CN105787588B - Dynamic peak-valley time-of-use electricity price method for improving new energy consumption capability - Google Patents
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Abstract
The invention discloses a dynamic peak-valley time-of-use electricity price method for improving new energy consumption capacity, which dynamically guides a user to reasonably use electricity, establishes a demand response evaluation model considering the dynamic peak-valley electricity price, and simulates dynamic response of the user according to the predicted output change of new energy, thereby promoting the new energy consumption. The invention achieves the following beneficial effects: (1) carrying out cluster analysis on the day-ahead net load of the system, and dynamically dividing peak-valley leveling time periods so as to obtain peak-valley time-of-use electricity prices; (2) judging the classification of each data sample according to the maximum membership principle by adopting an FCM clustering algorithm, thereby effectively carrying out peak-to-valley fuzzy classification on the system net load in each time period; (3) a demand response evaluation model considering the dynamic peak-valley electricity price is established, the unit operation cost and the minimum abandoned wind are taken as optimization targets, corresponding constraints are introduced, the corresponding system abandoned wind electricity quantity can be obtained, and the application range is wide.
Description
Technical Field
The invention relates to a method for dynamically adjusting peak-valley time-sharing division, in particular to a dynamic peak-valley time-sharing electricity price method for improving new energy consumption capacity.
Background
With the large-scale wind power, photovoltaic and other new energy online power generation, the randomness and the fluctuation of the new energy output bring huge challenges to the operation of a power system. When the output of the new energy exceeds the bearing capacity of a power grid, in order to meet the real-time balance of power generation and load of a system, wind and light abandonment is difficult to avoid.
At present, measures for consuming new energy are actively explored in all countries in the world. In the policy aspect, a plurality of laws are set in the United states, the subsidy of new energy technology is increased, the dependence degree on fossil energy is reduced, and an electric vehicle is introduced to absorb new energy; in the aspect of market mechanism, Denmark realizes wind power consumption by developing cross-country power market transactions with Norway in Nordic, Sweden and Germany in European continent; in the technical aspect, in order to promote new energy electric power consumption, China mainly improves the performances of wind power and photovoltaic generator sets, such as the low-voltage ride through capability of a fan connected to an electric power system, the construction of a double-fed fan wind power plant, the enhancement of photovoltaic power generation stability and the like. However, the total wind power abandoned wind amount in China in 2013 still exceeds 200 hundred million kilowatt-hours. It has been realized that the problem of large-scale new energy consumption is difficult to solve by means of technical means only, without using market mechanisms. Therefore, it is urgently needed to introduce a flexible market mechanism to guide the interaction of demand-side resources so as to promote the consumption of new energy.
The real-time electricity price mechanism can efficiently solve the problem of real-time balance of a power system, however, the problems of insufficient infrastructure and large reformation resistance are faced when the real-time electricity price is implemented in China. And the peak-valley time-of-use electricity price is relatively simple and easy to implement, and is widely applied in China. The fact proves that reasonable peak-valley time-of-use electricity price can effectively cut peaks and fill valleys and optimize resource allocation. In the traditional peak-valley time-of-use electricity price time interval division, the influence of new energy output change is not considered, but a system load curve is used as a research object, and the peak-valley time interval of the system load curve is divided through cluster analysis, so that the corresponding time-of-use electricity price time interval is obtained. On the basis of the traditional division method, the time-of-use electricity price can guide the demand side to reasonably use the electricity, and a user can respond to the electricity price to generate the benefits of shifting peaks and filling valleys, so that the system load curve is 'ironed', the peak-valley difference of the system load is reduced, and the utilization rate of power generation resources is improved. However, after large-scale new energy power generation by internet, the peak-to-valley difference of the "net load" (difference between the system load and the new energy power generation) supplied by the conventional power supply is significantly increased compared to the system load curve, and the peak-to-valley period is significantly changed. The traditional peak-valley time-of-use electricity price produces the effect of overmodulation, which leads to the increase of the operation cost of the conventional power supply and reduces the utilization rate of power generation resources. Therefore, under the current electricity price policy of China, based on the system load curve and the new energy output curve predicted in the day, the peak valley leveling time period is scientifically and reasonably divided dynamically, so that the user is dynamically guided to use electricity rationally, the new energy consumption is promoted, and the method has strong practical value and practical significance.
