CN-122026386-A - Building group virtual power plant real-time regulation and control method and system based on unmanned aerial vehicle cooperation

CN122026386ACN 122026386 ACN122026386 ACN 122026386ACN-122026386-A

Abstract

The invention discloses a real-time regulation and control method and a real-time regulation and control system for building group virtual power plants based on unmanned aerial vehicle cooperation. The method comprises the steps of enabling an unmanned aerial vehicle cluster to fly to preset positions outside each building, acquiring real-time temperature data acquired by building indoor temperature sensors through a wireless network, enabling a virtual power plant control center to take the temperature data as a building indoor temperature representative value, combining real-time adjustable potential of each building air conditioning system, acquiring power grid dispatching requirements, calculating and generating optimal power adjustment quantity of each building through solving a preset optimization model, calculating perception priority scores of each building according to the optimal power adjustment quantity of each building and a data acquisition time interval, and distributing perception target buildings of the next regulation period for the unmanned aerial vehicle cluster based on the scores with the aim of minimizing total task execution cost. The invention realizes flexible collection of building group temperature data and accurate regulation and control of air conditioner load, improves real-time performance and reliability of virtual power plant response to power grid dispatching, and improves resource utilization rate.

Inventors

XUE MEIZI
YANG YONGHUA

Assignees

上海永天科技股份有限公司

Dates

Publication Date: 20260512
Application Date: 20260225

Claims (9)

