CN114092278A - Energy scheduling method and system for distributed energy storage equipment and storage medium - Google Patents
Energy scheduling method and system for distributed energy storage equipment and storage medium Download PDFInfo
- Publication number
- CN114092278A CN114092278A CN202111388384.5A CN202111388384A CN114092278A CN 114092278 A CN114092278 A CN 114092278A CN 202111388384 A CN202111388384 A CN 202111388384A CN 114092278 A CN114092278 A CN 114092278A
- Authority
- CN
- China
- Prior art keywords
- energy storage
- equipment
- day
- predicted
- power consumption
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000004146 energy storage Methods 0.000 title claims abstract description 311
- 238000000034 method Methods 0.000 title claims abstract description 25
- 238000003860 storage Methods 0.000 title claims abstract description 9
- 230000005611 electricity Effects 0.000 claims abstract description 104
- 238000012545 processing Methods 0.000 claims description 9
- 238000004364 calculation method Methods 0.000 claims description 8
- 230000000694 effects Effects 0.000 description 4
- 230000005284 excitation Effects 0.000 description 3
- 238000003062 neural network model Methods 0.000 description 3
- 238000004458 analytical method Methods 0.000 description 2
- 238000001514 detection method Methods 0.000 description 2
- 238000004519 manufacturing process Methods 0.000 description 2
- 210000002569 neuron Anatomy 0.000 description 2
- 230000002159 abnormal effect Effects 0.000 description 1
- 238000013528 artificial neural network Methods 0.000 description 1
- 230000004888 barrier function Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000009826 distribution Methods 0.000 description 1
- 230000008030 elimination Effects 0.000 description 1
- 238000003379 elimination reaction Methods 0.000 description 1
- 238000012886 linear function Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06315—Needs-based resource requirements planning or analysis
Landscapes
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Engineering & Computer Science (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Entrepreneurship & Innovation (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Marketing (AREA)
- General Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- Tourism & Hospitality (AREA)
- Health & Medical Sciences (AREA)
- Development Economics (AREA)
- Game Theory and Decision Science (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Public Health (AREA)
- Primary Health Care (AREA)
- Water Supply & Treatment (AREA)
- Educational Administration (AREA)
- General Health & Medical Sciences (AREA)
- Supply And Distribution Of Alternating Current (AREA)
- Charge And Discharge Circuits For Batteries Or The Like (AREA)
Abstract
The invention relates to the field of energy storage system scheduling, and particularly discloses a distributed energy storage equipment energy scheduling method, a system and a storage medium, wherein the method comprises the steps of predicting the current consumption, the current consumption on the second day and the electricity price value of the last three days according to an electric equipment group of a user side to obtain the current predicted consumption, the current predicted consumption on the second day and the predicted electricity value of the last three days; acquiring corresponding on-day predicted power consumption, next-day predicted power consumption, predicted electric values of the last three days and the on-day energy storage total amount of the energy storage equipment group; and predicting whether the energy storage equipment group is charged on the same day and the corresponding charging amount according to the predicted power consumption on the same day, the predicted power consumption on the next day, the predicted electric value in the last three days and the total energy storage amount of the energy storage equipment group on the same day. This scheme can carry out accurate judgement to energy storage equipment's charge time and charge volume, realizes carrying out reasonable planning to the produced charges of electricity of user's consumer, and greatly reduced corresponds charges of electricity.
Description
Technical Field
The invention relates to the field of energy storage system scheduling, in particular to a distributed energy storage equipment energy scheduling method, a distributed energy storage equipment energy scheduling system and a storage medium.
Background
Along with the continuous development of society, the consumer that people used is more and more, and corresponding power consumption is also increasing constantly, leads to the charges of electricity that people paid and also increases along with it, simultaneously because the charges of electricity is in the state of constantly fluctuating, the problem that the charges of electricity has the price unstability promptly, this has just caused the charges of electricity that correspond when carrying out the equipment power consumption in different time to be different.
Therefore, on the premise of ensuring the electricity consumption of the user, the reasonable planning is carried out on the electricity charge of all the electricity consumption of the user, and the electricity charge to be paid is reduced, which is a new problem faced by the current customers.
In order to solve the problem, an energy storage device is usually used for storing electricity, when the electricity price is higher, a user reduces the corresponding electricity fee by using the stored electricity in the energy storage device, but the existing energy storage device cannot accurately judge the charging time and the charging amount, so that the electricity fee of electricity consumption generated by the electricity utilization device of the user cannot be reasonably planned, and although the electricity fee is reduced, the reduction effect is not good.
Based on this, a distributed energy storage device energy scheduling method, system and storage medium are needed, which can accurately determine the charging time and charging amount of the energy storage device, realize reasonable planning of the electric charge generated by the electric equipment of the user, and greatly reduce the corresponding electric charge.
Disclosure of Invention
The invention aims to provide a distributed energy storage equipment energy scheduling method, a distributed energy storage equipment energy scheduling system and a storage medium, which can accurately judge the charging time and the charging amount of energy storage equipment, realize reasonable planning of electric charge generated by electric equipment of a user and greatly reduce the corresponding electric charge.
