CN115313378A - Day-ahead active output optimal scheduling method and system for wind-solar power storage power station - Google Patents

Day-ahead active output optimal scheduling method and system for wind-solar power storage power station Download PDF

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CN115313378A
CN115313378A CN202211037421.2A CN202211037421A CN115313378A CN 115313378 A CN115313378 A CN 115313378A CN 202211037421 A CN202211037421 A CN 202211037421A CN 115313378 A CN115313378 A CN 115313378A
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wind
day
output
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魏远
张欢畅
刘世友
高华
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Northwest Electric Power Design Institute of China Power Engineering Consulting Group
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/004—Generation forecast, e.g. methods or systems for forecasting future energy generation
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/008—Circuit arrangements for ac mains or ac distribution networks involving trading of energy or energy transmission rights
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/24—Arrangements for preventing or reducing oscillations of power in networks
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • H02J3/466—Scheduling the operation of the generators, e.g. connecting or disconnecting generators to meet a given demand
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • H02J3/48—Controlling the sharing of the in-phase component
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/22—The renewable source being solar energy
    • H02J2300/24—The renewable source being solar energy of photovoltaic origin
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/28—The renewable source being wind energy

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Abstract

The invention provides a day-ahead active power output optimal scheduling method and system for a wind-solar power storage station, which comprises the following steps: acquiring single-day historical output data of a target wind power and photovoltaic power station to obtain a power prediction curve of the wind power and photovoltaic power day ahead; and inputting the wind power and photovoltaic power day-ahead power prediction curves into a pre-established day-ahead optimized dispatching target model, respectively taking the tracked dispatching planned output and the smooth output fluctuation as dispatching targets, and solving the wind-light-storage day-ahead optimized dispatching output curve. The method has clear targets, the optimization objective function and parameters can be selected and set according to the actual operation condition of the wind-light-storage new energy power station, the application scene requirements of the wind-light-storage new energy power station can be met, and the method has guiding significance and practical value for new energy engineering.

Description

Day-ahead active output optimal scheduling method and system for wind-solar power storage power station
Technical Field
The invention belongs to the technical field of new energy, and particularly relates to a day-ahead active output optimal scheduling method and system for a wind and light storage power station.
Background
With the rapid development of power electronic technology, new energy power stations represented by wind power and photovoltaic have higher installation proportion in a power grid, and the power generation capacity proportion is larger. Wind power and photovoltaic output have the characteristics of randomness, intermittence, volatility and the like, and a plurality of challenges are brought to the safe and stable operation of a power grid. The battery energy storage has important functions in the fields of matching wind power and photovoltaic power generation by virtue of flexible charge and discharge characteristics, quick response speed, higher technical maturity and low requirements on engineering construction site selection. The controllable and adjustable output of the wind-light-storage power station is a premise that large-scale new energy is connected into a power grid and is an important guarantee for improving the consumption level of the new energy, and the method is mainly used for solving the problems of output prediction of wind power and photovoltaic power generation and charge and discharge optimization scheduling of stored energy. In the prior art, the problems that controllable and schedulable output can not be realized in optimized scheduling of the wind-solar power storage station exist.
Disclosure of Invention
The invention provides a method and a system for optimizing and scheduling the day-ahead active power output of a wind-light-storage power station, aiming at solving the problems of controllable and schedulable output, and the method has clear target, and the optimization objective function and parameters can be selected and set according to the actual operation condition of the wind-light-storage new energy power station.
In order to achieve the purpose, the invention adopts the following technical scheme:
a method for optimizing and scheduling day-ahead active output of a wind-solar power storage station comprises the following steps:
acquiring single-day historical output data of a target wind power and photovoltaic power station to obtain a wind power and photovoltaic power day-ahead power prediction curve;
and inputting the day-ahead power prediction curves of the wind power and the photovoltaic power into a pre-established day-ahead optimization scheduling target model, respectively taking tracking scheduling planned output and smooth output fluctuation as scheduling targets, and solving a wind-light-storage day-ahead optimization scheduling output curve.
