Energy control method and controller for energy management of renewable energy

Abstract

An energy control method and a controller for energy management are provided. In the method, a first output power of a target energy source within a monitoring time interval is obtained. A second output power of the target energy source within an extended time interval is determined. The extended time interval corresponds to a time interval after the monitoring time interval. Filter weights are determined based on the first output power and the second output power. The filter weights are used for smoothing filtering. The first output power and the second output power are smoothed based on the filter weights. Therefore, the degree of smooth control could be improved and the time delay could be reduced.

Description

Technical Field

[0001] This invention relates to an energy management technology, and more particularly to an energy control method and a controller for energy management. Prior Technology

[0002] In recent years, there has been increasing global attention on the development and application of renewable energy, especially solar photovoltaic (PV) technology. However, solar power generation is intermittent, which can cause power system failures or outages. Therefore, combining solar power with battery energy storage systems (BESS) to improve grid stability has become a solution.

[0003] Due to regulatory restrictions and the high cost of energy storage systems, combining smoothing filters with battery energy storage systems to reduce costs and stabilize output power has become a widely recognized approach. However, while existing filters effectively smooth power fluctuations, they introduce significant time delays. Summary of the Invention

[0004] This invention provides an energy control method and a controller for energy management, which can effectively reduce time delays.

[0005] The energy control method of this invention includes (but is not limited to) the following steps: obtaining the first output power of the target energy within a monitoring time interval; determining the second output power of the target energy within an extended time interval, wherein the extended time interval corresponds to the time interval after the monitoring time interval; determining the filtering weight based on the first output power and the second output power, wherein the filtering weight is used for smoothing filtering; and smoothing the first output power and the second output power based on the filtering weight.

[0006] The controller for energy management in this embodiment of the invention is configured to: obtain a first output power of a target energy source within a monitoring time interval; determine a second output power of the target energy source within an extended time interval; determine a filtering weight based on the first output power and the second output power; and smooth the first output power and the second output power based on the filtering weight. The extended time interval corresponds to the time interval following the monitoring time interval, and the filtering weight is used for smoothing filtering.

[0007] Based on the above, the energy control method and controller for energy management in this embodiment of the invention extend the output power (i.e., the second output power) after the monitoring time interval and smooth the original output power (i.e., the first output power) and the extended output power. This solves the filter boundary problem and reduces filtering delay.

[0008] To make the above features and advantages of the present invention more apparent and understandable, specific embodiments are described below in conjunction with the accompanying drawings for detailed explanation. Simple Explanation of the Diagram

[0009] Figure 1 is a block diagram of the components of an overall system according to an embodiment of the present invention. Figure 2 is a schematic diagram of renewable energy control according to an embodiment of the present invention. Figure 3 is a flowchart of an energy control method according to an embodiment of the present invention. Figure 4 is a schematic diagram of a high-pass filter according to an embodiment of the present invention. Figure 5 is a schematic diagram of signal spread according to an embodiment of the present invention. Figure 6 is a flowchart of a variable Gaussian filter smoothing according to an embodiment of the present invention. Figure 7 is a flowchart of energy storage power control according to an embodiment of the present invention. Figure 8 is a comparison diagram of the variable Gaussian filter and the moving average method according to an embodiment of the present invention. Figure 9 is a comparison diagram of the variable Gaussian filter and the double moving average method according to an embodiment of the present invention. Figure 10 is a comparison chart of the variable Gaussian filter and the exponential moving average method according to an embodiment of the present invention. Implementation

[0010] Figure 1 is a block diagram of the components of an overall system 1 according to an embodiment of the present invention. Referring to Figure 1, the overall system 1 includes (but is not limited to) a power supply system 50 and an energy management system 100.

[0011] The power supply system 50 is, for example, various types of power generation systems, power converters, generators, or other devices or combinations thereof that provide electricity. The power supply system 50 can provide electricity from a target energy source. In one embodiment, the target energy source is, for example, solar energy, wind power, hydropower, or other renewable / green energy. In another embodiment, the target energy source may also be thermal power or nuclear power. The type of target energy source can vary depending on different design requirements, and this embodiment of the invention does not limit this.

[0012] The energy management system 100 includes (but is not limited to) a controller 110 and an energy storage device 120.

