storage battery cluster. Assuming that the power dispatching instruction of the energy storage station remains unchanged during this period, the sum of the charging and discharging energy during this period and the energy of each energy storage battery cluster itself is the total energy to be allocated, and after the shortest charging and discharging state balancing time, the energy of each energy storage battery cluster is consistent, thereby calculating the power dispatching instruction of each energy storage battery cluster and using it as the issued instruction value. If the power dispatching instruction of the energy storage station changes during this period, the shortest charging and discharging state balancing time changes in real time, and the power dispatching instructions of each energy storage battery cluster are also updated in real time. This method takes into account the charge and discharge limit power of the battery cluster, that is, the influence of the charge and discharge rate, and calculates the charging power or discharging power of each energy storage battery cluster in real time online according to the power dispatch instruction of the energy storage power station, the charge state of each group of energy storage battery clusters, and the charge and discharge limit power, and estimates the online balancing time in real time. This method realizes the online balancing function of the charge state power dispatch of the energy storage battery cluster.

[0084] 1. This control method makes algorithm improvements on the original circuit topology and does not require additional hardware circuits;

[0085] 2. This control method can be used in large-scale energy storage power stations or energy storage battery clusters with high consistency requirements;

[0086] 3. This control method is applicable to SOC balancing of parallel battery clusters containing power converters;

[0087] 4. This control method has strong real-time performance and high precision, and can achieve charging and discharging balance during power scheduling;

[0088] 5. This control method can determine the charging balance or discharging balance state according to the power dispatch instruction of the energy storage power station, estimate the online balance time in real time, and calculate the power instruction value issued by each energy storage battery cluster according to the online balance time.

[0089] 6. This control method can be applied to a DC-DC converter or a DC-AC converter. BRIEF DESCRIPTION OF THE DRAWINGS

[0090] Figure 1 1 is a structural diagram of a motor system according to an embodiment of the present invention.

[0091] Figure 2 This is a flow chart of active balancing control of power scheduling of an energy storage battery cluster according to an embodiment of the present invention.

[0092] Figure 3 This is a discharge state equilibrium time diagram of an embodiment of the present invention.

[0093] Figure 4 This is a diagram of the SOC state and power scheduling instructions of a battery cluster in a discharge state according to an embodiment of the present invention.

[0094] Figure 5 This is a comparison between the value issued by the power scheduling instruction in the discharge state and the actual value in an embodiment of the present invention.

[0095] Figure 6 This is a charging state equalization time diagram of an embodiment of the present invention.

[0096] Figure 7 This is a diagram of the state of charge (SOC) state and power scheduling instructions of a battery cluster according to an embodiment of the present invention.

[0097] Figure 8 This is a comparison between the value issued by the charging state power scheduling instruction and the actual value in an embodiment of the present invention. DETAILED DESCRIPTION

[0098] This section is an embodiment of the present invention, which is used to explain and illustrate the technical solution of the present invention.

[0099] Figure 1 This is a structural diagram of an energy storage system according to an embodiment of the present invention.

[0100] Figure 2 This is a flow chart of active balancing control of power scheduling of energy storage battery clusters according to an embodiment of the present invention. The steps of the control method of the present invention are as follows:

[0101] An online balancing method for power scheduling of a state of charge of an energy storage battery cluster, characterized in that the steps of the online balancing method for power scheduling of a state of charge of an energy storage battery cluster are as follows:

[0102] Step S1: Obtain the capacity S of the energy storage battery cluster in the energy storage power station rate , the number of energy storage battery clusters n, the power dispatch instruction P of the energy storage power station ref ;

[0103] Get the state of charge (SOC) of each energy storage battery cluster in the xth group respectively x , Charging limit power P limc_x , discharge limit power P limd_x ;

[0104] Among them, P limc_x ≥0, P limd_x ≥0, soc x ∈[0,1], n≥2 and n is a positive integer, x is a positive integer and x∈[1,n];

[0105] In this embodiment, the capacity of the energy storage battery clusters in the energy storage power station is S rate =270V·Ah, the number of energy storage battery clusters n=3, the state of charge of the three energy storage battery clusters soc1=68%, soc2=66.8%, soc3=67.5% and the charging limit power P of the three energy storage battery clusters limc_1 =P limc_2 =P limc_n =5400W, discharge limit power P limd_1 =P limd_2 =P limd_3 =5400W.

