CN104485681A - Monitoring method for wind power plant energy storage system - Google Patents

Abstract

The invention provides a monitoring method for a wind power plant energy storage system. According to the method, the generated power of a wind power plant can be predicted, the load changing situation is predicted, the battery capacity of a storage battery module is detected in real time, the running situation of a power distribution network is obtained in real time, the most appropriate control strategy is formulated and implemented, it is guaranteed that the wind power plant stably outputs power, the safety of the energy storage system is improved, and the service life of the energy storage system is prolonged.

Description

A kind of method for supervising of wind energy turbine set energy-storage system

Art

The present invention relates to a kind of method for supervising of wind energy turbine set energy-storage system.

Background technology

In recent years, wind power generation relies on its advantage such as environmental protection, aboundresources, obtains the attention of countries in the world, becomes the important sources of non-fossil fuel generating.But wind energy has randomness and intermittent feature, independently wind generator system be difficult to provide stable, continuous print power stage, fluctuation is comparatively large, is directly incorporated into the safe and stable operation that electrical network will inevitably affect electric power system.Therefore, consider from power grid security angle, introduce energy storage device for wind energy turbine set and stabilize its power fluctuation, set up the inexorable trend that wind storing cogeneration system is following wind power generation.

Wind storage system absorbs dump energy rapidly by energy storage or supplemental capacity vacancy stabilizes the power fluctuation of wind energy turbine set, so when utilizing energy-storage system to stabilize the power fluctuation of wind energy turbine set, cannot ensure to carry out regular discharge and recharge to it, easily there is super-charge super-discharge, this not only can affect its useful life, to increase input cost, and its charging and discharging capabilities may be made when power fluctuation is violent not enough, affect the safety of wind-electricity integration operation.

Wind storage system absorbs dump energy rapidly by energy storage or supplemental capacity vacancy stabilizes the power fluctuation of wind energy turbine set, so when utilizing energy-storage system to stabilize the power fluctuation of wind energy turbine set, cannot ensure to carry out regular discharge and recharge to it, easily there is super-charge super-discharge, this not only can affect its useful life, to increase input cost, and its charging and discharging capabilities may be made when power fluctuation is violent not enough, affect the safety of wind-electricity integration operation.If when formulating energy-storage system discharge and recharge strategy, add the extra control to its SOC (State ofCharge, state-of-charge), just can while stabilizing wind power fluctuation, avoid the super-charge super-discharge of energy-storage system, can the power output of smooth wind power field for a long time.

Summary of the invention

The invention provides a kind of method for supervising of wind energy turbine set energy-storage system, the method can predict the generated output of wind energy turbine set, the situation of change of prediction load, the ruuning situation of the battery module battery capacity of real-time detection and the power distribution network of Real-time Obtaining, formulate and implement optimum control strategy, ensure the steady power output of wind energy turbine set, promote fail safe and the useful life of energy-storage system.

To achieve these goals, the invention provides a kind of method for supervising of wind energy turbine set energy-storage system, the method realizes based on following supervising device, and this supervising device comprises:

Wind-powered electricity generation monitoring module, for monitoring wind-powered electricity generation module in real time, and predicts the generated output of wind-powered electricity generation module;

Battery monitor module, for monitoring battery module in real time;

Load monitoring module, for monitoring the load in wind energy turbine set energy-storage system in real time, and predicts the changed power situation of load;

Power distribution network contact module, knows the ruuning situation of power distribution network and relevant schedule information for real-time from power distribution network regulation and control center;

Be incorporated into the power networks monitoring module, connects or isolation power distribution network for controlling wind energy turbine set energy-storage system;

Middle control module, for determining the operation reserve of wind energy turbine set energy-storage system, and sends instruction to each module in above-mentioned supervising device, to perform this operation reserve;

Bus module, for the liaison of the modules of this supervising device;

This method for supervising comprises the steps:

(1) service data of wind-powered electricity generation monitoring module Real-time Obtaining wind-powered electricity generation module, and store data, the load variations situation of load monitoring module Real-time Obtaining load;

(2) according to the service data of wind-powered electricity generation module, the power output of the wind-powered electricity generation module in following predetermined instant is predicted, according to the load variations situation of wind energy turbine set load, the workload demand of load is predicted;

(3) SOC obtaining battery module is detected in real time, the parameter of Real-time Obtaining power distribution network and schedule information;

(4) using the SOC of the schedule information of power distribution network, current batteries to store energy, following wind-powered electricity generation module power output and to the change of following workload demand as constraints, realize the optimal control of battery module SOC.

Preferably, predict the power output of wind-powered electricity generation module in step (2) in the following way, described wind-powered electricity generation module comprises wind-driven generator and SVG:

(201) gather in wind-powered electricity generation module that current all kinds of electricity measured value is as the initial value of the predicted value of all kinds of electricity, predicted value comprises: blower fan is gained merit predicted value predicted value that blower fan is idle blower fan set end voltage predicted value predicted value that SVG is idle sVG set end voltage predicted value wind-powered electricity generation module site (PCC) prediction of busbar voltage value

(202) set up the MPC optimizing control models be made up of optimization object function and constraints according to described predicted value, and solve the predicted value of the meritorious of wind-powered electricity generation module and idle output:

The target function of MPC optimizing control models is such as formula shown in (1):

$\underset{Q_{\mathrm{WTG}}^{\mathrm{set}},V_{\mathrm{SVG}}^{\mathrm{set}}}{\min }(\underset{i=0}{\overset{N-1}{\mathrm{\Σ}}}\underset{j=0}{\overset{M-1}{\mathrm{\Σ}}}{\mathrm{\ρ}}^{t_{i,j}}{F}_1,\underset{i=0}{\overset{N-1}{\mathrm{\Σ}}}\underset{j=0}{\overset{M-1}{\mathrm{\Σ}}}{\mathrm{\ρ}}^{t_{i,j}}{F}_2)---\left(1\right)$

In formula (1) with for optimized variable, with implication is respectively the idle set point of blower fan and SVG voltage setting value; N is the number in time window Coverage Control cycle; M is the number containing future position under single control cycle; ρ is attenuation coefficient, value ρ < 1; Time variable ti, j=(Mi+j) Δ t meaning is the jth future position that current time plays in i-th control cycle, and Δ t is future position interval, and Δ t is determined by wind-powered electricity generation modular power predicted time interval;

F1 is wind-powered electricity generation module and the variance level of site busbar voltage and set point, and F1 expression is such as formula (2):

$F_1\left(t_{i,j}\right)={[V_{\mathrm{PCC}}^{\mathrm{pre}}\left(t_{i,j}\right)-V_{\mathrm{PCC}}^{\mathrm{ref}}]}^2---\left(2\right)$

In formula (2) represent the reference value of PCC voltage, setting after extracting from main website control command;

F2 is the idle level of reserve of SVG, and F2 expression is such as formula (3):

$F_2\left(t_{i,j}\right)={[Q_{\mathrm{SVG}}^{\mathrm{pre}}\left(t_{i,j}\right)-Q_{\mathrm{SVG}}^{\mathrm{opr}}]}^2---\left(3\right)$

In formula (3) for the idle best operating point of SVG;

The constraints of MPC optimizing control models, specifically comprises:

Blower fan is gained merit prediction-constraint condition:

$P_{\mathrm{WTG}}^{\mathrm{pre}}\left(t_{i,j}\right)=\underset{k=1}{\overset{N_a}{\mathrm{\&Sigma;}}}{\mathrm{\&phi;}}_k{P}_{\mathrm{WTG}}^{\mathrm{pre}}\left(t_{i,j-k}\right)+{\mathrm{\&epsiv;}}_{\mathrm{WTG}}^{\mathrm{pre}}\left(t_{i,j}\right)-\underset{k-1}{\overset{N_m}{\mathrm{\&Sigma;}}}{\mathrm{\&theta;}}_k{\mathrm{\&epsiv;}}_{\mathrm{WTG}}^{\mathrm{pre}}\left(t_{i,j-k}\right)---\left(4\right)$