In order to achieve the above object, the technical solution of the present invention provides a method for scheduling energy of distributed energy storage devices, including the following steps:
s100: predicting the current power consumption, the next day power consumption and the electricity price values of the last three days according to the electric equipment group of the user side to obtain the current predicted power consumption, the next day predicted power consumption and the predicted electricity value of the last three days;
s200: acquiring the total energy storage amount of the energy storage equipment group on the same day;
s300: and predicting whether the energy storage equipment group is charged on the same day and the corresponding charging amount according to the predicted power consumption on the same day, the predicted power consumption on the next day, the predicted electric value in the last three days and the total energy storage amount of the energy storage equipment group on the same day.
The principle and the effect of the scheme are as follows: and obtaining the current day predicted power consumption, the next day predicted power consumption and the predicted electric value of the last three days corresponding to the power consumption equipment group of the user through prediction, and simultaneously obtaining the current day energy storage total amount of the corresponding energy storage equipment group. And then, according to the information, namely the predicted power consumption on the same day, the predicted power consumption on the next day, the predicted electric value of the last three days and the total energy storage amount on the same day, whether the energy storage equipment group needs to be charged on the same day and the corresponding charging amount are predicted, and the charging time and the charging amount of the energy storage equipment are accurately judged through various data, so that reasonable charging is carried out at the proper electricity price value at the proper time. Therefore, the reasonable planning of the electric charge generated by the electric equipment is completed, and the corresponding electric charge is greatly reduced.
Further, the S100 includes:
s100-1: predicting the current power consumption and the next-day power consumption of each electronic equipment of the electronic equipment group of the user side and the electricity price values of the last three days to obtain the current predicted power consumption of the user electronic equipment, the second predicted power consumption of the user electronic equipment and the predicted electricity value of the last three days;
s100-2: and correspondingly superposing the current predicted power consumption of the user sub-equipment of each electronic equipment and the second predicted power consumption of the user sub-equipment to obtain the current predicted power consumption and the second predicted power consumption.
Through the day power consumption and the next day power consumption of each electronic equipment, the day power consumption and the next day power consumption of all the user terminal equipment are superposed to obtain the corresponding day predicted power consumption and the next day predicted power consumption, and the day predicted power consumption and the next day predicted power consumption obtained in the way can be more accurate.
Further, the S300 includes:
s300-1: judging the relationship between the current-day energy storage total amount of the energy storage equipment group and the current-day predicted power consumption to obtain a first judgment result according to the current-day predicted power consumption, the current-day predicted power consumption and the current-day energy storage total amount of the energy storage equipment group;
meanwhile, according to the predicted electric values of the last three days, comparing and judging the predicted electric values of the same day, the predicted electric values of the second day and the predicted electric values of the third day to obtain a second judgment result;
s300-2: predicting whether the energy storage equipment group needs to be charged on the same day according to the first judgment result and the second judgment result;
s300-3: and when the prediction result is that the energy storage equipment needs to be charged in the same day, calculating the charging amount of the energy storage equipment group by using the preset maximum total energy storage amount and the total energy storage amount in the same day.
The situation of the total energy storage amount of the energy storage equipment group on the day is known through the three information of the total energy storage amount of the energy storage equipment group on the day, the electricity consumption amount predicted on the next day and the total energy storage amount of the energy storage equipment group on the day, the electricity value predicted on the last three days is known, the electricity price distribution situation of the energy storage equipment group on the day on the last three days is known, and then according to the two judgment results, whether the energy storage equipment group needs to be charged on the day is predicted, and when the energy storage equipment group needs to be charged again, the corresponding charging amount is obtained through calculation. After the initial judgment, whether the energy storage device group needs to be charged or not in the same day is predicted according to the judgment result, so that the whole judgment process is more rigorous, and the obtained judgment result is more accurate.
Further, the method further comprises the step of S400: acquiring the predicted power consumption of the user sub-equipment of each user sub-equipment of the user equipment group, the second predicted power consumption of the user sub-equipment and the energy storage energy of the corresponding energy storage sub-equipment on the same day, and judging whether each energy storage sub-equipment is charged on the same day and the corresponding charging amount;
the S400 further includes the steps of:
s400-1: acquiring the current predicted power consumption of the user sub-equipment of each user sub-equipment of the user equipment group and the second predicted power consumption of the user sub-equipment;
s400-2: matching the energy storage sub-equipment in the energy storage equipment group connected with the user sub-equipment of the corresponding user equipment group, and acquiring the energy stored by the energy storage sub-equipment of the corresponding energy storage sub-equipment on the same day;
s400-3: judging whether the energy storage sub-equipment needs to be charged in the same day according to the obtained predicted electricity consumption of the user sub-equipment in the same day, the second predicted electricity consumption of the user sub-equipment and the energy storage of the energy storage sub-equipment in the same day;
s400-4: when the judgment result is that the energy storage sub-equipment needs to be charged in the same day, calculating the charging amount of the energy storage sub-equipment by using the preset maximum energy storage amount of the energy storage sub-equipment and the total energy storage amount of the energy storage sub-equipment in the same day;
s400-5: and generating a corresponding scheduling report according to the calculation result of each energy storage sub-device.
After the overall judgment of the energy storage equipment group is completed, the charging time and the charging amount of each energy storage sub-equipment are judged, so that each energy storage sub-equipment can be specifically used, after all, the power consumption of each user sub-equipment is different, so that the use of each user sub-equipment can be ensured to normally operate, and meanwhile, the judgment is performed on the user sub-equipment of each user side, so that the electricity charge generated by the electricity consumption of each user sub-equipment can be reasonably reduced, and the condition problem that the electricity charge is unreasonable can be avoided from occurring on a certain user sub-equipment.
