CN116384834A - Energy storage-regional power grid coordination peak shaving capacity assessment method based on multi-objective fusion - Google Patents
Energy storage-regional power grid coordination peak shaving capacity assessment method based on multi-objective fusion Download PDFInfo
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Abstract
The invention discloses an energy storage-regional power grid coordination peak shaving capacity assessment method based on multi-objective fusion, and belongs to the technical field of new energy power generation. Constructing an energy storage-regional power grid coordination peak regulation capacity assessment index system from the angles of energy storage, generator set and the like, wherein the index system comprehensively considers the influence of factors such as economy, technology, reliability and the like on the peak regulation capacity; meanwhile, a multi-level evaluation method is established according to the characteristics of the evaluation index system, wherein a weighted rank sum ratio evaluation method is established for the characteristics of the secondary index, and an improved radar graph method is established for the characteristics of the primary index. Not only gives consideration to subjective and objective factors, but also eliminates interference of factors such as dimension, magnitude and the like on an evaluation result; the method for evaluating the coordination peak shaving capacity of the energy storage-regional power grid fully considers the characteristics of the energy storage and the regional power grid in the peak shaving process, quantifies the economical efficiency, the technical performance and the reliability, and evaluates the coordination peak shaving capacity of the energy storage-regional power grid through a multi-level evaluation method.
Description
Technical Field
The invention belongs to the technical field of new energy power generation, and particularly relates to an energy storage-regional power grid coordination peak regulation capability assessment method based on multi-objective fusion.
Background
Energy storage is one of the most effective ways to solve the influence of wind and light and other new energy intermittence, fluctuation and inaccuracy predictability on the system as a schedulable resource. The energy storage system has the function of space-time translation of energy, has high response speed, can rapidly store the energy through a certain form when the energy in the system is surplus, and can rapidly release the stored energy when the energy in the system is insufficient. The energy storage system is matched with the wind power plant and the photovoltaic power station to effectively reduce the influence of wind power and photovoltaic volatility, improves the absorption capacity of the power grid on wind and light power generation, and becomes a technical means for supporting high-efficiency grid connection of large-scale wind and light power generation. The input of energy storage is equivalent to adding a flexible link with rapid and controllable power in the power grid, and the purposes of peak clipping, valley filling and improving the quality of electric energy are achieved by properly storing and releasing electric energy and smoothing intermittent power supply output. After the wind-solar energy storage is combined, the controllability of wind power and photoelectricity is enhanced, the standby capacity set due to the prediction error of wind power and photoelectricity is reduced, the loss of wind abandoning and light abandoning is reduced, and the utilization rate of wind power and photoelectricity is improved. After the consumption and utilization level of wind power and photoelectricity is improved, the dependence of electricity on traditional fossil energy sources can be reduced, the coal consumption and pollutant discharge of traditional thermal power are reduced, and the method has important practical significance for realizing clean transformation of the electricity in China.
At present, the research on the energy storage-regional power grid coordination peak shaving capacity evaluation mainly has the following defects: 1. at present, less research is conducted on peak shaving capacity, most of the research is an intuitive experience method, but not a strict system method, and the research is focused on calculating wind power accommodation through the peak shaving capacity; 2. the energy storage-regional power grid coordination peak regulation capacity is estimated too singly, and most of the capacity of a certain aspect is estimated, so that a comprehensive estimation system is built without comprehensively considering various factors; 3. solving a multi-objective capacity optimization configuration model, performing normalization processing on each sub-objective in the existing research, and then converting multi-objective optimization into single-objective optimization by using a linear weighting method, wherein the normalization processing processes of most researches are different, artificial subjectivity cannot be avoided in weight determination, the optimal value of the weight of each objective function is difficult to determine, and the accuracy of an optimization result is influenced.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides an energy storage-regional power grid coordination peak regulation capacity assessment method based on multi-objective fusion.
The technical scheme adopted by the invention is as follows:
the energy storage-regional power grid coordination peak shaving capacity assessment method based on multi-objective fusion comprises the following steps: the method comprises the following steps:
step A: establishing a multi-level coordination peak shaving capacity evaluation index system according to an energy storage-regional power grid coordination peak shaving process, wherein the multi-level coordination peak shaving capacity evaluation index system comprises a plurality of evaluation indexes, and the evaluation indexes comprise primary indexes and secondary indexes under the primary indexes;
and (B) step (B): establishing an evaluation method of weighted rank combination ratio aiming at the characteristics of the secondary index, and evaluating the secondary index by adopting the evaluation method of weighted rank combination ratio;
step C: b, establishing an improved radar chart evaluation method based on an analytic hierarchy process aiming at the characteristics of the first-level indexes, solving the weight of the first-level indexes by adopting the improved radar chart evaluation method based on the analytic hierarchy process, and obtaining an improved radar chart according to the evaluation result obtained in the step B and the solved weight;
step D: and (3) extracting feature vectors according to the improved radar chart to establish an evaluation function, and taking a geometric average value of the evaluation function as a final peak regulation capacity evaluation result.
