CN105633950A - Multi-target random, fuzzy and dynamic optimal power flow considering wind power injection uncertainty - Google Patents

Abstract

The invention relates to a multi-target random, fuzzy and dynamic optimal power flow considering wind power injection uncertainty, belonging to the field of research of a day-ahead scheduling plan of a power system. The multi-target random, fuzzy and dynamic optimal power flow comprises the following steps of acquiring system associated data in a next scheduling period; describing a wind speed variable and corresponding wind power output by a random fuzzy model and a chance measurement function; building a multi-target random, fuzzy and dynamic optimal power flow model by taking minimum system power generation consumption, minimum pollutant discharge quantity and minimum active power network loss as targets and considering static constraints of safety voltage of a power system node, reactive power output, system standby application and the like and dynamic constraints of machine set climbing; generating wind power output by a random and fuzzy simulation method; and acquiring a Pareto solution set and an optimal compromise solution of the optimal module by applying NSGA-II and maximum satisfaction method. According to the multi-target random, fuzzy and dynamic optimal power flow, the wind power output is described by the random and fuzzy model, the multi-target random, fuzzy and dynamic optimal power flow model is built, and the multi-target optimal scheduling process of the power system under multiple uncertain wind power injection is reasonably described and solved.

Description

Consider that wind-powered electricity generation injects probabilistic multiple target Random-fuzzy Dynamic Optimal Power Flow Problem

Technical field

The invention belongs to power system operation plan research field a few days ago, relate to a kind of consideration wind-powered electricity generation and inject probabilistic multiple target Random-fuzzy Dynamic Optimal Power Flow Problem.

Background technology

Along with new energy development utilizes, electric power system dispatching is brought numerous impact and challenge by the uncertain injection of large-scale wind power, and its modeling is one of the matter of utmost importance and difficult point of correlational study.

It is randomness or ambiguity that its uncertainty is looked in wind power output modeling in current scheduling problem research more. Stochastic model stochastic modeling is broadly divided into two kinds, and one is generally first describe wind speed with Weibull distribution function, and recycling wind speed obtains Power Output for Wind Power Field with power relation, also has research employing Beta distribution to describe; Two is the analysis method adopting Random time sequence, and conventional mainly has Markov-chain model and ARMA model etc. In scheduling problem, Monte Carlo simulation is generally adopted to produce emulation wind power output. Obscurity model building also can divide two kinds, and one is directly wind power output is carried out fuzzy membership modeling; Two is the ambiguity that the ambiguity of wind-powered electricity generation is converted into forecast error, and error is carried out obscurity model building.

It is true that wind speed has randomness because being affected by numerous natural causes, usable probability distribution describes, but its distributed constant matching is limit by limited historical data and be there is ambiguity, thus wind speed has random and fuzzy dual uncertain feature concurrently. Scale wind power output is also had multiple uncertainty by leading impact of wind speed, its more science model accurately and scheduling problem urgently studied based on the wind power output simulation method of the multiple ambiguous model of wind speed.

Consider that wind-powered electricity generation injects multiple target Dynamic Optimal Power Flow Problem and is intended to based on the reasonable prediction to situations such as power system next of loads dispatching cycle, in formulation system, scalable means such as wind-powered electricity generation abandons air quantity, fired power generating unit is exerted oneself, the operation plan of set end voltage, reactive-load compensation input etc., with realize power system economy under meeting unit climbing and the constraint such as voltage security, environmental protection, energy-conservation etc. in multiobjective Dynamic Optimization run. Such issues that be the Complex multi-target nonlinear optimal problem with a large amount of hybrid variable and constraints. Quick non-dominated sorted genetic algorithm (non-dominatedsortinggeneticalgorithm-II based on genetic idea, NSGA-II) there is good nonlinear optimization ability and robustness, multiple target Pareto optimal solution set can be obtained, ensure that optimum individual multiformity is thus providing difference preference to select for policymaker, becomes one of outstanding intelligent algorithm solving multi-objective optimization question. Maximum satisfaction degree method is calculated thus obtaining the comprehensive optimal compromise solution of multiple target by fuzzy satisfactory degree and comprehensive satisfaction, provides a kind of method and approach for decision-making.

