CN113054649A

CN113054649A - Demand control method, system and storage medium

Info

Publication number: CN113054649A
Application number: CN201911362977.7A
Authority: CN
Inventors: 王又佳; 吴帅; 徐中华
Original assignee: Shanghai Envision Innovation Intelligent Technology Co Ltd; Envision Digital International Pte Ltd
Current assignee: Shanghai Envision Innovation Intelligent Technology Co Ltd; Envision Digital International Pte Ltd
Priority date: 2019-12-26
Filing date: 2019-12-26
Publication date: 2021-06-29
Also published as: WO2021133257A1

Abstract

The embodiment of the application provides a demand control method, a demand control system and a storage medium, and relates to the technical field of electric energy control. The method is applied to a demand control system, and the demand control system comprises a controller, energy storage equipment, a monitoring ammeter and an energy storage cloud platform. The energy storage cloud platform calls a load prediction model to perform load prediction to obtain the predicted demand of the ith charging unit; sending the predicted demand of the ith charging unit to a controller; the monitoring electric meter sends the actual demand of the (i-1) charging units to the controller; the controller determines whether the ith charging unit needs to perform demand control; and if the ith charging unit needs to perform demand control, controlling the energy storage equipment to discharge. According to the technical scheme provided by the embodiment of the application, the accuracy of demand control can be improved.

Description

Demand control method, system and storage medium

Technical Field

The embodiment of the application relates to the technical field of electric energy control, in particular to a demand control method, a demand control system and a storage medium.

Background

For large-scale cities, the capacity of the power distribution network available to the user side is increasingly tight due to the limited capacity of the power distribution network in the urban area and the continuous increase of the total power demand of the user.

In the related art, the electricity fee of commercial electricity is charged in two parts: basic electricity charges and electricity charge. The base electricity rate is based on the monthly maximum demand, which is the average power (in kilowatts) provided by the distribution grid measured over a period of time (e.g., 15 minutes). The electricity consumption rate is collected according to the total electricity consumption amount (unit is kilowatt-hour, commonly called degree) and the unit electricity price in the electricity consumption period, the unit electricity price is higher in the electricity consumption peak period, and the unit electricity price is lower in the electricity consumption valley period. To reduce the basic electricity charge, the skilled person can empirically control the monthly maximum demand, e.g. determine empirically when demand control is required and at what power the energy storage device is discharged during demand control.

In the above related art, the related-art person makes a decision on demand control only based on manual experience, resulting in low accuracy of demand control.

Disclosure of Invention

The embodiment of the application provides a demand control method, a demand control system and a storage medium, and can solve the technical problem of low accuracy of demand control. The technical scheme is as follows:

on one hand, the embodiment of the application provides a demand control method, which is applied to a demand control system, wherein the demand control system comprises a controller, an energy storage device, a monitoring ammeter and an energy storage cloud platform; the method comprises the following steps:

the energy storage cloud platform calls a load prediction model to carry out load prediction, and the predicted demand of the ith charging unit is obtained through historical load data; sending the predicted demand of the ith charging unit to the controller, wherein the charging period comprises n charging units, n is a positive integer, and i is a positive integer smaller than or equal to n;

the monitoring ammeter sends the actual demand of (i-1) charging units to the controller, wherein the (i-1) charging units are the first (i-1) charging units in the charging period;

the controller determines the maximum predicted demand of the ith charging unit according to the predicted demand of the ith charging unit; determining the maximum actual demand of the (i-1) charging units according to the actual demand of the (i-1) charging units; if the maximum predicted demand of the ith charging unit is greater than the maximum actual demand of the (i-1) charging unit, determining that the ith charging unit needs to perform demand control; and after the fact that the ith charging unit needs to perform demand control is determined, controlling the energy storage equipment to discharge.

Optionally, the load prediction model is obtained by:

obtaining at least one group of training samples, wherein each group of training samples comprises predicted load data in a historical time period and historical condition data corresponding to the historical predicted load data, and the historical condition data is used for representing the estimated environmental characteristics in the historical time period;

training the load prediction model by adopting the training samples;

and when the training of the load prediction model meets the training stopping condition, stopping the training of the load prediction model to obtain the trained load prediction model.

Optionally, the stop training condition comprises:

the value of a loss function of the load prediction model is smaller than a first threshold value, and the value of the loss function is obtained according to predicted load data in the historical time period and actual load data in the historical time period;

or,

the training times of the load prediction model are larger than a second threshold value.

Optionally, the method further comprises:

the controller controls the discharge power of the energy storage device to be target power;

the target power is used to control the actual demand of the ith charging unit to be less than or equal to the target demand of the ith charging unit, the actual demand of the ith charging unit refers to the demand generated in the ith charging unit in real time, and the target demand of the ith charging unit refers to the demand threshold of the ith charging unit.

Optionally, the method further comprises:

the controller acquires the maximum discharge power of the energy storage device; calculating the difference value between the maximum predicted demand of the ith charging unit and the maximum discharge power to obtain a first demand difference; and determining the larger value of the first demand difference and the maximum actual demand of the (i-1) charging units as the target demand of the ith charging unit.

