Pumped storage microgrid energy storage capacity optimization configuration method, system, device and medium

Abstract

The application discloses a kind of pumped storage micro-grid energy storage capacity optimization configuration method, system, equipment, medium, relating to the optimization of grid energy storage capacity, its purpose is to solve the problem of low accuracy in prior art when capacity configuration is carried out in mixed energy storage system, configuration capacity and comprehensive cost are higher.It includes collecting data;Calculate the total power of mixed energy storage, carry out CEEMDAN decomposition, obtain IMF component;Instantaneous power is calculated using Hilbert transform;Frequency division is carried out on instantaneous power;Objective function is written and mixed energy storage capacity optimization model is established;The mixed energy storage capacity optimization model established and the data collected are written to MATLAB platform, and the SCIP solver in OptiToolbox is called to solve, and the optimization variable is obtained and output.Based on CEEMDAN, the capacity of the mixed energy storage system is configured, which has better accuracy than EEMD, thereby reducing the configuration capacity and comprehensive cost of the mixed energy storage, and compared with the system without pumped storage, CEEMDAN has more advantages in the scheme.

Description

Technical Field

[0001] This invention belongs to the field of power grid energy storage technology, and relates to the optimization of power grid energy storage capacity, and particularly to a method, system, equipment and medium for optimizing the configuration of energy storage capacity of pumped storage microgrids. Background Technology

[0002] In recent years, guided by national policies, my country's new energy industry has developed rapidly. By controlling the total amount of fossil fuels, focusing on improving utilization efficiency, implementing renewable energy substitution initiatives, and deepening power system reform, China strives to achieve peak carbon emissions by 2030 and carbon neutrality by 2060, building a new power system with new energy as the mainstay. Energy storage systems have the advantages of rapid power regulation and energy storage capacity, thus playing a crucial role in smoothing renewable energy power fluctuations, improving power quality, and serving as backup power sources. They are a vital link in increasing grid energy penetration and optimizing network operation. Therefore, research on energy storage technologies under the new power system is particularly important.

[0003] Regarding hybrid energy storage, some technical solutions propose hybrid energy storage systems based on cogeneration units, such as energy storage batteries, electric boilers, and thermal storage tanks, which alleviate the problem of wind curtailment. However, the thermal storage tanks suffer from heat loss, resulting in lower energy efficiency. Other solutions utilize hybrid energy storage composed of lithium batteries and supercapacitors to smooth power fluctuations from distributed sources, fully leveraging the advantages of both types of energy storage and improving system operating efficiency. However, due to the high cost of supercapacitors, this model lacks economic viability. Alternatively, a hybrid energy storage model combining hydrogen energy storage and batteries can be used, balancing system economy and low carbon emissions. However, efficient operation of hydrogen energy storage requires the system to maintain a high power level. Other technical solutions have verified the effectiveness of hybrid energy storage systems composed of supercapacitors, batteries, and compressed air energy storage, but this model requires coordination with gas turbines and can cause pollution under high-temperature conditions. These technical studies have demonstrated the characteristics and advantages of hybrid energy storage systems in multiple scenarios. However, pumped hydro storage, as a typical representative of energy storage, is technologically mature and low-cost, but models incorporating pumped hydro storage into hybrid energy storage systems are still rare and warrant further investigation.

[0004] Regarding capacity optimization, one technical solution proposes a decomposition method based on Discrete Fourier Transform (DFT), which analyzes signals through simple time-frequency transformation. However, this method decomposes the frequency characteristics over a long time scale and cannot determine the specific time period when a particular power output signal occurs. To address this, some researchers have used wavelet packet decomposition to perform multi-scale decomposition and reconstruction of wind power signals, resulting in a more refined power decomposition. However, the energy storage configuration results of this method are highly dependent on the manually determined number of wavelet packet decomposition layers. Therefore, to overcome the influence of subjective factors, some technologies use adaptive moving average filtering to process power signals, avoiding human interference. However, using the average power of all sampling points within a sliding window as the smoothing target can easily cause the target power curve to deviate from the original power curve. More powerful techniques, such as ensemble empirical mode decomposition (EMD), effectively solve the mode aliasing problem of signals, resulting in smaller deviations during signal reconstruction. However, its performance in handling white noise is somewhat inferior to that of complete ensemble empirical mode decomposition.

[0005] Patent application number 202211578592.6 discloses a method and terminal for optimizing energy storage configuration in microgrids. The method first establishes an objective function constraining the energy storage capacity of the microgrid, then transforms this objective function into an augmented objective function by incorporating frequency safety constraints. Next, it establishes virtual inertia constraints, virtual damping constraints, the energy storage capacity optimization interval, and the differential-algebraic equations of the microgrid system's generator units for the virtual synchronous generators. Then, it establishes an energy storage capacity optimization model by combining the augmented objective function, virtual inertia constraints, virtual damping constraints, the energy storage capacity optimization interval, and the differential-algebraic equations. Finally, it uses this energy storage capacity optimization model to optimize the energy storage configuration of the microgrid. This method establishes an energy storage capacity optimization model based on an objective function constraining the energy storage capacity of the microgrid. Under constraints, it matches parameters such as virtual inertia, virtual damping, and energy storage capacity between different virtual synchronous generators, thereby ensuring that the microgrid system maintains sufficient inertia and damping support, preventing frequency instability during microgrid operation, and simultaneously achieving optimized energy storage capacity configuration, enabling the system to operate safely and stably.

