CN103904695A - Nearby-island multi-microgrid dynamic scheduling method based on MCS-PSO - Google Patents

Abstract

The invention relates to a nearby-island multi-microgrid dynamic scheduling method based on MCS-PSO. At present, the research for the multi-microgrid interconnection technology is intensive step by step. However, the research for multi-microgrid active scheduling is less. On the basis of generation and load prediction, an optimization model for each microgrid interconnection line power control is built on the basis of the indexes of reliability and economy, and the solution is carried out through the MCS-PSO. Due to the fact that generation and load prediction information in microgrids is constantly updated as time goes by, dynamic optimization scheduling updated in an online-rolling mode is adopted so as to improve the precision of a scheduling plane. According to the nearby-island multi-microgrid dynamic scheduling method based on the MCS-PSO, by the combination of the reality of interconnection operation of multiple nearby islands, energy of the microgrid of each island can be reasonably scheduled and distributed, various uncertainty factors are fully taken into account, and the running cost and loss can be reduced to the minimum.

Description

A kind ofly close on island many microgrids dynamic dispatching method based on MCS-PSO

Technical field

The invention belongs to technical field of power systems, be specifically related to a kind ofly close on island many microgrids dynamic dispatching method based on MCS-PSO.

Background technology

The powerup issue that develops into solution China remote districts of distributed power generation and micro-electric power network technique provides new approaches.The development of micro-electrical network, not only can effectively develop local regenerative resource, and can solve powerup issue from far-off regions.The isolated micro-electrical network supporting for dereliction electrical network, due to fluctuation and the randomness of honourable resource, needs to rely on energy-storage system to maintain the balance of generating and load, and its power supply reliability is subject to the restriction of Self-regulation ability.

Large Grid interconnected operation can supply mutually electric power, for subsequent use each other between each department, strengthens the accident ability of resisting, and improves power grid security level and power supply reliability.China island is numerous, is referred from the interconnected strategy in region of large electrical network, isolated island contiguous in the group of island can be formed to many micro-grid systems from net type island group by cable interconnect.

Summary of the invention

The object of the invention is to for the deficiencies in the prior art, provide a kind of and close on island many microgrids dynamic dispatching method based on MCS-PSO.

The present invention includes following steps:

Step 1: in conjunction with reliability and economic index, set up Optimal Operation Model, specifically:

1, the mathematical description of index:

1.1, reliability lacks the index λ of amount of power supply by expectation _cPdescribe:

${\mathrm{\λ}}_{\mathrm{CP}}=\∑p_{\mathrm{cut}}\·P_{\mathrm{cut}}$

In formula: P _cutthe load of excision during for supply of electric power deficiency; p _cutfor the probability of this state appearance.

1.2, economic index λ _tPby because abandoning the generated output that wind/light reduces, cost of electricity-generating and the loss of SER service life of diesel engine generator described:

${\mathrm{\λ}}_{\mathrm{TP}}=\∑p_{\mathrm{tune}}\·P_{\mathrm{tune}}$

In formula: P _tunefor the generated output reducing because abandoning wind/light; p _tunefor the probability of this state appearance.

${\mathrm{\λ}}_{\mathrm{OP}}=\∑p_{\mathrm{oil}}\·a\·P_{\mathrm{oil}}$

In formula: the fuel coefficient that a is diesel engine generator; P _oilfor its generated output; p _oilfor diesel engine generator is in this shape probability of state.

${\mathrm{\λ}}_{\mathrm{DOD}}=\∑p_{\mathrm{DOD}}\·\mathrm{\α}\·{\mathrm{\η}}_{\mathrm{DOD}}$

${\mathrm{\λ}}_{\mathrm{rate}}=\∑p_{\mathrm{rate}}\·\mathrm{\β}\·\left|P_S\right|$

In formula: η _dODfor SER depth of discharge; p _dODfor SER is in η _dODshape probability of state; α is the life consumption coefficient of depth of discharge; | P _s| for SER discharges and recharges watt level; p _ratefor SER is in | P _s| shape probability of state; β is the life consumption coefficient that discharges and recharges watt level.

2, Optimized Operation target and constraint:

In the many micro-grid systems of new forms of energy, scheduling strategy need be taken into account reliability and economy, therefore the target of dynamic economic dispatch is to seek optimum interconnection power P _line, make each microgrid under the inner SER of microgrid adjusts, make operating cost and minimization of loss, reach the optimum of on-road efficiency:

$\begin{array}{cc}\min & \underset{m}{\∑}\underset{n}{\∑}[c_{\mathrm{CP}}\·{\mathrm{\λ}}_{\mathrm{CP}}+c_{\mathrm{TP}}\·{\mathrm{\λ}}_{\mathrm{TP}}+c_{\mathrm{OP}}\·{\mathrm{\λ}}_{\mathrm{OP}}+c_{\mathrm{LOS}}\·({\mathrm{\λ}}_{\mathrm{DOD}}+{\mathrm{\λ}}_{\mathrm{rate}})]\end{array}$

In formula: the time hop count of m for comprising in the dispatching cycle; N is microgrid number contained in many micro-grid systems; c _cPduring for supply of electric power deficiency, excise the economic loss that specific load causes; λ _cPfor the expectation in microgrid lacks amount of power supply; c _tPfor abandoning the economic loss of wind/light time unit energy output; λ _tPfor abandoning the desired value of wind/light in microgrid; c _oPfor the unit cost of diesel generation fuel; λ _oPfor the fuel consumption desired value of diesel engine generator; c _lOSfor SER life unit cost; λ _dODand λ _ratebe respectively depth of discharge and discharge and recharge the SER life consumption desired value that power causes.

