CN111723975A - Power distribution network electric power tight balance method based on distributed power supply output time sequence - Google Patents
Power distribution network electric power tight balance method based on distributed power supply output time sequence Download PDFInfo
- Publication number
- CN111723975A CN111723975A CN202010421721.5A CN202010421721A CN111723975A CN 111723975 A CN111723975 A CN 111723975A CN 202010421721 A CN202010421721 A CN 202010421721A CN 111723975 A CN111723975 A CN 111723975A
- Authority
- CN
- China
- Prior art keywords
- power supply
- power
- load
- capacity
- hour
- 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 39
- 238000010606 normalization Methods 0.000 claims abstract description 22
- 238000004364 calculation method Methods 0.000 claims description 28
- 230000005540 biological transmission Effects 0.000 claims description 15
- 238000003064 k means clustering Methods 0.000 claims description 5
- 238000012216 screening Methods 0.000 claims description 3
- 238000010248 power generation Methods 0.000 claims 2
- 238000005457 optimization Methods 0.000 description 3
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000005611 electricity Effects 0.000 description 1
- 238000007726 management method Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 229920006395 saturated elastomer Polymers 0.000 description 1
- 238000012795 verification Methods 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
- G06Q10/04—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
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/23—Clustering techniques
- G06F18/232—Non-hierarchical techniques
- G06F18/2321—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions
- G06F18/23213—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions with fixed number of clusters, e.g. K-means clustering
-
- 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
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Physics & Mathematics (AREA)
- Economics (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Strategic Management (AREA)
- Human Resources & Organizations (AREA)
- Tourism & Hospitality (AREA)
- General Business, Economics & Management (AREA)
- Marketing (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Quality & Reliability (AREA)
- Evolutionary Biology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- General Health & Medical Sciences (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
- Bioinformatics & Computational Biology (AREA)
- Artificial Intelligence (AREA)
- Probability & Statistics with Applications (AREA)
- Evolutionary Computation (AREA)
- Development Economics (AREA)
- Game Theory and Decision Science (AREA)
- Entrepreneurship & Innovation (AREA)
- Operations Research (AREA)
- Primary Health Care (AREA)
- Supply And Distribution Of Alternating Current (AREA)
- General Engineering & Computer Science (AREA)
Abstract
The invention discloses a power distribution network electric power tight balance method based on a distributed power supply output time sequence, which comprises the following steps: extracting 24-hour active load values of a transformer in a historical year, clustering according to the class of power supply loads of the transformer to form a load four-season typical day curve, and calculating a 24-hour load normalization value of the four-season typical day; predicting typical daily load values of all types of land properties in four seasons, calculating corresponding 24-hour load values, and calculating 24-hour load values of typical days in four seasons in an area in an overlapping manner; extracting 24-hour output values of distributed power supplies in the historical years, setting confidence probability initial values of the output of the distributed power supplies, clustering to form four-season typical confidence output curves, and calculating 24-hour confidence output normalized values of the four-season typical days; knowing the total installed capacity of the distributed power supply, calculating a 24-hour confidence output value of a four-season typical day; based on 24-hour confidence output, 24-hour load, conventional power output and current power supply capacity of a distributed power supply in a four-season typical day, four-season power tight balance is respectively carried out to obtain a demand curve of power supply capacity needing to be newly added.
Description
Technical Field
The invention relates to the technical field of power grid planning, in particular to a power distribution network electric power tight balance method based on a distributed power supply output time sequence.
Background
In order to implement structural reform and deployment of a supply side, achieve cost reduction, cost saving, quality improvement and efficiency improvement of state resource committee, improve the operation performance of companies and guarantee the sustainable development of a power grid, a state grid company issues a notice about further strictly controlling the investment of the power grid (state grid office (2019) No. 826). The investment construction idea of 'three strictly forbidden, two strictly forbidden and two no longer' is provided, and the importance and the urgency of fully knowing and strictly controlling the power grid investment are provided; the investment scale of the power grid is strictly controlled according to the output fixed investment; the strong grab loss is managed, and no additional investment is added to the loss unit; efficiency benefits are focused, and power grid investment management is enhanced.