As a further improvement of the invention, the pre-established day-ahead optimized scheduling objective model is a day-ahead active power output optimized scheduling objective model of the wind-solar energy storage power station based on a mixed integer linear programming model, which is constructed according to a battery energy storage operation cost model, a power selling trading model, power balance constraints and a defined day-ahead optimized scheduling objective function.
As a further improvement of the present invention, the battery energy storage operation cost model includes an energy storage device acquisition cost and an operation maintenance cost, and the energy storage device acquisition cost is converted in the device life cycle, and the obtained energy storage operation cost is:
Figure BDA0003815911900000021
wherein, C ES,t Representing the operating cost of the energy storage system in the period t; k ES The converted energy storage charging and discharging cost per unit time step length;
Figure BDA0003815911900000022
and
Figure BDA0003815911900000023
the discharge power and the charging power stored in the t time period are respectively; eta dis And η ch The energy storage efficiency and the charging efficiency in the t time period are respectively, and delta t is the scheduling time step;
the energy storage operation constraint conditions are as follows:
Figure BDA0003815911900000024
Figure BDA0003815911900000025
Figure BDA0003815911900000026
wherein, the first and the second end of the pipe are connected with each other,
Figure BDA0003815911900000027
and
Figure BDA0003815911900000028
respectively representing the maximum allowable charging and discharging power of stored energy; b is ES,t Is a binary variable representing the charge-discharge state of the stored energy, when B ES,t When the time is not less than 1, the energy is stored in a discharge state in the time period of t, and when B is in the discharge state ES,t When =0, the energy is stored in the charging state in the t period;
Figure BDA0003815911900000031
and
Figure BDA0003815911900000032
respectively the minimum electric quantity and the maximum electric quantity allowed by energy storage; e ES,t Is the energy stored during the stored energy t period, gives the stored energy at time 0 of the stored energy, and gives E ES,t The power in each period is expressed as:
Figure BDA0003815911900000033
wherein, E ES,0 Representing the stored energy at time 0 of the stored energy.
As a further improvement of the invention, the electricity selling transaction model is that the wind-light-storage new energy power station obtains the income by selling electricity to the power grid, and the income of electricity selling transaction in each time period is as follows:
Figure BDA0003815911900000034
wherein S is MG,t The income of the power station and the power selling transaction of the superior power grid in the time period t;
Figure BDA0003815911900000035
trading the electricity price for the power station at the moment t to sell electricity to the power grid;
Figure BDA0003815911900000036
respectively selling power of the power station in the time period t; u shape PV,t 、U WD,t And U LD,t Respectively representing the abandoned light power, the abandoned wind power and the unsatisfied load power in the t period;
Figure BDA0003815911900000037
the cost of light abandonment and the cost of wind abandonment are respectively.
As a further improvement of the invention, the power balance constraint:
Figure BDA0003815911900000038
0≤U PV,t ≤P PV,t (formula 8)
0≤U WD,t ≤P WD,t (formula 9)
Wherein, P PV,t And P WD,t And respectively representing the predicted values of the photovoltaic output and the wind power output in the t period.
As a further improvement of the invention, the defined day-ahead optimized scheduling target is used for tracking a scheduling plan and smoothing output fluctuation as an optimized scheduling target of the wind-light-storage new energy power station;
the total cost caused by tracking dispatch plan outages is:
Figure BDA0003815911900000039
wherein, P plan,t Represents the planned output issued by the power grid in the period t,
Figure BDA0003815911900000041
the absolute value, K, of the deviation between the power station's t-time output and the planned output devi Penalty cost per power deviation; under the condition of tracking a scheduling plan target, the lowest deviation total cost caused by the output of the tracking scheduling plan is taken as the scheduling target;
for a smooth output fluctuation target, the upper-level power grid evaluates the active power variation of the power station, and the output of the wind-light-storage new energy power station after smoothing is calculated by adopting a moving average algorithm, wherein the smooth algorithm for obtaining the output is as follows:
Figure BDA0003815911900000042
the penalty cost is set for the tracking error:
Figure BDA0003815911900000043
wherein, the first and the second end of the pipe are connected with each other,
Figure BDA0003815911900000044
the absolute value, K, of the deviation between the output and the smoothed power at the time t of the power station smoo Penalty cost per power deviation; and under the smooth output fluctuation target, taking the lowest deviation total cost caused by smooth output fluctuation as a scheduling target.
