CN103580044B - A kind of capacity collocation method of tackling many wind farm energy storage device of wind power fluctuation - Google Patents

Abstract

The invention provides a kind of capacity collocation method of tackling many wind farm energy storage device of wind power fluctuation, comprise S1 and set up minimum energy storage power demand Optimized model so that the energy storage power demand of all nodes of electric power system is minimum for target; S2 is according to minimum energy storage power demand Optimized model and obtain the wind energy turbine set energy storage minimum capacity value under most strong wind power fluctuation scope in conjunction with robust linear optimization method; S3 when minimum capacity value is zero, then does not need to configure energy storage device; When minimum capacity value is non-vanishing, then at non-vanishing Nodes, according to wind energy turbine set energy storage minimum capacity value configuration energy storage device.Contemplated by the invention the layout of multiple wind energy turbine set in electric power system and the network topology structure of connecting system itself, the objective fact of multiple wind energy turbine set access in current power system can be reflected; The robust linear optimization method based on stochastic variable distributed intelligence introduced, that can consider multiple wind energy turbine set goes out fluctuation simultaneously, and the Stochastic Programming Model being difficult to solve is converted to certainty linear programming model.

Description

A kind of capacity collocation method of tackling many wind farm energy storage device of wind power fluctuation

Technical field

The invention belongs to technical field of wind power generation, more specifically, relate to a kind of capacity collocation method of tackling many wind farm energy storage device of wind power fluctuation.

Background technology

Energy storage technology can realize initiatively causing technical role that is steady and power-balance in conventional electric power system, but along with the development of large-scale wind power and grid-connected, the novel electric power system containing windy electric field presents stronger randomness and fluctuation.For ensureing the safe operation of electrical network, it is the energy storage device of wind energy turbine set configuration certain capacity in the electrical network containing windy electric field, can effectively weaken short time/long-time output of wind electric field fluctuates the harmful effect brought system cloud gray model, changes the uncontrollability of output of wind electric field even to a certain extent.

Be in the text of CN103023066A, disclose a kind of Optimal Configuration Method of energy storage power of electrical power system be suitable for containing wind-powered electricity generation at Chinese invention patent application file publication number, the energy storage power that the method uses with electric power system in dispatching cycle is minimum for target function, set up Chance-constrained Model, the positive and negative spinning reserve of system is obtained, to determine to tackle the optimum energy storage power configuration needed for net load predicated error based on containing the electric power system wind power of wind-powered electricity generation and the sample data of load and energy storage power configuration model.This inventive method based on solve traffic department for reply wind power and load prediction error needed for minimum energy storage power and spinning reserve problem, owing to only taking into account all output of wind electric field sums accessed in system, namely position and electrical network self grid structure of multiple wind energy turbine set access electrical network is ignored, only set up stored energy capacitance allocation models from the angle of system power balance, therefore can not reflect the practical topology of windy electric field connecting system completely.

Be in the text of CN103078338A, disclose a kind of wind energy turbine set energy-storage system collocation method improving Wind Power Utilization level at Chinese invention patent application file publication number, the method utilizes energy-storage system to reduce wind energy turbine set and abandons air quantity to improve the receiving ability of electrical network to output of wind electric field, and considered cost of investment and the economic benefit of energy-storage system, maximize to realize energy storage comprehensive benefit.This invention is only for the energy storage allocation problem of single wind energy turbine set, the given wind farm grid-connected upper limit of exerting oneself, energy storage reduces action effect that wind energy turbine set abandons air quantity and is reflected in actual wind power and grid-connectedly exerts oneself in the difference of the upper limit, and therefore the power/energy capacity of energy-storage system receives electricity directly related with wind-powered electricity generation.But this invention can not expand the energy storage Research on configuration into multiple wind energy turbine set.

Summary of the invention

For above defect or the Improvement requirement of prior art, the invention provides a kind of capacity collocation method of tackling many wind farm energy storage device of wind power fluctuation, its object is to the layout structure and the connecting system own net topological structure that consider multiple wind energy turbine set access electric power system, and consider the fluctuation of multiple output of wind electric field simultaneously, solve the practical topology that prior art does not consider windy electric field connecting system thus, and do not consider the technical problem that the energy storage of multiple wind energy turbine set configures simultaneously.

The invention provides a kind of capacity collocation method of tackling many wind farm energy storage device of wind power fluctuation, comprise the steps:

S1: set up minimum energy storage power demand Optimized model for target so that the energy storage power demand of all nodes of electric power system is minimum;

The target function of described minimum energy storage power demand Optimized model is: n represents the node number in electric power system, and i represents electric power system interior joint sequence number; 1≤i≤N; e _irepresent the energy storage power capacity of i-th node in electric power system;

The constraints of described minimum energy storage power demand Optimized model comprises: the constraint of node power Constraints of Equilibrium, Line Flow, the constraint of circuit transmission power limit restraint, unit operation and the constraint of RANDOM WIND power fluctuation;

S2: obtain the wind energy turbine set energy storage minimum capacity value under most strong wind power fluctuation scope in conjunction with robust linear optimization method according to described minimum energy storage power demand Optimized model;

S3: when described minimum capacity value is zero, then do not need to configure energy storage device; When described minimum capacity value is non-vanishing, then at non-vanishing Nodes, according to described wind energy turbine set energy storage minimum capacity value configuration energy storage device.

