Day-ahead bidding method and device for new energy station configured with hybrid energy storage

Abstract

The invention provides a day-ahead bidding method and device for a new energy station configured with hybrid energy storage, and belongs to the technical field of new energy station operation and energy storage operation. The method comprises the following steps: for a wind storage station configured with a hybrid energy storage system, establishing an objective function of a wind storage complex day-ahead bidding optimization model, the objective function being for maximizing the total operation income; wherein the hybrid energy storage system comprises lithium battery energy storage and super capacitor energy storage; the optimization model considers the condition of asymmetrically declared secondary frequency modulation capacity of energy storage resources; establishing constraint conditions of the day-ahead bidding optimization model of the wind storage complex; and solving the day-ahead bidding optimization model of the wind storage complex to obtain an optimization result of the day-ahead declaration amount of the wind storage station. The method can solve the problem that in the day-ahead market bidding stage, the decision of the new energy station configured with hybrid energy storage participates in the declaration decision of the electric energy market and the secondary frequency modulation market at the same time, and improves the operation flexibility of the new energy station.

Description

Technical Field

[0001] This invention belongs to the field of new energy power station operation and energy storage operation technology, and specifically relates to a day-ahead bidding method and device for new energy power stations equipped with hybrid energy storage. It is particularly suitable for day-ahead bidding scenarios in which power stations equipped with power-type and energy-type energy storage such as lithium batteries and flywheels / supercapacitors participate in the electricity market and provide secondary frequency regulation auxiliary services. Background Technology

[0003] In recent years, the installed capacity of new energy sources, represented by wind power and photovoltaics, has increased rapidly, placing greater emphasis on the flexible regulation and high-quality grid connection of the power system. For power plant sites, single energy storage solutions struggle to balance response speed, continuous discharge capacity, and lifespan economics: sudden power surges can easily exceed the unit's rate capability, daily peak shifting requires significant energy support, and long-term operation necessitates consideration of thermal management and degradation costs. To address these contradictions, engineering practice has introduced a hybrid energy storage architecture combining energy and power: lithium batteries provide hourly energy support, while hybrid supercapacitors / flywheels / supercapacitors handle millisecond-minute rapid responses, forming a collaborative "fast channel + energy channel." This architecture, on the one hand, traps peak and high-frequency fluctuations within the power-type unit, reducing lithium battery rate stress and equivalent cycle depth, delaying capacity decay, and optimizing levelized cost of electricity (LCOE); on the other hand, it improves round-trip efficiency and reduces heat dissipation load in the low-to-medium rate range, resulting in superior system energy efficiency and reliability. At the market operation level, it is becoming increasingly common for new energy power plants to integrate ancillary services such as electricity trading and secondary frequency regulation. Hybrid energy storage can release available power and energy while meeting grid connection smoothing and schedule tracking requirements, thereby improving frequency tracking quality and bidding competitiveness. Against this backdrop, there is an urgent need to develop a day-ahead bidding technical framework for new energy power plants equipped with hybrid energy storage: under a unified operation and management approach, standardize power-energy coordination, SoC operating boundaries, and lifetime cost measurement, support the ability to obtain diversified revenue from "electricity revenue + ancillary service revenue," and promote a better balance between safety, economy, and feasibility for new energy power plants.

[0004] An existing capacity bidding method for hybrid energy storage participating in the frequency regulation ancillary services market (application number CN202210792960.0) uses the VMD-ST-QF algorithm to decompose the day-ahead frequency regulation command prediction signal into high-frequency and low-frequency components, which are then allocated to the supercapacitor and lithium battery systems, respectively. Considering the frequency regulation capacity benefits, mileage benefits, and aging costs of energy storage participating in the frequency regulation market, an optimization objective function oriented towards maximizing the economic benefits of energy storage is constructed. Integrating the physical performance constraints of energy storage itself and the performance index constraints for participating in the frequency regulation market, a set of constraints for the optimization model is constructed. Since the intraday real-time frequency regulation command is adjusted compared to the day-ahead frequency regulation command prediction curve, leading to uncertainty in the frequency regulation command, conditional value of risk is introduced to improve the objective function of the optimization model, reducing the risk coefficient for energy storage operators participating in the day-ahead bidding market. The limitations of this method are that it fails to establish a relevant model for the impact of excessively high or low state of charge (SOC) of the energy storage system on the effectiveness of frequency regulation ancillary services. Furthermore, it does not consider the flexible participation of the energy storage system in the electricity market, its operation through peak-valley arbitrage and secondary frequency regulation, or its ability to adjust its SOC by separately applying for upper and lower frequency regulation capacities to improve the overall operating revenue of the power plant. Additionally, this invention is only suitable for independent energy storage power plants and not for joint bidding of wind-storage combined power plants. Summary of the Invention

[0005] The purpose of this invention is to address the shortcomings of existing technologies by proposing a day-ahead bidding method and apparatus for renewable energy power plants equipped with hybrid energy storage. This invention can solve the problem of simultaneously participating in the bidding decisions for both the electricity market and the secondary frequency regulation market for renewable energy power plants equipped with hybrid energy storage during the day-ahead market bidding stage, thereby improving the operational flexibility of renewable energy power plants.

