CN113762785A - Method for realizing motor-pumped well load flexible scheduling based on multi-data fusion - Google Patents
Method for realizing motor-pumped well load flexible scheduling based on multi-data fusion Download PDFInfo
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Abstract
The invention relates to the technical field of motor-pumped well load scheduling methods, in particular to a motor-pumped well load flexible scheduling method based on multi-data fusion. According to the invention, by collecting user soil moisture content data, motor-pumped well operation data and line load data, performing user control priority analysis calculation and combining with user control priority, motor-pumped well group control ordered power load regulation is realized, the traditional simple way of drawing a circuit to limit power is changed, the effective power supply capacity of the circuit is utilized to the maximum extent, the social power demand is met to the maximum extent while the agricultural irrigation power demand is considered, the active sensing and decision control capacity of the operation state of the medium and low voltage power distribution network is comprehensively improved, and the lean management level of the power distribution network is effectively supported to be improved.
Description
Technical Field
The invention relates to the technical field of motor-pumped well load scheduling methods, in particular to a motor-pumped well load flexible scheduling method based on multi-data fusion.
Background
The tower city agricultural irrigation distribution line has the characteristic of overlapping seasonal and peak loads, short-term overload of the line is serious, the agricultural irrigation load characteristic in summer is strong in seasonality, the peak-valley difference is large, a motor-pumped well manager manages the motor-pumped well in a way of drawing a way to limit electricity during the peak period of irrigation so as to meet the load demand of the motor-pumped well, the conditions of unbalanced load and sudden increase of load exist, the problems cannot be solved in the traditional 'source following load movement' scheduling mode, and accurate regulation and intelligent use of a platform area oriented to network load coordination can become an important technical means for improving the peak-valley regulation capability of a power grid and the utilization rate of assets of the power grid.
Disclosure of Invention
The invention provides a method for realizing flexible dispatching of motor-pumped well loads based on multi-data fusion, which overcomes the defects of the prior art, solves the problem of seasonal peak loads of a line caused by disorder of power utilization of an agricultural motor-pumped well, takes a motor-pumped well of a farmer as a controllable flexible load, participates in regulation and control of the line load, realizes peak clipping and valley filling of the line load, group control of the motor-pumped well and ordered power utilization load regulation of the farmer, and realizes ordered power utilization of the farmer.
The technical scheme of the invention is realized by the following measures: a method for realizing motor-pumped well load flexible scheduling based on multi-data fusion comprises the following steps: collecting load data of line sections and branch switches in real time, collecting energy consumption load data of each motor-pumped well in real time, collecting soil moisture content data, irrigation area and water consumption data in real time, calculating user control priority based on the collected data,
the calculation formula of the user control priority is as follows:
P= Pa*( Pb + Pc + Pd+ Pe + Pf – Pg) （1）
in the formula (1), P represents a user control priority level, Pa represents a user controllable level, Pb represents an important level of a user, Pc represents a power consumption and demand capacity level applied by the user, Pd represents a level of a ratio of actual water consumption to irrigation area of the user within three days, Pe represents a user soil moisture content level, Pf represents an accumulated power consumption time level of the user within three days, and Pg represents a down-regulated level;
and scheduling the motor-pumped well load by adopting an autonomous regulation and control strategy or a peak shaving response control strategy according to the actual load of each current user and the control priority of the user.
In equation (1), the parameter description is calculated:
(A) whether the user is controllable: whether a user signs a controlled agreement with a power supply company, if yes, Pa =1, and if not, the user does not take a control regulation object, and Pa = 0;
(B) importance level of user: the users are classified into 5 grades, rated on a 1 to 5 scale, with users of lower grades having higher controlled priority. Pb = (5-B) × 10, B representing user rating;
(C) the electricity application and installation capacity applied by the user is as follows: under the condition of line overload, the control target requirement can be quickly met by cutting off the large-capacity load, so that the user with large capacity is controlled to have high priority. Pc = 30 when the installed capacity > =200kVA, Pc =20 when the installed capacity > =150kVA, Pc =10 when the installed capacity > =100kVA, and Pc = 5 when the installed capacity < 100;
(D) the ratio of actual water usage to the area irrigated by the user in nearly three days: the higher the proportion is, the farmland is irrigated sufficiently in the near term, and the regulation priority is high. Pd = D50, D represents the ratio of actual water usage to the area irrigated by the user over three days;
(E) the soil moisture data of the user soil mainly is the average humidity of the soil: and if the average soil humidity is high, the regulation priority is high. Pe = E, E represents the user's soil average moisture level;
(F) cumulative electricity usage time (hours) by the user in the last three days: the longer the power consumption time, the higher the regulation priority. Pf = F5, F representing the cumulative electricity usage time of the user over the last three days;
(G) controlled number of times and time of user in the last week: if the user is subjected to regulation in the near term, the priority is reduced, and in order to express the requirement that the priority is lower when the user is subjected to regulation in the near term, the priority is expressed by a Fibonacci number sequence: {2, 3, 5, 8, 13, 21, 34, 55, 89, 144}, corresponding to the relationship between the recent regulation days and the priority, if the current day is regulated, the down-regulation priority coefficient is 144, yesterday is regulated, the down-regulation priority coefficient is 89, and finally the down-regulation priority is coefficient multiplied by the regulation time (hour), Pg = Σ (G × T)/10, where T is the limited hours and G represents the down-regulation priority coefficient.
Distribution automation is a reliable communication, so load monitoring data for line sections and branch switches is considered to be on-line, and each switch is unconditionally controllable.
Considering the factor that the real-time data of part of users is not visible due to communication, that is, the communication is not on line, and part of load on the line is rigid load (public change) or users who do not sign controlled protocol, so the load of the controllable users is less than the actual line load.
The control output comprises line section, branch switch, and low voltage main switch of user distribution transformer.
The following is further optimization or/and improvement of the technical scheme of the invention:
when the feeder line, the branch line and the local load of the platform area are out of limit and alarmed (namely, the load overload reaches the control threshold), an autonomous regulation and control strategy is executed, wherein the autonomous regulation and control strategy comprises the following steps:
(1) analyzing the reason of the overload of the load, whether the overload of the branch is overload or the overload of the section, and starting a corresponding control strategy;
(2) branch overload control strategy: calculating the real-time load of the user under the overload branch, if the real-time load is greater than the load reduction regulation and control requirement, selecting the users participating in the control according to the user control priority, and generating a control command; if the load reduction requirement cannot be met, directly controlling the branch switch to generate a branch switch control strategy;
(3) segmented overload: firstly, considering load transfer, and seeing whether the contacted opposite side feeder line can receive the transferred load, if the transferred load can be transferred, a transfer strategy is generated, otherwise, a local control strategy is carried out;
(4) an in-place control strategy: deleting and selecting according to the control priority of the user, the branch line and the main line, according to the real-time load of the real-time online user and in combination with the control priority of the user, implementing the control strategy of the user under the condition of meeting the total regulation and control requirement, and generating a user control command (user control output).
According to a preset regulation strategy, active regulation is realized, and the local overload condition is eliminated. And recording the regulation and control process.
The peak shaving response control strategy is executed according to the peak shaving requirement scheduled by the main network, and the peak shaving response control strategy comprises the following steps:
(1) when the main network dispatching has no peak regulation response requirement, firstly considering a load transfer strategy to see whether the opposite side line can receive the transfer load, if the transfer load can be transferred, generating a load transfer strategy (load transfer control output), otherwise, performing a local control strategy; when the main network has a peak shaving response requirement, the interior of the line is controlled (by adopting a local control strategy);
(2) and (3) load transfer strategy: analyzing the load of the current section switch according to the regulation load requirement, determining which section load is transferred to meet the regulation load requirement, and generating a corresponding control output command (a contact switch and a section switch);
(3) an in-place control strategy: deleting and selecting according to the control priority of the user, the branch line and the main line, according to the real-time load of the real-time online user and in combination with the control priority of the user, implementing the control strategy of the user and generating a user control command under the condition of meeting the total regulation and control requirement.
When the main network scheduling puts forward a peak regulation requirement, implementing peak regulation control according to the peak regulation load; meanwhile, a process monitoring and regulation feedback evaluation mechanism is established, and a means for further intervention of a dispatcher is provided.
The control outputs (control commands) include line segments, branch switches, and low voltage master switches for the customer distribution.
The invention provides a flexible load control strategy based on big data fusion aiming at the characteristics of agricultural irrigation loads in Tower city area of Xinjiang, and by acquiring soil moisture content data of users, operation data of motor-pumped wells and line load data and analyzing and calculating user control priority, the user control priority is a core flexible control algorithm of the invention, so that orderly power load adjustment of motor-pumped wells and flexible dispatching control of motor-pumped wells are realized, the simple way of pulling circuits and limiting power in the past is changed, the effective power supply capacity of the circuits is utilized to the maximum extent, the social power demand is met to the maximum extent while the agricultural irrigation power demand is met, the active perception and decision control capacity of the operation state of the medium and low voltage distribution network is comprehensively improved, and the lean management level of the distribution network is effectively supported to be improved.
Drawings
FIG. 1 is a flow chart of an autonomous regulatory strategy.
Fig. 2 is a flow chart of a peak shaver response control strategy.
Detailed Description
The present invention is not limited by the following examples, and specific embodiments may be determined according to the technical solutions and practical situations of the present invention.
The invention is further described below with reference to the following examples:
example (b): the method for realizing motor-pumped well load flexible scheduling based on multi-data fusion comprises the following steps: collecting load data of line sections and branch switches in real time, collecting energy consumption load data of each motor-pumped well in real time, collecting soil moisture content data, irrigation area and water consumption data in real time, calculating user control priority based on the collected data,
the calculation formula of the user control priority is as follows:
P= Pa*( Pb + Pc + Pd+ Pe + Pf – Pg) （1）
in the formula (1), P represents a user control priority level, Pa represents a user controllable level, Pb represents an important level of a user, Pc represents a power consumption and demand capacity level applied by the user, Pd represents a level of a ratio of actual water consumption to irrigation area of the user within three days, Pe represents a user soil moisture content level, Pf represents an accumulated power consumption time level of the user within three days, and Pg represents a down-regulated level;
and scheduling the motor-pumped well load by adopting an autonomous regulation and control strategy or a peak shaving response control strategy according to the actual load of each current user and the control priority of the user.
Further, as shown in fig. 1, when the feeder, the branch line, and the local load of the distribution room are out of limit and alarmed (i.e. the load overload reaches the control threshold), an autonomous regulation and control policy is implemented, where the autonomous regulation and control policy includes:
(1) analyzing the reason of the overload of the load, whether the overload of the branch is overload or the overload of the section, and starting a corresponding control strategy;
(2) branch overload control strategy: calculating the real-time load of the user under the overload branch, if the real-time load is greater than the load reduction regulation and control requirement, selecting the users participating in the control according to the user control priority, and generating a control command; if the load reduction requirement cannot be met, directly controlling the branch switch to generate a branch switch control strategy;
(3) segmented overload: firstly, considering load transfer, and seeing whether the contacted opposite side feeder line can receive the transferred load, if the transferred load can be transferred, a transfer strategy is generated, otherwise, a local control strategy is carried out;
(4) an in-place control strategy: deleting and selecting according to the control priority of the user, the branch line and the main line, according to the real-time load of the real-time online user and in combination with the control priority of the user, implementing the control strategy of the user under the condition of meeting the total regulation and control requirement, and generating a user control command (user control output); if the current visible and controllable user can not meet the regulation and control requirement, firstly selecting each branch switch load, and if the control branch switch can meet the regulation and control requirement, generating a branch switch control strategy (branch switch control output); if all branches are cut off and the regulation requirement cannot be met, the section switch can be selected for control, the switch with the actual load closest to the switch meeting the regulation requirement is selected, and a control command (section switch control output) is generated.
According to a preset regulation strategy, active regulation is realized, and the local overload condition is eliminated. And recording the regulation and control process.
Further, as shown in fig. 2, according to the peak shaving requirement of the master network scheduling, a peak shaving response control strategy is executed, where the peak shaving response control strategy includes:
(1) when the main network dispatching has no peak regulation response requirement, firstly considering a load transfer strategy to see whether the opposite side line can receive the transfer load, if the transfer load can be transferred, generating a load transfer strategy (load transfer control output), otherwise, performing a local control strategy; when the main network has a peak shaving response requirement, the interior of the line is controlled (by adopting a local control strategy);
(2) and (3) load transfer strategy: analyzing the load of the current section switch according to the regulation load requirement, determining which section load is transferred to meet the regulation load requirement, and generating a corresponding control output command (a contact switch and a section switch);
(3) an in-place control strategy: deleting and selecting according to the control priorities of the users, the branch lines and the main lines, according to the real-time loads of real-time online users and in combination with the control priorities of the users, implementing the control strategy of the users under the condition of meeting the total regulation and control requirements, and generating a user control command; if the current visible and controllable user can not meet the regulation and control requirement, firstly selecting each branch switch load, and if the control branch switch can meet the regulation and control requirement, generating a branch switch control strategy (branch switch control output); if all branches are cut off and the regulation requirement cannot be met, the section switch can be selected for control, the switch with the actual load closest to the switch meeting the regulation requirement is selected, and a control command (section switch control output) is generated.
Based on the general technical thought of a power distribution station intelligent Internet of things system, the problem of seasonal peak load of a line caused by the disorder of power utilization of an agricultural motor-pumped well in a tower city area is combined, the technical framework advantages of the Internet of things of an intelligent terminal in the station area are fully exerted, the cloud edge collaborative computing technology is utilized, the ordered management and control of regional power utilization taking the station area as a unit are constructed, a brand-new collaborative means and an optimization capability are provided for the power utilization of the agricultural motor-pumped well, and accurate irrigation, energy utilization and soil moisture content three-in-one balance service is provided for each user.
The invention is based on flexible load control, fully releases the power supply capacity of the existing distribution network line, efficiently utilizes the redundant capacity of the distribution network, realizes mutual power compensation between the distribution networks in different areas and different transformer areas, and orderly controls the energy utilization, thereby ensuring the safety of line operation and the energy utilization requirements of agricultural irrigation users.
The technical characteristics form an embodiment of the invention, which has strong adaptability and implementation effect, and unnecessary technical characteristics can be increased or decreased according to actual needs to meet the requirements of different situations.
Claims (3)
1. A method for realizing motor-pumped well load flexible scheduling based on multi-data fusion is characterized by comprising the following steps: collecting load data of line sections and branch switches in real time, collecting energy consumption load data of each motor-pumped well in real time, collecting soil moisture content data, irrigation area and water consumption data in real time, calculating user control priority based on the collected data,
the calculation formula of the user control priority is as follows:
P= Pa*( Pb + Pc + Pd+ Pe + Pf – Pg) （1）
in the formula (1), P represents a user control priority level, Pa represents a user controllable level, Pb represents an important level of a user, Pc represents a power consumption and demand capacity level applied by the user, Pd represents a level of a ratio of actual water consumption to irrigation area of the user within three days, Pe represents a user soil moisture content level, Pf represents an accumulated power consumption time level of the user within three days, and Pg represents a down-regulated level;
and scheduling the motor-pumped well load by adopting an autonomous regulation and control strategy or a peak shaving response control strategy according to the actual load of each current user and the control priority of the user.
2. The method for achieving flexible motor-pumped well load scheduling based on multi-data fusion as claimed in claim 1, wherein when a feeder line, a branch line and a platform area local load are out-of-limit and alarmed, an autonomous regulation and control strategy is implemented, the autonomous regulation and control strategy comprising:
(1) analyzing the reason of the overload of the load, whether the overload of the branch is overload or the overload of the section, and starting a corresponding control strategy;
(2) branch overload control strategy: calculating the real-time load of the user under the overload branch, if the real-time load is greater than the load reduction regulation and control requirement, selecting the users participating in the control according to the user control priority, and generating a control command; if the load reduction requirement cannot be met, directly controlling the branch switch to generate a branch switch control strategy;
(3) segmented overload: firstly, considering load transfer, if the side feeder can receive the load, generating a transfer strategy, otherwise, performing an in-situ control strategy;
(4) an in-place control strategy: deleting and selecting according to the control priority of the user, the branch line and the main line, according to the real-time load of the real-time online user and in combination with the control priority of the user, implementing the control strategy of the user and generating a user control command under the condition of meeting the total regulation and control requirement.
3. The method for achieving flexible dispatching of motor-pumped well load based on multi-data fusion as claimed in claim 1 or 2, wherein a peak shaving response control strategy is executed according to the peak shaving requirement of the main network dispatching, and the peak shaving response control strategy comprises:
(1) when the main network dispatching has no peak regulation response requirement, firstly considering a load transfer strategy, and judging whether the opposite side line can receive the transfer load, if so, generating the load transfer strategy, otherwise, performing a local control strategy; when the main network has a peak shaving response requirement, the interior of the line is controlled;
(2) and (3) load transfer strategy: analyzing the load of the current section switch according to the regulation load requirement, determining which section load is transferred to meet the regulation load requirement, and generating a corresponding control output command;
(3) an in-place control strategy: deleting and selecting according to the control priority of the user, the branch line and the main line, according to the real-time load of the real-time online user and in combination with the control priority of the user, implementing the control strategy of the user and generating a user control command under the condition of meeting the total regulation and control requirement.
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