Further, described node power Constraints of Equilibrium is-B θ+g+ ω+e=d; Described Line Flow is constrained to P _l=A _lθ, circuit transmission power limit restraint be | P _l|≤P _lMAX; Described unit operation is constrained to g _min≤ g≤g _max; RANDOM WIND power fluctuation is constrained to ω _min≤ ω≤ω _max; E is wind energy turbine set energy storage power configuration requirement vector; B is containing N × N rank admittance matrix of N number of node system; θ is that N maintains system phase angle column vector; G is system unit output column vector, unit number JG; ω wind power column vector, wind energy turbine set number JW; D system loading column vector, load number JL; P _lm ties up branch road active power column vector; A _lm × N rank relational matrix of branch power and phase angle; P _lMAXbranch road transmission power limit column vector; g _minunit minimum load column vector; g _maxunit heap(ed) capacity column vector; ω _minwind power fluctuation range lower limit column vector; ω _maxwind power fluctuation range upper limit column vector.

Further, step S2 specifically comprises:

S21: the linear restriction and the system power Constraints of Equilibrium that obtain Branch Power Flow and node injecting power according to node power Constraints of Equilibrium and Line Flow constraint;

S22: obtain the inequality constraints of circuit transmission power according to the linear restriction of described Branch Power Flow and node injecting power and circuit transmission power limit restraint;

The inequality constraints of system power balance is obtained according to system power Constraints of Equilibrium and the constraint of described unit operation;

S23: probability statistics are carried out to the historical data of each wind energy turbine set and obtains the active power average of each wind energy turbine set, the upper limit of exerting oneself and lower limit of exerting oneself, and based on robust linear optimization method in conjunction with the set of the active power average of wind energy turbine set, the upper limit of exerting oneself and lower limit of exerting oneself acquisition for describing many output power fluctuation of wind farms scope;

S24: the target function target function of described minimum energy storage power demand Optimized model being converted to described certainty robust peer-to-peer model;

The constraints of the certainty robust peer-to-peer model of described minimum energy storage power demand Optimized model is obtained according to the inequality constraints of described circuit transmission power, the balance inequality constraints of described system power and described set;

S25: the wind energy turbine set energy storage minimum capacity value under most strong wind power fluctuation scope described in obtaining according to the target function of described certainty robust peer-to-peer model and constraints.

Further, the linear restriction of described Branch Power Flow and node injecting power is P _l=A ＇ _lb ' (g+ ω+e-d); Described system power Constraints of Equilibrium is wherein: A ＇ _lfor leaving out the m after balancing machine node respective column vector × (N-1) rank Relationship of Coefficients matrix; B ' is for leaving out the N-1 rank admittance square formation after balance node corresponding row column vector; g _i, ω _i, e _iand d _ibe respectively the generator output, output of wind electric field, energy storage power demand and the node load that access with branch road l related node, subscript i is under the jurisdiction of different power supply node set respectively.

Further, the inequality constraints of described circuit transmission power is: $\underset{i\∈\mathrm{JG}}{\mathrm{\Σ}}{s}_{\mathrm{li}}{g}_i+\underset{i\∈\mathrm{JW}}{\mathrm{\Σ}}{s}_{\mathrm{li}}{\mathrm{\ω}}_i+\underset{i\∈\mathrm{JW}}{\mathrm{\Σ}}{s}_{\mathrm{li}}{e}_i-\underset{i\∈\mathrm{JL}}{\mathrm{\Σ}}{s}_{\mathrm{li}}{d}_i\≤P_{L\max ,l}$ With $-\underset{i\∈\mathrm{JG}}{\mathrm{\Σ}}{s}_{\mathrm{li}}{g}_i-\underset{i\∈\mathrm{JW}}{\mathrm{\Σ}}{s}_{\mathrm{li}}{\mathrm{\ω}}_i-\underset{i\∈\mathrm{JW}}{\mathrm{\Σ}}{s}_{\mathrm{li}}{e}_i+\underset{i\∈\mathrm{JL}}{\mathrm{\Σ}}{s}_{\mathrm{li}}{d}_i\≤P_{L\max ,l};$ The inequality constraints of described system power balance is: with wherein: matrix S=A ＇ _lb ', s _lifor the element in matrix S, represent the coefficient of relationship of l article of Line Flow and i-th node injecting power; P _{lmax, l}be the l article of circuit transmission power limit; g _0max, g _0minfor balancing the upper and lower limit of unit output.

Further, described set is: in set wherein: be i-th active power of wind power field average; w _{i, max}, w _{i, min}be that i-th wind energy turbine set is maximum meritoriously to exert oneself and minimumly meritoriously to exert oneself; be i-th output of wind electric field fluctuation upper and lower bound.

Further, described target function is wherein: e _i=U _i-V _i; U _iand V _ifor there is no the nonnegative number of actual physics meaning.

Further, the constraints of described certainty robust peer-to-peer model is:

$\underset{i\∈\mathrm{JW}}{\mathrm{\Σ}}{s}_{\mathrm{li}}(U_i-V_i)+{\mathrm{\Γz}}_l+\underset{i\∈\mathrm{JW}}{\mathrm{\Σ}}{p}_{\mathrm{li}}\≤P_{L\max ,l}-\underset{i\∈\mathrm{JG}}{\mathrm{\Σ}}{s}_{\mathrm{li}}{g}_i+\underset{i\∈\mathrm{JL}}{\mathrm{\Σ}}{s}_{\mathrm{li}}{d}_i-\underset{i\∈\mathrm{JW}}{\mathrm{\Σ}}{s}_{\mathrm{li}}\stackrel{\‾}{w_i},l=1,...,m,$

