CN114928054B - Energy storage multi-objective coordination optimization method and system considering uncertainty of new energy - Google Patents

Energy storage multi-objective coordination optimization method and system considering uncertainty of new energy Download PDF

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CN114928054B
CN114928054B CN202210838805.8A CN202210838805A CN114928054B CN 114928054 B CN114928054 B CN 114928054B CN 202210838805 A CN202210838805 A CN 202210838805A CN 114928054 B CN114928054 B CN 114928054B
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CN114928054A (en
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朱文广
张华�
王伟
钟士元
王欣
陈俊志
陈会员
郑春
李映雪
杨超
薄明明
欧阳斌
马瑞
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State Grid Corp of China SGCC
Economic and Technological Research Institute of State Grid Jiangxi Electric Power Co Ltd
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Economic and Technological Research Institute of State Grid Jiangxi Electric Power Co Ltd
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Abstract

The invention discloses an energy storage multi-target coordination optimization method and system considering new energy uncertainty, wherein the method comprises the following steps: constructing an energy storage configuration optimization model by using the maximum comprehensive benefit of energy storage as a first objective function to obtain optimized rated capacity and rated power; correcting the rated capacity and the rated power to obtain real-time power after energy storage optimization; calculating the new energy consumption rate and the cost recovery age limit after energy storage optimization based on the real-time power, and judging whether the new energy consumption rate is smaller than a first preset threshold value and whether the cost recovery age limit is larger than a second preset threshold value; and if the new energy consumption rate is smaller than a first preset threshold and the cost recovery year is larger than a second preset threshold, correcting the yield coefficient and the cost coefficient according to the difference value of the new energy consumption rate and the first preset threshold to obtain the target rated capacity and rated power. The problem of energy storage configuration under long-term return is effectively solved in the smelting industry of high-proportion new energy access, and benefit maximization of users is achieved.

Description

Energy storage multi-target coordination optimization method and system considering new energy uncertainty
Technical Field
The invention belongs to the technical field of electrical automation, and particularly relates to an energy storage multi-target coordination optimization method and system considering uncertainty of new energy.
Background
Changing an energy structure, introducing a higher proportion of new energy is a key means for energy conservation and emission reduction, but as the permeability of the new energy in a power grid system is continuously increased, the randomness of the new energy and the unpredictability of load bring huge peak load pressure to the system, so that the consumption rate of the new energy is not high, and the phenomena of wind abandonment and light abandonment are more obvious. The energy storage is the best choice for solving the problem of new energy consumption and improving the peak regulation, frequency regulation and voltage regulation capabilities of a power grid, so the energy storage has attracted extensive attention in recent years.
The existing energy storage power station construction scheme mainly plans the power grid side, most of the energy storage power station construction scheme only researches the peak load regulation, frequency regulation and other capabilities of the power grid system, and influences of distributed energy storage on the income of industrial users and the consumption rate of new energy are ignored.
Disclosure of Invention
The invention provides an energy storage multi-target coordination optimization method and system considering new energy uncertainty, which are used for solving the technical problems of low income of industrial users and low consumption rate of new energy.
In a first aspect, the invention provides an energy storage multi-objective coordination optimization method considering uncertainty of new energy, which comprises the following steps: establishing an energy storage configuration optimization model by taking the maximum comprehensive income of energy storage installation of industrial users as a first objective function to obtain the rated capacity and rated power of energy storage optimization, wherein the comprehensive income is the sum of the energy storage income and the carbon emission reduction income minus the energy storage cost, and the expression of the first objective function is as follows:
Figure 311934DEST_PATH_IMAGE001
in the formula (I), wherein,
Figure 979676DEST_PATH_IMAGE002
for the peak clipping and valley filling income of the j month of energy storage,
Figure 979380DEST_PATH_IMAGE003
for the defense gains of the basic electricity price in the j month of energy storage,
Figure 707165DEST_PATH_IMAGE004
in order to save the construction cost of the energy storage,
Figure 320549DEST_PATH_IMAGE005
、
Figure 526402DEST_PATH_IMAGE006
respectively a gain factor and a cost factor,
Figure 643263DEST_PATH_IMAGE007
the number of the months is the number of the months,
Figure 807528DEST_PATH_IMAGE008
the carbon emission reduction benefit for month j; correcting the rated capacity and the rated power according to a preset intra-day operation optimization model to obtain real-time power after energy storage optimization; calculating a new energy consumption rate and a cost recovery year limit based on the real-time power after energy storage optimization, and judging whether the new energy consumption rate is smaller than a first preset threshold and whether the cost recovery year limit is larger than a second preset threshold; if the new energy consumption rate is smaller than a first preset threshold and the cost recovery year is larger than a second preset threshold, calculating the difference value between the new energy consumption rate and the first preset threshold
Figure 252416DEST_PATH_IMAGE009
And according to the difference
Figure 386594DEST_PATH_IMAGE009
And correcting the yield coefficient and the cost coefficient to obtain the rated capacity and the rated power of the corrected energy storage target.