After the technical scheme is adopted, the energy storage-regional power grid coordination peak shaving capacity assessment index system fully considers the conditions of the power grid side, the power generation measurement and the energy storage side, comprises a primary index and a secondary index, establishes a weighted rank combination ratio considering subjective and objective factors and an improved radar chart assessment method aiming at the characteristics of the system, and achieves the assessment of the energy storage-regional power grid peak shaving capacity.
Preferably, three primary indexes in the step A are respectively an economic index, a technical index and a reliability index, thirteen secondary indexes are respectively a dynamic recovery period index, an investment yield index and a daily peak regulation cost index under the economic index, and a peak clipping and valley filling rate index, a load smoothness index, a load fluctuation rate index, a peak Gu Chalv index, a peak regulation demand index, a peak regulation capacity ratio index, a new energy permeability index and a annual failure frequency index, a annual power failure frequency index and a annual electricity shortage quantity index under the technical index are respectively used as the thirteen secondary indexes.
Preferably, the calculation formula of the dynamic recovery period index (1) is:
in the formula (1): t (T) p Is a dynamic recovery period; c (C) I For the inflow of funds to the system, i.e. annual returns to regional power grids and energy storage, C O For the system's capital outflow, i.e., sum of investment cost, annual operation maintenance cost, annual failure loss cost and retirement disposal cost, (C) I -C O ) t The net benefit of the energy storage device in the t year is obtained, and r is the discount rate;
(2) The calculation formula of the investment yield index is as follows:
in the formula (2): n (N) B The total energy storage investment, namely the total life cycle cost; c (C) lcc Is the annual average net benefit in the life cycle of the system, namely the average value of the total benefit of energy storage in the whole life cycle, C lcc The calculation formula of (2) is as follows:
in the formula (3): r (i) is the total revenue of the ith year of regional power grid configuration energy storage;
(3) The calculation formula of the daily peak regulation cost index is as follows:
in the formula (4): r is R fs (t) is the peak regulation cost of the thermal power generating unit at the moment t; r is R bs (t) is the peak regulation cost of the energy storage device at the moment of t, R s Is the daily peak regulation cost.
Preferably, (1) the calculation formula of the daily peak shaving cost index is as follows:
in formula (5): l (L) Bp And L Bv Respectively the peak and trough of the load when the energy storage participates in peak shaving, L p And L v Load peaks and troughs without energy storage, I safr Peak shaving cost for day;
(2) The calculation formula of the load smoothness index is as follows:
in formula (6): l (t) and L (t-1) are the load sizes at times t and t-1, I slr Load smoothness in a T time period from the moment T;
(3) The calculation formula of the load fluctuation rate index is as follows:
in the formula (7): l (L) ave L (T) is the load size at time T, I, which is the average value of the load over time T lfr The load fluctuation rate in the T time period from the moment T is set;
(4) The calculation formula of the peak Gu Chalv index is as follows:
in formula (8): l (L) max And L min Respectively the maximum value and the minimum value of the load curve, I pvdr Peak Gu Chalv;
(5) The calculation formula of the peak shaving demand index is as follows:
in the formula (9): p (P) max The maximum output of the generator set is the rated output, P min To minimum output of the generator set, P bs And P bc For rated discharge power and charging power of energy storage, L (t) is the load size at time t, I psd Peak shaving demand in a T time period from a time T;
(6) The calculation formula of the peak regulation capacity ratio index is as follows:
in the formula (10): p (P) max The maximum output of the generator set is the rated output, P min To minimum output of the generator set, P bs And P bc Rated discharge power and charging power for energy storage, I pscr Peak-to-peak capacity ratio;
(7) The calculation formula of the new energy permeability index is as follows:
in the formula (11): p (P) w (t) and P l (t) is grid-connected power of wind power and photovoltaic at t moment, L (t) is load size at t moment, I nepr And the new energy permeability in the T time period from the moment T is obtained.
Preferably, the annual fault frequency index is the frequency of faults of equipment in the system within one year and is used for evaluating the reliability degree of the energy storage-regional power grid system equipment; the annual power failure frequency index is the power failure frequency of the regional power grid of the system in one year and is used for quantifying the power supply reliability of the energy storage-regional power grid system; the annual electricity shortage index is the sum of electricity interruption caused by power interruption in one year and is used for representing the severity of the power interruption.
Preferably, the method for evaluating the weighted rank sum ratio in the step B includes the steps of:
(1) Calculating weights;
(2) Rank-ordering;
(3) Calculating a weighted rank sum;
(4) Calculating probability units;
(5) A linear regression equation is calculated.
