Energy storage power station scheduling model establishment method and device and energy storage power station scheduling method and device

Abstract

The invention provides an energy storage power station scheduling model establishment method and device and an energy storage power station scheduling method and device. The method comprises the steps that the electricity price of a power transaction center, the capacity compensation electricity charge, the power transmission and distribution electricity charge, the government fund and additional item electricity charge, the power grid on efficiency, the power grid off efficiency, the loss rate, the charging power, the discharging power and operation parameters are obtained; establishing a target function, an upper limit constraint formula, a lower limit constraint formula, a charging and discharging constraint formula, a capacity SOC constraint formula of the energy storage system and a power balance constraint based on the data; and establishing an energy storage power station scheduling model based on the objective function, the upper limit constraint formula, the lower limit constraint formula, the charging and discharging constraint formula, the capacity SOC constraint formula of the energy storage system and the power balance constraint. Therefore, by designing and controlling the objective function and the constraint condition, different energy storage power station scheduling models can be established according to actual design requirements and scheduling requirements, so that automatic and personalized scheduling of the energy storage power station is realized, the scheduling efficiency is improved, and the scheduling cost is reduced.

Description

Technical Field

[0001] The present disclosure relates to the technical field of energy storage power station scheduling, and in particular to establishing an energy storage power station scheduling model and an energy storage power station scheduling method and device. Background Art

[0002] In the current power system, with the rapid growth of renewable energy such as wind power and photovoltaic power and the increasing volatility of electricity peaks and valleys, the power market has also increased its demand for the regulatory capabilities of the participants in power trading. Independent shared energy storage, as a new type of power resource, can improve the flexibility and reliability of the power grid, especially in the power spot market environment. This energy storage system can not only provide the necessary frequency regulation, peak regulation and backup services for the power grid, but also maximize the economic benefits of energy storage power stations by participating in power spot market transactions.

[0003] At present, a lot of research has been carried out on the economic dispatch of energy storage power stations at home and abroad, but the operation mode of independent shared energy storage power stations in the electricity spot market is still lacking in exploration, and it is urgently needed to be optimized to adapt to market changes and fully utilize 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 method for establishing a scheduling model for an energy storage power station.

[0006] The second objective of the present disclosure is to provide a method for scheduling an energy storage power station.

[0007] The third objective of the present disclosure is to provide a device for establishing a scheduling model for an energy storage power station.

[0008] The fourth objective of the present disclosure is to provide a scheduling device for an energy storage power station.

[0009] A fifth objective of the present disclosure is to provide an electronic device.

[0010] A sixth object of the present disclosure is to provide a non-transitory computer-readable storage medium.

[0011] A seventh objective of the present disclosure is to provide a computer program product.

[0012] To achieve the above-mentioned purpose, the first aspect of the present disclosure proposes a method for establishing a scheduling model of an energy storage power station, including: obtaining the power trading center electricity price, capacity compensation electricity fee, transmission and distribution electricity fee, government fund and additional electricity fee, grid efficiency, grid efficiency, loss rate, charging power, discharging power and operating parameters of the target energy storage power station; establishing an objective function based on the power trading center electricity price, capacity compensation electricity fee, transmission and distribution electricity fee, government fund and additional electricity fee, grid efficiency, grid efficiency, loss rate, charging power and discharging power of the target energy storage power station, and establishing an upper limit constraint formula, a lower limit constraint formula, a charge and discharge constraint formula, a capacity SOC constraint formula and a power balance constraint of the energy storage system based on the operating parameters of the target energy storage power station; and establishing an energy storage power station scheduling model of the target energy storage power station based on the objective function, the upper limit constraint formula, the lower limit constraint formula, the charge and discharge constraint formula, the capacity SOC constraint formula of the energy storage system and the power balance constraint.

[0013] According to one embodiment of the present disclosure, the objective function 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.

[0014] According to one embodiment of the present disclosure, the operating parameters include the maximum value of the charge and discharge power, and the upper limit constraint formula is: state_max *I state (t)-P state (t)≥0, where P state_mas is the maximum value of the charge and discharge power, the I xtate (t) is the current value at time t, and the P state (t) is the power value at time t.