In a second aspect, the present invention provides an energy storage multi-objective coordination optimization system considering uncertainty of new energy, including: the building module is configured to build an energy storage configuration optimization model by taking the maximum comprehensive income of energy storage installation of industrial users as a first objective function to obtain the rated capacity and the rated power of energy storage optimization, wherein the comprehensive income is the sum of the energy storage income and the carbon emission reduction income minus the energy storage cost, and the expression of the first objective function is as follows:
Figure 498906DEST_PATH_IMAGE001
in the formula (I), wherein,
Figure 693127DEST_PATH_IMAGE002
for the peak clipping and valley filling income of the j month of energy storage,
Figure 890890DEST_PATH_IMAGE003
for the defense gains of the basic electricity price in the j month of energy storage,
Figure 563180DEST_PATH_IMAGE010
in order to save the construction cost of the energy storage,
Figure 795578DEST_PATH_IMAGE005
、
Figure 770488DEST_PATH_IMAGE006
respectively a gain factor and a cost factor,
Figure 580181DEST_PATH_IMAGE007
for the number of months,
Figure 197107DEST_PATH_IMAGE008
the carbon emission reduction benefit for month j; the first correction module is configured to correct the rated capacity and the rated power according to a preset intra-day operation optimization model to obtain real-time power after energy storage optimization; the judging module is configured to calculate a new energy consumption rate and a cost recovery year based on the real-time power after energy storage optimization, and judge whether the new energy consumption rate is smaller than a first preset threshold and whether the cost recovery year is larger than a second preset threshold; a second correction module configured to calculate a difference between the new energy consumption rate and a first preset threshold if the new energy consumption rate is less than the first preset threshold and the cost recovery period is greater than a second preset threshold
Figure 143066DEST_PATH_IMAGE009
And according to the difference
Figure 554456DEST_PATH_IMAGE009
And correcting the yield coefficient and the cost coefficient to obtain the rated capacity and the rated power of the corrected energy storage target.
In a third aspect, an electronic device is provided, comprising: the energy storage multi-objective coordination optimization method comprises at least one processor and a memory which is in communication connection with the at least one processor, wherein the memory stores instructions which can be executed by the at least one processor, and the instructions are executed by the at least one processor, so that the at least one processor can execute the steps of the energy storage multi-objective coordination optimization method considering the uncertainty of the new energy source according to any embodiment of the invention.
In a fourth aspect, the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the program instructions, when executed by a processor, cause the processor to perform the steps of the method for energy storage multi-objective coordination optimization considering new energy uncertainty according to any embodiment of the present invention.
The energy storage multi-objective coordination optimization method and system considering the uncertainty of the new energy aim at improving economy and the consumption rate of the new energy and reducing the carbon emission of a user, energy storage is optimized for configuration and operation of energy storage on the user side, during operation optimization, energy storage can be optimized and scheduled according to the electricity price condition and the load condition of the user, energy storage charging time is distributed in the time period of low electricity price and high new energy output, energy storage discharging is mainly concentrated in the late peak period of high electric power load and photovoltaic failure, the maximum energy storage benefit is obtained under the condition of maximum consumption of the new energy, the problem of energy storage configuration under the condition of long-term return in the smelting industry accessed by the high-proportion new energy can be effectively solved, and benefit maximization of the user is achieved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the description below are some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
Fig. 1 is a flowchart of an energy storage multi-objective coordination optimization method considering uncertainty of new energy according to an embodiment of the present invention;
fig. 2 is a structural block diagram of an energy storage multi-objective coordination optimization system considering uncertainty of new energy according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
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. All other embodiments, which can be obtained by a person skilled in the art without inventive step based on the embodiments of the present invention, are within the scope of protection of the present invention.
Referring to fig. 1, a flowchart of an energy storage multi-objective coordination optimization method considering uncertainty of new energy according to the present application is shown.
As shown in fig. 1, in step S101, an energy storage configuration optimization model is constructed by using a first objective function with the maximum comprehensive profit of industrial user installation and energy storage, so as to obtain a rated capacity and a rated power of energy storage optimization.
It should be noted that the comprehensive benefit is the sum of the energy storage benefit and the carbon emission reduction benefit minus the energy storage cost, and the expression of the first objective function is as follows:
Figure 726811DEST_PATH_IMAGE001
，
in the formula (I), the compound is shown in the specification,
Figure 737974DEST_PATH_IMAGE002
for peak clipping and valley filling gains in the j th month of energy storage,
Figure 679385DEST_PATH_IMAGE003
for the defense gains of the basic electricity price in the j month of energy storage,
Figure 386310DEST_PATH_IMAGE010
in order to save the construction cost of the energy storage,
Figure 780382DEST_PATH_IMAGE005
、
Figure 739111DEST_PATH_IMAGE006
respectively a gain factor and a cost factor,
Figure 659662DEST_PATH_IMAGE007
for the number of months,
Figure 678434DEST_PATH_IMAGE008
the carbon emission reduction benefit of the j month.
In this embodiment, when the new energy permeability is high, considering the limitation of the energy storage construction cost, in the prior art, considering that the energy storage capacity obtained by optimizing two gains (peak clipping and valley filling and basic electricity price defending gain) is not enough to consume so much new energy, the phenomena of wind abandoning and light abandoning are likely to occur. At the moment, the carbon emission reduction benefit is considered, the game is carried out between the three benefits and the energy storage cost, and the energy storage capacity is properly increased to reduce or avoid the phenomena of wind and light abandonment by the optimization result.
Step S102, correcting the rated capacity and the rated power according to a preset intra-day operation optimization model to obtain real-time power after energy storage optimization;
step S103, calculating a new energy consumption rate and a cost recovery year based on the real-time power after energy storage optimization, and judging whether the new energy consumption rate is smaller than a first preset threshold and whether the cost recovery year is larger than a second preset threshold;
step S104, if the new energy consumption rate is less than a first preset threshold and the cost recovery time is greater than a second preset threshold, calculating the difference value between the new energy consumption rate and the first preset threshold
Figure 684436DEST_PATH_IMAGE009
And according to the difference
Figure 915697DEST_PATH_IMAGE009
And correcting the yield coefficient and the cost coefficient to obtain the rated capacity and the rated power of the corrected energy storage target.