Further, in S300-1, according to the current-day predicted power consumption, and the current-day energy storage total amount of the energy storage device group, the relationship between the current-day energy storage total amount of the energy storage device group, the current-day predicted power consumption, and the current-day predicted power consumption is determined, and the determination logic for obtaining the first determination result is:
if the total energy storage amount of the energy storage equipment on the same day is smaller than the predicted electricity consumption amount on the same day, the first judgment result is that the total energy storage amount of the energy storage equipment group on the same day is insufficient;
if the total energy storage amount of the energy storage equipment on the same day is larger than the predicted power consumption amount on the same day but smaller than the sum of the predicted power consumption amount on the same day and the predicted power consumption amount on the next day, the first judgment result indicates that the total energy storage amount of the energy storage equipment group on the same day is enough to be used on the same day but insufficient in energy storage on the next day;
and if the total energy storage amount of the energy storage equipment on the same day is larger than the sum of the predicted power consumption in the same day and the predicted power consumption on the next day, the first judgment result is that the total energy storage amount of the energy storage equipment group on the same day is enough for two days.
Through the relation between the total amount of energy storage of the energy storage equipment group on the day that can be fine and the power consumption of two days last, the total amount of energy storage on the day that can be accurate is analyzed and judged well.
Further, the second judgment result in S300-1 includes three types, which are respectively the predicted electric value of the current day is the minimum value; the predicted electricity value of the current day is not the minimum value, but the predicted electricity value of the current day is smaller than the predicted electricity value of the next day; and the predicted electric value of the current day is larger than the predicted electric value of the next day.
Through the comparison between the electricity prices of three days, the predicted electricity value of the same day can be well positioned, and the accurate analysis of the predicted electricity value of the same day is realized.
The invention also provides a distributed energy storage equipment energy scheduling system, which comprises:
the parameter prediction module is used for predicting the current power consumption, the second power consumption and the electricity price value of the last three days of each electronic equipment of the electronic equipment group of the user side to obtain the current predicted power consumption, the second predicted power consumption and the predicted electricity value of the last three days of the user electronic equipment;
the parameter processing module is used for correspondingly superposing the predicted power consumption of the user sub-equipment of each electronic equipment on the same day and the second predicted power consumption of the user sub-equipment to obtain the predicted power consumption on the same day and the predicted power consumption on the second day;
the first parameter acquisition module is used for acquiring corresponding current predicted power consumption, current predicted power consumption on the second day, predicted electric value of the last three days and current energy storage total amount of the energy storage equipment group;
the first judgment module is used for judging the relation between the current-day energy storage total amount of the energy storage equipment group and the current-day predicted power consumption according to the current-day predicted power consumption, the current-day predicted power consumption and the current-day energy storage total amount of the energy storage equipment group to obtain a first judgment result;
the device is also used for comparing and judging the predicted electric value of the current day, the predicted electric value of the second day and the predicted electric value of the third day according to the predicted electric value of the last three days to obtain a second judgment result;
the charging prediction module is used for predicting whether the energy storage equipment group needs to be charged in the same day according to the first judgment result and the second judgment result;
and the first processing module is used for calculating the charging amount of the energy storage equipment group by utilizing the preset maximum total energy storage amount and the total energy storage amount of the energy storage equipment group in the same day when the prediction result is that the energy storage equipment needs to be charged in the same day.
The principle and the effect of the scheme are as follows: the method comprises the steps of predicting the electricity value of three recent days while predicting the electricity consumption of each user sub-device of an electricity device group of a user side on the same day and the electricity consumption of the next day, obtaining the predicted electricity consumption of the electricity device group on the same day and the predicted electricity consumption of the next day through superposition calculation according to the predicted electricity consumption of each user sub-device on the same day and the predicted electricity consumption of the next day, and then obtaining the total energy storage amount of the energy storage device group on the same day. The above steps complete the acquisition of the front-end data.
And then, analyzing the relationship between the total energy storage amount of the day and the predicted power consumption amount of the day by the predicted power consumption amount of the day, the predicted power consumption amount of the next day and the total energy storage amount of the energy storage equipment group of the day, so that a relative reference object is available for analyzing and judging the total energy storage amount of the day, and the total energy storage amount of the day can be known clearly according to the judgment result obtained at the moment. And through the comparison and judgment among the predicted electric values of the last three days, the judgment on the predicted electric values of the same day is more accurate and is convincing.
And then, combining the judgment results obtained by the two judgments to judge whether the energy storage equipment group needs to be charged in the same day, and then calculating the corresponding charging amount.
This application is through judging the back to the same day energy storage total amount of same day energy storage equipment group and the predicted electric value of same day respectively, come to charge whether the same day energy storage equipment group according to judged result to calculate the corresponding charge volume when needs charge, can carry out accurate judgement to energy storage equipment's charging time and charge volume, realize carrying out reasonable planning to the produced charges of electricity of user's consumer, greatly reduced corresponding charges of electricity.
