CN111126665A - Energy storage configuration method based on rolling load prediction and minimized daily load variance - Google Patents

Energy storage configuration method based on rolling load prediction and minimized daily load variance Download PDF

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CN111126665A
CN111126665A CN201911169029.1A CN201911169029A CN111126665A CN 111126665 A CN111126665 A CN 111126665A CN 201911169029 A CN201911169029 A CN 201911169029A CN 111126665 A CN111126665 A CN 111126665A
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周先哲
曹伟
叶桂南
龚舒
唐羿轩
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Guangxi Power Grid Co Ltd
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Abstract

The invention discloses an energy storage configuration method based on rolling load prediction and minimized daily load variance, which comprises the following steps: based on the algorithm of rolling load prediction, grouping historical load data of the same time in the last five days, and predicting the current load data; combining the historical load data and the current load data to list an objective function of minimizing daily load variance; analyzing constraints in the energy storage configuration by using a target function of minimizing daily load variance, and listing constraint conditions; and determining the type of the model based on the constraint conditions, and solving the model to obtain the optimal energy storage configuration capacity. In the implementation of the invention, the energy storage configuration method based on rolling load prediction and minimized daily load variance can be applied to any type of energy storage system.

Description

Energy storage configuration method based on rolling load prediction and minimized daily load variance
Technical Field
The invention relates to the technical field of energy storage at a user side of a power system, in particular to an energy storage configuration method based on rolling load prediction and minimized daily load variance.
Background
In recent years, with the development of load demand and the access of renewable energy, the great difference in peak-to-valley load has prompted power companies to upgrade existing power systems. However, due to the relatively short peak load duration, the investment may be uneconomical, resulting in extremely low equipment utilization.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides an energy storage configuration method based on rolling load prediction and minimized daily load variance, which can be applied to any type of energy storage system.
In order to solve the above technical problem, an embodiment of the present invention provides an energy storage configuration method based on rolling load prediction and minimized daily load variance, where the method includes:
based on the algorithm of rolling load prediction, grouping historical load data of the same time in the last five days, and predicting the current load data;
combining the historical load data and the current load data to list an objective function of minimizing daily load variance;
analyzing constraints in the energy storage configuration by using a target function of minimizing daily load variance, and listing constraint conditions;
and determining the type of the model based on the constraint conditions, and solving the model to obtain the optimal energy storage configuration capacity.
Optionally, the algorithm based on rolling load prediction is configured to group historical load data of the same time in the previous five days, and predict current load data, where the predicting includes: and evaluating the accuracy of the prediction by adopting an average percentage error, wherein the average percentage error is calculated by the following formula:
Figure BDA0002288217670000021
wherein MAPE represents the mean percent error; x (i) and
Figure BDA0002288217670000022
respectively representing the actual value and the predicted value of the target day; n represents the number of samples for the target day.
Optionally, the combining the historical load data and the current load data to list an objective function that minimizes the daily load variance includes:
combining the historical load data and the current load data to list the variance of the minimized daily load curve;
listing an additional term representing an adjacent time deviation based on the variance of the minimized daily load curve;
and combining the variance of the minimized daily load curve and the additional term of the adjacent time deviation to obtain an objective function of the minimized daily load variance.
Optionally, the formula for minimizing the variance of the daily load curve is as follows:
Figure BDA0002288217670000023
the formula of the additional term representing the adjacent time offset is as follows:
Figure BDA0002288217670000024
the formula of the objective function for minimizing daily load variance is as follows:
minf＝βf1+(1-β)f1；
wherein f is1To minimize the variance of the daily load curve; f. of2An additional term for adjacent time offsets; minf is an objective function of the minimum daily load variance;
Figure BDA0002288217670000025
is the average of the load over the day;
Figure BDA0002288217670000026
and
Figure BDA0002288217670000027
respectively the discharging power and the charging power of the energy storage system at the time k; pL(k) Load power obtained by the energy storage system at time k;
Figure BDA0002288217670000028
and
Figure BDA0002288217670000029
respectively the discharging power and the charging power of the energy storage system at the time k + 1; pL(k +1) is the load power of the energy storage system obtained at the time k +1, β is the weight of two terms in the function, β epsilon [0,1 ∈ ]]If β is 1, only variance is considered, otherwise, only adjacent time offset is considered.
