A method and system for coordinated planning of power grid source, grid, load and storage considering multiple types of energy storage

Abstract

The present invention relates to the field of power grid technology, and specifically to a method and system for coordinated planning of power grid sources, grids, loads and storages taking into account multiple types of energy storage, including: obtaining future load data of the power grid using a preset load forecasting model based on historical load data of the power grid; obtaining future energy supply data using a preset energy forecasting algorithm; obtaining future multiple types of energy storage data using a preset multiple types of energy storage forecasting algorithm; and using the future load data of the power grid, the future energy supply data and the future multiple types of energy storage data to coordinate planning based on the goal of maximizing load satisfaction. The present invention can better cope with load fluctuations and uncertainties in energy supply by making multi-faceted predictions on the energy side, the load side and the energy storage side, and making coordinated planning based on the prediction results. It ensures that the electricity demand of users can be met in all situations, reduces power outages, and promotes the development of clean energy.

Description

Technical Field

[0001] The present invention relates to the field of power grid technology, and in particular to a power grid source-grid-load-storage coordination planning method and system considering multiple types of energy storage. Background Art

[0002] With the rapid development of renewable energy, the power grid is facing more and more challenges, especially in terms of supply and demand balance, system stability and energy efficiency. Traditional grid planning methods often focus on the independent optimization of the power generation side and the load side, while ignoring the coordinating role of energy storage systems in the power grid. However, energy storage systems, including gas storage, thermal storage and electric storage, are gradually becoming an indispensable part of the power grid because of their advantages such as balancing supply and demand, alleviating grid pressure and improving energy utilization efficiency.

[0003] Renewable energy sources such as wind and solar are intermittent and unstable, which may cause the power grid to face the risk of insufficient power supply during peak hours and excess power during off-peak hours. In order to cope with these challenges, the power grid needs more flexible and intelligent adjustment methods.

[0004] As an important tool for grid regulation, energy storage systems can store energy when there is an oversupply of electricity and release energy when demand peaks, thereby achieving a balance between supply and demand. Gas energy storage technology is a technology that converts electrical energy into gaseous fuel. Hydrogen is produced by electrolyzing water, and then hydrogen is synthesized with substances such as carbon dioxide into methane or other gases to achieve the storage and conversion of electrical energy. Thermal energy storage technology stores thermal energy, which can be stored by utilizing the heat absorbed or released during the phase change of substances. It stores thermal energy when the grid load is low and releases thermal energy to generate electricity when needed. Electric energy storage technology, especially battery energy storage systems, uses batteries to store electrical energy. With its fast response and high energy density, it provides the grid with a variety of services such as frequency modulation and peak-valley cutting.

[0005] The existing methods cannot comprehensively consider the power generation, power consumption and various types of energy storage of the power grid, and the flexibility and reliability of the power grid need to be improved. Summary of the invention

[0006] (I) Purpose of the invention

[0007] The purpose of the present invention is to provide a system that can comprehensively consider the power generation, power consumption and various types of energy storage of the power grid, thereby improving the flexibility and reliability of the power grid.

[0008] (II) Technical solution

[0009] To solve the above problems, the present invention provides a method for coordinated planning of power grid sources, grids, loads and storages considering multiple types of energy storage, including:

[0010] According to the historical load data of the power grid, the preset load forecasting model is used to obtain the future load data of the power grid;

[0011] Use the preset energy forecasting algorithm to obtain future energy supply data;

[0012] Use the preset multi-type energy storage prediction algorithm to obtain future multi-type energy storage data;

[0013] Utilizing the grid's future load data, future energy supply data, and future multi-type energy storage data, coordinated planning is carried out based on the goal of maximizing load satisfaction.

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

[0015] According to the historical load data of the power grid, using the preset load forecasting model, the future load data of the power grid is obtained, including:

[0016] The preset load forecasting model includes obtaining future load data of the power grid using the following formula:

[0017]

[0018] Among them, y i represents the load data of the power grid in the future i-th time period. The preset load forecasting model consists of n layers, including input layer, fully connected layer and output layer. U represents the activation function of the fully connected layer, W1 represents the weight coefficient of the fully connected layer, and b d represents the bias vector of the fully connected layer, f represents the DBI-LSTM activation function, Represents the weight coefficient matrix of the forward LSTM network, Represents the weight coefficient matrix of the reverse LSTM network, x i represents the i-th historical load data; represents the input of the nth layer, and W2 is the weight coefficient of the output layer.

