CN117595332A - Power distribution network balanced power supply method based on energy storage system - Google Patents
Power distribution network balanced power supply method based on energy storage system Download PDFInfo
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- CN117595332A CN117595332A CN202410078187.0A CN202410078187A CN117595332A CN 117595332 A CN117595332 A CN 117595332A CN 202410078187 A CN202410078187 A CN 202410078187A CN 117595332 A CN117595332 A CN 117595332A
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- 238000004146 energy storage Methods 0.000 title claims abstract description 49
- 238000000034 method Methods 0.000 title claims abstract description 26
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- 230000007613 environmental effect Effects 0.000 claims description 8
- 238000013528 artificial neural network Methods 0.000 claims description 2
- 230000009286 beneficial effect Effects 0.000 description 1
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- 238000012986 modification Methods 0.000 description 1
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- 238000006467 substitution reaction Methods 0.000 description 1
Classifications
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/28—Arrangements for balancing of the load in a network by storage of energy
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/003—Load forecast, e.g. methods or systems for forecasting future load demand
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
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- Engineering & Computer Science (AREA)
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Abstract
The invention discloses a power distribution network balanced power supply method based on an energy storage system, which comprises the following steps: s1, constructing a power supply model of a power distribution network: dividing a power supply range of a power distribution network into a plurality of power supply areas, wherein each power supply area is internally provided with an energy storage system, and the power distribution network charges the energy storage systems of the power supply areas and supplies power to the power supply areas; s2, collecting historical electricity consumption information and historical environment information in each power supply area, and constructing an electricity consumption prediction model in each area based on the collected information; s3, acquiring future environment systems of all power supply areas, and obtaining future power consumption prediction results in all areas based on a power consumption prediction model; s4, power distribution network power supply scheduling is conducted based on future power consumption prediction results of all the areas. The method can predict the power demand based on historical power consumption data and weather data, and can ensure the consistency of power supply and charging of the energy storage systems in different power supply areas.
Description
Technical Field
The invention relates to power supply scheduling of a power distribution network, in particular to a power distribution network balanced power supply method based on an energy storage system.
Background
At present, the electricity consumption of each power supply area has huge difference at different time, and in general, the electricity supply is increased in the period of peak electricity consumption period of users, and the increase of the electricity supply is difficult to realize under the background that the electricity supply and demand relationship is increasingly tense, so that the electricity is stored in different areas when the electricity demand is lower, and the circuit stored when the electricity demand is high is an effective solution, but the prediction of the electricity demand is difficult to realize more accurately, and the electricity storage and the electricity supply of energy storage equipment in different power supply areas are inconsistent, so that the management is difficult.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, and provides a power distribution network balanced power supply method based on an energy storage system, which can predict power demand based on historical power consumption data and weather data, and can ensure the consistency of power supply and charging of the energy storage system in different power supply areas.
The aim of the invention is realized by the following technical scheme: an energy storage system-based power distribution network balanced power supply method comprises the following steps:
s1, constructing a power supply model of a power distribution network: dividing a power supply range of a power distribution network into a plurality of power supply areas, wherein each power supply area is internally provided with an energy storage system, and the power distribution network charges the energy storage systems of the power supply areas and supplies power to the power supply areas;
s2, collecting historical electricity consumption information and historical environment information in each power supply area, and constructing an electricity consumption prediction model in each area based on the collected information;
s3, acquiring future environment systems of all power supply areas, and obtaining future power consumption prediction results in all areas based on a power consumption prediction model;
s4, power distribution network power supply scheduling is conducted based on future power consumption prediction results of all the areas.
The beneficial effects of the invention are as follows: the power demand prediction method can predict the power demand based on historical power consumption data and weather data, can ensure the consistency of power supply and charging of the energy storage systems in different power supply areas, realizes the charge and discharge balance of the energy storage systems in each power supply area, and completes the balanced power supply of the power distribution network.
