CN118134076A - Distributed energy storage system evaluation method based on analytic hierarchy process - Google Patents
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Abstract
The invention discloses a distributed energy storage system evaluation method based on an analytic hierarchy process, which aims at typical application scenes when energy storage is used as a voltage support, a standby power supply and a peak clipping and valley filling function, establishes an energy storage system evaluation index matched with the typical application scenes, establishes an evaluation index system comprising energy storage economy, reliability and functionality, and evaluates the application performance of the energy storage system by dividing weights of various indexes under different application scenes through the analytic hierarchy process. The invention fully considers different requirements of actual application scenes, classifies and gives reasonable indexes, and compared with the prior art, the accuracy and rationality of energy storage system performance evaluation are improved.
Description
Technical Field
The invention relates to the technical field of energy storage system evaluation, in particular to a distributed energy storage system evaluation method based on an analytic hierarchy process.
Background
Under the double-carbon target, a large number of new installers of distributed photovoltaics bring great challenges to a low-voltage distribution network, an energy storage system becomes an effective mode for relieving the fluctuation of new energy output, however, typical application scenes such as peak clipping and valley filling, voltage quality improvement, standby power supply and the like have different functional requirements on the energy storage system, and according to the requirements in different application scenes, how to effectively evaluate the application mode and the performance of the energy storage system becomes a difficult problem. The comprehensive benefit evaluation method for the distributed energy storage system is single in application scene diversity and energy storage system composition analysis evaluation. For various application modes, the lack of comprehensive evaluation indexes for energy storage economy, reliability and functionality is included.
Disclosure of Invention
Aiming at the defects existing in the prior art, the invention aims to establish an evaluation system for the distributed energy storage system in three main application scenes, wherein the application scenes comprise: voltage support, standby power supply, peak clipping and valley filling, and discloses three energy storage system performance evaluation indexes in aspects of economy, reliability and functionality, and a comprehensive evaluation method of a distributed energy storage system in each scene.
In order to achieve the above object, the present invention is realized by the following technical scheme: the distributed energy storage system evaluation method based on the analytic hierarchy process is characterized by comprising the following steps of:
1) Selecting an evaluation index according to the application requirements of the distributed energy storage system in an application scene;
2) Constructing a low-voltage power distribution network tide model accessed by a distributed energy storage system, and calculating parameters required by selecting evaluation indexes;
3) Establishing an analytic hierarchy process model, and determining weights step by step for indexes in a scene through grading;
4) Calculating a result according to the weight proportion occupied by the rating index;
5) Normalizing to obtain various scores;
6) And combining the scores to obtain the final score of the energy storage system in the scene.
The evaluation index comprises economic, reliability and functional evaluation indexes,
In the case of an application to which the present invention is applied,
The economic evaluation index is selected from the following indexes: leveling energy storage cost, delaying investment of a power distribution network and energy arbitrage;
The reliability evaluation index is selected from the following indexes: voltage deviation, voltage balance, energy storage utilization rate and load coverage rate;
The functional evaluation index is selected from the following indexes: voltage imbalance reduction rate, isolated network average power supply time and peak clipping and valley filling rate.