In sum, research considers that wind-powered electricity generation injects probabilistic multiple target Random-fuzzy Dynamic Optimal Power Flow Problem, effectively to model and to solve containing wind-powered electricity generation power system multiple target scheduling problem a few days ago, for adapting to wind-powered electricity generation infiltration access and lifting power system scheduling level a few days ago, there is positive effect.

Summary of the invention

For the deficiencies in the prior art, the present invention " considers that wind-powered electricity generation injects probabilistic multiple target Random-fuzzy Dynamic Optimal Power Flow Problem ", propose to adopt the dual uncertain mathematical model of Random-fuzzy to describe wind speed and wind power output in scheduling problem, power system multiple target Random-fuzzy Dynamic Optimal Power Flow Problem model is set up based on this, generate emulation wind power output with Random-fuzzy simulation method, take NSGA-II and maximum satisfaction degree method hybrid algorithm to solve the Pareto disaggregation and optimal compromise solution obtaining multiple target Dynamic Optimal Power Flow Problem.

The present invention adopts the following technical scheme that consideration wind-powered electricity generation injects probabilistic multiple target Random-fuzzy Dynamic Optimal Power Flow Problem, and the method comprises the steps:

Step 1: obtain the power system data in the next full schedule cycle, and carry out load prediction.

Step 2: with the dual ambiguous model of Random-fuzzy and chance measure function representation wind speed variable thereof and corresponding wind power output.

Step 3: generating electricity with system, consuming is minimum, pollutant discharge amount is minimum, active power loss is minimum for target, consider power system node security voltage, idle exert oneself, the dynamic constrained of the static constraint such as system reserve and unit climbing, set up multiple target Random-fuzzy Dynamic Optimal Power Flow Problem model.

Step 4: by Random-fuzzy simulation method generation day air speed data and corresponding wind power output, adopt NSGA-II and maximum satisfaction degree method hybrid algorithm to obtain Pareto disaggregation and the optimal compromise solution of multiple target Random-fuzzy Dynamic Optimal Power Flow Problem.

Accompanying drawing explanation

Fig. 1: the present invention considers that wind-powered electricity generation injects the whole implementation flow chart of probabilistic multiple target Random-fuzzy Dynamic Optimal Power Flow Problem;

Fig. 2: the Random-fuzzy simulation wind power output flow chart of the present invention;

Fig. 3: the type extent function curve less than normal of the present invention;

The wiring schematic diagram of Fig. 4: IEEE30 node system;

The typical load curve figure of Fig. 5: IEEE30 node system.

Detailed description of the invention

Below in conjunction with drawings and the specific embodiments, the present invention is further described.

The consideration wind-powered electricity generation that the present invention proposes injects probabilistic multiple target Random-fuzzy Dynamic Optimal Power Flow Problem, and its whole implementation flow process is shown in Fig. 1, is described in detail with IEEE30 node system for specific embodiment below, and its wiring condition is shown in Fig. 4. Embodiment is used for illustrating but is not limited to the present invention.

Step 1: obtain the power system data in the next full schedule cycle, and carry out load prediction.

The present embodiment is directly inputted to the data of IEEE30 node system. Load prediction curve adopts Fig. 5 typical double-peak curve to be example.

Step 2: with the dual ambiguous model of Random-fuzzy and chance measure function representation wind speed variable thereof and corresponding wind power output.

