Optimization Method and System for Photovoltaic Energy Storage Loss

Abstract

The present invention provides an optimization method and system for photovoltaic energy storage loss, relating to the technical field of photovoltaic energy. The method includes: obtaining a first storage scheme from a combined energy storage device based on target energy characteristic information, simulating the process of storing the photovoltaic energy to be stored, and monitoring to obtain a first storage log, respectively obtaining first supercapacitor characteristic information and first battery characteristic information. An energy storage loss fusion model analyzes the first supercapacitor characteristic information, the first battery characteristic information, and the first storage log to obtain a first energy storage loss prediction index as an optimization evaluation benchmark, and combines the combined energy storage device to adjust and optimize the first storage scheme, solving the technical problem in the prior art of lacking control over photovoltaic energy during the energy storage process, resulting in high photovoltaic energy storage loss, realizing precise control over photovoltaic energy during the energy storage process, and reducing photovoltaic energy storage loss.

Description

Technical Field

[0001] The present invention relates to the technical field of photovoltaic energy, and particularly to an optimization method and system for photovoltaic energy storage loss. Background Art

[0002] With the rapid development of modernization, traditional energy gradually appears difficult to meet the economic and social needs. Against this background, photovoltaic energy storage technology has emerged, providing new impetus for the sustainable development of the economy and society.

[0003] Photovoltaic energy storage technology is a technology that converts light energy into electrical energy and stores it in a battery, achieving the effective utilization of energy through energy storage. This technology consists of a solar panel, a photovoltaic inverter, and a storage battery. The solar panel converts light energy into electrical energy, which is then converted from direct current to alternating current by the photovoltaic inverter and finally stored in the battery. This technology has the characteristics of environmental protection, energy conservation, high efficiency, stability, etc., and has a long energy storage time and a large energy storage capacity, making it one of the important technical means to solve the energy bottleneck.

[0004] However, in the prior art, due to the lack of control over photovoltaic energy during the energy storage process, there is a technical problem of high photovoltaic energy storage loss. Summary of the Invention

[0005] The present application provides an optimization method and system for photovoltaic energy storage loss, aiming to solve the technical problem in the prior art that due to the lack of control over photovoltaic energy during the energy storage process, the photovoltaic energy storage loss is high.

[0006] In view of the above problems, the present application provides an optimization method and system for photovoltaic energy storage loss.

[0007] In a first aspect, the present application provides an optimization method for photovoltaic energy storage losses. The method includes: collecting the energy characteristics of the photovoltaic energy to be stored to obtain target energy characteristic information, where the target energy characteristic information includes target distributed storage requirements; based on the target distributed storage requirements, obtaining a first storage scheme from the combined energy storage device, where the first storage scheme includes a first set of storage supercapacitors and a first set of storage batteries; simulating the process of storing the photovoltaic energy to be stored according to the first storage scheme, and monitoring to obtain a first storage log; sequentially collecting the device characteristics of the first set of storage supercapacitors and the first set of storage batteries to obtain first supercapacitor characteristic information and first battery characteristic information respectively; constructing an energy storage loss fusion model based on the integrated fusion principle; analyzing the first supercapacitor characteristic information, the first battery characteristic information, and the first storage log through the energy storage loss fusion model to obtain a first energy storage loss prediction index; using the first energy storage loss prediction index as an optimization evaluation benchmark, and combining with the combined energy storage device to adjust and optimize the first storage scheme.

[0008] In a second aspect, the present application provides an optimization system for photovoltaic energy storage losses. The system includes: an information acquisition module for collecting the energy characteristics of the photovoltaic energy to be stored to obtain target energy characteristic information, where the target energy characteristic information includes target distributed storage requirements; a scheme acquisition module for obtaining a first storage scheme from the combined energy storage device based on the target distributed storage requirements, where the first storage scheme includes a first set of storage supercapacitors and a first set of storage batteries; a simulation module for simulating the process of storing the photovoltaic energy to be stored according to the first storage scheme and monitoring to obtain a first storage log; a characteristic collection module for sequentially collecting the device characteristics of the first set of storage supercapacitors and the first set of storage batteries to obtain first supercapacitor characteristic information and first battery characteristic information respectively; a model construction module for constructing an energy storage loss fusion model based on the integrated fusion principle; an analysis module for analyzing the first supercapacitor characteristic information, the first battery characteristic information, and the first storage log through the energy storage loss fusion model to obtain a first energy storage loss prediction index; an adjustment and optimization module for using the first energy storage loss prediction index as an optimization evaluation benchmark and combining with the combined energy storage device to adjust and optimize the first storage scheme.