In formula (4) for blower fan is gained merit predicated error; Na and Nm is respectively the exponent number of AR and MA model, and φ k and θ k is associated weight, and exponent number and weight are all determined according to blower fan history value of gaining merit; Ti, j-k (comprise for participating in calculated data in prediction ) the corresponding moment, subscript k pushes away the k Δ t time before characterizing the prediction moment, works as ti, and during j-k≤0, meritorious predicted value should get corresponding moment history value;

Prediction-constraint condition that blower fan is idle:

Blower fan is idle reaches set point before controlling next time:

$Q_{\mathrm{WTG}}^{\mathrm{pre}}\left(t_{i,0}\right)=Q_{\mathrm{WTG}}^{\mathrm{set}}\left(t_{i-\mathrm{1,0}}\right)---\left(5\right)$

Each future position in i-th control cycle, the change procedure of blower fan reactive power is with exponential function matching:

$Q_{\mathrm{WTG}}^{\mathrm{pre}}\left(t_{i,j}\right)=\frac{1-e^{-(t_{i,j}-t_{i,0})/T_s}}{1-e^{-\mathrm{M\&Delta;t}/T_s}}{Q}_{\mathrm{WTG}}^{\mathrm{set}}\left(t_{i,0}\right)+\frac{e^{-(t_{i,j}-t_{i,0})/T_s}-e^{-\mathrm{M\&Delta;t}/T_s}}{1-e^{-\mathrm{M\&Delta;t}/T_s}}{Q}_{\mathrm{WTG}}^{\mathrm{pre}}\left(t_{i,0}\right)---\left(6\right)$

In formula (6), Ts is blower fan Reactive-power control time constant, can obtain according to blower fan Reactive-power control testing experiment.

Prediction-constraint condition that SVG is idle:

Reference value that SVG is idle shown in (7):

$\begin{array}{c}{Q}_{\mathrm{SVG}}^{\mathrm{ref}}\left(t_{i,j}\right)=K_P[V_{\mathrm{SVG}}^{\mathrm{pre}}\left(t_{i,j}\right)-V_{\mathrm{SVG}}^{\mathrm{set}}\left(t_{i,0}\right)]+K_I\mathrm{\&Delta;t}\underset{k=0}{\overset{i\&times;M+j}{\mathrm{\&Sigma;}}}[V_{\mathrm{SVG}}^{\mathrm{pre}}\left(t_{i,j-k}\right)-V_{\mathrm{SVG}}^{\mathrm{set}}\left(t_{i,-k}\right)]\\ +Q_{\mathrm{SVG}}^{\mathrm{pre}}\left(t_{\mathrm{0,0}}\right)-K_P[V_{\mathrm{SVG}}^{\mathrm{pre}}\left(t_{\mathrm{0,0}}\right)-V_{\mathrm{SVG}}^{\mathrm{set}}\left(t_{\mathrm{0,0}}\right)]\end{array}---\left(7\right)$

In formula (7), KI and KP is respectively the coefficient of proportional component and integral element;

Predicted value that SVG is idle is such as formula shown in (8):

$Q_{\mathrm{SVG}}^{\mathrm{pre}}\left(t_{i,j}\right)=Q_{\mathrm{SVG}}^{\mathrm{ref}}\left(t_{i,j-1}\right)+{[Q_{\mathrm{SVG}}^{\mathrm{pre}}\left(t_{i,j-1}\right)-Q_{\mathrm{SVG}}^{\mathrm{ref}}\left(t_{i,j-1}\right)]e}^{-(t_{i,j}-t_{i,j-1})/T_d}---\left(8\right)$

In formula (8), time constant Td is SVG power electronic equipment action delay;

Voltage prediction constraints:

$V^{\mathrm{pre}}\left(t_{i,j}\right)-V^{\mathrm{pre}}\left(t_{\mathrm{0,0}}\right)=S\left[\begin{array}{c}{P}_{\mathrm{WTG}}^{\mathrm{pre}}\left(t_{i,j}\right)-P_{\mathrm{WTG}}^{\mathrm{pre}}\left(t_{\mathrm{0,0}}\right)\\ Q_{\mathrm{WTG}}^{\mathrm{pre}}\left(t_{i,j}\right)-Q_{\mathrm{WTG}}^{\mathrm{pre}}\left(t_{\mathrm{0,0}}\right)\\ Q_{\mathrm{SVG}}^{\mathrm{pre}}\left(t_{i,j}\right)-Q_{\mathrm{SVG}}^{\mathrm{pre}}\left(t_{\mathrm{0,0}}\right)\end{array}\right]---\left(9\right)$

V in formula (9) ^prefor the vector that blower fan machine end, SVG machine end and PCC prediction of busbar voltage value are formed, S is sensitivity matrix;

The constraints that system voltage, generator operation and SVG run:

$\left\{\begin{array}{c}{V}^{\min }\&le;V^{\mathrm{pre}}\left(t_{i,j}\right)\&le;V^{\max }\\ Q_{\mathrm{WTG}}^{\min }\&le;Q_{\mathrm{WTG}}^{\mathrm{pre}}\left(t_{i,j}\right)\&le;Q_{\mathrm{WTG}}^{\max }\\ Q_{\mathrm{SVG}}^{\min }\&le;Q_{\mathrm{SVG}}^{\mathrm{pre}}\left(t_{i,j}\right)\&le;Q_{\mathrm{SVG}}^{\max }\\ {\mathrm{\&Delta;Q}}_{\mathrm{WTG}}^{\min }\&le;Q_{\mathrm{WTG}}^{\mathrm{pre}}\left(t_{i,0}\right)-Q_{\mathrm{WTG}}^{\mathrm{pre}}\left(t_{i-\mathrm{1,0}}\right)\&le;{\mathrm{\&Delta;Q}}_{\mathrm{WTG}}^{\max }\\ {\mathrm{\&Delta;Q}}_{\mathrm{SVG}}^{\min }\&le;Q_{\mathrm{SVG}}^{\mathrm{pre}}\left(t_{i,0}\right)-Q_{\mathrm{SVG}}^{\mathrm{pre}}\left(t_{i-\mathrm{1,0}}\right){\mathrm{\&Delta;Q}}_{\mathrm{SVG}}^{\max }\end{array}\right.---\left(10\right)$

V in formula (11) ^maxand V ^minbe respectively by the upper and lower bound of PCC, blower fan and SVG voltage prediction value construction system voltage vector, wherein PCC voltage limits is provided by power distribution network control centre, and the normal range of operation that blower fan and SVG voltage limits provide according to device fabrication manufacturer is determined; with be respectively the idle operation bound of blower fan, with wei the idle operation bound of SVG, the normal range of operation all provided according to device fabrication manufacturer is determined; with be respectively the idle climbing bound of blower fan, with be respectively the idle climbing bound of SVG, all need to determine through reactive speed experimental results.

Preferably, in step (4), the optimal control of above-mentioned battery module SOC comprises the following steps:

(41) solve optimum SOC scope, concrete steps are:

The target function of optimum SOC scope Optimized model is:

$\begin{array}{c}\min F={\mathrm{\&lambda;}}_1\underset{i=1}{\overset{N}{\mathrm{\&Sigma;}}}{u}_{\mathrm{optSOC}\min }\left(t_i\right)\mathrm{\&Delta;t}+{\mathrm{\&lambda;}}_2\underset{i=1}{\overset{N}{\mathrm{\&Sigma;}}}{u}_{\mathrm{optSOC}\max }\left(t_i\right)\mathrm{\&Delta;t}\\ +{\mathrm{\&lambda;}}_3|{\mathrm{SOC}}_{\mathrm{opt}\_\min }-{\mathrm{SOC}}_{\min }|+{\mathrm{\&lambda;}}_4|{\mathrm{SOC}}_{\mathrm{opt}\_\max }-{\mathrm{SOC}}_{\max }|\end{array}$

Constraints is:

