CN104201723A - Off-network microgrid reliability assessment method based on timing simulation - Google Patents

Abstract

The invention discloses an off-network microgrid reliability assessment method based on timing simulation. The off-network microgrid reliability assessment method comprises the steps of firstly, on the basis of an ARMA model, calculating the timing power output of a wind power generation unit based on the output power characteristic curve of the wind power generation unit, secondly, building a timing charge/discharge model of an energy storage system and providing three operation strategies of the energy storage system, and finally, thoroughly assessing the reliability of an off-network microgrid based on the operation characteristics of distributed generation and the energy storage system. The method is used for performing reliability simulation assessment on the existing off-network microgrid, and is convenient in calculation and visual in result; as a result, the power supply reliability of the off-network microgrid can be effectively controlled.

Description

Based on timing simulation from net type microgrid reliability estimation method

Technical field

The present invention relates to Model in Reliability Evaluation of Power Systems technology, specifically, be a kind of based on timing simulation from net type microgrid reliability estimation method.

Background technology

Along with society and economic fast development, the energy and environmental problem receive people's concern day by day.Conventional energy resource is resource-constrained not only, even causes serious environmental pollution, runs counter to the Scientific Strategy of sustainable development, energy-conserving and environment-protective.In order to reduce the impact on environment, meet ever-increasing energy demand, micro-electrical network that the generation of electricity by new energy of take is basis also receives electric power enterprise and user's concern, and the reliability of micro-electrical network and electrical network, user's impact is become to one of important subject of micro-power network development and application in recent years.In net type microgrid, load is only powered by internal electric source, generally comprises the new forms of energy such as distributed power generation and energy-storage system.From net type microgrid have at random, the characteristic such as sequential, traditional reliability estimation method is difficult to exactly the microgrid under island mode be carried out to reliability assessment, needs the Reliability Evaluation Algorithm based on islet operation characteristic badly.On the other hand, energy storage is as the important part of microgrid, and its operation strategy and charge and discharge process directly affect system operation, the impact of the operation strategy that also must take into account energy storage device when reliability assessment on microgrid reliability.

Summary of the invention

For the deficiencies in the prior art, propose herein a kind of based on timing simulation from net type microgrid reliability estimation method, the method can be passed through historical wind speed data, the reliability from net type microgrid that wind-powered electricity generation unit, diesel engine unit and energy-storage system are formed is assessed, by setting up the sequential charging and recharging model of energy-storage system and from net type microgrid storage energy operation strategy, studied the reliability index of micro-electrical network, made from the reliability assessment of net type microgrid more intuitively, concrete technical scheme is as follows:

Based on timing simulation from a net type microgrid reliability estimation method, its key is to carry out according to following steps:

Step 1: according to the failure rate of each element in electrical network, generate at random the time between failures of each element, and select the element of time between failures minimum as fault element;

Step 2: according to the repair rate of the selected fault element of step 1, determine repair time;

Step 3: according to the selected fault element of step 1, utilize fault traversal search, the load that fault element is affected is divided into a, b two classes, wherein:

A type load: lose with microgrid power supply the load being connected, this type load interruption duration is the element repair time that step 2 calculates;

B type load: keep with microgrid power supply the load being connected, by selecting any control the in following three kinds of storage energy operation strategies, be specially: order is from network operation time t=1;

Strategy 1:

1-1: calculate the wind-powered electricity generation unit output P of t hour _w(t), the diesel engine unit P that exerts oneself _d(t), workload demand P _l(t) the wind-powered electricity generation unit average output power P and in simulated time _a;

1-2: if P _w(t)>=P _a, energy-storage system charging P _bat(t)=P _w(t)-P _a, accumulative total stored energy capacitance E _bat(t+1)=E _bat(t)+P _bat(t), the common power output P of wind-powered electricity generation unit and energy storage now _{w & bat}(t)=P _a, go to 1-4; If not, carry out 1-3;