Optionally, the analyzing constraints in the energy storage configuration by using the objective function of minimizing daily load variance, and listing constraint conditions includes:
analyzing the energy storage system power capacity constraint in the energy storage configuration by using the objective function of the minimized daily load variance, and listing out the energy storage system power capacity constraint conditions;
analyzing energy capacity constraint of an energy storage system in energy storage configuration by using a target function of the minimized daily load variance, and listing energy capacity constraint conditions of the energy storage system;
and analyzing the peak regulation constraint of the energy storage system in the energy storage configuration by using the objective function of the minimized daily load variance, and listing the peak regulation constraint conditions of the energy storage system.
Optionally, the energy storage system power capacity constraint conditions are as follows:
let PcapThe maximum discharge power is the power capacity of the energy storage system
Figure BDA0002288217670000031
Maximum charging power
Figure BDA0002288217670000032
Let EcapIs the energy capacity of the energy storage system; the energy storage system has a power capacity of aboutThe bundles are as follows:
Figure BDA0002288217670000033
in addition, in the formula (I),
Figure BDA0002288217670000034
and
Figure BDA0002288217670000035
the following equation should be satisfied:
Figure BDA0002288217670000036
wherein the content of the first and second substances,
Figure BDA0002288217670000037
and
Figure BDA0002288217670000038
respectively representing the discharge and charge states of the energy storage system; if it is not
Figure BDA0002288217670000039
Then the energy storage system is charging; if it is not
Figure BDA00022882176700000310
The energy storage system is discharging.
Optionally, the energy capacity constraint conditions of the energy storage system are as follows:
let E (k) be the remaining energy of the energy storage system at k, E (0) be the initial remaining energy, E (n) be the remaining energy at the end of the day, EcapIs the energy capacity of the energy storage system; the energy capacity constraints of the energy storage system are as follows:
εlowEcap≤E(k)≤εhighEcap；
wherein epsilonlowAnd εhighRespectively, the lowest and highest percentages of remaining energy; epsilonlowAnd εhighThe range of (A) is 0 to 100 percentAnd is epsilonlow≤εhigh；
To achieve continuous peak shaving, the remaining energy at the end of the day should be the same as the remaining energy at the beginning of the day, which is expressed as:
E(n)＝E(0)；
in addition, the relationship between the power output of the energy storage system and the remaining energy is as follows:
Figure BDA00022882176700000311
wherein, η+For discharge efficiency, η-For charging efficiency, Δ t is a time step.
Optionally, the peak shaving constraint conditions of the energy storage system are as follows:
Figure BDA0002288217670000041
wherein, PL1(k) Is the load at time k;
Figure BDA0002288217670000042
is the peak load value of the correlation period; this constraint ensures that the power capacity P is met at the amount of peak clippingcap；γ1And gamma2Are between 0 and 1, and minimize the peak load hours and the valley load hours.
Optionally, the determining the type of the model based on the constraint condition, and solving the model to obtain the optimal energy storage configuration capacity includes:
determining a model based on the constraint condition, wherein the model adopts a mixed integer programming model;
solving the mixed integer programming model based on an artificial intelligence algorithm;
and acquiring the optimal energy storage configuration capacity by utilizing CPLEX software based on the result of solving the mixed integer programming model.
In the implementation of the invention, a new Mixed Integer Programming (MIP) method of energy storage configuration is adopted, and the method not only considers a typical method of physical constraint of the energy storage configuration, but also considers operation constraint. At the same time, the model is designed to minimize the load variance, or both; furthermore, the number of charge-discharge cycles is considered as a constraint condition that more significantly affects the use of the energy storage system, rather than the number of charge-discharge cycles; the method proposed by the invention can be applied to any type of energy storage system.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic flow chart of a method for energy storage configuration based on rolling load prediction and minimization of daily load variance in an embodiment of the present invention;
FIG. 2 is a schematic diagram of a load sequence update algorithm in an embodiment of the present invention;
fig. 3 is a graph of energy storage peak clipping and valley filling results in an embodiment of the present invention.
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 only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Examples
Referring to fig. 1, fig. 1 is a flowchart illustrating an energy storage configuration method based on rolling load prediction and minimization of daily load variance according to an embodiment of the present invention.
As shown in fig. 1, a method for energy storage configuration based on rolling load prediction and minimization of daily load variance, the method includes:
s11: based on the algorithm of rolling load prediction, grouping historical load data of the same time in the last five days, and predicting the current load data;
in the specific implementation process of the present invention, the algorithm based on rolling load prediction groups historical load data of the same time in the last five days, and predicting the current load data includes: and evaluating the accuracy of the prediction by adopting an average percentage error, wherein the average percentage error is calculated by the following formula:
Figure BDA0002288217670000051
wherein MAPE represents the mean percent error; x (i) and
Figure BDA0002288217670000052
respectively representing the actual value and the predicted value of the target day; n represents the number of samples for the target day.