$\begin{array}{c}\underset{j=\mathrm{1,2},...,N_k}{\max }{P}_{\mathrm{out}}\left(t_{i-j}\right)-\underset{j=\mathrm{1,2},...,N_k}{\min }{P}_{\mathrm{out}}\left(t_{i-j}\right)\&le;{\mathrm{\&gamma;}}_k,k=\mathrm{1,2},...,K\\ -P_{\mathrm{ch}\_\max }\&le;P_{B\_\mathrm{ref}}\left(t_i\right)\&le;P_{\mathrm{disch}\_\max }\\ {\mathrm{SOC}}_{\min }\&le;{\mathrm{SOC}}_{\mathrm{opt}\_\min }\&le;{\mathrm{SOC}}_{\max }\\ {\mathrm{SOC}}_{\min }\&le;{\mathrm{SOC}}_{\mathrm{opt}\_\max }\&le;{\mathrm{SOC}}_{\max }\\ {\mathrm{SOC}}_{\left(t_i\right)}={\mathrm{SOC}}_{\left(t_{i-1}\right)}-\frac{P_{B\_\mathrm{ref}}\left(t_i\right)\mathrm{\&Delta;t}}{E_{\mathrm{cap}}}\\ P_{\mathrm{out}}\left(t_i\right)=P_{B\_\mathrm{ref}}\left(t_i\right)+P_{w\_\mathrm{pre}}\left(t_i\right)\end{array}---\left(12\right)$

Wherein, SOC _{opt_min}represent the lower limit of the optimum working range of energy-storage system, SOC _{opt_max}represent the upper limit of the optimum working range of energy-storage system, SOC _minrepresent the lower limit of energy-storage system normal range of operation, SOC _maxrepresent the upper limit of energy-storage system normal range of operation, λ ₁, λ ₂, λ ₃, λ ₄be respectively corresponding weight coefficient, be positive number and weight coefficient and be 1, SOC (t _i) and SOC (t _i-1) be respectively t _imoment and t _i-1the energy-storage system state-of-charge in moment, P _{b_ref}(t _i) for energy-storage system is at t _ithe setting power in moment, E _capfor the capacity of energy-storage system, P _out(t _i) be the grid-connected power of wind energy turbine set after energy-storage system is stabilized, u _optSOCmin(t _i) represent initial time state-of-charge SOC (t ₀) be SOC _{opt_min}time, t _iwhether moment energy-storage system there is super-charge super-discharge; u _optSOCmax(t _i) represent initial time state-of-charge SOC (t ₀) be SOC _{opt_max}time, t _iwhether moment energy-storage system there is super-charge super-discharge; P _{ch_max}for the maximum charge power that energy-storage system allows, P _{disch_max}for the maximum discharge power that energy-storage system allows; N _krepresent the number of time step Δ t in a kth undulated control time range, K represents the quantity of undulated control time range, γ _krepresent the maximum variable quantity of power allowed in a kth undulated control time range;

The attribute of setting particle is SOC _{opt_min}, SOC _{opt_max}and P _{b_ref (ti)}, adopt particle cluster algorithm to solve optimum SOC scope Optimized model and can obtain optimum SOCopt_min, SOCopt_max;

(42) wind storage system is in running, according to the setting power of real-time state-of-charge shift ratio and energy-storage system, periodically regulates time constant filter, and each concrete grammar regulated is:

(421) shift ratio calculates:

According to the optimum SOC scope (SOC obtained _{opt_min}, SOC _{opt_max}) and the real-time SOC of energy-storage system calculate state-of-charge shift ratio pro _{Δ SOC}, computing formula is:

${\mathrm{pro}}_{\mathrm{\&Delta;SOC}}=\frac{\mathrm{SOC}-\frac{1}{2}({\mathrm{SOC}}_{\mathrm{opt}\_\min }+{\mathrm{SOC}}_{\mathrm{opt}\_\max })}{{\mathrm{SOC}}_{\mathrm{opt}\_\max }-{\mathrm{SOC}}_{\mathrm{opt}\_\min }}---\left(13\right)$

(422) by the setting power P of state-of-charge shift ratio pro Δ SOC and energy-storage system current time _{b_ref}as input, time constant filter T, as output, according to the fuzzy control rule preset, adopts fuzzy control strategy to obtain time constant filter T;

(423), in this regulating cycle, the time constant filter T obtained according to step (422) is to the real output P of wind energy turbine set _wcarry out low-pass filtering, the grid-connected power of the expectation after stabilizing is designated as P _{out_exp}, calculate the goal-setting power of energy-storage system and according to following formula, limit value process is carried out to goal-setting power, obtain final setting power P _{b_ref}, restriction process formula is:

$\begin{array}{c}{\tilde{P}}_{B\_\mathrm{ref}}\&le;\frac{(\mathrm{SOC}-{\mathrm{SOC}}_{\mathrm{protect}})*E_{\mathrm{cap}}}{\mathrm{\&Delta;k}}\\ -P_{\mathrm{ch}\_\max }\&le;{\tilde{P}}_{B\_\mathrm{ref}}\&le;P_{\mathrm{disch}\_\max }\end{array}---\left(14\right)$

Wherein, SOC _protectrepresent the state-of-charge protection of setting, Δ k represents the control cycle that time constant filter regulates.

Method for supervising tool of the present invention has the following advantages: the changed power situation of (1) Accurate Prediction wind energy turbine set; (2) control strategy takes into account the workload demand of power distribution network scheduling requirement, energy-storage system ruuning situation and load, meets user simultaneously, has taken into account power supply reliability, ensure the fail safe of energy-storage system, extend the useful life of system stored energy system.

Accompanying drawing explanation

Fig. 1 shows the block diagram of a kind of wind energy turbine set energy-storage system that the inventive method uses and supervising device thereof;

Fig. 2 shows the flow chart of the inventive method.

Embodiment

Fig. 1 shows a kind of wind energy turbine set energy-storage system supervising device 11 of the present invention, and this device 11 comprises: wind-powered electricity generation monitoring module 114, for monitoring the wind-powered electricity generation module 12 in wind energy turbine set energy-storage system 10 in real time, and predicts the generated output of wind-powered electricity generation module 12; Battery monitor module 115, for monitoring the battery module 13 in wind energy turbine set energy-storage system 10 in real time; Load monitoring module 116, for monitoring the load 17 in wind energy turbine set energy-storage system 10 in real time, and predicts the changed power situation of load 17; Power distribution network contact module 112, regulates and controls center from power distribution network 20 know the ruuning situation of power distribution network 20 and relevant schedule information for real-time; Parallel control module 113, connects or isolates power distribution network 20 for wind energy turbine set energy-storage system 10; Middle control module 117, for determining the operation reserve of wind energy turbine set energy-storage system 10, and sends instruction to above-mentioned each module, to perform this power supply strategy; Bus module 111, for the liaison of the modules of this supervising device 11.

Communication module 111, for the communication between above-mentioned modules, described bus communication module 111 is connected with other modules by redundancy dual CAN bus.

Wind-powered electricity generation module comprises multiple wind-driven generator and SVG equipment.Wind-powered electricity generation monitoring module 114 at least comprises wind-driven generator level pressure, electric current, frequency detection equipment, wind speed measurement equipment, and SVG voltage and current checkout equipment.The power output of wind-driven generator determined by the wind speed of wind-driven generator site, wind direction and unique characteristics.

Battery monitor module 116 at least comprises accumulator voltage, electric current, SOC checkout equipment and temperature testing equipment.For monitoring the SOC of battery module in real time.

Middle control module 117 at least comprises CPU element, data storage cell and display unit.

Power distribution network contact module 112 at least comprises Wireless Telecom Equipment.This Wireless Telecom Equipment can be wireline equipment or wireless device.