Wherein, the evaluation index is specifically as follows:
1) Leveling energy storage cost index
In order to accurately quantify the cost required by the energy storage participating in an application scene, the research adopts the standardized energy storage cost (Levelized Cost Of Storage, LCOS) as a cost index, wherein the cost index comprises investment, operation maintenance, charging and scrapping costs, and the formula is as follows:
a) Investment cost
Wherein I cost is the investment cost of the energy storage equipment; a is the operation maintenance cost; b, charging cost; c, scrapping cost; r is the discount rate; n is the nth year of operation of the energy storage device; n is the total service life of the energy storage equipment; t c is the construction time of the energy storage device; e dc, n is the discharge electric quantity in the nth service life;
b) Cost of operation and maintenance
F cost is the fixed operation maintenance cost of the energy storage device; r is the discount rate; n is the nth year of operation of the energy storage device; n is the total service life of the energy storage equipment; t c is the construction time of the energy storage device;
c) Cost of charging
C cost is the charging cost of the energy storage device; r is the discount rate; n is the nth year of operation of the energy storage device; n is the total service life of the energy storage equipment; t c is the construction time of the energy storage device;
d) Cost of scrapping
S cost is the scrapping cost of the energy storage equipment; r is the discount rate; n is the nth year of operation of the energy storage device; n is the total service life of the energy storage equipment, and the formula of the variable calculation formula in the formula is as follows:
Wherein C P is the power cost of the energy storage device; c pP is the rated power of the energy storage device; CE is the energy cost of the energy storage device; c pE is the rated capacity of the energy storage device; c PF is the power cost of operation and maintenance of the energy storage device; c EF is the energy cost of operation and maintenance of the energy storage device; c y is the annual average charge and discharge times of the energy storage device; d gy is the cyclic discharge aging rate of the energy storage device; d gT is the age of the energy storage device; d oD is the depth of discharge of the energy storage device; p el is the charging electricity price (CNY/(kW.h)) of the nth working year; t charged is the charging time of the nth working period; FEOL is the proportionality coefficient to the cost of the primary investment; η is the total efficiency of external charging and discharging of the energy storage device; η RT is the charge-discharge cycle efficiency of the energy storage device; η self is the self-discharge rate of the energy storage device; when the application scene is determined, C pE is the rated capacity of the energy storage device when the continuous discharge duration of the energy storage device at rated power is t app each time; t app is the required discharge time length of the application scene;
2) Delay of equipment investment of distribution network
The calculation of the investment benefit of deferred power distribution network equipment upgrade is as follows:
Wherein M delay is the income for delaying the investment of equipment of the power distribution network; c inv is the one-time investment cost required by the transformation and upgrading of the power grid; n is the total service life of the energy storage equipment; r is the discount rate;
3) Energy arbitrage index
The energy storage technology participates in the profit calculation of the energy arbitrage as follows:
M ea is the benefit obtained by the arbitrage service when the total time length of energy storage participation is H; Δq ea,h is the amount of energy stored in the h period of time to participate in energy arbitrage; m ea,h is the energy arbitrage profit in the h time period; p h,peak is the peak electricity price in the h period; p h,valley is the valley electricity price in the h time period;
4) Voltage deviation
Voltage deviation refers to the difference between the energy storage system output voltage and the grid rated voltage, and is generally used to describe the stability of the voltage. The calculation formula is as follows:
Wherein U is the output voltage of the energy storage system; the UE is the rated voltage of the power grid;
5) Degree of voltage balance
The voltage balance is an index for measuring the degree of difference between the voltages of all nodes in the power grid. It is used to evaluate the voltage stability and the power quality of the power system. The calculation of the voltage balance U D may be calculated by a positive sequence voltage unbalance calculation method:
For each time point t, for each node i, recording a voltage value U t,i,Uavgm as an average voltage value of the ith node, U i as voltage at the moment of the ith node t, D i as a voltage deviation rate of the node i, n as a total number of time slices in a selected time period, and m as a total number of nodes in a power grid;
6) Energy storage utilization rate
The energy storage utilization rate is the ratio of available capacity to average load, and the degree of the energy supply capacity actually provided by the energy storage system relative to the average load of the power grid;
7) Load coverage rate
The load coverage rate is the ratio of the output of the energy storage system to the maximum load, the coverage degree of the output capacity of the energy storage system relative to the maximum load of the power grid is measured, the load coverage rate is an important index for evaluating how much power demand the energy storage system can provide during peak load, and the load coverage rate reflects the matching degree between the output capacity of the energy storage system and the load demand of the power grid;
8) Rate of voltage offset reduction
The voltage imbalance reduction rate represents an improvement effect of the energy storage system on the problem of unbalanced power grid voltage. The higher the voltage imbalance reduction rate is, the stronger the correction capability of the energy storage system to the voltage imbalance problem in the power grid is. The energy storage system can effectively balance the problems of negative sequence voltage, zero sequence voltage or unbalanced load in the power grid, and the like, and improves the voltage quality and stability of the power grid. The voltage offset reduction rate Δu ％ is calculated as follows:
Wherein B 1 represents the imbalance before intervention in the energy storage system; b 2 represents the unbalance after intervention in the energy storage system;
9) Isolated net average power supply time
The isolated network average power supply time refers to the time that the energy storage system can continuously provide power for an isolated power grid under the condition that the power grid is powered off or isolated. The calculation method of the isolated network average power supply time T comprises the following steps:
Wherein C is the capacity of the energy storage system; Running average load demand for island;
10 Peak clipping and valley filling rate
The peak clipping rate (PEAK SHAVING RATE, PSR) and the valley filling rate (VALLEY FILLING RATE, VFR) of the energy storage system refer to the ratio of power provided by the energy storage system in the peak clipping and valley filling processes, and the peak clipping rate and the valley filling rate are generally expressed by the percentage of power, and the calculation formula is as follows:
Wherein P h is the peak grid load; p l is the grid load valley, and P C is the energy storage system output power.
