CN112001598A - Energy storage configuration evaluation and operation optimization method for different users based on energy storage type selection - Google Patents
Energy storage configuration evaluation and operation optimization method for different users based on energy storage type selection Download PDFInfo
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Abstract
The invention discloses an energy storage configuration evaluation and operation optimization method for different users based on energy storage type selection, which comprises the steps of checking energy storage technology and performance index parameters based on battery energy storage market indexes, constructing a battery energy storage type selection evaluation model based on an inter-zone analytic method, and evaluating to obtain an energy storage battery; selecting an energy storage battery, and establishing an energy storage full-life cycle configuration model based on a time-of-use electricity price mechanism, an energy storage battery technology and performance parameters; selecting annual average load curves of different users for energy storage configuration, and calculating corresponding user energy storage evaluation index parameters; evaluating energy storage configurations of different users based on an analytic hierarchy process; and establishing a day-ahead energy storage optimization scheduling model for the energy storage users suitable for configuration, and optimizing the energy storage charging/discharging power in a day scheduling period based on LSTM day-ahead load prediction data. The invention can evaluate and analyze the energy storage configured by different typical users, provides guidance for installing the energy storage for different users and plays a role in promoting the development of the energy storage at the user side.
Description
Technical Field
The invention relates to the field of electric power, in particular to a method for evaluating and optimizing energy storage configuration of different users based on energy storage type selection.
Background
In recent years, along with the rapid development of social economy and the continuous improvement of the living standard of people, the frequency of peak load in a power system is continuously increased, the load of a power user has the characteristic of larger peak-valley difference, and the battery energy storage (hereinafter referred to as energy storage) can realize the space-time translation of electric energy by virtue of the flexible throughput capacity of the battery energy storage to electric power, and has the potential of coping with the load fluctuation. Meanwhile, in recent years, a series of policies such as guidance opinions about promotion of energy storage industry and technology development, southern regional electrochemical energy storage power station grid-connected operation management and auxiliary service management implementation rules and the like are provided by the country, so that the energy storage development at the user side is actively encouraged, and the main direction of the energy storage market is expected to extend to the user side in the future. The user side energy storage direct action is in the user, under the condition that does not change user power consumption action, can directly enjoy the power price system and reform the bonus, benefits through "storing up the height" and overlapping the profit, realizes that the peak clipping of user demand side management and load fills in the millet to can also provide incessant power supply for the user under the power failure condition, improve the power supply reliability. With the deep reformation of the power system in the future, power users enter the power market, and the development prospect of energy storage at the user side is very wide.
The energy storage equipment required by the user side has the characteristics of small occupied area, flexible installation, high energy density, high response speed and the like. The battery energy storage technology can effectively meet the requirements. At present, for a user side using a Battery Energy Storage System (BESS), the biggest obstacle is not in an energy storage body technology and a grid-connected technology, but the problems of high investment cost, single low-efficiency income, no effective mechanism formed in the user side energy storage market and the like are solved. The research related to the energy storage type selection is mostly based on availability and price factors and is obtained by simple comparison, so that a more reasonable method needs to be adopted to select an energy storage battery suitable for a user, and the economic benefit of BESS is maximized. Before a user plans and installs, an energy storage battery with the best performance is selected, and the research on the problems of adaptability and economy of energy storage configuration of the user is of great importance.
In the user-side energy storage research, most of the existing researches aim at energy storage configuration planning of a single industrial user, and the operation optimization stage is considered in part of the researches, so that the consideration on other typical users is not perfect; meanwhile, time scale difference is mostly ignored in the energy storage configuration process, the current rate and the currency expansion rate in the actual planning process are not comprehensively considered, and the optimal configuration capacity of energy storage cannot be accurately obtained. Therefore, the load characteristics of different typical users are greatly different, and in addition, the electricity price difference exists in the time-of-use electricity price environment of the existing market, so that the energy storage configuration adaptability of different typical users needs to be evaluated and analyzed under the condition of considering the time scale difference, and the problem of comprehensive optimization of energy storage configuration and operation is researched.
