A capacity configuration method for energy storage systems based on artificial potential field algorithm

Abstract

The present invention belongs to the field of energy storage technology, and specifically relates to a method for configuring the capacity of an energy storage system based on an artificial potential field algorithm, comprising: obtaining a preselected scheme for the capacity configuration of an electric energy storage device and a hydrogen energy storage device; running the artificial potential field algorithm using the preselected scheme, and verifying whether the power allocation result corresponding to the output of the artificial potential field algorithm meets the capacity constraint of the preselected scheme, so as to confirm whether the preselected scheme meets the operating conditions of the artificial potential field algorithm, and using the preselected scheme that meets the operating conditions of the artificial potential field algorithm as the optimal scheme for capacity configuration; wherein, when there are multiple preselected schemes that meet the operating conditions of the artificial potential field algorithm, the one with the lowest procurement cost is selected from the multiple preselected schemes that meet the operating conditions of the artificial potential field algorithm as the optimal scheme for capacity configuration, so as to construct the most economical energy storage system based on the one. The present invention obtains the most economical capacity configuration scheme for the energy storage system by combining offline and online calculations.

Description

Technical Field

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

[0002] With the growing development of renewable energy, energy storage has become a vital component of power systems. Furthermore, as one of the most promising solutions for adapting to the intermittent and uncertain nature of renewable energy, energy storage is widely used to improve system stability and economic efficiency. Electrical energy storage is suitable for short-term (i.e., hours to days) and small- to medium-sized energy storage applications. Supercapacitors and batteries are commonly used as short-term energy storage devices. Supercapacitors primarily include double-layer capacitors, lithium-ion capacitors, and sodium-ion capacitors, while batteries primarily include lithium-ion batteries, sodium-ion batteries, lithium metal batteries, semi-solid-state batteries, and solid-state batteries. Energy storage devices such as batteries and supercapacitors offer significant advantages in power density. For long-term (i.e., weekly, monthly, and seasonal) and large-scale applications, hydrogen energy storage technology may be a better choice. Hydrogen energy storage technology typically includes devices such as electrolyzers, fuel cells, and hydrogen storage tanks, used to convert electrical energy into hydrogen energy for storage and hydrogen energy for utilization. Hydrogen energy storage offers significant advantages in energy density. Considering the differences between electric energy storage and hydrogen energy storage technologies in physical properties such as energy density, power density, cycle life and energy efficiency, hybrid energy storage systems combining the two technologies are under research.

[0003] To improve the economic efficiency of energy storage systems, appropriate capacity configuration is required for energy storage components. Capacity configuration and energy management are actually two closely coupled issues, mutually reinforcing and interdependent to achieve global system economic optimization. However, energy management strategies based on filtering methods are typically not combined with component capacity optimization because the former is real-time while the latter is offline. Therefore, offline capacity configuration solutions have certain limitations. Summary of the Invention

[0004] In view of the shortcomings of the prior art described above, the purpose of the present invention is to propose a capacity configuration method for an energy storage system, which integrates an optimized energy management strategy based on the filtering method (using an artificial potential field algorithm to optimize the energy management strategy based on the filtering method) and obtains the most economical capacity configuration plan for the energy storage system through a combination of offline and online calculations.

[0005] To achieve the above-mentioned objectives and other related objectives, the present invention provides a method for configuring the capacity of an energy storage system based on an artificial potential field algorithm, comprising: obtaining a preselected scheme for capacity configuration of an electric energy storage device and a hydrogen energy storage device; running the artificial potential field algorithm using the preselected scheme, and verifying whether the power allocation result corresponding to the output of the artificial potential field algorithm meets the capacity constraint of the preselected scheme, so as to confirm whether the preselected scheme meets the operating conditions of the artificial potential field algorithm, and using the preselected scheme that meets the operating conditions of the artificial potential field algorithm as the optimal scheme for capacity configuration; wherein, when there are multiple preselected schemes that meet the operating conditions of the artificial potential field algorithm, the one with the lowest procurement cost is selected from the multiple preselected schemes that meet the operating conditions of the artificial potential field algorithm as the optimal scheme for capacity configuration, so as to construct the most economical energy storage system based on the one.

