Hybrid energy storage collaborative planning method and system for power system

Abstract

The application relates to the technical field of power system planning, in particular to a hybrid energy storage collaborative planning method and system for a power system, which comprises the following steps: predicting energy storage system demand; constructing a hybrid energy storage operation cost model, and respectively calculating the operation cost of each energy storage system in the hybrid energy storage; determining preliminary planning data of each type of energy storage system according to the energy storage system demand and the operation cost of each energy storage system in the hybrid energy storage; obtaining historical planning data of each type of energy storage system; and correcting the preliminary planning data of each type of energy storage system according to the historical planning data and the preliminary planning data, and obtaining current planning data. According to the energy storage system demand and the hybrid energy storage operation cost model, various factors influencing the operation cost of the energy storage system are comprehensively considered to determine the current planning data of each type of energy storage system. An optimal energy storage scheme in terms of economy is determined, and the overall operation cost of the energy storage system is reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power system planning, in particular to a hybrid energy storage collaborative planning method and system for a power system. BACKGROUND

[0002] With the transformation of global energy structure and the rapid development of the electricity market, the power system is facing unprecedented challenges and opportunities. On the one hand, the intermittency and instability of renewable energy sources such as solar and wind energy pose higher requirements for the stable operation of the power system; on the other hand, the expectations of power users for power supply quality and reliability are also continuously improving. Therefore, the power system needs more flexible, efficient and reliable energy storage technology to cope with these challenges.

[0003] Traditional single energy storage technology such as electric energy storage system, although to a certain extent, can alleviate the contradiction between supply and demand of the power system, but its limitations are increasingly prominent. The electric energy storage system usually faces the problems of limited capacity, limited charging and discharging rate, high investment cost, etc. In order to overcome these limitations, the power system begins to explore hybrid energy storage technology, that is, through the collaborative work of multiple types of energy storage systems, to realize the overall improvement of energy storage efficiency, economy and reliability.

[0004] However, the implementation of hybrid energy storage technology is not easy. The technical characteristics, economy, reliability and other factors of different energy storage systems differ significantly. How to reasonably plan and configure each type of energy storage system to achieve the overall optimal performance of the energy storage system has become an important issue faced by the power system. SUMMARY

[0005] (I) Invention purpose

[0006] The purpose of the present application is to provide a hybrid energy storage collaborative planning method and system for a power system which can enhance the stability of the power system while reducing the operating cost.

[0007] (II) Technical solution

[0008] To solve the above problems, the present application provides a hybrid energy storage collaborative planning method for a power system, the hybrid energy storage includes multiple types of energy storage systems, the energy storage systems include electric energy storage systems, thermal energy storage systems and hydrogen energy storage systems, and the method comprises:

[0009] predicting energy storage system demand;

[0010] constructing a hybrid energy storage operating cost model, and calculating the operating cost of each energy storage system in the hybrid energy storage based on the hybrid energy storage operating cost model;

[0011] determining the preliminary planning data of each type of energy storage system according to the energy storage system demand and the operating cost of each energy storage system in the hybrid energy storage;

[0012] obtain historical planning data of each type of energy storage system;

[0013] correct the preliminary planning data of each type of energy storage system according to the historical planning data and the preliminary planning data, and obtain current planning data.

[0014] In another aspect of the present application, preferably,

[0015] The prediction of the energy storage system demand comprises:

[0016] obtain historical load data and new energy historical generation data of the power system;

[0017] predict the new energy generation amount in the predetermined period according to the new energy historical generation data;

[0018] predict the load of the power system in the predetermined period according to the historical load data;

[0019] predict the energy storage system demand according to the load of the power system and the new energy generation amount.

[0020] In another aspect of the present application, preferably, the new energy generation amount in the predetermined period is predicted by the following formula:

[0021]

[0022] wherein S represents the similarity value, X i represents the i-th feature vector of the predetermined period affecting the new energy generation amount, and n represents the number of feature vectors of the predetermined period affecting the new energy generation amount, represents the mean value of the feature vectors of the predetermined period affecting the new energy generation amount, Y i represents the i-th feature vector of the historical period affecting the new energy generation amount, represents the mean value of the feature vectors of the historical period affecting the new energy generation amount;

[0023] The new energy generation amount of the historical period with the highest similarity value is taken as the predicted new energy generation amount in the predetermined period.

