CN112668918A - Energy storage model selection method based on data model algorithm - Google Patents
Energy storage model selection method based on data model algorithm Download PDFInfo
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Abstract
The invention discloses an energy storage model selection method based on a data model algorithm, which comprises the following steps: according to the power data of the user, performing first model construction to obtain a load model; performing secondary model construction based on the energy storage data of the user to obtain a battery model; building a third model based on the profit mode of the user to obtain an economic model; based on a data model algorithm, performing data model algorithm training according to the load model, the battery model and the economic model to construct an energy storage model selection model; and generating a user energy storage type selection analysis report according to the energy storage type selection model so as to realize planning guidance of energy storage type selection. The method solves the problem of difficult matching of energy storage strategy type selection, and carries out accurate user-side energy storage type selection based on user historical data, thereby realizing high cost performance planning of energy storage equipment, avoiding low-efficiency waste, reducing cost and increasing profit margin.
Description
Technical Field
The invention relates to the technical field of energy storage, in particular to an energy storage model selection method based on a data model algorithm.
Background
The energy storage is a new strategic industry of China, is an important component and a key supporting technology of an energy internet, has the technical advantages of quick response, bidirectional adjustment, strong environmental adaptability, short construction period and the like, and can have great influence on energy transformation, a power grid pattern and a power supply structure when being applied in large scale.
With the breakthrough of energy storage technology and the rapid decrease of cost, the prospect of energy storage application will become wider, and many technologies have entered the commercial demonstration stage and have shown certain economy in some fields. The electrochemical energy storage technology represented by lithium batteries, lead-acid and liquid flows is developed continuously and maturely, and the electrochemical energy storage technology with the cost further reduced is the energy storage technology with the widest application range and the greatest development potential. Mechanical energy storage technologies represented by flywheels and compressed air overcome difficulties in material and the like, and the industrialization speed is accelerated; and new energy storage technologies represented by lithium sulfur, lithium air, all-solid-state batteries, and sodium ions are also continuously developed, and technological progress is made.
According to the application research report of energy storage industry of 2019 published by the association of chemical and physical power supply industries of China, the method comprises the following steps: in 2018, from the aspect of the installed power proportion, in the aspect of installed power proportion, installed distributions of 5 types of application scenes such as a centralized new energy source + energy storage, a power supply side frequency modulation, a power grid side energy storage, a distributed micro-grid and user side (peak clipping and valley filling, demand side response and the like) are relatively even, and the proportion is respectively 18.5%, 16.4%, 24%, 16.9% and 24.6%; in the aspect of the installed capacity ratio, the user side (industrial and commercial peak clipping and valley filling, demand side response and the like) energy storage capacity installed machine is exclusively used for the crabbing, and the installed scale is up to 1583.0MWh and accounts for 51.0%.
From domestic perspective, the relatively mature business modes comprise peak-valley price difference arbitrage, auxiliary frequency modulation service charging, construction of large-scale energy storage power stations by matching with renewable energy sources and distributed energy storage application. Under a mature business model, the business interests of a user side become key factors for pushing the energy storage project to land, so that the energy storage type selection method is very important.
The existing energy storage type selection method mainly focuses on storage capacity optimization configuration and control strategies, and researches related to the aspect of energy storage type selection are mostly based on the availability of energy storage equipment and consideration of price factors, and are obtained by simple comparison without consideration of requirements of an end user side.
With the further development of energy storage battery technology, the performance and control strategy of energy storage devices are not the key factors for energy storage type selection, and the commercial benefits of the energy storage user side become more important.
Meanwhile, the current model selection method still stays in a horizontal comparison stage between the same attributes, and cannot be used for processing decisions among multiple mutually advantageous energy storage types, and a set of systematic energy storage model selection scheme suitable for a user side is not provided.
At present, a plurality of technical problems exist in the aspects of energy storage type selection and energy storage profit. In the power system, the load size shows a corresponding peak-valley rule along with the living, working and resting characteristics of people, and the load peak-valley is intensified along with the increase of the number and the types of the loads of the power system. The aggravation of the load peak-valley difference enables the conventional unit to be in the unfavorable state of frequent starting, and the economy is reduced.