[0006] This optimization configuration method introduces an optimization algorithm when using an energy storage capacity optimization model to optimize the energy storage configuration of a microgrid. This ensures that the output of the energy storage capacity optimization model yields the optimal solution, thereby achieving optimal frequency security and stability and optimal energy storage capacity configuration for the system. However, this method has relatively low accuracy when configuring capacity for hybrid energy storage systems, and the configuration capacity and overall cost of hybrid energy storage are relatively high. Summary of the Invention

[0007] The purpose of this invention is to solve the problems of low accuracy in capacity configuration, high configuration capacity and overall cost in existing hybrid energy storage systems, and to provide a method, system, equipment and medium for optimizing the configuration of energy storage capacity in pumped storage microgrids.

[0008] To achieve the above objectives, the present invention specifically adopts the following technical solution:

[0009] A method for optimizing the energy storage capacity of a microgrid containing pumped storage includes the following steps when optimizing the capacity configuration of a microgrid containing pumped storage hybrid energy storage:

[0010] Step S1, collect data

[0011] Collect net load power and tie-line protocol power;

[0012] Step S2: Calculate the total power of the hybrid energy storage system, perform CEEMDAN decomposition, and obtain the IMF component.

[0013] Using the net load power and tie-line protocol power collected in step S1, the total hybrid energy storage power is calculated, and the total hybrid energy storage power is decomposed into CEEMDAN components to obtain multiple IMF components.

[0014] Step S3: Calculate instantaneous power using Hilbert transform.

[0015] Based on the IMF components obtained in step S2, the instantaneous power of each IMF component is calculated, resulting in multiple instantaneous powers:

[0016] Step S4, Instantaneous power frequency division

[0017] The multiple instantaneous power obtained in step S3 is divided into high-frequency components, mid-frequency components, and low-frequency components. The high-frequency components are smoothed by supercapacitors, the mid-frequency components are smoothed by batteries, and the low-frequency components are smoothed by pumped hydro storage.

[0018] Step S5, Write the objective function

[0019] A target function is written with the goal of minimizing the annual comprehensive cost of the energy storage system, which is used for capacity optimization configuration; step S6, establish a hybrid energy storage capacity optimization model.

[0020] Using the objective function and constraints written in step S5, establish a hybrid energy storage capacity optimization model; in step S7, call SCIP to solve the problem.

[0021] The hybrid energy storage capacity optimization model established in step S6 and the data collected in step S1 are written into the MATLAB platform and the SCIP solver in OptiToolbox is called to solve the problem and obtain the optimization variables.

[0022] Step S8, output variables

[0023] Output the optimized variables obtained in step S7 and update the parameters of the microgrid containing pumped storage hybrid energy storage.

[0024] Furthermore, the pumped storage hybrid energy storage microgrid includes a microgrid central controller, pumped storage modules connected to the main power grid, battery modules, supercapacitor modules, wind and solar power generation modules, and power load modules. The pumped storage modules, battery modules, supercapacitor modules, wind and solar power generation modules, and power load modules are connected to the microgrid central controller via signals.

[0025] Further, in step S3, the instantaneous power f is calculated. k (t):

[0026]

[0027] IMF k (t)=F k (t)+jHF k (t)

[0028]

[0029]

[0030] Where j represents a complex number, k represents the k-th calculation, t represents the t-th time, and τ represents the time constant. IMF k The phase of (t), express The differential, IMF k (t) represents a complex function, F k (t) represents the k-th order IMF component, F k (τ) represents the k-th order IMF component at time τ, HF k (t) represents F k The Hilbert transform of (τ), where h(t) represents the impulse function.

[0031] Furthermore, in step S4, when dividing the instantaneous power into high-frequency, mid-frequency, and low-frequency components, the high-mid-low frequency division point is:

[0032]

[0033] Among them, f m (t) represents the crossover point between the high and intermediate frequencies, f l (t) represents the crossover point between the mid-frequency and low-frequency ranges.

[0034] Furthermore, in step S4, when the high-frequency component is suppressed by a supercapacitor, the mid-frequency component by a battery, and the low-frequency component by pumped hydro storage, the reference power of the supercapacitor, the battery, and the pumped hydro storage are respectively P C (t), P B (t), P P (t), and based on the reference power P C (t), P B (t), P P (t) Configure energy storage capacity; where the reference power P C (t), P B (t), P P (t) are respectively represented as:

[0035]

[0036] Where k represents the k-th calculation, t represents the t-th time, m represents the order of the IMF component of the supercapacitor, l represents the sum of the orders of the IMF components of the supercapacitor and the battery, K represents the sum of the orders of the IMF components of the supercapacitor, the battery, and the pumped storage, and P C (t), P B (t), P P (t) represents the reference power of the supercapacitor, battery, and pumped storage, respectively. k (t) represents the k-th order IMF component, r k (t) represents the residual component.