Optionally, the method further comprises:

if the actual demand of the ith charging unit is greater than the target demand of the ith charging unit when the target power reaches the maximum discharge power, the controller makes the target demand of the ith charging unit equal to the actual demand of the ith charging unit.

Optionally, the method further comprises:

the controller determines whether the ith charging unit is finished; and if the ith charging unit is finished, determining the maximum actual demand of the i charging units, wherein the i charging units are the first i charging units in the charging period.

Optionally, the method further comprises:

the controller determines the maximum actual demand of the ith charging unit; determining whether the maximum actual demand of the ith charging unit is greater than the maximum actual demand of the (i-1) charging unit; and if the maximum actual demand of the ith charging unit is greater than the maximum actual demand of the (i-1) charging unit, making the maximum actual demand of the i charging units equal to the maximum actual demand of the ith charging unit.

On the other hand, the embodiment of the application provides a demand control system, which comprises a controller, energy storage equipment, a monitoring electric meter and an energy storage cloud platform;

the energy storage cloud platform is used for calling a load prediction model to perform load prediction and obtaining the predicted demand of the ith charging unit through historical load data; sending the predicted demand of the ith charging unit to the controller, wherein i is a positive integer;

the monitoring electric meter is used for sending the actual demand of the (i-1) charging units to the controller;

the controller is used for determining the maximum predicted demand of the ith charging unit according to the predicted demand of the ith charging unit; determining the maximum actual demand of the (i-1) charging units according to the actual demand of the (i-1) charging units; if the maximum predicted demand of the ith charging unit is greater than the maximum actual demand of the (i-1) charging unit, determining that the ith charging unit needs to perform demand control; and after the fact that the ith charging unit needs to perform demand control is determined, controlling the energy storage equipment to discharge.

In yet another aspect, an embodiment of the present application provides a computer-readable storage medium, in which a computer program is stored, and the computer program is loaded and executed by a processor to implement the method steps of the controller side in the demand control method as described above.

The technical scheme provided by the embodiment of the application at least comprises the following beneficial effects:

and (3) carrying out load prediction through a load prediction model to obtain the maximum predicted demand of the ith charging unit, determining whether the ith charging unit needs to carry out demand control or not by combining the maximum actual demand of the (i-1) charging units, and if the ith charging unit needs to carry out demand control, carrying out the demand control by controlling the discharge of the energy storage equipment. Compared with the prior art, the demand control is decided by the related art personnel according to manual experience. In the technical scheme provided by the embodiment of the application, on one hand, the demand control system predicts the demand according to the historical data and judges whether to perform the demand control according to the predicted demand, and how to control the energy storage equipment to perform the demand control, so that the automation of the demand control is realized, and the accuracy of the demand control is improved; on the other hand, the load prediction is carried out by adopting the load prediction model, so that the prediction accuracy is improved, and the accuracy of demand control is further improved.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application.

Drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.

FIG. 1 is a schematic diagram of a demand control system provided by an embodiment of the present application;

FIG. 2 is a flow chart of a demand control method provided by an embodiment of the present application;

FIG. 3 is a flow chart of a demand control method provided in another embodiment of the present application;

FIG. 4 is a graph illustrating a real-time demand, a target demand, and a discharge power of the energy storage system;

FIG. 5 illustrates a flow chart of a demand control method;

fig. 6 is a block diagram of a controller according to an embodiment of the present application.

Detailed Description

Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Rather, they are merely examples of methods consistent with aspects of the present application, as detailed in the appended claims.

First, terms related to embodiments of the present application will be briefly described.

The demand is the average power (unit is kilowatt) provided by the distribution network measured in a measuring period, and is charged according to the maximum demand, namely, the maximum value of the average power of the electricity used by a client is charged in a certain settlement period. Because the instantaneous maximum power is meaningless and difficult to detect, power supply companies often measure the power consumption demand of users by using a "slip" measurement mode. At present, the power supply company adopts the following measurement rule that the smart meter enters a measurement period (15 minutes) every 1 minute. Thus, 1440 measurements per day, the smart meter only accessed the maximum value for the day, and if the value measured the next day was greater than the value recorded the previous day, the smart meter recorded the value that was automatically overwritten with the new value.

An energy storage device is a device that stores energy and can collect and output energy. The stored energy and the output energy of different energy storage devices may be different, for example, the energy storage device using solar energy may convert solar energy into chemical energy first, and then convert the chemical energy into electric energy for output. In the embodiment of the application, the energy storage device can input electric energy and convert the electric energy into energy in other forms (such as chemical energy, potential energy, heat energy and the like) for storage, and according to actual conditions, the stored energy is converted into electric energy to be discharged when needed or at places.

The load prediction means that load data at a certain future moment is determined according to a plurality of factors such as the operating characteristics, capacity increase decision, natural conditions and social influence of a system under the condition of meeting a certain precision requirement, wherein the load refers to the demand or power consumption of electric energy. The method for load prediction application can comprise a unit consumption method, a trend extrapolation method, an elastic coefficient method, a regression analysis method, a time sequence method, a gray model method, a preferred combination prediction method, a wavelet analysis prediction method and the like; the load prediction may be classified into an ultra-short term, a medium term, and a long term according to purposes.