A distributed energy storage system evaluation method based on analytic hierarchy process is applied to a voltage support scene,
(1) Analyzing application scenes to select economic evaluation indexes and calculating results: leveling energy storage cost and delaying investment of a power distribution network;
(2) Analyzing application scenes to provide reliability evaluation indexes and calculating results: voltage deviation, voltage balance;
(3) Analyzing application scenes to provide functional evaluation indexes and calculating results: a voltage imbalance reduction rate;
(4) Constructing a low-voltage distribution network power flow model accessed by distributed energy storage, and calculating parameters required by selecting evaluation indexes;
(5) Establishing an analytic hierarchy process model, dividing weights of various indexes in a voltage supporting scene according to the analytic hierarchy process, establishing a judgment matrix, calculating, determining a comparison scale value among various elements in a form of expert scoring, establishing the judgment matrix, and carrying out consistency test by using the matrix and calculating various weights W i;
(6) Normalizing the calculation results of all indexes, and taking the reciprocal after normalizing the cost indexes;
(7) And superposing the normalized index values to obtain a final score.
A distributed energy storage system assessment method based on analytic hierarchy process is applied to an electric standby power supply scene, (1) an economic evaluation index is selected by analyzing an application scene and a calculation result is obtained: leveling energy storage cost indexes and energy arbitrage;
(2) Analyzing application scenes to provide reliability evaluation indexes and calculating results: energy storage utilization rate and load coverage rate;
(3) Analyzing application scenes to provide functional evaluation indexes and calculating results: the isolated network average power supply time;
(4) Constructing a low-voltage distribution network power flow model accessed by distributed energy storage, and calculating parameters required by selecting evaluation indexes;
(5) Establishing an analytic hierarchy process model, dividing weights of various indexes in a voltage supporting scene according to the analytic hierarchy process, establishing a judgment matrix, calculating, determining a comparison scale value among various elements in a form of expert scoring, establishing the judgment matrix, and carrying out consistency test by using the matrix and calculating various weights W i;
(6) Normalizing the calculation results of all indexes, and taking the reciprocal after normalizing the cost indexes;
(7) And superposing the normalized index values to obtain a final score.
A distributed energy storage system evaluation method based on analytic hierarchy process is applied to peak clipping and valley filling scenes,
(1) Analyzing application scenes to select economic evaluation indexes and calculating results: leveling energy storage cost indexes and energy arbitrage;
(2) Analyzing application scenes to provide reliability evaluation indexes and calculating results: energy storage utilization rate and load coverage rate;
(3) Analyzing application scenes to provide functional evaluation indexes and calculating results: peak clipping and valley filling rate;
(4) Constructing a low-voltage distribution network power flow model accessed by distributed energy storage, and calculating parameters required by selecting evaluation indexes;
(5) Establishing an analytic hierarchy process model, dividing weights of various indexes in a voltage supporting scene according to the analytic hierarchy process, establishing a judgment matrix, calculating, determining a comparison scale value among various elements in a form of expert scoring, establishing the judgment matrix, and carrying out consistency test by using the matrix and calculating various weights W i;
(6) Normalizing the calculation results of all indexes, and taking the reciprocal after normalizing the cost indexes;
(7) And superposing the normalized index values to obtain a final score.
Compared with the prior art, the energy storage system has the advantages that when facing different application scenes of the energy storage system, multiple complex influences are comprehensively considered, the economic requirements of the energy storage system are built under different application scenes, and the energy storage system has beneficial effects on a power grid and use safety of the energy storage system. The evaluation index is transformed by determining the contribution degree of the evaluation index to the superior index of the corresponding class in different scenes through specific analysis of the index content of each scheme layer according to the actual scenes. Therefore, the evaluation method provided by the invention has better flexibility, and the specific evaluation index is more attached to the actual application scene, so that the evaluation accuracy is higher.