[0009] One or more technical solutions provided in the present application have at least the following technical effects or advantages:

[0010] The optimization method and system for photovoltaic energy storage loss provided by this application relate to the technical field of photovoltaic energy, solve the technical problem in the prior art that due to the lack of control over photovoltaic energy during the energy storage process, the storage loss of photovoltaic energy is high, and achieve precise control over photovoltaic energy during the energy storage process, reducing the storage loss of photovoltaic energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 It is a schematic flow chart of the optimization method for photovoltaic energy storage loss provided by this application;

[0012] Figure 2 It is a schematic flow chart of the first storage scheme in the optimization method for photovoltaic energy storage loss provided by this application;

[0013] Figure 3 It is a schematic flow chart of obtaining the first energy storage loss prediction index in the optimization method for photovoltaic energy storage loss provided by this application;

[0014] Figure 4 It is a schematic structural diagram of the optimization system for photovoltaic energy storage loss provided by this application.

[0015] Description of reference numerals: Information acquisition module 1, scheme acquisition module 2, simulation module 3, feature acquisition module 4, model construction module 5, analysis module 6, adjustment and optimization module 7. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0016] This application provides an optimization method and system for photovoltaic energy storage loss to solve the technical problem in the prior art that due to the lack of control over photovoltaic energy during the energy storage process, the storage loss of photovoltaic energy is high.

[0017] Embodiment 1

[0018] As Figure 1 shown, the embodiment of this application provides an optimization method for photovoltaic energy storage loss. This method is applied to the optimization system for photovoltaic energy storage loss. The optimization system for photovoltaic energy storage loss is communicatively connected to a combined energy storage device. This method includes:

[0019] Step S100: Collect the energy characteristics of the photovoltaic energy to be stored to obtain target energy characteristic information, where the target energy characteristic information includes target distributed storage requirements;

[0020] Specifically, the optimization method for photovoltaic energy storage loss provided by the embodiment of this application is applied to the optimization system for photovoltaic energy storage loss. The optimization system for photovoltaic energy storage loss is communicatively connected to a combined energy storage device, and the combined energy storage device is used for collecting energy parameters.

[0021] To ensure the accuracy of the energy loss caused during the later-stage energy storage of photovoltaic energy, the characteristics of the photovoltaic energy to be stored are first collected. Photovoltaic energy refers to the conversion of solar radiant energy into electrical energy based on the photovoltaic effect. The characteristics of photovoltaic energy include no pollution, no noise, low maintenance cost, long service life, etc. Based on this, the target energy characteristic information is obtained, and the target energy characteristic information includes the target distributed storage requirements. The target distributed storage requirements refer to the quantity requirements for distribution in several storage batteries or several supercapacitors, which serve as an important reference basis for optimizing the loss during the later-stage storage of photovoltaic energy.

[0022] Step S200: Based on the target distributed storage requirements, obtain a first storage solution from the combined energy storage device, where the first storage solution includes a first set of storage supercapacitors and a first set of storage batteries.

[0023] Furthermore, as Figure 2 shown, step S200 of the present application further includes:

[0024] Step S210: The combined energy storage device includes a supercapacitor bank and a battery bank.

[0025] Step S220: Extract the first set of storage supercapacitors from the supercapacitor bank according to the supercapacitor requirements in the target distributed storage requirements.

[0026] Step S230: Extract the first set of storage batteries from the battery bank according to the battery requirements in the target distributed storage requirements.

[0027] Step S240: Combine the first set of storage supercapacitors and the first set of storage batteries to obtain the first storage solution.

[0028] Specifically, using the above-mentioned target distribution storage requirements as the basic data for storing photovoltaic energy, a first storage scheme for photovoltaic energy is then generated from the combined energy storage device communicatively connected to the system. The combined energy storage device is used to store photovoltaic energy in a battery pack or a supercapacitor bank for combined storage of photovoltaic energy, which means that the combined energy storage device includes a supercapacitor bank and a battery pack. The supercapacitor bank refers to a new energy package formed by connecting multiple supercapacitor monomers in series, cooperating with a voltage equalization and discharge voltage stabilization system, and using an aluminum alloy casing. The battery pack refers to a power source composed of multiple batteries connected in series, which can include lead-acid batteries and nickel-metal hydride batteries. Further, the supercapacitor requirements included in the obtained target distribution storage requirements are extracted, and at the same time, the extracted supercapacitor requirements are used as the screening criteria to screen the supercapacitor data for storing photovoltaic energy in the supercapacitor bank. The supercapacitor data that meets the supercapacitor requirements in the supercapacitor bank can be screened, integrated, and summarized according to the service life requirements, temperature range requirements, energy-saving requirements, lighting brightness requirements, etc. in the supercapacitor requirements, and then recorded as the first storage supercapacitor set. Further, the battery requirements included in the obtained target distribution storage requirements are extracted, and at the same time, the extracted battery requirements are used as the screening criteria to screen the battery data for storing photovoltaic energy in the battery pack. The battery data that meets the battery requirements in the battery pack can be screened, integrated, and summarized according to the efficiency requirements, long-life requirements, safety requirements, environmental protection requirements, etc. in the battery requirements, and then recorded as the first storage battery set. Finally, the combination of the storage requirements of the first storage supercapacitor set obtained according to the supercapacitor requirements and the first storage battery set obtained according to the battery requirements is performed, which means taking any n different elements from the first storage supercapacitor set and any m elements from the first storage battery set as a group, and outputting the combined data as the first storage scheme, thereby ensuring the loss optimization during the storage process of photovoltaic energy.

