A method and system for day-ahead active power output optimization scheduling of a wind-solar-storage power station

Abstract

The application provides a kind of wind and light storage power station day-to-day active power optimization scheduling method and system, comprising: obtaining target wind power, photovoltaic power station single-day historical output data to obtain wind power, photovoltaic power day-to-day power prediction curve;The wind power, photovoltaic power day-to-day power prediction curve is input into the day-to-day optimization scheduling target model established in advance, respectively with tracking scheduling plan output and smooth output fluctuation as scheduling target, solve wind-light-storage day-to-day optimization scheduling output curve.The method target is clear and distinct, optimization objective function and parameter can be selected and set according to the actual operation of wind-light-storage new energy power station, can meet the demand of wind-light-storage new energy power station application scene, has guiding significance and practical value for new energy engineering.

Description

Technical Field

[0001] This invention belongs to the field of new energy technology, specifically relating to a method and system for optimizing the day-ahead active power output of wind, solar and energy storage power stations. Background Technology

[0002] With the rapid development of power electronics technology, the installed capacity and power generation of new energy power plants, represented by wind and solar power, are increasing in the power grid. However, the randomness, intermittency, and volatility of wind and solar power output pose significant challenges to the safe and stable operation of the power grid. Battery energy storage, with its flexible charging and discharging characteristics, rapid response speed, high technological maturity, and low requirements for engineering site selection, plays a crucial role in complementing wind and solar power generation. Controllable and dispatchable output of wind-solar-storage power plants is a prerequisite for large-scale grid integration of new energy sources and an important guarantee for improving the level of new energy consumption. Its core lies in solving the problems of predicting wind and solar power output and optimizing the charging and discharging scheduling of energy storage. Current technologies for optimizing the scheduling of wind-solar-storage power plants cannot achieve controllable and dispatchable output. Summary of the Invention

[0003] To address the shortcomings of existing technologies, this invention proposes a method and system for optimizing the day-ahead active power output of wind-solar-storage power stations. This method achieves controllable and dispatchable power output, with a clear objective. The optimization objective function and parameters can be selected and set according to the actual operation of the wind-solar-storage new energy power station.

[0004] To achieve the above objectives, the present invention adopts the following technical solution:

[0005] A method for optimizing the day-ahead active power output scheduling of wind, solar, and energy storage power stations includes:

[0006] Obtain the daily historical output data of the target wind power and photovoltaic power station to obtain the day-ahead power prediction curves of wind power and photovoltaic power;

[0007] The day-ahead power prediction curves of wind power and photovoltaic power are input into the pre-established day-ahead optimal scheduling target model. The day-ahead optimal scheduling output curves of wind-solar-storage are solved with the scheduling objectives of tracking the scheduled output and smoothing the output fluctuations, respectively.

[0008] As a further improvement of the present invention, the pre-established day-ahead optimization scheduling target model is constructed based on the battery energy storage operation cost model, the electricity sales transaction model, the power balance constraints and the defined day-ahead optimization scheduling target function to obtain the day-ahead active power output optimization scheduling target model of wind, solar and energy storage power stations based on the mixed integer linear programming model.

[0009] As a further improvement of the present invention, the battery energy storage operating cost model includes the purchase cost of energy storage equipment and the operation and maintenance cost. The purchase cost of energy storage equipment is discounted over the entire life cycle of the equipment to obtain the energy storage operating cost as follows:

[0010]

[0011] Among them, C ES,t K represents the operating cost of the energy storage system during time period t; ES This represents the converted cost of energy storage charging and discharging per unit time step; and These are the discharge power and charging power of the energy stored during time period t, respectively; η dis and η ch These are the discharge efficiency and charging efficiency of energy storage during time period t, respectively, and Δt is the scheduling time step.

[0012] The constraints for energy storage operation are:

[0013]

[0014]

[0015]

[0016] in, and These represent the maximum permissible charging and discharging power of energy storage, respectively; B ES,t It is a binary variable representing the charging and discharging state of energy storage. When B ES,t =1 indicates that the stored energy is in a discharge state during time period t. When B ES,t =0 indicates that the energy storage is in a charging state during time period t; and These are the minimum and maximum allowable energy storage capacities, respectively; E ES,t It is the energy stored in the energy storage during time period t, given the energy stored at time 0, and E ES,t The power at different time periods is expressed as follows:

[0017]

[0018] Among them, E ES,0 This represents the energy stored at time 0.