[0106] Step S2: The energy storage power station power dispatch instruction P obtained in step S1 ref , determine the status flag of the energy storage power station state ;

[0107] The status flag bit judgment method is as follows:

[0108]

[0109] Among them, flag state =1 means the energy storage battery is in discharge state. If flag state=1, then execute steps S3-S5; flag state =0 means the energy storage battery is in charging state. If flag state =0, then execute steps S6-S8;

[0110] Step S3: According to the power dispatch instruction P of the energy storage power station ref The discharge limit power of each of the n energy storage battery clusters is used to calculate the discharge state power scheduling instruction

[0111] In some optional implementations, in step S3, the discharge state power scheduling instruction The calculation steps are as follows:

[0112] Step S31, calculating the total discharge limit power P limd ,

[0113] Step S32, calculating the discharge state power scheduling instruction

[0114]

[0115] Step S4: Calculate the energy storage battery cluster discharge state balance time T BalancedDischarge ;

[0116] In some optional implementations, in step S4, the energy storage battery cluster discharge state balancing time T BalancedDischarge The calculation steps are as follows:

[0117] Step S41, respectively calculating the minimum balancing time T of the discharge state of the x-th group of energy storage battery clusters minDisc_x :

[0118]

[0119] Wherein, m is a positive integer and m∈[1,n], ΔP is a bias power to prevent division by zero, generally, ΔP∈(-∞,1], in this embodiment, ΔP=0.001W;

[0120] Step S42, calculating the minimum balance time T of the discharge state minBalancedDischarge :

[0121] T minBalancedDischarge =max(0,T minDisc_1 ,…,T minDisc_n );

[0122] Step S43, respectively calculating the maximum balancing time T of the x-th group of energy storage battery clusters in the discharge state maxDisc_x ,

[0123]

[0124] Among them, T maxD is a constant, here the value is T minDisc_x The maximum value of the selected data type. In this example, T minDisc_x For unsigned hexadecimal data format, T max D takes the unsigned hexadecimal number 0xFFFF;

[0125] Step S44, calculating the maximum balance time T of the discharge state maxBalancedDischarge :

[0126] T maxBalancedDischarge =min(T maxDisc_1 ,…,T maxDisc_n )

[0127] Step S45, discharge state active equalization flag flag DisBalance :

[0128]

[0129] Step S46, calculating the discharge state equilibrium time T BalancedDischarge :

[0130]

[0131] Among them, ΔT is the charge and discharge state balance time offset value. Generally, ΔT∈(-∞,1], T maxD is a constant, here the value is T BalancedDischarge The maximum value of the selected data type, in this embodiment, ΔT = 0.001s, T BalancedDischarge For unsigned hexadecimal data format, T maxD Get the unsigned hexadecimal number 0xFFFF.

[0132] Step S5: Calculate the power dispatch instruction P of the x-th group of energy storage battery cluster in the discharge state discharge_refx , execute step S9;

[0133] In some optional implementations, in step S5, the discharge state power scheduling instruction P of the xth group of energy storage battery clusters is discharge_refx The calculation steps are as follows:

[0134] Step S51, calculating the power dispatch instruction reference value of the xth group of energy storage battery cluster in the discharge state

[0135]

[0136] Step S52, calculating the discharge state power dispatch instruction P of the xth group of energy storage battery clusters discharge_refx :

[0137]

[0138] Step S6: According to the power dispatch instruction P of the energy storage power station ref and the charging limit power calculation of each of the n energy storage battery clusters to calculate the charging state power scheduling instruction

[0139] In some optional implementations, in step S6, the charging state power scheduling instruction The calculation steps are as follows:

[0140] Step S61, calculating the total charging limit power P limc :

[0141]

[0142] Step S62: Calculate the power dispatch instruction of the energy storage battery cluster charging state

[0143]

[0144] Step S7: Calculate the energy storage battery cluster charging state equalization time T BalancedCharge ;

[0145] In some optional implementations, in step S7, the energy storage battery cluster charging state equalization time T BalancedCharge The calculation steps are as follows:

[0146] Step S71: Calculate the minimum balancing time T of the x-th group of energy storage battery clusters minC_x :

[0147]

[0148] Step S72, calculate the minimum equalization time T of the charging state minBalancedCharge :

[0149] T minBalancedCharge =max(0,T minC_1 ,…,T minC_n );

[0150] Step S73, respectively calculate the maximum balancing time T of the x-th group of energy storage battery clusters maxC_x :

[0151]

[0152] Among them, T max C is a constant, here it is taken as T minC_x The maximum value of the selected data type. In this example, T minC_xFor unsigned hexadecimal data format, T maxC Get the unsigned hexadecimal number 0xFFFF;

[0153] Step S74, calculating the maximum equalization time T of the charging state maxBalancedDischarge :

[0154] T maxBalancedDischarge =min(T maxDisc_1 ,…,T maxDisc_n );

[0155] Step S75, calculate the active equalization flag of the charging state CBalance :

[0156]

[0157] Step S76, calculate the charging state equalization time T BalancedCharge :

[0158]

[0159] Among them, ΔT is the charge and discharge state balance time offset value, T maxC is a constant, here the value is T BalancedCharge The maximum value of the selected data type. In this embodiment, ΔT = 0.001s, T BalancedCharge For unsigned hexadecimal data format, T maxC Get the unsigned hexadecimal number 0xFFFF.

