CN117856355A - Wind-solar energy storage station power generation control method and system - Google Patents
Wind-solar energy storage station power generation control method and system Download PDFInfo
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Abstract
In the day-ahead stage, according to an energy storage plan electric quantity accumulation value calculated based on an energy storage life model and wind-light power short-term prediction data corrected according to an overhaul plan, calculating a peak or peak regulation capacity by using the energy storage plan electric quantity accumulation value; determining the reported peak or peak shaving capacity according to the calculated duty ratio of the peak or peak shaving capacity to the energy storage capacity; the power grid dispatching returns a demand power curve; based on wind-light power short-term prediction data and a required power curve, carrying out daily optimization calculation on the generated power of the wind-light storage station; in the intra-day stage, performing intra-day optimization calculation of wind-solar storage power generation power based on the corrected wind-solar power ultra-short term prediction data, the required power curve and a day-ahead optimization calculation result; in the real-time stage, based on the maximum possible power generation capability returned by the wind-solar power generation unit, real-time optimization calculation is carried out on the daily optimization result. The wind-solar energy storage active power optimization control is realized, and the peak shaving capacity is improved.
Description
Technical Field
The invention relates to the technical field of control of wind-solar energy storage power generation systems, in particular to a method and a system for controlling power generation of a wind-solar energy storage station under the condition of an electric power market.
Background
With the continuous improvement of the capacity ratio of wind-light and other new energy power generation in a power system, the fluctuation of wind-light power generation has an increasing influence on a power grid. Because the energy storage system has the capability of stabilizing the fluctuation of wind and light power generation, the translation of energy on a time scale is realized, the grid-connection friendliness of wind and light power generation is fully improved, and the wind and light storage combined grid connection becomes an important direction in the field of new energy power generation in recent years.
The energy storage system has the advantages of high response speed, capability of rapidly controlling bidirectional power, strong power tracking capability, capability of meeting frequency modulation requirements under various scenes, capability of reducing the influence of wind power generation and photovoltaic power generation on the operation of a power grid, and peak clipping and valley filling effects. The overall idea of energy storage control is to charge and discharge along with the change of natural conditions of wind and light so as to achieve the targets of various frequency modulation, peak clipping, valley filling and the like. The disadvantage of energy storage is the cost of power generation, and the random use of energy storage results in a rapid increase in overall power generation cost.
In the prior art, for actual engineering, the configuration proportion of the energy storage capacity in the wind-solar energy storage station to the total capacity is generally not more than 15%, the energy storage capacity is relatively low, the power generation control method mainly comprises active power smoothing and primary frequency modulation, peak shaving is not planned in advance for peak shaving functions, peak shaving is performed on the basis of the energy storage real-time electric quantity, and enough peak shaving capacity cannot be provided for a power grid. Also, under the condition that the power generation price changes with time, the comprehensive control of wind and solar energy storage has uneconomical problems because the energy storage plan is insufficient or the energy storage plan cannot integrate the conditions of energy storage life, new energy station maintenance plan, energy storage electric quantity distribution and the like.
Disclosure of Invention
In order to solve the defects in the prior art, the invention provides a wind-light storage station power generation control method and system, which are based on the requirements of wind-light storage coordinated operation and actual system engineering operation, a plurality of wind-light storage system grid-connected power generation modes are established, and the wind-light storage station power generation control method and system are provided based on each power generation mode model, so that wind-light storage active power optimization control is realized, and peak-to-peak capacity is improved.
The invention adopts the following technical scheme.
The invention provides a power generation control method of a wind-solar energy storage station, which sequentially comprises a day-ahead stage, a day-in stage and a real-time stage;
in the day-ahead phase, comprising:
step 1, calculating an energy storage plan electric quantity accumulated value in a normal operation time period of an energy storage system based on an energy storage life model;
step 2, correcting wind-light power short-term prediction data according to a wind-light unit maintenance plan; calculating the peak capacity or peak regulation capacity by using the energy storage plan electric quantity cumulative value according to the magnitude relation between the energy storage plan electric quantity cumulative value and the corrected wind-solar power short-term prediction data; the reported peak capacity or peak shaving capacity is determined according to the calculated duty ratio of the peak capacity or peak shaving capacity to the energy storage capacity;
step 3, the power grid dispatching returns a required power curve according to the reported peak capacity or peak shaving capacity; based on wind-light power short-term prediction data and a required power curve, carrying out daily optimization calculation on the generated power of the wind-light storage station;
in the intra-day phase, comprising:
step 4, carrying out optimization calculation on wind-solar-energy storage generating power in a day based on wind-solar-power ultra-short-term prediction data, a required power curve and a day-ahead optimization calculation result after correction;
in the real-time phase, it includes:
step 5, carrying out real-time optimization calculation on the daily optimization result based on the maximum possible power returned by the wind-solar power generation unit; and obtaining real-time output instructions of all the wind-light storage stations, and carrying out peak-shaving or peak-shaving operation on all the wind-light storage stations according to the real-time output instructions.