[0015] According to one embodiment of the present disclosure, the operating parameters include the minimum value of the charge and discharge power, and the lower limit constraint formula is: state_min *I state (t)-Pstate (t)≤0, where P state_min is the minimum value of the charge and discharge power, the I xtate (t) is the current value at time t, and the P state (t) is the power value at time t.

[0016] According to one embodiment of the present disclosure, the charge and discharge constraint formula is: c (t)+I d (t)≤1, where I c (t) is the charging value at time t, and I d (t) is the discharge value at time t.

[0017] According to one embodiment of the present disclosure, the operating parameters include a maximum energy storage capacity and a minimum energy storage capacity, and the capacity SOC constraint formula of the energy storage system is: SOC min ≤SOC(t)≤SOC max Among them, the SOC min is the minimum energy storage capacity, the SOC max is the maximum value of the energy storage capacity, and SOC(t) is the capacity value at time t.

[0018] According to one embodiment of the present disclosure, the power balance constraint formula is: Among them, the P pure (t) is the net power at time t, and η on is the grid efficiency, the ηo ff is the lower grid efficiency, the P c (t) is the power at time t.

[0019] To achieve the above-mentioned purpose, the second aspect embodiment of the present disclosure proposes a method for scheduling an energy storage power station, which is characterized by comprising: obtaining the input electricity price, operating parameters and initial electricity value of the target energy storage power station, and obtaining the energy storage power station scheduling model of the target energy storage power station, wherein the energy storage power station scheduling model is obtained by the method described in the first aspect embodiment; inputting the input electricity price, the operating parameters and the initial electricity value of the target energy storage power station into the energy storage power station scheduling model to determine the target scheduling data; and scheduling changes to the operating parameters of the target energy storage power station based on the target scheduling data.

[0020] To achieve the above-mentioned purpose, the third aspect of the present disclosure proposes a device for establishing a scheduling model of an energy storage power station, including: an acquisition module, used to obtain the power trading center electricity price, capacity compensation electricity fee, transmission and distribution electricity fee, government fund and additional electricity fee, upper grid efficiency, lower grid efficiency, loss rate, charging power, discharging power and operating parameters of the target energy storage power station; a determination module, used to establish an objective function based on the power trading center electricity price, the capacity compensation electricity fee, the transmission and distribution electricity fee, the government fund and additional electricity fee, the upper grid efficiency, the lower grid efficiency, the loss rate, the charging power and the discharging power of the target energy storage power station, and to establish an upper limit constraint formula, a lower limit constraint formula, a charge and discharge constraint formula, a capacity SOC constraint formula and a power balance constraint of the energy storage system based on the operating parameters of the target energy storage power station; an establishment module, used to establish the energy storage power station scheduling model of the target energy storage power station based on the objective function, the upper limit constraint formula, the lower limit constraint formula, the charge and discharge constraint formula, the capacity SOC constraint formula of the energy storage system and the power balance constraint.

[0021] To achieve the above-mentioned purpose, the fourth aspect of the present disclosure proposes a scheduling device for an energy storage power station, including: an acquisition module, used to obtain the input electricity price, operating parameters and starting electricity value of the target energy storage power station, and obtain the energy storage power station scheduling model of the target energy storage power station, wherein the energy storage power station scheduling model is obtained by the method described in the first aspect of the embodiment; an input module, used to input the input electricity price, the operating parameters and the starting electricity value of the target energy storage power station into the energy storage power station scheduling model to determine the target scheduling data; a scheduling module, used to schedule changes to the operating parameters of the target energy storage power station based on the target scheduling data

[0022] To achieve the above-mentioned purpose, the fifth 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 method for establishing a scheduling model of an energy storage power station as described in the first aspect embodiment of the present disclosure, or to implement the scheduling method of an energy storage power station as described in the second aspect embodiment.

[0023] To achieve the above-mentioned purpose, the sixth 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 method for establishing a scheduling model of an energy storage power station as described in the first aspect embodiment of the present disclosure, or to implement the energy storage power station scheduling method as described in the second aspect embodiment.

[0024] To achieve the above-mentioned purpose, the fifth aspect 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 method for establishing a scheduling model of an energy storage power station as described in the first aspect embodiment of the present disclosure, or to implement the energy storage power station scheduling method as described in the second aspect embodiment.