[0006] A first aspect of this invention proposes a day-ahead bidding method for new energy power stations equipped with hybrid energy storage, comprising:

[0007] For wind-storage power plants equipped with hybrid energy storage systems, an objective function is established for the day-ahead bidding optimization model of the wind-storage consortium, wherein the objective function is to maximize the total operating revenue; wherein the hybrid energy storage system includes lithium battery energy storage and supercapacitor energy storage; the optimization model considers the situation of asymmetric bidding for secondary frequency regulation capacity of energy storage resources;

[0008] Establish the constraints for the day-ahead bidding optimization model of the aforementioned wind-storage consortium;

[0009] The optimization model for day-ahead bidding of the wind and storage consortium is solved to obtain the optimization results of the day-ahead bidding volume of the wind and storage sites.

[0010] In one specific embodiment of the present invention, it further includes:

[0011] Obtain the predicted maximum wind power output curve for the second day prior to the bidding. Initial state of charge of the lithium battery energy storage system at 0:00 on the second day and the initial state of charge of the supercapacitor energy storage system Maximum output of wind storage and collection station Lower limit Among them, when the predicted power is lower than the upper limit of the wind storage station's output. When the predicted power is higher than the lower limit of the wind-storage power station output, the hybrid energy storage system participates in the up-regulation; when the predicted power is higher than the lower limit of the wind-storage power station output, the hybrid energy storage system participates in the down-regulation.

[0012] In one specific embodiment of the present invention, it further includes:

[0013] The hybrid energy storage system applied for frequency regulation capacity. and down-modulation capacity The calculation expression is as follows:

[0014] (1)

[0015] (2)

[0016] in, and These represent the lithium battery energy storage capacity participating in up- and down-frequency regulation, respectively. and These represent the supercapacitor's energy storage capacity participating in up- and down-frequency regulation, respectively.

[0017] In a specific embodiment of the present invention, the objective function of the day-ahead bidding optimization model of the wind-storage consortium is:

[0018] (3)

[0019] in, For revenue generated from electricity used for internet access, In order to participate in the frequency modulation revenue, Costs related to the lifespan depletion of energy storage;

[0020] (4)

[0021] (5)

[0022] In the formula, For the period t, the on-grid electricity price and These represent the charging power and discharging power of lithium-ion battery energy storage during time period t, respectively. This represents the output power of the wind farm during time period t; The total output power of wind storage during time period t;

[0023] (6)

[0024] In the formula, , Here, represents the capacity and mileage price for time period t, respectively; m represents the average mileage.

[0025] (7)

[0026] In the formula, To reduce the lifespan cost of lithium battery energy storage per unit charge / discharge capacity during wind curtailment. The cost of lifespan loss per unit charge / discharge capacity when lithium battery energy storage participates in frequency regulation, where Δt is the length of a single operating time period.

[0027] In a specific embodiment of the present invention, the constraints of the day-ahead bidding optimization model of the wind-storage consortium include:

[0028] 1) Power constraint;

[0029] (8)

[0030] 2) Lithium battery energy storage charging and discharging power constraints;

[0031] (9)

[0032] In the formula, This represents a 0-1 variable representing the energy storage capacity of a lithium battery during charging in time period t. This represents a 0-1 variable representing the energy storage capacity of a lithium battery during discharge in time period t. This represents a 0-1 variable representing the charging time period t of the supercapacitor energy storage. This represents a 0-1 variable representing the energy stored in a supercapacitor during discharge in time period t. This indicates the rated charging power of the lithium battery energy storage. This indicates the rated discharge power of the lithium battery for energy storage. This indicates the rated charging power of the supercapacitor energy storage. This indicates the rated discharge power of the supercapacitor's energy storage.

[0033] 3) Power constraints when hybrid energy storage participates in frequency regulation;

[0034] (10)

[0035] 4) Power constraints;

[0036] (11)

[0037] (12)

[0038] In the formula, The remaining energy stored in the lithium battery during time period t; The rated capacity for energy storage in lithium batteries; and These represent the charging and discharging efficiencies of lithium-ion battery energy storage, respectively. The self-discharge rate of lithium-ion batteries for energy storage; and These represent the minimum and maximum states of charge allowed for lithium battery energy storage, respectively. This indicates the state of charge of a lithium battery energy storage device during time period t.

[0039] 5) FM performance limitations;

[0040] The frequency regulation performance of lithium battery energy storage and supercapacitor energy storage is simplified into a piecewise function with SOC as the variable; when the lithium battery SOC is too low or too high, the frequency regulation performance score is... In other cases, the score is 1. When the supercapacitor's state of charge (SOC) is too low or too high, the frequency modulation performance score is: In other cases, the score is 1. ;

[0041] (13)

[0042] (14)

[0043] in, The performance score of lithium battery energy storage during time period t. The supercapacitor's performance score at time interval t; This indicates the percentage by which the SOC (State of Charge) of a lithium battery deviates from the median value. This indicates the percentage by which the SOC of a supercapacitor deviates from the median value;

[0044] The overall frequency regulation performance is then a weighted average of the frequency regulation performances of lithium battery energy storage and supercapacitor energy storage:

[0045] (15)

[0046] (16)

[0047] in, The average score for performance during the FM broadcast period. This is the preset lower limit value.