Flowing out direction taking power from microgrid is positive direction, and when SER charging, power direction is positive direction.Subscript i represents i period; Subscript j represents j microgrid; Subscript l represents l article of circuit.Ignore after network loss, main constraints can be expressed as follows:

1) power-balance constraint

$\begin{array}{cc}{P}_{\mathrm{DG}}{|}_i^j-P_{\mathrm{tune}}{|}_i^j+P_{\mathrm{oil}}{|}_i^j=P_L{|}_i^j-P_{\mathrm{cut}}{|}_i^j+P_S{|}_i^j+\underset{k^j}{\∑}{P}_{\mathrm{line}}{|}_i^j,& i=\mathrm{1,2,3},...,m;j=\mathrm{1,2,3},...,n\end{array}$

In formula: P _dGfor the current maximum of DG can generated output; P _tunefor the generated output reducing because abandoning wind/light; P _oilfor the generated output of diesel engine generator; P _lfor load power; P _cutfor the load excising because of supply of electric power deficiency; P _sfor SER discharges and recharges power; P _linefor interconnection flows out power; k ^jfor the interconnection number being connected with j microgrid.

2) SER carrying capacity equality constraint

$\begin{array}{cc}S{|}_i^j=S{|}_{i-1}^j+P_S{|}_i^j\·\mathrm{\ΔT},& i=\mathrm{1,2,3},...,m;j=\mathrm{1,2,3},...,n\end{array}$

In formula: S is the carrying capacity of current period Mo SER; Δ T is the interval duration of period.

3) SER discharges and recharges power constraint

$\begin{array}{cc}{\underset{\‾}{P}}_S{|}^j\≤P_S{|}_i^j\≤{\stackrel{\‾}{P}}_S{|}^j,& i=\mathrm{1,2,3},...,m;j=\mathrm{1,2,3},...,n\end{array}$

In formula: P _sfor SER in microgrid discharges and recharges power;

with

be respectively electric discharge and the charge power limit of SER.

4) SER carrying capacity bound constraint

$\begin{array}{cc}{\mathrm{\η}}_{\lim }\·\stackrel{\‾}{S}{|}^j\≤S{|}_i^j\≤\stackrel{\‾}{S}{|}^j,& i=\mathrm{1,2,3},...,m;j=\mathrm{1,2,3},...,n\end{array}$

In formula: η _limfor the restriction of SER depth of discharge;

for the rating load electric weight of SER.

5) tie-line power transmission constraint

$\begin{array}{cc}{\underset{\‾}{P}}_{\mathrm{line}}{|}_i^l\≤P_{\mathrm{line}}{|}_i^l\≤{\stackrel{\‾}{P}}_{\mathrm{line}}{|}_i^l,& i=\mathrm{1,2,3},...,m;l=\mathrm{1,2,3},...,h\end{array}$

In formula: h represents interconnection sum;

with

represent respectively the Power Limitation of circuit input and output.

6) diesel engine generator power constraint

${\underset{\‾}{P}}_{\mathrm{oil}}{|}^j\≤P_{\mathrm{oil}}{|}_j^i\≤{\stackrel{\‾}{P}}_{\mathrm{oil}}{|}^j$

In formula:

with

the minimum operation that represents respectively diesel engine generator is exerted oneself and maximum output.

Step 2: consider the uncertainty of generating, load and equipment operation, ask for target function, specifically:

1, analyze and in many microgrids cooperation, consider operating uncertainty:

The uncertainty of 1.1 generating predictions

While adopting Beta distribution to describe the probability distribution of DG generating, have

$f\left(P_{\mathrm{DG}}\right)=\frac{P_{\mathrm{DG}}^{\mathrm{\α}-1}\·{(1-P_{\mathrm{DG}})}^{\mathrm{\β}-1}}{B(\mathrm{\α},\mathrm{\β})}$

In formula: P _dGfor the generated output after normalization, fiducial value is its specified generated output; The value of B (α, β) is passed through

$B(\mathrm{\α},\mathrm{\β})={\∫}_0^1{P}^{\mathrm{\α}-1}\·{(1-P)}^{\mathrm{\β}-1}\mathrm{dP}$

Can be calculated; α and β can obtain by the statistical parameter of generating predicted value and predicated error.

