CN115313441A - New energy station energy storage configuration calculation method, system, medium and equipment - Google Patents

New energy station energy storage configuration calculation method, system, medium and equipment Download PDF

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CN115313441A
CN115313441A CN202211016973.5A CN202211016973A CN115313441A CN 115313441 A CN115313441 A CN 115313441A CN 202211016973 A CN202211016973 A CN 202211016973A CN 115313441 A CN115313441 A CN 115313441A
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new energy
power
energy
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王尧
陈洁
刘子拓
王建学
梁燕
刘海丞
武中
王凯凯
王皑
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Xian Jiaotong University
Economic and Technological Research Institute of State Grid Shanxi Electric Power Co Ltd
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Economic and Technological Research Institute of State Grid Shanxi Electric Power Co Ltd
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Abstract

The invention discloses a new energy station energy storage configuration calculation method, a system, a medium and equipment, wherein a new energy station energy storage configuration calculation model based on a tracking load curve and a normalization ratio considers the matching relation between a new energy-energy combined output curve and a load curve in practice, the invention aims to research the theoretical optimal ratio between new energy and energy storage instead of the optimal capacity, and if the optimal ratio between new energy is required to be obtained, only the per unit value of the output of the load curve and the new energy is required to be given, and only the curve shape characteristic is considered, so that the requirement on the aspect of data input is looser; according to the invention, the balance between the energy storage investment cost and the supply curve deviation cost is comprehensively considered, the optimal wind-light energy storage capacity ratio is formulated for the power system, the peak shaving pressure brought to the power system by the uncertainty of new energy is reduced, the output fluctuation of the new energy is reduced, and the new energy consumption is promoted.

Description

New energy station energy storage configuration calculation method, system, medium and equipment
Technical Field
The invention belongs to the technical field of multi-energy complementary power supply planning in a new energy station, and particularly relates to a method, a system, a medium and equipment for calculating energy storage configuration of the new energy station.
Background
Renewable energy power generation has clearance and fluctuation characteristics, and with the large-scale grid connection of renewable new energy and the reduction of the proportion of a traditional power supply, the impact of the randomness of the power supply side on a power system is larger and larger, and the peak load regulation pressure is increased, so that the system planning and operation are challenged greatly.
The wind-solar output has a space-time smoothing effect and a resource complementation effect (generally speaking, the wind-solar output is large at night, and the photovoltaic output is zero at night), and the complementation characteristic is more and more obvious with the increase of the regional scale. Aiming at the new energy station, the random fluctuation of the new energy output can be reduced by fully mining the space-time complementary action of the wind and light output. In addition, the energy storage system can effectively overcome the fluctuation of photovoltaic and wind power output power by virtue of the chargeable and dischargeable operating characteristics, and the consumption of renewable energy sources is improved. The large-scale configuration of electrochemical energy storage is an effective way for stabilizing the output fluctuation of new energy and improving the utilization rate of the new energy. Because the construction cost of the existing chemical energy storage is still high, the wind and light energy storage capacity needs to be reasonably and optimally configured on the basis of utilizing the flexible adjustment capacity of the energy storage.
The wind-solar hybrid configuration can reduce the fluctuation of output, however, from the perspective of the system, the matching relationship between the source load and the load can highlight the multi-energy complementary effect. Because the load is fluctuant, the fluctuation of the load is borne by the controllable power supply, but if the total output of the new energy is not stable but has a similar fluctuation condition with the load, the change of the load can be borne by the new energy, so that the peak shaving pressure of the controllable power supply is greatly reduced, and simultaneously, the consumption of the new energy is increased to some extent, thereby meeting the requirement of the cleanness of power generation.
At present, most researches on capacity planning of new energy and electrochemical energy storage are conducted on multi-energy collaborative planning based on a traditional power Generation Extension Planning (GEP) model, and the relation between the new energy and the electrochemical energy storage is rarely considered independently.
Disclosure of Invention
The technical problem to be solved by the present invention is to provide a method, a system, a medium and a device for calculating energy storage configuration of a new energy station, which are used for configuring the most suitable new energy and energy storage ratio for different new energy stations, so as to reduce peak shaving pressure brought by the new energy to the system on the premise of ensuring economy.
The invention adopts the following technical scheme:
a new energy station energy storage configuration calculation method includes obtaining a load curve and an output curve of a new energy unit according to load requirements and historical data of new energy output; correcting the load curve by adopting a proportion method, keeping the peak-to-valley difference rate of the load curve before and after correction unchanged, and defining the corrected load curve as a planned output curve; taking the product of the output curve of the new energy unit and the new energy installation as a new energy output curve; establishing a minimum deviation model of the tracking planned output curve by taking the minimum deviation of the new energy output curve and the planned output curve as a target function; solving to obtain the wind-solar configuration with the optimal system peak regulation characteristic and the optimal follow-up load capacity; establishing a new energy station energy storage configuration model taking economic optimization as a target on the basis of wind and light configuration with optimal system peak regulation characteristics and following load capacity and with optimal investment cost, operation cost and deviation cost as target functions; and solving to obtain an energy storage proportioning result of the new energy station.
Specifically, the method for correcting the load curve by adopting the proportion method specifically comprises the following steps:
converting the new energy output curve into a per unit value curve, and determining the planned output of the wind-solar-energy storage complementary system at the tth typical day by adopting a proportional method
Figure BDA0003810046040000021
The following were used:
Figure BDA0003810046040000022
wherein,
Figure BDA0003810046040000023
is the load per unit value at the time t of the d typical day, K i The number of installed power plants is the number of installed power plants,
Figure BDA0003810046040000024
for the predicted capacity of the wind farm i at the time t on the typical day d,
Figure BDA0003810046040000025
the predicted output of the photovoltaic power station i at the time t on the typical day d is obtained.
Specifically, the objective function of the model for tracking the minimum deviation of the planned output curve is as follows:
Figure BDA0003810046040000031
wherein, ω is d Is the weight of a typical day d, and lambda (P) is the unit power deviation assessment cost,
Figure BDA0003810046040000032
for the power generation power of the wind-solar-energy storage complementary system at the tth time of the d typical day,
Figure BDA0003810046040000033
and the planned output of the wind-solar-energy storage complementary system at the tth typical day is obtained.
