CN103971172A - Optimal configuration method for microgrid under condition of grid faults - Google Patents

Abstract

The invention provides an optimal configuration method for a microgrid under the condition of grid faults. The optimal configuration method comprises the steps of defining optimization variables, determining constraint conditions, building an objective function, and carrying out optimal configuration of the microgrid according to the genetic algorithm. According to the optimal configuration method for the microgrid under the condition of the grid faults, more users outside the microgrid are prevented from being influenced by power outages by receiving distributed power sources and loads outside the microgrid. The optimal configuration method is used for supplying power to the microgrid of islands and remote areas.

Description

The Optimal Configuration Method of micro-electrical network under a kind of electric network fault condition

Technical field

The present invention relates to a kind of reconstructing method, be specifically related to the Optimal Configuration Method of micro-electrical network under a kind of electric network fault condition.

Background technology

Island electrical network and rural power grids, in electric system end, are directly connected with power consumer, are the important steps to user's supply of electrical energy and distribution electric energy.But there is the features such as distribute wide, density is little, circuit is long, poor reliability due to island electrical network and rural power grids load, be prone to fault and be difficult for recovery.Along with micro-electrical network and increasing distributed power source access island electrical network and rural power grids, in electric network fault situation, how micro-electrical network makes the more user of load outside micro-electrical network avoid the impact that has a power failure in the power supply of maintenance self load under normal circumstances, is the problem that present stage mainly should consider and solve.

In recent years, intelligent grid is developed country and regional concern extremely, and the U.S., Europe and Japan have carried out positive exploration, and have made some progress.Under this background, China has also proposed the target of " building strong intelligent grid ".For further changing power network development mode, push forward the coordinated development of extra-high voltage and electrical networks at different levels comprehensively, the world-class electrical network of Accelerating The Construction, meet better socio-economic development needs, will be from ensureing energy security, Optimization of Energy Structure, promotion energy-saving and emission-reduction, development low-carbon economy, the requirement of improving service level, the unified strong intelligent grid of all-round construction.

Unified strong intelligent grid is that taking extra-high voltage grid as key rack, electric network coordinations at different levels develop, and have the China national electrical network of informationization, robotization, interactive feature taking unified planning, unified standard, unified construction as principle.Rural power grids are important component parts of China national electrical network, and county town and electricity for rural use have accounted for the more than 52 of Analyzing Total Electricity Consumption, and speed of development is swift and violent, and rural power grids intelligent construction is that intelligent national grid is built indispensable important component part.Accelerating The Construction taking strong as basis, intelligence is as the novel rural power grids of feature, is target and the challenge that new period rural power work faces, be also to realize the inevitable requirement that rural power grids and electric network coordination at different levels develop.

The backward phenomenons such as at present, the rural power grids of China still exist grid structure weakness, power supply reliability is low, loss is high, the quality of power supply is poor, equipment is backward, automatization level is low, electricity price level is higher, transformation funds breach is larger.Building Socialist New, villages and small townsization are built and the enforcement of the policy such as " household electrical appliances are gone to the countryside " causes rural electricity consumption load rapid growth, and existing rural power grids power supply capacity and condition of power supply far can not meet the growing need for electricity in rural area.

Summary of the invention

In order to overcome above-mentioned the deficiencies in the prior art, the invention provides the Optimal Configuration Method of micro-electrical network under a kind of electric network fault condition, by receiving distributed power source and the load outside micro-electrical network, make the more user outside micro-electrical network avoid the impact having a power failure, be applied to the micro-electrical network in island and remote districts power supply.

In order to realize foregoing invention object, the present invention takes following technical scheme:

The Optimal Configuration Method that the invention provides micro-electrical network under a kind of electric network fault condition, said method comprising the steps of:

Step 1: definition optimized variable;

Step 2: clear and definite constraint condition;

Step 3: set up objective function;

Step 4: by genetic algorithm, carry out distributing rationally of micro-electrical network.

