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

Abstract

The present application belongs to the technical field of energy storage, and particularly relates to an energy management optimization method for an energy storage system based on an artificial potential field algorithm, comprising: obtaining the value of an experience parameter in the artificial potential field algorithm; using the value of the experience parameter to run the artificial potential field algorithm to manage the energy of the energy storage system, so as to check whether the value of the experience parameter can make the energy storage system run normally; for the value meeting the normal operation condition of the energy storage system, optimizing the artificial potential field algorithm according to the value, so as to minimize the loss cost of the energy storage system. The present application fully considers the loss cost of the energy storage system, reasonably optimizes the energy management strategy based on the artificial potential field algorithm through the combination of offline operation and online operation, and further improves the economy of the energy storage system under the premise of ensuring the stable operation of the energy storage system.

Description

Technical Field

[0001] This invention belongs to the field of energy storage technology, specifically relating to an energy management optimization method for energy storage systems based on an artificial potential field algorithm. Background Technology

[0002] With the increasing development of renewable energy, energy storage has become an important component of the power system. Furthermore, as one of the most promising solutions to adapt to the intermittency and uncertainty of renewable energy, energy storage is widely used to improve system stability and economics. Electrical energy storage is suitable for short-term (a few hours to a few days) and small-to-medium-sized applications. Supercapacitors and batteries are commonly used as short-term electrical energy storage devices. Supercapacitors mainly include double-layer capacitors, lithium-ion capacitors, and sodium-ion capacitors, while batteries mainly include lithium-ion batteries, sodium-ion batteries, lithium metal batteries, semi-solid-state batteries, and solid-state batteries. Both batteries and supercapacitors have significant advantages in power density. For long-term (weekly, monthly, quarterly) 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 to convert electrical energy into hydrogen for storage and to convert hydrogen energy back into electrical energy for utilization. Hydrogen storage has significant advantages in energy density. Considering the differences between electric energy storage and hydrogen energy storage technologies in terms of physical characteristics such as energy density, power density, cycle life, and energy efficiency, hybrid energy storage systems combining these two technologies are under investigation.

[0003] Energy management methods for energy storage systems are constantly being developed, and can be broadly categorized into rule-based methods, optimization-based methods, and learning-based methods. In particular, rule-based methods primarily utilize high-pass filters or low-pass filters (LPFs). For example, an LPF decomposes the load into high-frequency and low-frequency components, which are then allocated to electrical and hydrogen energy storage devices respectively. However, a drawback of LPFs with fixed cutoff frequencies is that their cutoff frequencies cannot be adjusted accordingly when system operating conditions change, potentially leading to overcharging or over-discharging of the energy storage devices. Correspondingly, artificial potential field algorithms are used to optimize these methods, thereby maintaining the stability of the State of Charge (SOC) of the electrical energy storage devices. However, current energy management methods based on artificial potential field algorithms only consider the stable control of SOC and do not adequately consider the economic aspects of the energy storage system, such as device lifespan, thus having certain shortcomings. Summary of the Invention

[0004] In view of the shortcomings of the prior art described above, the purpose of this invention is to propose an optimization method for energy management strategy based on artificial potential field algorithm, which optimizes the empirical parameters in artificial potential field algorithm considering the loss cost of energy storage system, so as to improve the economy of energy storage system.

[0005] To achieve the above and other related objectives, this invention provides an energy management optimization method for an energy storage system based on an artificial potential field algorithm, comprising: obtaining the values ​​of empirical parameters in the artificial potential field algorithm; using the values ​​of the empirical parameters to run the artificial potential field algorithm to manage the energy of 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 values ​​that meet the conditions for normal operation of the energy storage system, optimizing the artificial potential field algorithm accordingly, so as to minimize the loss cost of the energy storage system.

[0006] According to a specific embodiment of the present invention, the steps of running an artificial potential field algorithm to manage the energy of an energy storage system include: pre-defining a corresponding virtual force based on the state of charge of the electric energy storage device; determining a corresponding power allocation factor based on the virtual force of the electric energy storage device with respect to its state of charge, and using the power allocation factor to adjust the cutoff frequency of the low-pass filter in the energy storage system to adjust the input and output power of the electric energy storage device; and synchronously adjusting the input and output power of the hydrogen energy storage device according to the input and 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 the pre-set shaping parameters of the energy storage device with respect to the virtual force of its state of charge and the pre-set initial parameters of the corresponding power allocation factor.

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

[0009] ,

[0010] ,

[0011] in, The pre-set shaping parameters for virtual forces. This represents the minimum preset state of charge for the energy storage device. This indicates the maximum preset state of charge of the energy storage device. This represents the preset intermediate value of the state of charge of the energy storage device, i.e. .

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

[0013] ,

[0014] in, This represents the initial parameters preset for the power allocation factor. This represents the virtual force corresponding to the state of charge of an electric energy storage device.