Preferably, the specific step of (1) calculating the weight includes:
setting m evaluation objects, n evaluation indexes and j index value of the ith evaluation object as b ij Then an evaluation matrix b= (B) is formed ij ) m×n And (3) carrying out standardization treatment on each index by adopting standard 0-1 conversion, wherein when the index is benefit, the standardization formula is as follows:
when the index is of the cost type, the standardized formula is:
according to the standardized decision matrix, the characteristic specific gravity p of the ith evaluation sample of the jth index is calculated ij Wherein 0 < p ij < 1, characteristic specific gravity p ij The calculation formula of (2) is as follows:
entropy value e of jth index j The calculation formula of (2) is as follows:
when p is ij When=0, take lnp ij ＝0；
Coefficient of difference g of jth index j The calculation formula of (2) is as follows:
g j ＝1-e j j＝1,2,…,n (16)
entropy weight w of jth index j The calculation formula of (2) is as follows:
(2) The specific steps of the rank ordering include: establishing an evaluation matrix for the secondary indexes under each primary index of the evaluation object, and compiling each index rank of each evaluation object, wherein the benefit indexes are ranked from small to large, the cost indexes are ranked from large to small, and if the same index data are ranked equally, the obtained rank matrix is recorded as: r= (R ij ) m×n ；
(3) The specific steps for calculating the weighted rank combination include: calculate the WRSR of the ith evaluation object, record as
(4) The specific steps of calculating the probability units comprise: a WRSR frequency distribution table is compiled to list the frequency f of each group i Calculate the cumulative frequency f of each group i Determining the rank range R and average rank of each group of WRSRs
Calculating downward cumulative frequency, estimating the last cumulative frequency by 1-1/4m, and adding p i Probability unit P converted into ith evaluation object robiti The value is the percentage p i Adding 5 to the corresponding standard normal dispersion;
(5) The specific steps for calculating the linear regression equation include: in units of probability corresponding to cumulative frequency
As an independent variable, the estimated value +.wrsr calculated by regression analysis with the i-th evaluation object>
For dependent variables, a linear regression equation is calculated, i.e. +.>
Wherein a, b are coefficients, ">
And evaluating the result for the secondary index.
Preferably, the step C includes subjective weight calculation based on the analytic hierarchy process, and the subjective weight calculation based on the analytic hierarchy process includes the following steps:
(1) Constructing a judgment matrix: judging the relative importance degree among the indexes, and displaying the relative importance degree by adopting a 1-9 scale method to form a judging matrix;
(2) One-time inspection of the judgment matrix: introducing a characteristic quantity CI into the analytic hierarchy process to carry out consistency test, wherein the larger the value of the characteristic quantity CI is, the larger the degree of deviation from the complete consistency of the judgment matrix is; the smaller the value of the feature quantity CI is, the better the consistency of the judgment matrix is, and the calculation formula of the feature quantity CI is as follows:
in formula (19): z is the order of the judgment matrix, lambda max To judge the maximum characteristic root of the matrix.
(3) Weight calculation: and obtaining and normalizing the eigenvectors of the judgment matrix M, wherein the numerical value of the eigenvectors is the weight of each index.
Preferably, the specific steps of the radar chart improving method in the step C include: and determining the included angle of the index axes by using the index weights obtained by the analytic hierarchy process, and making a sector area by using the corresponding index values as the radius of the sector to obtain the radar improvement graph.
Preferably, in step D, the sum of the arc lengths and the sum of the sector areas of each segment are extracted as a feature vector u according to the obtained improved radar chart i The evaluation vector calculates the corresponding evaluation function value, and then carries out comprehensive evaluation, wherein the characteristic vector u i ＝[S i ,L i ],i＝1,2,…,m，u i The calculation formula of (2) is as follows:
in the formula (20): s is S i 、L i The area and the perimeter of the sector radar chart of the ith evaluation object are respectively; a, a ij Radius of the jth sector of the ith evaluation object; alpha j Is the central angle of the jth sector; w (W) j ＝α j /2π。
The evaluation vector calculation formula is:
v i1 ＝S i /S
in the formula (21): s is the area maximum of the sector radar map of all evaluation objects, s=max { S i |i＝1,2,…,m}；v i1 The value is used for reflecting the overall advantage of the evaluation object, and the larger the value is, the higher the overall level is, and the lower the value is conversely; v i2 The method is used for reflecting the development coordination degree of each aspect of the evaluation object, and the larger the numerical value is, the better the balanced development degree of each aspect is, and the worse the balanced development degree of each aspect is;
the calculation formula of the evaluation function is constructed by adopting a geometric averaging method, and comprehensive evaluation is carried out according to the evaluation function value as follows:
in summary, due to the adoption of the technical scheme, the beneficial effects of the invention are as follows:
1. based on the actual operation condition of energy storage-regional power grid coordinated peak shaving, the main factors influencing the peak shaving capacity effect, namely the technical, economical and reliable, are analyzed from the two angles of a power supply side and an energy storage side, and a multi-level evaluation system is established to quantify the technical, economical and reliable.
2. The method eliminates the influence of factors such as evaluation index dimension and orders of magnitude, constructs an improved radar chart, and visualizes evaluation data.
3. In the aspect of economy, the influence of medium-and-long-term electric quantity and spot electric quantity on system income is considered, and the power discarding operation and the load shedding operation caused by the power exchange limitation between the multi-energy complementary power generation system and the power grid are considered, and the cost expense generated by the power discarding operation and the load shedding operation is also considered to be in the range of economy.
