CN105787588A - Dynamic state peak-valley time-of-use tariff method for improving new energy absorption capability - Google Patents

Abstract

The present invention discloses a dynamic state peak-valley time-of-use tariff method for improving new energy absorption capability. The method provided by the invention is characterized by dynamically guiding user rational electricity consumption, building a demand response assessment model taking a dynamic state peak-valley price into account and simulating that a user predicate the changing of force according to the new energy to dynamically respond so as to prompt the new energy absorption capability. The method comprises: (1) performing cluster analysis of the current payload of a system and dynamically dividing a peak balka period to obtain the peak-valley time-of-use tariff; (2) determining the classification of each data sample according to the maximum membership principle through adoption of a FCM cluster algorithm to perform effective peak balka fuzzy classification of the system payload of each period; and (3) building a demand response assessment model taking the dynamic state peak-valley price into account to take the set operation cost and the minimum wind abandoning as an optimal object, and introducing corresponding constraints to obtain corresponding system wind abandoning electric quantity. The dynamic state peak-valley time-of-use tariff method for improving the new energy absorption capability has a wide range of application.

Description

A kind of dynamic Peak-valley TOU power price method for promoting new energy digestion capability

Technical field

The present invention relates to a kind of method that dynamic adjustment peak interval of time divides, be specifically related to a kind of dynamic Peak-valley TOU power price method for promoting new energy digestion capability.

Background technology

Along with the online generating of the new forms of energy such as large-scale wind power, photovoltaic, randomness and undulatory property that new forms of energy are exerted oneself are that Operation of Electric Systems brings huge challenge.Exert oneself when new forms of energy and exceed electrical network bearing capacity, for meeting system generating and the Real-time Balancing of load, abandon wind, abandon light and will be difficult to avoid that.

At present, countries in the world are all in the measure of new forms of energy of trying to explore to dissolve.Policy aspect, the U.S. sets up multinomial bill, strengthens and new energy technology is subsidized dynamics, alleviates fossil energy degree of dependence, introduces electric automobile with new forms of energy of dissolving；Market mechanism aspect, Denmark is by carrying out transnational power market transaction with the Germany of the Norway in Northern Europe, Sweden and Continental Europe, it is achieved wind electricity digestion；Technical elements, for promoting new forms of energy power consumption, China mainly improves wind-powered electricity generation, photovoltaic generation unit performance, accesses the low-voltage crossing ability of power system as improved blower fan, builds double-fed blower fan wind energy turbine set, enhancing photovoltaic generation stability etc..But, within 2013, China's wind-powered electricity generation abandons wind total amount still above 20,000,000,000 kilowatt hours.It has been realized that rely solely on technological means, and do not utilize market mechanism, it is difficult to solve the difficult problem that extensive new forms of energy are dissolved.Therefore, in the urgent need to introducing market mechanism flexibly, guide demand side resources interaction, to promote that new forms of energy are dissolved.

Spot Price mechanism can efficiently solve the problem of power system Real-time Balancing, but, China implements the problem that Spot Price faces infrastructure insufficiency, reform resistance is bigger.And Peak-valley TOU power price is relatively easy easy, obtain relatively broad application in China.Fact proved that rational Peak-valley TOU power price, it is possible to effectively peak load shifting is optimized allocation of resources.Traditional Peak-valley TOU power price Time segments division, does not consider that new forms of energy are exerted oneself the impact of change, but with system loading curve for object of study, by cluster analysis, Pinggu, peak period of dividing system load curve, thus obtaining the price period of the tou power price of correspondence.On the basis of tradition division methods, tou power price can the resource rationality electricity consumption of guide demand side, user will respond electricity price, produce the benefit of peak load shifting, thus " ironing " system loading curve, reduction system loading peak-valley difference, lifting generation assets utilization rate.But, after the online generating of extensive new forms of energy, compare system loading curve, normal power supplies the peak-valley difference of " net load " (difference of system loading and generation of electricity by new energy) supplied dramatically increases, peak interval of time generation significant change.Tradition Peak-valley TOU power price produces the effect of " ovennodulation ", causes that normal power supplies operating cost increases, reduces the utilization rate of generation assets.Therefore, under the electrovalence policy that China is current, based on the system loading curve predicted and new forms of energy power curve a few days ago, dynamically divide Pinggu, peak period scientifically and rationally, thus dynamic guiding user's rationality electricity consumption, promote that new forms of energy are dissolved, there is very strong practical value and realistic meaning.

