CN102402725A - Multi-target optimized energy management information processing method for virtual power plant - Google Patents

Abstract

The invention relates to a multi-target optimized energy management information processing method for a virtual power plant. The method comprises the following steps of: 1) acquiring power generation information of power supplies administered by a virtual power plant company and power utilization information of power utilization users, determining credits of users according to the acquired power generation and power utilization information and the historical information of the power supplies and the users, and determining the regulating ranges of power generation quantity and power utilization quantity according to the credits by using a fuzzy function; 2) constructing a multi-target united scheduling model; 3) substituting the power generation information and the power utilization information acquired in the step 1) into the multi-target united scheduling model with a priority in the step 2), and solving with an optimization algorithm; and 4) performing multi-target optimization and priority coordinating processing. Compared with the prior art, the method has the advantages of effective optimization and management of an assembly of a distributed generator and a controllable load and the like.

Description

The multiple-objection optimization energy management information processing method that is used for virtual plant

Technical field

The present invention relates to a kind of energy management information processing method, especially relate to a kind of multiple-objection optimization energy management information processing method that is used for virtual plant.

Background technology

Along with the development of intelligent grid technology, (virtual power plants VPP) obtains attention as a kind of electrical network of realizing to virtual plant with the intelligent dispatching method of power supply, user interaction.Virtual plant is meant by the distributed generator of the some of supervision of distributed power management system and optimization and the set of controllable load to have required functions such as load prediction, renewable energy power generation prediction, unit combination, generating and load management and exchange supervision.VPP is the set that is disperseed genset by the small-sized and microminiature of EMS supervision and control, and its owner and operator can be through being obtained the information of technology, economy and ecological aspect by the operation planning scheduling (being called the dispersion energy management system) of computer computing.

The management and running of conventional electric power systematic economy generally are target with economy, perhaps decrease minimum etc. comprehensively as objective function with economy and net.And in virtual plant, the dispatcher also need be concerned about environmental benefit, and guarantees technical indicators such as minimum reliability of supplying power with the user of actual power and generation schedule deviation and power supply quality as far as possible according to agreement.Therefore, the angle from scheduling proposes the dispatching requirement that a plurality of regulation goals more meet virtual plant.

In traditional scheduling, when having a plurality of target, the mode of employing weighting forms an objective function and comes the constitution optimization model, does not consider the satisfaction of single target and preferentially satisfies rank.Under the situation that has customization electric power, the satisfaction that needs to consider each target separately requires and priority.

Summary of the invention

The object of the invention is exactly for the defective that overcomes above-mentioned prior art existence a kind of multiple-objection optimization energy management information processing method that is used for virtual plant to be provided.

The object of the invention can be realized through following technical scheme:

A kind of multiple-objection optimization energy management information processing method that is used for virtual plant is characterized in that, may further comprise the steps:

1) gathers the generating information of the power supply that virtual electric utility administers and electricity consumption user's power information; Historical information according to the generating that collects and power information and power supply and user is confirmed its credit value; According to this credit value, confirm the range of adjustment of generated energy and power consumption by ambiguity function;

2) structure multiple goal combined dispatching model comprises customization objective function and function priority;

3) with the generating information of being obtained in the step 1) and power information substitution to step 2) in the multiple goal combined dispatching model of band priority of structure; Adopt optimized Algorithm to find the solution; Obtain the voltage magnitude and the phase angle of each node; Meritorious and the reactive power of power supply point output, the meritorious and reactive power of load point, each target function value;

4) multiple-objection optimization and priority Coordination Treatment.

The generating information of the power supply in the described step 1) comprises that the load voltage value of power supply, minimum and maximum technology exert oneself, predict generated energy, the signatory generated energy of plan, the generating credit of customization generating power supply and the historical data of above information.

Electricity consumption user's in the described step 1) power information comprises the historical data of user's load voltage value, prediction power consumption, the signatory power consumption of plan and above information.

Described step 2) structure multiple goal combined dispatching model concrete steps are:

(1) make up optimization aim: setting up a plurality of is the function of target with economical, security, the quality of power supply, contract customization electric weight;

(2) make up constraint condition, comprising: the power constraint of power supply output, the power constraint of power transmission line, voltage, restriction of current, the bound constraint of exerting oneself that power supply is adjustable, system's operation constraint;

(3) customization priority provides the satisfactory value grading system according to the importance scheduler of objective function.

