Scheduling method and device of energy storage power station based on electricity price prediction

Abstract

The invention provides an energy storage power station scheduling method and device based on electricity price prediction, and relates to the technical field of energy storage power station scheduling, and the method comprises the steps: obtaining historical electricity price data and operation parameters of a target energy storage power station; establishing a target electricity price prediction model of the target energy storage power station based on the historical electricity price data; predicting the electricity price of the target energy storage power station at the target timestamp based on a target electricity price prediction model to obtain a predicted electricity price; and determining target scheduling data of the target energy storage power station based on the predicted electricity price and the operation parameters, and performing scheduling change on the operation parameters of the target energy storage power station at the target timestamp. Therefore, accurate price prediction can be performed on the target energy storage power station through the target electricity price prediction model of the target energy storage power station, so that an accurate data basis is provided for subsequent determination of target scheduling data at future moments, and the economic benefits of the target energy storage power station are improved.

Description

Technical Field

[0001] The present disclosure relates to the technical field of energy storage power station scheduling, and in particular to a scheduling method and device for an energy storage power station based on electricity price prediction. Background Art

[0002] With the rapid development of the power market and the increasing proportion of renewable energy generation, the operation of the power system is facing more and more uncertainties and challenges. As an important means of regulation, independent energy storage system (IES) can play a bridging role between power supply and demand, storing electricity when electricity prices are low and releasing electricity when electricity prices are high, thus achieving the temporal and spatial balance of electricity. However, in order to give full play to the economic benefits of IEs, accurate electricity price forecasting and optimized scheduling methods are essential.

[0003] At present, a lot of research has been done on electricity price forecasting and economic dispatch of energy storage power stations at home and abroad. However, the scheme of forecasting and dispatch linkage and the operation mode of independent shared energy storage stations in the electricity spot market are still lacking in-depth exploration, and need to be optimized to adapt to market changes and give full play to the potential of energy storage technology. Summary of the invention

[0004] The present disclosure aims to solve one of the technical problems in the related art at least to some extent.

[0005] To this end, an object of the present disclosure is to propose a scheduling method for an energy storage power station based on electricity price prediction.

[0006] The second objective of the present disclosure is to propose a scheduling device for an energy storage power station based on electricity price prediction.

[0007] A third objective of the present disclosure is to provide an electronic device.

[0008] A fourth object of the present disclosure is to provide a non-transitory computer-readable storage medium.

[0009] A fifth object of the present disclosure is to provide a computer program product.

[0010] To achieve the above-mentioned purpose, the first aspect of the present disclosure proposes a scheduling method for an energy storage power station based on electricity price prediction, including: obtaining historical electricity price data and operating parameters of a target energy storage power station; establishing a target electricity price prediction model for the target energy storage power station based on the historical electricity price data; predicting the electricity price of the target energy storage power station at a target timestamp based on the target electricity price prediction model to obtain a predicted electricity price; determining target scheduling data for the target energy storage power station based on the predicted electricity price and the operating parameters, and scheduling changes to the operating parameters of the target energy storage power station at the target timestamp.

[0011] According to one embodiment of the present disclosure, establishing a target electricity price prediction model for the target energy storage power station based on the historical electricity price data includes: establishing a prediction objective function; establishing an initial electricity price prediction model based on the prediction objective function; and training the initial electricity price prediction model based on the historical electricity price data to generate the target electricity price prediction model.

[0012] According to one embodiment of the present disclosure, the prediction objective function is: t =φ1X t-1 +φ2X t-2 +…+φ k X t-p +∈ t Among them, the X t is the predicted electricity value at time t, φ1, φ2…φ k is the weight value of the past timestamp corresponding to the time t, ∈ t It is a random error.

[0013] According to one embodiment of the present disclosure, determining the target scheduling data of the target energy storage power station based on the predicted electricity price and the operating parameters includes: obtaining an energy storage power station scheduling model of the target energy storage power station; inputting the predicted electricity price and the operating parameters into the energy storage power station scheduling model to obtain the target scheduling data of the target energy storage power station.

[0014] According to one embodiment of the present disclosure, the objective function of the energy storage power station scheduling model is: Wherein, T is the total number of sampling points in the scheduling period, and P predict / real (t) is the electricity price of the power trading center, and P cap (t) is the capacity compensation electricity fee, the P trans is the electricity fee for the amount of power transmitted and distributed, and the P gov is the government fund and additional electricity fee, and the η on is the grid efficiency, the η off For the lower grid efficiency, the C cost (t) is the loss rate, the P c (t) is the charging power, the P d (t) is the discharge power.