In summary, firstly, an inter-regional analytic method is constructed for energy storage type selection, and an optimal energy storage battery is obtained through evaluation; secondly, establishing different user energy storage configuration evaluation models based on the whole life cycle, and evaluating the energy storage users suitable for configuration; and finally, establishing a day-ahead scheduling optimization model, and optimizing a day-ahead energy storage charging and discharging strategy of the user.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, and provides a different-user energy storage configuration evaluation and operation optimization method based on energy storage battery type selection, which simultaneously considers the problems of energy storage type selection and configuration optimization of different-user energy storage, checks the technology and performance index parameters of different energy storage based on battery energy storage market indexes, constructs a battery energy storage type selection evaluation model based on an inter-regional analytic method, and evaluates the battery to obtain an energy storage battery with the best performance; secondly, constructing two-stage models of different user energy storage configuration evaluation and operation optimization on the basis of market time-of-use electricity price; in the configuration evaluation stage, an energy storage battery with the best performance is selected as a medium, the best energy storage capacity and power of different users are obtained through optimization by adding energy storage operation performance and state constraints, an analytic hierarchy process evaluation model is constructed according to the best energy storage capacity and power, and the advantages and the disadvantages of energy storage configuration of different users are evaluated; in the operation optimization stage, for a user suitable for configuring energy storage, based on LSTM day-ahead load prediction data, energy storage charging/discharging power in a daily scheduling period is optimized; the method comprises the following specific steps:
step 1: energy storage battery model selection
The method comprises the steps of verifying different energy storage technologies and performance index parameters based on battery energy storage market indexes, constructing a battery energy storage type selection evaluation model based on an inter-zone analytic method, and evaluating to obtain an energy storage battery with the best performance for different typical user energy storage configurations;
step 2: energy storage configuration model
Selecting the energy storage medium with the best performance in the energy storage evaluation based on a time-of-use electricity price mechanism and a user annual average load curve, establishing an energy storage full-life cycle configuration model, and performing energy storage configuration on different users;
and step 3: energy storage operating performance and state constraints
Establishing performance and state constraint conditions related to the operation of the energy storage battery according to the actual requirement of the operation of the energy storage battery; the method comprises energy storage charging and discharging power constraint, energy storage battery capacity constraint, energy storage multiplying power constraint and energy storage charge state continuity constraint;
and 4, step 4: different-user energy storage evaluation model
Optimizing the battery capacity and the battery power of the energy storage configuration based on the energy storage full-life cycle configuration model, and calculating corresponding optimization index parameters; establishing an energy storage evaluation model of an analytic hierarchy process based on the evaluation result, constructing a judgment matrix, carrying out consistency check on the energy storage evaluation model, evaluating energy storage configurations of different users, judging whether the energy storage configuration is proper or not, and if so, entering the step 5;
and 5: day-ahead energy storage optimization scheduling model
Carrying out energy storage configuration on users suitable for configuring energy storage, establishing a day-ahead energy storage optimization scheduling model, and optimizing energy storage charging and discharging power in a day scheduling period based on LSTM day-ahead load prediction data;
step 6: model solution
And performing energy storage configuration and operation optimization solving based on a CPLEX solver, wherein the energy storage configuration and the operation optimization model solving are both performed on the Matlab platform by adopting the CPLEX solver, and an optimization result is obtained.
Furthermore, the energy storage battery model selection can be divided into quantitative indexes and qualitative indexes according to the decision index types, and the qualitative indexes are converted into the quantitative indexes by adopting a 9-level scaling method of AHP (advanced high performance packet) so as to construct a scheme judgment matrix. For energy storage battery scheme CiBy means of the decision index UjMeasure to obtain CiAbout UjIs determined as the index value CijThe number is the number of intervals and is represented as Cij＝(CL ij,CU ij) L and U are the lower and upper limits, respectively. Thereby forming a decision matrix C ═ (C)ij)m×n
Weight coefficient calculation step
1) Aiming at the technical, economic and environmental protection properties of the first layer of indexes of the criterion layer, constructing a judgment matrix by adopting a 9-level scaling method of an AHP method according to expert experience, and carrying out consistency inspection;
2) determining the weight of the qualitative index in the second layer in each layer by an entropy method;
3) before the entropy method is utilized, the decision matrix needs to be quantized;
based on the interval number phase separation degree, the decision matrix C is equal to (C)ij)m×nConverting into a phase separation matrix D ═ D (D)ij)m×nWherein
Is an index Gj(j-1, 2, …, n).