The ultra-short term load prediction refers to load prediction within 1 hour in the future, under a safety monitoring state, a prediction value of 5-10 seconds or 1-5 minutes is generally required, and a prediction value of 10 minutes to 1 hour is generally required for preventive control and emergency state treatment.

The short-term load prediction refers to daily load prediction and weekly load prediction and is respectively used for arranging a daily scheduling plan and a weekly scheduling plan, and comprises the steps of determining unit start and stop, water, fire and electricity coordination, tie line exchange power, load economic distribution, reservoir scheduling, equipment maintenance and the like, wherein the short-term prediction value is closely related to factors such as weather factors, daily types and short-term load changes.

The medium-term load forecast refers to load forecast from month to year, and mainly determines unit operation modes, equipment overhaul plans and the like.

The long-term load prediction refers to load prediction within a period of 3-5 years or even longer in the future, and is mainly a prospective plan of power grid transformation and extension work performed by a power grid planning department according to the development of national economy and the demand on power load. The impact of national economic development, national policy and the like is especially researched for medium and long-term load prediction.

Referring to fig. 1, a schematic diagram of a demand control system according to an embodiment of the present application is shown. As shown in fig. 1, the demand control system 100 may include an energy storage device 110, a controller 120, an energy storage cloud platform 130, and a monitoring meter 140. Wherein:

the energy storage device 110 is used for storing electric energy and discharging the stored electric energy when demand management is required, and the charging power and the discharging power of the energy storage device can be controlled. The energy storage device 110 may send its operating status (e.g., state of charge and state of discharge) and operating parameters (e.g., operating power, operating time, amount of stored power, amount of discharged power, amount of remaining power, etc.) to the controller 120.

The energy storage cloud platform 130 is used for load prediction to obtain a load prediction result. The energy storage cloud platform 130 may send the load prediction result to the controller 120, so that the controller 120 can control the operation state and the operation parameters of the energy storage device 110. The load prediction is a method of predicting load data at a specific time in the future, and the load prediction result indicates the load data at the specific time in the future.

The monitoring meter 140 may be used to monitor power consumption information of the power consuming equipment, such as power consumption time, real-time demand, running equipment, and the like of the power consuming equipment. The electric equipment may be one electric equipment, or may include a plurality of electric equipments. For enterprises, the electric equipment may include production equipment such as machine tools and blast furnaces, and auxiliary production equipment such as lighting and overhead cranes. The demand referred to in the embodiments of the present application is the sum of the demands of the electric devices. The monitoring meter 140 may transmit the power usage condition of the electric device to the controller 120.

The controller 120 is configured to control the operating state and the operating parameters of the energy storage device 110 according to the operating state and the operating parameters of the energy storage device 110, the load prediction result, and the power consumption information of the power consumption device. The controller 120 may control the operating state and the operating parameters of the energy storage device 110 according to the power consumption information and the load prediction result of the power consumption device. The power utilization condition of the electric equipment monitored by the monitoring electric meter 140 may also be transmitted to the energy storage cloud platform 130 through the controller 120, so that the energy storage cloud platform 130 can perform load prediction according to the power utilization condition of the electric equipment.

Referring to fig. 2, a flow chart of a demand control method according to an embodiment of the present application is shown. In this embodiment, the method is mainly applied to the demand control system described in the embodiment of fig. 1 above for illustration. The method may include the steps of:

step 201, the energy storage cloud platform calls a load prediction model to perform load prediction, and obtains the predicted demand of the ith charging unit according to historical load data.

The charging period comprises n charging units, n is a positive integer, and i is a positive integer less than or equal to n.

And paying the basic electric charge once in each charging period, wherein the basic electric charge in each charging period is paid according to the maximum demand generated in the charging period.

Optionally, the n charging units are consecutive time units.

In some possible embodiments, the charging period may be one month, two months, or three months, which is not limited in this application.

In some possible embodiments, a charging unit may be 4 hours, 6 hours, 12 hours, one day, two days, or one week, which is not limited in this application.

In the demand control system, the energy storage cloud platform is used for load prediction to obtain the predicted demand of the ith charging unit. The load prediction is explained in the above noun introduction and will not be described herein. The predicted demand of the ith charging unit refers to the predicted demand of the ith charging unit.

The energy storage cloud platform can call a load prediction model to perform load prediction so as to obtain the predicted demand of the ith charging unit through historical load data. The historical load data is load data of a historical time period, and the historical load data comprises historical actual demand.

Alternatively, the load prediction model may be an LSTM (Long Short Term Memory) model, an RNN (Recurrent Neural Networks) model, a DRN (Deep Residual Networks) model, an ARIMA (automated Integrated Moving Average) model, or the like. The load prediction model may be another model, which is not limited in the embodiments of the present application.

Optionally, before the load prediction model is called to perform load prediction, the historical load data may be preprocessed to remove abnormal data therein, so as to improve the accuracy of the prediction result. In the embodiment of the application, the load prediction can be carried out in a mode of combining short-term prediction (daily prediction) and middle-term prediction (monthly prediction), and the accuracy of the load prediction is improved.