The uncertainty of 1.2 load predictions

The load prediction error of electric power system is described with normal distribution, the probability density function of load

$f\left(P_L\right)=\frac{1}{\sqrt{2\mathrm{\π}}\·{\mathrm{\σ}}_L}{e}^{-\frac{{(P_L-P_{\mathrm{Lf}})}^2}{2{\mathrm{\σ}}_L^2}}$

In formula: σ _lfor the standard deviation of load prediction error; P _lffor load prediction value; P _lfand P _lfall normalization, fiducial value and P _dGidentical.

The uncertainty of 1.3 equipment operations

Distributed power generation, diesel engine generator, SER and interconnection in microgrid, the uncertainty that unplanned stoppage in transit occurs because of relevant device fault, protection action can be described with forced outage rate γ.

State for equipment u at period i, gets the random real number ξ on [0,1] interval, represents that u is in malfunction if ξ is less than γ, otherwise represents that u normally moves.

2, Monte Carlo simulation calculates

The uncertain problem of this scheduling model, the stochastic variable distributing except containing multiple different probability, and between each variable, have nonlinear constrained, therefore adopt Monte Carlo simulation.

For shortening the algorithm time, do to set as follows: (1), abandon the generation when different with excision load of wind/light; (2) while, only depending on SER to be not enough to guarantee load, just start diesel engine generator.After calculating by sampled analog, acquired results is added up and drawn target desired value.

Step 3: ask for optimum interconnection power ratio control P by PSO algorithm _line, the step of PSO Optimization Solution is as follows:

STEP1. tie-line power transmission in microgrid is set to reference direction, set cycle-index N _cir, particle number N _p;

STEP2. the Position And Velocity of the particle in initialization population:

in the random tie-line power transmission P that produces _lineinitial value, particle rapidity v in [1,1] at random produce;

STEP3. the individual optimal value of initialization and all optimal values: getting sampling scale is N _mcs, adopt Monte-carlo Simulation Method in step 2, ask for the fitness of each particle.By current P _linevalue is as the individual optimal value p of particle _best, choose optimal value wherein as all optimal value g _best;

STEP4. basis two formulas renewal v and P below _linevalue:

$\begin{array}{c}v=w\·v+c_1\·r_u\·(p_{\mathrm{best}}-P_{\mathrm{line}})\\ +c_2\·r_u\·(g_{\mathrm{best}}-P_{\mathrm{line}})\\ P_{\mathrm{line}}=P_{\mathrm{line}}+v\end{array}$

In formula: w is inertia weight; c ₁, c ₂for the study factor; r _ufor the random real number on [0,1] interval;

STEP5. adopt Monte-carlo Simulation Method to recalculate particle fitness

upgrade individual optimal value p _bestwith all optimal value g _best;

STEP6. carry out mutation operation according to following formula, and judgement whether be less than g _best.If $g_{\mathrm{best}}^{*}<g_{\mathrm{best}},$ ? $g_{\mathrm{best}}=g_{\mathrm{best}}^{*};$

$g_{\mathrm{best}}^{*}=g_{\mathrm{best}}\&CenterDot;[1+r_g]$

In formula: r _gfor obeying the random real number of standardized normal distribution;

STEP7. calculate the variance of particle fitness if

be less than convergence definite value ε, convergence Output rusults.Otherwise carry out STEP4;

Step 4: the rolling of dynamic dispatching

In the time period of specifying, according to new predicted value, operation plan is upgraded to correction.

Beneficial effect of the present invention: the present invention closes on the reality of interconnected operation in conjunction with many islands, can reasonably carry out dispatching distribution to the energy of each island microgrid.In invention, take into full account various uncertain factors, can the cost of operation and loss dropped to minimum.This invention not only has certain meaning for the construction of many islands microgrid, also can be used for the management and running in actual motion.

Brief description of the drawings

Fig. 1 is island many micro-grid systems of new forms of energy schematic diagram;

Fig. 2 is Monte Carlo simulation calculation flow chart.

Embodiment

Below in conjunction with accompanying drawing, the invention will be further described.

As shown in Figure 1, in the many micro-grid systems of island new forms of energy, micro-grid system internal structure unit is mainly:

1) distributed power generation: in the microgrid of island, distributed power generation (DG) mainly comprises wind-force and photovoltaic generation, and renewable energy power generation is main generation mode.Its generated output is determined by real-time meteorological condition, adopts suitable Forecasting Methodology can carry out the prediction of certain hour section.

2) load: by load forecasting method, consider, after the many factors such as user type, temperature, festivals or holidays, can predict the workload demand in certain period.In the time of power supply shortage, can load to maintain system operation by cut-out.

3) energy storage device: in net type micro-grid system, energy storage device (SER-Stored Energy Resources) is being born the task of regulating system frequency.For extending the service life of energy storage device, depth of discharge (DOD, Depth of Discharge) need be set and limit and discharge and recharge Power Limitation.