[0019] As a further improvement of the present invention, the electricity sales transaction model is that the wind-solar-storage renewable energy power station obtains revenue by selling electricity to the grid, and the revenue from electricity sales transactions in each time period is as follows:

[0020]

[0021] Among them, SMG,t This refers to the revenue generated by the power plant from its electricity sales transactions with the upstream power grid during time period t. This refers to the day-ahead transaction price of electricity sold by the power plant to the grid during time period t. These represent the power output of the power plant during time period t; U PV,t U WD,t and U LD,t These represent the curtailed solar power, curtailed wind power, and unmet load power during time period t, respectively. These are the costs of curtailing solar power and the costs of curtailing wind power.

[0022] As a further improvement of the present invention, the power balance constraint is as follows:

[0023]

[0024] 0≤U PV,t ≤P PV,t (Equation 8)

[0025] 0≤U WD,t ≤P WD,t (Equation 9)

[0026] Among them, P PV,t With P WD,t These represent the predicted values ​​of photovoltaic power output and wind power output during time period t, respectively.

[0027] As a further improvement of the present invention, the defined day-ahead optimization scheduling objective is to track the scheduling plan and smooth out power output fluctuations as the optimization scheduling objective of wind-solar-storage new energy power plants;

[0028] The total cost resulting from deviations in the output of the tracking and scheduling plan is:

[0029]

[0030] Among them, P plan,t This represents the planned power output issued by the power grid during time period t. K represents the absolute value of the deviation between the power output and the planned power output of the power plant during time period t. devi The penalty cost per unit power deviation; under the objective of tracking the scheduling plan, the scheduling objective is to minimize the total cost of deviation caused by tracking the scheduling plan output.

[0031] To achieve the goal of smoothing power output fluctuations, the upper-level power grid will assess the changes in active power of the power plants. A moving average algorithm is used to calculate the smoothed power output of the wind-solar-storage renewable energy power plants. The smoothing algorithm for the power output is as follows:

[0032]

[0033] Setting penalty costs for tracking errors:

[0034]

[0035] in, K represents the absolute value of the deviation between the power output of the power plant during time period t and the smoothed power output. smoo The penalty cost per unit power deviation; under the objective of smoothing output fluctuations, the scheduling objective is to minimize the total cost of deviations caused by smoothing output fluctuations.

[0036] As a further improvement of the present invention, the pre-established day-ahead optimization scheduling target model includes:

[0037] Regarding the tracking plan's output targets:

[0038]

[0039] C devi,t The total cost caused by the deviation of the tracking and scheduling plan output is C. ES,t S represents the operating cost of the energy storage system during time period t; MG,t This refers to the revenue generated by the power plant from its electricity sales transactions with the upstream power grid during time period t.

[0040] For the objective of a smooth output curve:

[0041]

[0042] C smoo,t To set a penalty cost for tracking error, C devi,t The total cost caused by the deviation of the tracking and scheduling plan output is C. ES,t S represents the operating cost of the energy storage system during time period t; MG,t This represents the revenue generated by the power plant from its electricity sales transactions with the upstream power grid during time period t.

[0043] As a further improvement of the present invention, the wind-solar-storage day-ahead optimal scheduling output curve is obtained by using the MATLAB cplex mixed integer linear programming solver.

[0044] As a further improvement of the present invention, the data interval of the day-ahead power prediction curves for wind power and photovoltaic power is 15 min / point, and the data span is 1 day.

[0045] A new active power output optimization dispatch system for wind, solar, and energy storage power stations includes:

[0046] The acquisition module is used to obtain the daily historical output data of the target wind power and photovoltaic power station to obtain the day-ahead power prediction curve of wind power and photovoltaic power.

[0047] The solution module is used to input the day-ahead power prediction curves of wind power and photovoltaic power into the pre-established day-ahead optimal scheduling target model, and solve the day-ahead optimal scheduling output curves of wind-solar-storage with the scheduling objectives of tracking the scheduled output and smoothing the output fluctuations, respectively.