4. The energy storage-regional power grid coordination peak shaving evaluation index system and the evaluation method can evaluate the construction and operation results of energy storage engineering, determine weak links in the peak shaving process, and provide scientific guidance for power grid improvement operation and later planning.
Drawings
The invention will now be described by way of example and with reference to the accompanying drawings in which:
FIG. 1 is a schematic flow chart of the present invention.
Fig. 2 is a graph of an evaluation index system of the coordinated peak shaving capacity of the energy storage-regional power grid with multi-objective fusion according to the invention.
Fig. 3 is a weighted rank combination ratio evaluation flow of the present invention.
Fig. 4 is an improved radar chart evaluation flow of the present invention.
Fig. 5 is a diagram illustrating a multiple retrofit radar of the present invention.
Fig. 6 is an improved radar chart of a simulation scenario of the present invention.
Detailed Description
For the purposes of making the objects, technical solutions and advantages of the embodiments of the present application more clear, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is apparent that the described embodiments are only some embodiments of the present application, but not all embodiments. The components of the embodiments of the present application, which are generally described and illustrated in the figures herein, may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present application, as provided in the accompanying drawings, is not intended to limit the scope of the application, as claimed, but is merely representative of selected embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present application without making any inventive effort, are intended to be within the scope of the present application.
As shown in fig. 1-2, the energy storage-regional power grid coordination peak shaving capacity assessment method based on multi-objective fusion comprises the following steps: step A: establishing a multi-level coordination peak shaving capacity assessment index system according to the coordination peak shaving process of the energy storage-regional power grid, wherein the multi-level coordination peak shaving capacity assessment index system comprises a first-level index and a second-level index under the first-level index;
the three primary indexes are respectively an economic index, a technical index and a reliability index, the thirteen secondary indexes are respectively a dynamic recovery period index, an investment yield index and a daily peak regulation cost index under the economic index, and the peak clipping and filling rate index, the load smoothness index, the load fluctuation rate index, the peak Gu Chalv index, the peak regulation demand index, the peak regulation capacity ratio index, the new energy permeability index and the annual failure frequency index, the annual power failure frequency index and the annual power shortage index under the reliability index under the technical index.
(1) The calculation formula of the dynamic recovery period index is as follows:
in the formula (1): t (T) p Is a dynamic recovery period; c (C) I For the inflow of funds to the system, i.e. annual returns to regional power grids and energy storage, C O For the system's capital outflow, i.e., sum of investment cost, annual operation maintenance cost, annual failure loss cost and retirement disposal cost, (C) I -C O ) t The net benefit of the energy storage device in the t year is obtained, and r is the discount rate;
(2) The calculation formula of the investment yield index is as follows:
in the formula (2): n (N) B The total energy storage investment, namely the total life cycle cost; c (C) lcc Is the annual average net benefit in the life cycle of the system, namely the average value of the total benefit of energy storage in the whole life cycle, C lcc The calculation formula of (2) is as follows:
in the formula (3): r (i) is the total profit of the ith year of regional power grid configuration energy storage, N y The energy storage life cycle is the year;
(3) The calculation formula of the daily peak regulation cost index is as follows:
in the formula (4): r is R fs (t) is the peak regulation cost of the thermal power generating unit at the moment t; r is R bs (t) is the peak regulation cost of the energy storage device at the moment of t, R s Is the daily peak regulation cost.
(1) The calculation formula of the daily peak regulation cost index is as follows:
in formula (5): l (L) Bp And L Bv Respectively the peak and trough of the load when the energy storage participates in peak shaving, L p And L v Load peaks and troughs without energy storage, I safr Peak shaving cost for day;
(2) The calculation formula of the load smoothness index is as follows:
in formula (6): l (t) and L (t-1) are the load sizes at times t and t-1, I slr Load smoothness in a T time period from the moment T;
(3) The calculation formula of the load fluctuation rate index is as follows:
in the formula (7): l (L) ave L (T) is the load size at time T, I, which is the average value of the load over time T lfr The load fluctuation rate in the T time period from the moment T is set;
(4) The calculation formula of the peak Gu Chalv index is as follows:
in formula (8): l (L) max And L min Respectively the maximum value and the minimum value of the load curve, I pvdr Peak Gu Chalv;
(5) The calculation formula of the peak shaving demand index is as follows:
in the formula (9): p (P) max The maximum output of the generator set is the rated output, P min To minimum output of the generator set, P bs And P bc For rated discharge power and charging power of energy storage, L (t) is the load size at time t, I psd Peak shaving demand in a T time period from a time T;
(6) The calculation formula of the peak regulation capacity ratio index is as follows:
in the formula (10): p (P) max The maximum output of the generator set is the rated output, P min To minimum output of the generator set, P bs And P bc Rated discharge power and charging power for energy storage, I pscr Peak-to-peak capacity ratio;
(7) The calculation formula of the new energy permeability index is as follows:
in the formula (11): p (P) w (t) and P l (t) is grid-connected power of wind power and photovoltaic at t moment, L (t) is load size at t moment, I nepr The new energy permeability in the T time period from the moment T is set;
the secondary indexes included under the reliability indexes in the energy storage-regional power grid coordination peak shaving capacity evaluation index system are as follows:
1) Number of annual failures:
the annual failure times are times of failure of equipment in the annual system, and are used for evaluating the reliability degree of the energy storage-regional power grid system equipment.