[0006] According to a specific embodiment of the present invention, the step of obtaining a preselected solution for capacity configuration of the electric energy storage device and the hydrogen energy storage device includes: using a particle swarm optimization algorithm to calculate the most ideal preselected solution for capacity configuration of the electric energy storage device and the hydrogen energy storage device.

[0007] According to a specific embodiment of the present invention, the step of using a particle swarm optimization algorithm to calculate the most ideal pre-selected solution for the capacity configuration of the electric energy storage device and the hydrogen energy storage device includes: obtaining the capacity configuration parameters of the electric energy storage device and the hydrogen energy storage device in the built energy storage system as the initial values ​​of the particle swarm optimization algorithm; using the procurement 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 most ideal pre-selected solution for the capacity configuration of the electric energy storage device and the hydrogen energy storage device.

[0008] According to a specific embodiment of the present invention, the steps of running an artificial potential field algorithm using the preselected scheme and verifying whether a power allocation result corresponding to the output of the artificial potential field algorithm meets the capacity constraint of the preselected scheme to confirm whether the preselected scheme meets the operating conditions of the artificial potential field algorithm, and using the preselected scheme that meets the operating conditions of the artificial potential field algorithm as the optimal scheme for capacity configuration include: for a preselected scheme, running the artificial potential field algorithm according to the capacity configuration parameters of the electric energy storage device and the hydrogen energy storage device, and calculating the power allocation results of the electric energy storage device and the hydrogen energy storage device; wherein the power allocation results include an input power range interval or an output power range interval of the electric energy storage device within a preset time period, and an input power range interval or an output power range interval of the hydrogen energy storage device within the preset time period; judging the input power range interval or the output power range interval of the electric energy storage device Whether the interval exceeds the capacity configuration parameters of the electric energy storage device in the preselected solution, and whether the input power range interval or the output power range interval of the hydrogen energy storage device exceeds the capacity configuration parameters of the hydrogen energy storage device in the preselected solution: if the input power range interval or the output power range interval of the electric energy storage device and the input power range interval or the output power range interval of the hydrogen energy storage device do not exceed the corresponding capacity configuration parameters, then the power allocation result is deemed to meet the capacity constraints of the preselected solution, and the preselected solution meets the operating conditions of the artificial potential field algorithm; if the input power range interval or the output power range interval of the electric energy storage device and / or the input power range interval or the output power range interval of the hydrogen energy storage device exceed the corresponding capacity configuration parameters, then the power allocation result is deemed to not meet the capacity constraints of the preselected solution, and the preselected solution does not meet the operating conditions of the artificial potential field algorithm.

[0009] According to a specific embodiment of the present invention, the step of adjusting the power distribution between the electric energy storage device and the hydrogen energy storage device based on the artificial potential field algorithm includes: pre-defining a corresponding virtual force based on the state of charge of the electric energy storage device; determining a corresponding power distribution factor based on the virtual force of the electric energy storage device relative to its state of charge, and using the power distribution factor to adjust the cutoff frequency of a low-pass filter in the energy storage system to adjust the input power or output power of the electric energy storage device; and synchronously adjusting the input power or output power of the hydrogen energy storage device based on the input power or output power of the electric energy storage device.

[0010] According to a specific embodiment of the present invention, the virtual force F sc as follows:

[0011]

[0012] x = SOC - SOC mid ,

[0013] Among them, a is the shaping parameter preset by the virtual force, SOCmin 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, namely SOC min <SOC mid <SOC max .

[0014] According to a specific embodiment of the present invention, the power allocation factor K sc as follows:

[0015]

[0016] Among them, K scmid Indicates the initial parameters of the power allocation factor, F sc Indicates the virtual force corresponding to the state of charge of the energy storage device.