[0025] Therefore, by designing and controlling the objective function and constraints, different energy storage power station scheduling models can be established according to actual design needs and scheduling requirements, thereby realizing automated and personalized scheduling of energy storage power stations, improving scheduling efficiency and reducing scheduling costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 is a schematic diagram of a method for establishing a scheduling model for an energy storage power station according to an embodiment of the present disclosure;

[0027] Figure 2 is a schematic diagram of a method for dispatching an energy storage power station according to an embodiment of the present disclosure;

[0028] Figure 3 is a schematic diagram of an electricity planning curve according to an embodiment of the present disclosure;

[0029] Figure 4 is a schematic diagram of another power planning curve according to an embodiment of the present disclosure;

[0030] Figure 5 It is a schematic diagram of a device for establishing a scheduling model of an energy storage power station according to one embodiment of the present disclosure;

[0031] Figure 6 is a schematic diagram of an energy storage power station scheduling device according to one embodiment of the present disclosure;

[0032] Figure 7 is a schematic diagram of an electronic device according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0033] 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.

[0034] 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.

[0035] 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.

[0036] Figure 1 is a schematic diagram of a method for establishing a scheduling model for an energy storage power station according to an embodiment of the present disclosure, such as Figure 1 As shown, the method for establishing the energy storage power station scheduling model includes the following steps:

[0037] S101, obtaining the electricity price of the power trading center, capacity compensation electricity fee, transmission and distribution electricity fee, government fund and additional electricity fee, grid efficiency, grid efficiency, loss rate, charging power, discharging power and operating parameters of the target energy storage power station.

[0038] 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.

[0039] It should be noted that there are many ways to obtain the target energy storage power station's electricity price from the power trading center, capacity compensation charges, transmission and distribution charges, government funds and additional charges, grid efficiency, grid efficiency, loss rate, charging power, discharging power and operating parameters, and no limitation is made here.

[0040] In one possible implementation, the above data may be obtained through manual input.

[0041] Optionally, the historical operation data of the target energy storage power station may be obtained and analyzed.

[0042] 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.

[0043] It should be noted that government funds and additional electricity charges are established by the government to support specific policy goals (such as renewable energy development, energy efficiency improvement, subsidies for low-income households, etc.). They are collected from electricity users through electricity bills and then used to fund related projects and programs.

[0044] The upper grid efficiency and lower grid efficiency refer to the upper grid efficiency and lower grid efficiency including the estimated DC-AC conversion loss of the power conversion system (PCS) and the plant power loss respectively.

[0045] S102, establish an objective function based on the power trading center electricity price, capacity compensation electricity fee, transmission and distribution electricity fee, government fund and additional electricity fee, grid efficiency, grid efficiency, loss rate, charging power, and discharging power of the target energy storage power station, and establish an upper limit constraint formula, a lower limit constraint formula, a charging and discharging constraint formula, a capacity SOC constraint formula of the energy storage system, and a power balance constraint based on the operating parameters of the target energy storage power station.

[0046] In the embodiment of the present disclosure, the objective function is a scheduling strategy function for the energy storage power station, which is designed in advance and can be changed according to actual design needs, and is not limited here.

[0047] In one possible implementation, the objective function may focus on the economic benefits of the target energy storage power station. Optionally, the efficiency of the target energy storage power station may be focused on by changing the objective function. Optionally, the efficiency and economic benefits may be assigned different proportions to establish the objective function.

[0048] It should be noted that the upper limit constraint formula and the lower limit constraint formula are formulas for constraining the charging and discharging power of the target energy storage power station, the charging and discharging constraint formula is a formula for constraining the charging and discharging state of the target energy storage power station, the capacity charge state (State of Charge, SOC) constraint formula of the energy storage system is a formula for constraining the capacity of the target energy storage power station, and the power balance constraint is a formula for constraining the operating power of the target energy storage power station.

[0049] In the embodiments of the present disclosure, the upper limit constraint formula, the lower limit constraint formula, the charge and discharge constraint formula, the capacity SOC constraint formula of the energy storage system, and the power balance constraint are designed in advance and can be changed according to actual design requirements or the configuration of the energy storage power station. No limitation is made here.