[0029] Step S300: Simulate the process of storing the photovoltaic energy to be stored according to the first storage scheme, and monitor to obtain a first storage log;

[0030] Specifically, to ensure the storage efficiency of the first storage scheme generated above, the first storage scheme is first used as the storage requirement to simulate the energy storage of the photovoltaic energy to be stored. The simulation process can be to first perform electrochemical polarization on the photovoltaic energy to be stored based on the supercapacitor through the electrolyte, and then store the electric energy generated by the photovoltaic energy to be stored into the storage battery based on the storage battery to achieve this. At the same time, monitor the storage data and storage steps during the process of simulating the storage of the photovoltaic energy to be stored, so as to use the storage dynamics obtained by the monitoring as the first storage log, and there is a charge-discharge cycle sequence with a depth identifier in the first storage log. The depth identifier refers to the measure of the number of sampling points that can be saved when the photovoltaic energy to be stored is storing photovoltaic energy during the process of simulating the storage of the photovoltaic energy to be stored. The charge-discharge cycle sequence refers to the number of charge-discharge cycles that the supercapacitor or battery pack has performed during the process of simulating the storage of the photovoltaic energy to be stored. Each charge-discharge will increase the cycle serial number, laying a foundation for subsequent optimization of the losses during the storage process of the photovoltaic energy.

[0031] Step S400: Sequentially collect the device characteristics of the first storage supercapacitor set and the first storage battery set to obtain first supercapacitor characteristic information and first battery characteristic information respectively;

[0032] Specifically, in order to determine the storage characteristics of the photovoltaic energy to be stored in the supercapacitor and the storage battery, it is necessary to collect the device characteristics of the first storage supercapacitor set and the first storage battery set in sequence. The device characteristics collected can be the supercapacitor operation duration characteristic, the supercapacitor life characteristic, the supercapacitor performance index characteristic, the storage battery operation duration characteristic, the storage battery life characteristic, the storage battery performance index characteristic, etc. The supercapacitor operation duration characteristic refers to the fluctuation of the energy storage time during the energy storage process of the supercapacitor. The supercapacitor life characteristic refers to the time period from the first energy storage or charge-discharge of the photovoltaic energy to be stored until it can no longer be energy stored or charge-discharged. The performance index characteristic of the supercapacitor refers to the characteristics that can evaluate the energy storage situation or performance of the supercapacitor during the energy storage process by using evaluation techniques and indicators such as rated capacity, rated voltage, rated current, and power density. The storage battery operation duration characteristic refers to the fluctuation of the energy storage time during the energy storage process of the storage battery. The storage battery life characteristic refers to the time period from the first energy storage or charge-discharge of the photovoltaic energy to be stored until it can no longer be energy stored or charge-discharged. The storage battery performance index characteristic refers to the characteristics that can evaluate the energy storage situation or performance of the storage battery during the energy storage process by using evaluation techniques and indicators such as open circuit voltage and working voltage. Then, the characteristics corresponding to the supercapacitor and the characteristics corresponding to the storage battery are respectively summarized and output as the first supercapacitor characteristic information and the first storage battery characteristic information, which plays a role in promoting the loss optimization during the storage process of photovoltaic energy.

[0033] Step S500: Construct an energy storage loss fusion model based on the integrated fusion principle;

[0034] Specifically, to accurately estimate the energy loss of photovoltaic energy during the energy storage process, it is first necessary to construct an energy storage loss fusion model based on the integrated fusion principle. The model processing flow of the energy storage loss fusion model includes:

[0035] Based on the principle of integrated fusion, the first storage log of the photovoltaic energy to be stored is trained using the principle of integrated fusion. First, a weak learner 1 is trained from the training dataset with initial weights. According to the learning error rate performance of the weak learner 1, the weights of the training samples are updated, so that the weights of the training sample points with high learning error rates in the previous weak learner 1 become higher, making these points with high error rates receive more attention in the subsequent weak learner 2. Then, the weak learner 2 is trained based on the training set with adjusted weights. This process is repeated until the number of weak learners reaches the pre-specified number T. Finally, these T weak learners are integrated through an ensemble strategy to obtain the final strong learner. Then, using high-performance information processing and computing technologies, the final strong learner is analyzed and comprehensively processed, which means that each group of training data in the training dataset is input into the energy storage loss fusion model, and the output supervision of the energy storage loss fusion model is adjusted through the corresponding supervision data of this group of training data. The supervision dataset is the supervision data corresponding one-to-one to the training dataset. When the output result of the energy storage loss fusion model is consistent with the supervision data, the training of the current group is completed. When all the training data in the training dataset have been trained, the fully connected neural network training is completed.

