CN117856314A - Distributed energy storage management method - Google Patents
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Abstract
The application relates to a distributed energy storage management method, which is applied to a distributed energy storage system and comprises a cloud server and charging stations distributed at different positions, wherein each charging station comprises a photovoltaic module, an energy storage module, a control module and a charging module; the method comprises the following management methods: load information and power generation information are obtained, wherein the load information is the total power consumption speed of load charging, and the power generation information is the power generation speed of a photovoltaic module; judging whether the load information is smaller than or equal to the power generation information; if yes, storing the electric energy with the power generation information exceeding the load information into an energy storage module; if not, acquiring time information and interval information, wherein the time information is the current time, and the interval information is a time period corresponding to a power consumption peak period and a power consumption valley period of the power grid; judging whether the time information is in a power consumption peak period or not; if yes, the energy storage module is called to charge the load; if not, the commercial power is called to charge the energy storage module and the load. The method has the effect of reducing the electricity fee cost.
Description
Technical Field
The present application relates to the field of energy storage management systems, and in particular, to a distributed energy storage management method.
Background
With the popularization of electric vehicles, charging stations for charging electric vehicles are rapidly laid.
Currently, related art discloses a charging station, which is generally provided with a charging module including a charging stake and a control box. When the electric automobile is connected with the charging pile for charging, the control box is connected with the charging pile and the commercial power, so that the electric automobile is conveniently charged.
For the above related technologies, the inventor considers that the consumed electric quantity comes from the commercial power when the charging pile charges the electric automobile, thereby increasing the electric charge cost.
Disclosure of Invention
In order to reduce the cost of electricity charge, the application provides a distributed energy storage system and a distributed energy storage management method.
The application provides a distributed energy storage system and a distributed energy storage management method, which adopts the following technical scheme:
the utility model provides a distributed energy storage system, includes cloud ware and distributes in the charging station of different positions, the charging station includes photovoltaic module, energy storage module, control module and charge module, photovoltaic module and energy storage module all have inserted the commercial power, photovoltaic module is used for converting light energy into electric energy, energy storage module is used for storing the electric energy of photovoltaic module and commercial power output, charge module is used for charging the electric energy of commercial power, photovoltaic module and energy storage module output for the load, cloud ware is used for sending control command to control module, control module controls energy storage module charge and discharge according to control command, control module still opens and close charge module and charge for the load according to control command.
Through adopting above-mentioned technical scheme, photovoltaic module converts light energy into electric energy, and the electric energy of photovoltaic module output is through charging module power supply to the load afterwards to reduce the charges of electricity cost. And when the electricity generation speed of the photovoltaic module is greater than the electricity consumption speed of the load, the photovoltaic module stores redundant electric energy in the energy storage module. When the electricity generation speed of the photovoltaic module is smaller than the electricity consumption speed of the load, the energy storage module and the photovoltaic module supply power for the power supply module together, so that the electricity cost is reduced.
A distributed energy storage management method, comprising the following management methods:
load information and power generation information are obtained, wherein the load information is the total power consumption speed of load charging, and the power generation information is the power generation speed of a photovoltaic module;
judging whether the load information is smaller than or equal to the power generation information; if yes, storing the electric energy with the power generation information exceeding the load information into an energy storage module; if not, acquiring time information and interval information, wherein the time information is the current time, and the interval information is a time period corresponding to a power consumption peak period and a power consumption valley period of the power grid;
judging whether the time information is in a power consumption peak period or not; if yes, the energy storage module is called to charge the load; if not, the commercial power is called to charge the energy storage module and the load.
By adopting the technical scheme, the electricity price in the electricity consumption peak period is higher than that in the electricity consumption valley period. And in the power consumption peak period, if the power generation speed of the photovoltaic module is smaller than the power consumption speed of the load, the energy storage module charges the load. And when the electricity consumption period is in the valley period, if the electricity generation speed of the photovoltaic module is smaller than the electricity consumption speed of the load, the commercial power is called to supply power for the load, so that the electricity cost is reduced.
Optionally, after the energy storage module is called to charge the load, the method further comprises the following steps:
acquiring first electric quantity information and first threshold value information, wherein the first electric quantity information is the residual electric quantity of an energy storage module, and the first threshold value information is a preset electric energy reserved quantity;
judging whether the first electric quantity information is larger than first threshold information or not; if yes, returning to acquire the first electric quantity information and the first threshold value information; if not, stopping the energy storage module from charging the load, and transferring to the power supply to charge the load.
Through adopting above-mentioned technical scheme, when energy storage module's electric energy is not enough, call the commercial power and charge for the load to conveniently charge for the load.
Optionally, after judging that the time information is in the electricity consumption valley period and calling the commercial power to charge the energy storage module and the load, the method comprises the following steps:
acquiring a first electric loss ratio K1, wherein the first electric loss ratio K1 is the electric energy loss ratio of charging and discharging of the energy storage module;
acquiring a first electricity price A1 and a second electricity price A2, wherein the first electricity price A1 is the electricity price in the electricity consumption peak period, and the second electricity price A2 is the electricity price in the electricity consumption valley period;
judging whether A2/1-K1 < A1 is satisfied; if yes, keeping the commercial power to charge the energy storage module and the load; if not, only the commercial power is called to charge the load.
Through adopting above-mentioned technical scheme, when the electricity consumption is in the valley period, the commercial power charges to energy storage module, and energy storage module charges to the load again, and partial electric energy can be lost to form first electricity loss ratio K1. A2.2.1-K1 forms the electricity consumption valley period to charge the energy storage module, and the electricity price of the energy storage module when the energy storage module charges the load. When A2/1-K1 is less than A1, the utility power is called to charge the energy storage module, so that the electricity cost is reduced.
