CN104319768A - Micro-grid power supply and monitoring method - Google Patents

Abstract

The invention relates to a micro-grid power supply and a monitoring method. According to the monitoring method, generated power of a photovoltaic module and a wind power module of a micro-grid can be predicted, the change situation of loads is predicted, based on the energy storage condition, detected in real time, of a storage battery module and the running condition, obtained in real time, of a large grid, a power supply strategy is formulated, and therefore the micro-grid can be in a safe and economical running state making users satisfied.

Description

A kind of micro-capacitance sensor is powered and method for supervising

Art

The present invention relates to a kind of micro-capacitance sensor and powers and method for supervising.

Background technology

Along with continuing to increase of global energy crisis, low-carbon (LC), clean regenerative resource become study hotspot, and the micro-capacitance sensor research project containing regenerative resource has all been carried out in current countries in the world.Micro-capacitance sensor refers to the network that multiple distributed power source and related load thereof form according to certain topological structure, and is associated to normal grid by static switch.

A large amount of wind/Light distribation formula generating accesses from low-voltage network using the form of micro-capacitance sensor as a two-way schedulable unit, forms the microgrid group of multiple micro-capacitance sensor composition or active power distribution network.But, due to the height random of the renewable power supply such as wind power generation, photovoltaic generation, the uncertainty of cleaner power sources such as tradition load, fuel cell etc., and elastic load is controllable, make micro-capacitance sensor become uncertain load in public electric wire net, huge impact is brought with optimizing to the stable of public electric wire net.

Therefore, micro-capacitance sensor safety be ensured, reliably, economically run, realize the promotion and application of micro-capacitance sensor, with regard to needs, micro-capacitance sensor power supply management problem is studied.Compared with traditional electrical network power supply management of load passive participation operation of power networks, the operation of the power supply management of micro-capacitance sensor more desired user load active participate electrical network, realizes the positive consumption to electric energy.But along with the increase of intelligent subscriber quantity, the electricity consumption quantity become during user and random electricity consumption time bring disturbance by giving the energy distribution of load side.On the other hand, the feeder ear of micro-capacitance sensor introduces the new forms of energy of renewable, energy-conserving and environment-protective in a large number, and the randomness of new forms of energy brings the impact of disturbance also to the power supply management of micro-capacitance sensor.

Micro-capacitance sensor both by power distribution network and the parallel running of large-scale power net, can form the combined operation system of a large-scale power grid and small grids, also can independently for local load provides electricity needs.Under main electrical network normal condition, micro-capacitance sensor needs steady in a long-term operation; When bulk power grid fault, micro-capacitance sensor must depart from main electrical network fast, enters and is held in islet operation, and after bulk power grid failture evacuation, auto-parallel runs again; When micro-capacitance sensor breaks down, same needs excise micro-capacitance sensor fast.Due to some distributed power sources in micro-capacitance sensor, as wind power generation and solar power generation, affect very large by outside climatic condition, frequency often fluctuates and causes the error of sampling, therefore, how stablizing, reliably implementing micro-grid connection is problem in the urgent need to address at present.

Summary of the invention

For solving the problem, the invention provides a kind of micro-capacitance sensor to power and method for supervising, this method for supervising can predict the photovoltaic module of micro-capacitance sensor, the generated output of wind-powered electricity generation module, the situation of change of prediction load, and based on the real-time energy storage situation of battery module of detection and the ruuning situation of the bulk power grid of Real-time Obtaining, formulate power supply strategy, make micro-capacitance sensor be in safety, economy, make under customer satisfaction system running status.