[0036] To ensure the convergence and accuracy of the energy storage loss fusion model, its convergence process can be that when the output data in the energy storage loss fusion model converges to a point and approaches a certain value, it is considered convergence. Its accuracy can be tested by using the test dataset to process the energy storage loss fusion model. For example, the test accuracy can be set to 80%. When the test accuracy of the test dataset meets 80%, the energy storage loss fusion model is constructed, so as to be used as reference data for the later loss optimization of photovoltaic energy during the storage process.

[0037] Step S600: Analyze the first supercapacitor feature information, the first battery feature information, and the first storage log through the energy storage loss fusion model to obtain the first energy storage loss prediction index.

[0038] Specifically, when dealing with the loss of photovoltaic energy storage of the photovoltaic energy to be stored, it is necessary to perform charge and discharge analysis on the first supercapacitor feature information, the first battery feature information, and the first storage log obtained through device feature collection through the above-mentioned constructed energy storage loss fusion model. The specific operation is not elaborated here, so as to output the first energy storage loss prediction index according to the energy storage calculation fusion model, and improve the accuracy of the later loss optimization of photovoltaic energy during the storage process.

[0039] Furthermore, as Figure 3 shown, step S600 of this application further includes:

[0040] Step S610: The first storage log includes m sets of charge and discharge sequences for m simulation operation cycles;

[0041] Step S620: Extract the target cycle from the m simulation operation cycles, and match the target charge and discharge sequence group of the target cycle in the m sets of charge and discharge sequences;

[0042] Step S630: The target charge and discharge sequence group includes a target supercapacitor charge and discharge sequence and a target battery charge and discharge sequence;

[0043] Step S640: Analyze the target supercapacitor charge and discharge sequence and the first supercapacitor characteristic information through the first loss prediction unit in the energy storage loss fusion model to obtain a first loss prediction index;

[0044] Step S650: Analyze the target battery charge and discharge sequence and the first battery characteristic information through the second loss prediction unit in the energy storage loss fusion model to obtain a second loss prediction index;

[0045] Step S660: Perform weighted calculation on the first loss prediction index and the second loss prediction index to obtain the first energy storage loss prediction index.

[0046] Furthermore, step S640 of the present application includes:

[0047] Step S641: The target supercapacitor charge and discharge sequence includes n charge and discharge sequences of n supercapacitors;

[0048] Step S642: Extract the first supercapacitor from the n supercapacitors, and match the first sequence of the first supercapacitor in the n charge and discharge sequences;

[0049] Step S643: Among them, the first sequence includes p pieces of supercapacitor charge and discharge depth information with time identifiers;

[0050] Step S644: Match the first information of the first supercapacitor in the first supercapacitor characteristic information;

[0051] Step S645: Among them, the first information includes the first operation duration, the first service life, and the first performance level;

[0052] Step S646: Use the p pieces of supercapacitor charge and discharge depth information with time identifiers, the first operation duration, the first service life, and the first performance level as the first input information;

[0053] Step S647: Analyze the first input information through the first loss prediction unit, and calculate the first loss prediction index.

[0054] Furthermore, step S650 of the present application includes:

[0055] Step S651: The target battery charge and discharge sequence includes q charge and discharge sequences of q batteries;

[0056] Step S652: Extract the first battery among the q batteries, and match the second sequence of the first battery in the q charge and discharge sequences;

[0057] Step S653: Among them, the second sequence includes r supercapacitor charge and discharge depth information with time stamps;

[0058] Step S654: Match the second information of the first battery in the first battery characteristic information;

[0059] Step S655: Among them, the second information includes the second operation duration, the second lifespan, and the second performance level;

[0060] Step S656: Use the r supercapacitor charge and discharge depth information with time stamps, the second operation duration, the second lifespan, and the second performance level as the second input information;

[0061] Step S657: Analyze the second input information through the second loss prediction unit, and calculate the second loss prediction index.

