CN111126703A - Method and device for predicting maximum power consumption demand of enterprise - Google Patents
Method and device for predicting maximum power consumption demand of enterprise Download PDFInfo
- Publication number
- CN111126703A CN111126703A CN201911362287.1A CN201911362287A CN111126703A CN 111126703 A CN111126703 A CN 111126703A CN 201911362287 A CN201911362287 A CN 201911362287A CN 111126703 A CN111126703 A CN 111126703A
- Authority
- CN
- China
- Prior art keywords
- month
- maximum
- enterprise
- demand
- electricity
- 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
- 238000000034 method Methods 0.000 title claims abstract description 51
- 230000005611 electricity Effects 0.000 claims abstract description 143
- 230000006870 function Effects 0.000 claims description 19
- 238000004590 computer program Methods 0.000 claims description 17
- 230000035945 sensitivity Effects 0.000 claims description 17
- 238000003860 storage Methods 0.000 claims description 7
- 238000012512 characterization method Methods 0.000 claims 1
- 238000004364 calculation method Methods 0.000 abstract description 18
- 238000010586 diagram Methods 0.000 description 11
- 230000000694 effects Effects 0.000 description 2
- 238000013277 forecasting method Methods 0.000 description 2
- 238000005286 illumination Methods 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 230000002159 abnormal effect Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000009826 distribution Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 239000002699 waste material Substances 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
- 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
- G06Q10/0637—Strategic management or analysis, e.g. setting a goal or target of an organisation; Planning actions based on goals; Analysis or evaluation of effectiveness of goals
- G06Q10/06375—Prediction of business process outcome or impact based on a proposed change
-
- 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
-
- 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
Landscapes
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Engineering & Computer Science (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Entrepreneurship & Innovation (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Marketing (AREA)
- General Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- Tourism & Hospitality (AREA)
- Game Theory and Decision Science (AREA)
- Educational Administration (AREA)
- Development Economics (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Health & Medical Sciences (AREA)
- Water Supply & Treatment (AREA)
- Primary Health Care (AREA)
- Public Health (AREA)
- General Health & Medical Sciences (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention is suitable for the technical field of power application, and provides a method and a device for predicting the maximum demand of power consumption of an enterprise, wherein the method comprises the following steps: calculating and obtaining the sum of the maximum power consumption demand of all power consumption outlet loads of the enterprise in the forecast month; and acquiring the maximum electricity utilization simultaneous coefficient of the enterprise in the reference month, and determining the monthly electricity utilization maximum demand of the enterprise in the forecast month based on the product of the maximum electricity utilization simultaneous coefficient and the sum of the maximum electricity utilization demands of all electricity outlet line loads of the enterprise in the forecast month. According to the method for predicting the maximum power consumption demand of the enterprise, the occupation ratio of each outlet load in the maximum power consumption demand of the enterprise in the month power consumption calculation prediction of the month is taken into consideration by referring to the month maximum power consumption simultaneous coefficient, so that the condition that the calculation result of the month power consumption maximum demand of the enterprise in the month is greatly subjected to error due to the fact that each outlet load comprises various different loads is avoided, and the prediction accuracy is improved.
Description
Technical Field
The invention belongs to the technical field of electric power application, and particularly relates to a method and a device for predicting maximum power consumption demand of an enterprise.
Background
In industrial electricity, the cost of electricity consumption for enterprises is a huge expense. According to the current regulation of general industrial and commercial electricity prices, enterprises can choose to pay electricity charges according to the maximum demand or the capacity of a transformer according to own needs, and the charging modes need to be reported to a power supply department between the beginning of each month, so that the actual paid charges have larger gaps due to the selection of different charging modes. At present, enterprises hope to have a scheme which can reasonably predict the power consumption of the enterprises and guide the enterprises to select the optimal electric charge pricing mode so as to achieve the purpose of saving electric charge expenditure.
Disclosure of Invention
In view of this, the embodiment of the invention provides a method and a device for predicting the maximum power consumption demand of an enterprise, so as to solve the problem that it is difficult to confirm which electric charge pricing scheme is declared most economical in the existing enterprise power consumption process.
The first aspect of the embodiment of the invention provides a method for predicting the maximum power consumption demand of an enterprise, which comprises the following steps: calculating and obtaining the sum of the maximum power consumption demand of all power consumption outlet loads of the enterprise in the forecast month; acquiring a maximum power utilization simultaneous coefficient of a maximum power utilization demand of an enterprise of a reference month, wherein the reference month comprises a month which is the same as the month number of the forecast month or a previous month adjacent to the forecast month in at least one previous year; and determining to obtain the enterprise monthly electricity maximum demand of the forecast month based on the product of the maximum electricity utilization simultaneous coefficient and the sum of the maximum electricity utilization demand of all electricity outlet line loads of the forecast month enterprise.
In some optional embodiments, after the step of determining the maximum monthly power demand of the enterprise for the forecast month, the method further comprises the steps of: and comparing the total amount of the electric charges of the maximum monthly electricity demand of the enterprise under at least two preset electric charge pricing schemes, and taking the electric charge pricing scheme with the minimum total amount of the electric charges as a target scheme of the forecast month.
In some optional embodiments, the comparing the total amount of the electric charges of the maximum monthly electricity demand of the enterprise under at least two preset electric charge pricing schemes, and using the electric charge pricing scheme with the minimum total amount of the electric charges as the target scheme of the forecast month includes the following steps: determining a receiving capacity of a power consumer; comparing the maximum monthly power demand of the enterprise with the specified percentage of the receiving capacity to determine the maximum demand value for calculating the basic power charge; calculating a first total amount of electricity charges based on the maximum demand value and a first electricity charge pricing unit price when the basic electricity charges are paid according to the electricity receiving capacity; calculating a second electric charge total amount based on the maximum demand value and a second electric charge pricing unit price when the basic electric charge is paid according to the maximum demand; and comparing the first electric charge total amount with the second electric charge total amount, and determining the electric charge pricing scheme corresponding to the minimum electric charge total amount as a target scheme.