[0048] In one specific embodiment of the present invention, it further includes:

[0049] The optimization model is linearized, wherein equations (13)-(14) are transformed into the following linear constraints by the Big M method:

[0050] (17)

[0051] (18)

[0052] (19)

[0053] (20)

[0054] In the formula, , These represent the upper and lower bounds of the state of charge that make the frequency regulation performance of lithium battery energy storage equal to 1. , These represent the upper and lower bounds of the state of charge that make the frequency modulation performance of supercapacitor energy storage equal to 1; It is a large number; 0-1 variables represent The segment position it is in A value of 1 indicates Located at the corresponding interval segment position , A value of 0 indicates Not in the corresponding interval segment position ; 0-1 variables represent The segment position it is in A value of 1 indicates Located at the corresponding interval segment position , A value of 0 indicates Not in the corresponding interval segment position .

[0055] In one specific embodiment of the present invention, it further includes:

[0056] Solving the linearized optimization model yields the power reporting curves for wind storage and wind farms in the electricity market. And the reported frequency regulation capacity of wind storage stations in the secondary frequency regulation ancillary service market. and down-modulation capacity .

[0057] To achieve the above embodiments, a second aspect of the present invention proposes a day-ahead bidding device for a new energy power station configured with hybrid energy storage, comprising:

[0058] The objective function construction module is used to establish the objective function of the wind-storage consortium day-ahead bidding optimization model for wind-storage farms with hybrid energy storage systems. The objective function is to maximize the total operating revenue. The hybrid energy storage system includes lithium battery energy storage and supercapacitor energy storage. The optimization model considers the case of asymmetric bidding for secondary frequency regulation capacity of energy storage resources.

[0059] The constraint construction module is used to establish the constraints of the day-ahead bidding optimization model of the wind-storage consortium.

[0060] The day-ahead bidding module is used to solve the day-ahead bidding optimization model of the wind-storage consortium to obtain the optimized results of the day-ahead declaration volume of the wind-storage stations.

[0061] A third aspect of the present invention provides an electronic device comprising:

[0062] At least one processor; and a memory communicatively connected to said at least one processor;

[0063] The memory stores instructions that can be executed by the at least one processor, and the instructions are configured to execute the day-ahead bidding method for a new energy power station with a hybrid energy storage configuration as described above.

[0064] A fourth aspect of the present invention provides a computer-readable storage medium storing computer instructions for causing the computer to execute the above-described day-ahead bidding method for a new energy power station configured with hybrid energy storage.

[0065] The features and beneficial effects of this invention are as follows:

[0066] This invention additionally considers the relationship between the initial state of charge and frequency regulation performance of supercapacitors in the day-ahead bidding optimization model, and takes into account multiple sources of revenue (grid-connected electricity, secondary frequency regulation ancillary services) and the situation where energy storage resources can be asymmetrically declared for secondary frequency regulation capacity under certain regional rules. It establishes the relationship between the state of charge and frequency regulation performance of energy storage over a long period of time, and models the day-ahead bidding decision model as MILP and solves it.

[0067] This invention can guide wind farms equipped with hybrid energy storage to participate in bidding decisions in both the power market and the secondary frequency regulation ancillary service market under more flexible electricity market rules (allowing them to participate in different types of markets simultaneously and to separately declare the upper and lower frequency regulation capacities for secondary frequency regulation ancillary services), thereby improving the flexibility of wind and energy storage farms. Attached Figure Description

[0068] Figure 1 This is an overall flowchart of a day-ahead bidding method for a new energy power station with hybrid energy storage according to an embodiment of the present invention. Detailed Implementation

[0069] This invention proposes a day-ahead bidding method and apparatus for new energy power stations equipped with hybrid energy storage, which will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0070] A first aspect of this invention proposes a day-ahead bidding method for new energy power stations equipped with hybrid energy storage, comprising:

[0071] For wind-storage power plants equipped with hybrid energy storage systems, an objective function is established for the day-ahead bidding optimization model of the wind-storage consortium, wherein the objective function is to maximize the total operating revenue; wherein the hybrid energy storage system includes lithium battery energy storage and supercapacitor energy storage; the optimization model considers the situation of asymmetric bidding for secondary frequency regulation capacity of energy storage resources;

[0072] Establish the constraints for the day-ahead bidding optimization model of the aforementioned wind-storage consortium;

[0073] The optimization model for day-ahead bidding of the wind and storage consortium is solved to obtain the optimization results of the day-ahead bidding volume of the wind and storage sites.

[0074] In a specific embodiment of the present invention, the overall process of the day-ahead bidding method for a new energy power station configured with hybrid energy storage is as follows: Figure 1 As shown, it includes the following steps:

[0075] 1) Obtain forecasts and market information on the operation of wind power storage stations with hybrid energy storage systems before submitting the bid.