[0062] Specifically, before analyzing the first supercapacitor characteristic information, the first battery characteristic information, and the first storage log, first obtain the charge and discharge sequences, which means using the first storage log as the basic data, and the first storage log contains m sets of charge and discharge sequences of m simulation operation cycles, where m is a positive integer greater than 1. The simulation operation cycle refers to the time period for one charge and discharge of the supercapacitor and the battery. The m simulation operation cycles refer to the time periods of m charge and discharges. In the m sets of charge and discharge sequences of the m simulation cycles, each operation cycle corresponds to the charge and discharge sequences of two storage devices, namely the supercapacitor and the battery. Whenever a charge and discharge cycle is performed, the serial number of the charge and discharge sequence will be increased accordingly. If the serial number is 1, it means that the supercapacitor or the battery has not undergone a cyclic charge and discharge. Further, arbitrarily select one cycle from the m simulation operation cycles as the target cycle and extract it. At the same time, match the m simulation cycles corresponding to the m sets of charge and discharge sequences with the target charge and discharge sequence group included in the target cycle. The target charge and discharge sequence group includes the target supercapacitor charge and discharge sequence and the target battery charge and discharge sequence, the target supercapacitor charge and discharge sequence.

[0063] Further, the first loss prediction unit included in the energy storage loss fusion model analyzes the target supercapacitor charge-discharge sequence and the first supercapacitor characteristic information. The first loss prediction unit is a unit for predicting the energy storage loss of the supercapacitor. Among them, the target supercapacitor charge-discharge sequence includes the charge-discharge sequences of n supercapacitors, where n is a positive integer greater than or equal to 1. At the same time, a supercapacitor is randomly selected from the n supercapacitors as the first supercapacitor. Then, the sequence corresponding to the first capacitor is matched with the n charge-discharge sequences, and the first sequence is obtained according to the matching result. The first sequence refers to the simulated charge-discharge sequence of any supercapacitor in the first storage supercapacitor set. Among them, the first sequence includes p pieces of supercapacitor charge-discharge depth information with time stamps, where p is a positive integer greater than 0. Further, a piece of information randomly selected from the first supercapacitor is used as the first information and matched with the first supercapacitor characteristic information. Among them, the first information includes the first operation duration, the first life, and the first performance level of the supercapacitor. Further, the p pieces of supercapacitor charge-discharge depth information with time stamps, the first operation duration, the first life, and the first performance level are used as the first input information, and the first input information is input into the first loss prediction unit for analysis. The first loss prediction unit analyzes the first input information based on the following calculation formula:

[0064]

[0065] where, f sc refers to the first loss prediction index, (Δt) refers to the mth cycle among the m simulation operation cycles, i refers to the ith supercapacitor among the n supercapacitors, j refers to the jth piece of supercapacitor charge-discharge depth information with time stamps among the p pieces of supercapacitor charge-discharge depth information with time stamps, and α sc (i) refers to the loss coefficient of the ith supercapacitor, where, α1(i), α2(i), α3(i) are respectively the first operation duration, the first life, and the first performance level of the ith supercapacitor, a and b are respectively the first adjustment coefficient and the second adjustment coefficient, and P sc (j) refers to the value of the charge-discharge depth of the jth supercapacitor with a time stamp.

[0066] Thus, the calculation result obtained by the calculation is recorded as the first loss prediction index of the supercapacitor and output by the first loss prediction unit.

[0067] Further, the second loss prediction unit included in the energy storage loss fusion model analyzes the target battery charge-discharge sequence and the first battery characteristic information. The second loss prediction unit is a unit used to predict the energy storage loss of the battery. Among them, the target battery charge-discharge sequence includes the charge-discharge sequences of q batteries, where q is a positive integer greater than or equal to 1. A battery is randomly selected from the q batteries as the first battery. At the same time, the sequence corresponding to the first battery is matched with the q charge-discharge sequences, and a second sequence is obtained according to the matching result. The second sequence refers to the simulated charge-discharge sequence of any battery in the first storage supercapacitor set. Among them, the second sequence includes r pieces of supercapacitor charge-discharge depth information with time stamps, and r is a positive integer greater than 0.

[0068] Further, a piece of information randomly selected from the first battery is used as the second information and matched with the first battery characteristic information. Among them, the second information includes the second operation duration, the second life, and the second performance level of the battery. The r pieces of supercapacitor charge-discharge depth information with time stamps, the second operation duration, the second life, and the second performance level are used as the second input information, and the second input information is input into the second loss prediction unit for analysis. The second loss prediction unit analyzes the second input information based on the following calculation formula:

[0069]

[0070] Among them, f B refers to the second loss prediction index, (Δt) refers to the mth cycle in the m simulation operation cycles, x refers to the xth battery among the q batteries, y refers to the yth piece of supercapacitor charge-discharge depth information with time stamps among the r pieces of supercapacitor charge-discharge depth information with time stamps, and β B (x) refers to the loss coefficient of the xth battery. Among them, β1(x), β2(x), β3(x) are respectively the second operation duration, the second life, and the second performance level of the xth battery, c and d are the third adjustment coefficient and the fourth adjustment coefficient respectively, and P B (y) refers to the value of the charge-discharge depth of the yth piece of supercapacitor charge-discharge depth information with time stamps.

