Frequency modulation method and device for power system

Abstract

The present disclosure relates to a frequency modulation method and device for a power system, the method comprising: obtaining first actual output power data, the first actual output power data being actual output power of an energy storage system in a first time period after a collection time point, the energy storage system comprising a power-type flywheel and an energy-type flywheel; performing empirical mode decomposition according to the first actual output power data to determine a plurality of power components; determining a plurality of power distribution strategies according to the plurality of power components, each of the power distribution strategies comprising a first power component distributed to the power-type flywheel and a second power component distributed to the energy-type flywheel; inputting the plurality of power distribution strategies into a first prediction model to output a target power distribution strategy; and controlling the power-type flywheel and the energy-type flywheel to charge or discharge according to the target power distribution strategy to adjust a frequency of a power system to which the energy storage system belongs.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of power generation, and in particular to a frequency modulation method and device of a power system. BACKGROUND

[0002] Wind energy, as a renewable energy source, is green, environmentally friendly, and widely distributed, and is the main energy for new energy development and utilization. Wind power generation is an important way to utilize wind energy. With the continuous increase of wind power installed capacity, the problem of stable operation of the power system caused by the strong randomness and weak controllability of wind energy has become an important factor restricting the development of wind power. Flywheel energy storage, as a mechanical energy storage method with fast response speed, high conversion efficiency and no pollution, is the best choice for a wind storage combined system to realize power system frequency modulation.

[0003] In related technologies, a single energy storage device, such as a single flywheel, can be used to realize power system frequency modulation. However, the frequency modulation effect of a single flywheel is not good, and the cost of a single flywheel is very high. SUMMARY

[0004] To overcome the problems in the related art, the present disclosure provides a frequency modulation method and device of a power system.

[0005] According to a first aspect of an embodiment of the present disclosure, a frequency modulation method of a power system is provided, comprising:

[0006] acquiring first actual output power data, the first actual output power data being actual output power of an energy storage system in a first time period after a collection time point, the energy storage system comprising a power flywheel and an energy flywheel;

[0007] performing empirical mode decomposition according to the first actual output power data to determine a plurality of power components;

[0008] determining a plurality of power distribution strategies according to the plurality of power components, each power distribution strategy comprising a first power component allocated to the power flywheel and a second power component allocated to the energy flywheel;

[0009] inputting the plurality of power distribution strategies into a first prediction model to output a target power distribution strategy, the first prediction model being pre-constructed based on a first constraint condition and a first target loss function, the first constraint condition being used to constrain the power distribution strategy, and the first target loss function being used to determine the target power distribution strategy from the power distribution strategies satisfying the first constraint condition, such that a total state of charge of the energy storage system in the first time period and an actual total output power of the energy storage system in the first time period satisfy a first preset condition;

[0010] According to the target power distribution strategy, the power-type flywheel and the energy-type flywheel are controlled to charge or discharge to adjust the frequency of a power system to which the energy storage system belongs.

[0011] In some embodiments, the first constraint condition comprises at least one of:

[0012] The first power component in the power distribution strategy is between the maximum discharging power and the maximum charging power of the power-type flywheel, the second power component in the power distribution strategy is between the maximum discharging power and the maximum charging power of the energy-type flywheel, the first state of charge of the power-type flywheel based on the power distribution strategy is between the minimum state of charge and the maximum state of charge of the power-type flywheel, and the second state of charge of the energy-type flywheel based on the power distribution strategy is between the minimum state of charge and the maximum state of charge of the energy-type flywheel.

[0013] In some embodiments, the total state of charge comprises the first state of charge and the second state of charge, and the first preset condition comprises:

[0014] The first target loss function is obtained by weighting and summing a first difference between the first state of charge and a target state of charge in the first period, a second difference between the second state of charge and the target state of charge in the first period, and a third difference between the actual total output power in the first period and a required total output power of the energy storage system in the first period.

[0015] In some embodiments, the first actual output power data is obtained by:

[0016] Second required output power data is obtained, the second required output power data being a required output power of the energy storage system in a second period after the collection time, the second period having a time length greater than the first period;

[0017] The second required output power data is input into a second prediction model, and second actual output power data is output, the second actual output power data being an actual output power of the energy storage system in the second period, the second prediction model being pre-constructed based on a second constraint condition and a second target loss function, the second constraint condition being used to constrain at least one of a total capacity of the energy storage system, the actual total output power, and the total state of charge, and the second target loss function being used to output the second actual output power data under the second constraint condition, so that the total state of charge of the energy storage system in the second period and the actual total output power of the energy storage system in the second period satisfy a second preset condition;

[0018] acquire the first actual output power data from the second actual output power data.

[0019] In some embodiments, the second constraint condition comprises at least one of:

[0020] the total capacity is equal to a sum of a first capacity of the power-type flywheel and a second capacity of the energy-type flywheel, the total state of charge satisfies a system dynamics equation, the actual total output power is between a maximum total discharge power and a maximum total charge power of the energy storage system, the maximum total discharge power is equal to a sum of a maximum discharge power of the power-type flywheel and a maximum discharge power of the energy-type flywheel, the maximum total charge power is equal to a sum of a maximum charge power of the power-type flywheel and a maximum charge power of the energy-type flywheel, the total state of charge is between a minimum total state of charge and a maximum total state of charge of the energy storage system.

[0021] In some embodiments, the second preset condition comprises:

[0022] a fourth difference between the total state of charge of the second time period and the target state of charge, and a fifth difference between the actual total output power of the second time period and a demand total output power of the energy storage system in the second time period are weighted and summed, and a value of the second target loss function obtained after the weighting and summing is minimum.

[0023] In some embodiments, the acquiring the second demand output power data comprises:

[0024] acquiring wind power fluctuation data and frequency fluctuation data of the power system in a historical time period before the collection time point;

[0025] processing the wind power fluctuation data and the frequency fluctuation data in the historical time period according to a pre-trained machine learning model, and outputting predicted wind power fluctuation data and predicted frequency fluctuation data in the second time period after the collection time point;

[0026] determining the second demand output power data according to the predicted wind power fluctuation data and the predicted frequency fluctuation data, the second demand output power data being the demand output power of the energy storage system in the second time period after the collection time point.

[0027] In some embodiments, the collection time point comprises a plurality of collection time points determined at every preset time interval, and the method further comprises:

[0028] The step of obtaining the first actual output power data to the frequency of the power system to which the energy storage system belongs is performed for each of the collection time points until the collection time point is the last one of the collection time points.