Drawings
FIG. 1 is a general flow chart of an energy storage system evaluation method;
FIG. 2 is a graph of voltage support scenario simulation model loads and individual source power curves;
FIG. 3 is a graph of power curves of various parts of a power grid in a backup power scene simulation model when power failure occurs;
FIG. 4 is a graph of power absorbed and compensated by the energy storage to the grid system using the peak clipping and valley filling scenario simulation model;
Detailed Description
In order to make the technical means, the creation characteristics, the achievement of the purposes and the effects of the implementation of the present invention easy to understand, the present invention is further described below by implementing the present evaluation mode in conjunction with the specific three typical application scenarios.
The specific embodiment evaluates the calculation example parameter data of the index system:
Some relevant parameters of the economic evaluation are shown in the following table 1, and the rest parameters are determined according to specific application scenes. The fixed investment cost calculation energy arbitrage is simulated by referring to a power consumption time-of-use electricity price meter of residents in Jiangsu province, and the specific table is shown in table 2.
TABLE 1 energy storage cost index parameter
Meter 22023 7-month Jiangsu time-sharing electricity price meter
Scene analysis one: distributed energy storage voltage support scene
1) Establishing a simulation model
When the load increases in the system, its demand exceeds the power supply capacity, resulting in the power supply not providing sufficient power support and thus causing a voltage drop. Let us examine the access of 40MW/300MWh energy storage at node 1 to provide voltage support for the grid. Fig. 2 is a graph of a 24h total power curve.
And comparing the voltage change conditions of the front node and the rear node through load flow calculation and accessing an energy storage system, wherein the voltage change conditions are shown in tables 5 and 6.
TABLE 5 Power grid flow calculation results before energy storage
TABLE 6 Power grid load flow calculation results after energy storage
2) Energy storage system performance evaluation based on analytic hierarchy process
The weights of the indexes in the voltage supporting scene are divided according to the analytic hierarchy process, and the hierarchical structure and the corresponding matrix are shown in table 7:
TABLE 7 energy storage system voltage support performance evaluation architecture
Establishing a judgment matrix, calculating, determining a comparison scale value among the elements in a form of expert scoring, establishing the judgment matrix, and carrying out consistency test by using the matrix and calculating each weight W i. The judgment matrix A-B between the target layer and the criterion layer is shown in table 8, and the obtained lambda max = 3.0001 is solved; c.r. =0.00008 < 0.1, the consistency test passes. The judgment matrix B-C between the criterion layer and the scheme layer is shown in the table 9 and the table 10, and λmax=2 in the table 9; r= -5 < 0.1, consistency check pass, lambda max = 2 in table 10; c.r. =0 < 0.1, and the consistency test passed.
Table 8 judgment matrix A-B under voltage support scene
Table 9 judgment matrix B 1 -C under voltage support scenario
Table 10 judgment matrix B 2 -C under voltage support scene
Further calculating the combination weight of the scheme layer to the target layer, and sequencing, namely 5 influence weights of the performance indexes to the performance indexes of the energy storage system are respectively as follows: investment cost (17.31%), deferred distribution network investment (5.77%), voltage deviation (7.69%), voltage balance (23.08%), voltage offset reduction rate (30.77%).
And calculating the parameters of the other index values according to the specific scene information by combining the simulation result and the established energy storage system standby power supply performance evaluation system. The economic index parameters C pP are 40MW, C pE is 300MWh, D oD is 86.7%, t charged is 2190h, F EOL is 1, t app is 80MWh, and t app is 13h; the reliability index parameter U is 1.021/pu, and U E is 1/pu; the functional index parameter B 1 is 2.507 percent, and the B 2 is 2.233 percent; calculating the flattening energy storage cost of 35403.81 ten thousand yuan according to the parameters, and normalizing to obtain the score of 0.6488 of the index in the analytic hierarchy process; delaying the investment income of the distribution network in the scene to be 15709.035 ten thousand yuan, wherein the score after normalization is 0.9633; the voltage deviation was 4% and the resulting score was 0.96; the voltage balance was 0.21% and the score was 0.86; the voltage disorder reduction rate was 10.93% and the score was 0.891. The final composite score for the energy storage system in the scenario is 0.5354 according to the weight.