4) And finally, calculating the comprehensive weight of each index.
Further, the objective function of the energy storage full life cycle configuration model is as follows:
max F＝f1+f2+f3-C1-C2+C3
in the formula: f is the net gain over the life cycle of the BESS; f. of1Earnings for the profit of BESS 'low storage and high release'; f. of2The basic electric charge income of the user is reduced; f. of3Subsidizing income for government electricity prices; c1Initial investment cost for BESS; c2For operating maintenance costs; c3The recycling value of the BESS is high.
Further, the energy storage operation performance and state constraint conditions of the energy storage full life cycle configuration model include:
the system satisfies power balance constraints
Pgrid(i)＝Pload(i)+Pess(i)
In the formula: pgrid(i) Exchanging power with the power grid for the period i; pload(i) Load power for the user in period i; pess(i) The output power is the output power in the energy storage period i, and the output power is negative when the energy storage is discharged;
energy storage state of charge (SOC) constraints
SOCmin、SOCmaxRespectively corresponding to BESS and scheduling energy storage capacity upper and lower limits;
SOCmin≤SOC(i)≤SOCmax
in the formula: SOC (i) is the BESS state of charge at time i;
the energy storage charge state satisfies the energy storage performance continuity constraint
In the formula: etacAnd ηdRespectively corresponding to BESS charging/discharging efficiency;
energy storage charge/discharge state constraint
Bch(i)、Bdis(i) Is a variable from 0 to 1, wherein 1 represents the state of charge; 0 represents the discharge state, satisfying the constraint:
Bdis(i)+Bch(i)≤1
energy storage charge/discharge confinement
In order to ensure the health state of the battery, BESS controls the charging/discharging power of each time not to exceed the rated value in the operation process, and the total discharging power does not exceed the energy storage rated capacity;
constraint of multiplying power characteristics between energy storage capacity and power
The multiplying power of the battery energy storage meets the requirement of a certain charge and discharge rate, and the energy storage rated capacity and the rated power are assumed to be in direct proportion:
Emax＝βPmax
in the formula: beta is an energy multiplying factor;
load restriction for peak clipping
The peak clipping and valley filling functions can be achieved after the energy storage configuration, the equivalent load of the system is smaller than the load peak value after the peak clipping, namely:
Pload(i)+Pch(i)-Pdis(i)≤(1-μ)Pl,max
in the formula: pl,maxLoad maximum for 24 periods of 1 day; mu is the peak clipping rate.
Further, the energy storage evaluation model-based method comprises the following steps:
and establishing an evaluation model based on an analytic hierarchy process model, wherein the hierarchical structure is divided into a target layer, a criterion layer and a decision layer. The target layer is an optimal user energy storage configuration scheme; the criterion layer takes total income, net income, investment recovery years and investment return rate in the BESS whole life cycle as decision indexes; and the decision layer configures schemes for different user energy storage. The criterion layer has 4 decision indexes, and a 4 multiplied by 4 judgment matrix is constructed; if the scheme layer has 4 decision schemes, 4 judgment matrixes of 4 multiplied by 4 are constructed; wherein the judgment matrix form is:
in the formula: a iskjThe comparison scale value represents the importance degree of the index k to the index j;
further, the building of the energy storage day-ahead scheduling optimization phase model comprises the following steps:
step 1: configuring a user with the highest comprehensive benefit based on the energy storage in the first stage, and establishing an energy storage day-ahead optimization scheduling model by taking the lowest electricity purchasing cost in a scheduling period of the day after the user additionally installs the energy storage as an objective function;
step 2: energy storage SOC constraint, energy storage SOC continuity constraint, energy multiplying power constraint, charging/discharging state and power constraint are required to be met;
and step 3: based on the ultra-short term prediction method of the deep long-short term memory network, the historical load data is used for predicting the day-ahead load data, the energy storage charging/discharging power in the day scheduling period is optimized, and the day-ahead energy storage charging/discharging strategy of the user is output.