[0029] According to a second aspect of the embodiments of the present disclosure, a frequency regulation device of a power system is provided, comprising:

[0030] An obtaining module is configured to obtain first actual output power data, the first actual output power data being actual output power of an energy storage system in a first time period after a collection time point, the energy storage system comprising a power-type flywheel and an energy-type flywheel;

[0031] A decomposition module is configured to perform empirical mode decomposition according to the first actual output power data to determine a plurality of power components;

[0032] A determination module is configured to determine a plurality of power distribution strategies according to the plurality of power components, each of the power distribution strategies comprising a first power component distributed to the power-type flywheel and a second power component distributed to the energy-type flywheel;

[0033] An output module is configured to input the plurality of power distribution strategies into a first prediction model to output a target power distribution strategy, the first prediction model being pre-constructed based on a first constraint condition and a first target loss function, the first constraint condition being used to constrain the power distribution strategy, and the first target loss function being used to determine the target power distribution strategy from the power distribution strategies satisfying the first constraint condition, so that a total state of charge of the energy storage system in the first time period and actual total output power of the energy storage system in the first time period satisfy a first preset condition;

[0034] A control module is configured to control the power-type flywheel and the energy-type flywheel to charge or discharge according to the target power distribution strategy, so as to adjust the frequency of the power system to which the energy storage system belongs.

[0035] According to a third aspect of the embodiments of the present disclosure, a frequency regulation device of a power system is provided, comprising:

[0036] A processor;

[0037] A memory for storing processor-executable instructions;

[0038] The processor is configured to:

[0039] Obtain first actual output power data, the first actual output power data being actual output power of an energy storage system in a first time period after a collection time point, the energy storage system comprising a power-type flywheel and an energy-type flywheel;

[0040] performing empirical mode decomposition on the first actual output power data to determine a plurality of power components;

[0041] determining a plurality of power distribution strategies according to the plurality of power components, each of the power distribution strategies including a first power component distributed to the power-type flywheel and a second power component distributed to the energy-type flywheel;

[0042] inputting the plurality of power distribution strategies into a first prediction model to output a target power distribution strategy, the first prediction model being pre-constructed based on a first constraint condition and a first target loss function, the first constraint condition being used to constrain the power distribution strategy, and the first target loss function being used to determine the target power distribution strategy from the power distribution strategies satisfying the first constraint condition, such that the total state of charge of the energy storage system in the first time period and the actual total output power of the energy storage system in the first time period satisfy a first preset condition;

[0043] controlling the power-type flywheel and the energy-type flywheel to charge or discharge according to the target power distribution strategy, so as to adjust the frequency of the power system to which the energy storage system belongs.

[0044] According to a fourth aspect of the embodiments of the present disclosure, a computer readable storage medium is provided, which stores computer program instructions, and the computer program instructions are executed by a processor to implement the steps of the frequency regulation method of the power system provided in the first aspect of the present disclosure.

[0045] The technical solutions provided by the embodiments of the present disclosure can have the following beneficial effects: by setting the energy storage system to include the power-type flywheel and the energy-type flywheel, the power-type flywheel has a fast response speed and a small capacity, and the energy-type flywheel has a slow response speed and a large capacity. By combining the use of the power-type flywheel and the energy-type flywheel with complementary relationship, compared with the case of using only one flywheel in the related art, the energy storage system of the present disclosure has a better effect on regulating the frequency of the power system, and the power required by the two flywheels of the present disclosure is not higher than the power required by one flywheel, so that the cost of the energy storage system of the present disclosure is low under the condition of ensuring the same energy storage efficiency. In addition, the present disclosure can dynamically determine the power components respectively distributed to the power-type flywheel and the energy-type flywheel according to the collection time, i.e., dynamically determine the target power distribution strategy, so as to better control the two flywheels in the energy storage system to charge or discharge, and further improve the frequency regulation effect of the power system in which the energy storage system is located.

[0046] It should be understood that the foregoing general description and the following detailed description are only exemplary and explanatory, and cannot limit the present disclosure. BRIEF DESCRIPTION OF DRAWINGS

[0047] The accompanying drawings, which are incorporated herein and constitute part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, further serve to explain the principles of the present disclosure.

[0048] Figure 1 is a flowchart of a frequency modulation method of a power system according to an exemplary embodiment.

[0049] Figure 2 is a structural schematic diagram of a power system according to an exemplary embodiment.

[0050] Figure 3 is a flowchart of acquiring first actual output power data according to an exemplary embodiment.

[0051] Figure 4 is a block diagram of a frequency modulation device of a power system according to an exemplary embodiment.

[0052] Figure 5 is a block diagram of a device for frequency modulation of a power system according to an exemplary embodiment. DETAILED DESCRIPTION

[0053] The exemplary embodiments will be described in detail herein with reference to the attached drawings. The following description is made with reference to the accompanying drawings in which like reference numerals represent like elements, unless the context dictates otherwise. The following description of exemplary embodiments is not representative of all embodiments consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with some aspects of the present disclosure as detailed in the appended claims.

[0054] Figure 1 is a flowchart of a frequency modulation method of a power system according to an exemplary embodiment, as shown in Figure 1 the method can include the following steps.

[0055] Step 110, acquiring first actual output power data, the first actual output power data being actual output power of the energy storage system in a first time period after the collection time, the energy storage system including a power-type flywheel and an energy-type flywheel.

[0056] In some embodiments, the collection time can be determined according to actual conditions. In some embodiments, the collection time can be the current time, or a time determined every preset time interval starting from the current time. For example, taking the current time as 0:00am and the preset time interval as 1h, the first collection time is 0:00am, the second collection time is 1:00am, the third collection time is 2:00am, and so on.

[0057] In some embodiments, the first time period can be determined according to actual conditions, and the first time period can be 1h. For example, if the collection time is 0:00am, the first actual output power data can be the actual output power of the energy storage system in the 1h after 0:00am.

[0058] In the case where the collection time is the current time, since the first actual output power data is the actual output power in the first time period after the collection time, the first actual output power data can be predicted. For details of obtaining the first actual output power, see the following Figure 2 and related descriptions, which are not repeated here. In some embodiments, the first actual output power can also be obtained from a memory that stores the first actual output power obtained in advance.