Drawings
FIG. 1 is a flow chart of the method of the present invention.
Detailed Description
The technical solution of the present invention will be described in further detail with reference to the accompanying drawings, but the scope of the present invention is not limited to the following description.
As shown in fig. 1, a power distribution network balanced power supply method based on an energy storage system includes the following steps:
s1, constructing a power supply model of a power distribution network: dividing a power supply range of a power distribution network into a plurality of power supply areas, wherein each power supply area is internally provided with an energy storage system, and the power distribution network charges the energy storage systems of the power supply areas and supplies power to the power supply areas;
s2, collecting historical electricity consumption information and historical environment information in each power supply area, and constructing an electricity consumption prediction model in each area based on the collected information;
s3, acquiring future environment systems of all power supply areas, and obtaining future power consumption prediction results in all areas based on a power consumption prediction model;
s4, power distribution network power supply scheduling is conducted based on future power consumption prediction results of all the areas.
The step S2 includes:
s201, for any power supply area, collecting historical power utilization data and historical environment data of the power supply area in a period T:
let the period T include N time periods, then:
the historical electricity consumption data is recorded asWherein->Historical electricity usage data representing an nth time period;
the historical environmental data is recorded asWherein->Representing historical environmental data within an nth time period, wherein +.>Represents the average temperature in the nth time period,/-, for example>Indicating weather during the nth time period, < +.>Indicating that there is rain, snow, hail or haze in the period of time,/->Indicating that there is no rain, snow, hail or haze in the time period;
s202, constructing a training sample in a current period T;
in each training sample, the number n of the time period and the corresponding environmental data are taken as sample characteristics and recorded asHistorical electricity consumption data in the nth time period +.>As sample tag, wherein->A total of N training samples were obtained, noted:
s203, repeatedly executing the steps S201-S202 in M periods T, obtaining M times N training samples, and adding the M times N training samples into the same set to obtain a training sample set of a current power supply area;
s204, constructing an initial electricity consumption prediction model of the current power supply area based on a BP neural network algorithm, and training the initial electricity consumption prediction model by utilizing a training sample set of the current power supply area to obtain the electricity consumption prediction model of the current power supply area;
s205, repeatedly executing steps S201-S204 for each power supply area to obtain a power consumption prediction model of each power supply area.
In the embodiment of the present application, the one period T is one week, that is, from the monday 0 point to the sunday 24 point. The time length of each time period in the period T is equal to 15 minutes, 30 minutes or one hour.
When the training sample set of the current power supply area is used for training the initial power consumption prediction model in step S204, sample characteristics of each training sample in the training sample set of the current power supply area are used as input, and corresponding sample labels are used as expected output for training until all training samples are trained, and then the power consumption prediction model of the current power supply area is obtained.
The step S3 includes:
s301, for any power supply area, acquiring environment information of the area in a future period T, and recording as follows:
wherein,representing environmental data in the nth time period of one period T in the future,/day>；
S302, constructing an input feature vector in a current power supply area, and marking the input feature vector asAnd respectively sending the power consumption prediction results into a power consumption prediction model of a current power supply area to obtain power consumption prediction results of each time period in a future period T, and marking the power consumption prediction results as follows: />The method comprises the steps of carrying out a first treatment on the surface of the Wherein the method comprises the steps of/>And the power consumption prediction result of the nth time period of the current power supply area in the future period T is shown.
S303, repeatedly executing the steps S301-S302 to obtain a future electricity consumption prediction result of each power supply area in each time period in a future period T, and marking the electricity consumption prediction structure of the kth power supply area as:
；
wherein,representing the result of prediction of the power consumption of the kth power supply region in the nth time period within one period T in the future,/th power supply region>，/>K represents the number of power supply areas.