$\underset{i\∈\mathrm{JW}}{\mathrm{\Σ}}-s_{\mathrm{li}}(U_i-V_i)+{\mathrm{\Γz}}_l^{\text{'}}+\underset{i\∈\mathrm{JW}}{\mathrm{\Σ}}{p}_{\mathrm{li}}^{\text{'}}\≤P_{L\max ,l}+\underset{i\∈\mathrm{JG}}{\mathrm{\Σ}}{s}_{\mathrm{li}}{g}_i-\underset{i\∈\mathrm{JL}}{\mathrm{\Σ}}{s}_{\mathrm{li}}{d}_i+\underset{i\∈\mathrm{JW}}{\mathrm{\Σ}}{s}_{\mathrm{li}}\stackrel{\‾}{w_i},l=1,...,m,$

$\underset{i\∈\mathrm{JW}}{\mathrm{\Σ}}-(U_i-V_i)+{\mathrm{\Γz}}_0+\underset{i\∈\mathrm{JW}}{\mathrm{\Σ}}{p}_{0i}\≤g_{0\max }+\underset{i\∈\mathrm{JG}}{\mathrm{\Σ}}{g}_i-\underset{i\∈\mathrm{JL}}{\mathrm{\Σ}}{d}_i+\underset{i\∈\mathrm{JW}}{\mathrm{\Σ}}\stackrel{\‾}{w_i},$

$\underset{i\∈\mathrm{JW}}{\mathrm{\Σ}}{U}_i-V_i+{\mathrm{\Γz}}_0^{\text{'}}+\underset{i\∈\mathrm{JW}}{\mathrm{\Σ}}{p}_{0i}^{\text{'}}\≤{-g}_{0\max }-\underset{i\∈\mathrm{JG}}{\mathrm{\Σ}}{g}_i+\underset{i\∈\mathrm{JL}}{\mathrm{\Σ}}{d}_i-\underset{i\∈\mathrm{JW}}{\mathrm{\Σ}}\stackrel{\‾}{w_i},$

$\left\{\begin{array}{c}{z}_l+p_{\mathrm{li}}\≥-s_{\mathrm{li}}(\stackrel{\‾}{w_i}-w_{i,\min })\\ z_l+p_{\mathrm{li}}\≥s_{\mathrm{li}}(w_{i,\max }-\stackrel{\‾}{w_i})\end{array}\right.,i\∈\mathrm{JW},l=1,...,m,$

$\left\{\begin{array}{c}{z}_l^{\text{'}}+p_{\mathrm{li}}^{\text{'}}\≥s_{\mathrm{li}}(\stackrel{\‾}{w_i}-w_{i,\min })\\ z_l^{\text{'}}+p_{\mathrm{li}}^{\text{'}}\≥{-s}_{\mathrm{li}}(w_{i,\max }-\stackrel{\‾}{w_i})\end{array}\right.,i\∈\mathrm{JW},l=1,...,m,$

$\left\{\begin{array}{c}{z}_0+p_{0i}\≥\stackrel{\‾}{w_i}-w_{i,\min }\\ z_0+p_{0i}\≥-(w_{i,\max }-\stackrel{\‾}{w_i})\end{array}\right.,i\∈\mathrm{JW},$

$\left\{\begin{array}{c}{z}_0^{\′}+p_{0i}^{\′}\≥-(\stackrel{\‾}{w_i}-w_{i,\min })\\ z_0^{\′}+p_{0i}^{\′}\≥(w_{i,\max }-\stackrel{\‾}{w_i})\end{array}\right.,i\∈\mathrm{JW},$

$\left\{\begin{array}{c}{z}_l,z_l^{\text{'}},z_0,z_0^{\text{'}}\≥0\\ p_{\mathrm{li}},p_{\mathrm{li}}^{\text{'}},p_{0i},p_{0i}^{\text{'}}\≥0,\\ U_i,V_i\≥0\end{array}\right.i\∈\mathrm{JW},l=1,...,m;$

Wherein: U _i, V _i, z _i, p _ikbe the nonnegative number variable of introducing, do not possess physical significance.

In general, the above technical scheme conceived by the present invention compared with prior art, because system stored energy collocation method after the windy electric field access that the present invention proposes considers the layout of multiple wind energy turbine set in electric power system and the network topology structure of connecting system itself, the objective fact of multiple wind energy turbine set access in current power system can be reflected.Meanwhile, the robust linear optimization method based on stochastic variable distributed intelligence of introducing, that can consider multiple wind energy turbine set goes out fluctuation simultaneously, and the Stochastic Programming Model being difficult to solve is converted to certainty linear programming model.Determine the maximum power fluctuation range of each wind energy turbine set based on each wind energy turbine set historical data sample, obtain energy storage configuration minimum capacity value, and according to minimum capacity value configuration energy storage device.The robustness of the adjustable energy storage configuration result of change bound variable introduced and optimality, provide advisory opinion for policymaker weighs allocation plan.Therefore, there is positive beneficial effect.

Accompanying drawing explanation

Fig. 1 is the capacity collocation method realization flow figure of many wind farm energy storage device of the reply wind power fluctuation that the embodiment of the present invention provides;

Fig. 2 is certain system typical case active power of wind power field frequency histogram schematic diagram that the embodiment of the present invention provides;

Fig. 3 is that certain system typical wind energy turbine set wind power experience that the embodiment of the present invention provides distributes, kernel function estimation distributes and exponential distribution fitting function schematic diagram;

Fig. 4 is the relation schematic diagram that certain system wind energy turbine set energy storage configuration result of providing of the embodiment of the present invention and robust control change bound variable.

Embodiment

In order to make object of the present invention, technical scheme and advantage clearly understand, below in conjunction with drawings and Examples, the present invention is further elaborated.Should be appreciated that specific embodiment described herein only in order to explain the present invention, be not intended to limit the present invention.In addition, if below in described each execution mode of the present invention involved technical characteristic do not form conflict each other and just can mutually combine.