[0024] In another aspect of the present application, preferably, the prediction of the load of the power system in the predetermined period comprises:

[0025] obtain historical load data of the power system in several years before the predetermined period;

[0026] obtain historical periods with the same time characteristics as the predetermined period in the historical load data in several years;

[0027] obtain the load of the power system in the predetermined period according to the several historical periods by the following formula:

[0028] R pre =c+φ1·R his-(1) +φ2·R his-(2) +…+φ i ·R his-(i)

[0029] Among them, R pre represents the load of the power system within the predicted predetermined period, φ1, φ2, ..., φ i is the model parameter, Rhis-(i) represents the historical load data of the i-th historical period, and c represents the constant term.

[0030] In another aspect of the present invention, preferably, the hybrid energy storage operating cost model uses the following formula to calculate the operating cost of the electric energy storage system:

[0031]

[0032] Among them, C B is the operating cost of the electric energy storage system; C initial-B is the initial investment cost of the energy storage system, T1 is the life of the energy storage system, r B represents the first discount rate, g B represents the first maintenance cost growth rate, R B is the rate of performance degradation of the energy storage system, t1 represents the usage time of the energy storage system, C om-B is the initial maintenance cost of the electric energy storage system.

[0033] In another aspect of the present invention, preferably,

[0034] The hybrid energy storage operating cost model uses the following formula to calculate the operating cost of the thermal energy storage system:

[0035]

[0036] Among them, C Q is the operating cost of the thermal energy storage system; C initial-Q is the initial investment cost of the thermal energy storage system, T2 is the life of the thermal energy storage system, r Q represents the second discount rate, g Q represents the second maintenance cost growth rate, R Q is the rate of performance degradation of the thermal energy storage system, t2 represents the usage time of the thermal energy storage system, C om-Q is the initial maintenance cost of the thermal energy storage system.

[0037] In another aspect of the present invention, preferably,

[0038] The hybrid energy storage operating cost model uses the following formula to calculate the operating cost of the hydrogen energy storage system:

[0039]

[0040] wherein C G is the operation cost of the hydrogen energy storage system; C initial-G is the initial investment cost of the hydrogen energy storage system, T3 is the service life of the hydrogen energy storage system, r G represents the third discount rate, g G represents the third maintenance cost growth rate, R G is the ratio of performance degradation of the hydrogen energy storage system, t3 represents the service time of the hydrogen energy storage system, C om-G is the initial maintenance cost of the hydrogen energy storage system.

[0041] In another aspect of the present application, preferably, the determining the preliminary planning data of each type of energy storage system according to the energy storage system demand and the operation cost of each energy storage system in the hybrid energy storage comprises:

[0042] determining the planning proportion of each type of energy storage system with the cost minimization as the objective function;

[0043] determining the preliminary planning data of each type of energy storage system according to the energy storage system demand and the planning proportion of each type of energy storage system;

[0044] The objective function is represented by the following formula:

[0045] I = min{ω1C B + ω2C Q + ω3C G}

[0046] wherein I represents the objective function, C B is the operation cost of the electric energy storage system, ω1 is the planning proportion of the electric energy storage system; C Q is the operation cost of the thermal energy storage system, ω2 is the planning proportion of the thermal energy storage system, C G is the operation cost of the hydrogen energy storage system, and ω3 is the planning proportion of the hydrogen energy storage system.

[0047] In another aspect of the present application, preferably, the current planning data is calculated by the following formula:

[0048]

[0049] wherein D represents the current planning data, ω j represents the planning proportion of the jth energy storage system, D initial represents the preliminary planning data of the energy storage system, D j-k represents the historical planning data of the jth energy storage system, wherein k = 1, 2,..., m, represents different historical years, and R j-kD represents the jth energy storage system j-k remaining life of the jth energy storage system.

[0050] In another aspect of the present application, preferably, a hybrid energy storage collaborative planning system of a power system, the hybrid energy storage includes multiple types of energy storage systems, the energy storage systems include electrical energy storage systems, thermal energy storage systems and hydrogen energy storage systems, the system includes:

[0051] a prediction module: predicting energy storage system demand;

[0052] a model construction module: constructing a hybrid energy storage operation cost model, and calculating the operation cost of each energy storage system in the hybrid energy storage based on the hybrid energy storage operation cost model;

[0053] a planning module: determining preliminary planning data of each type of energy storage system according to the energy storage system demand and the operation cost of each energy storage system in the hybrid energy storage;

[0054] an acquisition module: acquiring historical planning data of each type of energy storage system;

[0055] a correction module: correcting the preliminary planning data of each type of energy storage system according to the historical planning data and the preliminary planning data, and obtaining current planning data.