Step 202, the energy storage cloud platform sends the predicted demand of the ith charging unit to the controller.

After obtaining the predicted demand of the ith charging unit, the energy storage cloud platform may send the predicted demand of the ith charging unit to the controller.

Optionally, data may be transmitted between the energy storage cloud platform and the controller through network connection, or data may be transmitted through electrical connection, which is not limited in the embodiment of the present application.

Optionally, the energy storage cloud platform may send the predicted demand of the ith charging unit to the controller before or just before the ith charging unit.

And step 203, the monitoring electric meter sends the actual demand of the (i-1) charging units to the controller.

Wherein, the (i-1) charging units are the first (i-1) charging units of the charging period.

The monitoring electric meter can acquire the electricity utilization information of the electric equipment through monitoring the electric equipment, the electricity utilization information comprises the actual demand of the (i-1) charging units, and then the monitoring electric meter can send the actual demand of the (i-1) charging units to the controller. The actual demand of the (i-1) charging units is the demand actually generated by the (i-1) charging units.

Optionally, data may be transmitted between the monitoring electric meter and the controller through a network connection, or data may be transmitted through an electrical connection, which is not limited in this application.

Optionally, data may be transmitted between the energy storage cloud platform and the monitoring electric meter through network connection, or data may be transmitted through electrical connection, which is not limited in the embodiment of the present application.

And step 204, the controller determines the maximum predicted demand of the ith charging unit according to the predicted demand of the ith charging unit.

The maximum predicted demand of the ith charging unit is the predicted maximum demand of the ith charging unit.

Optionally, the predicted demand of each time period of the ith charging unit may be obtained through load prediction, and then the maximum value of the predicted demand of each time period of the ith charging unit is determined as the maximum predicted demand of the ith charging unit.

Optionally, a predicted demand curve of the ith charging unit may be obtained through load prediction, where the predicted demand curve of the ith charging unit represents a predicted demand change condition of the ith charging unit, and a demand corresponding to a highest point of the predicted demand curve of the ith charging unit is determined as a maximum predicted demand of the ith charging unit.

And step 205, the controller determines the maximum actual demand of the (i-1) charging units according to the actual demand of the (i-1) charging units.

Wherein, the maximum actual demand of (i-1) charging units refers to the maximum value of the demand actually generated by (i-1) charging units.

Alternatively, the controller may obtain the actual demand of each time period of (i-1) charging units, and then determine the maximum value of the actual demand of each time period of (i-1) charging units as the maximum actual demand of (i-1) charging units.

Alternatively, the controller may obtain an actual demand curve of (i-1) charging units, where the actual demand curve of (i-1) charging units represents a change situation of the actual demand of (i-1) charging units, and determine a demand corresponding to a highest point of the actual demand curve of (i-1) charging units as a maximum actual demand of (i-1) charging units.

Alternatively, i is equal to 1, the maximum actual demand of (i-1) charging units may be set to 0.

In step 206, if the maximum predicted demand of the ith charging unit is greater than the maximum actual demand of the (i-1) charging unit, the controller determines that the ith charging unit needs to perform demand control.

After the maximum predicted demand of the ith charging unit and the maximum actual demand of the (i-1) charging unit are obtained, the maximum predicted demand of the ith charging unit and the maximum actual demand of the (i-1) charging unit can be compared, and if the maximum predicted demand of the ith charging unit is larger than the maximum actual demand of the (i-1) charging unit, the controller determines that the ith charging unit needs to perform demand control.

The maximum predicted demand of the ith charging unit is used to represent the maximum demand that the ith charging unit will generate, and based on the maximum predicted demand of the ith charging unit, it can be determined whether the ith charging unit needs to perform demand control.

When the maximum predicted demand of the ith charging unit is greater than the maximum actual demand of the (i-1) charging units, if demand control is not performed, the maximum demand actually generated by the ith charging unit is likely to exceed the originally recorded maximum actual demand of the (i-1) charging units, and then higher basic electric charge may need to be paid in the charging period; that is, in this case, it is necessary to reduce the basic electricity charge by controlling the demand so that the maximum actual demand of the ith charging unit does not exceed the maximum actual demand of the (i-1) charging units as much as possible.

In addition, if the maximum predicted demand of the ith charging unit is less than or equal to the maximum actual demand of the (i-1) charging unit, determining that the ith charging unit does not need to perform demand control.

And step 207, after determining that the ith charging unit needs to perform demand control, controlling the energy storage device to discharge by the controller.

The demand control is carried out by controlling the energy storage equipment, and the demand control can be carried out by controlling the discharge power of the energy storage equipment.

Optionally, when it is determined that the ith charging unit needs to perform demand control, the energy storage device outputs power to fill up power consumed by the electric equipment, so that the generated real-time demand does not exceed the maximum actual demand of the (i-1) charging unit as much as possible.

If the demand control is predicted to be required by the ith charging unit, part of electric quantity can be reserved for the energy storage device to use in the future demand control in the period of time when the demand control is not required but the peak electricity price is reached.