[0013] Controller 110 may be a mobile phone, tablet computer, laptop computer, desktop computer, server, voice assistant device, smart home appliance, wearable device, vehicle system, or other electronic device. Alternatively, controller 110 may be a central processing unit (CPU), graphics processing unit (GPU), or other programmable general-purpose or special-purpose microprocessor, digital signal processor (DSP), programmable controller, field-programmable gate array (FPGA), application-specific integrated circuit (ASIC), neural network accelerator, or other similar components or combinations thereof.

[0014] In some application scenarios, controller 110 can be referred to as a smooth controller.

[0015] The energy storage device 120 is coupled to the controller 110. The energy storage device 120 can be a battery energy storage system (BESS), a supercapacitor energy storage system, or a hybrid energy storage system.

[0016] In some applications, energy storage device 120 is also coupled to other power grids and power supply systems 50. Energy storage device 120 can supply / discharge to the power grid, and / or power supply system 50 can charge energy storage device 120.

[0017] Figure 2 is a schematic diagram of renewable energy control according to an embodiment of the present invention. Referring to Figure 2, in the application scenario of renewable energy, the power supply system 50 is, for example, a solar power generation system. The power supply system 50 and the energy storage device 120 are respectively coupled to the power grid EN (e.g., the public power grid) via a common coupling point CCP. The controller 110 can capture the output power provided by the power supply system 50 via the common coupling point CCP. As shown, the output power is captured over 24 hours. The controller 110 can control the energy storage device 120 to perform charging or discharging operations. The charging source is, for example, the power supply system 50 and / or the power grid EN. The discharging target is, for example, the power grid EN.

[0018] It should be noted that the above application scenarios are only illustrative examples, and users can modify the device or function according to their actual needs.

[0019] The method described in the embodiments of the present invention will be explained in conjunction with the various systems, devices, components and modules in the overall system 1. The various processes of this method may be adjusted according to the implementation situation, and are not limited thereto.

[0020] Figure 3 is a flowchart of an energy control method according to an embodiment of the present invention. Referring to Figure 3, the controller 110 obtains the first output power of the target energy within a monitoring time interval (step S310). Specifically, the target energy is, for example, the energy provided by the power supply system 50. The target energy is used to generate electricity. The monitoring time interval can be 5 minutes, 30 minutes, 1 hour, 12 hours, or 24 hours, and is not limited thereto. As shown in Figure 2, the controller 110 can obtain the output power of the power supply system 50 within 24 hours through the electricity meter EM. The method of obtaining / measuring / obtaining the output power value is, for example, by sensor measurement, parsing packets in the communication protocol of the power system, or recording power data through a data acquisition system.

[0021] In one embodiment, the first output power within the monitoring time interval may include multiple first power values ​​corresponding to multiple sampling points.

[0022] Controller 110 determines the second output power of the target energy source within the extended time interval (step S320). Specifically, the first output power acquired / measured / captured by controller 110 within the monitoring time interval includes the latest output power. The latest output power can be the output power acquired at the last / most recent sampling time point or capture time point within the monitoring time interval. That is, the latest output power can represent the instantaneous / current output power of the target energy source. The extended time interval corresponds to the time interval after the monitoring time interval. In other words, the extended time interval represents the future time interval corresponding to the current time.

[0023] In one embodiment, the second output power over the extended time interval may include multiple second power values ​​corresponding to multiple sampling points.

[0024] The following further explains why a second output power is needed over the extended time interval.

[0025] It is worth noting that the power variability rate (RR) is used to measure the degree of power variation of renewable energy systems such as solar or wind power systems per unit time. RR=|ΔP| / ΔT (% / minute)…(1) ΔP is the change in power over time, and ΔT is the time required for the change, measured in minutes. Power variability is commonly used to assess the stability and reliability of energy systems and to determine system operating limits and regulation strategies. Currently, the standard range for power variability in common solar energy systems internationally is 40-10% per minute, meaning the system's output power can increase or decrease by a maximum of 4-10% per minute over a short period. To reduce the impact of rapid power fluctuations caused by intermittent renewable energy generation on the power grid, the output power of the renewable energy power converter at the common coupling point must be smoothed.

