Energy storage system energy management optimization method based on artificial potential field algorithm

Abstract

The invention belongs to the technical field of energy storage, and particularly relates to an energy storage system energy management optimization method based on an artificial potential field algorithm, and the method comprises the steps: obtaining the value of an empirical parameter in the artificial potential field algorithm; using the value of the empirical parameter to operate an artificial potential field algorithm to perform energy management on the energy storage system so as to verify whether the value of the empirical parameter can enable the energy storage system to operate normally; and for the value meeting the normal operation condition of the energy storage system, the artificial potential field algorithm is optimized according to the value, so that the loss cost of the energy storage system is minimum. According to the method, the loss cost of the energy storage system is fully considered, and an energy management strategy based on an artificial potential field algorithm is reasonably optimized through combination of offline operation and online operation, so that the economical efficiency of the energy storage system is improved on the premise of ensuring stable operation of the energy storage system.

Description

Technical Field

[0001] The present invention belongs to the technical field of energy storage, and in particular relates to an energy storage system energy management optimization method based on an artificial potential field algorithm. Background Art

[0002] With the increasing development of renewable energy, energy storage has become an important component of the power system. Moreover, as one of the most promising solutions to adapt to the intermittent and uncertain nature of renewable energy, energy storage is widely used to improve the stability and economy of the system. Electric energy storage is suitable for short-term (i.e., a few hours to a few days) and small and medium-sized energy storage applications. Among them, supercapacitors and batteries are usually used as short-term electric energy storage devices to store energy. Supercapacitors mainly include double-layer capacitors, lithium-ion capacitors, sodium-ion capacitors and other types. Batteries mainly include lithium-ion batteries, sodium-ion batteries, lithium metal batteries, semi-solid batteries, solid-state batteries and other types. Electric energy storage devices such as batteries and supercapacitors have significant advantages in power density. For long-term (i.e., weeks, months, seasons) and large-scale applications, hydrogen energy storage technology may be a better choice. Hydrogen energy storage technology usually includes devices such as electrolyzers, fuel cells, and hydrogen storage tanks to convert electrical energy into hydrogen energy storage, and convert hydrogen energy into electrical energy utilization. Hydrogen energy storage has significant advantages in energy density. Considering the differences between electric and hydrogen energy storage technologies in terms of physical properties such as energy density, power density, cycle life and energy efficiency, hybrid energy storage systems combining the two technologies are under study.

[0003] Among them, energy management methods for energy storage systems are constantly being developed, which can be roughly divided into rule-based methods, optimization-based methods, and learning-based methods. In particular, rule-based methods are mainly implemented using high-pass filters or low-pass filters (LPF). For example, the load is decomposed into high-frequency components and low-frequency components by LPF, and then allocated to the electric energy storage device and the hydrogen energy storage device respectively. However, the disadvantage of LPF with a fixed cutoff frequency is that when the system operating conditions change, its cutoff frequency cannot be adjusted accordingly, so that the energy storage device may be overcharged or over-discharged. Accordingly, the above method is optimized by an artificial potential field algorithm to maintain the stability of the state of charge (SOC) of the electric energy storage device. However, the above energy management method for energy storage systems based on the artificial potential field algorithm only considers the stable control of SOC, and does not take into account the economy of the energy storage system, such as the life of the device, and there are certain deficiencies. Summary of the invention

[0004] In view of the above-mentioned shortcomings of the prior art, the purpose of the present invention is to propose an optimization method for energy management strategy based on artificial potential field algorithm, and optimize the empirical parameters in the artificial potential field algorithm taking into account the loss cost of the energy storage system to improve the economy of the energy storage system.

[0005] To achieve the above-mentioned purpose and other related purposes, the present invention provides an energy management optimization method for an energy storage system based on an artificial potential field algorithm, comprising: obtaining the value of an empirical parameter in the artificial potential field algorithm; using the value of the empirical parameter to run the artificial potential field algorithm to perform energy management on the energy storage system to verify whether the value of the empirical parameter can enable the energy storage system to operate normally; for the value that meets the normal operating conditions of the energy storage system, optimizing the artificial potential field algorithm based on the value to minimize the loss cost of the energy storage system.