[0019] In another aspect of the present invention, preferably,

[0020] Using the preset energy prediction algorithm, obtain future energy supply data, including using the following formula to predict photovoltaic power generation:

[0021]

[0022] Among them, E PVi represents the photovoltaic power generation in the future i-th time period, P PVi represents the photovoltaic power generation power in the future i-th time period, η sys Represents the efficiency of the photovoltaic system, P rated-s Indicates the rated power of the photovoltaic array, G tirepresents the solar irradiance forecast for the i-th time period in the future, G std represents the standard sunshine quantity, and Δt represents the time cycle step.

[0023] In another aspect of the present invention, preferably,

[0024] Using the preset energy forecasting algorithm, obtain future energy supply data, including predicting wind power generation using the following formula:

[0025]

[0026] Among them, E wind (V i ) represents the wind power generation under the wind speed V in the future i-th time period, P wind (V i ) represents the wind power generation under the wind speed V in the future i-th time period, ρ represents the air density, A represents the swept area, C p represents the power coefficient, λ represents the cusp speed ratio, β represents the pitch angle, V cut-in Indicates the wind speed at which power generation starts, V cut-out Indicates the wind speed at which power generation stops, P rated-V represents the rated power of the wind turbine, and Δt represents the time cycle step.

[0027] In another aspect of the present invention, preferably,

[0028] Using the preset multi-type energy storage prediction algorithm, future multi-type energy storage data is obtained, including using the following formula to obtain the predicted electric energy storage data of the electric energy storage system:

[0029] R pre-i =c+φ1·R pre-(i-1) +φ2·R pre-(i-2) +…+φ p ·R pre-(i-p)

[0030] Among them, R pre-i represents the predicted remaining capacity of the energy storage in the i-th time period, φ1, φ2, …, φ P is the model parameter, R pre-(i-p) It represents the remaining power data of the ipth time period, and c represents a constant term.

[0031] In another aspect of the present invention, preferably,

[0032] Using the preset multi-type energy storage prediction algorithm, obtaining future multi-type energy storage data includes obtaining predicted thermal energy storage data of the thermal energy storage system using the following formula:

[0033] Q stored-i =Q stored-(i-1)+Q in-(i-1) ·η charge -Q out-(i-1) ·η discharge -α·U·(T stored-(i-1) -T ambient )

[0034] Among them, Q stored-i represents the predicted heat storage medium heat in the ith time period, Q stored-(i-1) represents the heat of the heat storage medium in the i-1th time period, Q in-(i-1) represents the input heat of the i-1th time period, Q out-(i-1) represents the output heat of the i-1th time period, U represents the total heat capacity of the heat storage medium, α represents the heat loss coefficient, T stored-(i-1) represents the temperature of the heat storage medium at the end of the i-1th time period, T ambient represents the ambient temperature, η charge and η discharge Represents thermal energy storage efficiency and release efficiency.

[0035] In another aspect of the present invention, preferably,

[0036] Using the preset multi-type energy storage prediction algorithm, obtaining future multi-type energy storage data includes obtaining predicted gas energy storage data of the gas energy storage system using the following formula:

[0037] G gs-i =G gs-(i-1) +(η gsc G gsc -G gsd / η gsd )Δt

[0038] Among them, G gs-i represents the predicted gas storage capacity of the i-th time period, η gsc and η gsd represents the natural gas storage efficiency and release efficiency, G gs-(i-1) represents the predicted gas storage capacity for the i-1th time period, G gsc and G gsd represents the natural gas storage and release power, and Δt represents the time cycle step.

[0039] In another aspect of the present invention, preferably,

[0040] Using the future load data of the power grid, future energy supply data and future multi-type energy storage data, coordinated planning is carried out based on the load maximization goal, including:

[0041] According to the load data of the power grid predicted in the i-th time period, the predicted photovoltaic power generation and the predicted wind power generation, the difference between the load and the power generation is calculated;

[0042] If the difference is equal to zero, the predicted photovoltaic power generation and the predicted wind power generation are coordinated and planned according to the predicted load data of the power grid;

[0043] If the difference is less than zero, storing the difference;

[0044] If the difference is greater than zero, respectively calculating the amount of electrical storage, thermal storage and gas storage required for the difference;

[0045] Coordinated planning is carried out in the order of electric energy storage system, thermal energy storage system and gas energy storage system.