4) diesel engine generator: on island etc. remote districts from net type microgrid, owing to departing from main electrical network, the diesel engine generator of configuration start and stop is at any time as emergency power supply, to improve power supply reliability conventionally.

5) interconnect circuit: many micro-grid systems connect each microgrid by interconnect circuit between island, the energy between each microgrid is coordinated to realize by the power of controlling interconnection.

The Optimized Operation target of many microgrids cooperation is considered reliability and economy.Reliability is described by expecting the index that lacks amount of power supply:

${\mathrm{\&lambda;}}_{\mathrm{CP}}=\&Sum;p_{\mathrm{cut}}\&CenterDot;P_{\mathrm{cut}}$

In formula: P _cutthe load of excision during for supply of electric power deficiency; p _cutfor the probability of this state appearance.

Economy by because abandoning the generated output that wind/light reduces, cost of electricity-generating and the loss of SER service life of diesel engine generator described:

${\mathrm{\&lambda;}}_{\mathrm{TP}}=\&Sum;p_{\mathrm{tune}}\&CenterDot;P_{\mathrm{tune}}$

In formula: P _tunefor the generated output reducing because abandoning wind/light; p _tunefor the probability of this state appearance.

${\mathrm{\&lambda;}}_{\mathrm{OP}}=\&Sum;p_{\mathrm{oil}}\&CenterDot;a\&CenterDot;P_{\mathrm{oil}}$

In formula: the fuel coefficient that a is diesel engine generator; P _oilfor its generated output; p _oilfor diesel engine generator is in this shape probability of state.

${\mathrm{\&lambda;}}_{\mathrm{DOD}}=\&Sum;p_{\mathrm{DOD}}\&CenterDot;\mathrm{\&alpha;}\&CenterDot;{\mathrm{\&eta;}}_{\mathrm{DOD}}$

${\mathrm{\&lambda;}}_{\mathrm{rate}}=\&Sum;p_{\mathrm{rate}}\&CenterDot;\mathrm{\&beta;}\&CenterDot;\left|P_S\right|$

In formula: η _dODfor SER depth of discharge; p _dODfor SER is in η _dODshape probability of state; α is the life consumption coefficient of depth of discharge; | P _s| for SER discharges and recharges watt level; p _ratefor SER is in | P _s| shape probability of state; β is the life consumption coefficient that discharges and recharges watt level.

In the many micro-grid systems of new forms of energy, scheduling strategy need be taken into account reliability and economy, therefore the target of dynamic economic dispatch is to seek optimum interconnection power P _line, make each microgrid under the inner SER of microgrid adjusts, make operating cost and minimization of loss, reach the optimum of on-road efficiency:

$\begin{array}{cc}\min & \underset{m}{\&Sum;}\underset{n}{\&Sum;}[c_{\mathrm{CP}}\&CenterDot;{\mathrm{\&lambda;}}_{\mathrm{CP}}+c_{\mathrm{TP}}\&CenterDot;{\mathrm{\&lambda;}}_{\mathrm{TP}}+c_{\mathrm{OP}}\&CenterDot;{\mathrm{\&lambda;}}_{\mathrm{OP}}+c_{\mathrm{LOS}}\&CenterDot;({\mathrm{\&lambda;}}_{\mathrm{DOD}}+{\mathrm{\&lambda;}}_{\mathrm{rate}})]\end{array}$

In formula: the time hop count of m for comprising in the dispatching cycle; N is microgrid number contained in many micro-grid systems; c _cPduring for supply of electric power deficiency, excise the economic loss that specific load causes; λ _cPfor the expectation in microgrid lacks amount of power supply; c _tPfor abandoning the economic loss of wind/light time unit energy output; λ _tPfor abandoning the desired value of wind/light in microgrid; c _oPfor the unit cost of diesel generation fuel; λ _oPfor the fuel consumption desired value of diesel engine generator; c _lOSfor SER life unit cost; λ _dODand λ _ratebe respectively depth of discharge and discharge and recharge the SER life consumption desired value that power causes.

For convenience of discussing, direction is flowed out in the present invention taking power from microgrid be positive direction, and when SER charging, power direction is positive direction.Subscript i represents i period; Subscript j represents j microgrid; Subscript l represents l article of circuit.Ignore after network loss, main constraints can be expressed as follows:

Power-balance constraint

$\begin{array}{cc}{P}_{\mathrm{DG}}{|}_i^j-P_{\mathrm{tune}}{|}_i^j+P_{\mathrm{oil}}{|}_i^j=P_L{|}_i^j-P_{\mathrm{cut}}{|}_i^j+P_S{|}_i^j+\underset{k^j}{\&Sum;}{P}_{\mathrm{line}}{|}_i^j,& i=\mathrm{1,2,3},...,m;j=\mathrm{1,2,3},...,n\end{array}$

In formula: P _dGfor the current maximum of DG can generated output; P _tunefor the generated output reducing because abandoning wind/light; P _oilfor the generated output of diesel engine generator; P _lfor load power; P _cutfor the load excising because of supply of electric power deficiency; P _sfor SER discharges and recharges power; P _linefor interconnection flows out power; k ^jfor the interconnection number being connected with j microgrid.