[0076] In this embodiment, various types of data required for day-ahead bidding need to be collected before the bidding process begins. Regarding operational forecasting, the predicted maximum wind power output curve for the second day needs to be obtained. Initial state of charge of the lithium battery energy storage system at 0:00 on the second day and the initial state of charge of the supercapacitor energy storage system Among them, the footer Indicates lithium battery energy storage, subscript This refers to supercapacitor energy storage. Regarding market information, it's necessary to collect market demands for the maximum output capacity of wind power storage stations. Lower limit When the predicted power is lower than the market-required upper limit of the wind storage station's output. When the predicted power is higher than the lower limit of the wind-storage station output, the hybrid energy storage system is allowed to participate in the up-regulation; when the predicted power is higher than the lower limit of the wind-storage station output, the hybrid energy storage system is allowed to participate in the down-regulation.

[0077] 2) Extract day-ahead bidding decision variables for wind power plants equipped with hybrid energy storage systems.

[0078] In this embodiment, wind farms equipped with hybrid energy storage systems simultaneously utilize energy storage resources to participate in secondary frequency regulation, and it is considered that frequency regulation capacity can be applied for simultaneously during the application stage. and down-modulation capacity .

[0079] (1)

[0080] (2)

[0081] in, and These represent the lithium battery energy storage capacity participating in up- and down-frequency regulation, respectively. and These represent the supercapacitor's energy storage capacity participating in up- and down-frequency regulation, respectively. In this embodiment, it is required to declare this during the application process. and During the bidding stage, the wind power curve also needs to be determined. Lithium battery energy storage charging power curve and lithium battery energy storage discharge power curve .

[0082] 3) Based on the results of steps 1) and 2), construct an optimization model for day-ahead bidding by the wind-storage consortium; the specific steps are as follows:

[0083] 3-1) Constructing the objective function of the day-ahead bidding optimization model for wind-storage consortiums

[0084] In this embodiment, the wind-storage consortium consists of a wind farm and an energy storage facility. The objective function of the day-ahead bidding optimization model for the wind-storage consortium is to maximize total operating revenue, which includes three parts: revenue from grid-connected electricity. Participation in frequency modulation revenue Energy storage lifespan depreciation costs The expression is as follows:

[0085] (3)

[0086] in,

[0087] Considering the total grid-connected power of the wind and energy storage consortium, the revenue from grid-connected electricity is calculated using the following formula:

[0088] (4)

[0089] (5)

[0090] In the formula, For the period t, the on-grid electricity price and These represent the charging and discharging power of lithium-ion battery energy storage during time period t, respectively. The charging and discharging power of supercapacitor energy storage during time period t are ignored because the supercapacitor only performs secondary frequency modulation function. The secondary frequency modulation signal is usually a neutral signal. Over a longer period (one day), the total energy released by the supercapacitor is 0. This represents the output power of the wind farm during time period t; The total output power of the wind-storage system during time period t is given.

[0091] Benefits of energy storage participating in frequency regulation It includes two parts: capacity compensation and performance compensation.

[0092] (6)

[0093] In the formula, , These represent the capacity and mileage price for time period t, respectively; m represents the average mileage; the average mileage can be obtained by averaging historical operating data over multiple days. , , , Consideration should be given to allowing asymmetric reporting in the ancillary services market; for market rules requiring symmetrical reporting of frequency regulation capacity, simply add an constraint that the upstream and downstream frequency regulation capacities are equal.

[0094] In this embodiment, the aging cost of the supercapacitor is ignored. The lifespan loss of the lithium battery energy storage system is related to the number of charge-discharge cycles, which is defined by the total charge-discharge capacity. Therefore, the lifespan loss is directly proportional to the charge-discharge capacity of the stored energy. When the energy storage system participates in the frequency regulation market, if the energy storage reports frequency regulation power... In actual operation, energy storage does not continuously maintain maximum charging or discharging power, but rather charges and discharges in response to a constantly changing frequency modulation signal with a mean of zero. Assuming that the cumulative charging or discharging power used within a time period Δt (the length of a single operating period, which in this invention can be set to 15 minutes or 1 hour according to actual electricity market rules) is... The amount of electricity, so that it can be used This represents the ratio of the actual charge / discharge amount of the energy storage system participating in frequency regulation to the reported power within the time interval Δt. Since the ratio of lifetime loss to charge / discharge amount is related to the depth of charge / discharge, and the average depth of charge / discharge differs when the energy storage system reduces wind curtailment and participates in frequency regulation, the lifetime loss cost per unit charge / discharge capacity for lithium battery energy storage under these two applications is respectively... and Then we have:

[0095] (7)

[0096] In the formula, To reduce the lifespan cost of lithium battery energy storage per unit charge / discharge capacity during wind curtailment. Cost of lifespan loss per unit charge / discharge capacity when lithium battery energy storage is used for frequency regulation.

[0097] 3-2) Constraints for constructing the day-ahead bidding optimization model for wind-storage consortiums.

[0098] In this embodiment, the constraints of the optimization model mainly include the power constraints of the wind-storage complex, the energy storage system's power constraints, and the frequency regulation performance constraints of the energy storage system, as detailed below:

[0099] 3-2-1) Power constraints.

[0100] In this embodiment, the power constraints are the maximum wind power output constraint and the total wind and energy storage power limit constraint:

[0101] (8)

[0102] 3-2-2) Lithium battery energy storage charging and discharging power constraints.

