CN115829141A - Energy storage system optimal configuration method based on short-term intelligent ammeter data - Google Patents
Energy storage system optimal configuration method based on short-term intelligent ammeter data Download PDFInfo
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Abstract
The invention relates to an energy storage system optimal configuration method based on short-term intelligent electric meter data. Firstly, designing an energy storage system optimization configuration model; then, processing the data of the intelligent electric meter, the monthly total electricity consumption data of the traditional electric meter and the time-of-use electricity price to obtain the parameter constraints of the energy storage capacity, the power and the investment cost; establishing an energy storage optimization configuration model: based on the electric quantity data of the intelligent ammeter, establishing an optimized energy storage configuration equation by taking the maximum electricity price benefit and the minimum investment cost of an energy storage system as objective functions, and considering constraint conditions including the optimal charge and discharge power of energy storage, the optimal capacity of a battery and the optimal power; and solving energy storage optimization configuration parameters by utilizing a particle swarm algorithm.
Description
Technical Field
The invention belongs to the technical field of energy storage systems, and particularly relates to an energy storage system optimal configuration method based on short-term intelligent electric meter data.
Background
With the development of battery related industries, the cost of the battery is continuously reduced, and more medium-sized and small enterprises and home users deploy energy storage systems to achieve the purposes of saving energy and reducing expenses. By additionally arranging the energy storage system with proper capacity and designing the optimal configuration scheme of the energy storage system, the effects of peak clipping, valley filling and cost saving can be achieved, and meanwhile, the reliability of power supply can be improved.
The intelligent electric meter data acquisition process is a starting point of energy storage system construction. However, the traditional energy storage system optimal configuration method needs to be based on historical smart meter data of months or even whole years. For some enterprise users, waiting months to obtain the complete smart meter data is unacceptable in terms of time and cost. Therefore, the energy storage system configuration method based on short-term smart meter data is designed, the construction period of the battery energy storage device can be greatly shortened, the cost of a user side is reduced, and the method has high application value.
The Chinese patent application numbers are: CN202010843305.4, name: a micro-grid energy storage optimization configuration method based on a particle swarm algorithm is disclosed. Firstly, designing a battery energy storage system model; clustering daily load data by using a K-means clustering algorithm to obtain a typical daily load curve, and further obtaining parameter constraints of energy storage capacity, power and investment cost; establishing an energy storage optimization configuration model; solving an energy storage optimization configuration model by utilizing a particle swarm algorithm; and calculating energy storage optimization parameters to obtain a final micro-grid energy storage optimization configuration method. The method uses a K-means clustering algorithm to cluster the daily load data, and does not consider the condition that the data quantity acquired by a user is insufficient. Under the condition that the daily load data volume is insufficient, a typical daily load curve cannot be obtained by using a clustering algorithm, so that the optimal parameter of the configuration of the energy storage system cannot be obtained.
The Chinese patent application numbers are: CN201911249186.3, name: a comprehensive energy storage optimal configuration method and a system. The method combines the framework of a user-side comprehensive energy system, and gives the energy demand of the system in each time period and the maximum output of an energy production unit in the system in each time period; establishing physical models of an energy conversion unit and an energy storage unit in the system; and establishing a comprehensive energy storage optimal configuration model aiming at the minimum load variance, the minimum reduction of renewable energy sources and the minimum comprehensive energy storage capacity, wherein the optimal solution of the model is the optimal capacity which should be configured for each type of energy storage device. The method is suitable for the condition that the energy demands at the same time interval in different seasons are the same or similar, and for medium-sized and small-sized enterprises and family users, the energy demands are greatly influenced by seasons under the common condition, and an optimal energy storage system cannot be constructed only according to data in a single season.
The battery energy storage system is a sharp tool in the electric energy allocation activity and plays an important role in avoiding resource waste. However, the power consumption behavior of the user side is more and more diversified, so that the reasonable configuration of the battery energy storage system becomes difficult. Meanwhile, for a user, the user waits for a long time to obtain complete electricity data of the intelligent electric meter, and the financial cost and the time cost are unacceptable.