[0037] Furthermore, in step S5, the objective function is written as follows:

[0038] min C t =min{C1+C2}

[0039] C1 = C iB +C iB +C iP

[0040] C2 = r B C iB +r C C iB +r P C iP

[0041]

[0042]

[0043]

[0044] Where C1 represents the annual investment cost of hybrid energy storage, C2 represents the annual operation and maintenance cost of hybrid energy storage, and C iB C iC C iP These represent the annual investment costs of batteries, supercapacitors, and pumped storage, respectively. B r C r P These represent the annual operation and maintenance cost coefficients for batteries, supercapacitors, and pumped storage, respectively; δ represents the discount rate, and Y represents the annual operation and maintenance cost coefficients for pumped storage, respectively. B Y C Y P These represent the actual service life of the battery, supercapacitor, and pumped storage, respectively. BP e CP e PP These represent the unit power cost of batteries, supercapacitors, and pumped hydro storage, respectively. BE e CE e PE P represents the unit capacity cost of batteries, supercapacitors, and pumped storage, respectively. BN P CN E represents the rated power of the battery and the supercapacitor, respectively. BN E CN E PN These represent the rated capacities of the storage battery, supercapacitor, and pumped storage, respectively; P PumN P GenN These represent the rated pumping power and rated generating power of pumped storage, respectively.

[0045] Furthermore, in step S6, the constraints include charging and discharging power constraints, energy conservation constraints, tie-line power offset constraints, and battery life constraints.

[0046] A system for optimizing the energy storage capacity of a pumped-storage microgrid includes:

[0047] The data collection module is used to collect net load power and tie-line protocol power.

[0048] The IMF component generation module is used to calculate the total hybrid energy storage power using the net load power and tie-line protocol power collected by the data collection module, and to perform CEEMDAN decomposition on the total hybrid energy storage power to obtain multiple IMF components.

[0049] The instantaneous power generation module is used to obtain IMF components from the IMF component generation module, calculate the instantaneous power of each IMF component, and obtain multiple instantaneous power values.

[0050] The instantaneous power frequency divider module is used to divide the multiple instantaneous power obtained by the instantaneous power generation module into high-frequency components, medium-frequency components, and low-frequency components. The high-frequency components are smoothed by supercapacitors, the medium-frequency components are smoothed by batteries, and the low-frequency components are smoothed by pumped hydro storage.

[0051] The objective function writing module is used to write objective functions with the minimum annual comprehensive cost of the energy storage system as the objective, for capacity optimization configuration.

[0052] The hybrid energy storage capacity optimization model building module is used to build a hybrid energy storage capacity optimization model using the objective function and constraints written in the objective function writing module.

[0053] The SCIP solver module is used to compile the hybrid energy storage capacity optimization model established by the hybrid energy storage capacity optimization model building module and the data collected by the data collection module into the MATLAB platform, and call the SCIP solver in OptiToolbox to solve it and obtain the optimization variables.

[0054] The variable output module is used to output the optimized variables obtained from the SCIP solver module and update the parameters of the microgrid containing pumped-storage hybrid energy storage.

[0055] A computer device includes a memory and a processor, the memory storing a computer program that, when executed by the processor, causes the processor to perform the steps of the method described above.

[0056] A computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the steps of the above-described method.

[0057] The beneficial effects of this invention are as follows:

[0058] 1. In this invention, the hybrid energy storage system effectively smooths the power of the interconnect line between the microgrid and the main grid. The smoothed fluctuation rate is much smaller than the original load fluctuation rate. Compared with the system without pumped storage, it improves the system economy and extends the service life of the energy storage equipment.

[0059] 2. In this invention, the capacity configuration of the hybrid energy storage system based on CEEMD has better accuracy than that of EEMD, thereby reducing the configuration capacity and overall cost of hybrid energy storage. Moreover, compared with the system without pumped storage, the structure proposed in this technical solution has more advantages in using CEEMD. Attached Figure Description

[0060] Figure 1 This is a flowchart illustrating the present invention;

[0061] Figure 2This is a schematic diagram of the microgrid containing pumped-storage hybrid energy storage in this invention;

[0062] Figure 3 This is a schematic diagram of the net load power, tie-line protocol power, and total hybrid energy storage power in this invention;

[0063] Figure 4 This is a CEEMDAN exploded view of the total power of the hybrid energy storage in this invention;

[0064] Figure 5 This is the reference output power of the hybrid energy storage in this invention;

[0065] Figure 6 This is a comparison chart of lifespan and cost under different offset rates in this invention;

[0066] Figure 7 This is a schematic diagram of the hybrid energy storage effect in this invention. Detailed Implementation

[0067] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments.

[0068] Therefore, all other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0069] Example 1

[0070] This embodiment provides a method for optimizing the energy storage capacity configuration of a microgrid containing pumped-storage, which includes a microgrid with pumped-storage hybrid energy storage, such as... Figure 2 As shown, the microgrid with pumped storage hybrid energy storage includes a microgrid central controller, pumped storage modules connected to the main power grid, battery modules, supercapacitor modules, wind and solar power generation modules, and power load modules. The pumped storage modules, battery modules, supercapacitor modules, wind and solar power generation modules, and power load modules are connected to the microgrid central controller via signals. G Power output of combined wind and solar power; P L For load output; P NL Net load power; P A The tie-line protocol power is preset by the microgrid dispatching department. This system can utilize energy-type energy storage characteristics to compensate for insufficient power-type energy storage, smoothing the net load power in the microgrid to tie-line protocol power, ensuring the security of interaction with the main grid, and improving system performance.

[0071] The power balance relationship of this system is as follows:

[0072] P G =P L +P NL

[0073] P HESS =P A -P NL

[0074] In the formula, P HESS The total power of the system's hybrid energy storage is given by the battery's reference output P. B Supercapacitor reference output P C Pumped storage reference output P P The three components are as follows:

[0075] P HESS =P B +P C +P P

[0076] At a certain time t, when P P When (t)≤0, the reference pumping output of the pumped storage is P. Pum (t)=P P (t); when P P When (t)>0, the reference power output of pumped storage hydroelectric power generation is P. Gen (t)=P P (t).