Scene analysis II: standby power supply
1) Establishing a simulation model
The micro-grid park photovoltaic energy storage system consists of 23 nodes, wherein the 1 node is connected with a grid, and the 12, 13, 15, 21 and 23 nodes are respectively connected with photovoltaic systems with rated powers of 18kW, 11kW, 87kW and 87 kW. 17. The 18, 19, 20 and 22 nodes are respectively connected with loads, and the 14 and 16 nodes are respectively connected with 50kW/100kWh energy storage devices. And when the simulation scene is a power failure of the power grid, the energy storage system is used as a standby power supply to supply power for the off-grid park. Figure 3 is a graph showing the power curve of the system for 24 hours with photovoltaic output power and load consumption power during normal operation of the park system, and the power outage of the grid at 17-19, with the energy storage system being used as a backup power source for power supply.
2) Energy storage system performance evaluation based on analytic hierarchy process
The weights of the indexes in the standby power scene are divided according to the analytic hierarchy process, and the hierarchical structure and the corresponding matrix are shown in table 11:
table 11 energy storage system backup power performance evaluation architecture
And establishing a judgment matrix and calculating. And determining the scale value of comparison among the elements through the form of expert scoring, establishing a judgment matrix, carrying out consistency test by using the matrix, and calculating each weight W i. Wherein, the judgment matrix A-B between the target layer and the rule layer is shown in table 12, lambda max =3.0858, C.R. =0.0825 < 0.1, the consistency test passes, the judgment matrix B-C between the rule layer and the scheme layer is shown in table 13 and table 14, lambda max =2, C.R. =0 < 0.1 in the judgment matrix B 1 -C, lambda max =2, C.R. =0 < 0.1 in the judgment matrix B 2 -C, and the consistency test passes.
Table 12 judgment matrix A-B in standby power scene
Table 13 judges matrix B 1 -C
Table 14 judges matrix B 2 -C
Further calculating the combination weight of the scheme layer to the target layer, and sequencing, namely 5 influence weights of the performance indexes to the performance indexes of the energy storage system are respectively as follows: leveling energy storage cost index (15.03%), energy arbitrage (7.5%), energy storage utilization (22.46%), load coverage (15.03%), isolated net average maintenance time (10.07%).
And calculating the rest index values according to the specific scene information by combining the simulation result and the established energy storage system standby power supply performance evaluation system. The economic index parameters C pP are 100kW, C pE is 200kWh, D oD is 65.67%, t charged is 730h, F EOL is 1, t app is 100kW, and t app is 2h; functional index parameter65.67KW; calculating the flattening energy storage cost of 201160.45 yuan, and normalizing to obtain the score of 0.5747 of the index in the analytic hierarchy process; the energy arbitrage in the scene is 239.4791 yuan, and the score after normalization is 0.7464; the energy storage utilization rate is 95.35%, and the obtained score is 0.9536; load coverage was 62.24% with a score 0.6224; the isolated net average maintenance time was 1.907h and scored 0.0795. The final composite score for the energy storage system in the scenario is 0.4581 according to the weight.
Scene analysis three: distributed energy storage peak clipping and valley filling scene
And (3) establishing a simulation model, wherein the system is composed of 9 nodes, and generators G1, G2 and G3 are respectively arranged at the 1 node, the 2 node and the 3 node to provide electric energy for a power grid. And a photovoltaic power station with rated power of 40MW is connected to the 5 nodes. And the 4-node is connected with a large-scale energy storage power station of 200MW/400 MWh. 5. The 7 and 9 nodes are loads. Due to the fluctuation of renewable energy sources and the change of load electricity demand, peak-valley areas are generated in the power grid. Therefore, the energy storage power station is adopted to balance the power supply and demand, reduce the pressure during peak load and fully utilize the idle capacity during valley. Fig. 4 shows a 24 hour power profile for normal operation of the system. The flow calculation results of 8 points, 13 points and 18 points of the system are shown in tables 15, 16 and 17.
Table 15 grid tide calculation 8 hours data
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Table 16 Power grid flow calculation 12 time data
Table 17 grid trend calculation 18 hours data
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2) Energy storage system performance evaluation based on analytic hierarchy process
The weights of the indexes in the energy storage peak clipping and valley filling scene are divided according to the analytic hierarchy process, and the hierarchical structure and the corresponding matrix are shown in table 18:
peak clipping and valley filling performance evaluation system structure of table 18 energy storage system
And establishing a judgment matrix and calculating. And determining the scale value of comparison among the elements through the form of expert scoring, establishing a judgment matrix, carrying out consistency test by using the matrix, and calculating each weight W i. Wherein the judgment matrix a-B between the target layer and the criterion layer is shown in table 19, wherein λ max =2.9993, c.r. = 0.000064 < 0.1, the consistency check passes, the judgment matrix B 1 -C between the criterion layer and the scheme layer is shown in table 20, corresponding λ max =2, c.r. =0 < 0.1, the consistency check passes, the judgment matrix B 2 -C is shown in table 21, corresponding λ max =2; r=0 < 0.1, the consistency check passes, the decision matrix B 3 -C is shown in table 22, corresponding λ max =2; c.r. =0 < 0.1, and the consistency test passed.
