Shared energy storage auxiliary wind power plant group grid-connected double-layer online optimization control method

Abstract

The invention relates to the field of shared energy storage, in particular to a shared energy storage auxiliary wind power plant group grid-connected double-layer online optimization control method, which comprises the following steps of: establishing a shared energy storage instruction power decision model which comprises a decision model objective function and decision model constraint conditions; establishing a shared energy storage instruction power distribution model which comprises a distribution model objective function and a distribution model constraint condition; and constructing a distributed online optimization algorithm, including constructing an augmented Lagrange function, establishing a simplified online optimization operation model of the shared energy storage unit, updating active power and reactive power of the shared energy storage unit, and creating an evaluation index. According to the method, the internal tidal current state of the shared energy storage system and the output voltage of the energy storage unit are comprehensively considered, and the instruction power of the shared energy storage system is quickly distributed under the condition that the shared energy storage system has sufficient regulation capability; and the reliability and continuity of the operation of the shared energy storage system are considered while the grid-connected power of the wind power plant group is controlled within an allowable range.

Description

Technical Field

[0001] The present invention relates to the technical field of shared energy storage, and in particular to a double-layer online optimization control method for grid-connected wind farm groups assisted by shared energy storage. Background Art

[0002] With the rapid development of energy storage technology and policy support, the installed capacity of energy storage systems is also increasing, taking on the important task of supporting the safe and stable operation of the frequency of new power systems. However, due to the high cost of energy storage systems and their use costs, they are currently mostly used to assist wind farms in connecting to the grid. How to ensure the healthy status of shared energy storage systems and energy storage units during the process of shared energy storage assisting wind farms in connecting to the grid is still an issue that needs to be optimized urgently. Summary of the invention

[0003] The purpose of the present invention is to overcome the defects of the existing technology and propose a shared energy storage assisted wind farm group grid-connected double-layer online optimization control method, which can balance the output of the wind farm group and the shared energy storage system, while meeting the qualified grid-connected power of the wind farm group and improving the remaining power of the shared energy storage system.

[0004] To achieve the above object, the present invention adopts the following specific technical solutions:

[0005] The shared energy storage-assisted wind farm group grid-connected double-layer online optimization control method provided by the present invention comprises the following steps:

[0006] S1. Establish a shared energy storage command power decision model, including a decision model objective function and a decision model constraint condition. The decision model objective function is used to optimize the grid-connected power qualification rate of the wind farm group and the health status of the shared energy storage system. The decision model constraint condition is used to constrain the health status of the shared energy storage system and the relationship between the wind power suppression power and the command power.

[0007] S2. Establish a shared energy storage command power allocation model, including an allocation model objective function and allocation model constraints. The allocation model objective function is used to reduce the energy storage unit charging and discharging loss cost and life depreciation cost, and optimize the health status of the energy storage unit. The allocation model constraints are used to constrain the charging and discharging power and state of charge (SOC) of the energy storage unit, as well as the shared energy storage power balance;

[0008] S3. Construct a distributed online optimization algorithm, including constructing an augmented Lagrangian function, establishing a simplified online optimization operation model of a shared energy storage unit, updating the active power and reactive power of the shared energy storage unit, and creating evaluation indicators.

[0009] Furthermore, in step S1, the expression of the objective function of the decision model is specifically as follows:

[0010]

[0011] In the formula, F t is the objective function of the decision model at time t; is the power consumption of the wind farm group at time t; is the command power of shared energy storage at time t; is the health status value of the shared energy storage system at time t; SOC h,idea is the ideal health status of the shared energy storage system at time t, which is a constant value; is the planned output power of wind farm i at time t; is the actual output power of wind farm i at time t; α is the deviation coefficient; and is the deviation state of wind farm i at time t; and is the discharge buffer and charge buffer that the wind farm group can provide at time t; NF is the number of wind farm groups; is the charging health factor of the shared energy storage unit i at time t; is the discharge health factor of the shared energy storage unit i at time t; SOC max and SOC min The maximum and minimum values ​​allowed for the energy storage unit SOC; is the state of charge of the shared energy storage unit i at time t; E N,i is the rated capacity of the shared energy storage unit i; Δt is the time interval; and are the normalized charging health factor and discharging health factor of the shared energy storage unit i at time t; P1 and P2 are the health coefficients of the shared energy storage system, and P1+P2=1; N is the energy storage unit number set; is the health status of the shared energy storage system at time t.