[0160] Step S8: Calculate the charging state power dispatch instruction P of the x-th group of energy storage battery clusters charge_refx , execute step S9;

[0161] In some optional implementations, in step S8, the charging state power scheduling instruction P of the xth group of energy storage battery clusters is charge_refx The calculation steps are as follows:

[0162] Step S81, calculating the charging state power dispatch instruction reference value of the x-th group of energy storage battery clusters

[0163]

[0164] Step S82, calculating the charging state power scheduling instruction P of the x-th group of energy storage battery clusters charge_refx :

[0165]

[0166] Step S9: according to the discharge state power dispatch instruction P of the xth group of energy storage battery clusters discharge_refxand / or the xth group of energy storage battery cluster charging state power scheduling instruction P charge_refx Get the power dispatch instruction P of the xth group of energy storage battery clusters refx ;

[0167] In some optional implementations, in step S9, the power scheduling instruction P of the xth group of energy storage battery clusters is refx The calculation formula is as follows:

[0168]

[0169] Power balancing time T according to discharge state BalancedDischarge and charging state power balancing time T BalancedCharge Get the charge state equalization time T of the energy storage battery cluster BalancedSOC ;

[0170] In some optional implementations, in step S9, the energy storage battery cluster charge state equalization time T BalancedSOC :

[0171]

[0172] Step S10: Send the xth group of energy storage battery cluster power scheduling instruction P refx The power converter of the x-th energy storage battery cluster controls the charging or discharging of the x-th energy storage battery cluster.

[0173] Figures 3 to 5 The simulation waveforms of various parameters of the energy storage power station under the discharge state. The upper system of the energy storage power station operates the active balancing method of energy storage battery cluster power scheduling.

[0174] Figure 3 It is the balancing time in the discharge state of the embodiment of the present invention. Before 0.2s, the balancing time is expected to reach 260s. After 0.2s, the balancing time is expected to reach less than 2s. At 0.4s, the balancing time is expected to be 0.8s. At 0.6s, the balancing time is expected to be 0.4s. Finally, the charge state of each battery cluster is balanced before 1s.

[0175] Figure 4 This is a diagram of the SOC state and power scheduling instruction of the battery cluster in the discharge state of an embodiment of the present invention. Before 0.2s, the charge state balance is very small, and the deviations of the three power instructions are small. Then, as the upper-level power scheduling instruction changes, the deviations of the three power instructions gradually increase, and the three SOCs gradually balance. After 0.95s, the SOCs gradually balance.

[0176] Figure 5This is a comparison between the power dispatch instruction value and the actual value in the discharge state of the embodiment of the present invention. Before 0.2s, since the power dispatch instruction value of the energy storage power station is greater than the allowable discharge value of the energy storage battery cluster, there is a deviation between the power dispatch instruction value and the actual value, and then they are completely consistent.

[0177] Figures 6 to 8 It is the simulated waveform of each parameter of the energy storage power station under charging state. The upper system of the energy storage power station runs the active balancing method of power scheduling of energy storage battery cluster.

[0178] Figure 6 It is the equalization time under the charging state of the embodiment of the present invention. Before 0.2s, the equalization time is expected to reach 260s, after 0.2s, the equalization time is expected to reach 1.85s, at 0.4s, the equalization time is expected to be 0.85s, at 0.6s, the equalization time is expected to be 0.4s, and finally, the charge state of each battery cluster is balanced before 1s.

[0179] Figure 7 This is a diagram of the SOC state and power scheduling instructions of the charging state battery cluster according to an embodiment of the present invention. Before 0.2s, the charge state balance is very small and the deviations of the three power instructions are small. Then, as the upper-level power scheduling instructions change, the deviations of the three power instructions gradually increase, and the three SOCs gradually balance. After 0.95s, the SOCs gradually balance.

[0180] Figure 8 This is a comparison between the power dispatch instruction value and the actual value of the charging state in the embodiment of the present invention. Before 0.2s, since the power dispatch instruction value of the energy storage power station is greater than the allowable charging value of the energy storage battery cluster, there is a deviation between the power dispatch instruction value and the actual value, and then they are completely consistent.