Wind speed Follow Weibull Distribution

f_v(v)=k/c (v/c)^k-1exp[-(v/c)^k](1)

Excavated by historical data and parameter fitting, obtain the fuzzy membership of the scale parameter c and form parameter k of historical wind speed Weibull distribution, used fuzzy variable ��_cAnd ��_kRepresent. Thus wind speed being described as random fuzzy variable ��_v, namely

$f_{{\mathrm{\ξ}}_v}\left({\mathrm{\ξ}}_v\right)={\mathrm{\ξ}}_k/{\mathrm{\ξ}}_c{({\mathrm{\ξ}}_v/{\mathrm{\ξ}}_c)}^{k-1}\exp \[-{({\mathrm{\ξ}}_v/{\mathrm{\ξ}}_c)}^{{\mathrm{\ξ}}_k}\]---\left(2\right)$

Chance measure distribution function is

$F\left({\mathrm{\&xi;}}_v\right)=Ch(v<{\mathrm{\&xi;}}_v)=1-\exp \&lsqb;-{({\mathrm{\&xi;}}_v/{\mathrm{\&xi;}}_c)}^{{\mathrm{\&xi;}}_k}\&rsqb;---\left(3\right)$

The meritorious model of exerting oneself of scale wind energy turbine set is approximately multiple stage wind-driven generator and exerts oneself superposition

$P_{WG}^{fore}=\left\{\begin{array}{cc}0,& {\mathrm{\&xi;}}_v<v_{ci}{\mathrm{or\&xi;}}_v\&GreaterEqual;v_{co}\\ N\&CenterDot;P_{WGr}({{\mathrm{\&xi;}}_v}^3-v_{ci}^3)/(v_r^3-v_{ci}^3),& v_{ci}\&le;{\mathrm{\&xi;}}_v\&le;v_r\\ N\&CenterDot;P_{WGr},& {\mathrm{\&xi;}}_v\&GreaterEqual;v_r\end{array}\right.---\left(4\right)$

Wherein v_ci, v_coAnd v_rRespectively incision, excision and rated wind speed, P_WGrBeing that the specified meritorious of single wind-driven generator is exerted oneself, N is wind-driven generator number.

Dynamic scheduling problem considers abandon air quantity P_WC,t, wind-powered electricity generation is meritorious exert oneself into

$P_{WG,t}=P_{WG,t}^{fore}-P_{WC,t}---\left(5\right)$

Consider that wind energy turbine set runs on leading phaseConstant power factor control model under, then its absorption is idle

Step 3: generating electricity with system, consuming is minimum, pollutant discharge amount is minimum, active power loss is minimum for target, consider power system node security voltage, idle exert oneself, the dynamic constrained of the static constraint of system reserve etc. and unit climbing, set up multiple target Random-fuzzy Dynamic Optimal Power Flow Problem model.

Multiple target Random-fuzzy Dynamic Optimal Power Flow Problem model in the present invention is as follows:

Decision variable vectorWherein P_Gi,tBe fired power generating unit i meritorious exert oneself (i=1,2 ... n_G), U_Gi,tIt is the voltage of fired power generating unit i place node, B_k,t(k=1,2 ... n_SC) it is reactive-load compensator k input amount, P_WC,tIt is that wind energy turbine set abandons air quantity.

In this example, tradition fired power generating unit 6, reactive-load compensator 2, wind energy turbine set 1, meritorious the exerting oneself of balance unit does not include decision variable in, then have 14 decision variables.

Object function:

$f_1\&lsqb;x_t\&rsqb;=min\underset{t=1}{\overset{T}{\&Sigma;}}\{\underset{i=1}{\overset{n_G}{\&Sigma;}}\&lsqb;a_i{P}_{Gi,t}^2+b_i{P}_{Gi,t}+c_i\&rsqb;\}---\left(7\right)$

$f_2\&lsqb;x_t\&rsqb;=min\underset{t=1}{\overset{T}{\&Sigma;}}\{\underset{i=1}{\overset{n_G}{\&Sigma;}}\&lsqb;{\mathrm{\&alpha;}}_i{P}_{Gi,t}^2+{\mathrm{\&beta;}}_i{P}_{Gi,t}+{\mathrm{\&gamma;}}_i\&rsqb;\}---\left(8\right)$

