CN-122000961-A - New energy consumption optimizing method for power distribution network considering service life loss of transformer

CN122000961ACN 122000961 ACN122000961 ACN 122000961ACN-122000961-A

Abstract

A new energy consumption optimizing method for a power distribution network, which takes service life loss of a transformer into consideration, calculates real-time load rate and power deviation through real-time state sensing and ultra-short term prediction, introduces event trigger zone bit, carries out event trigger judgment, namely triggers a forced safety mode when the transformer is overloaded forward or backward, and when the load rate of the transformer is in a safety threshold, the system is in an economic tracking mode, namely energy storage finely adjusts the power deviation according to a reference scheduling plan so as to maintain economy of a plan in the future and not adjust flexible load. According to the invention, through the Arrhenius reaction dynamics theory, the quantitative relation between the temperature of the hot spot of the transformer winding and the service life loss is established, the service life loss is converted into specific economic cost, the specific economic cost is incorporated into the whole system optimization target, the deep fusion of the health state and the operation economy of equipment is realized, the forced correction strategy is respectively executed for forward overload and reverse overload, the prediction error risk caused by the randomness of the photovoltaic output is effectively solved, and the decoupling and the cooperation of the economic dispatch and the physical safety control are realized.

Inventors

ZUO JUAN
Hu Fengtong
JIAO YE
JI YU
WANG WENBO
XU CHONGXIN
LV GUANGXIAN
FA WEI
YANG HONGLEI
MA SHENGKUI
ZHANG YING

Assignees

国网上海能源互联网研究院有限公司

Dates

Publication Date: 20260508
Application Date: 20260207

Claims (8)