[0015] According to an embodiment of the present disclosure, before training the initial electricity price prediction model based on the historical electricity price data, it includes: preprocessing the historical electricity price data; dividing the preprocessed historical electricity price data according to preset time intervals to generate multiple training data.

[0016] According to an embodiment of the present disclosure, the preprocessing includes one or more of data cleaning, data conversion and data integration.

[0017] To achieve the above-mentioned purpose, the second aspect embodiment of the present disclosure proposes a scheduling device for an energy storage power station based on electricity price prediction, including: an acquisition module, used to obtain historical electricity price data and operating parameters of a target energy storage power station; an establishment module, used to establish a target electricity price prediction model of the target energy storage power station based on the historical electricity price data; a prediction module, used to predict the electricity price of the target energy storage power station at a target timestamp based on the target electricity price prediction model to obtain the predicted electricity price; a scheduling module, used to determine the target scheduling data of the target energy storage power station based on the predicted electricity price and the operating parameters, and to schedule changes to the operating parameters of the target energy storage power station at the target timestamp.

[0018] To achieve the above-mentioned purpose, the third aspect embodiment of the present disclosure proposes an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to implement the scheduling method of the energy storage power station based on electricity price prediction as described in the first aspect embodiment of the present disclosure.

[0019] To achieve the above-mentioned purpose, the fourth aspect embodiment of the present disclosure proposes a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to implement the scheduling method of the energy storage power station based on electricity price prediction as described in the first aspect embodiment of the present disclosure.

[0020] To achieve the above-mentioned purpose, the fifth embodiment of the present disclosure proposes a computer program product, including a computer program, which, when executed by a processor, is used to implement the scheduling method of the energy storage power station based on electricity price prediction as described in the first embodiment of the present disclosure.

[0021] Therefore, through the target electricity price prediction model of the target energy storage power station, accurate price prediction of the target energy storage power station can be achieved, thereby providing an accurate data basis for the subsequent determination of target scheduling data at future moments and improving the economic benefits of the target energy storage power station. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 It is a schematic diagram of a scheduling method of an energy storage power station based on electricity price prediction according to an embodiment of the present disclosure;

[0023] Figure 2 is a schematic diagram of another method for scheduling an energy storage power station based on electricity price prediction according to an embodiment of the present disclosure;

[0024] Figure 3is a schematic diagram of another method for scheduling an energy storage power station based on electricity price prediction according to an embodiment of the present disclosure;

[0025] Figure 4 is a schematic diagram of a dispatching device of an energy storage power station based on electricity price prediction according to an embodiment of the present disclosure;

[0026] Figure 5 is a schematic diagram of an electronic device according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0027] Embodiments of the present disclosure are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present disclosure, and should not be construed as limiting the present disclosure.

[0028] The acquisition, storage, use, and processing of data in the technical solution disclosed in this disclosure are in compliance with the relevant provisions of relevant laws and regulations.

[0029] It should be noted that in the embodiments of the present application, certain software, components, models and other existing solutions in the industry may be mentioned, and they should be considered as exemplary. Their purpose is only to illustrate the feasibility of implementing the technical solution of the present application, but it does not mean that the applicant has or will necessarily use the solution.

[0030] Figure 1 is a schematic diagram of a method for dispatching an energy storage power station based on electricity price prediction according to an embodiment of the present disclosure, such as Figure 1 As shown, the scheduling method of the energy storage power station based on electricity price prediction includes the following steps:

[0031] S101, obtaining historical electricity price data and operating parameters of a target energy storage power station.

[0032] The method for establishing a scheduling model for an energy storage power station of an embodiment of the present application can be applied to the scenario of scheduling an energy storage power station. The executor for establishing the scheduling model for an energy storage power station of an embodiment of the present application can be the device for establishing a scheduling model for an energy storage power station of an embodiment of the present application, and the device for establishing a scheduling model for an energy storage power station can be set on an electronic device.

[0033] It should be noted that the operating parameters may include multiple types, which are not limited here. For example, the operating parameters may include the rated power and energy storage value of the target energy storage power station.

[0034] The historical electricity price data may be electricity price data for a period of time, or may be all past electricity price data of the target energy storage power station.

[0035] S102, establishing a target electricity price prediction model for a target energy storage power station based on historical electricity price data.