[0012] Furthermore, in step S1, the expression of the constraint condition of the decision model is specifically as follows:

[0013]

[0014] The wind farm group's absorption capacity is not 0:

[0015]

[0016] The wind farm group's absorption capacity is 0, and the wind farm group's buffer capacity meets the shared energy storage recovery:

[0017]

[0018] The wind farm group's absorption capacity is 0, and the wind farm group's buffer capacity does not meet the shared energy storage recovery requirements:

[0019]

[0020] In the formula, SOC h,min and SOC h,max is the lower and upper limits of the health status of the shared energy storage system; and The discharge and charge capacity required by the shared energy storage system at time t; and It is the discharge buffer and charge buffer that the wind farm group can provide at time t.

[0021] Furthermore, in step S2, the expression of the objective function of the allocation model is specifically as follows:

[0022]

[0023] In the formula, F cost is the objective function of the allocation model; is the charge and discharge loss cost of energy storage unit i at time t; is the life loss cost of energy storage unit i at time t; is the output power of energy storage unit i at time t; c e is the on-grid electricity price, which is 520 yuan / (MW·h); and is the charging state and discharging state of the energy storage unit at time t; and is the charging and discharging efficiency of energy storage unit i; c FR,i and P N,i are the unit power cost and rated power of energy storage unit i respectively; N 0,i is the equivalent cycle number of energy storage unit i at 100% charge and discharge depth; K p is a constant, which can be obtained by fitting the relationship between the number of energy storage cycles and the depth of discharge using the actual operating data provided by the battery manufacturer. It is generally between 0.8 and 2.1; T is the total scheduling time.

[0024] Furthermore, in step S2, the expression of the allocation model constraint condition is specifically as follows:

[0025]

[0026]

[0027] In the formula, and are the active power and reactive power output by each energy storage unit i in the shared energy storage system at time t respectively; It is the permissible operating range in which the active power and reactive power of the energy storage unit after power electronic conversion are mutually restrained by voltage and current; and are the lower and upper limits of the active power of energy storage unit i at time t; is the maximum charge and discharge power of energy storage unit i; and are the upper and lower limits of battery energy respectively; is the remaining power of energy storage unit i at time t; and are the charge states of the shared energy storage unit i at time t-1 and time t respectively; and are the lower limit and upper limit of the state of charge of energy storage unit i at time t respectively; is the charge state deviation of the shared energy storage unit i at time t.

[0028] Furthermore, in step S3, the augmented Lagrangian function is constructed as follows:

[0029]

[0030] Where τ1, τ2 and τ3 are the penalty coefficients of the state of charge deviation of the shared energy storage unit, the health factor penalty coefficient and the penalty coefficient of the command power tracking, respectively.

[0031] Furthermore, in step S3, a simplified online optimization operation model of the shared energy storage unit is established as follows:

[0032]

[0033] Where proj{} is a range-limiting function that limits the active power and reactive power of the shared energy storage unit to Within the range; K is the iterative update step length; and They are the collection of active power and reactive power of the shared energy storage unit at time t and time t+1 respectively.

[0034] Furthermore, in step S3, the active power and reactive power of the shared energy storage unit are updated as follows:

[0035]

[0036] In the formula, It is the partial differential of the command power at time t with respect to the active power and reactive power of energy storage unit i.

[0037] Furthermore, in step S3, the evaluation index is created as follows:

[0038]

[0039] In the formula, RMSE is the root mean square evaluation index. The smaller the RMSE, the better the effect of shared energy storage tracking the command power. MAPE is the mean absolute percentage error index. The smaller the MAPE, the better the effect of shared energy storage tracking the command power.