In some optional embodiments, the comparing the maximum monthly power demand of the enterprise with the specified percentage of the receiving capacity to determine the maximum demand value for calculating the basic power charge includes the following steps: judging the maximum monthly power demand of the enterprise and the size of the specified percentage of the power receiving capacity; if the maximum monthly power demand of the enterprise is larger than the specified percentage of the receiving capacity, determining the maximum monthly power demand of the enterprise as a maximum demand value; and if the maximum monthly power demand of the enterprise is less than the specified percentage of the receiving capacity, determining that the product of the receiving capacity of the enterprise and the specified percentage is the maximum demand value.
In some optional embodiments, the obtaining the maximum power consumption simultaneous coefficient of the enterprise with reference to the month includes the following steps: acquiring the maximum power consumption demand of an enterprise with a reference month; calculating the sum of the maximum power consumption of each outlet wire of the enterprise; and determining the maximum electricity utilization simultaneous coefficient of the enterprise in the reference month based on the ratio of the maximum electricity utilization demand of the enterprise to the sum of the maximum electricity utilization demands of the outgoing lines.
In some optional embodiments, the calculating and obtaining the sum of the maximum power demand for predicting all power outlet loads of the monthly enterprise comprises the following steps: determining a forecast month, and acquiring meteorological data and product yield data of the forecast month. And selecting a reference month of the predicted month, and acquiring meteorological data, product yield data and power utilization data of the reference month. And constructing a characteristic function of the enterprise power utilization outlet load according to a load sensitivity coefficient set based on the load characteristics of each power utilization outlet load of the enterprise, the meteorological data and the product yield data of the forecast month, and the meteorological data and the product yield data of the reference month, and calculating the sum of the maximum power utilization demand of all the power utilization outlet loads of the forecast month enterprise by adopting the characteristic function and the power utilization data of the reference month.
A second aspect of the embodiments of the present invention provides an enterprise maximum power demand prediction apparatus, including: the outlet load electricity utilization prediction module is configured to calculate and obtain the sum of the maximum electricity utilization demand of all electricity outlet loads of the enterprise in the forecast month; the power utilization simultaneous coefficient determining module is configured to obtain a maximum power utilization simultaneous coefficient of the maximum power utilization demand of the enterprise in a reference month, wherein the reference month comprises a month which is the same as the month of the forecast month or a last month adjacent to the forecast month in at least one previous year; and the maximum electricity utilization prediction module is configured to determine the maximum monthly electricity utilization demand of the enterprise in the predicted month based on the product of the maximum electricity utilization simultaneous coefficient and the sum of the maximum electricity utilization demands of all electricity outlet line loads of the predicted month enterprise.
A third aspect of the embodiments of the present invention provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor executes the computer program to implement the steps of the enterprise maximum electricity demand prediction method according to any one of the first aspect.
A fourth aspect of the embodiments of the present invention provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the computer program implements the steps of the method for predicting the maximum power demand of an enterprise according to any one of the first aspect.
Compared with the prior art, the embodiment of the invention has the following beneficial effects: according to the enterprise maximum power consumption demand forecasting method, in the calculation and forecasting of the enterprise monthly power consumption maximum demand of the forecast month, the occupation ratio of each outlet load in the power consumption maximum demand is taken into consideration by referring to the maximum power consumption simultaneous coefficient, so that the condition that the calculation result of the enterprise monthly power consumption maximum demand of the forecast month has a large error due to the fact that each outlet load comprises various different loads is avoided, and the forecasting accuracy is improved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed for the embodiments or the prior art descriptions will be briefly described 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 without creative efforts.
Fig. 1 shows a flowchart of an embodiment of a method for predicting maximum power demand of an enterprise provided by the present application.
Fig. 2 is a flowchart illustrating another embodiment of a method for predicting the maximum power demand of an enterprise provided by the present application.
Fig. 3 shows a flow chart of an embodiment of step S201 in the embodiment shown in fig. 2.
Fig. 4 shows a flowchart of an embodiment of determining a maximum demand value for calculating a base electricity rate provided by the present application.
Fig. 5 shows a flowchart of an embodiment of step S102 in the embodiment shown in fig. 1.
Fig. 6 shows a flow chart of an embodiment of step S101 in the embodiment shown in fig. 1.
Fig. 7 shows a schematic structural diagram of an embodiment of the maximum power demand prediction apparatus for an enterprise provided by the present application.
Fig. 8 is a schematic structural diagram illustrating another embodiment of the maximum power demand prediction of the enterprise provided by the present application.
Fig. 9 is a view showing a configuration of an embodiment of the electricity fee pricing scheme determining module in the embodiment of fig. 7.
Fig. 10 is a schematic structural diagram of an embodiment of the maximum demand value determination unit in the embodiment shown in fig. 9.
Fig. 11 is a schematic structural diagram of an embodiment of the power consumption simultaneous coefficient determination module in the embodiment shown in fig. 7.
Fig. 12 is a schematic structural diagram of an embodiment of the power consumption prediction module for the outgoing line load in the embodiment of fig. 7.
Fig. 13 illustrates an exemplary electronic device to which some embodiments of the enterprise maximum electricity demand prediction method or enterprise electricity prediction apparatus of the present application may be applied.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the invention. It will be apparent, however, to one skilled in the art that the present invention may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.