[0006] According to a specific embodiment of the present invention, the step of running an artificial potential field algorithm to perform energy management on an energy storage system includes: pre-defining a corresponding virtual force based on the state of charge of an electric energy storage device; determining a corresponding power allocation factor according to the virtual force of the electric energy storage device regarding its state of charge, and using the power allocation factor to adjust the cutoff frequency of a low-pass filter in the energy storage system to adjust the input / output power of the electric energy storage device; and synchronously adjusting the input / output power of the hydrogen energy storage device according to the input / output power of the electric energy storage device.

[0007] According to a specific embodiment of the present invention, the empirical parameters in the artificial potential field algorithm include shaping parameters preset for the virtual force of the electric energy storage device with respect to its state of charge and initial parameters preset for the corresponding power allocation factor.

[0008] According to a specific embodiment of the present invention, the virtual force F sc as follows:

[0009]

[0010] x = SOC - SOC mid ,

[0011] Among them, a is the shaping parameter preset by the virtual force, SOC min Indicates the preset minimum value of the state of charge of the energy storage device, SOC max Indicates the preset maximum value of the state of charge of the energy storage device, SOC mid Indicates the preset intermediate value of the state of charge of the energy storage device, i.e. SOC min <SOC mid <SOC max .

[0012] According to a specific embodiment of the present invention, the power allocation factor K sc as follows:

[0013]

[0014] Among them, K scmid represents the preset initial parameters of the power allocation factor, F sc Indicates the virtual force corresponding to the charge state of the electric energy storage device.

[0015] According to a specific embodiment of the present invention, the step of obtaining the value of the empirical parameter in the artificial potential field algorithm includes: using a particle swarm optimization algorithm to calculate the optimal value of the empirical parameter in the artificial potential field algorithm.

[0016] According to a specific embodiment of the present invention, the step of using a particle swarm optimization algorithm to calculate the optimal value of an empirical parameter in an artificial potential field algorithm includes: obtaining an initial value of the empirical parameter in the artificial potential field algorithm as the initial value of the particle swarm optimization algorithm; using the loss cost of the energy storage system as the fitness value of the particle swarm optimization algorithm, and iteratively calculating the particle swarm optimization algorithm to obtain the optimal value of the empirical parameter in the artificial potential field algorithm.

[0017] According to a specific embodiment of the present invention, the loss cost of the energy storage system includes the degradation cost of the hydrogen energy storage device and the deviation cost of the charge state of the electric energy storage device from a preset value.

[0018] According to a specific embodiment of the present invention, the hydrogen energy storage device includes an electrolyzer, and the degradation cost of the hydrogen energy storage device is calculated according to the following formula:

[0019]

[0020] Among them, J eldeg represents the degradation cost of the electrolyzer, J elsize represents the purchase cost of the electrolyzer, U elbeol Indicates the voltage drop of the electrolytic cell before reaching the end of its service life, ΔU elb Indicates the actual voltage drop of the electrolytic cell.

[0021] According to a specific embodiment of the present invention, the electric energy storage device includes a supercapacitor, and the deviation cost of the state of charge of the electric energy storage device from a preset value is calculated according to the following formula:

[0022] J scele =β ele E sc |SOC end -SOC mid |,

[0023] Among them, J scele Indicates the deviation cost of the supercapacitor charge state from the preset value, β ele represents the current electricity price, E sc Indicates the capacity of the supercapacitor, SOC midIndicates the preset intermediate value of the supercapacitor state of charge, SOC end Indicates the final value of the supercapacitor state of charge.