SER carrying capacity equality constraint

$\begin{array}{cc}S{|}_i^j=S{|}_{i-1}^j+P_S{|}_i^j\&CenterDot;\mathrm{\&Delta;T},& i=\mathrm{1,2,3},...,m;j=\mathrm{1,2,3},...,n\end{array}$

In formula: S is the carrying capacity of current period Mo SER; Δ T is the interval duration of period.

SER discharges and recharges power constraint

$\begin{array}{cc}{\underset{\&OverBar;}{P}}_S{|}^j\&le;P_S{|}_i^j\&le;{\stackrel{\&OverBar;}{P}}_S{|}^j,& i=\mathrm{1,2,3},...,m;j=\mathrm{1,2,3},...,n\end{array}$

In formula: P _sfor SER in microgrid discharges and recharges power;

with

be respectively electric discharge and the charge power limit of SER.

The constraint of SER carrying capacity bound

$\begin{array}{cc}{\mathrm{\&eta;}}_{\lim }\&CenterDot;\stackrel{\&OverBar;}{S}{|}^j\&le;S{|}_i^j\&le;\stackrel{\&OverBar;}{S}{|}^j,& i=\mathrm{1,2,3},...,m;j=\mathrm{1,2,3},...,n\end{array}$

In formula: η _limfor the restriction of SER depth of discharge; for the rating load electric weight of SER.

Tie-line power transmission constraint

$\begin{array}{cc}{\underset{\&OverBar;}{P}}_{\mathrm{line}}{|}_i^l\&le;P_{\mathrm{line}}{|}_i^l\&le;{\stackrel{\&OverBar;}{P}}_{\mathrm{line}}{|}_i^l,& i=\mathrm{1,2,3},...,m;l=\mathrm{1,2,3},...,h\end{array}$

In formula: h represents interconnection sum;

with

represent respectively the Power Limitation of circuit input and output.

Diesel engine generator power constraint

${\underset{\&OverBar;}{P}}_{\mathrm{oil}}{|}^j\&le;P_{\mathrm{oil}}{|}_j^i\&le;{\stackrel{\&OverBar;}{P}}_{\mathrm{oil}}{|}^j$

In formula: with

the minimum operation that represents respectively diesel engine generator is exerted oneself and maximum output.

In many microgrids cooperation, consider operating uncertainty:

While adopting Beta distribution to describe the probability distribution of DG generating, have

$f\left(P_{\mathrm{DG}}\right)=\frac{P_{\mathrm{DG}}^{\mathrm{\&alpha;}-1}\&CenterDot;{(1-P_{\mathrm{DG}})}^{\mathrm{\&beta;}-1}}{B(\mathrm{\&alpha;},\mathrm{\&beta;})}$

In formula: P _dGfor the generated output after normalization, fiducial value is its specified generated output; The value of B (α, β) is passed through

$B(\mathrm{\&alpha;},\mathrm{\&beta;})={\&Integral;}_0^1{P}^{\mathrm{\&alpha;}-1}\&CenterDot;{(1-P)}^{\mathrm{\&beta;}-1}\mathrm{dP}$

Can be calculated; α and β can obtain by the statistical parameter of generating predicted value and predicated error.

The load prediction error of electric power system is generally described with normal distribution, the probability density function of load

$f\left(P_L\right)=\frac{1}{\sqrt{2\mathrm{\&pi;}}\&CenterDot;{\mathrm{\&sigma;}}_L}{e}^{-\frac{{(P_L-P_{\mathrm{Lf}})}^2}{2{\mathrm{\&sigma;}}_L^2}}$

In formula: σ _lfor the standard deviation of load prediction error; P _lffor load prediction value; P _lfand P _lfall normalization, fiducial value and P _dGidentical.

Distributed power generation, diesel engine generator, SER and interconnection in microgrid, the uncertainty that unplanned stoppage in transit occurs because of relevant device fault, protection action can be described with forced outage rate γ.

State for equipment u at period i, gets the random real number ξ on [0,1] interval, represents that u is in malfunction if ξ is less than γ, otherwise represents that u normally moves.

The uncertain problem of this scheduling model, the stochastic variable distributing except containing multiple different probability, and between each variable, have nonlinear constrained, and be difficult to describe with traditional optimized algorithm, adopt Monte Carlo simulation can well solve.Particle cluster algorithm (PSO) in conjunction with Monte Carlo simulation (MCS) can solve the complex nonlinear optimization problem that contains stochastic variable, in the problems such as Power System Reliability Analysis, has obtained application.This model also adopts MCS-PSO Algorithm for Solving, first for probabilistic part, adopts MCS to carry out calculating target function value, is translated into certain problem; Then by PSO algorithm, it is optimized.