[0059] The flywheel can be a kind of energy storage device, and the energy storage system of the present disclosure can include various energy storage devices. In some embodiments, the energy storage system can include a power flywheel and an energy flywheel, the power flywheel has fast response speed and small capacity, and the energy flywheel has slow response speed and large capacity. By combining the use of power flywheel and energy flywheel with complementary relationship, compared with the case of using only one flywheel in the related art, the energy storage system of the present disclosure realizes better effect of power system frequency modulation, and the power required by the two flywheels of the present disclosure is not higher than the power required by one flywheel, so that the cost of the energy storage system of the present disclosure is low under the condition of ensuring the same energy storage efficiency.

[0060] In step 120, the first actual output power data is subjected to empirical mode decomposition to determine a plurality of power components.

[0061] Empirical mode decomposition (EMD) is a signal decomposition method based on the time scale characteristics of the data itself, without pre-setting the basis function. In the processing of non-stationary and nonlinear data, it has obvious advantages and is suitable for analyzing nonlinear and non-stationary signal sequences.

[0062] EMD decomposition of data can obtain intrinsic mode function (IMF) components and residual components. In some embodiments, the plurality of power components can include a plurality of IMF components and a residual component.

[0063] In step 130, a plurality of power distribution strategies are determined according to the plurality of power components, each power distribution strategy including a first power component allocated to the power flywheel and a second power component allocated to the energy flywheel.

[0064] In some embodiments, the plurality of power components can be randomly combined to determine a plurality of power allocation strategies. In some embodiments, a high-frequency component in the plurality of power components can also be allocated to the power-type flywheel, and a low-frequency component in the plurality of power components can also be allocated to the energy-type flywheel, to obtain the plurality of power allocation strategies. Since the power-type flywheel responds to high-frequency frequency fluctuations, and the energy-type flywheel responds to low-frequency frequency fluctuations, allocating the high-frequency component to the power-type flywheel and the low-frequency component to the energy-type flywheel can maximize the efficiency of the energy storage system, thereby improving the frequency modulation effect of the power system.

[0065] For example, taking the plurality of power components including the IMF1-9 and the residual component obtained in the order from high frequency to low frequency as an example, the plurality of power allocation strategies can include the following 8 strategies, which are respectively: power allocation strategy 1: IMF1 is allocated to the power-type flywheel, and IMF2-IMF8 and the residual component are allocated to the energy-type flywheel; power allocation strategy 2: IMF1 and IMF2 are allocated to the power-type flywheel, and IMF3-IMF8 and the residual component are allocated to the energy-type flywheel; power allocation strategy 3: IMF1-IMF3 are allocated to the power-type flywheel, and IMF4-IMF8 and the residual component are allocated to the energy-type flywheel; power allocation strategy 4: IMF1-IMF4 are allocated to the power-type flywheel, and IMF5-IMF8 and the residual component are allocated to the energy-type flywheel; power allocation strategy 5: IMF1-IMF5 are allocated to the power-type flywheel, and IMF6-IMF8 and the residual component are allocated to the energy-type flywheel; power allocation strategy 6: IMF1-IMF6 are allocated to the power-type flywheel, and IMF7-IMF8 and the residual component are allocated to the energy-type flywheel; power allocation strategy 7: IMF1-IMF7 are allocated to the power-type flywheel, and IMF8 and the residual component are allocated to the energy-type flywheel; and power allocation strategy 8: IMF1-IMF8 are allocated to the power-type flywheel, and the residual component is allocated to the energy-type flywheel.

[0066] In step 140, the plurality of power allocation strategies are input into a first prediction model, and a target power allocation strategy is output. The first prediction model is pre-constructed based on a first constraint condition and a first target loss function. The first constraint condition is used to constrain the power allocation strategy, and the first target loss function is used to determine the target power allocation strategy from the power allocation strategies satisfying the first constraint condition, so that the total state of charge of the energy storage system in the first time period and the actual total output power of the energy storage system in the first time period satisfy a first preset condition.

[0067] In some embodiments, the first constraint condition can include at least one of: the first power component in the power allocation strategy being between the maximum discharging power and the maximum charging power of the power-type flywheel, the second power component in the power allocation strategy being between the maximum discharging power and the maximum charging power of the energy-type flywheel, the first state of charge of the power-type flywheel based on the power allocation strategy being between the minimum state of charge and the maximum state of charge of the power-type flywheel, and the second state of charge of the energy-type flywheel based on the power allocation strategy being between the minimum state of charge and the maximum state of charge of the energy-type flywheel.

[0068] In some embodiments, the first constraint condition can be represented by the following formulas (1)-(4):

[0069] u min,1 ≤u1(k,i)≤u max,1 ,i=0,1,……,n1 (1)

[0070] wherein u min,1 represents the maximum discharging power of the power-type flywheel, u max,1 represents the maximum charging power of the power-type flywheel, u1(k,i) represents the power component allocated to the power-type flywheel at the i-th time, u1(k,i) can be obtained according to the first power component in the power allocation strategy, and n1 represents the total time points included in the first time period obtained in seconds, for example, taking 1 h as the first time period, then n1 = 1*60*60 = 3600, and for example, taking 15 min as the first time period, then n1 = 15*60 = 900.

[0071] u min,2 ≤u2(k,i)≤u max,2 ,i=0,1,……,n1 (2)

[0072] wherein u min,2 represents the maximum discharging power of the energy-type flywheel, u max,2 represents the maximum charging power of the energy-type flywheel, u2(k,i) represents the power component allocated to the energy-type flywheel at the i-th time, u2(k,i) can be obtained according to the second power component in the power allocation strategy, and the description about n1 can be referred to the related description in formula (1), which will not be repeated here.

[0073] y min,1 ≤y1(k,i)≤y max,1 ,i=0,1,……,n1 (3)

[0074] wherein y min,1 represents the minimum state of charge of the power-type flywheel, y max,1y1(k, i) represents the state of charge of the power-type flywheel at the i-th moment, which can be determined according to the first state of charge of the power-type flywheel based on the power distribution strategy. For details of n1, refer to the related description in formula (1), which will not be repeated here.

[0075] y min,2 ≤y2(k,i)≤y max,2 ,i=0,1,……,n1 (4)

[0076] wherein y min,2 represents the minimum state of charge of the energy-type flywheel, y max,2 represents the maximum state of charge of the energy-type flywheel, and y2(k, i) represents the state of charge of the energy-type flywheel at the i-th moment, which can be determined according to the second state of charge of the energy-type flywheel based on the power distribution strategy. For details of n1, refer to the related description in formula (1), which will not be repeated here.