The power balance of the traditional power grid planning has two main characteristics, namely the power balance at the peak maximum load single moment of the whole grid is met, and the distributed power supply does not participate in the balance. Due to the fact that a large number of distributed power supplies are connected and diversified loads are generated, the maximum value of the superposed output and the load is greatly changed and cannot be expressed in a traditional single-time-point balance mode. The traditional power balance is characterized by loose balance, a large amount of power grid capital investment is needed, but the peak load duration is short, so that the overall utilization efficiency of equipment is low. In the large environment of power grid tightening investment, the traditional mode is difficult to continue.
In view of the above technical problems, no effective solution has been proposed at present.
Disclosure of Invention
Aiming at the technical problems in the related art, the invention provides a power distribution network power tight balancing method based on a distributed power supply output time sequence, which can solve the problems of huge power grid investment and low equipment utilization efficiency caused by the fact that the contribution of distributed power supply output is not considered and the annual maximum load is met as a power balancing standard in the prior art, and further realize the planning problem of global optimization.
In order to achieve the technical purpose, the technical scheme of the invention is realized as follows:
a power distribution network power tight balance method based on a distributed power supply output time sequence comprises the following steps:
s1: extracting 24-hour daily active load values of the transformer in history of 1-3 years, clustering to form typical daily characteristic curves of various loads according to different types of power supply loads of the transformer, and calculating 24-hour load normalization values of the typical daily load of the four seasons;
s2: predicting the four-season typical daily load values of various land parcel properties by adopting a space load density method, calculating corresponding 24-hour load values according to the four-season typical daily 24-hour load normalization value calculated in the step S1, and superposing and calculating the 24-hour load values of the four-season typical days in the region;
s3: extracting 24-hour-per-day output values of the distributed power supplies in the history of 1-3 years, setting confidence probability initial values of the output of the distributed power supplies, clustering to form four-season typical confidence output characteristic curves, and calculating 24-hour confidence output normalized values of the four-season typical day;
s4: knowing the total installed capacity of the distributed power supply, calculating a 24-hour confidence output value of a typical day in four seasons according to the confidence output normalization value calculated in the step S3;
s5: based on 24-hour confidence output, 24-hour load, conventional power output and current power supply capacity of a distributed power supply on a four-season typical day, four-season power tight balance is respectively carried out, and a characteristic curve of the demand of the power supply capacity needing to be newly added is obtained through calculation.
Further, in step S1, the transformer power supply load category includes residential, commercial, administrative, and industrial.
Further, in the steps S1 and S3, the clustering method includes K-Means clustering and mean shift clustering.
Further, in step S1, the calculation formula of the four season typical day 24 hour load normalization value is:
a point-in-time load normalization value = a point-in-time load value/the daily load maximum value.
Further, in step S2, the calculation formula of the typical daily load value of the plot for four seasons is:
the typical daily load value of the land for four seasons = land building area × load density per building area.
Further, in step S3, the distributed power source includes photovoltaic power and wind power.
In step S3, the initial value of the confidence probability is a confidence probability that the distributed power supply is involved in the balance, and is 90% or more and 90% or more.
Further, in step S4, the calculation step of the four season typical day 24 hour confidence force value is as follows:
s41: counting the total installed capacity of various distributed power supplies;
s42: calculating the 24-hour confidence output value of the four-season typical day by adopting the following calculation formula:
the confidence output value at a certain time point = the total installed capacity of the distributed power supply, the theoretical maximum output ratio and the 24-hour confidence output normalized value of the four seasons typical day.