Disclosure of Invention
The invention aims to solve the problems and provides an energy storage system optimal configuration method based on short-term intelligent electric meter data, which comprises the steps of firstly, constructing a compensation data set through short-term electricity utilization data of an intelligent electric meter and monthly total electricity consumption data of a traditional electric meter; secondly, establishing a battery energy storage system configuration optimization equation under a multi-constraint condition; and finally, solving the configuration parameters of the optimal battery energy storage system by adopting a particle swarm algorithm.
In order to achieve the purpose, the technical scheme of the invention is as follows: an energy storage system optimal configuration method based on short-term smart meter data comprises the following steps:
step 1, carrying out data preprocessing on short-term detailed electricity consumption data collected by an intelligent ammeter and monthly total electricity consumption data of an existing traditional ammeter; complementing missing values by methods including discarding, complementing, true value converting and feature selecting, and selectively removing abnormal values;
step 2, constructing an initial data set based on the traditional electric meter data and the intelligent electric meter data preprocessed in the step 1;
step 3, constructing a battery energy storage system configuration model from the three aspects of construction cost, net profit and objective constraint of the battery energy storage system;
and 4, solving the optimal configuration parameters of the battery energy storage system by utilizing a particle swarm algorithm.
In an embodiment of the present invention, the step 2 is specifically implemented as follows:
step 2.1, clustering the daily electricity data of the intelligent electric meter by applying a K-means clustering algorithm to obtain typical daily electricity data of the intelligent electric meter, wherein the sampling frequency of the intelligent electric meter is half an hour once, namely 48 daily load data points exist in 1 day, and the daily load data points are marked as P i (i＝1,2,…,48)；
Step 2.2, calculating the average daily electricity Tra of the traditional electricity meter per Average daily electric quantity Sma of intelligent electric meter per The proportionality coefficient of (a); average half-hour electricity consumption D of typical day of intelligent electric meter i Multiplying the ratio coefficient by the ratio coefficient to calculate the approximate typical daily electricity consumption of the traditional electric meter; the mathematical expression is shown as follows:
the construction of the initial typical daily electricity data set is accomplished by steps 2.1-2.2.
In an embodiment of the present invention, the step 2.1 is specifically implemented as follows:
step 2.1.1, performing clustering analysis on the data of the intelligent electric meter by adopting a K-means clustering algorithm, randomly selecting K pieces of data from the obtained daily load sample data, and taking each time period of each piece of data as an initial clustering center; the Euclidean distance is used as a distance function of a K-means clustering algorithm, wherein the Euclidean distance d (x, y) between x and y two points in the n-dimensional space is calculated according to the following formula:
in the formula: n is the spatial dimension, x i And y i The ith attribute feature value of x and y respectively;
step 2.1.2, respectively calculating the distance from each sample point to K cluster centers in a corresponding time period, finding the cluster center closest to the sample point, and attributing the sample point to a corresponding cluster;
step 2.1.3, after all the data are divided into K clusters, recalculating the gravity center of each cluster, and taking the calculated cluster gravity center as a new cluster center;
step 2.1.4, circulating step 2.1.1-2.1.3 until reaching the termination condition; and (4) connecting the final cluster centers in the step 2.1.3 by using a smooth curve to obtain typical daily electric meter data of the intelligent electric meter.