[0026] However, in existing smoothing filtering techniques (e.g., moving average filters, double moving average filters, exponential moving average filters, or one-dimensional Gaussian filters), the filtered signal may be delayed or distorted in order to improve the smoothness. Alternatively, a one-dimensional Gaussian filter may produce boundary phenomena because the instantaneous data corresponds to the center point, which in turn causes signal delay or distortion.

[0027] For example, Figure 4 is a schematic diagram of a Gaussian filter (GF) according to an embodiment of the present invention. Referring to Figure 4, the one-dimensional Gaussian filter (GF) eliminates high-frequency oscillations and preserves edge information through weighted averaging. Based on the normal distribution function, weighted coefficients are used to average the input data, giving higher weights to data closer to the center point. As shown in the figure, the weight of the center point is 1, and the weight decreases as the data moves further away from the center point. In the Gaussian filter, the filter / window size and standard deviation are important parameters. The filter / window size determines the coverage of the convolution operation, and the standard deviation affects the distribution of the weighted coefficients; adjusting the standard deviation can control the smoothness. The one-dimensional Gaussian filter calculates the weighted coefficients using mathematical formulas and applies them to the convolution operation. The formula for the one-dimensional Gaussian filter is as follows: …(2) ,in These are weighting coefficients. Represents the center position of the filter. Represents the standard deviation of the filter. This represents the input data that needs to be filtered.

[0028] However, when the center point of the filter represents updated data (e.g., the latest output power), the right half (corresponding to the extended or future time interval) is empty. Current industry practice automatically pads the right half with zeros (as shown in Figure 4). This results in the smoothed value after passing through the Gaussian filter being smaller than the original value, causing time delays and distortion. The lack of edge data affects the smoothing effect of the filter, and this effect becomes more pronounced with larger filter or window widths, leading to decreased accuracy and even abnormal oscillations. Therefore, without improvements to the one-dimensional Gaussian filter, there is no advantage for real-time computation.

[0029] In one embodiment, the controller 110 may set the second output power to be greater than zero. Compared to setting it to zero, a second output power greater than zero will make the Gaussian filter smoothed value closer to the original value.

[0030] In one embodiment, the second output power is generated based on the first output power. For example, FIG5 is a schematic diagram of signal spread according to an embodiment of the present invention. Referring to FIG5, the controller 110 can determine the first signal waveform of the first output power FP within a monitoring time interval. The first signal waveform is the amplitude shape of the first output power FP changing with time within the monitoring time interval.

[0031] Furthermore, the controller 110 can generate a second signal waveform based on the first signal waveform. The second signal waveform can represent the amplitude shape of the second output power SP changing over time within an extended time interval.

[0032] In one embodiment, the controller 110 may use the mirrored first signal waveform as the second signal waveform. With the vertical axis (e.g., the axis corresponding to amplitude / power magnitude) passing through the latest output power NP of the first output power FP as the central axis, the controller 110 flips the first signal waveform from left to right (assuming the left half corresponds to the monitoring time interval and the right half corresponds to the extended time interval), thus generating the second signal waveform.

[0033] Next, the controller 110 can determine the second output power SP based on the second signal waveform. For example, multiple second power values ​​at multiple sampling points can be set as the amplitude of the corresponding time point on the second signal waveform.

[0034] This embodiment proposes an Improved Gaussian Filter (IGF) FW, one of its objectives being to improve upon the traditional one-dimensional Gaussian filter. Filter / window size and standard deviation remain important input calculation parameters for weight allocation. A mirror compensation method (also known as symmetric expansion or reflection expansion) is used to address boundary issues, thereby mitigating the time delay phenomenon. This is achieved by mirroring the boundary of the original signal (i.e., the first signal waveform) and expanding the boundary with data opposite to the original signal, resulting in an expanded signal (i.e., the second signal waveform) that fills the gap in the original filter size.

[0035] For example, …(3) ,in The original signal (e.g., the signal with the first output power within the monitoring time interval). For the extended signal (e.g., a combination of the first output power signal within the monitoring time interval and the second output power signal within the extended time interval), L is the number of sampling points in the extended range (e.g., the extended time interval), and N is the length of the original signal (e.g., the number of sampling points in the monitoring time interval). In other words, the L first power values ​​to the left of the center point are mirrored and filled into the L second power values ​​to the right of the center point to complete the Gaussian window signal. This avoids edge effects during signal filtering, effectively reduces the impact of boundary effects on the calculation results, and improves the accuracy of signal processing.