Optimized Algorithm in the described step 3) is simplicial method or interior point method.

Multiple-objection optimization in the described step 4) and priority Coordination Treatment concrete steps are following:

(1) target function value being carried out gray processing handles;

(2) confirm the satisfaction of largest global, promptly get the minimum value in the maximum satisfactory value of each objective function;

(3) whether the system operation mode under the check largest global satisfaction satisfies priority; If do not satisfy, reduce maximum satisfaction, up to the operating scheme that finds near the priority that sets.

Can customize energy source and user, a plurality of objective functions of customization and priority level; Can consider independent target capabilities; Can a plurality of targets of Coordination Treatment with priority between conflict, realize man-machine interaction neatly.

Compared with prior art, the present invention has the set of effective optimization and managing distributed generator and controllable load, can customize energy source and user, a plurality of objective functions of customization and priority level; When a plurality of targets and priority clash, can Coordination Treatment obtain suboptimal solution, realize man-machine interaction flexibly.

Description of drawings

Fig. 1 is a process flow diagram of the present invention;

Fig. 2 is the synoptic diagram of constraint condition of the present invention.

Embodiment

Below in conjunction with accompanying drawing and specific embodiment the present invention is elaborated.

Embodiment

1-2 is described further the present invention below in conjunction with accompanying drawing.But content of the present invention not only is confined to this.Please with reference to Fig. 1, the workflow of this method is specific as follows:

The generating information of the power supply that the collection virtual electric utility is administered and electricity consumption user's power information; Confirm its credit value c according to power supply and user's historical information _j, confirm the scope of actual power amount and power consumption, represent with the mode of fuzzy set: α (x)={ X _F1≤x≤X _F2, wherein, X _F1And X _F1Be to change limit value.

The structure Model for Multi-Objective Optimization comprises definite objective function, restriction range and function priority;

Customize following 5 objective functions:

(1) maximum economic benefit

$f_1=\underset{j=1}{\overset{\mathrm{ns}}{\mathrm{\Σ}}}{P}_{\mathrm{sup\; ply}}^j{k}_{\mathrm{sup\; ply}}^j+\underset{j=1}{\overset{\mathrm{nv}}{\mathrm{\Σ}}}{P}_{\mathrm{vpp}}^j{k}_{\mathrm{vpp}}^j+\underset{j=1}{\overset{\mathrm{nt}}{\mathrm{\Σ}}}{P}_{\mathrm{storage}}^j{k}_{\mathrm{storage}}^j-\underset{j=1}{\overset{\mathrm{nl}}{\mathrm{\Σ}}}{P}_{\mathrm{load}}^j{k}_{\mathrm{load}}^j---\left(1\right)$

In the formula, ns is the quantity of power supply; Nt is the quantity of memory device; Nl is the quantity of load point; Nv is the quantity of power supply at one's command; is the power supply that has the contract constraint, and

is the sale electricity price of this power supply;

is the load power demand, and

buys electric market price; is the output power of power supply at one's command, and

is its production cost;

is the power that need be stored in storage facilities, and is the carrying cost of considering equipment loss.

(2) network of minimum is decreased

$f_2=\underset{i=1}{\overset{N}{\mathrm{\Σ}}}\underset{j=1}{\overset{N}{\mathrm{\Σ}}}{G}_{\mathrm{ij}}(U_i^2+U_j^2-{2U}_i{U}_j\cos {\mathrm{\θ}}_{\mathrm{ij}})---\left(2\right)$

In the formula, N is the bar number of power transmission line; G _IjThe electricity that is the circuit of connection bus i and j is led; U _iAnd U _jBe the voltage of bus i and bus j; θ _IjIt is the phase angle difference of bus i and bus j voltage.

(3) fluctuation of voltage node is stable

$f_3=\sqrt{\underset{i=1}{\overset{n_l}{\mathrm{\Σ}}}{(1-u_i)}^2}---\left(3\right)$

In the formula,

n _lIt is the bus number;

It is the voltage magnitude of bus i;

It is the voltage rating of bus i.