According to the method, the power and capacity of the stored energy are obtained through the energy storage configuration optimization modelAnd the energy storage real-time power is used as a boundary constraint condition for operation optimization within an energy storage day, so that the energy storage real-time power of 1000 days is optimized, the cost recovery period of the energy storage in the optimization period (1000 days) is calculated, and the new energy consumption rate is obtained by dividing the actual output of the new energy by the theoretical output. Coefficient in energy storage configuration objective function is judged through new energy consumption rate and energy storage cost recovery age
Figure 831701DEST_PATH_IMAGE005
、
Figure 880428DEST_PATH_IMAGE006
And (4) whether to perform correction.
In a specific embodiment, the energy storage multi-objective coordination optimization method considering the uncertainty of the new energy specifically comprises the following steps:
step 1: calculating user side energy storage gain
For a large industrial user, the paid electric charge comprises 2 parts of basic electric charge and electric power charge. The basic electricity charge major users select actual demand charging (monthly maximum power demand), namely, the actual demand in the month is multiplied by the basic electricity charge unit price; the electricity rate and the electricity charge are the commonly known electricity quantity actually used by the user and the unit electricity price at the corresponding moment. In order to reduce the load pressure of the power grid, a user is guided to autonomously transfer a part of load in the peak period of the power load, and a power company also provides a power rate and electricity charge pricing rule of time-of-use electricity price.
The energy storage device has the characteristic of peak clipping and valley filling, when the power load is in the valley period or the new energy is abundant, the energy storage is charged to store the electric energy, and when the power load is in the peak period, the energy storage outputs the electric energy, so that the electric power obtained by a user from a power grid is reduced, the electric power charge of the user is reduced, the maximum power of monthly demand is reduced, and the basic electric power charge of the user is reduced. When the new energy is surplus and the user can not consume the new energy, the stored energy is stored and then released in a proper time period, and the electric quantity obtained by the user from the power grid is reduced. When the permeability of the new energy is high, the phenomena of wind abandonment and light abandonment are likely to occur only by considering the limitation of energy storage construction cost and considering that the energy storage capacity obtained by the two kinds of yield optimization is not enough to consume so much new energy. At the moment, the carbon emission reduction benefits are considered, the three benefits and the energy storage cost are in a game, and the energy storage capacity is properly increased to reduce or avoid the phenomena of wind and light abandonment by optimizing the result. Specifically, the method comprises the following steps:
Figure 514672DEST_PATH_IMAGE011
，
in the formula (I), the compound is shown in the specification,
Figure 674258DEST_PATH_IMAGE012
the number of days of the j-th month,
Figure 179188DEST_PATH_IMAGE013
the electricity rate at time t for the industrial user,
Figure 539763DEST_PATH_IMAGE014
is the stored energy power at the time point t,
Figure 520357DEST_PATH_IMAGE015
for peak clipping and valley filling gains in the j th month of energy storage,
Figure 359000DEST_PATH_IMAGE016
is the maximum value of the load power at month j,
Figure 108650DEST_PATH_IMAGE017
for the equivalent load of the simultaneous discontinuity after the optimization of the energy storage month j,
Figure 374546DEST_PATH_IMAGE018
in order to be the basic electricity price,
Figure 717803DEST_PATH_IMAGE019
in order to be a step of time,
Figure 487700DEST_PATH_IMAGE008
for the carbon emission reduction benefit of month j,
Figure 967223DEST_PATH_IMAGE020
in order to achieve the unit cost of carbon dioxide emissions,
Figure 794234DEST_PATH_IMAGE021
the carbon dioxide amount discharged for one degree of electricity of the replaced coal,
Figure 359207DEST_PATH_IMAGE022
for the total carbon dioxide emission of the user before the energy storage optimization at the k day,
Figure 539653DEST_PATH_IMAGE023
and (5) storing the optimized total carbon dioxide emission of the user for the k day.
Specifically, the expression for calculating the total carbon dioxide emission of the user before the k-th energy storage optimization is as follows:
Figure 263895DEST_PATH_IMAGE024
，
in the formula (I), the compound is shown in the specification,
Figure 137174DEST_PATH_IMAGE025
the total amount of the carbon dioxide emission is,
Figure 314077DEST_PATH_IMAGE026
the proportion of carbon emission in the total carbon emission is consumed by power;
calculating the expression of the total carbon dioxide emission of the user after the k-th energy storage optimization is as follows:
Figure 32634DEST_PATH_IMAGE027
，
in the formula (I), the compound is shown in the specification,
Figure 752329DEST_PATH_IMAGE028
the amount of power consumed before optimization for the k-th day of energy storage,
Figure 655563DEST_PATH_IMAGE029
is as followsThe consumed electric quantity after k days of energy storage optimization;
wherein, the first and the second end of the pipe are connected with each other,
Figure 195128DEST_PATH_IMAGE030
，
Figure 107589DEST_PATH_IMAGE031
the amount of carbon dioxide emitted to burn fossil fuels,
Figure 416211DEST_PATH_IMAGE032
the carbon dioxide amount discharged in the production process of industrial users,
Figure 631292DEST_PATH_IMAGE033
the amount of carbon dioxide emitted for net purchased power and heat consumption,
Figure 517208DEST_PATH_IMAGE034
the amount of carbon dioxide emitted as a raw material,
Figure 843147DEST_PATH_IMAGE035
to the activity level of the ith fossil fuel,
Figure 396488DEST_PATH_IMAGE036
is the carbon dioxide emission factor of the ith fossil fuel,
Figure 516891DEST_PATH_IMAGE037
average lower calorific value of the i-th fossil fuel,
Figure 296628DEST_PATH_IMAGE038
Net consumption of the ith fossil fuel,
Figure 282383DEST_PATH_IMAGE039
The carbon content per unit calorific value of the ith fossil fuel,
Figure 300018DEST_PATH_IMAGE040
The rate of carbon oxidation for the ith fossil fuel,
Figure 247114DEST_PATH_IMAGE041
a unit carbon emission factor for the electricity supplied by the grid,
Figure 982989DEST_PATH_IMAGE042
n is the total number of fossil fuels for the net load of electricity.