Further, the method also comprises the following steps:
the second parameter acquisition module is used for acquiring the predicted electricity consumption of the user sub-equipment of each user sub-equipment of the user equipment group, the second predicted electricity consumption of the user sub-equipment and the current energy storage of the energy storage sub-equipment;
the correlation module is used for correlating the predicted power consumption of the user sub-equipment of each user sub-equipment and the second predicted power consumption of the user sub-equipment with the daily energy storage of the energy storage sub-equipment correspondingly connected with the correlation module;
the second judgment module is used for matching the energy storage sub-equipment of the corresponding energy storage sub-equipment with the current-day energy storage according to the obtained current predicted power consumption of the user sub-equipment of the user equipment and the second predicted power consumption of the user sub-equipment, and judging whether the energy storage sub-equipment needs to be charged in the current day;
the second processing module is used for calculating the charging amount of the energy storage sub-equipment by utilizing the preset maximum energy storage amount of the energy storage sub-equipment and the total energy storage amount of the energy storage sub-equipment in the same day when the judgment result indicates that the energy storage sub-equipment needs to be charged in the same day;
and the scheduling report generating module is used for generating a corresponding scheduling report according to the calculation result of each energy storage sub-device.
Whether each energy storage sub-device needs to be charged and the corresponding charging amount in the same day is judged, so that the corresponding electricity charge of each corresponding user sub-device can be greatly reduced while normal operation can be realized when the corresponding user sub-device uses electricity.
The energy scheduling storage medium of the distributed energy storage device is used for storing computer executable instructions, and when the computer executable instructions are executed, the energy scheduling method of the distributed energy storage device is realized.
Drawings
Fig. 1 is a flowchart of a method for scheduling energy of a distributed energy storage device according to an embodiment of the present invention.
Fig. 2 is a logic block diagram of an energy scheduling system of a distributed energy storage device according to an embodiment of the present invention.
Detailed Description
The following is further detailed by way of specific embodiments:
example one
An embodiment substantially as shown in figure 1: a distributed energy storage equipment energy scheduling method comprises the following steps:
s100: and predicting the current consumption, the current consumption on the next day and the electricity price values of the last three days according to the electric equipment group of the user side to obtain the current predicted power consumption, the current predicted power consumption on the next day and the predicted electricity value of the last three days.
The S100 includes:
s100-1: predicting the current power consumption and the next-day power consumption of each electronic equipment of the electronic equipment group of the user side and the electricity price values of the last three days to obtain the current predicted power consumption of the user electronic equipment, the second predicted power consumption of the user electronic equipment and the predicted electricity value of the last three days;
s100-2: and correspondingly superposing the current predicted power consumption of the user sub-equipment of each electronic equipment and the second predicted power consumption of the user sub-equipment to obtain the current predicted power consumption and the second predicted power consumption.
For example, the electric equipment group includes equipment a, equipment B, equipment C, and equipment D, and it is predicted that the current-day power consumption and the next-day power consumption corresponding to each of the electric equipment are respectively: 100 and 100 kilowatt-hours for device a, 50 and 50 kilowatt-hours for device B, 10 and 20 kilowatt-hours for device C, and 200 and 100 kilowatt-hours for device D. The last three days in this embodiment include the current day, the second day and the third day.
The predicted electricity consumption of the obtained electric equipment group is 360 kilowatt hours on the same day, and the predicted electricity consumption is 270 kilowatt hours on the next day.
S200: and acquiring the total energy storage amount of the energy storage equipment group on the same day.
S300: and predicting whether the energy storage equipment group is charged on the same day and the corresponding charging amount according to the predicted power consumption on the same day, the predicted power consumption on the next day, the predicted electric value in the last three days and the total energy storage amount of the energy storage equipment group on the same day.
The S300 includes:
s300-1: judging the relation between the current-day energy storage total amount of the energy storage equipment group, the current-day predicted electricity consumption and the current-day predicted electricity consumption of the energy storage equipment group according to the current-day predicted electricity consumption, the current-day predicted electricity consumption and the current-day energy storage total amount of the energy storage equipment group to obtain a first judgment result;
the second judgment result has the judgment logic: if the total energy storage amount of the energy storage equipment on the same day is smaller than the predicted electricity consumption amount on the same day, the first judgment result is that the total energy storage amount of the energy storage equipment on the same day is insufficient;
if the total energy storage amount of the energy storage equipment on the same day is larger than the predicted power consumption amount on the same day but smaller than the sum of the predicted power consumption amount on the same day and the predicted power consumption amount on the next day, the first judgment result indicates that the total energy storage amount of the energy storage equipment on the same day is enough to be used on the same day but insufficient in energy storage on the next day;
and if the daily energy storage total amount of the daily energy storage equipment is larger than the sum of the daily predicted power consumption and the next day predicted power consumption, the first judgment result is that the daily energy storage total amount of the energy storage equipment is enough to be used for two days.
Meanwhile, according to the predicted electric values of the last three days, comparing and judging the predicted electric values of the same day, the predicted electric values of the second day and the predicted electric values of the third day to obtain a second judgment result;
the second judgment results comprise three types, and the predicted electric value of the current day is the minimum value; the predicted electricity value of the current day is not the minimum value, but the predicted electricity value of the current day is smaller than the predicted electricity value of the next day; and the predicted electric value of the current day is larger than the predicted electric value of the next day.
S300-2: and predicting whether the energy storage equipment group needs to be charged on the same day according to the first judgment result and the second judgment result. In this embodiment, whether the energy storage device group needs to be charged on the same day is predicted through a BP neural network model.