Optionally, the reserved electric quantity may be 15%, 20%, or 25% of the total capacity of the energy storage device, and specifically, the reserved electric quantity is set by a relevant technician according to an actual requirement, which is not limited in this embodiment of the application.

To sum up, in the embodiment of the present application, load prediction is performed through a load prediction model to obtain the maximum predicted demand of the ith charging unit, and whether the ith charging unit needs to perform demand control is determined by combining the maximum actual demand of the (i-1) charging units, and if the ith charging unit needs to perform demand control, the demand control is performed by controlling the discharge of the energy storage device. Compared with the prior art, the demand control is decided by the related art personnel according to manual experience. In the technical scheme provided by the embodiment of the application, on one hand, the demand control system predicts the demand according to the historical data and judges whether to perform the demand control according to the predicted demand, and how to control the energy storage equipment to perform the demand control, so that the automation of the demand control is realized, and the accuracy of the demand control is improved; on the other hand, the load prediction is carried out by adopting the load prediction model, so that the prediction accuracy is improved, and the accuracy of demand control is further improved.

Referring to fig. 3, a flow chart of a demand control method according to an embodiment of the present application is shown. In the present embodiment, the method is mainly exemplified by being applied to the demand control system described above. The method may include the steps of:

step 301, the energy storage cloud platform calls a load prediction model to perform load prediction, and obtains the predicted demand of the ith charging unit according to historical load data.

This step is the same as or similar to the step 201 in the embodiment of fig. 2, and is not described here again.

Alternatively, the load prediction model may be obtained by:

1. at least one set of training samples is obtained.

Each set of training samples includes predicted load data over a historical time period and historical condition data corresponding to the historical predicted load data. The above-mentioned historical time period represents a past time period, and each time period may be spaced at a fixed time. The historical condition data is used for representing environmental characteristics in the estimated historical time period, and the historical condition data may include external temperature (such as minimum temperature, maximum temperature, real-time temperature, and the like), humidity (such as relative humidity, absolute humidity, real-time humidity, and the like), weather type (such as sunny, rain, snow, cloudy, and the like) and other data corresponding to the historical time period.

2. And training the load prediction model by adopting the training samples.

After the training samples are obtained, the training samples can be used for training a load prediction model, and various parameters of the load prediction model are adjusted.

3. And when the training of the load prediction model meets the training stopping condition, stopping the training of the load prediction model to obtain the trained load prediction model.

Optionally, the stop training condition may include: the value of the loss function of the load prediction model is less than a first threshold value; alternatively, the number of training times of the load prediction model is greater than the second threshold.

The value of the loss function is used for representing the difference degree between the predicted load data and the actual load data, and the value of the loss function is obtained according to the predicted load data in the historical time period and the actual load data in the historical time period.

The threshold value may also be referred to as a critical value, and refers to the lowest value or the highest value that an effect can produce. In the present application, the first threshold value is the minimum value of the loss function of the load prediction model when the training stopping condition is satisfied; the second threshold value is the maximum value of the number of times of training of the load prediction model when the training stop condition is satisfied.

The first threshold and the second threshold may be set according to practical experience, and the embodiment of the present application is not limited thereto.

When the value of the loss function is greater than or equal to the first threshold value, the parameters of the load prediction model are adjusted, and the load prediction model is continuously trained until the value of the loss function is less than the first threshold value.

In the embodiment of the application, the load prediction is carried out through the load prediction model, and the load prediction model is obtained by training the predicted load data in the historical time period and the historical condition data corresponding to the historical predicted load data, so that the accuracy of the result of the load prediction is improved, and the efficiency of the load prediction is improved.

And step 302, the energy storage cloud platform sends the predicted demand of the ith charging unit to the controller.

This step is the same as or similar to the step 202 in the embodiment of fig. 2, and is not described here again.

And step 303, the monitoring electric meter sends the actual demand of the (i-1) charging units to the controller.

This step is the same as or similar to the step 203 in the embodiment of fig. 2, and is not described here again.

And step 304, the controller determines the maximum predicted demand of the ith charging unit according to the predicted demand of the ith charging unit.

This step is the same as or similar to the step 204 in the embodiment of fig. 2, and is not described here again.

And 305, the controller determines the maximum actual demand of the (i-1) charging units according to the actual demand of the (i-1) charging units.

This step is the same as or similar to the step 205 in the embodiment of fig. 2, and is not described here again.

Step 306, if the maximum predicted demand of the ith charging unit is greater than the maximum actual demand of the (i-1) charging unit, the controller determines that the ith charging unit needs to perform demand control.

This step is the same as or similar to the step 206 in the embodiment of fig. 2, and is not described here again.

And 307, after the ith charging unit is determined to need to perform demand control, controlling the energy storage device to discharge by the controller.

The target demand of the ith charging unit can be determined first, and then the energy storage device is controlled to discharge according to the target demand of the ith charging unit.

Alternatively, determining the target demand of the ith charging unit may comprise the sub-steps of:

1. the controller acquires the maximum discharge power of the energy storage equipment;

2. calculating the difference value between the maximum predicted demand and the maximum discharge power of the ith charging unit to obtain a first demand difference;

3. and determining the larger value of the first demand difference and the maximum actual demand of the (i-1) charging units as the target demand of the ith charging unit.