1. The real-time building group virtual power plant regulation and control method based on unmanned aerial vehicle cooperation is characterized by comprising the following steps of: The unmanned aerial vehicle cluster collects indoor real-time temperature information of a target building cluster; the virtual power plant control center acquires the operation data of each building air conditioning system, and takes the indoor real-time temperature information acquired by the unmanned aerial vehicle cluster as an indoor temperature representative value of a corresponding building; The virtual power plant control center obtains power grid dispatching requirements, and generates optimal power adjustment quantity of each building by solving a preset optimization model based on the indoor temperature representative value of each building and the real-time adjustable potential range of the air conditioning system; And the virtual power plant control center calculates the perception priority score of each building according to the optimal power adjustment quantity of each building and the information representing the data state of each building, and distributes the perception target building of the next regulation period for the unmanned aerial vehicle cluster based on the perception priority score.
2. The method of claim 1, wherein the unmanned aerial vehicle cluster mining Indoor real-time temperature information of a target building group is collected, which comprises the following steps: the unmanned aerial vehicle cluster flies to a preset position outside the target building according to the flight instruction received in the current regulation period, and the real-time temperature sensed by the temperature sensor arranged in the target building is obtained through the wireless network.
3. The method of claim 1, further comprising pre-building the optimization model, comprising: The method comprises the steps of taking the power adjustment quantity of each building as an optimization variable, taking the total adjustment requirement of a power grid, the indoor temperature representative value of each building, the corresponding comfort reference value, the real-time power adjustable range and the maximum power change rate of each building as input parameters, constructing an objective function J taking the minimum comprehensive adjustment cost as a target, wherein the objective function J is a weighted sum of a normalized economic adjustment cost item and a normalized temperature comfort deviation cost item; And setting constraint conditions for the optimized variables, wherein the constraint conditions comprise power balance constraint that the sum of all building power adjustment amounts is equal to the total requirement of a power grid, adjustment capability constraint that each building power adjustment amount does not exceed the real-time adjustable range of each building power adjustment amount, and adjustment rate constraint that the difference between the current power adjustment amount and the last adjustment period power adjustment amount of each building is not more than the maximum allowable change rate of each building power adjustment amount.
4. A method according to claim 3, wherein the mathematical expression of the optimization model is: the constraint conditions are as follows: Wherein, the For the building number index, ; The total number of the buildings participating in regulation and control; For issuing to be solved Optimal power adjustment of the building air conditioning system; is the first A power adjustment cost coefficient of the building; in order to integrate the cost weight coefficients, ; Normalizing the reference value for the economic cost; is the first Indoor temperature representative value of building; is the first An indoor temperature comfort reference value of a building; Normalized reference value for temperature deviation; The total power regulation requirement of the virtual power plant is issued to the power grid dispatching center; 、 respectively the first The lower limit and the upper limit of the power regulation quantity of the building in the current regulation period; Issue the last regulation period to the first The power adjustment amount of the building; is the first Maximum rate of change of power allowed by the building air conditioning system; Is the duration of the regulation cycle.
5. The method of claim 1, wherein the information characterizing the status of the building data includes at least an absolute value of an optimal power adjustment for the building and a time interval since the last successful acquisition of temperature data by the drone.
6. The method of claim 5, wherein the virtual power plant control center calculating a perceived priority score for each building based on the optimal power adjustment for each building and information characterizing its data state, comprising: the virtual power plant control center obtains the first The absolute value of the optimal power regulating quantity calculated in the current regulating period of the building is obtained, the maximum value of the absolute values in all the participating regulating buildings is obtained, and the first building is Dividing the absolute value of the optimal power adjustment quantity of the building by the maximum value to obtain the first The importance index coefficient of the building is used for representing the importance degree of the ith building in the aspect of power adjustment; Acquiring a time interval from the last time that the unmanned aerial vehicle successfully acquires indoor temperature data of the ith building to the current moment; according to a preset weight coefficient, the first step The importance index coefficient and the state uncertainty index of the building are weighted and summed to obtain the first Perception priority scoring of a building.
7. The method of claim 1, wherein assigning the next regulatory period of perceived target buildings to the cluster of drones based on the perceived priority score comprises: According to the method, the system comprises the steps of determining a candidate building set which can be reached by each unmanned aerial vehicle in a next regulation period according to the remaining endurance time of each unmanned aerial vehicle and the geographic position of each building, generating one or more unmanned aerial vehicle building task allocation schemes based on all unmanned aerial vehicles and all buildings needing to be perceived, calculating the total task execution cost of each allocation scheme, wherein the total task execution cost is the sum of single task execution cost coefficients of all paired unmanned aerial vehicle building combinations under the allocation scheme, selecting the allocation scheme with the minimum total task execution cost as a final scheme, and issuing a perception target building instruction of the next regulation period to each unmanned aerial vehicle according to the final scheme.
8. The method of claim 7, wherein the method of determining the single task execution cost coefficient comprises: Determining unmanned aerial vehicle Fly from its current position against Dividing the flight time by a single-pass maximum flight time threshold allowed by the unmanned plane k to obtain the unmanned plane Flying to the first Normalized time-of-flight cost coefficients for a building; Acquisition of the first The perception priority grade of the building is determined, and the highest perception priority grade of all the buildings to be perceived is determined Dividing the perception priority score of the building by the highest perception priority score to obtain a first Subtracting the first value from 1 A first numerical value of the building is obtained Task urgency cost coefficients for a building; According to a preset weight coefficient, the unmanned aerial vehicle is arranged Flying to the first Normalized time-of-flight cost coefficient for building Weighting and summing task urgency cost coefficients of the building to obtain unmanned aerial vehicle This pair of combined individual tasks performs a cost factor with the i-th building.
9. Real-time regulation and control system of virtual power plant of building crowd based on unmanned aerial vehicle is cooperated, a serial communication port, including unmanned aerial vehicle crowd and virtual power plant control center, wherein: the unmanned aerial vehicle cluster is used for collecting indoor real-time temperature information of the target building cluster; The virtual power plant control center is used for acquiring the operation data of each building air conditioning system and taking the indoor real-time temperature information acquired by the unmanned aerial vehicle clusters as an indoor temperature representative value of the corresponding building; The virtual power plant control center is further used for acquiring power grid dispatching requirements, and generating optimal power adjustment quantity of each building by solving a preset optimization model based on the indoor temperature representative value of each building and the real-time adjustable potential range of the air conditioning system; The virtual power plant control center is further used for calculating a perception priority grade of each building according to the optimal power adjustment quantity of each building and information representing the data state of each building, and distributing a perception target building of the next regulation and control period for the unmanned aerial vehicle cluster based on the perception priority grade.