[0024] The present invention provides an energy management optimization method for an energy storage system based on an artificial potential field algorithm, which fully considers the loss cost of the energy storage system and reasonably optimizes the energy management strategy based on the artificial potential field algorithm through the combination of offline calculation and online calculation, thereby improving the economy of the energy storage system while ensuring its stable operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 A flowchart of a specific embodiment of an energy storage system energy management optimization method based on an artificial potential field algorithm provided by the present invention. DETAILED DESCRIPTION

[0026] The following will describe the embodiments of the present invention with reference to the accompanying drawings and preferred embodiments. Those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be understood that the preferred embodiments are only for illustrating the present invention, not for limiting the scope of protection of the present invention.

[0027] It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present invention, and thus the drawings only show components related to the present invention rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed arbitrarily, and the component layout may also be more complicated.

[0028] In the following description, numerous details are discussed to provide a more thorough explanation of the embodiments of the present invention. However, it is obvious to those skilled in the art that the embodiments of the present invention can be implemented without these specific details. In other embodiments, structures and devices known to the public are shown in the form of block diagrams rather than in detail to avoid making the embodiments of the present invention difficult to understand.

[0029] See also Figure 1 An energy storage system energy management optimization method based on an artificial potential field algorithm is shown, comprising:

[0030] Step S100, obtaining the value of the empirical parameter in the artificial potential field algorithm.

[0031] Step S200, using the values ​​of the empirical parameters to run an artificial potential field algorithm to perform energy management on the energy storage system, so as to verify whether the values ​​of the empirical parameters can enable the energy storage system to operate normally.

[0032] Step S300, for the values ​​that meet the normal operating conditions of the energy storage system, the artificial potential field algorithm is optimized according to the values ​​to minimize the loss cost of the energy storage system.

[0033] First of all, it should be noted that the energy management strategy based on the artificial potential field algorithm is to use the artificial potential field algorithm to adjust the cutoff frequency of the low-pass filter, and the low-pass filter can allocate the high-frequency part of the power to the electric energy storage device and the low-frequency part to the hydrogen energy storage device, or use the low-pass filter to make the electric energy storage device provide high-frequency power and the hydrogen energy storage device provide low-frequency power. Since the above-mentioned process of adjusting the power distribution between the electric energy storage device and the hydrogen energy storage device through the low-pass filter requires adjusting the input / output power of the electric energy storage device, in order to avoid overcharging or over-discharging, it is necessary to maintain the SOC of the electric energy storage device stable. Therefore, the artificial potential field algorithm is used to optimize the power distribution of the electric energy storage device.

[0034] In a specific embodiment, a supercapacitor is used as an example for an electric energy storage device. First, the virtual force of the supercapacitor is determined using an artificial potential field, and the virtual force F sc It can be expressed as:

[0035]

[0036] Where x = SOC-SOC mid , while SOC mid is a preset minimum SOC min and the preset maximum SOC max The value between is the SOC that the supercapacitor expects to operate, which can be adjusted accordingly according to the actual situation. a is the shaping parameter preset by the virtual force, which determines the virtual force F sc The rate of change.

[0037] Secondly, the virtual force F can be used sc To calculate the power allocation factor K corresponding to the supercapacitor sc , and the calculation formula is as follows:

[0038]

[0039] Among them, K scmid It is the preset initial parameter used to adjust the input / output power of the supercapacitor.

[0040] Finally, the power allocation factor K can be calculated according to scThe input power of the supercapacitor during charging or the output power during discharging is adjusted, that is, the cutoff frequency of the low-pass filter is correspondingly adjusted, so that the net load power of the system can be reasonably distributed and the overcharging or over-discharging of the supercapacitor can be fully avoided.

[0041] However, in order to avoid overcharging or overdischarging of the energy storage device, the energy management strategy based on the artificial potential field algorithm only considers the SOC of the energy storage device during calculation, and does not consider the loss and life of the device. In this regard, in this embodiment, the artificial potential field algorithm is further optimized based on the loss cost of the energy storage system, that is, the empirical parameters therein are optimized, thereby improving the economy of the energy storage system.