2) Number of annual blackouts:
the annual power failure times are the power failure times of the regional power grid of the annual system, and the power supply reliability of the energy storage-regional power grid system is quantified.
3) Year electricity shortage:
the annual electricity shortage is the sum of electricity interruption caused by annual electricity interruption, characterizes the severity of the electricity interruption, and visually reflects the power supply reliability of the energy storage-regional power grid system;
and (B) step (B): the evaluation method for establishing the weighted rank sum ratio aiming at the characteristics of the secondary index is shown in fig. 3, the evaluation method for the weighted rank sum ratio comprises the steps of evaluating the secondary index by adopting the evaluation method for the weighted rank sum ratio,
(1) The specific steps of calculating the weight comprise:
setting m evaluation objects, n evaluation indexes and j index value of the ith evaluation object as b ij Then an evaluation matrix b= (B) is formed ij ) m×n And (3) carrying out standardization treatment on each index by adopting standard 0-1 conversion, wherein when the index is benefit, the standardization formula is as follows:
when the index is of the cost type, the standardized formula is:
according to the standardized decision matrix, the characteristic specific gravity p of the ith evaluation sample of the jth index is calculated ij Wherein 0 < p ij < 1, characteristic specific gravity p ij The calculation formula of (2) is as follows:
entropy value e of jth index j The calculation formula of (2) is as follows:
when p is ij When=0, take lnp ij ＝0；
Coefficient of difference g of jth index j The calculation formula of (2) is as follows:
g j ＝1-e j j＝1,2,…,n (16)
entropy weight w of jth index j The calculation formula of (2) is as follows:
(2) The specific steps of the rank ordering include: establishing an evaluation matrix for the secondary indexes under each primary index of the evaluation object, and compiling each index rank of each evaluation object, wherein the benefit indexes are ranked from small to large, the cost indexes are ranked from large to small, and if the same index data are ranked equally, the obtained rank matrix is recorded as: r= (R ij ) m×n ；
(3) The specific steps for calculating the weighted rank combination include: calculate the WRSR of the ith evaluation object, record as
(4) The specific steps of calculating the probability units comprise: a WRSR frequency distribution table is compiled to list the frequency f of each group i Calculate the cumulative frequency f of each group i Determining the rank range R and average rank of each group of WRSRs
Calculating downward cumulative frequency, estimating the last cumulative frequency by 1-1/4m, and adding p i Probability unit converted into the ith evaluation object +.>
The value is the percentage p i Adding 5 to the corresponding standard normal dispersion;
(5) The specific steps for calculating the linear regression equation include: in units of probability corresponding to cumulative frequency
As an independent variable, the estimated value +.wrsr calculated by regression analysis with the i-th evaluation object>
For dependent variables, a linear regression equation is calculated, i.e. +.>
Wherein a, b are coefficients, ">
The secondary index evaluation result is obtained;
step C: b, establishing an improved radar chart evaluation method based on an analytic hierarchy process aiming at the characteristics of the first-level indexes, wherein the improved radar chart evaluation method based on the analytic hierarchy process is shown in fig. 4, calculating the weight of the first-level indexes, and obtaining an improved radar chart according to the evaluation result obtained in the step B and the calculated weight; the improved radar chart evaluation is shown in fig. 5, the included angle of the index axes is determined by each index weight obtained through the analytic hierarchy process, and the corresponding index value is used as the radius of the sector to make a sector area.
The subjective weight calculation based on the analytic hierarchy process is included in the step C, and includes the following steps:
(1) Constructing a judgment matrix: judging the relative importance degree among the indexes, and displaying the relative importance degree by adopting a 1-9 scale method to form a judging matrix;
(2) One-time inspection of the judgment matrix: introducing a characteristic quantity CI into the analytic hierarchy process to carry out consistency test, wherein the larger the value of the characteristic quantity CI is, the larger the degree of deviation from the complete consistency of the judgment matrix is; the smaller the value of the feature quantity CI (close to 0), the better the consistency of the judgment matrix is, and the feature quantity CI is calculated according to the following formula:
in formula (19): z is the order of the judgment matrix, lambda max Judging the maximum characteristic root of the matrix;
(3) Weight calculation: the eigenvector of the judgment matrix M is obtained and normalized, and the numerical value of the eigenvector is the weight of each index;
step D: according to the improved radar chart, extracting feature vectors to establish an evaluation function, and taking a geometric average value of the evaluation function as a final peak regulation capacity evaluation result;
in the step D, the sum of the arc lengths and the sum of the sector areas of each segment are extracted as a feature vector u according to the obtained improved radar chart i The evaluation vector calculates the corresponding evaluation function value, and then carries out comprehensive evaluation, wherein the characteristic vector u i ＝[S i ,L i ],i＝1,2,…,m，u i The calculation formula of (2) is as follows:
in the formula (20): s is S i 、L i The area and the perimeter of the sector radar chart of the ith evaluation object are respectively; a, a ij Radius of the jth sector of the ith evaluation object; alpha j Is the central angle of the jth sector; w (W) j ＝α j /2π。
The evaluation vector calculation formula is:
v i1 ＝S i /S
in the formula (21): s is the area maximum of the sector radar map of all evaluation objects, s=max { S i |i＝1,2,…,m}；v i1 The value is used for reflecting the overall advantage of the evaluation object, and the larger the value is, the higher the overall level is, and the lower the value is conversely; v i2 The method is used for reflecting the development coordination degree of each aspect of the evaluation object, and the larger the numerical value is, the better the balanced development degree of each aspect is, and the worse the balanced development degree of each aspect is;
the calculation formula of the evaluation function is constructed by adopting a geometric averaging method, and comprehensive evaluation is carried out according to the evaluation function value as follows:
taking a simulation scene as an example, the energy storage-regional power grid coordination peak shaving capacity assessment method is used for describing different energy storage control modes, different energy storage rated capacities and rated powers in detail. The following examples are only for more clearly illustrating the technical solution of the present invention and do not limit the scope of the present invention.