[0048] Compared with the prior art, the present invention has the following advantages:

[0049] This invention takes a wind-solar-storage renewable energy power plant as the research scenario, comprehensively considering the daily scheduling and operation needs of such power plants. With tracking the scheduling plan and smoothing output fluctuations as control objectives, a day-ahead optimized scheduling target model is obtained. The scheduling method only requires obtaining the daily historical output data of the target wind and solar power plants and inputting it into the pre-established day-ahead optimized scheduling target model. The day-ahead optimized scheduling output curve of the wind-solar-storage power plant is solved, with tracking the planned output and smoothing output fluctuations as the scheduling objectives respectively. This method has clear and explicit objectives, and the optimization objective function and parameters can be selected and set according to the actual operation of the wind-solar-storage renewable energy power plant. It can meet the application scenario requirements of wind-solar-storage renewable energy power plants and has guiding significance and practical value for renewable energy engineering.

[0050] Furthermore, this method takes the 15-minute wind and solar day-ahead power prediction curves as input, and calculates the economically optimal dispatch plan for the day-ahead active power output of wind-solar-storage new energy power plants by introducing economic indicators such as curtailment / wind curtailment penalties, power fluctuation penalties, output deviation penalties, and energy storage dispatch costs, based on the station operation control objectives. Attached Figure Description

[0051] Figure 1 This is a flowchart of the day-ahead active power output optimization scheduling calculation for wind, solar and energy storage power stations according to the present invention.

[0052] Figure 2 This is the wind and solar day-ahead power prediction curve of this invention;

[0053] Figure 3 To adjust the planned power and actual grid-connected power curves of this invention;

[0054] Figure 4 The energy storage charge and discharge power curves for each time period of this invention are shown below.

[0055] Figure 5 This invention aims to smooth the power output versus actual grid-connected power curve.

[0056] Figure 6 The energy storage charge and discharge power curves for each time period of this invention are shown below.

[0057] Figure 7 This invention relates to a method and system for optimizing the day-ahead active power output scheduling of wind, solar and energy storage power stations. Detailed Implementation

[0058] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0059] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0060] This invention provides a method for optimizing the day-ahead active power output scheduling of wind, solar, and energy storage power stations, comprising:

[0061] Obtain the daily historical output data of the target wind power and photovoltaic power station to obtain the day-ahead power prediction curves of wind power and photovoltaic power;

[0062] The day-ahead power prediction curves of wind power and photovoltaic power are input into the pre-established day-ahead optimal scheduling target model. The day-ahead optimal scheduling output curves of wind-solar-storage are solved with the scheduling objectives of tracking the scheduled output and smoothing the output fluctuations, respectively.

[0063] This invention takes a wind-solar-storage renewable energy power plant as the research scenario, comprehensively considers the daily scheduling and operation requirements of the wind-solar-storage renewable energy power plant, and proposes a day-ahead active power output optimization scheduling method based on a mixed integer linear programming (MILP) model with the control objectives of tracking the scheduling plan and smoothing power output fluctuations.

[0064] As an optional solution, the pre-established day-ahead optimization scheduling target model is constructed based on the battery energy storage operation cost model, the electricity sales transaction model, the power balance constraints, and the defined day-ahead optimization scheduling objective function to obtain the day-ahead active power output optimization scheduling target model of wind, solar and energy storage power stations based on a mixed integer linear programming model.

[0065] As an optional approach, the wind-solar-storage day-ahead optimal scheduling output curve is obtained by using the MATLAB cplex mixed integer linear programming solver.

[0066] Example

[0067] The calculation process of this invention is as follows: Figure 1 A method for optimizing the day-ahead active power output scheduling of wind, solar, and energy storage power stations based on a mixed-integer linear programming model includes the following steps:

[0068] 1) Establish a day-ahead optimal scheduling model

[0069] Establish a battery energy storage operating cost model. The operating cost of energy storage mainly includes the purchase cost of the energy storage equipment and the operation and maintenance cost. The energy storage operating cost is obtained by discounting the purchase cost of the energy storage equipment over its entire life cycle:

[0070]