[0103] (9)

[0104] In the formula, This represents a 0-1 variable representing the energy storage capacity of a lithium battery during charging in time period t. This represents a 0-1 variable representing the energy storage capacity of a lithium battery during discharge in time period t. This represents a 0-1 variable representing the charging time period t of the supercapacitor energy storage. This represents a 0-1 variable representing the energy stored in the supercapacitor during discharge in time period t. This indicates the rated charging power of the lithium battery energy storage. This indicates the rated discharge power of the lithium battery for energy storage. This indicates the rated charging power of the supercapacitor energy storage. This indicates the rated discharge power of the supercapacitor energy storage. This constraint stipulates that the energy storage system cannot be charged and discharged simultaneously, and the charging and discharging power must not exceed the power limit of the energy storage system.

[0105] 3-2-3) Power constraints when hybrid energy storage participates in frequency regulation.

[0106] This constraint ensures that the total power of the energy storage system does not exceed its power limit:

[0107] (10)

[0108] 3-2-4) Power constraints.

[0109] The state of charge (SOC) changes of lithium-ion battery energy storage cells and supercapacitors are described by the following expression:

[0110] (11)

[0111] (12)

[0112] In the formula, The remaining energy stored in the lithium battery during time period t; This refers to the rated capacity of the lithium battery energy storage. In this embodiment, after one operating cycle, the remaining energy storage capacity should return to its initial state. and These represent the charging and discharging efficiencies of lithium-ion battery energy storage, respectively. The self-discharge rate of lithium-ion batteries for energy storage; and These represent the minimum and maximum states of charge allowed for lithium battery energy storage, respectively. This indicates the state of charge of the lithium battery energy storage during time period t.

[0113] In this embodiment, supercapacitor energy storage is not represented by energy, but by power, which describes the SoC change over a period of time under different up and down frequency modulation capacities.

[0114] 3-2-5) Frequency modulation performance constraints.

[0115] In this embodiment, when entering the frequency regulation period, an initial SOC value that is too high or too low for lithium battery energy storage or supercapacitor energy storage will affect the performance of energy storage in frequency regulation. If the SOC of lithium battery energy storage or supercapacitor energy storage deviates significantly from the median value, the energy storage may be unable to respond to the system's frequency regulation commands due to excessively high or low power levels. This embodiment simplifies the frequency regulation performance of lithium battery energy storage and supercapacitor energy storage as a piecewise function with SOC as the variable. When the lithium battery SOC is too low or too high, the frequency regulation performance score is... In other cases, the score is 1. Similarly, when the supercapacitor's state of charge (SOC) is too low or too high, the frequency modulation performance score is... In other cases, the score is 1. ;

[0116] The following two formulas are used to calculate the performance scores of lithium battery energy storage and supercapacitor energy storage during the frequency regulation period t and all frequency regulation periods, respectively:

[0117] (13)

[0118] (14)

[0119] in, The performance score of lithium battery energy storage during time period t. The supercapacitor's performance score at time interval t; This indicates the percentage by which the SOC deviates from the median value; This represents a function that calculates the average value.

[0120] The overall frequency regulation performance is then a weighted average of the frequency regulation performances of lithium battery energy storage and supercapacitor energy storage:

[0121] (15)

[0122] (16)

[0123] FM performance constraint (16) requires the average score of the performance during the FM period. Greater than a lower limit value This ensures that the energy storage's state of charge is as far away from the boundary as possible when participating in frequency regulation, thereby guaranteeing high availability during the frequency regulation ancillary service period.

[0124] 4) Linearize and solve the optimization model established in step 3). The specific steps are as follows:

[0125] 4-1) Linearize the optimization model established in step 3).

[0126] In this embodiment, the optimization model is a mixed integer programming model, and all constraints except for equations (13)-(14) are linear. Equations (13)-(14) can be transformed into the following linear constraints using the Big M method:

[0127] (17)

[0128] (18)

[0129] (19)

[0130] (20)

[0131] In the formula, , These represent the upper and lower bounds of the state of charge that make the frequency regulation performance of lithium battery energy storage equal to 1. , These represent the upper and lower bounds of the state of charge that make the frequency modulation performance of supercapacitor energy storage equal to 1. For a very large number, the model only needs to ensure that M is simultaneously greater than... maximum value The opposite of the minimum value That's all. 0-1 variables represent The segment position it is in A value of 1 indicates Located at the corresponding interval segment position , A value of 0 indicates Not in the corresponding interval segment position . 0-1 variables represent The segment position it is in A value of 1 indicates Located at the corresponding interval segment position , A value of 0 indicates Not in the corresponding interval segment position .

[0132] Thus, we can obtain a mixed-integer linear programming model for energy storage to reduce wind curtailment and participate in frequency regulation, which can be solved using mature optimization software.

[0133] 4-2) Solve the linearized model from step 4-1) to obtain the power reporting curve of the wind storage station in the electricity market. And the reported frequency regulation capacity of wind storage stations in the secondary frequency regulation ancillary service market. and down-modulation capacity .