In the step 1, the accumulated value of the energy storage planning electric quantity meets the following relation:
in the method, in the process of the invention,
EDT bat for the accumulated value of the energy storage planning electric quantity,
C bat for the capacity of the energy storage system,
l c the service life is designed for the charge and discharge times of the energy storage system,
l d the life of the energy storage system is designed for,
SOH avg (i) For the average battery health value calculated on day i,
ET bat the accumulated electric quantity is charged and discharged for energy storage,
d is the number of days the energy storage system has been put into operation normally.
In step 2, correcting the wind-solar power short-term prediction data according to the wind-solar unit maintenance plan includes: generating a unit overhaul influence coefficient sequence according to the capacity occupation ratio and time of the unit overhaul plan contained in the overhaul plan; short-term prediction and sequence correction of wind-light power are carried out by utilizing a unit overhaul influence coefficient sequence, and the following relational expression is satisfied:
WSM i ＝WS i ·KM i
in the formula, the subscript i represents an ith wind-solar power prediction point and a WSM i WS for the ith element in the corrected short-term prediction and sequence of wind-solar power i KM for the ith element in the power short-term prediction and sequence i For the ith element in the unit maintenance influence coefficient sequence, representing the time corresponding to the ith wind-solar power prediction pointThe capacity of the wind-light unit which does not participate in maintenance occupies the ratio of the total capacity of the wind-light unit.
In the step 2, according to the magnitude relation between the accumulated value of the energy storage plan electric quantity and the short-term prediction data of the wind-solar power after correction, the peak capacity is calculated by using the accumulated value of the energy storage plan electric quantity, and the following relational expression is satisfied:
if it isThen C p ＝k p ·EDT bat Otherwise
In the method, in the process of the invention,
C p in order to calculate the peak capacity of the result,
k p is the peak capacity coefficient of the energy storage system.
According to the calculated peak capacity to energy storage capacity ratio, determining the reported peak capacity, and satisfying the following relation:
if it isThen C p ＝6C bat 。
In the step 2, according to the magnitude relation between the accumulated value of the energy storage plan electric quantity and the short-term prediction data of the wind-solar power after correction, the peak regulation capacity is calculated by using the accumulated value of the energy storage plan electric quantity, and the following relation is satisfied:
if it isThen C a ＝k a ·EDT bat Otherwise
In the method, in the process of the invention,
k a for energy storageThe coefficient of peak capacity is set to be,
C a the peak shaving capacity is calculated.
According to the calculated duty ratio of the peak shaving capacity to the energy storage capacity, the reported peak shaving capacity is determined, and the following relation is satisfied:
if it isThen C a ＝14C bat 。
The step 3 comprises the following steps:
step 3.1, determining a target and constraint conditions for optimizing the generating power of the wind-solar storage station in the future;
step 3.2, correcting an inertia weight coefficient of a particle swarm algorithm, and establishing a variable inertia optimizing model; and optimizing and solving the variable inertia optimizing model target and the constraint condition.
The step 5 comprises the following steps:
the optimized result before day is set to comprise the current power value P of wind power w (i) Current power value P of photovoltaic s (i) Stored energy current power value P b (i) Setting the current maximum power of wind and light as P' w 、P' s The actual power of wind and light generated at the upper moment is P w (i-1)、P s (i-1) setting the maximum change rate of wind power and photovoltaic power generation as R respectively w 、R s Then store real-time power P for wind and light w 、P s 、P b The calculation model of (2) satisfies the following relation:
if it isThen P w ＝P' w Otherwise P w ＝(R w +1)·P w (i-1)
If it isThen P s ＝P' s Otherwise P s ＝(R s +1)·P s (i-1)
P b ＝P b +[P w (i)+P s (t)-P w -P w ]
In the formula, a subscript i represents an ith wind-solar power prediction point;
based on energy storage capacity C bat And carrying out safety check on the stored energy power, and meeting the following relation:
if |P b |>C bat P is then b >sign(P b )·C bat
Wherein C is bat To the capacity of the energy storage system sign (P b ) In order to take the sign of the current power value of the stored energy, the charge is positive, and the discharge is negative.
The invention also provides a power generation control system of the wind-solar energy storage station, which comprises the following steps: the system comprises a day-ahead stage control module, a day-in stage control module and a real-time stage control module;
the day-ahead stage control module is used for calculating an energy storage planning electric quantity accumulated value in a normal operation time period of the energy storage system based on the energy storage life model; correcting wind-light power short-term prediction data according to a wind-light unit maintenance plan; calculating the peak capacity or peak regulation capacity by using the energy storage plan electric quantity cumulative value according to the magnitude relation between the energy storage plan electric quantity cumulative value and the corrected wind-solar power short-term prediction data; the reported peak capacity or peak shaving capacity is determined according to the calculated duty ratio of the peak capacity or peak shaving capacity to the energy storage capacity; the power grid dispatching returns a demand power curve according to the reported peak capacity or peak regulation capacity; based on wind-light power short-term prediction data and a required power curve, carrying out daily optimization calculation on the generated power of the wind-light storage station;
the in-day stage control module is used for carrying out in-day optimization calculation on wind-solar storage power generation power based on wind-solar power ultra-short term prediction data, a required power curve and a day-ahead optimization calculation result;
the real-time stage control module is used for carrying out real-time optimization calculation on the daily optimization result based on the maximum possible power generation capability returned by the wind-light power generation unit; and obtaining real-time output instructions of all the wind-light storage stations, and carrying out peak-shaving or peak-shaving operation on all the wind-light storage stations according to the real-time output instructions.