Claims

1. An online balancing method for power scheduling of a storage battery cluster state of charge, characterized in that: The method comprises the following steps: Step S1: Obtain the capacity S of the energy storage battery cluster in the energy storage power station rate , the number of energy storage battery clusters n, the power dispatch instruction P of the energy storage power station ref ; Get the state of charge (SOC) of each energy storage battery cluster in the xth group respectively x , Charging limit power P limc_x , discharge limit power P limd_x ; Among them, P limc_x ≥0, P limd_x ≥0, soc x ∈[0,1], n≥2 and n is a positive integer, x is a positive integer and x∈[1,n]; Step S2: The energy storage power station power dispatch instruction P obtained in step S1 ref , determine the status flag of the energy storage power station state ; If flag state =1, then execute steps S3-S5; if flag state =0, then execute steps S6-S8; Step S3: According to the power dispatch instruction P of the energy storage power station ref The discharge limit power of each of the n energy storage battery clusters is used to calculate the discharge state power scheduling instruction P limd The total discharge limit power; Step S4: Calculate the energy storage battery cluster discharge state balance time T BalancedDischarge , the steps are as follows: Step S41, respectively calculating the minimum balancing time T of the discharge state of the x-th group of energy storage battery clusters minDisc_x : Wherein, m is a positive integer and m∈[1,n], ΔP is the bias power to prevent division by zero, ΔP∈(-∞,1]; Step S42, calculating the minimum balance time T of the discharge state minBalancedDischarge : T minBalancedDischarge =max(0,T minDisc_1 ,…,T minDisc_n ); Step S43, respectively calculating the maximum balancing time T of the x-th group of energy storage battery clusters in the discharge state maxDisc_x , Among them, T maxD is a constant, here the value is T minDisc_x The maximum value of the selected data type; Step S44, calculating the maximum balance time T of the discharge state maxBalancedDischarge : T maxBalancedDischarge =min(T maxDisc_1 ,…,T maxDisc_n ) Step S45, discharge state active equalization flag flag DisBalance : Step S46, calculating the discharge state equilibrium time T BalancedDischarge : Among them, ΔT is the charge and discharge state balance time offset value, ΔT∈(-∞,1], T maxD is a constant, here the value is T BalancedDischarge The maximum value of the selected data type; Step S5: Calculate the power dispatch instruction P of the x-th group of energy storage battery cluster in the discharge state discharge_refx , the steps are as follows: Step S51, calculating the power dispatch instruction reference value of the xth group of energy storage battery cluster in the discharge state Step S52, calculating the discharge state power dispatch instruction P of the xth group of energy storage battery clusters discharge_refx : Execute step S9; Step S6: According to the power dispatch instruction P of the energy storage power station ref and the charging limit power calculation of each of the n energy storage battery clusters to calculate the charging state power scheduling instruction P limc Limit power for total charging; Step S7: Calculate the energy storage battery cluster charging state equalization time T BalancedCharge , the steps are as follows: Step S71: Calculate the minimum balancing time T of the x-th group of energy storage battery clusters minC_x : Step S72, calculate the minimum equalization time T of the charging state minBalancedCharge : T minBalancedCharge =max(0,T minC_1 ,…,T minC_n ); Step S73, respectively calculate the maximum balancing time T of the x-th group of energy storage battery clusters maxC_x : Among them, T maxC is a constant, here the value is T minC_x The maximum value of the selected data type; Step S74, calculating the maximum equalization time T of the charging state maxBalancedDischarge : T maxBalancedDischarge =min(T maxDisc_1 ,…,T maxDisc_n ); Step S75, calculate the active equalization flag of the charging state CBalance : Step S76, calculate the charging state equalization time T BalancedCharge : Among them, ΔT is the charge and discharge state balance time offset value, T maxC is a constant, here the value is T BalancedCharge The maximum value of the selected data type; Step S8: Calculate the charging state power dispatch instruction P of the x-th group of energy storage battery clusters charge_refx , the steps are as follows: Step S81, calculating the charging state power dispatch instruction reference value of the x-th group of energy storage battery clusters Step S82, calculating the charging state power scheduling instruction P of the x-th group of energy storage battery clusters charge_refx : Execute step S9; Step S9: according to the discharge state power dispatch instruction P of the xth group of energy storage battery clusters discharge_refx and / or the xth group of energy storage battery cluster charging state power scheduling instruction P charge_refx Get the power dispatch instruction P of the xth group of energy storage battery clusters refx , Power balancing time T according to discharge state BalancedDischarge and charging state power balancing time T BalancedCharge Get the charge state equalization time T of the energy storage battery cluster BalancedSOC , Step S10: Send the xth group of energy storage battery cluster power scheduling instruction P refx The power converter of the x-th energy storage battery cluster controls the charging or discharging of the x-th energy storage battery cluster.

2. The method for online balancing of state of charge power scheduling of energy storage battery cluster according to claim 1, characterized in that: In the step S3, Total discharge limit power P limd , 3. The method for online balancing of state of charge power scheduling of energy storage battery cluster according to claim 2 is characterized in that: In step S6, the total charging limit power P limc :

Images