CN104348188A - Distributed generation running and monitoring method - Google Patents

Abstract

The invention relates to a distributed generation running and monitoring method. The method is capable of predicting the generated output of a photovoltaic module and a wind power module of a distributed generation and predicting the change condition of load, and upon the real-time detected energy storage condition of a storage battery module, the running condition of a diesel generator and the real-time acquired running condition of a power distribution network, the optimum control strategy is implemented.

Description

A kind of distributed power source runs and method for supervising

Art

The present invention relates to a kind of distributed power source and runs and method for supervising.

Background technology

Along with economic development, electricity needs increases rapidly, and electrical network scale constantly expands, and the drawback of ultra-large electric power system also shows especially day by day: operation stability is poor, is difficult to adapt to user to electric power safety and reliability requirement and diversified power demands.

Distributed power source (distributed generation, DG) is a kind of emerging electric power energy, comprises photovoltaic generating system, wind generator system, generation system of micro turbine etc.Distributed generation technology has environmental protection, economic dispatch series of advantages, can meet people well and stablize the requirement with economic environmental protection to electric power safety, cause extensive concern, and be promoted gradually and develop.

But these small-sized renewable energy power generation modes basically, exist the shortcomings such as capacity is little, product efficiency is low, power supply reliability is poor, cost of electricity-generating is high, greatly hinder the large-scale promotion application of such electricity generation system and further develop.

In addition, DG accesses power distribution network and also brings new problem and challenge to the protection of power distribution network and control.Photovoltaic and the wind-powered electricity generation energy are a kind of intermittent energy source, inevitably there is the shortcoming that generated output fluctuates along with the fluctuation of illumination power and wind-force power when being incorporated into the power networks.Therefore, the distributed power source access power distribution network of high permeability, brings a lot of negative effect will inevitably to the operation of power distribution network, scheduling and management.In order to the grid integration making power distribution network can adapt to distributed power source, in the urgent need to furtheing investigate distributed power source operation control and access control key technology, thus the adverse effect that reduction distributed power source grid integration brings, play the booster action that it is positive simultaneously.

Summary of the invention

For solving the problem, the invention provides a kind of distributed power source to run and method for supervising, the method can the photovoltaic module of prediction distribution formula power supply, the generated output of wind-powered electricity generation module, the situation of change of prediction load, and based on the ruuning situation of the energy storage situation of the battery module detected in real time, the ruuning situation of diesel engine generator and the power distribution network of Real-time Obtaining, implement optimum control strategy.

To achieve these goals, the invention provides a kind of distributed power source and run and method for supervising, the method realizes based on following supervisory control system, and this supervisory control system comprises:

Photovoltaic generation monitoring module, for the photovoltaic generating module in real-time monitoring distributed power supply, and predicts the generated output of photovoltaic generating module;

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

Battery monitor module, for the battery module in real-time monitoring distributed power supply;

Diesel engine generator monitoring module, for diesel engine generator in real-time monitoring distributed power supply;

Load monitoring module, for the load in real-time monitoring distributed power supply, 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 distributed power source;

Middle control module, for determining the operation reserve of distributed power source, and sends instruction to each module in above-mentioned supervisory control system, to perform this operation reserve;

Bus module, for the liaison of the modules of this supervisory control system;

This method for supervising comprises the steps:

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

(2) according to the service data of photovoltaic generating module and wind-powered electricity generation module in existing distributed power source, regenerative resource power output in distributed power source in following predetermined instant is predicted, according to the load variations situation of the load in existing distributed power source, the workload demand of load is predicted;

(3) the energy storage dischargeable capacity obtaining battery module is detected in real time, the service data of Real-time Obtaining diesel engine generator and the parameter of power distribution network and schedule information;

(4) using the power output of the renewable energy resources power output in the dischargeable capacity of the schedule information of power distribution network, current batteries to store energy, following distributed power source, diesel engine generator and to the change of following workload demand as constraints, set up the target function of distributed power source operation reserve;

(5) above-mentioned power supply policy goals function is optimized, determines operation reserve;

(6) above-mentioned operation reserve is performed.