Optionally, the cloud server forms charging and discharging information according to charging and discharging of the energy storage module and stores the charging and discharging information to obtain a first electric loss ratio K1, and the method includes the following steps:
acquiring charge and discharge information, wherein the charge and discharge information comprises a stored electric quantity Q1, a charge quantity C, a consumed electric quantity Q2 and a discharge quantity F, the stored electric quantity Q1 is an increase quantity of stored electric energy when an energy storage module is charged, the charge quantity C is an electric quantity actually charged into the energy storage module when the energy storage module is charged, the consumed electric quantity Q2 is a decrease quantity of stored electric energy when the energy storage module is discharged, and the discharge quantity F is an electric quantity actually discharged when the energy storage module is discharged;
obtaining a first electric loss ratio K1, K1=1- (Q1/C) x (F/Q2) according to the charge/discharge information
By adopting the technical scheme, the ratio of Q1 to C is formed into the ratio of charging the energy storage module, and the ratio of F to Q2 is formed into the ratio of discharging the energy storage module. The conversion ratio of charge and discharge of the energy storage module is obtained by (Q1/C) × (F/Q2). The first loss ratio K1 is obtained by 1- (Q1/C) × (F/Q2).
Optionally, after the charge and discharge information is obtained, the method further comprises the following steps:
acquiring a second electric loss ratio K2, wherein the second electric loss ratio K2 is the electric energy loss ratio of charging and discharging of a preset energy storage module;
acquiring statistical information and second threshold information, wherein the statistical information is the duration of statistical charge and discharge information, and the second threshold information is the preset duration;
judging whether the statistical information is smaller than second threshold information or not; if so, acquiring a first electric loss ratio K1 and K1=K2 according to a second electric loss ratio K2; if not, acquiring a first electric loss ratio K1 according to the charge-discharge information
By adopting the technical scheme, after the battery is replaced, the charging and discharging information statistics time is short, and the charging and discharging times of the energy storage module are few. When the statistics time of the charging and discharging information is short, the second electric loss ratio K2 is used as the first electric loss ratio K1, so that whether the commercial power is used for charging the energy storage module is conveniently selected in the electricity consumption valley period.
Optionally, the day is uniformly divided into a plurality of time points, and the total electricity consumption M1 for charging the load and the total electricity generation N1 of the photovoltaic module at different time points every day are stored in the cloud server;
after the utility power is called to charge the energy storage module and the load, the method comprises the following steps:
acquiring a total power consumption average value M2 and a total power generation average value N2 of each time point, wherein the total power consumption average value M2 is an average value of total electric energy consumed by charging a load at the same time point every day, and the total power generation average value N2 is a total power generation average value of a photovoltaic module at the same time point every day;
acquiring total capacity L1, wherein the total capacity L1 is the rated capacity of the energy storage module;
acquiring an electric energy surplus and deficit value P1, P1=M2-N2 according to the total electricity consumption average value M2 and the total electricity generation average value N2;
selecting a minimum electric energy surplus and deficit value P1 to form a demand surplus and deficit value P2;
acquiring a required capacity L2 according to the total capacity L1 and the required excess and deficiency value P2, wherein l2=l1+p2;
acquiring second electric quantity information, wherein the second electric quantity information is the residual electric quantity of the energy storage module;
judging whether the second electric quantity information is smaller than the required capacity L2, if yes, calling the commercial power to charge the energy storage module, and returning to acquire the second electric quantity information; if not, stopping charging the energy storage module.
Through adopting above-mentioned technical scheme, when the electricity consumption valley period charges to energy storage module, stop charging after energy storage module's residual capacity reaches demand capacity L2 to make things convenient for energy storage module to have sufficient space to store the electric energy that photovoltaic module sent at daytime electricity consumption peak period.
Optionally, selecting the minimum electric energy profit and loss value P1 to form the required profit and loss value P2, including the following steps:
the electric energy surplus and deficit value P1 with a positive value is selected to obtain an actual surplus and deficit value P3, and P3=P1×Q2/F;
judging whether an actual profit-loss value P3 larger than the total capacity L1 exists or not; if so, selecting an actual excess and deficiency value P3 which is larger than the total capacity L1 to form an excess and deficiency value P4, and selecting an electric energy excess and deficiency value P1 which is the smallest before each excess and deficiency value P4 at a time point to form a demand excess and deficiency value P2; if not, the minimum electric energy surplus and deficit value P1 is selected to form a demand surplus and deficit value P2.
By adopting the technical scheme, when the electricity generation speed of the photovoltaic module is smaller than the electricity utilization speed of load charging, the energy storage module can charge the load. And when the excess and excess value P4 is larger than the total capacity L1, the electric quantity of the energy storage module is exhausted. The time point is selected to form a demand profit and loss value P2 before each exceeding and loss value P4 and at the minimum electric energy profit and loss value P1, so that the energy storage module can charge the load conveniently.
Optionally, before the total electricity consumption average value M2 and the total electricity production average value N2 of each time point are obtained, the method further includes the following steps:
acquiring first weather information, wherein the first weather information is the weather of a second day, and the first weather information comprises a sunny day or a cloudy day;
judging whether the first weather information is a sunny day or not; if yes, acquiring a total power consumption average value M2 and a total power generation average value N2 of each time point; if not, the utility power is called to be full of the energy storage module, and the utility power is called to be charged for the load.
Through adopting above-mentioned technical scheme, when the weather of the next day is the cloudy day, be full of energy storage module to make things convenient for energy storage module to charge for the load in daytime.