Table 19 shows the judgment matrix A-B under the scene of peak clipping and valley filling
Table 20 judges matrix B 1 -C under peak clipping and valley filling scene
Table 21 shows judgment matrix B 2 -C under peak clipping and valley filling scene
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Table 22 judges matrix B 3 -C under peak clipping and valley filling scene
Further calculating the combination weight of the scheme layer to the target layer, and sequencing, namely, the influence weights of the 6 performance indexes to the performance indexes of the energy storage system are respectively as follows: leveling energy storage cost index (14.81%), energy arbitrage (7.41%), energy storage utilization rate (2.78%), voltage deviation (8.33%), peak clipping rate (33.34%), valley filling rate (33.34%).
And combining the simulation result in the previous section and the established energy storage system standby power supply performance evaluation system, and calculating other index values according to the specific scene information. Economic index parameters C pP are 200MW, C pE are 400MWh, D oD are 57.23%, t charged are 4745h, F EOL are 1, t app are 400MWh, and t app are 11h; the reliability index parameter U is 1.032/pu, and U E is 1/pu; the functional index parameter P h is 341.62MW, P l is 202.03MW, and P C is 200MW; calculating the flattening energy storage cost of 78243.5 ten thousand yuan, and normalizing to obtain the score of 0.2167 of the index in the analytic hierarchy process; peak clipping and valley filling energy arbitrage profit is 14.3181 ten thousand yuan in the scene, and the normalized score is 0.3254; the energy storage utilization rate is 72.63%, and the obtained score is 0.7263; voltage deviation was 3.933% and score 0.9607; peak clipping was 58.54% and score 0.5854. The filling rate is 98.99%, and the grading is 0.9899. Therefore, according to the weight, the final comprehensive score of the energy storage system in the scene is 0.6816, and the distributed energy storage capacity and the site selection are still to be optimized.
The foregoing has shown and described the basic principles and main features of the present invention and the advantages of the present invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, and that the above embodiments and descriptions are merely illustrative of the principles of the present invention, and various changes and modifications may be made without departing from the spirit and scope of the invention, which is defined in the appended claims. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (6)
1. The distributed energy storage system evaluation method based on the analytic hierarchy process is characterized by comprising the following steps of:
1) Selecting an evaluation index according to the application requirements of the distributed energy storage system in an application scene;
2) Constructing a low-voltage power distribution network tide model accessed by a distributed energy storage system, and calculating parameters required by selecting evaluation indexes;
3) Establishing an analytic hierarchy process model, and determining weights step by step for indexes in a scene through grading;
4) Calculating a result according to the weight proportion occupied by the rating index;
5) Normalizing to obtain various scores;
6) And combining the scores to obtain the final score of the energy storage system in the scene.
2. The analytic hierarchy process-based distributed energy storage system assessment method of claim 1, wherein the assessment indicators comprise economic, reliability and functional assessment indicators,
In the case of an application to which the present invention is applied,
The economic evaluation index is selected from the following indexes: leveling energy storage cost, delaying investment of a power distribution network and energy arbitrage;
The reliability evaluation index is selected from the following indexes: voltage deviation, voltage balance, energy storage utilization rate and load coverage rate;
The functional evaluation index is selected from the following indexes: voltage imbalance reduction rate, isolated network average power supply time and peak clipping and valley filling rate.
