CN117035158A - System and method for evaluating energy storage optimization configuration of user side based on multiple profit modes - Google Patents
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Abstract
The application relates to a user side energy storage optimal configuration evaluation system based on multiple profit modes, which comprises the following modules: the data processing module is used for analyzing historical load data of a user and reading parameters such as a annual load curve, an electricity price curve, an energy storage technical index and the like; the user side energy storage optimizing configuration module firstly establishes a user side energy storage optimizing configuration model participating in different comprehensive profit modes based on three profit modes such as peak valley arbitrage, demand management and demand response, simultaneously determines parameter values participating in auxiliary service, then optimizes capacity and power of energy storage configuration based on the established energy storage full life cycle configuration model and input parameters, and calculates corresponding evaluation index parameters; the modeling module is used for establishing an AHP-based energy storage comprehensive evaluation model according to the acquired evaluation index parameters; and the comprehensive weight calculation module is used for constructing a judgment matrix, carrying out consistency test, evaluating and analyzing various energy storage configuration schemes participating in different comprehensive profit modes, and giving out good and bad sequencing.
Description
Technical Field
The application relates to a user side energy storage optimal configuration evaluation system and method based on multiple profit modes, and belongs to the technical field of energy storage.
Background
In recent years, along with the maturation of energy storage technology and gradual reduction of energy storage construction cost, the energy storage technology plays a great role in stabilizing new energy power fluctuation, peak clipping and valley filling, improving voltage quality, demand side response, frequency modulation and the like, and the commercial development of energy storage at a user side is widely focused, so that intelligent new modes of electricity utilization such as peak valley arbitrage, demand management, demand response, emergency power supply, peak regulation and the like based on energy storage become effective ways for reducing electricity utilization cost of industrial and commercial users and improving user income. The advantages of the energy storage system are seen, but the commercial value of the energy storage system is not well reflected in the current market environment. For the configuration energy storage of the user side, the problems of high investment cost, low economic benefit, long investment recovery years and the like are the main factors for restricting the commercialized development of the energy storage.
Currently, research on energy storage configuration is mainly based on a time-of-use price policy, and the energy storage configuration is optimally calculated by establishing a battery energy storage system (battery energy storage system, BESS) economical model or a full life cycle life model. Most of the current energy storage configuration researches are based on a single profit mode to construct a full life cycle cost benefit model of energy storage, but few researches are conducted to analyze multi-profit modes and user side energy storage optimization configuration of multi-profit mode comparison, and few researches are conducted to analyze the economical efficiency of the energy storage life cycle, investment cost, investment recovery period, return on investment and other multi-attribute comprehensive evaluation indexes under different application scenes. Therefore, on the basis of analysis of the energy storage income at the user side, according to different application scenes and user demands, a set of effective energy storage system capacity configuration and evaluation algorithm is designed by comprehensively considering a plurality of economic indexes such as the initial investment, the net income of the whole life cycle, the return on investment, the investment recovery period and the like of the energy storage system, the user can be helped to improve the utilization value of the energy storage system, and the economic potential of the current energy storage system market application can be maximized.
Disclosure of Invention
In order to solve the problems in the prior art, the application provides a system and a method for evaluating energy storage optimization configuration at a user side based on multiple profit modes.
The technical scheme of the application is as follows:
in one aspect, the application provides a system for evaluating energy storage optimization configuration of a user side based on multiple profit modes, which comprises the following modules:
the data processing module is used for analyzing historical load data of a user and acquiring annual load curve, electricity price curve and energy storage technical index parameters;
the user side energy storage optimizing configuration module is used for calculating the profit situation of a user in different profit modes according to the parameters acquired by the data processing module, arranging and combining each profit mode to obtain a plurality of comprehensive profit modes, combining the profit situation of each profit mode with each comprehensive profit mode, establishing a user side energy storage optimizing configuration model participating in the different comprehensive profit modes, optimizing the capacity and the power of the energy storage configuration based on the established configuration model, and calculating corresponding evaluation index parameters;
the modeling module is used for establishing an energy storage comprehensive evaluation model based on the AHP according to the calculated evaluation index parameters;
the evaluation module is used for constructing a judgment matrix and carrying out consistency check through an AHP-based energy storage comprehensive evaluation model, evaluating and analyzing various energy storage configuration schemes participating in different comprehensive profit modes and carrying out quality sorting.
As a preferred embodiment of the application, a user side energy storage optimization configuration model participating in different comprehensive profit modes is established by combining the profit conditions of the three profit modes of peak valley arbitrage, demand management and demand response with the participation conditions of the three profit modes, and an objective function is established by taking the net profit in the whole life cycle of the user side energy storage as an objective, wherein the objective function is shown in the following formula:
F＝k 1 C 1 +k 2 C 2 +k 3 C 3 -f 1 -f 2
wherein: f is the net benefit in the energy storage life cycle; c (C) 1 For peak Gu Taoli benefit, C 2 To manage profits for demand, C 3 Response revenue for the demand side; f (f) 1 To store energy at initial investment cost f 2 The operation and maintenance cost is saved in the whole life cycle; k (k) 1 、k 2 、k 3 The full life cycle benefit coefficients of the three benefit modes of peak Gu Tao benefit, demand management, demand response, respectively, are 0 if the mode is engaged with 1, and if not engaged.
As a preferred embodiment of the application, the energy storage initial investment cost f 1 The calculation method is as follows:
f 1 ＝C p P+C e E
wherein: c (C) p 、C e The cost of the charge/discharge power of the energy storage unit and the cost of the unit capacity are respectively; p is the rated power value of energy storage; e is the rated capacity of energy storage;
the energy storage annual operation maintenance cost f 2 The calculation method is as follows:
f 2 ＝f m PK r
wherein: f (f) m Annual operation maintenance cost for the charging/discharging power of the energy storage unit; k (K) r Calculating a coefficient for the whole life cycle of the energy storage system, namely calculating the current value according to complex profit in the whole life cycle of the energy storage system; t is the service life of the energy storage system; t is t r Is the inflation rate; d, d r The discount rate.