1-3: energy storage system discharges P now _bat(t)≤P _dch-maxif, P _w(t)+P _bat(t)>=P _a, make the common power output P of wind-powered electricity generation unit and energy storage _{w & bat}(t)=P _a; Otherwise, P _{w & bat}(t)=P _w(t)+P _bat(t), accumulative total energy storage residual capacity E _bat(t+1)=E _bat(t)-P _bat(t); Go to 1-4;

1-4: if P _{w & bat}(t)+P _d(t)>=P _l(t), system is reliable; Otherwise the reduction of loading;

1-5: upgrade the load lshed and the residue load lremain that cut down, accumulative total is cut down frequency of power cut and the interruption duration of load;

1-6: if t<T _w+ T _tTR, make t=t+1, return to 1-1; Otherwise carry out step 4;

Strategy 2:

2-1: calculate respectively the wind-powered electricity generation unit output P of t hour _w(t), the diesel engine unit P that exerts oneself _d(t), workload demand P _l(t);

2-2: if P _w(t)>=x%P _l(t), energy-storage system charging P _bat(t)=P _w(t)-x%P _l(t), accumulative total stored energy capacitance E _bat(t+1)=E _bat(t)+P _bat(t); If the diesel engine unit P that exerts oneself now _d(t)>=(1-x%) P _l(t), system is reliable; Otherwise the reduction of loading, upgrades load lshed and residue load lremain, and accumulative total is cut down frequency of power cut and the interruption duration of load; Go to 2-4; If P _w(t) <x%P _l(t), carry out 2-3;

2-3: control diesel engine unit and exert oneself, if P _d(t)+P _w(t)>=P _l(t), system is reliable; Otherwise energy storage system discharges P _bat(t)=min{P _dch-max, x%P _l(t)-P _w(t) }, accumulative total energy storage residual capacity E _bat(t+1)=E _bat(t)-P _bat(t); If P now _d(t)+P _w(t)+P _bat(t)>=P _l(t), system is reliable, otherwise load lshed and residue load lremain are upgraded in the reduction of loading, and accumulative total is cut down frequency of power cut and the interruption duration of load;

2-4: if t<T _w+ T _tTR, make t=t+1, return to 2-1; Otherwise carry out step 4;

Strategy 3:

3-1: calculate respectively the wind-powered electricity generation unit output P of t hour _w(t), the diesel engine unit P that exerts oneself _d(t), workload demand P _l(t);

3-2: if P _w(t)>=P _l(t), system is reliable, energy-storage system charging P _bat(t)=P _w(t)-P _l(t), accumulative total stored energy capacitance E _bat(t+1)=E _bat(t)+P _bat(t); Go to 3-4; Otherwise carry out 3-3;

3-3: diesel engine unit is exerted oneself, if P _d(t)+P _w(t)>=P _l(t), system is reliable; Otherwise energy storage system discharges P _bat(t)≤P _dch-max, accumulative total stored energy capacitance E _bat(t+1)=E _bat(t)-P _bat(t);

3-4: if P _d(t)+P _w(t)+P _bat(t)>=P _l(t), system is reliable; Otherwise the reduction of loading, upgrades load lshed and residue load lremain, and accumulative total is cut down frequency of power cut and the interruption duration of load;

3-5: if t<T _w+ T _tTR, make t=t+1, return to 3-1; Otherwise carry out step 4;

Step 4: according to the frequency of power cut of each load point counting and interruption duration, determine the reliability index of each load point and micro-electrical network islet operation.

Based on above-mentioned control strategy, can appreciate that

Strategy 1:

1) wind-powered electricity generation unit average output power in during calculating simulation, when wind-powered electricity generation unit output is greater than average output power, energy storage charging; Otherwise energy storage electric discharge, makes both reach average output power by gross capability.

2) when wind-powered electricity generation unit and energy storage gross output are less than load, diesel engine unit is exerted oneself, if three exerts oneself, still can not meet workload demand, the reduction of loading; Otherwise system is reliable.

Strategy 2:

1) when wind-powered electricity generation unit output is greater than the x% of load, energy storage charging, diesel engine unit is exerted oneself simultaneously.If diesel engine unit power output is less than the 1-x% of load, the reduction of loading; Otherwise system is reliable.

