Hybrid energy storage collaborative planning method and system of power system

Abstract

The invention relates to the technical field of power system planning, in particular to a hybrid energy storage collaborative planning method and system of a power system. Constructing a hybrid energy storage operation cost model, and respectively calculating the operation cost of each energy storage system in hybrid energy storage; determining preliminary planning data of each type of energy storage system according to the requirements of the energy storage system and the operation cost of each energy storage system in hybrid energy storage; acquiring historical planning data of each type of energy storage system; and according to the historical planning data and the preliminary planning data, correcting the preliminary planning data of each type of energy storage system to obtain 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, and the current planning data of each type of energy storage system are determined. And the energy storage scheme with the optimal economical efficiency is determined, and the overall operation cost of the energy storage system is reduced.

Description

Technical Field

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

[0002] With the transformation of the global energy structure and the rapid development of the power market, the power system is facing unprecedented challenges and opportunities. On the one hand, the intermittent and unstable nature of renewable energy such as solar and wind power has put forward higher requirements for the stable operation of the power system; on the other hand, power users' expectations for power supply quality and reliability are also increasing. Therefore, the power system needs more flexible, efficient and reliable energy storage technology to meet these challenges.

[0003] Although traditional single energy storage technologies such as electric energy storage systems can alleviate the contradiction between supply and demand in the power system to a certain extent, their limitations are becoming increasingly prominent. Electric energy storage systems usually face problems such as limited capacity, limited charging and discharging rates, and high investment costs. In order to overcome these limitations, the power system has begun to explore hybrid energy storage technology, that is, through the coordinated work of multiple types of energy storage systems to achieve a comprehensive improvement in energy storage efficiency, economy, and reliability.

[0004] However, the implementation of hybrid energy storage technology is not easy. Different energy storage systems have significant differences in technical characteristics, economic efficiency, reliability and other factors. How to reasonably plan and configure various types of energy storage systems to achieve the overall optimal performance of the energy storage system has become an important issue facing the power system. Summary of the invention

[0005] (I) Purpose of the invention

[0006] The object of the present invention is to provide a hybrid energy storage collaborative planning method and system for an electric power system that can enhance the stability of the electric power system while reducing the operating cost.

[0007] (II) Technical solution

[0008] To solve the above problems, the present invention provides a hybrid energy storage collaborative planning method for a power system, wherein 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:

[0009] Forecasting energy storage system demand;

[0010] 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;

[0011] 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;

[0012] Obtain historical planning data for various types of energy storage systems;

[0013] 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 the current planning data.

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

[0015] Forecasting energy storage system needs includes:

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

[0017] Based on the historical power generation data of the new energy, predict the power generation of the new energy within a predetermined period;

[0018] Predicting the load of the power system within a predetermined period based on the historical load data;

[0019] The demand for energy storage system is predicted based on the load of the power system and the power generation of renewable energy.

[0020] In another aspect of the present invention, preferably, the amount of electricity generated by renewable energy within a predetermined period is predicted using the following formula:

[0021]

[0022] 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, Represents the mean of the characteristic vectors that affect the generation of renewable energy in a predetermined period, Y i The ith eigenvector that represents the impact of renewable energy generation in the historical period, Represents the mean of the characteristic vectors that affect the generation of renewable energy in the historical period;

[0023] 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.

[0024] In another aspect of the present invention, preferably, predicting the load of the power system within a predetermined period includes:

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

[0026] Obtain historical cycles with the same time characteristics as the predetermined cycle in the historical load data of several years;

[0027] The load of the power system within a predetermined period is obtained using the following formula based on several historical periods:

[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 operation cost model calculates the operation cost of the electric energy storage system using the following formula:

[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 operation cost model uses the following formula to calculate the operation 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 operation cost model uses the following formula to calculate the operating cost of the hydrogen energy storage system:

[0039]

[0040] 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.