3. The method for analytic hierarchy-process-based distributed energy storage system assessment of claim 2,
1) Leveling energy storage cost index
In order to accurately quantify the cost required by the energy storage participating in an application scene, a leveling energy storage cost (LevelizedCost Of Storage, LCOS) is adopted as a cost index, wherein the cost index comprises investment, operation maintenance, charging and scrapping costs, and the formula is as follows:
a) Investment cost
Wherein I cost is the investment cost of the energy storage equipment; a is the operation maintenance cost; b, charging cost; c, scrapping cost; r is the discount rate; n is the nth year of operation of the energy storage device; n is the total service life of the energy storage equipment; t c is the construction time of the energy storage device; e dc, n is the discharge electric quantity in the nth service life;
b) Cost of operation and maintenance
F cost is the fixed operation maintenance cost of the energy storage device; r is the discount rate; n is the nth year of operation of the energy storage device; n is the total service life of the energy storage equipment; t c is the construction time of the energy storage device;
c) Cost of charging
C cost is the charging cost of the energy storage device; r is the discount rate; n is the nth year of operation of the energy storage device; n is the total service life of the energy storage equipment; t c is the construction time of the energy storage device;
d) Cost of scrapping
S cost is the scrapping cost of the energy storage equipment; r is the discount rate; n is the nth year of operation of the energy storage device; n is the total service life of the energy storage equipment, and the formula of the variable calculation formula in the formula is as follows:
Wherein C P is the power cost of the energy storage device; c pP is the rated power of the energy storage device; c E is the energy cost of the energy storage device; c pE is the rated capacity of the energy storage device; c PF is the power cost of operation and maintenance of the energy storage device; c EF is the energy cost of operation and maintenance of the energy storage device; c y is the annual average charge and discharge times of the energy storage device; d gy is the cyclic discharge aging rate of the energy storage device; d gT is the age of the energy storage device; d oD is the depth of discharge of the energy storage device; p el is the charging electricity price (CNY/(kW.h)) of the nth working year; t charged is the charging time of the nth working period; f EOL is the proportionality coefficient of the relative disposable investment cost; η is the total efficiency of external charging and discharging of the energy storage device; η RT is the charge-discharge cycle efficiency of the energy storage device; η self is the self-discharge rate of the energy storage device; when the application scene is determined, C pE is the rated capacity of the energy storage device when the continuous discharge duration of the energy storage device at rated power is t app each time; t app is the required discharge time length of the application scene;
2) Delay of equipment investment of distribution network
The calculation of the investment benefit of deferred power distribution network equipment upgrade is as follows:
Wherein M delay is the income for delaying the investment of equipment of the power distribution network; c inv is the one-time investment cost required by the transformation and upgrading of the power grid; n is the total service life of the energy storage equipment; r is the discount rate;
3) Energy arbitrage index
The energy storage technology participates in the profit calculation of the energy arbitrage as follows:
M ea is the benefit obtained by the arbitrage service when the total time length of energy storage participation is H; Δq ea,h is the amount of energy stored in the h period of time to participate in energy arbitrage; m ea,h is the energy arbitrage profit in the h time period; p h,peak is the peak electricity price in the h period; p h,valley is the valley electricity price in the h time period;
4) Voltage deviation
Voltage deviation refers to the difference between the energy storage system output voltage and the grid rated voltage, and is generally used to describe the stability of the voltage. The calculation formula is as follows:
Wherein U is the output voltage of the energy storage system; the UE is the rated voltage of the power grid;
5) Degree of voltage balance
The voltage balance is an index for measuring the degree of difference between the voltages of all nodes in the power grid. It is used to evaluate the voltage stability and the power quality of the power system. The calculation of the voltage balance U D may be calculated by a positive sequence voltage unbalance calculation method:
For each time point t, for each node i, recording a voltage value U t,i,Uavgm as an average voltage value of the ith node, U i as voltage at the moment of the ith node t, D i as a voltage deviation rate of the node i, n as a total number of time slices in a selected time period, and m as a total number of nodes in a power grid;
6) Energy storage utilization rate
The energy storage utilization rate is the ratio of available capacity to average load, and the degree of the energy supply capacity actually provided by the energy storage system relative to the average load of the power grid;
7) Load coverage rate
The load coverage rate is the ratio of the output of the energy storage system to the maximum load, the coverage degree of the output capacity of the energy storage system relative to the maximum load of the power grid is measured, the load coverage rate is an important index for evaluating how much power demand the energy storage system can provide during peak load, and the load coverage rate reflects the matching degree between the output capacity of the energy storage system and the load demand of the power grid;
8) Rate of voltage offset reduction
The voltage imbalance reduction rate represents an improvement effect of the energy storage system on the problem of unbalanced power grid voltage. The higher the voltage imbalance reduction rate is, the stronger the correction capability of the energy storage system to the voltage imbalance problem in the power grid is. The energy storage system can effectively balance the problems of negative sequence voltage, zero sequence voltage or unbalanced load in the power grid, and the like, and improves the voltage quality and stability of the power grid. The voltage offset reduction rate Δu ％ is calculated as follows:
Wherein B 1 represents the imbalance before intervention in the energy storage system; b 2 represents the unbalance after intervention in the energy storage system;
9) Isolated net average power supply time
The isolated network average power supply time refers to the time that the energy storage system can continuously provide power for an isolated power grid under the condition that the power grid is powered off or isolated. The calculation method of the isolated network average power supply time T comprises the following steps:
Wherein C pE is the energy storage system capacity; Running average load demand for island;
10 Peak clipping and valley filling rate
The peak clipping rate (PEAKSHAVINGRATE, PSR) and the valley filling rate (VALLEYFILLINGRATE, VFR) of the energy storage system refer to the ratio of power provided by the energy storage system in the peak clipping and valley filling processes, and the peak clipping rate and the valley filling rate are generally expressed by the percentage of power, and the calculation formula is as follows:
Wherein P h is the peak grid load; p l is the grid load valley, and P C is the energy storage system output power.