2) when wind-powered electricity generation unit output is less than the x% of load, diesel engine unit is exerted oneself.If diesel engine unit power output is greater than residue load, system is reliable; Otherwise, energy storage electric discharge, but the gross output of wind-powered electricity generation unit and energy storage can not be greater than the x% of system loading, and x% is taken as 30% conventionally.

3) if now three exerts oneself and still can not meet workload demand, the reduction of loading; Otherwise system is reliable.

Strategy 3:

1) when wind-powered electricity generation unit output is greater than load, energy storage charging, system is reliable; Otherwise diesel engine unit is exerted oneself.

2) if wind-powered electricity generation unit and diesel engine unit output gross power are greater than load, system is reliable; Otherwise, energy storage electric discharge.

3) if energy storage electric discharge still can not meet workload demand, the reduction of loading; Otherwise system is reliable.

This programme has utilized the state duration sampling of sequential Monte carlo algorithm to all elements, thereby forms the time sequence status of system, then each state is carried out to fail-safe analysis.Obviously, when all elements normally move, for conventional power distribution network or grid type microgrid, this system is in normal operating conditions, and all steady loads are powered.But, for for net type microgrid, its load is only powered by internal electric source, the characteristics such as random, intermittence due to distributed power source, if a certain moment power supply is exerted oneself in the time of can not meeting workload demand, even if all elements normally move, in microgrid, still have sub-load to need to cut down, therefore, this programme is when carrying out reliability assessment from net type microgrid, not only will consider the state of all elements, also take into account each power supply constantly under all states of system and exerted oneself and workload demand, its assessment result is more reliable.

As further describing, in described step 1, according to number of elements in electrical network, generate one group of random number δ ₁, δ ₂,, δ _n, and according to T _i=-ln δ _i/ λ _icalculate the time between failures of each element, wherein, λ _ibe the failure rate of i element, i=1～n, n is number of elements in electrical network.

Further describe again, in step 2, after fault element is selected, produce a random number x, according to T _r=-lnx/ μ calculates the repair time of fault element, the repair rate that wherein μ is selected fault element.

In this programme, wind-powered electricity generation unit power output P _wTG(t) account form is as follows:

First, take wind speed historical data as basis, adopt arma modeling in time series method to analyze wind series, be specially:

In formula: x _t=(v _t-μ _t)/σ _t, μ wherein _tand σ _trepresent respectively wind series in t average and variance constantly; β _i(i=1～q) is respectively autoregression and moving average parameter, ε _tthat average is 0, variance is σ _a ²white Gaussian noise, i.e. ε _t∈ (0, σ _a ²);

The wind speed of simulating by arma modeling is: v _t=μ _t+ x _t* σ _t;

Then, according to:

Set up the wind-powered electricity generation unit sequential model of exerting oneself, wherein, V _ci, V _r, V _cobe respectively incision wind speed, rated wind speed and the cut-out wind speed of wind-powered electricity generation unit; P _rthe rated power of wind-powered electricity generation unit, A, B and C are parameter type, account form is:

$\left\{\begin{array}{c}A=\frac{1}{{(V_{\mathrm{ci}}-V_r)}^2}[V_{\mathrm{ci}}(V_{\mathrm{ci}}+V_r)-4V_{\mathrm{ci}}{V}_r{\left(\frac{V_{\mathrm{ci}}+V_r}{2V_r}\right)}^3]\\ B=\frac{1}{{(V_{\mathrm{ci}}-V_r)}^2}[4(V_{\mathrm{ci}}+V_r){\left(\frac{V_{\mathrm{ci}}+V_r}{2V_r}\right)}^3-(3V_{\mathrm{ci}}+V_r)\\ C=\frac{1}{{(V_{\mathrm{ci}}-V_r)}^2}[2-4{\left(\frac{V_{\mathrm{ci}}+V_r}{2V_r}\right)}^3]\end{array}\right..$

In conjunction with the constraints of energy-storage system, in this programme, energy-storage system management of charging and discharging mode is as follows:

Charged state: $\left\{\begin{array}{c}{P}_{\mathrm{bat}}\left(t\right)\&le;P_{\mathrm{ch}-\max }\\ E_{\mathrm{bat}}\left(t\right)+P_{\mathrm{bat}}\left(t\right)\&le;E_{\max }\\ E_{\mathrm{bat}}(t+1)=E_{\mathrm{bat}}\left(t\right)+P_{\mathrm{bat}}\left(t\right)\end{array}\right.,$

Discharge condition: $\left\{\begin{array}{c}{P}_{\mathrm{bat}}\left(t\right)\&le;P_{\mathrm{dch}-\max }\\ E_{\mathrm{bat}}\left(t\right)-P_{\mathrm{bat}}\left(t\right)\&GreaterEqual;E_{\min }\\ E_{\mathrm{bat}}(t+1)=E_{\mathrm{bat}}\left(t\right)-P_{\mathrm{bat}}\left(t\right)\end{array}\right.,$

P wherein _batand E (t) _bat(t) represent respectively energy-storage system charge and discharge power and the storage power of t hour, P _ch-maxand P _dch-maxthe maximum charge power and the maximum discharge power that represent respectively energy-storage system; E _minand E _maxrepresent respectively minimum capacity and the heap(ed) capacity restriction of energy-storage system.

Remarkable result of the present invention is: first take wind speed autoregression rolling average (auto regressive and moving average, ARMA) model as basis, the sequential based on wind-powered electricity generation unit characteristics of output power curve calculation wind-powered electricity generation unit is exerted oneself; Next sets up the sequential charging and recharging model of energy-storage system, proposes three kinds of operation strategies of energy-storage system; Based on distributed power source and energy-storage system operation characteristic, fully, to assessing from the reliability of net type microgrid, utilize the method from net type microgrid, to carry out reliability simulation assessment, its convenience of calculation to existing, visual result, can effectively grasp from the power supply reliability of net type microgrid.

Accompanying drawing explanation

Fig. 1 is the electric network model figure of specific embodiment;

Fig. 2 is the failure rate of load point under different control strategies;

Fig. 3 is the annual interruption duration of load point under different control strategies.

Embodiment

Below in conjunction with accompanying drawing, the specific embodiment of the present invention and operation principle are described in further detail.

The present embodiment adopts shown in Fig. 1 from net type microgrid circuit structure, as can be seen from the figure, microgrid internal electric source mainly consists of wind-powered electricity generation unit, diesel generating set and energy-storage system, have 8 loads, on part branch road, intelligent switch is housed, can effectively cut off load current, wind-powered electricity generation unit incision, specified and cut-out wind speed are respectively 3m/s, 8m/s and 15m/s, all the other related datas are as table 1, shown in table 2.

Table 1 microgrid internal electric source and energy-storage system parameter

Specifically according to following steps, carry out:

Step 1: generate one group of random number δ according to number of elements n in electrical network ₁, δ ₂,, δ _n, and according to T _i=-ln δ _i/ λ _icalculate the time between failures of each element, wherein, λ _ibe the failure rate of i element, i=1～n; Then select the element of time between failures minimum as fault element, the random number is here selected the random number of Normal Distribution between (0,1) conventionally;

Step 2: after fault element is selected, produce the random number x of Normal Distribution between one (0,1), according to T _r=-lnx/ μ calculates the repair time of fault element, the repair rate that wherein μ is selected fault element;

Step 3: according to the selected fault element of step 1, utilize fault traversal search, the load that fault element is affected is divided into a, b two classes, wherein:

A type load: lose with microgrid power supply the load being connected, this type load interruption duration is the element repair time that step 2 calculates;

B type load: keep with microgrid power supply the load being connected, by selecting any control the in following three kinds of storage energy operation strategies, be specially: order is from network operation time t=1;

Strategy 1:

1-1: calculate the wind-powered electricity generation unit output P of t hour _w(t), the diesel engine unit P that exerts oneself _d(t), workload demand P _l(t) the wind-powered electricity generation unit average output power P and in simulated time _a;