The inventor of the application finds out through research that: according to the related regulation of 'notice of reducing general industrial and commercial electricity price by the State development and reform Commission', two power consumers can voluntarily select to pay the electricity fee according to the capacity of the transformer or the maximum demand of the contract. The power consumer determines a check value according to the time consumption of the maximum demand of the contract, and when the actual maximum demand exceeds 105% of the contract determination value, the basic electricity charge of the part exceeding 105% is charged by one time; collecting the data which do not exceed the contractual determined value by 105 percent according to the contractual determined value; and when the applied maximum demand checking value is lower than 40% of the sum of the transformer capacity and the high-voltage motor capacity, checking the contracted maximum demand according to 40% of the sum of the capacities.
Therefore, when the user selects different pricing manners, the basic electricity charges to be paid are different, and under the condition that the electricity consumption is constant and the electricity degree and electricity charge are fixed, how to optimally select the appropriate pricing manner of the basic electricity charges reduces the basic electricity charges, so that the method becomes the key for saving the electricity cost of enterprises. For example, at present, an enterprise reports the pricing mode of the electric charge to a power supply department every month, and the reasonable pricing mode of the electric charge means that considerable expenses can be saved for the enterprise. To select which basic electricity charging method is more economical, determining the monthly maximum demand of the enterprise is a very important first step. However, current enterprise users estimate the maximum demand of electricity in the future month according to their own experiences, and the error of the maximum demand of electricity obtained by the estimation method is usually large, so that the enterprise is very limited in the aspects of saving electricity cost and reducing energy waste.
Therefore, in view of the above technical state, the present application provides the following embodiments for helping enterprises select a reasonable electricity fee pricing manner to reduce electricity cost expenditure of the enterprises.
Exemplary embodiments of the methods
Fig. 1 shows a flowchart of an embodiment of an enterprise maximum electricity demand prediction method provided by the present application, which may be applied to a terminal device or a client.
Referring to fig. 1, the method for predicting the maximum power demand of the enterprise specifically includes the following process 100.
And S101, calculating and obtaining the sum of the maximum power consumption demand of all power consumption outlet loads of the enterprise in the forecast month.
In the above step S101, the predicted month means one of the months in accordance with one year.
S102, obtaining a maximum electricity utilization simultaneous coefficient of the maximum electricity utilization demand of the enterprise in a reference month, wherein the reference month comprises a month which is the same as the month of the forecast month or a previous month which is adjacent to the forecast month in at least one previous year.
In the above step S102, the reference month includes a month identical to the predicted month share of the predicted month or a previous month adjacent to the predicted month in at least one previous year. For example, if the current date is 2019, year 5, and the predicted month is 2019, year 6, the reference month may be 2018, year 5, year 6, or year 5, year 2018, year 5, month 2017, year 6, month 2018, month 6, year 6, month 2018, and the like. Of course, the reference month may be one or more months of the reference history, and is not limited to the above-mentioned exemplary cases.
And S103, determining the monthly maximum demand of the enterprise for the forecast month based on the product of the maximum power utilization simultaneous coefficient and the sum of the maximum power utilization demands of all power utilization outlet line loads of the forecast month enterprise.
According to the enterprise maximum power consumption demand forecasting method, in the calculation and forecasting of the enterprise monthly power consumption maximum demand of the forecast month, the occupation ratio of each outlet load in the power consumption maximum demand is taken into consideration by referring to the maximum power consumption simultaneous coefficient, so that the condition that the calculation result of the enterprise monthly power consumption maximum demand of the forecast month has a large error due to the fact that each outlet load comprises various different loads is avoided, and the forecasting accuracy is improved.
Fig. 2 is a flowchart illustrating another embodiment of a method for predicting the maximum power demand of an enterprise provided by the present application.
Referring to fig. 2, the present embodiment is different from the embodiment shown in fig. 1 in that after step S103, the following process 200 is further included.
S201, comparing the total amount of the electric charge of the maximum monthly electricity demand of the enterprise under at least two preset electric charge pricing schemes, and taking the electric charge pricing scheme with the minimum total amount of the electric charge as a target scheme of the forecast month.
According to the method, the maximum monthly power demand of the enterprise is calculated and predicted, the calculation is carried out by combining the current power fee pricing scheme, and then which power fee pricing scheme is most economical is compared, and then the power fee pricing scheme is used as a target scheme for reference selection of the enterprise, so that an administrator of the enterprise can directly carry out power fee pricing declaration according to the target scheme, and great convenience is brought to the power fee declaration work of the enterprise while the enterprise effectively plans the power fee expenditure.
Specifically, the current electricity fee pricing scheme includes two schemes, one scheme is to pay electricity fee according to the transformer capacity, and the other scheme is to pay electricity fee according to the maximum demand. The calculation of the total amount of electricity charge for electricity based on the above-described electricity charge pricing scheme will be described in detail below.
In an exemplary embodiment, it is assumed that the at least two preset electricity fee pricing schemes include a first electricity fee pricing unit and a second electricity fee pricing unit, wherein the first electricity fee pricing unit is a unit for paying an electricity fee according to the capacity of the transformer and is represented by a parameter P1; the second electricity charge price is a price for paying the electricity charge in accordance with the maximum demand, and is represented by a parameter P2.
Fig. 3 shows a flow chart of an embodiment of step S201 in the embodiment shown in fig. 2.
Referring to fig. 3, based on the above assumptions, step S201 compares the total amount of the electric charges of the enterprise with the maximum demand of the electric power under at least two preset electric charge pricing schemes, and takes the electric charge pricing scheme with the minimum total amount of the electric charges as a target scheme, which may specifically include the following process 300.
S301, the power receiving capacity of the power consumer is determined.