Specifically, the constraint of tracking the minimum model of the planned output curve deviation includes:
and (4) production variable constraint:
Figure BDA0003810046040000034
constraint of multi-energy complementary operation strategy:
Figure BDA0003810046040000035
Figure BDA0003810046040000036
Figure BDA0003810046040000037
Ω Total ＝Ω WT +Ω PV +Ω BES
Figure BDA0003810046040000038
operation constraint of the wind and light power station:
Figure BDA0003810046040000039
Figure BDA00038100460400000310
wherein, K i The installed number of power plants i, Z is an integer set, delta max For maximum permissible deviation of output, P i,d,t For the generated power of various power plants i in the energy base at the t moment of the d typical day, omega WT For a set of wind farms, Ω PV For photovoltaic power station set, omega BES Is a set of electrochemical energy storage power stations,
Figure BDA00038100460400000311
for the actual contribution of the wind farm i at the time t on the typical day d,
Figure BDA00038100460400000312
for the predicted capacity of the wind farm i at the time t on the typical day d,
Figure BDA00038100460400000313
for the actual output of the photovoltaic plant i at the time t on a typical day d,
Figure BDA00038100460400000314
the predicted output of the photovoltaic power station i at the time t on the typical day d is obtained.
Specifically, the objective function of the new energy station energy storage configuration model is as follows:
min f＝C inv +C oper +C dev
wherein, C inv As investment cost of the system, C oper To running costs, C dev The output deviation checking cost is shown.
Further, the investment cost C of the system inv And running cost C oper Respectively as follows:
Figure BDA0003810046040000041
Figure BDA0003810046040000042
wherein omega Total 、Ω BES For the aggregate of all power plants, the aggregate of electrochemical energy storage, K i The number of installed power plants of the power plant i,
Figure BDA0003810046040000043
the capacity of a single unit in a power plant i,
Figure BDA0003810046040000044
the maximum energy storage capacity of a single energy storage device in the electrochemical energy storage i,
Figure BDA0003810046040000045
and
Figure BDA0003810046040000046
investment cost per unit capacity and investment cost per unit electric quantity of the unit in the power plant i, omega WT 、Ω PV Is the collection of wind power plants and photovoltaic power plants, D is the collection of typical days, T is the collection of time within typical days, FO i Fixed operating cost per unit capacity, omega, for a power plant i d Is the weight of the typical day d,
Figure BDA0003810046040000047
and
Figure BDA0003810046040000048
for the predicted output of wind power plants and photovoltaic plants i at a typical moment of day d t,
Figure BDA0003810046040000049
and
Figure BDA00038100460400000410
the actual output of the wind power plant and the photovoltaic power plant i at the time t of the typical day d.
Specifically, the constraints of the new energy station energy storage configuration model include:
the electrochemical energy storage operation constraints are specifically as follows:
Figure BDA00038100460400000411
Figure BDA00038100460400000412
Figure BDA00038100460400000413
Figure BDA00038100460400000414
Figure BDA00038100460400000415
Figure BDA00038100460400000416
Figure BDA00038100460400000417
Figure BDA00038100460400000418
energy storage proportioning constraint:
Figure BDA00038100460400000419
each type of power utilization hours constraint:
Figure BDA0003810046040000051
Figure BDA0003810046040000052
Figure BDA0003810046040000053
Figure BDA0003810046040000054
Figure BDA0003810046040000055
Figure BDA0003810046040000056
Ω Total ＝Ω WT +Ω PV +Ω BES
Figure BDA0003810046040000057
Figure BDA0003810046040000058
Figure BDA0003810046040000059
wherein,
Figure BDA00038100460400000510
for the charging power of the energy storage station i at the typical time d,
Figure BDA00038100460400000511
for the discharge power of the energy storage plant i at the typical time d,
Figure BDA00038100460400000512
for the charging state of the energy storage plant i at the typical time of day d,
Figure BDA00038100460400000513
for the discharge state of the energy storage plant i at the typical time d,
Figure BDA00038100460400000514
the maximum single-machine charging power of the energy storage power station i,
Figure BDA00038100460400000515
is the maximum single-machine discharge power of the energy storage power station i,
Figure BDA00038100460400000516
for the energy storage capacity of the energy storage power station i at the typical time d,
Figure BDA00038100460400000517
and
Figure BDA00038100460400000518
respectively the self-loss coefficient, the charging efficiency and the discharging efficiency of the energy storage power station i,
Figure BDA00038100460400000519
the minimum single machine electricity storage quantity of the energy storage power station i,
Figure BDA00038100460400000520
is the maximum single-machine electricity storage quantity of the energy storage power station i,
Figure BDA00038100460400000521
generating power of energy storage power station i at typical day d，ψ BES In order to match the lowest capacity of stored energy with respect to new energy, the identifier of the power type is lambada, and comprises thermal power, hydroelectric power, wind power and photovoltaic power,
Figure BDA00038100460400000522
the minimum annual hours of use of the power type a,
Figure BDA00038100460400000523
the maximum annual hours of use of the power type a,
Figure BDA00038100460400000524
to be the annual minimum number of discharge hours of stored energy,
Figure BDA00038100460400000525
the annual maximum number of discharge hours of stored energy.
In a second aspect, an embodiment of the present invention provides a new energy station energy storage configuration computing system, including:
the correction module is used for obtaining a load curve and a new energy output curve according to the load demand and the historical data of the output of the new energy unit; correcting the load curve by adopting a proportional method, and keeping the peak-to-valley difference rate of the load curve before and after correction unchanged; defining the corrected load curve as a planned output curve, and taking the product of the output curve of the new energy unit and the new energy installation as a new energy output curve;
the deviation module is used for establishing a minimum deviation model of the tracking planned output curve by taking the minimum deviation of the new energy output curve and the planned output curve obtained by the correction module as a target function; solving to obtain the wind-solar configuration with the optimal system peak regulation characteristic and the optimal follow-up load capacity;
the calculation module is used for establishing a new energy station energy storage configuration model taking economic optimization as a target on the basis of the wind and light configuration with the optimal system peak regulation characteristic and the optimal follow-up load capacity obtained by the deviation module and taking the optimal investment cost, the optimal operation cost and the optimal deviation cost as a target function; and solving to obtain an energy storage proportioning result of the new energy station.
In a third aspect, a computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the steps of the new energy station energy storage configuration calculation method when executing the computer program.
In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, which includes a computer program, and when the computer program is executed by a processor, the computer program implements the steps of the new energy station energy storage configuration calculation method described above.
Compared with the prior art, the invention at least has the following beneficial effects:
a new energy station energy storage configuration calculation method includes designing different standardized plan supply curves aiming at different regions, namely configuring a new energy output curve before energy storage; then, according to the new energy output curve and the planned power generation curve, a load curve is subjected to standardization processing by adopting a proportional method; then, the energy storage unit is not considered, the wind-solar ratio is optimized by taking the minimum deviation between the new energy output curve and the planned output curve as a target, and the wind-solar ratio with the optimal system peak regulation characteristic and the optimal load following capacity is obtained; then, on the basis of wind-solar ratio, with optimal economy as a target, optimizing the ratio of the stored energy to new energy, and comprehensively considering the cost of the stored energy and the output regulating capacity of the stored energy to obtain a relatively balanced stored energy ratio result; and finally, defining evaluation indexes from two dimensions of volatility and inverse peak regulation characteristics, and analyzing a planning result. Based on the method provided by the invention, the balance between the energy storage investment cost and the supply curve deviation cost is comprehensively considered, and the optimal wind-light storage capacity ratio is formulated for the new energy station, so that the peak load pressure brought by the uncertainty of the new energy to the power system is reduced, the output fluctuation of the new energy is reduced, and the new energy consumption is promoted.
Furthermore, a standardized load curve and a new energy supply curve are formulated according to historical data, and the load curve is corrected by adopting a proportion method and a strategy that the planned power generation amount is the same as the new energy power generation amount, so that necessary conditions are provided for the following optimal matching.
Furthermore, the deviation of the planned output curve and the new energy output curve is used as a target curve, and a piecewise deviation punishment coefficient is formulated, so that on one hand, a nonlinear problem can be converted into a linear problem, and the solving efficiency is greatly improved; on the other hand, under the condition of reasonable parameter setting, the solution can achieve a good effect, the net load is close to a stable straight line, the fluctuation degree is minimum, and the peak regulation of the system is facilitated.
Furthermore, adding production variable constraints to ensure that the installed number in the new energy station is used for optimization, so that the result can be matched with an actual system; adding multi-energy complementary operation strategy constraints to ensure that the total electric quantity of the new energy can match the total electric quantity requirement, and making necessary preparation for subsequent energy storage planning; and adding the constraint of the wind and light power station to ensure that the wind and light output according to the new energy output curve.
Furthermore, the investment cost, the operation cost and the output deviation assessment cost are taken as targets, so that the planned output curve can be tracked as much as possible under the condition that the system economy is fully considered by the new energy matching energy storage.
Furthermore, in order to avoid the situation that the system economy is deteriorated due to the fact that a new energy source is matched with an energy storage blind tracking planned output curve, system investment cost and operation cost are added.
Furthermore, the investment cost of energy storage is added into the objective function, so that unreasonable results of excessive or insufficient investment are avoided; the running cost and the running constraint of energy storage are added, the running condition of the energy storage is described in detail, and the reasonability of a model result is ensured.
It is understood that the beneficial effects of the second to fourth aspects can be seen from the description of the first aspect, and are not described herein again.
In conclusion, compared with the traditional power supply planning method, the method has stronger applicability, and the method focuses on independently researching the relationship between new energy and electrochemical energy storage and considering the matching relationship between a new energy-energy storage combined output curve and an actual load curve so as to correct the load curve; when a new energy configuration model which does not contain stored energy is established, a method of a segmented penalty coefficient is adopted, so that the calculation efficiency is improved; then, when a new energy-energy storage configuration model is established, balance among energy storage investment, operation cost and supply curve deviation cost is comprehensively considered, so that a calculation result is more reasonable.
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
Drawings
FIG. 1 is a schematic flow diagram of the present invention;
FIG. 2 is a schematic diagram of a computer device provided in an embodiment of the present invention;
FIG. 3 is a graph of power generation curves of new energy and stored energy of scene 1 in Area _ 5;
FIG. 4 is a graph of power generation curves of new energy and stored energy of scene 1 in Area _ 3;
FIG. 5 is a graph of power generation curves for scene 2 new energy and stored energy in Area _ 7.
Detailed Description
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 some, not all, embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention.
In the description of the present invention, it should be understood that the terms "comprises" and/or "comprising" indicate the presence of the stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It is also to be understood that the terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the specification of the present invention and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should be further understood that the term "and/or" as used in this specification and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and including such combinations, e.g., a and/or B, may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the character "/" herein generally indicates that the former and latter associated objects are in an "or" relationship.
It should be understood that although the terms first, second, third, etc. may be used to describe preset ranges, etc. in embodiments of the present invention, these preset ranges should not be limited to these terms. These terms are only used to distinguish preset ranges from each other. For example, the first preset range may also be referred to as a second preset range, and similarly, the second preset range may also be referred to as the first preset range, without departing from the scope of the embodiments of the present invention.
The word "if" as used herein may be interpreted as "at 8230; \8230;" or "when 8230; \8230;" or "in response to a determination" or "in response to a detection", depending on the context. Similarly, the phrases "if determined" or "if detected (a stated condition or event)" may be interpreted as "when determined" or "in response to a determination" or "when detected (a stated condition or event)" or "in response to a detection (a stated condition or event)", depending on the context.
Various structural schematics according to the disclosed embodiments of the invention are shown in the drawings. The figures are not drawn to scale, wherein certain details are exaggerated and some details may be omitted for clarity of presentation. The shapes of various regions, layers and their relative sizes and positional relationships shown in the drawings are merely exemplary, and deviations may occur in practice due to manufacturing tolerances or technical limitations, and a person skilled in the art may additionally design regions/layers having different shapes, sizes, relative positions, according to actual needs.
The invention provides a new energy station energy storage configuration calculation method which is used for configuring the most appropriate new energy and energy storage proportion for different areas, so that the peak shaving pressure of the new energy on a power system is reduced on the premise of ensuring the economy; the new energy station energy storage configuration model based on the tracking load curve and the normalization ratio considers the matching relationship between the new energy-energy storage combined output curve and the load curve in practice, and the fluctuation of the non-traditional new energy-energy storage combined output is minimum, so that the calculation method is flexible; besides, the invention aims to research the theoretically optimal ratio between the new energy and the stored energy rather than the optimal capacity, and if the optimal ratio between the new energy is only required to be obtained, only the curve shape characteristic is considered and the load curve and the per unit value of the new energy output are required, so that the requirement on the aspect of data input is looser. Based on the method provided by the invention, the balance between the energy storage investment cost and the supply curve deviation cost is comprehensively considered, and the optimal wind-light energy storage capacity ratio is formulated for the power system, so that the peak load pressure brought by the uncertainty of new energy to the power system is reduced, the output fluctuation of the new energy is reduced, and the new energy consumption is promoted.
Referring to fig. 1, the present invention provides a method for calculating energy storage configuration of a new energy station, including the following steps:
s1, obtaining basic technical data of a system from a power system planning department;
when the method of the invention is applied, firstly, the required data is obtained from the relevant departments, which comprises the following steps:
the system basic technical data comprises: technical parameters of various types of power supplies, load requirements and historical information of new energy power generation.
And after the information is obtained from the relevant departments, the energy storage configuration calculation of the new energy station based on the tracking load curve and the normalization ratio is carried out according to the step shown in the figure 1.
The system basic technical data comprise: technical parameters of new energy and energy storage, load demand and historical information of new energy output.
The method comprises the steps that basic technical data of the system are obtained from a power system planning department, on one hand, new energy and basic technical data of energy storage to be built can provide economic technical parameters for an energy storage configuration scheme and are used for measuring investment economy of energy storage, and on the other hand, historical operation data of the system can be used for measuring deviation of a new energy supply curve and a planned output curve and operation economy of the energy storage configuration scheme, so that key data support is provided for formulating the final new energy and energy storage configuration scheme.
S2, according to the load demand and the historical data of the new energy output obtained in the step S1, a standardized load curve and a new energy supply curve are made; correcting the load curve by a proportional method, and keeping the peak-to-valley difference rate unchanged; carrying out secondary correction on the load curve by adopting a strategy that the planned generated energy is the same as the generated energy of the new energy;
converting the new energy curve into a per unit value curve, and converting the planned output curve by adopting a proportional method, wherein the specific steps are as follows:
Figure BDA0003810046040000101
wherein,
Figure BDA0003810046040000102
is the per unit value of the city original load curve at the typical time d and t.
S3, the energy storage unit is not considered, and the minimum deviation between the total output curve and the load curve is taken as a target function; constructing production variable constraints, multi-energy complementary operation strategy constraints and operation constraints of the wind and light power station; optimizing the wind-light ratio, and establishing a model for tracking the minimum deviation of the planned output curve; solving the minimum model of the output curve deviation of the tracking plan by adopting a branch-and-bound method to obtain the optimal wind-light ratio of the peak load regulation characteristic and the load following capacity of the system;
establishing a minimum deviation model according to the plan output curve corrected in the step S2;
the objective function of the minimum deviation model comprises the output deviation assessment cost of the system, and specifically comprises the following steps:
min f＝C dev (2)
Figure BDA0003810046040000111
wherein, C dev In order to evaluate the output deviation of the system, lambda (P) is the evaluation cost of the unit power deviation, the invention adopts a sectional evaluation mode, namely, the larger the output deviation is, the higher the corresponding evaluation cost is,
Figure BDA0003810046040000112
for the power generation of the wind-solar-energy storage complementary system at the t moment of the d typical day,
Figure BDA0003810046040000113
and the planned output of the wind-solar-energy storage complementary system at the tth typical day is obtained.
The production variable constraint is mainly that the number of production units of each power plant of the wind-solar-energy storage complementary system is an integer, and specifically comprises the following steps:
Figure BDA0003810046040000114
the multi-energy complementary operation strategy constrains that mainly in the aspect of peak regulation characteristics, the wind-solar-energy storage complementary system outputs power according to a given planned power generation curve, limits that the output deviation of the wind-solar-energy storage complementary system does not exceed a set maximum output deviation, further limits the total annual power generation amount of the system to enable the total annual power generation amount of the system to be larger than or equal to the electric quantity of the planned power generation curve, and specifically comprises the following steps:
Figure BDA0003810046040000115
Figure BDA0003810046040000116
Figure BDA0003810046040000117
Ω Total ＝Ω WT +Ω PV +Ω BES (8)
Figure BDA0003810046040000118
the operation constraint of the wind power plant and the photovoltaic power station mainly means that the output of the wind power plant and the photovoltaic power station at each moment is limited by resource level, and specifically comprises the following steps:
Figure BDA0003810046040000121
Figure BDA0003810046040000122
wherein, K i The installed number of power plants i, Z is an integer set, delta max To maximum permissible deviation of output, P i,d,t For the generated power of each type of power plant i in the energy base at the t moment of the d typical day, omega WT For a set of wind farms, Ω PV For photovoltaic power station set, omega BES Is a set of electrochemical energy storage power stations,
Figure BDA0003810046040000123
for the actual contribution of the wind farm i at the time t on the typical day d,
Figure BDA0003810046040000124
for the predicted capacity of the wind farm i at the time t on the typical day d,
Figure BDA0003810046040000125
for the actual output of the photovoltaic plant i at the time t on a typical day d,
Figure BDA0003810046040000126
the predicted output of the photovoltaic power station i at the time t on the typical day d is obtained.
S4, based on the wind-solar ratio obtained in the step S3, optimally taking investment cost, operation cost and deviation cost as a target function; adding electrochemical energy storage operation constraint, energy storage proportioning constraint and various types of power supply utilization hour constraint on the basis of the original constraint; establishing an energy storage configuration model of the new energy station; solving the energy storage configuration optimization model by adopting a commercial solver so as to optimize the proportion of the energy storage to new energy, and comprehensively considering the cost of the energy storage and the output adjustment capacity of the energy storage to obtain a relatively balanced energy storage proportion result;
and (4) according to the wind-solar ratio data obtained in the step (S3), constructing an economic optimal model considering energy storage, and optimizing the ratio of the energy storage to new energy.
The objective function of the economic optimum model is:
min f＝C inv +C oper +C dev (12)
wherein, C inv As investment cost of the system, C oper For operating costs, C dev The output deviation checking cost is shown.
Investment cost C of the system inv And an operating cost C oper Respectively as follows:
Figure BDA0003810046040000127
Figure BDA0003810046040000128
wherein omega Total 、Ω BES For the aggregate of all power plants, the aggregate of electrochemical energy storage, K i The number of installed power plants of the power plant i,
Figure BDA0003810046040000129
the capacity of a single unit in a power plant i,
Figure BDA00038100460400001210
the maximum energy storage capacity of a single energy storage device in the electrochemical energy storage i,
Figure BDA00038100460400001211
and
Figure BDA00038100460400001212
the investment cost per unit capacity and the investment cost per unit electric quantity of the unit in the i-type power plant, omega WT 、Ω PV The method is a collection of a wind power plant and a photovoltaic power station, D is a typical day collection, the value range is from 1 to | D |, | | represents the cardinal number of the collection, T is a typical day time collection, the value range is from 1 to | T |, FO i For fixed operating cost per unit capacity of plant i, ω d Is the weight of the typical day d and,
Figure BDA0003810046040000131
and
Figure BDA0003810046040000132
for the predicted output of wind power plants and photovoltaic plants i at a typical moment of day d t,
Figure BDA0003810046040000133
and
Figure BDA0003810046040000134
the actual output of the wind power plant and the photovoltaic power plant i at the time t of the typical day d.
The electrochemical energy storage operation constraint is mainly the limitation of the operation state (including charge and discharge state, charge and discharge power and energy storage capacity) of electrochemical energy storage, and specifically comprises the following steps:
Figure BDA00038100460400001314
Figure BDA0003810046040000135
Figure BDA0003810046040000136
Figure BDA0003810046040000137
Figure BDA0003810046040000138
Figure BDA0003810046040000139
Figure BDA00038100460400001310
Figure BDA00038100460400001311
since the equations (15) and (16) contain such a nonlinear term that a variable of 0 to 1 is multiplied by an independent variable, the constraint is linearized by the BigM method to obtain the equations (23), (24), (25), and (26).
The energy storage proportioning constraint mainly limits the proportion of electrochemical energy storage relative to a new energy installation machine so as to meet the requirement of an energy storage proportioning policy of a new project, and specifically comprises the following steps:
Figure BDA00038100460400001312
the constraint on the number of hours of use of each type of power supply mainly means that the number of hours of use of each type of power supply needs to satisfy the constraint, and the constraint should be greater than a given minimum number of hours of use and less than a given maximum number of hours of use, specifically:
Figure BDA00038100460400001313
Figure BDA0003810046040000141
s.t(4)～(11)
wherein,
Figure BDA0003810046040000142
for the charging power of the energy storage station i at the typical time d,
Figure BDA0003810046040000143
for the discharge power of the energy storage power station i at the typical time d,
Figure BDA0003810046040000144
for the charging state of the energy storage plant i at the typical time d,
Figure BDA0003810046040000145
for the discharge state of the energy storage power station i at the typical time d,
Figure BDA0003810046040000146
for the maximum single machine charging power of the energy storage power station i,
Figure BDA0003810046040000147
is the maximum single-machine discharge power of the energy storage power station i,
Figure BDA0003810046040000148
for the energy storage capacity of the energy storage power station i at the typical time d,
Figure BDA0003810046040000149
and
Figure BDA00038100460400001410
respectively the self-loss coefficient, the charging efficiency and the discharging efficiency of the energy storage power station i,
Figure BDA00038100460400001411
the minimum single machine electricity storage quantity of the energy storage power station i,
Figure BDA00038100460400001412
for energy-storage power stations iThe maximum single-machine electricity storage capacity is realized,
Figure BDA00038100460400001413
for the generated power, psi, of the energy storage plant i at a typical time d BES In order to match the lowest capacity of stored energy relative to new energy, the lambda is an identifier of a power supply type, including thermal power, hydroelectric power, wind power and photovoltaic power,
Figure BDA00038100460400001414
the minimum annual hours of use of the power type a,
Figure BDA00038100460400001415
the maximum annual hours of use of the power type a,
Figure BDA00038100460400001416
for the annual minimum number of discharge hours of stored energy,
Figure BDA00038100460400001417
the maximum number of discharge hours per year of stored energy.
And S5, according to the wind-solar energy storage ratio data obtained in the step S4, defining evaluation indexes from two dimensions of volatility and anti-peak-shaving characteristics, and comparing the indexes before and after optimal configuration to prove the effectiveness of the method.
The output fluctuation rate of the single-moment new energy system is as follows:
Figure BDA00038100460400001418
the output fluctuation of the new energy system is measured by adopting the annual average fluctuation rate, and the method specifically comprises the following steps:
Figure BDA00038100460400001419
the peak-to-valley difference rate of the output deviation is adopted as the inverse peak regulation characteristic, and specifically comprises the following steps:
Figure BDA00038100460400001420
Figure BDA00038100460400001421
Figure BDA00038100460400001422
where ρ is t The output change rate P of the wind-solar-energy storage complementary system at the time t t The output of the wind-solar storage complementary system at the moment t is represented by rho which is the annual average fluctuation rate of the wind-solar storage complementary system d,t The output, eta, of the wind-solar hybrid system at the typical d-time t d The ratio of typical day d is 0 to 1, D is a typical day set, T is a typical time set in the day, the value range is from 1 to | T |,
Figure BDA0003810046040000152
is the power-out deviation power at time t,
Figure BDA0003810046040000151
for the planned generated power at time t, P t WP The actual generating power, xi, of the wind-solar hybrid system at the moment t d Is the peak-to-valley difference of the output deviation curve on a typical day d, S is the total installed capacity of the new energy, xi is the peak-to-valley difference rate of the output deviation curve, eta d The ratio of the typical day d is 0 to 1.
In another embodiment of the present invention, a new energy station energy storage configuration calculation system is provided, which can be used to implement the new energy station energy storage configuration calculation method described above, and specifically, the new energy station energy storage configuration calculation system includes a correction module, a deviation module, and a calculation module.
The correction module is used for obtaining a load curve and an output curve of a new energy unit according to the load demand and historical data of new energy output; correcting the load curve by adopting a proportion method, and keeping the peak-to-valley difference rate of the load curve before and after correction unchanged; defining the corrected load curve as a planned output curve, and taking the product of the output curve of the new energy unit and the new energy installation as a new energy output curve;
the deviation module is used for establishing a minimum deviation model of the tracking planned output curve by taking the minimum deviation of the new energy output curve and the planned output curve obtained by the correction module as a target function; solving to obtain the wind-solar configuration with the optimal system peak regulation characteristic and the optimal follow-up load capacity;
the calculation module is used for establishing a new energy station energy storage configuration model taking economic optimization as a target on the basis of the wind and light configuration with the optimal system peak regulation characteristic and the optimal follow-up load capacity obtained by the deviation module and taking the optimal investment cost, the optimal operation cost and the optimal deviation cost as a target function; and solving to obtain an energy storage proportioning result of the new energy station.
In yet another embodiment of the present invention, a terminal device is provided that includes a processor and a memory for storing a computer program comprising program instructions, the processor being configured to execute the program instructions stored by the computer storage medium. The Processor may be a Central Processing Unit (CPU), or may be other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, a discrete Gate or transistor logic device, a discrete hardware component, etc., which is a computing core and a control core of the terminal, and is adapted to implement one or more instructions, and is specifically adapted to load and execute one or more instructions to implement a corresponding method flow or a corresponding function; the processor provided by the embodiment of the invention can be used for the operation of the energy storage configuration calculation method of the new energy station, and comprises the following steps:
obtaining a load curve and an output curve of a new energy unit according to the load demand and historical data of new energy output; correcting the load curve by adopting a proportion method, keeping the peak-to-valley difference rate of the load curve before and after correction unchanged, and defining the corrected load curve as a planned output curve; taking the product of the output curve of the new energy unit and the new energy installation as a new energy output curve; establishing a tracking planned output curve deviation minimum model by taking the minimum deviation of the new energy output curve and the planned output curve as a target function, and solving to obtain the wind-light configuration with the optimal system peak regulation characteristic and the optimal follow-up load capacity; establishing a new energy station energy storage configuration model taking economic optimization as a target on the basis of wind and light configuration with optimal system peak regulation characteristics and following load capacity and with optimal investment cost, operation cost and deviation cost as target functions; and solving to obtain an energy storage proportioning result of the new energy station.
In still another embodiment of the present invention, the present invention further provides a storage medium, specifically a computer-readable storage medium (Memory), which is a Memory device in a terminal device and is used for storing programs and data. It is understood that the computer readable storage medium herein may include a built-in storage medium in the terminal device, and may also include an extended storage medium supported by the terminal device. The computer-readable storage medium provides a storage space storing an operating system of the terminal. Also, one or more instructions, which may be one or more computer programs (including program code), are stored in the memory space and are adapted to be loaded and executed by the processor. It should be noted that the computer-readable storage medium may be a high-speed RAM Memory, or may be a Non-Volatile Memory (Non-Volatile Memory), such as at least one disk Memory.
The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of the method for calculating the energy storage configuration of the new energy station in the above embodiment; one or more instructions in the computer readable storage medium are loaded by the processor and perform the steps of:
obtaining a load curve and an output curve of a new energy unit according to the load demand and historical data of new energy output; correcting the load curve by adopting a proportion method, keeping the peak-to-valley difference rate of the load curve before and after correction unchanged, and defining the corrected load curve as a planned output curve; taking the product of the output curve of the new energy unit and the new energy installation as a new energy output curve; establishing a tracking planned output curve deviation minimum model by taking the minimum deviation of the new energy output curve and the planned output curve as a target function, and solving to obtain the wind-light configuration with the optimal system peak regulation characteristic and the optimal follow-up load capacity; establishing a new energy station energy storage configuration model taking economic optimization as a target on the basis of wind and light configuration with optimal system peak regulation characteristics and following load capacity and with optimal investment cost, operation cost and deviation cost as target functions; and solving to obtain an energy storage proportioning result of the new energy station.
Referring to fig. 2, the computer apparatus 60 of this embodiment includes: the processor 61, the memory 62, and the computer program 63 stored in the memory 62 and capable of running on the processor 61, where the computer program 63 is executed by the processor 61 to implement the energy storage configuration calculation method of the new energy station in the embodiment, and in order to avoid repetition, details are not repeated herein. Alternatively, the computer program 63 is executed by the processor 61 to implement the functions of each model/unit in the new energy station energy storage configuration computing system according to the embodiment, which are not described herein again to avoid repetition.
The computing device 60 may be a desktop computer, a notebook, a palm top computer, a cloud server, or other computing device. The computer device 60 may include, but is not limited to, a processor 61, a memory 62. Those skilled in the art will appreciate that fig. 2 is merely an example of a computer device 60 and is not intended to limit the computer device 60 and that it may include more or less components than those shown, or some of the components may be combined, or different components, e.g., the computer device may also include input output devices, network access devices, buses, etc.
The Processor 61 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The storage 62 may be an internal storage unit of the computer device 60, such as a hard disk or a memory of the computer device 60. The memory 62 may also be an external storage device of the computer device 60, such as a plug-in hard disk provided on the computer device 60, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like.
Further, memory 62 may also include both internal and external storage devices for computer device 60. The memory 62 is used to store computer programs and other programs and data required by the computer device. The memory 62 may also be used to temporarily store data that has been output or is to be output.
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, 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 some, but not all embodiments of the present invention. The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. 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.
In order to verify the effectiveness of the method, a certain test system is selected for calculation and analysis.
The test system was divided into 11 zones, and the boundary conditions of the 11 zones are shown in table 1:
TABLE 1 regional boundary conditions
Figure BDA0003810046040000181
Figure BDA0003810046040000191
The energy storage configuration proportion is limited, the minimum proportion is 5%, the maximum proportion is 20%, the retirement of the unit is not considered, the minimum value of the proportion of wind power and photovoltaic in the total new energy capacity is 10%, and the energy storage parameters are shown in table 2:
TABLE 2 energy storage cell parameters
Figure BDA0003810046040000192
And under the boundary conditions and the parameter settings, optimizing the wind power installed capacity, the photovoltaic installed capacity and the energy storage installed capacity by using the optimal planning model. For convenience of demonstration and analysis, the planning results of wind power generation and photovoltaic power generation are converted into a relative ratio, so that the ratio of the energy storage capacity to the new energy capacity is obtained, as shown in table 3:
TABLE 3 regional planning results
Figure BDA0003810046040000193
Figure BDA0003810046040000201
The main factors influencing the energy storage structure are two, namely the existing new energy structure and the load curve characteristic. On the new energy structure, wind power generation generally has the characteristics of large output at night and small output at day, and needs photovoltaic power generation to make up for the deficiency of output at day. However, if the existing photovoltaic power generation capacity is too large, the total output of the new energy exceeds the allowable deviation range, and energy storage is needed to participate in adjustment. From the characteristic of the load curve, due to the randomness of the new energy resources, the new energy station cannot flexibly adjust the power generation amount. The larger the peak-to-valley difference of the load curve, the higher the requirement on the capacity of stored energy.
Referring to fig. 3, the area _5conventional photovoltaic capacity 1150MW has reached 87% of the RE conventional total capacity. In the planning scheme, the photovoltaic power generation capacity is not increased, and the RE energy which is planned to be increased is provided by newly added wind power. As can be seen from fig. 3, the ESS duty is 20% because the photovoltaic duty is too high and the midday peak shaver pressure increases sharply, necessitating extensive ESS regulation.