Wherein, the maximum discharge power of the energy storage device, i.e. the maximum value of the electric power that the energy storage device can output; the first demand difference is used for representing the minimum value of the predicted maximum demand of the ith charging unit under the condition that the energy storage device is discharged; the target demand of the ith charging unit is the demand threshold of the ith charging unit; the target demand is the set ideal upper limit of the demand, and the demand control aims to control the real-time demand of the ith charging unit not to exceed the set target demand.

The target demand of the ith charging unit can be calculated by the following formula:

MDtarget＝max(MDforecast–Pstorage,MD)

the method comprises the steps of obtaining an MDtarget, an MDforecast and an MD, wherein the MDtarget represents the target demand of the ith charging unit, the MDforecast represents the maximum predicted demand of the ith charging unit, the Pstorage represents the maximum discharge power of the energy storage device, and the MD represents the maximum actual demand of the (i-1) charging units.

The maximum discharge power of a certain energy storage device is also determined. For the demand control of the ith charging unit, the target demand must be greater than or equal to the maximum actual demand of (i-1) charging units, but if the maximum predicted demand of the ith charging unit is too large, so that the demand (i.e. the first demand difference) that needs to be obtained from the power distribution network is still greater than the maximum actual demand of (i-1) charging units when the energy storage device is expected to output electric energy at the maximum power. In this case, it is not appropriate to set the target demand to the maximum actual demand of (i-1) charging units because the maximum actual demand of the charging period is greater than or equal to the first demand difference. Therefore, when the first demand difference is larger than the maximum actual demand of (i-1) charging units, the first demand difference is determined as the target demand.

Optionally, the controlling the energy storage device to discharge includes controlling a discharge power of the energy storage device to be a target power by the controller.

The target power is used for controlling the actual demand of the ith charging unit to be less than or equal to the target demand of the ith charging unit; the actual demand of the ith charging unit refers to the demand generated in real time in the ith charging unit.

Optionally, the minimum value of the target power is a minimum discharge power of the energy storage device.

In some possible embodiments, further determining the minimum discharge power of the energy storage device may comprise the sub-steps of:

1. acquiring real-time power consumption;

2. when the real-time power consumption is larger than the target demand, the real-time power consumption is different from the target demand of the ith charging unit to obtain a second demand difference;

3. determining the second demand difference as the minimum discharge power of the energy storage device when the second demand difference is less than or equal to the maximum discharge power of the energy storage device;

4. and circulating the steps to adjust the minimum discharge power of the energy storage equipment in real time.

The real-time power consumption can be instantaneous power, so that the time for acquiring the real-time power consumption is far shorter than a demand period, appropriate demand control is carried out in time, and the situation that the real-time demand of the ith charging unit exceeds the target demand of the ith charging unit can be avoided as far as possible.

For substep 3 above, the energy storage device may be discharged at its maximum discharge power when the second demand difference is greater than the maximum discharge power of the energy storage device.

In some specific embodiments, for a period when the energy storage residual capacity is large and/or at the peak electricity price, the discharge power of the energy storage device may be greater than the minimum discharge power of the energy storage device; the discharge power of the energy storage device may be equal to the minimum discharge power of the energy storage device when the energy storage remaining capacity is small and/or during the valley price period.

Step 308, if the target power reaches the maximum discharge power, and the actual demand of the ith charging unit is greater than the target demand of the ith charging unit, the controller makes the target demand of the ith charging unit equal to the actual demand of the ith charging unit.

When the real-time demand of the ith charging unit is greater than the target demand of the ith charging unit, the original target demand of the ith charging unit is maintained, and the total electric charge cannot be reduced as much as possible, so that the target demand of the ith charging unit needs to be updated, the generated maximum demand of the charging period is set as the current target demand, and the demand control is continued according to the current target demand.

Step 309, the controller determines whether the ith charging unit is finished; and if the ith charging unit is finished, determining the maximum actual demand of the i charging units.

Wherein, i charging units are the first i charging units of the charging cycle.

Alternatively, the manner for determining whether the ith charging unit is finished may be timer counting, and may also be clock counting. Exemplarily, taking one charging unit as one day, when a timer counts for 24 hours, it indicates that the ith charging unit is finished; or, when the point 24 of the ith charging unit is reached, the ith charging unit is finished.

If the ith charging unit is not finished, the steps are continuously executed for demand control; if the ith charging unit is finished, the energy storage device can stop running.

Determining the maximum actual demand for i charging units may comprise the sub-steps of:

1. the controller determines the maximum actual demand of the ith charging unit;

2. the controller determines whether the maximum actual demand of the ith charging unit is greater than the maximum actual demand of the (i-1) charging unit;

3. and if the maximum actual demand of the ith charging unit is greater than the maximum actual demand of the (i-1) charging units, the controller makes the maximum actual demand of the i charging units equal to the maximum actual demand of the ith charging unit.

The maximum actual demand of the i charging units is determined, and the current maximum demand of the charging period can be determined, so that the current maximum demand of the charging period is used as a reference for demand control of the (i +1) th charging unit.