[0077] When optimizing the capacity configuration of a microgrid containing pumped storage and hybrid energy storage, such as Figure 1 As shown, the specific steps include:

[0078] Step S1, collect data

[0079] Collect net load power and tie-line protocol power.

[0080] The data collected in this embodiment comes from the data recorded in the publicly published paper "Optimization of Hybrid Energy Storage Capacity in AC / DC Hybrid Microgrids Based on Ensemble Empirical Mode Decomposition".

[0081] Step S2: Calculate the total power of the hybrid energy storage system, perform CEEMDAN decomposition, and obtain the IMF component.

[0082] Using the net load power and tie-line protocol power collected in step S1, the total hybrid energy storage power is calculated, and the total hybrid energy storage power is decomposed into CEEMDAN components to obtain multiple IMF components.

[0083] Since ensemble empirical mode decomposition (EEMD) introduces white noise interference into the original signal, altering the extreme point characteristics of the signal and thus suppressing aliasing, this method addresses the issue of high-to-low frequency transfer and propagation associated with white noise. To resolve this, this embodiment proposes an improved method—complete ensemble empirical mode decomposition (CEEMDAN). This involves re-introducing white noise into the residual after each IMF component calculation and recalculating the mean of the IMF components, repeating this process until the function becomes monotonic and cannot be decomposed further. The specific calculations are as follows:

[0084] P x (t)+w i (x)=F 1,i (t)+r i (t)

[0085] In the formula: t is time; i is the number of times white noise is added; P x (t) represents the original power decomposition signal; w i (x) represents the added white noise perturbation; F 1,i (t) represents the i-th order IMF component in the initial calculation; ri(t) represents the residual component.

[0086] The mean of the IMF components is calculated as follows:

[0087]

[0088] In the formula: N is the total order; F1(t) is the first-order IMF component.

[0089] The residual value is updated as follows:

[0090] r1(t)=P x (t)-F1(t)

[0091] After K calculations, the function cannot be decomposed. Therefore, the final result is:

[0092]

[0093] Step S3: Calculate instantaneous power using Hilbert transform.

[0094] Based on the IMF components obtained in step S2, the instantaneous power of each IMF component is calculated to obtain multiple instantaneous powers.

[0095] Instantaneous power f k The calculation method for (t) is as follows:

[0096]

[0097] IMF k (t)=F k (t)+jHF k (t)

[0098]

[0099]

[0100] Where j represents a complex number, k represents the k-th calculation, t represents the t-th time, and τ represents the time constant. IMF k The phase of (t), express The differential, IMF k (t) represents a complex function, F k (t) represents the k-th order IMF component, F k (τ) represents the k-th order IMF component at time τ, HF k (t) represents F k The Hilbert transform of (τ), where h(t) represents the impulse function.

[0101] Step S4, Instantaneous power frequency division

[0102] The multiple instantaneous power obtained in step S3 is divided into high-frequency components, mid-frequency components, and low-frequency components. The high-frequency components are smoothed by supercapacitors, the mid-frequency components are smoothed by batteries, and the low-frequency components are smoothed by pumped hydro storage.

[0103] The crossover points for high, mid, and low frequencies are:

[0104]

[0105] Among them, f m (t) represents the crossover point between the high and intermediate frequencies, f l (t) represents the crossover point between the mid-frequency and low-frequency ranges.

[0106] That is, the instantaneous power of IMF components with a power greater than or equal to 0.017 is classified as high-frequency components, the instantaneous power of IMF components with a frequency in the range of 0.0022 (inclusive) to 0.017 is classified as mid-frequency components, and the instantaneous power of IMF components with a power less than 0.0022 is classified as low-frequency components.

[0107] When high-frequency components are suppressed using supercapacitors, mid-frequency components using batteries, and low-frequency components using pumped storage, the reference power for the supercapacitor, battery, and pumped storage is respectively P C (t), P B (t), P P (t), and based on the reference power PC (t), P B (t), P P (t) Configure energy storage capacity; where the reference power P C (t), P B (t), P P (t) are respectively represented as:

[0108]

[0109] Where k represents the k-th calculation, t represents the t-th time, m represents the order of the IMF component of the supercapacitor, l represents the sum of the orders of the IMF components of the supercapacitor and the battery, K represents the sum of the orders of the IMF components of the supercapacitor, the battery, and the pumped storage, and P C (t), P B (t), P P (t) represents the reference power of the supercapacitor, battery, and pumped storage, respectively. k (t) represents the k-th order IMF component, r k (t) represents the residual component.

[0110] Step S5, Write the objective function

[0111] An objective function is written with the goal of minimizing the annual comprehensive cost of the energy storage system, which is used for capacity optimization configuration.