[0077] In some embodiments, y min,1 and y max,1 may be determined according to actual needs, for example, y min,1 may be 0.1, y max,1 may be 0.9, y min,2 may be 0.2, y max,2 may be 0.8, and the like.

[0078] In some embodiments, the first state of charge can be obtained based on the following formula (5):

[0079]

[0080] wherein y1(k, i+1) represents the state of charge of the power-type flywheel at the i+1-th moment, y1(k, i) represents the state of charge of the power-type flywheel at the i-th moment, u(k, i) represents the actual output power of the energy storage system at the i-th moment, which can be the sum of the actual output power of the power-type flywheel at the i-th moment and the actual output power of the energy-type flywheel at the i-th moment, t=1s, C1 is the rated capacity of the power-type flywheel, and for details of n1, refer to the related description in formula (1), which will not be repeated here.

[0081] In some embodiments, the second state of charge can be obtained based on the following formula (6):

[0082]

[0083] wherein k2(k,i+1) represents the state of charge of the energy-type flywheel obtained at the i+1th time, y2(k,i) represents the state of charge of the energy-type flywheel obtained at the ith time, u(k,i) represents the actual output power obtained by the energy storage system at the ith time, the u(k,i) can be the sum of the actual output power obtained by the power-type flywheel at the ith time and the actual output power obtained by the energy-type flywheel at the ith time, t=1s, C2 is the rated capacity of the energy-type flywheel, and n1 can refer to the related description in formula (1) and will not be described here.

[0084] In some embodiments, the u(k,i) can be obtained based on the first power component and the second power component.

[0085] In some embodiments, the total state of charge includes the first state of charge and the second state of charge, and the first preset condition includes: after the first difference between the first state of charge of the first time period and the target state of charge, the second difference between the second state of charge of the first time period and the target state of charge, and the third difference between the actual total output power of the first time period and the required total output power of the energy storage system of the first time period are weighted and summed, the value of the first target loss function obtained is the minimum.

[0086] In some embodiments, the value of the first target loss function can be obtained by formula (7) as follows:

[0087] J=a1J1+a2J2+a3J3 (7)

[0088] wherein a1, a2 and a3 can represent weight coefficients. J1 can represent the value of the first difference, J2 represents the value of the second difference, and J3 represents the value of the third difference.

[0089] In some embodiments, the target state of charge can be 0.5, and correspondingly, J1 and J2 can be obtained by formula (8) and (9) as follows:

[0090]

[0091] wherein J1 represents the value of the first difference, y1(k) represents the first state of charge at the kth time, and n1 represents the total time included in the first time period obtained in seconds.

[0092] In some embodiments, J2 can be obtained by formula (9) as follows:

[0093]

[0094] wherein J2 represents the value of the second difference, y2(k) represents the second state of charge at the kth time, and n1 represents the total time included in the first time period obtained in seconds.

[0095] In some embodiments, J3 can be obtained by formula (10) as follows:

[0096]

[0097] wherein J3 represents the value of the third difference, u1(k) represents the actual output power of the power-type flywheel at time k, u2(k) represents the actual output power of the energy-type flywheel at time k, u1(k)+u2(k) represents the actual total output power of the energy storage system at time k, p(k) represents the total demand output power of the energy storage system at time k, and n1 represents the total time included in the first time period in seconds.

[0098] In some embodiments, the power allocation strategy that minimizes the value of the first target loss function (i.e., the function value of the aforementioned J function) can be determined as the target power allocation strategy. For example, among the aforementioned eight power allocation strategies, the power allocation strategy that minimizes the value of the first target loss function can be determined as the target power allocation strategy. In some embodiments, the total demand output power of the energy storage system in the first time period can be pre-set or can be obtained from the second demand output power. For details of obtaining the second demand output power, please refer to step 210 and its related description below, which will not be repeated here.

[0099] In the embodiments of the present disclosure, the power-type flywheel and the energy-type flywheel constitute the energy storage system. Since the actual total output power of the energy storage system can be less than the total demand output power due to the constraint of the first constraint condition, the third difference is constructed in the first target loss function, so as to make the actual total output power equal to the total demand output power as much as possible. In this way, the rationality and accuracy of the target power allocation strategy can be improved.

[0100] In step 150, the power-type flywheel and the energy-type flywheel are controlled to charge or discharge according to the target power allocation strategy, so as to adjust the frequency of the power system to which the energy storage system belongs.

[0101] In some embodiments, whether the power-type flywheel and the energy-type flywheel are charged or discharged can be determined according to the total demand output power of the energy storage system in the first time period. For example, when the total demand output power > 0, the power-type flywheel and the energy-type flywheel are controlled to charge; when the total demand output power < 0, the power-type flywheel and the energy-type flywheel are controlled to discharge.

[0102] As shown in FIG. 1, Figure 2 The power system to which the energy storage system belongs can include a fan, an AC / DC converter (i.e., an alternating current to direct current converter), an energy management system, a DC / DC converter (i.e., a direct current to direct current converter), a DC / AC converter (i.e., a direct current to alternating current converter), a power-type flywheel, an energy-type flywheel, and a power grid. The power-type flywheel and the energy-type flywheel constitute the energy storage system.Figure 2 The functions and connection relationships of other devices other than the power-type flywheel and the energy-type flywheel can be referred to the related art, and will not be described herein.

[0103] In some embodiments, frequency adjustment, also known as frequency control, is a main measure to maintain the balance between active power supply and demand in a power system, and is used to ensure the frequency stability of the power system. By controlling the charging and discharging of the flywheel in the power system, the load can be adjusted, so as to adjust the frequency of the power system. The power system can be a wind storage combined power generation system.

[0104] By controlling the charging and discharging of the flywheel included in the power system to adjust the frequency of the power system, the embodiments of the present disclosure can make the best use of the resources included in the power system itself, and avoid wasting resources.

[0105] Figure 3 is a flowchart for acquiring first actual output power data according to an exemplary embodiment, as shown in Figure 3 The flowchart can include the following steps.

[0106] In step 310, second demand output power data is acquired. The second demand output power data is the demand output power of the energy storage system in a second period after the acquisition time. The length of time of the second period is greater than that of the first period.