[0050] S103, establishing an energy storage power station scheduling model for the target energy storage power station based on the objective function, the upper limit constraint formula, the lower limit constraint formula, the charge and discharge constraint formula, the SOC constraint formula of the energy storage system, and the power balance constraint.

[0051] In the disclosed embodiment, after obtaining the energy storage power station dispatching model of the target energy storage power station, the energy storage power station dispatching model can also be trained. It can be understood that the training of the model is a repeated iterative process, and the training is performed 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.

[0052] In the disclosed embodiment, firstly, the electricity price of the power trading center, the capacity compensation electricity fee, the transmission and distribution electricity fee, the government fund and additional electricity fee, the upper grid efficiency, the lower grid efficiency, the loss rate, the charging power, the discharging power and the operating parameters of the target energy storage power station are obtained, and then the objective function is established based on the electricity price of the power trading center, the capacity compensation electricity fee, the transmission and distribution electricity fee, the government fund and additional electricity fee, the upper grid efficiency, the lower grid efficiency, the loss rate, the charging power and the discharging power of the target energy storage power station, and the upper limit constraint formula, the lower limit constraint formula, the charging and discharging constraint formula, the capacity SOC constraint formula and the power balance constraint of the energy storage system are established based on the operating parameters of the target energy storage power station, and finally, the energy storage power station scheduling model of the target energy storage power station is established based on the objective function, the upper limit constraint formula, the lower limit constraint formula, the charging and discharging constraint formula, the capacity SOC constraint formula and the power balance constraint of the energy storage system. Therefore, by designing and controlling the objective function and the constraint conditions, different energy storage power station scheduling models can be established according to the actual design needs and scheduling requirements, so as to realize the automation and personalized scheduling of the energy storage power station, improve the scheduling efficiency and reduce the scheduling cost.

[0053] In one possible implementation, the objective function may be:

[0054]

[0055] 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.

[0056] 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.

[0057] 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, Ccost (t) is the loss rate, P c (t) is the charging power, P d (t) is the discharge power.

[0058] P state_max *I state (t)-P state (t)≥0

[0059] 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.

[0060] The operating parameters include the minimum charge and discharge power, and the lower limit constraint formula is:

[0061] P state_min *I state (t)-P state (t)≤0

[0062] 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.

[0063] The charge and discharge constraint formula is:

[0064] I c (t)+I d (t)≤1

[0065] Among them, I c (t) is the charging value at time t, I d (t) is the discharge value at time t.

[0066] The operating parameters include the maximum and minimum energy storage capacity. The capacity SOC constraint formula of the energy storage system is:

[0067] SOC min ≤SOC(t)≤SOC max

[0068] 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.

[0069] The power balance constraint formula is:

[0070]

[0071] Among them, P pure(t) is the net power at time t, η on is the grid efficiency, η off 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.

[0072] Figure 2 is a schematic diagram of a method for dispatching an energy storage power station according to an embodiment of the present disclosure, such as Figure 2 As shown, the energy storage power station scheduling method includes the following steps:

[0073] S201, obtaining the input electricity price, operating parameters and initial electricity value of the target energy storage power station, and obtaining the energy storage power station scheduling model of the target energy storage power station.

[0074] It should be noted that the operating parameters may be various and are not limited here. In one possible implementation, the operating parameters may be as shown in the following table:

[0075]

[0076]

[0077] It should be noted that the energy storage power station scheduling model of the target energy storage power station in the embodiment of the present disclosure is as follows: Figure 1 The energy storage power station scheduling model is obtained by the method of establishing the energy storage power station scheduling model shown.

[0078] S202, inputting the input electricity price, operating parameters and initial electricity value of the target energy storage power station into the energy storage power station scheduling model to determine the target scheduling data.

[0079] It should be noted that the input electricity price, operating parameters and starting electricity value of the target energy storage power station can be input manually or automatically after the above data are obtained, and there is no limitation here.

[0080] 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.

[0081] S203: Schedule changes to the operating parameters of the target energy storage power station based on the target scheduling data.