[0041] In another aspect of the present invention, preferably, the determining of preliminary planning data for 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 comprises:

[0042] Taking the lowest cost as the objective function, determine the planning proportion of each type of energy storage system;

[0043] Determine preliminary planning data for each type of energy storage system based on the energy storage system demand and the planned proportions of each type of energy storage system;

[0044] The objective function is expressed using the following formula:

[0045] I=min{ω1C B +ω2C Q +ω3C G}

[0046] Where 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 proportion of the thermal energy storage system, C G is the operating cost of the hydrogen energy storage system, and ω3 is the planned proportion of the hydrogen energy storage system.

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

[0048]

[0049] 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-krepresents the D of the j-th energy storage system j-k remaining years.

[0050] In another aspect of the present invention, preferably, a hybrid energy storage collaborative planning system for a power system, wherein the hybrid energy storage includes multiple types of energy storage systems, wherein the energy storage system includes an electric energy storage system, a thermal energy storage system, and a hydrogen energy storage system, and wherein the system includes:

[0051] Prediction module: predicts energy storage system demand;

[0052] Model building module: building 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] Planning module: determining preliminary planning data for each type of energy storage system according to the energy storage system requirements and the operating costs of each energy storage system in the hybrid energy storage;

[0054] Acquisition module: obtains historical planning data of various types of energy storage systems;

[0055] Correction module: Corrects the preliminary planning data of various types of energy storage systems according to the historical planning data and preliminary planning data to obtain current planning data.

[0056] (III) Beneficial effects

[0057] The above technical solution of the present invention has the following beneficial technical effects:

[0058] The present invention predicts the total demand for energy storage systems in the future based on factors such as the load demand of the power system, renewable energy power generation, and power market conditions; and considers factors such as the initial investment cost, operation and maintenance cost, and energy conversion cost of various energy storage systems to construct a hybrid energy storage operation cost model. Through model calculation, the cost of various energy storage systems during operation can be obtained. According to the energy storage system demand and the hybrid energy storage operation cost model, various factors affecting the operating cost of the energy storage system are comprehensively considered to determine the preliminary planning data for various types of energy storage systems. 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, and determine the most economical energy storage solution by comprehensively considering the cost factors of various energy storage systems, thereby reducing the overall operating cost of the energy storage system. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 It is an overall flow chart of an embodiment of the present invention. DETAILED DESCRIPTION

[0060] In order to make the purpose, technical scheme and advantages of the present invention clearer, the present invention is further described in detail below in conjunction with specific embodiments and with reference to the accompanying drawings. It should be understood that these descriptions are only exemplary and are not intended to limit the scope of the present invention. In addition, in the following description, the description of well-known structures and technologies is omitted to avoid unnecessary confusion of the concept of the present invention.

[0061] Obviously, the described embodiments are only some embodiments of the present invention, but not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0062] In the description of the present invention, it should be noted that the terms “first”, “second” and “third” are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0063] In addition, the technical features involved in the different embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0064] The present invention will be described in more detail below with reference to the accompanying drawings. In each of the accompanying drawings, the same elements are represented by similar reference numerals. For the sake of clarity, the various parts in the accompanying drawings are not drawn to scale.

[0065] Embodiment 1

[0066] A hybrid energy storage collaborative planning method for power systems,In the development of power systems, hybrid energy storage technology has attracted much attention due to its high efficiency, flexibility and environmental protection.,Hybrid energy storage systems combine multiple energy storage technologies such as electrical, thermal and hydrogen storage,,aiming to meet the energy storage needs of power systems in different time scales and application scenarios. Figure 1 FIG. 1 shows an overall flow chart of an embodiment of the present invention, as shown in FIG. Figure 1 As shown, the method includes:

[0067] Predicting the demand for energy storage systems: Predicting the demand for energy storage in the power system can be a comprehensive analysis of factors such as the load demand of the power system, renewable energy generation, and power market conditions in the future. Through prediction, it is possible to clarify the energy storage capacity that the hybrid energy storage system needs to meet, providing basic data for subsequent planning.

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

[0069] 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;

[0070] Obtain historical planning data for various types of energy storage systems;

[0071] 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, such as the capacity that needs to be increased.