[0107] In some embodiments, the second period can be determined according to actual conditions. For example, taking the first period of 1h as an example, the second period can be 4h.

[0108] In some embodiments, acquiring the second demand output power data can include: acquiring wind power fluctuation data and frequency fluctuation data of the power system in a historical period before the acquisition time; processing the wind power fluctuation data and the frequency fluctuation data in the historical period according to a pre-trained machine learning model to output predicted wind power fluctuation data and predicted frequency fluctuation data in a second period after the acquisition time; and determining the second demand output power data according to the predicted wind power fluctuation data and the predicted frequency fluctuation data, the second demand output power data being the demand output power of the energy storage system in the second period after the acquisition time.

[0109] In some embodiments, the historical period can be determined according to actual conditions. For example, the historical period can be 24h before the acquisition time. In some embodiments, the pre-trained machine learning model can be used to process the wind power fluctuation data and the frequency fluctuation data in the historical period before the acquisition time to output the wind power fluctuation data and the frequency fluctuation data in the second period after the acquisition time.

[0110] In some embodiments, the pre-trained machine learning model can be trained by, for example,Figure 2 The energy management system shown obtains wind power fluctuation data and frequency fluctuation data in a historical period. In some embodiments, the second demand output power data can be obtained by the following formula (11):

[0111]

[0112] wherein P represents the second demand output power, k f represents the active frequency modulation coefficient, Δf represents the power system frequency deviation, i.e. the frequency fluctuation data, f N represents the power system rated power, P t represents the wind farm active power, i.e. the wind power fluctuation data. Wherein the wind farm refers to a batch of power stations composed of wind turbine generators or wind turbine generator groups, collection lines, main step-up transformers and other equipment.

[0113] In some embodiments, the active frequency modulation coefficient k f , the power system rated power f N can be set according to actual needs, for example, k f can be selected from 10-50, f N may be 50Hz. As can be seen from the above, the second demand output power data in the second period can be obtained according to the predicted wind power fluctuation data and frequency fluctuation data in the second period and the above formula (11).

[0114] In some embodiments, the machine learning model can be trained according to a plurality of training sample data, each training sample data including actual wind power fluctuation data and actual frequency fluctuation data of a sample period. For details of training of the machine learning model, please refer to the end-to-end training method in the related art, which will not be described here.

[0115] In some embodiments, the second demand output power data can also be obtained by other means. For example, in some embodiments, obtaining the second demand output power data can include: obtaining actual demand output power data of the energy storage system in a historical period before the collection time; processing the actual demand output power data in the historical period according to the pre-trained machine learning model to output the second demand output power data, the second demand output power data being the demand output power of the energy storage system in a second period after the collection time.

[0116] In this case, the pre-trained machine learning model can be used to process the actual demand output power data in the historical period before the collection time, output the demand output power in the second period after the collection time, i.e., output the second actual output power data, which can be predicted by the machine learning model. Correspondingly, the machine learning model can be trained according to a plurality of training sample data, each training sample data including actual demand output power data of a sample period. Details of training of the machine learning model can refer to an end-to-end training manner in the related art, which will not be described here.

[0117] In step 320, the second demand output power data is input into a second prediction model to output second actual output power data, the second actual output power data being actual output power of the energy storage system in the second period, the second prediction model being pre-constructed based on a second constraint condition and a second target loss function, the second constraint condition being used to constrain at least one of a total capacity, an actual total output power and a total state of charge of the energy storage system, and the second target loss function being used to output the second actual output power data under the second constraint condition, so that the total state of charge of the energy storage system in the second period and the actual total output power of the energy storage system in the second period satisfy a second preset condition.

[0118] In some embodiments, the second constraint condition can include at least one of the following: the total capacity being equal to a sum of the first capacity of the power flywheel and the second capacity of the energy flywheel, the total state of charge satisfying a system dynamics equation, the actual total output power being between a maximum total discharge power and a maximum total charge power of the energy storage system, the maximum total discharge power being equal to a sum of a maximum discharge power of the power flywheel and a maximum discharge power of the energy flywheel, the maximum total charge power being equal to a sum of a maximum charge power of the power flywheel and a maximum charge power of the energy flywheel, and the total state of charge being between a minimum total state of charge and a maximum total state of charge of the energy storage system.

[0119] In some embodiments, the second constraint condition can be obtained by the following formulas (12)-(17):

[0120] C = C1 + C2 (12)

[0121] wherein C represents the total capacity of the energy storage system, C1 represents the first capacity, i.e., the rated capacity of the power flywheel, and C2 represents the second capacity, i.e., the rated capacity of the energy flywheel.

[0122]

[0123] where y(k+i+1) represents the total state of charge of the energy storage system at the i+1 time, y(k+i) represents the total state of charge of the energy storage system at the i time, u(k+i) represents the actual total output power of the energy storage system at the i time, t = 1 s, C represents the total capacity of the energy storage system, and n2 represents the total time included in the second time period in seconds. For example, if the second time period is 1 h, then n2 = 1 * 60 * 60 = 3600.

[0124] u min ≤u(k+i)≤u max ,i=0,1,……,n2-1 (14)

[0125] where u(k+i) represents the actual total output power of the energy storage system at the i time, u min represents the maximum discharging power of the energy storage system, and u max represents the maximum charging power of the energy storage system. For n2, refer to the related description above, which will not be repeated here.

[0126] u min =u min,1 +u min,2 (15)

[0127] where u min represents the maximum discharging power of the energy storage system, u min,1 represents the maximum discharging power of the power-type flywheel, and u min,2 represents the maximum discharging power of the energy-type flywheel.

[0128] u max =u max,1 +u max,2 (16)

[0129] where u max represents the maximum charging power of the energy storage system, u max,1 represents the maximum charging power of the power-type flywheel, and u max,2 represents the maximum charging power of the energy-type flywheel.

[0130] y min ≤y(k+i)≤y max ,i=0,1,……,n2-1 (17)

[0131] where y(k+i) represents the total state of charge at the i time, y min represents the minimum state of charge of the energy storage system, and y max represents the maximum state of charge of the energy storage system. For n2, refer to the related description above, which will not be repeated here. In some embodiments, y min and y maxmay be determined according to actual conditions, for example, y min may be 0.1, y max may be 0.9. In some embodiments, the total state of charge can satisfy the system dynamics equation by the above formula (13).