[0112] The objective function is as follows:

[0113] min C t =min{C1+C2}

[0114] C1 = C iB +C iC +C iP

[0115] C2 = r B C iB +r C C iB +r P C iP

[0116]

[0117]

[0118]

[0119] Where C1 represents the annual investment cost of hybrid energy storage, C2 represents the annual operation and maintenance cost of hybrid energy storage, and C iB C iCC iP These represent the annual investment costs of batteries, supercapacitors, and pumped storage, respectively. B r C r P These represent the annual operation and maintenance cost coefficients for batteries, supercapacitors, and pumped storage, respectively; δ represents the discount rate, and Y represents the annual operation and maintenance cost coefficients for pumped storage, respectively. B Y C Y P These represent the actual service life of the battery, supercapacitor, and pumped storage, respectively. BP e CP e PP These represent the unit power cost of batteries, supercapacitors, and pumped hydro storage, respectively. BE e CE e PE P represents the unit capacity cost of batteries, supercapacitors, and pumped storage, respectively. BN P CN E represents the rated power of the battery and the supercapacitor, respectively. BN E CN E PN These represent the rated capacities of the storage battery, supercapacitor, and pumped storage, respectively; P PumN P GenN These represent the rated pumping power and rated generating power of pumped storage, respectively.

[0120] Step S6: Establish a hybrid energy storage capacity optimization model

[0121] Using the objective function and constraints written in step S5, a hybrid energy storage capacity optimization model is established.

[0122] The constraints include charging and discharging power constraints, energy conservation constraints, tie-line power offset constraints, and battery life constraints.

[0123] The process of establishing an optimization model using the objective function and constraints is existing technology. Those skilled in the art can directly establish a hybrid energy storage capacity optimization model based on the objective function and constraints in this embodiment.

[0124] Step S7, call SCIP to solve.

[0125] The hybrid energy storage capacity optimization model established in step S6 and the data collected in step S1 are written into the MATLAB platform and the SCIP solver in OptiToolbox is called to solve the problem and obtain the optimization variables.

[0126] Step S8, output variables

[0127] Output the optimized variables obtained in step S7 and update the parameters of the microgrid containing pumped storage hybrid energy storage.

[0128] In this embodiment, based on data obtained from the paper "Optimization Configuration of Hybrid Energy Storage Capacity in AC / DC Hybrid Microgrids Based on Ensemble Empirical Mode Decomposition", the daily net load power, tie-line protocol power, and total hybrid energy storage power curves are as follows: Figure 3 As shown, the sampling interval was 7.5 minutes, and a total of 192 sampling points were collected per day. From Figure 3 As can be seen, the net load power fluctuates approximately between -400kW and +400kW, exhibiting a relatively high fluctuation frequency. Direct grid connection without energy storage would significantly impact the stability of the main grid. Based on microgrid connection requirements, this embodiment sets the tie-line power offset rate target at 0-0.2 and the maximum interactive power at 50% of the net load power, i.e., -200kW to +200kW. A hybrid energy storage system is used to smooth out the tie-line protocol power of the net load power, thereby reducing its fluctuation frequency.

[0129] The total power of the hybrid energy storage system after CEEMDAN decomposition is as follows: Figure 4 As shown. From Figure 4 It can be seen that the total power of the hybrid energy storage is decomposed into eight IMF components with different frequencies. IMF1 is the highest frequency component, and r7 is the lowest frequency component.

[0130] Reference output power of batteries, supercapacitors, and pumped storage, such as Figure 5 As shown. From Figure 5 It can be seen that supercapacitors are responsible for smoothing out the part with the fastest power change, pumped hydro storage is responsible for smoothing out the part with the slowest power change, and the remaining part is smoothed out by batteries.

[0131] Changes in battery life and annual comprehensive cost of energy storage under different offset rates are as follows: Figure 6 As shown. From Figure 6 It can be seen that the system achieves the highest overall efficiency when the power offset rate is 0.163. At this point, the annual comprehensive cost of energy storage is minimized, and the battery life is extended.

[0132] Selecting "power offset rate of 0.163" as the optimal solution yields the following smoothing effect: Figure 7 As shown. By Figure 7 It can be seen that the total power fluctuation after the hybrid energy storage has been greatly improved compared with the net load power, achieving the expected effect.

[0133] To demonstrate the advantages of combining the proposed model with the CEEMDAN method in this embodiment, a comparison scheme is set up as shown in Table 1.

[0134] Table 1 Scheme Setup

[0135]

[0136] The capacity configuration results for the four schemes are shown in Tables 2 to 5.

[0137] Table 2. Capacity optimization configuration results for Scheme 1

[0138]

[0139] Table 3. Capacity optimization configuration results for Scheme 2

[0140]

[0141] Table 4. Capacity optimization configuration results for Scheme 3

[0142]

[0143] Table 5. Capacity optimization configuration results for Scheme 4

[0144]

[0145]

[0146] Comparing the data in Tables 2 and 3, when using EEMD for power allocation, without pumped storage, the power offset rate of the system tie line is much greater than the preset range; the addition of pumped storage reduces the offset rate and the annual comprehensive cost of energy storage, but the offset rate is still greater than 0.2 and fluctuates greatly.

[0147] Comparing the data in Tables 4 and 5, when using CEEMDAN for power distribution, the addition of pumped hydro storage can further reduce the power offset rate of the tie line and improve the stability of the connection between the microgrid and the main grid; it can also reduce the annual comprehensive cost of energy storage to a certain extent, extend the service life of the battery, and achieve both technical and economic optimization.

[0148] Comparing the data in Tables 3 and 5, compared with EEMD, CEEMDAN results in a lower system tie-line power offset rate, better smoothing effect, and smaller energy storage capacity requirement, thus improving system economy. This is because CEEMDAN, through updating residuals and repeating calculations, makes the decomposed and reconstructed signal closer to the original power, resulting in a more accurate reference output power for hybrid energy storage, thereby reducing hybrid energy storage capacity and ultimately reducing the overall annual cost of energy storage.