[0036] In one embodiment, the center point of the weight allocation of the variable Gaussian filter FW corresponds to the latest output power NP of the first output power FP within the monitoring time interval, and the window size of the variable Gaussian filter FW corresponds to / is equal to / approximately the sum of the monitoring time interval and the extended time interval. For example, if the monitoring time interval is the time interval corresponding to 10 first power values ​​and the extended time interval is the time interval corresponding to 9 second power values, then the window size is 19.

[0037] In another embodiment, the center point of the weight allocation of the variable Gaussian filter FW corresponds to the other output power values ​​of the first output power FP within the monitoring time interval, and / or the window size of the variable Gaussian filter FW is smaller than the sum of the monitoring time interval and the extended time interval. For example, the center point corresponds to the first power value of the second-to-last sampling point. As another example, if the monitoring time interval is the time interval corresponding to 8 first power values ​​and the extended time interval is the time interval corresponding to 7 second power values, then the window size is 13.

[0038] Referring to Figure 3, the controller 110 determines the filtering weights based on the first output power and the second output power (step S330), and smooths the first output power and the second output power based on the filtering weights (step S340). Specifically, the filtering weights are used for smoothing filtering. The smoothing filtering uses the filtering weights to weight the power values ​​of the first output power and the second output power. As shown in the Gaussian filter in Figure 4, the input data [4 3 1 4 6] are weighted with the filtering weights [0.1 0.3 1 0.3 0.1] respectively to obtain the smoothed output power corresponding to the center point (e.g., the latest output power).

[0039] Figure 6 is a flowchart of a variable Gaussian filter smoothing process according to an embodiment of the present invention. Referring to Figure 6, the variable Gaussian filter includes a first filter, a second filter, and a third filter. The controller 110 can acquire the original signal. (For example, the signal of the first output power within the monitoring time interval) (step S611).

[0040] The controller 110 can determine a (first) filter with a standard deviation of α (step S621). α is σ / n, where n is a positive integer between 2 and 5, and σ is the original signal. The standard deviation of the Gaussian filter (e.g., the standard deviation of the Gaussian filter corresponds to the first output power). Controller 110 can smooth the original signal using the first filter. (i.e., the signal of the first output power), and generate the third output power. Next, the controller 110 multiplies the signal of the third output power by γ (step S622) and generates the fourth output power. γ is a value between 2 and 4. Next, the controller 110 outputs the signal y (i.e., the signal of the fourth output power) (step S623).

[0041] On the other hand, controller 110 can determine a (second) filter with a standard deviation of β (step S631). β is σ. That is, the same as the original signal. The standard deviation of the Gaussian filter. Controller 110 can smooth the original signal through the second filter. (i.e., the signal of the first output power), and generate the fifth output power. Then, the controller 110 outputs signal z (i.e., the signal of the fifth output power) (step S632).

[0042] The controller 110 can calculate the power difference (yz) between the signal y (i.e., the signal of the fourth output power) and the fifth output power z (i.e., the signal of the fifth output power) (step S641). The controller 110 can determine the smoothed output power (e.g., the output power smoothed by a variable Gaussian filter for the latest output power) based on this power difference.

[0043] The controller 110 can determine a (third) filter with a standard deviation of δ (step S642). δ is σ / m, and m is a positive integer between 2 and 5. The controller 110 can smooth the power difference signal through the third filter and generate the output of a variable Gaussian filter (i.e., the smoothed output power) (step S643).

[0044] In one embodiment, n is 2, γ is 4, and m is 2. This corresponds to the embodiment shown in Figure 5. That is, the center point of the weight allocation of the variable Gaussian filter FW corresponds to the latest output power NP of the first output power FP within the monitoring time interval, and the window size of the variable Gaussian filter FW corresponds to the sum of the monitoring time interval and the extended time interval.

[0045] However, the parameters used in the variable Gaussian filter can still be adjusted according to actual needs.

[0046] Figure 7 is a flowchart of energy storage power control according to an embodiment of the present invention. Referring to Figure 7, the controller 110 can set a monitoring time interval (step S710). As described in step S310, and the length of the monitoring time interval can be defined according to actual needs, it will not be repeated here. The controller 110 can obtain the first output power PPV within the monitoring interval (step S720). As described in step S310, it will not be repeated here. For example, the power value of solar energy fed into the grid is obtained. The controller 110 can obtain the smoothed output power Prefer (step S730). As described in steps S330 to S340, it will not be repeated here.