[0132] In some embodiments, the second preset condition can include: a fourth difference between the total state of charge of the second period and the target state of charge, and a fifth difference between the actual total output power of the second period and the required total output power of the energy storage system of the second period, and the value of the second target loss function obtained after weighted summation of the fourth difference and the fifth difference is minimum.

[0133] In some embodiments, the value of the second target loss function can be obtained by the following formula (18):

[0134] J' = a4J4 + a5J5 (18)

[0135] Wherein, J' represents the value of the second target loss function, a4 and a5 can represent weight coefficients. J4 can represent the value of the fourth difference, and J5 represents the value of the fifth difference.

[0136] As described above, the target state of charge can be 0.5, and correspondingly, J4 and J5 can be obtained by the following formulas (19) and (20) respectively:

[0137]

[0138] Wherein, J4 represents the value of the fourth difference, y(k) represents the total state of charge at time k, and n2 represents the total time included in the second period in seconds. By summing the difference between the total state of charge at each time k and the target state of charge, the fourth difference between the total state of charge of the second period and the target state of charge can be obtained.

[0139]

[0140] Wherein, J5 represents the value of the fifth difference, u(k) represents the actual total output power of the energy storage system at time k, p(k) represents the required total output power of the energy storage system at time k, and n2 represents the total time included in the second period in seconds. By summing the difference between the actual total output power at each time k and the required total output power, the fifth difference between the actual total output power of the second period and the required total output power of the energy storage system of the second period can be obtained.

[0141] Step 330, obtaining the first actual output power data from the second actual output power data.

[0142] As described above, the second actual output power data is the actual output power of the energy storage system in a second time period after the collection time, and the first actual output power data is the actual output power of the energy storage system in a first time period after the collection time, and the length of the second time period is greater than the length of the first time period.

[0143] In some embodiments, the actual output power data of the first time period in the second actual output power data can be determined as the first actual output power data. For example, if the collection time is 0:00 am, and the second actual output power data is the actual output power of the energy storage system from 0:00 am to 4:00 am, then the first actual output power data is the actual output power of the energy storage system from 0:00 am to 1:00 am.

[0144] As described above, in some embodiments, the collection time includes a plurality of collection times determined at a preset time interval, and the method further comprises: for each collection time, performing the steps of obtaining the first actual output power data to adjusting the frequency of the power system to which the energy storage system belongs, until the collection time is the last collection time in the plurality of collection times. Thus, the frequency regulation method of the power system of the present disclosure can be a cyclic frequency regulation.

[0145] For example, the foregoing steps 310-320-330-120-130-140-150 can be sequentially performed at each collection time, wherein the target power distribution strategy output by step 150 is used to control the charging and discharging of the power flywheel and the energy flywheel between the current collection time and the next collection time in a preset time interval to adjust the frequency of the power system at the preset time interval. Then, when the collection time is not the last collection time, the foregoing steps 310-320-330-120-130-140-150 are sequentially performed at the next collection time until the last collection time stops performing the foregoing steps, completing the frequency regulation method of the power system of the present disclosure.

[0146] Figure 4 is a block diagram of a frequency regulation device of a power system according to an exemplary embodiment. Referring to Figure 4 The device 400 includes an acquisition module 410, a decomposition module 420, a determination module 430, an output module 440, and a control module 450.

[0147] The acquisition module 410 is configured to acquire first actual output power data, the first actual output power data being the actual output power of an energy storage system in a first time period after a collection time, the energy storage system including a power flywheel and an energy flywheel;

[0148] The decomposition module 420 is configured to perform empirical mode decomposition according to the first actual output power data to determine a plurality of power components;

[0149] The determining module 430 is configured to determine a plurality of power allocation strategies according to a plurality of power components, each of the power allocation strategies including a first power component allocated to the power-type flywheel and a second power component allocated to the energy-type flywheel.

[0150] The output module 440 is configured to input the plurality of power allocation strategies into a first prediction model to output a target power allocation strategy, the first prediction model being pre-constructed based on a first constraint condition and a first target loss function, the first constraint condition being used to constrain the power allocation strategy, and the first target loss function being used to determine the target power allocation strategy from the power allocation strategies satisfying the first constraint condition, such that a total state of charge of the energy storage system in the first time period and an actual total output power of the energy storage system in the first time period satisfy a first preset condition.

[0151] The control module 450 is configured to control the power-type flywheel and the energy-type flywheel to charge or discharge according to the target power allocation strategy, so as to adjust a frequency of a power system to which the energy storage system belongs.

[0152] In some embodiments, the first constraint condition includes at least one of:

[0153] the first power component in the power allocation strategy is between a maximum discharging power and a maximum charging power of the power-type flywheel, the second power component in the power allocation strategy is between a maximum discharging power and a maximum charging power of the energy-type flywheel, a first state of charge of the power-type flywheel based on the power allocation strategy is between a minimum state of charge and a maximum state of charge of the power-type flywheel, and a second state of charge of the energy-type flywheel based on the power allocation strategy is between a minimum state of charge and a maximum state of charge of the energy-type flywheel.

[0154] In some embodiments, the total state of charge includes the first state of charge and the second state of charge, and the first preset condition includes:

[0155] a first difference between the first state of charge in the first time period and a target state of charge, a second difference between the second state of charge in the first time period and the target state of charge, and a third difference between the actual total output power in the first time period and a required total output power of the energy storage system in the first time period are weighted and summed to obtain a minimum value of the first target loss function.

[0156] In some embodiments, the acquisition module 310 is further configured to:

[0157] obtain second demand output power data, the second demand output power data being demand output power of the energy storage system in a second time period after the collection time point, a time length of the second time period being greater than that of the first time period;

[0158] input the second demand output power data into a second prediction model, and output second actual output power data, the second actual output power data being actual output power of the energy storage system in the second time period, the second prediction model being pre-constructed based on a second constraint condition and a second target loss function, the second constraint condition being used to constrain at least one of a total capacity of the energy storage system, the actual total output power and the total state of charge, the second target loss function being used to output the second actual output power data under the second constraint condition, so that the total state of charge of the energy storage system in the second time period and the actual total output power of the energy storage system in the second time period satisfy a second preset condition;

[0159] obtain the first actual output power data from the second actual output power data.