Parallel control module 113 at least comprises checkout equipment, data acquisition unit and data processing unit for detecting power distribution network and wind energy turbine set energy-storage system voltage, electric current and frequency.Data acquisition unit comprises collection preliminary treatment and A/D modular converter, gathers eight tunnel telemetered signal amounts, comprises grid side A phase voltage, electric current, three-phase voltage, the electric current of wind energy turbine set energy-storage system side.Remote measurement amount changes strong ac signal (5A/110V) into inner weak electric signal without distortion by the high-precision current in terminal and voltage transformer, after filtering process, enter A/D chip carry out analog-to-digital conversion, digital signal after conversion calculates through data processing unit, obtains three-phase voltage current value and the power distribution network 20 side phase voltage current value of wind energy turbine set energy-storage system 10 side.The process of this telemetered signal amount have employed high-speed and high-density synchronized sampling, automatic frequency tracking technology also has the fft algorithm improved, so precision is fully guaranteed, the measurement and process that gain merit in wind energy turbine set energy-storage system 10 side, idle and electric energy is from first-harmonic to higher harmonic components can be completed.

See accompanying drawing 2, method of the present invention comprises the steps:

S1. the service data of wind-powered electricity generation monitoring module Real-time Obtaining wind-powered electricity generation module, and store data, the load variations situation of load monitoring module Real-time Obtaining load;

S2. according to the service data of wind-powered electricity generation module, the power output of the wind-powered electricity generation module in following predetermined instant is predicted, according to the load variations situation of wind energy turbine set load, the workload demand of load is predicted;

S3. the SOC obtaining battery module is detected in real time, the parameter of Real-time Obtaining power distribution network and schedule information;

S4. using the SOC of the schedule information of power distribution network, current batteries to store energy, following wind-powered electricity generation module power output and to the change of following workload demand as constraints, realize the optimal control of battery module SOC.

Preferably, predict the power output of wind-powered electricity generation module in step S2. in the following way, described wind-powered electricity generation module comprises wind-driven generator and SVG:

S201. gather in wind-powered electricity generation module that current all kinds of electricity measured value is as the initial value of the predicted value of all kinds of electricity, predicted value comprises: blower fan is gained merit predicted value predicted value that blower fan is idle blower fan set end voltage predicted value predicted value that SVG is idle sVG set end voltage predicted value wind-powered electricity generation module site (PCC) prediction of busbar voltage value

S202. set up the MPC optimizing control models be made up of optimization object function and constraints according to described predicted value, and solve the predicted value of the meritorious of wind-powered electricity generation module and idle output:

The target function of MPC optimizing control models is such as formula shown in (1):

$\underset{Q_{\mathrm{WTG}}^{\mathrm{set}},V_{\mathrm{SVG}}^{\mathrm{set}}}{\min }(\underset{i=0}{\overset{N-1}{\mathrm{\&Sigma;}}}\underset{j=0}{\overset{M-1}{\mathrm{\&Sigma;}}}{\mathrm{\&rho;}}^{t_{i,j}}{F}_1,\underset{i=0}{\overset{N-1}{\mathrm{\&Sigma;}}}\underset{j=0}{\overset{M-1}{\mathrm{\&Sigma;}}}{\mathrm{\&rho;}}^{t_{i,j}}{F}_2)---\left(1\right)$

In formula (1) with for optimized variable, with implication is respectively the idle set point of blower fan and SVG voltage setting value; N is the number in time window Coverage Control cycle; M is the number containing future position under single control cycle; ρ is attenuation coefficient, value ρ < 1; Time variable ti, j=(Mi+j) Δ t meaning is the jth future position that current time plays in i-th control cycle, and Δ t is future position interval, and Δ t is determined by wind-powered electricity generation modular power predicted time interval;

F1 is wind-powered electricity generation module and the variance level of site busbar voltage and set point, and F1 expression is such as formula (2):

$F_1\left(t_{i,j}\right)={[V_{\mathrm{PCC}}^{\mathrm{pre}}\left(t_{i,j}\right)-V_{\mathrm{PCC}}^{\mathrm{ref}}]}^2---\left(2\right)$

In formula (2) represent the reference value of PCC voltage, setting after extracting from main website control command;

F2 is the idle level of reserve of SVG, and F2 expression is such as formula (3):

$F_2\left(t_{i,j}\right)={[Q_{\mathrm{SVG}}^{\mathrm{pre}}\left(t_{i,j}\right)-Q_{\mathrm{SVG}}^{\mathrm{opr}}]}^2---\left(3\right)$

In formula (3) for the idle best operating point of SVG;

The constraints of MPC optimizing control models, specifically comprises:

Blower fan is gained merit prediction-constraint condition:

$P_{\mathrm{WTG}}^{\mathrm{pre}}\left(t_{i,j}\right)=\underset{k=1}{\overset{N_a}{\mathrm{\&Sigma;}}}{\mathrm{\&phi;}}_k{P}_{\mathrm{WTG}}^{\mathrm{pre}}\left(t_{i,j-k}\right)+{\mathrm{\&epsiv;}}_{\mathrm{WTG}}^{\mathrm{pre}}\left(t_{i,j}\right)-\underset{k-1}{\overset{N_m}{\mathrm{\&Sigma;}}}{\mathrm{\&theta;}}_k{\mathrm{\&epsiv;}}_{\mathrm{WTG}}^{\mathrm{pre}}\left(t_{i,j-k}\right)---\left(4\right)$

In formula (4) for blower fan is gained merit predicated error; Na and Nm is respectively the exponent number of AR and MA model, and φ k and θ k is associated weight, and exponent number and weight are all determined according to blower fan history value of gaining merit; Ti, j-k (comprise for participating in calculated data in prediction ) the corresponding moment, subscript k pushes away the k Δ t time before characterizing the prediction moment, works as ti, and during j-k≤0, meritorious predicted value should get corresponding moment history value;

Prediction-constraint condition that blower fan is idle:

Blower fan is idle reaches set point before controlling next time:

$Q_{\mathrm{WTG}}^{\mathrm{pre}}\left(t_{i,0}\right)=Q_{\mathrm{WTG}}^{\mathrm{set}}\left(t_{i-\mathrm{1,0}}\right)---\left(5\right)$

Each future position in i-th control cycle, the change procedure of blower fan reactive power is with exponential function matching:

$Q_{\mathrm{WTG}}^{\mathrm{pre}}\left(t_{i,j}\right)=\frac{1-e^{-(t_{i,j}-t_{i,0})/T_s}}{1-e^{-\mathrm{M\&Delta;t}/T_s}}{Q}_{\mathrm{WTG}}^{\mathrm{set}}\left(t_{i,0}\right)+\frac{e^{-(t_{i,j}-t_{i,0})/T_s}-e^{-\mathrm{M\&Delta;t}/T_s}}{1-e^{-\mathrm{M\&Delta;t}/T_s}}{Q}_{\mathrm{WTG}}^{\mathrm{pre}}\left(t_{i,0}\right)---\left(6\right)$

In formula (6), Ts is blower fan Reactive-power control time constant, can obtain according to blower fan Reactive-power control testing experiment.

Prediction-constraint condition that SVG is idle:

Reference value that SVG is idle shown in (7):

$\begin{array}{c}{Q}_{\mathrm{SVG}}^{\mathrm{ref}}\left(t_{i,j}\right)=K_P[V_{\mathrm{SVG}}^{\mathrm{pre}}\left(t_{i,j}\right)-V_{\mathrm{SVG}}^{\mathrm{set}}\left(t_{i,0}\right)]+K_I\mathrm{\&Delta;t}\underset{k=0}{\overset{i\&times;M+j}{\mathrm{\&Sigma;}}}[V_{\mathrm{SVG}}^{\mathrm{pre}}\left(t_{i,j-k}\right)-V_{\mathrm{SVG}}^{\mathrm{set}}\left(t_{i,-k}\right)]\\ +Q_{\mathrm{SVG}}^{\mathrm{pre}}\left(t_{\mathrm{0,0}}\right)-K_P[V_{\mathrm{SVG}}^{\mathrm{pre}}\left(t_{\mathrm{0,0}}\right)-V_{\mathrm{SVG}}^{\mathrm{set}}\left(t_{\mathrm{0,0}}\right)]\end{array}---\left(7\right)$

In formula (7), KI and KP is respectively the coefficient of proportional component and integral element;

Predicted value that SVG is idle is such as formula shown in (8):