Summary of the invention

For solving the deficiencies in the prior art, it is an object of the invention to provide a kind of to promote the Peak-valley TOU power price Time segments division method that new energy digestion capability is target, thus dynamic guiding user's rationality electricity consumption, establish the demand response assessment models considering dynamic time-of-use tariffs, analog subscriber predicts the change and dynamic response exerted oneself according to new forms of energy, thus promoting that new forms of energy are dissolved.

In order to realize above-mentioned target, the present invention adopts the following technical scheme that:

A kind of dynamic Peak-valley TOU power price method for promoting new energy digestion capability, is characterized in that, comprise the following steps:

Step 1: prediction system loading curve a few days ago and new forms of energy power curve, computing system net load curve, adopt fuzzy C-mean algorithm (FCM) clustering algorithm, system net load curve is carried out cluster analysis, obtain the degree of membership in Pinggu, day part peak, according to maximum membership grade principle, divide Pinggu, peak period, obtain Peak-valley TOU power price；

Step 2: set up the demand response assessment models considering dynamic time-of-use tariffs, with unit operation expense with to abandon wind minimum for optimization aim, introduces account load balancing constraints, the constraint of unit output bound and Climing constant, Line Flow constraint, unit Constraint；And consider machine group quantity of electricity constraints, power plant's quantity of electricity constraints and section tidal current constraints, and after solving demand response assessment models, obtaining system and abandon wind-powered electricity generation amount, measuring and calculating user participates in the benefit that system new forms of energy are dissolved by demand response.

A kind of aforesaid dynamic Peak-valley TOU power price method for promoting new energy digestion capability, is characterized in that, described step 1) in, definition system net load sequence is:In formula: hop count when T is every day,For system loading sequence；Exert oneself sequence for new forms of energy.

A kind of aforesaid dynamic Peak-valley TOU power price method for promoting new energy digestion capability, is characterized in that, described step 1) specifically include following steps:

1.1) initialization date variable, makes d=1；

1.2) predict that the system loading sequence a few days ago predicted and new forms of energy are exerted oneself sequence；

1.3) based on step 1.2) generate system net load sequence；

1.4) adopt FCM clustering algorithm, system net load curve is carried out the cluster analysis of Pinggu, peak；

1.5) according to cluster analysis result, divide Pinggu, peak period of net load, update and issue the Peak-valley TOU power price of next day；

1.6) judge whether current date reaches the date upper limit, if reached, then Flow ends；Otherwise, go to step 1.2), start to roll a few days ago；

A kind of aforesaid dynamic Peak-valley TOU power price method for promoting new energy digestion capability, is characterized in that, described step 1.4) in, FCM clustering algorithm is n vector x_i(i=1,2 ..., n) it is divided into c ambiguity group (1 < c < n), and seeks the cluster centre v often organized_iSo that the cost function J of non-similarity index_mReach minimum:In formula: d_ijIt is data sample x_iWith cluster centre v_jEuclidean distance, d_ij=| | x_i-v_j||；u_jiBeing the i-th data sample degree of membership to jth cluster centre, m is a Weighted Index；Constraints is:

A kind of aforesaid dynamic Peak-valley TOU power price method for promoting new energy digestion capability, is characterized in that, described step 2) in, the demand response assessment models of dynamic time-of-use tariffs is:In formula: f_g(P_g,t) for unit operation cost function, G is generating set set, and T is period sequence, and M is a big number, represents minimum for primary optimization aim to abandon wind, and empirically value is chosen, ε_tAir quantity is abandoned for t period system.

A kind of aforesaid dynamic Peak-valley TOU power price method for promoting new energy digestion capability, is characterized in that, the constraints set when the demand response assessment models of described dynamic time-of-use tariffs solves includes:

Account load balancing constraints is:T ∈ T, in formula: P_d,tP () is the workload demand under Price Mechanisms p, D represents load bus set；

Unit output bound is constrained to: P_g,min≤P_g,t≤P_g,max,g∈G,t∈T；

Climing constant is :-R_g≤P_g,t-P_g,t-1≤R_g,g∈G,t∈T；

Line Flow retrains:In formula, G_d-kAnd G_g-kFor transfer distribution factor；

Unit Constraint:

A kind of aforesaid dynamic Peak-valley TOU power price method for promoting new energy digestion capability, it is characterized in that, described step 2) in for different time-of-use tariffs pricing methods, response characteristic according to load obtains load curve, substitutes into model solution and can obtain abandoning wind-powered electricity generation amount C under this time-of-use tariffs system:Wherein day part abandons air quantity is nonnegative value, i.e. ε_t≥0,t∈T。

The beneficial effect that the present invention reaches: (1), by system net load a few days ago is carried out cluster analysis, dynamically divides Pinggu, peak period, thus obtaining Peak-valley TOU power price.This division methods can after extensive new forms of energy access electrical network, dynamic guiding user's rationality electricity consumption, promote that new forms of energy are dissolved, and then promote the economy of Operation of Electric Systems；(2) analysis of system net load characteristics clustering adopts FCM (fuzzy C-mean algorithm) clustering algorithm, this algorithm can solve the degree of membership information drawing each load sample to each cluster centre (Pinggu, peak), judge to classify belonging to each data sample according to maximum membership grade principle, thus effectively the system net load of day part being carried out Pinggu, peak fuzzy classification；(3) the demand response assessment models considering dynamic time-of-use tariffs is established, with unit operation expense with to abandon wind minimum for optimization aim, introduce corresponding constraint, for different Peak-valley TOU power price mechanism, after solving demand response assessment models, corresponding system can be obtained and abandon wind-powered electricity generation amount, applied widely.

Accompanying drawing explanation

Fig. 1 is Peak-valley TOU power price Time segments division flow chart.

Detailed description of the invention

Below in conjunction with accompanying drawing, the invention will be further described.Following example are only for clearly illustrating technical scheme, and can not limit the scope of the invention with this.

The present invention proposes a kind of dynamic Peak-valley TOU power price method for promoting new energy digestion capability, mainly includes following key step:

Step 1: the system loading curve that prediction is predicted a few days ago and new forms of energy power curve, computing system net load curve, adopt FCM (fuzzy C-mean algorithm) clustering algorithm, system net load curve is carried out cluster analysis, obtain the degree of membership in Pinggu, day part peak, according to maximum membership grade principle, divide Pinggu, peak period, obtain Peak-valley TOU power price.

It is TD day that Fig. 1 show time scale, dynamically divides the flow chart of Peak-valley TOU power price period day by day, and its concrete solution procedure is as follows:

1) initialization date variable, makes d=1.

2) the system loading sequence a few days ago predicted is predictedExert oneself sequence with new forms of energy

3) based on step 2) generate system net load sequence

4) adopt FCM clustering algorithm, system net load curve is carried out the cluster analysis of Pinggu, peak.

5) according to cluster analysis result, divide Pinggu, peak period of net load, update and issue the Peak-valley TOU power price of next day.

6) judge whether current date reaches the date upper limit, if reached, then Flow ends；Otherwise, go to step 2), start to roll a few days ago.

Step 4) in FCM clustering algorithm n vector x_i(i=1,2 ..., n) it is divided into c ambiguity group (1 < c < n), and seeks the cluster centre often organized so that the cost function of non-similarity index reaches minimum:

$\min J_m=\underset{i=1}{\overset{n}{\&Sigma;}}\underset{j=1}{\overset{c}{\&Sigma;}}{u}_{ji}^m{d}_{ij}^2$

In formula: d_ijIt is data sample x_iWith cluster centre v_jEuclidean distance, d_ij=| | x_i-v_j||；u_jiIt it is the i-th data sample degree of membership to jth cluster centre；M is a Weighted Index.

Constraints is:

$\underset{j=1}{\overset{c}{\&Sigma;}}{u}_{ji}=1,1\&le;i\&le;n$

$0<\underset{i=1}{\overset{n}{\&Sigma;}}{u}_{ji}<n,1\&le;j\&le;c$

0≤u_ji≤1,1≤j≤c,1≤i≤n

It is as follows that FCM clustering algorithm solves the concrete solution procedure of flow process:

1) initialize obfuscation variable, Weighted Index is set, generally makes m=2.

2) according to classification demand, arranging cluster centre number c, generally corresponding Pinggu, peak arranges 3 cluster centres.According to accuracy requirement, convergence threshold ε is set.