Specifically, referring to fig. 2, fig. 2 is a schematic diagram illustrating a load sequence update algorithm in an embodiment of the present invention; the embodiment of the invention refers to the same time period from the current time to the previous day as a virtual day; considering that the regression parameters calculated for a certain prediction are not always optimal, embodiments of the present invention refresh the regression parameters each time a new prediction is made for the next prediction time; the scrolling algorithm is shown in FIG. 2; y for each predicted pointi(i ═ 1,2, …, n), calculated using the following formula:
Figure BDA0002288217670000053
wherein, the optimal regression parameter r calculated each timejiJ is updated to 1,2, …, m.
S12: combining the historical load data and the current load data to list an objective function of minimizing daily load variance;
in a specific implementation process of the present invention, the step of listing an objective function that minimizes the daily load variance by combining the historical load data and the current load data includes: combining the historical load data and the current load data to list the variance of the minimized daily load curve; listing an additional term representing an adjacent time deviation based on the variance of the minimized daily load curve; and combining the variance of the minimized daily load curve and the additional term of the adjacent time deviation to obtain an objective function of the minimized daily load variance.
Specifically, the energy storage can effectively fail to carry out peak clipping and valley filling, and reduce the load fluctuation; the objective of the optimization is written as minimizing the variance of the daily load curve, which is formulated as follows:
Figure BDA0002288217670000061
in order to smooth the curve, an additional term representing the adjacent time offset may be listed, the formula of which is as follows:
Figure BDA0002288217670000062
the formula of the objective function for minimizing daily load variance is as follows:
minf＝βf1+(1-β)f1；
wherein f is1To minimize the variance of the daily load curve; f. of2An additional term for adjacent time offsets; minf is an objective function of the minimum daily load variance;
Figure BDA0002288217670000063
is the average of the load over the day;
Figure BDA0002288217670000064
and
Figure BDA0002288217670000065
respectively the discharging power and the charging power of the energy storage system at the time k; pL(k) Load power obtained by the energy storage system at time k;
Figure BDA0002288217670000066
and
Figure BDA0002288217670000067
respectively the discharging power and the charging power of the energy storage system at the time k + 1; pL(k +1) is the load power of the energy storage system obtained at the time k +1, β is the weight of two terms in the function, β epsilon [0,1 ∈ ]]If β is 1, only variance is considered, otherwise, only adjacent time offset is considered.
S13: analyzing constraints in the energy storage configuration by using a target function of minimizing daily load variance, and listing constraint conditions;
in the specific implementation process of the invention, the analyzing the constraint in the energy storage configuration by using the objective function of minimizing daily load variance and listing out the constraint conditions comprises: analyzing the energy storage system power capacity constraint in the energy storage configuration by using the objective function of the minimized daily load variance, and listing out the energy storage system power capacity constraint conditions; analyzing energy capacity constraint of an energy storage system in energy storage configuration by using a target function of the minimized daily load variance, and listing energy capacity constraint conditions of the energy storage system; and analyzing the peak regulation constraint of the energy storage system in the energy storage configuration by using the objective function of the minimized daily load variance, and listing the peak regulation constraint conditions of the energy storage system.
Specifically, the energy storage system power capacity constraint conditions are as follows:
let PcapThe maximum discharge power is the power capacity of the energy storage system
Figure BDA0002288217670000071
Maximum charging power
Figure BDA0002288217670000072
Let EcapIs the energy capacity of the energy storage system; the energy storage system power capacity constraints are as follows:
Figure BDA0002288217670000073
in addition, in the formula (I),
Figure BDA0002288217670000074
and
Figure BDA0002288217670000075
the following equation should be satisfied:
Figure BDA0002288217670000076
wherein the content of the first and second substances,
Figure BDA0002288217670000077
and
Figure BDA0002288217670000078
respectively representing the discharge and charge states of the energy storage system; if it is not
Figure BDA0002288217670000079
Then the energy storage system is charging; if it is not
Figure BDA00022882176700000710
The energy storage system is discharging.
Specifically, the energy capacity constraint conditions of the energy storage system are as follows:
let E (k) be the remaining energy of the energy storage system at k, E (0) be the initial remaining energy, E (n) be the remaining energy at the end of the day, EcapIs the energy capacity of the energy storage system; the energy capacity constraints of the energy storage system are as follows:
εlowEcap≤E(k)≤εhighEcap；
wherein epsilonlowAnd εhighRespectively, the lowest and highest percentages of remaining energy; epsilonlowAnd εhighIn the range of 0% to 100%, and εlow≤εhigh；
To achieve continuous peak shaving, the remaining energy at the end of the day should be the same as the remaining energy at the beginning of the day, which is expressed as:
E(n)＝E(0)；
in addition, the relationship between the power output of the energy storage system and the remaining energy is as follows:
Figure BDA00022882176700000711
wherein, η+For discharge efficiency, η-For charging efficiency, Δ t is a time step.