Optionally, the cloud server stores first state information, second weather information and first temperature information of each day in advance, wherein the first state information comprises working days or non-working days, the second weather information comprises sunny days or cloudy days, and the first temperature information is average temperature of the same day;
the method for obtaining the total power consumption average value M2 and the total power generation average value N2 of each time point comprises the following steps of:
selecting the second weather information as the total power consumption M1 and the total power generation N1 on a sunny day to form a first aggregate;
acquiring first day information and second day information, wherein the first day information is the number of days when the total power consumption M1 and the total power generation N1 need to be called, and the second day information is the number of days when the total power consumption M1 and the total power generation N1 in the first aggregate set cross;
judging whether the first day information is larger than the second day information or not; if yes, calculating the total electricity consumption M1 average value of the same time point in the first aggregate to form a total electricity consumption average value M2, and calculating the total electricity production N1 average value of the same time point in the first aggregate to form a total electricity production average value N2; if not, acquiring second state information of the second day, wherein the second state information comprises working days and non-working days;
selecting the total power consumption M1 and the total power generation N1 which are the same as the first state information and the second state information in the first aggregate to form a second aggregate;
acquiring third day information, wherein the third day information is the number of days spanned by the total power consumption M1 and the total power generation N1 in the second combined set;
judging whether the first day information is larger than the third day information or not; if yes, calculating the total electricity consumption M1 average value of the same time point in the second aggregate to form a total electricity consumption average value M2, and calculating the total electricity production N1 average value of the same time point in the second aggregate to form a total electricity production average value N2; if not, acquiring second temperature information of the second day, wherein the second temperature information is the average temperature of the second day;
sequentially selecting the total power consumption M1 and the total power generation N1 corresponding to the first temperature information from small to large according to the difference value of the first temperature information and the second temperature information to form a third combined set, wherein the number of selected days is equal to the first number of days information;
and calculating the total electricity consumption M1 average value of the same time point in the third aggregate to form a total electricity consumption average value M2, and calculating the total electricity production N1 average value of the same time point in the third aggregate to form a total electricity production average value N2.
By adopting the technical scheme, the weather corresponding to the predicted total power consumption M1 and the total power generation N1 and the weather on the next day are sunny days, and the average temperature corresponding to the predicted total power consumption M1 and the total power generation N1 is close to the average temperature on the next day, so that the total power generation average value N2 is improved to reflect the accurate information of the power generation amount of the photovoltaic module on the next day. The first state information and the second state information of the predicted sample total power consumption M1 and the total power generation N1 are the same, so that the accuracy of the total power consumption average value M2 for reflecting the electric energy consumed by load charging in the next day is improved.
In summary, the present application includes at least one of the following beneficial technical effects:
the photovoltaic module is used for converting the light energy into the electric energy to charge the load, and meanwhile, when the electricity generation speed of the photovoltaic module is higher than the electricity consumption speed of the load, the photovoltaic module charges the load, and when the electricity generation speed of the photovoltaic module is lower than the electricity consumption speed of the load, the energy storage module and the photovoltaic module charge the load together, so that the electricity charge cost is reduced;
and in the electricity consumption valley period, the energy storage module is charged by using the commercial power, so that the energy storage module charges a load in the electricity consumption peak period, and the electricity charge cost is further reduced.
Drawings
FIG. 1 is a schematic diagram of a wiring diagram of an embodiment of the present application;
FIG. 2 is a flow chart of steps S1-S12 of an embodiment of the present application;
FIG. 3 is a specific flowchart of step S11 in the embodiment of the present application;
FIG. 4 is a flowchart of steps S12-S19 and steps S28-S31 of an embodiment of the present application;
FIG. 5 is a flowchart of steps S19-S27 of an embodiment of the present application;
FIG. 6 is a flow chart of steps S1901-S1910 and step S20 of an embodiment of the present application;
FIG. 7 is a flow chart of steps S1910-S1912 and step S20 of the embodiments of the present application;
fig. 8 is a specific flowchart of step S22 in the embodiment of the present application.
Reference numerals illustrate: 1. a photovoltaic module; 11. a photovoltaic panel; 12. an inverter; 2. an energy storage module; 21. a battery pack; 22. a current transformer; 3. a control module; 4. a charging module; 41. a control box; 42. and (5) charging the pile.
Detailed Description
The present application is described in further detail below in conjunction with figures 1-8.
The embodiment of the application discloses a distributed energy storage system and a distributed energy storage management method.
Referring to fig. 1, a distributed energy storage system includes a cloud server and charging stations distributed at different locations, the charging stations including a photovoltaic module 1, an energy storage module 2, a control module 3, and a charging module 4.
The photovoltaic module 1 includes a photovoltaic panel 11 and an inverter 12, and the photovoltaic panel 11 is electrically connected to the inverter 12. The photovoltaic panel 11 converts light energy into electric energy, and then inputs the electric energy into the inverter 12, and the inverter 12 performs ac-dc conversion on the electric energy output by the photovoltaic panel 11 and then outputs the electric energy.
The charging module 4 comprises a control box 41 and a plurality of charging piles 42, the charging piles 42 being for connection with a load. In this embodiment, the load is an electric vehicle. The inverter 12 is electrically connected to a control box 41, and the control box 41 is electrically connected to each charging pile 42. After the load is connected to the charging pile 42, the control box 41 connects the charging pile 42 to the inverter 12, so that the inverter 12 inputs electric energy into the charging pile 42, and the charging pile 42 is convenient for supplying power to the load.
The energy storage module 2 includes a battery pack 21 and a current transformer 22, the battery pack 21 is electrically connected to the current transformer 22, and the current transformer 22 is electrically connected to the control box 41 and the inverter 12. The electric energy output by the battery pack 21 is converted by the converter 22 and then input into the charging pile 42 for charging. When the power consumption speed of the load is smaller than the power generation speed of the photovoltaic module 1, the redundant electric energy output by the photovoltaic module 1 is subjected to AC-DC conversion by the inverter 12 and then is charged into the battery pack 21.
The control box 41, the inverter 12 and the converter 22 are all connected with commercial power. When the electricity generation speed of the photovoltaic module 1 is greater than the electricity consumption speed of the load and the energy storage module 2 is full of electricity, the photovoltaic module 1 inputs redundant electric energy into a power grid, so that the waste of electric energy is reduced. When the electricity generation speed of the photovoltaic module 1 is smaller than the electricity consumption speed of the load and the electric quantity of the energy storage module 2 is insufficient, the charging module 4 is used for calling electric energy from the mains supply to charge the load.
The photovoltaic module 1, the energy storage module 2, the control module 3 and the charging module 4 are all in communication connection with the cloud server. Photovoltaic data such as electricity generation speed and electricity generation quantity of the photovoltaic module 1 can be uploaded to the cloud server for storage, the BMS management system is generally arranged in the energy storage module 2, and energy storage data such as charge and discharge quantity of the energy storage module 2 and residual quantity of the energy storage module 2 can be uploaded to the cloud server for storage. Load data such as the load power consumption speed of the charging module 4 is also uploaded to the cloud server.