In this step S301, it is assumed that the power receiving capacity of the power consumer is represented by a parameter S, the transformer capacity is represented by a parameter S1, and the high-voltage motor capacity is represented by a parameter S2. Generally, if the power consumer does not have a high-voltage motor that is not connected through a dedicated transformer, the power receiving capacity S is determined to be the transformer capacity S1; if the power consumer has a high-voltage motor that is not connected through the dedicated transformer, the power receiving capacity S is determined to be the transformer capacity S1+ the high-voltage motor capacity S2 (i.e., the power corresponds to the same capacity).
And S302, comparing the maximum monthly power demand of the enterprise with the specified percentage of the receiving capacity to determine a maximum demand value for calculating the basic electric charge.
In step S302, according to the current electric charge pricing scheme, the specified percentage is 40%, and of course, if the electric charge pricing scheme changes, the specified percentage may be modified accordingly, and the application does not limit the specific proportional value of the specified percentage.
Specifically, the parameter M may be used to represent a basic electricity rate value, and the declared maximum demand value may be obtained as long as the maximum monthly electricity demand and the maximum receiving capacity S of the enterprise are determined.
Specifically, in an exemplary implementation, see fig. 4, which shows a flowchart of an embodiment of determining a maximum demand value provided in the present application, please refer to fig. 4, which specifically includes a process 400.
S401, judging the maximum monthly power demand of the enterprise and the size of the specified percentage of the power receiving capacity.
In step S401, a parameter P is assumednRepresents the maximum monthly power demand of the enterprise, and the specified percentage of the receiving capacity is 40% S, so the two values can be compared.
S402, if the actual maximum monthly power demand of the enterprise is larger than the specified percentage of the receiving capacity, determining that the maximum monthly power demand of the enterprise is the declared maximum demand value.
In step S402, the maximum demand value M is declared as the maximum monthly power demand P of the enterprisen。
And S403, if the maximum monthly power demand of the enterprise is less than the specified percentage of the receiving capacity, determining that the product of the receiving capacity of the enterprise and the specified percentage is the declared maximum demand value.
In step S403, the maximum demand value M is reported to be 40% of the receiving capacity S.
The embodiment prominently realizes the effect of automatically determining the maximum demand value declared by the enterprise. Because, the maximum monthly power demand P of the enterprise is obtained in the predictionnIn the case of the above, the declared maximum demand values for calculating the electricity charges may not be the same, and if the artificial calculation is performed, the calculation is relatively complex and is prone to error.
And S303, calculating a first electric charge total amount based on the maximum demand value and the first electric charge pricing unit price when the basic electric charge is paid according to the receiving capacity.
In step S303, according to the embodiment shown in fig. 4, when the enterprise pays the electricity according to the transformer capacity, the calculation formula of the first total electricity fee is E1 ═ P1 × S.
And S304, calculating a second electric charge total amount based on the maximum demand value and the second electric charge pricing unit price when the basic electric charge is paid according to the maximum demand.
In step S304, according to the embodiment shown in fig. 4, when the enterprise pays the electricity according to the actual maximum demand, the calculation formula of the second total electricity fee is E2 ═ P2 × M.
S305, comparing the first electric charge total amount with the second electric charge total amount, and determining the electric charge pricing scheme corresponding to the minimum electric charge total amount as a target scheme.
In step S305, assuming that the first total amount of electric power E1 is smaller than the second total amount of electric power E2, an electric power rate scheme of paying electric power according to the capacity of the transformer is selected as a target scheme; assuming that the first electric charge total E1 is equal to or greater than the second electric charge total E2, an electric charge pricing scheme for paying the electric charge in accordance with the maximum demand is selected as a target scheme.
Therefore, the embodiment prominently achieves the effect of automatically obtaining the electric charge pricing scheme according to the maximum monthly power demand or the receiving capacity of the enterprise, helps the enterprise to save a complex calculation process, effectively shortens the determination time of the declaration scheme, and can enable the electric charge declaration work to be more efficient.
Fig. 5 shows a flowchart of an embodiment of step S102 in the embodiment shown in fig. 1.
Referring to fig. 5, in the step S102, the step of obtaining the maximum power consumption simultaneous coefficient in the production period before and after the maximum power consumption of the enterprise in the reference month specifically includes the following process 500.
S501, acquiring the maximum power consumption demand of the enterprise in the reference month.
In step S501, the maximum power demand is the maximum value of the power demand in the current month of the reference month, and the maximum value may obviously be a certain time point appearing in the current month of the reference month, or the maximum value may also be a certain time period or a plurality of time periods appearing in the current month of the reference month.
And S502, calculating the sum of the maximum demand of each outlet line month of the reference month.
In step S502, the outgoing lines are also power outgoing lines or power outgoing line loads, the maximum demand of each outgoing month in the reference month is also the maximum power demand of each outgoing month in the reference month, and the maximum power demand of each outgoing month may be outgoing within the same time or may not be outgoing within the same time, and the maximum power demand of each outgoing line in each month needs to be recorded. .
And S503, determining the maximum electricity utilization simultaneous coefficient of the enterprise in the reference month based on the ratio of the total maximum electricity utilization demand of the enterprise and the sum of the maximum demand of each outgoing line.
According to the embodiment, the abnormal power utilization peaks can be filtered through the consideration of the maximum power utilization simultaneous coefficient, so that the prediction accuracy of the monthly maximum demand of power utilization is improved.
Fig. 6 shows a flow chart of an embodiment of step S101 in the embodiment shown in fig. 1.
Referring to fig. 6, in the step S101, the calculation of the sum of the maximum power consumption of all the power outlet loads of the enterprise in the month includes the following process 600.
S601, determining a forecast month, and acquiring meteorological data and product yield data of the forecast month.