[0036] In the embodiments of the present disclosure, there may be multiple target electricity price prediction models, which are not limited here.

[0037] In one possible implementation, the target electricity price prediction model may be a time series model, a regression model, a classification model, etc.

[0038] It should be noted that the target electricity price prediction model of the target energy storage power station is established based on the historical electricity price data. The historical electricity price data can be preprocessed first, and training data can be established based on the historical electricity price data. Then, the initial model is trained with the training data until the training is completed, and the target electricity price prediction model is obtained.

[0039] It can be understood that model training is an iterative process, which is carried out by continuously adjusting the network parameters of the model until the overall loss function value of the model is less than the preset value, or the overall loss function value of the model no longer changes or changes slowly, the model converges, and a trained model is obtained.

[0040] S103, predicting the electricity price of the target energy storage power station at the target timestamp based on the target electricity price prediction model to obtain a predicted electricity price.

[0041] It should be noted that since the model generated by the final training is designed in advance, when making electricity price predictions, it is necessary to input historical electricity price data according to the input requirements of the model.

[0042] Optionally, electricity price data within a set time before the target timestamp to be predicted may be input, and the set time may be changed according to actual design requirements to modify the model.

[0043] Optionally, the input electricity price data may be converted into a specific format or made into a specific template, etc., so as to predict the electricity price at the target timestamp.

[0044] S104, determining target scheduling data of the target energy storage power station based on the predicted electricity price and the operating parameters, and making scheduling changes to the operating parameters of the target energy storage power station at a target timestamp.

[0045] It should be noted that the target scheduling data may be multiple, and no limitation is given here. For example, the target scheduling data may be the target discharge power, target charging power and target SOC of the target energy storage power station.

[0046] In the embodiments of the present disclosure, there may be multiple methods for determining the target scheduling data of the target energy storage power station based on the predicted electricity price and the operating parameters, which are not limited here.

[0047] In one possible implementation, the predicted electricity price and operating parameters can be predicted using a target dispatch data algorithm to calculate and obtain the target dispatch data.

[0048] In the disclosed embodiment, the historical electricity price data and operating parameters of the target energy storage power station are first obtained, and then a target electricity price prediction model of the target energy storage power station is established based on the historical electricity price data, and then the electricity price of the target energy storage power station at the target timestamp is predicted based on the target electricity price prediction model to obtain the predicted electricity price, and finally the target scheduling data of the target energy storage power station is determined based on the predicted electricity price and operating parameters, and the operating parameters of the target energy storage power station are scheduled and changed at the target timestamp. Thus, through the target electricity price prediction model of the target energy storage power station, accurate price prediction of the target energy storage power station can be achieved, thereby providing an accurate data basis for the subsequent determination of the target scheduling data at future times, and improving the economic benefits of the target energy storage power station.

[0049] It should be noted that before training the initial electricity price prediction model based on the historical electricity price data, the historical electricity price data needs to be preprocessed, and then the preprocessed historical electricity price data is divided according to preset time intervals to generate multiple training data.

[0050] It should be noted that preprocessing includes one or more of data cleaning, data conversion and data integration.

[0051] In the above embodiment, the target electricity price prediction model of the target energy storage power station is established based on the historical electricity price data. Figure 2 Explaining further, the method includes:

[0052] S201, establishing a prediction objective function.

[0053] It should be noted that the prediction objective function in this disclosure is:

[0054] X t =φ1X t-1 +φ2X t-2 +…+φ k X t-p +∈ t

[0055] Among them, X t is the predicted electricity value at time t, φ1, φ2…φ k is the weight value of the past timestamp corresponding to time t, ∈ t It is a random error.

[0056] For example, assuming that the Fth day is the day to be predicted, take the historical M-day electricity price data set QF-1, QF-2, QF-3, QF-3…, QF-P, input the AR prediction model, and use the historical time data of the variable itself to predict itself according to the relationship between the current value and the historical spot electricity price. The model training can obtain that the predicted value Xt is a linear combination of the previous k values. The k value can be obtained by grid search, and the result with the minimum Bayesian Information Criterion (BIC) is taken. The day-ahead electricity spot forecast result is obtained, which is a linear combination of the electricity spot prices from the F-1th day to the FPth day. Finally, the next day's hourly or 15-minute electricity spot transaction price is output.

[0057] S202: Establishing an initial electricity price prediction model based on the prediction objective function.