To achieve these goals, the invention provides a kind of micro-capacitance sensor and power and method for supervising, the method realizes based on following supervisory control system, and this supervisory control system comprises:

Photovoltaic generation monitoring module, for monitoring the photovoltaic generating module in micro-capacitance sensor in real time, and predicts the generated output of photovoltaic generating module;

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

Battery monitor module, for monitoring the battery module in micro-capacitance sensor in real time;

Load monitoring module, for monitoring the load in micro-capacitance sensor in real time, and predicts the changed power situation of load;

Bulk power grid contact module, knows the ruuning situation of bulk power grid and relevant schedule information for real-time from bulk power grid regulation and control center;

Circuit breaker, for connecting or isolating micro-capacitance sensor and bulk power grid;

Parallel control module, connects for controlling circuit breaker or isolates micro-capacitance sensor and bulk power grid;

Middle control module, for determining the power supply strategy of micro-capacitance sensor, and sends instruction to above-mentioned each module, to perform this power supply strategy;

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 micro-capacitance sensor, 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 micro-capacitance sensor, power output in micro-capacitance sensor in following predetermined instant is predicted, according to the load variations situation of the load in existing micro-capacitance sensor, the workload demand of load is predicted;

(3) the energy storage dischargeable capacity obtaining battery module is detected in real time, and the schedule information of bulk power grid;

(4) using the power output in the dischargeable capacity of the schedule information of bulk power grid, current batteries to store energy, following micro-capacitance sensor and to the change of following workload demand as constraints, set up the target function of micro-capacitance sensor power supply management;

(5) above-mentioned power supply management target function is optimized, determines power supply strategy;

(6) above-mentioned power supply strategy is performed.

Preferably, in step (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, with micro-capacitance sensor controllable output power, batteries to store energy discharge and recharge and discharge and recharge time, be performance variable to load demand, with the optimum capacity of energy storage device for set point, minimum as target using the energy supply and demand error of micro-capacitance sensor, meeting micro-capacitance sensor controllable output power, under the discharge and recharge of batteries to store energy and capacity physical constraint condition, adjustment micro-capacitance sensor 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 micro-capacitance sensor.

Preferably, in step (2), power output in micro-capacitance sensor in following predetermined instant is predicted, specifically realize in the following way, the power stage of photovoltaic generating module is predicted, according to the power output of air speed data prediction wind-powered electricity generation module according to intensity of illumination data and temperature data.

Preferably, in step (4), micro-capacitance sensor 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 micro-capacitance sensor can absorb from bulk power grid, P _{g, max}for micro-capacitance sensor can to the maximum power of bulk power grid 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), micro-capacitance sensor 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:

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

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.

Preferably, in step (6), when micro-capacitance sensor is accessed bulk power grid by needs, access in the following way:

(61) link together having based on the micro-grid connection monitoring module of technology of frequency tracking and grid-connected point breaker;

(62) micro-grid connection monitoring module gathers bulk power grid side and micro-capacitance sensor side frequency by frequency senser, calculates the frequency-splitting of both sides during simultaneous interconnecting and all wave frequencies;

(63) micro-grid connection monitoring module obtains the three-phase voltage current value of micro-capacitance sensor side and bulk power grid side phase voltage current value by data acquisition unit acquires, and carries out that gain merit in micro-capacitance sensor side, the idle and measurement processing of electric energy from first-harmonic to higher harmonic components;

(64) micro-grid connection monitoring module calculates voltage difference, phase difference value, the frequency-splitting of micro-capacitance sensor and bulk power grid side, meets difference on the frequency at the same time and is not more than the frequency-splitting F same period _hQmax, voltage difference is not more than synchronous voltage difference operating value U _hQmax, the same period angle be not more than the differential seat angle operating value Ang same period _hQmaxcondition under, receive grid-connected combined floodgate order, carry out grid-connected combined floodgates process.