The step S4 includes:
s401, constructing a power supply model of an nth time period in a future period T:
giving the maximum power supply quantity of the power distribution network in a time period as D, initializing the energy storage capacity of an energy storage system of each power supply area as Q, and enabling the initial energy storage to be Q/2;
when the distribution network is arranged to directly supply power to the power supply area, the power supply loss rates are respectively as followsWhen the power distribution network charges the energy storage system of each power supply area, the charging loss rate is +.>When the power supply area where the energy storage system of each power supply area is positioned supplies power, the power supply loss rate is +.>；
Then at time n the total loss of power supply is noted as:
；
wherein the method comprises the steps ofIndicating the amount of power supplied by the distribution network to the kth power supply area during the nth time period,/for the kth power supply area>Representing the power supply of the power distribution network to the energy storage system of the kth power supply area during the nth time period,/>Representing the power supply quantity of the energy storage system in the k power supply areas in the nth time period;
s402, determining a power supply scheduling strategy: in the nth time period, if the sum D1 of the power consumption prediction results of all the power supply areas is not greater than D, the power distribution network directly supplies power to all the areas and charges the energy storage systems of all the areas, and the charging quantity is equal to the charging quantityAt this time->(energy storage system is not powered),>；
in the nth time period, if the sum D1 of the circuit prediction results of each power supply area is larger than D, calculating the power supply of the area by the energy storage system and the power distribution network of each power supply area, wherein the power supply quantity of the energy storage systemAt this time->(the distribution network does not charge the energy storage system),>；
wherein,、/>scheduling parameters to be determined in a scheduling strategy;
s403, establishing a power supply objective function as follows:
given the following constraints:
(1) For any arbitrary，/>Satisfies the following conditions；
(2) In the S-th period of time,for any->The method comprises the following steps:
；
(3) For any oneSatisfy->；
By passing throughUnder the constraint condition, solving an objective function to obtain、/>Thereby obtaining a power supply scheduling scheme in a future period of time T, and performing power supply scheduling according to the scheduling parameters of step S402 and the constraint conditions in step S403.
The above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (7)
1. An energy storage system-based power distribution network balanced power supply method is characterized by comprising the following steps of: the method comprises the following steps:
s1, constructing a power supply model of a power distribution network: dividing a power supply range of a power distribution network into a plurality of power supply areas, wherein each power supply area is internally provided with an energy storage system, and the power distribution network charges the energy storage systems of the power supply areas and supplies power to the power supply areas;
s2, collecting historical electricity consumption information and historical environment information in each power supply area, and constructing an electricity consumption prediction model in each area based on the collected information;
s3, acquiring future environment systems of all power supply areas, and obtaining future power consumption prediction results in all areas based on a power consumption prediction model;
s4, power distribution network power supply scheduling is conducted based on future power consumption prediction results of all the areas.
2. The energy storage system-based power distribution network balanced power supply method according to claim 1, wherein the method comprises the following steps: the step S2 includes:
s201, for any power supply area, collecting historical power utilization data and historical environment data of the power supply area in a period T:
let the period T include N time periods, then:
the historical electricity consumption data is recorded asWherein->Historical electricity usage data representing an nth time period;
the historical environmental data is recorded asWherein->Representing historical environmental data within an nth time period, wherein +.>Represents the average temperature in the nth time period,/-, for example>Indicating the weather in the nth time period,indicating that there is rain, snow, hail or haze in the period of time,/->Indicating that there is no rain, snow, hail or haze in the time period;
s202, constructing a training sample in a current period T;
in each training sample, the number n of the time period and the corresponding environmental data are taken as sample characteristics and recorded asHistorical electricity consumption data in the nth time period +.>As sample tag, wherein->A total of N training samples were obtained, noted:
s203, repeatedly executing the steps S201-S202 in M periods T, obtaining M times N training samples, and adding the M times N training samples into the same set to obtain a training sample set of a current power supply area;
s204, constructing an initial electricity consumption prediction model of the current power supply area based on a BP neural network algorithm, and training the initial electricity consumption prediction model by utilizing a training sample set of the current power supply area to obtain the electricity consumption prediction model of the current power supply area;
s205, repeatedly executing steps S201-S204 for each power supply area to obtain a power consumption prediction model of each power supply area.