1-2: if P _w(t)>=P _a, energy-storage system charging P _bat(t)=P _w(t)-P _a, accumulative total stored energy capacitance E _bat(t+1)=E _bat(t)+P _bat(t), the common power output P of wind-powered electricity generation unit and energy storage now _{w & bat}(t)=P _a, go to 1-4; If not, carry out 1-3;

1-3: energy storage system discharges P now _bat(t)≤P _dch-maxif, P _w(t)+P _bat(t)>=P _a, make the common power output P of wind-powered electricity generation unit and energy storage _{w & bat}(t)=P _a; Otherwise, P _{w & bat}(t)=P _w(t)+P _bat(t), accumulative total energy storage residual capacity E _bat(t+1)=E _bat(t)-P _bat(t); Go to 1-4;

1-4: if P _{w & bat}(t)+P _d(t)>=P _l(t), system is reliable; Otherwise the reduction of loading;

1-5: upgrade the load lshed and the residue load lremain that cut down, accumulative total is cut down frequency of power cut and the interruption duration of load;

1-6: if t<T _w+ T _tTR, make t=t+1, return to 1-1; Otherwise carry out step 4;

Strategy 2:

2-1: calculate respectively the wind-powered electricity generation unit output P of t hour _w(t), the diesel engine unit P that exerts oneself _d(t), workload demand P _l(t);

2-2: if P _w(t)>=x%P _l(t), energy-storage system charging P _bat(t)=P _w(t)-x%P _l(t), accumulative total stored energy capacitance E _bat(t+1)=E _bat(t)+P _bat(t); If the diesel engine unit P that exerts oneself now _d(t)>=(1-x%) P _l(t), system is reliable; Otherwise the reduction of loading, upgrades load lshed and residue load lremain, and accumulative total is cut down frequency of power cut and the interruption duration of load; Go to 2-4; If P _w(t) <x%P _l(t), carry out 2-3;

2-3: control diesel engine unit and exert oneself, if P _d(t)+P _w(t)>=P _l(t), system is reliable; Otherwise energy storage system discharges P _bat(t)=min{P _dch-max, x%P _l(t)-P _w(t) }, accumulative total energy storage residual capacity E _bat(t+1)=E _bat(t)-P _bat(t); If P now _d(t)+P _w(t)+P _bat(t)>=P _l(t), system is reliable, otherwise load lshed and residue load lremain are upgraded in the reduction of loading, and accumulative total is cut down frequency of power cut and the interruption duration of load;

2-4: if t<T _w+ T _tTR, make t=t+1, return to 2-1; Otherwise carry out step 4;

Strategy 3:

3-1: calculate respectively the wind-powered electricity generation unit output P of t hour _w(t), the diesel engine unit P that exerts oneself _d(t), workload demand P _l(t);

3-2: if P _w(t)>=P _l(t), system is reliable, energy-storage system charging P _bat(t)=P _w(t)-P _l(t), accumulative total stored energy capacitance E _bat(t+1)=E _bat(t)+P _bat(t); Go to 3-4; Otherwise carry out 3-3;

3-3: diesel engine unit is exerted oneself, if P _d(t)+P _w(t)>=P _l(t), system is reliable; Otherwise energy storage system discharges P _bat(t)≤P _dch-max, accumulative total stored energy capacitance E _bat(t+1)=E _bat(t)-P _bat(t);

3-4: if P _d(t)+P _w(t)+P _bat(t)>=P _l(t), system is reliable; Otherwise the reduction of loading, upgrades load lshed and residue load lremain, and accumulative total is cut down frequency of power cut and the interruption duration of load;

3-5: if t<T _w+ T _tTR, make t=t+1, return to 3-1; Otherwise carry out step 4;

Step 4: according to the frequency of power cut of each load point counting and interruption duration, determine the reliability index of each load point and micro-electrical network islet operation.