For solving above-mentioned problems of the prior art, the invention provides a kind of minimum energy storage power capacity collocation method being suitable for tackling containing the electric power system of windy electric field wind power fluctuation, by the minimum energy storage power robust allocation models that reply multiple wind power fluctuation of setting up affects system power balance and network configuration, a kind of energy storage configuration method simultaneously considering safe operation and the multiple wind energy turbine set different capacity fluctuating level of reply under system determination Run-time scenario is provided, and obtains half-way house between robustness and economy.

The present invention is based on the energy storage allocation problem after windy electric field connecting system, collocation method considers the layout of multiple wind energy turbine set in electric power system and the network topology structure of connecting system itself, to reflect impact on power transmitting capability after the access of windy electric field, with actual physical background of fitting.

Fig. 1 shows the capacity collocation method realization flow of many wind farm energy storage device of the reply wind power fluctuation that the embodiment of the present invention provides, and specifically comprises:

S1: minimum for target function with the energy storage power demand of all nodes of system, set up minimum energy storage power demand Optimized model, method is as follows:

Target function: $\min \underset{i\∈N}{\mathrm{\Σ}}\left|e_i\right|---\left(1\right)$

In formula, N represents intrasystem node number; I represents electric power system interior joint sequence number; e _irepresent the energy storage power configuration demand of system node i, energy storage power capacity e _ican just can bear, to reflect the effect of energy storage device when participating in system power balance: e _i>0 illustrates that energy storage electric discharge is equivalent to source element, otherwise, e _i<0 illustrates that energy storage charging shows as load cell.E _i=0 illustrates do not have energy storage configuration needs.

S2: the system cloud gray model constraints setting up vector form, comprising:

Node power Constraints of Equilibrium :-B θ+g+ ω+e=d(2)

Line Flow retrains: P _l=A _lθ (3)

Circuit transmission power limit restraint: | P _l|≤P _lMAX(4)

Unit operation retrains: g _min≤ g≤g _max(5)

RANDOM WIND power fluctuation retrains: ω _min≤ ω≤ω _max(6)

Wherein:

E wind energy turbine set energy storage power configuration requirement vector (unit is: MW);

B is containing N × N rank admittance matrix of N number of node system;

θ N maintains system phase angle vector;

G system unit output vector (MW), unit number JG;

ω wind power vector (MW), wind energy turbine set number JW;

D system loading vector (MW), load number JL;

P _lm ties up branch road active power vector (MW);

A _lm × N rank relational matrix of branch power and phase angle;

P _lMAXbranch road transmission power limit vector (MW);

G _minunit minimum load vector (MW);

G _maxunit heap(ed) capacity vector (MW);

ω _minwind power fluctuation range lower limit vector (MW);

ω _maxwind power fluctuation range upper limit vector (MW);

The physical significance of constraints is described as follows:

Equation (2) is the constraint of system node power-balance, and wherein output of wind electric field ω is stochastic variable, represents with Greek alphabet.System node power balance equation reflects the layout of multiple wind energy turbine set in electric power system and the network topology structure of connecting system itself.

Equation (3) is the constraint of system line trend, and establishing the transformational relation of Branch Power Flow and node phase angle, is the connection constraints of model conversion and model solution.

Inequality (4) is circuit transmission power limit restraint, and circuit nonoverload is the constraints ensureing system safety operation.

Inequality (5) is unit operation constraint, the generating regulating power of reflection fired power generating unit.

Inequality (6) is the constraint of RANDOM WIND power fluctuation, describes the fluctuation range of stochastic variable Power Output for Wind Power Field, by limiting the fluctuation range of exerting oneself, utilizes robust linear optimization method process stochastic variable, solving of implementation model.

In conjunction with constraints in target function in S1 and S2, constitute a kind of minimum energy storage power capacity collocation method being suitable for tackling containing the electric power system of windy electric field wind power fluctuation that the present invention proposes, this model is Stochastic Programming Model.

S3: introduce the robust linear optimum theory process stochastic variable based on stochastic variable distributed intelligence, realize solving of Stochastic Programming Model.Thought based on robust linear optimum theory solving model is as follows: for each inequality containing stochastic variable introduces change bound variable Γ _iknown mean information and the stochastic variable of bound information can be described as the value set relevant with changing bound variable, different change bound variable value likely changing uncertain parameter in Controlling model, controls optimality and the robustness of optimal solution with this.Linear programming model containing uncertain parameter passes through and transforms and process, forms deterministic linear programming model, can conveniently solve.Change bound variable Γ _ibe defined as the robust level index of inequality i, span is 0≤Γ _i≤ J _i, wherein J _ifor the number of stochastic variable in inequality i.Γ _iget maximum J _irepresent that in this inequality, all stochastic variables all can reach maximum fluctuation scope, corresponding optimal solution can tackle the change of all stochastic variables, and namely robustness is the strongest.Γ _iget minimum value 0 and represent that all stochastic variables get average, model no longer possesses random nature, is converted into deterministic models completely, and the robustness of corresponding optimal solution is minimum.Γ _iin span during value, corresponding optimal solution will be weighed between robustness and optimality.

The step by the windy electric field minimum energy storage power configuration model conversation containing stochastic variable being certainty linear model is as follows:

S31: cancellation Partial Variable and constraints, forms inequality constraints.