In an embodiment of the present invention, the step 3 is specifically implemented as follows:
step 3.1, construction cost: the typical battery energy storage system BESS consists of a plurality of parts including a battery system and a power conversion system, and the cost of each subsystem jointly constitutes the construction cost of the battery energy storage system, which is marked as C 1 Which is related to the amount of energy storage capacity of the battery, expressed in annual conversion after the system lands, as follows:
C 1 ＝r w K w W
in the formula: r is w The average annual comprehensive depreciation rate, K, of the battery energy storage system w W is the unit construction cost and the battery energy storage capacity of the battery energy storage system respectively;
step 3.2, maintenance cost: the maintenance cost is related to the self-power of the battery energy storage system and is marked as C 2 The calculation method is as follows:
C 2 ＝C m P
in the formula: c m The maintenance cost (year) of unit capacity is taken as P, and the charging and discharging power of the battery energy storage system is taken as P;
step 3.3, electricity price income: the resulting electricity price benefit is denoted as E and is expressed by the following formula:
in the formula: e.g. of a cylinder i Represents the electricity prices of the ith time period,
is the charging power of the battery energy storage system in the ith half hour of the day d,
is the discharge power of the battery energy storage system in the ith half hour of the day d;
step 3.4, net profit: the battery energy storage system BESS utilizes the time-of-use electricity price difference to earn electricity price income, and the part of deducting the system cost is net profit T and is also an optimization target of the battery energy storage system optimization model:
Max T
T＝E-C 1 -C 2
step 3.5, objective constraint: (1) At any moment, the residual electric quantity in the battery energy storage system is less than or equal to the optimal capacity of the system design; (2) the charging and discharging power of the system is less than or equal to the design power; (3) The conversion efficiency limitation exists in the storage and release of the electric energy of the battery energy storage system, and the total charging amount and the total discharging amount in the system are balanced; the specific formulas of constraints (1) to (3) are as follows:
s.t.w j ≤W
in the formula: w is a group of j Refers to the remaining capacity of the battery energy storage system at any time j,
respectively representing the charging and discharging power, w, of the battery energy storage system t Representing the electric quantity used by a user from the battery energy storage system in any time period, and the conversion efficiency of the electric energy is eta.
In an embodiment of the present invention, the step 4 is specifically implemented as follows:
step 4.1, initializing an algorithm, randomly generating N numbers of particle groups Xi = (X1, X2, …, XN), randomly generating the position Xi = (Xi 1, xi2, …, xid) of each particle, wherein the position of each particle represents different capacity and power configurations, and giving the initial velocity vector of the particle as Vi = (Vi 1, vi2, …, vid); setting a particle swarm optimization boundary, namely setting the maximum value and the minimum value of the charge-discharge power P and the energy storage capacity of the battery energy storage system; the particles follow objective constraints in both the initialization and update stages;
step 4.2, the objective function value of each individual is evaluated based on the particle position, and the individual optimal particle Pi = (P) can be obtained i1 ,P i2 ,…,P id ) Particle P optimal to the population g ＝(P g1 ,P g2 ,…,P gd )；
4.3, iterating the particle swarm optimization, respectively updating the speed and the position of the d-dimension of each particle i according to the following formula, and recording the optimal capacity and power represented by the individual particles and the global particles;
in the formula, w is inertia weight; c. C 1 And c 2 Is an acceleration factor, i.e. a learning factor;
and
is two [0,1]A random number over a span;
step 4.4, updating the configuration parameters represented by the particles in a given iteration number, stopping iteration if the current iteration number reaches a preset maximum number or a minimum error requirement, and recording the position of the overall optimal particles, namely the optimal configuration parameters of the battery energy storage system; otherwise go to step 4.2 to continue execution.
Compared with the prior art, the invention has the following beneficial effects:
1. based on the unspecified short-term intelligent ammeter value and the historical total electricity consumption value of the traditional ammeter, the parameters of the energy storage optimization configuration model are solved through an intelligent optimization algorithm, so that the electricity consumption cost of a user side can be reduced, and the load capacity and the utilization power of the battery energy storage system can be improved.
2. The battery energy storage system can be constructed by only collecting a small amount of intelligent electric meter data and combining historical traditional electric meter data, so that the time cost is saved, the profit is guaranteed, and the battery energy storage capacity, the energy storage rated power information, the lowest cost investment under the optimal configuration and the maximum profit of the electricity price benefit of the battery energy storage system are matched.