[0017] According to a specific embodiment of the present invention, the optimized artificial potential field algorithm is run using the preselected scheme, and the power allocation result corresponding to the output of the optimized artificial potential field algorithm is verified to see whether it meets the capacity constraint of the preselected scheme, so as to confirm whether the preselected scheme meets the operating conditions of the optimized artificial potential field algorithm, and the preselected scheme that meets the operating conditions of the optimized artificial potential field algorithm is used as the optimal scheme for capacity configuration; wherein, when there are multiple preselected schemes that meet the operating conditions of the optimized artificial potential field algorithm, the one with the lowest procurement cost is selected from the multiple preselected schemes that meet the operating conditions of the optimized artificial potential field algorithm as the optimal scheme for capacity configuration, so as to construct the most economical energy storage system based on it.

[0018] According to a specific embodiment of the present invention, the artificial potential field algorithm is optimized according to the following steps: a particle swarm optimization algorithm is used to calculate the optimized parameter values ​​of the shaping parameters preset for the virtual force of the charge state of the electric energy storage device in the artificial potential field algorithm, as well as the optimized parameter values ​​of the initial parameters of the power distribution factor corresponding to the electric energy storage device, so as to operate the artificial potential field algorithm according to the optimized parameter values.

[0019] According to a specific embodiment of the present invention, the particle swarm optimization algorithm uses the original parameter values ​​of the initial parameters of the shaping parameters and the power allocation factor as initial values, and the loss cost of the energy storage system as the fitness value, and performs iterative calculation to obtain the optimized parameter values ​​of the initial parameters of the shaping parameters and the power allocation factor.

[0020] The present invention provides a capacity configuration method for an energy storage system based on an artificial potential field algorithm, which obtains the most economical capacity configuration scheme for the energy storage system by combining offline calculations with online calculations. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 This is a flow chart of a specific embodiment of a method for configuring energy storage system capacity based on an artificial potential field algorithm provided by the present invention. DETAILED DESCRIPTION

[0022] The following describes the embodiments of the present invention with reference to the accompanying drawings and preferred embodiments. Those skilled in the art will readily appreciate the other advantages and benefits of the present invention from the disclosure herein. The present invention may also be implemented or applied through various other specific embodiments, and the various details in this specification may be modified or altered based on different viewpoints and applications without departing from the spirit of the present invention. It should be understood that the preferred embodiments are intended only to illustrate the present invention and are not intended to limit the scope of protection of the present invention.

[0023] It should be noted that the illustrations provided in the following embodiments are merely schematic illustrations of the basic concept of the present invention. Therefore, the illustrations only show components related to the present invention and are not 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 complex.

[0024] 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, publicly known structures and devices are shown in block diagram form rather than in detail to avoid making the embodiments of the present invention difficult to understand.

[0025] See Figure 1 A method for configuring energy storage system capacity based on an artificial potential field algorithm is shown, comprising:

[0026] Step S100: obtaining a preselected solution for capacity configuration of the electric energy storage device and the hydrogen energy storage device.

[0027] Step S200: running the artificial potential field algorithm using the preselected solution, and verifying whether the power allocation result outputted by the artificial potential field algorithm meets the capacity constraint of the preselected solution, so as to confirm whether the preselected solution meets the operating conditions of the artificial potential field algorithm, and taking the preselected solution that meets the operating conditions of the artificial potential field algorithm as the optimal solution for capacity allocation;

[0028] Among them, when there are multiple pre-selected solutions that meet the operating conditions of the artificial potential field algorithm, the one with the lowest procurement cost is selected from the multiple pre-selected solutions that meet the operating conditions of the artificial potential field algorithm as the optimal solution for capacity configuration, so as to build the most economical energy storage system based on it.

[0029] First, for energy storage systems that have not yet been built, whether considering the economic benefits of hydrogen production or the corresponding construction costs, the capacity of the hydrogen energy storage device and the capacity of the electric energy storage device must be reasonably configured. In this regard, in a specific embodiment, the following objective function is constructed for the capacity configuration scheme of the hydrogen energy storage device and the electric energy storage device:

[0030] J cost =J elsize +J scsize , and J elsize =x el P elbmax C el , J scsize =E sc C sc ,

[0031] Among them, J cost Represents the objective function, and the objective function corresponds to the procurement cost of hydrogen energy storage devices and electric energy storage devices, J elsize represents the purchase cost of the electrolytic cell, J scsize represents the purchase cost of supercapacitors, x el Indicates the number of cells in the electrolytic cell, P elbmax Indicates the maximum input power of the electrolytic cell, that is, x el P elbmax Indicates the capacity of the electrolytic cell, C el Indicates the purchase cost per unit capacity of the electrolytic cell, E sc Indicates the capacity of the supercapacitor, C sc Indicates the purchase cost of supercapacitor per unit capacity.