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

[0016] According to a specific embodiment of the present invention, the step of calculating the optimal value of the empirical parameter in the artificial potential field algorithm using the particle swarm optimization algorithm includes: obtaining the 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 optimal value of the empirical parameter in the artificial potential field algorithm through the particle swarm optimization 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 state of charge 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] in, This represents the degradation cost of the electrolytic cell. This indicates the purchase cost of the electrolytic cell. This indicates the voltage drop of a single cell in an electrolyzer before it reaches the end of its service life. This indicates the actual voltage drop of a single cell in the electrolytic cell.

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

[0022] ,

[0023] in, This represents the cost of deviation from the preset value of the supercapacitor's state of charge. This indicates the current electricity price. This indicates the capacitance of the supercapacitor. This represents the preset intermediate value of the supercapacitor's state of charge. This represents the final value of the supercapacitor's state of charge.

[0024] This invention provides an energy management optimization method for energy storage systems based on an artificial potential field algorithm. Taking into full account the loss cost of the energy storage system, the method combines offline and online calculations to reasonably optimize the energy management strategy based on the artificial potential field algorithm, thereby improving the economic efficiency of the energy storage system while ensuring its stable operation. Attached Figure Description

[0025] Figure 1 This is a flowchart illustrating a specific embodiment of an energy management optimization method for energy storage systems based on an artificial potential field algorithm provided by the present invention. Detailed Implementation

[0026] The embodiments of the present invention will be described below 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 content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed 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 and 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 representations of the basic concept of the present invention. Therefore, the drawings only show the components related to the present invention and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0028] In the following description, numerous details are explored to provide a more thorough explanation of embodiments of the invention. However, it will be apparent to those skilled in the art that embodiments of the invention may be practiced without these specific details. In other embodiments, publicly known structures and devices are shown in block diagram form rather than in detail to avoid obscuring embodiments of the invention.

[0029] Please see Figure 1 The energy management optimization method for an energy storage system based on an artificial potential field algorithm, as shown, includes:

[0030] Step S100: Obtain the values ​​of the empirical parameters in the artificial potential field algorithm.

[0031] Step S200: Use the values ​​of the empirical parameters to run the artificial potential field algorithm to manage the energy of 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 values ​​that meet the normal operating conditions of the energy storage system, optimize the artificial potential field algorithm to minimize the loss cost of the energy storage system.

[0033] First, it's important to clarify that the energy management strategy based on the artificial potential field algorithm adjusts the cutoff frequency of the low-pass filter. The low-pass filter can allocate the high-frequency portion of power to the electric energy storage device and the low-frequency portion to the hydrogen energy storage device, or it can enable the electric energy storage device to provide high-frequency power and the hydrogen energy storage device to provide low-frequency power. Since this process of adjusting the power distribution between the electric and hydrogen energy storage devices via the low-pass filter requires regulating the input and output power of the electric energy storage device, maintaining a stable State of Charge (SOC) is crucial to prevent overcharging or over-discharging. Therefore, the artificial potential field algorithm is used to optimize the power distribution to the electric energy storage device.

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

[0035] ,

[0036] In the formula ,and It is a value between the preset minimum value and the minimum value. and the preset maximum value The values ​​between these ranges represent the expected SOC (State of Charge) of the supercapacitor, and can be adjusted accordingly based on the actual situation. The pre-defined shaping parameters for virtual force determine the virtual force. The rate of change.

[0037] Secondly, this virtual force can be utilized. To calculate the power allocation factor corresponding to the supercapacitor The specific calculation formula is as follows:

[0038] ,

[0039] in, These are the preset initial parameters used to adjust the input and output power of the supercapacitor.

[0040] Finally, the calculated power allocation factor can be used as a guide. 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. This allows for the reasonable allocation of the system's net load power and effectively prevents the supercapacitor from being overcharged or over-discharged.

[0041] However, the aforementioned energy management strategy based on the artificial potential field algorithm, in order to avoid overcharging or over-discharging of the energy storage device, only considers the state of charge (SOC) of the energy storage device during calculation, without taking into account device losses and lifespan. Therefore, this embodiment further optimizes the artificial potential field algorithm based on the loss cost of the energy storage system, i.e., optimizes its empirical parameters, thereby improving the economics of the energy storage system.

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

[0043] ,and ,

[0044] in, Let represent the objective function, and let represent the total loss cost of the energy storage system. This represents the degradation cost of the electrolytic cell. This indicates the cost of the deviation of the supercapacitor's SOC from the preset value. This indicates the voltage drop of a single cell in an electrolyzer before it reaches the end of its service life. This indicates the real-time voltage drop of a single cell in the electrolytic cell. Indicates electricity price, This represents the final value during the actual testing of the supercapacitor. This is the aforementioned preset intermediate value.