Disclosure of Invention
In order to solve the defects of the prior art, the invention aims to provide a peak-valley time-of-use electricity price time-interval division method aiming at improving the new energy consumption capability, so that the rational electricity utilization of a user is dynamically guided, a demand response evaluation model considering the dynamic peak-valley electricity price is established, and the dynamic response of the user is simulated according to the output change of the new energy prediction, so that the new energy consumption is promoted.
In order to achieve the above object, the present invention adopts the following technical solutions:
a dynamic peak-valley time-of-use electricity price method for improving new energy consumption capability is characterized by comprising the following steps:
step 1: predicting a system load curve and a new energy output curve in the day ahead, calculating a system net load curve, carrying out cluster analysis on the system net load curve by adopting a Fuzzy C Mean (FCM) clustering algorithm to obtain the membership degree of peak valley periods in each period, and dividing the peak valley periods according to a maximum membership degree principle to obtain peak valley time-of-use electricity price;
step 2: establishing a demand response evaluation model considering dynamic peak-valley electricity prices, and introducing load balance constraint, unit output upper and lower limit constraint, climbing constraint, line current constraint and unit electricity quantity constraint by taking unit operation cost and minimum abandoned wind as optimization targets; and considering a power and electricity quantity constraint condition of the machine group, a power plant power and electricity quantity constraint condition and a section flow constraint condition, solving a demand response evaluation model to obtain the system abandoned wind electricity quantity, and calculating the benefit of the user participating in demand response on system new energy consumption.
The dynamic peak-valley time-of-use electricity price method for improving the new energy consumption capability is characterized in that, in the step 1), the system payload sequence is defined as:
in the formula: t is the number of time periods per day,
is a system load sequence;
and is a new energy output sequence.
The dynamic peak-valley time-of-use electricity price method for improving the new energy consumption capability is characterized in that the step 1) specifically comprises the following steps:
1.1) initializing a date variable, making d equal to 1;
1.2) predicting a system load sequence and a new energy output sequence predicted in the day ahead;
1.3) generating a system payload sequence based on step 1.2);
1.4) carrying out peak-to-valley clustering analysis on a system net load curve by adopting an FCM clustering algorithm;
1.5) dividing the peak-valley time interval of the net load according to the clustering analysis result, updating and issuing the peak-valley time-of-use electricity price of the next day;
1.6) judging whether the current date reaches the date upper limit, and if so, terminating the process; otherwise, turning to the step 1.2) and starting rolling in the day ahead;
the dynamic peak-valley time-of-use electricity price method for improving the new energy consumption capability is characterized in that in the step 1.4), the FCM clustering algorithm carries out n vectors xi(i ═ 1,2, …, n) was divided into c fuzzy groups (1 < c < n) and the cluster center v was determined for each groupiSo that the value function J of the non-similarity indexmThe minimum value is reached:
in the formula: dijIs a data sample xiAnd a clustering center vjEuclidean distance of dij＝||xi-vj||； ujiIs the membership from the ith data sample to the jth clustering center, and m is a weighting index; the constraint conditions are as follows:
one of the foregoing is used forThe dynamic peak-valley time-of-use electricity price method for improving the new energy consumption capability is characterized in that in the step 2), a demand response evaluation model of the dynamic peak-valley electricity price is as follows:
in the formula: f. ofg(Pg,t) The method comprises the steps of taking a unit operation cost function, G as a generator set, T as a time interval sequence, M as a large number, representing that the minimum wind curtailment is taken as a primary optimization target, selecting according to an empirical value, and epsilontAnd (5) abandoning the air volume of the system for the period t.