Node power Constraints of Equilibrium (2) is substituted into Line Flow constraint (3), obtains linear restriction (7) and the system power Constraints of Equilibrium (8) of Branch Power Flow and node injecting power:

The linear restriction of Branch Power Flow and node injecting power: P _l=A ＇ _lb ' (g+ ω+e-d) (7);

System power Constraints of Equilibrium: $\underset{i\&Element;\mathrm{JG}}{\mathrm{\&Sigma;}}{g}_i+\underset{i\&Element;\mathrm{JW}}{\mathrm{\&Sigma;}}{\mathrm{\&omega;}}_i+\underset{i\&Element;\mathrm{JL}}{\mathrm{\&Sigma;}}{e}_i=\underset{i\&Element;\mathrm{JL}}{\mathrm{\&Sigma;}}{d}_i---\left(8\right);$

Wherein, A ＇ _lfor leaving out the m after balancing machine node respective column vector × (N-1) rank Relationship of Coefficients matrix, B ' is for leaving out the N-1 rank admittance square formation after balance node corresponding row column vector.Comprise balancing machine in system balancing equation (13) to exert oneself g ₀.Set up by the linear restriction (7) of Branch Power Flow and node injecting power and system power Constraints of Equilibrium (8) annexation that system rack and node inject, institute's Prescribed Properties is all converted into joint form and solves.

S32: the mathematical constraint model forming joint form.

Order matrix S=A ＇ _lb '.By the linear restriction (7) of Branch Power Flow and node injecting power with system power Constraints of Equilibrium (8) substitutes into circuit transmission power limit restraint (4) respectively and unit operation retrains (5), cancellation equality constraint, obtain circuit transmission power inequality constraints (9), (10) and system power balance inequality constraints (11), (12), constraints is all expressed as system node form:

The inequality constraints of circuit transmission power: $\underset{i\&Element;\mathrm{JG}}{\mathrm{\&Sigma;}}{s}_{\mathrm{li}}{g}_i+\underset{i\&Element;\mathrm{JW}}{\mathrm{\&Sigma;}}{s}_{\mathrm{li}}{\mathrm{\&omega;}}_i+\underset{i\&Element;\mathrm{JW}}{\mathrm{\&Sigma;}}{s}_{\mathrm{li}}{e}_i-\underset{i\&Element;\mathrm{JL}}{\mathrm{\&Sigma;}}{s}_{\mathrm{li}}{d}_i\&le;P_{L\max ,l}---\left(9\right)$

The inequality constraints of circuit transmission power: $-\underset{i\&Element;\mathrm{JG}}{\mathrm{\&Sigma;}}{s}_{\mathrm{li}}{g}_i-\underset{i\&Element;\mathrm{JW}}{\mathrm{\&Sigma;}}{s}_{\mathrm{li}}{\mathrm{\&omega;}}_i-\underset{i\&Element;\mathrm{JW}}{\mathrm{\&Sigma;}}{s}_{\mathrm{li}}{e}_i+\underset{i\&Element;\mathrm{JL}}{\mathrm{\&Sigma;}}{s}_{\mathrm{li}}{d}_i\&le;P_{L\max ,l}---\left(10\right)$

The inequality constraints of system power balance: $-(\underset{i\&Element;\mathrm{JG}}{\mathrm{\&Sigma;}}{g}_i+\underset{i\&Element;\mathrm{JW}}{\mathrm{\&Sigma;}}{\mathrm{\&omega;}}_i+\underset{i\&Element;\mathrm{JW}}{\mathrm{\&Sigma;}}{e}_i-\underset{i\&Element;\mathrm{JL}}{\mathrm{\&Sigma;}}{d}_i)\&le;g_{0\min }---\left(11\right)$

The inequality constraints of system power balance: $\underset{i\&Element;\mathrm{JG}}{\mathrm{\&Sigma;}}{g}_i+\underset{i\&Element;\mathrm{JW}}{\mathrm{\&Sigma;}}{\mathrm{\&omega;}}_i+\underset{i\&Element;\mathrm{JW}}{\mathrm{\&Sigma;}}{e}_i-\underset{i\&Element;\mathrm{JL}}{\mathrm{\&Sigma;}}{d}_i\&le;-g_{0\min }---\left(12\right)$

Wherein, s _lifor the element in matrix S, represent the coefficient of relationship of l article of Line Flow and i-th node injecting power.G _i, ω _i, e _iand d _ibe respectively and access generator output, output of wind electric field, energy storage power and node load with circuit l related node, subscript i is under the jurisdiction of different power supply node set respectively.P _{lmax, l}be the l article of maximum permission transmission power of branch road.G _0maxand g _0minbe respectively the bound of balance unit output.

S33: the mean information and the bound information that obtain stochastic variable.

According to the probability statistical analysis of wind energy turbine set operation history data, obtain the average of wind energy turbine set i ∈ JW active power with the upper limit w that exerts oneself under certain probabilistic confidence level _{i, max}with lower limit w _{i, min}.The fluctuation upper limit of wind energy turbine set i ∈ JW active power is fluctuation lower limit is based on robust linear optimization method, introduce change bound variable Γ, definition set (13) describes the fluctuation range of many Power Output for Wind Power Field:

The set of windy electric field power fluctuation scope:

In the Stochastic Programming Model that the present invention proposes, stochastic variable is the power output of each wind energy turbine set, and therefore in model, the change bound variable of each inequality constraints is equal, and meets Γ=JW.

S34: the linear representation of absolute value non-linear objective function transforms.

E _irepresent the energy storage power configuration demand of system node i, can get on the occasion of or negative value.Introduce the nonnegative number U not having actual physics meaning _iand V _i, make e _i=U _i-V _i, then in target function, absolute value variable can be expressed as | e _i|=U _i+ V _i;

The linear representation of target function: $\min \underset{i\&Element;\mathrm{JW}}{\mathrm{\&Sigma;}}{U}_i+V_i---\left(14\right)$

S35: the conversion of certainty linear programming model.