[0056] (Three) beneficial effects

[0057] The above technical solutions of the present application have the following beneficial technical effects:

[0058] The present application predicts the total demand of the energy storage system in a future period of time according to the load demand of the power system, the renewable energy generation situation, the power market situation and other factors, and constructs a hybrid energy storage operation cost model by considering the initial investment cost, operation and maintenance cost, energy conversion cost and other factors of various energy storage systems. Through model calculation, the cost situation of various energy storage systems in the operation process can be obtained. According to the energy storage system demand and the hybrid energy storage operation cost model, various factors affecting the operation cost of the energy storage system are comprehensively considered to determine the preliminary planning data of each type of energy storage system. The hybrid energy storage system can flexibly adjust the output of various energy storage systems according to different power demand scenarios, improve the regulation ability and response speed of the power system, determine the most economical energy storage scheme by comprehensively considering the cost factors of various energy storage systems, and reduce the overall operation cost of the energy storage system. BRIEF DESCRIPTION OF DRAWINGS

[0059] Figure 1 is the overall flowchart of an embodiment of the present application. DETAILED DESCRIPTION

[0060] In order to make the objects, technical solutions and advantages of the present application clearer, further detailed description will be made to the present application with reference to the specific embodiments and the accompanying drawings. It should be understood that the description is only exemplary and is not intended to limit the scope of the present application. In addition, in the following description, the description of the known structures and technologies is omitted to avoid unnecessary confusion of the concept of the present application.

[0061] Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0062] In the description of the present application, it should be noted that the terms "first", "second", "third" are only for the purpose of description, and cannot be understood as indicating or implying relative importance.

[0063] In addition, the technical features involved in the different embodiments of the present application described below can be combined with each other as long as there is no conflict between them.

[0064] The present application will be described in more detail below with reference to the accompanying drawings. In each of the drawings, the same elements are denoted by similar reference numerals. Each part in the drawings is not drawn to scale for the sake of clarity.

[0065] Embodiment one

[0066] A hybrid energy storage collaborative planning method for a power system. In the development of power systems, hybrid energy storage technology has attracted much attention due to its efficiency, flexibility and environmental friendliness. Hybrid energy storage systems combine various energy storage technologies such as electrical energy storage, thermal energy storage and hydrogen energy storage, aiming to meet the energy storage needs of power systems at different time scales and application scenarios. Figure 1 The overall flowchart of one embodiment of the present application is shown as Figure 1 The method comprises:

[0067] Predicting energy storage system demand; predicting the energy storage demand of the power system, which can be a comprehensive analysis of factors such as power system load demand, renewable energy generation, power market conditions, etc. within a certain period of time in the future. Through prediction, the energy storage capacity that the hybrid energy storage system needs to meet can be determined, providing basic data for subsequent planning.

[0068] Constructing a hybrid energy storage operation cost model, and calculating the operation cost of each energy storage system in the hybrid energy storage based on the hybrid energy storage operation cost model; comprehensively considering factors such as initial investment cost, operation and maintenance cost, energy conversion cost, etc. of various energy storage systems. Through model calculation, the cost of different energy storage systems under specific operating conditions can be obtained, providing economic basis for planning decisions.

[0069] According to the energy storage system demand and the operation cost of each energy storage system in the hybrid energy storage, preliminary planning data of each type of energy storage system is determined;

[0070] The historical planning data of each type of energy storage system is obtained;

[0071] According to the historical planning data and the preliminary planning data, the preliminary planning data of each type of energy storage system is corrected to obtain the current planning data, such as the capacity to be increased.

[0072] In this embodiment, the prediction of the energy storage system demand includes:

[0073] The historical load data and the historical new energy generation data of the power system are obtained; the load data reflects the power demand of the power system in different time periods. By collecting and analyzing the historical load data, the load variation law of the power system can be understood, such as seasonal variation and daily variation. The new energy generation (such as wind power and photovoltaic power) has intermittency and uncertainty, and its generation is affected by many factors such as weather conditions and geographical location. By collecting the historical new energy generation data, the volatility and change trend of new energy generation can be analyzed.