[0032] Furthermore, the purchase cost per unit capacity of the electrolyzer can be expressed as:

[0033]

[0034] C elpc represents the unit purchase price of the electrolytic cell, y el represents the service life of the electrolytic cell, r represents the corresponding annual interest rate, T year Indicates the number of days in a year, T test Indicates the number of days in a year that the electrolyzer is actually tested.

[0035] The purchase cost of supercapacitor per unit capacity can be expressed as:

[0036]

[0037] C scpc Indicates the unit purchase price of supercapacitor, y sc represents the service life of the supercapacitor, r represents the corresponding annual interest rate, Ttest Indicates the number of days in a year that the supercapacitor is actually tested.

[0038] It should be noted that in this embodiment, the hydrogen energy storage device is specifically taken as an electrolyzer as an example, but it is not intended 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 is specifically taken as an example of a supercapacitor. In actual applications, the electric energy storage device can also use a battery, or be composed of a mixture of supercapacitors and batteries. No excessive restrictions are made on this. 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.

[0039] Based on the above, by considering the procurement cost of the energy storage system, that is, using this objective function, multiple possible pre-selected solutions for the capacity configuration of the electric energy storage device and the hydrogen energy storage device can be calculated accordingly. In this embodiment, in order to directly obtain the most ideal pre-selected solution, a particle swarm optimization algorithm is used to calculate the optimal capacity configuration of the electric energy storage device and the hydrogen energy storage device. The specific key steps can be referred to as follows:

[0040] Step 1: Initialize a set of particles with random positions and velocities, where the position of a specific particle represents the parameters related to the capacity of the electric energy storage device and the hydrogen energy storage device that need to be obtained, that is, x in the above formula el and E sc .

[0041] It should be noted that during the particle swarm optimization algorithm operation, the corresponding initial value needs to be added to the variable parameter obtained, so that the target value of the variable parameter can be obtained through multiple iterative calculations. el and E sc It is necessary to use the capacity configuration parameters of the electrolyzer and supercapacitor in the existing energy storage system as the initial value, that is, to use the x el and E sc The actual parameter value is used as the x in the particle swarm optimization algorithm el and E sc The initial value of .

[0042] Step 2: Since x el and E sc are respectively related to the capacity of the electric energy storage device and the hydrogen energy storage device. According to the above x el and E sc The initial value of can determine the power distribution between the electrolyzer and the supercapacitor in actual applications. 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 power or output power of the supercapacitor, and the state of charge (SOC) of the supercapacitor can be output.

[0043] Step 3: Calculate the fitness value of the particle according to the output of the energy management strategy. Specifically, the procurement cost of the energy storage system, that is, the above J cost As the fitness value. For each particle, calculate the corresponding fitness value.

[0044] Step 4: By comparing the current fitness value of each particle with the previous best fitness value, update the best fitness value of each particle, that is, the minimum J cost For a particular particle, the position associated with its best fitness value is denoted as X p .

[0045] Step 5: Update the best fitness value of the group by comparing the current fitness values ​​of all particles with the best fitness value of the group before. For a group, the position associated with the best fitness value of the group is recorded as X g .

[0046] Step 6: For each particle, update its velocity and position as follows:

[0047] V(i+1)=wV(i)+c1r1(X p (i)-X(i))+c2r2(X g (i)-X(i)),

[0048] X(i+1)=X(i)+V(i+1),

[0049] Where V and X are the velocity and position of a specific particle, i is the current iteration index, w is the inertia weight, c1 and c2 are two acceleration constants, r1 and r2 are two random numbers in [0,1]. If the iteration termination condition is not met, loop back to step 2 for the next iteration, otherwise, terminate the particle swarm optimization algorithm and return x el and E sc target value, that is, the optimal capacity configuration parameters of the electric energy storage device and the hydrogen energy storage device.