As a further improvement of the invention, the pre-established day-ahead optimization scheduling objective model comprises:
for tracking planned contribution targets:
Figure BDA0003815911900000045
C devi,t the total cost due to tracking dispatch plan outages is C ES,t Representing the operating cost of the energy storage system in the period t; s MG,t The income of the power station and the power selling transaction of the superior power grid in the time period t;
for the smoothed output curve target:
Figure BDA0003815911900000046
C smoo,t a penalty cost is set for the tracking error,C devi,t the total cost due to tracking dispatch plan outtake bias is C ES,t Representing the operating cost of the energy storage system in the period t; s MG,t And (4) the income of the power station and the power selling transaction of the superior power grid in the time period t.
As a further improvement of the invention, the wind-light-storage day-ahead optimized scheduling output curve is obtained by calculating by using an MATLAB cplex mixed integer linear programming solver.
As a further improvement of the method, the data interval of the day-ahead power prediction curves of the wind power and the photovoltaic power is 15 min/point, and the data span is 1 day.
A day-ahead active output optimal scheduling system of a wind-solar storage power station comprises:
the acquisition module is used for acquiring single-day historical output data of a target wind power and photovoltaic power station to obtain a wind power and photovoltaic power day-ahead power prediction curve;
and the solving module is used for inputting the wind power and photovoltaic power day-ahead power prediction curves into a pre-established day-ahead optimization scheduling target model, respectively taking the tracking scheduling planned output and the smooth output fluctuation as scheduling targets, and solving the wind-light-storage day-ahead optimization scheduling output curve.
Compared with the prior art, the invention has the following advantages:
the method takes a power supply side wind-light-storage new energy power station as a research scene, comprehensively considers the daily scheduling operation requirements of the wind-light-storage new energy power station, takes a tracking scheduling plan and smooth output fluctuation as control targets to obtain a day-ahead optimized scheduling target model, and the scheduling method only needs to obtain single-day historical output data of the target wind power station and the photovoltaic power station, inputs the single-day historical output data into the pre-established day-ahead optimized scheduling target model, and respectively takes the tracking scheduling plan output and the smooth output fluctuation as scheduling targets to solve a wind-light-storage day-ahead optimized scheduling output curve. The method has clear targets, the optimization objective function and parameters can be selected and set according to the actual operation condition of the wind-light-storage new energy power station, the application scene requirements of the wind-light-storage new energy power station can be met, and the method has guiding significance and practical value for new energy engineering.
Further, the method uses 15min wind and light pre-day power prediction curves as input, and calculates and provides a 15min wind-light-storage new energy power station pre-day active power output economic optimal scheduling plan according to a station operation control target by introducing economic indexes such as light abandon/wind abandon punishment, power fluctuation punishment, output deviation punishment, energy storage scheduling cost and the like.
Drawings
FIG. 1 is a flow chart of the day-ahead active power output optimization scheduling calculation of a wind-solar power storage station;
FIG. 2 is a power prediction curve before wind and light days according to the present invention;
FIG. 3 is a plot of measured power versus actual on-line power for the present invention;
FIG. 4 is a graph of energy storage charging and discharging power at each time interval according to the present invention;
FIG. 5 is a graph of planned smooth output versus actual on-grid power for the present invention;
FIG. 6 is a graph of energy storage charge and discharge power at each time interval according to the present invention;
FIG. 7 shows a day-ahead active power output optimization scheduling method and system for a wind-solar energy storage power station.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Moreover, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
The invention provides a day-ahead active power output optimal scheduling method for a wind and light storage power station, which comprises the following steps:
acquiring single-day historical output data of a target wind power and photovoltaic power station to obtain a power prediction curve of the wind power and photovoltaic power day ahead;
and inputting the wind power and photovoltaic power day-ahead power prediction curves into a pre-established day-ahead optimized dispatching target model, respectively taking the tracked dispatching planned output and the smooth output fluctuation as dispatching targets, and solving the wind-light-storage day-ahead optimized dispatching output curve.