[0074] According to the new energy historical generation data, the new energy generation in the predetermined period is predicted; in this embodiment, the new energy generation in the predetermined period is predicted by the following formula:

[0075]

[0076] wherein S represents the similarity value, X i represents the i-th feature vector of the new energy generation in the predetermined period, and n represents the number of feature vectors of the new energy generation in the predetermined period, represents the mean value of the feature vector of the new energy generation in the predetermined period, Y i represents the i-th feature vector of the new energy generation in the historical period, represents the mean value of the feature vector of the new energy generation in the historical period;

[0077] The new energy generation in the historical period with the highest similarity value is taken as the predicted new energy generation in the predetermined period.

[0078] The relevant data of the new energy power generation system in the historical period are collected, including feature vectors and corresponding power generation. The feature vectors can include multiple factors related to the performance of the new energy power generation system, such as date type, season, weather condition, etc. These factors can affect the power generation behavior of the new energy power generation system, thereby affecting its power generation. By considering multiple feature vectors, the influencing factors of the performance of the new energy power generation system can be more comprehensively captured, thereby improving the prediction accuracy of the power generation. This method avoids the training process of complex physical models or machine learning models, reducing the computational complexity and time cost. Since multiple feature vectors are considered, this method can adapt to the performance differences and seasonal changes of new energy power generation systems in different regions.

[0079] According to the historical load data, the load of the power system in the predetermined period is predicted; in the embodiment, the load of the power system in the predetermined period in the predetermined period includes:

[0080] Obtain the historical load data of the power system in the years before the predetermined period; collect the historical load data of the power system in the years (such as 3 years, 5 years or more) before the predetermined period. These data should cover the load changes in different time periods (such as day, week, month, season, year);

[0081] Obtain the historical period with the same time characteristics as the predetermined period in the historical load data of several years; after obtaining the historical load data of several years, the historical period with the same time characteristics as the predetermined period needs to be selected. It can include the same day of the week, month, season or year, etc. Through screening, it can be ensured that the load data of the selected historical period is comparable in time with the load data of the predetermined period.

[0082] According to the historical period of several years, the load of the power system in the predetermined period is obtained by using the following formula:

[0083] R pre = c + φ1·R his-(1) + φ2·R his-(2) + … + φ i ·R his-(i)

[0084] wherein R pre represents the predicted load of the power system in the predetermined period, φ1, φ2, …, φ i are model parameters, Rhis-(i) represents the historical load data of the i-th historical period, and c represents a constant term.

[0085] According to the load of the power system and the new energy power generation, the demand of the energy storage system is predicted. After obtaining the new energy power generation and the load prediction result of the power system in the predetermined period, the difference or imbalance between the two is calculated, thereby obtaining the demand of the energy storage system.

[0086] Further, in the embodiment, the hybrid energy storage operation cost model calculates the operation cost of the electric energy storage system using the following formula:

[0087]

[0088] wherein C B is the operation cost of the electric energy storage system; C initial-B is the initial investment cost of the electric energy storage system, T1 is the lifetime of the electric energy storage system, r B represents the first discount rate, g B represents the first maintenance cost growth rate, R B is the rate of performance degradation of the electric energy storage system, t1 represents the usage time of the electric energy storage system, C om-B is the initial maintenance cost of the electric energy storage system.

[0089] The hybrid energy storage operation cost model calculates the operation cost of the thermal energy storage system using the following formula:

[0090]

[0091] wherein C Q is the operation cost of the thermal energy storage system; C initial-Q is the initial investment cost of the thermal energy storage system, T2 is the lifetime of the thermal energy storage system, r Q represents the second discount rate, g Q represents the second maintenance cost growth rate, R Q is the rate of performance degradation of the thermal energy storage system, t2 represents the usage time of the thermal energy storage system, C om-Q is the initial maintenance cost of the thermal energy storage system.