[0047] Next, controller 110 determines the output power difference between the latest output power and the smoothed output power in the first output power (step S740). As explained above, the latest output power is the last sampled value of the first output power within the monitoring time interval, and the smoothed output power is the smoothed output power of the first output power and the second output power. Controller 110 obtains the output power difference, for example, by subtracting the latest output power from the smoothed output power. For example, first output power PPV - smoothed output power Prefer.

[0048] The controller 110 can determine the energy storage output power of the energy storage device 120 based on the output power difference. The energy storage output power is one of a charging power value and a discharging power value. The charging power value corresponds to a charging operation. For example, charging the energy storage device 120 with power supplied by the power supply system 50. The discharging power value corresponds to a discharging operation. For example, providing power to a coupled power grid or other power system.

[0049] The controller 110 can store energy and output power to generate control commands. These control commands are used to instruct the energy storage device 120 to perform corresponding charging or discharging operations. In response to the control command indicating a charging power value, the energy storage device 120 requests a charging operation from the power supply system 50 at that charging power value (e.g., issues a corresponding command). In response to the control command indicating a discharging power value, the energy storage device 120 performs a discharging operation with the indicated charging power value to the coupled power grid or other power system.

[0050] Taking Figure 7 as an example, the controller 110 determines whether the output power difference is that the latest output power of the first output power is greater than the smoothed output power (step S750). For example, whether the first output power PPV - the smoothed output power Prefer is greater than zero.

[0051] In response to the latest output power being greater than the smoothed output power, the controller 110 controls the charging power value to be, for example, the absolute value of the first output power PPV minus the smoothed output power Prefer (step S760).

[0052] On the other hand, in response to the latest output power not being equal to the smoothed output power, the controller 110 controls the discharge power value to be, for example, the absolute value of the first output power PPV - the smoothed output power Prefer (step S770).

[0053] Next, the controller 110 determines the energy storage output power (step S780). For example, if the latest output power is greater than the smoothed output power, the controller 110 determines the energy storage output power as the charging power value and controls the energy storage device 120 to perform a charging operation at the specified charging power value. Alternatively, if the latest output power is not greater than the smoothed output power, the controller 110 determines the energy storage output power as the discharging power value and controls the energy storage device 120 to perform a charging operation at the specified discharging power value.

[0054] In one application scenario, an energy storage system (corresponding to energy storage device 120), a solar photovoltaic system (corresponding to power supply system 50), and loads are integrated into a three-zone microgrid system. Through the smooth control of this embodiment of the invention, the output power of the three-zone microgrid is integrated, ensuring that the overall output of the three-zone microgrid system back to the grid is effectively smoothed and conforms to the power company's set output fluctuation rate. The obtained allocation ratio effectively balances and maintains the battery capacity of the three-zone microgrid. This control strategy not only effectively improves the power quality at the grid end but also maintains power balance, thereby extending the overall grid's lifespan.

[0055] The variable Gaussian filtering algorithm proposed in this invention is compared with common smoothing filtering methods for smoothing time-varying data. The differences in smoothing results between different filtering methods are judged through graphs, and it can be used to evaluate the performance and benefits of different methods.

[0056] Figure 8 is a comparison diagram of the variable Gaussian filter 805 and moving average methods 802, 803, and 804 according to an embodiment of the present invention. Referring to Figure 8, compared to the instantaneous output power 801, although moving average methods 802, 803, and 804 with window sizes of 12, 24, and 48 can all smooth the values, the smoothing performance improves significantly with increasing window size. However, using the moving average method 804 with a window size of 48 leads to a longer delay, resulting in a significant deviation in the smoothed data and thus relatively poor power tracking capability. The variable Gaussian filter 805 also provides a better smoothing effect and effectively reduces time delay.