[0149] Based on the data in Tables 2 to 5, the advantages of CEEMDAN in systems with and without pumped storage are analyzed, as shown in Table 6.

[0150] Table 6. Advantages of CEEMDAN in Systems with and without Pumped Storage

[0151]

[0152] As can be seen from Table 6, CEEMDAN has greater value in the hybrid energy storage model of microgrids with pumped storage.

[0153] Example 2

[0154] This embodiment provides a system for optimizing the energy storage capacity of a pumped-storage microgrid, specifically including:

[0155] The data collection module is used to collect net load power and tie-line protocol power.

[0156] The data collected in this embodiment comes from the data recorded in the publicly published paper "Optimization of Hybrid Energy Storage Capacity in AC / DC Hybrid Microgrids Based on Ensemble Empirical Mode Decomposition".

[0157] The IMF component generation module is used to calculate the total hybrid energy storage power using the net load power and tie-line protocol power collected by the data collection module, and to perform CEEMDAN decomposition on the total hybrid energy storage power to obtain multiple IMF components.

[0158] Since ensemble empirical mode decomposition (EEMD) introduces white noise interference into the original signal, altering the extreme point characteristics of the signal and thus suppressing aliasing, this method addresses the issue of high-to-low frequency transfer and propagation associated with white noise. To resolve this, this embodiment proposes an improved method—complete ensemble empirical mode decomposition (CEEMDAN). This involves re-introducing white noise into the residual after each IMF component calculation and recalculating the mean of the IMF components, repeating this process until the function becomes monotonic and cannot be decomposed further. The specific calculations are as follows:

[0159] P x (t)+w i (x)=F 1,i (t)+r i (t)

[0160] In the formula: t is time; i is the number of times white noise is added; P x (t) represents the original power decomposition signal; w i (x) represents the added white noise perturbation; F 1,i (t) represents the i-th order IMF component in the initial calculation; ri(t) represents the residual component.

[0161] The mean of the IMF components is calculated as follows:

[0162]

[0163] In the formula: N is the total order; F1(t) is the first-order IMF component.

[0164] The residual value is updated as follows:

[0165] r1(t)=P x (t)-F1(t)

[0166] After K calculations, the function cannot be decomposed. Therefore, the final result is:

[0167]

[0168] The instantaneous power generation module is used to obtain IMF components from the IMF component generation module, calculate the instantaneous power of each IMF component, and obtain multiple instantaneous powers.

[0169] Instantaneous power f k The calculation method for (t) is as follows:

[0170]

[0171] IMF k (t)=F k (t)+jHF k (t)

[0172]

[0173]

[0174] Where j represents a complex number, k represents the k-th calculation, t represents the t-th time, and τ represents the time constant. IMF k The phase of (t), express The differential, IMF k (t) represents a complex function, F k (t) represents the k-th order IMF component, F k (τ) represents the k-th order IMF component at time τ, HF k (t) represents F k The Hilbert transform of (τ), where h(t) represents the impulse function.

[0175] The instantaneous power frequency divider module is used to divide the multiple instantaneous power obtained by the instantaneous power generation module into high-frequency components, medium-frequency components, and low-frequency components. The high-frequency components are smoothed by supercapacitors, the medium-frequency components are smoothed by batteries, and the low-frequency components are smoothed by pumped hydro storage.

[0176] The crossover points for high, mid, and low frequencies are:

[0177]

[0178] Among them, f m (t) represents the crossover point between the high and intermediate frequencies, f l (t) represents the crossover point between the mid-frequency and low-frequency ranges.

[0179] That is, the instantaneous power of IMF components with a power greater than or equal to 0.017 is classified as high-frequency components, the instantaneous power of IMF components with a frequency in the range of 0.0022 (inclusive) to 0.017 is classified as mid-frequency components, and the instantaneous power of IMF components with a power less than 0.0022 is classified as low-frequency components.

[0180] When high-frequency components are suppressed using supercapacitors, mid-frequency components using batteries, and low-frequency components using pumped storage, the reference power for the supercapacitor, battery, and pumped storage is respectively P C (t), P B (t), P P (t), and based on the reference power P C (t), P B (t), P P (t) Configure energy storage capacity; where the reference power P C (t), P B (t), P P (t) are respectively represented as:

[0181]

[0182] Where k represents the k-th calculation, t represents the t-th time, m represents the order of the IMF component of the supercapacitor, l represents the sum of the orders of the IMF components of the supercapacitor and the battery, K represents the sum of the orders of the IMF components of the supercapacitor, the battery, and the pumped storage, and P C (t), P B (t), P P (t) represents the reference power of the supercapacitor, battery, and pumped storage, respectively. k (t) represents the k-th order IMF component, r k (t) represents the residual component.

[0183] The objective function writing module is used to write objective functions with the goal of minimizing the annual comprehensive cost of the energy storage system, for capacity optimization configuration.