Power Output for Wind Power Field is considered as coefficient of combination matrix element, corresponding certainty multiplier item is considered as decision variable.Inequality i is introduced to the new decision variable z without physical significance _iand p _ik, and output power fluctuation of wind farm scope set (13) is applied to the inequality constraints of circuit transmission power and the inequality constraints of system power balance, obtain constraints (15)-(23) of certainty robust peer-to-peer model:

$\underset{i\&Element;\mathrm{JW}}{\mathrm{\&Sigma;}}{s}_{\mathrm{li}}(U_i-V_i)+{\mathrm{\&Gamma;z}}_l+\underset{i\&Element;\mathrm{JW}}{\mathrm{\&Sigma;}}{p}_{\mathrm{li}}\&le;P_{L\max ,l}-\underset{i\&Element;\mathrm{JG}}{\mathrm{\&Sigma;}}{s}_{\mathrm{li}}{g}_i+\underset{i\&Element;\mathrm{JL}}{\mathrm{\&Sigma;}}{s}_{\mathrm{li}}{d}_i-\underset{i\&Element;\mathrm{JW}}{\mathrm{\&Sigma;}}{s}_{\mathrm{li}}\stackrel{\&OverBar;}{w_i},l=1,...,m---\left(15\right)$

$\underset{i\&Element;\mathrm{JW}}{\mathrm{\&Sigma;}}-s_{\mathrm{li}}(U_i-V_i)+{\mathrm{\&Gamma;z}}_l^{\text{'}}+\underset{i\&Element;\mathrm{JW}}{\mathrm{\&Sigma;}}{p}_{\mathrm{li}}^{\text{'}}\&le;P_{L\max ,l}+\underset{i\&Element;\mathrm{JG}}{\mathrm{\&Sigma;}}{s}_{\mathrm{li}}{g}_i-\underset{i\&Element;\mathrm{JL}}{\mathrm{\&Sigma;}}{s}_{\mathrm{li}}{d}_i+\underset{i\&Element;\mathrm{JW}}{\mathrm{\&Sigma;}}{s}_{\mathrm{li}}\stackrel{\&OverBar;}{w_i},l=1,...,m---\left(16\right)$

$\underset{i\&Element;\mathrm{JW}}{\mathrm{\&Sigma;}}-(U_i-V_i)+{\mathrm{\&Gamma;z}}_0+\underset{i\&Element;\mathrm{JW}}{\mathrm{\&Sigma;}}{p}_{0i}\&le;g_{0\max }+\underset{i\&Element;\mathrm{JG}}{\mathrm{\&Sigma;}}{g}_i-\underset{i\&Element;\mathrm{JL}}{\mathrm{\&Sigma;}}{d}_i+\underset{i\&Element;\mathrm{JW}}{\mathrm{\&Sigma;}}\stackrel{\&OverBar;}{w_i}---\left(17\right)$

$\underset{i\&Element;\mathrm{JW}}{\mathrm{\&Sigma;}}{U}_i-V_i+{\mathrm{\&Gamma;z}}_0^{\text{'}}+\underset{i\&Element;\mathrm{JW}}{\mathrm{\&Sigma;}}{p}_{0i}^{\text{'}}\&le;{-g}_{0\max }-\underset{i\&Element;\mathrm{JG}}{\mathrm{\&Sigma;}}{g}_i+\underset{i\&Element;\mathrm{JL}}{\mathrm{\&Sigma;}}{d}_i-\underset{i\&Element;\mathrm{JW}}{\mathrm{\&Sigma;}}\stackrel{\&OverBar;}{w_i}---\left(18\right)$

$\left\{\begin{array}{c}{z}_l+p_{\mathrm{li}}\&GreaterEqual;-s_{\mathrm{li}}(\stackrel{\&OverBar;}{w_i}-w_{i,\min })\\ z_l+p_{\mathrm{li}}\&GreaterEqual;s_{\mathrm{li}}(w_{i,\max }-\stackrel{\&OverBar;}{w_i})\end{array}\right.,i\&Element;\mathrm{JW},l=1,...,m---\left(19\right)$

$\left\{\begin{array}{c}{z}_l^{\text{'}}+p_{\mathrm{li}}^{\text{'}}\&GreaterEqual;s_{\mathrm{li}}(\stackrel{\&OverBar;}{w_i}-w_{i,\min })\\ z_l^{\text{'}}+p_{\mathrm{li}}^{\text{'}}\&GreaterEqual;{-s}_{\mathrm{li}}(w_{i,\max }-\stackrel{\&OverBar;}{w_i})\end{array}\right.,i\&Element;\mathrm{JW},l=1,...,m---\left(20\right)$

$\left\{\begin{array}{c}{z}_0+p_{0i}\&GreaterEqual;\stackrel{\&OverBar;}{w_i}-w_{i,\min }\\ z_0+p_{0i}\&GreaterEqual;-(w_{i,\max }-\stackrel{\&OverBar;}{w_i})\end{array}\right.,i\&Element;\mathrm{JW}---\left(21\right)$

$\left\{\begin{array}{c}{z}_0^{\&prime;}+p_{0i}^{\&prime;}\&GreaterEqual;-(\stackrel{\&OverBar;}{w_i}-w_{i,\min })\\ z_0^{\&prime;}+p_{0i}^{\&prime;}\&GreaterEqual;(w_{i,\max }-\stackrel{\&OverBar;}{w_i})\end{array}\right.,i\&Element;\mathrm{JW}---\left(22\right)$

$\left\{\begin{array}{c}{z}_l,z_l^{\text{'}},z_0,z_0^{\text{'}}\&GreaterEqual;0\\ p_{\mathrm{li}},p_{\mathrm{li}}^{\text{'}},p_{0i},p_{0i}^{\text{'}}\&GreaterEqual;0,\\ U_i,V_i\&GreaterEqual;0\end{array}\right.i\&Element;\mathrm{JW},l=1,...,m---\left(23\right)$

Wherein, U _i, V _i, z _i, p _ikbeing the nonnegative number variable of introducing, not possessing physical significance, only introducing to change Stochastic Programming Model.