1. The utility model provides a new energy consumption optimizing method of distribution network, which is characterized by comprising the following steps: step 1, real-time state sensing and ultra-short term prediction calculation of real-time load rate and power deviation; Step 2, introducing event trigger zone bit And triggering a forced safety mode when the transformer is overloaded in the forward direction or the reverse direction, and when the transformer load rate is within a safety threshold, enabling the system to be in an economic tracking mode, namely enabling the energy storage to finely adjust the power deviation according to a reference scheduling plan so as to maintain the economy of a day-ahead plan and not adjust the flexible load.
2. The method for optimizing new energy consumption of a power distribution network according to claim 1, wherein the forced safety mode comprises: When the transformer is in forward overload, giving up economic constraint of a project before the day, and performing emergency control according to the principle of energy storage discharge and load shedding, namely, the energy storage system performs discharge operation with maximum capacity, and based on a day-ahead instruction, the minimum discharge power required by eliminating out-of-limit is overlapped and limited by the maximum discharge power of equipment, and when the maximum discharge power of the energy storage reaches the maximum discharge power, the out-of-limit cannot be eliminated, the cutting of the interruptible load is forcedly triggered; and when the transformer is reversely overloaded, emergency control is performed according to an energy storage charging and discarding strategy, namely energy storage forced charging is performed, and when the energy storage is full, the photovoltaic output is limited.
3. The method for optimizing new energy consumption of power distribution network according to claim 1, wherein the fine adjustment means that the system is in an economic tracking mode when the transformer load rate is within a safety threshold, that is, the energy storage fine adjusts the power deviation according to a reference scheduling plan, so that the economy of a future plan is maintained while the flexible load is not adjusted.
4. The method for optimizing new energy consumption of power distribution network according to claim 1, wherein the reference scheduling plan is obtained by: step i, after collecting prediction data, transformer parameters and basic data, calculating hot spot temperature and acceleration factors based on an Arrhenius reaction dynamics model; And ii, constructing a multi-objective optimization function containing the life loss cost of the transformer in the step i, and solving to obtain a reference scheduling plan after applying constraint conditions.
5. The power distribution network new energy consumption optimizing method considering life loss of transformer according to any one of claims 1 to 4, characterized by comprising the following specific steps: step 1, calculating the real-time load rate and power deviation by real-time state sensing and ultra-short term prediction, wherein the deviation is necessarily generated in the real-time operation stage by a day-ahead scheduling plan due to the random fluctuation of photovoltaic output, and the real-time load rate is calculated by collecting the source load operation data in real time through an intelligent sensing terminal deployed in a distribution area Power deviation Wherein, superscript 、 Representing real-time value and day-ahead optimized value respectively; 、 Respectively real-time load and photovoltaic power, Representing the time; Rated capacity of the transformer; Step 2, introducing event trigger zone bit And (3) performing event triggering judgment according to the comparison result of the real-time load rate and the load rate threshold value obtained in the step (1), wherein the event triggering judgment specifically comprises the following steps: 2.1 calculating event trigger flag bit Wherein: Is a load factor threshold; 2.2, judging event trigger zone bit, specifically comprising: ① When (when) Determining that the transformer is overloaded forward and triggering a forced safety mode, wherein economic constraints planned before the day are abandoned, and emergency control is carried out according to the principle of energy storage discharging and load shedding, namely the energy storage system carries out discharging operation with maximum capacity, and the minimum discharging power required for eliminating out-of-limit is overlapped and eliminated on the basis of the instructions before the day and is limited by the maximum discharging power of equipment When the maximum discharge power of the stored energy is still unable to eliminate out-of-limit, the cutting off of the interruptible load is forcedly triggered, and the corrected load instruction is obtained as follows: Wherein, response amount ; 、 Respectively storing energy and discharging power in real time and in the day before; The maximum discharge power is stored; 、 the real-time and day-ahead interruptible load power respectively; ② When (when) The reverse overload of the transformer is judged, and a forced safety mode is triggered, wherein the photovoltaic inversion is serious at the moment, and the emergency control is carried out according to an energy storage charging and discarding strategy, namely the energy storage forced charging: When the energy storage is full, the photovoltaic output is required to be limited, and the reduced photovoltaic output command is required to be obtained Wherein the photovoltaic reduction ; 、 Respectively storing energy and charging power in real time and in the day before; maximum charging power for energy storage; 、 the real-time and day-ahead interruptible load power respectively; In the real-time correction stage, the transferable load is always unchanged, and the load cannot finish the rescheduling of the energy conservation in a short time, so that the load (IL) can be interrupted to participate in the emergency response of the forward overload; ③ When (when) And judging that the transformer load rate is in a safety threshold and the system is in an economic tracking mode, namely, the energy storage finely adjusts the power deviation according to the reference scheduling plan so as to maintain the economical efficiency of the day-ahead plan and not adjust the flexible load: Wherein: To smooth the coefficients, take the values 0,1, To track the power for the stored energy, Wherein: 、 The load can be transferred in real time and day before; 、 real-time and day-ahead interruptible loads, respectively.
6. The method for optimizing new energy consumption of power distribution network according to claim 5, wherein the reference scheduling plan is obtained by: step i, after collecting prediction data, transformer parameters and basic data, calculating hot spot temperature and acceleration factors based on an Arrhenius reaction dynamics model, wherein the method specifically comprises the following steps of carrying out economic quantification on life loss to obtain life loss cost of the transformer, and specifically comprises the following steps: Wherein: is investment cost and total life loss of transformer , The expected insulation life of the transformer; And ii, constructing a multi-objective optimization function containing the life loss cost of the transformer in the step i, and solving to obtain a reference scheduling plan after applying constraint conditions.
7. The method for optimizing new energy consumption of power distribution network according to claim 6, wherein said step ii comprises the steps of: Constructing an objective function, wherein the optimization objective is to minimize the total cost of the system in a dispatching period, including electricity purchasing cost, energy storage full period cost, light discarding punishment cost and transformer life loss cost, and the optimization objective is specifically as follows: Wherein: The total number of scheduling time periods; Is that Time period electricity purchase and sale costs; Is that Time period energy storage full period cost; Is that The time period light discarding punishment cost; Is that Time-interval flexible load scheduling costs; b, setting constraint conditions including power balance constraint conditions Physical constraint of energy storage 、、、 Flexible load restraint 、、 Wherein: Is that Time period rigid load power; c, solving through mixed integer nonlinear solution by adopting a CPLEX solver to obtain a reference scheduling plan, wherein the method specifically comprises the following steps: 、、 And 。
8. The method for optimizing new energy consumption of power distribution network according to claim 7, wherein the electricity purchasing and selling cost is specifically as follows: Wherein: 、 Respectively is Time-period electricity purchase and electricity selling price; 、 Respectively is Time period electricity purchasing and electricity selling power; the total period cost of energy storage is specifically as follows: Wherein: investment cost per unit capacity; is the energy storage life; scheduling a number of time periods for a year; The unit power running cost; the operation and maintenance cost is per unit capacity; Is the rated capacity of energy storage; The light discarding punishment cost is specifically as follows: Wherein: the unit light rejection penalty coefficient; Is that The period of available photovoltaic power; Is that The photovoltaic power is actually utilized in the period; The flexible load scheduling cost is specifically as follows: Wherein: 、 A load compensation unit price and a load scheduling unit price which can be transferred for the interruption; Is the original reference power.