[0071] The calculation result obtained by calculation is recorded as the second loss prediction index of the supercapacitor and output through the second loss prediction unit.

[0072] Preferably, a weighted calculation is performed on the first loss prediction index and the second loss prediction index. The weighted calculation needs to be based on a large amount of data aggregation and accurate determination of weights before targeted calculation. Exemplarily, the weight ratio of the first loss prediction index and the second loss prediction index can be that the first influence coefficient: the second influence coefficient is 4:6. Then, the influence parameters after the weighted calculation process are respectively the first influence parameter * 0.4 and the second influence parameter * 0.6. According to the weighted calculation result, the final value of the first energy storage loss prediction index is obtained, achieving the technical effect of providing an important basis for optimizing the loss during the storage process of photovoltaic energy in the later stage.

[0073] Step S700: Use the first energy storage loss prediction index as the optimization evaluation benchmark, and combine the combined energy storage device to adjust and optimize the first storage plan.

[0074] Specifically, to ensure the accuracy of optimizing the loss during the storage process of photovoltaic energy, it is necessary to use the first energy storage loss prediction index output by the energy storage loss fusion model as the reference data, and conduct an optimization evaluation on the possible storage energy loss of the photovoltaic energy to be stored. It means taking the energy loss prediction data corresponding to the first energy storage loss prediction index as the energy loss upper limit, improving the storage device characteristics with energy loss when using a supercapacitor and a battery for energy storage. The larger the difference between the storage loss value and the energy loss upper limit during the photovoltaic storage of the photovoltaic energy to be stored, the higher the optimization evaluation. At the same time, according to the optimization evaluation, combined with the combined energy storage device communicatively connected to the system, the first storage plan obtained above is adjusted and optimized to minimize the photovoltaic storage loss, so as to ensure better loss optimization during the storage process of photovoltaic energy in the later stage.

[0075] In summary, the optimization method for photovoltaic energy storage loss provided by the embodiments of the present application has at least the following technical effects: realizing precise control during the energy storage process of photovoltaic energy and reducing the storage loss of photovoltaic energy.

[0076] Embodiment 2

[0077] Based on the same inventive concept as the optimization method for photovoltaic energy storage loss in the foregoing embodiments, as Figure 4 shown, the present application provides an optimization system for photovoltaic energy storage loss, and the system includes:

[0078] An information acquisition module 1, which is used to collect the energy characteristics of the photovoltaic energy to be stored to obtain target energy characteristic information, where the target energy characteristic information includes target distributed storage requirements;

[0079] A solution acquisition module 2, which is configured to obtain a first storage solution from the combined energy storage device based on the target distribution storage requirement, where the first storage solution includes a first set of storage supercapacitors and a first set of storage batteries;

[0080] A simulation module 3, which is configured to simulate the process of storing the photovoltaic energy to be stored according to the first storage solution and monitor and obtain a first storage log;

[0081] A feature acquisition module 4, which is configured to sequentially perform device feature acquisition on the first set of storage supercapacitors and the first set of storage batteries to obtain first supercapacitor feature information and first battery feature information respectively;

[0082] A model construction module 5, which is configured to construct an energy storage loss fusion model based on the integrated fusion principle;

[0083] An analysis module 6, which is configured to analyze the first supercapacitor feature information, the first battery feature information and the first storage log through the energy storage loss fusion model to obtain a first energy storage loss prediction index;

[0084] An adjustment and optimization module 7, which is configured to use the first energy storage loss prediction index as an optimization evaluation benchmark and combine the combined energy storage device to perform adjustment and optimization on the first storage solution.

[0085] Furthermore, the system further includes:

[0086] A device module, where the device module is for the combined energy storage device including a supercapacitor bank and a battery bank;

[0087] A first extraction module, which is configured to extract the first set of storage supercapacitors from the supercapacitor bank according to the supercapacitor requirement in the target distribution storage requirement;

[0088] A second extraction module, which is configured to extract the first set of storage batteries from the battery bank according to the battery requirement in the target distribution storage requirement;

[0089] A combination module, which is configured to combine the first set of storage supercapacitors and the first set of storage batteries to obtain the first storage solution.

