CN-122015242-A - Commercial building air conditioner load regulation and control method and device based on user response behaviors

CN122015242ACN 122015242 ACN122015242 ACN 122015242ACN-122015242-A

Abstract

The invention relates to the field of load regulation and control, and particularly provides a method and a device for regulating and controlling commercial building air conditioner load based on user response behaviors. The method comprises the steps of constructing a contract response cost function based on contract response cost data, constructing a difference response cost function based on difference response cost data, carrying out weighted summation on the contract response cost function and the difference response cost function according to preset weight coefficients to obtain a comprehensive response cost function, constructing an dissatisfaction cost function based on deviation between accumulated comprehensive response electric quantity of each user in each sub-preset period and ideal response electric quantity in a building, solving an objective function based on preset ranges of contract compensation cost data and electric power purchase cost data, and carrying out load regulation and control on the building based on the solution of the objective function. The technical scheme provided by the invention realizes the balance between the user satisfaction and the power grid regulation target to a certain extent.

Inventors

CHEN HAO
CHEN AIKANG
MA YAHUI
CHEN GUANG
JIANG HAIYAN
LI CHENYANG
MENG SHIYU
SHI WENBO
XU HAIHUA

Assignees

国网(苏州)城市能源研究院有限责任公司

Dates

Publication Date: 20260512
Application Date: 20260210

Claims (11)

1. A method for regulating and controlling the air conditioning load of a commercial building based on user response behaviors, which is characterized by comprising the following steps: constructing a contract response cost function based on contract response cost data for the air conditioner load in each sub-preset time period within the preset time period, wherein the contract response cost data is the product of the power purchase cost data and the power use quantity of the sub-preset time period minus the product of contract compensation cost data and the responsive power output quantity; Constructing a differential response cost function based on differential response cost data of the air conditioner load in each sub-preset time period in the preset time period, wherein the differential response cost data is the product of the result of subtracting the differential response electric quantity from the electric power consumption of the sub-preset time period and the electric power purchase cost data of the sub-preset time period; the contract response cost function and the difference response cost function of each user in the commercial building are weighted and summed according to a preset weight coefficient to obtain a comprehensive response cost function; multiplying the result of performing high-order Taylor expansion on the deviation between the accumulated comprehensive response electric quantity and the ideal response electric quantity of each user in each sub-preset period in the commercial building by the user dissatisfaction coefficient to obtain a dissatisfaction cost function; And solving an objective function by taking a preset range of the contract compensation cost data and a preset range of the electric power purchase cost data as constraint conditions, and carrying out load regulation and control on the commercial building based on an air conditioner load corresponding to an optimal solution of the objective function, wherein the objective function is the minimum sum of the comprehensive response cost function and the dissatisfaction cost function.
2. The method of claim 1, wherein constructing a contract response cost function based on contract response cost data for the air conditioning load for each sub-predetermined period within the predetermined period comprises: Multiplying the power price adjustment proportion of different sub-preset time periods by reference power price data to obtain power purchase cost data of different sub-preset time periods, wherein the power price adjustment proportion comprises a peak time price adjustment proportion and a valley time price adjustment proportion, the peak time price adjustment proportion is greater than 1, and the Gu Shijia grid adjustment proportion is less than 1; Subtracting the product of contract compensation cost data and the response power output quantity in the sub-preset time period from the product of the power purchase cost data and the power consumption quantity in the sub-preset time period to obtain contract response cost data in the sub-preset time period, wherein the response power output quantity and the contract compensation cost data are in a linear relation; and accumulating the contract response cost data of each sub-preset time period in the preset time period to construct a contract response cost function.
3. The method of claim 1, wherein constructing a differential response cost function based on differential response cost data for the air conditioning load for each sub-predetermined period within the predetermined period comprises: Multiplying the power price adjustment proportion of different sub-preset time periods by reference power price data to obtain power purchase cost data of different sub-preset time periods, wherein the power price adjustment proportion comprises a peak time price adjustment proportion and a valley time price adjustment proportion, the peak time price adjustment proportion is greater than 1, and the Gu Shijia grid adjustment proportion is less than 1; Sequentially multiplying the power consumption, the price change sensitivity coefficient and the differential response electric quantity ratio to obtain differential response electric quantity, wherein the differential response electric quantity ratio is the ratio of the difference of the power purchase cost data and the reference power price data to the reference power price data in a sub-preset period; multiplying the power purchase cost data of the sub-preset time period by the difference between the power consumption of the sub-preset time period and the deficit response electric quantity to obtain deficit response cost data; and accumulating the difference response cost data of each sub-preset time period in the preset time period to construct a difference response cost function.
4. The method of claim 1, wherein multiplying the result of the high-order taylor expansion of the deviation between the integrated total response power and the ideal response power for each sub-predetermined period of each user in the commercial building by the user dissatisfaction coefficient to obtain the dissatisfaction cost function comprises: Based on basic punishment coefficients And user willingness factor Determining a user dissatisfaction factor Wherein, the method comprises the steps of, Is a shape parameter, and ; According to the preset weight coefficient, carrying out weighted summation on the responsive power output quantity and the differential response power quantity in the preset time period to obtain comprehensive response power quantity; adding the comprehensive response electric quantity of each user in the commercial building in each sub-preset time period in the preset time period to obtain an accumulated comprehensive response electric quantity; And multiplying the result of performing high-order Taylor expansion on the difference between the accumulated comprehensive response electric quantity and the ideal response electric quantity by the user dissatisfaction coefficient to obtain a dissatisfaction cost function.
5. The method of claim 4, wherein the penalty coefficients are based on a basis And user willingness factor Determining a user dissatisfaction factor Before the step of (a), the method comprises: Based on the user's educational level influence coefficient Coefficient of household income And user family average age influence coefficient Determining comprehensive subjective influence coefficients Wherein, the method comprises the steps of, ; Based on objective influence coefficient in case that the power purchase cost data is smaller than the reference power price data Duration of on-off state of air conditioner And minimum start-up and shut-down time of air conditioner Calculating objective willingness degree Wherein, the method comprises the steps of, The user willingness factor is expressed as: Wherein, the method comprises the steps of, In order for the power purchase price data to be available, As the reference power price data, N () is a normal distribution function for the highest power purchase cost data; based on objective influence coefficient in case that the power purchase cost data is larger than the reference power price data Duration of on-off state of air conditioner And minimum start-up and shut-down time of air conditioner Calculating objective willingness degree Wherein, the method comprises the steps of, The user willingness factor is expressed as: 。
6. The method of claim 1, wherein the power purchase cost data includes peak-time power cost data and valley-time power cost data, and solving an objective function with a preset range of the contract compensation cost and a preset range of the power purchase cost data as constraints comprises: setting the contract compensation cost data, the peak-time power cost data, and the valley-time power cost data as branch variables; dividing each branch variable into a plurality of discrete values according to a preset numerical range and constructing a multi-level decision tree according to a preset step length, wherein each level represents a branch variable of one dimension; Determining a reference decision chain in the decision tree, wherein the reference decision chain comprises initial contract compensation cost, initial peak-time power cost and initial valley-time power cost; And searching a target optimal solution in the decision tree by taking the reference decision chain as a reference, wherein the target optimal solution is the numerical value of the contract compensation expense data, the peak-time power cost data and the valley-time power cost data under the condition that the target function value is minimum.
7. The method of claim 6, wherein searching the decision tree for a target optimal solution based on the reference decision chain comprises: Updating the current decision chain into a reference decision chain under the condition that the objective function value corresponding to the current decision chain is smaller than the objective function value corresponding to the reference decision chain, and pruning the reference decision chain before updating; And pruning the current decision chain under the condition that the objective function value corresponding to the current decision chain is larger than the objective function value corresponding to the reference decision chain.
8. The method according to claim 6 or 7, characterized in that after the step of searching the decision tree for a target optimal solution based on the reference decision chain, the method further comprises: Determining sub-air conditioner power of each user in each sub-preset period based on the target optimal solution; and under the condition that the power of each sub-air conditioner is larger than or equal to the preset minimum air conditioner power and the power of each sub-air conditioner is smaller than or equal to the preset maximum air conditioner power, carrying out sectional load regulation and control on the commercial building based on the accumulated air conditioner power of each user in each sub-preset period.
9. The utility model provides a regulation and control device of commercial building air conditioner load based on user response action which characterized in that, the regulation and control device of commercial building air conditioner load based on user response action includes: The contract cost construction module is used for constructing a contract response cost function based on contract response cost data of the air conditioner load in each sub-preset time period in the preset time period, wherein the contract response cost data is obtained by subtracting the product of contract compensation cost data and the responsive power output from the product of the power purchase cost data and the power consumption in the sub-preset time period; The system comprises a response cost construction module, a response cost calculation module and a control module, wherein the response cost construction module is used for constructing a difference response cost function based on difference response cost data of air conditioner loads in each sub-preset time period in a preset time period, wherein the difference response cost data is the product of the result of subtracting difference response electric quantity from the electric power consumption of the sub-preset time period and the electric power purchase cost data of the sub-preset time period; The comprehensive cost construction module is used for carrying out weighted summation on the contract response cost function and the difference response cost function of each user in the commercial building according to a preset weight coefficient to obtain a comprehensive response cost function; the dissatisfaction cost construction module is used for multiplying a result of performing high-order Taylor expansion on the deviation between the accumulated comprehensive response electric quantity and the ideal response electric quantity of each user in each sub-preset period in the commercial building by the dissatisfaction coefficient of the user to obtain a dissatisfaction cost function; And the objective function solving module is used for solving an objective function by taking a preset range of the contract compensation cost and a preset range of the electric power purchase cost data as constraint conditions, and carrying out load regulation and control on the commercial building based on an air conditioning load corresponding to an optimal solution of the objective function, wherein the objective function is the minimum sum of the comprehensive response cost function and the dissatisfaction cost function.
10. An electronic device comprising a memory and a processor, the memory storing a computer program, the processor implementing the method of regulating commercial building air conditioning load based on user response behavior of any of claims 1 to 8 when the computer program is executed.
11. A computer storage medium having stored thereon a computer program which when executed by a processor implements the method for regulating the load of a commercial building air conditioner based on user response behaviour as claimed in any one of claims 1 to 8.

Description

Commercial building air conditioner load regulation and control method and device based on user response behaviors Technical Field The invention relates to the field of load regulation and control, in particular to a method and a device for regulating and controlling the load of a commercial building air conditioner based on user response behaviors. Background In a wide variety of urban load resources, the power consumption of commercial building air conditioning load systems accounts for a high proportion of the total power consumption of cities. Especially, in the face of a summer high-temperature environment, the load ratio of the air conditioning system is obviously increased, and the peak load of the power grid can be more than 30%. With the continuous development of smart grid and demand side management technologies, air conditioning loads are considered as very potential demand response resources due to their thermal inertia and adjustability. In the related art, demand response research for commercial building air conditioning loads is mainly focused on centralized control of an air conditioning system or automatic regulation based on preset strategies. For example, the set temperature of the air conditioner is uniformly increased in the electricity utilization peak period, the compressor or the fan is periodically started and stopped, or the thermal inertia of the building envelope structure and the indoor objects is utilized for carrying out the pre-cooling and the like. However, in practical applications, individual preferences of users within commercial buildings in terms of power usage and cost may create uncertainty in their response power, thereby affecting the accuracy of the aggregator in terms of power reporting. The existing rigid regulation strategies tend to ignore this variability, which can easily lead to reduced user satisfaction. Disclosure of Invention The invention creates the regulation and control method, the device, the electronic equipment, the storage medium and the computer program product of the commercial building air conditioner load based on the user response behavior, so as to realize the balance between the user satisfaction degree and the power grid regulation target to a certain extent. In a first aspect, the present invention provides a method for regulating and controlling a commercial building air conditioner load based on user response behavior, the method comprising: constructing a contract response cost function based on contract response cost data for the air conditioner load in each sub-preset time period within the preset time period, wherein the contract response cost data is the product of the power purchase cost data and the power use quantity of the sub-preset time period minus the product of contract compensation cost data and the responsive power output quantity; Constructing a differential response cost function based on differential response cost data of the air conditioner load in each sub-preset time period in the preset time period, wherein the differential response cost data is the product of the result of subtracting the differential response electric quantity from the electric power consumption of the sub-preset time period and the electric power purchase cost data of the sub-preset time period; the contract response cost function and the difference response cost function of each user in the commercial building are weighted and summed according to a preset weight coefficient to obtain a comprehensive response cost function; multiplying the result of performing high-order Taylor expansion on the deviation between the accumulated comprehensive response electric quantity and the ideal response electric quantity of each user in each sub-preset period in the commercial building by the user dissatisfaction coefficient to obtain a dissatisfaction cost function; And solving an objective function by taking a preset range of the contract compensation cost data and a preset range of the electric power purchase cost data as constraint conditions, and carrying out load regulation and control on the commercial building based on an air conditioner load corresponding to an optimal solution of the objective function, wherein the objective function is the minimum sum of the comprehensive response cost function and the dissatisfaction cost function. In one embodiment of the present invention, constructing a contract response cost function based on contract response cost data for an air conditioner load in each sub-predetermined period within a predetermined period includes: Multiplying the power price adjustment proportion of different sub-preset time periods by reference power price data to obtain power purchase cost data of different sub-preset time periods, wherein the power price adjustment proportion comprises a peak time price adjustment proportion and a valley time price adjustment proportion, the peak time price adjustment proportion is greater than 1, an