[0184] The objective function is as follows:

[0185] min C t =min{C1+C2}

[0186] C1 = C iB +CiC +C iP

[0187] C2 = r B C iB +r C C iB +r P C iP

[0188]

[0189]

[0190]

[0191] Where C1 represents the annual investment cost of hybrid energy storage, C2 represents the annual operation and maintenance cost of hybrid energy storage, and C iB C iC C iP These represent the annual investment costs of batteries, supercapacitors, and pumped storage, respectively. B r C r P These represent the annual operation and maintenance cost coefficients for batteries, supercapacitors, and pumped storage, respectively; δ represents the discount rate, and Y represents the annual operation and maintenance cost coefficients for pumped storage, respectively. B Y C Y P These represent the actual service life of the battery, supercapacitor, and pumped storage, respectively. BP e CP e PP These represent the unit power cost of batteries, supercapacitors, and pumped hydro storage, respectively. BE e CE e PE P represents the unit capacity cost of batteries, supercapacitors, and pumped storage, respectively. BN P CN E represents the rated power of the battery and the supercapacitor, respectively. BN E CN E PN These represent the rated capacities of the storage battery, supercapacitor, and pumped storage, respectively; P PumN P GenN These represent the rated pumping power and rated generating power of pumped storage, respectively.

[0192] The hybrid energy storage capacity optimization model building module is used to build a hybrid energy storage capacity optimization model using the objective function and constraints written in the objective function writing module.

[0193] The constraints include charging and discharging power constraints, energy conservation constraints, tie-line power offset constraints, and battery life constraints.

[0194] The process of establishing an optimization model using the objective function and constraints is existing technology. Those skilled in the art can directly establish a hybrid energy storage capacity optimization model based on the objective function and constraints in this embodiment.

[0195] The SCIP solver module is used to compile the hybrid energy storage capacity optimization model established by the hybrid energy storage capacity optimization model building module and the data collected by the data collection module into the MATLAB platform, and call the SCIP solver in OptiToolbox to solve it and obtain the optimization variables.

[0196] The variable output module is used to output the optimized variables obtained from the SCIP solver module and update the parameters of the microgrid containing pumped-storage hybrid energy storage.

[0197] Example 3

[0198] A computer device includes a memory and a processor, the memory storing a computer program that, when executed by the processor, causes the processor to perform steps including a method for optimizing the energy storage capacity configuration of a pumped-storage microgrid.

[0199] The computer device can be a desktop computer, laptop, handheld computer, or cloud server, etc. The computer device can interact with the user via a keyboard, mouse, remote control, touchpad, or voice control.

[0200] The memory includes at least one type of readable storage medium, including flash memory, hard disk, multimedia card, card-type memory (e.g., SD or D-interface display memory), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, disk, optical disk, etc. In some embodiments, the memory may be an internal storage unit of the computer device, such as the hard disk or memory of the computer device. In other embodiments, the memory may also be an external storage device of the computer device, such as a plug-in hard disk, smart media card (SMC), secure digital card (SD), flash card, etc., equipped on the computer device. Of course, the memory may include both the internal storage unit and the external storage device of the computer device. In this embodiment, the memory is often used to store the operating system and various application software installed on the computer device, such as the program code of the method for optimizing the energy storage capacity of the pumped-storage microgrid. In addition, the memory can also be used to temporarily store various types of data that have been output or will be output.

[0201] In some embodiments, the processor may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chip. The processor is typically used to control the overall operation of the computer device. In this embodiment, the processor is used to run program code stored in the memory or process data, for example, to run program code containing the method for optimizing the energy storage capacity configuration of a pumped-storage microgrid.

[0202] Example 4

[0203] A computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform steps including a method for optimizing the energy storage capacity configuration of a pumped-storage microgrid.

[0204] The computer-readable storage medium stores an interface display program, which can be executed by at least one processor to cause the at least one processor to perform the steps of the above-described method for optimizing the energy storage capacity of a pumped-storage microgrid.

[0205] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the energy storage capacity optimization configuration method of pumped storage microgrids described in the embodiments of this application.

Claims

1. A method for optimizing the energy storage capacity configuration of a microgrid containing pumped-storage, characterized in that: When optimizing the capacity configuration of a microgrid containing pumped-storage hybrid energy storage, the following steps are included: Step S1, collect data Collect net load power and tie-line protocol power; Step S2: Calculate the total power of the hybrid energy storage system, perform CEEMDAN decomposition, and obtain the IMF component. Using the net load power and tie-line protocol power collected in step S1, the total hybrid energy storage power is calculated, and the total hybrid energy storage power is decomposed into CEEMDAN components to obtain multiple IMF components. Step S3: Calculate instantaneous power using Hilbert transform. Based on the IMF components obtained in step S2, the instantaneous power of each IMF component is calculated, resulting in multiple instantaneous powers: Step S4, Instantaneous power frequency division The multiple instantaneous power obtained in step S3 is divided into high-frequency components, mid-frequency components, and low-frequency components. The high-frequency components are smoothed by supercapacitors, the mid-frequency components are smoothed by batteries, and the low-frequency components are smoothed by pumped hydro storage. Step S5, Write the objective function An objective function is written with the goal of minimizing the annual comprehensive cost of the energy storage system, which is used for capacity optimization configuration. Step S6: Establish a hybrid energy storage capacity optimization model Using the objective function and constraints written in step S5, a hybrid energy storage capacity optimization model is established; Step S7, call SCIP to solve. The hybrid energy storage capacity optimization model established in step S6 and the data collected in step S1 are written into the MATLAB platform and the SCIP solver in OptiToolbox is called to solve the problem and obtain the optimization variables. Step S8, output variables Output the optimized variables obtained in step S7 and update the parameters of the microgrid containing pumped storage hybrid energy storage.

2. The method for optimizing the energy storage capacity of a microgrid containing pumped storage as described in claim 1, characterized in that: A microgrid with pumped storage and hybrid energy storage includes a microgrid central controller, pumped storage modules, battery modules, supercapacitor modules, wind and solar power generation modules, and power load modules connected to the main power grid. The pumped storage modules, battery modules, supercapacitor modules, wind and solar power generation modules, and power load modules are connected to the microgrid central controller via signals.