In described step 1, select to receive the photovoltaic array outside micro-electrical network to count N _pV, wind turbine several N that organize a performance _wT, distribute the running time T of rear micro-electrical network rationally _op, receive number of users N _fas optimized variable X, and do to give a definition:

In described step 2, constraint condition comprises battery charging and discharging constraint, power-balance constraint and micro-electric network source units limits.

Described battery charging and discharging constraint comprises following two constraints:

A. for guaranteeing life-span and the security of operation of battery, when the residual capacity of battery reaches battery max cap., energy storage controller control battery stops charging; In the time that the residual capacity of battery reaches battery minimum state of charge, energy storage controller control battery stops electric discharge, that is:

S _min≤S _SOC≤S _max（2）

Wherein, S _sOCfor the residual capacity of battery, S _masfor battery max cap., S _minfor battery minimum state of charge, and S _max=100%, S _max=20%;

B. the battery electric weight that charges and discharge hourly can not exceed 20% of battery discharge electric weight, that is:

$\left\{\begin{array}{c}{P}_{+}\≤{0.2}^{E_{\mathrm{bat}}}/\mathrm{\Δt}\\ P_{-}\≤{0.2}^{E_{\mathrm{bat}}}/\mathrm{\Δt}\end{array}\right.---\left(3\right)$

Wherein, Δ t is 1h, P ₊and P _-be respectively the power that discharges and recharges in unit hour, E _batfor battery electric quantity;

Described power-balance constraint is as follows:

$P_{\mathrm{PV}}^t+P_{\mathrm{WT}}^t+P_{\mathrm{BAT}}^t\≥P_d^t+P_{\mathrm{loss}}^t---\left(4\right)$

Wherein, with be respectively the photovoltaic array of t period and the active power of blower fan output, with be respectively active power, load consumed power and the line loss of the output of t period battery.

Described micro-electric network source units limits is as follows:

$P_i^{g\min }\≤P_i^g\≤P_i^{g\max }---\left(5\right)$

Wherein, be i meritorious the exerting oneself of micro-electric network source, with be respectively i meritorious lower limit and the upper limit of exerting oneself of micro-electric network source.

Described step 3 comprises the following steps:

Step 3-1: according to the power supply reliability R maximum of the micro-electrical network after distributing rationally, set up following objective function:

maxR＝R _LPSP（6）

Wherein, R _lPSPfor short of electricity probability, it is micro-electric network reliability index of islet operation;

Step 3-2: suppose that blower fan and the active power of photovoltaic array output per hour and user's power consumption are constant, the micro-electrical network after calculation optimization configuration is in running time T _opinterior short of electricity probability R _lPSP.

Described step 3-2 comprises the following steps:

Step 3-2-1: calculate t hour blower fan and photovoltaic array gross generation;

T hour blower fan and photovoltaic array gross generation E _{r, t}represent, have:

E _R，t＝N _WTE _WT，t+N _PVE _PV，t（7）

Wherein, E _{wT, t}and E _{pV, t}be respectively blower fan and the photovoltaic array generated energy of t hour;

Step 3-2-2: the capacity E that calculates t hour battery _{bat, t};

1) as t hour blower fan and photovoltaic array gross generation E _{r, t}while being greater than user power utilization amount, battery charging; Have:

E _Bat，t≈E _Bat，t-1(1-σ)+(E _R，t-E _load，t/η _inv)η _Bat（8）

Wherein, E _{load, t}be the user power utilization amount of t hour, E _{bat, t-1}be the capacity of t-1 hour battery, σ is battery discharge coefficient, η _invfor transducer effciency, η _batfor battery charge efficiency;

2) as t hour blower fan and photovoltaic array gross generation E _{r, t}while being less than user power utilization amount, battery discharge; Have:

E _Bat，t≈E _Bat，t-1(1-σ)-(E _R，t-E _load，t/η _inv) （9）

Step 3-2-3: in the time that all micro-electric network sources and battery remaining power still can not meet user power utilization amount demand, the short of electricity amount E of t hour _{lPS, t}be expressed as:

Step 3-2-4: the micro-electrical network after distributing rationally is in running time T _opinterior short of electricity probability R _lPSPbe expressed as:

$R_{\mathrm{LPSP}}={\mathrm{\Σ}}_{t=1}^{T_{\mathrm{op}}}{E}_{\mathrm{LPS},t}/{\mathrm{\Σ}}_{t=1}^{T_{\mathrm{op}}}{E}_{\mathrm{load},t}---\left(11\right).$

Described step 4 comprises the following steps:

Step 4-1: be that the constraint condition of 24 hours forms the initial population that number of individuals is 300～600 according to receiving maximum photovoltaic array number outside micro-electrical network, maximum wind unit number of units, the maximum maximum working time of receiving number of users and distributing rear micro-electrical network rationally, and each genes of individuals information is by receiving the photovoltaic array outside micro-electrical network to count N _pV, wind turbine several N that organize a performance _wT, receive number of users N _fand distribute the running time T of rear micro-electrical network rationally _opcomposition;

Step 4-2: calculating target function and fitness value, according to the gene information of each individuality, calculate the short of electricity probability of each individuality, and then known target function value is the fitness value of each individuality;

Step 4-3: obtaining after the adaptive value of each individuality in colony, colony is chosen in mating pond by wheel disc method, then from mating pond, get two individualities hybridizes according to probability or makes a variation and produce interim new colony at every turn, in again will interim new colony, each individuality be according to probability selection or directly copy to the next generation, or individuality corresponding to previous generation is at war with, and optimum individual in every generation is preserved, each generation after new colony, in Ruo Xin colony, there is the individuality more better than the adaptive value of existing optimum individual, replace current optimum individual with it;

Step 4-4: output optimum results, reaches after iterations 150～300 times the scheme of the power supply reliability maximum of the micro-electrical network after output is distributed rationally.

Compared with prior art, beneficial effect of the present invention is:

The Optimal Configuration Method of micro-electrical network under electric network fault condition provided by the invention, can be under island electrical network and rural power grids failure condition, micro-electrical network can be by receiving distributed power source and the load outside micro-electrical network, make the outer more user of micro-electrical network avoid the impact that has a power failure, in addition, this programme is target by again distributing the power supply reliability of rear micro-electrical network rationally, consider blower fan, photovoltaic is exerted oneself and the temporal characteristics of the inside and outside load of micro-electrical network and discharging and recharging of micro-electrical network accumulator, power-balance and the micro-power supply constraint condition such as exert oneself, make prioritization scheme have more security and confidence level.

Brief description of the drawings

Fig. 1 is distributed generation system schematic diagram in the embodiment of the present invention;

Fig. 2 adopts genetic algorithm to carry out the process flow diagram of distributing rationally of micro-electrical network in the embodiment of the present invention.

Embodiment

Below in conjunction with accompanying drawing, the present invention is described in further detail.

The Optimal Configuration Method that the invention provides micro-electrical network under a kind of electric network fault condition, said method comprising the steps of:

Step 1: definition optimized variable;

Step 2: clear and definite constraint condition;

Step 3: set up objective function;

Step 4: by genetic algorithm, carry out distributing rationally of micro-electrical network.

In described step 1, the distributed generation system that the present invention adopts, as Fig. 1, selects to receive the photovoltaic array outside micro-electrical network to count N _pV, wind turbine several N that organize a performance _wT, distribute the running time T of rear micro-electrical network rationally _op, receive number of users N _fas optimized variable X, and do to give a definition:

In described step 2, constraint condition comprises battery charging and discharging constraint, power-balance constraint and micro-electric network source units limits.