Monte Carlo simulation calculates

Consider the uncertain factor in microgrid, calculate the corresponding target function value of given Pline by monte carlo method.For shortening the algorithm time, can do following setting in conjunction with reality: (1), abandon the generation when different with excision load of wind/light; (2) while, only depending on SER to be not enough to guarantee load, just start diesel engine generator.After calculating by sampled analog, acquired results is added up and drawn target desired value, idiographic flow as shown in Figure 2.

PSO Optimization Solution

STEP1. tie-line power transmission in microgrid is set to reference direction, set cycle-index N _cir, particle number N _p;

STEP2. the Position And Velocity of the particle in initialization population: in the random tie-line power transmission P that produces _lineinitial value, particle rapidity v in [1,1] at random produce;

STEP3. the individual optimal value of initialization and all optimal values: getting sampling scale is N _mcs, adopt Monte-carlo Simulation Method in step 2, ask for the fitness of each particle.By current P _linevalue is as the individual optimal value p of particle _best, choose optimal value wherein as all optimal value g _best;

STEP4. basis two formulas renewal v and P below _linevalue:

$\begin{array}{c}v=w\&CenterDot;v+c_1\&CenterDot;r_u\&CenterDot;(p_{\mathrm{best}}-P_{\mathrm{line}})\\ +c_2\&CenterDot;r_u\&CenterDot;(g_{\mathrm{best}}-P_{\mathrm{line}})\\ P_{\mathrm{line}}=P_{\mathrm{line}}+v\end{array}$

In formula: w is inertia weight; c ₁, c ₂for the study factor; r _ufor the random real number on [0,1] interval;

STEP5. adopt Monte-carlo Simulation Method to recalculate particle fitness upgrade individual optimal value p _bestwith all optimal value g _best;

STEP6. carry out mutation operation according to following formula, and judgement whether be less than g _best.If

? $g_{\mathrm{best}}=g_{\mathrm{best}}^{*};$

$g_{\mathrm{best}}^{*}=g_{\mathrm{best}}\&CenterDot;[1+r_g]$

In formula: r _gfor obeying the random real number of standardized normal distribution;

STEP7. calculate the variance of particle fitness

if be less than convergence definite value ε, convergence Output rusults.Otherwise carry out STEP4;

The rolling of dynamic dispatching

DG generating prediction and load prediction are to following system operational parameters, estimating within certain period (as 24h).Due to reasons such as the stochastic volatilities of regenerative resource, predicted value and actual motion value can not be in full accord, and the precision of operation plan is along with the passing of time period declines gradually.

For improving the precision of operation plan, the present invention rolls to upgrade to Optimized Operation and proofreaies and correct: (as 30min) at set intervals, upgrades correction according to new predicted value to operation plan.

Claims (1)

1. Based on MCS-PSO close on island many microgrids dynamic dispatching method, it is characterized in that the method comprises the following steps:

   Step 1: in conjunction with reliability and economic index, set up Optimal Operation Model, specifically:

   1, the mathematical description of index:

   1.1, reliability lacks the index λ of amount of power supply by expectation _cPdescribe:

   ${\mathrm{\&lambda;}}_{\mathrm{CP}}=\&Sum;p_{\mathrm{cut}}\&CenterDot;P_{\mathrm{cut}}$

   In formula: P _cutthe load of excision during for supply of electric power deficiency; p _cutfor the probability of this state appearance;

   1.2, economic index λ _tPby because abandoning the generated output that wind/light reduces, cost of electricity-generating and the loss of SER service life of diesel engine generator described:

   ${\mathrm{\&lambda;}}_{\mathrm{TP}}=\&Sum;p_{\mathrm{tune}}\&CenterDot;P_{\mathrm{tune}}$

   In formula: P _tunefor the generated output reducing because abandoning wind/light; p _tunefor the probability of this state appearance;

   ${\mathrm{\&lambda;}}_{\mathrm{OP}}=\&Sum;p_{\mathrm{oil}}\&CenterDot;a\&CenterDot;P_{\mathrm{oil}}$

   In formula: the fuel coefficient that a is diesel engine generator; P _oilfor its generated output; p _oilfor diesel engine generator is in this shape probability of state;

   ${\mathrm{\&lambda;}}_{\mathrm{DOD}}=\&Sum;p_{\mathrm{DOD}}\&CenterDot;\mathrm{\&alpha;}\&CenterDot;{\mathrm{\&eta;}}_{\mathrm{DOD}}$

   ${\mathrm{\&lambda;}}_{\mathrm{rate}}=\&Sum;p_{\mathrm{rate}}\&CenterDot;\mathrm{\&beta;}\&CenterDot;\left|P_S\right|$

   In formula: η _dODfor SER depth of discharge; p _dODfor SER is in η _dODshape probability of state; α is the life consumption coefficient of depth of discharge; | P _s| for SER discharges and recharges watt level; p _ratefor SER is in | P _s| shape probability of state; β is the life consumption coefficient that discharges and recharges watt level;