[0090] Furthermore, the system further includes:

[0091] A first sequence module, where the first storage log includes m sets of charge and discharge sequences in m simulation operation cycles;

[0092] A first matching module, which is used to extract a target cycle from the m simulation operation cycles and match a target charge-discharge sequence group of the target cycle in the m groups of charge-discharge sequences;

[0093] A second sequence module, which is used for the target charge-discharge sequence group to include a target supercapacitor charge-discharge sequence and a target battery charge-discharge sequence;

[0094] A first index module, which is used to analyze the target supercapacitor charge-discharge sequence and the first supercapacitor characteristic information through a first loss prediction unit in the energy storage loss fusion model to obtain a first loss prediction index;

[0095] A second index module, which is used to analyze the target battery charge-discharge sequence and the first battery characteristic information through a second loss prediction unit in the energy storage loss fusion model to obtain a second loss prediction index;

[0096] A weighted calculation module, which is used to perform weighted calculation on the first loss prediction index and the second loss prediction index to obtain the first energy storage loss prediction index.

[0097] Furthermore, the system further includes:

[0098] A third sequence module, which is used for the target supercapacitor charge-discharge sequence to include n charge-discharge sequences of n supercapacitors;

[0099] A second matching module, which is used to extract a first supercapacitor from the n supercapacitors and match a first sequence of the first supercapacitor in the n charge-discharge sequences;

[0100] A first charge-discharge depth information module, where the first sequence includes p pieces of supercapacitor charge-discharge depth information with time identifiers;

[0101] A third matching module, which is used to match a first piece of information of the first supercapacitor in the first supercapacitor characteristic information;

[0102] A first information module, where the first information includes a first operation duration, a first service life, and a first performance level;

[0103] The first input information module is used to take the charge and discharge depth information of the p supercapacitors with time stamps, the first operation duration, the first lifespan, and the first performance level as the first input information;

[0104] The first analysis module is used to analyze the first input information through the first loss prediction unit and calculate the first loss prediction index.

[0105] Furthermore, the system further includes:

[0106] The charge and discharge sequence module is used for the charge and discharge sequences of the target battery, including the charge and discharge sequences of q batteries;

[0107] The fourth matching module is used to extract the first battery from the q batteries and match the second sequence of the first battery in the q charge and discharge sequences;

[0108] The second charge and discharge depth information module is used for, among which, the second sequence includes r charge and discharge depth information of supercapacitors with time stamps;

[0109] The second information module is used to match the second information of the first battery in the first battery characteristic information;

[0110] The second information module is used for, among which, the second information includes the second operation duration, the second lifespan, and the second performance level;

[0111] The second input information module is used to take the r charge and discharge depth information of supercapacitors with time stamps, the second operation duration, the second lifespan, and the second performance level as the second input information;

[0112] The third index module is used to analyze the second input information through the second loss prediction unit and calculate the second loss prediction index.

[0113] Through the foregoing detailed description of the optimization method for photovoltaic energy storage losses in this specification, those skilled in the art can clearly know the optimization system for photovoltaic energy storage losses in this embodiment. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple. For the relevant parts, reference can be made to the description in the method part.

[0114] The foregoing description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. An optimization method for photovoltaic energy storage losses, characterized in that, The method is applied to an optimization system for photovoltaic energy storage loss. The system is communicatively connected to a combined energy storage device. The method includes: Collecting the energy characteristics of the photovoltaic energy to be stored to obtain target energy characteristic information, where the target energy characteristic information includes target distributed storage requirements; Based on the target distributed storage requirements, obtaining a first storage scheme from the combined energy storage device, where the first storage scheme includes a first set of storage supercapacitors and a first set of storage batteries; Simulating the process of storing the photovoltaic energy to be stored according to the first storage scheme and monitoring to obtain a first storage log; Successively collecting the device characteristics of the first set of storage supercapacitors and the first set of storage batteries to obtain first supercapacitor characteristic information and first battery characteristic information respectively; Constructing an energy storage loss fusion model based on the integrated fusion principle; Analyzing the first supercapacitor characteristic information, the first battery characteristic information and the first storage log through the energy storage loss fusion model to obtain a first energy storage loss prediction index; Using the first energy storage loss prediction index as an optimization evaluation benchmark and combining with the combined energy storage device to adjust and optimize the first storage scheme; Among them, the analyzing the first supercapacitor characteristic information, the first battery characteristic information and the first storage log through the energy storage loss fusion model to obtain a first energy storage loss prediction index includes: The first storage log includes m sets of charge-discharge sequences in m simulation operation cycles; Extracting a target cycle from the m simulation operation cycles and matching a target charge-discharge sequence group of the target cycle in the m sets of charge-discharge sequences; The target charge-discharge sequence group includes a target supercapacitor charge-discharge sequence and a target battery charge-discharge sequence; Analyzing the target supercapacitor charge-discharge sequence and the first supercapacitor characteristic information through a first loss prediction unit in the energy storage loss fusion model to obtain a first loss prediction index; Analyzing the target battery charge-discharge sequence and the first battery characteristic information through a second loss prediction unit in the energy storage loss fusion model to obtain a second loss prediction index; Performing weighted calculation on the first loss prediction index and the second loss prediction index to obtain the first energy storage loss prediction index; Among them, the obtaining the first loss prediction index includes: The target supercapacitor charge-discharge sequence includes n charge-discharge sequences of n supercapacitors; Extracting a first supercapacitor from the n supercapacitors and matching a first sequence of the first supercapacitor in the n charge-discharge sequences; Among them, the first sequence includes p pieces of supercapacitor charge-discharge depth information with time identifiers; Matching the first information of the first supercapacitor in the first supercapacitor characteristic information; Among them, the first information includes a first operation duration, a first lifespan, and a first performance level; Take the charge and discharge depth information of the p supercapacitors with time stamps, the first operation duration, the first lifespan, and the first performance level as the first input information; Analyze the first input information through the first loss prediction unit and calculate the first loss prediction index; Among them, obtaining the second loss prediction index includes: The target battery charge and discharge sequence includes q charge and discharge sequences of q batteries; Extract the first battery from the q batteries and match the second sequence of the first battery in the q charge and discharge sequences; Among them, the second sequence includes r battery charge and discharge depth information with time stamps; Match the second information of the first battery in the first battery characteristic information; Among them, the second information includes the second operation duration, the second lifespan, and the second performance level; Take the r battery charge and discharge depth information with time stamps, the second operation duration, the second lifespan, and the second performance level as the second input information; Analyze the second input information through the second loss prediction unit and calculate the second loss prediction index.