S300-3: and when the prediction result is that the energy storage equipment needs to be charged in the same day, calculating the charging amount of the energy storage equipment group by using the preset maximum total energy storage amount and the total energy storage amount in the same day. For example, when the total amount of energy stored in the day is 360 kilowatt-hours, and the preset maximum total energy stored is 500 kilowatt-hours, the corresponding calculated charging amount of the energy storage device group is 140 kilowatt-hours.
Further comprising S400: acquiring the predicted power consumption of the user sub-equipment of each user sub-equipment of the user equipment group, the second predicted power consumption of the user sub-equipment and the energy storage energy of the corresponding energy storage sub-equipment on the same day, and judging whether each energy storage sub-equipment is charged on the same day and the corresponding charging amount;
the S400 further includes the steps of:
s400-1: and acquiring the current predicted power consumption of the user sub-equipment of each user sub-equipment of the user equipment group and the second predicted power consumption of the user sub-equipment.
S400-2: and matching the energy storage sub-equipment in the energy storage equipment group connected with the user sub-equipment of the corresponding user equipment group, and acquiring the energy storage of the energy storage sub-equipment of the corresponding energy storage sub-equipment on the same day.
S400-3: and judging whether each energy storage sub-device needs to be charged in the same day according to the obtained predicted power consumption of the user sub-device in the same day, the second predicted power consumption of the user sub-device and the energy storage sub-device in the same day.
S400-4: and when the judgment result shows that a certain energy storage sub-device needs to be charged in the same day, calculating the charging amount of the energy storage sub-device by using the preset maximum energy storage amount of the energy storage sub-device and the total energy storage amount of the energy storage sub-device in the same day.
S400-5: and generating a corresponding scheduling report according to the calculation result of each energy storage sub-device.
For example, the predicted power consumption of the user sub-equipment corresponding to the equipment a is 100 kilowatt-hours on the same day, and the second predicted power consumption of the user sub-equipment is 100 kilowatt-hours; if the energy storage sub-device corresponding to the device A has energy storage of 200 kilowatt hour, the energy storage sub-device corresponding to the device A is judged not to be charged in the same day, and if the energy storage sub-device corresponding to the device A has energy storage of 50 kilowatt hour, the energy storage sub-device corresponding to the device A is judged to need to be charged in the same day, and the corresponding charging amount is calculated to be 150 kilowatt hour according to the judgment that the preset maximum energy storage of the energy storage sub-device corresponding to the device A has energy storage of 200 kilowatt hour.
As shown in fig. 2, this embodiment also discloses a distributed energy storage device energy scheduling system, which includes:
and the parameter prediction module is used for predicting the current power consumption and the second-day power consumption of each electronic equipment of the electronic equipment group of the user side and the electricity price values of the last three days to obtain the current predicted power consumption of the user sub-equipment, the second predicted power consumption of the user sub-equipment and the predicted electricity value of the last three days.
And the parameter processing module is used for correspondingly superposing the current predicted power consumption of the user sub-equipment of each electronic equipment and the second predicted power consumption of the user sub-equipment to obtain the current predicted power consumption and the second predicted power consumption.
And the first parameter acquisition module is used for acquiring the predicted power consumption on the corresponding day, the predicted power consumption on the next day, the predicted electric value in the last three days and the total energy storage amount of the energy storage equipment group on the day.
And the first judging module is used for judging the relation between the current-day energy storage total amount of the energy storage equipment group and the current-day predicted power consumption to obtain a first judging result according to the current-day predicted power consumption, the current-day predicted power consumption and the current-day energy storage total amount of the energy storage equipment group.
And the device is also used for comparing and judging the predicted electric value of the current day, the predicted electric value of the second day and the predicted electric value of the third day according to the predicted electric value of the last three days to obtain a second judgment result.
And the charging prediction module is used for predicting whether the energy storage equipment group needs to be charged in the same day according to the first judgment result and the second judgment result. In this embodiment, the charging prediction module is a BP neural network model, specifically, a three-layer BP neural network model is first constructed, and includes an input layer, a hidden layer, and an output layer, in this embodiment, the first determination result and the second determination result are input, so the input layer has 2 nodes, and the output of this model is whether to charge the energy storage device group on the same day, so the output layer has 1 node, and for the hidden layer, this embodiment uses the following formula to determine the number of hidden layer nodes:
where l is the number of nodes of the hidden layer, n is the number of nodes of the input layer, m is the number of nodes of the output layer, and a is a number between 1 and 10, which is taken as 4 in this embodiment, so that the hidden layer has 5 nodes in total. BP neural networks typically employ Sigmoid differentiable functions and linear functions as the excitation function of the network. The S-type tangent function tansig is chosen herein as the excitation function for hidden layer neurons. The prediction model selects an S-shaped logarithmic function tansig as an excitation function of neurons of an output layer.
In another embodiment, the judgment condition of judging whether to store energy can be adjusted according to the condition of the next day, or the judgment condition can be adjusted according to the generation stability of the enterprise.
And the first processing module is used for calculating the charging amount of the energy storage equipment group by utilizing the preset maximum total energy storage amount and the total energy storage amount of the energy storage equipment group in the same day when the prediction result is that the energy storage equipment needs to be charged in the same day.
And the second parameter acquisition module is used for acquiring the predicted electricity consumption of the user sub-equipment of each user sub-equipment of the user equipment group, the second predicted electricity consumption of the user sub-equipment and the current energy storage of the energy storage sub-equipment.