$f_2\&lsqb;x_t\&rsqb;=min\underset{t=1}{\overset{T}{\&Sigma;}}\{\underset{bi=1}{\overset{n_{bus}}{\&Sigma;}}{U}_{bi}\left(t\right)\underset{bj\&Element;\mathrm{\&Gamma;}}{\mathrm{\&Sigma;}}{U}_{bj}\left(t\right)\&lsqb;G_{bi,bj,t}{\mathrm{cos\&delta;}}_{bi,bj,t}+B_{bi,bj,t}{\mathrm{sin\&delta;}}_{bi,bj,t}\&rsqb;\}---\left(9\right)$

Constraints:

1. static constraint

(1) power-balance constraint

$P_{Gbi,t}-P_{Lbi,t}=U_{bi,t}\underset{bj\&Element;\mathrm{\&Gamma;}}{\mathrm{\&Sigma;}}{U}_{bj,t}\&lsqb;G_{bi,bj,t}{\mathrm{cos\&delta;}}_{bi,bj,t}+B_{bi,bj,t}{\mathrm{sin\&delta;}}_{bi,bj,t}\&rsqb;\},\&ForAll;bi,t---\left(10\right)$

$Q_{Gbi,t}-Q_{Lbi,t}=U_{bi,t}\underset{bj\&Element;\mathrm{\&Gamma;}}{\mathrm{\&Sigma;}}{U}_{bj,t}\&lsqb;G_{bi,bj,t}{\mathrm{sin\&delta;}}_{bi,bj,t}-B_{bi,bj,t}{\mathrm{cos\&delta;}}_{bi,bj,t}\&rsqb;\},\&ForAll;bi,t---\left(11\right)$

(2) generating constraint

$0\&le;P_{WC,t}\&le;P_{WC,t}^{fore}$

$P_{Gi}^{\min }\&le;P_{Gi,t}\&le;P_{Gi}^{max},\&ForAll;i,t---\left(12\right)$

$Q_{Gi}^{min}\&le;Q_{Gi,t}\&le;Q_{Gi}^{\max },\&ForAll;i,t---\left(13\right)$

(3) node voltage constraint

$U_{bi}^{min}\&le;U_{bi,t}\&le;U_{bi}^{\max },\&ForAll;bi,t---\left(14\right)$

(4) system spinning reserve constraint

$\underset{i=1}{\overset{n_G}{\&Sigma;}}\&lsqb;P_{Gi}^{\max }-P_{Gi,t}\&rsqb;\&GreaterEqual;{\mathrm{\&mu;P}}_L^{max},\&ForAll;t---\left(15\right)$

Take ��=5%

2. dynamic constrained

The Climing constant of fired power generating unit

$-{\mathrm{\&Delta;P}}_{Gi}^{down}\&le;P_{Gi,t+1}-P_{Gi,t}\&le;{\mathrm{\&Delta;P}}_{Gi}^{up},\&ForAll;i,t---\left(16\right)$

Consider that the bound of exerting oneself of the fired power generating unit after Climing constant is determined by following formula

$\max \{P_{Gi}^{min},P_{Gi,t-1}-{\mathrm{\&Delta;P}}_{Gi}^{down}\}\&le;P_{Gi,t}\&le;\min \{P_{Gi}^{\max },P_{Gi,t-1}+{\mathrm{\&Delta;P}}_{Gi}^{up}\}---\left(17\right)$

Step 4: by Random-fuzzy simulation method generation day air speed data and corresponding wind power output, adopt NSGA-II and maximum satisfaction degree method hybrid algorithm to obtain Pareto disaggregation and the optimal compromise solution of multiple target Random-fuzzy Dynamic Optimal Power Flow Problem.

Adopting Random-fuzzy simulation method to produce the meritorious power curve of available wind-powered electricity generation, its flow chart is as shown in Figure 2.

Adopt NSGA-II to ask for Pareto optimal solution set, maximum satisfaction degree method decision-making optimal compromise solution, optimization object function adds dynamic penalty function and realizes static equality constraint.

