CN103490410B - Micro-grid planning and capacity allocation method based on multi-objective optimization - Google Patents

Abstract

The invention discloses a micro-grid planning and capacity allocation method based on multi-objective optimization. The method is characterized in that the method comprises the steps of (1) setting the operation mode of a micro-grid, wherein the operation mode of the micro-grid comprises an independent mode and a grid-connected mode; (2) inputting basic data which comprise the system condition, electrovalence parameters, load parameters, photovoltaic parameters, wind electricity parameters and storage battery parameters; (3) pre-processing the basic data; (4) optimizing a distributed power supply and an energy storage system. According to the micro-grid planning and capacity allocation method, distributed power supply capacity and energy storage system capacity in micro-grid planning can be solved jointly, and optimizing allocation can be carried out at the same time.

Description

A kind of micro-Electric Power Network Planning and capacity collocation method based on multiple-objection optimization

Technical field

The present invention relates to a kind of micro-Electric Power Network Planning and capacity collocation method, belong to micro-electric power network technique field.

Background technology

The proposition of micro-electric power network technique and distributed power generation (Distributed Generation, DG) application of technology is closely related with development, compared with relying on the conventional power source of remote conveying distribution, distributed generation technology has many-sided advantages such as the high and environmental friendliness of energy form variation, efficiency of energy utilization.Distributed generation system infield feature flexible, that disperse has adapted to dispersion electricity needs and resource distribution admirably, delay the required huge investment that upgrades of defeated, power distribution network, simultaneously, it and large the electrical network each other power supply reliability that also makes for subsequent use are improved, and efficiently solve many potential problems of large-scale centralized electrical network.But distributed generation system access electrical network also exists problems, as high in distributed power source unit cost of access, control is difficult etc.

For coordinating the contradiction of electrical network and distributed power source, solving a large amount of positions disperses, various informative, the simple grid-connected impact that electrical network and user are caused of the different distributed power source of characteristic, reducing it accesses the quality of power supply, system protection, the adverse effect that system operation etc. brings, fully excavating distributed power source is value and the benefit that electrical network and user bring, R.H.Lasseter delivered Microgrids mono-literary composition in 2002 on IEEE Power Engineering Society Winter Meeting, the concept of micro-electrical network has been proposed first, and provide the frame structure of micro-electrical network.Micro-electrical network, from systematic point of view, by integrated to distributed power source, load, energy storage device and control device etc., forms a controlled unit, simultaneously to user's supply of electrical energy and heat energy.Both can with the operation of large grid network, also can or need at electric network fault time with major network disconnection isolated operation.The contradiction between large electrical network and distributed power source has effectively been coordinated in the proposition of micro-electrical network concept, has fully excavated value and benefit that distributed energy brings for electrical network and user.

The diversity of distributed power source and the uncertainty of exerting oneself thereof, make planning and the capacity configuration problem of micro-electrical network very complicated, and the capacity configuration of distributed power source and energy-storage system is subject to the impact of many-sided conditions such as available energy resources (wind energy, solar energy etc.), load importance, power supply reliability, renewable energy utilization rate and economy.Therefore, how realizing in micro-electrical network that the capacity of distributed power source and energy-storage system distributes rationally is the problem that need solve in micro-Electric Power Network Planning.

The retrieval of existing documents and materials and patent description is found, the paper " micro-Study on Power Grid Planning method and software " (the 32nd the 25th phase of volume of Proceedings of the CSEE) that the people such as Xiao Jun deliver has been realized independent micro-electrical network and the distributed power source of the micro-electrical network of grid type and the capacity collocation method of energy-storage system, and has developed a set of micro-Study on Power Grid Planning software; The paper " considering the micro-network optimization configuration of self of different control strategies " (Automation of Electric Systems the 37th volume o. 11th) that the people such as Chen Jian deliver has been set up the micro-network optimization allocation models of self wind-solar-diesel storage based on different control strategies, take power supply economics and the feature of environmental protection as optimization aim, seek the capacity configuration scheme under Optimal Control Strategy; The paper " based on the micro-network optimization method for designing of multiobject independence " (the 36th the 17th phase of volume of Automation of Electric Systems) that the people such as Liu Mengxuan deliver is for the independent micro-electrical network of wind-solar-diesel storage, propose to consider the multi-objective optimization design of power model of life cycle management net charge, renewable energy utilization rate and pollutant emission level, under set control strategy, realized the capacity configuration of distributed power source and energy-storage system; Paper that the people such as Ma Xiyuan deliver " adopt improve look for food wind/light/storage of algorithm of bacterium mix micro-electric network source and distribute rationally " (the 31st the 25th phase of volume of Proceedings of the CSEE) has proposed to improve bacterium and has looked for food algorithm in the application of the capacity configuration of distributed power source and energy-storage system, has realized the economy configuring.