As a preferred embodiment of the present application, the peak Gu Taoli yields C 1 The calculation method is as follows:
C 1 ＝Y 1 S 1 K r
wherein: s is S 1 A benefit of peak Gu Tao benefit in a day; i is a certain period of the day; Δt is the duration of the i period, set to 1 hour; y is Y 1 The operation days are the energy storage year; p is p i Electricity price in the i period; p (P) c,i 、P dis,i The actual charge/discharge power for the stored energy during period i; b (B) c,i 、B dis,i Charge/discharge state for energy storage during period i;
the demand management benefit C 2 The calculation method of (2) is shown as follows:
C 2 ＝Y 2 S 2 K r
S 2 ＝α(P md -P d )
wherein: y is Y 2 Number of operating months per year; s is S 2 Basic capacity electricity charge saved for each month of energy storage users; alpha is the unit price of basic capacity electricity fee; p (P) md The maximum load power value of the user before the energy storage is not installed; p (P) d The maximum demand value reported by the user after the energy storage is installed;
the demand response C 3 The calculation method of (2) is shown as follows:
β p ＝s p v p
wherein: beta p The electricity price of the load can be interrupted for the p-th participation demand response; s is(s) p The electricity price standard corresponding to the p-th regulation time length; v p The p-th response speed coefficient; p (P) DSM,p Reporting success rate for participating in the contract response; p is p t Is the number of demand responses.
As a preferred implementation mode of the application, the user side energy storage optimization configuration model participating in different comprehensive profit modes further comprises constraint conditions, wherein the constraint conditions are as follows:
state of charge constraints are represented by the following formula:
S min ≤S i ≤S max
wherein: s is S i The energy storage charge state is in the period i; s is S min And S is max A lower limit and an upper limit of the state of charge, respectively;
the charge/discharge state constraint is as follows:
B c,i +B dis,i ≤1
in the process of charging and discharging the energy storage, the actual power and the capacity are within the rated value range of the energy storage, and the energy storage charging and discharging power is constrained, as shown in the following formula:
0≤P dis,i ≤B dis,i P
0≤P c,i ≤B c,i P
setting the energy storage daily throughput in the model to be 2 times of the maximum capacity, namely the energy storage can be charged and discharged 2 times a day;
the stored state of charge continuity constraint is represented by the following formula:
wherein: η is the charge/discharge efficiency of the stored energy;
peak clipping load constraints are shown by the following formula:
P Load,i +P c,i -P dis,i ≤1.05P d
wherein: after the energy storage operation, the equivalent load is not more than 1.05 times of the maximum value of the optimized demand; p (P) Load,i The actual running power value of a user in the period i before the energy storage is installed;
demand response constraints are shown by the following formula:
max(Load k +P c,k -P d,k )≤max(Load j +P c,j -P d,j )
max(Load j +P c,j -P d,j )-max(Load k +P c,k -P d,k )≤0.8P DSM
wherein: k is the response time of the demand response day; j is the corresponding time to baseline; p (P) c,k And P c,j Charging power of the energy storage demand response and the period corresponding to the base line respectively; p (P) d,k And P d,j Respectively the discharge power of the time corresponding to the energy storage demand response and the baseline; load k For loads engaged in demand response periods; load j In response to a load at a time corresponding to 5 days before day; p (P) DSM Reporting power values for participating in the contract response;maximum peak load for the last year user;constraint is carried out on the range of the appointed response power; max (Load) k +P c,k -P d,k )≤max(Load j +P c,j -P d,j ) Maximum load does not exceed the baseline maximum load for the response period; max (Load) j +P c,j -P d,j )-max(Load k +P c,k -P d,k )≤0.8P DSM Average load constraint for response time period;
and multiplying power constraint between the energy storage capacity and the power is shown as follows:
E＝β×P
wherein: the energy storage multiplying power characteristic range is set to be more than or equal to 1 and less than or equal to 10.
As a preferred embodiment of the application, a CPLEX12.9 solver in MATLAB is used for solving an objective function of a user side energy storage optimizing configuration model participating in different comprehensive profit modes, the energy storage capacity and the power are obtained, and the energy storage investment cost, the net profit in the whole life cycle, the investment recovery period and the return on investment are used as decision indexes, so that corresponding evaluation index parameters are calculated.
As a preferred embodiment of the present application, the calculation method of the energy storage investment cost is as follows:
f＝f 1 + 2
wherein: f is energy storage investment cost, and consists of energy storage initial investment cost and energy storage maintenance investment cost, wherein the former is used for purchasing and installing energy storage equipment, and the latter is used for running and maintaining the energy storage equipment;
the investment recovery years calculation formula is as follows:
the calculation formula of the return on investment is as follows:
as a preferred embodiment of the present application, the AHP-based energy storage comprehensive evaluation model includes a target layer, a criterion layer and a scheme layer;
the target layer is the optimal comprehensive effect of the energy storage configuration scheme and is used for giving an evaluation target of the decision-making problem corresponding to the energy storage configuration scheme;
the evaluation indexes of the criterion layer comprise initial investment cost, net income, return on investment age and return on investment rate, and are used for giving the criterion for influencing the evaluation target;
the scheme layer is used for evaluating the optimization effect of the user side energy storage configuration scheme participating in different comprehensive profit modes.
