CN110717694A - Energy storage configuration random decision method and device based on new energy consumption expected value - Google Patents
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Abstract
The application relates to an energy storage configuration random decision method and device based on a new energy consumption expected value, aiming at the uncertainty of flexible adjustment requirements in new energy consumption, and establishing a new energy consumption typical scene and a distribution probability thereof based on output scene historical sample data. And then respectively obtaining the required energy storage configuration for each typical scene, and comprehensively considering the respective probability and the energy storage configuration of each typical scene to obtain a final energy storage configuration scheme. The method and the device can well quantify the uncertainty of the demand scene caused by the power fluctuation of the new energy, realize the optimization of the target expected value, and have positive effects on improving the quantitative decision level of energy storage configuration and promoting the new energy consumption.
Description
Technical Field
The application belongs to the field of planning and design of power systems, and particularly relates to a random decision-making method and device for energy storage configuration based on a new energy consumption expected value.
Background
In recent years, the generation of new energy in China is rapidly developed, and the installed capacity is gradually increased year by year. By 12 months in 2018, the installed capacity of wind power reaches 1.46 hundred million kilowatts and the installed capacity of solar power generation reaches 1.53 hundred million kilowatts in China. The problem of consumption of new energy is gradually highlighted while the new energy is developed on a large scale, and the phenomena of wind abandoning/light abandoning and electricity limiting are frequent in certain areas due to the influence of the power grid delivery capacity and the flexible system regulation capacity. Therefore, in recent years, from various aspects such as power grid construction, scheduling operation, market trading and the like, with large-scale popularization and application of new energy power generation technologies such as wind power generation, photovoltaic power generation and the like, the proportion of the installed capacity of new energy to the total installed capacity of a power system is continuously increased. By the end of 2018, the installed capacity of wind power reaches 1.8 hundred million kilowatts and the installed capacity of solar power generation reaches 1.7 hundred million kilowatts in China. The new energy power generation is influenced by natural resource conditions such as wind speed, illumination and the like, so that the output is intermittent, random and fluctuating. While the installed capacity of new energy power generation is increased on a large scale, the flexible regulation demand of the power system is also continuously increased. In order to improve the flexible adjustment capability of the system and promote the consumption of new energy, a large pumped storage power station is built, and a novel power energy storage technology represented by a battery is widely applied to a user side and a station side. At the power grid side, construction and demonstration operation of the power grid side energy storage power station are successively carried out in Henan, Jiangsu and the like at present, and the purpose is to fully excavate and utilize the aggregation effect of the multipoint distributed energy storage power station through reasonable layout and optimal configuration so as to meet the flexible adjustment requirements of the power grid in different scenes. Regarding the optimal configuration problem of energy storage, the energy storage configuration problem mainly focuses on the user side, the station side and other independent application scenarios at present, and there is no relevant technology for how to perform the configuration of the energy storage at the power grid side.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: in order to solve the defects in the prior art, the energy storage configuration random decision method and the device based on the new energy consumption expected value are provided.
The technical scheme adopted by the invention for solving the technical problems is as follows: an energy storage configuration random decision-making method based on new energy consumption expectation value,
s1: collecting historical sample data of an output scene of the new energy power generation system, performing cluster analysis on the historical sample data to establish a plurality of new energy consumption typical scenes, and acquiring the corresponding distribution probability of each typical scene;
s2: configuring an energy storage system to a new energy consumption typical scene to obtain a new energy grid-connected consumption production simulation model aiming at each typical scene, and solving energy storage configuration requirements under the model to obtain an energy storage configuration scheme set comprising all scenes;
s3: and calculating an expected value of the energy storage configuration according to the distribution probability of each typical scene based on the energy storage configuration scheme set, and determining a final energy storage configuration scheme.
Preferably, in the energy storage configuration random decision method based on the new energy consumption expected value, in the step S1, the clustering analysis method is a k-means clustering algorithm or a central point clustering algorithm.