[0046] In another aspect of the present invention, preferably,

[0047] Storing the difference includes:

[0048] respectively obtaining the chargeable amounts of the electric energy storage system, the thermal energy storage system and the gas energy storage system;

[0049] If the difference is less than or equal to the chargeable amount of the electric energy storage system, storing the difference using the electric energy storage system;

[0050] If the difference is greater than the chargeable amount of the electric energy storage system, storing the difference using the electric energy storage system and the thermal energy storage system;

[0051] If the difference is greater than the chargeable amount of the electric energy storage system and the thermal energy storage system, the difference is stored using the electric energy storage system, the thermal energy storage system and the gas energy storage system;

[0052] Coordinated planning in the order of electric energy storage system, thermal energy storage system and gas energy storage system includes:

[0053] If the amount of storage required by the difference is less than or equal to the remaining amount of the electric energy storage, then coordinate planning of the electric energy storage system;

[0054] If the amount of storage required by the difference is greater than the remaining amount of the electric energy storage, then coordinate planning of the electric energy storage system and the thermal energy storage system;

[0055] If the amount of storage power required by the difference is greater than the remaining power of the electric energy storage and the heat of the heat storage medium of the thermal energy storage system, the electric energy storage system, the thermal energy storage system and the gas energy storage system are coordinated and planned.

[0056] In another aspect of the present invention, preferably,

[0057] A power grid source-grid-load-storage coordination planning system considering multiple types of energy storage, including:

[0058] The first prediction module: based on the historical load data of the power grid, using the preset load prediction model, obtains the future load data of the power grid;

[0059] The second prediction module: uses the preset energy prediction algorithm to obtain future energy supply data;

[0060] The third prediction module: uses the preset multi-type energy storage prediction algorithm to obtain future multi-type energy storage data;

[0061] Coordination planning module: Utilizes the future load data of the power grid, future energy supply data and future multi-type energy storage data to carry out coordinated planning based on the goal of maximizing load satisfaction.

[0062] (III) Beneficial effects

[0063] The above technical solution of the present invention has the following beneficial technical effects:

[0064] The present invention can better cope with load fluctuations and energy supply uncertainties by making multi-faceted predictions on the energy side, load side, and energy storage side, and making coordinated plans based on the prediction results. It ensures that the user's electricity demand can be met in all situations and power outages can be reduced. Through accurate prediction and intelligent coordinated planning, grid resources can be used more effectively and waste can be reduced. Through the use of energy storage systems, renewable energy such as wind and solar energy can be better integrated to promote the development of clean energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 It is an overall flow chart of an embodiment of the present invention. DETAILED DESCRIPTION

[0066] 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.

[0067] 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.

[0068] 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.

[0069] 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.

[0070] Embodiment 1

[0071] A coordinated planning method for power grid sources, grids, loads and storage considering multiple types of energy storage. Figure 1 FIG. 1 shows an overall flow chart of an embodiment of the present invention, as shown in FIG. Figure 1 As shown, including:

[0072] According to the historical load data of the power grid, the preset load forecasting model is used to obtain the future load data of the power grid; by analyzing the historical load data of the power grid, the seasonality, periodicity, trend and other characteristics of the load can be identified. The preset load forecasting model predicts the future load data of the power grid based on these characteristics. Accurate load forecasting helps the power grid to prepare for the coordinated planning of energy supply and energy storage equipment in advance. The specific content of the preset load forecasting model is not limited here. Optionally, in this embodiment, the preset load forecasting model includes obtaining the future load data of the power grid using the following formula:

[0073]

[0074] Among them, y i represents the load data of the power grid in the future i-th time period. The preset load forecasting model consists of n layers, including input layer, fully connected layer and output layer. U represents the activation function of the fully connected layer, W1 represents the weight coefficient of the fully connected layer, and b d represents the bias vector of the fully connected layer, f represents the DBI-LSTM activation function, Represents the weight coefficient matrix of the forward LSTM network, Represents the weight coefficient matrix of the reverse LSTM network, x i represents the i-th historical load data; represents the input of the nth layer, and W2 is the weight coefficient of the output layer.