[0042] Therefore, the following objective function is constructed regarding the loss cost of the energy storage system:

[0043] J cost =J eldeg +J scele ,and J scele =β ele E sc |SOC end -SOC mid

[0044] Among them, J cost Represents the objective function, and the objective function corresponds to the total loss cost of the energy storage system, J elsize represents the degradation cost of the electrolyzer, J scele The deviation cost of the supercapacitor’s SOC from the preset value, U elbeol Indicates the voltage drop of the electrolytic cell before reaching the end of its service life, ΔU elb Indicates the real-time voltage drop of the electrolytic cell, β ele Indicates electricity price, SOC end Indicates the final value of the supercapacitor during actual testing, SOC mid This is the middle value of the above preset.

[0045] It should be noted that in this embodiment, the hydrogen energy storage device specifically takes an electrolyzer as an example, but it is not used to limit the components included in the hydrogen energy storage device. For example, it can also include a fuel cell, a hydrogen storage tank, etc. Similarly, the electric energy storage device takes a supercapacitor as an example. In practical applications, the electric energy storage device can also use a battery, or a mixture of a supercapacitor and a battery. There are no excessive restrictions on this. The modifications and embellishments made to the embodiments of the present invention by those skilled in the art without departing from the spirit of the present invention still fall within the scope of the invention application of the present invention.

[0046] In this embodiment, how to optimize the artificial potential field algorithm, that is, by optimizing the shaping parameter a and the initial parameter K of the power allocation factor scmid To achieve this, we need to obtain a and K accordingly. scmid The optimized parameter values ​​are used to optimize the artificial potential field algorithm.

[0047] To this end, the particle swarm optimization algorithm can be used to calculate a and K scmid The optimal value of , the specific key steps can be referred to as follows:

[0048] Step 1: Initialize a set of particles with random positions and velocities, where the position of a specific particle represents the a and K that need to be obtained scmid Optimized parameter values.

[0049] It should be noted that during the particle swarm optimization algorithm operation, the corresponding initial value needs to be added to the obtained variable parameter, so as to obtain the target value of the variable parameter through multiple iterations. scmid It is necessary to use its initial value, that is, the current energy storage system a and K in the artificial potential field algorithm scmid The actual parameter values ​​are used as the initial values ​​for particle swarm optimization algorithm calculation.

[0050] Step 2: Based on the above a and K scmid The initial value of can determine the power distribution between the electrolyzer and the supercapacitor. When the energy management strategy is used to adjust the power distribution between the electrolyzer and the supercapacitor, the input power of the electrolyzer, the input / output power of the supercapacitor, and the supercapacitor SOC can be output.

[0051] Step 3: Calculate the fitness value of the particle according to the output of the energy management strategy. Specifically, the loss cost of the energy storage system, i.e., the above J cost As the fitness value. For each particle, calculate the corresponding fitness value.

[0052] Step 4: Update the best fitness value of each particle, i.e., the minimum J, by comparing the current fitness value of each particle with the previous best fitness value. cost For a particular particle, the position associated with its best fitness value is denoted by X p .

[0053] Step 5: Update the best fitness value of the group by comparing the current fitness values ​​of all particles with the previous best fitness value of the group. For the group, the position associated with the best fitness value of the group is recorded as X g .

[0054] Step 6: For each particle, update its velocity and position as follows:

[0055] V(i+1)=wV(i)+c 1 r 1 (X p (i)-X(i))+c 2 r 2 (X g (i)-X(i)),

[0056] X(i+1)=X(i)+V(i+1),

[0057] Among them, V and X are the speed and position of a specific particle, i is the current iteration index, w is the inertia weight, c 1 and c 2 are two acceleration constants, r 1 and r 2 are two random numbers in [0,1]. If the iteration termination condition is not met, loop to step 2 for the next iteration, otherwise, terminate the particle swarm optimization algorithm and return a and K scmid The optimal value of .

[0058] It should be noted that in this embodiment, the particle swarm optimization algorithm is used to directly obtain a and K scmid The optimal parameter value is not limited to the particle swarm optimization algorithm, and other intelligent algorithms can also be used, such as the whale optimization algorithm, etc. The modifications and modifications made to the embodiments of the present invention by those skilled in the art without departing from the spirit of the present invention still fall within the scope of the invention application patent of the present invention.