$Q_{\mathrm{SVG}}^{\mathrm{pre}}\left(t_{i,j}\right)=Q_{\mathrm{SVG}}^{\mathrm{ref}}\left(t_{i,j-1}\right)+{[Q_{\mathrm{SVG}}^{\mathrm{pre}}\left(t_{i,j-1}\right)-Q_{\mathrm{SVG}}^{\mathrm{ref}}\left(t_{i,j-1}\right)]e}^{-(t_{i,j}-t_{i,j-1})/T_d}---\left(8\right)$

In formula (8), time constant Td is SVG power electronic equipment action delay;

Voltage prediction constraints:

$V^{\mathrm{pre}}\left(t_{i,j}\right)-V^{\mathrm{pre}}\left(t_{\mathrm{0,0}}\right)=S\left[\begin{array}{c}{P}_{\mathrm{WTG}}^{\mathrm{pre}}\left(t_{i,j}\right)-P_{\mathrm{WTG}}^{\mathrm{pre}}\left(t_{\mathrm{0,0}}\right)\\ Q_{\mathrm{WTG}}^{\mathrm{pre}}\left(t_{i,j}\right)-Q_{\mathrm{WTG}}^{\mathrm{pre}}\left(t_{\mathrm{0,0}}\right)\\ Q_{\mathrm{SVG}}^{\mathrm{pre}}\left(t_{i,j}\right)-Q_{\mathrm{SVG}}^{\mathrm{pre}}\left(t_{\mathrm{0,0}}\right)\end{array}\right]---\left(9\right)$

V in formula (9) ^prefor the vector that blower fan machine end, SVG machine end and PCC prediction of busbar voltage value are formed, S is sensitivity matrix;

The constraints that system voltage, generator operation and SVG run:

$\left\{\begin{array}{c}{V}^{\min }\&le;V^{\mathrm{pre}}\left(t_{i,j}\right)\&le;V^{\max }\\ Q_{\mathrm{WTG}}^{\min }\&le;Q_{\mathrm{WTG}}^{\mathrm{pre}}\left(t_{i,j}\right)\&le;Q_{\mathrm{WTG}}^{\max }\\ Q_{\mathrm{SVG}}^{\min }\&le;Q_{\mathrm{SVG}}^{\mathrm{pre}}\left(t_{i,j}\right)\&le;Q_{\mathrm{SVG}}^{\max }\\ {\mathrm{\&Delta;Q}}_{\mathrm{WTG}}^{\min }\&le;Q_{\mathrm{WTG}}^{\mathrm{pre}}\left(t_{i,0}\right)-Q_{\mathrm{WTG}}^{\mathrm{pre}}\left(t_{i-\mathrm{1,0}}\right)\&le;{\mathrm{\&Delta;Q}}_{\mathrm{WTG}}^{\max }\\ {\mathrm{\&Delta;Q}}_{\mathrm{SVG}}^{\min }\&le;Q_{\mathrm{SVG}}^{\mathrm{pre}}\left(t_{i,0}\right)-Q_{\mathrm{SVG}}^{\mathrm{pre}}\left(t_{i-\mathrm{1,0}}\right){\mathrm{\&Delta;Q}}_{\mathrm{SVG}}^{\max }\end{array}\right.---\left(10\right)$

V in formula (11) ^maxand V ^minbe respectively by the upper and lower bound of PCC, blower fan and SVG voltage prediction value construction system voltage vector, wherein PCC voltage limits is provided by power distribution network control centre, and the normal range of operation that blower fan and SVG voltage limits provide according to device fabrication manufacturer is determined; with be respectively the idle operation bound of blower fan, with wei the idle operation bound of SVG, the normal range of operation all provided according to device fabrication manufacturer is determined; with be respectively the idle climbing bound of blower fan, with be respectively the idle climbing bound of SVG, all need to determine through reactive speed experimental results.

Preferably, in step S4, the optimal control of above-mentioned battery module SOC comprises the following steps:

S41. solve optimum SOC scope, concrete steps are:

The target function of optimum SOC scope Optimized model is:

$\begin{array}{c}\min F={\mathrm{\&lambda;}}_1\underset{i=1}{\overset{N}{\mathrm{\&Sigma;}}}{u}_{\mathrm{optSOC}\min }\left(t_i\right)\mathrm{\&Delta;t}+{\mathrm{\&lambda;}}_2\underset{i=1}{\overset{N}{\mathrm{\&Sigma;}}}{u}_{\mathrm{optSOC}\max }\left(t_i\right)\mathrm{\&Delta;t}\\ +{\mathrm{\&lambda;}}_3|{\mathrm{SOC}}_{\mathrm{opt}\_\min }-{\mathrm{SOC}}_{\min }|+{\mathrm{\&lambda;}}_4|{\mathrm{SOC}}_{\mathrm{opt}\_\max }-{\mathrm{SOC}}_{\max }|\end{array}$

Constraints is:

$\begin{array}{c}\underset{j=\mathrm{1,2},...,N_k}{\max }{P}_{\mathrm{out}}\left(t_{i-j}\right)-\underset{j=\mathrm{1,2},...,N_k}{\min }{P}_{\mathrm{out}}\left(t_{i-j}\right)\&le;{\mathrm{\&gamma;}}_k,k=\mathrm{1,2},...,K\\ -P_{\mathrm{ch}\_\max }\&le;P_{B\_\mathrm{ref}}\left(t_i\right)\&le;P_{\mathrm{disch}\_\max }\\ {\mathrm{SOC}}_{\min }\&le;{\mathrm{SOC}}_{\mathrm{opt}\_\min }\&le;{\mathrm{SOC}}_{\max }\\ {\mathrm{SOC}}_{\min }\&le;{\mathrm{SOC}}_{\mathrm{opt}\_\max }\&le;{\mathrm{SOC}}_{\max }\\ {\mathrm{SOC}}_{\left(t_i\right)}={\mathrm{SOC}}_{\left(t_{i-1}\right)}-\frac{P_{B\_\mathrm{ref}}\left(t_i\right)\mathrm{\&Delta;t}}{E_{\mathrm{cap}}}\\ P_{\mathrm{out}}\left(t_i\right)=P_{B\_\mathrm{ref}}\left(t_i\right)+P_{w\_\mathrm{pre}}\left(t_i\right)\end{array}---\left(12\right)$

Wherein, SOC _{opt_min}represent the lower limit of the optimum working range of energy-storage system, SOC _{opt_max}represent the upper limit of the optimum working range of energy-storage system, SOC _minrepresent the lower limit of energy-storage system normal range of operation, SOC _maxrepresent the upper limit of energy-storage system normal range of operation, λ ₁, λ ₂, λ ₃, λ ₄be respectively corresponding weight coefficient, be positive number and weight coefficient and be 1, SOC (t _i) and SOC (t _i-1) be respectively t _imoment and t _i-1the energy-storage system state-of-charge in moment, P _{b_ref}(t _i) for energy-storage system is at t _ithe setting power in moment, E _capfor the capacity of energy-storage system, P _out(t _i) be the grid-connected power of wind energy turbine set after energy-storage system is stabilized, u _optSOCmin(t _i) represent initial time state-of-charge SOC (t ₀) be SOC _{opt_min}time, t _iwhether moment energy-storage system there is super-charge super-discharge; u _optSOCmax(t _i) represent initial time state-of-charge SOC (t ₀) be SOC _{opt_max}time, t _iwhether moment energy-storage system there is super-charge super-discharge; P _{ch_max}for the maximum charge power that energy-storage system allows, P _{disch_max}for the maximum discharge power that energy-storage system allows; N _krepresent the number of time step Δ t in a kth undulated control time range, K represents the quantity of undulated control time range, γ _krepresent the maximum variable quantity of power allowed in a kth undulated control time range;

The attribute of setting particle is SOC _{opt_min}, SOC _{opt_max}and P _{b_ref (ti)}, adopt particle cluster algorithm to solve optimum SOC scope Optimized model and can obtain optimum SOCopt_min, SOCopt_max;

S42. wind storage system is in running, according to the setting power of real-time state-of-charge shift ratio and energy-storage system, periodically regulates time constant filter, and each concrete grammar regulated is:

S421. shift ratio calculates:

According to the optimum SOC scope (SOC obtained _{opt_min}, SOC _{opt_max}) and the real-time SOC of energy-storage system calculate state-of-charge shift ratio pro _{Δ SOC}, computing formula is:

${\mathrm{pro}}_{\mathrm{\&Delta;SOC}}=\frac{\mathrm{SOC}-\frac{1}{2}({\mathrm{SOC}}_{\mathrm{opt}\_\min }+{\mathrm{SOC}}_{\mathrm{opt}\_\max })}{{\mathrm{SOC}}_{\mathrm{opt}\_\max }-{\mathrm{SOC}}_{\mathrm{opt}\_\min }}---\left(13\right)$

S422. by the setting power P of state-of-charge shift ratio pro Δ SOC and energy-storage system current time _{b_ref}as input, time constant filter T, as output, according to the fuzzy control rule preset, adopts fuzzy control strategy to obtain time constant filter T;

S423., in this regulating cycle, the time constant filter T obtained according to step (422) is to the real output P of wind energy turbine set _wcarry out low-pass filtering, the grid-connected power of the expectation after stabilizing is designated as P _{out_exp}, calculate the goal-setting power of energy-storage system and according to following formula, limit value process is carried out to goal-setting power, obtain final setting power P _{b_ref}, restriction process formula is:

$\begin{array}{c}{\tilde{P}}_{B\_\mathrm{ref}}\&le;\frac{(\mathrm{SOC}-{\mathrm{SOC}}_{\mathrm{protect}})*E_{\mathrm{cap}}}{\mathrm{\&Delta;k}}\\ -P_{\mathrm{ch}\_\max }\&le;{\tilde{P}}_{B\_\mathrm{ref}}\&le;P_{\mathrm{disch}\_\max }\end{array}---\left(14\right)$

Wherein, SOC _protectrepresent the state-of-charge protection of setting, Δ k represents the control cycle that time constant filter regulates.

In S2, adopt Neural Network model predictive workload demand, concrete steps are as follows:

S211. gather 12 groups of active power and reactive power every day, altogether continuous acquisition 8 days, have 96 groups of data P (k) and Q (k), k=1 like this, 2 ..., 96.

S212. 96 groups of data P (k) and Q (k) are normalized, make n=1,2 ..., 96; First using 12 of every day active-power Ps (k) as one group of input vector R (m), 12 reactive power Qs (k) as one group of input vector S (m), m=1,2,, 8, m represents the frequency of training of neural net; Simultaneously suppose the output vector R ' of 12 active-power P ' (k) of the 9th day as predicted power in advance, 12 reactive power Q ' (k) of the 9th day are as the output vector S ' of predicted power; The active power input vector of front like this 8 days is just

R (1), R (2), R (3), R (4), R (5), R (6), R (7), R (8), the output vector of the 9th day prediction active power is R '; The reactive power input vector of first 8 days is just

S (1), S (2), S (3), S (4), S (5), S (6), S (7), S (8), the output vector of the 9th day prediction active power is S '.

S213. using 8 groups of input vectors R (m) and the input layer of S (m) as neural net, the transfer function of hidden layer neuron adopts S type tan tansig, the neuronic transfer function of output layer adopts S type logarithmic function logsig, as shown in Figure 2, like this after 8 neural metwork trainings, just determine the weights of each connection weight in neural net.

S214. for 8 active power input vector R (m), a is had in hidden layer neuron ¹=tansig (IW ¹r+b ¹), wherein a ¹for hidden layer neuron exports, IW ¹for the weights of hidden layer neuron, b ¹for the threshold value of hidden layer neuron; A is had at output layer neuron ²=log sig (LW ²a ¹+ b ²), wherein a ²for output layer neuron exports, IW ²for the neuronic weights of output layer, b ²for the neuronic threshold value of output layer.

S215. for 8 active power input vector S (m), c is had in hidden layer neuron ¹=tansig (IW ¹s+b ¹), wherein c ¹for hidden layer neuron exports, IW ¹for the weights of hidden layer neuron, b ¹for the threshold value of hidden layer neuron; C is had at output layer neuron ²=log sig (LW ²c1+b ²), wherein c ²for output layer neuron exports, IW ²for the neuronic weights of output layer, b ²for the neuronic threshold value of output layer.

S216. using the input vector R (8) of the 8th day and S (8) again as the input layer of neural net, the output vector R ' of the predicted power now exported in neural net and S ' is the power prediction normalized value of the 9th day, use renormalization algorithm again, namely k=1,2 ..., 96, the vector value R (9) of output and S (9) is exactly 12 active-power P ' (k) of the 9th day predicted power and 12 reactive power Q ' (k).So by that analogy, the step that can repeat above utilizes the data prediction of second day to the 9th day to the power of the tenth day, and the power of every day can be out predicted so below.

In step s 4 which, being constrained to of wind energy turbine set energy-storage system gross power Pg:

Non-response scheduling slot 1 time, P _{g, min}≤ P _{g (l)}≤ P _{g, max}, P _{g, min}for the maximum power that wind energy turbine set energy-storage system 10 can absorb from power distribution network 20, P _{g, max}for wind energy turbine set energy-storage system 10 can to the maximum power of power distribution network 20 transmission power;

Response scheduling period 2 times, P _{g (2)}=P _set, P _setfor the dominant eigenvalues that the response scheduling period requires for 2 times.

Above content is in conjunction with concrete preferred implementation further description made for the present invention, can not assert that specific embodiment of the invention is confined to these explanations.For general technical staff of the technical field of the invention, without departing from the inventive concept of the premise, make some equivalent to substitute or obvious modification, and performance or purposes identical, all should be considered as belonging to protection scope of the present invention.

Claims (3)

1. a method for supervising for wind energy turbine set energy-storage system, the method realizes based on following supervising device, and this supervising device comprises:

Wind-powered electricity generation monitoring module, for monitoring wind-powered electricity generation module in real time, and predicts the generated output of wind-powered electricity generation module;

Battery monitor module, for monitoring battery module in real time;

Load monitoring module, for monitoring the load in wind energy turbine set energy-storage system in real time, and predicts the changed power situation of load;

Power distribution network contact module, knows the ruuning situation of power distribution network and relevant schedule information for real-time from power distribution network regulation and control center;

Be incorporated into the power networks monitoring module, connects or isolation power distribution network for controlling wind energy turbine set energy-storage system;

Middle control module, for determining the operation reserve of wind energy turbine set energy-storage system, and sends instruction to each module in above-mentioned supervising device, to perform this operation reserve;

Bus module, for the liaison of the modules of this supervising device;

This method for supervising comprises the steps:

(1) service data of wind-powered electricity generation monitoring module Real-time Obtaining wind-powered electricity generation module, and store data, the load variations situation of load monitoring module Real-time Obtaining load;

(2) according to the service data of wind-powered electricity generation module, the power output of the wind-powered electricity generation module in following predetermined instant is predicted, according to the load variations situation of wind energy turbine set load, the workload demand of load is predicted;

(3) SOC obtaining battery module is detected in real time, the parameter of Real-time Obtaining power distribution network and schedule information;

(4) using the SOC of the schedule information of power distribution network, current batteries to store energy, following wind-powered electricity generation module power output and to the change of following workload demand as constraints, realize the optimal control of battery module SOC.