Described battery charging and discharging constraint comprises following two constraints:

A. for guaranteeing life-span and the security of operation of battery, when the residual capacity of battery reaches battery max cap., energy storage controller control battery stops charging; In the time that the residual capacity of battery reaches battery minimum state of charge, energy storage controller control battery stops electric discharge, that is:

S _min≤S _sOC≤S _max（2）

Wherein, S _sOCfor the residual capacity of battery, S _maxfor battery max cap., S _minfor battery minimum state of charge, and

S _max＝100％，S _max＝20％；

B. the battery electric weight that charges and discharge hourly can not exceed 20% of battery discharge electric weight, that is:

$\left\{\begin{array}{c}{P}_{+}\≤{0.2}^{E_{\mathrm{bat}}}/\mathrm{\Δt}\\ P_{-}\≤{0.2}^{E_{\mathrm{bat}}}/\mathrm{\Δt}\end{array}\right.---\left(3\right)$

Wherein, Δ t is 1h, O ₊and P _-be respectively the power that discharges and recharges in unit hour, E _batfor battery electric quantity;

Described power-balance constraint is as follows:

$P_{\mathrm{PV}}^t+P_{\mathrm{WT}}^t+P_{\mathrm{BAT}}^t\≥P_d^t+P_{\mathrm{loss}}^t---\left(4\right)$

Wherein, with be respectively the photovoltaic array of t period and the active power of blower fan output, with be respectively active power, load consumed power and the line loss of the output of t period battery.

Described micro-electric network source units limits is as follows:

$P_i^{g\min }\≤P_i^g\≤P_i^{g\max }---\left(5\right)$

Wherein, be i meritorious the exerting oneself of micro-electric network source, with be respectively i meritorious lower limit and the upper limit of exerting oneself of micro-electric network source.

Described step 3 comprises the following steps:

Step 3-1: according to the power supply reliability R maximum of the micro-electrical network after distributing rationally, set up following objective function:

maxR＝R _LPSP（6）

Wherein, R _lPSPfor short of electricity probability, it is micro-electric network reliability index of islet operation;

Step 3-2: suppose that blower fan and the active power of photovoltaic array output per hour and user's power consumption are constant, the micro-electrical network after calculation optimization configuration is in running time T _opinterior short of electricity probability R _lPSP.

Described step 3-2 comprises the following steps:

Step 3-2-1: calculate t hour blower fan and photovoltaic array gross generation;

T hour blower fan and photovoltaic array gross generation E _{r, t}represent, have:

E _R，t＝N _WTE _WT，t+N _PVE _PV，t（7）

Wherein, E _{wT, t}and E _{pV, t}be respectively blower fan and the photovoltaic array generated energy of t hour;

Step 3-2-2: the capacity E that calculates t hour battery _{bat, t};

1) as t hour blower fan and photovoltaic array gross generation E _{r, t}while being greater than user power utilization amount, battery charging; Have:

E _Bat，t≈E _Bat，t-1(1-σ)+(E _R，t-E _load，t/η _inv)η _Bat（8）

Wherein, E _{load, t}be the user power utilization amount of t hour, E _{bat, t-1}be the capacity of t-1 hour battery, σ is battery discharge coefficient, η _invfor transducer effciency, η _batfor battery charge efficiency;

2) as t hour blower fan and photovoltaic array gross generation E _{r, t}while being less than user power utilization amount, battery discharge; Have:

E _Bat，t≈E _Bat，t-1(1-σ)-(E _R，t-E _load，t/η _inv) （9）

Step 3-2-3: in the time that all micro-electric network sources and battery remaining power still can not meet user power utilization amount demand, the short of electricity amount E of t hour _{lPS, T}be expressed as:

Step 3-2-4: the micro-electrical network after distributing rationally is in running time T _opinterior short of electricity probability R _lPSPbe expressed as:

$R_{\mathrm{LPSP}}={\mathrm{\Σ}}_{t=1}^{T_{\mathrm{op}}}{E}_{\mathrm{LPS},t}/{\mathrm{\Σ}}_{t=1}^{T_{\mathrm{op}}}{E}_{\mathrm{load},t}---\left(11\right).$

As Fig. 2, described step 4 comprises the following steps:

Step 4-1: be that the constraint condition of 24 hours forms the initial population that number of individuals is 300～600 according to receiving maximum photovoltaic array number outside micro-electrical network, maximum wind unit number of units, the maximum maximum working time of receiving number of users and distributing rear micro-electrical network rationally, and each genes of individuals information is by receiving the photovoltaic array outside micro-electrical network to count N _pV, wind turbine several N that organize a performance _wT, receive number of users N _fand distribute the running time T of rear micro-electrical network rationally _opcomposition;

Step 4-2: calculating target function and fitness value, according to the gene information of each individuality, calculate the short of electricity probability of each individuality, and then known target function value is the fitness value of each individuality;

Step 4-3: obtaining after the adaptive value of each individuality in colony, colony is chosen in mating pond by wheel disc method, then from mating pond, get two individualities hybridizes according to probability or makes a variation and produce interim new colony at every turn, in again will interim new colony, each individuality be according to probability selection or directly copy to the next generation, or individuality corresponding to previous generation is at war with, and optimum individual in every generation is preserved, each generation after new colony, in Ruo Xin colony, there is the individuality more better than the adaptive value of existing optimum individual, replace current optimum individual with it;

Step 4-4: output optimum results, reaches after iterations 150～300 times the scheme of the power supply reliability maximum of the micro-electrical network after output is distributed rationally.

Finally should be noted that: above embodiment is only in order to illustrate that technical scheme of the present invention is not intended to limit, although the present invention is had been described in detail with reference to above-described embodiment, those of ordinary skill in the field are to be understood that: still can modify or be equal to replacement the specific embodiment of the present invention, and do not depart from any amendment of spirit and scope of the invention or be equal to replacement, it all should be encompassed in the middle of claim scope of the present invention.

Claims (9)

1. an Optimal Configuration Method for micro-electrical network under electric network fault condition, is characterized in that: said method comprising the steps of:

Step 1: definition optimized variable;

Step 2: clear and definite constraint condition;

Step 3: set up objective function;

Step 4: by genetic algorithm, carry out distributing rationally of micro-electrical network.

2. the Optimal Configuration Method of micro-electrical network under electric network fault condition according to claim 1, is characterized in that: in described step 1, select to receive the photovoltaic array outside micro-electrical network to count N _pV, wind turbine several N that organize a performance _wT, distribute the running time T of rear micro-electrical network rationally _op, receive number of users N _fas optimized variable X, and do to give a definition:

3. the Optimal Configuration Method of micro-electrical network under electric network fault condition according to claim 1, is characterized in that: in described step 2, constraint condition comprises battery charging and discharging constraint, power-balance constraint and micro-electric network source units limits.

4. the Optimal Configuration Method of micro-electrical network under electric network fault condition according to claim 3, is characterized in that: described battery charging and discharging constraint comprises following two constraints:

A. for guaranteeing life-span and the security of operation of battery, when the residual capacity of battery reaches battery max cap., energy storage controller control battery stops charging; In the time that the residual capacity of battery reaches battery minimum state of charge, energy storage controller control battery stops electric discharge, that is:

S _min≤S _SOC≤S _max（2）

Wherein, S _sOCfor the residual capacity of battery, S _masfor battery max cap., S _minfor battery minimum state of charge, and

S _max＝100％，S _max＝20％；

B. the battery electric weight that charges and discharge hourly can not exceed 20% of battery discharge electric weight, that is:

Wherein, Δ t is 1h, P ₊and P _-be respectively the power that discharges and recharges in unit hour, E _batfor battery electric quantity.

5. the Optimal Configuration Method of micro-electrical network under electric network fault condition according to claim 3, is characterized in that: described power-balance constraint is as follows:

Wherein, with be respectively the photovoltaic array of t period and the active power of blower fan output, with be respectively active power, load consumed power and the line loss of the output of t period battery.