   2, Optimized Operation target and constraint:

   In the many micro-grid systems of new forms of energy, scheduling strategy need be taken into account reliability and economy, therefore the target of dynamic economic dispatch is to seek optimum interconnection power P _line, make each microgrid under the inner SER of microgrid adjusts, make operating cost and minimization of loss, reach the optimum of on-road efficiency:

   $\begin{array}{cc}\min & \underset{m}{\&Sum;}\underset{n}{\&Sum;}[c_{\mathrm{CP}}\&CenterDot;{\mathrm{\&lambda;}}_{\mathrm{CP}}+c_{\mathrm{TP}}\&CenterDot;{\mathrm{\&lambda;}}_{\mathrm{TP}}+c_{\mathrm{OP}}\&CenterDot;{\mathrm{\&lambda;}}_{\mathrm{OP}}+c_{\mathrm{LOS}}\&CenterDot;({\mathrm{\&lambda;}}_{\mathrm{DOD}}+{\mathrm{\&lambda;}}_{\mathrm{rate}})]\end{array}$

   In formula: the time hop count of m for comprising in the dispatching cycle; N is microgrid number contained in many micro-grid systems; c _cPduring for supply of electric power deficiency, excise the economic loss that specific load causes; λ _cPfor the expectation in microgrid lacks amount of power supply; c _tPfor abandoning the economic loss of wind/light time unit energy output; λ _tPfor abandoning the desired value of wind/light in microgrid; c _oPfor the unit cost of diesel generation fuel; λ _oPfor the fuel consumption desired value of diesel engine generator; c _lOSfor SER life unit cost; λ _dODand λ _ratebe respectively depth of discharge and discharge and recharge the SER life consumption desired value that power causes;

   Flowing out direction taking power from microgrid is positive direction, and when SER charging, power direction is positive direction; Subscript i represents i period; Subscript j represents j microgrid; Subscript l represents l article of circuit; Ignore after network loss, main constraints can be expressed as follows:

   1) power-balance constraint

   $P_{\mathrm{DG}}{|}_i^j-P_{\mathrm{tune}}{|}_i^j+P_{\mathrm{oil}}{|}_i^j=P_L{|}_i^j-P_{\mathrm{cut}}{|}_i^j+P_S{|}_i^j+\underset{k^j}{\&Sum;}{P}_{\mathrm{line}}{|}_i^j,i=\mathrm{1,2,3},...,m;j=\mathrm{1,2,3},...,n$

   In formula: P _dGfor the current maximum of DG can generated output; P _tunefor the generated output reducing because abandoning wind/light; P _oilfor the generated output of diesel engine generator; P _lfor load power; P _cutfor the load excising because of supply of electric power deficiency; P _sfor SER discharges and recharges power; P _linefor interconnection flows out power; k ^jfor the interconnection number being connected with j microgrid;

   2) SER carrying capacity equality constraint

   $\begin{array}{cc}S{|}_i^j=S{|}_{i-1}^j+P_S{|}_i^j\&CenterDot;\mathrm{\&Delta;T},& i=\mathrm{1,2,3},...,m;j=\mathrm{1,2,3},...,n\end{array}$

   In formula: S is the carrying capacity of current period Mo SER; Δ T is the interval duration of period;

   3) SER discharges and recharges power constraint

   $\begin{array}{cc}{\underset{\&OverBar;}{P}}_S{|}^j\&le;P_S{|}_i^j\&le;{\stackrel{\&OverBar;}{P}}_S{|}^j,& i=\mathrm{1,2,3},...,m;j=\mathrm{1,2,3},...,n\end{array}$

   In formula: P _sfor SER in microgrid discharges and recharges power;

   

   with

   

   be respectively electric discharge and the charge power limit of SER;

   4) SER carrying capacity bound constraint

   $\begin{array}{cc}{\mathrm{\&eta;}}_{\lim }\&CenterDot;\stackrel{\&OverBar;}{S}{|}^j\&le;S{|}_i^j\&le;\stackrel{\&OverBar;}{S}{|}^j,& i=\mathrm{1,2,3},...,m;j=\mathrm{1,2,3},...,n\end{array}$

   In formula: η _limfor the restriction of SER depth of discharge;

   

   for the rating load electric weight of SER;

   5) tie-line power transmission constraint

   $\begin{array}{cc}{\underset{\&OverBar;}{P}}_{\mathrm{line}}{|}_i^l\&le;P_{\mathrm{line}}{|}_i^l\&le;{\stackrel{\&OverBar;}{P}}_{\mathrm{line}}{|}_i^l,& i=\mathrm{1,2,3},...,m;l=\mathrm{1,2,3},...,h\end{array}$

   In formula: h represents interconnection sum;

   

   with

   

   represent respectively the Power Limitation of circuit input and output;