The simulated scene settings are shown in table 1.
Table 1 scene configuration scheme
The evaluation index data of the five configurations are shown in table 2.
Table 2 evaluation index calculation results
Taking economy as an example, obtaining index weight w by using entropy weight method 21 ＝[0.21,0.18,0.61]Calculating a weighted rank sum ratio
As shown in table 3.
Table 3 weighted rank mix calculation results
Converting the percentage into probability units, and calculating a regression equation through a regress statement to obtain an estimated value delta WRSRfit ＝-0.360+0.180P robit The calculation results are shown in table 4, in which the number of scenes to be evaluated m=5.
Table 4 weighted rank mix distribution
The technical and reliability secondary index evaluation results are calculated by the same method and are shown in table 5.
TABLE 5 technical and reliability evaluation results
Obtaining the subjective weight w of the first-level index according to the analytic hierarchy process 1 ＝[0.359,0.517,0.124]An improved radar chart is made based on the weight results and the secondary index evaluation results, as shown in fig. 6.
The evaluation vectors and final evaluation results were obtained from the improved radar chart as shown in table 6.
Table 6 evaluation results
The foregoing examples merely represent specific embodiments of the present application, which are described in more detail and are not to be construed as limiting the scope of the present application. It should be noted that, for those skilled in the art, several variations and modifications can be made without departing from the technical solution of the present application, which fall within the protection scope of the present application.
Claims (10)
1. The energy storage-regional power grid coordination peak shaving capacity assessment method based on multi-objective fusion is characterized by comprising the following steps of: the method comprises the following steps:
step A: establishing a multi-level coordination peak shaving capacity evaluation index system according to an energy storage-regional power grid coordination peak shaving process, wherein the multi-level coordination peak shaving capacity evaluation index system comprises a plurality of evaluation indexes, and the evaluation indexes comprise primary indexes and secondary indexes under the primary indexes;
and (B) step (B): establishing an evaluation method of weighted rank combination ratio aiming at the characteristics of the secondary index, and evaluating the secondary index by adopting the evaluation method of weighted rank combination ratio;
step C: b, establishing an improved radar chart evaluation method based on an analytic hierarchy process aiming at the characteristics of the first-level indexes, solving the weight of the first-level indexes by adopting the improved radar chart evaluation method based on the analytic hierarchy process, and obtaining an improved radar chart according to the evaluation result obtained in the step B and the solved weight;
step D: and (3) extracting feature vectors according to the improved radar chart to establish an evaluation function, and taking a geometric average value of the evaluation function as a final peak regulation capacity evaluation result.
2. The energy storage-regional power grid coordination peak shaving capacity assessment method based on multi-objective fusion according to claim 1, wherein the method is characterized by comprising the following steps of: the first-level indexes in the step A are three, namely an economical index, a technical index and a reliability index, the second-level indexes are thirteen, and the thirteen second-level indexes are a dynamic recovery period index, an investment yield index and a daily peak regulation cost index under the economical index, a peak clipping and valley filling rate index, a load smoothness index, a load fluctuation rate index, a peak Gu Chalv index, a peak regulation demand amount index, a peak regulation capacity ratio index, a new energy permeability index, a annual failure frequency index, an annual power failure frequency index and an annual electricity shortage amount index under the technical index.