The invention provides a method for optimizing and scheduling the day-ahead active power output of a wind-light-storage new energy power station based on a Mixed Integer Linear Programming (MILP) model, which takes a power supply side wind-light-storage new energy power station as a research scene, comprehensively considers the daily scheduling operation requirements of the wind-light-storage new energy power station, and takes a tracking scheduling plan and smooth output fluctuation as control targets.
As an alternative scheme, the pre-established day-ahead optimized scheduling objective model is a day-ahead active power output optimized scheduling objective model of the wind and light storage power station based on a mixed integer linear programming model, which is established according to a battery energy storage operation cost model, a power selling transaction model, a power balance constraint and a defined day-ahead optimized scheduling objective function.
As an alternative, the wind-light-storage day-ahead optimized scheduling output curve is obtained by calculating by adopting an MATLAB cplex mixed integer linear programming solver.
Examples
The calculation flow of the invention is shown in figure 1, and the method for optimizing and scheduling the day-ahead active power output of the wind-solar power storage station based on the mixed integer linear programming model comprises the following steps:
1) Establishing day-ahead optimization scheduling model
And establishing a battery energy storage operation cost model. The energy storage operation cost mainly comprises energy storage equipment acquisition cost and operation maintenance cost, and the energy storage equipment acquisition cost is converted in the whole life cycle of the equipment to obtain the energy storage operation cost:
Figure BDA0003815911900000081
wherein, C ES,t Representing the operating cost of the energy storage system in the period t; k is ES The converted energy storage charging and discharging cost per unit time step length;
Figure BDA0003815911900000082
and
Figure BDA0003815911900000083
the discharge power and the charging power stored in the time period t are stored respectively; eta dis And η ch The discharge efficiency and the charge efficiency of the stored energy in the t period are respectively. Δ t is a scheduling time step, and is set to 1h. In order to limit the charging and discharging power and prevent the energy storage from over charging and discharging, the energy storage operation needs to satisfy the following constraint conditions:
Figure BDA0003815911900000084
Figure BDA0003815911900000085
Figure BDA0003815911900000086
wherein the content of the first and second substances,
Figure BDA0003815911900000087
and
Figure BDA0003815911900000088
respectively representing the maximum allowable charging and discharging power of stored energy; b is ES,t Is a binary variable representing the charge-discharge state of stored energy, when B ES,t Indicates that the energy is stored in a discharge state in the period t when the value is 1 and B ES,t When =0, the energy is stored in the charging state in the t period;
Figure BDA0003815911900000089
and
Figure BDA00038159119000000810
respectively the minimum electric quantity and the maximum electric quantity allowed by the stored energy; e ES,t Is the energy stored in the energy storage period t, can give the stored energy at the time of 0, and will be E ES,t Expressed in terms of power per time period as:
Figure BDA00038159119000000811
wherein, E ES,0 Representing the stored energy of the stored energy at time 0.
And establishing an electricity selling transaction model. The wind-light-storage new energy power station obtains income by selling electricity to the power grid. The profit of the electricity-selling transaction at each time period can be expressed as:
Figure BDA00038159119000000812
wherein S is MG,t The income of the power station and the power selling transaction of the superior power grid in the time period t;
Figure BDA0003815911900000091
trading the electricity price for the power station at the moment t to sell electricity to the power grid;
Figure BDA0003815911900000092
respectively selling power of the power station in the time period t; u shape PV,t 、U WD,t And U LD,t Respectively representing the abandoned light power, the abandoned wind power and the unsatisfied load power in the t period;
Figure BDA0003815911900000093
the cost of light abandonment and the cost of wind abandonment are respectively. The intra-site power needs to satisfy the power balance constraint:
Figure BDA0003815911900000094
0≤U PV,t ≤P PV,t (formula 8)
0≤U WD,t ≤P WD,t (formula 9)
Wherein, P PV,t And P WD,t And respectively representing the predicted values of the photovoltaic output and the wind power output in the t time period.