Compared with the prior art, the method has the beneficial effects that an interaction model for dispatching with the power grid is realized, the balance of the peak load and the peak load of the wind-light storage station is realized through interaction with the power grid, the peak load regulation target is further determined, and the peak load and peak load regulation functions of taking the power grid requirement, the wind-light storage station capacity, the energy storage service life and the economic benefit of the wind-light storage station are realized through a daily-real-time staged progressive optimization strategy.
According to the invention, by optimizing the architecture in real time in the daytime-in-day stage, the power generation economy of the wind-light storage station is taken as an optimization target, the power balance, the energy storage life and the operation safety constraint are met, the active power of the wind-light storage station is optimally controlled based on an improved particle swarm algorithm, and the economic benefit of the station is optimal
Drawings
FIG. 1 is a flow chart of a method of controlling power generation at a wind turbine farm in accordance with an embodiment of the present invention;
FIG. 2 is a flow chart of a method for controlling the generation of a wind-solar energy storage station according to the present invention;
FIG. 3 is a flow chart of peak control in the wind-solar energy storage station power generation control method proposed by the invention;
FIG. 4 is a schematic diagram of peak interaction in the wind-solar energy storage station power generation control method;
FIG. 5 is a flow chart of peak shaving control in the wind-solar energy storage station power generation control method provided by the invention;
fig. 6 is a schematic diagram of peak shaving interaction in the method for controlling power generation of the wind-solar energy storage station.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. The embodiments described herein are merely some, but not all embodiments of the invention. All other embodiments, which can be made by those skilled in the art without inventive faculty, are within the scope of the invention, based on the spirit of the invention.
As shown in fig. 1, the invention provides a method for controlling power generation of a wind-solar storage station, which comprises a day-ahead stage, a day-in stage and a real-time stage; in the day-ahead stage, based on a safety constraint day-ahead power generation plan optimization model, a day-ahead wind-solar energy storage output plan curve and an energy storage capacity allocation plan which are obtained by utilizing input data are input into the day-ahead stage; in the intra-day stage, based on a safety constraint intra-day rolling plan optimization model, wind and light ultra-short-term prediction data are utilized to optimize a wind and light storage output plan curve and an energy storage capacity distribution plan; in a real-time stage, based on a safety constraint real-time scheduling optimization model, real-time updating is carried out on an optimized day-ahead wind-solar energy storage output planning curve and an energy storage capacity allocation plan by utilizing a real-time power instruction, so that a wind-solar energy storage station real-time output instruction and energy storage capacity allocation are obtained; and each wind-solar energy storage station is regulated to operate according to the instruction.
Specifically, the input data includes: wind and light short-term prediction data, SCADA data, equipment state data, unit maintenance plans, function configuration weights and energy storage operation constraints.
In a non-limiting preferred embodiment, the day-ahead is 0 point of the target day, the future 4-hour optimization result is generated by rolling every 15 minutes, and the real-time control is triggered according to a real-time instruction issued by the power grid dispatching, and is generally controlled every 1 minute.
Specifically, the power grid dispatching issues a peak shaving interaction instruction and a real-time power instruction;
the wind-light power prediction system distributes wind-light ultra-short-term prediction data and wind-light short-term prediction data; the wind-solar ultra-short-term prediction data refer to prediction of wind power and photovoltaic power generation power for 4 hours in the future, the data interval is 15 minutes, and the prediction result is 16 power data points; the wind-solar short-term prediction data refer to prediction of wind power and photovoltaic power generation power of 7 days in the future, the data interval is 15 minutes, the prediction result is 96 points per day, and 672 power data points are taken as total 7 days.
The basic centralized control system issues SCADA data and equipment state data;
the overhaul plan system issues a unit overhaul plan;
each function configuration parameter system issues each function configuration weight, the meaning of the weight is that the total discharge capacity of energy storage is distributed among the functions, and the peak function weight coefficient is 0.7, which means that 70% of the total discharge capacity of energy storage in the future day needs to be reserved for the peak function, and other functions do not occupy part of the electric quantity during discharge; the energy storage operating parameter system issues energy storage operating constraints including, but not limited to, energy storage power limits.