The cloud server sends a control instruction to the control module 3 according to the photovoltaic data, the energy storage data and the load data, the control module 3 controls the energy storage module 2 to charge and discharge according to the control instruction, and the control module 3 also opens and closes the charging module 4 to charge the load according to the control instruction.
Referring to fig. 2, a distributed energy storage management method, applied to a distributed energy storage system in an embodiment of the present application, includes the following management methods:
s1, load information and power generation information are acquired. Step S2 is then performed.
When the load is connected to the charging module 4 for charging, the charging module 4 sends the power consumption speed of each load charging to the cloud server, and the cloud server adds the power consumption speeds of each load to form load information. The electricity generation speed of the photovoltaic module 1 is sent to the cloud server, so that the cloud server obtains electricity generation information.
S2, judging whether the load information is smaller than or equal to the power generation information; if yes, executing step S3; if not, step S4 is performed.
When the load information is smaller than the power generation information, the power generation speed of the photovoltaic module 1 is enough to consume power of the load; when the load information is greater than the power generation information, the power generation speed of the photovoltaic module 1 cannot satisfy the power consumption speed of the load.
And S3, storing the electric energy with the power generation information exceeding the load information into the energy storage module 2.
When the electricity generation speed of the photovoltaic module 1 is enough to consume the electricity of the load, the cloud server sends a control instruction to the control module 3. The control module 3 is connected with the energy storage module 2 and the photovoltaic module 1 according to the control instruction, so that the photovoltaic module 1 can conveniently charge redundant electric energy into the energy storage module 2. After the energy storage module 2 is fully charged, the cloud server sends a control instruction to the control module 3, and the control module 3 is connected with the photovoltaic module 1 and the mains supply, so that redundant electric energy of the photovoltaic module 1 is sent into a power grid, and the waste of the electric energy is reduced.
S4, acquiring time information and interval information. Step S5 is then performed.
The time information is the current time, and the interval information is a time period corresponding to the electricity consumption peak period and the electricity consumption valley period of the power grid.
S5, judging whether the time information is in a power consumption peak period or not; if yes, executing step S6; if not, step S10 is performed.
S6, the energy storage module 2 is called to charge the load. Step S7 is then performed.
When the electricity generation speed of the photovoltaic module 1 is insufficient to consume electricity of the load and the current time is in the electricity consumption peak period, the cloud server sends a control instruction to the control module 3, and the control module 3 opens the energy storage module 2 to charge the load, so that the electricity cost is reduced.
S7, acquiring first electric quantity information and first threshold information. Step S8 is then performed.
In the process that the energy storage module 2 charges the load, the cloud server obtains the residual electric quantity of the energy storage module 2 to form first electric quantity information. The cloud server is pre-recorded with first threshold information which needs to be reserved by the energy storage module 2.
S8, judging whether the first electric quantity information is larger than first threshold information or not; if yes, returning to the execution step S7; if not, step S9 is performed.
And S9, stopping the energy storage module 2 from charging the load, and transferring to the power supply to charge the load.
When the remaining power of the energy storage module 2 is greater than the first threshold information, the energy storage module 2 still has sufficient power, and the energy storage module 2 continues to charge the load. When the remaining power of the energy storage module 2 is smaller than the first threshold value information, the energy storage module 2 is insufficient in power, and is converted into commercial power to charge a load.
And S10, calling the commercial power to charge the energy storage module 2 and the load. Step S11 is then performed.
When the electricity generation speed of the photovoltaic module 1 is insufficient to consume electricity of the load and the current time is in the electricity consumption low-valley period, the cloud server sends a control instruction to the control module 3, and the control module 3 is connected with the mains supply and the charging module 4, so that the mains supply charges the load. And in the electricity consumption low-valley period, the electricity fee is low in price. The energy of the energy storage module 2 is reserved for charging the load during the peak period of electricity consumption, thereby reducing the electricity fee cost.
Referring to fig. 3, S11, a first loss ratio K1 is acquired. Step S12 is then performed.
The first electric loss ratio K1 is an electric loss ratio of charging and discharging of the energy storage module 2.
Step S11 includes the steps of:
s111, acquiring charging and discharging information. Step S112 is then performed.
The charge-discharge information includes a stored charge amount Q1, a charged amount C, a consumed charge amount Q2, and a discharged amount F. When the energy storage module 2 is charged, the increment of the electric energy stored by the energy storage module 2 is sent to the cloud server for storage, and the cloud server calls the increment of the electric energy stored when the energy storage module 2 is charged recently to form the stored electric quantity Q1. The electric quantity actually charged into the energy storage module 2 is sent to the cloud server for storage, and the cloud server invokes the electric quantity actually charged when the energy storage module 2 is charged to form a charging quantity C.
When the energy storage module 2 discharges, the reduction amount of the electric energy stored by the energy storage module 2 is sent to the cloud server for storage, the cloud server calls the reduction amount of the electric energy stored by the energy storage module 2 when discharging to form consumed electric quantity Q2, the electric quantity actually output by the energy storage module 2 is sent to the cloud server for storage, and the cloud server calls the actual electric quantity discharged by the energy storage module 2 when discharging to form discharge quantity F.
In the embodiment of the application, the cloud server retrieves the stored power Q1, the charge amount C, the consumed power Q2 and the discharge amount F of the day to form the charge-discharge information. After the cloud server transfers the stored electric quantity Q1, the charged quantity C, the consumed electric quantity Q2 and the discharged electric quantity F of the near day to form charging and discharging information, if the stored electric quantity Q1 and the consumed electric quantity Q2 are smaller than the rated capacity of the energy storage module 2, the cloud server can transfer the charging and discharging information of the near two days, and if the charging and discharging information of the near two days is not consistent, the cloud server can transfer the near three days, and the process is repeated until the stored electric quantity Q1 and the consumed electric quantity Q2 are both larger than the rated capacity of the energy storage module 2.
S112, obtaining a second electric loss ratio K2. Step S113 is then performed.