Step 2: establishing constraint conditions for operation of energy storage battery
In order to ensure that the configured energy storage device and the power grid system can stably operate and achieve the optimal operation effect, the following constraint conditions are given:
1) Constraint of power equation
In order to ensure normal and stable operation of various operations of an enterprise, power balance needs to be met at any moment. The equation is constrained as follows:
Figure 509785DEST_PATH_IMAGE043
，
in the formula (I), the compound is shown in the specification,
Figure 913085DEST_PATH_IMAGE044
is the load power at the time point t,
Figure 640869DEST_PATH_IMAGE045
the energy storage power at the t moment is charged to be positive, the discharge is negative,
Figure 988674DEST_PATH_IMAGE046
is the photovoltaic power at the time t,
Figure 460107DEST_PATH_IMAGE047
is the wind power at the t-th moment,
Figure 717913DEST_PATH_IMAGE048
and exchanging power between the user and the power grid at the t moment.
2) Energy storage state of charge confinement
The energy storage battery can work for a longer service life only when the state of charge (SOC) of energy storage is within a certain range, and the service life of energy storage can be greatly shortened if the energy storage battery works for a long time in a small state of charge. The constraint inequality is as follows:
Figure 475653DEST_PATH_IMAGE049
，
in the formula (I), the compound is shown in the specification,
Figure 186120DEST_PATH_IMAGE050
the state of charge of the stored energy at time t.
3) Energy storage battery performance constraints
The energy storage charging and discharging power cannot exceed the energy storage rated charging and discharging power, and the energy storage battery always runs in a reasonable charge state, so the service life of the energy storage battery is closely related to the electric energy throughput, and the smaller the throughput is, the longer the service life of the energy storage battery is. In order to maximize the benefit of the energy storage device, the daily throughput of the energy storage battery is reasonably limited based on the time-of-use electricity price policy, and meanwhile, the switching times of charging and discharging states of the energy storage battery in one day are reduced, so that the energy storage loss is reduced. The constraint inequality is as follows:
Figure 585877DEST_PATH_IMAGE051
，
Figure 432611DEST_PATH_IMAGE052
，
in the formula (I), the compound is shown in the specification,
Figure 626832DEST_PATH_IMAGE053
is the rated charge-discharge power of the stored energy,
Figure 824595DEST_PATH_IMAGE054
the daily throughput of stored battery power for the kth day,
Figure 637830DEST_PATH_IMAGE055
is the maximum value of the electric energy throughput, the value of which depends onThe rated capacity is 2 times of the user load, the time-of-use electricity price distribution condition, the PCS (energy storage battery) performance requirement and other factors.
4) Power back-off constraint
The actual load of the user and the running power of the stored energy cannot be smaller than 0, namely, the new energy and the stored energy at the user side are not reversely injected into the power grid and cannot exceed the maximum load value of the user, otherwise, the energy storage device does not play a role of preventing the user from defending the moon and needing the maximum value. The constraint inequality is as follows:
Figure 729283DEST_PATH_IMAGE056
，
in the formula (I), the compound is shown in the specification,
Figure 969771DEST_PATH_IMAGE057
is the maximum value of the load power.
And 3, step 3: building energy storage configuration optimization model
The configuration optimization of the energy storage is based on the load characteristics of a user and the output condition of new energy under the simulation period, the rated power of the wind and light new energy which is installed or planned to be installed is determined, the real output data of the wind and light in the last year is correspondingly selected or the output power of each day in the optimization period (one year) is randomly generated through Weibull distribution and Beta distribution, then one or one typical daily load curve in each season of a smelting enterprise is selected as a load curve in the optimization process, the minimum energy storage cost, the maximum energy storage income (including peak load-off income, basic electricity price defense income) and the maximum carbon emission reduction income of the user for installing the energy storage are multiple targets, and the weighted sum is integrated into a single target function
Figure 782394DEST_PATH_IMAGE058
The specific calculation formula is as follows. And solving the energy storage configuration model, and optimizing to obtain the rated power and the rated capacity of the energy storage.