Referring to fig. 4, in 12-17 h of Area _3, the planned yield curve is in the valley, and the total new energy yield is greater than the upper limit. The stored energy reduces the total output to a position close to the upper limit by charging. And the actual output power of the wind power is lower than the planned output power at night. The stored energy should be discharged periodically to make the deviation of each period uniform and ensure the actual output curve to track the planned output curve.
Referring to fig. 5, the ratio of the wind power capacity to the photovoltaic capacity of area 7 is 0.9:0.1. due to the complementarity of the wind power and the photovoltaic power, the total output of the new energy is relatively stable under the condition of a proper wind power and photovoltaic capacity ratio. As can be seen from fig. 5, if there is no stored energy, the actual production curve itself fluctuates around the planned production curve within a limited range. Therefore, it is not economical to configure the stored energy from the viewpoint of cost. But with consideration of policy and the like, 5% of the stored energy is put into the configuration.
And finally, evaluating the planning result by using the complementary indexes defined in the previous step.
First, the annual fluctuation rate ρ of the 11 region optimization results is calculated 1 Annual peak-to-valley difference rate xi of sum net load 1 (ii) a Then, the ratio of the wind power to the photovoltaic capacity is kept unchanged (equal to the existing ratio), the ESS is not configured, and the RE capacity of each region in the target year is optimized again. Calculating annual fluctuation ratio rho 2 Net load peak-to-valley difference rate xi of year 2 。
Through comparison, the improvement degree and the peak reversal regulation and control characteristics of new energy and energy storage optimization on the fluctuation rate are analyzed, and the characteristics are shown in table 4:
TABLE 4 complementary Effect of optimization models
Figure BDA0003810046040000211
By optimizing the new energy and energy storage planning model, the annual fluctuation rate of the complementary system is obviously reduced to 37.5-87.5%, and the annual peak-valley difference rate of the net load of the complementary system is also obviously reduced to 54.6-98.6%. Generally, the larger the energy storage configuration proportion, the greater the rate of change of the annual peak-to-valley rate of net load.
In summary, according to the new energy station energy storage configuration calculation method, the system, the medium and the equipment, the new energy station energy storage configuration model based on the tracking load curve and the normalization ratio considers the matching relationship between the new energy-energy combined output curve and the load curve in the system, and the fluctuation of the new energy-energy combined output is not the minimum in the traditional new energy-energy combined output, so that the calculation method is flexible.
Besides, the invention aims to research the theoretical optimal ratio between new energy and stored energy of the new energy station instead of the optimal capacity, and if the optimal ratio between the new energy is only required to be obtained, only the per unit value of the load curve and the new energy output is required to be given, and only the curve shape characteristic is considered, so that the requirement on the aspect of data input is looser.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application 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 application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. 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.
The above-mentioned contents are only for illustrating the technical idea of the present invention, and the protection scope of the present invention is not limited thereby, and any modification made on the basis of the technical idea of the present invention falls within the protection scope of the claims of the present invention.

Claims (10)

1. A new energy station energy storage configuration calculation method is characterized in that a load curve and an output curve of a new energy unit are obtained according to load requirements and historical data of new energy output; correcting the load curve by adopting a proportion method, keeping the peak-to-valley difference rate of the load curve before and after correction unchanged, and defining the corrected load curve as a planned output curve; taking the product of the output curve of the new energy unit and the new energy installation as a new energy output curve; establishing a tracking planned output curve deviation minimum model by taking the minimum deviation of the new energy output curve and the planned output curve as a target function, and solving to obtain the optimal wind and light configuration of the peak load regulation characteristic and the load following capacity of the system; establishing a new energy station energy storage configuration model taking economic optimization as a target on the basis of wind and light configuration with optimal system peak regulation characteristics and following load capacity and with optimal investment cost, operation cost and deviation cost as target functions; and solving to obtain an energy storage proportioning result of the new energy station.
2. The method for calculating the energy storage configuration of the new energy station according to claim 1, wherein the step of correcting the load curve by using a proportional method specifically comprises the following steps:
converting the new energy output curve into a per unit value curve, and determining the planned output of the wind-solar-energy storage complementary system at the tth typical day by adopting a proportional method
Figure FDA0003810046030000011
The following were used:
Figure FDA0003810046030000012
wherein,
Figure FDA0003810046030000013
is the load per unit value at the time t of the d typical day, K i The number of installed power plants is the number of installed power plants,
Figure FDA0003810046030000014
for the predicted capacity of the wind farm i at the time t on the typical day d,
Figure FDA0003810046030000015
and (4) predicting the output of the photovoltaic power station i at the typical day d and t.
3. The new energy station energy storage configuration calculation method of claim 1, wherein the objective function of the model for tracking the minimum deviation of the planned output curve is as follows:
Figure FDA0003810046030000016
wherein, ω is d Is the weight of a typical day d, and lambda (P) is the unit power deviation assessment cost,
Figure FDA0003810046030000017
for the power generation of the wind-solar-energy storage complementary system at the t moment of the d typical day,
Figure FDA0003810046030000018
and the planned output of the wind-solar-energy storage complementary system at the tth typical day is obtained.
4. The new energy station energy storage configuration calculation method of claim 1, wherein tracking constraints of the model with minimum projected output curve deviation comprises:
and (3) restricting production variables:
Figure FDA0003810046030000021
constraint of multi-energy complementary operation strategy:
Figure FDA0003810046030000022
Figure FDA0003810046030000023
Figure FDA0003810046030000024
Ω Total ＝Ω WT +Ω PV +Ω BES
Figure FDA0003810046030000025
operation constraint of the wind and light power station:
Figure FDA0003810046030000026
Figure FDA0003810046030000027
wherein, K i The installed number of power plants i, Z is an integer set, delta max For maximum permissible deviation of output, P i,d,t For the generated power of each type of power plant i in the energy base at the t moment of the d typical day, omega WT For a set of wind farms, Ω PV For a collection of photovoltaic power stations, Ω BES Is a set of electrochemical energy storage power stations,
Figure FDA0003810046030000028
for the actual contribution of the wind farm i at the time t on the typical day d,
Figure FDA0003810046030000029
for the predicted capacity of the wind farm i at the time t on the typical day d,
Figure FDA00038100460300000210
for the actual output of the photovoltaic plant i at the time t on a typical day d,
Figure FDA00038100460300000211
the predicted output of the photovoltaic power station i at the time t on the typical day d is obtained.