[0092] The hybrid energy storage operation cost model calculates the operation cost of the hydrogen energy storage system using the following formula:

[0093]

[0094] wherein C G is the operation cost of the hydrogen energy storage system; C initial-G is the initial investment cost of the hydrogen energy storage system, T3 is the lifetime of the hydrogen energy storage system, r G represents the third discount rate, g G represents the third maintenance cost growth rate, R G is the rate of performance degradation of the hydrogen energy storage system, t3 represents the usage time of the hydrogen energy storage system, C om-G is the initial maintenance cost of the hydrogen energy storage system.

[0095] Further, in this embodiment, the preliminary planning data of each type of energy storage system is determined according to the energy storage system demand and the operation cost of each energy storage system in the hybrid energy storage system, which includes:

[0096] The planning proportion of each type of energy storage system is determined as the objective function with the lowest cost;

[0097] The preliminary planning data of each type of energy storage system is determined according to the energy storage system demand and the planning proportion of each type of energy storage system.

[0098] The objective function is represented by the following formula:

[0099] I = min{ω1C B + ω2C Q + ω3C G}

[0100] Wherein, I represents the objective function, C B is the operation cost of the electric energy storage system, and ω1 is the planning proportion of the electric energy storage system; C Q is the operation cost of the thermal energy storage system, and ω2 is the planning proportion of the thermal energy storage system; C G is the operation cost of the hydrogen energy storage system, and ω3 is the planning proportion of the hydrogen energy storage system.

[0101] The operation costs of the electric energy storage system, the thermal energy storage system and the hydrogen energy storage system are calculated respectively, and the factors such as the service life, the discount rate, the maintenance cost growth rate, the performance decline rate and the use time of the energy storage system are comprehensively considered, so that the long-term operation cost of the energy storage system can be accurately reflected. The planning proportion is a key decision variable in the energy storage system planning, which determines the proportion of each type of energy storage system in the hybrid energy storage system. In this embodiment, the lowest cost is taken as the objective, and the optimal planning proportion of each type of energy storage system is obtained by solving the objective function through an optimization algorithm (such as genetic algorithm, particle swarm algorithm, etc.). The planning proportion meets the energy storage system demand and realizes the minimization of the total operation cost.

[0102] After the planning proportion of each type of energy storage system is determined, the preliminary planning data of each type of energy storage system can be calculated according to the total demand of the energy storage system and the planning proportion of each type of energy storage system. These data include the capacity, the number, the layout and other key parameters of the energy storage system, which are important basis for the construction and operation of the energy storage system.

[0103] The preliminary planning data of each type of energy storage system is corrected according to the historical planning data and the preliminary planning data, and the current planning data is obtained,

[0104] The current planning data is calculated by the following formula:

[0105]

[0106] wherein, represents the current planning data, ω j represents the planning proportion of the jth energy storage system, D initial represents the preliminary planning data of the energy storage system, D j-k represents the historical planning data of the jth energy storage system, wherein k = 1, 2, …, m, represents different historical years, R j-k represents the remaining life of the jth energy storage system D j-k The remaining life of the historical planning data is considered, so that the planning data is more accurate.

[0107] The present application predicts the total demand for energy storage systems in the future according to factors such as load demand of the power system, renewable energy generation, and power market conditions; considers factors such as initial investment cost, operation and maintenance cost, and energy conversion cost of various energy storage systems to build a hybrid energy storage operation cost model. Through model calculation, the cost of various energy storage systems in the operation process can be obtained. According to the energy storage system demand and the hybrid energy storage operation cost model, the technical characteristics, economy, reliability and other factors of various energy storage systems are comprehensively considered to determine the preliminary planning data of each type of energy storage system. The hybrid energy storage system can flexibly adjust the output of various energy storage systems according to different power demand scenarios, improve the regulation ability and response speed of the power system, determine the most economical energy storage scheme by comprehensively considering the cost factors of various energy storage systems, and reduce the overall operation cost of the energy storage system.

[0108] Example two

[0109] A hybrid energy storage collaborative planning system for a power system, the hybrid energy storage including multiple types of energy storage systems, the energy storage systems including electrical energy storage systems, thermal energy storage systems, and hydrogen energy storage systems, the system comprising:

[0110] A prediction module for predicting energy storage system demand;

[0111] A model construction module for constructing a hybrid energy storage operation cost model and calculating the operation cost of each energy storage system in the hybrid energy storage based on the hybrid energy storage operation cost model;

[0112] A planning module for determining preliminary planning data of each type of energy storage system according to the energy storage system demand and the operation cost of each energy storage system in the hybrid energy storage;

[0113] An acquisition module for acquiring historical planning data of each type of energy storage system;

[0114] A correction module for correcting the preliminary planning data of each type of energy storage system according to the historical planning data and the preliminary planning data to obtain current planning data.