2) Defining a day-ahead optimization scheduling objective
The method takes the tracking scheduling plan and the smooth output fluctuation as the optimal scheduling target of the wind-light-storage new energy power station.
For tracking a scheduling plan target, when an upper-level power grid issues a planned output curve to a power station according to the power generation capacity and the power grid state of the wind-solar energy storage new energy power station, the power station realizes optimization of economic benefits through an energy storage system in the scheduling station or reasonably abandoning wind/light. Therefore, the total cost resulting from tracking dispatch plan export deviations is:
Figure BDA0003815911900000095
wherein, P plan,t Represents the planned output transmitted by the power grid in the period of t,
Figure BDA0003815911900000096
is the absolute value of the deviation between the power station t time period output and the planned output, K devi Is the penalty cost per power deviation. And under the condition of tracking the dispatching plan target, the lowest deviation total cost caused by the output of the tracking dispatching plan is taken as the dispatching target.
For a smooth output fluctuation target, an upper-level power grid examines the active power variation of a power station, a moving average algorithm is adopted to calculate the output of a wind-light-storage new energy power station after smoothing, and taking a scheduling step length delta T =1h and a scheduling period T =24h as examples, the output smoothing algorithm is obtained as follows:
Figure BDA0003815911900000101
in order to enable the wind-light-storage new energy power station to track the smoothed output power, a penalty cost is set for a tracking error:
Figure BDA0003815911900000102
wherein the content of the first and second substances,
Figure BDA0003815911900000103
the absolute value, K, of the deviation between the output and the smoothed power at the time t of the power station smoo Is the penalty cost per power deviation. And under the smooth output fluctuation target, taking the lowest deviation total cost caused by smooth output fluctuation as a scheduling target.
3) Establishing a day-ahead optimization scheduling objective function
Establishing a power station day-ahead optimization scheduling mixed integer linear programming model according to the battery energy storage operation cost model, the electricity selling trading model and the power balance constraint established in the step 1) and the day-ahead optimization scheduling target defined in the step 2), wherein the optimization target is that the maximum operation yield is the target, and for a tracking plan output target:
Figure BDA0003815911900000104
S MG,t 、C ES,t 、C devi,t respectively shown as (formula 6), (formula 1) and (formula 10).
For the smoothed output curve target:
Figure BDA0003815911900000105
S MG,t 、C ES,t and C smoo,t Are respectively shown in formula (formula 6), (formula 1) and (formula 12).
4) Solving wind-light-storage day-ahead optimized dispatching output curve
According to the day-ahead optimized scheduling mixed integer linear programming model of the wind-light-storage new energy power station established in the step 3), taking a day-ahead power prediction curve (15 min/point) of a wind power and photovoltaic power prediction system as input, taking parameters such as energy storage capacity, power, charge state, charge and discharge efficiency as constraint conditions, setting economic parameters such as light abandoning cost, wind abandoning cost, energy storage and charge and discharge operation cost, power deviation punishment cost and the like according to the operation condition of an electric power market in the region where the wind-light-storage new energy power station is located,
and (3) calculating and solving a wind-light-storage day-ahead optimized dispatching output curve (15 min/point) by adopting an MATLAB cplex mixed integer linear programming solver.