In addition, random peak loads may cause overload of part of the power flow sections, and the stability of the system is reduced. The energy storage technology is widely applied to the fields of smooth renewable energy output, peak clipping and valley filling and the like at present due to the flexible electric power throughput characteristic, and has the potential of economic benefit compared with some conventional peak clipping and valley filling means except the quick response characteristic.
Energy storage type selection difficulty caused by fluctuation of power utilization conditions, and the power utilization rule of a user is unclear and is difficult to match with a corresponding energy storage type selection model; meanwhile, the profit mode is unreasonable, and the energy storage strategy model selection model cannot be established without a responding electric power market environment and a user-side energy storage time-sharing profit sharing mechanism.
Disclosure of Invention
The invention aims to provide an energy storage model selection method based on a data model algorithm. The method aims to solve the problem that matching is difficult in energy storage strategy type selection, and the method is applied to a mature analysis method and an analysis tool, utilizes a relevant theory, and carries out accurate user-side energy storage type selection based on user historical data, so that high cost performance planning of energy storage equipment is realized, inefficient waste is avoided, cost is reduced, and profit margin is increased.
In order to achieve the purpose, the invention provides an energy storage model selection method based on a data model algorithm, which comprises the following steps:
step 1: according to the power data of the user, performing first model construction to obtain a load model;
step 2: performing secondary model construction based on the energy storage data of the user to obtain a battery model;
and step 3: building a third model based on the profit mode of the user to obtain an economic model;
and 4, step 4: based on a data model algorithm, training the data model algorithm according to the load model, the battery model and the economic model to construct an energy storage model selection model;
and 5: and generating a user energy storage type selection analysis report according to the energy storage type selection model so as to realize planning guidance of energy storage type selection.
Most preferably, before the first model building, the electricity utilization data of the user needs to be researched to obtain the electricity data of the user.
Most preferably, the power data of the user includes: basic information of users, electric power files, electric power metering and electricity prices.
Most preferably, the basic information of the user includes: user name, user number, electricity usage category.
Most preferably, the energy storage data is basic information of the energy storage device.
Most preferably, the energy storage device is a battery.
Most preferably, the basic information includes: battery capacity, maximum discharge power, maximum discharge rate, charge efficiency, discharge efficiency, and charge-discharge depth.
Most preferably, the third model building comprises energy storage modeling of the economic model and data processing of the profit model.
Most preferably, the customer energy storage typing analysis report is generated based on the return on investment of customer demand.
By applying the method and the device, the problem that matching is difficult in energy storage strategy type selection is solved, a mature analysis method and an analysis tool are applied, and a relevant theory is utilized to perform accurate user-side energy storage type selection based on user historical data, so that high cost performance planning of energy storage equipment is realized, low-efficiency waste is avoided, cost is reduced, and profit margin is increased.
Compared with the prior art, the invention has the following beneficial effects:
1. the energy storage type selection method provided by the invention is applied to a mature analysis method and an analysis tool, utilizes a correlation theory, carries out accurate user-side energy storage type selection based on user historical data, combines the current electric power market environmental data and various energy storage project profit modes, develops energy storage modeling and yield analysis, actually carries out measurement and calculation according to the actual energy consumption requirement of the user side, deeply excavates the data value, and provides an actually usable user energy storage type selection analysis report for the user-side energy storage type selection project.
2. The energy storage type selection method provided by the invention helps a user scientifically plan the type selection of the energy storage equipment, realizes the high cost performance planning of the energy storage equipment and avoids the inefficient waste. The reasonable planning of energy storage can help users balance peak-valley electricity prices, so that the cost is reduced, the profit margin is increased, the peak-valley price difference can help enterprise users to realize energy storage profit as soon as possible in the reasonable planning, and the economy is improved.
3. The energy storage type selection method provided by the invention can be used for large industrial enterprises to store energy in the valley of power utilization and release the energy in the peak of power utilization, so that the highest power utilization is reduced, the capacity price charged according to capacity in two power generation prices is reduced under the condition of not influencing normal production, and the reasonable energy storage type selection can help the large enterprises to determine the energy storage scale and the battery technology according to annual income and internal income rate.