[0082] In the disclosed embodiment, the input electricity price, operating parameters and initial electricity value of the target energy storage power station are first obtained, and the energy storage power station scheduling model of the target energy storage power station is obtained, and then the input electricity price, operating parameters and initial electricity value of the target energy storage power station are input into the energy storage power station scheduling model to determine the target scheduling data, and finally the operating parameters of the target energy storage power station are scheduled and changed based on the target scheduling data. Thus, the target energy storage power station is automatically scheduled through the energy storage power station scheduling model, thereby realizing the automation and personalized scheduling of the energy storage power station, improving the scheduling efficiency, and reducing the scheduling cost.

[0083] In the embodiment of the present disclosure, after the target scheduling data is obtained, the target scheduling data may be further processed. In a possible implementation method, an electricity plan curve may be generated based on the target scheduling data. For example, Figure 3 As shown, an active power planning curve can also be generated based on the target scheduling data, for example, Figure 4 As shown, Figure 3 and Figure 4 The horizontal axis time is a time scale of 15 minutes per point. Figure 3 The vertical axis is the power value. Figure 4 The vertical axis is the predicted dispatch electricity price. Figure 4 Curve 1 is the comprehensive dispatching electricity price curve, and curve 2 is the comprehensive dispatching output power curve. Negative numbers are charging power, and positive numbers are discharging power, and the unit is kilowatt.

[0084] In another possible implementation method, the revenue of the target energy storage power station can also be estimated based on the target scheduling data. For example, it can be as follows:

[0085] Revenue from day-one settlement bills

[0086]

[0087] Net income from day-ahead settlement (considering network efficiency)

[0088]

[0089] Objective function net benefit (considering unit KWh charging and discharging cost)

[0090]

[0091] Among them, P input (t) is the input power at time t, P c (t) is the charging power at time t, P d (t) is the discharge power at time t, η on is the grid efficiency, η off is the grid efficiency, C cost (t) is the loss rate.

[0092] Corresponding to the energy storage power station scheduling model establishment method provided by the above-mentioned embodiments, an embodiment of the present disclosure further provides an energy storage power station scheduling model establishment device. Since the energy storage power station scheduling model establishment device provided by the embodiment of the present disclosure corresponds to the energy storage power station scheduling model establishment method provided by the above-mentioned embodiments, the implementation method of the above-mentioned energy storage power station scheduling model establishment method is also applicable to the energy storage power station scheduling model establishment device provided by the embodiment of the present disclosure, and will not be described in detail in the following embodiments.

[0093] Figure 5 is a schematic diagram of a device for establishing a scheduling model for an energy storage power station according to an embodiment of the present disclosure. Figure 5 As shown, the energy storage power station scheduling model establishment device 500 includes: an acquisition module 510, a determination module 520 and a determination module 530.

[0094] Among them, the acquisition module 510 is used to obtain the power trading center electricity price, capacity compensation electricity fee, transmission and distribution electricity fee, government fund and additional electricity fee, grid efficiency, grid efficiency, loss rate, charging power, discharging power and operating parameters of the target energy storage power station.

[0095] Determination module 520 is used to establish an objective function based on the electricity price of the power trading center, capacity compensation electricity fee, transmission and distribution electricity fee, government fund and additional electricity fee, grid efficiency, grid efficiency, loss rate, charging power, and discharging power of the target energy storage power station, as well as to establish an upper limit constraint formula, a lower limit constraint formula, a charge and discharge constraint formula, a capacity SOC constraint formula and a power balance constraint of the energy storage system based on the operating parameters of the target energy storage power station.

[0096] Establishing module 530, for establishing an energy storage power station scheduling model of a target energy storage power station based on an objective function, an upper limit constraint formula, a lower limit constraint formula, a charge and discharge constraint formula, a capacity SOC constraint formula of an energy storage system, and a power balance constraint.

[0097] In the embodiment of the present disclosure, the objective function 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, Pc (t) is the charging power, P d (t) is the discharge power.

[0100] In the embodiment of the present disclosure, the operating parameters include the maximum value of the charge and discharge power, and the upper limit constraint formula is:

[0101] P state_max *I state (t)-P state (t)≥0

[0102] 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.