The invention has the beneficial effects that: the optimal selection is carried out on different types of energy storage batteries, the energy storage configured by different typical users is evaluated and analyzed, and the optimal energy storage battery corresponds to the optimal configuration user and provides guidance for different users to install the energy storage; meanwhile, the invention can also realize the load prediction in the day ahead, optimize the energy storage day ahead scheduling and reduce the electricity purchasing cost of users.
Drawings
FIG. 1 is a process technology roadmap;
FIG. 2 is a schematic diagram of a user energy storage configuration;
FIG. 3 is a block diagram of an energy storage option;
FIG. 4 is a flow diagram illustrating various exemplary user energy storage optimization processes;
FIG. 5 is a graph of customer load and electricity rates;
FIG. 6 user A before energy storage charge-discharge strategy;
figure 7 shows the load change of user a before and after the energy storage is added.
Detailed Description
The technical solutions of the present invention are further described in detail below with reference to the accompanying drawings, but the scope of the present invention is not limited to the following.
The technical route of the method is shown in figure 1, the structure diagram of energy storage configuration is shown in figure 2, the structure diagram of energy storage selection is shown in figure 3, the specific flow chart is shown in figure 4, table 1 is the comprehensive weight parameter of the energy storage battery, and table 2 is the relevant parameter of the energy storage battery; table 3 shows the scheme layer alignment rule layer comprehensive weight coefficients; the method comprises the following steps:
step 1: energy storage battery model selection
The method comprises the steps of verifying different energy storage technologies and performance index parameters based on battery energy storage market indexes, constructing a battery energy storage type selection evaluation model based on an inter-zone analytic method, and evaluating to obtain an energy storage battery with optimal performance for energy storage configuration of different typical users;
step 2: energy storage configuration model
Selecting the energy storage medium with the best performance in the energy storage evaluation based on a time-of-use electricity price mechanism and a user annual average load curve, establishing an energy storage full-life cycle configuration model, and performing energy storage configuration on different users;
and step 3: energy storage operating performance and state constraints
Establishing energy storage operation performance and state related constraint conditions aiming at the actual requirements of the operation of the energy storage battery; the method comprises energy storage charging and discharging power constraint, energy storage battery capacity constraint, energy storage multiplying power constraint and energy storage charge state continuity constraint;
and 4, step 4: different-user energy storage evaluation model
Calculating corresponding optimization index parameters based on the battery capacity and power obtained by the energy storage full life cycle configuration model; establishing an energy storage evaluation model of an analytic hierarchy process based on the evaluation result, constructing a judgment matrix, carrying out consistency check on the energy storage evaluation model, evaluating energy storage configurations of different users, judging whether the energy storage configuration is proper or not, and if so, entering the step 5;
and 5: day-ahead energy storage optimization scheduling model
Carrying out energy storage configuration on users suitable for configuring energy storage, establishing a day-ahead energy storage optimization scheduling model, and optimizing energy storage charging and discharging power in a day scheduling period based on LSTM day-ahead load prediction data;
step 6: model solution
And performing energy storage configuration and operation optimization solving based on a CPLEX solver, wherein the energy storage configuration and the operation optimization model solving are both performed on the Matlab platform by adopting the CPLEX solver, and an optimization result is obtained.