In step S601, the weather data of the predicted month includes a predicted average temperature value of the weather of the month and a predicted sunshine hours of the weather of the month, and the data of the predicted product yield of the month includes a predicted product type of the month.
S602, selecting a reference month of the prediction month, and acquiring meteorological data, product yield data and power utilization data of the reference month.
In this step S602, the meteorological data of the reference month includes a month average temperature value and a month illumination hour of the reference month, the product yield data of the reference month includes a month product category of the reference month, and the electricity consumption data of the reference month includes an enterprise month maximum electricity demand and a respective outgoing month maximum demand of the reference month.
S603, according to the load sensitivity coefficient set based on the load characteristics of each power utilization outlet load of the enterprise, the meteorological data and product yield data of the forecast month and the meteorological data and product yield data of the reference month, a characteristic function of the power utilization outlet load of the enterprise is constructed, and the sum of the maximum power utilization demand of all the power utilization outlet loads of the enterprise in the forecast month is calculated by adopting the characteristic function and the power utilization data of the reference month.
In step S603, the load characteristics of the outgoing line loads of the enterprise are classified according to the characteristics of the loads. For example, loads can be classified into four categories according to their sensitivity based on the electrical loads for each outlet:
the A-type load is a load sensitive to weather and temperature, such as an air conditioner load;
the type B load is a load sensitive to the intensity of sunlight, such as an illumination load;
class C loads are loads sensitive to product yield, such as power loads;
the D-type load is fixed and other loads, such as machine room loads.
Based on the load characteristics, load sensitivity coefficients consistent with the classification types can be set for the loads of the power utilization outgoing lines respectively.
According to the load characteristics of each power utilization outlet load, 4 characteristic sensitivity coefficients are set for each power utilization outlet load, wherein the characteristic sensitivity coefficients are as follows: class A load sensitivity coefficient, class B load sensitivity coefficient, class C load sensitivity coefficient, and class D load sensitivity coefficient.
Wherein, let Ki1Class A load sensitivity coefficient, K, of the ith power utilization outlet loadi2Class B load sensitivity coefficient, K, of the ith power utilization outlet loadi3Class C load sensitivity coefficient, K, for the ith power utilization outlet loadi4The class D load sensitivity coefficient of the ith power utilization outlet load. Then, if the ith power utilization outlet load is the A-type load, K is takeni1＝1，Ki2＝Ki3＝Ki4If the ith power utilization outlet load is a B-type load, taking Ki2＝1,Ki1＝Ki3＝Ki4If the ith power utilization outlet load is a C-type load, taking Ki3＝1,Ki1＝Ki2＝Ki4If the ith power utilization outlet load is a D-type load, taking Ki4＝1,Ki1＝Ki2＝Ki3＝0。
In some exemplary embodiments, based on the load sensitivity factor of the power utilization line load provided above, and the weather data and product yield data of the forecast month and the reference month, a characteristic function of the power utilization line load may be constructed, that is:
wherein, f (i) represents the characteristic function of the ith power utilization outlet load in the enterprise, T1Predicting a temperature average, T, for a monthly weather forecast of a month0As a mean value of the monthly temperature of the reference historical month, H1Predicting monthly sunshine hours for predicting monthly weather of a month, H0For reference to the number of hours of the month and day of the historical month, W1Predicting product yield for a month of the predicted month, W0Reference is made to the monthly product yield of the historical month.
Specifically, according to the characteristic function provided above, the maximum demand of the ith outgoing load in the predicted month can be calculated:
Pin＝Pih×f(i)，
wherein P isinFor predicting the maximum monthly demand, P, of the ith outgoing line of a monthly enterpriseihThe maximum monthly demand of the ith outgoing line of the enterprise of the reference month.
Then, the sum of the maximum power demand for predicting all power outlet loads of the monthly enterprise is as follows:
wherein, PtThe sum of the maximum demand of all the electric outlet loads in the enterprise, and m is the total number of the electric outlet loads in the enterprise.
According to the embodiment, the sum of the maximum power demand of all the power outlet loads of the enterprise can be rapidly calculated.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.
Exemplary embodiments of the product
Based on the same inventive concept of the enterprise maximum power demand prediction method, the embodiment also provides an enterprise power consumption prediction device.
Fig. 7 shows a schematic structural diagram of an embodiment of the maximum power demand prediction apparatus for an enterprise provided by the present application.
Referring to fig. 7, the device 700 for predicting the maximum power consumption demand of an enterprise includes an outgoing line load power consumption prediction module 701, a power consumption simultaneous coefficient determination module 702 and a maximum power consumption prediction module 703, where the outgoing line load power consumption prediction module 701 is configured to calculate and obtain a sum of maximum power consumption demands of all power consumption outgoing line loads of a forecast month enterprise; a power consumption simultaneous coefficient determination module 702 configured to obtain a maximum power consumption simultaneous coefficient of an enterprise maximum power consumption of a reference month including a month identical to the predicted month in at least one previous year or a previous month adjacent to the predicted month; the maximum electricity utilization predicting module 703 is configured to determine the maximum monthly electricity utilization demand of the enterprise for the prediction month based on the product of the maximum electricity utilization simultaneous coefficient and the sum of the maximum electricity utilization demands of all the electricity outlet line loads of the enterprise for the prediction month.
Fig. 8 is a schematic structural diagram illustrating another embodiment of an enterprise maximum power demand prediction apparatus 700 provided by the present application.
Referring to fig. 8, the maximum power demand forecasting apparatus 700 further includes: and the electric charge pricing scheme determining module 802 is configured to compare the electric charge total of the maximum monthly electricity demand of the enterprise under at least two preset electric charge pricing schemes, and use the electric charge pricing scheme corresponding to the minimum electric charge total as the target scheme of the forecast month.