[0050] As can be seen, the aforementioned preselected capacity configurations for the electric and hydrogen energy storage devices were obtained through offline calculations. To confirm their applicability in practical applications, they can be verified using the energy management strategies of existing energy storage systems running online to confirm their feasibility. In this embodiment, the optimal preselected capacity configurations for the electric and hydrogen energy storage devices obtained above were verified using an energy management strategy based on an artificial potential field algorithm.

[0051] It should be noted here that the energy management strategy based on the artificial potential field algorithm uses the artificial potential field algorithm to adjust the cutoff frequency of the low-pass filter. The low-pass filter can allocate the high-frequency portion of the power to the electric energy storage device and the low-frequency portion to the hydrogen energy storage device, or through the low-pass filter, the electric energy storage device provides high-frequency power and the hydrogen energy storage device provides 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 power or 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.

[0052] In a specific embodiment, a supercapacitor is used as an example of 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:

[0053]

[0054] Where x = SOC - SOC mid , and SOC mid is a value between the preset minimum SOC min and the preset maximum SOC max The value between is the SOC that the supercapacitor expects to operate at, which can be adjusted accordingly based on the actual situation. a is the shaping parameter preset by the virtual force, which determines the virtual force F sc The rate of change.

[0055] Secondly, the virtual force F can be used sc To calculate the power distribution factor K corresponding to the supercapacitor sc , and the calculation formula is as follows:

[0056]

[0057] Among them, K scmid It is the preset initial parameter used to adjust the input power or output power of the supercapacitor.

[0058] Finally, the power allocation factor K can be calculated sc Adjusting the input power of the supercapacitor during charging or the output power during discharging is achieved by adjusting the cutoff frequency of the low-pass filter accordingly, thereby reasonably allocating the net load power of the system and fully avoiding overcharging or over-discharging of the supercapacitor.

[0059] Furthermore, considering the loss cost of the energy storage system, the energy management strategy based on the artificial potential field algorithm can be further optimized. Accordingly, the optimized energy management strategy can be used to verify the most ideal pre-selected scheme for capacity configuration of the above-mentioned electric energy storage device and hydrogen energy storage device.

[0060] Specifically, the optimization of the energy management strategy based on the artificial potential field algorithm is achieved by optimizing the empirical parameters in the artificial potential field algorithm, that is, for the shaping parameter a mentioned in the artificial potential field algorithm, and the initial parameter K of the power allocation factor scmid To this end, the optimization goal is to reduce the loss cost of the energy storage system as much as possible. The particle swarm optimization algorithm can be used to directly calculate a and K scmid The optimal parameter value is used as the optimized parameter value.

[0061] It should be noted that the loss cost of the energy storage system can be expressed as J eldeg +J scele , where J eldeg represents the degradation cost of the electrolyzer, and J scele Indicates the deviation cost of the supercapacitor's SOC from the preset value.

[0062] Correspondingly, J eldeg It can be expressed as:

[0063] Among them, 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.

[0064] J scele It can be expressed as: J scele =β ele E sc |SOC end -SOC mid |,

[0065] Among them, β ele Indicates electricity price, SOC end Indicates the final value of the supercapacitor, while SOC mid The preset intermediate value for the supercapacitor SOC.

[0066] Based on the above, the particle swarm optimization algorithm is used to calculate the initial parameters K of the shaping parameter a and the power allocation factor: scmid The steps to find the optimal parameter values ​​are as follows:

[0067] 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.

[0068] Similarly, using a and K in the artificial potential field algorithm scmid The original parameter values ​​are used as the initial values ​​of the particle swarm optimization algorithm.

[0069] Step 2: Based on 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 power or output power of the supercapacitor, and the supercapacitor SOC can be output.

[0070] Step 3: Calculate the particle fitness value based on the output of the energy management strategy. Specifically, use the loss cost of the energy storage system as the fitness value. For each particle, calculate the corresponding fitness value.

[0071] Step 4: By comparing the current fitness value of each particle with the previous best fitness value, update the best fitness value of each particle, that is, minimize the loss cost of the energy storage system. For a specific particle, the position associated with its best fitness value is recorded as X p .