The specific implementation mode is as follows:
energy storage battery model selection model establishment
Step 1: the energy storage battery model selection can be divided into quantitative indexes and qualitative indexes according to decision index types, and the qualitative indexes are converted into the quantitative indexes by adopting a 9-level scaling method of AHP to construct a scheme judgment matrix. For energy storage battery scheme CiBy means of the decision index UjMeasure to obtain CiAbout UjIs determined as the index value CijThe number is the number of intervals and is represented as Cij＝(CL ij,CU ij) L and U are the lower and upper limits, respectively. Thereby forming a decision matrix C ═ (C)ij)m×n
Weight coefficient calculation step
1) Aiming at the technical, economic and environmental protection properties of the first layer of indexes of the criterion layer, constructing a judgment matrix by adopting a 9-level scaling method of an AHP method according to expert experience, and carrying out consistency inspection;
2) determining the weight of the qualitative index in the second layer in each layer by an entropy method;
3) before the entropy method is utilized, the decision matrix needs to be quantized;
based on the interval number phase separation degree, the decision matrix C is equal to (C)ij)m×nConverting into a phase separation matrix D ═ D (D)ij)m×nWherein
Is an index Gj(j-1, 2, …, n).
4) And finally, calculating the comprehensive weight of each index.
The steps of determining the index weight by the entropy method are as follows:
1. converting the decision matrix C to (C)ij)m×nConverting into a phase separation matrix D ═ D (D)ij)m×n；
2. Normalized phase separation matrix D ═ Dij)m×nBy using
Standardizing the matrix D as P ═ P (P)ij)m×n；
3. Find the index GjEntropy of the lower
GjCoefficient d of deviation degree of each solution index valuej＝1-SjJ is 1,2, …, m. The local weight coefficient of each decision index is as follows:
multiplying index local weight of an entropy value method by one layer of weight based on an AHP method to obtain comprehensive weight w 'of each index'j。
4. And finally, calculating the comprehensive weight of the performance of each energy storage battery scheme by adopting the following formula.
Weighting factor and consistency check thereof
The check for consistency is performed by calculating a consistency ratio CR and a consistency index CI.
In the formula: CR is a random consistency index. When the consistency ratio CR is less than 0.1, the judgment matrix passes through the consistency check comprehensive weight coefficient and the consistency check thereof
The overall weight coefficient consistency ratio formula is:
similarly, when the consistency ratio CR is less than 0.1, the comprehensive weight coefficient of the scheme layer passes the consistency check.
And finally, comparing the comprehensive weight coefficients of different energy storage batteries according to the result, wherein the larger the value of the comprehensive weight coefficient is, the optimal comprehensive performance of the energy storage battery is shown, and the evaluation result is shown in table 1. As can be seen from table 1, the lithium battery has the largest comprehensive weight coefficient, and is most suitable for the energy storage configuration at the user side.
TABLE 1 energy storage cell comprehensive weight parameters
And establishing a model in the energy storage configuration evaluation stage.
Step 2: and the user side BESS configuration model optimizes the energy storage configuration power and capacity by selecting the lithium battery with the best energy storage performance within the energy storage life cycle with the net income as the maximum objective function. Benefits including BESS "low-reserve high-discharge" arbitrage benefits, user-base electricity charge benefits, government electricity price subsidy benefits, and BESS recycling values, including BESS initial investment costs and operational maintenance costs, are received over the BESS life cycle. The concrete expression is as follows:
max F＝f1+f2+f3-C1-C2+C3 (5)
in the formula: f is the net income over the life cycle of the BESS; f. of1Earnings for profit sharing of BESS 'low storage and high release'; f. of2To reduce the basic electricity charge income of users; f. of3Subsidizing income for government electricity prices; c1Initial investment cost for BESS; c2For operating maintenance costs; c3The recycling value of the BESS is high.
Energy storage operating performance and state constraints
And step 3: in order to ensure the conservation of system power, a system power balance constraint is established; in order to ensure safe and stable operation of energy storage, related constraint conditions of energy storage operation are established, wherein the related constraint conditions comprise state of charge (SOC) constraint, energy storage SOC continuity constraint, charging/discharging state constraint, energy storage charging/discharging constraint and multiplying power constraint between energy storage capacity and power.
1) The system satisfies power balance constraints
Pgrid(i)＝Pload(i)+Pess(i) (6)
In the formula: pgrid(i) Exchanging power with the power grid for the period i; pload(i) Load power for the user in period i; pess(i) The output power is in the energy storage period i, and the energy storage is negative when discharging.