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

[0073] Obtain the historical load data of the power system and the historical power generation data of new energy; the load data reflects the power demand of the power system in different time periods. By collecting and analyzing the historical load data, we can understand the load change pattern of the power system, such as seasonal changes, daily changes, etc. New energy power generation (such as wind power, photovoltaics, etc.) is intermittent and uncertain, and its power generation is affected by many factors such as weather conditions and geographical location. By collecting the historical power generation data of new energy, we can analyze the volatility and change trend of new energy power generation.

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

[0075]

[0076] 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, Represents the mean of the characteristic vectors that affect the generation of renewable energy in a predetermined period, Y i The ith eigenvector that represents the impact of renewable energy generation in the historical period, Represents the mean of the characteristic vectors that affect the generation of renewable energy in the historical period;

[0077] 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.

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

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

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

[0081] Obtain the historical load data of several years that have the same time characteristics as the predetermined period; after obtaining the historical load data of several years, it is necessary to filter out the historical periods that have the same time characteristics as the predetermined period. The same day of the week, month, season or year as the predetermined period can be included. By filtering, it can be ensured that the load data of the selected historical period is comparable with the load data of the predetermined period in terms of time.

[0082] The load of the power system within a predetermined period is obtained using the following formula based on several historical periods:

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

[0084] 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.

[0085] The energy storage system demand is predicted based on the load of the power system and the renewable energy generation. After the renewable energy generation and the power system load forecast results within a predetermined period are obtained, the difference or imbalance between the two is calculated to obtain the energy storage system demand.

[0086] Furthermore, in this embodiment, the hybrid energy storage operation cost model uses the following formula to calculate the operation cost of the electric energy storage system:

[0087]

[0088] 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.

[0089] The hybrid energy storage operation cost model uses the following formula to calculate the operation cost of the thermal energy storage system:

[0090]

[0091] 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.

[0092] The hybrid energy storage operation cost model uses the following formula to calculate the operating cost of the hydrogen energy storage system:

[0093]

[0094] 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.

[0095] Furthermore, 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 operating cost of each energy storage system in the hybrid energy storage, including:

[0096] Taking the lowest cost as the objective function, determine the planning proportion of each type of energy storage system;

[0097] According to the energy storage system demand and the planned proportions of various types of energy storage systems, preliminary planning data for various types of energy storage systems are determined.

[0098] The objective function is expressed using the following formula:

[0099] I=min{ω1C B +ω2C Q +ω3C G}

[0100] Where 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 proportion of the thermal energy storage system, C G is the operating cost of the hydrogen energy storage system, and ω3 is the planned proportion of the hydrogen energy storage system.

[0101] Previously, the operating costs of electric energy storage systems, thermal energy storage systems, and hydrogen energy storage systems were calculated separately, taking into account factors such as the life of the energy storage system, discount rate, maintenance cost growth rate, performance degradation rate, and usage time, which can more accurately reflect the long-term operating costs of the energy storage system. The planning ratio is a key decision variable in energy storage system planning, which determines the proportion of each type of energy storage system in the hybrid energy storage system. In this embodiment, with the lowest cost as the goal, the objective function is solved by an optimization algorithm (such as a genetic algorithm, a particle swarm algorithm, etc.) to obtain the optimal planning ratio of each type of energy storage system. The planning ratio minimizes the total operating cost while meeting the needs of the energy storage system.

[0102] After determining the planning ratio of each type of energy storage system, the preliminary planning data of each type of energy storage system can be calculated based on the total demand for energy storage systems and the planning ratio of each type of energy storage system. These data include key parameters such as capacity, quantity, and layout of energy storage systems, which are important bases for the construction and operation of energy storage systems.

[0103] 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.

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

[0105]

[0106] 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 life of historical planning data is taken into account, making the planning data more accurate.