Further, in step S5, the step of obtaining the characteristic curve of the power supply capacity requirement to be newly added is as follows:
s51: evaluating the current power supply capacity of a regional power grid, wherein the power supply capacity of a 110kV transformer substation is the power supply capacity without load loss when any main transformer fails according to the requirements of the power supply safety standard of the power grid, and the calculation formula of the power supply capacity of the 110kV transformer substation is as follows:
the power supply capacity of the 110kV transformer substation = transformer capacity of the transformer substation-main transformer capacity with the maximum capacity;
the power supply capacity of the 10kV feeder line is that no load is lost when any feeder line in the feeder line group is in power failure, and the calculation formula of the power supply capacity of the medium-voltage feeder line is as follows:
the feeder line power supply capacity of 10kV = present power factor x (the sum of feeder line maximum transmission capacity of a feeder line group-the maximum transmission capacity of a single feeder line)/the number of feeder line groups;
s52: calculating the newly-added power supply capacity required by the 110kV power grid at a certain moment, wherein the calculation formula is as follows:
the newly added power supply capacity required by a 110kV power grid in a certain moment region = the moment load predicted value-the moment low-voltage internet distributed power supply confidence output value-the moment low-voltage internet stable power supply output value-the moment 110kV and above special line user load-the current 110kV power grid power supply capacity,
the current 110kV power grid power supply capacity = the accumulated sum of the power supply capacities of 110kV transformer substations in the region;
s53: calculating the newly-added power supply capacity required by the 10kV power grid at a certain moment, wherein the calculation formula is as follows:
the required power supply capacity of a 10kV power grid in a certain time region = the load predicted value at the time-the confidence output value of the low-voltage online distributed power supply at the time-the output value of the low-voltage online stable power supply at the time-the load of a special line user at the time of 10kV and above-the power supply capacity of a current 10kV feeder line,
the current 10kV feeder line power supply capacity = the accumulated sum of the 10kV feeder line power supply capacities in the region;
s54: screening the maximum value of the capacity increment curve required 24 hours in a typical day of the four seasons, calculating the number of transformer substation seats required to be newly added and the number of medium-voltage feeder lines, acquiring a characteristic curve required by the power supply capacity required to be newly added,
the number of newly-built 110kV transformer substation seats = newly-added maximum power supply capacity/typical configuration capacity of a 110kV transformer substation required by a 110kV power grid in a certain time zone,
the typical configuration capacity of the 110kV transformer substation is 100 MVA;
the number of 10kV feeders needs to be increased = the maximum value of the power supply capacity required by a 10kV power grid in a certain time region/the transmission capacity of a single feeder,
wherein, the single feeder line transmission capacity is 4MVA according to the typical LGJ-240 safe transmission capacity.
The invention has the beneficial effects that: the method forms characteristic curves of various load four-season typical days through clustering, and obtains a load normalization value; based on the saturated load prediction of different load properties, drawing a load characteristic curve, and overlapping to form a regional four-season typical hourly load value; forming a four-season confidence output characteristic curve of the distributed power supply through clustering, and acquiring an output normalization value; the method is characterized in that confidence output of the distributed power supply, output of the conventional power supply and load are considered, the power balance of the section of the four seasons of 24 hours in a typical day is calculated, and the problems of huge power grid investment and low equipment utilization efficiency caused by the fact that the contribution of the output of the distributed power supply is not considered and the annual maximum load is met as a power balance standard are solved, so that the global optimization planning is realized.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed in the embodiments 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 is a flowchart of a method for tightly balancing power of a power distribution network based on a distributed power output timing sequence according to an embodiment of the present invention;
FIG. 2 is a typical daily electricity consumption characteristic curve of commercial load in summer of a power distribution network power tight balance method based on a distributed power supply output time sequence according to an embodiment of the invention;
fig. 3 is a summer distributed photovoltaic confidence output characteristic curve of a power distribution network power tight balancing method based on a distributed power output timing sequence according to an embodiment of the present invention;
fig. 4 is a summer distributed photovoltaic typical sunrise characteristic curve of a power tight balancing method for a power distribution network based on a distributed power supply power sequence according to an embodiment of the invention;
fig. 5 is a characteristic curve of demand for newly added power supply capacity of a tight balance calculation result of a power tight balance method for a power distribution network based on a distributed power output timing sequence according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments that can be derived by one of ordinary skill in the art from the embodiments given herein are intended to be within the scope of the present invention.