Adopt Fuzzy satisfaction computing formula less than normal, its schematic diagram such as Fig. 3. Pareto being solved to each non-domination solution concentrated, the satisfaction of calculating each of which desired value, then calculate the comprehensive satisfaction of each non-domination solution, choosing the maximum non-domination solution of comprehensive satisfaction is multiobjective optimization compromise solution.

Embodiment of above is merely to illustrate the present invention; and it is not limitation of the present invention; those of ordinary skill about technical field; without departing from the spirit and scope of the present invention; can also making a variety of changes and modification, therefore all equivalent technical schemes fall within the protection category of the present invention.

Claims (3)

1. considering that wind-powered electricity generation injects probabilistic multiple target Random-fuzzy Dynamic Optimal Power Flow Problem, is characterized in that, the method comprises the steps:

Step 1: obtain the power system data in the next full schedule cycle, and carry out load prediction;

Step 2: with the dual ambiguous model of Random-fuzzy and chance measure function representation wind speed variable thereof and corresponding wind power output;

Step 3: generating electricity with system, consuming is minimum, pollutant discharge amount is minimum, active power loss is minimum for target, consider power system node security voltage, idle exert oneself, the dynamic constrained of the static constraint of system reserve etc. and unit climbing, set up multiple target Random-fuzzy Dynamic Optimal Power Flow Problem model;

Step 4: by Random-fuzzy simulation method generation day air speed data and corresponding wind power output, adopt NSGA-II and maximum satisfaction degree method hybrid algorithm to obtain Pareto disaggregation and the optimal compromise solution of multiple target Random-fuzzy Dynamic Optimal Power Flow Problem.

2. consideration wind-powered electricity generation according to claim l injects probabilistic multiple target Random-fuzzy Dynamic Optimal Power Flow Problem, it is characterized in that, the wind speed in step 2 and wind power output model, is the dual ambiguous model of Random-fuzzy, it may be assumed that

Wind speed ��_vDistribution function $f_{{\mathrm{\&xi;}}_v}\left({\mathrm{\&xi;}}_v\right)={\mathrm{\&xi;}}_k/{\mathrm{\&xi;}}_c{({\mathrm{\&xi;}}_v/{\mathrm{\&xi;}}_c)}^{k-1}\exp \&lsqb;-{({\mathrm{\&xi;}}_v/{\mathrm{\&xi;}}_c)}^{{\mathrm{\&xi;}}_k}\&rsqb;$

Wherein ��_kAnd ��_cRepresent form parameter fuzzy variable and the scale parameter fuzzy variable of Weibull distribution respectively;

Chance measure distribution function $F\left({\mathrm{\&xi;}}_v\right)=Ch(v<{\mathrm{\&xi;}}_v)=1-\exp \&lsqb;-{({\mathrm{\&xi;}}_v/{\mathrm{\&xi;}}_c)}^{{\mathrm{\&xi;}}_k}\&rsqb;$

Scale wind energy turbine set is meritorious exerts oneself

$P_{WG}^{fore}=\left\{\begin{array}{cc}0,& {\mathrm{\&xi;}}_v<v_{ci}{\mathrm{or\&xi;}}_v\&GreaterEqual;v_{co}\\ N\&CenterDot;P_{WGr}({{\mathrm{\&xi;}}_v}^3-v_{ci}^3)/(v_r^3-v_{ci}^3),& v_{ci}\&le;{\mathrm{\&xi;}}_v\&le;v_r\\ N\&CenterDot;P_{WGr},& {\mathrm{\&xi;}}_v\&GreaterEqual;v_r\end{array}\right.$

Wherein v_ci, v_coAnd v_rRespectively incision, excision and rated wind speed, P_WGrBeing that the specified meritorious of single wind-driven generator is exerted oneself, N is wind-driven generator number.

3. the consideration wind-powered electricity generation belonging to any one claim in claim 1 and claim 2 injects probabilistic multiple target Random-fuzzy Dynamic Optimal Power Flow Problem, it is characterized in that, the model solution in step 4 emulates data and corresponding wind power output with the Random-fuzzy simulation method generation day wind speed based on chance measure distribution function and inverse transformation method.