Specifically, the invention provides a method for controlling power generation of a wind-solar energy storage station to realize peak control, which comprises the following steps as shown in fig. 2:
in the day-ahead stage, based on a safety constraint day-ahead power generation plan optimization model, a day-ahead wind-light storage output plan curve and an energy storage capacity allocation plan are obtained by utilizing wind-light short-term prediction data, each function configuration weight, a unit maintenance plan and equipment state data, peak capacity is reported to a power grid dispatching, and the power grid dispatching confirms peak demands; in the daytime, based on a safety constraint daily rolling plan optimization model, wind and light ultra-short-term prediction data, SCADA data and equipment state data are utilized to optimize a solar and light storage output plan curve and an energy storage capacity allocation plan before the day, so that an optimized daily power curve is obtained; in a real-time stage, based on a safety constraint real-time scheduling optimization model, updating an optimized daily power curve by using a real-time power instruction issued by scheduling, SCADA data and equipment state data to obtain real-time output instructions and energy storage capacity allocation of each wind-solar storage station; and each wind-solar energy storage station performs peak operation according to the instruction.
Specifically, as shown in fig. 3, in the previous day, the wind-solar energy storage station power generation control system reports the peak capacity of the second day to the power grid schedule, and the power grid schedule returns to the peak time period and the power curve; in the target day, in the peak process, the power grid dispatching issues a peak power instruction to the wind-light storage station power generation control system.
Specifically, the invention provides a method for controlling power generation of a wind-solar energy storage station to realize peak regulation control, which comprises the following steps as shown in fig. 2:
step 1, in the day-ahead stage, based on an energy storage life model, calculating an energy storage plan electric quantity accumulated value in a normal operation time period of the energy storage system, and representing a chargeable and dischargeable value of the energy storage system according to a plan.
Specifically, step 1 includes:
step 1.1, establishing a calculation model of the current average battery health of the energy storage system in a normal operation time period of the energy storage system;
specifically, the first 30 days of the battery health degree sequence of the energy storage system is set as SOH, the sequence SOH is set to comprise 30 data points, the average battery health degree value of the last 30 days is represented, and then the current average battery health degree SOH of the energy storage system is set avg The calculation model of (2) satisfies the following relation:
in the method, in the process of the invention,
SOH avg indicating the current average battery health of the energy storage system,
SOH i representing the i-th element in the sequence SOH.
Step 1.2, establishing a calculation model of a current daily charge and discharge electric quantity planning value of the energy storage system;
specifically, let the design life of the energy storage system be l d The unit is day, and the design life of the charge and discharge times of the energy storage system is l c The unit is times, which indicates that the energy storage system can be completely charged and discharged c Next, the total electric energy of the energy storage system is set as E bat Then define the current daily charge and discharge electric quantity planning value ED of the energy storage system bat And satisfies the following relationship:
and 1.3, establishing a calculation model of the accumulated value of the energy storage planning electric quantity.
Set up energy storage system to put into operation normally D day, SOH avg (i) For the average battery health value calculated on the ith day, the accumulated energy ET of the stored energy is charged and discharged bat Then the energy storage planning electric quantity accumulated value EDT bat Is a computational model of (a):
and the follow-up steps are all calculated based on the accumulated value of the energy storage planning electric quantity, so that an energy storage full-life-cycle energy distribution model is realized, and the control relation between the energy storage use and the energy storage life is optimized and balanced.
Step 2, correcting wind-light power short-term prediction data according to a wind-light unit maintenance plan; calculating peak capacity or peak regulation capacity by using the accumulated value of the energy storage plan electric quantity according to the magnitude relation between the accumulated value of the current energy storage plan electric quantity and the short-term prediction data of the wind-solar power after correction; and determining the reported peak capacity or peak shaving capacity according to the calculated duty ratio of the peak capacity or peak shaving capacity to the energy storage capacity.
Specifically, the sum of the short-term prediction curves of wind power and photovoltaic power on the target day is set as a sequence WS, and each curve contains 96 data points which represent power prediction values of 24 hours every 15 minutes on the future day. Generating a wind and light unit overhaul influence coefficient sequence KM according to the planned overhaul unit capacity occupation ratio and time interval contained in an overhaul plan, wherein the sequence contains 96 points and represents the occupation ratio of the wind and light unit which does not participate in overhaul every 15 minutes in 24 hours in the future to the total capacity of the unit; and (3) utilizing a unit overhaul influence coefficient sequence to obtain a WSM sequence by short-term prediction of wind and light power and sequence WS correction, wherein the following relation is satisfied:
WSM i ＝WS i ·KM i
in the formula, the subscript i represents an ith wind-solar power prediction point, and in the embodiment, the value of i is consistent with the number of data points contained in each curve; WSM (Wireless sensor module) i WS for the ith element in the corrected short-term prediction and sequence of wind-solar power i KM for the ith element in the power short-term prediction and sequence i And for the ith element in the unit overhaul influence coefficient sequence, representing the ratio of the capacity of the wind-light unit which does not participate in overhaul at the moment corresponding to the ith wind-light power prediction point to the total capacity of the wind-light unit.
According to the wind-light unit maintenance plan, the wind-light power short-term prediction data are corrected, a model which considers the equipment state and maintenance plan during wind-light storage power generation control is realized, so that wind-light storage power generation optimization results are more practical, and the wind-light storage power generation optimization method has long-term reliable operation capability on an engineering site.