The cloud server presets the electric energy loss ratio of charging and discharging of one energy storage module 2, so that a second electric energy loss ratio K2 is formed.
S113, acquiring statistical information and second threshold information. Step S114 is then performed.
The cloud server counts the charge and discharge time of the energy storage module 2 to form statistical information. The cloud server presets a duration to form second threshold information.
S114, judging whether the statistical information is smaller than the second threshold information; if yes, go to step S115; if not, step S116 is performed.
S115, obtaining a first loss ratio K1, k1=k2 according to the second loss ratio K2. Step S12 is then performed.
S116, acquiring a first electric loss ratio K1 according to the charge and discharge information. Step S12 is then performed.
The calculation formula of the first electric loss ratio K1 is as follows: k1 =1- (Q1 ≡c) × (F ≡q2). (Q1/C) forms the conversion ratio when the energy storage module 2 is charged, and (F/Q2) is the conversion ratio when the energy storage module 2 is discharged. (Q1/C) x (F/Q2) is the conversion ratio of the charge and discharge of the energy storage module. 1- (Q1/C) x (F/Q2) forms a first loss ratio K1.
When part of the energy storage modules 2 are replaced or just installed, the time for storing the charging and discharging information by the cloud server is short. When the statistical information is smaller than the second threshold information, a preset second electric loss ratio K2 is adopted as a first electric loss ratio K1; when the statistical information is greater than the second threshold information, the first electric loss ratio K1 is obtained according to the charging and discharging information, so that the accuracy of the electric loss ratio of the charging and discharging of the energy storage module 2 reflected by the first electric loss ratio K1 is improved.
Referring to fig. 4, S12, a first electricity rate A1 and a second electricity rate A2 are acquired. Step S13 is then performed.
And inputting the electricity prices of the charging station in the electricity consumption peak period and the electricity consumption valley period into the cloud server, wherein the electricity price in the electricity consumption peak period forms a first electricity price A1, and the electricity price in the electricity consumption valley period forms a second electricity price A2.
S13, judging whether A2/1-K1 is less than A1; if yes, go to step S14; if not, step S15 is performed.
And S14, keeping the commercial power to charge the energy storage module 2 and the load. Step S16 is then performed.
S15, only the commercial power is called to charge the load. Step S28 is then performed.
The energy storage module 2 is charged and discharged with loss, and electricity price required by discharging the energy storage module 2 is obtained through A2/1-K1, so that the energy storage module 2 is charged by the mains supply in the electricity consumption valley period. When A2/1-K1 is less than A1, the energy storage module 2 is charged by using the mains supply in the electricity consumption valley period, and the cost of the energy storage module 2 for charging the load is lower than that of the energy storage module 2 for directly charging the load by using the mains supply in the electricity consumption peak period, so that the electricity charge cost is reduced.
S16, acquiring first weather information. Step S17 is then performed.
The first weather information is weather of the second day, and the first weather information comprises sunny days or cloudy days.
S17, judging whether the first weather information is a sunny day or not; if yes, go to step S19; if not, step S18 is performed.
And S18, calling the mains supply to be full of the energy storage module 2, and calling the mains supply to charge the load.
Referring to fig. 5, S19, a total electricity consumption average value M2 and a total electricity generation average value N2 for each time point are acquired. Step S20 is then performed.
In the present example, a day is divided into 24 time points at one hour intervals. And at each time point, the cloud server counts the total electric energy consumed by the load charging on the same day, and the total electric energy generated by the photovoltaic module 1 on the same day. The total electricity consumption M1 for charging the load at different time points every day and the total electricity production N1 of the photovoltaic module 1 are stored in the server, and then the cloud server calculates the average value of the total electricity consumption M1 at the same time point every day to form a total electricity consumption average value M2, and the average value of the total electricity production N1 at the same time point every day to form a total electricity production average value N2.
Meanwhile, the cloud server also stores first state information, second weather information and first temperature information of each day in advance, wherein the first state information comprises working days or non-working days, the second weather information comprises sunny days or cloudy days, and the first temperature information is the average temperature of the same day.
Referring to fig. 6 and 7, step S19 includes the steps of:
s1901, selecting the second weather information as the total electricity consumption M1 and the total electricity production N1 of the sunny day to form a first combined set. Step S1902 is then executed.
And integrating the second weather information corresponding to the total electricity consumption M1 and the total electricity production N1 in the cloud server on a sunny day, so as to form a first aggregate. The energy storage module 2 is generally filled directly on overcast days. The next day is a sunny day, and the electric quantity which needs to be stored by the energy storage module 2 is predicted. The total electricity production mean value N2 of each time point in the next day is predicted by the total electricity production N1 in the sunny day, so that the accuracy of the total electricity production mean value N2 reflecting the total electricity production of each time point of the photovoltaic module 1 in the next day is improved.
S1902, acquiring first day information and second day information. Step S1903 is then performed.
The first day information is the number of days when the total power consumption M1 and the total power generation N1 need to be called, and the second day information is the number of days when the total power consumption M1 and the total power generation N1 in the first combination set cross.
S1903, judging whether the first day information is larger than the second day information; if yes, go to step S1904; if not, step S1905 is performed.
S1904, calculating the average value of the total electricity consumption M1 at the same time point in the first aggregate to form a total electricity consumption average value M2, and calculating the average value of the total electricity production N1 at the same time point in the first aggregate to form a total electricity production average value N2. Step S20 is then performed.
When the first day information is larger than the second day information, the total power consumption M1 and the total power generation N1 in the first aggregate span less days, and the sample size is insufficient, so that the continuous screening is stopped. And then, directly predicting the total power consumption average value M2 and the total power generation average value N2 of different time points on the second day by adopting the total power consumption M1 and the total power generation N1 in the first aggregate, so that the total power consumption average value M2 and the total power generation average value N2 are conveniently calculated.
S1905, obtaining the second status information of the next day. Step S1906 is then performed.
The second status information includes a workday and a non-workday. When the first day information is smaller than the second day information, the total power consumption M1 and the total power generation N1 in the first aggregate set are sufficient in sample. The screening is further carried out on the working day and the non-working day, so that the accuracy of the total power consumption average value M2 for reflecting the total power consumption of the load charging at different time points in the next day is improved.