3. The energy storage system-based power distribution network balanced power supply method as claimed in claim 2, wherein: the one period T is one week, i.e., from monday 0 to sunday 24.
4. A power distribution network balanced power supply method based on an energy storage system according to claim 3, characterized in that: the time length of each time period in the period T is equal to 15 minutes, 30 minutes or one hour.
5. The energy storage system-based power distribution network balanced power supply method as claimed in claim 2, wherein: when the training sample set of the current power supply area is used for training the initial power consumption prediction model in step S204, sample characteristics of each training sample in the training sample set of the current power supply area are used as input, and corresponding sample labels are used as expected output for training until all training samples are trained, and then the power consumption prediction model of the current power supply area is obtained.
6. The energy storage system-based power distribution network balanced power supply method as claimed in claim 2, wherein: the step S3 includes:
s301, for any power supply area, acquiring environment information of the area in a future period T, and recording as follows:
wherein,representing environmental data in the nth time period of one period T in the future,/day>；
S302, constructing an input feature vector in a current power supply area, and marking the input feature vector asAnd respectively sending the power consumption prediction results into a power consumption prediction model of a current power supply area to obtain power consumption prediction results of each time period in a future period T, and marking the power consumption prediction results as follows: />The method comprises the steps of carrying out a first treatment on the surface of the Wherein->The power consumption prediction result of the nth time period of the current power supply area in a future period T is shown;
s303, repeatedly executing the steps S301-S302 to obtain a future electricity consumption prediction result of each power supply area in each time period in a future period T, and marking the electricity consumption prediction structure of the kth power supply area as:
；
wherein,representing the power consumption prediction result of the kth power supply region in the nth time period in the future period T,，/>k represents the number of power supply areas.
7. The energy storage system-based power distribution network balanced power supply method as claimed in claim 2, wherein: the step S4 includes:
s401, constructing a power supply model of an nth time period in a future period T:
giving the maximum power supply quantity of the power distribution network in a time period as D, initializing the energy storage capacity of an energy storage system of each power supply area as Q, and enabling the initial energy storage to be Q/2;
when the distribution network is arranged to directly supply power to the power supply area, the power supply loss rates are respectively as followsWhen the power distribution network charges the energy storage system of each power supply area, the charging loss rate is +.>When the power supply area where the energy storage system of each power supply area is positioned supplies power, the power supply loss rate is +.>；
Then at time n the total loss of power supply is noted as:
；
wherein the method comprises the steps ofIndicating the amount of power supplied by the distribution network to the kth power supply area during the nth time period,/for the kth power supply area>Representing the power supply of the power distribution network to the energy storage system of the kth power supply area during the nth time period,/>Representing the power supply quantity of the energy storage system in the k power supply areas in the nth time period;
s402, determining a power supply scheduling strategy: in the nth time period, if the sum D1 of the power consumption prediction results of all the power supply areas is not greater than D, the power distribution network directly supplies power to all the areas and charges the energy storage systems of all the areas, and the charging quantity is equal to the charging quantityAt this time->，/>；
In the nth time period, if the sum D1 of the circuit prediction results of each power supply area is larger than D, calculating the power supply of the area by the energy storage system and the power distribution network of each power supply area, wherein the power supply quantity of the energy storage systemAt this time/>，/>；
Wherein,、/>scheduling parameters to be determined in a scheduling strategy;
s403, establishing a power supply objective function as follows:
given the following constraints:
(1) For any arbitrary，/>Satisfies the following conditions；
(2) In the S-th period of time,for any->The method comprises the following steps:
；
(3) For any oneSatisfy->；
Solving the objective function under the constraint condition to obtain、/>Thereby obtaining a power supply scheduling scheme within a future period of time T.
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