3. The energy storage-regional power grid coordination peak shaving capacity assessment method based on multi-objective fusion according to claim 2, wherein the method is characterized by comprising the following steps of:
(1) The calculation formula of the dynamic recovery period index is as follows:
in the formula (1): t (T) p Is a dynamic recovery period; c (C) I The fund inflow for the system, namely the annual income of regional power grid and energy storage; c (C) O The method is characterized in that the funds of the system flow out, namely the sum of investment cost, annual operation maintenance cost, annual fault loss cost and retirement disposal cost; (C) I -C O ) t The net benefit of the energy storage device in the t year is obtained, and r is the discount rate;
(2) The investment yield index R inv The calculation formula of (2) is as follows:
in the formula (2): n (N) B The total energy storage investment, namely the total life cycle cost; c (C) lcc The annual average net benefit in the life cycle of the system, namely the average value of the total benefit of energy storage in the whole life cycle; r is R inv For investment return rate, C lcc The calculation formula of (2) is as follows:
in the formula (3): r (i) is the total revenue of the ith year of regional power grid configuration energy storage; n (N) y The energy storage life cycle is the unit of year;
(3) The calculation formula of the daily peak regulation cost index is as follows:
in the formula (4): r is R fs (t) is the peak regulation cost of the thermal power generating unit at the moment t; r is R bs (t) the peak shaver cost at the moment of the energy storage device t; r is R s Is the daily peak regulation cost.
4. The energy storage-regional power grid coordination peak shaving capacity assessment method based on multi-objective fusion according to claim 2, wherein the method is characterized by comprising the following steps of:
(1) The calculation formula of the daily peak regulation cost index is as follows:
in formula (5): l (L) Bp And L Bv Respectively the wave crest and the wave trough of the load when the energy storage participates in peak shaving; l (L) p And L v Load wave peaks and wave troughs without energy storage; i safr Peak shaving cost for day;
(2) The calculation formula of the load smoothness index is as follows:
in formula (6): l (t) and L (t-1) are the load sizes at times t and t-1; i slr Load smoothness in a T time period from the moment T;
(3) The calculation formula of the load fluctuation rate index is as follows:
in the formula (7): l (L) ave Is the average value of the load over time T; l (t) is the load size at time t; i lfr The load fluctuation rate in the T time period from the moment T is set;
(4) The calculation formula of the peak Gu Chalv index is as follows:
in formula (8): l (L) max And L min The maximum value and the minimum value of the load curve are respectively; i pvdr Peak Gu Chalv;
(5) The calculation formula of the peak shaving demand index is as follows:
in the formula (9): p (P) max The maximum output of the generator set is the rated output; p (P) min Minimum output of the generator set; p (P) bs And P bc Rated discharge power and charging power for energy storage; l (t) is the load size at time t; i psd Peak shaving demand in a T time period from a time T;
(6) The calculation formula of the peak regulation capacity ratio index is as follows:
in the formula (10): p (P) max The maximum output of the generator set is the rated output; p (P) min Minimum output of the generator set; p (P) bs And P bc Rated discharge power and charging power for energy storage; i pscr Peak-to-peak capacity ratio;
(7) The calculation formula of the new energy permeability index is as follows:
in the formula (11): p (P) w (t) and P l (t) respectively obtaining grid-connected power of wind power and photovoltaic at the moment t; l (t) is the load size at time t; i nepr For time T from time TNew energy permeability in the water.
5. The energy storage-regional power grid coordination peak shaving capacity assessment method based on multi-objective fusion according to claim 2, wherein the method is characterized by comprising the following steps of: the annual fault frequency index is the frequency of faults of equipment in the system in one year and is used for evaluating the reliability degree of the energy storage-regional power grid system equipment; the annual power failure frequency index is the power failure frequency of the regional power grid of the system in one year and is used for quantifying the power supply reliability of the energy storage-regional power grid system; the annual electricity shortage index is the sum of electricity interruption caused by power interruption in one year and is used for representing the severity of the power interruption.
6. The energy storage-regional power grid coordination peak shaving capacity assessment method based on multi-objective fusion according to claim 2, wherein the method is characterized by comprising the following steps of: the evaluation method of the weighted rank sum ratio in the step B comprises the following steps:
(1) Calculating weights;
(2) Rank-ordering;
(3) Calculating a weighted rank sum;
(4) Calculating probability units;
(5) A linear regression equation is calculated.
7. The energy storage-regional power grid coordination peak shaving capacity assessment method based on multi-objective fusion according to claim 6, wherein the method is characterized by comprising the following steps of: the specific step of calculating the weight in (1) comprises the following steps:
setting m evaluation objects, n evaluation indexes and j index value of the ith evaluation object as b ij Then an evaluation matrix b= (B) is formed ij ) m×n And (3) carrying out standardization treatment on each index by adopting standard 0-1 conversion, wherein when the index is benefit, the standardization formula is as follows:
when the index is of the cost type, the standardized formula is:
according to the standardized decision matrix, the characteristic specific gravity p of the ith evaluation sample of the jth index is calculated ij Wherein 0 < p ij < 1, characteristic specific gravity p ij The calculation formula of (2) is as follows:
entropy value e of jth index j The calculation formula of (2) is as follows:
when p is ij When=0, take lnp ij ＝0；
Coefficient of difference g of jth index j The calculation formula of (2) is as follows:
g j ＝1-e j j＝1,2,…,n (16)
entropy weight w of jth index j The calculation formula of (2) is as follows:
(2) The specific steps of the rank ordering include: establishing an evaluation matrix for the secondary indexes under each primary index of the evaluation object, and compiling each index rank of each evaluation object, wherein the benefit indexes are ranked from small to large, the cost indexes are ranked from large to small, and if the same index data are ranked equally, the obtained rank matrix is recorded as: r= (R ij ) m×n ；
(3) The specific steps for calculating the weighted rank combination include: calculate the WRSR of the ith evaluation object, record as
(4) The specific steps of calculating the probability units comprise: a WRSR frequency distribution table is compiled to list the frequency f of each group i Calculate the cumulative frequency f of each group i Determining the rank range R and average rank of each group of WRSRs
Calculating downward cumulative frequency, estimating the last cumulative frequency by 1-1/4m, and adding p i Probability unit P converted into ith evaluation object robiti The value is the percentage p i Adding 5 to the corresponding standard normal dispersion;
(5) The specific steps for calculating the linear regression equation include: in units of probability corresponding to cumulative frequency
As an independent variable, the estimated value +.wrsr calculated by regression analysis with the i-th evaluation object>
For dependent variables, the linear regression equation is calculated, i.e
Wherein a, b are coefficients, ">
And evaluating the result for the secondary index.