S1906, selecting the total power consumption M1 and the total power generation N1 with the same first state information and the same second state information in the first aggregate set to form a second aggregate set. Step S1907 is then performed.
And S1907, acquiring third day information. Step S1908 is then performed.
The third day information is the number of days spanned by the total electricity consumption M1 and the total electricity production N1 in the second aggregate.
S1908, judging whether the first day information is larger than the third day information; if yes, go to step S1909; if not, step S1910 is performed.
S1909, calculating the average value of the total electricity consumption M1 at the same time point in the second aggregate to form a total electricity consumption average value M2, and calculating the average value of the total electricity production N1 at the same time point in the second aggregate to form a total electricity production average value N2. Step S20 is then performed.
When the first day number information is larger than the third day number information, the total power consumption M1 and the total power generation N1 in the second aggregate span less days, and the sample size is insufficient, so that the continuous screening is stopped. And then, directly predicting the total power consumption average value M2 and the total power generation average value N2 of different time points on the second day by adopting the total power consumption M1 and the total power generation N1 in the second aggregate, so that the total power consumption average value M2 and the total power generation average value N2 are conveniently calculated.
S1910, acquiring second temperature information of the next day. Step S1911 is then performed.
The second temperature information is the average temperature of the next day. When the first day information is smaller than the third day information, the total power consumption M1 and the total power generation N1 in the second aggregate are sufficient. And the average temperature is further used for screening, so that the accuracy of the total power generation amount of the photovoltaic module 1 at different time points in the next day of the total power generation average value N2 reaction is improved.
S1911, sequentially selecting the total power consumption M1 and the total power generation N1 corresponding to the first temperature information from small to large according to the difference value of the first temperature information and the second temperature information to form a third combined set, wherein the number of selected days is equal to the first number of days information. Step S1912 is then performed.
If the first day number information is three days, selecting three days with the smallest difference between the first temperature information and the second temperature information to form a third aggregate. If the first day number information is ten days, selecting ten days with the smallest difference between the first temperature information and the second temperature information to form a third aggregate.
S1912, calculating the average value of the total electricity consumption M1 at the same time point in the third aggregate to form a total electricity consumption average value M2, and calculating the average value of the total electricity production N1 at the same time point in the third aggregate to form a total electricity production average value N2. Step S20 is then performed.
Referring to fig. 5, S20, the total capacity L1 is acquired. Step S21 is then performed.
The total capacity L1 is the rated capacity of the energy storage module 2.
S21, acquiring an electric energy surplus and deficit value P1 according to the total electricity consumption average value M2 and the total electricity generation average value N2. Step S22 is then performed.
The calculation formula of the electric energy surplus and deficit value P1 is as follows: p1=m2-N2. When the electric energy surplus and deficit value P1 is a positive value, the generated energy of the photovoltaic module 1 is smaller than the power consumption of the load; when the electric energy surplus and deficit value P1 is a negative value, the generated energy of the photovoltaic module 1 is larger than the power consumption of the load.
S22, selecting the minimum electric energy surplus and deficit value P1 to form a demand surplus and deficit value P2. Step S23 is then performed.
Referring to fig. 8, step S22 includes the steps of:
s221, selecting an electric energy surplus and deficit value P1 as a positive value to obtain an actual surplus and deficit value P3. Step S222 is then performed.
The calculation formula of the actual profit and loss value P3 is as follows: p3=p1×q2++f. When the energy storage module 2 discharges, loss exists, the energy storage module 2 discharges an electric energy shortage and shortage value P1 which is a positive value, and the actual reduction amount of the electric energy stored in the energy storage module 2 is an actual shortage and shortage value P3.
S222, judging whether an actual profit-loss value P3 larger than the total capacity L1 exists or not; if yes, go to step S223; if not, step S224 is performed.
S223, selecting an actual excess and deficiency value P3 larger than the total capacity L1 to form an excess and deficiency value P4, and selecting an electric energy excess and deficiency value P1 with the minimum time point before the excess and deficiency value P4 to form a demand excess and deficiency value P2. Step S23 is then performed.
S224, selecting the minimum electric energy profit and loss value P1 to form a demand profit and loss value P2. Step S23 is then performed.
When there is an actual surplus and deficit value P3 greater than the total capacity L1, the electric energy stored in the energy storage module 2 is exhausted. After the energy storage module 2 has been depleted of power, the energy storage module 2, which has a time point after the excess of the limit value P4, has sufficient space for storing the excess power of the photovoltaic module 1.
Before each excess filling defect value P4, a situation may occur in which the energy storage module 2 is full of electricity and cannot be charged when the photovoltaic module 1 charges the energy storage module 2. When the electric energy surplus and deficit value P1 is a negative value, the electricity generation speed of the photovoltaic module 1 is greater than the electricity consumption speed of the load, and the smaller the electric energy surplus and deficit value P1 is, the more electric energy is charged into the energy storage module 2.
Referring to fig. 5, S23 obtains a required capacity L2 from the total capacity L1 and the required excess and deficiency value P2. Step S24 is then performed.
The calculation formula of the required capacity L2 is: l2=l1+p2.
S24, obtaining second electric quantity information. Step S25 is then performed.
The second electric quantity information is the residual electric quantity of the energy storage module 2.
S25, judging whether the second electric quantity information is smaller than the required capacity L2, if so, executing a step S26; if not, step S27 is performed.
S26, the commercial power is called to charge the energy storage module 2, and the step S24 is executed.
And S27, stopping charging the energy storage module 2.
Referring to fig. 4, S28, third threshold information is obtained, where the third threshold information is that the electric loss ratio of charging and discharging of the energy storage module 2 reaches the crippling critical point. Step S29 is then performed.
S29, judging whether the first electric loss ratio K1 is larger than third threshold information; if yes, go to step S30; if not, step S31 is performed.
S30, reminding of replacing the energy storage module 2.
S31, standby.