2) Energy storage state of charge (SOC) constraints
SOCmin、SOCmaxRespectively corresponding to BESS schedulable energy storage capacity upper and lower limits.
SOCmin≤SOC(i)≤SOCmax (7)
In the formula: SOC (i) is the BESS state of charge at time i.
3) Energy storage state of charge continuity constraint
In the formula: etacAnd ηdCorresponding to the BESS charge/discharge efficiencies, respectively.
4) Energy storage charge/discharge state constraint
Bch(i)、Bdis(i) Is a variable from 0 to 1, wherein 1 represents the state of charge; 0 represents the discharge state, satisfying the constraint:
Bdis(i)+Bch(i)≤1 (9)
5) energy storage charge/discharge confinement
In order to meet the energy storage performance constraint and prolong the energy storage service life in the operation process of the BESS, the charging/discharging power at each time is controlled not to exceed the rated value, and the total discharging power does not exceed the energy storage rated capacity.
6) Power ratio constraint between energy storage capacity and power
Assuming a direct ratio between the energy storage rated capacity and the rated power:
Emax＝βPmax (12)
in the formula: beta is the energy multiplying factor.
7) Load restriction for peak clipping
The stored energy has the functions of peak clipping and valley filling, and the equivalent load of the system obtained after the stored energy is configured is smaller than the load peak value after the peak clipping, namely:
Pload(i)+Pch(i)-Pdis(i)≤(1-μ)Pl,max (13)
in the formula: pl,maxLoad maximum for 24 periods of 1 day; mu is the peak clipping rate.
Based on the annual average load curve and the electricity price curve of different typical users as shown in fig. 5, the technical index parameters of the lithium battery with the best performance are selected as shown in table 2. And calling a CPLEX solver in MATLAB software to optimize the energy storage configuration to obtain the capacity and power of the energy storage configuration of different typical users.
TABLE 2 lithium cell-related parameters
Different-user energy storage evaluation model
And 4, step 4: and establishing an AHP energy storage evaluation model based on the result obtained by the energy storage configuration model and the corresponding evaluation parameters.
The user energy storage scheme evaluation model based on the AHP comprises the following steps:
1) establishing a hierarchical structure model, wherein the target layer is comprehensively optimal for the energy storage configuration result of the user side; the criterion layer takes BESS full life cycle net income, total income, investment recovery years and investment return rate as decision indexes; the scheme layer is different types of user energy storage schemes.
2) Structural judgment matrix
The criterion layer has 4 decision indexes, and for the target layer, a 4 x 4 judgment matrix needs to be constructed; there are 4 decision schemes in the scheme layer, and for the criterion layer, 4 × 4 judgment matrices need to be constructed. Wherein the judgment matrix A is in the form of:
in the formula: a iskjThe degree of importance of the index k to the index j is compared with the scale value.
3) Criterion layer weight coefficients and consistency check thereof
The check for consistency is performed by calculating a consistency ratio CR and a consistency index CI.
In the formula: CR is a random consistency index. When the consistency ratio CR is less than 0.1, the judgment matrix passes the consistency check
4) Comprehensive weight coefficient and consistency check thereof
Then the formula of the consistency ratio of the comprehensive weight coefficients of the scheme layer is:
similarly, when the consistency ratio CR is less than 0.1, the comprehensive weight coefficient of the scheme layer passes the consistency check.
Finally, the comprehensive weight coefficients of different scheme layers are compared according to the results, the larger the value of the comprehensive weight coefficient is, the better the scheme is, and the evaluation results are shown in table 3. As can be seen from table 3, the industrial user a has the largest comprehensive weight coefficient, which indicates that the comprehensive benefit of energy storage configuration is the best, and is suitable for installation and popularization of energy storage.