Preferably, in step (3), the power distribution network parameter of acquisition comprises: bus numbering, title, load are gained merit, the branch road of reactive load, circuit number, headend node and endpoint node numbering, series resistance, series reactance.

Preferably, in step (5), above-mentioned target function is optimized in accordance with the following steps: utilize the PREDICTIVE CONTROL optimized algorithm based on MINLP model belt restraining, power supply controllable output power in a distributed manner, batteries to store energy discharge and recharge and discharge and recharge time, diesel engine generator controllable output power and be performance variable to load demand, with the optimum capacity of energy storage device for set point, the energy supply and demand error of power supply is minimum as target in a distributed manner, meeting distributed power source controllable output power, diesel engine generator controllable output power, under the discharge and recharge of batteries to store energy and capacity physical constraint condition, adjustment distributed power source controllable output power, diesel engine generator controllable output power, energy storage discharge and recharge and discharge and recharge time, and the regulating action to load demand, to reach the minimum target function value of distributed power source.

Preferably, in step (2), the regenerative resource power output in the distributed power source in following predetermined instant is predicted, comprise the prediction power stage of photovoltaic generating module and the power output of wind-powered electricity generation module.

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:

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

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):

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

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:

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

V in formula (10) ^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), distributed power source gross power Pg is constrained to:

Non-response scheduling slot 1 time, P _{g, min}≤ P _{g (l)}≤ P _{g, max}, P _{g, min}for the maximum power that distributed power source can absorb from power distribution network, P _{g, max}for distributed power source can to the maximum power of power distribution network 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.

Preferably, in step (4), distributed power source battery capacity is constrained to:

The energy flow of storage battery can be described as,

[Soc ^ref-Soc(k+1)]＝a[Soc ^ref-Soc(k)]+ηEs(k)

Soc _min≤Soc(k)≤Soc _max

Wherein, Soc (k) is the capacity status of k moment storage battery, Soc ^refbe reliability for ensureing energy-storage battery work and a set point arranging, Es (k) represents the electricity flowed between energy storage device and other power equipment, the physical deterioration coefficient a ∈ (0,1) of energy storage, η is the efficiency for charge-discharge of storage battery, and charge efficiency is designated as η _c, discharging efficiency is designated as η _d, and between them, meet following relation:

$\mathrm{\&eta;}=\left\{\begin{array}{cc}{\mathrm{\&eta;}}_c,& \mathrm{if\; Es}\left(k\right)>0\\ {\mathrm{\&eta;}}_d,& \mathrm{else}.\end{array}\right.$

The charge and discharge process of energy storage can be regarded as the dynamic process that comprises continuous variable and discrete variable simultaneously, here mixed logical dynamics processing method is adopted, the operating state of energy storage at current time is represented by introducing binary variable δ (k)

Z(k)＝δ(k)Es(k)

Z (k) represents the electricity of current time energy storage charge/discharge, then the dynamic characteristic of storage battery can be described as:

[Soc ^ref-Soc(k+1)]＝a[Soc ^ref-Soc(k)]+(η _c-η _d)Z(k)+η _dEs(k)

Meet following constraints: E ₁ ^δ(k)+E ₂z (k)≤E ₃es (k)+E ₄

Wherein, coefficient matrix E ₁, E ₂, E ₃and E ₄be the linear inequality constraint that binary variable and continuous variable will meet when logical proposition being converted to linear inequality, the derivation by mathematical formulae obtains.

Method for supervising tool of the present invention has the following advantages: the changed power situation of (1) Accurate Prediction distributed power source; (2) power supply strategy takes into account the workload demand of power distribution network scheduling requirement, distributed power source ruuning situation and load, meets user simultaneously, has taken into account power supply reliability, improve power supply benefit simultaneously.