[0075] Among them, the weight coefficient W1 of the fully connected layer, the weight coefficient W2 of the output layer and the bias vector b of the fully connected layer d The acquisition method is obtained through model training. Before the training starts, all weight coefficients and bias vectors will be randomly initialized, or initialized according to some heuristic methods, using the current weight coefficients and bias vectors, and the input data x i , obtain the model output and compare it with the actual future load data y iCompare and calculate the value of the loss function; use the chain rule to calculate the gradient of the loss function with respect to the model parameters. Use optimization algorithms (such as gradient descent, Adam, RMSprop, etc.) and the calculated gradient to update the model's weight coefficients and bias vector. Repeat the above steps until the stop condition is met (such as reaching the maximum number of iterations, the loss function value is lower than a certain threshold, the performance on the validation set is no longer improved, etc.) to obtain the weight coefficient W1 of the fully connected layer, the weight coefficient W2 of the output layer, and the bias vector b of the fully connected layer d .

[0076] The preset energy prediction algorithm is used to obtain future energy supply data. The prediction of energy supply is crucial to the stable operation of the power grid. The preset energy prediction algorithm is used to comprehensively consider factors such as the weather pattern of energy generation to predict future energy supply. The energy supply prediction helps the power grid take measures in advance when energy supply is tight to ensure that the load is met. In this embodiment, the future energy supply comes from wind energy and solar energy. The specific content of the preset energy prediction algorithm is not limited here. Optionally, in this embodiment, the preset energy prediction algorithm is used to obtain future energy data, including predicting photovoltaic power generation.

[0077]

[0078] Among them, E PVi represents the photovoltaic power generation in the future i-th time period, P PVi represents the photovoltaic power generation power in the future i-th time period, η sys Represents the efficiency of the photovoltaic system, P rated-s Indicates the rated power of the photovoltaic array, G ti represents the solar irradiance forecast for the i-th time period in the future, G std represents the standard sunshine quantity, and Δt represents the time cycle step.

[0079] Using the preset energy forecasting algorithm, we can obtain future energy data including wind power generation forecast.

[0080]

[0081] Among them, E wind (V i ) represents the wind power generation under the wind speed V in the future i-th time period, P wind (V i ) represents the wind power generation under the wind speed V in the future i-th time period, ρ represents the air density, A represents the swept area, C p represents the power coefficient, λ represents the cusp speed ratio, β represents the pitch angle, V cut-in Indicates the wind speed at which power generation starts, V cut-out Indicates the wind speed at which power generation stops, Prated-V represents the rated power of the wind turbine, and Δt represents the time cycle step.

[0082] The swept area is the area swept by the wind turbine blades when they rotate. The swept area is determined by the blade length (radius) and the number of blades of the wind turbine. For a wind turbine with N blades, each with a length of R, the swept area A can be approximated as A = NπR 2 ; Tip speed ratio, which is the ratio of the tip speed of the wind turbine blade to the wind speed. Tip speed ratio λ is a function of blade design and wind speed, and it affects the efficiency and power output of the wind turbine. Pitch angle, which is the angle between the wind turbine blade and the horizontal plane. The pitch angle β changes the wind turbine's efficiency in capturing wind by adjusting the angle of the blades. Adjusting the pitch angle can help optimize the performance of the wind turbine when the wind speed changes.

[0083] Using the preset multi-type energy storage prediction algorithm, future multi-type energy storage data is obtained; with the gradual increase in the penetration of new energy, large-scale random incorporation brings operational economy and safety issues to the integrated energy system. Energy storage of different energy types can achieve energy transfer in time and space, alleviating the overall system pressure. In this embodiment, multi-type energy storage includes electric energy storage system, thermal energy storage system and gas energy storage system; in this embodiment, the following formula is used to obtain the predicted electric energy storage data of the electric energy storage system:

[0084] Using the preset multi-type energy storage prediction algorithm, future multi-type energy storage data is obtained, including using the following formula to obtain the predicted electric energy storage data of the electric energy storage system:

[0085] R pre-i =c+φ1·R pre-(i-1) +φ2·R pre-(i-2) +…+φ p ·R pre-(i-p)