The peak is the amount that when the power consumption of the power grid is in peak time (18:00-20:00), the energy storage can discharge to support the power grid, so 72 wind-light power prediction points are selected, and the peak can be filled up to 18:00.
Setting the peak capacity coefficient k of the energy storage system p Peak capacity C p The following relation is satisfied:
if it isThen C p ＝k p ·EDT bat Otherwise
In EDT of bat For the current energy storage plan power accumulated value,
the energy storage capacity C is calculated according to the peak capacity bat The reported peak capacity is determined, and the following relation is satisfied:
if it isThen C p ＝6C bat 。
The time range of peak shaving period is wider, and the peak shaving period is generally considered to be after 10 am in engineering, so 40 wind-light power prediction points are selected to represent the quantity which can be fully required by peak shaving before 10 am every day.
Set energy storage peak regulation capacity coefficient k a Peak shaving ability C a The following relation is satisfied:
if it isThen C a ＝k a ·EDT bat Otherwise
The energy storage capacity C is calculated according to the peak regulation capacity bat The reported peak regulation capacity is determined, and the following relation is satisfied:
if it isThen C a ＝14C bat 。
Step 3, the power grid dispatching returns a required power curve according to the reported peak capacity or peak shaving capacity; and carrying out daily optimization calculation on the generated power of the wind-light storage station based on the wind-light power short-term prediction data and the required power curve.
Specifically, the overall power demand of the grid dispatching on the wind-light storage station is a unified curve, the curve comprises a total power curve of peak and peak shaving, and a curve sequence is defined as R G 。
The wind power generation power day-ahead optimization calculation result of the wind power storage station is a wind power generation power curve, a light power generation power curve and a stored power generation power curve, wherein each curve comprises 96 points, and represents the respective power generation power plans of target solar wind, light and stored.
Specifically, the calculation process of step 3 is as follows:
step 3.1, determining a target and constraint conditions for optimizing the generating power of the wind-solar storage station in the future;
let the ith points in the wind, light and stored power curves be P respectively w (i)、P s (i)、P b (i) The ith point of the demand power curve is R G (i) The ith point of the short-term prediction curve of wind power and optical power is PF respectively w (i)、PF s (i) The power curves are all 15 minutes intervals, 96 points are taken every day, the value range of i is 1-96, and the power curves correspond to all times in one day.
The day-ahead optimization calculation of wind-solar storage generation power can be defined as the following optimization problem:
the aim of day-ahead optimization of the generating power of the wind-solar storage station meets the following relation:
Min(R G (i)-[P w (t)+P s (i)+P b (i)])
Min(PF w (i)-P w (i))
Min(PF s (i)-P s (i))
constraint conditions of day-ahead optimization of wind-solar energy storage station power generation power meet the following relation:
R G (i)-[P w (i)+P s (i)+P b (i)]≥0
PF w (i)-P w (i)≥0
PF s (i)-P s (i)≥0
P w (i)≥0
P s (i)≥0
P b (i)≤C bat
and 3.2, optimizing and solving the target and the constraint condition.
Specifically, an improved particle swarm algorithm (PSO) is adopted for optimizing solving calculation, and the specific process is as follows:
1) Reading input data and setting initial values, including inertia weight, knowledge factors, maximum speed, particle number, maximum iteration number and speed and position of particles in a particle swarm;
2) Calculating each optimization target value by combining constraint conditions;
3) And carrying out iterative search to find out an optimal result.
In the iterative search, in order to overcome the defects that the basic conventional particle swarm algorithm is easy to fall into local optimum and has poor searching capability, the particle swarm algorithm is improved, and a variable inertia optimizing model is provided:
let the current inertia weight coefficient of the particle swarm be w, and the maximum inertia weight coefficient be w max The minimum inertia weight coefficient is w min Current iteration number i, maximum iteration number i max The update model of w is proposed as follows:
and 4, performing the optimization calculation of the wind-solar-energy storage generating power in the day based on the corrected wind-solar-power ultra-short-term prediction data, the required power curve and the day-ahead optimization calculation result.
Specifically, the optimization is calculated every 15 minutes before the day, and the difference between the optimization and the optimization is that the optimization in the day only optimizes the wind and light storage power for 4 hours in the future each time, namely 16 power points in the future, and the optimization calculation method is the same as that in the step 3.
And 5, optimizing the daily optimization result in real time based on the maximum possible power generation capacity returned by the wind-solar power generation unit.
Specifically, the day-ahead optimization result comprises a current power value P of wind power w (i) Current power value P of photovoltaic s (i) Stored energy current power value P b (i) Setting the current maximum power of wind and light as P' w 、P' s The actual power of wind and light generated at the upper moment is P w (i-1)、P s (i-1) setting the maximum change rate of wind power and photovoltaic power generation as R respectively w 、R s Then store real-time power P for wind and light w 、P s 、P b The calculation model of (2) satisfies the following relation:
if it isThen P w ＝P' w Otherwise P w ＝(R w +1)·P w (i-1)
If it isThen P s ＝P' s Otherwise P s ＝(R s +1)·P s (i-1)
P b ＝P b +[P w (i)+P s (t)-P w -P w ]
Further, based on energy storage capacity C bat To store energy powerAnd (3) safety check:
if |P b |>C bat P is then b >sign(P b )·C bat 。
Wherein C is bat To the capacity of the energy storage system sign (P b ) In order to take the sign of the current power value of the stored energy, the charge is positive, and the discharge is negative.