In the present embodiment, wind-powered electricity generation unit power output P _wTG(t) account form is as follows:

First, take wind speed historical data as basis, adopt arma modeling in time series method to analyze wind series, be specially:

In formula: x _t=(v _t-μ _t)/σ _t, μ wherein _tand σ _trepresent respectively wind series in t average and variance constantly; β _i(i=1～q) is respectively autoregression and moving average parameter, ε _tthat average is 0, variance is σ _a ²white Gaussian noise, i.e. ε _t∈ (0, σ _a ²);

The wind speed of simulating by arma modeling is: v _t=μ _t+ x _t* σ _t;

Then, according to:

Set up the wind-powered electricity generation unit sequential model of exerting oneself, wherein, V _ci, V _r, V _cobe respectively incision wind speed, rated wind speed and the cut-out wind speed of wind-powered electricity generation unit; P _rthe rated power of wind-powered electricity generation unit, A, B and C are parameter type, account form is:

$\left\{\begin{array}{c}A=\frac{1}{{(V_{\mathrm{ci}}-V_r)}^2}[V_{\mathrm{ci}}(V_{\mathrm{ci}}+V_r)-4V_{\mathrm{ci}}{V}_r{\left(\frac{V_{\mathrm{ci}}+V_r}{2V_r}\right)}^3]\\ B=\frac{1}{{(V_{\mathrm{ci}}-V_r)}^2}[4(V_{\mathrm{ci}}+V_r){\left(\frac{V_{\mathrm{ci}}+V_r}{2V_r}\right)}^3-(3V_{\mathrm{ci}}+V_r)\\ C=\frac{1}{{(V_{\mathrm{ci}}-V_r)}^2}[2-4{\left(\frac{V_{\mathrm{ci}}+V_r}{2V_r}\right)}^3]\end{array}\right..$

Energy-storage system management of charging and discharging mode is as follows:

Charged state: $\left\{\begin{array}{c}{P}_{\mathrm{bat}}\left(t\right)\&le;P_{\mathrm{ch}-\max }\\ E_{\mathrm{bat}}\left(t\right)+P_{\mathrm{bat}}\left(t\right)\&le;E_{\max }\\ E_{\mathrm{bat}}(t+1)=E_{\mathrm{bat}}\left(t\right)+P_{\mathrm{bat}}\left(t\right)\end{array}\right.,$

Discharge condition: $\left\{\begin{array}{c}{P}_{\mathrm{bat}}\left(t\right)\&le;P_{\mathrm{dch}-\max }\\ E_{\mathrm{bat}}\left(t\right)-P_{\mathrm{bat}}\left(t\right)\&GreaterEqual;E_{\min }\\ E_{\mathrm{bat}}(t+1)=E_{\mathrm{bat}}\left(t\right)-P_{\mathrm{bat}}\left(t\right)\end{array}\right.,$

P wherein _batand E (t) _bat(t) represent respectively energy-storage system charge and discharge power and the storage power of t hour, P _ch-maxand P _dch-maxthe maximum charge power and the maximum discharge power that represent respectively energy-storage system; E _minand E _maxrepresent respectively minimum capacity and the heap(ed) capacity restriction of energy-storage system.

Based on said method, carry out switching and control and statistics, can obtain the various evaluation indexes shown in Fig. 2, Fig. 3 and table 3.

Table 3 micro-grid system reliability index

From the above results, the characteristics such as system is random owing to having taken into full account in reliability assessment process, sequential, therefore use this method to calculate comparatively exactly from the power supply reliability of net type microgrid.

Claims (5)

1. Based on timing simulation from a net type microgrid reliability estimation method, it is characterized in that carrying out according to following steps:

   Step 1: according to the failure rate of each element in electrical network, generate at random the time between failures of each element, and select the element of time between failures minimum as fault element;

   Step 2: according to the repair rate of the selected fault element of step 1, determine repair time;

   Step 3: according to the selected fault element of step 1, utilize fault traversal search, the load that fault element is affected is divided into a, b two classes, wherein:

   A type load: lose with microgrid power supply the load being connected, this type load interruption duration is the element repair time that step 2 calculates;

   B type load: keep with microgrid power supply the load being connected, by selecting any control the in following three kinds of storage energy operation strategies, be specially: order is from network operation time t=1;

   Strategy 1:

   1-1: calculate the wind-powered electricity generation unit output P of t hour _w(t), the diesel engine unit P that exerts oneself _d(t), workload demand P _l(t) the wind-powered electricity generation unit average output power P and in simulated time _a;

   1-2: if P _w(t)>=P _a, energy-storage system charging P _bat(t)=P _w(t)-P _a, accumulative total stored energy capacitance E _bat(t+1)=E _bat(t)+P _bat(t), the common power output P of wind-powered electricity generation unit and energy storage now _{w & bat}(t)=P _a, go to 1-4; If not, carry out 1-3;

   1-3: energy storage system discharges P now _bat(t)≤P _dch-maxif, P _w(t)+P _bat(t)>=P _a, make the common power output P of wind-powered electricity generation unit and energy storage _{w & bat}(t)=P _a; Otherwise, P _{w & bat}(t)=P _w(t)+P _bat(t), accumulative total energy storage residual capacity E _bat(t+1)=E _bat(t)-P _bat(t); Go to 1-4;

   1-4: if P _{w & bat}(t)+P _d(t)>=P _l(t), system is reliable; Otherwise the reduction of loading;

   1-5: upgrade the load lshed and the residue load lremain that cut down, accumulative total is cut down frequency of power cut and the interruption duration of load;

   1-6: if t<T _w+ T _tTR, make t=t+1, return to 1-1; Otherwise carry out step 4;

   Strategy 2:

   2-1: calculate respectively the wind-powered electricity generation unit output P of t hour _w(t), the diesel engine unit P that exerts oneself _d(t), workload demand P _l(t);

   2-2: if P _w(t)>=x%P _l(t), energy-storage system charging P _bat(t)=P _w(t)-x%P _l(t), accumulative total stored energy capacitance E _bat(t+1)=E _bat(t)+P _bat(t); If the diesel engine unit P that exerts oneself now _d(t)>=(1-x%) P _l(t), system is reliable; Otherwise the reduction of loading, upgrades load lshed and residue load lremain, and accumulative total is cut down frequency of power cut and the interruption duration of load; Go to 2-4; If P _w(t) <x%P _l(t), carry out 2-3;

   2-3: control diesel engine unit and exert oneself, if P _d(t)+P _w(t)>=P _l(t), system is reliable; Otherwise energy storage system discharges P _bat(t)=min{P _dch-max, x%P _l(t)-P _w(t) }, accumulative total energy storage residual capacity E _bat(t+1)=E _bat(t)-P _bat(t); If P now _d(t)+P _w(t)+P _bat(t)>=P _l(t), system is reliable, otherwise load lshed and residue load lremain are upgraded in the reduction of loading, and accumulative total is cut down frequency of power cut and the interruption duration of load;

   2-4: if t<T _w+ T _tTR, make t=t+1, return to 2-1; Otherwise carry out step 4;

   Strategy 3:

   3-1: calculate respectively the wind-powered electricity generation unit output P of t hour _w(t), the diesel engine unit P that exerts oneself _d(t), workload demand P _l(t);

   3-2: if P _w(t)>=P _l(t), system is reliable, energy-storage system charging P _bat(t)=P _w(t)-P _l(t), accumulative total stored energy capacitance E _bat(t+1)=E _bat(t)+P _bat(t); Go to 3-4; Otherwise carry out 3-3;

   3-3: diesel engine unit is exerted oneself, if P _d(t)+P _w(t)>=P _l(t), system is reliable; Otherwise energy storage system discharges P _bat(t)≤P _dch-max, accumulative total stored energy capacitance E _bat(t+1)=E _bat(t)-P _bat(t);

   3-4: if P _d(t)+P _w(t)+P _bat(t)>=P _l(t), system is reliable; Otherwise the reduction of loading, upgrades load lshed and residue load lremain, and accumulative total is cut down frequency of power cut and the interruption duration of load;

   3-5: if t<T _w+ T _tTR, make t=t+1, return to 3-1; Otherwise carry out step 4;