Method for supervising tool of the present invention has the following advantages: the changed power situation of (1) Accurate Prediction micro-capacitance sensor; (2) power supply strategy takes into account the workload demand of bulk power grid scheduling requirement, micro-capacitance sensor 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 micro-capacitance sensor 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 micro-capacitance sensor supervisory control system 100 of the present invention, and this system 100 comprises: photovoltaic generation monitoring module 104, for monitoring the photovoltaic generating module 201 in micro-capacitance sensor 200 in real time, and predicts the generated output of photovoltaic generating module 201; Wind-powered electricity generation monitoring module 105, for monitoring the wind-powered electricity generation module 202 in wind power grid 200 in real time, and predicts the generated output of wind-powered electricity generation module 202; Battery monitor module 106, for monitoring the battery module 203 in micro-capacitance sensor 200 in real time; Load monitoring module 108, for monitoring the load 204 in micro-capacitance sensor 200 in real time, and predicts the changed power situation of load 204; Bulk power grid contact module 102, regulates and controls center from bulk power grid 300 know the ruuning situation of bulk power grid 300 and relevant schedule information for real-time; Circuit breaker 109, for connecting or isolating micro-capacitance sensor 200 and bulk power grid 300; Parallel control module 103, connects or isolates micro-capacitance sensor 200 and bulk power grid 300 for controlling circuit breaker 109; Middle control module 107, for determining the power supply strategy of micro-capacitance sensor 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.

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 generating module 201 at least comprises voltage, current detecting equipment and sunlight intensity checkout equipment and temperature testing equipment.Wind-powered electricity generation monitoring module 105 at least comprises wind-driven generator level pressure, electric current, frequency detection equipment, and wind speed measurement 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, during to illuminance, forecasting wind speed, can predict by the method similar with temperature prediction, the Mathematical Modeling then utilizing photovoltaic module and blower fan to exert oneself is predicted 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:

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

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:

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

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

Bulk power grid 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 bulk power grid and micro-capacitance sensor 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 micro-capacitance sensor side, 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 bulk power grid 300 side phase voltage current value of micro-capacitance sensor 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 micro-capacitance sensor 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 micro-capacitance sensor 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 micro-capacitance sensor 200, power output in micro-capacitance sensor 200 in following predetermined instant is predicted, according to the load variations situation of the load 204 in existing micro-capacitance sensor 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 bulk power grid contact module 102 detects the schedule information of bulk power grid in real time;

S4. using the power output in the schedule information of bulk power grid 300, the dischargeable capacity of current battery module 203 energy storage, following micro-capacitance sensor and to the change of following workload demand as constraints, set up the target function of micro-capacitance sensor 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 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, with micro-capacitance sensor 200 controllable output power (comprising photovoltaic generation power output and wind power 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, minimum as target using the energy supply and demand error of micro-capacitance sensor 200, meeting micro-capacitance sensor 200 controllable output power, under the discharge and recharge of battery module 203 energy storage and capacity physical constraint condition, adjustment micro-capacitance sensor 200 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 micro-capacitance sensor.

In step s 2, power output in micro-capacitance sensor 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 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, vector value and 12 active-power P ' (k) and 12 reactive power Q ' (k) that are exactly the 9th day predicted power of output.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 micro-capacitance sensor 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 micro-capacitance sensor 200 can absorb from bulk power grid 300, P _{g, max}for micro-capacitance sensor 200 can to the maximum power of bulk power grid 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.

In step s 6, when micro-capacitance sensor 200 is accessed bulk power grid 300 by needs, access in the following way:

S61. the micro-grid connection monitoring module 103 had based on technology of frequency tracking is linked together with grid-connected point breaker 109;

S62. micro-grid connection monitoring module 103 gathers bulk power grid side 300 and micro-capacitance sensor side 200 frequency by frequency senser, calculates the frequency-splitting of both sides during simultaneous interconnecting and all wave frequencies;

S63. micro-grid connection monitoring module leads to 103 and crosses and comprise three-phase voltage current value and the bulk power grid side 300 phase voltage current value that data acquisition unit acquires in it obtain micro-capacitance sensor side 200, and carries out that gain merit in micro-capacitance sensor 200 side, the idle and measurement processing of electric energy from first-harmonic to higher harmonic components;

S64. micro-grid connection monitoring module 103 calculates voltage difference, phase difference value, the frequency-splitting of micro-capacitance sensor and bulk power grid side, meets difference on the frequency at the same time and is not more than the frequency-splitting F same period _hQmax, voltage difference is not more than synchronous voltage difference operating value U _hQmax, the same period angle be not more than the differential seat angle operating value Ang same period _hQmaxcondition under, receive grid-connected combined floodgate order, carry out grid-connected combined floodgates process.