As shown in fig. 1, a method for tightly balancing power of a power distribution network based on a distributed power supply output timing sequence according to an embodiment of the present invention includes the following steps:
s1: extracting 24-hour daily active load values of the transformer in history of 1-3 years, clustering to form typical daily characteristic curves of various loads according to different types of power supply loads of the transformer, and calculating 24-hour load normalization values of the typical daily load of the four seasons;
s2: predicting the four-season typical daily load values of various land parcel properties by adopting a space load density method, calculating corresponding 24-hour load values according to the four-season typical daily 24-hour load normalization value calculated in the step S1, and superposing and calculating the 24-hour load values of the four-season typical days in the region;
s3: extracting 24-hour-per-day output values of the distributed power supplies in the history of 1-3 years, setting confidence probability initial values of the output of the distributed power supplies, clustering to form four-season typical confidence output characteristic curves, and calculating 24-hour confidence output normalized values of the four-season typical day;
s4: knowing the total installed capacity of the distributed power supply, calculating a 24-hour confidence output value of a typical day in four seasons according to the confidence output normalization value calculated in the step S3;
s5: based on 24-hour confidence output, 24-hour load, conventional power output and current power supply capacity of a distributed power supply on a four-season typical day, four-season power tight balance is respectively carried out, and a characteristic curve of the demand of the power supply capacity needing to be newly added is obtained through calculation.
In one embodiment, in step S1, the transformer power supply load category includes residential, commercial, administrative, and industrial.
In one embodiment, the clustering methods in steps S1 and S3 include K-Means clustering and mean shift clustering.
In one embodiment, as shown in fig. 2, in step S1, the formula for calculating the four season typical day 24 hour load normalization value is:
a point-in-time load normalization value = a point-in-time load value/the daily load maximum value.
In one embodiment, in step S2, the calculation formula of the typical daily load value of the plot for four seasons is:
the typical daily load value of the land for four seasons = land building area × load density per building area.
Preferably, the value of the load density of the unit building area can be selected according to the technical guide rule of planning and designing of a power distribution network DL/T5729-2016 or other related guide rules in places.
In a specific embodiment, in step S3, the distributed power source includes photovoltaic power and wind power.
Preferably, in step S3, the initial value of the confidence probability is the confidence probability that the distributed power supply is involved in the balance, and is generally 90% or more.
In one embodiment, as shown in fig. 3, in step S4, the calculation of the 24-hour confidence value of the four-season typical day includes the following steps:
s41: counting the total installed capacity of various distributed power supplies;
s42: calculating the 24-hour confidence output value of the four-season typical day by adopting the following calculation formula:
the confidence output value at a certain time point = the total installed capacity of the distributed power supply, the theoretical maximum output ratio, the 24-hour confidence output normalized value of the four seasons typical day,
the 24-hour confidence output value of the four-season typical day = 24-hour confidence output value/maximum sunrise output value of the four-season typical day, and the sunrise output value is shown in fig. 4.
Preferably, the theoretical maximum output is 90% in winter, 95% in summer and 97% in spring and autumn.