6. the Optimal Configuration Method of micro-electrical network under electric network fault condition according to claim 3, is characterized in that: described micro-electric network source units limits is as follows:

Wherein, be i meritorious the exerting oneself of micro-electric network source, with be respectively i meritorious lower limit and the upper limit of exerting oneself of micro-electric network source.

7. the Optimal Configuration Method of micro-electrical network under electric network fault condition according to claim 1, is characterized in that: described step 3 comprises the following steps:

Step 3-1: according to the power supply reliability R maximum of the micro-electrical network after distributing rationally, set up following objective function:

maxR＝R _LPSP（6）

Wherein, R _lPSPfor short of electricity probability, it is micro-electric network reliability index of islet operation;

Step 3-2: suppose that blower fan and the active power of photovoltaic array output per hour and user's power consumption are constant, the micro-electrical network after calculation optimization configuration is in running time T _opinterior short of electricity probability R _lPSP.

8. the Optimal Configuration Method of micro-electrical network under electric network fault condition according to claim 7, is characterized in that: described step 3-2 comprises the following steps:

Step 3-2-1: calculate t hour blower fan and photovoltaic array gross generation;

T hour blower fan and photovoltaic array gross generation E _{r, t}represent, have:

E _R，t＝N _WTE _WT，t+N _PVE _PV，t（7）

Wherein, E _{wT, t}and E _{pV, t}be respectively blower fan and the photovoltaic array generated energy of t hour;

Step 3-2-2: the capacity E that calculates t hour battery _{bat, t};

1) as t hour blower fan and photovoltaic array gross generation E _{r, T}while being greater than user power utilization amount, battery charging; Have:

E _Bat，t≈E _Bat，t- ₁(1-σ)+(E _R，t-E _load，t/η _inv)η _Bat（8）

Wherein, E _{load, t}be the user power utilization amount of t hour, E _{bat, t-1}be the capacity of t-1 hour battery, σ is battery discharge coefficient, η _invfor transducer effciency, η _batfor battery charge efficiency;

2) as t hour blower fan and photovoltaic array gross generation E _{r, t}while being less than user power utilization amount, battery discharge; Have:

E _Bat，t≈E _Bat，t-1(1-σ)-(E _R，t-E _load，t/η _inv) （9）

Step 3-2-3: in the time that all micro-electric network sources and battery remaining power still can not meet user power utilization amount demand, the short of electricity amount E of t hour _{lPS, t}be expressed as:

Step 3-2-4: the micro-electrical network after distributing rationally is in running time T _opinterior short of electricity probability R _lPSPbe expressed as:

9. the Optimal Configuration Method of micro-electrical network under electric network fault condition according to claim 1, is characterized in that: described step 4 comprises the following steps:

Step 4-1: be that the constraint condition of 24 hours forms the initial population that number of individuals is 300～600 according to receiving maximum photovoltaic array number outside micro-electrical network, maximum wind unit number of units, the maximum maximum working time of receiving number of users and distributing rear micro-electrical network rationally, and each genes of individuals information is by receiving the photovoltaic array outside micro-electrical network to count N _pV, wind turbine several N that organize a performance _wT, receive number of users N _fand distribute the running time T of rear micro-electrical network rationally _opcomposition;

Step 4-2: calculating target function and fitness value, according to the gene information of each individuality, calculate the short of electricity probability of each individuality, and then known target function value is the fitness value of each individuality;

Step 4-3: obtaining after the adaptive value of each individuality in colony, colony is chosen in mating pond by wheel disc method, then from mating pond, get two individualities hybridizes according to probability or makes a variation and produce interim new colony at every turn, in again will interim new colony, each individuality be according to probability selection or directly copy to the next generation, or individuality corresponding to previous generation is at war with, and optimum individual in every generation is preserved, each generation after new colony, in Ruo Xin colony, there is the individuality more better than the adaptive value of existing optimum individual, replace current optimum individual with it;

Step 4-4: output optimum results, reaches after iterations 150～300 times the scheme of the power supply reliability maximum of the micro-electrical network after output is distributed rationally.