(4) particular power source power supply deviation

$f_4=\sqrt{\underset{i=1}{\overset{n_m}{\mathrm{\Σ}}}{(1-m_i)}^2}---\left(4\right)$

In the formula,

n _mBe the sale of electricity power supply point of agreement output,

Be the actual active power of output of power supply point,

It is the agreement active power of output of power supply point.

(5) power supply quality of specific load

$f_5=\max \left\{\right|\sqrt{{(1-\frac{u_c^i}{u_{\mathrm{cset}}^i})}^2}{-\mathrm{\ϵ}}_{\mathrm{set}}^i\left|\right\}---\left(5\right)$

In the formula,

is the busbar voltage amplitude of user access point;

is user access point busbar voltage amplitude ratings;

is the busbar voltage amplitude deviation value of the user access point of agreement.

According to practical operation situation, the restriction range of confirming objective function is shown in dash area among Fig. 2.Formulate:

$F=\left\{\begin{array}{c}{P}_{\min }^i\≤P^i\≤P_{\max }^i\\ Q_{\min }^i\≤Q^i\≤Q_{\max }^i\\ U_{\min }^i\≤U^i\≤U_{\max }^i\end{array}\right.---\left(6\right)$

Among Fig. 2, I representes the power limit of access point operation characteristic; II representes the transmitted power limit value of transmission line of electricity; III representes the specified meritorious output limit value of power supply; IV is illustrated in the output power of power supply under the situation that access point is a rated voltage; V is illustrated in the output power of power supply under 95% the situation that access point is a rated voltage; VI is illustrated in the output power of power supply under 105% the situation that access point is a rated voltage.

Confirming function priority, is example with three priority, representes as follows with the mode of satisfaction:

$\left\{\begin{array}{c}{\mathrm{\μ}}_{f_i}\left(x\right)\{f_i\∈{\mathrm{level}}^1\}-{\mathrm{\μ}}_{f_i^{\′}}\left(x\right)\{f_i^{\′}\∈{\mathrm{level}}^2\}>k_1\\ {\mathrm{\μ}}_{f_i^{\′}}\left(x\right)\{f_i^{\′}\∈{\mathrm{level}}^2\}-{\mathrm{\μ}}_{f_i^{\′\′}}\left(x\right)\{f_i^{\′\′}\∈{\mathrm{level}}^3\}>k_2\end{array}\right.---\left(7\right)$

In the formula, f _i(i=1-5) expression objective function;

Satisfaction for objective function; k ₁∈ [0,1] and k ₂∈ [0,1] representes the satisfaction gap between the first order and the second level and the second level and the third level respectively.

According to generated energy and power consumption scope, calculate trend; At definite objective function, after range of operation constraint and function priority require, confirm the maximal value and the minimum value of objective function according to calculation of tidal current, objective function to be carried out gray processing handle, the satisfaction that obtains each objective function is represented as follows:

${\mathrm{\&mu;}}_{f_i}\left(x\right)=\left\{\begin{array}{cc}1& \mathrm{if}{f}_i\left(x\right)\&le;f_i^{*}\\ 1-\frac{f_i^{*}-f_i\left(x\right)}{f_i^{*}-f_i^{\max }}& \mathrm{if}{f}_i^{*}<f_i\left(x\right)\&le;f_i^{\max }\\ 0& \mathrm{if}{f}_i\left(x\right)>f_i^{\max }\end{array}\right.---\left(8\right)$

In the formula,

Be objective function f _iExpectation value;

Be f _iMaximal value in feasible zone.When

f _i(x) do not meet the demands fully; When

f _i(x) satisfy specified criteria fully.

The maximum satisfaction of the overall situation is β ^t=min (β ₁..., β _i..., β _k), solving model is following:

$\left\{\begin{array}{c}\max \mathrm{\&beta;}\\ s.t.{\mathrm{\&mu;}}_{f_i}\left(x\right)\&GreaterEqual;\mathrm{\&beta;}\\ x\&Element;F\end{array}\right.---\left(9\right)$

In the formula;

is the satisfaction of each objective function, and F is a restriction range.

Adopt two to go on foot the method for decomposing for the Coordination Treatment under the conflict between satisfaction and the multiobject priority, concrete steps are following:

At first do not consider priority, at range of operation constraint and α (x)={ X _F1≤x≤X _F2In, confirm the satisfactory value of each objective function, get minimum value in the maximum satisfactory value of each objective function as the largest global satisfaction.