[0059] Therefore, after obtaining a and K scmid After obtaining the optimal value, it is used to verify whether the artificial potential field algorithm can operate normally, thereby maintaining the normal operation of the energy storage system, so as to avoid the parameter value calculated in the offline state being unsuitable for the actual online scenario.

[0060] In this regard, the a and K in the artificial potential field algorithm of the energy storage system are running. scmid The initial value of is replaced with the optimal value calculated above to verify whether the energy storage system can operate normally when the artificial potential field algorithm is used.

[0061] Correspondingly, in the above a and K scmid After the optimal value of is verified, it can be used to optimize the artificial potential field algorithm, thereby minimizing the loss cost of the energy storage system. Of course, if it fails to pass the verification, continue to use a and K scmid The artificial potential field algorithm is run with the initial value of , thereby ensuring that the energy storage system currently operates with a more economical energy management strategy.

[0062] It can be understood that the above is to use the particle swarm optimization algorithm to directly obtain a and K scmidThe optimal value of a and K can also be enumerated one by one. scmid All possible parameter values ​​can be used to obtain multiple optimization solutions for the artificial potential field algorithm. To this end, we still need to first verify whether the artificial potential field algorithm can run normally. At the same time, we can also screen multiple parameter values, that is, the a and K in the artificial potential field algorithm currently running in the energy storage system. scmid The initial values ​​of are replaced with possible parameter values ​​in turn to verify the use of a and K scmid Whether the artificial potential field algorithm can operate normally with different parameter values ​​and retain the values ​​that meet the normal operating conditions of the energy storage system.

[0063] Furthermore, for the values ​​that meet the normal operation conditions of the energy storage system, the loss cost of the energy storage system can be calculated according to the corresponding operation results when the artificial potential field algorithm is used, so as to select one of the values ​​that meet the normal operation conditions of the energy storage system to minimize the loss cost of the energy storage system and use it as the most economical optimization solution for the energy storage system. Accordingly, the artificial potential field algorithm is optimized using it.

[0064] It can be seen that by optimizing the energy management strategy based on the artificial potential field algorithm through the combination of offline and online calculations, the normal operation of the energy storage system can be maintained while minimizing the loss cost of the energy storage system, thereby improving its economy.

[0065] It should be noted that the step division of the above methods is only for clear description. When implemented, they can be combined into one step or some steps can be split and decomposed into multiple steps. As long as they contain the same logical relationship, they are all within the scope of protection of this application; adding insignificant modifications to the algorithm or process or introducing insignificant designs without changing the core design of the algorithm and process are all within the scope of protection of this application.

[0066] In summary, the present invention provides an energy management optimization method for an energy storage system based on an artificial potential field algorithm, which fully takes into account the loss cost of the energy storage system, and through the combination of offline calculations and online calculations, reasonably optimizes the energy management strategy based on the artificial potential field algorithm, thereby improving the economy of the energy storage system while ensuring its stable operation.

[0067] The above embodiments are merely illustrative of the principles and effects of the present invention, and are not intended to limit the present invention. Anyone familiar with the technology may modify or change the above embodiments without violating the spirit and scope of the present invention. Therefore, all equivalent modifications or changes made by a person of ordinary skill in the art without departing from the spirit and technical ideas disclosed by the present invention shall still be covered by the claims of the present invention.

Claims

1. A method for optimizing energy management of an energy storage system based on an artificial potential field algorithm, characterized in that: include: Get the value of the empirical parameter in the artificial potential field algorithm; Using the values ​​of the empirical parameters to run an artificial potential field algorithm to perform energy management on the energy storage system, so as to verify whether the values ​​of the empirical parameters can enable the energy storage system to operate normally; For the values ​​that meet the normal operating conditions of the energy storage system, the artificial potential field algorithm is optimized based on them to minimize the loss cost of the energy storage system.