3) cluster centre of kth time iteration is calculated respectively according to following two formulasAnd Euclidean distance

$v_j^{(k+1)}=\frac{\underset{i=1}{\overset{n}{\&Sigma;}}{\left(u_{ji}^{\left(k\right)}\right)}^m{x}_i}{\underset{i=1}{\overset{n}{\&Sigma;}}{\left(u_{ji}^{\left(k\right)}\right)}^m}$

$d_{ij}^{\left(k\right)}=\left|\right|x_i-v_j^{\left(k\right)}\left|\right|$

4) subordinated-degree matrix of kth time iteration is calculated according to following formula

$u_{ji}^{(k+1)}={\left(\underset{l=1}{\overset{c}{\&Sigma;}}\frac{{\left(d_{ij}^{\left(k\right)}\right)}^2}{{\left(d_{il}^{\left(k\right)}\right)}^2}\right)}^{-\frac{1}{m-1}}$

5) for given convergence threshold ε, if target function value meets required precision, namely

$|J_m^{(k+1)}-J_m^{\left(k\right)}|<\mathrm{\&epsiv;}$

Then derivation algorithm iteration ends.Otherwise return step 3).

When derivation algorithm terminates, FCM clustering algorithm can generate cluster centre matrix V=[v₁,v₂…v_c] and subordinated-degree matrix U=[u that dimension is c × n_ji]。

Pass through subordinated-degree matrix, it is possible to obtain each data sample degree of membership information to each cluster centre.According to maximum membership grade principle, it can be determined that classification belonging to each data sample, thus reaching the purpose of cluster.

Step 2: set up the demand response assessment models considering dynamic time-of-use tariffs, with unit operation expense with to abandon wind minimum for optimization aim, introduce account load balancing constraints, the constraint of unit output bound and Climing constant, Line Flow constraint, unit Constraint, in addition, model further contemplates the constraints considered in the practical engineering application such as machine group quantity of electricity, power plant's quantity of electricity constraint, section tidal current constraint, after solving demand response assessment models, obtaining system and abandon wind-powered electricity generation amount, measuring and calculating user participates in the benefit that system new forms of energy are dissolved by demand response.

According to the dynamic time-of-use tariffs period set in step 1, it is considered to the power load transfer of user, setting up the demand response assessment models considering dynamic time-of-use tariffs, model is as follows:

$min\left(\underset{G}{\&Sigma;}\underset{T}{\&Sigma;}{f}_g\right(P_{g,t})+M\underset{T}{\&Sigma;}{\mathrm{\&epsiv;}}_t)$

Wherein, f_g(P_g) for unit operation cost function, be generally adopted linear function or secondary convex function represents, G is generating set set, and T is period sequence, and M is a big number, ε_tAbandoning air quantity for t period system, model is minimum for primary optimization aim to abandon wind, is askMinima, owing to abandoning air quantityFront weight coefficient M is very big, and model is minimum for primary optimization aim to abandon wind thus.

The constraints of optimization problem includes:

A. account load balancing constraints:

$\underset{G}{\&Sigma;}{P}_{g,t}-{\mathrm{\&epsiv;}}_t=\underset{D}{\&Sigma;}{P}_{d,t}\left(p\right),t\&Element;T$

Wherein, P_d,tP () is the workload demand under Price Mechanisms p, D represents load bus set.

B. unit output bound constraint:

P_g,min≤P_g,t≤P_g,max,g∈G,t∈T

C. Climing constant:

-R_g≤P_g,t-P_g,t-1≤R_g,g∈G,t∈T

D. Line Flow constraint:

$F_{k,min}\&le;\underset{G}{\&Sigma;}{G}_{g-k}{P}_{g,t}-\underset{D}{\&Sigma;}{G}_{d-k}{P}_{d,t}\left(p\right)\&le;F_{k,max}$

In formula: G_d-kAnd G_g-kFor transfer distribution factor.

E. unit Constraint:

$E_{g,min}\&le;\underset{T}{\&Sigma;}{P}_{g,t}\&le;E_{g,max},t\&Element;T,g\&Element;G$

Additionally, model further contemplates the constraints considered in the practical engineering application such as machine group quantity of electricity, power plant's quantity of electricity constraint, section tidal current constraint.

For different time-of-use tariffs pricing methods, obtain load curve according to the response characteristic of load, substitute into model solution and can obtain abandoning wind-powered electricity generation amount C under this time-of-use tariffs system:The system that this value can be weighed under different time-of-use tariffs pricing method abandons landscape condition, and wherein day part abandons air quantity is nonnegative value, i.e. ε_t≥0,t∈T。

The above is only the preferred embodiment of the present invention; it should be pointed out that, for those skilled in the art, under the premise without departing from the technology of the present invention principle; can also making some improvement and deformation, these improve and deformation also should be regarded as protection scope of the present invention.