[0058] It should be noted that the initial electricity price prediction model in the embodiment of the present disclosure is a time series model. The electricity price prediction and optimization scheduling method based on time series analysis can provide high-precision short-term electricity price forecasts, capture seasonal, cyclical and random changes in electricity prices, and optimize scheduling to enable energy storage systems with different configurations to actively respond to the electricity spot market, discharge when electricity prices are peak and charge when prices are low, thereby improving the stability of the power system and maximizing the economic benefits of the energy storage power station.

[0059] S203: training an initial electricity price prediction model based on historical electricity price data to generate a target electricity price prediction model.

[0060] It can be understood that model training is an iterative process, which is carried out by continuously adjusting the network parameters of the model until the overall loss function value of the model is less than the preset value, or the overall loss function value of the model no longer changes or changes slowly, the model converges, and a trained model is obtained.

[0061] It should be noted that the training end condition is set in advance and can be changed according to actual design needs, and no limitation is made here. For example, the training end condition can be that the training reaches a set number of times, the training time reaches a set time, or the loss value of the training result meets the preset loss threshold, etc.

[0062] In the above embodiment, the target dispatching data of the target energy storage power station is determined based on the predicted electricity price and operating parameters, and the target dispatching data of the target energy storage power station can also be determined by Figure 3 Explaining further, the method includes:

[0063] S301, obtaining an energy storage power station scheduling model of a target energy storage power station.

[0064] In the embodiment of the present disclosure, the energy storage power station scheduling model can be obtained through training, and the objective function of the energy storage power station scheduling model is:

[0065]

[0066] Where T is the total number of sampling points in the scheduling period, P predict / real (t) is the electricity price of the power trading center, P cap (t) is the capacity compensation electricity fee, P trans is the electricity fee for transmission and distribution, P gov is the government fund and additional electricity charges, η on is the grid efficiency, η off is the grid efficiency, C cost (t) is the loss rate, P c (t) is the charging power, P d (t) is the discharge power.

[0067] As shown in the above objective function, the model established through the objective function can fully tap the profit space of peak-valley electricity differences and policy subsidies, and greatly improve the economic efficiency of independent shared energy storage in participating in the electricity spot market.

[0068] Where T is the total number of sampling points in the scheduling period, P predict / real (t) is the electricity price of the power trading center, P cap (t) is the capacity compensation electricity fee, P trans is the electricity fee for transmission and distribution, P gov is the government fund and additional electricity charges, η on is the grid efficiency, η off is the grid efficiency, C cost (t) is the loss rate, P c (t) is the charging power, P d (t) is the discharge power.

[0069] P state_max *I state (t)-P state (t)≥0

[0070] Among them, P state_max is the maximum charge and discharge power, I state (t) is the current value at time t, P state (t) is the power value at time t.

[0071] The operating parameters include the minimum charge and discharge power, and the lower limit constraint formula is:

[0072] P state_min *I state (t)-P state (t)≤0

[0073] Among them, P state_min is the minimum charge and discharge power, I state(t) is the current value at time t, P state (t) is the power value at time t.

[0074] The charge and discharge constraint formula is:

[0075] I c (t)+I d (t)≤1

[0076] Among them, I c (t) is the charging value at time t, I d (t) is the discharge value at time t.

[0077] The operating parameters include the maximum and minimum energy storage capacity. The capacity SOC constraint formula of the energy storage system is:

[0078] SOC min ≤SOC(t)≤SOC maX

[0079] Among them, SOC min is the minimum energy storage capacity, SOC max is the maximum energy storage capacity, and SOC(t) is the capacity value at time t.

[0080] The power balance constraint formula is:

[0081]

[0082] Among them, P pure (t) is the net power at time t, η on is the grid efficiency, ηo ff is the grid efficiency, P c (t) is the power at time t, and P c (t) is the charging power at time t, and P d (t) is the discharge power at time t.

[0083] Based on the above objective function, upper limit constraint formula, lower limit constraint formula, charge and discharge constraint formula, SOC constraint formula of the energy storage system and power balance constraint, an energy storage power station scheduling model of the target energy storage power station is established.

[0084] S302, inputting the predicted electricity price and operating parameters into the energy storage power station scheduling model to obtain target scheduling data of the target energy storage power station.

[0085] In the disclosed embodiment, the energy storage power station dispatching model of the target energy storage power station is first obtained, and then the predicted electricity price and operating parameters are input into the energy storage power station dispatching model to obtain the target dispatching data of the target energy storage power station. Thus, the target dispatching data can be automatically generated through the energy storage power station dispatching model, and combined with the target electricity price prediction model in the above embodiment, the target energy storage power station can be automatically controlled in terms of economic benefits, thereby improving the control effect and dispatching efficiency.