Accompanying drawing explanation

Fig. 1 shows the block diagram of a kind of distributed power source supervisory control system that the inventive method uses;

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

Embodiment

Fig. 1 shows a kind of distributed power source supervisory control system 100 of the present invention, this system 100 comprises: photovoltaic generation monitoring module 104, for the photovoltaic generating module 201 in real-time monitoring distributed power supply 200, and the generated output of photovoltaic generating module 201 is predicted; Wind-powered electricity generation monitoring module 105, for the wind-powered electricity generation module 202 in real-time monitoring distributed power supply 200, and predicts the generated output of wind-powered electricity generation module 202; Diesel engine generator monitoring module 109, for diesel engine generator 207 in real-time monitoring distributed power supply; Battery monitor module 106, for the battery module 203 in real-time monitoring distributed power supply 200; Load monitoring module 108, for the load 204 in real-time monitoring distributed power supply 200, and predicts the changed power situation of load 204; Power distribution network contact module 102, regulates and controls center from power distribution network 300 know the ruuning situation of power distribution network 300 and relevant schedule information for real-time; Parallel control module 103, connects or isolates power distribution network 300 for distributed power source 200; Middle control module 107, for determining the power supply strategy of distributed power source 200, and sends instruction to above-mentioned each module, to perform this power supply strategy; Bus module 101, for the liaison of the modules of this supervisory control system 100.

Communication module 101, for the communication between above-mentioned modules, described bus communication module 101 is connected with other modules by redundancy dual CAN bus.

Photovoltaic power generation module 201 comprises multiple photovoltaic array.Photovoltaic generating module 201 at least comprises voltage, current detecting equipment and sunlight intensity checkout equipment and temperature testing equipment.

Wind-powered electricity generation module comprises multiple wind-driven generator and SVG equipment.Wind-powered electricity generation monitoring module 105 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.

Based on photovoltaic generation, the blower fan generating prediction of real time meteorological data, need predict temperature, illuminance, wind speed.Wherein, temperature prediction can adopt following methods:

Temperature data T1 [24] in sample, T2 [24] ... Tm [24], Δ T1 [23], Δ T2 [23], Δ T3 [23], Δ T4 [23], Δ T5 [23]; Run to the temperature T [t] of t monitoring, Δ T [t-1]; The similarity of accounting temperature variation tendency; Similarity is normalized; Temperature after t is predicted, obtains T [24-t].In addition, to illuminance, can predict by the method similar with temperature prediction, then utilize the Mathematical Modeling of photovoltaic module to predict its power output.

Battery monitor module 106 at least comprises accumulator voltage, current detecting equipment and temperature testing equipment.For monitoring the charge and discharge process of storage battery rice card in real time.By regulating the charge/discharge of storage battery to store/supplement the energy have more than needed/lacked, the energy flow of storage battery can be described as,

The energy flow of storage battery can be described as,

[Soc ^ref-Soc(k+1)]＝a[Soc ^ref-Soc(k)]+ηEs(k)

Soc _min≤Soc(k)≤Soc _max

Wherein, Soc (k) is the capacity status of k moment storage battery, Soc ^refbe reliability for ensureing energy-storage battery work and a set point arranging, Es (k) represents the electricity flowed between energy storage device and other power equipment, the physical deterioration coefficient a ∈ (0,1) of energy storage, η is the efficiency for charge-discharge of storage battery, and charge efficiency is designated as η _c, discharging efficiency is designated as η _d, and between them, meet following relation:

$\mathrm{\&eta;}=\left\{\begin{array}{cc}{\mathrm{\&eta;}}_c,& \mathrm{if\; Es}\left(k\right)>0\\ {\mathrm{\&eta;}}_d,& \mathrm{else}.\end{array}\right.$

The charge and discharge process of energy storage can be regarded as the dynamic process that comprises continuous variable and discrete variable simultaneously, here mixed logical dynamics processing method is adopted, the operating state of energy storage at current time is represented by introducing binary variable δ (k)