TABLE 3 scheme layer versus criteria layer composite weight coefficients
Energy storage day-ahead scheduling optimization stage model establishment
And 5: and obtaining an industrial user A with the highest comprehensive benefit based on the energy storage configuration evaluation result of the last stage, and establishing an energy storage day-ahead optimization scheduling model by taking the lowest electricity purchasing cost in a scheduling period of the day after the user A is additionally provided with energy storage as an objective function. The expression is as follows:
in the formula: f1The electricity purchase cost in the daily scheduling period.
The energy storage day-ahead optimization scheduling model also needs to meet energy storage SOC constraints, energy storage SOC continuity constraints, energy rate constraints, charge/discharge states, power constraints and the like. As shown in formulas (6) to (13). Except that in the state-of-charge constraint and the energy storage power constraint, the power limit and capacity of the stored energy are the corresponding values obtained in the energy storage configuration.
And then, based on a deep long short term memory network (LSTM) ultra-short term prediction method, predicting the load data before the day by using the historical load data, optimizing the energy storage charging/discharging power in the daily scheduling period, and finally outputting a user energy storage charging/discharging strategy before the day.
The charge and discharge strategy of the user A is shown in fig. 6, and the load curve of the user A before and after the installation of the stored energy is shown in fig. 7.
The difference between the energy stored by the user and the energy stored by the user can be intuitively obtained from the graph 6 and the graph 7, the energy storage charging and discharging curve and the electricity price have strong coupling, the energy storage is controlled to be charged when the electricity price is low, the energy storage is controlled to be discharged when the electricity price is high, and the profit is benefited by the peak-valley electricity price difference. After the energy storage is configured, the peak clipping and valley filling effects on the load can be well achieved, the load demand during peak is reduced, and the stability of the power grid is improved.
The battery which is best suitable for energy storage configuration is obtained through battery type selection and is used for energy storage configuration evaluation, selection can be provided for different users to configure energy storage, and the configuration result can be accurate and reliable by taking the full life cycle as configuration time and considering the discount rate and the currency expansion rate; and finally, carrying out day-ahead charging and discharging strategy optimization on the energy storage users suitable for configuration, and exerting the maximum benefit of energy storage at the user side. And finally, the energy storage type selection is matched with the user pairing, and guidance is provided for the development of the energy storage at the user side.
The foregoing is illustrative of the preferred embodiments of this invention, and it is to be understood that the invention is not limited to the precise form disclosed herein and that various other combinations, modifications, and environments may be resorted to, falling within the scope of the concept as disclosed herein, either as described above or as apparent to those skilled in the relevant art. And that modifications and variations may be effected by those skilled in the art without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (6)
1. The method is characterized in that the method simultaneously considers the configuration optimization problems of energy storage type selection and different user energy storage, obtains different energy storage technologies and performance index parameters based on battery energy storage market indexes, constructs a battery energy storage type selection evaluation model based on a zonal analysis method, evaluates and obtains an energy storage battery with optimal performance, and constructs two-stage models of energy storage configuration evaluation and operation optimization of different users based on market time-sharing electricity price; in the configuration evaluation stage, the energy storage battery obtained by evaluation is selected as a medium, the optimal energy storage capacity and power of different users are obtained through optimization by adding energy storage operation performance and state constraints, an analytic hierarchy process evaluation model is constructed according to the optimal energy storage capacity and power, and the advantages and the disadvantages of energy storage configuration of different users are evaluated; in the operation optimization stage, for a user suitable for configuring energy storage, based on LSTM day-ahead load prediction data, energy storage charging/discharging power in a daily scheduling period is optimized; the method comprises the following specific steps:
step 1: energy storage battery model selection
And (3) checking different energy storage technologies and performance index parameters based on the battery energy storage market index, constructing a battery energy storage type selection evaluation model based on the inter-zone analytic method, and evaluating to obtain an energy storage battery with the best performance for energy storage configuration.