[0045] It should be noted that, in this embodiment, the hydrogen energy storage device is specifically taken as an example of an electrolyzer, but this is not intended to limit the components included in the hydrogen energy storage device. For example, it may also include fuel cells, hydrogen storage tanks, etc. Similarly, regarding the electric energy storage device, taking a supercapacitor as an example, in practical applications, the electric energy storage device may also use batteries, or be composed of a combination of supercapacitors and batteries. No further restrictions are imposed on this. Modifications and refinements made by those skilled in the art to the embodiments of this invention without departing from the spirit of this invention still fall within the scope of the invention application patent of this invention.

[0046] In this embodiment, the optimization of the artificial potential field algorithm is achieved by optimizing the shaping parameters. and the initial parameters of the power allocation factor To achieve this, the corresponding information needs to be obtained. and The optimized parameter values ​​are used to optimize the artificial potential field algorithm.

[0047] This can be calculated using the particle swarm optimization algorithm. and To find the optimal value, the key steps are as follows:

[0048] Step 1: Initialize a set of particles with random positions and velocities, where the position of a specific particle represents the target particle. and Optimized parameter values.

[0049] It's important to note that during the particle swarm optimization algorithm's computation, initial values ​​need to be added to the acquired variable parameters to obtain their target values ​​through multiple iterations. Therefore, for and It is necessary to utilize its initial value, that is, the value of the energy storage system in the current artificial potential field algorithm. and The actual parameter values ​​are used as the initial values ​​for the particle swarm optimization algorithm calculation.

[0050] Step 2: Based on the above and The initial value can determine the power distribution between the electrolytic cell and the supercapacitor. When adjusting the power distribution between the electrolytic cell and the supercapacitor using the energy management strategy, the input power of the electrolytic cell, the input and output power of the supercapacitor, and the SOC of the supercapacitor can be output.

[0051] Step 3: Calculate the fitness value of the particles based on the output of the energy management strategy. Specifically, this involves calculating the loss cost of using the energy storage system, as mentioned above. This serves as the fitness value. For each particle, the corresponding fitness value is calculated.

[0052] Step 4: Update the optimal fitness value (minimum fitness value) of each particle by comparing its current fitness value with its previous best fitness value. For a given particle, the position associated with its optimal fitness value is denoted as... .

[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 population, the position associated with the population's best fitness value is denoted as... .

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

[0055] ,

[0056] ,

[0057] in, V and X For the velocity and position of a specific particle, i For the current iteration metric, w For inertial weights, and For two acceleration constants, and Given two random numbers in the range [0,1]. If the iteration termination condition is not met, the loop continues to step 2 for the next iteration; otherwise, the particle swarm optimization algorithm terminates and returns. and The optimal value.

[0058] It should be noted that in this embodiment, the particle swarm optimization algorithm was used to directly obtain the results. and The optimal parameter values ​​are not limited to particle swarm optimization; other intelligent algorithms, such as whale optimization, can also be used. Modifications and refinements made by those skilled in the art to the embodiments of this invention without departing from the spirit of this invention still fall within the scope of the invention application.

[0059] Therefore, after obtaining and After obtaining the optimal value, it is necessary to verify whether using it can enable the artificial potential field algorithm to operate normally, thereby maintaining the normal operation of the energy storage system, so as to avoid the fact that the parameter values ​​calculated in the offline state are not applicable to the actual online scenario.

[0060] In response, the artificial potential field algorithm currently running in the energy storage system... and The initial value is replaced with the optimal value obtained from the above calculation to verify whether the energy storage system can operate normally when the artificial potential field algorithm is run using it.

[0061] Accordingly, in the above and Once the optimal value 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 the verification fails, the algorithm will continue to be used... and The initial values ​​are used to run the artificial potential field algorithm, thereby ensuring that the energy storage system is currently operating with a more economical energy management strategy.

[0062] Understandably, the above was obtained directly using the particle swarm optimization algorithm. and The optimal value can be found, but it can also be listed one by one using enumeration. and For all possible parameter values, multiple optimized schemes for the artificial potential field algorithm are obtained. However, it is still necessary to first verify whether using these schemes will enable the artificial potential field algorithm to operate normally. Simultaneously, multiple parameter values ​​can be filtered, meaning the artificial potential field algorithm currently running in the energy storage system is selected. and The initial value is replaced sequentially with possible parameter values ​​to verify its use. and Whether different parameter values ​​can enable the artificial potential field algorithm to operate normally and retain values ​​that meet the normal operating conditions of the energy storage system.

[0063] Furthermore, for values ​​that satisfy the normal operating conditions of the energy storage system, the loss cost of the energy storage system can be calculated based on the corresponding calculation results when using the artificial potential field algorithm. Thus, one value that minimizes the loss cost of the energy storage system can be selected from multiple values ​​that satisfy the normal operating conditions, and this value becomes the most economical optimization scheme for the energy storage system. Correspondingly, the artificial potential field algorithm is optimized using this value.