5. The new energy station energy storage configuration calculation method according to claim 1, wherein the objective function of the new energy station energy storage configuration model is as follows:
min f＝C inv +C oper +C dev
wherein, C inv For investment costs of the system, C oper For operating costs, C dev The output deviation checking cost is shown.
6. The method for calculating the energy storage configuration of the new energy station as claimed in claim 5, wherein the investment cost C of the system inv And an operating cost C oper Respectively as follows:
Figure FDA0003810046030000031
Figure FDA0003810046030000032
wherein omega Total 、Ω BES For the aggregate of all power plants, the aggregate of electrochemical energy storage, K i The number of installed power plants of the power plant i,
Figure FDA0003810046030000033
the capacity of a single unit in a power plant i,
Figure FDA0003810046030000034
the maximum energy storage capacity of a single energy storage device in the electrochemical energy storage i,
Figure FDA0003810046030000035
and
Figure FDA0003810046030000036
the investment cost per unit capacity and the investment cost per unit electric quantity of the unit in the i-type power plant, omega WT 、Ω PV Is a collection of wind power plants and photovoltaic power plants, and D is a typical day setIn sum, T is a typical set of time of day, FO i For fixed operating cost per unit capacity of plant i, ω d Is the weight of the typical day d,
Figure FDA0003810046030000037
and
Figure FDA0003810046030000038
for the predicted output of wind power plants and photovoltaic plants i at a typical moment of day d t,
Figure FDA0003810046030000039
and
Figure FDA00038100460300000310
the actual output of the wind power plant and the photovoltaic power plant i at the time t of the typical day d.
7. The new energy station energy storage configuration calculation method according to claim 1, wherein the constraints of the new energy station energy storage configuration model include:
the electrochemical energy storage operation constraints are specifically as follows:
Figure FDA00038100460300000311
Figure FDA00038100460300000312
Figure FDA00038100460300000313
Figure FDA00038100460300000314
Figure FDA00038100460300000315
Figure FDA00038100460300000316
Figure FDA00038100460300000317
Figure FDA00038100460300000318
energy storage proportioning constraint:
Figure FDA00038100460300000319
each type of power utilization hours constraint:
Figure FDA0003810046030000041
Figure FDA0003810046030000042
Figure FDA0003810046030000043
Figure FDA0003810046030000044
Figure FDA0003810046030000045
Figure FDA0003810046030000046
Ω Total ＝Ω WT +Ω PV +Ω BES
Figure FDA0003810046030000047
Figure FDA0003810046030000048
Figure FDA0003810046030000049
wherein,
Figure FDA00038100460300000410
for the charging power of the energy storage plant i at the typical time d,
Figure FDA00038100460300000411
for the discharge power of the energy storage power station i at the typical time d,
Figure FDA00038100460300000412
for the charging state of the energy storage plant i at the typical time of day d,
Figure FDA00038100460300000413
for the discharge state of the energy storage power station i at the typical time d,
Figure FDA00038100460300000414
charging the largest single machine of the energy storage power station iThe power of the electric motor is controlled by the power controller,
Figure FDA00038100460300000415
is the maximum single-machine discharge power of the energy storage power station i,
Figure FDA00038100460300000416
for the energy storage of the energy storage plant i at the typical moment of day d t,
Figure FDA00038100460300000417
and
Figure FDA00038100460300000418
respectively the self-loss coefficient, the charging efficiency and the discharging efficiency of the energy storage power station i,
Figure FDA00038100460300000419
the minimum single machine electricity storage quantity of the energy storage power station i,
Figure FDA00038100460300000420
is the maximum single-machine electricity storage quantity of the energy storage power station i,
Figure FDA00038100460300000421
for the generated power of the energy storage station i at the typical day d at time t, psi BES In order to match the lowest capacity of stored energy with respect to new energy, the identifier of the power type is lambada, and comprises thermal power, hydroelectric power, wind power and photovoltaic power,
Figure FDA00038100460300000422
the minimum annual hours of use of the power type a,
Figure FDA00038100460300000423
the maximum annual hours of use of the power type a,
Figure FDA00038100460300000424
annual minimum number of discharge hours for energy storage，
Figure FDA00038100460300000425
The maximum number of discharge hours per year of stored energy.
8. A new energy station energy storage configuration computing system, comprising:
the correction module is used for obtaining a load curve and an output curve of a new energy unit according to the load demand and historical data of new energy output; correcting the load curve by adopting a proportion method, and keeping the peak-to-valley difference rate of the load curve before and after correction unchanged; defining the corrected load curve as a planned output curve, and taking the product of the output curve of the new energy unit and the new energy installation as a new energy output curve;
the deviation module is used for establishing a minimum deviation model of the tracking planned output curve by taking the minimum deviation of the new energy output curve and the planned output curve obtained by the correction module as a target function; solving to obtain the wind-solar configuration with the optimal system peak regulation characteristic and the optimal follow-up load capacity;
the calculation module is used for establishing a new energy station energy storage configuration model taking economic optimization as a target on the basis of the wind and light configuration with the optimal system peak regulation characteristic and the optimal follow-up load capacity obtained by the deviation module and taking the optimal investment cost, the optimal operation cost and the optimal deviation cost as a target function; and solving to obtain an energy storage proportioning result of the new energy station.
9. A computer readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by a computing device, cause the computing device to perform any of the methods of claims 1-7.
10. A computing device, comprising:
one or more processors, memory, and one or more programs stored in the memory and configured for execution by the one or more processors, the one or more programs including instructions for performing any of the methods of claims 1-7.
CN202211016973.5A 2022-08-22 2022-08-22 New energy station energy storage configuration calculation method, system, medium and equipment Pending CN115313441A (en)

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115940152A (en) * 2023-02-22 2023-04-07 国网山东省电力公司东营市垦利区供电公司 New energy installed capacity optimal allocation method, system, terminal and medium
CN116231764A (en) * 2023-05-08 2023-06-06 厦门晶晟能源科技有限公司 Source network charge storage coordination control method and system

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115940152A (en) * 2023-02-22 2023-04-07 国网山东省电力公司东营市垦利区供电公司 New energy installed capacity optimal allocation method, system, terminal and medium
CN115940152B (en) * 2023-02-22 2023-08-18 国网山东省电力公司东营市垦利区供电公司 New energy installed capacity optimal allocation method, system, terminal and medium
CN116231764A (en) * 2023-05-08 2023-06-06 厦门晶晟能源科技有限公司 Source network charge storage coordination control method and system

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