Claims (7)

1., for promoting a dynamic Peak-valley TOU power price method for new energy digestion capability, it is characterized in that, comprise the following steps:

Step 1: prediction system loading curve a few days ago and new forms of energy power curve, computing system net load curve, adopt fuzzy C-mean algorithm (FCM) clustering algorithm, system net load curve is carried out cluster analysis, obtain the degree of membership in Pinggu, day part peak, according to maximum membership grade principle, divide Pinggu, peak period, obtain Peak-valley TOU power price；

Step 2: set up the demand response assessment models considering dynamic time-of-use tariffs, with unit operation expense with to abandon wind minimum for optimization aim, introduces account load balancing constraints, the constraint of unit output bound and Climing constant, Line Flow constraint, unit Constraint；And consider machine group quantity of electricity constraints, power plant's quantity of electricity constraints and section tidal current constraints, and after solving demand response assessment models, obtaining system and abandon wind-powered electricity generation amount, measuring and calculating user participates in the benefit that system new forms of energy are dissolved by demand response.

2. a kind of dynamic Peak-valley TOU power price method for promoting new energy digestion capability according to claim 1, is characterized in that, described step 1) in, definition system net load sequence is:In formula: hop count when T is every day,For system loading sequence；Exert oneself sequence for new forms of energy.

3. a kind of dynamic Peak-valley TOU power price method for promoting new energy digestion capability according to claim 2, is characterized in that, described step 1) specifically include following steps:

1.1) initialization date variable, makes d=1；

1.2) predict that the system loading sequence a few days ago predicted and new forms of energy are exerted oneself sequence；

1.3) based on step 1.2) generate system net load sequence；

1.4) adopt FCM clustering algorithm, system net load curve is carried out the cluster analysis of Pinggu, peak；

1.5) according to cluster analysis result, divide Pinggu, peak period of net load, update and issue the Peak-valley TOU power price of next day；

1.6) judge whether current date reaches the date upper limit, if reached, then Flow ends；Otherwise, go to step 1.2), start to roll a few days ago.

4. a kind of dynamic Peak-valley TOU power price method for promoting new energy digestion capability according to claim 3, is characterized in that, described step 1.4) in, FCM clustering algorithm is n vector x_i(i=1,2 ..., n) it is divided into c ambiguity group (1 < c < n), and seeks the cluster centre v often organized_iSo that the cost function J of non-similarity index_mReach minimum: minIn formula: d_ijIt is data sample x_iWith cluster centre v_jEuclidean distance, d_ij=| | x_i-v_j||；u_jiBeing the i-th data sample degree of membership to jth cluster centre, m is a Weighted Index；Constraints is:

5. a kind of dynamic Peak-valley TOU power price method for promoting new energy digestion capability according to claim 1, is characterized in that, described step 2) in, the demand response assessment models of dynamic time-of-use tariffs is:In formula: f_g(P_g,t) for unit operation cost function, G is generating set set, and T is period sequence；M is a big number, and empirically value is chosen；ε_tAbandoning air quantity for t period system, model is minimum for primary optimization aim to abandon wind.

6. a kind of dynamic Peak-valley TOU power price method for promoting new energy digestion capability according to claim 5, is characterized in that, the constraints set when the demand response assessment models of described dynamic time-of-use tariffs solves includes:

Account load balancing constraints is:T ∈ T, in formula: P_d,tP () is the workload demand under Price Mechanisms p, D represents load bus set；

Unit output bound is constrained to: P_g,min≤P_g,t≤P_g,max,g∈G,t∈T；

Climing constant is :-R_g≤P_g,t-P_g,t-1≤R_g,g∈G,t∈T；

Line Flow retrains:In formula, G_d-kAnd G_g-kFor transfer distribution factor；

Unit Constraint:

7. a kind of dynamic Peak-valley TOU power price method for promoting new energy digestion capability according to claim 1, it is characterized in that, described step 2) in for different time-of-use tariffs pricing methods, response characteristic according to load obtains load curve, substitutes into model solution and can obtain abandoning wind-powered electricity generation amount C under this time-of-use tariffs system:Wherein day part abandons air quantity is nonnegative value, i.e. ε_t≥0,t∈T。