[0086] Among them, R pre-i represents the predicted remaining capacity of the energy storage in the i-th time period, φ1, φ2, …, φ P is the model parameter, R pre-(i-p) represents the remaining power data of the ipth time period, and c represents the constant term. The model parameters and constant term are obtained through training;

[0087] The predicted thermal energy storage data of the thermal energy storage system is obtained using the following formula:

[0088] Q stored-i =Q stored-(i-1) +Q in-(i-1) ·η charge -Q out-(i-1) ·η discharge -α·U·(Tstored-(i-1) -T ambient )

[0089] Among them, Q stored-i represents the predicted heat storage medium heat in the ith time period, Q stored-(i-1) represents the heat of the heat storage medium in the i-1th time period, Q in-(i-1) represents the input heat of the i-1th time period, Q out-(i-1) represents the output heat of the i-1th time period, U represents the total heat capacity of the heat storage medium, α represents the heat loss coefficient, T stored-(i-1) represents the temperature of the heat storage medium at the end of the i-1th time period, T ambient represents the ambient temperature, η charge and η discharge Represents thermal energy storage efficiency and release efficiency.

[0090] By connecting the thermal energy storage system to the energy supply, the excess electricity generated by the wind and solar units can be stored in the form of thermal energy. Thermal energy can be stored in various ways, such as using phase change materials (PCMs), hydrothermal storage or rock thermal storage. The stored thermal energy can be released when needed. When the load is high, the thermal energy is converted into electrical energy using thermoelectric conversion technology.

[0091] The predicted gas energy storage data of the gas energy storage system is obtained using the following formula:

[0092] G gs-i =G gs-(i-1) +(η gsc G gsc -G gsd / η gsd )Δt

[0093] Among them, G gs-i represents the predicted gas storage capacity of the i-th time period, η gsc and η gsd represents the natural gas storage efficiency and release efficiency, G gs-(i-1) represents the predicted gas storage capacity for the i-1th time period, G gsc and G gsd represents the natural gas storage and release power, and Δt represents the time cycle step.

[0094] By connecting the gas energy storage system to the energy supply, the excess electricity generated by the wind and solar units can be converted into combustible gas for storage. Power-to-gas technology is a technology that converts electricity into gaseous fuel. Hydrogen is produced by electrolysis of water, and then hydrogen is synthesized with substances such as carbon dioxide into methane or other gases to achieve the storage and conversion of electricity. The basis of power-to-gas technology is the electrolysis of water, which decomposes water into hydrogen and oxygen through electrolysis. The energy conversion efficiency of this process can reach 75% to 85%. The hydrogen produced can be used in fuel cells, liquefied or stored in other ways, or it can be further synthesized into gases such as methane. Based on the electrolysis of hydrogen, carbon dioxide and hydrogen are used to react under high temperature and high pressure to generate natural gas, namely methane. The energy conversion rate of power-to-natural gas is usually between 45% and 60%.

[0095] Using the future load data of the power grid, future energy supply data and future multi-type energy storage data, coordinated planning is carried out based on the goal of maximizing load satisfaction. After obtaining the future load data of the power grid, future energy supply data and future multi-type energy storage data, with the goal of maximizing the load satisfaction rate, through reasonable energy allocation and coordinated planning of energy storage equipment, the stable operation of the power grid and the satisfaction of the load are ensured.

[0096] In this embodiment, using the future load data of the power grid, the future energy supply data and the future multi-type energy storage data, based on the load maximization goal, the coordinated planning includes:

[0097] According to the load data of the power grid predicted in the i-th time period, the predicted photovoltaic power generation and the predicted wind power generation, the difference between the load and the power generation is calculated;

[0098] If the difference is equal to zero, the predicted photovoltaic power generation and the predicted wind power generation are coordinated and planned according to the predicted load data of the power grid;

[0099] If the difference is less than zero, storing the difference;

[0100] If the difference is greater than zero, respectively calculating the amount of electrical storage, thermal storage and gas storage required for the difference;

[0101] Coordinated planning is carried out in the order of electric energy storage system, thermal energy storage system and gas energy storage system.