[0115] It is to be understood that the above specific embodiments of the present application are merely illustrative of the principles of the present application and are not intended to limit the scope of the present application. Any modification, equivalent substitution, improvement, etc. made without departing from the spirit and scope of the present application should be included in the scope of the present application. In addition, the appended claims of the present application are intended to cover all changes and modifications falling within the scope and boundary of the appended claims or the equivalent forms of such scope and boundary.

[0116] The present application has been described above with reference to the embodiments of the present application. However, these embodiments are merely for illustrative purposes and are not intended to limit the scope of the present application. The scope of the present application is defined by the appended claims and equivalents thereof. Those skilled in the art can make various substitutions and modifications without departing from the scope of the present application, and such substitutions and modifications should fall within the scope of the present application.

[0117] Although the embodiments of the present application have been described in detail, it should be understood that various changes, substitutions and alterations can be made to the embodiments of the present application without departing from the spirit and scope of the present application.

[0118] Obviously, the above-described embodiments are merely for illustrative purposes and are not intended to limit the embodiments. Based on the above description, those skilled in the art can make other different forms of changes or modifications. Here, it is not necessary and impossible to exhaust all the embodiments. The obvious changes or modifications derived therefrom are still within the scope of protection of the present application.

Claims

1. A hybrid energy storage collaborative planning method for a power system, characterized in that: The hybrid energy storage includes multiple types of energy storage systems, including an electric energy storage system, a thermal energy storage system, and a hydrogen energy storage system. The method includes: Forecasting energy storage system demand; Constructing a hybrid energy storage operation cost model, and calculating the operation cost of each energy storage system in the hybrid energy storage based on the hybrid energy storage operation cost model; Determine preliminary planning data for each type of energy storage system based on the energy storage system requirements and the operating costs of each energy storage system in the hybrid energy storage; Obtain historical planning data for various types of energy storage systems; According to the historical planning data and the preliminary planning data, the preliminary planning data of each type of energy storage system is revised to obtain current planning data; The determining of preliminary planning data for each type of energy storage system based on the energy storage system requirements and the operating costs of each energy storage system in the hybrid energy storage system includes: Taking the lowest cost as the objective function, determine the planning proportion of each type of energy storage system; Determine preliminary planning data for each type of energy storage system based on the energy storage system requirements and the planned proportions of each type of energy storage system; The objective function is expressed using the following formula: I=min{ω1C B +ω2C Q +ω3C G } Among them, I represents the objective function, C B is the operating cost of the electric energy storage system, ω1 is the planned proportion of the electric energy storage system; C Q is the operating cost of the thermal energy storage system, ω2 is the planned ratio of the thermal energy storage system, C G is the operating cost of the hydrogen energy storage system, ω3 is the planned ratio of the hydrogen energy storage system; The current planning data is calculated using the following formula: in, Represents the current planning data, ω j represents the planned proportion of the j-th energy storage system, D initial represents the preliminary planning data of the energy storage system, D j-k represents the historical planning data of the j-th energy storage system, where k = 1, 2, ..., m, representing different historical years, R j-k represents the D of the j-th energy storage system j-k the remaining years.

2. The method according to claim 1, wherein: Forecasting energy storage system needs includes: Obtain historical load data of the power system and historical power generation data of new energy sources; Predicting the amount of power generated by new energy within a predetermined period based on the historical power generation data of the new energy; forecasting the load of the power system within a predetermined period based on the historical load data; The demand for the energy storage system is predicted based on the load of the power system and the power generation of renewable energy.

3. The method according to claim 2, wherein: The renewable energy power generation within a predetermined period is predicted using the following formula: Among them, S represents the similarity value, X i represents the i-th eigenvector that affects the power generation of renewable energy in a predetermined period, n represents the number of eigenvectors that affect the power generation of renewable energy in a predetermined period, The mean of the eigenvectors that affect the generation of renewable energy in a predetermined period, Y i The i-th eigenvector that affects the generation of renewable energy in the historical period, The mean of the characteristic vectors that affect renewable energy generation in the historical period; The new energy power generation in the historical period with the highest similarity value is used as the predicted new energy power generation in the predetermined period.