The invention has the advantages that:
compared with the patent application of 'a micro-grid energy storage optimal configuration method based on particle swarm optimization', the method aims to combine short-term intelligent ammeter data with historical traditional ammeter data to construct a battery energy storage system. Firstly, designing an optimal configuration model of a battery energy storage system; then, processing the data of the intelligent ammeter, the monthly total electricity consumption data of the traditional ammeter and the time-of-use electricity price to obtain parameter constraints of energy storage capacity, power and investment cost; establishing an energy storage optimization configuration model: based on the electric quantity data of the intelligent electric meter, establishing an optimized energy storage configuration equation by taking the maximum electricity price benefit and the minimum investment cost of a battery energy storage system as objective functions, and considering constraint conditions including the optimal charge and discharge power of energy storage, the optimal capacity of the battery and the optimal power; and solving energy storage optimization configuration parameters by using a particle swarm algorithm.
Drawings
Fig. 1 is a flowchart of an optimal configuration model of a battery energy storage system according to the present invention.
FIG. 2 is a flow chart of a K-means clustering algorithm.
Fig. 3 is a flow chart of the PSO algorithm.
Detailed Description
The following describes in detail a specific embodiment of the present invention with reference to an electricity meter data example and the accompanying drawings, and an optimal configuration model process of a battery energy storage system is shown in fig. 1, and mainly includes the following steps:
step 1, selecting traditional electric meter data of 2019, 4,5 and 6 months and intelligent electric meter data research objects of 2019, 7 months, and performing data preprocessing on all electric meter data. Complementing missing values by methods of discarding, complementing, true value conversion, feature selection and the like; and carrying out selective elimination on the abnormal values. The average daily electricity usage data after pretreatment is shown in table 1.
TABLE 1 average daily power consumption data for intelligent and traditional electric meters
And 2, constructing an initial data set based on the traditional electric meter data and the intelligent electric meter data preprocessed in the step 1.
And 2.1, clustering the daily electricity data of the intelligent electric meter by using a K-means clustering algorithm to obtain typical daily electricity data of the intelligent electric meter so as to achieve more accurate configuration of the energy storage device. Wherein the sampling frequency of the intelligent electric meterThe rate is once in half an hour, namely 48 daily load data points are recorded as P in 1 day i (i =1,2, …, 48) as in table 2.
TABLE 2 typical daily electricity consumption data and approximate traditional electricity consumption data of intelligent electric meter
Step 2.1.1, performing clustering analysis on the data of the intelligent electric meter by adopting a K-means clustering algorithm, and enabling the number of clustering centers to be K =2, namely randomly selecting 2 pieces of data from daily load sample data, and taking each time period of each piece of data as an initial clustering center; using Euclidean distance as a distance function of a K-means clustering algorithm, as shown in a formula (2-1); the corresponding flow chart is shown in fig. 2.
Step 2.1.2, respectively calculating the distance from each sample point to 2 cluster centers of the corresponding time period according to the formula (2-1), finding the cluster center closest to the sample point, and attributing the sample point to the corresponding cluster;
step 2.1.3, after all the data are divided into 2 clusters, recalculating the gravity center of each cluster, and taking the calculated cluster gravity center as a new cluster center;
and 2.1.4, circulating the steps 2.1.1-2.1.3 until the termination condition is reached. Connecting the final cluster centers in the step 2.1.3 by using a smooth curve to obtain typical daily electricity consumption data of the intelligent ammeter, wherein the typical daily electricity consumption data are shown in columns 1-3 in a table 2;
step 2.2, calculating the average daily electricity Tra of the traditional electricity meter according to the formula (2-2) per Average daily electric quantity Sma of intelligent ammeter per Taking the data of the traditional electric meter in 2019 in month 6 and the data of the smart electric meter in month 7 as examples, the proportionality coefficient of 0.67 is obtained(ii) a And finally, the average half-hour power consumption of the intelligent ammeter is D i And the proportional coefficient is multiplied, so that the approximate typical daily electricity consumption of the traditional electricity meter is calculated according to the formula (2-2), as shown in the 5 th column in the table 2.
By the method, the construction of the initial typical daily electricity data set can be completed;
and 3, constructing an energy storage system configuration model from the three aspects of construction cost, net profit and objective constraint of the battery energy storage system.