[0072] Step 5: Update the best fitness value of the group by comparing the current fitness values ​​of all particles with the best fitness value of the group before. For a group, the position associated with the best fitness value of the group is recorded as X g .

[0073] Step 6: For each particle, update its velocity and position as follows:

[0074] V(i+1)=wV(i)+c1r1(X p (i)-X(i))+c2r2(X g (i)-X(i)),

[0075] X(i+1)=X(i)+V(i+1),

[0076] If the iteration termination condition is not met, loop back to step 2 for the next iteration; otherwise, terminate the particle swarm optimization algorithm and return a and K. scmid The optimal parameter values ​​are used to optimize the artificial potential field algorithm.

[0077] The steps for verifying the pre-selected solution using the artificial potential field algorithm can be referred to as follows:

[0078] When an existing energy storage system uses an artificial potential field algorithm to adjust the power distribution between the electric energy storage device and the hydrogen energy storage device, it can operate according to the most ideal pre-selected scheme obtained above, so as to calculate the power distribution result of the electric energy storage device and the hydrogen energy storage device, and verify whether the power distribution result meets the capacity constraint of the pre-selected scheme to confirm whether the pre-selected scheme meets the operating conditions of the artificial potential field algorithm.

[0079] When the power allocation result meets the capacity constraint of the pre-selected scheme, it means that the pre-selected scheme meets the operating conditions of the artificial potential field algorithm and can be used to build an energy storage system in practical applications; when the power allocation result does not meet the capacity constraint of the pre-selected scheme, it means that the pre-selected scheme does not meet the operating conditions of the artificial potential field algorithm and cannot support practical applications.

[0080] Specifically, the input power or output power of the online allocated electric energy storage device and hydrogen energy storage device is used to determine whether the calculated corresponding capacity can meet the input power or output power:

[0081] If the capacity configuration in the preselected scheme can meet the corresponding input power or output power calculated using the artificial potential field algorithm, that is, the input power range interval or the output power range interval of the electric energy storage device does not exceed the capacity configuration parameters in the preselected scheme, and the input power range interval or the output power range interval of the hydrogen energy storage device does not exceed the capacity configuration parameters in the preselected scheme, it means that the power allocation result meets the capacity constraints of the preselected scheme.

[0082] If the capacity configuration in the preselected scheme cannot meet the corresponding input power or output power calculated using the artificial potential field algorithm, that is, the input power range interval or the output power range interval of the electric energy storage device exceeds the capacity configuration parameters in the preselected scheme, and / or the input power range interval or the output power range interval of the hydrogen energy storage device exceeds the capacity configuration parameters in the preselected scheme, it means that the power allocation result does not meet the capacity constraints of the preselected scheme.

[0083] Accordingly, the pre-selected solutions that do not meet the operating conditions of the artificial potential field algorithm are directly discarded. Since the particle swarm optimization algorithm is used in this embodiment to directly obtain the most ideal pre-selected solution, if this solution meets the operating conditions of the artificial potential field algorithm, then it is the optimal solution for capacity configuration.

[0084] It should be noted that the aforementioned process of verifying the capacity configuration in the preselected solution using the artificial potential field algorithm involves obtaining the range of fluctuations in the input power or output power of the electric energy storage device and the hydrogen energy storage device calculated within a preset time period, and using the capacity configuration parameters in the preselected solution as the upper and lower power limits. Specifically, when the artificial potential field algorithm is used to calculate the power allocation results for the electric energy storage device and the hydrogen energy storage device, the algorithm records the input power or output power that needs to be adjusted for the electric energy storage device at multiple different time points to form a continuous power range, and the input power or output power that needs to be adjusted for the hydrogen energy storage device at multiple different time points to form another continuous power range. The algorithm then identifies whether the power range of the electric energy storage device exceeds the capacity configuration parameters of the electric energy storage device in the preselected solution, and whether the power range of the hydrogen energy storage device exceeds the capacity configuration parameters of the hydrogen energy storage device in the preselected solution. If at least one of the electric energy storage device and the hydrogen energy storage device exceeds the capacity configuration parameters of the hydrogen energy storage device in the preselected solution, the preselected solution does not meet the operating conditions of the artificial potential field algorithm.