4. The method according to claim 2, wherein: The load of the power system within a predetermined period is predicted to include: Obtain historical load data of the power system several years before a predetermined period; Obtain historical periods with the same time characteristics as the predetermined period from historical load data of several years; The load of the power system within a predetermined period is obtained using the following formula based on several historical periods: R pre =c+φ1·R his-(1) +φ2·R his-(2) +…+φ i ·R his-(i) Among them, R pre represents the load of the power system within the predicted predetermined period, φ1, φ2, ..., φ i is the model parameter, R his- (i) represents the historical load data of the i-th historical period, and c represents the constant term.

5. The method according to claim 1, wherein: The hybrid energy storage operating cost model uses the following formula to calculate the operating cost of the electric energy storage system: Among them, C B is the operating cost of the electric energy storage system; C initial-B is the initial investment cost of the energy storage system, T1 is the life of the energy storage system, r B represents the first discount rate, g B represents the first maintenance cost growth rate, R B is the rate of performance degradation of the energy storage system, t1 represents the usage time of the energy storage system, C om-B is the initial maintenance cost of the electric energy storage system.

6. The method according to claim 1, wherein: The hybrid energy storage operating cost model uses the following formula to calculate the operating cost of the thermal energy storage system: Among them, C Q is the operating cost of the thermal energy storage system; C initial-Q is the initial investment cost of the thermal energy storage system, T2 is the life of the thermal energy storage system, r Q represents the second discount rate, g Q represents the second maintenance cost growth rate, R Q is the rate of performance degradation of the thermal energy storage system, t2 represents the usage time of the thermal energy storage system, C om-Q is the initial maintenance cost of the thermal energy storage system.

7. The method according to claim 1, wherein: The hybrid energy storage operating cost model uses the following formula to calculate the operating cost of the hydrogen energy storage system: Among them, C G is the operating cost of the hydrogen energy storage system; C initial-G is the initial investment cost of the hydrogen energy storage system, T3 is the life of the hydrogen energy storage system, r G represents the third discount rate, g G represents the third maintenance cost growth rate, R G is the performance degradation rate of the hydrogen energy storage system, t3 represents the usage time of the hydrogen energy storage system, C om-G is the initial maintenance cost of the hydrogen energy storage system.

8. A hybrid energy storage collaborative planning system for a power system, characterized in that: The hybrid energy storage system includes multiple types of energy storage systems, including an electric energy storage system, a thermal energy storage system and a hydrogen energy storage system. The system includes: Prediction module: predicts energy storage system demand; Model construction module: constructing a hybrid energy storage operation cost model, and calculating the operation cost of each energy storage system in the hybrid energy storage based on the hybrid energy storage operation cost model; Planning module: Determines preliminary planning data for each type of energy storage system based on the energy storage system requirements and the operating costs of each energy storage system in the hybrid energy storage; Acquisition module: obtains historical planning data of various types of energy storage systems; Correction module: Correcting the preliminary planning data of each type of energy storage system based on the historical planning data and preliminary planning data to obtain current planning data; The determining of preliminary planning data for each type of energy storage system based on the energy storage system requirements and the operating costs of each energy storage system in the hybrid energy storage system includes: Taking the lowest cost as the objective function, determine the planning proportion of each type of energy storage system; Determine preliminary planning data for each type of energy storage system based on the energy storage system requirements and the planned proportions of each type of energy storage system; The objective function is expressed using the following formula: I=min{ω1C B +ω2C Q +ω3C G } Among them, I represents the objective function, C B is the operating cost of the electric energy storage system, ω1 is the planned proportion of the electric energy storage system; C Q is the operating cost of the thermal energy storage system, ω2 is the planned ratio of the thermal energy storage system, C G is the operating cost of the hydrogen energy storage system, ω3 is the planned ratio of the hydrogen energy storage system; The current planning data is calculated using the following formula: in, Represents the current planning data, ω j represents the planned proportion of the j-th energy storage system, D initial represents the preliminary planning data of the energy storage system, D j-k represents the historical planning data of the j-th energy storage system, where k = 1, 2, ..., m, representing different historical years, R j-k represents the D of the j-th energy storage system j-k the remaining years.

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