[0071] Among them, C ES,t K represents the operating cost of the energy storage system during time period t; ES This represents the converted cost of energy storage charging and discharging per unit time step; and These are the discharge power and charging power of the energy stored during time period t, respectively; η dis and η ch These represent the discharge efficiency and charging efficiency of the energy storage during time period t, respectively. Δt is the scheduling time step, set to 1 hour. To limit the charging and discharging power and prevent overcharging and over-discharging of the energy storage, the energy storage operation needs to meet the following constraints:

[0072]

[0073]

[0074]

[0075] in, and These represent the maximum permissible charging and discharging power of energy storage, respectively; B ES,t It is a binary variable representing the charging and discharging state of energy storage. When B ES,t =1 indicates that the stored energy is in a discharge state during time period t. When B ES,t =0 indicates that the energy storage is in a charging state during time period t; and These are the minimum and maximum allowable energy storage capacities, respectively; E ES,t It is the energy stored during time period t. The energy stored at time 0 can be given, and E is... ES,t The power at different time periods is expressed as follows:

[0076]

[0077] Among them, E ES,0 This represents the energy stored at time 0.

[0078] Establish an electricity sales trading model. Wind-solar-storage renewable energy power plants generate revenue by selling electricity to the grid. The revenue from electricity sales transactions at different times can be expressed as:

[0079]

[0080] Among them, S MG,t This refers to the revenue generated by the power plant from its electricity sales transactions with the upstream power grid during time period t. This refers to the day-ahead transaction price of electricity sold by the power plant to the grid during time period t. These represent the power output of the power plant during time period t; U PV,t U WD,t and U LD,t These represent the curtailed solar power, curtailed wind power, and unmet load power during time period t, respectively. These are the costs of curtailing solar power and wind power, respectively. The power output within the site must meet power balance constraints:

[0081]

[0082] 0≤U PV,t ≤P PV,t (Equation 8)

[0083] 0≤U WD,t ≤P WD,t (Equation 9)

[0084] Among them, P PV,t With P WD,t These represent the predicted values ​​of photovoltaic power output and wind power output during time period t, respectively.

[0085] 2) Define the day-ahead optimization scheduling objective

[0086] This invention takes tracking scheduling plans and smoothing output fluctuations as the optimization scheduling objectives of wind-solar-storage new energy power plants.

[0087] For tracking the dispatch plan target, when the upper-level power grid issues planned output curves to the power stations based on the power generation capacity of wind, solar, and energy storage power stations and the grid status, the power stations optimize economic benefits by using their internal energy storage systems or by rationally curtailing wind / solar power. Therefore, the total cost caused by the deviation from the tracking dispatch plan output is:

[0088]

[0089] Among them, P plan,tThis represents the planned power output issued by the power grid during time period t. K represents the absolute value of the deviation between the power output and the planned power output of the power plant during time period t. devi This represents the penalty cost per unit power deviation. Under the objective of tracking the scheduling plan, the scheduling goal is to minimize the total cost of deviation caused by tracking the scheduled output.

[0090] To smooth out power output fluctuations, the upper-level power grid will assess the changes in active power of the power plants. A moving average algorithm is used to calculate the smoothed power output of wind-solar-storage renewable energy power plants. Taking a scheduling step size Δt = 1 hour and a scheduling period T = 24 hours as an example, the power output smoothing algorithm is as follows:

[0091]

[0092] To enable wind-solar-storage renewable energy power plants to track the smoothed power output, a penalty cost is set for tracking errors:

[0093]

[0094] in, K represents the absolute value of the deviation between the power output of the power plant during time period t and the smoothed power output. smoo This represents the penalty cost per unit power deviation. Under the objective of smoothing output fluctuations, the scheduling objective is to minimize the total cost of deviations caused by smoothing output fluctuations.

[0095] 3) Establish the day-ahead optimization scheduling objective function

[0096] Based on the battery energy storage operating cost model, electricity sales transaction model, and power balance constraints established in step 1), and the day-ahead optimization scheduling objective defined in step 2), a mixed-integer linear programming model for day-ahead optimization scheduling of power plants is established. Its optimization objective is to maximize operating revenue. For tracking planned output targets:

[0097]

[0098] S MG,t C ES,t C devi,t As shown in Equation 6, Equation 1, and Equation 10 respectively.

[0099] For the objective of a smooth output curve:

[0100]

[0101] S MG,t C ES,t and C smoo,t As shown in Equations (6), (1), and (12) respectively.