And 3.1, construction cost. The typical Battery Energy Storage System (BESS) is composed of a plurality of parts, mainly including a battery system and a power conversion system, and the cost of each subsystem jointly constitutes the construction cost of the battery energy storage system, which is marked as C 1 . The method is mainly related to the energy storage capacity of the battery, and can be expressed as a formula (3-1) according to annual conversion after the system falls to the ground:
C 1 ＝r w K w W (3-1)
in the formula: average annual comprehensive depreciation rate r of energy storage system w 0.09, unit construction cost K of the battery energy storage system w The concentration was 1000 CNY/kW.h.
And 3.2, maintaining cost. After the battery energy storage system is started, regular equipment inspection and maintenance are required to ensure safe and reliable operation of the system. Generally, the maintenance cost is related to the self-power of the battery energy storage system and is marked as C 2 The calculation method is as the formula (3-2):
C 2 ＝C m P (3-2)
in the formula: maintenance cost C per unit capacity m Equal to 20CNY/kW.
And 3.3, obtaining the electricity price. The battery energy storage system can be implemented with a number of functions, such as: the stability of power supply is improved for users, and the electricity cost is saved by utilizing the electricity price difference in the time-of-use electricity price policy. Based on a reasonable charging and discharging strategy, a user can purchase electric energy to be stored in the battery energy storage system when the electricity price is low, and the electric energy stored in the battery energy storage system is used for supplying power when the electricity price is high. The electricity price benefit thus generated is denoted as E, and the electricity price benefit can be expressed as formula (3-3):
in the formula: time of use electricity price e i Charging power is given in Table 3
And
the maximum power constraint is satisfied.
And step 3.4, net profit. The Battery Energy Storage System (BESS) utilizes the time-of-use electricity price difference to earn electricity price income, and the part of deducting the system cost is net profit T and is also an optimization target of an energy storage system optimization model.
Max T (3-4)
T＝E-C 1 -C 2 (3-5)
And 3.5, objective constraint. In addition to the three points described above, the battery energy storage system is also subject to some objective constraints: (1) At any moment, the residual capacity in the battery energy storage system is less than or equal to the optimal capacity of the system design. (2) The charging and discharging power of the system is less than or equal to the design power (3), the electric energy storage and release of the battery energy storage system have conversion efficiency limitation, and the total charging amount and the total discharging amount in the system are balanced. The specific formula of the above constraint is as follows
s.t.wj≤W (3-6)
In the above formula: the value ranges of P and W are {10,60} and {50,200}, respectively, W j ，
And w t The above constraints are satisfied.
And 4, solving the optimal configuration parameters of the energy storage system by utilizing a particle swarm algorithm (shown in figure 3).
Step 4.1, initializing an algorithm, and randomly generating particle swarm x with the population number N =50 i ＝(x 1 ,x 2 ,…,x 50 ) Random position x of each particle i ＝(x i1 ,x i2 ) The position of each particle represents a different capacity and power configuration and gives the initial velocity vector of the particle V i ＝(V i1 ,V i2 ). And setting the optimization boundary of the particle swarm algorithm, namely setting the optimal power P of the battery energy storage system to be in the range of 10,60 and the optimal capacity W to be in the range of 50,200. The particles follow the constraints of equations (3-7) to (3-9) during both the initialization and update phases.
Step 4.2, evaluating the objective function value of each individual based on the particle position to obtain the individual optimal particle P i Particle P optimal to the population g 。
And 4.3, iterating the particle swarm optimization, respectively updating the speed and the position of the d-th dimension of each particle i according to the following formulas, and recording the optimal capacity and power represented by the individual particles and the global particles.
In the formula, the inertia weight w is belonged to {0.4,0.9}; acceleration factor c 1 And c 2 All are taken as 2;
and
is two [0,1]Random numbers over the interval.
Step 4.4, updating the configuration parameters represented by the particles in a given iteration number, stopping iteration if the current iteration number reaches the preset maximum number (or reaches the minimum error requirement), and recording the position of the globally optimal particles, namely the optimal configuration parameters of the energy storage system; otherwise go to step 4.2 to continue execution.