[0134] To achieve the above embodiments, a second aspect of the present invention proposes a day-ahead bidding device for a new energy power station configured with hybrid energy storage, comprising:

[0135] The objective function construction module is used to establish the objective function of the wind-storage consortium day-ahead bidding optimization model for wind-storage farms with hybrid energy storage systems. The objective function is to maximize the total operating revenue. The hybrid energy storage system includes lithium battery energy storage and supercapacitor energy storage. The optimization model considers the case of asymmetric bidding for secondary frequency regulation capacity of energy storage resources.

[0136] The constraint construction module is used to establish the constraints of the day-ahead bidding optimization model of the wind-storage consortium.

[0137] The day-ahead bidding module is used to solve the day-ahead bidding optimization model of the wind-storage consortium to obtain the optimized results of the day-ahead declaration volume of the wind-storage stations.

[0138] It should be noted that the aforementioned explanation of an embodiment of a day-ahead bidding method for a new energy power station with hybrid energy storage is also applicable to a day-ahead bidding device for a new energy power station with hybrid energy storage in this embodiment, and will not be repeated here. According to an embodiment of the present invention, a day-ahead bidding device for a new energy power station with hybrid energy storage establishes an objective function for a wind-storage consortium day-ahead bidding optimization model for a wind-storage power station with a hybrid energy storage system. The objective function is to maximize the total operating revenue. The hybrid energy storage system includes lithium battery energy storage and supercapacitor energy storage. The optimization model considers the case of asymmetric bidding for secondary frequency regulation capacity of energy storage resources. Constraints are established for the wind-storage consortium day-ahead bidding optimization model. Solving the wind-storage consortium day-ahead bidding optimization model yields the optimized result of the day-ahead bidding quantity for the wind-storage power station.

[0139] In one specific embodiment of the present invention, it further includes:

[0140] Obtain the predicted maximum wind power output curve for the second day prior to the bidding. Initial state of charge of the lithium battery energy storage system at 0:00 on the second day and the initial state of charge of the supercapacitor energy storage system Maximum output of wind storage and collection station Lower limit Among them, when the predicted power is lower than the upper limit of the wind storage station's output. When the predicted power is higher than the lower limit of the wind-storage power station output, the hybrid energy storage system participates in the up-regulation; when the predicted power is higher than the lower limit of the wind-storage power station output, the hybrid energy storage system participates in the down-regulation.

[0141] In one specific embodiment of the present invention, it further includes:

[0142] The hybrid energy storage system applied for frequency regulation capacity. and down-modulation capacity The calculation expression is as follows:

[0143] (1)

[0144] (2)

[0145] in, and These represent the lithium battery energy storage capacity participating in up- and down-frequency regulation, respectively. and These represent the supercapacitor's energy storage capacity participating in up- and down-frequency regulation, respectively.

[0146] In a specific embodiment of the present invention, the objective function of the day-ahead bidding optimization model of the wind-storage consortium is:

[0147] (3)

[0148] in, For revenue generated from electricity used for internet access, In order to participate in the frequency modulation revenue, Costs related to the lifespan depletion of energy storage;

[0149] (4)

[0150] (5)

[0151] In the formula, For the period t, the on-grid electricity price and These represent the charging power and discharging power of lithium-ion battery energy storage during time period t, respectively. This represents the output power of the wind farm during time period t; The total output power of wind storage during time period t;

[0152] (6)

[0153] In the formula, , Here, represents the capacity and mileage price for time period t, respectively; m represents the average mileage.

[0154] (7)

[0155] In the formula, To reduce the lifespan cost of lithium battery energy storage per unit charge / discharge capacity during wind curtailment. The cost of lifespan loss per unit charge / discharge capacity when lithium battery energy storage participates in frequency regulation, where Δt is the length of a single operating time period.

[0156] In a specific embodiment of the present invention, the constraints of the day-ahead bidding optimization model of the wind-storage consortium include:

[0157] 1) Power constraint;

[0158] (8)

[0159] 2) Lithium battery energy storage charging and discharging power constraints;

[0160] (9)

[0161] In the formula, This represents a 0-1 variable representing the energy storage capacity of a lithium battery during charging in time period t. This represents a 0-1 variable representing the energy storage capacity of a lithium battery during discharge in time period t. This represents a 0-1 variable representing the charging time period t of the supercapacitor energy storage. This represents a 0-1 variable representing the energy stored in a supercapacitor during discharge in time period t. This indicates the rated charging power of the lithium battery energy storage. This indicates the rated discharge power of the lithium battery for energy storage. This indicates the rated charging power of the supercapacitor energy storage. This indicates the rated discharge power of the supercapacitor's energy storage.

[0162] 3) Power constraints when hybrid energy storage participates in frequency regulation;

[0163] (10)

[0164] 4) Power constraints;

[0165] (11)

[0166] (12)

[0167] In the formula, The remaining energy stored in the lithium battery during time period t; The rated capacity for energy storage in lithium batteries; and These represent the charging and discharging efficiencies of lithium-ion battery energy storage, respectively. The self-discharge rate of lithium-ion batteries for energy storage; and These represent the minimum and maximum states of charge allowed for lithium battery energy storage, respectively. This indicates the state of charge of a lithium battery energy storage device during time period t.