According to the introduction of above-mentioned technical background, existing article stresses respectively different angles and carries out theoretical research, has obtained a series of achievement in research, but still in various degree there is following problem:

(1) existing micro-Electric Power Network Planning and capacity collocation method are often distributed distributed power source capacity with energy storage system capacity rationally and are distributed rationally as two and independently optimize computational process.

In the time carrying out the optimization of distributed power source capacity, energy storage system capacity configures as known conditions; In the time carrying out energy storage system capacity optimization, distributed power source capacity is as known conditions.Such computational methods, the impact of the control strategy of having simplified micro-electrical network in whole year operation simulation process on distributed power source and energy storage device, economic benefit that energy storage device peak load shifting brings, energy storage device whole year discharge and recharge number of times, distributed power source is abandoned wind and is abandoned the situations such as light.Therefore, the optimization of distributed power source capacity and energy storage system capacity optimization independently being optimized to computational process as two, may to cause result of calculation be not optimal solution.

(2) existing micro-Electric Power Network Planning and capacity collocation method are mostly for independent micro-electrical network, research to the micro-electrical network of grid type is little, in the document of the micro-electrical network of a few studies grid type, do not consider the receiving ability of outside large electrical network to distributed power source capacity in micro-electrical network yet.

Existing document great majority are planning and capacity configuration problems of the independent micro-electrical network of research, and in this case, micro-electrical network does not have Power Exchange with the interconnection of outside large electrical network, has simplified simulation model.

The planning of the micro-electrical network of existing a few studies grid type and the document of capacity configuration problem, also simplified the receiving ability of outside large electrical network to distributed power source capacity in micro-electrical network.

(3) existing micro-Electric Power Network Planning and capacity collocation method majority stress simple target optimization, and using other condition as constraint, finally obtain one group of optimal solution.

Existing document majority has stronger specific aim, for micro-electrical network or certain ad hoc hypothesis condition of certain structure, as need calculate other micro-electric network composition or revise assumed condition, needs corresponding adjustment algorithm.Therefore, there is to a certain extent limitation.

(4) existing micro-Electric Power Network Planning and capacity collocation method research lay particular emphasis on algorithm and theoretical research, are difficult to directly apply to engineering reality.

Summary of the invention

Technical problem to be solved by this invention is: the method for proposition, can consider at the same time, to combining and solve in the distributed power source capacity in micro-Electric Power Network Planning and energy storage system capacity situation, to be optimized configuration simultaneously; Both independent micro-Electric Power Network Planning and capacity configuration problem can have been solved, also can solve planning and the capacity configuration problem of the micro-electrical network of grid type, and in the planning and capacity collocation method of the micro-electrical network of grid type, the computational methods of the receiving ability of the large electrical network of actual engineering design peripheral to distributed power source capacity in micro-electrical network are provided in detail, incorporation engineering reality more; Adopt multiple constraint, multiobject algorithm, can obtain the many groups optimal solution under different condition, for designer's reference; Method of the present invention is to propose on the basis of the wind-powered electricity generation through a large amount of, photovoltaic distributed power station and connecting system design thereof, focuses on engineer's design experiences, and incorporation engineering reality more, can be used as aid decision-making system and carry out actual engineering design.