[0102] Further, in this embodiment, storing the difference includes:

[0103] respectively obtaining the chargeable amounts of the electric energy storage system, the thermal energy storage system and the gas energy storage system;

[0104] If the difference is less than or equal to the chargeable amount of the electric energy storage system, storing the difference using the electric energy storage system;

[0105] If the difference is greater than the chargeable amount of the electric energy storage system, storing the difference using the electric energy storage system and the thermal energy storage system;

[0106] If the difference is greater than the chargeable amount of the electric energy storage system and the thermal energy storage system, the difference is stored using the electric energy storage system, the thermal energy storage system and the gas energy storage system;

[0107] Coordinated planning in the order of electric energy storage system, thermal energy storage system and gas energy storage system includes:

[0108] If the amount of storage required by the difference is less than or equal to the remaining amount of the electric energy storage, then coordinate planning of the electric energy storage system;

[0109] If the amount of storage required by the difference is greater than the remaining amount of the electric energy storage, then coordinate planning of the electric energy storage system and the thermal energy storage system;

[0110] If the amount of storage power required by the difference is greater than the remaining power of the electric energy storage and the heat of the heat storage medium of the thermal energy storage system, the electric energy storage system, the thermal energy storage system and the gas energy storage system are coordinated and planned.

[0111] This embodiment ensures efficient conversion and utilization of energy between different forms of energy storage systems through reasonable coordinated planning. It can cope with the volatility and uncertainty of renewable energy and improve the grid's ability to respond to load changes. Through forecasting and coordinated planning, electricity, heat and gas resources can be more reasonably allocated to reduce waste. Through the use of energy storage systems, photovoltaic and wind power generation can be better integrated to promote the development of clean energy. It ensures that the load demand of the grid can be met under different circumstances, reduces power outages, and improves the stability of power supply.

[0112] Embodiment 2

[0113] A power grid source-grid-load-storage coordination planning system considering multiple types of energy storage, including:

[0114] The first prediction module: based on the historical load data of the power grid, using the preset load prediction model, obtains the future load data of the power grid;

[0115] The second prediction module: uses the preset energy prediction algorithm to obtain future energy supply data;

[0116] The third prediction module: uses the preset multi-type energy storage prediction algorithm to obtain future multi-type energy storage data;

[0117] Coordination planning module: Utilizes the future load data of the power grid, future energy supply data and future multi-type energy storage data to carry out coordinated planning based on the goal of maximizing load satisfaction.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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 method for coordinated planning of power grid sources, grids, loads and storage considering multiple types of energy storage, characterized in that: include: According to the historical load data of the power grid, the preset load forecasting model is used to obtain the future load data of the power grid; Use the preset energy forecasting algorithm to obtain future energy supply data; Use the preset multi-type energy storage prediction algorithm to obtain future multi-type energy storage data; Utilize the future load data of the power grid, future energy supply data and future multi-type energy storage data to carry out coordinated planning based on the goal of maximizing load satisfaction; Using the preset multi-type energy storage prediction algorithm, future multi-type energy storage data is obtained, including using the following formula to obtain the predicted electric energy storage data of the electric energy storage system: R pre-i =c+φ1·R pre-(i-1) +φ2·R pre-(i-2) +…+φ p ·R pre-(i-p) Among them, R pre-i represents the predicted remaining capacity of the energy storage in the i-th time period, φ1, φ2, …, φ P is the model parameter, R pre-(i-p) represents the remaining power data of the ipth time period, and c represents a constant term; Using the preset multi-type energy storage prediction algorithm, future multi-type energy storage data is obtained, including the predicted thermal energy storage data of the thermal energy storage system using the following formula: Q stored-i =Q stored-(i-1) +Q in-(i-1) ·or charge -Q out-(i-1) ·or discharge -a·U·(T stored-(i-1) -T ambient ) Among them, Q stored-i represents the predicted heat storage medium heat in the ith time period, Q stored-(i-1) represents the heat of the heat storage medium in the i-1th time period, Q in-(i-1) represents the input heat of the i-1th time period, Q out-(i-1) represents the output heat of the i-1th time period, U represents the total heat capacity of the heat storage medium, α represents the heat loss coefficient, T stored-(i-1) represents the temperature of the heat storage medium at the end of the i-1th time period, T ambient represents the ambient temperature, η charge and η discharge Indicates thermal energy storage efficiency and release efficiency; Using the preset multi-type energy storage prediction algorithm, obtaining future multi-type energy storage data includes obtaining predicted gas energy storage data of the gas energy storage system using the following formula: G gs-i =G gs-(i-1) +(the gsc G gsc -G gsd / or gsd )Δt Among them, G gs-i represents the predicted gas storage capacity of the i-th time period, η gsc and η gsd represents the natural gas storage efficiency and release efficiency, G gs-(i-1) represents the predicted gas storage capacity for the i-1th time period, G gsc and G gsd represents the natural gas storage and release power, Δt represents the time cycle step; Using the future load data of the power grid, future energy supply data and future multi-type energy storage data, coordinated planning is carried out based on the load maximization goal, including: According to the load data of the power grid predicted in the i-th time period, the predicted photovoltaic power generation and the predicted wind power generation, the difference between the load and the power generation is calculated; If the difference is equal to zero, the predicted photovoltaic power generation and the predicted wind power generation are coordinated and planned according to the predicted load data of the power grid; If the difference is less than zero, storing the difference; If the difference is greater than zero, respectively calculating the amount of electrical storage, thermal storage and gas storage required for the difference; Coordinate planning in the order of electric energy storage system, thermal energy storage system and gas energy storage system; Wherein, storing the difference includes: respectively obtaining the chargeable amounts of the electric energy storage system, the thermal energy storage system and the gas energy storage system; If the difference is less than or equal to the chargeable amount of the electric energy storage system, storing the difference using the electric energy storage system; If the difference is greater than the chargeable amount of the electric energy storage system, storing the difference using the electric energy storage system and the thermal energy storage system; If the difference is greater than the chargeable amount of the electric energy storage system and the thermal energy storage system, the difference is stored using the electric energy storage system, the thermal energy storage system and the gas energy storage system; Coordinated planning in the order of electric energy storage system, thermal energy storage system and gas energy storage system includes: If the amount of storage required by the difference is less than or equal to the remaining amount of the electric energy storage, then coordinate planning of the electric energy storage system; If the amount of storage required by the difference is greater than the remaining amount of the electric energy storage, then coordinate planning of the electric energy storage system and the thermal energy storage system; If the amount of storage power required by the difference is greater than the remaining power of the electric energy storage and the heat of the heat storage medium of the thermal energy storage system, the electric energy storage system, the thermal energy storage system and the gas energy storage system are coordinated and planned.