Simulation example
The invention is further described in detail below with a wind-light-storage new energy power station with a wind power installation of 400MW, a photovoltaic installation of 200MW and an energy storage installation of 100MW/100MWh as a research scene: (the values of the relevant parameters in the following calculation examples are only schematic and can be adjusted according to the actual operation conditions of the power station)
1) Establishing day-ahead optimization scheduling model
Substituting the battery energy storage operation cost model (formula 1) established in the step 1) into related calculation parameters, and assuming the energy storage discharge efficiency eta dis And charging efficiency η ch 0.92 is taken, and the energy storage charging and discharging cost K ES Taking 0.15 yuan/kWh, the battery energy storage operation cost model is as follows:
Figure BDA0003815911900000111
substituting the electricity selling transaction model (formula 6) established in the step 1) into the related calculation parameters, and assuming the electricity price of the electricity selling transaction before the day
Figure BDA0003815911900000112
Cost of 0.55 yuan/kWh of light discarded
Figure BDA0003815911900000113
Taking 0.25 yuan/kWh and discarding wind to obtain the cost
Figure BDA0003815911900000114
Taking 0.25 yuan/kWh, the electricity selling transaction model is as follows:
Figure BDA0003815911900000115
2) Defining a day-ahead optimization scheduling objective
Adopting the output dispatching target (formula 10) of the tracking dispatching plan established in the step 2), substituting the output dispatching target into related calculation parameters, and assuming the penalty cost K of the unit power deviation devi Taking 0.5 yuan/kWh, tracking the dispatch plan output bias results in the total cost as follows:
Figure BDA0003815911900000116
substituting the smooth output fluctuation scheduling target (formula 12) established in the step 2) into related calculation parameters, and assuming the penalty cost K of the unit power deviation smoo Taking 0.3 yuan/kWh, the total cost due to tracking dispatch plan export deviation is as follows:
Figure BDA0003815911900000121
3) Establishing day-ahead optimized scheduling objective function
And substituting the above formula into the day-ahead optimization scheduling mixed integer linear programming model (formula 13 and formula 14).
4) Solving wind-light-storage day-ahead optimal scheduling output curve
Collected single-day historical output data of a certain wind field and a photovoltaic power station are used as the day-ahead power prediction curves of the wind power and the photovoltaic power required in the step 4), the data interval is 15 min/point, and the data span is 1 day (96 data points). And respectively taking the tracked scheduling planned output and the smooth output fluctuation as scheduling targets, and calculating an optimal scheduling output curve before air supply, light supply and storage days, wherein the scheduling output curve is issued by a manual simulation scheduling plan instruction, 1 total output plan value is given every 15min, and 96 plan values are counted every day.
When the operation target of the wind-light-storage new energy power station is to track the scheduled output, a mixed integer linear programming model is adopted to solve the formula 13, the obtained wind-light-storage day-ahead optimized scheduled output curve is as follows, and the total power on line of the power station is very close to the issued power schedule curve by controlling the energy storage charge and discharge power at each time interval.
When the operation target of the wind-light-storage new energy power station is smooth output fluctuation, a mixed integer linear programming model is adopted to solve the formula 14, the obtained wind-light-storage day-ahead optimal scheduling output curve is as follows, and the total power-on-line power of the power station is very close to the planned smooth output curve by controlling the energy storage charge-discharge power at each time interval.
According to the implementation case, daily scheduling operation requirements of the wind-light-storage new energy power station are comprehensively considered, tracking scheduling plans and smooth output fluctuation are taken as control targets, a wind-light-storage new energy power station day-ahead active output optimization scheduling method based on a Mixed Integer Linear Programming (MILP) model is provided, expected effects of tracking planned output and a smooth power curve can be achieved, and benefits of the wind-light-storage new energy power station are improved.
As shown in fig. 7, the present invention further provides a system for optimizing and scheduling active power output of a wind-solar energy storage power station in the day ahead, including:
the acquisition module is used for acquiring single-day historical output data of a target wind power and photovoltaic power station to obtain a wind power and photovoltaic power day-ahead power prediction curve;
and the solving module is used for inputting the wind power and photovoltaic power day-ahead power prediction curves into a pre-established day-ahead optimized dispatching target model, respectively taking the tracked dispatching planned output and the smooth output fluctuation as dispatching targets, and solving the wind-light-storage day-ahead optimized dispatching output curve.
The invention provides electronic equipment which comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the computer program to realize the steps of the method for optimizing and scheduling the active power output of the wind-solar-storage power station before day.
The method for optimizing and scheduling the day-ahead active power output of the wind-solar storage power station comprises the following steps:
acquiring single-day historical output data of a target wind power and photovoltaic power station to obtain a wind power and photovoltaic power day-ahead power prediction curve;
and inputting the day-ahead power prediction curves of the wind power and the photovoltaic power into a pre-established day-ahead optimization scheduling target model, respectively taking tracking scheduling planned output and smooth output fluctuation as scheduling targets, and solving a wind-light-storage day-ahead optimization scheduling output curve.