3. The method for optimizing the energy storage capacity of a microgrid containing pumped storage as described in claim 1, characterized in that: In step S3, the instantaneous power f is calculated. k (t): IMF k (t)=F k (t)+jHF k (t) Where j represents a complex number, k represents the k-th calculation, t represents the t-th time, and τ represents the time constant. IMF k The phase of (t), express The differential, IMF k (t) represents a complex function, F k (t) represents the k-th order IMF component, F k (τ) represents the k-th order IMF component at time τ, HF k (t) represents F k The Hilbert transform of (τ), where h(t) represents the impulse function.

4. The method for optimizing the energy storage capacity of a microgrid containing pumped storage as described in claim 1, characterized in that: In step S4, when dividing the instantaneous power into high-frequency, mid-frequency, and low-frequency components, the high-mid-low frequency division point is: Among them, f m (t) represents the crossover point between the high and intermediate frequencies, f l (t) represents the crossover point between the mid-frequency and low-frequency ranges.

5. The method for optimizing the energy storage capacity of a microgrid containing pumped storage as described in claim 1, characterized in that: In step S4, when the high-frequency component is suppressed by a supercapacitor, the mid-frequency component by a battery, and the low-frequency component by pumped hydro storage, the reference power of the supercapacitor, battery, and pumped hydro storage are respectively P C (t), P B (t), P P (t), and based on the reference power P C (t), P B (t), P P (t) Configure energy storage capacity; Wherein, the reference power P C (t), P B (t), P P (t) are respectively represented as: Where k represents the k-th calculation, t represents the t-th time, m represents the order of the IMF component of the supercapacitor, l represents the sum of the orders of the IMF components of the supercapacitor and the battery, K represents the sum of the orders of the IMF components of the supercapacitor, the battery, and the pumped storage, and P C (t), P B (t), P P (t) represents the reference power of the supercapacitor, battery, and pumped storage, respectively. k (t) represents the k-th order IMF component, r k (t) represents the residual component.

6. The method for optimizing the energy storage capacity of a microgrid containing pumped storage as described in claim 1, characterized in that: In step S5, the objective function is written as follows: my C t =min{C1+C2} C1=C iB +C iC +C iP C2=r B C iB +r C C iB +r P C iP Where C1 represents the annual investment cost of hybrid energy storage, C2 represents the annual operation and maintenance cost of hybrid energy storage, and C iB C iC C iP These represent the annual investment costs of batteries, supercapacitors, and pumped storage, respectively. B r C r P These represent the annual operation and maintenance cost coefficients for batteries, supercapacitors, and pumped storage, respectively; δ represents the discount rate, and Y represents the annual operation and maintenance cost coefficients for pumped storage, respectively. B Y C Y P These represent the actual service life of the storage battery, supercapacitor, and pumped storage, respectively. BP e CP e PP These represent the unit power cost of batteries, supercapacitors, and pumped hydro storage, respectively. BE e CE e PE P represents the unit capacity cost of batteries, supercapacitors, and pumped storage, respectively. BN P CN E represents the rated power of the battery and the supercapacitor, respectively. BN E CN E PN These represent the rated capacities of the storage battery, supercapacitor, and pumped storage, respectively; P PumN P GenN These represent the rated pumping power and rated generating power of pumped storage, respectively.

7. The method for optimizing the energy storage capacity of a microgrid containing pumped storage as described in claim 1, characterized in that: In step S6, the constraints include charging and discharging power constraints, energy conservation constraints, tie line power offset constraints, and battery life constraints.

8. A system for optimizing the energy storage capacity of a pumped-storage microgrid, characterized in that, include: The data collection module is used to collect net load power and tie-line protocol power. The IMF component generation module is used to calculate the total hybrid energy storage power using the net load power and tie-line protocol power collected by the data collection module, and to perform CEEMDAN decomposition on the total hybrid energy storage power to obtain multiple IMF components. The instantaneous power generation module is used to obtain IMF components from the IMF component generation module, calculate the instantaneous power of each IMF component, and obtain multiple instantaneous power values. The instantaneous power frequency divider module is used to divide the multiple instantaneous power obtained by the instantaneous power generation module into high-frequency components, medium-frequency components, and low-frequency components. The high-frequency components are smoothed by supercapacitors, the medium-frequency components are smoothed by batteries, and the low-frequency components are smoothed by pumped hydro storage. The objective function writing module is used to write objective functions with the minimum annual comprehensive cost of the energy storage system as the objective, for capacity optimization configuration. The hybrid energy storage capacity optimization model building module is used to build a hybrid energy storage capacity optimization model using the objective function and constraints written in the objective function writing module. The SCIP solver module is used to compile the hybrid energy storage capacity optimization model established by the hybrid energy storage capacity optimization model building module and the data collected by the data collection module into the MATLAB platform, and call the SCIP solver in OptiToolbox to solve it and obtain the optimization variables. The variable output module is used to output the optimized variables obtained from the SCIP solver module and update the parameters of the microgrid containing pumped-storage hybrid energy storage.

9. A computer device, characterized in that: It includes a memory and a processor, the memory storing a computer program that, when executed by the processor, causes the processor to perform the steps of the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The device stores a computer program that, when executed by a processor, causes the processor to perform the steps of the method as described in any one of claims 1 to 7.

Images