[0160] In some embodiments, the second constraint condition comprises at least one of:

[0161] the total capacity being equal to a sum of a first capacity of the power-type flywheel and a second capacity of the energy-type flywheel, the total state of charge satisfying a system dynamics equation, the actual total output power being between maximum total discharging power and maximum total charging power of the energy storage system, the maximum total discharging power being equal to a sum of maximum discharging power of the power-type flywheel and maximum discharging power of the energy-type flywheel, the maximum total charging power being equal to a sum of maximum charging power of the power-type flywheel and maximum charging power of the energy-type flywheel, and the total state of charge being between minimum total state of charge and maximum total state of charge of the energy storage system.

[0162] In some embodiments, the second preset condition comprises:

[0163] a value of the second target loss function being minimum after a weighted sum of a fourth difference between the total state of charge in the second time period and the target state of charge and a fifth difference between the actual total output power in the second time period and demand total output power of the energy storage system in the second time period.

[0164] In some embodiments, the obtaining module 410 is further configured to:

[0165] obtain actual demand output power data of the energy storage system in a historical time period before the collection time point;

[0166] processing the actual demand output power data in the historical period according to the pre-trained machine learning model, to output the second demand output power data, the second demand output power data being the demand output power of the energy storage system in the second period after the collection time.

[0167] In some embodiments, the obtaining module 310 is further configured to:

[0168] obtain wind power fluctuation data and frequency fluctuation data of the power system in a historical period before the collection time;

[0169] processing the wind power fluctuation data and the frequency fluctuation data in the historical period according to the pre-trained machine learning model, to output predicted wind power fluctuation data and predicted frequency fluctuation data in the second period after the collection time;

[0170] determining the second demand output power data according to the predicted wind power fluctuation data and the predicted frequency fluctuation data, the second demand output power data being the demand output power of the energy storage system in the second period after the collection time.

[0171] In some embodiments, the collection time includes a plurality of collection times determined at every preset time interval, and the apparatus further includes:

[0172] the performing module is configured to, for each of the collection times, perform the steps of obtaining the first actual output power data to adjusting the frequency of the power system to which the energy storage system belongs, until the collection time is the last collection time in the plurality of collection times.

[0173] As to the apparatus in the above embodiments, the specific manners in which various modules perform operations have been described in detail in the embodiments of the method, and thus will not be described in detail here.

[0174] The present disclosure also provides a computer-readable storage medium having stored thereon computer program instructions, which, when executed by a processor, implement the steps of the frequency regulation method of the power system provided by the present disclosure.

[0175] Figure 5 is a block diagram of an apparatus 500 for frequency regulation of a power system according to an exemplary embodiment. For example, the apparatus 500 can be provided as a server. Refer to Figure 5The apparatus 500 also includes a processing component 522 that is configured to execute instructions and manipulate data to perform various operations, including the operations described above with regard to the power system frequency regulation method. The processing component 522 can be a general purpose central processing unit (CPU), processor cores, a semiconductor-based microprocessor, or any other device suitable for retrieval and execution of instructions. The apparatus 500 further includes a memory component 532 that is configured to store data and instructions for use by the processing component 522. The memory component 532 can include non-removable memory or removable memory that is readable and writable by the processing component 522. The memory component 522 can include solid state memory such as flash memory, magnetic disk drives, optical disks, or any other storage medium that is readable and writable by the processing component 522. The memory component 532 can include volatile memory such as random access memory (RAM) or static random access memory (SRAM), or non-volatile memory such as read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The memory component 532 can include one or more memory components 532 that are configured to store instructions and data for use by the processing component 522. The memory component 532 can be considered a computer-readable medium.

[0176] The apparatus 500 can also include a power component 526 that is configured to perform power management for the apparatus 500, a wired or wireless network interface 550 that is configured to connect the apparatus 500 to a network, and an input / output interface 558. The apparatus 500 can operate based on an operating system stored in the memory component 532, such as Windows Server TM TM TM TM TM Mac OS X

[0177] Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure. It is intended that the specification and examples be considered as exemplary only, with the true scope and spirit of the disclosure being indicated by the following claims.

[0178] It is to be understood that the disclosure is not limited to the precise construction described and shown in the drawings and that various modifications and changes can be made by those skilled in the art without departing from the scope of the disclosure. The scope of the disclosure is limited only by the claims that follow.​​​​

Claims

1. A method of frequency regulation of an electric power system, characterized by, The method comprises: acquiring first actual output power data, the first actual output power data being actual output power of a power storage system in a first time period after a collection time point, the power storage system comprising a power-type flywheel and an energy-type flywheel; performing empirical mode decomposition according to the first actual output power data to determine a plurality of power components; determining a plurality of power distribution strategies according to the plurality of power components, each power distribution strategy comprising a first power component distributed to the power-type flywheel and a second power component distributed to the energy-type flywheel; inputting the plurality of power distribution strategies into a first prediction model to output a target power distribution strategy, the first prediction model being pre-constructed based on a first constraint condition and a first target loss function, the first constraint condition being used to constrain the power distribution strategy, and the first target loss function being used to determine the target power distribution strategy from the power distribution strategies satisfying the first constraint condition, so that a total state of charge of the power storage system in the first time period and actual total output power of the power storage system in the first time period satisfy a first preset condition; controlling the power-type flywheel and the energy-type flywheel to charge or discharge according to the target power distribution strategy, so as to adjust a frequency of a power system to which the power storage system belongs; the first constraint condition comprises at least one of the following: the first power component in the power distribution strategy is between a maximum discharging power and a maximum charging power of the power-type flywheel, the second power component in the power distribution strategy is between a maximum discharging power and a maximum charging power of the energy-type flywheel, a first state of charge of the power-type flywheel based on the power distribution strategy is between a minimum state of charge and a maximum state of charge of the power-type flywheel, and a second state of charge of the energy-type flywheel based on the power distribution strategy is between a minimum state of charge and a maximum state of charge of the energy-type flywheel; the total state of charge comprises the first state of charge and the second state of charge, and the first preset condition comprises: a value of the first target loss function is minimum after weighted summation of a first difference between the first state of charge in the first time period and a target state of charge, a second difference between the second state of charge in the first time period and the target state of charge, and a third difference between the actual total output power in the first time period and a required total output power of the power storage system in the first time period.