4. The analytical hierarchy process-based distributed energy storage system assessment method according to claim 3, applied in a voltage support scenario,
(1) Analyzing application scenes to select economic evaluation indexes and calculating results: leveling energy storage cost and delaying investment of a power distribution network;
(2) Analyzing application scenes to provide reliability evaluation indexes and calculating results: voltage deviation, voltage balance;
(3) Analyzing application scenes to provide functional evaluation indexes and calculating results: a voltage imbalance reduction rate;
(4) Constructing a low-voltage distribution network power flow model accessed by distributed energy storage, and calculating parameters required by selecting evaluation indexes;
(5) Establishing an analytic hierarchy process model, dividing weights of various indexes in a voltage supporting scene according to the analytic hierarchy process, establishing a judgment matrix, calculating, determining a comparison scale value among various elements in a form of expert scoring, establishing the judgment matrix, and carrying out consistency test by using the matrix and calculating various weights W i;
(6) Normalizing the calculation results of all indexes, and taking the reciprocal after normalizing the cost indexes;
(7) And superposing the normalized index values to obtain a final score.
5. The analytical hierarchy process-based distributed energy storage system assessment method according to claim 3, applied in an electrical backup power scenario,
(1) Analyzing application scenes to select economic evaluation indexes and calculating results: leveling energy storage cost indexes and energy arbitrage;
(2) Analyzing application scenes to provide reliability evaluation indexes and calculating results: energy storage utilization rate and load coverage rate;
(3) Analyzing application scenes to provide functional evaluation indexes and calculating results: the isolated network average power supply time;
(4) Constructing a low-voltage distribution network power flow model accessed by distributed energy storage, and calculating parameters required by selecting evaluation indexes;
(5) Establishing an analytic hierarchy process model, dividing weights of various indexes in a voltage supporting scene according to the analytic hierarchy process, establishing a judgment matrix, calculating, determining a comparison scale value among various elements in a form of expert scoring, establishing the judgment matrix, and carrying out consistency test by using the matrix and calculating various weights W i;
(6) Normalizing the calculation results of all indexes, and taking the reciprocal after normalizing the cost indexes;
(7) And superposing the normalized index values to obtain a final score.
6. The analytical hierarchy process-based distributed energy storage system assessment method of claim 3 applied to peak clipping and valley filling scenarios, wherein,
(1) Analyzing application scenes to select economic evaluation indexes and calculating results: leveling energy storage cost indexes and energy arbitrage;
(2) Analyzing application scenes to provide reliability evaluation indexes and calculating results: energy storage utilization rate and load coverage rate;
(3) Analyzing application scenes to provide functional evaluation indexes and calculating results: peak clipping and valley filling rate;
(4) Constructing a low-voltage distribution network power flow model accessed by distributed energy storage, and calculating parameters required by selecting evaluation indexes;
(5) Establishing an analytic hierarchy process model, dividing weights of various indexes in a voltage supporting scene according to the analytic hierarchy process, establishing a judgment matrix, calculating, determining a comparison scale value among various elements in a form of expert scoring, establishing the judgment matrix, and carrying out consistency test by using the matrix and calculating various weights W i;
(6) Normalizing the calculation results of all indexes, and taking the reciprocal after normalizing the cost indexes;
(7) And superposing the normalized index values to obtain a final score.
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