2. The optimization method according to claim 1, wherein The obtaining the first storage scheme from the combined energy storage device includes: The combined energy storage device includes a supercapacitor bank and a battery bank; Extract the first storage supercapacitor set from the supercapacitor bank according to the supercapacitor demand in the target distribution storage demand; Extract the first storage battery set from the battery bank according to the battery demand in the target distribution storage demand; Combine the first storage supercapacitor set with the first storage battery set to obtain the first storage scheme.

3. The optimization method according to claim 1, characterized in that The analyzing the first input information through the first loss prediction unit and calculating the first loss prediction index, where the calculation formula is as follows: ； Among them, refers to the first loss prediction index, refers to the m-th cycle among the m simulation operation cycles, i refers to the i-th supercapacitor among the n supercapacitors, and j refers to the j-th supercapacitor charge and discharge depth information with time stamps among the p supercapacitor charge and discharge depth information with time stamps, refers to the loss coefficient of the i-th supercapacitor, where, , are respectively the first operation duration, the first life, and the first performance level of the i-th supercapacitor, are respectively the first adjustment coefficient and the second adjustment coefficient, refers to the value of the j-th supercapacitor charge and discharge depth with time stamps.

4. The optimization method according to claim 1, wherein The analyzing the second input information through the second loss prediction unit and calculating the second loss prediction index, where the calculation formula is as follows: ； Among them, refers to the second loss prediction index, refers to the m-th cycle among the m simulation operation cycles, x refers to the x-th battery among the q batteries, and y refers to the y-th battery charge and discharge depth information with time stamps among the r battery charge and discharge depth information with time stamps, refers to the loss coefficient of the x-th battery, where, , are respectively the second operation duration, the second life, and the second performance level of the x-th battery, are respectively the third adjustment coefficient and the fourth adjustment coefficient, refers to the value of the y-th battery charge and discharge depth information with time stamps.

5. An optimization system for photovoltaic energy storage losses, characterized in that, The system is used to execute the optimization method for photovoltaic energy storage loss according to any one of claims 1-4. The system is communicatively connected to the combined energy storage device. The system includes: An information acquisition module, which is used to collect the energy characteristics of the photovoltaic energy to be stored and obtain the target energy characteristic information. Among them, the target energy characteristic information includes the target distribution storage demand; A scheme acquisition module, which is used to obtain the first storage scheme from the combined energy storage device based on the target distribution storage demand. Among them, the first storage scheme includes the first storage supercapacitor set and the first storage battery set; A simulation module, which is used to simulate the process of storing the photovoltaic energy to be stored according to the first storage scheme and monitor and obtain the first storage log; A feature acquisition module, which is used to sequentially acquire device features of the first storage supercapacitor set and the first storage battery set, and respectively obtain first supercapacitor feature information and first battery feature information; A model construction module, which is used to construct an energy storage loss fusion model based on the principle of integrated fusion; An analysis module, which is used to analyze the first supercapacitor feature information, the first battery feature information and the first storage log through the energy storage loss fusion model to obtain a first energy storage loss prediction index; An adjustment and optimization module, which is used to use the first energy storage loss prediction index as an optimization evaluation benchmark, and combine the combined energy storage device to adjust and optimize the first storage scheme.

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