   6) diesel engine generator power constraint

   ${\underset{\&OverBar;}{P}}_{\mathrm{oil}}{|}^j\&le;P_{\mathrm{oil}}{|}_j^i\&le;{\stackrel{\&OverBar;}{P}}_{\mathrm{oil}}{|}^j$

   In formula:

   

   with the minimum operation that represents respectively diesel engine generator is exerted oneself and maximum output;

   Step 2: consider the uncertainty of generating, load and equipment operation, ask for target function, specifically:

   1, analyze and in many microgrids cooperation, consider operating uncertainty:

   The uncertainty of 1.1 generating predictions

   While adopting Beta distribution to describe the probability distribution of DG generating, have

   $f\left(P_{\mathrm{DG}}\right)=\frac{P_{\mathrm{DG}}^{\mathrm{\&alpha;}-1}\&CenterDot;{(1-P_{\mathrm{DG}})}^{\mathrm{\&beta;}-1}}{B(\mathrm{\&alpha;},\mathrm{\&beta;})}$

   In formula: P _dGfor the generated output after normalization, fiducial value is its specified generated output; The value of B (α, β) is passed through

   $B(\mathrm{\&alpha;},\mathrm{\&beta;})={\&Integral;}_0^1{P}^{\mathrm{\&alpha;}-1}\&CenterDot;{(1-P)}^{\mathrm{\&beta;}-1}\mathrm{dP}$

   Can be calculated; α and β can obtain by the statistical parameter of generating predicted value and predicated error;

   The uncertainty of 1.2 load predictions

   The load prediction error of electric power system is described with normal distribution, the probability density function of load

   $f\left(P_L\right)=\frac{1}{\sqrt{2\mathrm{\&pi;}}\&CenterDot;{\mathrm{\&sigma;}}_L}{e}^{-\frac{{(P_L-P_{\mathrm{Lf}})}^2}{2{\mathrm{\&sigma;}}_L^2}}$

   In formula: σ _lfor the standard deviation of load prediction error; P _lffor load prediction value; P _lfand P _lfall normalization, fiducial value and P _dGidentical;

   The uncertainty of 1.3 equipment operations

   Distributed power generation, diesel engine generator, SER and interconnection in microgrid, the uncertainty that unplanned stoppage in transit occurs because of relevant device fault, protection action can be described with forced outage rate γ;

   State for equipment u at period i, gets the random real number ξ on [0,1] interval, represents that u is in malfunction if ξ is less than γ, otherwise represents that u normally moves;

   2, Monte Carlo simulation calculates

   The uncertain problem of this scheduling model, the stochastic variable distributing except containing multiple different probability, and between each variable, have nonlinear constrained, therefore adopt Monte Carlo simulation;

   For shortening the algorithm time, do to set as follows: (1), abandon the generation when different with excision load of wind/light; (2) while, only depending on SER to be not enough to guarantee load, just start diesel engine generator; After calculating by sampled analog, acquired results is added up and drawn target desired value;

   Step 3: ask for optimum interconnection power ratio control P by PSO algorithm _line, the step of PSO Optimization Solution is as follows:

   STEP1. tie-line power transmission in microgrid is set to reference direction, set cycle-index N _cir, particle number N _p;

   STEP2. the Position And Velocity of the particle in initialization population:

   

   in the random tie-line power transmission P that produces _lineinitial value, particle rapidity v in [1,1] at random produce;

   STEP3. the individual optimal value of initialization and all optimal values: getting sampling scale is N _mcs, adopt Monte-carlo Simulation Method in step 2, ask for the fitness of each particle; By current P _linevalue is as the individual optimal value p of particle _best, choose optimal value wherein as all optimal value g _best;

   STEP4. basis two formulas renewal v and P below _linevalue:

   $\begin{array}{c}v=w\&CenterDot;v+c_1\&CenterDot;r_u\&CenterDot;(p_{\mathrm{best}}-P_{\mathrm{line}})\\ +c_2\&CenterDot;r_u\&CenterDot;(g_{\mathrm{best}}-P_{\mathrm{line}})\\ P_{\mathrm{line}}=P_{\mathrm{line}}+v\end{array}$

   In formula: w is inertia weight; c ₁, c ₂for the study factor; r _ufor the random real number on [0,1] interval;

   STEP5. adopt Monte-carlo Simulation Method to recalculate particle fitness

   

   upgrade individual optimal value p _bestwith all optimal value g _best;

   STEP6. carry out mutation operation according to following formula, and judgement

   

   whether be less than g _best; If

   

   ? $g_{\mathrm{best}}=g_{\mathrm{best}}^{*};$

   $g_{\mathrm{best}}^{*}=g_{\mathrm{best}}\&CenterDot;[1+r_g]$

   In formula: r _gfor obeying the random real number of standardized normal distribution;

   STEP7. calculate the variance of particle fitness

   

   if

   

   be less than convergence definite value ε, convergence Output rusults; Otherwise carry out STEP4;

   Step 4: the rolling of dynamic dispatching

   In the time period of specifying, according to new predicted value, operation plan is upgraded to correction.

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