According to the method provided by the invention, a day-ahead stage is based on a safety constraint day-ahead power generation plan optimization model, wind-light short-term prediction data, each function configuration weight, a unit maintenance plan and equipment state data are utilized to obtain a day-ahead wind-light storage output plan curve and an energy storage capacity distribution plan, peak regulation capacity is reported to power grid dispatching, and the power grid dispatching confirms peak regulation requirements; in the day-ahead stage, a day-ahead wind-solar energy storage output plan curve and an energy storage capacity allocation plan are optimized based on a safety constraint day-ahead rolling plan optimization model by using a peak regulation plan issued by power grid dispatching, wind-solar ultra-short-term prediction data, SCADA data and equipment state data, so that an optimized day-ahead power curve is obtained; in a real-time stage, based on a safety constraint real-time scheduling optimization model, updating an optimized daily power curve by using a real-time power instruction issued by scheduling, SCADA data and equipment state data to obtain real-time output instructions and energy storage capacity allocation of each wind-solar storage station; and each wind-solar energy storage station carries out peak shaving operation according to the instruction.
The invention also provides a power generation control system of the wind-solar energy storage station, which comprises the following steps: the system comprises a day-ahead stage control module, a day-in stage control module and a real-time stage control module;
the day-ahead stage control module is used for calculating an energy storage planning electric quantity accumulated value in a normal operation time period of the energy storage system based on the energy storage life model; correcting wind-light power short-term prediction data according to a wind-light unit maintenance plan; calculating the peak capacity or peak regulation capacity by using the energy storage plan electric quantity cumulative value according to the magnitude relation between the energy storage plan electric quantity cumulative value and the corrected wind-solar power short-term prediction data; the reported peak capacity or peak shaving capacity is determined according to the calculated duty ratio of the peak capacity or peak shaving capacity to the energy storage capacity; the power grid dispatching returns a demand power curve according to the reported peak capacity or peak regulation capacity; based on wind-light power short-term prediction data and a required power curve, carrying out daily optimization calculation on the generated power of the wind-light storage station;
the in-day stage control module is used for carrying out in-day optimization calculation on wind-solar storage power generation power based on wind-solar power ultra-short term prediction data, a required power curve and a day-ahead optimization calculation result;
the real-time stage control module is used for carrying out real-time optimization calculation on the daily optimization result based on the maximum possible power generation capability returned by the wind-light power generation unit; and obtaining real-time output instructions of all the wind-light storage stations, and carrying out peak-shaving or peak-shaving operation on all the wind-light storage stations according to the real-time output instructions.
The embodiment of the invention provides a peak interaction model for wind-light storage station and power grid dispatching, wherein the peak interaction model is shown in fig. 3, the peak control model is shown in fig. 4, and the specific control process comprises the following steps:
and in the day-ahead stage, reporting the peak capacity data obtained by the calculation method to the power grid dispatching, and returning the peak demand data by the power grid dispatching, wherein the data content comprises a peak time period and a peak power curve. And then, adopting the method to finish the optimization of the wind-solar power storage curve before the day.
And in the daytime, the solar energy storage power curve optimization in the daytime is completed by adopting the daytime optimization method. In a real-time stage, the power grid dispatching issues a real-time power instruction, a normal instruction is issued in a non-peak period, the numerical value of the issued instruction of the power grid dispatching comprises peak power requirements in a peak period, the wind-solar energy storage station executes according to the instruction, a peak process is completed, and the dispatching power instruction returns to a normal range after exceeding the peak period.
The invention provides a peak shaving interaction model and a peak shaving control model for wind-light storage station and power grid dispatching, wherein the peak shaving interaction model is shown in fig. 5, and the peak shaving control model is shown in fig. 6. The specific control process comprises the following steps:
and in the day-ahead stage, reporting the capacity regulating data obtained by the calculation method to the power grid dispatching, and returning the peak regulating demand data by the power grid dispatching, wherein the data content comprises the peak regulating total electric quantity. And then, adopting the method to finish the optimization of the wind-solar power storage curve before the day.
In the daytime, the power grid dispatching can issue peak shaving demands for multiple times, the content comprises peak shaving time periods and power curves, and the wind-light storage station adopts the daily optimization method to finish daily wind-light storage power curve optimization. In a real-time stage, the power grid dispatching issues a real-time power instruction, a normal instruction is issued in a non-peak-shaving period, the numerical value of the power grid dispatching issued instruction contains peak-shaving power requirements in the peak-shaving period, the wind-solar energy storage station executes according to the instruction to complete the peak-shaving process, and the dispatching power instruction returns to the normal range after exceeding the peak-shaving period. And the peak shaving demands are issued for a plurality of times in a day for power grid dispatching, and the peak shaving can be completed for a plurality of times.