The invention also provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the steps of the method for optimizing and scheduling the active power output of the wind-solar energy storage power station before day.
The method for optimizing and scheduling the day-ahead active output of the wind-solar power storage station comprises the following steps of:
acquiring single-day historical output data of a target wind power and photovoltaic power station to obtain a power prediction curve of the wind power and photovoltaic power day ahead;
and inputting the wind power and photovoltaic power day-ahead power prediction curves into a pre-established day-ahead optimized dispatching target model, respectively taking the tracked dispatching planned output and the smooth output fluctuation as dispatching targets, and solving the wind-light-storage day-ahead optimized dispatching output curve.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may 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, and the like) having computer-usable program code embodied therein.
The present 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 flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may 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, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.

Claims (10)

1. A day-ahead active output optimal scheduling method for a wind-solar power storage station is characterized by comprising the following steps:
acquiring single-day historical output data of a target wind power and photovoltaic power station to obtain a power prediction curve of the wind power and photovoltaic power day ahead;
and inputting the day-ahead power prediction curves of the wind power and the photovoltaic power into a pre-established day-ahead optimization scheduling target model, respectively taking tracking scheduling planned output and smooth output fluctuation as scheduling targets, and solving a wind-light-storage day-ahead optimization scheduling output curve.
2. The method for optimizing and scheduling the day-ahead active power output of the wind-solar power storage station according to claim 1, wherein the pre-established day-ahead optimization scheduling objective model is constructed according to a battery energy storage operation cost model, a power selling transaction model, power balance constraints and a defined day-ahead optimization scheduling objective function to obtain the mixed integer linear programming model-based day-ahead active power output optimization scheduling objective model of the wind-solar power storage station.
3. The method according to claim 2, wherein the model of the battery energy storage operation cost comprises an energy storage device acquisition cost and an operation maintenance cost, and the energy storage device acquisition cost is converted in the whole life cycle of the device, so that the obtained energy storage operation cost is:
Figure FDA0003815911890000011
wherein, C ES,t Representing the running cost of the energy storage system in the t period; k is ES The converted energy storage charging and discharging cost per unit time step length;
Figure FDA0003815911890000012
and
Figure FDA0003815911890000013
the discharge power and the charging power stored in the time period t are stored respectively; eta dis And η ch The energy storage efficiency and the charging efficiency in the t time period are respectively, and delta t is the scheduling time step;
the energy storage operation constraint conditions are as follows:
Figure FDA0003815911890000014
Figure FDA0003815911890000015
Figure FDA0003815911890000021
wherein, the first and the second end of the pipe are connected with each other,
Figure FDA0003815911890000022
and
Figure FDA0003815911890000023
respectively representing the maximum allowable charging and discharging power of stored energy; b is ES,t Is a binary variable representing the charge-discharge state of the stored energy, when B ES,t When the time is not less than 1, the energy is stored in a discharge state in the time period of t, and when B is in the discharge state ES,t When =0, the energy is stored in the charging state in the t period;
Figure FDA0003815911890000024
and
Figure FDA0003815911890000025
respectively the minimum electric quantity and the maximum electric quantity allowed by the stored energy; e ES,t Is stored for a period tGiven the stored energy at time 0, and E ES,t The power in each period is expressed as:
Figure FDA0003815911890000026
wherein, E ES,0 Representing the stored energy of the stored energy at time 0.
4. The method for optimizing and scheduling the day-ahead active output of the wind-solar-storage power station according to claim 2, wherein the electricity-selling transaction model is that the wind-solar-storage new energy power station obtains the profit by selling electricity to the power grid, and the profit of electricity-selling transaction in each period is as follows:
Figure FDA0003815911890000027
wherein S is MG,t The income of the power station and the power selling transaction of the superior power grid in the time period t;
Figure FDA0003815911890000028
trading the electricity price for the power station at the moment t to sell electricity to the power grid;
Figure FDA0003815911890000029
respectively selling power of the power station in the time period t; u shape PV,t 、U WD,t And U LD,t Respectively representing the abandoned light power, the abandoned wind power and the unsatisfied load power in the t period;
Figure FDA00038159118900000210
the light abandoning cost and the wind abandoning cost are respectively.