8. The energy storage-regional power grid coordination peak shaving capacity assessment method based on multi-objective fusion according to claim 1, wherein the method is characterized by comprising the following steps of: the subjective weight calculation based on the analytic hierarchy process is included in the step C, and includes the following steps:
(1) Constructing a judgment matrix: judging the relative importance degree among the indexes, and displaying the relative importance degree by adopting a 1-9 scale method to form a judging matrix;
(2) One-time inspection of the judgment matrix: introducing a characteristic quantity CI into the analytic hierarchy process to carry out consistency test, wherein the larger the value of the characteristic quantity CI is, the larger the degree of deviation from the complete consistency of the judgment matrix is; the smaller the value of the feature quantity CI is, the better the consistency of the judgment matrix is, and the calculation formula of the feature quantity CI is as follows:
in formula (19): z is the order of the judgment matrix, lambda max Judging the maximum characteristic root of the matrix;
(3) Weight calculation: and obtaining and normalizing the eigenvectors of the judgment matrix M, wherein the numerical value of the eigenvectors is the weight of each index.
9. The energy storage-regional power grid coordination peak shaving capacity assessment method based on multi-objective fusion according to claim 8, wherein the method comprises the following steps of: the specific steps of the radar chart improving method in the step C comprise: and determining the included angle of the index axes by using the index weights obtained by the analytic hierarchy process, and making a sector area by using the corresponding index values as the radius of the sector to obtain the radar improvement graph.
10. The energy storage-regional power grid coordination peak shaving capability assessment method based on multi-objective fusion according to any one of claims 1 to 9, wherein: in the step D, the sum of the arc lengths and the sum of the sector areas of each segment are extracted as a feature vector u according to the obtained improved radar chart i Firstly, calculating a corresponding evaluation function value according to an evaluation vector, and then performing comprehensive evaluation, wherein the characteristic vector u i ＝[S i ,L i ],i＝1,2,…,m，u i The calculation formula of (2) is as follows:
in the formula (20): s is S i 、L i The area and the perimeter of the sector radar chart of the ith evaluation object are respectively; a, a ij Radius of the jth sector of the ith evaluation object; alpha j Is the central angle of the jth sector; w (W) j ＝α j /2π；
The evaluation vector calculation formula is:
v i1 ＝S i /S
in the formula (21): s is the area maximum of the sector radar map of all evaluation objects, s=max { S i |i＝1,2,…,m}；v i1 The value is used for reflecting the overall advantage of the evaluation object, and the larger the value is, the higher the overall level is, and the lower the value is conversely; v i2 The method is used for reflecting the development coordination degree of each aspect of the evaluation object, and the larger the numerical value is, the better the balanced development degree of each aspect is, and the worse the balanced development degree of each aspect is;
the calculation formula of the evaluation function is constructed by adopting a geometric averaging method, and comprehensive evaluation is carried out according to the evaluation function value as follows:
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| CN118336836A (en) * | 2024-06-12 | 2024-07-12 | 国网安徽省电力有限公司经济技术研究院 | Multi-type renewable energy peak-to-peak demand analysis method based on risk elimination |
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| CN117691640A (en) * | 2023-12-13 | 2024-03-12 | 国网青海省电力公司清洁能源发展研究院 | Evaluation method and device for power grid side energy storage emergency peak regulation standby capability |
| CN117691640B (en) * | 2023-12-13 | 2024-05-24 | 国网青海省电力公司清洁能源发展研究院 | Evaluation method and device for power grid side energy storage emergency peak regulation standby capability |
| CN118336836A (en) * | 2024-06-12 | 2024-07-12 | 国网安徽省电力有限公司经济技术研究院 | Multi-type renewable energy peak-to-peak demand analysis method based on risk elimination |
| CN118336836B (en) * | 2024-06-12 | 2024-09-13 | 国网安徽省电力有限公司经济技术研究院 | Multi-type renewable energy peak-to-peak demand analysis method based on risk elimination |
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