Preferably, in the energy storage configuration random decision method based on the new energy consumption expected value, in the step S1, the typical scenarios are wind power output data and load data within a period of time.
Preferably, in the energy storage configuration random decision method based on the new energy consumption expected value, in the step S2, when the energy storage configuration requirement is solved, the energy storage system capacity and the energy storage system power required when the actual generated energy of the new energy in the new energy power generation system is maximized in order to meet the conditions of power balance constraint, unit output constraint, climbing constraint, rotation standby constraint, and energy storage battery charging and discharging constraint.
Preferably, in the energy storage configuration random decision method based on the new energy consumption expected value, in the step S3, the final energy storage configuration scheme is obtained by multiplying the energy storage configuration expected value by a correction coefficient greater than or equal to 1.
The invention also provides an energy storage configuration random decision device based on the new energy consumption expectation value, which comprises the following steps:
a typical scene acquisition module: collecting historical sample data of an output scene of the new energy power generation system, performing cluster analysis on the historical sample data to establish a plurality of new energy consumption typical scenes, and acquiring the corresponding distribution probability of each typical scene;
an energy storage configuration scheme set acquisition module: configuring an energy storage system to a new energy consumption typical scene to obtain a new energy grid-connected consumption production simulation model aiming at each typical scene, and solving energy storage configuration requirements under the model to obtain an energy storage configuration scheme set comprising all scenes;
an energy storage configuration scheme determination module: and calculating an expected value of the energy storage configuration according to the distribution probability of each typical scene based on the energy storage configuration scheme set, and determining a final energy storage configuration scheme.
Preferably, in the energy storage configuration random decision device based on the new energy consumption expected value, in the typical scene acquisition module, the clustering analysis method is a k-means clustering algorithm or a central point clustering algorithm.
Preferably, in the energy storage configuration random decision device based on the new energy consumption expected value, in the typical scene acquisition module, the typical scene is wind power output data and load data within a period of time.
Preferably, in the energy storage configuration scheme set obtaining module of the energy storage configuration random decision device based on the new energy consumption expected value, the energy storage system capacity and the energy storage system power required by the new energy power generation system when the actual generated energy of the new energy is maximized in order to meet the conditions of power balance constraint, unit output constraint, climbing constraint, rotation standby constraint and energy storage battery charging and discharging constraint when the energy storage configuration requirement is solved.
Preferably, in the energy storage configuration scheme determining module of the energy storage configuration random decision device based on the new energy consumption expected value, the final energy storage configuration scheme is obtained by multiplying the energy storage configuration expected value by a correction coefficient greater than or equal to 1.
The invention has the beneficial effects that:
according to the energy storage configuration random decision method and device based on the new energy consumption expected value, aiming at the uncertainty of flexible adjustment requirements in new energy consumption, a new energy consumption typical scene and the distribution probability thereof are established based on the output scene historical sample data. And then respectively obtaining the required energy storage configuration for each typical scene, and comprehensively considering the respective probability and the energy storage configuration of each typical scene to obtain a final energy storage configuration scheme. The method and the device can well quantify the uncertainty of the demand scene caused by the power fluctuation of the new energy, realize the optimization of the target expected value, and have positive effects on improving the quantitative decision level of energy storage configuration and promoting the new energy consumption.
Drawings
The technical solution of the present application is further explained below with reference to the drawings and the embodiments.
Fig. 1 is a structural diagram (provided with a picture) of an IEEE 30 node system in an experimental example of the present effect;
FIG. 2 is a graph showing the relationship between wind power processing and time in 4 typical output scenarios in the effect experimental example;
fig. 3 is a load characteristic curve of the IEEE 30 node system in the effect experimental example;
fig. 4 is a flowchart of an energy storage configuration random decision method based on a new energy consumption expected value in an embodiment.
Detailed Description
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict.