As a preferred implementation mode of the application, the evaluation module constructs a judgment matrix and performs consistency test through an AHP-based energy storage comprehensive evaluation model, evaluates and analyzes various energy storage configuration schemes participating in different comprehensive profit modes, and performs quality sorting, and the method comprises the following specific steps:
and constructing a judgment matrix by adopting a consistent matrix method, wherein the judgment matrix is used for determining weights among layers and among factors of an energy storage comprehensive evaluation model, and the constructed judgment matrix of a criterion layer is shown as follows:
wherein: a represents a criterion layer judgment matrix; a, a i,j A comparison weight representing the ith element of the layer relative to the jth factor;
calculating weight coefficient of criterion layer, calculating eigenvalue of judgment matrix A, and finding out maximum eigenvalue lambda max And corresponding feature vector alpha max Then to the characteristic vector alpha max After normalization processing, a weight coefficient vector omega of a criterion layer decision factor to a target layer can be obtained; feature vector alpha max The normalized formula of (2) is shown as follows:
wherein: alpha i For the feature vector alpha max Is the i-th element of (a);
the consistency check is carried out on the criterion layer judgment matrix, the consistency index corresponding to the maximum characteristic value is calculated firstly, then the consistency ratio is calculated by combining with the random consistency index, and finally the passing condition of the consistency check is judged according to the size, and the calculation formula is shown as follows:
wherein: c (C) I Is a consistency index; n is the order of the criterion layer judgment matrix; r is R I Is a random consistency index; c (C) R Is a consistency ratio;
when the consistency test is not passed, the criterion layer judgment matrix needs to be reconstructed, and the steps are repeated until the consistency test is passed;
calculating comprehensive weight coefficients, wherein the weight coefficients of the kth index of the scheme layer to the criterion layer are sequentially b k1 ,b k2 ,······,b kn The calculation formula of the comprehensive weight coefficient of the kth scheme in the scheme layer to the total target is shown as follows:
wherein: ρ k Representing the comprehensive weight coefficient of the kth scheme in the scheme layer to the total target; omega i Representing the weight coefficient of the ith factor in the criterion layer to the target layer; beta ki A weight coefficient representing the kth scheme of the scheme layer to the ith factor in the criterion layer;
the consistency index of the scheme in the scheme layer to the decision index k in the criterion layer is set asThe random disposable index isThe overall weight coefficient consistency ratio C of the scheme layer R The formula of (2) is as follows:
when the element of the pair comparison matrix with higher consistency ratio does not pass the inspection, the element value of the pair comparison matrix is required to be modified until the element passes the inspection;
and finally, sorting the comprehensive weight coefficients of the target layer according to the scheme layer, wherein the scheme with the highest weight coefficient value is the optimal scheme.
On the other hand, the application also provides a user side energy storage optimizing configuration evaluation method based on a plurality of profit modes, which comprises the following steps:
analyzing historical load data of a user, and reading annual load curve, electricity price curve and energy storage technical index parameters;
calculating the profit situation of the user in different profit modes according to the parameters, arranging and combining each profit mode to obtain a plurality of comprehensive profit modes, combining the profit situation of each profit mode with each comprehensive profit mode, establishing a user-side energy storage optimizing configuration model participating in the different comprehensive profit modes, optimizing the capacity and power of energy storage configuration based on the established configuration model, and calculating corresponding evaluation index parameters;
according to the calculated evaluation index parameters, an energy storage comprehensive evaluation model based on AHP is established;
and constructing a judgment matrix and carrying out consistency check through an AHP-based energy storage comprehensive evaluation model, evaluating and analyzing a plurality of energy storage configuration schemes participating in different comprehensive profit modes, and carrying out quality sequencing.
The application has the following beneficial effects:
1. according to the application, through knowing the electricity price and load change curve of each time of the historical time, the capacity and power of the energy storage configuration are optimized, corresponding evaluation index parameters are calculated, and the energy storage configuration participated in by the auxiliary service is obtained to have optimal economical efficiency.
2. The application comprises a data processing module, a user side energy storage optimizing configuration module, a modeling module, a comprehensive weight calculating module and other device modules, evaluates various energy storage configuration schemes participating in different comprehensive profit modes, gives out good and bad ordering, and provides an optimal profit mode participation scheme for clients.
3. The application can help the user to improve the utilization value of the energy storage system, balance the electric quantity supply and demand relation, smooth the fluctuation of the electricity price and improve the investment income of energy storage.
Drawings
FIG. 1 is a system block diagram of the present application;
FIG. 2 is a flow chart of the method of the present application;
FIG. 3 is a diagram of an AHP-based user energy storage scheme evaluation model of the present application;
FIG. 4 is a graph showing comparison of the results of comprehensive weight coefficients of the evaluation index of the user-side energy storage configuration scheme under different comprehensive profit modes in the embodiment of the application;
fig. 5 is a graph of load and electricity price according to an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
It should be understood that the step numbers used herein are for convenience of description only and are not limiting as to the order in which the steps are performed.
It is to be understood that the terminology used in the description of the application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in this specification and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
The terms "comprises" and "comprising" indicate the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
The term "and/or" refers to any and all possible combinations of one or more of the associated listed items, and includes such combinations.
Embodiment one:
referring to fig. 1, a system for evaluating a user-side energy storage optimization configuration based on multiple profit modes includes the following modules:
the data processing module is used for analyzing historical load data of a user and acquiring annual load curve, electricity price curve and energy storage technical index parameters;
the user side energy storage optimizing configuration module is used for calculating the profit situation of a user in different profit modes according to the parameters acquired by the data processing module, arranging and combining each profit mode to obtain a plurality of comprehensive profit modes, combining the profit situation of each profit mode with each comprehensive profit mode, establishing a user side energy storage optimizing configuration model participating in the different comprehensive profit modes, optimizing the capacity and the power of the energy storage configuration based on the established configuration model, and calculating corresponding evaluation index parameters;
the modeling module is used for establishing an energy storage comprehensive evaluation model based on the AHP according to the calculated evaluation index parameters;
the evaluation module is used for constructing a judgment matrix and carrying out consistency check through an AHP-based energy storage comprehensive evaluation model, evaluating and analyzing various energy storage configuration schemes participating in different comprehensive profit modes and carrying out quality sorting.
As a preferred implementation manner of the embodiment, a user side energy storage optimization configuration model participating in different comprehensive profit modes is established by combining the profit conditions of the three profit modes of peak valley arbitrage, demand management and demand response with the participation conditions of the three profit modes, and an objective function is established by taking the net profit in the whole life cycle of the user side energy storage as a target, wherein the objective function is shown as the following formula:
F＝k 1 C 1 +k 2 C 2 +k 3 C 3 -f 1 -f 2
wherein: f is the net benefit in the energy storage life cycle; c (C) 1 For peak Gu Taoli benefit, C 2 To manage profits for demand, C 3 Response revenue for the demand side; f (f) 1 To store energy at initial investment cost f 2 The operation and maintenance cost is saved in the whole life cycle; k (k) 1 、k 2 、k 3 The full life cycle benefit coefficients of the three benefit modes of peak Gu Tao benefit, demand management, demand response, respectively, are 0 if the mode is engaged with 1, and if not engaged.
As a preferred implementation of this example, the energy storage initial investment cost f 1 The calculation method is as follows:
f 1 ＝ p P+C e E
wherein: c (C) p 、C e The cost of the charge/discharge power of the energy storage unit and the cost of the unit capacity are respectively; p is the rated power value of energy storage; e is the rated capacity of energy storage;
the energy storage annual operation maintenance cost f 2 The calculation method is as follows:
f 2 ＝ m PK r
wherein: f (f) m Annual operation maintenance cost for the charging/discharging power of the energy storage unit; k (K) r Calculating a coefficient for the whole life cycle of the energy storage system, namely calculating the current value according to complex profit in the whole life cycle of the energy storage system; t is the service life of the energy storage system; t is t r Is the inflation rate; d, d r The discount rate.
As a preferred implementation of this example, the peak Gu Taoli yields C 1 The calculation method is as follows:
C 1 ＝ 1 S 1 K r
wherein: s is S 1 A benefit of peak Gu Tao benefit in a day; i is a certain period of the day; Δt is the duration of the i period, set to 1 hour; y is Y 1 =345 is the number of days of energy storage year operation; p is p i Electricity price in the i period; p (P) c, 、P dis,i At i for energy storageSegment actual charge/discharge power; b (B) c, 、B dis,i Charge/discharge state for energy storage during period i;
the demand management benefit C 2 The calculation method of (2) is shown as follows:
C 2 ＝Y 2 S 2 K r
S 2 ＝α(P md -P d )
wherein: y is Y 2 12 is the number of operating months per year; s is S 2 Basic capacity electricity charge saved for each month of energy storage users; alpha is the unit price of basic capacity electricity fee; p (P) md The maximum load power value of the user before the energy storage is not installed; p (P) d The maximum demand value reported by the user after the energy storage is installed;
the demand response C 3 The calculation method of (2) is shown as follows:
β p ＝s p v p
wherein: beta p The electricity price of the load can be interrupted for the p-th participation demand response; s is(s) p The electricity price standard corresponding to the p-th regulation time length; v p The p-th response speed coefficient; p (P) DSM,p Reporting success rate for participating in the contract response; p is p t Is the number of demand responses.
As a preferred implementation manner of this embodiment, the user-side energy storage optimization configuration model participating in different comprehensive profit modes further includes constraint conditions, where the constraint conditions are:
state of charge constraints are represented by the following formula:
S min ≤S i ≤S max
wherein: s is S i The energy storage charge state is in the period i; s is S min And S is max A lower limit and an upper limit of the state of charge, respectively;
the charge/discharge state constraint is as follows:
B c,i +B dis,i ≤1
in the process of charging and discharging the energy storage, the actual power and the capacity are within the rated value range of the energy storage, and the energy storage charging and discharging power is constrained, as shown in the following formula:
0≤P dis,i ≤B dis,i P
0≤P c, i≤B c,i P
setting the energy storage daily throughput in the model to be 2 times of the maximum capacity, namely the energy storage can be charged and discharged 2 times a day;
the stored state of charge continuity constraint is represented by the following formula:
wherein: η is the charge/discharge efficiency of the stored energy;
peak clipping load constraints are shown by the following formula:
P Load,i +P c,i -P dis,i ≤1.05P d
wherein: after the energy storage operation, the equivalent load is not more than 1.05 times of the maximum value of the optimized demand; p (P) Load,i The actual running power value of a user in the period i before the energy storage is installed;
demand response constraints are shown by the following formula:
max(Load k +P c,k -P d,k )≤max(Load j +P c,j -P d,j )
max(Load j +P c,j -P d,j )-max(Load k +P c,j -P d,k )≤0.8P DSM
wherein: k is the demand response dayIs a response time of (2); j is the corresponding time to baseline; p (P) c,k And P c,j Charging power of the energy storage demand response and the period corresponding to the base line respectively; p (P) d,k And P d,j Respectively the discharge power of the time corresponding to the energy storage demand response and the baseline; load k For loads engaged in demand response periods; load j In response to a load at a time corresponding to 5 days before day; p (P) DSM Reporting power values for participating in the contract response;maximum peak load for the last year user;constraint is carried out on the range of the appointed response power; max (Load) k +P c,k -P d,k )≤max(Load j +P c,j -P d,j ) Maximum load does not exceed the baseline maximum load for the response period; max (Load) j +P c,j -P d,j )-max(Load k +P c,k -P d,k )≤0.8P DSM Average load constraint for response time period;
and multiplying power constraint between the energy storage capacity and the power is shown as follows:
E＝β×P
wherein: the energy storage multiplying power characteristic range is set to be more than or equal to 1 and less than or equal to 10.
As a preferred implementation manner of this embodiment, a CPLEX12.9 solver in MATLAB is used to solve the objective function of the energy storage optimization configuration model at the user side participating in different comprehensive profit modes, calculate the energy storage capacity and power, and calculate the corresponding evaluation index parameters by taking the energy storage investment cost, the net profit in the whole life cycle, the investment recovery period and the return on investment as decision indexes.
As a preferred implementation manner of this embodiment, the calculation method of the energy storage investment cost is as follows:
f＝f 1 +f 2
wherein: f is energy storage investment cost, and consists of energy storage initial investment cost and energy storage maintenance investment cost, wherein the former is used for purchasing and installing energy storage equipment, and the latter is used for running and maintaining the energy storage equipment;
the investment recovery years calculation formula is as follows:
the calculation formula of the return on investment is as follows:
as a preferred implementation manner of this embodiment, the AHP-based energy storage comprehensive evaluation model includes a target layer, a criterion layer and a scheme layer;
the target layer is the optimal comprehensive effect of the energy storage configuration scheme and is used for giving an evaluation target of the decision-making problem corresponding to the energy storage configuration scheme;
the evaluation indexes of the criterion layer comprise initial investment cost, net income, return on investment age and return on investment rate, and are used for giving the criterion for influencing the evaluation target;
the scheme layer is used for evaluating the optimization effect of the user side energy storage configuration scheme participating in different comprehensive profit modes.
As a preferred implementation manner of the embodiment, the evaluation module constructs a judgment matrix and performs consistency test through an energy storage comprehensive evaluation model based on the AHP, evaluates and analyzes various energy storage configuration schemes participating in different comprehensive profit modes, and performs good and bad sorting, and specifically comprises the following steps:
and constructing a judgment matrix by adopting a consistent matrix method, wherein the judgment matrix is used for determining weights among layers and among factors of an energy storage comprehensive evaluation model, and the constructed judgment matrix of a criterion layer is shown as follows:
wherein: a represents a criterion layer judgment matrix; a, a i,j A comparison weight representing the ith element of the layer relative to the jth factor;
calculating weight coefficient of criterion layer, calculating eigenvalue of judgment matrix A, and finding out maximum eigenvalue lambda max And corresponding feature vector alpha max Then to the characteristic vector alpha max After normalization processing, a weight coefficient vector omega of a criterion layer decision factor to a target layer can be obtained; feature vector alpha max The normalized formula of (2) is shown as follows:
wherein: delta i For the feature vector alpha max Is the i-th element of (a);
the consistency check is carried out on the criterion layer judgment matrix, the consistency index corresponding to the maximum characteristic value is calculated firstly, then the consistency ratio is calculated by combining with the random consistency index, and finally the passing condition of the consistency check is judged according to the size, and the calculation formula is shown as follows:
wherein: c (C) I Is a consistency index; n is the order of the criterion layer judgment matrix; r is R I Is a random consistency index; c (C) R Is a consistency ratio;
when the consistency test is not passed, the criterion layer judgment matrix needs to be reconstructed, and the steps are repeated until the consistency test is passed;
calculating comprehensive weight coefficients, wherein the weight coefficients of the kth index of the scheme layer to the criterion layer are sequentially b k1 ,k2,······,b kn The calculation formula of the comprehensive weight coefficient of the kth scheme in the scheme layer to the total target is shown as follows:
wherein: ρ k Representing the comprehensive weight coefficient of the kth scheme in the scheme layer to the total target; omega i Representing the weight coefficient of the ith factor in the criterion layer to the target layer; beta ki A weight coefficient representing the kth scheme of the scheme layer to the ith factor in the criterion layer;
let the consistency index of the scheme in the scheme layer to the decision index k in the criterion layer be C Ik The random disposable index is R Ik Then the comprehensive weight coefficient consistency ratio C of the scheme layer R The formula of (2) is as follows:
when the element of the pair comparison matrix with higher consistency ratio does not pass the inspection, the element value of the pair comparison matrix is required to be modified until the element passes the inspection;
and finally, sorting the comprehensive weight coefficients of the target layer according to the scheme layer, wherein the scheme with the highest weight coefficient value is the optimal scheme.
Specifically, in this embodiment, a large industrial user in su zhou is taken as an example:
referring to fig. 5, for the real-time load and electricity price graph of the large industrial user on a certain day, one load data and electricity price data are corresponding to every other hour;
constructing a user side energy storage configuration scheme of the user participating in different comprehensive profit modes, wherein: scheme 1 is set to participate in a gain mode of peak valley arbitrage; scheme 2 is set to participate in two revenue modes of peak valley arbitrage and demand management; scheme 3 is set to participate in two gain modes of peak valley arbitrage and demand response; scheme 4 is set to participate in three revenue modes of peak valley arbitrage, demand response, demand management;
modeling demand response benefits based on power demand response implementation rules issued by Jiangsu provinces; the supplementary electricity price of the demand response and the speed coefficient of the demand response are shown in tables 1 and 2:
TABLE 1 demand response auxiliary price of electricity
TABLE 2 speed coefficient of demand response
Setting the auxiliary electricity price of the demand response to 15 yuan kW -1 The response coefficient is 1; wherein the response times of the demand response years are not more than 8 times;
the unit charge/discharge power cost: c (C) p =600 yuan/(.h);
the parameters input to the optimization model are as follows:
the unit capacity cost is as follows: c (C) e =1800 yuan/;
inflation rate: t is t r ＝0.02；
The discount rate: d, d r ＝0.08；
Energy storage life: t=10 years;
annual operation maintenance cost of energy storage unit charging/discharging power: f (f) m =97 yuan/(. Year);
the number of hours the battery was full: the time interval step length is 1h between 1h and 2 h;
monthly transformer base capacity electricity rate α=35 yuan/(kw·month);
charge-discharge efficiency=0.9;
initial value of SOC S 0 ＝0.4；
Lower limit of state of charge S min ＝0.3；
S on state of charge max ＝0.9；
The algorithm package CPLEX12.9 is adopted to solve the four established energy storage full life cycle configuration models, and rated capacity/power configuration results of four schemes are obtained, wherein the results are shown in the table 3:
TABLE 3 energy storage configuration rated power and rated capacity
The energy storage evaluation indexes of the four schemes are obtained, and the results are shown in table 4:
table 4 energy storage evaluation index
Referring to fig. 3, according to the corresponding evaluation index parameters, constructing an AHP energy storage comprehensive evaluation model, wherein the hierarchical structure model; the target layer is the optimal comprehensive effect of the energy storage configuration scheme and is used for giving an evaluation target of the decision-making problem corresponding to the energy storage configuration scheme; the criterion layer comprises all evaluation indexes such as initial investment cost, net income, return on investment age, investment reporting rate and the like, and is used for giving a criterion for influencing the evaluation target; the scheme layer is used for evaluating the optimization effect of the user side energy storage configuration scheme under different comprehensive profit modes;
based on the AHP energy storage comprehensive evaluation model, constructing a criterion layer and a scheme layer judgment matrix for constructing investment emphasis on net benefit, wherein the criterion layer judgment matrix is subjected to consistency test, as shown in table 5:
TABLE 5
The result of the criterion layer weight calculation of the analytic hierarchy process shows that the initial investment cost weight 29.832%, the return on investment weight 11.825%, the return on investment age weight 10.408%, and the net return weight 47.935%, wherein the maximum feature root is 4.049, and the corresponding RI is found to be 0.882 according to the RI table, thus C R ＝ I / I =0.018.ltoreq.0.1, passing one-time test;
constructing the scheme layer judgment matrix according to the corresponding evaluation index parameters as shown in tables 6 to 9:
TABLE 6
TABLE 7
TABLE 8
TABLE 9
The solution layer weight calculation result (i.e. the total hierarchical order) of the analytic hierarchy process analyzes the weights of the indexes, and the result of passing the consistency test is shown in table 10:
table 10
Referring to fig. 4, four schemes are evaluated, given a ranking of merit and inferiority, and analyzed and evaluated;
in this embodiment, the user can control the investment cost of energy storage, reduce the own investment cost, and obtain higher benefits in a manner of combining with the optimal auxiliary profit service, so that the net benefits obtained by the user can be maximized;
in the embodiment, the net benefit of the full life cycle of the energy storage in the scheme 4 is the highest; next, scheme 2, next, scheme 1; according to the comparison analysis of the evaluation results of fig. 4, the comprehensive evaluation result of the scheme 4 is optimal, the scheme 2 is next, and the importance of the net benefit to the target layer is higher than other indexes based on the energy storage comprehensive evaluation model.
Embodiment two:
referring to fig. 2, a method for evaluating energy storage optimization configuration at a user side based on multiple profit modes includes the following steps:
analyzing historical load data of a user, and reading annual load curve, electricity price curve and energy storage technical index parameters;
calculating the profit situation of the user in different profit modes according to the parameters, arranging and combining each profit mode to obtain a plurality of comprehensive profit modes, combining the profit situation of each profit mode with each comprehensive profit mode, establishing a user-side energy storage optimizing configuration model participating in the different comprehensive profit modes, optimizing the capacity and power of energy storage configuration based on the established configuration model, and calculating corresponding evaluation index parameters;
according to the calculated evaluation index parameters, an energy storage comprehensive evaluation model based on AHP is established;
constructing a judgment matrix through an AHP-based energy storage comprehensive evaluation model, carrying out consistency test, evaluating and analyzing various energy storage configuration schemes participating in different comprehensive profit modes, and carrying out quality sequencing;
the method is used to implement the functions in the first embodiment, and will not be described here again.
In the embodiments of the present application, "at least one" means one or more, and "a plurality" means two or more. "and/or", describes an association relation of association objects, and indicates that there may be three kinds of relations, for example, a and/or B, and may indicate that a alone exists, a and B together, and B alone exists. Wherein A, B may be singular or plural. The character "/" generally indicates that the context-dependent object is an "or" relationship. "at least one of the following" and the like means any combination of these items, including any combination of single or plural items. For example, at least one of a, b and c may represent: a, b, c, a and b, a and c, b and c or a and b and c, wherein a, b and c can be single or multiple.
Those of ordinary skill in the art will appreciate that the various elements and algorithm steps described in the embodiments disclosed herein can be implemented as a combination of electronic hardware, computer software, and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, and are not repeated herein.
In several embodiments provided by the present application, any of the functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a usb disk, a removable hard disk, a Read-Only Memory (hereinafter referred to as ROM), a random access Memory (Random Access Memory) and various media capable of storing program codes such as a magnetic disk or an optical disk.
The foregoing description is only illustrative of the present application and is not intended to limit the scope of the application, and all equivalent structures or equivalent processes or direct or indirect application in other related technical fields are included in the scope of the present application.
Claims (10)
1. The user side energy storage optimal configuration evaluation system based on the multiple profit modes is characterized by comprising the following modules:
the data processing module is used for analyzing historical load data of a user and acquiring annual load curve, electricity price curve and energy storage technical index parameters;
the user side energy storage optimizing configuration module is used for calculating the profit situation of a user in different profit modes according to the parameters acquired by the data processing module, arranging and combining each profit mode to obtain a plurality of comprehensive profit modes, combining the profit situation of each profit mode with each comprehensive profit mode, establishing a user side energy storage optimizing configuration model participating in the different comprehensive profit modes, optimizing the capacity and the power of the energy storage configuration based on the established configuration model, and calculating corresponding evaluation index parameters;
the modeling module is used for establishing an energy storage comprehensive evaluation model based on the AHP according to the calculated evaluation index parameters;
the evaluation module is used for constructing a judgment matrix and carrying out consistency check through an AHP-based energy storage comprehensive evaluation model, evaluating and analyzing various energy storage configuration schemes participating in different comprehensive profit modes and carrying out quality sorting.
2. The system for evaluating the optimal configuration of the user-side energy storage based on the multiple profit modes according to claim 1, wherein the model for optimizing the configuration of the user-side energy storage based on different comprehensive profit modes is built by combining the profit conditions of the peak valley arbitrage, the demand management and the demand response with the participation conditions of the three profit modes, and an objective function is built by taking the net profit in the whole life cycle of the user-side energy storage as a target, wherein the objective function is shown as the following formula:
F＝k 1 C 1 +k 2 C 2 +k 3 C 3 -f 1 -f 2
wherein: f is the net benefit in the energy storage life cycle; c (C) 1 For peak Gu Taoli benefit, C 2 To manage profits for demand, C 3 Response revenue for the demand side; f (f) 1 To store energy at initial investment cost f 2 The operation and maintenance cost is saved in the whole life cycle; k (k) 1 、k 2 、k 3 The full life cycle benefit coefficients of the three benefit modes of peak Gu Tao benefit, demand management, demand response, respectively, are 0 if the mode is engaged with 1, and if not engaged.
3. The system for evaluating a customer-side energy storage optimal configuration based on multiple profit models according to claim 2, wherein the energy storage initial investment cost f 1 The calculation method is as follows:
f 1 ＝C p P+C e E
wherein: c (C) p 、C e The cost of the charge/discharge power of the energy storage unit and the cost of the unit capacity are respectively; p is the rated power value of energy storage; e is the rated capacity of energy storage;
the energy storage annual operation maintenance cost f 2 The calculation method is as follows:
f 2 ＝f m PK r
wherein: f (f) m Annual operation maintenance cost for the charging/discharging power of the energy storage unit; k (K) r Calculating a coefficient for the whole life cycle of the energy storage system, namely calculating the current value according to complex profit in the whole life cycle of the energy storage system; t is the service life of the energy storage system; t is t r Is the inflation rate; d, d r The discount rate.
4. The system for evaluating a customer-side energy storage optimal configuration based on multiple profit models according to claim 2, wherein the peak valley setsBenefit C 1 The calculation method is as follows:
C 1 ＝Y 1 S 1 K r
wherein: s is S 1 A benefit of peak Gu Tao benefit in a day; i is a certain period of the day; Δt is the duration of the i period, set to 1 hour; y is Y 1 The operation days are the energy storage year; p is p i Electricity price in the i period; p (P) c，i 、P dis，i The actual charge/discharge power for the stored energy during period i; b (B) c，i 、B dis，i Charge/discharge state for energy storage during period i;
the demand management benefit C 2 The calculation method of (2) is shown as follows:
C 2 ＝Y 2 S 2 K r
S 2 ＝α(P md -P d )
wherein: y is Y 2 Number of operating months per year; s is S 2 Basic capacity electricity charge saved for each month of energy storage users; alpha is the unit price of basic capacity electricity fee; p (P) md The maximum load power value of the user before the energy storage is not installed; p (P) d The maximum demand value reported by the user after the energy storage is installed;
the demand response C 3 The calculation method of (2) is shown as follows:
β p ＝s p v p
wherein: beta p The electricity price of the load can be interrupted for the p-th participation demand response; s is(s) p The electricity price standard corresponding to the p-th regulation time length; v p The p-th response speed coefficient; p (P) DSM，p Reporting success rate for participating in the contract response; p is p t Is the number of demand responses.
5. The system for evaluating the energy storage optimization configuration at the user side based on the multiple profit modes according to claim 4, wherein the energy storage optimization configuration model at the user side participating in the different comprehensive profit modes further comprises constraint conditions, and the constraint conditions are as follows:
state of charge constraints are represented by the following formula:
S min ≤S i ≤S max
wherein: s is S i The energy storage charge state is in the period i; s is S min And S is max A lower limit and an upper limit of the state of charge, respectively;
the charge/discharge state constraint is as follows:
B c，i +B dis，i ≤1
in the process of charging and discharging the energy storage, the actual power and the capacity are within the rated value range of the energy storage, and the energy storage charging and discharging power is constrained, as shown in the following formula:
0≤P dis，i ≤B dis，i P
0≤P c，i ≤B c，i P
setting the energy storage daily throughput in the model to be 2 times of the maximum capacity, namely the energy storage can be charged and discharged 2 times a day;
the stored state of charge continuity constraint is represented by the following formula:
wherein: η is the charge/discharge efficiency of the stored energy;
peak clipping load constraints are shown by the following formula:
P Load，i +P c，i -P dis，i ≤1.05P d
wherein: after the energy storage operation, the equivalent load is not more than 1.05 times of the maximum value of the optimized demand; p (P) Load，i The actual running power value of a user in the period i before the energy storage is installed;
demand response constraints are shown by the following formula:
max(Load k +P c，k -P d，k )≤max(Load j +P c，j -P d，j )
max(Load j +P c，j -P d，j )-max(Load k +P c，k -P d，k )≤0.8P DSM
wherein: k is the response time of the demand response day; j is the corresponding time to baseline; p (P) c，k And P c，j Charging power of the energy storage demand response and the period corresponding to the base line respectively; p (P) d，k And P d，j Respectively the discharge power of the time corresponding to the energy storage demand response and the baseline; load k For loads engaged in demand response periods; load j In response to a load at a time corresponding to 5 days before day; p (P) DSM Reporting power values for participating in the contract response;maximum peak load for the last year user; />Constraint is carried out on the range of the appointed response power; max (Load) k +P c,k -P d,k )≤max(Load j +P c,j -P d,j ) Maximum load does not exceed the baseline maximum load for the response period; max (Load) j +P c，j -P d，j )-max(Load k +P c,k -P d,k )≤0.8P DSM Average load constraint for response time period;
and multiplying power constraint between the energy storage capacity and the power is shown as follows:
E＝β×P
wherein: the energy storage multiplying power characteristic range is set to be more than or equal to 1 and less than or equal to 10.