Fig. 9 is a schematic structural diagram showing an embodiment of the electricity fee pricing scheme determining module 801 according to the embodiment shown in fig. 7.
Referring to fig. 8, the electricity fee pricing scheme determining module 801 includes: a power receiving capacity determination unit 901 configured to determine a power receiving capacity of a power consumer; a maximum demand value determination unit 902 configured to compare the maximum monthly power demand of the enterprise with the specified percentage of the receiving capacity to determine a maximum demand value for calculating the basic power rate; a first electricity rate calculation unit 903 configured to calculate a first electricity rate total amount based on the maximum demand value and a first electricity rate pricing unit price when the basic electricity rate is paid by the power receiving capacity; a second electricity fee calculation unit 904 configured to calculate a second electricity fee total amount based on the maximum demand value and the second electricity fee charging unit price when the basic electricity fee is paid by the maximum demand; a target plan determination unit 905 configured to compare the first and second electricity rate amounts and determine the electricity rate scheme in which the minimum electricity rate amount is located as the target plan.
Fig. 10 is a schematic structural diagram of an embodiment of the maximum demand value determination unit in the embodiment shown in fig. 9.
Referring to fig. 10, the maximum demand value determining unit 902 includes: the power quantity comparison unit 101 is configured to judge the maximum monthly power demand of the enterprise and the size of the specified percentage of the receiving capacity; a first determination unit 102 configured to determine that the maximum monthly power demand of the enterprise is a maximum power demand value if the maximum monthly power demand is greater than a specified percentage of the receiving capacity; a second determining unit 103 configured to determine that the product of the enterprise power capacity and the specified percentage is the maximum demand value if the maximum monthly power demand of the enterprise is less than the specified percentage of the power capacity.
Fig. 11 is a schematic structural diagram of an embodiment of the power consumption simultaneous determination module 702 in the embodiment shown in fig. 7.
Referring to fig. 11, the power consumption simultaneous determination module 702 includes: a power consumption peak data acquisition unit 111 configured to acquire a maximum power consumption demand of a reference month enterprise; the outgoing line maximum demand calculation unit 112 is configured to calculate the sum of the maximum demands of the outgoing lines of the enterprise in a reference month; and the electricity utilization simultaneous coefficient calculation unit 113 is configured to determine the maximum electricity utilization simultaneous coefficient of the enterprise in the reference month based on the ratio of the maximum electricity utilization demand to the sum of the maximum outgoing demand of each of the outgoing lines.
Fig. 12 is a schematic structural diagram illustrating an embodiment of the power utilization prediction module 701 for the outgoing line load in the embodiment illustrated in fig. 7.
Referring to fig. 12, the outgoing line load electricity consumption prediction module 701 includes: a predicted month data acquiring unit 121 configured to determine a predicted month, and acquire weather data and product yield data of the predicted month. The reference month data acquiring unit 122 is configured to select a reference month of the predicted month, and acquire the weather data, the product yield data, and the electricity consumption data of the reference month. And the power utilization calculating unit 123 is configured to construct a feature function of the enterprise power utilization outlet loads according to the load sensitivity coefficient set based on the load characteristics of each power utilization outlet load of the enterprise, the meteorological data and the product yield data of the forecast month, and the meteorological data and the product yield data of the reference month, and calculate the sum of the maximum power utilization demand of all the power utilization outlet loads of the enterprise in the forecast month by using the feature function and the power utilization data of the reference month.
It should be understood that the technical solution in the apparatus embodiment and the solution in the method embodiment belong to the same technical concept, so that the specific implementation manner of each module or unit in the foregoing embodiments may refer to the above method embodiment, which is not described herein again.
Terminal device embodiment
Fig. 13 illustrates an exemplary electronic device to which some embodiments of the enterprise maximum electricity demand prediction method or enterprise electricity prediction apparatus of the present application may be applied.
Referring to fig. 13, the electronic device 13 includes: a processor 131, a memory 132, and a computer program 133 stored in the memory 132 and executable on the processor 131. The processor 131, when executing the computer program 133, implements the steps in the above-described embodiments of the enterprise maximum electricity demand prediction method, such as the steps 101 to 103 shown in fig. 1. Alternatively, the processor 131, when executing the computer program 133, implements the functions of the modules/units in the above-described device embodiments, such as the functions of the modules 701 to 703 shown in fig. 7.
Illustratively, the computer program 133 may be divided into one or more modules/units, which are stored in the memory 132 and executed by the processor 131 to carry out the invention. The one or more modules/units may be a series of computer program instruction segments capable of performing specific functions, which are used to describe the execution of the computer program 133 in the electronic device 13. For example, the computer program 133 may be divided into an outgoing load electricity utilization prediction module, an electricity utilization simultaneous coefficient determination module, and a maximum electricity utilization prediction module (a module in a virtual device), and the specific functions of each module are as follows: the outlet load electricity utilization prediction module is configured to calculate and obtain the sum of the maximum electricity utilization demand of all electricity outlet loads of the enterprise in the forecast month; the power utilization simultaneous coefficient determining module is configured to obtain a power utilization simultaneous coefficient of a reference month enterprise, wherein the reference month comprises a month which is the same as the month number of the predicted month or a last month which is adjacent to the predicted month in at least one previous year; and the maximum electricity utilization forecasting module is configured to determine the maximum monthly electricity utilization demand of the enterprise in the forecast month based on the product of the electricity utilization simultaneous coefficient and the sum of the maximum electricity utilization demands of all the electricity outlet loads of the enterprise in the forecast month.