[0064] Therefore, by combining offline and online computation to optimize energy management strategies based on artificial potential field algorithms, it is possible to maintain the normal operation of energy storage systems while minimizing their loss costs, thereby improving their economic efficiency.

[0065] It should be noted that the steps of the various methods described above are only for clarity. In practice, they can be combined into one step or some steps can be split 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 or introducing insignificant designs to the algorithm or process, but without changing the core design of the algorithm and process, are also within the scope of protection of this application.

[0066] In summary, this invention provides an energy management optimization method for energy storage systems based on an artificial potential field algorithm. It fully considers the loss cost of the energy storage system and optimizes the energy management strategy based on the artificial potential field algorithm by combining offline and online calculations, thereby improving the economic efficiency 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 invention. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in the present invention should still be covered by the claims of the present invention.

Claims

1. An energy management optimization method for energy storage systems based on artificial potential field algorithm, characterized in that, include: Obtain the values ​​of empirical parameters in the artificial potential field algorithm; The energy management of the energy storage system is performed using an artificial potential field algorithm based on the values ​​of the empirical parameters, to verify whether the values ​​of the empirical parameters enable the energy storage system to operate normally. The steps of performing energy management using the artificial potential field algorithm include: pre-defining a corresponding virtual force based on the state of charge of the electric energy storage device; determining a corresponding power allocation factor based on the virtual force of the electric energy storage device with respect to its state of charge, and adjusting the cutoff frequency of the low-pass filter in the energy storage system using the power allocation factor to adjust the input and output power of the electric energy storage device; and synchronously adjusting the input and output power of the hydrogen energy storage device based on the input and output power of the electric energy storage device. For values ​​that meet the normal operating conditions of the energy storage system, optimize the artificial potential field algorithm to minimize the loss cost of the energy storage system.

2. The energy management optimization method for energy storage system based on artificial potential field algorithm according to claim 1, characterized in that, The empirical parameters in the artificial potential field algorithm include the pre-set shaping parameters of the energy storage device with respect to the virtual force of its state of charge and the pre-set initial parameters of the corresponding power allocation factor.

3. The energy management optimization method for energy storage systems based on artificial potential field algorithm according to claim 1, characterized in that, Virtual force As follows: ， ， wherein a shaping parameter preset for the virtual force, denotes a minimum value preset for the state of charge of the electrical energy storage device, denotes a maximum value preset for the state of charge of the electrical energy storage device, denotes an intermediate value preset for the state of charge of the electrical energy storage device, i.e. .

4. The energy management optimization method for energy storage systems based on artificial potential field algorithm according to claim 1, characterized in that, The power allocation factor as follows: ， in, This represents the initial parameters preset for the power allocation factor. This represents the virtual force corresponding to the state of charge of an electric energy storage device.

5. The energy management optimization method for energy storage systems based on artificial potential field algorithm according to claim 1, characterized in that, The steps to obtain the values ​​of empirical parameters in the artificial potential field algorithm include: The optimal values ​​of the empirical parameters in the artificial potential field algorithm are calculated using the particle swarm optimization algorithm.

6. The energy management optimization method for energy storage systems based on artificial potential field algorithm according to claim 5, characterized in that, The steps for calculating the optimal values ​​of empirical parameters in the artificial potential field algorithm using the particle swarm optimization algorithm include: Obtain the initial values ​​of the empirical parameters in the artificial potential field algorithm, and use them as the initial values ​​for 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 parameters in the artificial potential field algorithm is obtained by iterative calculation through the particle swarm optimization algorithm.

7. The energy management optimization method for energy storage systems based on artificial potential field algorithm according to claim 1 or 6, characterized in that, The loss cost of an energy storage system includes the degradation cost of hydrogen energy storage devices and the deviation cost of the state of charge of electrical energy storage devices from preset values.

8. The energy management optimization method for energy storage systems based on artificial potential field algorithm according to claim 7, 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: ， in, This indicates the degradation cost of the electrolytic cell. This indicates the purchase cost of the electrolytic cell. This indicates the voltage drop of a single cell in an electrolyzer before it reaches the end of its service life. This indicates the actual voltage drop of a single cell in the electrolytic cell.

9. The energy management optimization method for energy storage systems based on artificial potential field algorithm according to claim 7, characterized in that, The energy storage device includes a supercapacitor, and the deviation cost of the energy storage device's state of charge from a preset value is calculated according to the following formula: ， in, This represents the cost of deviation from the preset value of the supercapacitor's state of charge. This indicates the current electricity price. This indicates the capacitance of the supercapacitor. This represents the preset intermediate value of the supercapacitor's state of charge. This represents the final value of the supercapacitor's state of charge.

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