Drawings
Fig. 1 is a flow chart of the energy storage model selection method provided by the present invention.
Detailed Description
The invention will be further described by the following specific examples in conjunction with the drawings, which are provided for illustration only and are not intended to limit the scope of the invention.
The invention provides an energy storage model selection method based on a data model algorithm, which comprises the following steps of:
step 1: researching power consumption data of a user in a certain period to obtain power data of the user; according to the power data of the user, performing first model construction to obtain a load model;
in this embodiment, the power data of the user includes: basic information of users, electric power files, electric power metering, electricity price and the like; the basic information of the user includes: user name, user number, electricity usage category, etc.; the power data for a particular customer is shown in the following table:
in the embodiment, the electricity consumption data of the user in about 12 months is researched to obtain the electricity consumption data of the user; wherein, the electricity consumption data of the user in about 12 months comprises: total electric quantity of nearly 12 months, peak electric quantity duty ratio of nearly 12 months, peak-to-valley ratio maximum value of nearly 12 months, and annual average load rate.
Wherein, the total electric quantity of nearly 12 months is EPtotal, and satisfies:
EPtotal＝∑EPmonth；
the peak electric quantity of nearly 12 months is EPtotalpAnd satisfies the following conditions:
the peak electric quantity accounts for EPratio in about 12 monthspAnd satisfies the following conditions:
the maximum value of the peak-to-valley ratio of the monthly electricity of nearly 12 months is EPmaxratioAnd satisfies the following conditions:
the annual average load rate in the last 12 months is LyearAnd satisfies the following conditions:
wherein days is the total days of approximately 12 months.
The power data of the user includes: the method comprises the steps of positive active daily peak electric quantity correction, positive active daily average electric quantity correction, maximum value of daily electric quantity peak-valley difference of nearly 12 months and average value of daily electric quantity peak-valley difference of nearly 12 months.
Wherein the forward active daily peak power is corrected to EPpAnd satisfies the following conditions:
wherein, summer is the summer electricity price carried out between 7 months 1 day and 9 months 30 days;
forward active daily average power correction to EPfAnd satisfies the following conditions:
the maximum value of the peak-to-valley difference of the daily electric quantity in the last 12 months is as follows:
EPmaxdiff＝max(EPp-EPv)；
the average value of the peak-to-valley difference of the daily electricity quantity of the nearly 12 months is
And satisfies the following conditions:
step 2: and performing secondary model construction based on energy storage data of energy storage equipment in the existing electric power market to obtain a battery model.
In this embodiment, the energy storage data is basic information of the energy storage device; the energy storage device is a storage battery; the basic information of the battery includes: battery capacity, maximum discharge power, maximum discharge rate, charge efficiency, discharge efficiency, charge and discharge depth, and the like.
In this embodiment, the energy storage data is shown in the following table:
in the present embodiment, the battery model includes: the capacity of the battery for storing energy, the predicted service life of the energy storage, the capacity of the battery in the ith year (i is more than or equal to 1 and less than or equal to N), the 1-time charge amount of the battery in the ith year (i is more than or equal to 1 and less than or equal to N), the 1-time discharge amount of the battery in the ith year (i is more than or equal to 1 and less than or equal to N) and the net present value.
Wherein the capacity of the energy storage battery is CbatAnd satisfies the following conditions:
wherein ceil is50Rounding up by an integral multiple of 50;
the estimated service life of the stored energy is N and satisfies the following conditions:
wherein ceil is rounding up;
the battery capacity in the ith year (i is more than or equal to 1 and less than or equal to N) is
And satisfies the following conditions:
1-time charging quantity of battery in ith year (i is more than or equal to 1 and less than or equal to N)
And satisfies the following conditions:
1-time discharge capacity of battery in ith year (i is more than or equal to 1 and less than or equal to N)
And satisfies the following conditions:
and step 3: building a third model based on the current electric power market environment and a profit mode of user-side energy storage time-sharing profit sharing to obtain an economic model; and the third model construction comprises energy storage modeling of the economic model and data processing of the profit model.
In this embodiment, the economic model includes: investment cost, income in the ith year (i is more than or equal to 1 and less than or equal to N), net profit, annual average income, investment recovery period, return on investment, annual income rate and net present value.