Figure 133741DEST_PATH_IMAGE059
，
In the formula (I), the compound is shown in the specification,
Figure 79700DEST_PATH_IMAGE002
for peak clipping and valley filling gains in the j th month of energy storage,
Figure 491090DEST_PATH_IMAGE003
for the defense gains of the basic electricity price in the j month of energy storage,
Figure 397866DEST_PATH_IMAGE010
in order to save the construction cost of the energy storage,
Figure 943117DEST_PATH_IMAGE005
、
Figure 618949DEST_PATH_IMAGE006
respectively a gain factor and a cost factor,
Figure 325874DEST_PATH_IMAGE005
and
Figure 985525DEST_PATH_IMAGE006
and the sum is 1, and the sum is,
Figure 678675DEST_PATH_IMAGE007
for the number of months,
Figure 864805DEST_PATH_IMAGE008
for the carbon emission reduction benefit of month j,
Figure 617998DEST_PATH_IMAGE060
，
in the formula (I), the compound is shown in the specification,
Figure 499366DEST_PATH_IMAGE061
the price per unit of the energy storage device,
Figure 120840DEST_PATH_IMAGE062
in order to save the unit operation and maintenance cost of energy,
Figure 771264DEST_PATH_IMAGE053
is the rated charge-discharge power of the stored energy,
Figure 85571DEST_PATH_IMAGE063
in order to save the unit investment cost of energy storage,
Figure 454236DEST_PATH_IMAGE064
is the rated capacity of the stored energy.
And 4, step 4: construction of an intra-day operation optimization model
The optimization principle of the operation of the energy storage device in the day is mainly 3: firstly, peak clipping and valley filling, low storage and high release. The charging is carried out at the valley value of the power load, namely the low-price time period, the discharging is carried out at the peak value of the power load, namely the high-price time period, and the power price difference is obtained. And secondly, high-proportion new energy is consumed, and carbon emission is reduced. Because the new energy has the random fluctuation characteristic, the output condition is random and is not easy to predict, and the power grid system is more inclined to a stable power output device, the stored energy is charged when the new energy meets the load and has surplus, the electric energy is stored, when the output of the new energy is deficient, the energy storage device discharges and supplements in time, the new energy is maximally consumed, and the carbon emission is greatly reduced under the condition of the same load. Third, the maximum value is needed for defending the moon. Because the new energy has volatility, the peak value of the power load of the user is reduced through energy storage, so that the maximum value required by the user in a month is defended, and the power consumption cost of industrial users is reduced.
Based on the above 3 optimization principles, the optimization model is operated in the day to fully consider the uncertainty of the new energy, and the capacity and power of the energy storage obtained in the last step are corrected. First, based on the known rated power of wind and light, a force curve is randomly generated by monte carlo for multiple times (1000 times). And then, selecting a user typical load curve, and carrying out day-to-day operation optimization on the rated capacity obtained in the step 3. And adding a constraint condition of energy storage operation according to the maximum objective function of the sum of the peak clipping and valley filling income of the user and the monthly demand defense income, and optimizing to obtain the real-time power of the stored energy. The objective function is as follows:
Figure 754767DEST_PATH_IMAGE065
，
in the formula (I), the compound is shown in the specification,
Figure 384331DEST_PATH_IMAGE002
for the peak clipping and valley filling income of the j month of energy storage,
Figure 744906DEST_PATH_IMAGE003
for the defense gains of the basic electricity price in the j month of energy storage,
Figure 456991DEST_PATH_IMAGE007
in parts per month.
And 5: calculating the new energy consumption rate under the optimization period according to the real-time power of the step 4
Figure 295634DEST_PATH_IMAGE066
（
Figure 186230DEST_PATH_IMAGE067
) Judging whether the new energy consumption rate is greater than or equal to a first threshold value or not and judging whether the energy storage cost recovery age limit is larger than or equal to a first threshold value or not under the optimization period
Figure 576760DEST_PATH_IMAGE068
Whether the new energy consumption rate is less than or equal to a second threshold value or not is judged, and if the new energy consumption rate is less than the first threshold value and the cost recovery time is greater than a second preset threshold value, the difference value between the new energy consumption rate and the first threshold value is calculated
Figure 654437DEST_PATH_IMAGE069
Modifying the objective function
Figure 421405DEST_PATH_IMAGE070
Coefficient of performance
Figure 900928DEST_PATH_IMAGE071
Coefficient of construction cost of stored energy
Figure 462359DEST_PATH_IMAGE072
Reduce
Figure 355229DEST_PATH_IMAGE073
（
Figure 801254DEST_PATH_IMAGE074
The present application takes 0.01),
Figure 525496DEST_PATH_IMAGE075
increase of
Figure 398774DEST_PATH_IMAGE073
. And jumping to the step 3, and repeatedly running and optimizing until the absorption rate is greater than or equal to the first threshold value to obtain the corrected optimal rated capacity and rated power of the stored energy.
In conclusion, the method of the embodiment aims at improving economy and new energy consumption rate and reducing carbon emission of users, energy storage configuration and operation of the energy storage at the user side are optimized, during operation optimization, energy storage can be optimized and scheduled according to electricity price conditions and load conditions of the users, energy storage charging time is distributed in time periods with low electricity price and high new energy output, energy storage discharging is mainly concentrated in late peak periods with high electric load and photovoltaic failure, the maximum energy storage income is obtained under the condition of maximum new energy consumption, the problem of energy storage configuration under long-term return in the smelting industry with high-proportion new energy access can be effectively solved, and benefit maximization of the users is realized.
Referring to fig. 2, a block diagram of an energy storage multi-objective coordination optimization system considering uncertainty of new energy according to the present application is shown.
As shown in FIG. 2, the energy storage multi-objective coordination optimization system 200 includes a building module 210, a first modification module 220, a judgment module 230, and a second modification module 240.
The building module 210 is configured to build an energy storage configuration optimization model by using a maximum comprehensive profit of energy storage installed by an industrial user as a first objective function to obtain a rated capacity and a rated power of energy storage optimization, wherein the comprehensive profit is the sum of energy storage profit and carbon emission reduction profit minus energy storage cost, and the first profit is the sum of energy storage profit and carbon emission reduction profitThe expression of the objective function is:
Figure 185464DEST_PATH_IMAGE001
in the formula (I), wherein,
Figure 294235DEST_PATH_IMAGE002
for peak clipping and valley filling gains in the j th month of energy storage,
Figure 13929DEST_PATH_IMAGE003
for the defense gains of the basic electricity price in the j month of energy storage,
Figure 917163DEST_PATH_IMAGE010
in order to save the construction cost of the energy storage,
Figure 456729DEST_PATH_IMAGE005
、
Figure 244556DEST_PATH_IMAGE006
respectively a gain factor and a cost factor,
Figure 680741DEST_PATH_IMAGE007
in parts per month; the first correction module 220 is configured to correct the rated capacity and the rated power according to a preset intra-day operation optimization model to obtain real-time power after energy storage optimization; the judging module 230 is configured to calculate a new energy consumption rate and a cost recovery year based on the real-time power after the energy storage optimization, and judge whether the new energy consumption rate is smaller than a first preset threshold and whether the cost recovery year is larger than a second preset threshold; a second modification module 240 configured to calculate a difference between the new energy consumption rate and a first preset threshold if the new energy consumption rate is less than the first preset threshold and the cost recovery year is greater than a second preset threshold
Figure 630243DEST_PATH_IMAGE009
And according to the difference
Figure 922684DEST_PATH_IMAGE009
For coefficient of profitAnd correcting the cost coefficient to obtain the rated capacity and rated power of the corrected energy storage target.
It should be understood that the modules depicted in fig. 2 correspond to various steps in the method described with reference to fig. 1. Thus, the operations and features described above for the method and the corresponding technical effects are also applicable to the modules in fig. 2, and are not described again here.
In other embodiments, the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the program instructions, when executed by a processor, cause the processor to execute the method for energy storage multi-objective coordination optimization considering uncertainty of new energy in any of the method embodiments described above;
as one embodiment, the computer-readable storage medium of the present invention stores computer-executable instructions configured to:
constructing an energy storage configuration optimization model by using the maximum comprehensive profit of industrial user installation energy storage as a first objective function to obtain the rated capacity and the rated power of energy storage optimization;
correcting the rated capacity and the rated power according to a preset intra-day operation optimization model to obtain real-time power after energy storage optimization;
calculating a new energy consumption rate and a cost recovery year limit based on the real-time power after energy storage optimization, and judging whether the new energy consumption rate is smaller than a first preset threshold and whether the cost recovery year limit is larger than a second preset threshold;
if the new energy consumption rate is smaller than a first preset threshold and the cost recovery year is larger than a second preset threshold, calculating the difference value between the new energy consumption rate and the first preset threshold
Figure 107677DEST_PATH_IMAGE009
And according to the difference
Figure 801964DEST_PATH_IMAGE009
The gain coefficient and the cost coefficient are corrected to obtain the rated capacity of the corrected energy storage targetAnd a rated power.
The computer-readable storage medium may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created from use of the energy storage multi-objective coordinated optimization system considering uncertainty of new energy, and the like. Further, the computer-readable storage medium may include high speed random access memory, and may also include memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid state storage device. In some embodiments, the computer readable storage medium optionally includes a memory remotely located from the processor, and the remote memory may be connected to the energy storage multi-objective coordination optimization system over a network that accounts for new energy uncertainty. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
Fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present invention, and as shown in fig. 3, the electronic device includes: a processor 310 and a memory 320. The electronic device may further include: an input device 330 and an output device 340. The processor 310, the memory 320, the input device 330, and the output device 340 may be connected by a bus or other means, such as the bus connection in fig. 3. The memory 320 is the computer-readable storage medium described above. The processor 310 executes various functional applications of the server and data processing by running nonvolatile software programs, instructions and modules stored in the memory 320, namely, implementing the energy storage multi-objective coordination optimization method of the above method embodiment in consideration of uncertainty of new energy. The input device 330 may receive input numeric or character information and generate key signal inputs related to user settings and function control of the energy storage multi-objective coordination optimization system considering uncertainty of new energy. The output device 340 may include a display device such as a display screen.
The electronic device can execute the method provided by the embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method. For technical details that are not described in detail in this embodiment, reference may be made to the method provided by the embodiment of the present invention.
As an embodiment, the electronic device is applied to an energy storage multi-objective coordination optimization system considering uncertainty of new energy, and is used for a client, and the system includes: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to cause the at least one processor to:
constructing an energy storage configuration optimization model by taking the maximum comprehensive profit of the industrial user for installing energy storage as a first objective function to obtain the rated capacity and the rated power of energy storage optimization;
correcting the rated capacity and the rated power according to a preset intra-day operation optimization model to obtain real-time power after energy storage optimization;
calculating a new energy consumption rate and a cost recovery year limit based on the real-time power after energy storage optimization, and judging whether the new energy consumption rate is smaller than a first preset threshold and whether the cost recovery year limit is larger than a second preset threshold;
if the new energy consumption rate is smaller than a first preset threshold and the cost recovery year is larger than a second preset threshold, calculating the difference value between the new energy consumption rate and the first preset threshold
Figure 47001DEST_PATH_IMAGE009
And according to the difference
Figure 561158DEST_PATH_IMAGE009
And correcting the yield coefficient and the cost coefficient to obtain the rated capacity and the rated power of the corrected energy storage target.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment may be implemented by software plus a necessary general hardware platform, and may also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium, such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods of the various embodiments or some parts of the embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, and not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.