[0103] In the embodiment of the present disclosure, the operating parameters include the minimum value of the charge and discharge power, and the lower limit constraint formula is:

[0104] P state_min *I state (t)-P state (t)≤0

[0105] 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.

[0106] In the embodiment of the present disclosure, the charge and discharge constraint formula is:

[0107] I c (t)+I d (t)≤1

[0108] Among them, I c (t) is the charging value at time t, I d (t) is the discharge value at time t.

[0109] In the embodiment of the present disclosure, the operating parameters include a maximum energy storage capacity and a minimum energy storage capacity, and the capacity SOC constraint formula of the energy storage system is:

[0110] SOC min ≤SOC(t)≤SOC maX

[0111] 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.

[0112] In the embodiment of the present disclosure, the power balance constraint formula is:

[0113]

[0114] Among them, P pure (t) is the net power at time t, η on is the grid efficiency, η off is the grid efficiency, P c (t) is the charging power at time t, P d (t) is the discharge power at time t.

[0115] Therefore, by designing and controlling the objective function and constraints, different energy storage power station scheduling models can be established according to actual design needs and scheduling requirements, thereby realizing automated and personalized scheduling of energy storage power stations, improving scheduling efficiency and reducing scheduling costs.

[0116] Corresponding to the energy storage power station scheduling methods provided in the above-mentioned embodiments, an embodiment of the present disclosure further provides an energy storage power station scheduling device. Since the energy storage power station scheduling device provided in the embodiment of the present disclosure corresponds to the energy storage power station scheduling methods provided in the above-mentioned embodiments, the implementation methods of the above-mentioned energy storage power station scheduling methods are also applicable to the energy storage power station scheduling device provided in the embodiment of the present disclosure, and will not be described in detail in the following embodiments.

[0117] Figure 6 is a schematic diagram of a scheduling device for an energy storage power station according to an embodiment of the present disclosure, such as Figure 6 As shown, the energy storage power station scheduling device 600 includes: an acquisition module 610 , a determination module 620 and a determination module 630 .

[0118] The acquisition module 610 is used to obtain the input electricity price, operating parameters and initial electricity value of the target energy storage power station, and obtain the energy storage power station scheduling model of the target energy storage power station.

[0119] The input module 620 is used to input the input electricity price, operating parameters and initial electricity value of the target energy storage power station into the energy storage power station scheduling model to determine the target scheduling data.

[0120] The scheduling module 630 is used to schedule changes to the operating parameters of the target energy storage power station based on the target scheduling data.

[0121] Therefore, the target energy storage power station is automatically dispatched through the energy storage power station dispatching model, thereby realizing automated and personalized dispatching of the energy storage power station, improving dispatching efficiency and reducing dispatching costs.

[0122] In order to implement the above embodiment, the present disclosure further provides an electronic device 700, Figure 7is a schematic diagram of an electronic device according to an embodiment of the present disclosure, such as Figure 7 As shown, the electronic device 700 includes: a processor 701 and a memory 702 that is communicatively connected to the processor, the memory 702 stores instructions that can be executed by at least one processor, and the instructions are executed by at least one processor 701 to implement the present disclosure. Figure 1 The method for establishing the energy storage power station scheduling model of the embodiment, or Figure 2 A method for dispatching an energy storage power station according to an embodiment.

[0123] 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 The method for establishing the energy storage power station scheduling model of the embodiment, or Figure 2 A method for dispatching an energy storage power station according to an embodiment.

[0124] 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 The method for establishing the energy storage power station scheduling model of the embodiment, or Figure 2 A method for dispatching an energy storage power station according to an embodiment.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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 establishing a scheduling model for an energy storage power station, characterized in that: include: Obtain the electricity price of the power trading center, capacity compensation electricity fee, transmission and distribution electricity fee, government fund and additional electricity fee, grid efficiency, grid efficiency, loss rate, charging power, discharging power and operating parameters of the target energy storage power station; Establish an objective function based on the electricity price of the power trading center, the capacity compensation electricity fee, the transmission and distribution electricity fee, the government fund and additional electricity fee, the upper grid efficiency, the lower grid efficiency, the loss rate, the charging power, and the discharging power of the target energy storage power station, and establish an upper limit constraint formula, a lower limit constraint formula, a charge and discharge constraint formula, a capacity SOC constraint formula of the energy storage system, and a power balance constraint based on the operating parameters of the target energy storage power station; An energy storage power station scheduling model of the target energy storage power station is established based on the objective function, the upper limit constraint formula, the lower limit constraint formula, the charge and discharge constraint formula, the capacity SOC constraint formula of the energy storage system and the power balance constraint.