5. The method for optimizing and scheduling the day-ahead active output of the wind-solar-storage power station according to claim 2, wherein the power balance constraint is as follows:
Figure FDA00038159118900000211
0≤U PV,t ≤P PV,t (formula 8)
0≤U WD,t ≤P WD,t (formula 9)
Wherein, P PV,t And P WD,t And respectively representing the predicted values of the photovoltaic output and the wind power output in the t period.
6. The method for optimizing and scheduling the active power output of the wind-light-storage power station in the day ahead according to claim 2, wherein the defined day ahead optimization scheduling target is used for tracking a scheduling plan and smoothing output fluctuation as an optimization scheduling target of the wind-light-storage new energy power station;
the total cost caused by tracking dispatch plan outages is:
Figure FDA0003815911890000031
wherein, P plan,t Represents the planned output issued by the power grid in the period t,
Figure FDA0003815911890000032
the absolute value, K, of the deviation between the power station's t-time output and the planned output devi Penalty cost per power deviation; under the condition of tracking a dispatching plan target, taking the lowest deviation total cost caused by the output of the tracking dispatching plan as the dispatching target;
for a smooth output fluctuation target, the upper-level power grid evaluates the active power variation of the power station, and the output of the wind-light-storage new energy power station after smoothing is calculated by adopting a moving average algorithm, wherein the smooth algorithm for obtaining the output is as follows:
Figure FDA0003815911890000033
the penalty cost is set for the tracking error:
Figure FDA0003815911890000034
wherein, the first and the second end of the pipe are connected with each other,
Figure FDA0003815911890000035
is the absolute value of the deviation of the output and smoothed power of the power station at the time t, K smoo Penalty cost per power deviation; and under the smooth output fluctuation target, taking the lowest deviation total cost caused by smooth output fluctuation as a scheduling target.
7. The method for optimizing and scheduling the active power output of the wind-solar-energy-storage power station in the day ahead according to claim 1, wherein the pre-established day ahead optimization scheduling objective model comprises:
for tracking the planned contribution target:
Figure FDA0003815911890000036
C devi,t the total cost due to tracking dispatch plan outtake bias is C ES,t Representing the running cost of the energy storage system in the t period; s MG,t The income of the power station and the power selling transaction of the superior power grid in the time period t;
for the smoothed output curve target:
Figure FDA0003815911890000041
C smoo,t setting penalty cost for tracking error, C devi,t The total cost due to tracking dispatch plan outages is C ES,t Representing the running cost of the energy storage system in the t period; s. the MG,t And (4) the income of the power station and the power selling transaction of the superior power grid in the time period t.
8. The method for optimizing and scheduling the day-ahead active power output of the wind-photovoltaic-storage power station according to claim 1, wherein an MATLAB cplex mixed integer linear programming solver is adopted to calculate the wind-photovoltaic-storage day-ahead optimized and scheduled output curve.
9. The method for optimizing and scheduling the day-ahead active power output of the wind-solar-energy storage power station according to claim 1, wherein the data interval of the day-ahead power prediction curve of the wind power and the photovoltaic power is 15 min/point, and the data span is 1 day.
10. The utility model provides a wind-solar energy storage power station active power output optimization dispatch system before day which characterized in that includes:
the acquisition module is used for acquiring single-day historical output data of a target wind power and photovoltaic power station to obtain a wind power and photovoltaic power day-ahead power prediction curve;
and the solving module is used for inputting the wind power and photovoltaic power day-ahead power prediction curves into a pre-established day-ahead optimization scheduling target model, respectively taking the tracking scheduling planned output and the smooth output fluctuation as scheduling targets, and solving the wind-light-storage day-ahead optimization scheduling output curve.
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CN117175632A (en) * 2023-06-26 2023-12-05 南京国电南自电网自动化有限公司 Method and device for improving wind-solar resource utilization rate and power prediction accuracy rate
CN116599160A (en) * 2023-07-17 2023-08-15 电力规划总院有限公司 Active sensing method and system for new energy station cluster and new energy station
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