And the association module is used for associating the predicted electricity consumption of the user sub-equipment of each user sub-equipment with the second predicted electricity consumption of the user sub-equipment on the same day and the energy storage sub-equipment of the energy storage sub-equipment correspondingly connected with the association module on the same day. In this embodiment, the predicted power consumption of the user sub-equipment of each user sub-equipment on the same day and the second predicted power consumption of the user sub-equipment are associated with the stored energy of the corresponding energy storage sub-equipment on the same day through the association module, so that the matching is more rapid and effective.
And the second judgment module is used for matching the energy storage sub-equipment corresponding to the energy storage sub-equipment with the current energy storage energy according to the current predicted power consumption and the current predicted user sub-equipment of the user equipment, and judging whether the energy storage sub-equipment needs to be charged in the current day.
And the second processing module is used for calculating the charging amount of the energy storage sub-equipment by utilizing the preset maximum energy storage amount of the energy storage sub-equipment and the total energy storage amount of the energy storage sub-equipment in the day when the judgment result indicates that the energy storage sub-equipment needs to be charged in the day.
And the scheduling report generating module is used for generating a corresponding scheduling report according to the calculation result of each energy storage sub-device.
The energy scheduling storage medium of the distributed energy storage device is used for storing computer executable instructions, and when the computer executable instructions are executed, the energy scheduling method of the distributed energy storage device is realized.
Example two
Compared with the first embodiment, the implementation further comprises an electricity consumption detection module in the same day, and the electricity consumption detection module is used for detecting the actual electricity consumption in the same day;
the third judgment module is used for acquiring the generated actual power consumption and judging the actual power consumption condition according to a preset judgment strategy, wherein the preset judgment strategy is as follows: comparing the obtained actual power consumption with the estimated power consumption, if the difference value between the obtained actual power consumption and the estimated power consumption is larger than a preset threshold value, judging that the actual power consumption exceeds the preset threshold value, otherwise, judging that the actual power consumption is less;
and the first analysis module is used for analyzing whether newly added equipment or corresponding equipment power is increased or not when the actual power consumption exceeds the judgment result, acquiring the power consumption of each equipment if the newly added equipment or the corresponding equipment power is not increased, analyzing the abnormal power consumption equipment, analyzing and judging whether the equipment has faults or not and generating a corresponding judgment report.
The scheme detects the actual electricity consumption of each device on the same day, and sets the electricity consumption condition of each device in advance, namely, the corresponding estimated electricity consumption is preset, and then the actual electricity consumption condition on the same day is judged by comparison. The monitoring to each equipment power consumption condition is realized through the mode, the corresponding fault equipment can be rapidly detected when the equipment breaks down, the time for fault elimination can be greatly reduced, the reason that the current power consumption exceeds the current power consumption can be clearly known through the judgment report generated correspondingly, so that the adjustment later is convenient, meanwhile, in the embodiment, the condition that the actual power consumption is less is realized, the corresponding production equipment can be properly increased according to the actual condition, more efficient production operation is realized, and the electric quantity can be maximally used.
The above are merely examples of the present invention, and the present invention is not limited to the field related to this embodiment, and the common general knowledge of the known specific structures and characteristics in the schemes is not described herein too much, and those skilled in the art can know all the common technical knowledge in the technical field before the application date or the priority date, can know all the prior art in this field, and have the ability to apply the conventional experimental means before this date, and those skilled in the art can combine their own ability to perfect and implement the scheme, and some typical known structures or known methods should not become barriers to the implementation of the present invention by those skilled in the art in light of the teaching provided in the present application. It should be noted that, for those skilled in the art, without departing from the structure of the present invention, several changes and modifications can be made, which should also be regarded as the protection scope of the present invention, and these will not affect the effect of the implementation of the present invention and the practicability of the patent. The scope of the claims of the present application shall be determined by the contents of the claims, and the description of the embodiments and the like in the specification shall be used to explain the contents of the claims.
Claims (9)
1. The energy scheduling method of the distributed energy storage equipment is characterized by comprising the following steps: the method comprises the following steps:
s100: predicting the current power consumption, the next day power consumption and the electricity price values of the last three days according to the electric equipment group of the user side to obtain the current predicted power consumption, the next day predicted power consumption and the predicted electricity value of the last three days;
s200: acquiring the total energy storage amount of the energy storage equipment group on the same day;
s300: and predicting whether the energy storage equipment group is charged on the same day and the corresponding charging amount according to the predicted power consumption on the same day, the predicted power consumption on the next day, the predicted electric value in the last three days and the total energy storage amount of the energy storage equipment group on the same day.
2. The distributed energy storage device energy scheduling method according to claim 1, wherein: the S100 includes:
s100-1: predicting the current power consumption and the next-day power consumption of each electronic equipment of the electronic equipment group of the user side and the electricity price values of the last three days to obtain the current predicted power consumption of the user electronic equipment, the second predicted power consumption of the user electronic equipment and the predicted electricity value of the last three days;
s100-2: and correspondingly superposing the current predicted power consumption of the user sub-equipment of each electronic equipment and the second predicted power consumption of the user sub-equipment to obtain the current predicted power consumption and the second predicted power consumption.