[0168] 5) FM performance limitations;

[0169] The frequency regulation performance of lithium battery energy storage and supercapacitor energy storage is simplified into a piecewise function with SOC as the variable; when the lithium battery SOC is too low or too high, the frequency regulation performance score is... In other cases, the score is 1. When the supercapacitor's state of charge (SOC) is too low or too high, the frequency modulation performance score is: In other cases, the score is 1. ;

[0170] (13)

[0171] (14)

[0172] in, The performance score of lithium battery energy storage during time period t. The supercapacitor's performance score at time interval t; This indicates the percentage by which the SOC (State of Charge) of a lithium battery deviates from the median value. This indicates the percentage by which the SOC of a supercapacitor deviates from the median value;

[0173] The overall frequency regulation performance is then a weighted average of the frequency regulation performances of lithium battery energy storage and supercapacitor energy storage:

[0174] (15)

[0175] (16)

[0176] in, The average score for performance during the FM broadcast period. This is the preset lower limit value.

[0177] In one specific embodiment of the present invention, it further includes:

[0178] The optimization model is linearized, wherein equations (13)-(14) are transformed into the following linear constraints by the Big M method:

[0179] (17)

[0180] (18)

[0181] (19)

[0182] (20)

[0183] In the formula, , These represent the upper and lower bounds of the state of charge that make the frequency regulation performance of lithium battery energy storage equal to 1. , These represent the upper and lower bounds of the state of charge that make the frequency modulation performance of supercapacitor energy storage equal to 1; It is a large number; 0-1 variables represent The segment position it is in A value of 1 indicates Located at the corresponding interval segment position , A value of 0 indicates Not in the corresponding interval segment position ; 0-1 variables represent The segment position it is in A value of 1 indicates Located at the corresponding interval segment position , A value of 0 indicates Not in the corresponding interval segment position .

[0184] In one specific embodiment of the present invention, it further includes:

[0185] Solving the linearized optimization model yields the power reporting curves for wind storage and wind farms in the electricity market. And the reported frequency regulation capacity of wind storage stations in the secondary frequency regulation ancillary service market. and down-modulation capacity .

[0186] This can solve the problem of deciding whether new energy power plants equipped with hybrid energy storage can participate in both the electricity market and the secondary frequency regulation market during the current market bidding stage, thus improving the operational flexibility of new energy power plants.

[0187] To implement the above embodiments, a third aspect of the present invention provides an electronic device, comprising:

[0188] At least one processor; and a memory communicatively connected to said at least one processor;

[0189] The memory stores instructions that can be executed by the at least one processor, and the instructions are configured to execute the day-ahead bidding method for a new energy power station with a hybrid energy storage configuration as described above.

[0190] To implement the above embodiments, a fourth aspect of the present invention provides a computer-readable storage medium storing computer instructions for causing the computer to execute the above-described method for bidding on a new energy power station with hybrid energy storage configuration.

[0191] It should be noted that the computer-readable medium described in this disclosure can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this disclosure, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in connection with an instruction execution system, apparatus, or device. In this disclosure, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0192] The aforementioned computer-readable medium may be included in the aforementioned electronic device; or it may exist independently and not assembled into the electronic device. The aforementioned computer-readable medium carries one or more programs, which, when executed by the electronic device, cause the electronic device to perform a day-ahead bidding method for a new energy power station with hybrid energy storage as described in the above embodiments.

[0193] Computer program code for performing the operations of this disclosure can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0194] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0195] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0196] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing a particular logical function or process, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the function involved, as will be understood by those skilled in the art to which embodiments of this application pertain.

[0197] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which programs can be printed, because programs can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0198] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0199] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0200] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0201] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A day-ahead bidding method for a new energy power station equipped with hybrid energy storage, characterized in that, include: For wind-storage power plants equipped with hybrid energy storage systems, an objective function is established for the day-ahead bidding optimization model of the wind-storage consortium, wherein the objective function is to maximize the total operating revenue; wherein the hybrid energy storage system includes lithium battery energy storage and supercapacitor energy storage; the optimization model considers the situation of asymmetric bidding for secondary frequency regulation capacity of energy storage resources; Establish the constraints for the day-ahead bidding optimization model of the aforementioned wind-storage consortium; The optimization model for day-ahead bidding of the wind and storage consortium is solved to obtain the optimization results of the day-ahead bidding volume of the wind and storage sites.

2. The method according to claim 1, characterized in that, Also includes: Obtain the predicted maximum wind power output curve for the second day prior to the bidding. Initial state of charge of the lithium battery energy storage system at 0:00 on the second day and the initial state of charge of the supercapacitor energy storage system Maximum output of wind storage and collection station Lower limit Among them, when the predicted power is lower than the upper limit of the wind storage station's output. When the predicted power is higher than the lower limit of the wind-storage power station output, the hybrid energy storage system participates in the up-regulation; when the predicted power is higher than the lower limit of the wind-storage power station output, the hybrid energy storage system participates in the down-regulation.

3. The method according to claim 2, characterized in that, Also includes: The hybrid energy storage system applied for frequency regulation capacity. and down-modulation capacity The calculation expression is as follows: （1） （2） in, and These represent the lithium battery energy storage capacity participating in up- and down-frequency regulation, respectively. and These represent the supercapacitor's energy storage capacity participating in up- and down-frequency regulation, respectively.