In an embodiment, in step S5, the step of obtaining the characteristic curve of the demand for new power supply capacity includes:
s51: evaluating the current power supply capacity of a regional power grid, wherein the power supply capacity of a 110kV transformer substation is the power supply capacity without load loss when any main transformer fails according to the requirements of the power supply safety standard of the power grid, and the calculation formula of the power supply capacity of the 110kV transformer substation is as follows:
the power supply capacity of the 110kV transformer substation = transformer capacity of the transformer substation-main transformer capacity with the maximum capacity;
the power supply capacity of the 10kV feeder line is that no load is lost when any feeder line in the feeder line group is in power failure, and the calculation formula of the power supply capacity of the medium-voltage feeder line is as follows:
the feeder line power supply capacity of 10kV = present power factor x (the sum of feeder line maximum transmission capacity of a feeder line group-the maximum transmission capacity of a single feeder line)/the number of feeder line groups;
s52: calculating the newly-added power supply capacity required by the 110kV power grid at a certain moment, wherein the calculation formula is as follows:
the newly added power supply capacity required by a 110kV power grid in a certain moment region = the moment load predicted value-the moment low-voltage internet distributed power supply confidence output value-the moment low-voltage internet stable power supply output value-the moment 110kV and above special line user load-the current 110kV power grid power supply capacity,
the current 110kV power grid power supply capacity = the accumulated sum of the power supply capacities of 110kV transformer substations in the region;
s53: calculating the newly-added power supply capacity required by the 10kV power grid at a certain moment, wherein the calculation formula is as follows:
the required power supply capacity of a 10kV power grid in a certain time region = the load predicted value at the time-the confidence output value of the low-voltage online distributed power supply at the time-the output value of the low-voltage online stable power supply at the time-the load of a special line user at the time of 10kV and above-the power supply capacity of a current 10kV feeder line,
the current 10kV feeder line power supply capacity = the accumulated sum of the 10kV feeder line power supply capacities in the region;
s54: screening the maximum value of the capacity increment curve required 24 hours in a typical day of the four seasons, calculating the number of transformer substation seats required to be newly added and the number of medium-voltage feeder lines, obtaining a characteristic curve required to be newly added with power supply capacity, as shown in figure 5,
the number of newly-built 110kV transformer substation seats = newly-added maximum power supply capacity/typical configuration capacity of a 110kV transformer substation required by a 110kV power grid in a certain time zone,
the typical configuration capacity of the 110kV transformer substation is 100 MVA;
the number of 10kV feeders needs to be increased = the maximum value of the power supply capacity required by a 10kV power grid in a certain time region/the transmission capacity of a single feeder,
the single feeder line transmission capacity is according to the typical LGJ-240 safe transmission capacity, and meanwhile, the requirement of N-1 verification is considered, and 4MVA is generally adopted.
In order to facilitate understanding of the above-described technical aspects of the present invention, the above-described technical aspects of the present invention will be described in detail below in terms of specific usage.
When the method is used specifically, according to the power tight balance method of the power distribution network based on the distributed power supply output time sequence, various historical power supply load values are collected, various load four-season typical day characteristic curves are formed through clustering methods such as a K-Means clustering method and a mean shift clustering method, and a load normalization value is calculated; then predicting the four-season typical daily maximum load values of various plots, calculating corresponding daily hour load values according to the calculated load normalization value, and overlapping various load calculation areas to calculate the four-season typical daily hour load values; continuously collecting the hourly output values of the distributed power supplies in the historical years, forming a four-season typical daily confidence output characteristic curve by using a K-Means clustering method, a mean shift clustering method and other clustering methods, and calculating a confidence output normalization value; knowing the total installed capacity of the distributed power supply, calculating a 24-hour confidence output value of a typical day in four seasons of the region according to the calculated confidence output normalization value; and finally, performing four-season electric power tight balance calculation based on the four-season typical 24-hour daily controlled output, 24-hour daily load, conventional power output and current power supply capacity of the distributed power supply to obtain a characteristic curve of the power supply capacity required to be newly added. According to the method, decision basis for optimizing planning can be provided for planning technicians and enterprise managers, and accurate investment is realized.