For the above embodiments, in some alternative embodiments, as shown in fig. 4, a graph of the actual demand, the target demand, and the discharge power of the energy storage device is exemplarily shown. In the time period 410, the energy storage device does not need to perform demand control and can discharge with any power without causing the actual demand to exceed the current target demand; in time period 420, the energy storage device is controlled to discharge so that the implementation power is equal to the current target demand; immediately after the time period 420 is finished, the energy storage device outputs electricity with the maximum discharge power, and the actual demand cannot be below the current target demand line, so that in the time period 430, the target demand line is immediately adjusted, the adjusted target demand is equal to the currently generated maximum demand, then the discharge power of the energy storage device is controlled according to the adjusted target demand, and the demand control is continued; during time period 440, the energy storage device may be discharged at any power without having to calculate a minimum discharge power for the energy storage device.

To sum up, in the embodiment of the present application, when the actual demand of the ith charging unit is greater than the target demand of the ith charging unit, the target demand is adjusted in time, so that the target demand of the ith charging unit is equal to the actual demand of the ith charging unit, and according to the current actual situation, the actual demand is as much as possible not higher than the maximum actual demand generated in the history, so that the demand control method is optimized, and the flexibility of demand control is improved.

The load prediction is carried out through a load prediction model, and the load prediction model is obtained by training predicted load data in a historical time period and historical condition data corresponding to the historical predicted load data, so that the accuracy of a load prediction result is improved, and the efficiency of the load prediction is improved.

Referring to fig. 5, a flow chart of a demand control method is illustrated. As shown in fig. 5, in the present embodiment, the method is mainly exemplified by the controller applied to the demand control system described above. The method may include the steps of:

step 501, determine whether the ith charging unit is the 1 st charging unit of the charging period, and if the ith charging unit is the 1 st charging unit of the charging period, make the maximum actual demand of the (i-1) charging unit equal to 0.

i indicates a specific charging unit of the charging period; the maximum actual demand of (i-1) charging units can be expressed as md (maximum demand).

Step 502, comparing the maximum actual demand of the (i-1) charging units with the maximum predicted demand of the ith charging unit, and if the maximum actual demand of the (i-1) charging units is less than the maximum predicted demand of the ith charging unit, the ith charging unit needs to perform demand control.

The maximum predicted Demand of the ith charging unit may be expressed as mdform (maximum Demand form).

Optionally, if the maximum actual demand of the (i-1) charging units is greater than or equal to the maximum predicted demand of the ith charging unit, the ith charging unit does not need to perform demand control.

Step 503, determine the target demand of the ith charging unit.

If the ith charging unit needs to perform demand control, calculating a difference value between the maximum predicted demand and the maximum discharge power of the ith charging unit to obtain a first demand difference; and determining the larger value of the first demand difference and the maximum actual demand of the (i-1) charging units as the target demand of the ith charging unit.

The target Demand of the ith charging unit may be denoted as mdtarget (maximum Demand target).

And step 504, controlling the demand by controlling the energy storage equipment.

Step 505, if the maximum actual demand of the ith charging unit is still greater than the target demand of the ith charging unit when the energy storage device is discharged with the maximum power, making the target demand of the ith charging unit equal to the maximum actual demand of the ith charging unit.

The actual Demand of the ith charging unit may be denoted as mdnew (maximum Demand new), and the maximum discharge power of the stored energy may be denoted as Pstorage.

Step 506, if the actual demand of the ith charging unit is less than the target demand of the ith charging unit, determining whether the ith charging unit is finished.

Step 507, if the ith charging unit is finished, the maximum actual demand of the i charging units is updated.

Optionally, determining whether the maximum value of the actual demand of the ith charging unit is greater than the maximum actual demand of the (i-1) charging unit, and if the maximum value of the actual demand of the ith charging unit is greater than the maximum actual demand of the (i-1) charging unit, making the maximum actual demand of the i charging units equal to the maximum value of the actual demand of the ith charging unit; and if the maximum value of the actual demand of the ith charging unit is less than or equal to the maximum actual demand of the (i-1) charging units, keeping the maximum actual demand of the i charging units unchanged.

And after the maximum actual demand of the i charging units is updated, the step is ended.

The embodiment of the application also provides another demand control system. As shown in fig. 1, the demand control system includes a controller, an energy storage device, a monitoring meter, and an energy storage cloud platform.

The energy storage cloud platform is used for calling a load prediction model to perform load prediction and obtaining the predicted demand of the ith charging unit through historical load data; and sending the predicted demand of the ith charging unit to the controller, wherein the charging period comprises n charging units, n is a positive integer, and i is a positive integer smaller than or equal to n.

The monitoring ammeter is used for sending the actual demand of (i-1) charging units to the controller, and the (i-1) charging units are the first (i-1) charging units in the charging period.

In an exemplary embodiment, the load prediction model is obtained by:

training the load prediction model by adopting the training samples;

In an exemplary embodiment, the stop training condition includes: the value of a loss function of the load prediction model is smaller than a first threshold value, and the value of the loss function is obtained according to predicted load data in the historical time period and actual load data in the historical time period; or the training times of the load prediction model are larger than a second threshold value.