[0057] Figure 9 is a comparison diagram of the variable Gaussian filter 905 and the dual moving average methods 902, 903, and 904 according to an embodiment of the present invention. Referring to Figure 9, compared to the instantaneous output power 901, although the dual moving average methods 902, 903, and 904 with window sizes of (12, 12), (24, 12), and (48, 12) can all smooth the values, the smoothing performance improves significantly with the increase of the window size. However, the dual moving average method 904 with a window size of (48, 12) exhibits the most severe delay, resulting in a significant shift in the smoothed data and relatively poor power tracking capability. Similarly, the variable Gaussian filter 905 also provides a better smoothing effect and effectively reduces time delay.

[0058] Figure 10 is a comparison diagram of the variable Gaussian filter 1104 and the exponential moving average methods 1102 and 1103 according to an embodiment of the present invention. Referring to Figure 10, compared to the instantaneous output power 1101, although the exponential moving average methods 1102 and 1103 with smoothing coefficients of 0.03, 0.06, and 0.1 can all smooth the value, the smoothing performance improves as the smoothing coefficient decreases. However, the exponential moving average method 1102 with a smoothing coefficient of 0.03 responds more slowly to the rise or fall of the instantaneous output power 1101, and the time delay phenomenon is the most obvious and severe. Similarly, the variable Gaussian filter 1104 also provides a better smoothing effect and effectively reduces the time delay phenomenon.

[0059] In summary, the energy control method and controller for energy management in this invention address the boundary issues of smooth filtering by extending the output power over the monitoring time interval. Furthermore, by mirroring the boundary of the original signal of the first output power within the monitoring time and expanding the boundary with data opposite to the original signal, an extended signal is generated, filling the gap in the original filter size. This improves the smoothness of control and reduces time delay.

[0060] Although the present invention has been disclosed above by way of embodiments, it is not intended to limit the present invention. Anyone skilled in the art can make some modifications and refinements without departing from the spirit and scope of the present invention. Therefore, the scope of protection of the present invention shall be determined by the appended claims.

[0061] 1: Overall System 50: Power Supply System 100: Energy Management System 110: Controller 120: Energy storage device CCP: Common Coupling Point EN: power grid EM: electricity meter GF: Gaussian filter FP: First Output Power SP: Second Output Power NP: Latest Output Power FW: Variable Gaussian Filter PPV: First Output Power Prefer: Smoothed output power S310~S340, S611~S643, S710~S780: Steps 801: Instantaneous Output Power 802, 803, 804: Moving Average Method 805: Variable Gaussian Filter 901: Instantaneous Output Power 902, 903, 904: Double Moving Average Method 905: Variable Gaussian Filter 1101: Instantaneous Output Power 1102, 1103: Exponential Moving Average Method 1104: Variable Gaussian Filter

Claims

1. An energy control method for renewable energy, comprising: Obtain a first output power of a target energy source within a monitoring time interval; determine a second output power of the target energy source within an extended time interval, wherein the extended time interval corresponds to a time interval following the monitoring time interval; determine a filtering weight based on the first output power and the second output power, wherein the filtering weight is used for smoothing filtering; and smooth the first output power and the second output power based on the filtering weight.

2. The energy control method for renewable energy as described in claim 1, wherein the step of determining the second output power of the target energy within the extended time interval includes: Set the second output power to be greater than zero.

3. The energy control method for renewable energy as described in claim 1, wherein the step of determining the second output power of the target energy within the extended time interval includes: The first output power is determined by a first signal waveform within the monitoring time interval; A second signal waveform is generated based on the first signal waveform; And determine the second output power based on the second signal waveform.

4. The energy control method for renewable energy as described in claim 3, wherein the step of generating the second signal waveform based on the first signal waveform includes: The first signal waveform, which is then mirrored and flipped, is used as the second signal waveform.

5. The energy control method for renewable energy as described in claim 1, wherein the step of determining the filter weight based on the first output power and the second output power includes: The filter weights are determined by a variable Gaussian filter, wherein the center point of the weight allocation of the variable Gaussian filter corresponds to the latest output power of the first output power within the monitoring time interval, and the window size of the variable Gaussian filter corresponds to the sum of the monitoring time interval and the extended time interval.

6. The energy control method for renewable energy as described in claim 5, wherein the variable Gaussian filter includes a first filter and a second filter, and the step of determining the filter weight through the variable Gaussian filter includes: The first filter, whose standard deviation is determined by α, where α is σ / n, n is a positive integer between 2 and 5, and σ is the standard deviation of the Gaussian filter corresponding to the first output power; and the second filter, whose standard deviation is determined by β, where β is σ.