The electronic device 13 may be a desktop computer, a notebook, a palm computer, a cloud server, or other computing devices. The electronic device 13 may include, but is not limited to, a processor 131, a memory 132. Those skilled in the art will appreciate that fig. 13 is merely an example of the electronic device 7 and does not constitute a limitation of the electronic device 13 and may include more or fewer components than shown, or combine certain components, or different components, e.g., the electronic device 13 may also include input-output devices, network access devices, buses, etc.
The Processor 131 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, a discrete Gate or transistor logic device, a discrete hardware component, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The storage 132 may be an internal storage unit of the electronic device 13, such as a hard disk or a memory of the electronic device 13. The memory 132 may also be an external storage device of the electronic device 13, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like provided on the electronic device 13. Further, the memory 132 may also include both internal storage units and external storage devices of the electronic device 13. The memory 132 is used to store computer programs and other programs and data required by the electronic device 13. The memory 132 may also be used to temporarily store data that has been output or is to be output.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-mentioned functions. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present invention, and are intended to be included within the scope of the present invention.
Claims (10)
1. A method for predicting the maximum power demand of an enterprise is characterized by comprising the following steps:
calculating and obtaining the sum of the maximum power consumption demand of all power consumption outlet loads of the enterprise in the forecast month;
acquiring a maximum power utilization simultaneous coefficient of a maximum power utilization demand of an enterprise of a reference month, wherein the reference month comprises a month which is the same as the month number of the forecast month or a previous month adjacent to the forecast month in at least one previous year;
and determining to obtain the enterprise monthly electricity maximum demand of the forecast month based on the product of the maximum electricity utilization simultaneous coefficient and the sum of the maximum electricity utilization demand of all electricity outlet line loads of the forecast month enterprise.
2. The method for forecasting maximum enterprise electricity demand according to claim 1, further comprising, after the step of determining the maximum monthly enterprise electricity demand for the forecast month, the steps of:
and comparing the total amount of the electric charges of the maximum monthly electricity demand of the enterprise under at least two preset electric charge pricing schemes, and taking the electric charge pricing scheme with the minimum total amount of the electric charges as a target scheme of the forecast month.
3. The method for predicting the maximum power demand of the enterprise according to claim 3, wherein the step of comparing the total amount of the electric charges of the maximum monthly power demand of the enterprise under at least two preset electric charge pricing schemes and using the electric charge pricing scheme with the minimum total amount of the electric charges as the target scheme of the predicted month comprises the following steps:
determining a receiving capacity of a power consumer;
comparing the maximum monthly power demand of the enterprise with the specified percentage of the receiving capacity to determine the maximum demand value for calculating the basic power charge;
calculating a first total amount of electricity charges based on the maximum demand value and a first electricity charge pricing unit price when the basic electricity charges are paid according to the electricity receiving capacity;
calculating a second electric charge total amount based on the maximum demand value and a second electric charge pricing unit price when the basic electric charge is paid according to the maximum demand;
and comparing the first electric charge total amount with the second electric charge total amount, and determining the electric charge pricing scheme corresponding to the minimum electric charge total amount as a target scheme.
4. The method for forecasting maximum power demand of an enterprise according to claim 4, wherein the step of comparing the maximum monthly power demand of the enterprise with the specified percentage of the receiving capacity to determine the maximum demand value for calculating the base power rate comprises the following steps:
judging the maximum monthly power demand of the enterprise and the size of the specified percentage of the power receiving capacity;
if the maximum monthly power demand of the enterprise is larger than the specified percentage of the receiving capacity, determining the maximum monthly power demand of the enterprise as a maximum demand value;
and if the maximum monthly power demand of the enterprise is less than the specified percentage of the receiving capacity, determining that the product of the receiving capacity of the enterprise and the specified percentage is the maximum demand value.
5. The method for predicting the maximum power demand of the enterprise as claimed in claim 1, wherein the step of obtaining the maximum power simultaneous coefficient of the enterprise with reference to the month comprises the following steps:
acquiring the maximum power consumption demand of an enterprise with a reference month;
calculating the sum of the maximum power consumption of each outlet wire of the enterprise;
and determining the maximum electricity utilization simultaneous coefficient of the enterprise in the reference month based on the ratio of the maximum electricity utilization demand to the sum of the maximum electricity utilization demands of the outgoing lines.
6. The method for predicting the maximum power demand of the enterprise according to claim 1, wherein the step of calculating and acquiring the sum of the maximum power demands for predicting all power outlet loads of the enterprise in the month comprises the following steps:
determining a forecast month, and acquiring meteorological data and product yield data of the forecast month.
And selecting a reference month of the predicted month, and acquiring meteorological data, product yield data and power utilization data of the reference month.
And constructing a characteristic function of the enterprise power utilization outlet load according to a load sensitivity coefficient set based on the load characteristics of each power utilization outlet load of the enterprise, the meteorological data and the product yield data of the forecast month, and the meteorological data and the product yield data of the reference month, and calculating the sum of the maximum power utilization demand of all the power utilization outlet loads of the forecast month enterprise by adopting the characteristic function and the power utilization data of the reference month.
7. The method of predicting maximum electricity demand for an enterprise of claim 1, wherein the characterization function is:
wherein f (i) represents the characteristics of the ith power utilization outlet load in the enterpriseFunction, T1Predicting a temperature average, T, for a monthly weather forecast of a month0As a mean value of the monthly temperature of the reference historical month, H1Predicting monthly sunshine hours for predicting monthly weather of a month, H0For reference to the number of hours of the month and day of the historical month, W1Predicting product yield for a month of the predicted month, W0Reference is made to the monthly product yield of the historical month.