In an exemplary embodiment, the controller is configured to control the discharge power of the energy storage device to be a target power.

In an exemplary embodiment, the controller is further configured to make the target demand of the ith charging unit equal to the actual demand of the ith charging unit if the actual demand of the ith charging unit is greater than the target demand of the ith charging unit when the target power reaches the maximum discharging power.

In an exemplary embodiment, the controller is further configured to determine whether the ith charging unit is finished; and if the ith charging unit is finished, determining the maximum actual demand of the i charging units, wherein the i charging units are the first i charging units in the charging period.

In an exemplary embodiment, the controller is configured to determine a maximum actual demand of the ith charging unit; determining whether the maximum actual demand of the ith charging unit is greater than the maximum actual demand of the i charging units; and if the maximum actual demand of the ith charging unit is greater than the maximum actual demand of the (i-1) charging unit, making the maximum actual demand of the ith charging unit equal to the maximum actual demand of the ith charging unit.

Fig. 6 is a block diagram of a controller according to an embodiment of the present application. In connection with fig. 6, in particular:

the computer apparatus 600 includes a CPU (Central Processing Unit) 601, a system Memory 604 including a RAM (Random Access Memory) 602 and a ROM (Read-Only Memory) 603, and a system bus 605 connecting the system Memory 604 and the Central Processing Unit 601. The computer device 600 also includes a basic I/O (Input/Output) system 606 to facilitate information transfer between various elements within the computer, and a mass storage device 607 for storing an operating system 613, application programs 614, and other program modules 612.

The basic input/output system 606 includes a display 608 for displaying information and an input device 609 such as a mouse, keyboard, etc. for a user to input information. Wherein the display 608 and the input device 609 are connected to the central processing unit 601 through an input output controller 610 connected to the system bus 605. The basic input/output system 606 may also include an input/output controller 610 for receiving and processing input from a number of other devices, such as a keyboard, mouse, or electronic stylus. Similarly, input/output controller 610 may also provide output to a display screen, a printer, or other type of output device.

The mass storage device 607 is connected to the central processing unit 601 through a mass storage controller (not shown) connected to the system bus 605. The mass storage device 607 and its associated computer-readable media provide non-volatile storage for the computer device 600. That is, the mass storage device 607 may include a computer-readable medium (not shown) such as a hard disk or a CD-ROM (Compact disk Read-Only Memory) drive.

Without loss of generality, the computer-readable media may comprise computer storage media and communication media. Computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Computer storage media includes RAM, ROM, EPROM (Erasable Programmable Read-Only Memory), EEPROM (Electrically Erasable Programmable Read-Only Memory), flash Memory or other solid state Memory technology, CD-ROM, DVD (Digital Video Disc) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices. Of course, those skilled in the art will appreciate that the computer storage media is not limited to the foregoing. The system memory 604 and mass storage device 607 described above may be collectively referred to as memory.

According to various embodiments of the present application, the computer device 600 may also operate as a remote computer connected to a network via a network, such as the Internet. That is, the computer device 600 may be connected to the network 612 through the network interface unit 611 connected to the system bus 605, or may be connected to other types of networks or remote computer systems (not shown) using the network interface unit 611.

In an exemplary embodiment, a computer-readable storage medium is also provided, in which a computer program is stored which, when being executed by a processor, is adapted to carry out the method steps of the controller side of the above-mentioned demand control method.

It should be understood that the step numbers described herein only exemplarily show one possible execution sequence among the steps, and in some other embodiments, the steps may also be executed out of the numbering sequence, for example, two steps with different numbers are executed simultaneously, or two steps with different numbers are executed in a reverse order to the illustration, which is not limited by the embodiment of the present application.

The above description is only exemplary of the present application and should not be taken as limiting the present application, and any modifications, equivalents, improvements and the like that are made within the spirit and principle of the present application should be included in the protection scope of the present application.

Claims

1. The demand control method is characterized by being applied to a demand control system, wherein the demand control system comprises a controller, energy storage equipment, a monitoring ammeter and an energy storage cloud platform;

the energy storage cloud platform calls a load prediction model to carry out load prediction, and the predicted demand of the ith charging unit in the charging period is obtained through historical load data; sending the predicted demand of the ith charging unit to the controller, wherein the charging period comprises n charging units, n is a positive integer, and i is a positive integer smaller than or equal to n;

2. The method of claim 1, wherein the load prediction model is derived by:

training the load prediction model by adopting the training samples;

3. The method of claim 2, wherein the stop training condition comprises:

or,

4. The method according to any one of claims 1 to 3, further comprising:

5. The method of claim 4, further comprising:

6. The method of claim 4, further comprising:

7. The method according to any one of claims 1 to 3, further comprising:

8. The method of claim 7, further comprising:

9. The demand control system is characterized by comprising a controller, energy storage equipment, a monitoring ammeter and an energy storage cloud platform;

10. A computer-readable storage medium, in which a computer program is stored which is loaded and executed by a processor to implement the method steps at the controller side of the demand control method according to any one of claims 1 to 8.