3. The distributed energy storage device energy scheduling method of claim 2, wherein: the S300 includes:
s300-1: judging the relation between the current-day energy storage total amount of the energy storage equipment group, the current-day predicted electricity consumption and the current-day predicted electricity consumption of the energy storage equipment group according to the current-day predicted electricity consumption, the current-day predicted electricity consumption and the current-day energy storage total amount of the energy storage equipment group to obtain a first judgment result;
meanwhile, according to the predicted electric values of the last three days, comparing and judging the predicted electric values of the same day, the predicted electric values of the second day and the predicted electric values of the third day to obtain a second judgment result;
s300-2: predicting whether the energy storage equipment group needs to be charged on the same day according to the first judgment result and the second judgment result;
s300-3: and when the prediction result is that the energy storage equipment needs to be charged in the same day, calculating the charging amount of the energy storage equipment group by using the preset maximum total energy storage amount and the total energy storage amount in the same day.
4. The distributed energy storage device energy scheduling method of claim 3, wherein: further comprising S400: acquiring the predicted power consumption of the user sub-equipment of each user sub-equipment of the user equipment group, the second predicted power consumption of the user sub-equipment and the energy storage energy of the corresponding energy storage sub-equipment on the same day, and judging whether each energy storage sub-equipment is charged on the same day and the corresponding charging amount;
the S400 further includes the steps of:
s400-1: acquiring the current predicted power consumption of the user sub-equipment of each user sub-equipment of the user equipment group and the second predicted power consumption of the user sub-equipment;
s400-2: matching the energy storage sub-equipment in the energy storage equipment group connected with the user sub-equipment of the corresponding user equipment group, and acquiring the energy stored by the energy storage sub-equipment of the corresponding energy storage sub-equipment on the same day;
s400-3: according to the obtained predicted electricity consumption of the user sub-equipment on the same day, the second predicted electricity consumption of the user sub-equipment and the stored energy of the energy storage sub-equipment on the same day, whether each energy storage sub-equipment needs to be charged on the same day is judged;
s400-4: when the judgment result is that a certain energy storage sub-device needs to be charged in the same day, calculating the charging amount of the energy storage sub-device by using the preset maximum energy storage amount of the energy storage sub-device and the total energy storage amount of the energy storage sub-device in the same day;
s400-5: and generating a corresponding scheduling report according to the calculation result of each energy storage sub-device.
5. The distributed energy storage device energy scheduling method according to claim 4, wherein: in the S300-1, according to the current-day predicted power consumption, and the current-day energy storage total amount of the energy storage device group, a relationship between the current-day energy storage total amount of the energy storage device group, the current-day predicted power consumption, and the current-day predicted power consumption is determined, and a determination logic of a first determination result is:
if the total energy storage amount of the energy storage equipment on the same day is smaller than the predicted electricity consumption amount on the same day, the first judgment result is that the total energy storage amount of the energy storage equipment on the same day is insufficient;
if the total energy storage amount of the energy storage equipment on the same day is larger than the predicted power consumption amount on the same day but smaller than the sum of the predicted power consumption amount on the same day and the predicted power consumption amount on the next day, the first judgment result indicates that the total energy storage amount of the energy storage equipment on the same day is enough to be used on the same day but insufficient in energy storage on the next day;
and if the total energy storage amount of the energy storage equipment on the same day is larger than the sum of the predicted power consumption in the same day and the predicted power consumption on the next day, the first judgment result is that the total energy storage amount of the energy storage equipment on the same day is enough for two days.
6. The distributed energy storage device energy scheduling method according to claim 5, wherein: the second judgment results in the S300-1 include three types, and the predicted electric value of the current day is the minimum value; the predicted electricity value of the current day is not the minimum value, but the predicted electricity value of the current day is smaller than the predicted electricity value of the next day; and the predicted electric value of the current day is larger than the predicted electric value of the next day.
7. Distributed energy storage equipment energy scheduling system, its characterized in that includes:
the parameter prediction module is used for predicting the current power consumption, the second power consumption and the electricity price value of the last three days of each electronic equipment of the electronic equipment group of the user side to obtain the current predicted power consumption, the second predicted power consumption and the predicted electricity value of the last three days of the user electronic equipment;
the parameter processing module is used for correspondingly superposing the current predicted power consumption of the user sub-equipment of each electronic equipment and the second predicted power consumption of the user sub-equipment to obtain the current predicted power consumption and the second predicted power consumption;
the first parameter acquisition module is used for acquiring corresponding predicted power consumption on the same day, predicted power consumption on the next day, predicted electric value of the last three days and total energy storage amount of the energy storage equipment group on the same day;
the first judgment module is used for judging the relation between the current-day energy storage total amount of the energy storage equipment group and the current-day predicted power consumption according to the current-day predicted power consumption, the current-day predicted power consumption and the current-day energy storage total amount of the energy storage equipment group to obtain a first judgment result;
the device is also used for comparing and judging the predicted electric value of the current day, the predicted electric value of the second day and the predicted electric value of the third day according to the predicted electric value of the last three days to obtain a second judgment result;
the charging prediction module is used for predicting whether the energy storage equipment group needs to be charged in the same day according to the first judgment result and the second judgment result;
and the first processing module is used for calculating the charging amount of the energy storage equipment group by utilizing the preset maximum total energy storage amount and the total energy storage amount of the energy storage equipment group in the same day when the prediction result is that the energy storage equipment needs to be charged in the same day.