[0102] 4) Solve for the day-ahead optimal scheduling output curve of wind-solar-storage

[0103] Based on the day-ahead optimal scheduling hybrid integer linear programming model for wind-solar-storage renewable energy power plants established in step 3), the day-ahead power prediction curves (15 min / point) of the wind and solar power prediction systems are used as inputs, and parameters such as energy storage capacity, power, state of charge, and charge / discharge efficiency are used as constraints. Economic parameters such as curtailment cost, wind curtailment cost, energy storage charge / discharge operation cost, and power deviation penalty cost are set according to the local electricity market operation conditions of the wind-solar-storage renewable energy power plants.

[0104] The MATLAB cplex mixed-integer linear programming solver was used to calculate and solve the wind-solar-storage day-ahead optimal scheduling output curve (15 min / point).

[0105] Simulation Example

[0106] The following describes the invention in further detail using a wind-solar-storage new energy power station with a wind power capacity of 400MW, a photovoltaic capacity of 200MW, and an energy storage capacity of 100MW / 100MWh as the research scenario: (The relevant parameter values ​​in the following examples are for illustrative purposes only and can be adjusted according to the actual operation of the power station)

[0107] 1) Establish a day-ahead optimal scheduling model

[0108] Using the battery energy storage operating cost model (Equation 1) established in step 1), substituting the relevant calculation parameters, and assuming the energy storage discharge efficiency η dis and charging efficiency η ch Take 0.92, energy storage charging and discharging cost K ES Taking 0.15 yuan / kWh, the battery energy storage operating cost model is as follows:

[0109]

[0110] Using the electricity sales transaction model (Equation 6) established in step 1), substituting the relevant calculation parameters, and assuming the day-ahead electricity sales transaction price... Taking 0.55 yuan / kWh as the cost of curtailed solar power. Taking 0.25 yuan / kWh as the cost of wind curtailment Taking 0.25 yuan / kWh, the electricity sales transaction model is as follows:

[0111]

[0112] 2) Define the day-ahead optimization scheduling objective

[0113] Using the power scheduling target of the tracking scheduling plan established in step 2) (Equation 10), substituting the relevant calculation parameters, and assuming the penalty cost K per unit power deviation... deviTaking 0.5 yuan / kWh, the total cost caused by the deviation in the power output of the tracking and scheduling plan is as follows:

[0114]

[0115] Using the smooth power output fluctuation scheduling target established in step 2) (Equation 12), substituting the relevant calculation parameters, and assuming the penalty cost K per unit power deviation... smoo Taking 0.3 yuan / kWh, the total cost caused by the output deviation of the tracking scheduling plan is as follows:

[0116]

[0117] 3) Establish the day-ahead optimization scheduling objective function

[0118] Substitute the above formulas into the day-ahead optimization scheduling mixed integer linear programming model (Equations 13 and 14).

[0119] 4) Solve for the day-ahead optimal scheduling output curve of wind-solar-storage

[0120] The collected historical daily output data of a wind farm and a photovoltaic power station are used as the day-ahead power prediction curves for wind and photovoltaic power required in step 4). The data interval is 15 minutes / point, and the data span is 1 day (96 data points). With the scheduling objectives of tracking the planned output and smoothing the output fluctuations, the day-ahead optimized scheduling output curves of wind-solar-storage are calculated and given. The scheduling plan curve is a simulated scheduling plan command issued manually, and one total planned output value is given every 15 minutes, for a total of 96 planned values ​​per day.

[0121] When the operating target of the wind-solar-storage new energy power station is to track the output of the dispatch plan, the mixed integer linear programming model is used to solve Equation 13. The resulting wind-solar-storage day-ahead optimized dispatch output curve is as follows. By controlling the energy storage charging and discharging power in each time period, the total grid-connected power of the power station is very close to the issued power plan curve.

[0122] When the operating objective of a wind-solar-storage new energy power station is to smooth out power output fluctuations, the mixed integer linear programming model is used to solve Equation 14, and the resulting wind-solar-storage day-ahead optimized scheduling power output curve is as follows. By controlling the energy storage charging and discharging power in each time period, the total grid-connected power of the power station is very close to the planned smooth power output curve.