[0086] Corresponding to the scheduling methods of energy storage power stations based on electricity price prediction provided in the above-mentioned embodiments, an embodiment of the present disclosure further provides a scheduling device of an energy storage power station based on electricity price prediction. Since the scheduling device of an energy storage power station based on electricity price prediction provided in the embodiment of the present disclosure corresponds to the scheduling methods of energy storage power stations based on electricity price prediction provided in the above-mentioned embodiments, the implementation methods of the above-mentioned scheduling methods of energy storage power stations based on electricity price prediction are also applicable to the scheduling device of an energy storage power station based on electricity price prediction provided in the embodiment of the present disclosure, and will not be described in detail in the following embodiments.

[0087] Figure 4 is a schematic diagram of a scheduling device of an energy storage power station based on electricity price prediction according to an embodiment of the present disclosure, such as Figure 4 As shown, the scheduling device 400 of the energy storage power station based on electricity price prediction includes: an acquisition module 410, an establishment module 420, a prediction module 430 and a scheduling module 440.

[0088] The acquisition module 410 is used to acquire the historical electricity price data and operating parameters of the target energy storage power station.

[0089] The establishment module 420 is used to establish a target electricity price prediction model for the target energy storage power station based on historical electricity price data.

[0090] The prediction module 430 is used to predict the electricity price of the target energy storage power station at the target timestamp based on the target electricity price prediction model to obtain the predicted electricity price.

[0091] The scheduling module 440 is used to determine the target scheduling data of the target energy storage power station based on the predicted electricity price and the operating parameters, and to schedule changes to the operating parameters of the target energy storage power station at a target timestamp.

[0092] In the embodiment of the present disclosure, the establishment module 420 is also used to: establish a prediction target function; establish an initial electricity price prediction model based on the prediction target function; and train the initial electricity price prediction model based on historical electricity price data to generate a target electricity price prediction model.

[0093] In the embodiment of the present disclosure, the objective function is:

[0094] X t =φ1Xt-1 +φ2X t-2 +…+φ k X t-p +∈ t

[0095] Among them, X t is the predicted electricity value at time t, φ1, φ2…φ k is the weight value of the past timestamp corresponding to time t, ∈ t It is a random error.

[0096] In the disclosed embodiment, the scheduling module 440 is further used to: obtain the energy storage power station scheduling model of the target energy storage power station; input the predicted electricity price and operating parameters into the energy storage power station scheduling model to obtain the target scheduling data of the target energy storage power station.

[0097] In the embodiment of the present disclosure, the objective function of the energy storage power station scheduling model is:

[0098]

[0099] Where T is the total number of sampling points in the scheduling period, P predict / real (t) is the electricity price of the power trading center, P cap (t) is the capacity compensation electricity fee, P trans is the electricity fee for transmission and distribution, P gov is the government fund and additional electricity charges, η on is the grid efficiency, η off is the grid efficiency, C cost (t) is the loss rate, P c (t) is the charging power, P d (t) is the discharge power.

[0100] In the embodiment of the present disclosure, the establishment module 420 is also used to: pre-process the historical electricity price data; divide the pre-processed historical electricity price data according to preset time intervals to generate multiple training data.

[0101] In the embodiment of the present disclosure, preprocessing includes one or more of data cleaning, data conversion and data integration.

[0102] In order to implement the above embodiment, the present disclosure further provides an electronic device 500, Figure 5 is a schematic diagram of an electronic device according to an embodiment of the present disclosure, such as Figure 5 As shown, the electronic device 500 includes: a processor 501 and a memory 502 that is communicatively connected to the processor, the memory 502 stores instructions that can be executed by at least one processor, and the instructions are executed by at least one processor 501 to implement the present disclosure. Figure 1-Figure 3A method for scheduling an energy storage power station based on electricity price prediction according to an embodiment of the present invention.

[0103] In order to implement the above embodiments, the present disclosure also provides a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to enable the computer to implement the above embodiments. Figure 1-Figure 3 A method for scheduling an energy storage power station based on electricity price prediction according to an embodiment of the present invention.

[0104] In order to implement the above embodiments, the present disclosure also provides a computer program product, including a computer program. When the computer program is executed by a processor, the computer program implements the above embodiments. Figure 1-Figure 3 A method for scheduling an energy storage power station based on electricity price prediction according to an embodiment of the present invention.