8. An enterprise maximum electricity demand prediction device, comprising:
the outlet load electricity utilization prediction module is configured to calculate and obtain the sum of the maximum electricity utilization demand of all electricity outlet loads of the enterprise in the forecast month;
the power utilization simultaneous coefficient determining module is configured to obtain a maximum power utilization simultaneous coefficient of the maximum power utilization demand of the enterprise in a reference month, wherein the reference month comprises a month which is the same as the month of the forecast month or a last month adjacent to the forecast month in at least one previous year;
and the maximum electricity utilization prediction module is configured to determine the maximum monthly electricity utilization demand of the enterprise in the predicted month based on the product of the maximum electricity utilization simultaneous coefficient and the sum of the maximum electricity utilization demands of all electricity outlet line loads of the predicted month enterprise.
9. An electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor when executing the computer program performs the steps of the method for forecasting maximum electricity demand for an enterprise as claimed in any one of claims 1 to 7.
10. A computer-readable storage medium, storing a computer program, wherein the computer program, when executed by a processor, implements the steps of the method for predicting maximum electricity demand for an enterprise according to any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911362287.1A CN111126703A (en) | 2019-12-26 | 2019-12-26 | Method and device for predicting maximum power consumption demand of enterprise |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911362287.1A CN111126703A (en) | 2019-12-26 | 2019-12-26 | Method and device for predicting maximum power consumption demand of enterprise |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111126703A true CN111126703A (en) | 2020-05-08 |
Family
ID=70502694
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911362287.1A Pending CN111126703A (en) | 2019-12-26 | 2019-12-26 | Method and device for predicting maximum power consumption demand of enterprise |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111126703A (en) |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111813832A (en) * | 2020-07-10 | 2020-10-23 | 广东电网有限责任公司计量中心 | Power data analysis method and device, electronic equipment and storage medium |
| CN111987715A (en) * | 2020-08-14 | 2020-11-24 | 新奥数能科技有限公司 | Load regulation and control method and device |
| CN112686463A (en) * | 2021-01-07 | 2021-04-20 | 合肥阳光新能源科技有限公司 | Demand data processing method and device and electronic equipment |
| CN114202339A (en) * | 2020-08-31 | 2022-03-18 | 中国移动通信集团重庆有限公司 | Early warning processing method and device |
| CN116436002A (en) * | 2023-06-13 | 2023-07-14 | 成都航空职业技术学院 | Building electricity utilization prediction method |
-
2019
- 2019-12-26 CN CN201911362287.1A patent/CN111126703A/en active Pending
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111813832A (en) * | 2020-07-10 | 2020-10-23 | 广东电网有限责任公司计量中心 | Power data analysis method and device, electronic equipment and storage medium |
| CN111987715A (en) * | 2020-08-14 | 2020-11-24 | 新奥数能科技有限公司 | Load regulation and control method and device |
| CN111987715B (en) * | 2020-08-14 | 2022-03-01 | 新奥数能科技有限公司 | Load regulation and control method and device |
| CN114202339A (en) * | 2020-08-31 | 2022-03-18 | 中国移动通信集团重庆有限公司 | Early warning processing method and device |
| CN112686463A (en) * | 2021-01-07 | 2021-04-20 | 合肥阳光新能源科技有限公司 | Demand data processing method and device and electronic equipment |
| CN116436002A (en) * | 2023-06-13 | 2023-07-14 | 成都航空职业技术学院 | Building electricity utilization prediction method |
| CN116436002B (en) * | 2023-06-13 | 2023-09-05 | 成都航空职业技术学院 | Building electricity utilization prediction method |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| AU2017364328B2 (en) | Method and apparatus for facilitating the operation of an on-site energy storage system to co-optimize battery dispatch | |
| CN111126703A (en) | Method and device for predicting maximum power consumption demand of enterprise | |
| Zhu et al. | The case for efficient renewable energy management in smart homes | |
| US11258259B2 (en) | Power control system, power control apparatus, and power control method | |
| WO2017155624A1 (en) | Optimal battery sizing for behind-the-meter applications considering participation in demand response programs and demand charge reduction | |
| EP3032489B1 (en) | Power adjustment device, power adjustment method, and program | |
| US8983673B2 (en) | Implementing power management systems using peak demand factor | |
| CN110544112B (en) | Method and device for clearing regional frequency modulation market in consideration of renewable energy | |
| US10680455B2 (en) | Demand charge minimization in behind-the-meter energy management systems | |
| CN109389437B (en) | Pricing method and device of electricity price and terminal | |
| CN112070301A (en) | Method, system and equipment for adjusting power consumption of user | |
| Lee et al. | Development of energy storage system scheduling algorithm for simultaneous self-consumption and demand response program participation in South Korea | |
| JP7297004B2 (en) | Power supply system and power management method | |
| CN117829881A (en) | Charging and discharging and electricity changing optimization method of electricity changing station, storage medium and terminal equipment | |
| CN110998643B (en) | Systems, devices, and methods for pattern-based energy storage management | |
| CN109492957B (en) | Method and device for evaluating operating efficiency of power distribution network line and storage medium | |
| CN115456346B (en) | Electric quantity monitoring and early warning method and device, electronic equipment and medium | |
| JP2003324846A (en) | Method for selecting electricity charge contract system and system for supporting selection | |
| CN115187061A (en) | User side green electricity configuration method, device, equipment and storage medium | |
| KR20150094428A (en) | Method and Apparatus for Trading Solar Energy | |
| JP7345371B2 (en) | Power management device, power management method, and power management program | |
| CN111178979A (en) | Regional energy power data processing method and device | |
| CN115204954A (en) | Charging policy selection method and device, electronic equipment and readable storage medium | |
| US20220036484A1 (en) | Methods of allocating energy generated by a community solar energy system | |
| CN112200609A (en) | Interruptible electricity price determination method and device based on industry classification and storage medium |
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 | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20200508 |
|
| RJ01 | Rejection of invention patent application after publication |