2. The method according to claim 1, characterized in that According to the historical load data of the power grid, using the preset load forecasting model, the future load data of the power grid is obtained, including: The preset load forecasting model includes obtaining future load data of the power grid using the following formula: Among them, y i represents the load data of the power grid in the future i-th time period. The preset load forecasting model consists of n layers, including input layer, fully connected layer and output layer. U represents the activation function of the fully connected layer, W1 represents the weight coefficient of the fully connected layer, and b d represents the bias vector of the fully connected layer, f represents the DBI-LSTM activation function, Represents the weight coefficient matrix of the forward LSTM network, Represents the weight coefficient matrix of the reverse LSTM network, x i represents the i-th historical load data; represents the input of the nth layer, and W2 is the weight coefficient of the output layer.

3. The method according to claim 2, characterized in that Using the preset energy forecasting algorithm, we can obtain future energy supply data, including predicting photovoltaic power generation using the following formula: Among them, E PVi represents the photovoltaic power generation in the future i-th time period, P PVi represents the photovoltaic power generation power in the future i-th time period, η sys Represents the efficiency of the photovoltaic system, P rated-s Indicates the rated power of the photovoltaic array, G ti represents the solar irradiance forecast for the i-th time period in the future, G std represents the standard sunshine quantity, and Δt represents the time cycle step.

4. The method according to claim 3, characterized in that Using the preset energy forecasting algorithm, we can obtain future energy supply data, including forecasting wind power generation using the following formula: Among them, E wind (V i ) represents the wind power generation under the wind speed V in the future i-th time period, P wind (V i ) represents the wind power generation under the wind speed V in the future i-th time period, ρ represents the air density, A represents the swept area, C p represents the power coefficient, λ represents the cusp speed ratio, β represents the pitch angle, V cut-in Indicates the wind speed at which power generation starts, V cut-out Indicates the wind speed at which power generation stops, P rated-V represents the rated power of the wind turbine, and Δt represents the time cycle step.