In the previous day, reporting the peak shaving capacity of the second day to the power grid dispatching by the wind-solar storage station power generation control system, and returning peak shaving requirements by the power grid dispatching; in the target day, the power grid schedule of the first 4 hours of the starting time of the peak shaving process issues a peak shaving time period and a peak shaving power curve to the wind-light storage station power generation control system, and the power grid schedule of the starting time of the peak shaving process issues a peak shaving power instruction to the wind-light storage station power generation control system.
The system provided by the invention realizes a dynamic optimization model of energy distribution among functions when the energy storage system is supported for multiple functions, and avoids the problem that the functions cannot reach expectations due to unordered preemption of energy storage resources among the multiple functions.
The present disclosure may be a system, method, and/or computer program product. The computer program product may include a computer readable storage medium having computer readable program instructions embodied thereon for causing a processor to implement aspects of the present disclosure.
The computer readable storage medium may be a tangible device that can hold and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium would include the following: portable computer disks, hard disks, random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static Random Access Memory (SRAM), portable compact disk read-only memory (CD-ROM), digital Versatile Disks (DVD), memory sticks, floppy disks, mechanical coding devices, punch cards or in-groove structures such as punch cards or grooves having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media, as used herein, are not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., optical pulses through fiber optic cables), or electrical signals transmitted through wires.
The computer readable program instructions described herein may be downloaded from a computer readable storage medium to a respective computing/processing device or to an external computer or external storage device over a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmissions, wireless transmissions, routers, firewalls, switches, gateway computers and/or edge servers. The network interface card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium in the respective computing/processing device.
Computer program instructions for performing the operations of the present disclosure can be assembly instructions, instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, c++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer readable program instructions may be executed entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, aspects of the present disclosure are implemented by personalizing electronic circuitry, such as programmable logic circuitry, field Programmable Gate Arrays (FPGAs), or Programmable Logic Arrays (PLAs), with state information of computer readable program instructions, which can execute the computer readable program instructions.
Finally, it should be noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the above embodiments, it should be understood by those skilled in the art that: modifications and equivalents may be made to the specific embodiments of the invention without departing from the spirit and scope of the invention, which is intended to be covered by the claims.
Claims (10)
1. The wind-solar energy storage station power generation control method sequentially comprises a day-ahead stage, a day-in stage and a real-time stage, and is characterized in that,
in the day-ahead phase, comprising:
step 1, calculating an energy storage plan electric quantity accumulated value in a normal operation time period of an energy storage system based on an energy storage life model;
step 2, correcting wind-light power short-term prediction data according to a wind-light unit maintenance plan; calculating the peak capacity or peak regulation capacity by using the energy storage plan electric quantity cumulative value according to the magnitude relation between the energy storage plan electric quantity cumulative value and the corrected wind-solar power short-term prediction data; the reported peak capacity or peak shaving capacity is determined according to the calculated duty ratio of the peak capacity or peak shaving capacity to the energy storage capacity;
step 3, the power grid dispatching returns a required power curve according to the reported peak capacity or peak shaving capacity; based on wind-light power short-term prediction data and a required power curve, carrying out daily optimization calculation on the generated power of the wind-light storage station;
in the intra-day phase, comprising:
step 4, performing wind-solar-energy storage generating power in-day optimization calculation based on the corrected wind-solar-power ultra-short-term prediction data, the required power curve and the day-ahead optimization calculation result;
in the real-time phase, it includes:
step 5, carrying out real-time optimization calculation on the daily optimization result based on the maximum possible power returned by the wind-solar power generation unit; and obtaining real-time output instructions of all the wind-light storage stations, and carrying out peak-shaving or peak-shaving operation on all the wind-light storage stations according to the real-time output instructions.
2. The method for controlling the power generation of the wind-solar energy storage station according to claim 1, wherein,
in the step 1, the accumulated value of the energy storage planning electric quantity meets the following relation:
in the method, in the process of the invention,
EDT bat for the accumulated value of the energy storage planning electric quantity,
C bat for the capacity of the energy storage system,
l c the service life is designed for the charge and discharge times of the energy storage system,
l d the life of the energy storage system is designed for,
SOH avg (i) For the average battery health value calculated on day i,
ET bat the accumulated electric quantity is charged and discharged for energy storage,
d is the number of days the energy storage system has been put into operation normally.
3. The method for controlling the power generation of the wind-solar energy storage station according to claim 2, wherein,
in step 2, correcting the wind-solar power short-term prediction data according to the wind-solar unit maintenance plan includes: generating a unit overhaul influence coefficient sequence according to the capacity occupation ratio and time of the unit overhaul plan contained in the overhaul plan; short-term prediction and sequence correction of wind-light power are carried out by utilizing a unit overhaul influence coefficient sequence, and the following relational expression is satisfied:
WSM i ＝WS i ·KM i
in the formula, the subscript i represents an ith wind-solar power prediction point and a WSM i WS for the ith element in the corrected short-term prediction and sequence of wind-solar power i KM for the ith element in the power short-term prediction and sequence i And for the ith element in the unit overhaul influence coefficient sequence, representing the ratio of the capacity of the wind-light unit which does not participate in overhaul at the moment corresponding to the ith wind-light power prediction point to the total capacity of the wind-light unit.