The dynamic peak-valley time-of-use electricity price method for improving the new energy consumption capability is characterized in that the constraint conditions set during the solution of the demand response evaluation model of the dynamic peak-valley electricity price include:
the load balancing constraint is:
t ∈ T, wherein: pd,t(p) load demand under the electricity price mechanism p, D represents a load node set;
the upper and lower limits of the unit output are restricted as follows: pg,min≤Pg,t≤Pg,max,g∈G,t∈T；
The climbing restriction is as follows: -Rg≤Pg,t-Pg,t-1≤Rg,g∈G,t∈T；
And (3) line power flow constraint:
in the formula, Gd-kAnd Gg-kIs a transfer distribution factor;
and (3) unit electric quantity constraint:
the dynamic peak-valley time-of-use electricity price method for improving the new energy consumption capability is characterized in that, in the step 2), for different peak-valley electricity price pricing methods, the electricity price is obtained according to the response characteristics of the loadAnd substituting the load curve into a model to solve to obtain the abandoned wind power C under the peak-valley electrovalence system:
wherein the air curtailment quantity in each time interval is a non-negative value, namely epsilont≥0,t∈T。
The invention achieves the following beneficial effects: (1) by carrying out cluster analysis on the daily net load of the system, the peak-valley leveling time period is dynamically divided, so that the peak-valley time-of-use electricity price is obtained. The division method can dynamically guide users to rationally use electricity after large-scale new energy is accessed into a power grid, promote consumption of the new energy and further improve the economical efficiency of operation of a power system; (2) the system net load cluster analysis adopts FCM (fuzzy C-means) clustering algorithm, the algorithm can solve and obtain the membership degree information of each load sample to each cluster center (peak-valley), and the classification of each data sample is judged according to the maximum membership degree principle, so that the peak-valley fuzzy classification is effectively carried out on the system net load in each time period; (3) a demand response evaluation model considering dynamic peak-valley electricity prices is established, corresponding constraints are introduced by taking the minimum unit operation cost and the minimum abandoned wind as optimization targets, and corresponding system abandoned wind electricity quantity can be obtained after the demand response evaluation model is solved for different peak-valley time-of-use electricity price mechanisms, so that the application range is wide.
Drawings
Fig. 1 is a flow chart of peak-valley time-of-use electricity rate period division.
Detailed Description
The invention is further described below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present invention is not limited thereby.
The invention provides a dynamic peak-valley time-of-use electricity price method for improving the new energy consumption capacity, which mainly comprises the following main steps:
step 1: predicting a system load curve and a new energy output curve predicted day ahead, calculating a system net load curve, carrying out cluster analysis on the system net load curve by adopting an FCM (fuzzy C mean) clustering algorithm to obtain the membership degree of the peak valley and the valley of each time period, and dividing the peak valley and the valley time periods according to the maximum membership degree principle to obtain the peak valley time-of-use electricity price.
Fig. 1 is a flowchart showing time scales of TD days, and time-of-day electricity price periods of peak-to-valley time-of-day dynamic division, and specifically includes the following solving steps:
1) the date variable is initialized and d is set to 1.
2) Predicting a system load sequence predicted by day ahead
Output sequence with new energy
3) Generating a system payload sequence based on step 2)
4) And (4) performing peak-to-valley clustering analysis on the system net load curve by adopting an FCM clustering algorithm.
5) And dividing the peak-valley time interval of the net load according to the clustering analysis result, and updating and releasing the peak-valley time-of-use electricity price of the next day.
6) Judging whether the current date reaches the date upper limit, and if so, terminating the process; otherwise, go to step 2), start scrolling day by day.