Wherein the investment cost is C0And satisfies the following conditions:
C0＝Cbat*pbat；
wherein p isbatFor maximum discharge rate, in this embodiment, pbatWas 0.5 c.
The yield of the year i (i is more than or equal to 1 and less than or equal to N) is CiAnd satisfies the following conditions:
net profit of CpureAnd satisfies the following conditions:
the annual average income is
And satisfies the following conditions:
investment recovery period TperiodThe number of years which is more than or equal to the total investment for the first time of accumulated annual return; the investment recovery period is also called "investment recovery year", and the time (year) required for the total amount of income obtained after investment project investment to reach the total amount of investment of the investment project investment; and the investment recovery period TperiodSatisfies the following conditions:
return On Investment (ROI) refers to the value that should be returned by an Investment, i.e., the overall economic Return that an enterprise receives from an Investment; and the return on investment ROI satisfies:
the annual rate of return is r, and satisfies:
the Internal Rate of Return (IRR) is the discount Rate when the total of the present value of the inflowing funds is equal to the present value of the outflowing funds, and the net present value is equal to zero. IRR is a desired return rate for investment, with larger indices being better. Generally, when the internal rate of return is equal to or greater than the reference rate of return, the project is possible.
The sum of the cash flow discount values of each year of the investment project is the net cash flow value of the project, and the discount rate when the net cash flow value is zero is the internal yield of the project; the net present value is NPV and satisfies:
wherein r isirrThe present sum of the earnings for the subsequent years is equal to the interest rate of the initial investment.
And 4, step 4: based on a data model algorithm, performing data model algorithm training according to the load model, the battery model and the economic model to construct an energy storage type selection model for accurately defining the energy storage type selection of the user;
and 5: and generating a user energy storage type selection analysis report according to the energy storage type selection model based on the return on investment required by the user so as to realize planning guidance of energy storage type selection.
The working principle of the invention is as follows:
according to the power data of the user, performing first model construction to obtain a load model; performing secondary model construction based on the energy storage data of the user to obtain a battery model; building a third model based on the profit mode of the user to obtain an economic model; based on a data model algorithm, performing data model algorithm training according to the load model, the battery model and the economic model to construct an energy storage model selection model; and generating a user energy storage type selection analysis report according to the energy storage type selection model so as to realize planning guidance of energy storage type selection.
In conclusion, the energy storage type selection method based on the data model algorithm solves the problem that the energy storage strategy type selection is difficult to match, applies mature analysis methods and analysis tools, utilizes relevant theories, and carries out accurate user-side energy storage type selection based on user historical data, so that high cost performance planning of energy storage equipment is realized, low-efficiency waste is avoided, cost is reduced, and profit space is increased.
While the present invention has been described in detail with reference to the preferred embodiments, it should be understood that the above description should not be taken as limiting the invention. Various modifications and alterations to this invention will become apparent to those skilled in the art upon reading the foregoing description. Accordingly, the scope of the invention should be determined from the following claims.
Claims (9)
1. An energy storage model selection method based on a data model algorithm is characterized by comprising the following steps:
step 1: according to the power data of the user, performing first model construction to obtain a load model;
step 2: performing secondary model construction based on the energy storage data of the user to obtain a battery model;
and step 3: building a third model based on the profit mode of the user to obtain an economic model;
and 4, step 4: based on a data model algorithm, performing data model algorithm training according to the load model, the battery model and the economic model to construct an energy storage model selection model;
and 5: and generating a user energy storage type selection analysis report according to the energy storage type selection model so as to realize planning guidance of energy storage type selection.
2. The energy storage model selection method based on the data model algorithm as claimed in claim 1, wherein before the first model building, the electricity consumption data of a user needs to be researched to obtain the electricity data of the user.
3. The method of claim 1, wherein the power data of the user comprises: basic information of users, electric power files, electric power metering and electricity prices.
4. The energy storage model selection method based on the data model algorithm as claimed in claim 3, wherein the basic information of the user comprises: user name, user number, electricity usage category.
5. The energy storage model selection method based on the data model algorithm as claimed in claim 1, wherein the energy storage data is basic information of the energy storage device.
6. The method for energy storage model selection based on the datamation model algorithm, according to claim 5, wherein the energy storage device is a storage battery.
7. An energy storage model selection method based on a data model algorithm as claimed in claim 6, characterized in that the basic information of the storage battery comprises: battery capacity, maximum discharge power, maximum discharge rate, charge efficiency, discharge efficiency, and charge-discharge depth.
8. The method for energy storage model selection based on the datamation model algorithm, according to claim 1, wherein the third model building comprises energy storage modeling of an economic model and data processing of a profit model.
9. The method of claim 1, wherein the user energy storage selection analysis report is generated based on a return on investment of user demand.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113157801A (en) * | 2021-04-21 | 2021-07-23 | 内蒙古电力（集团）有限责任公司乌兰察布电业局 | Power utilization time sequence data visual display method and system and readable medium |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107370170A (en) * | 2017-06-23 | 2017-11-21 | 浙江大学 | A kind of energy storage system capacity collocation method for considering capacity price of electricity and load prediction error |
| CN109193720A (en) * | 2018-09-12 | 2019-01-11 | 华南理工大学 | User side energy accumulation capacity configuration based on enterprise customer's typical day load curve |
| CN109390949A (en) * | 2018-11-09 | 2019-02-26 | 毕锐 | A kind of household photovoltaic, energy storage and with can control method |
| CN109787261A (en) * | 2019-03-14 | 2019-05-21 | 国网江苏省电力有限公司镇江供电分公司 | Grid side and user side energy storage system capacity configuration optimizing method |
| CN110717259A (en) * | 2019-09-25 | 2020-01-21 | 浙江大学 | User side-oriented battery energy storage configuration and operation optimization method |
| CN111092448A (en) * | 2019-11-20 | 2020-05-01 | 国网浙江省电力有限公司经济技术研究院 | Dynamic optimization method for optimal battery energy storage capacity configuration based on user side load |
| CN111555329A (en) * | 2020-06-05 | 2020-08-18 | 西安石油大学 | Energy storage capacity configuration method for autonomous micro-grid |
| CN112001598A (en) * | 2020-07-28 | 2020-11-27 | 四川大学 | Energy storage configuration evaluation and operation optimization method for different users based on energy storage type selection |
-
2021
- 2021-01-04 CN CN202110002978.1A patent/CN112668918A/en active Pending
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107370170A (en) * | 2017-06-23 | 2017-11-21 | 浙江大学 | A kind of energy storage system capacity collocation method for considering capacity price of electricity and load prediction error |
| CN109193720A (en) * | 2018-09-12 | 2019-01-11 | 华南理工大学 | User side energy accumulation capacity configuration based on enterprise customer's typical day load curve |
| CN109390949A (en) * | 2018-11-09 | 2019-02-26 | 毕锐 | A kind of household photovoltaic, energy storage and with can control method |
| CN109787261A (en) * | 2019-03-14 | 2019-05-21 | 国网江苏省电力有限公司镇江供电分公司 | Grid side and user side energy storage system capacity configuration optimizing method |
| CN110717259A (en) * | 2019-09-25 | 2020-01-21 | 浙江大学 | User side-oriented battery energy storage configuration and operation optimization method |
| CN111092448A (en) * | 2019-11-20 | 2020-05-01 | 国网浙江省电力有限公司经济技术研究院 | Dynamic optimization method for optimal battery energy storage capacity configuration based on user side load |
| CN111555329A (en) * | 2020-06-05 | 2020-08-18 | 西安石油大学 | Energy storage capacity configuration method for autonomous micro-grid |
| CN112001598A (en) * | 2020-07-28 | 2020-11-27 | 四川大学 | Energy storage configuration evaluation and operation optimization method for different users based on energy storage type selection |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113157801A (en) * | 2021-04-21 | 2021-07-23 | 内蒙古电力（集团）有限责任公司乌兰察布电业局 | Power utilization time sequence data visual display method and system and readable medium |
| CN113157801B (en) * | 2021-04-21 | 2023-07-11 | 内蒙古电力(集团)有限责任公司乌兰察布供电分公司 | Power utilization time sequence data visual display method, system and readable medium |
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