4. The method for controlling the power generation of the wind-solar energy storage station according to claim 3, wherein,
in the step 2, according to the magnitude relation between the accumulated value of the energy storage plan electric quantity and the short-term prediction data of the wind-solar power after correction, the peak capacity is calculated by using the accumulated value of the energy storage plan electric quantity, and the following relational expression is satisfied:
if it isThen C p ＝k p ·EDT bat Otherwise
In the method, in the process of the invention,
C p in order to calculate the peak capacity of the result,
k p is the peak capacity coefficient of the energy storage system.
5. The method for controlling the power generation of the wind-solar energy storage station according to claim 4, wherein,
according to the calculated peak capacity to energy storage capacity ratio, determining the reported peak capacity, and satisfying the following relation:
if it isThen C p ＝6C bat 。
6. The method for controlling the power generation of the wind-solar energy storage station according to claim 3, wherein,
in the step 2, according to the magnitude relation between the accumulated value of the energy storage plan electric quantity and the short-term prediction data of the wind-solar power after correction, the peak regulation capacity is calculated by using the accumulated value of the energy storage plan electric quantity, and the following relation is satisfied:
if it isThen C a ＝k a ·EDT bat Otherwise
In the method, in the process of the invention,
k a for the energy storage peak regulation capacity coefficient,
C a the peak shaving capacity is calculated.
7. The method for controlling the power generation of the wind-solar energy storage station according to claim 6, wherein,
according to the calculated duty ratio of the peak shaving capacity to the energy storage capacity, the reported peak shaving capacity is determined, and the following relation is satisfied:
if it isThen C a ＝14C bat 。
8. The method for controlling the power generation of the wind-solar energy storage station according to claim 1, wherein,
the step 3 comprises the following steps:
step 3.1, determining a target and constraint conditions for optimizing the generating power of the wind-solar storage station in the future;
step 3.2, correcting an inertia weight coefficient of a particle swarm algorithm, and establishing a variable inertia optimizing model; and optimizing and solving the variable inertia optimizing model target and the constraint condition.
9. The method for controlling the power generation of the wind-solar energy storage station according to claim 1, wherein,
the step 5 comprises the following steps:
the optimized result before day is set to comprise the current power value P of wind power w (i) Current power value P of photovoltaic s (i) Stored energy current power value P b (i) Setting the current maximum power of wind and light as P' w 、P′ s The actual power of wind and light generated at the upper moment is P w (i-1)、P s (i-1) setting the maximum change rate of wind power and photovoltaic power generation as R respectively w 、R s Then store real-time power P for wind and light w 、P s 、P b The calculation model of (2) satisfies the following relation:
if it isThen P w ＝P′ w Otherwise P w ＝(R w +1)·P w (i-1)
If it isThen P s ＝P′ s Otherwise P s ＝(R s +1)·P s (i-1)
P b ＝P b +[P w (i)+P s (i)-P w -P w ]
In the formula, a subscript i represents an ith wind-solar power prediction point;
based on energy storage capacity C bat And carrying out safety check on the stored energy power, and meeting the following relation:
if |P b |＞C bat P is then b ＞sign(P b )·C bat
Wherein C is bat To the capacity of the energy storage system sign (P b ) In order to take the sign of the current power value of the stored energy, the charge is positive, and the discharge is negative.
10. Wind-solar energy storage station power generation control system, characterized by comprising:
the system comprises a day-ahead stage control module, a day-in stage control module and a real-time stage control module;
the day-ahead stage control module is used for calculating an energy storage planning electric quantity accumulated value in a normal operation time period of the energy storage system based on the energy storage life model; correcting wind-light power short-term prediction data according to a wind-light unit maintenance plan; calculating the peak capacity or peak regulation capacity by using the energy storage plan electric quantity cumulative value according to the magnitude relation between the energy storage plan electric quantity cumulative value and the corrected wind-solar power short-term prediction data; the reported peak capacity or peak shaving capacity is determined according to the calculated duty ratio of the peak capacity or peak shaving capacity to the energy storage capacity; the power grid dispatching returns a demand power curve according to the reported peak capacity or peak regulation capacity; based on wind-light power short-term prediction data and a required power curve, carrying out daily optimization calculation on the generated power of the wind-light storage station;
the in-day stage control module is used for carrying out in-day optimization calculation on wind-solar storage power generation power based on wind-solar power ultra-short term prediction data, a required power curve and a day-ahead optimization calculation result;
the real-time stage control module is used for carrying out real-time optimization calculation on the daily optimization result based on the maximum possible power generation capability returned by the wind-light power generation unit; and obtaining real-time output instructions of all the wind-light storage stations, and carrying out peak-shaving or peak-shaving operation on all the wind-light storage stations according to the real-time output instructions.
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