The electricity rate table for the first half year and hour of 2019 is shown in table 3.
TABLE 3 electricity price table for the first half year, hour and year 2019
And substituting the optimal configuration parameters obtained by the particle swarm optimization into the energy storage optimization configuration model to obtain the electricity price benefit E and net profit T based on uncompensated intelligent electric meter data and the intelligent electric meter data based on the compensation strategy, wherein the electricity price benefit E and the net profit T are shown in the table 4.
TABLE 4 month-uncompensated and 7 month Smart Meter data and 4,5, 6 months compensated conventional Meter data Electricity price benefits E, net profit T
The above are preferred embodiments of the present invention, and all changes made according to the technical scheme of the present invention that produce functional effects do not exceed the scope of the technical scheme of the present invention belong to the protection scope of the present invention.
Claims (5)
1. An energy storage system optimal configuration method based on short-term smart meter data is characterized by comprising the following steps:
step 1, carrying out data preprocessing on short-term detailed electricity consumption data collected by an intelligent ammeter and monthly total electricity consumption data of an existing traditional ammeter; complementing missing values by methods including discarding, complementing, true value converting and feature selecting, and selectively removing abnormal values;
step 2, constructing an initial data set based on the traditional electric meter data and the intelligent electric meter data preprocessed in the step 1;
step 3, constructing a battery energy storage system configuration model from the three aspects of construction cost, net profit and objective constraint of the battery energy storage system;
and 4, solving the optimal configuration parameters of the battery energy storage system by utilizing a particle swarm algorithm.
2. The energy storage system optimal configuration method based on short-term smart meter data as claimed in claim 1, wherein the step 2 is implemented as follows:
step 2.1, clustering the daily electricity data of the intelligent electric meter by applying a K-means clustering algorithm to obtain typical daily electricity data of the intelligent electric meter, wherein the sampling frequency of the intelligent electric meter is half an hour once, namely 48 daily load data points exist in 1 day, and the daily load data points are marked as P i (i＝1,2,…,48)；
Step 2.2, calculating the average daily electricity Tra of the traditional electricity meter per Average daily electric quantity Sma of intelligent electric meter per The proportionality coefficient of (a); average half hour electricity consumption D of the intelligent ammeter in a typical day i Multiplying the ratio coefficient by the ratio coefficient to calculate the approximate typical daily electricity consumption of the traditional electric meter; the mathematical expression is shown as follows:
the construction of the initial typical daily electricity data set is accomplished by steps 2.1-2.2.
3. The method for optimizing and configuring the energy storage system based on the short-term smart meter data according to claim 2, wherein the step 2.1 is specifically implemented as follows:
step 2.1.1, performing clustering analysis on the data of the intelligent electric meter by adopting a K-means clustering algorithm, randomly selecting K pieces of data from the obtained daily load sample data, and taking each time period of each piece of data as an initial clustering center; the Euclidean distance is used as a distance function of a K-means clustering algorithm, wherein the Euclidean distance d (x, y) between x and y two points in the n-dimensional space is calculated according to the following formula:
in the formula: n is the spatial dimension, x i And y i The ith attribute feature value of x and y respectively;
step 2.1.2, respectively calculating the distance from each sample point to K cluster centers in a corresponding time period, finding the cluster center closest to the sample point, and attributing the sample point to a corresponding cluster;
step 2.1.3, after all the data are divided into K clusters, recalculating the gravity center of each cluster, and taking the calculated cluster gravity center as a new cluster center;
step 2.1.4, circulating step 2.1.1-2.1.3 until reaching the termination condition; and (4) connecting the final cluster centers in the step 2.1.3 by using a smooth curve to obtain typical daily electric meter data of the intelligent electric meter.