Specifically, the peak regulation constraint conditions of the energy storage system are as follows:
Figure BDA0002288217670000081
wherein, PL1(k) Is the load at time k;
Figure BDA0002288217670000082
is the peak load value of the correlation period; this constraint ensures that the power capacity P is met at the amount of peak clippingcap；γ1And gamma2Are between 0 and 1, and minimize the peak load hours and the valley load hours.
S14: and determining the type of the model based on the constraint conditions, and solving the model to obtain the optimal energy storage configuration capacity.
In a specific implementation process of the present invention, the determining the type of the model based on the constraint condition, and solving the model to obtain the optimal energy storage configuration capacity includes: determining a model based on the constraint condition, wherein the model adopts a mixed integer programming model; solving the mixed integer programming model based on an artificial intelligence algorithm; and acquiring the optimal energy storage configuration capacity by utilizing CPLEX software based on the result of solving the mixed integer programming model.
Specifically, the model is a mixed integer programming model, the model not only considers a typical method of energy storage configuration physical constraint, but also considers operation constraint, the model can be solved by adopting an artificial intelligence algorithm, such as a neural network algorithm, a particle swarm algorithm and the like, and by combining with the attached drawing 3, the drawing 3 shows an energy storage peak clipping and valley filling result diagram in the embodiment of the invention, the rolling load prediction result is accurate, the peak clipping and valley filling effect is obvious, and the curve is smoother when β is 0 compared with when β is 0.5.
In the implementation of the invention, a new Mixed Integer Programming (MIP) method of energy storage configuration is adopted, and the method not only considers a typical method of physical constraint of the energy storage configuration, but also considers operation constraint. At the same time, the model is designed to minimize the load variance, or both; furthermore, the number of charge-discharge cycles is considered as a constraint condition that more significantly affects the use of the energy storage system, rather than the number of charge-discharge cycles; the method proposed by the invention can be applied to any type of energy storage system.
Those skilled in the art will appreciate that all or part of the steps in the methods of the above embodiments may be implemented by associated hardware instructed by a program, which may be stored in a computer-readable storage medium, and the storage medium may include: a Read Only Memory (ROM), a Random Access Memory (RAM), a magnetic or optical disk, or the like.
In addition, the energy storage configuration method based on rolling load prediction and minimized daily load variance provided by the embodiment of the present invention is described in detail above, and a specific example should be adopted herein to explain the principle and the implementation manner of the present invention, and the description of the above embodiment is only used to help understanding the method of the present invention and the core idea thereof; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.

Claims (9)

1. An energy storage configuration method based on rolling load prediction and minimization of daily load variance, the method comprising:
based on the algorithm of rolling load prediction, grouping historical load data of the same time in the last five days, and predicting the current load data;
combining the historical load data and the current load data to list an objective function of minimizing daily load variance;
analyzing constraints in the energy storage configuration by using a target function of minimizing daily load variance, and listing constraint conditions;
and determining the type of the model based on the constraint conditions, and solving the model to obtain the optimal energy storage configuration capacity.
2. The energy storage configuration method based on rolling load prediction and minimization of daily load variance according to claim 1, wherein the algorithm based on rolling load prediction is used for grouping historical load data of the same time in the last five days, and predicting the current load data comprises: and evaluating the accuracy of the prediction by adopting an average percentage error, wherein the average percentage error is calculated by the following formula:
Figure FDA0002288217660000011
wherein MAPE represents the mean percent error; x (i) and
Figure FDA0002288217660000012
respectively representing the actual value and the predicted value of the target day; n represents the number of samples for the target day.
3. The energy storage configuration method based on rolling load prediction and minimization of daily load variance according to claim 1, wherein the combining the historical load data and the current load data to list an objective function of minimization of daily load variance comprises:
combining the historical load data and the current load data to list the variance of the minimized daily load curve;
listing an additional term representing an adjacent time deviation based on the variance of the minimized daily load curve;
and combining the variance of the minimized daily load curve and the additional term of the adjacent time deviation to obtain an objective function of the minimized daily load variance.