5. A power grid source-grid-load-storage coordination planning system considering multiple types of energy storage, characterized in that: include: The first prediction module: based on the historical load data of the power grid, using the preset load prediction model, obtains the future load data of the power grid; The second prediction module: uses the preset energy prediction algorithm to obtain future energy supply data; The third prediction module: uses the preset multi-type energy storage prediction algorithm to obtain future multi-type energy storage data; Coordination planning module: Use the future load data of the power grid, future energy supply data and future multi-type energy storage data to carry out coordination planning based on the goal of maximizing load satisfaction; Using the preset multi-type energy storage prediction algorithm, future multi-type energy storage data is obtained, including using the following formula to obtain the predicted electric energy storage data of the electric energy storage system: R pre-i =c+φ1·R pre-(i-1) +φ2·R pre-(i-2) +…+φ p ·R pre-(i-p) Among them, R pre-i represents the predicted remaining capacity of the energy storage in the i-th time period, φ1, φ2, …, φ P is the model parameter, R pre-(i-p) represents the remaining power data of the ipth time period, and c represents a constant term; Using the preset multi-type energy storage prediction algorithm, future multi-type energy storage data is obtained, including the predicted thermal energy storage data of the thermal energy storage system using the following formula: Q stored-i =Q stored-(i-1) +Q in-(i-1) ·or charge -Q out-(i-1) ·or discharge -a·U·(T stored-(i-1) -T ambient ) Among them, Q stored-i represents the predicted heat storage medium heat in the ith time period, Q stored-(i-1) represents the heat of the heat storage medium in the i-1th time period, Q in-(i-1) represents the input heat of the i-1th time period, Q out-(i-1) represents the output heat of the i-1th time period, U represents the total heat capacity of the heat storage medium, α represents the heat loss coefficient, T stored-(i-1) represents the temperature of the heat storage medium at the end of the i-1th time period, T ambient represents the ambient temperature, η charge and η discharge Indicates thermal energy storage efficiency and release efficiency; Using the preset multi-type energy storage prediction algorithm, obtaining future multi-type energy storage data includes obtaining predicted gas energy storage data of the gas energy storage system using the following formula: G gs-i =G gs-(i-1) +(the gsc G gsc -G gsd / or gsd )Δt Among them, G gs-i represents the predicted gas storage capacity of the i-th time period, η gsc and η gsd represents the natural gas storage efficiency and release efficiency, G gs-(i-1) represents the predicted gas storage capacity for the i-1th time period, G gsc and G gsd represents the natural gas storage and release power, Δt represents the time cycle step; Using the future load data of the power grid, future energy supply data and future multi-type energy storage data, coordinated planning is carried out based on the load maximization goal, including: According to the load data of the power grid predicted in the i-th time period, the predicted photovoltaic power generation and the predicted wind power generation, the difference between the load and the power generation is calculated; If the difference is equal to zero, the predicted photovoltaic power generation and the predicted wind power generation are coordinated and planned according to the predicted load data of the power grid; If the difference is less than zero, storing the difference; If the difference is greater than zero, respectively calculating the amount of electrical storage, thermal storage and gas storage required for the difference; Coordinate planning in the order of electric energy storage system, thermal energy storage system and gas energy storage system; Wherein, storing the difference includes: respectively obtaining the chargeable amounts of the electric energy storage system, the thermal energy storage system and the gas energy storage system; If the difference is less than or equal to the chargeable amount of the electric energy storage system, storing the difference using the electric energy storage system; If the difference is greater than the chargeable amount of the electric energy storage system, storing the difference using the electric energy storage system and the thermal energy storage system; If the difference is greater than the chargeable amount of the electric energy storage system and the thermal energy storage system, the difference is stored using the electric energy storage system, the thermal energy storage system and the gas energy storage system; Coordinated planning in the order of electric energy storage system, thermal energy storage system and gas energy storage system includes: If the amount of storage required by the difference is less than or equal to the remaining amount of the electric energy storage, then coordinate planning of the electric energy storage system; If the amount of storage required by the difference is greater than the remaining amount of the electric energy storage, then coordinate planning of the electric energy storage system and the thermal energy storage system; If the amount of storage power required by the difference is greater than the remaining power of the electric energy storage and the heat of the heat storage medium of the thermal energy storage system, the electric energy storage system, the thermal energy storage system and the gas energy storage system are coordinated and planned.

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