[0040] The present invention can achieve the following technical effects:

[0041] Aiming at the problems of over-limit of grid-connected power of wind farm groups and over-high / under-charged state of charge of energy storage units in shared energy storage power stations, the present invention proposes a two-layer online optimization control method for grid-connected wind farm groups assisted by shared energy storage. The method comprises a shared energy storage command power decision model and a shared energy storage command power allocation model, and introduces a shared energy storage system health status assessment and a shared energy storage unit health factor. A distributed online optimization algorithm is adopted to comprehensively consider the internal flow state of the shared energy storage system and the output voltage of the energy storage unit, and quickly allocate the command power of the shared energy storage system while ensuring that the shared energy storage system has sufficient regulation capability. By introducing the concept of shared energy storage health status, the grid-connected power control of the wind farm group is ensured to be within the allowable range while taking into account the reliability and sustainability of the operation of the shared energy storage system. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 It is a flow chart of a double-layer online optimization control method for grid-connected shared energy storage-assisted wind farm group provided according to an embodiment of the present invention.

[0043] Figure 2 It is a block diagram of a two-layer online optimization control for a shared energy storage-assisted wind farm group connected to the grid according to an embodiment of the present invention.

[0044] Figure 3 It is a double-layer online optimization control flow chart of a shared energy storage-assisted wind farm group connected to the grid according to an embodiment of the present invention.

[0045] Figure 4 This is a diagram showing the effect of energy storage-assisted wind farm group tracking planned output according to Solution 1 provided in an embodiment of the present invention.

[0046] Figure 5 This is a diagram showing the effect of energy storage-assisted wind farm group tracking planned output according to Solution 2 provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0047] Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings. In the following description, the same modules are represented by the same reference numerals. In the case of the same reference numerals, their names and functions are also the same. Therefore, the detailed description thereof will not be repeated.

[0048] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and do not constitute a limitation of the present invention.

[0049] The embodiment of the present invention provides a two-layer online optimization control method for grid-connected wind farm groups assisted by shared energy storage. First, at the command power decision layer of the shared energy storage system, the shared energy storage system actively tracks the power generation plan of the wind farm group, comprehensively considers the overall health status of the shared energy storage system, the leveling amount of the wind farm group, and the buffer amount of the wind farm group that can be restored by the shared energy storage system, so as to determine the command power that the shared energy storage system needs to bear with the goal of tracking the planned power of the wind farm group and optimizing the health of the shared energy storage system; the energy storage unit allocation layer is used to solve the problem of too high / too low SOC of the energy storage unit inside the energy storage power station, introduces the energy storage health factor to allocate the command power of the shared energy storage, and at the same time, by adopting a distributed online optimization algorithm, in the process of continuous issuance and execution of the shared energy storage command power, realizes the time-varying optimal rapid decision allocation of the command power.

[0050] Specifically, the embodiment of the present invention provides a shared energy storage-assisted wind farm group grid-connected double-layer online optimization control method, the process of which is as follows: Figure 1 As shown, the control block diagram is as follows Figure 2 As shown, the control flow is as follows Figure 3 As shown, the following steps are included:

[0051] S1. Establish a shared energy storage command power decision model, including the decision model objective function and decision model constraints. The decision model objective function is used to optimize the grid-connected power qualification rate of the wind farm group and the health status of the shared energy storage system. The decision model constraints are used to constrain the health status of the shared energy storage system and the relationship between the wind power suppression power and the command power.

[0052] 1. Objective function:

[0053]

[0054] In the formula, F t is the objective function of the shared energy storage command power decision model at time t; is the wind farm group’s power consumption at time t; is the command power of shared energy storage at time t; is the health status value of the shared energy storage system at time t; SOC h,idea is the ideal health status of the shared energy storage system at time t, which is a constant value; is the planned output power of wind farm i at time t; is the actual output power of wind farm i at time t; α is the deviation coefficient; and is the deviation state of wind farm i at time t; and is the discharge buffer and charge buffer that the wind farm group can provide at time t; NF is the number of wind farm groups; and is the charging health factor and discharging health factor of the shared energy storage unit i at time t; SOC max and SOC min The maximum and minimum values ​​allowed for the energy storage unit SOC; is the state of charge of the shared energy storage unit i at time t; E N,i is the rated capacity of the shared energy storage unit i; Δt is the time interval; and are the normalized charging health factor and discharging health factor of the shared energy storage unit i at time t; P1 and P2 are the health coefficients of the shared energy storage system, and P1+P2=1; N is the energy storage unit number set; is the health status of the shared energy storage system at time t.