The technical solutions of the present application will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
Examples
The embodiment provides an energy storage configuration random decision method based on a new energy consumption expected value, as shown in fig. 1, including:
s1: collecting historical sample data of an output scene of the new energy power generation system, performing cluster analysis on the historical sample data to establish a plurality of new energy consumption typical scenes, and acquiring the corresponding distribution probability of each typical scene;
typical clustering analysis methods include a K-MEANS clustering algorithm (K-MEANS), a center point clustering algorithm (K-MEDOIDS), and the like. The typical scenario is wind power output and load data over a period of time, which is usually one day, but may also be multiple days.
S2: configuring an energy storage system to a new energy consumption typical scene to obtain a new energy grid-connected consumption production simulation model aiming at each typical scene, and solving energy storage configuration requirements under the model to obtain an energy storage configuration scheme set comprising all scenes;
when the energy storage configuration requirement is solved, the capacity of the energy storage system and the power of the energy storage system are required when the actual generated energy of the new energy in the new energy power generation system is maximized under the conditions of power balance constraint, unit output constraint, climbing constraint, rotation standby constraint and energy storage battery charging and discharging constraint.
The specific method for solving the energy storage configuration requirement comprises the following steps:
s21: establishing an objective function
Aiming at maximizing the actual generated energy of the new energy, a new energy grid-connected consumption production simulation model is established, and the mathematical expression is as follows:
in the formula: n is the number of unit periods contained in the time cycle; m is the number of new energy stations; pijActual output of the jth new energy station in the ith time period; Δ t is the duration of a unit period in a typical scenario (1 hour as in fig. 2 and 3).
S22: constraint conditions
The constraint conditions of the new energy grid-connected consumption production simulation consideration mainly comprise: power balance constraint, unit output constraint, climbing constraint, rotation standby constraint, energy storage battery charging and discharging constraint and the like.
Power balance constraint
In the formula: g is the number of conventional units; pikThe actual output of the kth conventional unit in the ith time period; piDThe system load at the i-th time period; piLThe system loss in the ith period.
Conventional unit output constraints
Pk,min≤Pik≤Pk,max(3)
In the formula: pk,maxThe output limit of the kth conventional unit is set; pk,minAnd the lower limit of the output of the kth conventional unit.
(original regulating power supply) energy storage system climbing restraint
-Rk,-·tmax≤Pik-P(i-1),k≤Rk,+·tmax(4)
In the formula: t is tmaxMaximum allowable ramp time; rk,-Downward speed regulation is carried out on the kth energy storage system; rk,+The rate is adjusted upward for the kth energy storage system.
Rotational back-up restraint
In the formula: piR,+Rotating the system up for a standby request at the ith time period; piR,-Rotating the standby request downward for the system during the ith time period.
Energy storage system charge-discharge constraint
Pe,min≤|Pie|≤Pe,max(7)
In the formula: pe,minThe lower limit of the charging and discharging power of the e energy storage system is set; pe,maxThe upper limit of the charging and discharging power of the energy storage system is set; pieCharging and discharging power for the energy storage facility in the ith time period; discharging is positive and charging is negative.
SOCmin≤SOC≤SOCmax(9)
In the formula: eiThe current energy state of the energy storage system; erateThe rated energy state of the energy storage system; SOCmin，SOCmaxThe upper and lower limits of the charging and discharging depth of the energy storage system are set.
S3: and calculating an expected value of the energy storage configuration according to the distribution probability of each typical scene based on the energy storage configuration scheme set, and determining a final energy storage configuration scheme.
Calculating an expected value of the energy storage configuration scheme according to the random probability of various typical scenes and the reference value of the configuration scheme obtained by production simulation under each scene, namely:
Ps,refthe capacity of the energy storage system required by meeting the new energy consumption target under the S typical scene; es,refThe power of the energy storage system required by meeting the new energy consumption target under the S typical scene; q. q.ssIs the distribution probability of the S-th typical scene.