Claims (10)

1. An energy storage multi-objective coordination optimization method considering new energy uncertainty is characterized by comprising the following steps:
establishing an energy storage configuration optimization model by taking the maximum comprehensive income of energy storage installation of an industrial user as a first objective function to obtain the rated capacity and rated power of energy storage optimization, wherein the comprehensive income is the sum of the energy storage income and the carbon emission reduction income minus the energy storage cost, and the expression of the first objective function is as follows:
Figure 371502DEST_PATH_IMAGE001
，
in the formula (I), the compound is shown in the specification,
Figure 147697DEST_PATH_IMAGE002
for peak clipping and valley filling gains in the j th month of energy storage,
Figure 396276DEST_PATH_IMAGE003
for the defense gains of the basic electricity price in the j month of energy storage,
Figure 525906DEST_PATH_IMAGE004
for energy storage constructionThe cost of the manufacture is reduced, and the cost is reduced,
Figure 933753DEST_PATH_IMAGE005
、
Figure 225057DEST_PATH_IMAGE007
respectively a gain factor and a cost factor,
Figure 351145DEST_PATH_IMAGE009
for the number of months,
Figure 18887DEST_PATH_IMAGE010
the carbon emission reduction benefit for month j;
correcting the rated capacity and the rated power according to a preset intra-day operation optimization model to obtain real-time power after energy storage optimization;
calculating a new energy consumption rate and a cost recovery year based on the real-time power after energy storage optimization, and judging whether the new energy consumption rate is smaller than a first preset threshold and whether the cost recovery year is larger than a second preset threshold;
if the new energy consumption rate is smaller than a first preset threshold and the cost recovery year is larger than a second preset threshold, calculating the difference value between the new energy consumption rate and the first preset threshold
Figure 422187DEST_PATH_IMAGE012
And according to the difference
Figure 9026DEST_PATH_IMAGE012
And correcting the yield coefficient and the cost coefficient to obtain the rated capacity and the rated power of the corrected energy storage target.
2. The method as claimed in claim 1, wherein the energy storage gains comprise peak clipping and valley filling gains and basic electricity price defense gains.
3. The method for energy storage multi-objective coordination optimization considering uncertainty of new energy source according to claim 1, wherein,
Figure 232197DEST_PATH_IMAGE013
，
Figure 831193DEST_PATH_IMAGE014
days of month j, k day k,
Figure 823420DEST_PATH_IMAGE015
the electricity rate at time t for the industrial user,
Figure 987685DEST_PATH_IMAGE016
is the stored energy power at the time point t,
Figure 822785DEST_PATH_IMAGE017
is the maximum value of the load power in the j-th month,
Figure 832330DEST_PATH_IMAGE018
for the equivalent load of the discontinuity at the same time after the optimization of the energy storage month j,
Figure 69276DEST_PATH_IMAGE020
in order to be the basic electricity price,
Figure 138863DEST_PATH_IMAGE022
in order to be a step of time,
Figure 336626DEST_PATH_IMAGE023
in order to achieve the unit cost of carbon dioxide emissions,
Figure 274495DEST_PATH_IMAGE024
the carbon dioxide amount discharged for one degree of electricity of the replaced coal,
Figure 241314DEST_PATH_IMAGE025
for the total carbon dioxide emission of the user before the energy storage optimization at the k day,
Figure 747382DEST_PATH_IMAGE026
for the total carbon dioxide emission of the user after the k day energy storage optimization,
Figure 291496DEST_PATH_IMAGE027
for the unit price of the energy storage device,
Figure 908422DEST_PATH_IMAGE028
is the unit operation and maintenance cost of the energy storage,
Figure 854381DEST_PATH_IMAGE029
is the rated charge-discharge power of the stored energy,
Figure 265771DEST_PATH_IMAGE030
in order to save the unit investment cost of energy storage,
Figure 438126DEST_PATH_IMAGE031
is the rated capacity of the stored energy.
4. The energy storage multi-objective coordinated optimization method considering the uncertainty of new energy according to claim 3, wherein the expression for calculating the total carbon dioxide emission of the user before the k-th energy storage optimization is as follows:
Figure 452219DEST_PATH_IMAGE032
，
in the formula (I), the compound is shown in the specification,
Figure 659209DEST_PATH_IMAGE033
the total amount of the carbon dioxide emission is,
Figure 366134DEST_PATH_IMAGE034
the proportion of carbon emission in the total carbon emission is consumed by power;
calculating the expression of the total carbon dioxide emission of the user after the k-th energy storage optimization as follows:
Figure 25786DEST_PATH_IMAGE035
，
in the formula (I), the compound is shown in the specification,
Figure 718935DEST_PATH_IMAGE036
the amount of power consumed before optimization for the kth day of energy storage,
Figure 636557DEST_PATH_IMAGE037
the amount of electricity consumed after the energy storage optimization for the kth day;
wherein the content of the first and second substances,
Figure 389749DEST_PATH_IMAGE038
，
Figure 395751DEST_PATH_IMAGE039
the amount of carbon dioxide emitted to burn fossil fuels,
Figure 158171DEST_PATH_IMAGE040
the carbon dioxide amount discharged in the production process of industrial users,
Figure 808595DEST_PATH_IMAGE041
the amount of carbon dioxide emitted for net purchased power and heat consumption,
Figure 857323DEST_PATH_IMAGE042
the amount of carbon dioxide emitted as a raw material,
Figure 225987DEST_PATH_IMAGE043
to the activity level of the ith fossil fuel,
Figure 526518DEST_PATH_IMAGE044
is the carbon dioxide emission factor of the ith fossil fuel,
Figure 421662DEST_PATH_IMAGE045
average lower calorific value of the i-th fossil fuel,
Figure 516657DEST_PATH_IMAGE046
Net consumption of the ith fossil fuel,
Figure 497251DEST_PATH_IMAGE047
The carbon content per unit calorific value of the ith fossil fuel,
Figure 601474DEST_PATH_IMAGE048
The rate of carbon oxidation for the ith fossil fuel,
Figure 960911DEST_PATH_IMAGE049
a unit carbon emission factor for the electricity provided by the grid,
Figure 351441DEST_PATH_IMAGE050
n is the total number of fossil fuels for the net load of electricity.