[0105] It should be noted that personal information from users should be collected for legitimate and reasonable purposes and should not be shared or sold outside of these legitimate uses. In addition, such collection / sharing should be carried out after receiving the user's informed consent, including but not limited to notifying the user to read the user agreement / user notice and sign the agreement / authorization including authorization of relevant user information before the user uses the function. In addition, any necessary steps should be taken to protect and safeguard access to such personal information data and ensure that others who have access to personal information data comply with its privacy policy and procedures.

[0106] The present application is expected to provide an implementation scheme for users to selectively block the use or access of personal information data. That is, the present disclosure is expected to provide hardware and / or software to prevent or block access to such personal information data. Once the personal information data is no longer needed, the risk can be minimized by limiting data collection and deleting the data. In addition, when applicable, such personal information is de-identified to protect the privacy of the user.

[0107] In the description of the aforementioned embodiments, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0108] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0109] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0110] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purpose of this specification, "computer-readable medium" can be any device that contains, stores, communicates, propagates or transmits a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples (non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic device), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways if necessary, and then stored in a computer memory.

[0111] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0112] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0113] In addition, each functional unit in each embodiment of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0114] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present application. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A method for dispatching an energy storage power station based on electricity price prediction, characterized in that: include: Obtain historical electricity price data and operating parameters of the target energy storage power station; Establishing a target electricity price prediction model for the target energy storage power station based on the historical electricity price data; Predicting the electricity price of the target energy storage power station at a target timestamp based on the target electricity price prediction model to obtain a predicted electricity price; The target scheduling data of the target energy storage power station is determined based on the predicted electricity price and the operating parameters, and the operating parameters of the target energy storage power station are scheduled to be changed at the target timestamp.

2. The method according to claim 1, characterized in that: The establishing of a target electricity price prediction model for the target energy storage power station based on the historical electricity price data comprises: Establish prediction objective function; Establishing an initial electricity price prediction model based on the prediction objective function; The initial electricity price prediction model is trained based on the historical electricity price data to generate the target electricity price prediction model.

3. The method according to claim 2, characterized in that The prediction objective function is: X t =φ1X t-1 +φ2X t-2 +…+φ k X t-p +∈ t Among them, the X t is the predicted electricity value at time t, φ1, φ2…φ k is the weight value of the past timestamp corresponding to the time t, ∈ t It is a random error.

4. The method according to any one of claims 1 to 3, characterized in that The determining the target dispatching data of the target energy storage power station based on the predicted electricity price and the operating parameters includes: Obtaining an energy storage power station scheduling model for the target energy storage power station; The predicted electricity price and the operating parameters are input into the energy storage power station scheduling model to obtain target scheduling data of the target energy storage power station.

5. The method according to claim 4, characterized in that The objective function of the energy storage power station dispatching model is: Wherein, T is the total number of sampling points in the scheduling period, and P predict / real (t) is the electricity price of the power trading center, and P cap (t) is the capacity compensation electricity fee, the P trans is the electricity fee for the amount of power transmitted and distributed, and the P gov is the government fund and additional electricity fee, and the η on is the grid efficiency, the η off For the lower grid efficiency, the C cost (t) is the loss rate, the P c (t) is the charging power, the P d (t) is the discharge power.

6. The method according to claim 2, characterized in that Before training the initial electricity price prediction model based on the historical electricity price data, the method includes: Preprocess the historical electricity price data: The preprocessed historical electricity price data is divided into preset time intervals to generate a plurality of training data.

7. The method according to claim 6, characterized in that The preprocessing includes one or more of data cleaning, data conversion and data integration.

8. A dispatching device for an energy storage power station based on electricity price prediction, characterized in that: include: An acquisition module is used to obtain historical electricity price data and operating parameters of the target energy storage power station; An establishment module, used to establish a target electricity price prediction model for the target energy storage power station based on the historical electricity price data; A prediction module, used to predict the electricity price of the target energy storage power station at a target timestamp based on the target electricity price prediction model to obtain a predicted electricity price; A scheduling module is used to determine the target scheduling data of the target energy storage power station based on the predicted electricity price and the operating parameters, and to schedule changes to the operating parameters of the target energy storage power station at the target timestamp.

9. An electronic device, characterized in that: Including memory and processor; The processor runs a program corresponding to the executable program code by reading the executable program code stored in the memory, so as to implement the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 7 when executed by a processor.

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