4. The energy storage system optimal configuration method based on short-term smart meter data as claimed in claim 1, wherein the step 3 is implemented as follows:
step 3.1, construction cost: the typical battery energy storage system BESS consists of a plurality of parts including a battery system and a power conversion system, and the cost of each subsystem jointly constitutes the construction cost of the battery energy storage system, which is marked as C 1 It is related to the energy storage capacity of the battery and is expressed by annual conversion after the system falls to the groundIs of the formula:
C 1 ＝r w K w W
in the formula: r is a radical of hydrogen w The average annual comprehensive depreciation rate, K, of the battery energy storage system w W is the unit construction cost and the battery energy storage capacity of the battery energy storage system respectively;
step 3.2, maintenance cost: the maintenance cost is related to the self-power of the battery energy storage system and is marked as C 2 The calculation method is as follows:
C 2 ＝C m P
in the formula: c m The maintenance cost (year) of unit capacity, and P is the charging and discharging power of the battery energy storage system;
step 3.3, electricity price income: the resulting electricity price benefit is denoted as E and is expressed as:
in the formula: e.g. of a cylinder i Represents the electricity prices of the ith time period,
is the charging power of the battery energy storage system in the ith half hour of the day d,
is the discharge power of the battery energy storage system in the ith half hour of the day d;
step 3.4, net profit: the battery energy storage system BESS utilizes the time-of-use electricity price difference to earn electricity price income, and the part of deducting the system cost is net profit T and is also an optimization target of the battery energy storage system optimization model:
Max T
T＝E-C 1 -C 2
step 3.5, objective constraint: (1) At any moment, the residual electric quantity in the battery energy storage system is less than or equal to the optimal capacity of the system design; (2) the charging and discharging power of the system is less than or equal to the design power; (3) The conversion efficiency limitation exists in the storage and release of the electric energy of the battery energy storage system, and the total charging amount and the total discharging amount in the system are balanced; the specific formulas of constraints (1) - (3) are as follows:
s.t.w j ≤W
in the formula: w j Refers to the remaining capacity of the battery energy storage system at any time j,
respectively representing the charging and discharging power, w, of the battery energy storage system t Representing the electric quantity used by a user from the battery energy storage system in any time period, and the conversion efficiency of the electric energy is eta.
5. The energy storage system optimal configuration method based on short-term smart meter data as claimed in claim 4, wherein the step 4 is implemented as follows:
step 4.1, initializing an algorithm, randomly generating N numbers of particle groups Xi = (X1, X2, …, XN), randomly generating the position Xi = (Xi 1, xi2, …, xid) of each particle, wherein the position of each particle represents different capacity and power configurations, and giving an initial velocity vector of each particle as Vi = (Vi 1, vi2, …, vid); setting a particle swarm optimization boundary, namely setting the maximum value and the minimum value of the charge-discharge power P and the battery energy storage capacity of the battery energy storage system; the particles follow objective constraints in both the initialization and update stages;
step 4.2, evaluating the objective function value of each individual based on the particle position to obtain the individual optimal particle P i ＝(P i1 ，P i2 ，...，P id ) Particle P optimal to the population g ＝(P g1 ，P g2 ，...，P gd )；
4.3, iterating the particle swarm optimization, respectively updating the speed and the position of the d-dimension of each particle i according to the following formula, and recording the optimal capacity and power represented by the individual particles and the global particles;
in the formula, w is inertia weight; c. C 1 And c 2 Is an acceleration factor, i.e., a learning factor;
and
is two [0,1]A random number over the interval;
step 4.4, updating the configuration parameters represented by the particles in a given iteration number, stopping iteration if the current iteration number reaches a preset maximum number or a minimum error requirement, and recording the position of the overall optimal particles, namely the optimal configuration parameters of the battery energy storage system; otherwise go to step 4.2 to continue execution.
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| CN117458489A (en) * | 2023-12-26 | 2024-01-26 | 福建华鼎智造技术有限公司 | EMD-Bi-LSTM short-term prediction method for electricity price and multidimensional time sequence variable |
| CN117458489B (en) * | 2023-12-26 | 2024-03-12 | 福建华鼎智造技术有限公司 | EMD-Bi-LSTM short-term prediction method for electricity price and multidimensional time sequence variable |
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