2. The method of claim 1, wherein, the acquiring first actual output power data comprises: acquiring second required output power data, the second required output power data being required output power of the power storage system in a second time period after the collection time point, the second time period being longer than the first time period; inputting the second demand output power data into a second prediction model to output second actual output power data, the second actual output power data being actual output power of the energy storage system in the second time period, the second prediction model being pre-constructed based on a second constraint condition and a second target loss function, the second constraint condition being used to constrain at least one of a total capacity of the energy storage system, the actual total output power and the total state of charge, the second target loss function being used to output the second actual output power data under the second constraint condition, so that the total state of charge of the energy storage system in the second time period and the actual total output power of the energy storage system in the second time period satisfy a second preset condition; obtaining the first actual output power data from the second actual output power data.

3. The method of claim 2, wherein, The second constraint condition comprises at least one of: the total capacity being equal to a sum of a first capacity of the power-type flywheel and a second capacity of the energy-type flywheel, the total state of charge satisfying a system dynamics equation, the actual total output power being between a maximum total discharge power and a maximum total charge power of the energy storage system, the maximum total discharge power being equal to a sum of a maximum discharge power of the power-type flywheel and a maximum discharge power of the energy-type flywheel, the maximum total charge power being equal to a sum of a maximum charge power of the power-type flywheel and a maximum charge power of the energy-type flywheel, the total state of charge being between a minimum total state of charge and a maximum total state of charge of the energy storage system.

4. The method of claim 2, wherein, The second preset condition comprises: a value of the second target loss function being minimum after a fourth difference between the total state of charge in the second time period and the target state of charge and a fifth difference between the actual total output power in the second time period and a demand total output power of the energy storage system in the second time period are weighted and summed.

5. The method of claim 2, wherein, The obtaining the second demand output power data comprises: obtaining wind power fluctuation data and frequency fluctuation data of the power system in a historical time period before the collection time point; processing the wind power fluctuation data and the frequency fluctuation data in the historical time period according to a pre-trained machine learning model to output predicted wind power fluctuation data and predicted frequency fluctuation data in the second time period after the collection time point; determining the second demand output power data according to the predicted wind power fluctuation data and the predicted frequency fluctuation data, the second demand output power data being the demand output power of the energy storage system in the second time period after the collection time point.

6. The method of claim 1, wherein, The collection time point comprises a plurality of collection time points determined at every preset time interval, and the method further comprises: for each collection time point, performing the steps of obtaining the first actual output power data to adjusting the frequency of the power system to which the energy storage system belongs until the collection time point is the last collection time point in the plurality of collection time points.

7. A frequency regulating device for an electric power system, characterized by ​ An acquisition module configured to acquire first actual output power data, the first actual output power data being actual output power of an energy storage system in a first time period after a collection time, the energy storage system comprising a power flywheel and an energy flywheel; A decomposition module configured to perform empirical mode decomposition according to the first actual output power data to determine a plurality of power components; A determination module configured to determine a plurality of power distribution strategies according to the plurality of power components, each power distribution strategy comprising a first power component distributed to the power flywheel and a second power component distributed to the energy flywheel; An output module configured to input the plurality of power distribution strategies into a first prediction model to output a target power distribution strategy, the first prediction model being pre-constructed based on a first constraint condition and a first target loss function, the first constraint condition being used to constrain the power distribution strategy, and the first target loss function being used to determine the target power distribution strategy from the power distribution strategies satisfying the first constraint condition, such that a total state of charge of the energy storage system in the first time period and actual total output power of the energy storage system in the first time period satisfy a first preset condition; A control module configured to control the power flywheel and the energy flywheel to charge or discharge according to the target power distribution strategy, so as to adjust a frequency of a power system to which the energy storage system belongs. The first constraint condition comprises at least one of the following: the first power component in the power distribution strategy is between a maximum discharging power and a maximum charging power of the power flywheel, the second power component in the power distribution strategy is between a maximum discharging power and a maximum charging power of the energy flywheel, a first state of charge of the power flywheel based on the power distribution strategy is between a minimum state of charge and a maximum state of charge of the power flywheel, and a second state of charge of the energy flywheel based on the power distribution strategy is between a minimum state of charge and a maximum state of charge of the energy flywheel; The total state of charge comprises the first state of charge and the second state of charge, and the first preset condition comprises that a value of a first target loss function obtained by weighted sum of a first difference between the first state of charge in the first time period and a target state of charge, a second difference between the second state of charge in the first time period and the target state of charge, and a third difference between the actual total output power in the first time period and a required total output power of the energy storage system in the first time period is minimum.

8. A frequency regulating device for an electric power system, characterized by comprise: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to: acquire first actual output power data, the first actual output power data being actual output power of an energy storage system in a first time period after a collection time, the energy storage system comprising a power flywheel and an energy flywheel; perform empirical mode decomposition according to the first actual output power data to determine a plurality of power components; determining a plurality of power allocation strategies according to a plurality of power components, each of the power allocation strategies including a first power component allocated to the power-type flywheel and a second power component allocated to the energy-type flywheel; inputting the plurality of power allocation strategies into a first prediction model to output a target power allocation strategy, the first prediction model being pre-constructed based on a first constraint condition and a first target loss function, the first constraint condition being used to constrain the power allocation strategies, and the first target loss function being used to determine the target power allocation strategy from the power allocation strategies satisfying the first constraint condition, such that a total state of charge of the energy storage system in the first time period and an actual total output power of the energy storage system in the first time period satisfy a first preset condition; controlling the power-type flywheel and the energy-type flywheel to charge or discharge according to the target power allocation strategy, so as to adjust a frequency of a power system to which the energy storage system belongs; wherein the first constraint condition includes at least one of the following: the first power component in the power allocation strategy is between a maximum discharging power and a maximum charging power of the power-type flywheel, the second power component in the power allocation strategy is between a maximum discharging power and a maximum charging power of the energy-type flywheel, a first state of charge of the power-type flywheel based on the power allocation strategy is between a minimum state of charge and a maximum state of charge of the power-type flywheel, and a second state of charge of the energy-type flywheel based on the power allocation strategy is between a minimum state of charge and a maximum state of charge of the energy-type flywheel; the total state of charge includes the first state of charge and the second state of charge, and the first preset condition includes that a first difference between the first state of charge in the first time period and a target state of charge, a second difference between the second state of charge in the first time period and the target state of charge, and a third difference between the actual total output power in the first time period and a required total output power of the energy storage system in the first time period are weighted and summed to obtain a minimum value of the first target loss function.

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