Z(k)＝δ(k)Es(k)

Z (k) represents the electricity of current time energy storage charge/discharge, then the dynamic characteristic of storage battery can be described as:

[Soc ^ref-Soc(k+1)]＝a[Soc ^ref-Soc(k)]+(η _c-η _d)Z(k)+η _dEs(k)

Meet following constraints: E ₁δ (k)+E ₂z (k)≤E ₃es (k)+E ₄

Wherein, coefficient matrix E ₁, E ₂, E ₃and E ₄be the linear inequality constraint that binary variable and continuous variable will meet when logical proposition being converted to linear inequality, the derivation by mathematical formulae obtains.

When logical proposition being converted to linear inequality in binary variable and continuous variable process, the linear inequality constraint E that meet ₁δ (k)+E ₂z (k)≤E ₃es (k)+E ₄, wherein coefficient matrix E ₁, E ₂, E ₃and E ₄be respectively:

$\left\{\begin{array}{c}{E}_1={\left[\begin{array}{cccccc}{\mathrm{Soc}}^{\mathrm{ref}}& -({\mathrm{Soc}}^{\mathrm{ref}}+\mathrm{\&epsiv;})& {\mathrm{Soc}}^{\mathrm{ref}}& {\mathrm{Soc}}^{\mathrm{ref}}& -{\mathrm{Soc}}^{\mathrm{ref}}& -{\mathrm{Soc}}^{\mathrm{ref}}\end{array}\right]}^T\\ E_2={\left[\begin{array}{cccccc}0& 0& 1& -1& 1& -1\end{array}\right]}^T\\ E_3={\left[\begin{array}{cccccc}1& -1& 1& -1& 0& 0\end{array}\right]}^T\\ E_4={\left[\begin{array}{cccccc}{\mathrm{Soc}}^{\mathrm{ref}}& -\mathrm{\&epsiv;}& {\mathrm{Soc}}^{\mathrm{ref}}& {\mathrm{Soc}}^{\mathrm{ref}}& 0& 0\end{array}\right]}^T\end{array}\right..$

Middle control module 107 at least comprises CPU element, data storage cell and display unit.

Power distribution network contact module 102 at least comprises Wireless Telecom Equipment.This Wireless Telecom Equipment can be wireline equipment or wireless device.

Parallel control module 103 at least comprises checkout equipment, data acquisition unit and data processing unit for detecting power distribution network and distributed power source 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, the three-phase voltage of distributed electrical source, electric current.Remote measurement amount changes strong ac signal (5A/100V) 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 300 side phase voltage current value of distributed power source 200 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 distributed power source 200 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. obtain the service data of photovoltaic generating module 201 and wind-powered electricity generation module 202 in distributed power source 200 when photovoltaic generation monitoring module 104, wind-powered electricity generation monitoring module reality 105, and store data, the load variations situation of load monitoring module 108 Real-time Obtaining load 204;

S2. according to the service data of photovoltaic generating module 201 and wind-powered electricity generation module 202 in existing distributed power source 200, power output in distributed power source 200 in following predetermined instant is predicted, according to the load variations situation of the load 204 in existing distributed power source 200, the workload demand of load 204 is predicted;

S3. battery monitor module 106 detects the energy storage dischargeable capacity obtaining battery module 203 in real time, and power distribution network contact module 102 detects the schedule information of power distribution network in real time;

S4. using the power output in the schedule information of power distribution network 300, the dischargeable capacity of current battery module 203 energy storage, following distributed power source and to the change of following workload demand as constraints, set up the target function of distributed power source 200 power supply management;

S5. above-mentioned power supply management target function is optimized, determines power supply strategy;

S6. above-mentioned power supply strategy is performed.

In step s3, power distribution network 300 parameter of acquisition comprises: bus numbering, title, load are gained merit, the branch road of reactive load, circuit number, headend node and endpoint node numbering, series resistance, series reactance.