4. The energy storage configuration method based on rolling load prediction and minimization of daily load variance as claimed in claim 3, wherein the formula of minimizing the variance of daily load curve is as follows:
Figure FDA0002288217660000021
the formula of the additional term representing the adjacent time offset is as follows:
Figure FDA0002288217660000022
the formula of the objective function for minimizing daily load variance is as follows:
min f＝βf1+(1-β)f1；
wherein f is1To minimize the variance of the daily load curve; f. of2An additional term for adjacent time offsets; minf is an objective function of the minimum daily load variance;
Figure FDA0002288217660000027
is the average of the load over the day;
Figure FDA0002288217660000023
and
Figure FDA0002288217660000024
respectively the discharging power and the charging power of the energy storage system at the time k; pL(k) Load power obtained by the energy storage system at time k;
Figure FDA0002288217660000025
and
Figure FDA0002288217660000026
are respectively energy storageThe discharge power and the charge power of the system at time k + 1; pL(k +1) is the load power of the energy storage system obtained at the time k +1, β is the weight of two terms in the function, β epsilon [0,1 ∈ ]]If β is 1, only variance is considered, otherwise, only adjacent time offset is considered.
5. The energy storage configuration method based on rolling load prediction and minimization of daily load variance according to claim 1, wherein the analyzing constraints in the energy storage configuration by using the objective function of minimization of daily load variance and listing out the constraints comprises:
analyzing the energy storage system power capacity constraint in the energy storage configuration by using the objective function of the minimized daily load variance, and listing out the energy storage system power capacity constraint conditions;
analyzing energy capacity constraint of an energy storage system in energy storage configuration by using a target function of the minimized daily load variance, and listing energy capacity constraint conditions of the energy storage system;
and analyzing the peak regulation constraint of the energy storage system in the energy storage configuration by using the objective function of the minimized daily load variance, and listing the peak regulation constraint conditions of the energy storage system.
6. The energy storage configuration method based on rolling load prediction and minimization of daily load variance according to claim 5, characterized in that the energy storage system power capacity constraint conditions are as follows:
let PcapThe maximum discharge power is the power capacity of the energy storage system
Figure FDA0002288217660000034
Maximum charging power
Figure FDA0002288217660000035
Let EcapIs the energy capacity of the energy storage system; the energy storage system power capacity constraints are as follows:
Figure FDA0002288217660000031
in addition, in the formula (I),
Figure FDA0002288217660000036
and
Figure FDA0002288217660000037
the following equation should be satisfied:
Figure FDA0002288217660000032
wherein the content of the first and second substances,
Figure FDA0002288217660000038
and
Figure FDA0002288217660000039
respectively representing the discharge and charge states of the energy storage system; if it is not
Figure FDA00022882176600000310
Then the energy storage system is charging; if it is not
Figure FDA00022882176600000311
The energy storage system is discharging.
7. The energy storage configuration method based on rolling load prediction and minimization of daily load variance according to claim 5, characterized in that the energy storage system energy capacity constraint conditions are as follows:
let E (k) be the remaining energy of the energy storage system at k, E (0) be the initial remaining energy, E (n) be the remaining energy at the end of the day, EcapIs the energy capacity of the energy storage system; the energy capacity constraints of the energy storage system are as follows:
εlowEcap≤E(k)≤εhighEcap；
wherein epsilonlowAnd εhighRespectively, the lowest and highest percentages of remaining energy; epsilonlowAnd εhighIn the range of 0% to 100%, and εlow≤εhigh；
To achieve continuous peak shaving, the remaining energy at the end of the day should be the same as the remaining energy at the beginning of the day, which is expressed as:
E(n)＝E(0)；
in addition, the relationship between the power output of the energy storage system and the remaining energy is as follows:
Figure FDA0002288217660000033
wherein, η+For discharge efficiency, η-For charging efficiency, Δ t is a time step.
8. The energy storage configuration method based on rolling load prediction and minimization of daily load variance according to claim 5, characterized in that the energy storage system peak load regulation constraint conditions are as follows:
Figure FDA0002288217660000041
wherein, PL1(k) Is the load at time k;
Figure FDA0002288217660000042
is the peak load value of the correlation period; this constraint ensures that the power capacity P is met at the amount of peak clippingcap；γ1And gamma2Are between 0 and 1, and minimize the peak load hours and the valley load hours.
9. The energy storage configuration method based on rolling load prediction and minimization of daily load variance according to claim 1, wherein the determining the type of the model based on the constraint condition and solving the model to obtain the optimal energy storage configuration capacity comprises:
determining a model based on the constraint condition, wherein the model adopts a mixed integer programming model;
solving the mixed integer programming model based on an artificial intelligence algorithm;
and acquiring the optimal energy storage configuration capacity by utilizing CPLEX software based on the result of solving the mixed integer programming model.
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