In practical engineering application, the energy storage configuration needs to meet the requirements of a scene, and certain standby and reliability requirements are also considered. Therefore, the final energy storage configuration scheme selection should satisfy:
P′ref＝Pref·cp，cp＞1 (12)
E′ref＝Eref·ce，ce＞1 (13)
in the formula is cp，ceThe specific value for correcting the coefficient depends on the actual engineering application requirements.
The embodiment also provides an energy storage configuration random decision device based on a new energy consumption expected value, which corresponds to the method of the embodiment, and includes:
a typical scene acquisition module: collecting historical sample data of an output scene of the new energy power generation system, performing cluster analysis on the historical sample data to establish a plurality of new energy consumption typical scenes, and acquiring the corresponding distribution probability of each typical scene; typical clustering analysis methods include a K-MEANS clustering algorithm (K-MEANS), a center point clustering algorithm (K-MEDOIDS), and the like. The typical scenario is wind power output and load data over a period of time, which is usually one day, but may also be multiple days.
An energy storage configuration scheme set acquisition module: configuring an energy storage system to a new energy consumption typical scene to obtain a new energy grid-connected consumption production simulation model aiming at each typical scene, and solving energy storage configuration requirements under the model to obtain an energy storage configuration scheme set comprising all scenes; when the energy storage configuration requirement is solved, the capacity of the energy storage system and the power of the energy storage system are required when the actual generated energy of the new energy in the new energy power generation system is maximized under the conditions of power balance constraint, unit output constraint, climbing constraint, rotation standby constraint and energy storage battery charging and discharging constraint.
An energy storage configuration scheme determination module: and calculating an expected value of the energy storage configuration according to the distribution probability of each typical scene based on the energy storage configuration scheme set, and determining a final energy storage configuration scheme.
In the energy storage configuration scheme determining module, the final energy storage configuration scheme is obtained by multiplying an energy storage configuration expected value by a correction coefficient which is greater than or equal to 1. The specific value of the correction factor depends on the actual engineering application requirements.
Experimental examples of Effect
The effect experimental example adopts an IEEE 30 node system to verify the effectiveness of the energy storage configuration random decision method based on the new energy consumption expected value. Reference capacity 100MVA for IEEE 30 node systems; and under the condition of no special marking, the parameters of each device adopt per unit values (pu). Setting a node No. 1 as a balance node; the No. 20 node is a wind power plant grid-connected point and has the installed capacity of 1 pu; the rotation standby coefficient and the network loss coefficient are 5% of the system load. According to wind power generation historical data of a certain place, a typical sunrise power sample set of the wind power plant is established through scene clustering and merging. In this embodiment, four typical daily wind power output scenes are finally clustered, and the corresponding distribution probabilities are respectively: 0.3, 0.2, 0.2, 0.3 (random probability is the ratio of the number of clusters to the total number of sample sets for each typical scene), as shown in fig. 2. The system load characteristics are shown in fig. 3.
The wind power output characteristics and the load requirements of each time period in a typical scene are shown in table 1:
table 1 output data (pu) and load data (pu) for each time interval in a typical scenario
The conventional train parameters are shown in table 1. Wherein "H" represents a hydro-electric unit; "G" represents a coal-fired thermal power unit; "M" represents a gas turbine unit; "E" denotes a battery energy storage facility, the capacity of which is to be determined.
TABLE 2 conventional Unit parameters
Assuming that the wind power consumption target is that the wind curtailment power is controlled to be less than 5%, based on the above typical scene parameters, according to the algorithm flow in fig. 1, the production simulation and the solution of the energy storage configuration requirement under the typical scene are performed, and the result is shown in table 3.
TABLE 3 energy storage configuration requirements under different scenarios
In practical engineering, the energy storage configuration also needs to consider certain backup and reliability requirements and a fixed value of unit capacity/energy in the energy storage modular design, and revise the energy storage configuration scheme. In this example, the correction coefficient bpTake 1.02, beTaking 1.05, the final energy storage configuration requirement scheme is 24MW/192 MWh.
In light of the foregoing description of the preferred embodiments according to the present application, it is to be understood that various changes and modifications may be made without departing from the spirit and scope of the invention. The technical scope of the present application is not limited to the contents of the specification, and must be determined according to the scope of the claims.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Claims (10)
1. An energy storage configuration random decision method based on new energy consumption expectation value is characterized in that,
s1: collecting historical sample data of an output scene of the new energy power generation system, performing cluster analysis on the historical sample data to establish a plurality of new energy consumption typical scenes, and acquiring the corresponding distribution probability of each typical scene;
s2: configuring an energy storage system to a new energy consumption typical scene to obtain a new energy grid-connected consumption production simulation model aiming at each typical scene, and solving energy storage configuration requirements under the model to obtain an energy storage configuration scheme set comprising all scenes;
s3: and calculating an expected value of the energy storage configuration according to the distribution probability of each typical scene based on the energy storage configuration scheme set, and determining a final energy storage configuration scheme.
2. The energy storage configuration random decision method based on the new energy consumption expected value according to claim 1, wherein in the step S1, the clustering analysis method is a k-means clustering algorithm or a central point clustering algorithm.
3. The energy storage configuration random decision method based on the new energy consumption expectation value according to claim 2, wherein in the step S1, the typical scenarios are wind power output data and load data within a period of time.
4. The energy storage configuration random decision method based on the new energy consumption expected value according to any one of claims 1 to 3, characterized in that in the step S2, when the energy storage configuration requirement is solved, the energy storage system capacity and the energy storage system power required when the actual generated energy of the new energy in the new energy power generation system is maximized are met under the conditions of power balance constraint, unit output constraint, climbing constraint, rotation standby constraint and energy storage battery charging and discharging constraint.
5. The energy storage configuration random decision method based on the new energy consumption expectation value according to any one of claims 1 to 4, wherein in the step S3, the final energy storage configuration scheme is obtained by multiplying the energy storage configuration expectation value by a correction coefficient which is greater than or equal to 1.
6. An energy storage configuration random decision device based on a new energy consumption expectation value, comprising:
a typical scene acquisition module: collecting historical sample data of an output scene of the new energy power generation system, performing cluster analysis on the historical sample data to establish a plurality of new energy consumption typical scenes, and acquiring the corresponding distribution probability of each typical scene;
an energy storage configuration scheme set acquisition module: configuring an energy storage system to a new energy consumption typical scene to obtain a new energy grid-connected consumption production simulation model aiming at each typical scene, and solving energy storage configuration requirements under the model to obtain an energy storage configuration scheme set comprising all scenes;
an energy storage configuration scheme determination module: and calculating an expected value of the energy storage configuration according to the distribution probability of each typical scene based on the energy storage configuration scheme set, and determining a final energy storage configuration scheme.
7. The energy storage configuration random decision-making device based on the new energy consumption expected value according to claim 6, characterized in that in the typical scene acquisition module, the clustering analysis method is a k-means clustering algorithm or a central point clustering algorithm.
8. The energy storage configuration random decision device based on the new energy consumption expected value according to claim 7, wherein in the typical scenario acquisition module, the typical scenario is wind power output data and load data within a period of time.
9. The energy storage configuration random decision device based on the new energy consumption expected value according to any one of claims 6 to 8, characterized in that in the energy storage configuration scheme set acquisition module, when the energy storage configuration requirement is solved, the energy storage system capacity and the energy storage system power required when the new energy actually maximizes the generated energy in the new energy power generation system under the conditions of meeting power balance constraint, unit output constraint, climbing constraint, rotation standby constraint, and energy storage battery charging and discharging constraint.
10. The energy storage configuration random decision device based on the new energy consumption expectation value according to any one of claims 6 to 8, wherein in the energy storage configuration scheme determination module, the final energy storage configuration scheme is obtained by multiplying the energy storage configuration expectation value by a correction coefficient which is greater than or equal to 1.
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| CN112084652A (en) * | 2020-09-07 | 2020-12-15 | 中国电力工程顾问集团西北电力设计院有限公司 | Wind-solar-storage complementary power generation system capacity configuration calculation method and system |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20130030784A1 (en) * | 2011-04-28 | 2013-01-31 | Vestas Wind Systems A/S | Renewable energy configurator |
| CN104092241A (en) * | 2014-07-14 | 2014-10-08 | 国家电网公司 | Wind power consumption ability analysis method considering standby requirement |
| CN108233357A (en) * | 2016-12-15 | 2018-06-29 | 中国电力科学研究院 | Wind-powered electricity generation based on nonparametric probabilistic forecasting and risk expectation dissolves optimization method a few days ago |
| CN108879741A (en) * | 2018-06-29 | 2018-11-23 | 中国电力科学研究院有限公司 | A kind of energy accumulation capacity configuration and system of distributed generation resource on-site elimination |
| CN109149571A (en) * | 2018-09-21 | 2019-01-04 | 国网福建省电力有限公司 | A kind of energy storage Optimal Configuration Method of the combustion gas of consideration system and fired power generating unit characteristic |
| CN109217364A (en) * | 2018-09-10 | 2019-01-15 | 国网冀北电力有限公司张家口供电公司 | Photovoltaic-stored energy capacitance of large-scale distributed power supply consumption distributes strategy rationally |
| CN110224393A (en) * | 2019-05-24 | 2019-09-10 | 广东电网有限责任公司阳江供电局 | A kind of new energy consumption appraisal procedure based on minimum load shedding model |
-
2019
- 2019-10-28 CN CN201911028187.5A patent/CN110717694B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20130030784A1 (en) * | 2011-04-28 | 2013-01-31 | Vestas Wind Systems A/S | Renewable energy configurator |
| CN104092241A (en) * | 2014-07-14 | 2014-10-08 | 国家电网公司 | Wind power consumption ability analysis method considering standby requirement |
| CN108233357A (en) * | 2016-12-15 | 2018-06-29 | 中国电力科学研究院 | Wind-powered electricity generation based on nonparametric probabilistic forecasting and risk expectation dissolves optimization method a few days ago |
| CN108879741A (en) * | 2018-06-29 | 2018-11-23 | 中国电力科学研究院有限公司 | A kind of energy accumulation capacity configuration and system of distributed generation resource on-site elimination |
| CN109217364A (en) * | 2018-09-10 | 2019-01-15 | 国网冀北电力有限公司张家口供电公司 | Photovoltaic-stored energy capacitance of large-scale distributed power supply consumption distributes strategy rationally |
| CN109149571A (en) * | 2018-09-21 | 2019-01-04 | 国网福建省电力有限公司 | A kind of energy storage Optimal Configuration Method of the combustion gas of consideration system and fired power generating unit characteristic |
| CN110224393A (en) * | 2019-05-24 | 2019-09-10 | 广东电网有限责任公司阳江供电局 | A kind of new energy consumption appraisal procedure based on minimum load shedding model |
Non-Patent Citations (2)
| Title |
|---|
| LI YAN ET AL.: "Study on the key factors of regional power grid renewable energy accommodating capability", 《2016 IEEE PES ASIA-PACIFIC POWER AND ENERGY ENGINEERING CONFERENCE (APPEEC)》 * |
| 施涛等: "考虑功率预测不确定性的风电消纳随机调度", 《电网与清洁能源》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112084652A (en) * | 2020-09-07 | 2020-12-15 | 中国电力工程顾问集团西北电力设计院有限公司 | Wind-solar-storage complementary power generation system capacity configuration calculation method and system |
| CN112084652B (en) * | 2020-09-07 | 2023-06-20 | 中国电力工程顾问集团西北电力设计院有限公司 | Capacity configuration calculation method and system for wind-solar-energy-storage complementary power generation system |
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