Above-mentioned target function is optimized in accordance with the following steps: utilize the PREDICTIVE CONTROL optimized algorithm based on MINLP model belt restraining, power supply controllable output power in a distributed manner, batteries to store energy discharge and recharge and discharge and recharge time, diesel engine generator controllable output power and be performance variable to load demand, with the optimum capacity of energy storage device for set point, the energy supply and demand error of power supply is minimum as target in a distributed manner, meeting distributed power source controllable output power, diesel engine generator controllable output power, under the discharge and recharge of batteries to store energy and capacity physical constraint condition, adjustment distributed power source controllable output power, diesel engine generator controllable output power, energy storage discharge and recharge and discharge and recharge time, and the regulating action to load demand, to reach the minimum target function value of distributed power source.

In step s 5, the target function of above-mentioned power supply management is optimized in accordance with the following steps: utilize the PREDICTIVE CONTROL optimized algorithm based on MINLP model belt restraining, power supply 200 controllable output power (comprising photovoltaic generation power output and wind power output power) in a distributed manner, diesel engine generator 207 controllable output power, battery module 203 energy storage discharge and recharge and discharge and recharge time, be performance variable to load 204 workload demand, with energy storage device (battery module 203) optimum capacity for set point, the energy supply and demand error of power supply 200 is minimum as target in a distributed manner, meeting distributed power source 200 controllable output power, under the discharge and recharge of battery module 203 energy storage and capacity physical constraint condition, adjustment distributed power source 200 controllable output power, diesel engine generator 207 controllable output power, energy storage discharge and recharge and discharge and recharge time, and the regulating action to load demand, to reach the minimum target function value of distributed power source.

In step s 2, power output in distributed power source 200 in following predetermined instant is predicted, specifically realize in the following way, predict the power stage of photovoltaic generating module according to intensity of illumination data and temperature data, according to the power output of air speed data prediction wind-powered electricity generation module.

In step s 2, the regenerative resource power output in the distributed power source in following predetermined instant is predicted, comprise the prediction power stage of photovoltaic generating module and the power output of wind-powered electricity generation module.

Predict the power output of wind-powered electricity generation module in the following way in step S2, 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:

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

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):

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

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:

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

V in formula (10) ^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.

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=tan sig (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=tan sig (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 distributed power source 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 distributed power source 200 can absorb from power distribution network 300, P _{g, max}for distributed power source 200 can to the maximum power of power distribution network 300 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 (7)

1. distributed power source runs and a method for supervising, and the method realizes based on following supervisory control system, and this supervisory control system comprises:

Photovoltaic generation monitoring module, for the photovoltaic generating module in real-time monitoring distributed power supply, and predicts the generated output of photovoltaic generating module;

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

Battery monitor module, for the battery module in real-time monitoring distributed power supply;

Diesel engine generator monitoring module, for diesel engine generator in real-time monitoring distributed power supply;

Load monitoring module, for the load in real-time monitoring distributed power supply, 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 distributed power source;

Middle control module, for determining the operation reserve of distributed power source, and sends instruction to each module in above-mentioned supervisory control system, to perform this operation reserve;

Bus module, for the liaison of the modules of this supervisory control system;

This method for supervising comprises the steps:

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

(2) according to the service data of photovoltaic generating module and wind-powered electricity generation module in existing distributed power source, regenerative resource power output in distributed power source in following predetermined instant is predicted, according to the load variations situation of the load in existing distributed power source, the workload demand of load is predicted;

(3) the energy storage dischargeable capacity obtaining battery module is detected in real time, the service data of Real-time Obtaining diesel engine generator and the parameter of power distribution network and schedule information;

(4) using the power output of the renewable energy resources power output in the dischargeable capacity of the schedule information of power distribution network, current batteries to store energy, following distributed power source, diesel engine generator and to the change of following workload demand as constraints, set up the target function of distributed power source operation reserve;

(5) above-mentioned power supply policy goals function is optimized, determines operation reserve;

(6) above-mentioned operation reserve is performed.