5. The energy storage multi-objective coordination optimization method considering the uncertainty of the new energy source according to claim 1, wherein the step of correcting the rated capacity and the rated power according to a preset intra-day operation optimization model to obtain real-time power after energy storage optimization comprises the steps of:
randomly generating an output curve based on the rated power;
selecting a user typical load curve in the output curve;
and performing day-to-day operation optimization on the rated capacity based on the user typical load curve to obtain real-time power after energy storage optimization, wherein the day-to-day operation optimization on the rated capacity comprises the step of optimizing the rated capacity by adopting constraint conditions of energy storage operation and using a second objective function with the maximum energy storage profit.
6. The method for energy storage multi-objective coordination optimization considering new energy uncertainty according to claim 5, wherein a second objective function of the intra-day operation optimization model is the maximum energy storage profit, and an expression of the second objective function is as follows:
Figure 22594DEST_PATH_IMAGE051
，
wherein the content of the first and second substances,
Figure 664927DEST_PATH_IMAGE052
，
Figure 534663DEST_PATH_IMAGE014
the number of days of the j-th month,
Figure 971461DEST_PATH_IMAGE015
the electricity rate at time t for the industrial user,
Figure 802014DEST_PATH_IMAGE016
is the stored energy power at the t-th moment,
Figure 110023DEST_PATH_IMAGE018
for the equivalent load of the discontinuity at the same time after the optimization of the energy storage month j,
Figure 444052DEST_PATH_IMAGE017
is the maximum value of the load power in the j-th month,
Figure 441964DEST_PATH_IMAGE020
in order to be the basic electricity price,
Figure 494234DEST_PATH_IMAGE022
is a time step.
7. The method for energy storage multi-objective coordination optimization considering uncertainty of new energy source as claimed in claim 1, wherein said difference value is used as a basis
Figure 478370DEST_PATH_IMAGE012
Modifying the cost and gain factors includes increasing the cost and gain factors
Figure 57119DEST_PATH_IMAGE053
Difference of times
Figure 101298DEST_PATH_IMAGE012
And reducing the cost factor
Figure 765498DEST_PATH_IMAGE053
Difference of times
Figure 553325DEST_PATH_IMAGE012
。
8. An energy storage multi-objective coordination optimization system considering uncertainty of new energy sources is characterized by comprising the following steps:
the building module is configured to build an energy storage configuration optimization model by taking the maximum comprehensive income of energy storage installation of industrial users as a first objective function to obtain the rated capacity and the rated power of energy storage optimization, wherein the comprehensive income is the sum of the energy storage income and the carbon emission reduction income minus the energy storage cost, and the expression of the first objective function is as follows:
Figure 861947DEST_PATH_IMAGE001
，
in the formula (I), the compound is shown in the specification,
Figure 201661DEST_PATH_IMAGE002
for peak clipping and valley filling gains in the j th month of energy storage,
Figure 962944DEST_PATH_IMAGE003
for the defense gains of the basic electricity price in the j month of energy storage,
Figure 554462DEST_PATH_IMAGE054
in order to save the construction cost of the energy storage,
Figure 842224DEST_PATH_IMAGE005
、
Figure 228206DEST_PATH_IMAGE007
respectively a gain factor and a cost factor,
Figure 866998DEST_PATH_IMAGE009
the number of the months is the number of the months,
Figure 996628DEST_PATH_IMAGE010
the carbon emission reduction benefit for month j;
the first correction module is configured to correct the rated capacity and the rated power according to a preset intra-day operation optimization model to obtain real-time power after energy storage optimization;
the judging module is configured to calculate a new energy consumption rate and a cost recovery year based on the real-time power after energy storage optimization, and judge whether the new energy consumption rate is smaller than a first preset threshold and whether the cost recovery year is larger than a second preset threshold;
a second correction module configured to calculate a difference between the new energy consumption rate and a first preset threshold if the new energy consumption rate is smaller than the first preset threshold and the cost recovery year is greater than a second preset threshold
Figure 14263DEST_PATH_IMAGE012
And according to the difference
Figure 961359DEST_PATH_IMAGE012
And correcting the yield coefficient and the cost coefficient to obtain the rated capacity and the rated power of the corrected energy storage target.
9. An electronic device, comprising: at least one processor, and a memory communicatively coupled to the at least one processor, wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any of claims 1-7.
10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the method of any one of claims 1 to 7.
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