Description

New energy consumption optimizing method for power distribution network considering service life loss of transformer Technical Field The invention relates to a technology in the field, in particular to a new energy consumption optimization method for a power distribution network, which takes service life loss of a transformer into account. Background Along with the increasing emphasis of global energy crisis and environmental problems, the construction of a clean, low-carbon, safe and efficient energy system has become a national consensus. The distribution network is used as a key hub for connecting energy production and consumption, the unprecedented reform pressure and development opportunity are faced, the access requirement of large-scale distributed new energy (such as distributed photovoltaic and distributed wind power) must be adapted, and the bearing capacity and flexibility of the system are improved. Disclosure of Invention Aiming at the defects existing in the prior art, the invention provides a new energy consumption optimizing method of a power distribution network for considering the service life loss of a transformer, establishes a quantitative relation between the hot spot temperature of a transformer winding and the service life loss through Arrhenius reaction dynamics theory, converts the service life loss into specific economic cost, is incorporated into a whole system optimizing target, realizes deep fusion of the health state and the operation economy of equipment, and breaks through the limitation that the long-term economy and the instantaneous safety are difficult to be considered in the traditional single time scale scheduling. And in the real-time scale, introducing an event trigger mechanism based on a transformer safety domain, and respectively executing a forced correction strategy aiming at forward overload and reverse overload, thereby effectively solving the prediction error risk caused by the randomness of the photovoltaic output and realizing decoupling and coordination of economic dispatch and physical safety control. The invention is realized by the following technical scheme: the invention relates to a new energy consumption optimizing method of a power distribution network considering service life loss of a transformer, which comprises the following steps: step 1, real-time state sensing and ultra-short term prediction calculation of real-time load rate and power deviation, Step 2, introducing event trigger zone bitAnd triggering a forced safety mode when the transformer is overloaded in the forward direction or the reverse direction, and when the transformer load rate is within a safety threshold, enabling the system to be in an economic tracking mode, namely enabling the energy storage to finely adjust the power deviation according to a reference scheduling plan so as to maintain the economy of a day-ahead plan and not adjust the flexible load. The forced security mode includes: When the transformer is in forward overload, giving up economic constraint of a project before the day, and performing emergency control according to the principle of energy storage discharge and load shedding, namely, the energy storage system performs discharge operation with maximum capacity, and based on a day-ahead instruction, the minimum discharge power required by eliminating out-of-limit is overlapped and limited by the maximum discharge power of equipment, and when the maximum discharge power of the energy storage reaches the maximum discharge power, the out-of-limit cannot be eliminated, the cutting of the interruptible load is forcedly triggered; and when the transformer is reversely overloaded, emergency control is performed according to an energy storage charging and discarding strategy, namely energy storage forced charging is performed, and when the energy storage is full, the photovoltaic output is limited. In the real-time correction stage, the transferable load is always unchanged, so that the load cannot complete the rescheduling of the energy conservation in a short time, and the load (IL) can only be interrupted to participate in the emergency response of the forward overload. The fine adjustment means that the transformer load rate is in a safety threshold, the system is in an economic tracking mode, namely the energy storage fine adjusts the power deviation according to a reference scheduling plan, so that the economy of a day-ahead plan is maintained, and meanwhile, the flexible load is not adjusted. The reference scheduling plan is obtained by the following steps: step i, after collecting prediction data, transformer parameters and basic data, calculating hot spot temperature and acceleration factors based on an Arrhenius reaction dynamics model; And ii, constructing a multi-objective optimization function containing the life loss cost of the transformer in the step i, and solving to obtain a reference scheduling plan after applying constraint conditions. Drawings FI