Thus the windy electric field minimum energy storage power configuration problem containing random wind-powered electricity generation is converted into deterministic linear optimization problem.Energy storage power capacity configuration needs can be tried to achieve by linear programming method solving model (14)-(23).

After the windy electric field access that the present invention proposes, system stored energy collocation method considers the layout of multiple wind energy turbine set in electric power system and the network topology structure of connecting system itself, to reflect the objective fact of multiple wind energy turbine set access in current power system, set up windy electric field energy storage configuration Stochastic Programming Model with actual physical background of fitting.The present invention introduces the robust linear optimum theory based on stochastic variable distributed intelligence, describes stochastic variable, the Stochastic Programming Model being difficult to solve is converted to certainty linear programming model with average and bound information.Based on wind energy turbine set historical data sample determination wind power fluctuation scope, determine the wind energy turbine set energy storage minimum capacity configuration needs under most strong wind power fluctuation scope with this.The robustness of the adjustable energy storage configuration result of change bound variable introduced and optimality, provide advisory opinion for policymaker weighs allocation plan.

In order to reply that the embodiment of the present invention the provides energy storage configuration method containing windy electric field system wind power fluctuation is further described, now in conjunction with instantiation and accompanying drawing, details are as follows:

Wind-powered electricity generation sample data to be gained merit force data from the wind energy turbine set 1min interval wind-powered electricity generation on May 1st, 2012 to October 31 of the actual access of certain system.Wind power integration example system is the IEEE reliability test system revised.

Implementation step 1: all the people present's wind energy turbine set selecting equivalent annual utilization hours close is as typical wind energy turbine set, and wind energy turbine set basic parameter is as shown in table 1.Limit layouting at wind energy turbine set access node of energy storage device.

Table 1 wind energy turbine set basic parameter

Statistical analysis is carried out to exerting oneself of wind energy turbine set of all the people present typical case, utilizes the cumulative distribution probability curve of kernel function estimation method matching typical case output of wind electric field.For wind energy turbine set 3, the frequency histogram of wind power as shown in Figure 2; Matching active power of wind power field power producing characteristics is not suitable for due to existing mathematical probabilities distribution function, adopt the cumulative distribution probability that kernel function estimation method is exerted oneself to active power of wind power field, except the fitting effect of the low section of exerting oneself is poor, other regions of exerting oneself substantially can coincide former sequence experience distribution, as shown in Figure 3.

The fluctuation upper limit of getting wind power long time scale is 90% to exert oneself state, and fluctuation lower limit is zero.Inverse operation is carried out to cumulative probability fitting of distribution function, it is as shown in table 2 that connecting system wind energy turbine set long time scale wind power fluctuation range parameter is set.

Table 2 accesses the fluctuation parameters of wind energy turbine set

Implementation step 2: calculate the energy storage demand after wind energy turbine set access test macro.

Revised test macro load is 2809MW, and the load of its interior joint 3 is kept to 74MW, and node 17 increases load 65MW.Given generator capacity to be exerted oneself mode as its typical case, its interior joint 7 generator typical case exerts oneself and is reduced to 125MW, and node 19 increases 50MW generator.The transmission power of 230kV network line reduces by half.Suppose that other units run using set-point as typical case's mode of exerting oneself except balancing machine, node 13 is regulating units installation node, and maximum output is 500MW.

Γ=4 represent the limit scene that all output of wind electric field change within the scope of maximum fluctuation, and it is as shown in table 3 that substitution certainty linear programming model (16)-(24) solve the wind energy turbine set energy storage power capacity configuring condition obtained.

Table 3 energy storage configuration needs

Wind energy turbine set access node	17	19	20	23
					Energy storage demand/MW	-5.41	60.33	0	0

Under the system operation mode arranged, node 17 needs the charge power providing 5.41MW, and node 19 needs the discharge power providing 60.33MW.After considering the fluctuation range of Power Output for Wind Power Field, system branch trend is no longer determined value.After the power adjustments ability of system determination unit output and adjustable unit is determined, the fluctuation range of Power Output for Wind Power Field can affect the excursion of Branch Power Flow.But the changed power of adjustable unit is limited in scope, and transmission power is subject to the restriction of adjacent lines conveying capacity.If there is the sight that circuit conveying capacity is limited, adjustable unit cannot maximize and play wind power fluctuation regulating action, cause system to occur safe operation problem.The result of distributing rationally of energy storage shows, at the energy storage device of the certain power capacity of Joint Enterprise, by changing the injecting power of system node, the trend distribution of influential system branch road, makes wind power maximum changing range be received by the regulating power of adjustable unit completely the impact of system cloud gray model.

Implementation step 3: the energy storage configuration result under different robustness requirement.

With Γ=4 be initial value, ε=0.01 reduces change bound variable Γ for iteration step length, and substitutes in robust linear model (15)-(24) and solve wind energy turbine set energy storage configuration needs.The relation of change bound variable Γ and windy electric field energy storage configuration result, as shown in Figure 4.

Change change bound variable Γ and will change the robustness of optimal solution, the robustness of the maximum expression optimal solution of Γ is the strongest, and Γ is minimum represents that the robustness of corresponding optimal solution is the poorest.Scene Γ=4 represent that the fluctuation range of exerting oneself of all wind energy turbine set is maximum, and the energy storage configuration needs under this scene can tackle maximum wind power output fluctuation, and the capacity of configuration is also the result of robust the most.Progressively reduce robust control parameter Γ, the fluctuation range of all output of wind electric field diminishes at the same time, and also reduce the impact of system power balance and safe operation, corresponding energy storage configuration needs also reduces.