[0123] As can be seen from the implementation cases, this invention comprehensively considers the daily scheduling and operation needs of wind-solar-storage new energy power plants, and takes tracking the scheduling plan and smoothing the power output fluctuation as the control objectives. It proposes a day-ahead active power output optimization scheduling method for wind-solar-storage new energy power plants based on the mixed integer linear programming (MILP) model. This method can achieve the expected effect of tracking the planned power output and smoothing the power curve, and improve the revenue of wind-solar-storage new energy power plants.

[0124] like Figure 7 As shown, the present invention also provides a day-ahead active power output optimization scheduling system for wind, solar and energy storage power stations, comprising:

[0125] The acquisition module is used to obtain the daily historical output data of the target wind power and photovoltaic power station to obtain the day-ahead power prediction curve of wind power and photovoltaic power.

[0126] The solution module is used to input the day-ahead power prediction curves of wind power and photovoltaic power into the pre-established day-ahead optimal scheduling target model, and solve the day-ahead optimal scheduling output curves of wind-solar-storage with the scheduling objectives of tracking the scheduled output and smoothing the output fluctuations, respectively.

[0127] The present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the day-ahead active power output optimization scheduling method of the wind, solar and energy storage power station.

[0128] The optimized scheduling method for the day-ahead active power output of the wind, solar and energy storage power stations includes the following steps:

[0129] Obtain the daily historical output data of the target wind power and photovoltaic power station to obtain the day-ahead power prediction curves of wind power and photovoltaic power;

[0130] The day-ahead power prediction curves of wind power and photovoltaic power are input into the pre-established day-ahead optimal scheduling target model. The day-ahead optimal scheduling output curves of wind-solar-storage are solved with the scheduling objectives of tracking the scheduled output and smoothing the output fluctuations, respectively.

[0131] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the day-ahead active power output optimization scheduling method for the wind, solar and energy storage power station.

[0132] The optimized scheduling method for the day-ahead active power output of the wind, solar and energy storage power stations includes the following steps:

[0133] Obtain the daily historical output data of the target wind power and photovoltaic power station to obtain the day-ahead power prediction curves of wind power and photovoltaic power;

[0134] The day-ahead power prediction curves of wind power and photovoltaic power are input into the pre-established day-ahead optimal scheduling target model. The day-ahead optimal scheduling output curves of wind-solar-storage are solved with the scheduling objectives of tracking the scheduled output and smoothing the output fluctuations, respectively.

[0135] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0136] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0137] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0138] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0139] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for optimizing the day-ahead active power output scheduling of wind, solar, and energy storage power stations, characterized in that, include: Obtain the daily historical output data of the target wind power and photovoltaic power station to obtain the day-ahead power prediction curves of wind power and photovoltaic power; The day-ahead power prediction curves of wind power and photovoltaic power are input into the pre-established day-ahead optimal scheduling target model. The day-ahead optimal scheduling output curves of wind-solar-storage are solved with the scheduling objectives of tracking the scheduled output and smoothing the output fluctuations, respectively. The pre-established day-ahead optimization scheduling target model is constructed based on the battery energy storage operation cost model, electricity sales transaction model, power balance constraints and the defined day-ahead optimization scheduling target function, resulting in a day-ahead active power output optimization scheduling target model for wind, solar and energy storage power stations based on a mixed integer linear programming model. The battery energy storage operating cost model includes the purchase cost of energy storage equipment and the operation and maintenance cost. The purchase cost of the energy storage equipment is discounted over the entire life cycle of the equipment to obtain the energy storage operating cost: (Equation 1) in, Indicates that the energy storage system is in Operating costs for a given period; This represents the converted cost of energy storage charging and discharging per unit time step; and They are Discharge power and charging power of time-limited energy storage; and They are Discharge efficiency and charging efficiency of time-limited energy storage. It is the scheduling time step; The constraints for energy storage operation are: (Equation 2) (Equation 3) (Equation 4) in, and These represent the maximum allowable charging and discharging power of the energy storage, respectively. It is a binary variable representing the charging and discharging state of energy storage. Time indicates During the period of energy storage, the energy is in a discharge state, when Time indicates During the period, energy storage is in a charging state; and These are the minimum and maximum allowable energy storage capacities, respectively. It is energy storage The energy stored during a given time period, the energy stored at time 0, and the energy stored at time 0. The power at different time periods is expressed as follows: (Equation 5) in, This represents the energy stored at time 0.