2. The method according to claim 1, characterized in that The objective function 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.

3. The method according to claim 1, characterized in that The operating parameters include the maximum value of the charge and discharge power, and the upper limit constraint formula is: P state_max *I state (t)-P state (t)≥0 Among them, the P state_max is the maximum value of the charge and discharge power, the I state (t) is the current value at time t, and the P state (t) is the power value at time t.

4. The method according to claim 1, characterized in that: The operating parameters include the minimum value of charge and discharge power, and the lower limit constraint formula is: P state_min *I state (t)-P state (t)≤0 Among them, the P state_min is the minimum value of the charge and discharge power, the I state (t) is the current value at time t, and the P state (t) is the power value at time t.

5. The method according to claim 1, characterized in that The charge and discharge constraint formula is: I c (t)+I d (t)≤1 Among them, the I c (t) is the charging value at time t, and I d (t) is the discharge value at time t.

6. The method according to claim 1, characterized in that The operating parameters include a maximum energy storage capacity and a minimum energy storage capacity, and the capacity SOC constraint formula of the energy storage system is: SOC min ≤SOC(t)≤SOC max Among them, the SOC min is the minimum energy storage capacity, the SOC max is the maximum value of the energy storage capacity, and SOC(t) is the capacity value at time t.

7. The method according to claim 1, characterized in that The power balance constraint formula is: Among them, the P pure (t) is the net power at time t, and η on is the grid efficiency, the η off is the lower grid efficiency, the P c (t) is the charging power at time t, and P d (t) is the discharge power at time t.

8. A method for dispatching an energy storage power station, characterized in that: include: Obtaining the input electricity price, operating parameters and initial electricity value of the target energy storage power station, and obtaining the energy storage power station scheduling model of the target energy storage power station, wherein the energy storage power station scheduling model is obtained by the energy storage power station scheduling model establishment method according to any one of claims 1 to 7: Inputting the input electricity price of the target energy storage power station, the operating parameters and the starting power value into the energy storage power station scheduling model to determine target scheduling data; The operating parameters of the target energy storage power station are changed based on the target scheduling data.

9. A device for establishing a scheduling model for an energy storage power station, characterized in that: include: The acquisition module is used to obtain the electricity price of the power trading center, capacity compensation electricity fee, transmission and distribution electricity fee, government fund and additional electricity fee, grid efficiency, grid efficiency, loss rate, charging power, discharging power and operating parameters of the target energy storage power station; A determination module is used to establish an objective function based on the electricity price of the power trading center of the target energy storage power station, the capacity compensation electricity fee, the transmission and distribution electricity fee, the government fund and additional electricity fee, the upper grid efficiency, the lower grid efficiency, the loss rate, the charging power, and the discharging power, and to establish an upper limit constraint formula, a lower limit constraint formula, a charge and discharge constraint formula, a capacity SOC constraint formula of the energy storage system, and a power balance constraint based on the operating parameters of the target energy storage power station; A module is established to establish an energy storage power station scheduling model of the target energy storage power station based on the objective function, the upper limit constraint formula, the lower limit constraint formula, the charge and discharge constraint formula, the capacity SOC constraint formula of the energy storage system and the power balance constraint.

10. A dispatching device for an energy storage power station, characterized in that: include: A collection module, used to obtain the input electricity price, operating parameters and starting electricity value of the target energy storage power station, and obtain the energy storage power station scheduling model of the target energy storage power station, wherein the energy storage power station scheduling model is obtained by the energy storage power station scheduling model establishment method according to any one of claims 1 to 7; An input module, used for inputting the input electricity price of the target energy storage power station, the operating parameters and the starting electricity value into the energy storage power station scheduling model to determine the target scheduling data; A scheduling module is used to schedule changes to the operating parameters of the target energy storage power station based on the target scheduling data.

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