In summary, the invention provides a power distribution network power tight balancing method based on a distributed power supply output time sequence, and solves the problems of huge power grid investment and low equipment utilization efficiency caused by the fact that the contribution of the distributed power supply output is not considered and the annual maximum load is met as a power balancing standard, so that the global optimization planning problem is realized.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (9)
1. A power distribution network electric power tight balance method based on a distributed power supply output time sequence is characterized by comprising the following steps:
s1: extracting 24-hour daily active load values of the transformer in history of 1-3 years, clustering to form typical daily characteristic curves of various loads according to different types of power supply loads of the transformer, and calculating 24-hour load normalization values of the typical daily load of the four seasons;
s2: predicting the four-season typical daily load values of various land parcel properties by adopting a space load density method, calculating corresponding 24-hour load values according to the four-season typical daily 24-hour load normalization value calculated in the step S1, and superposing and calculating the 24-hour load values of the four-season typical days in the region;
s3: extracting 24-hour-per-day output values of the distributed power supplies in the history of 1-3 years, setting confidence probability initial values of the output of the distributed power supplies, clustering to form four-season typical confidence output characteristic curves, and calculating 24-hour confidence output normalized values of the four-season typical day;
s4: knowing the total installed capacity of the distributed power supply, calculating a 24-hour confidence output value of a typical day in four seasons according to the confidence output normalization value calculated in the step S3;
s5: based on 24-hour confidence output, 24-hour load, conventional power output and current power supply capacity of a distributed power supply on a four-season typical day, four-season power tight balance is respectively carried out, and a characteristic curve of the demand of the power supply capacity needing to be newly added is obtained through calculation.
2. The method for tightly balancing power of a power distribution network based on distributed power output timing of claim 1, wherein in step S1, the transformer power supply load category includes residential, commercial, administrative office, and industrial.
3. The method for tightly balancing power of the power distribution network based on the distributed power supply output time sequence of claim 1, wherein the clustering methods in the steps S1 and S3 include K-Means clustering and mean shift clustering.
4. The method for tightly balancing power of a power distribution network based on distributed power supply output time sequence of claim 1, wherein in step S1, the calculation formula of the four season typical day 24 hour load normalization value is:
a point-in-time load normalization value = a point-in-time load value/the daily load maximum value.
5. The method according to claim 1, wherein in step S2, the formula for calculating the typical daily load value of the district four seasons is as follows:
the typical daily load value of the land for four seasons = land building area × load density per building area.
6. The method for tightly balancing power of the power distribution network based on the distributed power generation output time sequence of claim 1, wherein in the step S3, the distributed power generation comprises photovoltaic power and wind power.
7. The method according to any one of claims 1 or 6, wherein in step S3, the initial value of the confidence probability is a confidence probability that the distributed power sources are involved in the balance, and is 90% or more and 90% or more.
8. The method for tightly balancing power of a power distribution network based on distributed power supply output time sequence of claim 1, wherein in the step S4, the calculation of 24-hour confidence output value of typical day in four seasons includes the following steps:
s41: counting the total installed capacity of various distributed power supplies;
s42: calculating the 24-hour confidence output value of the four-season typical day by adopting the following calculation formula:
the confidence output value at a certain time point = the total installed capacity of the distributed power supply, the theoretical maximum output ratio and the 24-hour confidence output normalized value of the four seasons typical day.