After the residual electric quantity of the energy storage module 2 reaches the required capacity L2, the energy storage module 2 is stopped from being charged by using the mains supply, so that the energy storage module 2 is convenient for sufficient electric energy charging of the photovoltaic module 1.
The implementation principle of the distributed energy storage system and the distributed energy storage management method in the embodiment of the application is as follows: the photovoltaic module 1 converts light energy into electrical energy to charge a load. When the electricity generation speed of the photovoltaic module 1 is greater than the electricity consumption speed of the load, redundant electric energy of the photovoltaic module 1 is stored in the energy storage module 2. When the electricity generation speed of the photovoltaic module 1 is smaller than the electricity consumption speed of the load, the energy storage module 2 and the photovoltaic module 1 supply power for the load together, so that the electricity fee cost is reduced.
The foregoing are all preferred embodiments of the present application, and are not intended to limit the scope of the present application in any way, therefore: all equivalent changes in structure, shape and principle of this application should be covered in the protection scope of this application.
Claims (7)
1. A distributed energy storage management method is applied to a distributed energy storage system, and is characterized in that: the energy storage system comprises a cloud server and charging stations distributed at different positions, the charging stations comprise a photovoltaic module (1), an energy storage module (2), a control module (3) and a charging module (4), the photovoltaic module (1) and the energy storage module (2) are connected with commercial power, the photovoltaic module (1) is used for converting light energy into electric energy, the energy storage module (2) is used for storing the electric energy output by the photovoltaic module (1) and the commercial power, the charging module (4) is used for charging the commercial power, the electric energy output by the photovoltaic module (1) and the energy storage module (2) into loads, the cloud server is used for sending control instructions to the control module (3), the control module (3) controls the energy storage module (2) to charge and discharge according to the control instructions, and the control module (3) is used for charging the loads by starting and stopping the charging module (4) according to the control instructions;
the method comprises the following management methods:
load information and power generation information are obtained, wherein the load information is the total power consumption speed of load charging, and the power generation information is the power generation speed of the photovoltaic module (1);
judging whether the load information is smaller than or equal to the power generation information; if yes, storing the electric energy with the power generation information exceeding the load information into an energy storage module (2); if not, acquiring time information and interval information, wherein the time information is the current time, and the interval information is a time period corresponding to a power consumption peak period and a power consumption valley period of the power grid;
judging whether the time information is in a power consumption peak period or not; if yes, the energy storage module (2) is called to charge the load; if not, the commercial power is called to charge the energy storage module (2) and the load;
the method comprises the steps that a day is uniformly divided into a plurality of time points, and the total electricity consumption M1 for charging loads at different time points every day and the total electricity generation N1 of the photovoltaic module (1) are stored in a cloud server;
after the utility power is called to charge the energy storage module (2) and the load, the method comprises the following steps:
acquiring a total power consumption average value M2 and a total power generation average value N2 of each time point, wherein the total power consumption average value M2 is an average value of total electric energy consumed by charging a load at the same time point every day, and the total power generation average value N2 is a total power generation average value of a photovoltaic module (1) at the same time point every day;
acquiring total capacity L1, wherein the total capacity L1 is the rated capacity of an energy storage module (2);
acquiring an electric energy surplus and deficit value P1, P1=M2-N2 according to the total electricity consumption average value M2 and the total electricity generation average value N2;
selecting a minimum electric energy surplus and deficit value P1 to form a demand surplus and deficit value P2;
acquiring a required capacity L2 according to the total capacity L1 and the required excess and deficiency value P2, wherein l2=l1+p2;
acquiring second electric quantity information, wherein the second electric quantity information is the residual electric quantity of the energy storage module (2);
judging whether the second electric quantity information is smaller than the required capacity L2, if so, calling the commercial power to charge the energy storage module (2), and returning to acquire the second electric quantity information; if not, stopping charging the energy storage module (2);
the cloud server pre-stores first state information, second weather information and first temperature information of each day, wherein the first state information comprises working days or non-working days, the second weather information comprises sunny days or cloudy days, and the first temperature information is average temperature of the same day;
the method for obtaining the total power consumption average value M2 and the total power generation average value N2 of each time point comprises the following steps of:
selecting the second weather information as the total power consumption M1 and the total power generation N1 on a sunny day to form a first aggregate;
acquiring first day information and second day information, wherein the first day information is the number of days when the total power consumption M1 and the total power generation N1 need to be called, and the second day information is the number of days when the total power consumption M1 and the total power generation N1 in the first aggregate set cross;
judging whether the first day information is larger than the second day information or not; if yes, calculating the total electricity consumption M1 average value of the same time point in the first aggregate to form a total electricity consumption average value M2, and calculating the total electricity production N1 average value of the same time point in the first aggregate to form a total electricity production average value N2; if not, acquiring second state information of the second day, wherein the second state information comprises working days and non-working days;
selecting the total power consumption M1 and the total power generation N1 which are the same as the first state information and the second state information in the first aggregate to form a second aggregate;
acquiring third day information, wherein the third day information is the number of days spanned by the total power consumption M1 and the total power generation N1 in the second combined set;
judging whether the first day information is larger than the third day information or not; if yes, calculating the total electricity consumption M1 average value of the same time point in the second aggregate to form a total electricity consumption average value M2, and calculating the total electricity production N1 average value of the same time point in the second aggregate to form a total electricity production average value N2; if not, acquiring second temperature information of the second day, wherein the second temperature information is the average temperature of the second day;
sequentially selecting the total power consumption M1 and the total power generation N1 corresponding to the first temperature information from small to large according to the difference value of the first temperature information and the second temperature information to form a third combined set, wherein the number of selected days is equal to the first number of days information;
and calculating the total electricity consumption M1 average value of the same time point in the third aggregate to form a total electricity consumption average value M2, and calculating the total electricity production N1 average value of the same time point in the third aggregate to form a total electricity production average value N2.