6. The system for evaluating the optimal configuration of the user side energy storage based on the multiple profit modes according to claim 1, wherein a CPLEX12.9 solver in MATLAB is used for solving an objective function participating in the optimal configuration model of the user side energy storage of the different comprehensive profit modes, solving the energy storage capacity and the power, and calculating corresponding evaluation index parameters by taking the investment cost of the energy storage, the net profit in the whole life cycle, the investment recovery period and the return on investment as decision indexes.
7. The system for evaluating the optimal configuration of energy storage on the user side based on a plurality of profit patterns according to claim 6, wherein the calculation method of the energy storage investment cost is as follows:
f＝f 1 +f 2
wherein: f is energy storage investment cost, and consists of energy storage initial investment cost and energy storage maintenance investment cost, wherein the former is used for purchasing and installing energy storage equipment, and the latter is used for running and maintaining the energy storage equipment;
the investment recovery years calculation formula is as follows:
the calculation formula of the return on investment is as follows:
8. the multi-benefit-mode-based user-side energy storage optimal configuration assessment system according to claim 1, wherein the AHP-based energy storage comprehensive assessment model comprises a target layer, a criterion layer and a scheme layer;
the target layer is the optimal comprehensive effect of the energy storage configuration scheme and is used for giving an evaluation target of the decision-making problem corresponding to the energy storage configuration scheme;
the evaluation indexes of the criterion layer comprise initial investment cost, net income, return on investment age and return on investment rate, and are used for giving the criterion for influencing the evaluation target;
the scheme layer is used for evaluating the optimization effect of the user side energy storage configuration scheme participating in different comprehensive profit modes.
9. The system for evaluating the energy storage optimization configuration of the user side based on the multiple profit modes according to claim 1, wherein the evaluation module constructs a judgment matrix and performs consistency check through an energy storage comprehensive evaluation model based on AHP, evaluates and analyzes the energy storage configuration schemes participating in the different comprehensive profit modes, and performs the following steps:
and constructing a judgment matrix by adopting a consistent matrix method, wherein the judgment matrix is used for determining weights among layers and among factors of an energy storage comprehensive evaluation model, and the constructed judgment matrix of a criterion layer is shown as follows:
wherein: a represents a criterion layer judgment matrix; a, a i，j A comparison weight representing the ith element of the layer relative to the jth factor;
calculating weight coefficient of criterion layer, calculating eigenvalue of judgment matrix A, and finding out maximum eigenvalue lambda max And corresponding feature vector alpha max Then to the characteristic vector alpha max After normalization processing, a weight coefficient vector omega of a criterion layer decision factor to a target layer can be obtained; feature vector alpha max The normalized formula of (2) is shown as follows:
wherein: alpha i For the feature vector alpha max Is the i-th element of (a);
the consistency check is carried out on the criterion layer judgment matrix, the consistency index corresponding to the maximum characteristic value is calculated firstly, then the consistency ratio is calculated by combining with the random consistency index, and finally the passing condition of the consistency check is judged according to the size, and the calculation formula is shown as follows:
wherein: c (C) I Is a consistency index; n is the order of the criterion layer judgment matrix; r is R I Is a random consistency index; c (C) R Is a consistency ratio;
when the consistency test is not passed, the criterion layer judgment matrix needs to be reconstructed, and the steps are repeated until the consistency test is passed;
calculating comprehensive weight coefficients, wherein the weight coefficients of the kth index of the scheme layer to the criterion layer are sequentially b k1 ，b k2 ，……，b kn The calculation formula of the comprehensive weight coefficient of the kth scheme in the scheme layer to the total target is shown as follows:
wherein: ρ k Representing the comprehensive weight coefficient of the kth scheme in the scheme layer to the total target; omega i Representing the weight coefficient of the ith factor in the criterion layer to the target layer; beta ki A weight coefficient representing the kth scheme of the scheme layer to the ith factor in the criterion layer;
the consistency index of the scheme in the scheme layer to the decision index k in the criterion layer is set asThe random disposable index is->The overall weight coefficient consistency ratio C of the scheme layer R The formula of (2) is as follows:
when the element of the pair comparison matrix with higher consistency ratio does not pass the inspection, the element value of the pair comparison matrix is required to be modified until the element passes the inspection;
and finally, sorting the comprehensive weight coefficients of the target layer according to the scheme layer, wherein the scheme with the highest weight coefficient value is the optimal scheme.
10. The user side energy storage optimal configuration evaluation method based on the multiple profit modes is characterized by comprising the following steps of:
analyzing historical load data of a user, and reading annual load curve, electricity price curve and energy storage technical index parameters;
calculating the profit situation of the user in different profit modes according to the parameters, arranging and combining each profit mode to obtain a plurality of comprehensive profit modes, combining the profit situation of each profit mode with each comprehensive profit mode, establishing a user-side energy storage optimizing configuration model participating in the different comprehensive profit modes, optimizing the capacity and power of energy storage configuration based on the established configuration model, and calculating corresponding evaluation index parameters;
according to the calculated evaluation index parameters, an energy storage comprehensive evaluation model based on AHP is established;
and constructing a judgment matrix and carrying out consistency check through an AHP-based energy storage comprehensive evaluation model, evaluating and analyzing a plurality of energy storage configuration schemes participating in different comprehensive profit modes, and carrying out quality sequencing.
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