[0107] The present invention predicts the total demand for energy storage systems in the future based on factors such as the load demand of the power system, the renewable energy power generation situation, and the power market situation; and considers factors such as the initial investment cost, operation and maintenance cost, and energy conversion cost of various energy storage systems to construct a hybrid energy storage operation cost model. Through model calculation, the cost situation of various energy storage systems during operation 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 various types of energy storage systems. 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, and determine the most economical energy storage solution by comprehensively considering the cost factors of various energy storage systems, thereby reducing the overall operation cost of the energy storage system.

[0108] Embodiment 2

[0109] A hybrid energy storage collaborative planning system for a power system, wherein the hybrid energy storage includes multiple types of energy storage systems, wherein the energy storage system includes an electric energy storage system, a thermal energy storage system, and a hydrogen energy storage system, and wherein the system includes:

[0110] Prediction module: predicts energy storage system demand;

[0111] Model building module: building 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] Planning module: determining preliminary planning data for each type of energy storage system according to the energy storage system requirements and the operating costs of each energy storage system in the hybrid energy storage;

[0113] Acquisition module: obtains historical planning data of various types of energy storage systems;

[0114] Correction module: Corrects the preliminary planning data of various types of energy storage systems according to the historical planning data and preliminary planning data to obtain current planning data.

[0115] It should be understood that the above specific embodiments of the present invention are only used to illustrate or explain the principles of the present invention, and do not constitute a limitation of the present invention. Therefore, any modifications, equivalent substitutions, improvements, etc. made without departing from the spirit and scope of the present invention should be included in the protection scope of the present invention. In addition, the appended claims of the present invention are intended to cover all changes and modifications that fall within the scope and boundaries of the appended claims, or the equivalent forms of such scope and boundaries.

[0116] The present invention has been described above with reference to the embodiments of the present invention. However, these embodiments are for illustrative purposes only and are not intended to limit the scope of the present invention. The scope of the present invention is defined by the appended claims and their equivalents. Without departing from the scope of the present invention, a person skilled in the art may make various substitutions and modifications, which should all fall within the scope of the present invention.

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

[0118] Obviously, the above embodiments are merely examples for the purpose of clear explanation, and are not intended to limit the implementation methods. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the implementation methods here. The obvious changes or modifications derived therefrom are still within the scope of protection of the invention.

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 the current planning data.

2. The method according to claim 1, characterized in that: Forecasting energy storage system needs includes: Obtain historical load data of the power system and historical power generation data of new energy sources; Based on the historical power generation data of the new energy, predict the power generation of the new energy within a predetermined period; Predicting the load of the power system within a predetermined period based on the historical load data; The demand for 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, characterized in that: The renewable energy power generation within the 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 the scheduled period, n represents the number of eigenvectors that affect the power generation of renewable energy in the scheduled period, X represents the mean of the eigenvectors that affect the power generation of renewable energy in the scheduled period, and Y i The ith eigenvector that represents the impact of renewable energy generation in the historical period, Represents the mean of the characteristic vectors that affect the generation of renewable energy 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, characterized in that: 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 cycles with the same time characteristics as the predetermined cycle in the 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, Rhis-(i) represents the historical load data of the ith historical period, and c represents the constant term.

5. The method according to claim 1, characterized in that: The hybrid energy storage operation cost model uses the following formula to calculate the operation 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, characterized in that: The hybrid energy storage operation cost model uses the following formula to calculate the operation 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, characterized in that: The hybrid energy storage operation 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. The method according to claim 1, characterized in that: Determining preliminary planning data for each type of energy storage system based on the energy storage system demand and the operating cost of each energy storage system in the hybrid energy storage 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 demand 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 proportion of the thermal energy storage system, C G is the operating cost of the hydrogen energy storage system, and ω3 is the planned proportion of the hydrogen energy storage system.

9. The method according to claim 8, characterized in that: 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 remaining years.

10. A hybrid energy storage collaborative planning system 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, and the system includes: Prediction module: predicts energy storage system demand; Model building module: building 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: determining preliminary planning data for each type of energy storage system according to 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: Corrects the preliminary planning data of various types of energy storage systems according to the historical planning data and preliminary planning data to obtain current planning data.

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