[0085] It should also be added that, since the particle swarm optimization algorithm is used in this embodiment to directly obtain the most ideal pre-selected solution, of course, other intelligent algorithms, such as the whale optimization algorithm, etc., can also be used to obtain the most ideal pre-selected solution. Accordingly, it is only necessary to verify whether it meets the operating conditions of the artificial potential field algorithm. In addition, when obtaining the pre-selected solutions for the capacity configuration of the electric energy storage device and the hydrogen energy storage device, the enumeration method can also be used to list all possible capacity configuration solutions one by one, that is, there are multiple pre-selected solutions. Accordingly, all the pre-selected solutions are verified one by one according to the above steps, and one of the pre-selected solutions that meet the operating conditions of the artificial potential field algorithm is selected as the optimal solution for capacity configuration, that is, the pre-selected solution with the lowest procurement cost is selected as the optimal solution for capacity configuration.

[0086] Similarly, when optimizing the artificial potential field algorithm, the initial parameters K of the shaping parameter a and the power allocation factor are calculated. scmid The optimized parameter values ​​are not limited to the particle swarm optimization algorithm, but can also be other intelligent algorithms.

[0087] Based on the optimal capacity configuration scheme obtained above, the most economical energy storage system can be constructed accordingly.

[0088] It should be noted that the step division of the various methods above 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.

[0089] In summary, the present invention provides a capacity configuration method for an energy storage system based on an artificial potential field algorithm, which obtains the most economical capacity configuration scheme for the energy storage system by combining offline calculations with online calculations.

[0090] 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 skilled in the art may modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, any equivalent modifications or alterations made by one of ordinary skill in the art without departing from the spirit and technical principles disclosed herein are intended to be covered by the claims of the present invention.

Claims

1. A method for configuring energy storage system capacity based on artificial potential field algorithm, characterized in that: include: Obtaining a preselected solution regarding capacity configuration of the electric energy storage device and the hydrogen energy storage device; Running the artificial potential field algorithm using the preselected solution, and verifying whether the power allocation result outputted by the artificial potential field algorithm complies with the capacity constraint of the preselected solution, so as to confirm whether the preselected solution meets the operating conditions of the artificial potential field algorithm, and using the preselected solution that meets the operating conditions of the artificial potential field algorithm as the optimal solution for capacity configuration; Among them, when there are multiple pre-selected solutions that meet the operating conditions of the artificial potential field algorithm, the one with the lowest procurement cost is selected from the multiple pre-selected solutions that meet the operating conditions of the artificial potential field algorithm as the optimal solution for capacity configuration, so as to build the most economical energy storage system based on it.

2. The energy storage system capacity configuration method based on the artificial potential field algorithm according to claim 1 is characterized in that: The steps of obtaining a preselected solution regarding capacity configuration of the electric energy storage device and the hydrogen energy storage device include: The particle swarm optimization algorithm is used to calculate the most ideal pre-selected plan for the capacity configuration of the electric energy storage device and the hydrogen energy storage device.

3. The energy storage system capacity configuration method based on the artificial potential field algorithm according to claim 2 is characterized in that: The steps of using the particle swarm optimization algorithm to calculate the optimal pre-selected solution for the capacity configuration of the electric energy storage device and the hydrogen energy storage device include: Obtain the capacity configuration parameters of the electric energy storage device and the hydrogen energy storage device in the built energy storage system as the initial values ​​of the particle swarm optimization algorithm; The procurement cost of the energy storage system is used as the fitness value of the particle swarm optimization algorithm, and the optimal pre-selected plan for the capacity configuration of the electric energy storage device and the hydrogen energy storage device is obtained through iterative calculation of the particle swarm optimization algorithm.