2. The method for optimizing the day-ahead active power output scheduling of a wind-solar-storage power station according to claim 1, characterized in that, The electricity sales transaction model described above involves wind-solar-storage renewable energy power plants generating revenue by selling electricity to the grid. The revenue from electricity sales transactions in different time periods is as follows: (Equation 6) in, For the power station Revenue from electricity sales transactions with the superior power grid during specific time periods; For the power station The day-ahead trading price for electricity sold to the grid during a specific time period; The power station is located at Electricity sales volume during a given time period; , Don't mean Curtailed solar power and wind power during specific time periods; , These are the costs of curtailing solar power and the costs of curtailing wind power.

3. The method for optimizing the day-ahead active power output scheduling of a wind-solar-storage power station according to claim 2, characterized in that, The power balance constraint: (Equation 7) (Equation 8) (Equation 9) in, and They represent Forecast values ​​of photovoltaic and wind power output for the specified time period.

4. The method for optimizing the day-ahead active power output scheduling of a wind-solar-storage power station according to claim 2, characterized in that, The defined day-ahead optimization scheduling objective is to track the scheduling plan and smooth out output fluctuations as the optimization scheduling objective for wind-solar-storage new energy power plants. The total cost resulting from deviations in the output of the tracking and scheduling plan is: (Equation 10) in, express The planned power output issued by the power grid during the specified time period. For power station The absolute value of the deviation between the output during a given time period and the planned output. The penalty cost per unit power deviation; under the objective of tracking the scheduling plan, the scheduling objective is to minimize the total cost of deviation caused by tracking the scheduling plan output. To achieve the goal of smoothing power output fluctuations, the upper-level power grid will assess the changes in active power of the power plants. A moving average algorithm is used to calculate the smoothed power output of the wind-solar-storage renewable energy power plants. The smoothing algorithm for the power output is as follows: (Equation 11) Setting penalty costs for tracking errors: (Equation 12) in, For power station The absolute value of the deviation between the output during the time period and the smoothed power. The penalty cost per unit power deviation; under the objective of smoothing output fluctuations, the scheduling objective is to minimize the total cost of deviations caused by smoothing output fluctuations.

5. The day-ahead active power output optimization scheduling method for a wind-solar-storage power station according to claim 1, characterized in that, The pre-established day-ahead optimization scheduling objective model includes: Regarding the tracking plan's output targets: (Equation 13) The total cost resulting from deviations in the tracking and scheduling plan output is, Indicates that the energy storage system is in Operating costs for a given period; For the power station Revenue from electricity sales transactions with the superior power grid during specific time periods; For the objective of a smooth output curve: (Equation 14) Set a penalty cost for tracking errors. The total cost resulting from deviations in the tracking and scheduling plan output is, Indicates that the energy storage system is in Operating costs for a given period; For the power station Revenue from electricity sales transactions with the superior power grid during specific time periods.

6. The method for optimizing the day-ahead active power output scheduling of a wind-solar-storage power station according to claim 1, characterized in that, The optimal power output curve for wind-solar-storage day-ahead scheduling was obtained by using the MATLAB cplex mixed integer linear programming solver.

7. The day-ahead active power output optimization scheduling method for a wind-solar-storage power station according to claim 1, characterized in that, The data interval for the day-ahead power forecast curves of wind power and photovoltaic power is 15 minutes / point, and the data span is 1 day.

8. A day-ahead active power output optimization scheduling system for a wind-solar-storage power station, comprising a day-ahead active power output optimization scheduling method for a wind-solar-storage power station according to any one of claims 1 to 7, characterized in that, include: The acquisition module is used to obtain the daily historical output data of the target wind power and photovoltaic power station to obtain the day-ahead power prediction curve of wind power and photovoltaic power. The solution module is used to input the day-ahead power prediction curves of wind power and photovoltaic power into the pre-established day-ahead optimal scheduling target model, and solve the day-ahead optimal scheduling output curves of wind-solar-storage with the scheduling objectives of tracking the scheduled output and smoothing the output fluctuations, respectively.

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