4. The method according to claim 3, characterized in that, The objective function of the day-ahead bidding optimization model for the wind and energy consortium is: （3） in, For revenue generated from electricity used for internet access, In order to participate in the revenue from frequency modulation, Costs related to the lifespan depletion of energy storage; （4） （5） In the formula, For the period t, the on-grid electricity price and These represent the charging power and discharging power of lithium-ion battery energy storage during time period t, respectively. This represents the output power of the wind farm during time period t; The total output power of wind storage during time period t; （6） In the formula, , Here, represents the capacity and mileage price for time period t, respectively; m represents the average mileage. （7） In the formula, To reduce the lifespan cost of lithium battery energy storage per unit charge / discharge capacity during wind curtailment. The cost of lifespan loss per unit charge / discharge capacity when lithium battery energy storage participates in frequency regulation, where Δt is the length of a single operating time period.

5. The method according to claim 4, characterized in that, The constraints of the day-ahead bidding optimization model for the wind-storage consortium include: 1) Power constraint; （8） 2) Lithium battery energy storage charging and discharging power constraints; （9） In the formula, This represents a 0-1 variable representing the energy storage capacity of a lithium battery during charging in time period t. This represents a 0-1 variable representing the energy storage capacity of a lithium battery during discharge in time period t. This represents a 0-1 variable representing the charging time period t of the supercapacitor energy storage. This represents a 0-1 variable representing the energy stored in a supercapacitor during discharge in time period t. This indicates the rated charging power of the lithium battery energy storage. This indicates the rated discharge power of the lithium battery for energy storage. This indicates the rated charging power of the supercapacitor energy storage. This indicates the rated discharge power of the supercapacitor's energy storage. 3) Power constraints when hybrid energy storage participates in frequency regulation; （10） 4) Power constraints; （11） （12） In the formula, The remaining energy stored in the lithium battery during time period t; The rated capacity for energy storage in lithium batteries; and These represent the charging and discharging efficiencies of lithium-ion battery energy storage, respectively. The self-discharge rate of lithium-ion batteries for energy storage; and These represent the minimum and maximum states of charge allowed for lithium battery energy storage, respectively. This indicates the state of charge of a lithium battery energy storage device during time period t. 5) FM performance limitations; The frequency regulation performance of lithium battery energy storage and supercapacitor energy storage is simplified into a piecewise function with SOC as the variable; when the lithium battery SOC is too low or too high, the frequency regulation performance score is... In other cases, the score is 1. When the supercapacitor's state of charge (SOC) is too low or too high, the frequency modulation performance score is: In other cases, the score is 1. ; （13） （14） in, The performance score of lithium battery energy storage during time period t. The supercapacitor's performance score at time t; This indicates the percentage by which the SOC (State of Charge) of a lithium battery deviates from the median value. This indicates the percentage by which the SOC of a supercapacitor deviates from the median value; The overall frequency regulation performance is then a weighted average of the frequency regulation performances of lithium battery energy storage and supercapacitor energy storage: （15） （16） in, The average score for performance during the FM broadcast period. This is the preset lower limit value.

6. The method according to claim 5, characterized in that, Also includes: The optimization model is linearized, wherein equations (13)-(14) are transformed into the following linear constraints by the Big M method: （17） （18） （19） （20） In the formula, , These represent the upper and lower bounds of the state of charge that make the frequency regulation performance of lithium battery energy storage equal to 1. , These represent the upper and lower bounds of the state of charge that make the frequency modulation performance of supercapacitor energy storage equal to 1; It is a large number; 0-1 variables represent The segment position it is in A value of 1 indicates Located at the corresponding interval segment position , A value of 0 indicates Not in the corresponding interval segment position ; 0-1 variables represent The segment position it is in A value of 1 indicates Located at the corresponding interval segment position , A value of 0 indicates Not in the corresponding interval segment position .

7. The method according to claim 6, characterized in that, Also includes: Solving the linearized optimization model yields the power reporting curves for wind storage and wind farms in the electricity market. And the reported frequency regulation capacity of wind storage stations in the secondary frequency regulation ancillary service market. and down-modulation capacity .

8. A day-ahead bidding device for a new energy power station equipped with hybrid energy storage, characterized in that, include: The objective function construction module is used to establish the objective function of the wind-storage consortium day-ahead bidding optimization model for wind-storage farms with hybrid energy storage systems. The objective function is to maximize the total operating revenue. The hybrid energy storage system includes lithium battery energy storage and supercapacitor energy storage. The optimization model considers the case of asymmetric bidding for secondary frequency regulation capacity of energy storage resources. The constraint construction module is used to establish the constraints of the day-ahead bidding optimization model of the wind-storage consortium. The day-ahead bidding module is used to solve the day-ahead bidding optimization model of the wind-storage consortium to obtain the optimized results of the day-ahead declaration volume of the wind-storage stations.

9. An electronic device, characterized in that, include: At least one processor; And, a memory communicatively connected to the at least one processor; The memory stores instructions executable by the at least one processor, the instructions being configured to perform the method described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to perform the method according to any one of claims 1-7.

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