4. The energy storage system capacity configuration method based on the artificial potential field algorithm according to claim 1 is characterized in that: The steps of running the artificial potential field algorithm using the preselected scheme and verifying whether the power allocation result outputted by the artificial potential field algorithm meets the capacity constraint of the preselected scheme to confirm whether the preselected scheme meets the operating conditions of the artificial potential field algorithm, and using the preselected scheme that meets the operating conditions of the artificial potential field algorithm as the optimal scheme for capacity configuration include: For a preselected solution, an artificial potential field algorithm is run according to the capacity configuration parameters of the electric energy storage device and the hydrogen energy storage device, and a power allocation result of the electric energy storage device and the hydrogen energy storage device is calculated; wherein the power allocation result includes an input power range or an output power range of the electric energy storage device within a preset time period, and an input power range or an output power range of the hydrogen energy storage device within the preset time period; Determine whether the input power range or the output power range of the electric energy storage device exceeds the capacity configuration parameters of the electric energy storage device in the preselected solution, and determine whether the input power range or the output power range of the hydrogen energy storage device exceeds the capacity configuration parameters of the hydrogen energy storage device in the preselected solution: If the input power range or output power range of the electric energy storage device and the input power range or output power range of the hydrogen energy storage device do not exceed the corresponding capacity configuration parameters, then the power allocation result is deemed to meet the capacity constraints of the pre-selected solution, and the pre-selected solution meets the operating conditions of the artificial potential field algorithm; If the input power range or output power range of the electric energy storage device and / or the input power range or output power range of the hydrogen energy storage device exceeds the corresponding capacity configuration parameters, the power allocation result is deemed to not meet the capacity constraints of the pre-selected scheme, and the pre-selected scheme does not meet the operating conditions of the artificial potential field algorithm.

5. The energy storage system capacity configuration method based on artificial potential field algorithm according to claim 1 is characterized in that: The steps of adjusting the power distribution between the electric energy storage device and the hydrogen energy storage device based on the artificial potential field algorithm include: Predefine a corresponding virtual force based on the state of charge of the electric energy storage device; determining a corresponding power allocation factor based on a virtual force of the electric energy storage device relative to its state of charge, and using the power allocation factor to adjust a cutoff frequency of a low-pass filter in the energy storage system to adjust an input power or an output power of the electric energy storage device; The input power or output power of the hydrogen energy storage device is synchronously adjusted according to the input power or output power of the electric energy storage device.

6. The energy storage system capacity configuration method based on the artificial potential field algorithm according to claim 5 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, namely SOC min <SOC mid <SOC max .

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

8. The energy storage system capacity configuration method based on artificial potential field algorithm according to claim 1 is characterized in that: The optimized artificial potential field algorithm is run using the preselected scheme, and the power distribution result corresponding to the output of the optimized artificial potential field algorithm is verified to see whether it meets the capacity constraint of the preselected scheme, so as to confirm whether the preselected scheme meets the operating conditions of the optimized artificial potential field algorithm, and the preselected scheme that meets the operating conditions of the optimized artificial potential field algorithm is used as the optimal scheme for capacity configuration; wherein, when there are multiple preselected schemes that meet the operating conditions of the optimized artificial potential field algorithm, the one with the lowest procurement cost is selected from the multiple preselected schemes that meet the operating conditions of the optimized artificial potential field algorithm as the optimal scheme for capacity configuration, so as to construct the most economical energy storage system based on it.

9. The energy storage system capacity configuration method based on the artificial potential field algorithm according to claim 8, characterized in that: The artificial potential field algorithm is optimized as follows: The particle swarm optimization algorithm is used to calculate the optimized parameter values ​​of the shaping parameters preset by the virtual force of the charge state of the electric energy storage device in the artificial potential field algorithm, as well as the optimized parameter values ​​of the initial parameters corresponding to the power distribution factor of the electric energy storage device, so as to run the artificial potential field algorithm according to the optimized parameter values.

10. The energy storage system capacity configuration method based on artificial potential field algorithm according to claim 9, characterized in that: The particle swarm optimization algorithm uses the original parameter values ​​of the initial parameters of the shaping parameters and the power allocation factor as initial values, and the loss cost of the energy storage system as the fitness value, and performs iterative calculation to obtain the optimized parameter values ​​of the initial parameters of the shaping parameters and the power allocation factor.

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