Description

Building group virtual power plant real-time regulation and control method and system based on unmanned aerial vehicle cooperation Technical Field The invention relates to the field of intelligent power plants, in particular to a real-time control method and system for building group virtual power plants based on unmanned aerial vehicle cooperation. Background With the rapid development of smart grids and distributed energy technologies, virtual power plants are increasingly receiving widespread attention as an important technical means for aggregating distributed energy resources to participate in grid scheduling. In building clusters such as commercial buildings, office parks and the like, an air conditioning system has remarkable load adjusting potential as a main adjustable load, and has become a key resource for realizing demand response and power grid auxiliary service of a virtual power plant. At present, a building group-oriented virtual power plant regulation and control system mainly depends on a pre-deployed wired sensor network or manual inspection mode and the like in the aspect of acquiring real-time data of indoor environments of buildings. The prior art has the defects or problems that on one hand, the deployment cost of a wired sensor network is high, the regulation period is long, the transformation is difficult, the expansibility and the flexibility are poor especially in the existing building group, and on the other hand, the real-time synchronous acquisition of the data of a plurality of buildings is difficult to realize by manual inspection or fixed monitoring, the data updating frequency is low, the timeliness is poor, and the real-time indoor environment state of each building cannot be mastered timely and accurately by a virtual power plant control center. The method further influences the accuracy of the air conditioner load regulation and control instruction, and is difficult to ensure the temperature comfort level of a user while meeting the power grid dispatching requirement, and the efficient utilization and quick response capability of the virtual power plant to the building group flexible load are restricted. In a word, the prior art lacks an effective means for dynamically, cooperatively and low-cost realizing real-time acquisition of indoor environment data of a plurality of buildings and closed-loop linkage with a virtual power plant regulation instruction, so that the overall regulation efficiency of the system is low and the resource utilization is insufficient. Disclosure of Invention In order to solve the problems in the prior art, the embodiment of the invention provides a real-time regulation and control method and system for building group virtual power plants based on unmanned aerial vehicle cooperation. As an aspect of the present invention, an embodiment of the present invention provides a real-time control method for building group virtual power plants based on unmanned aerial vehicle cooperation, including: The unmanned aerial vehicle cluster collects indoor real-time temperature information of a target building cluster; the virtual power plant control center acquires the operation data of each building air conditioning system, and takes the indoor real-time temperature information acquired by the unmanned aerial vehicle cluster as an indoor temperature representative value of a corresponding building; The virtual power plant control center obtains power grid dispatching requirements, and generates optimal power adjustment quantity of each building by solving a preset optimization model based on the indoor temperature representative value of each building and the real-time adjustable potential range of the air conditioning system; And the virtual power plant control center calculates the perception priority score of each building according to the optimal power adjustment quantity of each building and the information representing the data state of each building, and distributes the perception target building of the next regulation period for the unmanned aerial vehicle cluster based on the perception priority score. In one embodiment, the unmanned aerial vehicle cluster collects indoor real-time temperature information of a target building cluster, including: the unmanned aerial vehicle cluster flies to a preset position outside the target building according to the flight instruction received in the current regulation period, and the real-time temperature sensed by the temperature sensor arranged in the target building is obtained through the wireless network. In one embodiment, the method further comprises pre-building the optimization model, comprising: The method comprises the steps of taking the power adjustment quantity of each building as an optimization variable, taking the total adjustment requirement of a power grid, the indoor temperature representative value of each building, the corresponding comfort reference value, the real-time power adjustable ra