   Step 4: according to the frequency of power cut of each load point counting and interruption duration, determine the reliability index of each load point and micro-electrical network islet operation.
2. According to claim 1 based on timing simulation from net type microgrid reliability estimation method, it is characterized in that: in described step 1, according to number of elements in electrical network, generate one group of random number δ ₁, δ ₂,, δ _n, and according to T _i=-ln δ _i/ λ _icalculate the time between failures of each element, wherein, λ _ibe the failure rate of i element, i=1～n, n is number of elements in electrical network.
3. According to claim 1 and 2 based on timing simulation from net type microgrid reliability estimation method, it is characterized in that: in step 2, after fault element is selected, produce a random number x, according to T _r=-lnx/ μ calculates the repair time of fault element, the repair rate that wherein μ is selected fault element.
4. According to claim 1 based on timing simulation from net type microgrid reliability estimation method, it is characterized in that wind-powered electricity generation unit power output P _wTG(t) account form is as follows:

   First, take wind speed historical data as basis, adopt arma modeling in time series method to analyze wind series, be specially:

   In formula: x _t=(v _t-μ _t)/σ _t, μ wherein _tand σ _trepresent respectively wind series in t average and variance constantly; β _i(i=1～q) is respectively autoregression and moving average parameter, ε _tthat average is 0, variance is σ _a ²white Gaussian noise, i.e. ε _t∈ (0, σ _a ²);

   The wind speed of simulating by arma modeling is: v _t=μ _t+ x _t* σ _t;

   Then, according to:

   Set up the wind-powered electricity generation unit sequential model of exerting oneself, wherein, V _ci, V _r, V _cobe respectively incision wind speed, rated wind speed and the cut-out wind speed of wind-powered electricity generation unit; P _rthe rated power of wind-powered electricity generation unit, A, B and C are parameter type, account form is:

   $\left\{\begin{array}{c}A=\frac{1}{{(V_{\mathrm{ci}}-V_r)}^2}[V_{\mathrm{ci}}(V_{\mathrm{ci}}+V_r)-4V_{\mathrm{ci}}{V}_r{\left(\frac{V_{\mathrm{ci}}+V_r}{2V_r}\right)}^3]\\ B=\frac{1}{{(V_{\mathrm{ci}}-V_r)}^2}[4(V_{\mathrm{ci}}+V_r){\left(\frac{V_{\mathrm{ci}}+V_r}{2V_r}\right)}^3-(3V_{\mathrm{ci}}+V_r)\\ C=\frac{1}{{(V_{\mathrm{ci}}-V_r)}^2}[2-4{\left(\frac{V_{\mathrm{ci}}+V_r}{2V_r}\right)}^3]\end{array}\right..$
5. According to claim 1 based on timing simulation from net type microgrid reliability estimation method, it is characterized in that, energy-storage system management of charging and discharging mode is as follows:

   Charged state: $\left\{\begin{array}{c}{P}_{\mathrm{bat}}\left(t\right)\&le;P_{\mathrm{ch}-\max }\\ E_{\mathrm{bat}}\left(t\right)+P_{\mathrm{bat}}\left(t\right)\&le;E_{\max }\\ E_{\mathrm{bat}}(t+1)=E_{\mathrm{bat}}\left(t\right)+P_{\mathrm{bat}}\left(t\right)\end{array}\right.,$

   Discharge condition: $\left\{\begin{array}{c}{P}_{\mathrm{bat}}\left(t\right)\&le;P_{\mathrm{dch}-\max }\\ E_{\mathrm{bat}}\left(t\right)-P_{\mathrm{bat}}\left(t\right)\&GreaterEqual;E_{\min }\\ E_{\mathrm{bat}}(t+1)=E_{\mathrm{bat}}\left(t\right)-P_{\mathrm{bat}}\left(t\right)\end{array}\right.,$

   P wherein _batand E (t) _bat(t) represent respectively energy-storage system charge and discharge power and the storage power of t hour, P _ch-maxand P _dch-maxthe maximum charge power and the maximum discharge power that represent respectively energy-storage system; E _minand E _maxrepresent respectively minimum capacity and the heap(ed) capacity restriction of energy-storage system.