Step 2: energy storage configuration model
Selecting the energy storage medium with the best performance in the energy storage evaluation based on a time-of-use electricity price mechanism and a user annual average load curve, establishing an energy storage full-life cycle configuration model, and performing energy storage configuration on different users;
and step 3: energy storage operating performance and state constraints
Establishing performance and state constraint conditions related to the operation of the energy storage battery according to the actual requirement of the operation of the energy storage battery; the method comprises energy storage charging and discharging power constraint, energy storage battery capacity constraint, energy storage multiplying power constraint and energy storage charge state continuity constraint;
and 4, step 4: different-user energy storage evaluation model
Optimizing the battery capacity and the battery power of the energy storage configuration based on the energy storage full-life cycle configuration model, and calculating corresponding optimization index parameters; establishing an energy storage evaluation model of an analytic hierarchy process based on the evaluation result, constructing a judgment matrix, carrying out consistency check on the energy storage evaluation model, evaluating energy storage configurations of different users, judging whether the energy storage configuration is proper or not, and if so, entering the step 5;
and 5: day-ahead energy storage optimization scheduling model
Carrying out energy storage configuration on users suitable for configuring energy storage, establishing a day-ahead energy storage optimization scheduling model, and optimizing energy storage charging and discharging power in a day scheduling period based on LSTM day-ahead load prediction data;
step 6: model solution
And performing energy storage configuration and operation optimization solving based on a CPLEX solver, wherein the energy storage configuration and the operation optimization model solving are both performed on the Matlab platform by adopting the CPLEX solver, and an optimization result is obtained.
2. The method for evaluating and optimizing the energy storage configuration of different users based on the energy storage type selection according to claim 1, wherein the energy storage battery scheme based on the battery energy storage type selection comprises a lithium battery, a lead-acid battery, a sodium-sulfur battery and an all-vanadium battery; the decision indexes comprise three categories of technical, economic and environmental protection; the aim is that the performance of the energy storage battery is optimal; and the accuracy of the type selection scheme is improved by introducing the interval number and the analytic hierarchy process combined with the analytic hierarchy process.
3. The method for evaluating and optimizing the energy storage configuration of different users based on energy storage type selection according to claim 1, wherein the energy storage configuration considers the life cycle cost of energy storage and the current rate and the inflation rate of traffic in the actual planning process, so that the result is reliable and accurate. The objective function of the energy storage full life cycle configuration model is as follows:
max F＝f1+f2+f3-C1-C2+C3
in the formula: f is the net gain over the life cycle of the BESS; f. of1Earnings for profit sharing of BESS 'low storage and high release'; f. of2To reduce basic electricity charge revenue; f. of3Subsidizing income for government electricity prices; c1Initial investment cost for BESS; c2For operating maintenance costs; c3The recycling value of the BESS is high.
4. The method for evaluating and optimizing the energy storage configuration of different users based on energy storage selection according to claim 1, wherein the energy storage operation performance and state constraints comprise system power balance constraints, state of charge (SOC) constraints, energy storage SOC continuity constraints, charge/discharge state constraints, energy storage charge/discharge constraints, magnification constraints between energy storage capacity and power, and load clipping constraints.
5. The method for evaluating and optimizing the energy storage configuration of different users based on energy storage selection according to claim 1, wherein the evaluation model based on the analytic hierarchy process model is divided into a target layer, a criterion layer and a decision layer in a hierarchical structure. The target layer is an optimal user energy storage configuration scheme; the criterion layer takes total income, net income, investment recovery years and investment return rate in the BESS whole life cycle as decision indexes; and the decision layer configures schemes for different user energy storage.
6. The method for evaluating and optimizing the energy storage configuration of different users based on energy storage selection according to claim 1, wherein a user with the highest comprehensive benefit is configured based on the energy storage in the first stage, and an energy storage day-ahead optimization scheduling model is established by taking the lowest electricity purchasing cost in a scheduling period of a day after the user installs the energy storage as an objective function; the day-ahead optimized scheduling load data is from a deep long-short term memory network (LSTM) prediction method, and the energy storage charging/discharging power in the day-ahead scheduling period is optimized by predicting the day-ahead load data by using historical load data.
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| CN113657739A (en) * | 2021-08-06 | 2021-11-16 | 国网浙江嘉善县供电有限公司 | Method for evaluating quantitative energy storage under multiple scenes |
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