If the largest global satisfaction satisfies formula (priority), explain that satisfaction and multiobject priority do not have conflict, the scheduling scheme under the satisfaction of largest global is optimal case.If do not satisfy, reduce maximum satisfaction, at range of operation constraint and α (x)={ X _F1≤x≤X _F2And priority restrictions under search for, satisfy priority that each objective function sets or near the scheme of the priority that sets up to finding.Search model is following:

$\left\{\begin{array}{c}\min \left|\right|\mathrm{\&gamma;}\left|\right|\\ s.t.{\mathrm{\&mu;}}_{f_i}\left(x\right)\&GreaterEqual;{\mathrm{\&beta;}}_r^t\\ {\mathrm{\&mu;}}_{f_i}\left(x\right)\{f_i\&Element;{\mathrm{level}}^j\}-{\mathrm{\&mu;}}_{f_{i_i}^{\&prime;}}\left(x\right)\{f_i^{\&prime;}\&Element;{\mathrm{level}}^{j+1}\}>k_j+{\mathrm{\&gamma;}}_j\\ x\&Element;F\end{array}\right.---\left(10\right)$

In the formula, is the overall satisfaction after reducing; || γ || represent the gap between the satisfaction in the priority of actual satisfaction and customization.

Claims (7)

1. a multiple-objection optimization energy management information processing method that is used for virtual plant is characterized in that, may further comprise the steps:

1) gathers the generating information of the power supply that virtual electric utility administers and electricity consumption user's power information; Historical information according to the generating that collects and power information and power supply and user is confirmed its credit value; According to this credit value, confirm the range of adjustment of generated energy and power consumption by ambiguity function;

2) structure multiple goal combined dispatching model comprises customization objective function and function priority;

3) with the generating information of being obtained in the step 1) and power information substitution to step 2) in the multiple goal combined dispatching model of band priority of structure; Adopt optimized Algorithm to find the solution; Obtain the voltage magnitude and the phase angle of each node; Meritorious and the reactive power of power supply point output, the meritorious and reactive power of load point, each target function value;

4) multiple-objection optimization and priority Coordination Treatment.

2. a kind of multiple-objection optimization energy management information processing method that is used for virtual plant according to claim 1; It is characterized in that the generating information of the power supply in the described step 1) comprises that the load voltage value of power supply, minimum and maximum technology exert oneself, predict generated energy, the signatory generated energy of plan, the generating credit of customization generating power supply and the historical data of above information.

3. a kind of multiple-objection optimization energy management information processing method that is used for virtual plant according to claim 1; It is characterized in that the electricity consumption user's in the described step 1) power information comprises the historical data of user's load voltage value, prediction power consumption, the signatory power consumption of plan and above information.

4. a kind of multiple-objection optimization energy management information processing method that is used for virtual plant according to claim 1 is characterized in that described step 2) structure multiple goal combined dispatching model concrete steps are:

(1) make up optimization aim: setting up a plurality of is the function of target with economical, security, the quality of power supply, contract customization electric weight;

(2) make up constraint condition, comprising: the power constraint of power supply output, the power constraint of power transmission line, voltage, restriction of current, the bound constraint of exerting oneself that power supply is adjustable, system's operation constraint;

(3) customization priority provides grading system according to the importance scheduler of objective function.

5. a kind of multiple-objection optimization energy management information processing method that is used for virtual plant according to claim 1 is characterized in that the optimized Algorithm in the described step 3) is simplicial method or interior point method.

6. a kind of multiple-objection optimization energy management information processing method that is used for virtual plant according to claim 1 is characterized in that multiple-objection optimization in the described step 4) and priority Coordination Treatment concrete steps are following:

(1) target function value being carried out gray processing handles;

(2) confirm the satisfaction of largest global, promptly get the minimum value in the maximum satisfactory value of each objective function;

(3) whether the system operation mode under the check largest global satisfaction satisfies priority; If do not satisfy, reduce maximum satisfaction, up to the operating scheme that finds near the priority that sets.

7. a kind of multiple-objection optimization energy management information processing method that is used for virtual plant according to claim 1 is characterized in that, can customize energy source and user, a plurality of objective functions of customization and priority level; Can consider independent target capabilities; Can a plurality of targets of Coordination Treatment with priority between conflict, realize man-machine interaction neatly.

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