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 (6)

1. micro-capacitance sensor is powered 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 monitoring the photovoltaic generating module in micro-capacitance sensor in real time, and predicts the generated output of photovoltaic generating module;

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

Battery monitor module, for monitoring the battery module in micro-capacitance sensor in real time;

Load monitoring module, for monitoring the load in micro-capacitance sensor in real time, and predicts the changed power situation of load;

Bulk power grid contact module, knows the ruuning situation of bulk power grid and relevant schedule information for real-time from bulk power grid regulation and control center;

Circuit breaker, for connecting or isolating micro-capacitance sensor and bulk power grid;

Parallel control module, connects for controlling circuit breaker or isolates micro-capacitance sensor and bulk power grid;

Middle control module, for determining the power supply strategy of micro-capacitance sensor, and sends instruction to above-mentioned each module, to perform this power supply strategy;

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

It is characterized in that, 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 micro-capacitance sensor, 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 micro-capacitance sensor, power output in micro-capacitance sensor in following predetermined instant is predicted, according to the load variations situation of the load in existing micro-capacitance sensor, the workload demand of load is predicted;

(3) the energy storage dischargeable capacity obtaining battery module is detected in real time, and the schedule information of bulk power grid;

(4) using the power output in the dischargeable capacity of the schedule information of bulk power grid, current batteries to store energy, following micro-capacitance sensor and to the change of following workload demand as constraints, set up the target function of micro-capacitance sensor power supply management;

(5) above-mentioned power supply management target function is optimized, determines power supply strategy;

(6) above-mentioned power supply strategy is performed.

2. the method for claim 1, it is characterized in that, in step (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, with micro-capacitance sensor controllable output power, batteries to store energy discharge and recharge and discharge and recharge time, be performance variable to load demand, with the optimum capacity of energy storage device for set point, minimum as target using the energy supply and demand error of micro-capacitance sensor, meeting micro-capacitance sensor controllable output power, under the discharge and recharge of batteries to store energy and capacity physical constraint condition, adjustment micro-capacitance sensor 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 micro-capacitance sensor.

3. method as claimed in claim 1 or 2, it is characterized in that, in step (2), power output in micro-capacitance sensor in following predetermined instant is predicted, specifically realize in the following way, the power stage of photovoltaic generating module is predicted, according to the power output of air speed data prediction wind-powered electricity generation module according to intensity of illumination data and temperature data.

4. method as claimed in claim 3, it is characterized in that, in step (4), micro-capacitance sensor 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 micro-capacitance sensor can absorb from bulk power grid, P _{g, max}for micro-capacitance sensor can to the maximum power of bulk power grid 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.

5. method as claimed in claim 4, it is characterized in that, in step (4), micro-capacitance sensor 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:

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.

6. method as claimed in claim 5, is characterized in that, in step (6), when micro-capacitance sensor is accessed bulk power grid by needs, accesses in the following way:

(61) link together having based on the micro-grid connection monitoring module of technology of frequency tracking and grid-connected point breaker;

(62) micro-grid connection monitoring module gathers bulk power grid side and micro-capacitance sensor side frequency by frequency senser, calculates the frequency-splitting of both sides during simultaneous interconnecting and all wave frequencies;

(63) micro-grid connection monitoring module obtains the three-phase voltage current value of micro-capacitance sensor side and bulk power grid side phase voltage current value by data acquisition unit acquires, and carries out that gain merit in micro-capacitance sensor side, the idle and measurement processing of electric energy from first-harmonic to higher harmonic components;

(64) micro-grid connection monitoring module calculates voltage difference, phase difference value, the frequency-splitting of micro-capacitance sensor and bulk power grid side, meets difference on the frequency at the same time and is not more than the frequency-splitting F same period _hQmax, voltage difference is not more than synchronous voltage difference operating value U _hQmax, the same period angle be not more than the differential seat angle operating value Ang same period _hQmaxcondition under, receive grid-connected combined floodgate order, carry out grid-connected combined floodgates process.