7. The energy control method for renewable energy as described in claim 6, wherein the step of smoothing the first output power and the second output power according to the filter weight includes: The signal of the first output power is smoothed by the first filter, and a third output power is generated; the signal of the third output power is multiplied by γ, and a fourth output power is generated, where γ is a value between 2 and 4; the signal of the first output power is smoothed by the second filter, and a fifth output power is generated; a power difference between the fourth output power and the fifth output power is calculated; and a smoothed output power is determined based on the power difference.

8. The energy control method for renewable energy as described in claim 7, wherein the variable Gaussian filter further includes a third filter, and the step of determining the filter weight through the variable Gaussian filter includes: The third filter is determined by its standard deviation δ, where δ is σ / m and m is a positive integer between 2 and 5.

9. The energy control method for renewable energy as described in claim 8, wherein the step of determining the smoothed output power based on the power difference includes: The third filter smooths the power difference signal and generates the smoothed output power.

10. The energy control method for renewable energy as described in claim 1 further includes: Determine an output power difference between a latest output power and a smoothed output power, wherein the latest output power is the last sampled value of the first output power within the monitoring time interval, and the smoothed output power is the smoothed output power of the first output power and the second output power; and determine an energy storage output power based on the output power difference, wherein the energy storage output power is one of a charging power value and a discharging power value.

11. A controller for energy management of renewable energy, configured to: acquire a first output power of a target energy source within a monitoring time interval; determine a second output power of the target energy source within an extended time interval, wherein the extended time interval corresponds to a time interval following the monitoring time interval; determine a filtering weight based on the first output power and the second output power, wherein the filtering weight is used for smoothing filtering; and smooth the first output power and the second output power based on the filtering weight.

12. The controller for energy management of renewable energy as described in claim 11 is further configured to: set the second output power to be greater than zero.

13. The controller for energy management of renewable energy as described in claim 11 is further configured to: determine a first signal waveform of the first output power within the monitoring time interval; generate a second signal waveform based on the first signal waveform; and determine the second output power based on the second signal waveform.

14. The controller for energy management of renewable energy as described in claim 13 is further configured to: use the mirrored and inverted first signal waveform as the second signal waveform.

15. The controller for energy management of renewable energy as described in claim 11 is further configured to: determine the filter weights through a variable Gaussian filter, wherein the center point of the weight allocation of the variable Gaussian filter corresponds to the latest output power of the first output power within the monitoring time interval, and the window size of the variable Gaussian filter corresponds to the sum of the monitoring time interval and the extended time interval.

16. The controller for energy management of renewable energy as claimed in claim 15, wherein the variable Gaussian filter includes a first filter and a second filter, and the controller is further configured to: determine the first filter having a standard deviation of α, wherein α is σ / n, n is a positive integer between 2 and 5, and σ is the standard deviation of the Gaussian filter corresponding to the first output power; and determine the second filter having a standard deviation of β, wherein β is σ.

17. The controller for energy management of renewable energy as described in claim 16 is further configured to: smooth the signal of the first output power through the first filter and generate a third output power; multiply the signal of the third output power by a factor of γ and generate a fourth output power, wherein γ is a value between 2 and 4; smooth the signal of the first output power through the second filter and generate a fifth output power; calculate a power difference between the fourth output power and the fifth output power; and determine a smoothed output power based on the power difference.

18. The controller for energy management of renewable energy as claimed in claim 17, wherein the variable Gaussian filter further includes a third filter, and the controller is further configured to: determine the third filter having a standard deviation of δ, wherein δ is σ / m, and m is a positive integer between 2 and 5.

19. The controller for energy management of renewable energy as described in claim 18 is further configured to: smooth the signal of the power difference through the third filter and generate the smoothed output power.

20. The controller for energy management of renewable energy as described in claim 11 is further configured to: determine an output power difference between a latest output power and a smoothed output power, wherein the latest output power is the last sampled value of the first output power within the monitoring time interval, and the smoothed output power is the smoothed output power of the first output power and the second output power; determine an energy storage output power based on the output power difference, wherein the energy storage output power is one of a charging power value and a discharging power value; and generate a control command based on the energy storage output power.

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