The FCM clustering algorithm in the step 4) carries out clustering on n vectors xi(i ═ 1,2, …, n) was divided into c fuzzy groups (1 < c < n) and the cluster centers for each group were calculated to minimize the cost function of the non-similarity index:
in the formula: dijIs a data sample xiAnd a clustering center vjEuclidean distance of dij＝||xi-vj||；ujiIs the membership from the ith data sample to the jth cluster center; m isA weighted index.
The constraint conditions are as follows:
0≤uji≤1,1≤j≤c,1≤i≤n
the FCM clustering algorithm solving process comprises the following concrete solving steps:
1) the fuzzification variable is initialized and a weighting index is set, typically letting m be 2.
2) According to the classification requirement, the number c of the clustering centers is set, and generally 3 clustering centers are set corresponding to the peak-valley and the valley. And setting a convergence threshold epsilon according to the precision requirement.
3) Respectively calculating the clustering centers of the kth iteration according to the following two formulas
And the Euclidean distance
4) Calculating a membership matrix of the kth iteration according to the following formula
5) For a given convergence threshold ε, if the value of the objective function meets the accuracy requirement, i.e.
The solution algorithm iteration terminates. Otherwise, returning to the step 3).
When the solving algorithm is ended, the FCM clustering algorithm can generate a clustering center matrix V ═ V1,v2…vc]Membership matrix U ═ U with a sum dimension of c × nji]。
And obtaining the membership degree information of each data sample to each clustering center through the membership degree matrix. According to the maximum membership principle, the classification of each data sample can be judged, so that the clustering purpose is achieved.
Step 2: and establishing a demand response evaluation model considering the dynamic peak-valley electricity price, introducing load balance constraint, unit output upper and lower limit constraint and climbing constraint, line power flow constraint and unit power constraint by taking the minimum unit operating cost and the minimum waste wind as optimization targets, considering constraint conditions considered in practical engineering applications such as unit group power electric quantity, power plant power electric quantity constraint, section power flow constraint and the like in the model, solving the demand response evaluation model to obtain system waste wind electric quantity, and measuring and calculating the benefit of the user participating in demand response on system new energy consumption.
According to the dynamic peak-valley electricity price time period set in the step 1, considering the power load transfer of the user, and establishing a demand response evaluation model considering the dynamic peak-valley electricity price, wherein the model is as follows:
wherein f isg(Pg) Is a unit operation cost function generally expressed by a linear function or a quadratic convex function, G is a generator set, T is a time interval sequence, M is a large number, epsilontIs a period of tThe system abandoned wind volume is solved by taking the minimum abandoned wind as the primary optimization target of the model
Due to the air-reject rate
The former weighting coefficient M is very large, so the model takes the minimum wind curtailment as the primary optimization target.
The constraints of the optimization problem include:
a. and (3) load balance constraint:
wherein, Pd,t(p) load demand under the electricity price mechanism p, D represents a set of load nodes.
b. And (3) restraining the upper and lower limits of the unit output:
Pg,min≤Pg,t≤Pg,max,g∈G,t∈T
c. and (3) climbing restraint:
-Rg≤Pg,t-Pg,t-1≤Rg,g∈G,t∈T
d. and (3) line power flow constraint:
in the formula: gd-kAnd Gg-kIs a transfer distribution factor.
e. And (3) unit electric quantity constraint:
in addition, constraint conditions considered in practical engineering application such as machine group power and electricity quantity, power plant power and electricity quantity constraint, section flow constraint and the like are also considered in the model.
And aiming at different peak-valley electricity price pricing methods, obtaining a load curve according to the response characteristic of the load, substituting the load curve into a model for solving to obtain the abandoned wind electric quantity C under the peak-valley electricity price system:
the value can measure the system air abandonment condition under different peak-valley electricity price pricing methods, wherein the air abandonment quantity in each time period is a non-negative value, namely epsilont≥0,t∈T。
The above description is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, several modifications and variations can be made without departing from the technical principle of the present invention, and these modifications and variations should also be regarded as the protection scope of the present invention.