2. the method for claim 1, it is characterized in that, in step (3), the power distribution network parameter of acquisition comprises: bus numbering, title, load are gained merit, the branch road of reactive load, circuit number, headend node and endpoint node numbering, series resistance, series reactance.

3. method as claimed in claim 1 or 2, it is characterized in that, in step (5), above-mentioned target function is optimized in accordance with the following steps: utilize the PREDICTIVE CONTROL optimized algorithm based on MINLP model belt restraining, power supply controllable output power in a distributed manner, batteries to store energy discharge and recharge and discharge and recharge time, diesel engine generator controllable output power and be performance variable to load demand, with the optimum capacity of energy storage device for set point, the energy supply and demand error of power supply is minimum as target in a distributed manner, meeting distributed power source controllable output power, diesel engine generator controllable output power, under the discharge and recharge of batteries to store energy and capacity physical constraint condition, adjustment distributed power source controllable output power, diesel engine generator controllable output power, energy storage discharge and recharge and discharge and recharge time, and the regulating action to load demand, to reach the minimum target function value of distributed power source.

4. method as claimed in claim 3, it is characterized in that, in step (2), the regenerative resource power output in the distributed power source in following predetermined instant is predicted, comprise the prediction power stage of photovoltaic generating module and the power output of wind-powered electricity generation module.

5. method as claimed in claim 4, it is characterized in that, predict the power output of wind-powered electricity generation module in the following way in step (2), 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:

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

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:

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

V in formula (10) ^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.

6. method as claimed in claim 5, it is characterized in that, in step (4), distributed power source gross power Pg is constrained to:

Non-response scheduling slot 1 time, P _{g, min}≤ P _{g (l)}≤ P _{g, max}, P _{g, min}for the maximum power that distributed power source can absorb from power distribution network, P _{g, max}for distributed power source can to the maximum power of power distribution network 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.

7. method as claimed in claim 6, it is characterized in that, in step (4), distributed power source battery capacity is constrained to:

The energy flow of storage battery can be described as,

[Soc ^ref-Soc(k+1)]＝a[Soc ^ref-Soc(k)]+ηEs(k)

Soc _min≤Soc(k)≤Soc _max

Wherein, Soc (k) is the capacity status of k moment storage battery, Soc ^refbe reliability for ensureing energy-storage battery work and a set point arranging, Es (k) represents the electricity flowed between energy storage device and other power equipment, the physical deterioration coefficient a ∈ (0 of energy storage, 1), η is the efficiency for charge-discharge of storage battery, charge efficiency is designated as η c, discharging efficiency is designated as η d, and meets following relation between them:

$\mathrm{\&eta;}=\left\{\begin{array}{c}{\mathrm{\&eta;}}_c,\mathrm{ifEs}\left(k\right)>0\\ {\mathrm{\&eta;}}_d,\mathrm{else}.\end{array}\right.$

The charge and discharge process of energy storage can be regarded as the dynamic process that comprises continuous variable and discrete variable simultaneously, here mixed logical dynamics processing method is adopted, the operating state of energy storage at current time is represented by introducing binary variable δ (k)

Z(k)＝δ(k)Es(k)

Z (k) represents the electricity of current time energy storage charge/discharge, then the dynamic characteristic of storage battery can be described as:

[Soc ^ref-Soc(k+1)]＝a[Soc ^ref-Soc(k)]+(η _c-η _d)Z(k)+η _dEs(k)

Meet following constraints: E ₁ ^δ(k)+E ₂z (k)≤E ₃es (k)+E ₄

Wherein, coefficient matrix E ₁, E ₂, E ₃and E ₄be the linear inequality constraint that binary variable and continuous variable will meet when logical proposition being converted to linear inequality, the derivation by mathematical formulae obtains.