9. The method according to claim 1, wherein in step S5, the step of obtaining the characteristic curve of the demand for new power supply capacity includes:
s51: evaluating the current power supply capacity of a regional power grid, wherein the power supply capacity of a 110kV transformer substation is the power supply capacity without load loss when any main transformer fails according to the requirements of the power supply safety standard of the power grid, and the calculation formula of the power supply capacity of the 110kV transformer substation is as follows:
the power supply capacity of the 110kV transformer substation = transformer capacity of the transformer substation-main transformer capacity with the maximum capacity;
the power supply capacity of the 10kV feeder line is that no load is lost when any feeder line in the feeder line group is in power failure, and the calculation formula of the power supply capacity of the medium-voltage feeder line is as follows:
the feeder line power supply capacity of 10kV = present power factor x (the sum of feeder line maximum transmission capacity of a feeder line group-the maximum transmission capacity of a single feeder line)/the number of feeder line groups;
s52: calculating the newly-added power supply capacity required by the 110kV power grid at a certain moment, wherein the calculation formula is as follows:
the newly added power supply capacity required by a 110kV power grid in a certain moment region = the moment load predicted value-the moment low-voltage internet distributed power supply confidence output value-the moment low-voltage internet stable power supply output value-the moment 110kV and above special line user load-the current 110kV power grid power supply capacity,
the current 110kV power grid power supply capacity = the accumulated sum of the power supply capacities of 110kV transformer substations in the region;
s53: calculating the newly-added power supply capacity required by the 10kV power grid at a certain moment, wherein the calculation formula is as follows:
the required power supply capacity of a 10kV power grid in a certain time region = the load predicted value at the time-the confidence output value of the low-voltage online distributed power supply at the time-the output value of the low-voltage online stable power supply at the time-the load of a special line user at the time of 10kV and above-the power supply capacity of a current 10kV feeder line,
the current 10kV feeder line power supply capacity = the accumulated sum of the 10kV feeder line power supply capacities in the region;
s54: screening the maximum value of the capacity increment curve required 24 hours in a typical day of the four seasons, calculating the number of transformer substation seats required to be newly added and the number of medium-voltage feeder lines, acquiring a characteristic curve required by the power supply capacity required to be newly added,
the number of newly-built 110kV transformer substation seats = newly-added maximum power supply capacity/typical configuration capacity of a 110kV transformer substation required by a 110kV power grid in a certain time zone,
the typical configuration capacity of the 110kV transformer substation is 100 MVA;
the number of 10kV feeders needs to be increased = the maximum value of the power supply capacity required by a 10kV power grid in a certain time region/the transmission capacity of a single feeder,
wherein, the single feeder line transmission capacity is 4MVA according to the typical LGJ-240 safe transmission capacity.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010421721.5A CN111723975A (en) | 2020-05-18 | 2020-05-18 | Power distribution network electric power tight balance method based on distributed power supply output time sequence |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010421721.5A CN111723975A (en) | 2020-05-18 | 2020-05-18 | Power distribution network electric power tight balance method based on distributed power supply output time sequence |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111723975A true CN111723975A (en) | 2020-09-29 |
Family
ID=72564673
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010421721.5A Pending CN111723975A (en) | 2020-05-18 | 2020-05-18 | Power distribution network electric power tight balance method based on distributed power supply output time sequence |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111723975A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113887809A (en) * | 2021-10-11 | 2022-01-04 | 国网新疆电力有限公司巴州供电公司 | Power distribution network supply and demand balance method, system, medium and computing equipment under double-carbon target |
| CN113972697A (en) * | 2021-10-14 | 2022-01-25 | 国网天津市电力公司 | Cluster output characteristic evaluation method for large-scale grid-connected new energy |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140324532A1 (en) * | 2013-04-24 | 2014-10-30 | International Business Machines Corporation | System and method for modeling and forecasting cyclical demand systems with dynamic controls and dynamic incentives |