Claims (3)
1. A dynamic peak-valley time-of-use electricity price method for improving new energy consumption capability is characterized by comprising the following steps:
step 1: predicting a system load curve and a new energy output curve in the day ahead, calculating a system net load curve, carrying out cluster analysis on the system net load curve by adopting a fuzzy C mean FCM (fuzzy C mean) cluster algorithm to obtain the membership of the peak valley of each time period, dividing the peak valley time periods according to the maximum membership principle to obtain the peak valley time-of-use electricity price,
the method specifically comprises the following steps:
1.1) initializing a date variable, making d equal to 1;
1.2) predicting a system load sequence and a new energy output sequence predicted in the day ahead;
1.3) generating a system payload sequence based on step 1.2);
1.4) carrying out peak-to-valley clustering analysis on a system net load curve by adopting an FCM clustering algorithm;
1.5) dividing the peak-valley time interval of the net load according to the clustering analysis result, updating and issuing the peak-valley time-of-use electricity price of the next day;
1.6) judging whether the current date reaches the date upper limit, and if so, terminating the process; otherwise, turning to step 1.2);
step 2: establishing a demand response evaluation model considering dynamic peak-valley electricity prices, and introducing load balance constraint, unit output upper and lower limit constraint, climbing constraint, line current constraint and unit electricity quantity constraint by taking unit operation cost and minimum abandoned wind as optimization targets; considering a power and electricity quantity constraint condition of a machine group, a power plant power and electricity quantity constraint condition and a section flow constraint condition, solving a demand response evaluation model to obtain system abandoned wind electricity quantity, and measuring and calculating the benefit of the participation of a user in demand response on system new energy consumption;
the demand response evaluation model is as follows:
wherein f isg(Pg，t) Is a unit operation cost function generally expressed by a linear function or a quadratic convex function, G is a generator set, T is a time interval sequence, M is a large number, epsilontFor the system air abandon amount in the time period t, the model takes the minimum air abandon amount as the primary optimization target and is to solve
Due to the air-reject rate
The former weighting coefficient M is very large, so the model takes the minimum wind curtailment as the primary optimization target,
the constraint conditions set during the solution of the demand response evaluation model of the dynamic peak-valley electricity price comprise:
the load balancing constraint is:
t ∈ T, wherein: pd,t(p) load demand under the electricity price mechanism p, D represents a load node set;
the upper and lower limits of the unit output are restricted as follows: pg,min≤Pg,t≤Pg,max,g∈G,t∈T；
The climbing restriction is as follows: -Rg≤Pg,t-Pg,t-1≤Rg,g∈G,t∈T；
And (3) line power flow constraint:
in the formula, Gd-kAnd Gg-kIs a transfer distribution factor;
and (3) unit electric quantity constraint:
and aiming at different peak-valley electricity price pricing methods, obtaining a load curve according to the response characteristic of the load, substituting the load curve into a model for solving to obtain the abandoned wind electric quantity C under the peak-valley electricity price system:
wherein the air curtailment quantity in each time interval is a non-negative value, namely epsilont≥0,t∈T。
2. The method according to claim 1, wherein in step 1), the system payload sequence is defined as:
in the formula: t is a time period sequence, and T is a time period sequence,
is a system load sequence;
and is a new energy output sequence.
3. The method for improving dynamic peak-valley time-of-use electricity price of new energy consumption capability of claim 1, wherein in step 1.4), the FCM clustering algorithm is used for clustering n vectors xiDividing into c fuzzy groups, where 1 < c < n, i ═ 1,2, …, n, and finding the cluster center v of each groupiSo that the value function J of the non-similarity indexmThe minimum value is reached:
in the formula: dijIs a data sample xiAnd a clustering center vjEuclidean distance of dij＝||xi-vj||；ujiIs the membership from the ith data sample to the jth clustering center, and m is a weighting index; the constraint conditions are as follows:
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