[0055] 2. Constraints:

[0056]

[0057] The wind farm group's absorption capacity is not 0:

[0058]

[0059] The wind farm group's absorption capacity is 0, and the wind farm group's buffer capacity meets the shared energy storage recovery:

[0060]

[0061] The wind farm group's absorption capacity is 0, and the wind farm group's buffer capacity does not meet the shared energy storage recovery requirements:

[0062]

[0063] In the formula, SOC h,min and SOC h,max is the lower and upper limits of the health status of the shared energy storage system; and The discharge and charge capacity required by the shared energy storage system at time t; and It is the discharge buffer and charge buffer that the wind farm group can provide at time t.

[0064] S2. Establish a shared energy storage command power allocation model, including an allocation model objective function and allocation model constraints. The allocation model objective function is used to reduce the charging and discharging loss cost and life depreciation cost of the energy storage unit and optimize the health status of the energy storage unit. The allocation model constraints are used to constrain the charging and discharging power and state of charge (SOC) of the energy storage unit, as well as the shared energy storage power balance.

[0065] 1. Objective function:

[0066]

[0067] In the formula, F cost is the objective function of the allocation model; is the charge and discharge loss cost of energy storage unit i at time t; is the life loss cost of energy storage unit i at time t; is the output power of energy storage unit i at time t; c e is the on-grid electricity price, which is 520 yuan / (MW·h); and is the charging state and discharging state of the energy storage unit at time t; and is the charging and discharging efficiency of energy storage unit i; c FR,i and P N,i are the unit power cost and rated power of energy storage unit i respectively; N 0,i is the equivalent cycle number of energy storage unit i at 100% charge and discharge depth; K p is a constant, which can be obtained by fitting the relationship between the number of energy storage cycles and the depth of discharge using the actual operating data provided by the battery manufacturer. It is generally between 0.8 and 2.1; T is the total scheduling time.

[0068] 2. Constraints:

[0069]

[0070] In the formula, and are the active power and reactive power output by each energy storage unit i in the shared energy storage system at time t respectively; It is the permissible operating range in which the active power and reactive power of the energy storage unit after power electronic conversion are mutually restrained by voltage and current; and are the lower and upper limits of the active power of energy storage unit i at time t; is the maximum charge and discharge power of energy storage unit i; and are the upper and lower limits of battery energy respectively; is the remaining power of energy storage unit i at time t; and are the charge states of the shared energy storage unit i at time t-1 and time t respectively; and are the lower limit and upper limit of the state of charge of energy storage unit i at time t respectively; is the charge state deviation of the shared energy storage unit i at time t.

[0071] S3. Construct a distributed online optimization algorithm, including constructing an augmented Lagrangian function, establishing a simplified online optimization operation model of a shared energy storage unit, updating the active power and reactive power of the shared energy storage unit, and creating evaluation indicators.

[0072] 1. Construct augmented Lagrangian function:

[0073]

[0074] Where τ1, τ2 and τ3 are the penalty coefficients of the state of charge deviation of the shared energy storage unit, the health factor penalty coefficient and the penalty coefficient of the command power tracking, respectively.

[0075] 2. Simplified online optimization operation model of shared energy storage units:

[0076]

[0077] Where proj{} is a range-limiting function that limits the active power and reactive power of the shared energy storage unit to Within the range; K is the iterative update step length; and They are the collection of active power and reactive power of the shared energy storage unit at time t and time t+1 respectively.

[0078] 3. Update of active power and reactive power of shared energy storage unit:

[0079]

[0080] In the formula, It is the partial differential of the command power at time t with respect to the active power and reactive power of energy storage unit i.

[0081] 4. Evaluation indicators:

[0082]

[0083] In the formula, RMSE is the root mean square evaluation index. The smaller the RMSE, the better the effect of shared energy storage tracking the command power. MAPE is the mean absolute percentage error index. The smaller the MAPE, the better the effect of shared energy storage tracking the command power.

[0084] In order to verify the effect of the shared energy storage assisted wind farm group grid-connected double-layer online optimization control method provided by the present invention, it is compared with the traditional scheme, and the tracking effect of the shared energy storage assisted wind farm group tracking planned output and the shared energy storage system and the algorithm calculation tracking effect of the shared energy storage system are compared between the two schemes, as follows:

[0085] Option 1: There is no shared energy storage and the wind farms do not form a wind farm group. In the traditional way, each wind farm is equipped with energy storage separately, and the configuration ratio of the energy storage station is 15% of the rated power of the wind farm;

[0086] Solution 2: A two-layer online optimization control method for grid-connected wind farm groups assisted by shared energy storage provided in an embodiment of the present invention.

[0087] The output effects of energy storage-assisted wind farm group tracking plan in Scheme 1 and Scheme 2 are as follows: Figure 4 and Figure 5 As shown in the figure, in Scheme 1, when the wind farm is configured separately, the energy storage cannot meet the demand for wind power grid-connected power at most times, and the volatility of its wind power grid-connected power is large. In addition, the wind power grid-connected power cannot be within the allowable upper and lower limits at most times. In Scheme 2, since the health status of shared energy storage is taken into account, the wind farm group can be controlled within the allowable range.

[0088] The tracking effect of scheme 2 is shown in Table 1 below:

[0089] Table 1 Algorithm calculation and tracking effect of scheme 2 shared energy storage system

[0090]

[0091] The shared energy storage in Scheme 2 can effectively track the command power, with an RMSE value of only 9.54% and a MAPE value of only 0.006754%.

[0092] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0093] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

[0094] The above specific implementations of the present invention do not constitute a limitation on the protection scope of the present invention. Any other corresponding changes and modifications made based on the technical concept of the present invention should be included in the protection scope of the claims of the present invention.

Claims

1. A two-layer online optimization control method for grid-connected wind farm group assisted by shared energy storage, characterized in that: The steps include: S1. Establish a shared energy storage command power decision model, including a decision model objective function and a decision model constraint condition. The decision model objective function is used to optimize the grid-connected power qualification rate of the wind farm group and the health status of the shared energy storage system. The decision model constraint condition is used to constrain the health status of the shared energy storage system, the relationship between the wind power suppression power and the command power. S2. Establish a shared energy storage command power allocation model, including an allocation model objective function and allocation model constraints, wherein the allocation model objective function is used to reduce the energy storage unit charging and discharging loss cost and life depreciation cost, and optimize the energy storage unit health status, and the allocation model constraints are used to constrain the energy storage unit charging and discharging power and state of charge (SOC), as well as the shared energy storage power balance; S3. Construct a distributed online optimization algorithm, including constructing an augmented Lagrangian function, establishing a simplified online optimization operation model of a shared energy storage unit, updating the active power and reactive power of the shared energy storage unit, and creating evaluation indicators.

2. The shared energy storage assisted wind farm group grid-connected double-layer online optimization control method according to claim 1 is characterized in that: In step S1, the expression of the objective function of the decision model is specifically as follows: In the formula, F t is the objective function of the decision model at time t; is the power consumption of the wind farm group at time t; is the command power of shared energy storage at time t; is the health status value of the shared energy storage system at time t; SOC h,idea is the ideal health status of the shared energy storage system at time t, which is a constant value; is the planned output power of wind farm i at time t; is the actual output power of wind farm i at time t; α is the deviation coefficient; and is the deviation state of wind farm i at time t; and is the discharge buffer and charge buffer that the wind farm group can provide at time t; NF is the number of wind farm groups; is the charging health factor of the shared energy storage unit i at time t; is the discharge health factor of the shared energy storage unit i at time t; SOC max and SOC min The maximum and minimum values ​​allowed for the energy storage unit SOC; is the state of charge of the shared energy storage unit i at time t; E N,i is the rated capacity of the shared energy storage unit i; Δt is the time interval; and are the normalized charging health factor and discharging health factor of the shared energy storage unit i at time t; P1 and P2 are the health coefficients of the shared energy storage system, and P1+P2=1; N is the energy storage unit number set; is the health status of the shared energy storage system at time t.

3. The shared energy storage assisted wind farm group grid-connected double-layer online optimization control method according to claim 2 is characterized in that: In step S1, the expression of the constraint condition of the decision model is specifically as follows: The wind farm group's absorption capacity is not 0: The wind farm group's consumption is 0, and the wind farm group's buffer capacity meets the shared energy storage recovery: The wind farm group's absorption capacity is 0, and the wind farm group's buffer capacity does not meet the shared energy storage recovery requirements: In the formula, SOC h,min and SOC h,max is the lower and upper limits of the health status of the shared energy storage system; and The discharge and charge capacity required by the shared energy storage system at time t; and It is the discharge buffer and charge buffer that the wind farm group can provide at time t.

4. The shared energy storage assisted wind farm group grid-connected double-layer online optimization control method according to claim 3 is characterized in that: In step S2, the expression of the objective function of the allocation model is specifically as follows: In the formula, F cost is the objective function of the allocation model; is the charge and discharge loss cost of energy storage unit i at time t; is the life loss cost of energy storage unit i at time t; is the output power of energy storage unit i at time t; c e is the on-grid electricity price, which is 520 yuan / (MW·h); and is the charging state and discharging state of the energy storage unit at time t; and is the charging and discharging efficiency of energy storage unit i; c FR,i and P N,i are the unit power cost and rated power of energy storage unit i respectively; N 0,i K is the equivalent cycle number of energy storage unit i at 100% charge and discharge depth; p is a constant, which can be obtained by fitting the relationship between the number of energy storage cycles and the depth of discharge using the actual operating data provided by the battery manufacturer. It is generally between 0.8 and 2.1; T is the total scheduling time.

5. The shared energy storage-assisted wind farm group grid-connected double-layer online optimization control method according to claim 4 is characterized in that: In step S2, the expression of the allocation model constraint condition is specifically as follows: In the formula, and are the active power and reactive power output by each energy storage unit i in the shared energy storage system at time t respectively; It is the permissible operating range in which the active power and reactive power of the energy storage unit after power electronic conversion are mutually restrained by voltage and current; and are the lower and upper limits of the active power of energy storage unit i at time t; is the maximum charge and discharge power of energy storage unit i; and are the upper and lower limits of battery energy respectively; is the remaining power of energy storage unit i at time t; and are the charge states of the shared energy storage unit i at time t-1 and time t respectively; and are the lower limit and upper limit of the state of charge of energy storage unit i at time t respectively; is the charge state deviation of the shared energy storage unit i at time t.

6. The shared energy storage-assisted wind farm group grid-connected double-layer online optimization control method according to claim 5 is characterized in that: In step S3, the construction of the augmented Lagrangian function is specifically as follows: Where τ1, τ2 and τ3 are the penalty coefficients of the state of charge deviation of the shared energy storage unit, the health factor penalty coefficient and the penalty coefficient of the command power tracking, respectively.

7. The shared energy storage assisted wind farm group grid-connected double-layer online optimization control method according to claim 6 is characterized in that: In step S3, the establishment of a simplified online optimization operation model of the shared energy storage unit is specifically as follows: Where proj{} is a range-limiting function that limits the active power and reactive power of the shared energy storage unit to Within the range; K is the iterative update step length; and are the sets of active power and reactive power of the shared energy storage unit at time t and time t+1 respectively.

8. The shared energy storage-assisted wind farm group grid-connected double-layer online optimization control method according to claim 7 is characterized in that: In step S3, the active power and reactive power of the shared energy storage unit are updated as follows: In the formula, It is the partial differential of the command power at time τ with respect to the active power and reactive power of energy storage unit i.

9. The shared energy storage-assisted wind farm group grid-connected double-layer online optimization control method according to claim 8 is characterized in that: In step S3, the evaluation indicators are created as follows: In the formula, RMSE is the root mean square evaluation index. The smaller the RMSE, the better the effect of shared energy storage tracking the command power. MAPE is the mean absolute percentage error index. The smaller the MAPE, the better the effect of shared energy storage tracking the command power.

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