The energy storage configuration result of this example signal is the system operation mode hypothesis based on determining, only considers the complex optimum result of the regulating power of regulating units.For the electric power system containing windy electric field of reality, the typical operation modes formulated with classical scenario method, can reflect the typical operation of system.In fact, allocation models (1)-(6) contain the adjustable extent of all units.Therefore, this collocation method can provide certain reference information for programmed decision-making person.

Those skilled in the art will readily understand; the foregoing is only preferred embodiment of the present invention; not in order to limit the present invention, all any amendments done within the spirit and principles in the present invention, equivalent replacement and improvement etc., all should be included within protection scope of the present invention.

Claims (4)

1. tackle a capacity collocation method for many wind farm energy storage device of wind power fluctuation, it is characterized in that, comprise the steps:

S1: set up minimum energy storage power demand Optimized model for target so that the energy storage power demand of all nodes of electric power system is minimum;

The target function of described minimum energy storage power demand Optimized model is: n represents the node number in electric power system, and i represents electric power system interior joint sequence number; 1≤i≤N; e _irepresent the energy storage power capacity of i-th node in electric power system;

The constraints of described minimum energy storage power demand Optimized model comprises: the constraint of node power Constraints of Equilibrium, Line Flow, the constraint of circuit transmission power limit restraint, unit operation and the constraint of RANDOM WIND power fluctuation;

S2: obtain the wind energy turbine set energy storage minimum capacity value under most strong wind power fluctuation scope in conjunction with robust linear optimization method according to described minimum energy storage power demand Optimized model;

S3: when described minimum capacity value is zero, then do not need to configure energy storage device; When described minimum capacity value is non-vanishing, then at non-vanishing Nodes, according to described wind energy turbine set energy storage minimum capacity value configuration energy storage device;

Described node power Constraints of Equilibrium is-B θ+g+ ω+e=d; Described Line Flow is constrained to P _l=A _lθ, circuit transmission power limit restraint be | P _l|≤P _lMAX; Described unit operation is constrained to g _min≤ g≤g _max; RANDOM WIND power fluctuation is constrained to ω _min≤ ω≤ω _max;

E is wind energy turbine set energy storage power configuration requirement vector; B is containing N × N rank admittance matrix of N number of node system; θ is that N maintains system phase angle column vector; G is system unit output column vector, and JG is unit number; ω is wind power column vector, and JW is wind energy turbine set number; D is system loading column vector, and JL is load number; P _lfor m ties up branch road active power column vector; A _lfor m × N rank relational matrix of branch power and phase angle; P _lMAXfor branch road transmission power limit column vector; g _minfor unit minimum load column vector; g _maxfor unit heap(ed) capacity column vector; ω _minfor wind power fluctuation range lower limit column vector; ω _maxfor wind power fluctuation range upper limit column vector;

Step S2 specifically comprises:

S21: the linear restriction and the system power Constraints of Equilibrium that obtain Branch Power Flow and node injecting power according to node power Constraints of Equilibrium and Line Flow constraint;

S22: obtain the inequality constraints of circuit transmission power according to the linear restriction of described Branch Power Flow and node injecting power and circuit transmission power limit restraint;

The inequality constraints of system power balance is obtained according to system power Constraints of Equilibrium and the constraint of described unit operation;

S23: probability statistics are carried out to the historical data of each wind energy turbine set and obtains the active power average of each wind energy turbine set, the upper limit of exerting oneself and lower limit of exerting oneself, and based on robust linear optimization method in conjunction with the set of the active power average of wind energy turbine set, the upper limit of exerting oneself and lower limit of exerting oneself acquisition for describing many output power fluctuation of wind farms scope;

S24: the target function target function of described minimum energy storage power demand Optimized model being converted to certainty robust peer-to-peer model;

The constraints of the certainty robust peer-to-peer model of described minimum energy storage power demand Optimized model is obtained according to the inequality constraints of described circuit transmission power, the balance inequality constraints of described system power and described set;

S25: the wind energy turbine set energy storage minimum capacity value under most strong wind power fluctuation scope described in obtaining according to the target function of described certainty robust peer-to-peer model and constraints.

2. capacity collocation method as claimed in claim 1, it is characterized in that, the linear restriction of described Branch Power Flow and node injecting power is P _l=A ' _lb ' (g+ ω+e-d);

Described system power Constraints of Equilibrium is

A ' _lfor leaving out the m after balancing machine node respective column vector × (N-1) rank Relationship of Coefficients matrix; B ' is for leaving out the N-1 rank admittance square formation after balance node corresponding row column vector; g _i, ω _i, e _iand d _ibe respectively the generator output, output of wind electric field, energy storage power demand and the node load that access with branch road l related node, subscript i is under the jurisdiction of different power supply node set respectively.

3. capacity collocation method as claimed in claim 2, it is characterized in that, the inequality constraints of described circuit transmission power is:

with

The inequality constraints of described system power balance is:

with

Matrix S=A ' _lb ', s _lifor the element in matrix S, represent the coefficient of relationship of l article of Line Flow and i-th node injecting power; P _{lmax, l}be the l article of circuit transmission power limit; g _0max, g _0minfor balancing the upper and lower limit of unit output.

4. capacity collocation method as claimed in claim 3, it is characterized in that, described set is: in set

be i-th active power of wind power field average; w _{i, max}, w _{i, min}be that i-th wind energy turbine set is maximum meritoriously to exert oneself and minimumly meritoriously to exert oneself; be i-th output of wind electric field fluctuation upper and lower bound.