2. the method for claim 1, is characterized in that, in step (2), predict the power output of wind-powered electricity generation module in the following way, described wind-powered electricity generation module comprises wind-driven generator and SVG:

(201) gather in wind-powered electricity generation module that current all kinds of electricity measured value is as the initial value of the predicted value of all kinds of electricity, predicted value comprises: blower fan is gained merit predicted value predicted value that blower fan is idle blower fan set end voltage predicted value predicted value that SVG is idle sVG set end voltage predicted value wind-powered electricity generation module site (PCC) prediction of busbar voltage value

(202) set up the MPC optimizing control models be made up of optimization object function and constraints according to described predicted value, and solve the predicted value of the meritorious of wind-powered electricity generation module and idle output:

The target function of MPC optimizing control models is such as formula shown in (1):

$\underset{Q_{\mathrm{WTG}}^{\mathrm{set}},V_{\mathrm{SVG}}^{\mathrm{set}}}{\min }(\underset{i=0}{\overset{N-1}{\mathrm{\&Sigma;}}}\underset{j=0}{\overset{M-1}{\mathrm{\&Sigma;}}}{\mathrm{\&rho;}}^{t_{i,j}}{F}_1,\underset{i=0}{\overset{N-1}{\mathrm{\&Sigma;}}}\underset{j=0}{\overset{M-1}{\mathrm{\&Sigma;}}}{\mathrm{\&rho;}}^{t_{i,j}}{F}_2)---\left(1\right)$

In formula (1) with for optimized variable, with implication is respectively the idle set point of blower fan and SVG voltage setting value; N is the number in time window Coverage Control cycle; M is the number containing future position under single control cycle; ρ is attenuation coefficient, value ρ < 1; Time variable ti, j=(Mi+j) Δ t meaning is the jth future position that current time plays in i-th control cycle, and Δ t is future position interval, and Δ t is determined by wind-powered electricity generation modular power predicted time interval;

F1 is wind-powered electricity generation module and the variance level of site busbar voltage and set point, and F1 expression is such as formula (2):

$F_1\left(t_{i,j}\right)={[V_{\mathrm{PCC}}^{\mathrm{pre}}\left(t_{i,j}\right)-V_{\mathrm{PCC}}^{\mathrm{ref}}]}^2---\left(2\right)$

In formula (2) represent the reference value of PCC voltage, setting after extracting from main website control command;

F2 is the idle level of reserve of SVG, and F2 expression is such as formula (3):

$F_2\left(t_{i,j}\right)={[Q_{\mathrm{SVG}}^{\mathrm{pre}}\left(t_{i,j}\right)-Q_{\mathrm{SVG}}^{\mathrm{opr}}]}^2---\left(3\right)$

In formula (3) for the idle best operating point of SVG;

The constraints of MPC optimizing control models, specifically comprises:

Blower fan is gained merit prediction-constraint condition:

$P_{\mathrm{WTG}}^{\mathrm{pre}}\left(t_{i,j}\right)=\underset{k=1}{\overset{N_a}{\mathrm{\&Sigma;}}}{\mathrm{\&phi;}}_k{P}_{\mathrm{WTG}}^{\mathrm{pre}}\left(t_{i,j-k}\right)+{\mathrm{\&epsiv;}}_{\mathrm{WTG}}^{\mathrm{pre}}\left(t_{i,j}\right)-\underset{k=1}{\overset{N_m}{\mathrm{\&Sigma;}}}{\mathrm{\&theta;}}_k{\mathrm{\&epsiv;}}_{\mathrm{WTG}}^{\mathrm{pre}}\left(t_{i,j-k}\right)---\left(4\right)$

In formula (4) for blower fan is gained merit predicated error; Na and Nm is respectively the exponent number of AR and MA model, and φ k and θ k is associated weight, and exponent number and weight are all determined according to blower fan history value of gaining merit; Ti, j-k (comprise for participating in calculated data in prediction ) the corresponding moment, subscript k pushes away the k Δ t time before characterizing the prediction moment, works as ti, and during j-k≤0, meritorious predicted value should get corresponding moment history value;

Prediction-constraint condition that blower fan is idle:

Blower fan is idle reaches set point before controlling next time:

$Q_{\mathrm{WTG}}^{\mathrm{pre}}\left(t_{i,0}\right)=Q_{\mathrm{WTG}}^{\mathrm{set}}\left(t_{i-\mathrm{1,0}}\right)---\left(5\right)$

Each future position in i-th control cycle, the change procedure of blower fan reactive power is with exponential function matching:

$Q_{\mathrm{WTG}}^{\mathrm{pre}}\left(t_{i,j}\right)=\frac{1-e^{-(t_{i,j}-t_{i,0})/T_s}}{1-e^{-\mathrm{M\&Delta;t}/T_s}}{Q}_{\mathrm{WTC}}^{\mathrm{set}}\left(t_{i,0}\right)+\frac{e^{-(t_{i,j}-t_{i,0})/T_s}-e^{-\mathrm{M\&Delta;t}/T_s}}{1-e^{-\mathrm{M\&Delta;t}/T_s}}{Q}_{\mathrm{WTG}}^{\mathrm{pre}}\left(t_{i,0}\right)---\left(6\right)$

In formula (6), Ts is blower fan Reactive-power control time constant, can obtain according to blower fan Reactive-power control testing experiment.

Prediction-constraint condition that SVG is idle:

Reference value that SVG is idle shown in (7):

$\begin{array}{c}{Q}_{\mathrm{SVG}}^{\mathrm{ref}}\left(t_{i,j}\right)=K_P[V_{\mathrm{SVG}}^{\mathrm{pre}}\left(t_{i,j}\right)-V_{\mathrm{SVG}}^{\mathrm{set}}\left(t_{i,0}\right)]+K_I\mathrm{\&Delta;t}\underset{k=0}{\overset{i\&times;M+j}{\mathrm{\&Sigma;}}}[V_{\mathrm{SVG}}^{\mathrm{pre}}\left(t_{i,j-k}\right)-V_{\mathrm{SVG}}^{\mathrm{set}}\left(t_{i,-k}\right)]\\ +Q_{\mathrm{SVG}}^{\mathrm{pre}}\left(t_{\mathrm{0,0}}\right)-K_P[V_{\mathrm{SVG}}^{\mathrm{pre}}\left(t_{\mathrm{0,0}}\right)-V_{\mathrm{SVG}}^{\mathrm{set}}\left(t_{\mathrm{0,0}}\right)]\end{array}---\left(7\right)$

In formula (7), KI and KP is respectively the coefficient of proportional component and integral element;

Predicted value that SVG is idle is such as formula shown in (8):

$Q_{\mathrm{SVG}}^{\mathrm{pre}}\left(t_{i,j}\right)=Q_{\mathrm{SVG}}^{\mathrm{ref}}\left(t_{i,j-1}\right)+[Q_{\mathrm{SVG}}^{\mathrm{pre}}\left(t_{i,j-1}\right)-Q_{\mathrm{SVG}}^{\mathrm{ref}}\left(t_{i,j-1}\right)]e^{-(t_{i,j}-t_{i,j-1})/T_d}---\left(8\right)$

In formula (8), time constant Td is SVG power electronic equipment action delay;

Voltage prediction constraints:

$V^{\mathrm{pre}}\left(t_{i,j}\right)-V^{\mathrm{pre}}\left(t_{\mathrm{0,0}}\right)=S\left[\begin{array}{c}{P}_{\mathrm{WTG}}^{\mathrm{pre}}\left(t_{i,j}\right)-P_{\mathrm{WTG}}^{\mathrm{pre}}\left(t_{\mathrm{0,0}}\right)\\ Q_{\mathrm{WTG}}^{\mathrm{pre}}\left(t_{i,j}\right)-Q_{\mathrm{WTG}}^{\mathrm{pre}}\left(t_{\mathrm{0,0}}\right)\\ Q_{\mathrm{SVG}}^{\mathrm{pre}}\left(t_{i,j}\right)-Q_{\mathrm{SVG}}^{\mathrm{pre}}\left(t_{\mathrm{0,0}}\right)\end{array}\right]---\left(9\right)$

V in formula (9) ^prefor the vector that blower fan machine end, SVG machine end and PCC prediction of busbar voltage value are formed, S is sensitivity matrix;

The constraints that system voltage, generator operation and SVG run:

$\left\{\begin{array}{c}{V}^{\min }\&le;V^{\mathrm{pre}}\left(t_{i,j}\right)\&le;V^{\max }\\ Q_{\mathrm{WTG}}^{\min }\&le;Q_{\mathrm{WTG}}^{\mathrm{pre}}\left(t_{i,j}\right)\&le;Q_{\mathrm{WTG}}^{\max }\\ Q_{\mathrm{SVG}}^{\min }\&le;Q_{\mathrm{SVG}}^{\mathrm{pre}}\left(t_{i,j}\right)\&le;Q_{\mathrm{SVG}}^{\max }\\ \mathrm{\&Delta;}{Q}_{\mathrm{WTG}}^{\min }\&le;Q_{\mathrm{WTG}}^{\mathrm{pre}}\left(t_{i,0}\right)-Q_{\mathrm{WTG}}^{\mathrm{pre}}\left(t_{i-1,0}\right)\&le;\mathrm{\&Delta;}{Q}_{\mathrm{WTG}}^{\max }\\ {\mathrm{\&Delta;Q}}_{\mathrm{SVG}}^{\min }\&le;Q_{\mathrm{SVG}}^{\mathrm{pre}}\left(t_{i,0}\right)-Q_{\mathrm{SVG}}^{\mathrm{pre}}\left(t_{i-\mathrm{1,0}}\right)\&le;{\mathrm{\&Delta;Q}}_{\mathrm{SVG}}^{\max }\end{array}\right.---\left(10\right)$

V in formula (11) ^maxand V ^minbe respectively by the upper and lower bound of PCC, blower fan and SVG voltage prediction value construction system voltage vector, wherein PCC voltage limits is provided by power distribution network control centre, and the normal range of operation that blower fan and SVG voltage limits provide according to device fabrication manufacturer is determined; with be respectively the idle operation bound of blower fan, with wei the idle operation bound of SVG, the normal range of operation all provided according to device fabrication manufacturer is determined; with be respectively the idle climbing bound of blower fan, with be respectively the idle climbing bound of SVG, all need to determine through reactive speed experimental results.

3. method as claimed in claim 1 or 2, it is characterized in that, in step (4), the optimal control of above-mentioned battery module SOC comprises the following steps:

(41) solve optimum SOC scope, concrete steps are:

The target function of optimum SOC scope Optimized model is:

$\begin{array}{c}\min F={\mathrm{\&lambda;}}_1\underset{i=1}{\overset{N}{\mathrm{\&Sigma;}}}{u}_{\mathrm{optSOC}\min }\left(t_i\right)\mathrm{\&Delta;t}+{\mathrm{\&lambda;}}_2\underset{i=1}{\overset{N}{\mathrm{\&Sigma;}}}{u}_{\mathrm{optSOC}\max }\left(t_i\right)\mathrm{\&Delta;t}\\ +{\mathrm{\&lambda;}}_3|{\mathrm{SOC}}_{\mathrm{opt}\_\min }-\mathrm{SO}{C}_{\min }|+{\mathrm{\&lambda;}}_4|{\mathrm{SOC}}_{\mathrm{op}\_\max }-{\mathrm{SOC}}_{\max }|\end{array}$

Constraints is:

$\left\{\begin{array}{c}\underset{j=\mathrm{1,2},...,N_k}{\max }{P}_{\mathrm{out}}\left(t_{i-j}\right)-\underset{j=\mathrm{1,2},...,N_k}{\min }{P}_{\mathrm{out}}\left(t_{i-j}\right)\&le;{\mathrm{\&gamma;}}_k,k=\mathrm{1,2},...,K\\ -P_{\mathrm{ch}\_\max }\&le;P_{B\_\mathrm{ref}}\left(t_i\right)\&le;P_{\mathrm{disch}\_\max }\\ S{\mathrm{OC}}_{\min }\&le;{\mathrm{SOC}}_{\mathrm{opt}\_\min }\&le;{\mathrm{SOC}}_{\max }\\ {\mathrm{SOC}}_{\min }\&le;{\mathrm{SOC}}_{\mathrm{opt}\_\max }\&le;{\mathrm{SOC}}_{\max }\\ \mathrm{SOC}\left(t_i\right)=\mathrm{SOC}\left(t_{i-1}\right)-\frac{P_{B\_\mathrm{ref}}\left(t_i\right)\mathrm{\&Delta;t}}{E_{\mathrm{cap}}}\\ P_{\mathrm{out}}\left(t_i\right)=P_{B\_\mathrm{ref}}\left(t_i\right)+P_{w\_\mathrm{pre}}\left(t_i\right)\end{array}\right.---\left(12\right)$

Wherein, SOC _{opt_min}represent the lower limit of the optimum working range of energy-storage system, SOC _{opt_max}represent the upper limit of the optimum working range of energy-storage system, SOC _minrepresent the lower limit of energy-storage system normal range of operation, SOC _maxrepresent the upper limit of energy-storage system normal range of operation, λ ₁, λ ₂, λ ₃, λ ₄be respectively corresponding weight coefficient, be positive number and weight coefficient and be 1, SOC (t _i) and SOC (t _i-1) be respectively t _imoment and t _i-1the energy-storage system state-of-charge in moment, P _{b_ref}(t _i) for energy-storage system is at t _ithe setting power in moment, E _capfor the capacity of energy-storage system, P _out(t _i) be the grid-connected power of wind energy turbine set after energy-storage system is stabilized, u _optSOCmin(t _i) represent initial time state-of-charge SOC (t ₀) be SOC _{opt_min}time, t _iwhether moment energy-storage system there is super-charge super-discharge; u _optSOCmax(t _i) represent initial time state-of-charge SOC (t ₀) be SOC _{opt_max}time, t _iwhether moment energy-storage system there is super-charge super-discharge; P _{ch_max}for the maximum charge power that energy-storage system allows, P _{disch_max}for the maximum discharge power that energy-storage system allows; N _krepresent the number of time step Δ t in a kth undulated control time range, K represents the quantity of undulated control time range, γ _krepresent the maximum variable quantity of power allowed in a kth undulated control time range;

The attribute of setting particle is SOC _{opt_min}, SOC _{opt_max}and P _{b_ref (ti)}, adopt particle cluster algorithm to solve optimum SOC scope Optimized model and can obtain optimum SOCopt_min, SOCopt_max;

(42) wind storage system is in running, according to the setting power of real-time state-of-charge shift ratio and energy-storage system, periodically regulates time constant filter, and each concrete grammar regulated is:

(421) shift ratio calculates:

According to the optimum SOC scope (SOC obtained _{opt_min}, SOC _{opt_max}) and the real-time SOC of energy-storage system calculate state-of-charge shift ratio pro _{Δ SOC}, computing formula is:

${\mathrm{pro}}_{\mathrm{\&Delta;SOC}}=\frac{\mathrm{SOC}-\frac{1}{2}({\mathrm{SOC}}_{\mathrm{opt}\_\min }+\mathrm{SO}{C}_{\mathrm{opt}\_\max })}{{\mathrm{SOC}}_{\mathrm{opt}\_\max }-{\mathrm{SOC}}_{\mathrm{opt}\_\min }}---\left(13\right)$

(422) by the setting power P of state-of-charge shift ratio pro Δ SOC and energy-storage system current time _{b_ref}as input, time constant filter T, as output, according to the fuzzy control rule preset, adopts fuzzy control strategy to obtain time constant filter T;

(423), in this regulating cycle, the time constant filter T obtained according to step (422) is to the real output P of wind energy turbine set _wcarry out low-pass filtering, the grid-connected power of the expectation after stabilizing is designated as P _{out_exp}, calculate the goal-setting power of energy-storage system and according to following formula, limit value process is carried out to goal-setting power, obtain final setting power P _{b_ref}, restriction process formula is:

$\begin{array}{c}{\tilde{P}}_{B\_\mathrm{ref}}\&le;\frac{(\mathrm{SOC}-{\mathrm{SOC}}_{\mathrm{protect}})*E_{\mathrm{cap}}}{\mathrm{\&Delta;k}}\\ -P_{\mathrm{ch}\_\max }\&le;{\tilde{P}}_{B\_\mathrm{ref}}\&le;P_{\mathrm{disch}\_\max }\end{array}---\left(14\right)$

Wherein, SOC _protectrepresent the state-of-charge protection of setting, Δ k represents the control cycle that time constant filter regulates.