| CN109617057A (en) * | 2018-12-14 | 2019-04-12 | 国家电网有限公司 | Electric energy based on gridding subregion substitutes mating power grid spare capacity appraisal procedure |
| CN110717619A (en) * | 2019-09-11 | 2020-01-21 | 国网浙江省电力有限公司经济技术研究院 | Multi-scale space-time load prediction method and system for bottom-up power distribution network |
-
2020
- 2020-05-18 CN CN202010421721.5A patent/CN111723975A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140324532A1 (en) * | 2013-04-24 | 2014-10-30 | International Business Machines Corporation | System and method for modeling and forecasting cyclical demand systems with dynamic controls and dynamic incentives |
| CN109617057A (en) * | 2018-12-14 | 2019-04-12 | 国家电网有限公司 | Electric energy based on gridding subregion substitutes mating power grid spare capacity appraisal procedure |
| CN110717619A (en) * | 2019-09-11 | 2020-01-21 | 国网浙江省电力有限公司经济技术研究院 | Multi-scale space-time load prediction method and system for bottom-up power distribution network |
Non-Patent Citations (2)
| Title |
|---|
| 万川欢: "地区电网小区域负荷特性分析及负荷预测", 《CNKI优秀硕士学位论文全文库》, no. 7, 15 July 2017 (2017-07-15), pages 8 - 9 * |
| 郑伟民;叶承晋;张曼颖;王蕾;孙可;丁一;: "基于Softmax概率分类器的数据驱动空间负荷预测", 电力系统自动化, no. 09, 1 February 2019 (2019-02-01), pages 117 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113887809A (en) * | 2021-10-11 | 2022-01-04 | 国网新疆电力有限公司巴州供电公司 | Power distribution network supply and demand balance method, system, medium and computing equipment under double-carbon target |
| CN113972697A (en) * | 2021-10-14 | 2022-01-25 | 国网天津市电力公司 | Cluster output characteristic evaluation method for large-scale grid-connected new energy |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Bayer et al. | The German experience with integrating photovoltaic systems into the low-voltage grids | |
| CN110502814B (en) | Active power distribution network multi-target planning method considering energy storage and load management technology | |
| Begovic et al. | Impact of renewable distributed generation on power systems | |
| CN102427244B (en) | Large-scale photovoltaic wind power information accessing system | |
| Zhao et al. | Three representative island microgrids in the East China Sea: Key technologies and experiences | |
| Murty et al. | Impact of D‐STATCOM in distribution systems with load growth on stability margin enhancement and energy savings using PSO and GAMS | |
| JP2012217331A (en) | System and method for operating capacitor banks | |
| CN111092451B (en) | Photovoltaic consumption early warning method based on power distribution network grid | |
| CN111723975A (en) | Power distribution network electric power tight balance method based on distributed power supply output time sequence | |
| CN110783959A (en) | New forms of energy power generation system's steady state control system | |
| Kobayashi et al. | Coordinated operation of a battery energy storage system and thermal generators for supply–demand balance maintenance and efficient use of photovoltaic energy | |
| KR20130074045A (en) | A control method for controlling energy of building based ob microgrid and system for same | |
| Ogimoto et al. | Making renewables work: Operational practices and future challenges for renewable energy as a major power source in Japan | |
| CN111159619A (en) | Power distribution network planning method based on distributed power supply coordination mechanism | |
| CN108173291B (en) | Distributed new energy intelligent power distribution method based on weather factors | |
| CN102567645A (en) | Method for counting equipment of power grid and computing transmission losses on basis of on-line theoretical transmission loss computation | |
| CN111723974B (en) | Active power distribution network planning method based on power tight balance | |
| CN106651136B (en) | Day-ahead power generation plan compiling method and device for bilateral transaction | |
| CN106203742B (en) | Power grid equipment energy-saving evaluation and type selection method based on energy-saving return rate | |
| Yang et al. | Over-voltage mitigation control strategies for distribution system with high PV penetration | |
| Kadri et al. | Electrical power distribution status in West Africa: assessment and perspective overview | |
| Meyer-Huebner et al. | Dynamic optimal power flow for dimensioning and operating quarter based storage in low voltage grids | |
| Hoffmann et al. | Grid services with PV-converters in distribution systems | |
| CN111507605A (en) | Power distribution network construction benefit evaluation method and system and computer equipment | |
| Ertugrul et al. | Maximizing PV hosting capacity and community level battery storage |
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 |