2. A distributed energy storage management method according to claim 1, characterized in that after the energy storage module (2) is invoked to charge a load, it further comprises the steps of:
acquiring first electric quantity information and first threshold value information, wherein the first electric quantity information is the residual electric quantity of an energy storage module (2), and the first threshold value information is a preset electric energy reserved quantity;
judging whether the first electric quantity information is larger than first threshold information or not; if yes, returning to acquire the first electric quantity information and the first threshold value information; if not, stopping the energy storage module (2) from charging the load, and transferring to the utility power to charge the load.
3. The distributed energy storage management method according to claim 1, wherein after determining that the time information is in a low electricity consumption period, the method comprises the steps of:
acquiring a first electric loss ratio K1, wherein the first electric loss ratio K1 is the electric energy loss ratio of charging and discharging of the energy storage module (2);
acquiring a first electricity price A1 and a second electricity price A2, wherein the first electricity price A1 is the electricity price in the electricity consumption peak period, and the second electricity price A2 is the electricity price in the electricity consumption valley period;
judging whether A2/1-K1 < A1 is satisfied; if yes, keeping the mains supply to charge the energy storage module (2) and the load; if not, only the commercial power is called to charge the load.
4. A distributed energy storage management method according to claim 3, wherein the cloud server forms charge-discharge information according to charge-discharge of the energy storage module (2) and stores the charge-discharge information, and the method is characterized by obtaining a first power loss ratio K1, and comprises the following steps:
acquiring charge and discharge information, wherein the charge and discharge information comprises a stored electric quantity Q1, a charged quantity C, a consumed electric quantity Q2 and a discharged electric quantity F, the stored electric quantity Q1 is an increased quantity of stored electric energy when the energy storage module (2) is charged, the charged quantity C is an electric quantity actually charged into the energy storage module (2) when the energy storage module (2) is charged, the consumed electric quantity Q2 is a reduced quantity of stored electric energy when the energy storage module (2) is discharged, and the discharged electric quantity F is an electric quantity actually discharged when the energy storage module (2) is discharged;
the first loss ratio K1, k1=1- (Q1/C) × (F/Q2) is obtained from the charge/discharge information.
5. The method of claim 4, further comprising the steps of:
acquiring a second electric loss ratio K2, wherein the second electric loss ratio K2 is the electric energy loss ratio of charging and discharging of the preset energy storage module (2);
acquiring statistical information and second threshold information, wherein the statistical information is the duration of statistical charge and discharge information, and the second threshold information is the preset duration;
judging whether the statistical information is smaller than second threshold information or not; if so, acquiring a first electric loss ratio K1 and K1=K2 according to a second electric loss ratio K2; if not, the first electric loss ratio K1 is obtained according to the charge and discharge information.
6. The method as set forth in claim 1, wherein selecting the minimum power shortage and shortage value P1 to form the required shortage and shortage value P2 includes the steps of:
the electric energy surplus and deficit value P1 with a positive value is selected to obtain an actual surplus and deficit value P3, and P3=P1×Q2/F;
judging whether an actual profit-loss value P3 larger than the total capacity L1 exists or not; if so, selecting an actual excess and deficiency value P3 which is larger than the total capacity L1 to form an excess and deficiency value P4, and selecting an electric energy excess and deficiency value P1 which is the smallest before each excess and deficiency value P4 at a time point to form a demand excess and deficiency value P2; if not, the minimum electric energy surplus and deficit value P1 is selected to form a demand surplus and deficit value P2.
7. A distributed energy storage management method according to claim 1, further comprising the steps of, prior to obtaining the total power consumption average value M2 and the total power production average value N2 for each time point:
acquiring first weather information, wherein the first weather information is the weather of a second day, and the first weather information comprises a sunny day or a cloudy day;
judging whether the first weather information is a sunny day or not; if yes, acquiring a total power consumption average value M2 and a total power generation average value N2 of each time point; if not, the utility power is called to be full of the energy storage module (2), and the utility power is called to be charged for the load.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113691006A (en) * | 2021-08-04 | 2021-11-23 | 深圳索瑞德电子有限公司 | Photovoltaic energy storage inverter capable of intelligently distributing electric energy |
| CN114583823A (en) * | 2022-04-01 | 2022-06-03 | 杭州奔晷新能源科技有限公司 | Street lamp power supply management method and device, electronic equipment and storage medium |
| US11705752B1 (en) * | 2022-04-20 | 2023-07-18 | Shenzhen Jindian Electronic Technology Co., Ltd. | Low-voltage direct supply system capable of switching between solar energy and mains supply |
| CN116961204A (en) * | 2023-07-26 | 2023-10-27 | 苏州科盛纬邦新能源智能系统有限公司 | Intelligent photovoltaic commercial power hybrid energy-saving charging control method |
| CN117353454A (en) * | 2023-10-12 | 2024-01-05 | 杭州电力设备制造有限公司 | Power grid user side energy consumption management system and control method thereof |
| CN117498414A (en) * | 2023-11-17 | 2024-02-02 | 崔涛 | System for realizing electricity saving and cost reduction of communication base station by combining photovoltaic and energy storage |
-
2024
- 2024-03-04 CN CN202410242531.5A patent/CN117856314B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113691006A (en) * | 2021-08-04 | 2021-11-23 | 深圳索瑞德电子有限公司 | Photovoltaic energy storage inverter capable of intelligently distributing electric energy |
| CN114583823A (en) * | 2022-04-01 | 2022-06-03 | 杭州奔晷新能源科技有限公司 | Street lamp power supply management method and device, electronic equipment and storage medium |
| US11705752B1 (en) * | 2022-04-20 | 2023-07-18 | Shenzhen Jindian Electronic Technology Co., Ltd. | Low-voltage direct supply system capable of switching between solar energy and mains supply |
| CN116961204A (en) * | 2023-07-26 | 2023-10-27 | 苏州科盛纬邦新能源智能系统有限公司 | Intelligent photovoltaic commercial power hybrid energy-saving charging control method |
| CN117353454A (en) * | 2023-10-12 | 2024-01-05 | 杭州电力设备制造有限公司 | Power grid user side energy consumption management system and control method thereof |
| CN117498414A (en) * | 2023-11-17 | 2024-02-02 | 崔涛 | System for realizing electricity saving and cost reduction of communication base station by combining photovoltaic and energy storage |
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