2. The energy storage system energy management optimization method based on artificial potential field algorithm according to claim 1 is characterized in that: The steps of running the artificial potential field algorithm to manage the energy storage system include: Predefine a corresponding virtual force based on the state of charge of the electric energy storage device; Determine a corresponding power allocation factor according to the virtual force of the electric energy storage device with respect to its state of charge, and use the power allocation factor to adjust the cutoff frequency of the low-pass filter in the energy storage system to adjust the input / output power of the electric energy storage device; The input / output power of the hydrogen energy storage device is synchronously adjusted according to the input / output power of the electric energy storage device.

3. The energy storage system energy management optimization method based on artificial potential field algorithm according to claim 2 is characterized in that: The empirical parameters in the artificial potential field algorithm include the shaping parameters preset by the virtual force of the electric energy storage device regarding its state of charge and the initial parameters preset by the corresponding power allocation factor.

4. The energy storage system energy management optimization method based on artificial potential field algorithm according to claim 2 is characterized in that: Virtual force F sc as follows: x=SOC-SOC mid , Among them, a is the shaping parameter preset by the virtual force, SOC min Indicates the preset minimum value of the state of charge of the energy storage device, SOC max Indicates the preset maximum value of the state of charge of the energy storage device, SOC mid Indicates the preset intermediate value of the state of charge of the energy storage device, i.e. SOC min <SOC mid <SOC max .

5. The energy storage system energy management optimization method based on artificial potential field algorithm according to claim 2 is characterized in that: The power allocation factor K sc as follows: Among them, K scmid represents the preset initial parameters of the power allocation factor, F sc Indicates the virtual force corresponding to the charge state of the electric energy storage device.

6. The energy storage system energy management optimization method based on artificial potential field algorithm according to claim 1 is characterized in that: The steps of obtaining the values ​​of the empirical parameters in the artificial potential field algorithm include: The particle swarm optimization algorithm is used to calculate the optimal values ​​of the empirical parameters in the artificial potential field algorithm.

7. The energy storage system energy management optimization method based on artificial potential field algorithm according to claim 6 is characterized in that: The steps of using the particle swarm optimization algorithm to calculate the optimal value of the empirical parameter in the artificial potential field algorithm include: Obtain the initial values ​​of the empirical parameters in the artificial potential field algorithm as the initial values ​​of the particle swarm optimization algorithm; The loss cost of the energy storage system is used as the fitness value of the particle swarm optimization algorithm, and the optimal value of the empirical parameter in the artificial potential field algorithm is obtained through iterative calculation of the particle swarm optimization algorithm.

8. The energy storage system energy management optimization method based on artificial potential field algorithm according to claim 1 or 7, characterized in that: The loss cost of the energy storage system includes the degradation cost of the hydrogen energy storage device and the deviation cost of the charge state of the electric energy storage device from the preset value.

9. The energy storage system energy management optimization method based on artificial potential field algorithm according to claim 8 is characterized in that: The hydrogen energy storage device includes an electrolyzer, and the degradation cost of the hydrogen energy storage device is calculated according to the following formula: Among them, J eldeg represents the degradation cost of the electrolyzer, J elsize represents the purchase cost of the electrolyzer, U elbeol Indicates the voltage drop of the electrolytic cell before reaching the end of its service life, ΔU elb Indicates the actual voltage drop of the electrolytic cell.

10. The energy storage system energy management optimization method based on artificial potential field algorithm according to claim 8, characterized in that: The electric energy storage device includes a supercapacitor, and the deviation cost of the charge state of the electric energy storage device from the preset value is calculated according to the following formula: J scele =β ele IN sc |SOC end -SOC mid |, Among them, J scele Indicates the deviation cost of the supercapacitor charge state from the preset value, β ele represents the current electricity price, E sc Indicates the capacity of the supercapacitor, SOC mid Indicates the preset intermediate value of the supercapacitor state of charge, SOC end Indicates the final value of the supercapacitor state of charge.

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