8. The distributed energy storage device energy scheduling system of claim 7, further comprising:
the second parameter acquisition module is used for acquiring the predicted electricity consumption of the user sub-equipment of each user sub-equipment of the user equipment group, the second predicted electricity consumption of the user sub-equipment and the current energy storage of the energy storage sub-equipment;
the correlation module is used for correlating the predicted electricity consumption of the user sub-equipment of each user sub-equipment and the second predicted electricity consumption of the user sub-equipment with the daily energy storage of the energy storage sub-equipment correspondingly connected with the user sub-equipment;
the second judgment module is used for matching the energy storage sub-equipment of the corresponding energy storage sub-equipment with the current-day energy storage according to the predicted power consumption of the current-day user sub-equipment of the user equipment and the current-day energy storage of the user sub-equipment, and judging whether the current-day energy storage sub-equipment needs to be charged;
the second processing module is used for calculating the charging amount of the energy storage sub-equipment by utilizing the preset maximum energy storage amount of the energy storage sub-equipment and the total energy storage amount of the energy storage sub-equipment in the same day when the judgment result indicates that the energy storage sub-equipment needs to be charged in the same day;
and the scheduling report generating module is used for generating a corresponding scheduling report according to the calculation result of each energy storage sub-device.
9. A distributed energy storage device energy scheduling storage medium for storing computer executable instructions, characterized in that: the computer-executable instructions, when executed, implement the distributed energy storage device energy scheduling method of any of claims 1 to 7 above.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111388384.5A CN114092278A (en) | 2021-11-22 | 2021-11-22 | Energy scheduling method and system for distributed energy storage equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111388384.5A CN114092278A (en) | 2021-11-22 | 2021-11-22 | Energy scheduling method and system for distributed energy storage equipment and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114092278A true CN114092278A (en) | 2022-02-25 |
Family
ID=80302871
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111388384.5A Pending CN114092278A (en) | 2021-11-22 | 2021-11-22 | Energy scheduling method and system for distributed energy storage equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114092278A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115841191A (en) * | 2023-02-15 | 2023-03-24 | 广东南海电力设计院工程有限公司 | Energy storage device optimization method and system |
| CN117728566A (en) * | 2023-12-05 | 2024-03-19 | 国网安徽省电力有限公司黄山供电公司 | Mobile energy storage remote control method and system |
-
2021
- 2021-11-22 CN CN202111388384.5A patent/CN114092278A/en active Pending
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115841191A (en) * | 2023-02-15 | 2023-03-24 | 广东南海电力设计院工程有限公司 | Energy storage device optimization method and system |
| CN115841191B (en) * | 2023-02-15 | 2023-05-30 | 广东南海电力设计院工程有限公司 | Energy storage device optimization method and system |
| CN117728566A (en) * | 2023-12-05 | 2024-03-19 | 国网安徽省电力有限公司黄山供电公司 | Mobile energy storage remote control method and system |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Zheng et al. | Aggregation model-based optimization for electric vehicle charging strategy | |
| CN108470233B (en) | Demand response capability assessment method and computing device for smart power grid | |
| CN114092278A (en) | Energy scheduling method and system for distributed energy storage equipment and storage medium | |
| CN114372417A (en) | Electric vehicle battery health state and remaining life evaluation method based on charging network | |
| US20130158725A1 (en) | Adaptive stochastic controller for distributed electrical energy storage management | |
| CN116646965B (en) | Photovoltaic energy storage charging and discharging integrated management system | |
| CN113269372A (en) | Cluster electric vehicle schedulable capacity prediction evaluation method considering user will | |
| CN107565585A (en) | Energy storage device peak regulation report-back time Forecasting Methodology and its model creation method | |
| CN115481905A (en) | Power grid power demand response method participated by user and related device | |
| Lin et al. | Deep Q-learning network based trip pattern adaptive battery longevity-conscious strategy of plug-in fuel cell hybrid electric vehicle | |
| CN115130727A (en) | Night charging scheduling method for new-energy pure-electric bus | |
| CN110738368A (en) | electric energy declaration deviation amount income recovery calculation method and system | |
| CN116923168A (en) | Charging pile electric energy dispatching system and dispatching method based on transformer substation networking | |
| CN114140176B (en) | Adjustable capacity prediction method and device for load aggregation platform | |
| Coria et al. | Computational efficient approach to compute a prediction-of-use tariff for coordinating charging of plug-in electric vehicles under uncertainty | |
| Khwaja et al. | Performance analysis of LSTMs for daily individual EV charging behavior prediction | |
| CN112766579B (en) | Charging system planning method and device based on adaptive algorithm | |
| CN113255957A (en) | Quantitative optimization analysis method and system for uncertain factors of comprehensive service station | |
| Englberger | Optimized energy management for battery energy storage via multi-use and multi-storage operation | |
| Dyo | Behavior-Neutral Smart Charging of Plugin Electric Vehicles: Reinforcement Learning Approach | |
| Zhao et al. | Hierarchical Deep Learning Model for Degradation Prediction per Look-Ahead Scheduled Battery Usage Profile | |
| CN110298715A (en) | A kind of energy transaction system and method based on distributed energy storage | |
| CN113408886B (en) | Energy storage capacity configuration method and device | |
| CN115528687B (en) | Power system flexible response capability optimization method under limited cost constraint | |
| CN115860163B (en) | New energy power generation deviation evaluation method and system based on system operation index |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |