CN115130899B - Kmeas-GM-based air conditioner load day-ahead response capacity evaluation method - Google Patents
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Abstract
The invention discloses an air conditioner load day-ahead response capability assessment method based on Kmeas-GM, which comprises the following steps: collecting air conditioner operation information; according to the operating characteristics of different air conditioners, performing clustering analysis on the acquired data based on a Kmeas clustering algorithm, and dividing mass air conditioners into a plurality of classes; predicting the number of the air-conditioning users of different types in the day before the clustering based on a GM algorithm to obtain the number of the air-conditioning users of different types in the unused time period in the day before; predicting the number of users day by day based on the day of different types of air conditioners, and evaluating the maximum and minimum response capabilities of different types of air conditioners day by day; and finally, constructing an air conditioner load day-ahead response capability evaluation model, aggregating different types of air conditioner loads day-ahead based on the day-ahead response capabilities of different types of air conditioner users, and evaluating the day-ahead response capability of the air conditioner loads day-ahead. The method of the invention fully excavates the day-ahead response capability of the air conditioner load, grasps the upper and lower limit values of the day-ahead response power, supports the formulation of the day-ahead operation plan of the power grid and lightens the regulation and control pressure of the main grid.
Description
Technical Field
The invention relates to the field of power systems, in particular to a Kmeas-GM-based air conditioner load day-ahead response capacity evaluation method.
Background
The number of thermal power generating units taking coal as main power is gradually reduced, the thermal power generating units have good controllability, certain inertia and good disturbance resistance, and can well support the operation of a power grid. On the other hand, fossil energy such as coal is mainly used in the thermal power generating unit, and the thermal power generating unit can burn the coal in the power generating process to generate greenhouse gases such as carbon dioxide, so that the proportion of the thermal power generating unit in a power system is imperative to be reduced. The proportion of new energy accessed into the power system is gradually improved, wherein the new energy represented by wind power and photovoltaic has the characteristics of strong fluctuation, high randomness, high uncontrollable performance, poor disturbance rejection capability and the like, and great challenges are brought to the operation of the power system. For a novel power system taking new energy as a theme, the regulation capability of the novel power system is improved, the essential requirements of the operation of the novel power system are met by power fluctuation and uncertain disturbance, and the novel power system is particularly important for supporting the operation of the novel power system. However, the traditional power system has a small quantity of adjustable resources, and the excavation of adjustable resources on the load side is insufficient, so that the adjustability on the load side cannot be fully utilized. In recent years, the proportion of air conditioner load is gradually improved, the air conditioner mainly generates load for meeting the comfort of human bodies, and the human body comfort has a certain range, so that the air conditioner load has a certain adjusting range, the day-ahead adjusting capacity of the air conditioner is excavated, and the air conditioner has important significance for making a day-ahead operation plan for supporting a novel power system, supporting power grid dispatching and reducing regulation and control pressure of a main network.
Disclosure of Invention
In order to solve the problems, the invention provides a Kmeas-GM-based assessment method for the day-ahead response capability of the air conditioner load, which can fully exploit the day-ahead response capability of the air conditioner load.
In order to achieve the purpose, the invention is realized by the following technical scheme:
the invention relates to a Kmeas-GM-based method for evaluating day-ahead response capability of air conditioner load, which comprises the following steps:
step 1, collecting daily air conditioner operation information;
step 2, providing an air conditioner load classification model based on kmeas clustering, carrying out clustering analysis on air conditioner operation information based on a kmeas clustering algorithm, and classifying mass air conditioners into a plurality of classes;
step 3, training the number of different types of air conditioners in the day ahead based on a gray model GM, predicting the number of different types of air conditioners in the day ahead, and providing a prediction method of the number of different types of air conditioner users in the day ahead based on the GM;
and 5, constructing an air conditioner load day-ahead response capability evaluation model, aggregating different types of air conditioner loads in the day-ahead based on the day-ahead response capabilities of different types of air conditioner users, and evaluating the day-ahead response capability of the air conditioner loads.
The invention is further improved in that: the information collected in the step 1 comprises indoor temperature information, indoor temperature setting upper and lower limits, air conditioner starting period, air conditioner outage period and air conditioner load information at different time, and the expression is as follows:
in the formula: x i (T) is collected information of the ith air conditioner, T i n (T) indoor temperature information of the ith air conditioner at different time T, T i up (t),T i dn (t) upper and lower limit values respectively set for the indoor temperature of the ith air conditioner,
respectively as the starting period and the shutdown period of the air conditioner i i Is the load information of the air conditioner i.
The invention is further improved in that: the clustering process in step 2 is as follows:
step 2.1, inputting a sample set, and randomly selecting k samples from a data set D as initial k centroid vectors, wherein the expression is as follows:
{μ 1 ,μ 2 ,…,μ k }
wherein d is i For the ith sample set, clustering a cluster tree k, wherein m is the number of samples and the maximum iteration number N;
step 2.2, calculate sample d i And each centroid vector mu j A distance of (j =1,2, ..., k) is expressed as follows:
will d i L with smallest label ij Fall under C j ；
Step 2.3, recalculating the centroid of the sample, wherein the expression is as follows:
returning to the step 2.2 until all samples are calculated and classified;
and 2.4, outputting the final classification, wherein the expression is as follows:
C＝{c 1 ,c 2 ,…,c k }。
the invention is further improved in that: the data samples of the number of different types of air conditioners in step 3 are as follows:
in the formula:
for a set of data prior to the day of the kth class of air conditioner, be->
The air conditioner data volume of the kth class air conditioner at the nth time point is obtained; by accumulating the raw data to weaken the random orderThe column volatility and randomness result in a new sequence, and the expression is as follows:
in the formula:
the number of the air conditioners of the kth class at the moment of the day-ahead time t is predicted. and a and u are the grey prediction development coefficient and the grey acting quantity, and are obtained by a least square method.
The invention is further improved in that: the step 4 is specifically operated as follows:
step 4.1, predicting the number of different types of air conditioners in the day before based on GM algorithm
Calculating the maximum and minimum power values of different types of air conditioners>
The expression is as follows:
in the formula:
respectively, maximum and minimum response capacities at time t of the kth type air conditioner>
For the number at a time t before the day of the kth class of air conditioner>
The maximum and minimum temperatures, eta, set for the kth air conditioner before day and in the room respectively k For an average load power in a single period of a kth air conditioner>
Respectively, the start-stop operation period, P, of the kth air conditioner k Load power of the kth air conditioner;
step 4.2, the maximum and minimum power values of various air conditioners
Summing to obtain the day-ahead power upper limit and the day-ahead power lower limit of the load aggregation quotient control air conditioner, wherein the expression is as follows:
in the formula:
respectively the predicted total maximum and minimum adjusting capacity, N, of the k-type air conditioners under the control of the day-ahead load aggregators K Is the total category number of the k types of air conditioners.
The invention has the beneficial effects that: the method can master the day-ahead response capability of the air conditioner load, improves the formulation quality of the day-ahead operation plan of the novel power system, reduces the regulation and control pressure of the main network, and realizes the double-carbon target of the power-assisted power grid in the early days, thereby having important significance.
Drawings
FIG. 1 is a schematic flow chart of the operation of the method of the present invention.
Fig. 2 is a cluster analysis of air conditioner operation characteristics.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, 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, so that those skilled in the art can implement the technical solutions in reference to the description text.
As shown in fig. 1, the present invention is a method for evaluating the day-ahead response capability of an air conditioner load based on Kmeas-GM, comprising the following steps:
step 1, collecting daily air conditioner operation information including indoor temperature information, indoor temperature setting upper and lower limits, air conditioner starting period, air conditioner outage period and air conditioner load information, and providing support for air conditioner cluster analysis and prediction of the number of different types of air conditioners in the day ahead, wherein the collected information expression is as follows:
in the formula: x i (T) is collected information of the ith air conditioner, T i n (T) indoor temperature information of the ith air conditioner at different time T, T i up (t),T i dn (t) upper and lower limit values respectively set for the indoor temperature of the ith air conditioner,
respectively as the start-up period and the shutdown period of the air conditioner, P i Is the load information of the air conditioner i.
Step 2, providing an air conditioner load classification model based on kmeas clustering, and classifying massive air conditioner loads according to start-up/shut-down operation cycles and air conditioner loads of the air conditioners, wherein the air conditioner load classification model is shown in fig. 2;
the specific clustering process is as follows:
1) Input sample set, as shown in equation (2), d i For the ith sample set, clustering cluster tree k, m is the number of samples, maximum iteration number N, and randomly selecting k samples from data set D as initial k centroid vectors, as shown in formula (3):
{μ 1 ,μ 2 ,…,μ k } (3)
2) Calculating a sample d i And each centroid vector mu j The distance of (j =1,2, \ 8230;, k) is given by the formula (4), and d is expressed by i L with minimum mark ij Fall under C j ；
3) Recalculating the centroid of the samples as shown in equation (5), (j =1,2, ..., k), returning to step 2), until all samples are classified by calculation;
4) Outputting the final classification as shown in formula (6);
C＝{c 1 ,c 2 ,…,c k } (6)。
step 3, training the number of different types of air conditioners in the day ahead based on a grey model GM (gateway model, GM), predicting the number of different types of air conditioners in the day ahead, and providing a prediction method of the number of different types of air conditioner users in the day ahead based on the GM; wherein, the number data samples of different types of air conditioners in the day ahead are as follows:
in the formula:
for a day-ahead data set of a kth class of air conditioners>
The data volume of the air conditioner at the nth time point of the kth type air conditioner.
The original data is accumulated to weaken the fluctuation and randomness of the random sequence, so as to obtain a new sequence, as shown in formula (8):
in the formula:
for predicting the number of air conditioners of the kth class at the moment of the day-ahead time t. and a and u are the grey prediction development coefficient and the grey acting quantity, and are obtained by a least square method.
quantity of different types of air conditioners used in days predicted based on GM algorithm
Calculating the maximum and minimum power values of different types of air conditioners>
As shown in equations (9) - (10):
in the formula:
the maximum response capacity and the minimum response capacity of the kth type air conditioner at the moment t are respectively. />
The number of the kth class air conditioners at the time t before the day. />
The temperature of the kth air conditioner is the outdoor temperature before the day, and the maximum temperature and the minimum temperature set indoors are respectively. Eta k The average load power within a single cycle is the kth class air conditioner. />
Respectively, the start-stop operation period, P, of the kth air conditioner k Is the load power of the kth air conditioner.
Maximum and minimum power values of different air conditioners
The summation is carried out, so as to obtain the day-ahead power upper limit of the load aggregation provider controlled air conditioner, as shown in the formula (11):
in the formula:
the predicted total maximum and minimum adjusting capacities of the k-type air conditioners under the control of the day-ahead load aggregation provider are respectively. N is a radical of K Is the total number of types of k air conditioners.
And 5, constructing an air conditioner load day-ahead response capability evaluation model, aggregating different types of air conditioner loads in the day-ahead based on the day-ahead response capabilities of different types of air conditioner users, and evaluating the day-ahead response capability of the air conditioner loads.
The above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is intended to be covered by the claims.
Claims (3)
1. A Kmeas-GM-based assessment method for day-ahead response capability of air conditioner load is characterized by comprising the following steps: the method comprises the following steps:
step 1, collecting daily air conditioner operation information;
step 2, providing an air conditioner load classification model based on kmeas clustering, carrying out clustering analysis on air conditioner operation information based on a kmeas clustering algorithm, and classifying mass air conditioners into a plurality of classes;
step 3, training the number of different types of air conditioners in the day ahead based on a gray model GM, predicting the number of different types of air conditioners in the day ahead, and providing a prediction method of the number of different types of air conditioner users in the day ahead based on the GM;
step 4, constructing maximum and minimum response capability evaluation models of different types of air conditioners in the day ahead, predicting the number of users in the day ahead based on the different types of air conditioners in the day ahead, and evaluating the maximum and minimum response capabilities of the different types of air conditioners in the day ahead;
step 5, aggregating different types of air conditioner loads in the day ahead based on the day ahead response capacity of different types of air conditioner users, and evaluating the day ahead response capacity of the air conditioner loads;
the clustering process in step 2 is as follows:
step 2.1, inputting a sample set, and randomly selecting k samples from a data set D as initial k centroid vectors, wherein the expression is as follows:
{μ 1 ,μ 2 ,…,μ k }
wherein, d i For the ith sample set, clustering a cluster tree k, wherein m is the number of samples and the maximum iteration number N;
step 2.2, calculate sample d i And each centroid vector mu j A distance of (j =1,2, ..., k) is expressed as follows:
d is to be i L with smallest label ij Falling under C j ；
Step 2.3, the centroid of the sample is recalculated, and the expression is as follows:
returning to the step 2.2 until all samples are calculated and classified;
step 2.4, outputting the final classification, wherein the expression is as follows:
C＝{c 1 ,c 2 ,…,c k }；
the step 4 is specifically operated as follows:
step 4.1, predicting the number of different types of air conditioners in the day before based on GM algorithm
Calculating the maximum and minimum power values of different types of air conditioners>
The expression is as follows:
in the formula:
is the maximum and minimum response capability at the moment t of the kth air conditioner respectively>
For the number at a time t before the day of the kth class of air conditioner>
The maximum and minimum temperatures, eta, set by the kth air conditioner before day and in the room respectively k For an average load power in a single period of a kth class of air conditioner>
Respectively, the start-stop operation period, P, of the kth air conditioner k Load power of the kth air conditioner;
step 4.2, the maximum and minimum power values of various air conditioners
Summing to obtain the day-ahead power upper limit and the day-ahead power lower limit of the load aggregation quotient control air conditioner, wherein the expression is as follows:
in the formula:
the predicted total maximum and minimum regulating capacities, N, of the k-type air conditioners under the control of the day-ahead load aggregators K Is the total category number of the k types of air conditioners.
2. The method for evaluating the day-ahead response capability of the Kmeas-GM-based air conditioner load according to claim 1, wherein the method comprises the following steps: the information collected in the step 1 comprises indoor temperature information, indoor temperature setting upper and lower limits, air conditioner starting period, air conditioner outage period and air conditioner load information at different time, and the expression is as follows:
in the formula: x i (T) is collected information of the ith air conditioner, T i n (t) is indoor temperature information of the ith air conditioner at different time t,
an upper limit value and a lower limit value which are respectively set for the indoor temperature of the ith air conditioner>
Respectively as the starting period and the shutdown period of the air conditioner i i Is the load information of the air conditioner i.
3. The method for evaluating the day-ahead response capability of the Kmeas-GM-based air conditioner load according to claim 1, wherein the method comprises the following steps: the data samples of the number of different types of air conditioners before the day in the step 3 are as follows:
in the formula:
for a set of data prior to the day of the kth class of air conditioner, be->
The air conditioner data volume of the kth class air conditioner at the nth time point is obtained; accumulating the original data to weaken the volatility and randomness of the random sequence to obtain a new sequence, wherein the expression is as follows:
in the formula:
in order to predict the number of the k-th type air conditioners at the time t before the day, a and u are a grey prediction development coefficient and a grey acting amount, and are calculated by a least square method. />
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