For solving the problems of the technologies described above, the invention provides a kind of micro-Electric Power Network Planning and capacity collocation method based on multiple-objection optimization, it is characterized in that, comprise the following steps:

One) set micro-operation of power networks pattern: the operational mode of micro-electrical network comprises independent micro-electrical network and the micro-electrical network of grid type;

Two) basic data input: basic data input comprises system condition, electric price parameter, load parameter, photovoltaic parameter, wind-powered electricity generation parameter and accumulator parameter;

Micro-Electric Power Network Planning and capacity collocation method based on multiple-objection optimization according to claim 1, is characterized in that: described basic data specifically comprises:

(1) system condition input: only for the micro-electrical network of grid type, comprise the grid-connected electric pressure of micro-electrical network, upper level transformer capacity, interconnection wire type and length, PCC point capacity of short circuit;

(2) electric price parameter input: for independent micro-electrical network, electric price parameter stoichiometric point is arranged on distributed power station outlet side, electric price parameter input comprises all kinds distributed power source generating electricity price; For the micro-electrical network of grid type, be divided into two kinds of electricity price metering methods according to micro-power grid operation pattern, being respectively stoichiometric point is arranged on the interconnection that distributed power station outlet side and stoichiometric point be arranged on micro-electrical network and outside large electrical network, electric price parameter is input as purchase electricity price and sale of electricity electricity price, comprises simultaneously and distinguishes time-of-use tariffs and do not distinguish time-of-use tariffs;

(3) load parameter input: the power and the average short trouble time of electrical network that comprise annual load, sensitive load;

(4) photovoltaic parameter input: the electric parameter, cost parameter, capacity limit and the light resources parameter that comprise photovoltaic module;

(5) wind-powered electricity generation parameter input: the electric parameter, cost parameter, capacity limit and the wind-resources parameter that comprise blower fan;

(6) accumulator parameter input: the electric parameter and the cost parameter that comprise storage battery;

(7) other parameter input, comprises maximum Load Probability, minimum renewable energy utilization rate, the standard year interest rate of losing,

Wherein:

Three) basic data preliminary treatment:

(1) system restriction condition:

For the micro-electrical network of grid type:

1) interconnection transmission power limit constraint, interconnection is the grid-connected circuit of the outside large electrical network of micro-electrical network access:

P-P _lmin< P _linemaxformula (1)

In formula, P is the active power sum of various distributed power sources in micro-electrical network, P _lminfor load minimum power, P _linemaxfor interconnection limit transmission power, wherein, interconnection limit transmission power P _linemaxdetermined by interconnection wire type;

2) PCC point short circuit current retrains with the ratio of micro-interconnecting ties maximum normal current:

$\frac{I_k}{I_e}\&GreaterEqual;10$ Formula (2)

In formula, I _kfor system PCC point short circuit current, I _efor the rated current sum of various distributed power sources in micro-electrical network; Wherein, PCC point short circuit current is calculated by capacity of short circuit;

3) voltage loss of transmission line constraint:

formula (3)

In formula, the voltage loss that Δ U% is transmission line, U is grid-connected voltage, R, X are respectively resistance and the reactance of interconnection,

for photovoltaic plant output voltage and current and phase difference, power factor is got the active power sum that 0.98, P is various distributed power sources in micro-electrical network, and l is interconnection length;

4) voltage fluctuation constraint:

$U_{d\max }\%=\frac{\mathrm{PR}+\mathrm{QX}}{U}\&times;100\%\&le;U_{d\_\mathrm{limite}}\%$ Formula (4)

In formula, U _dmax% is the voltage fluctuation that microgrid causes at PCC point, and P is the active power sum of various distributed power sources in micro-electrical network, and Q is the reactive power sum of various distributed power sources in micro-electrical network, and R, X are respectively resistance and the reactance of interconnection, and U is grid-connected voltage, U _{d_limite}% is the voltage fluctuation limit value that PCC is ordered;

(2) an operation year photovoltaic power curve, year blower fan power curve of going into operation:

Photovoltaic power curve, year blower fan power curve of going into operation are according to the mathematics model of stable state of light resources, wind-resources situation and photovoltaic, blower fan, calculate by wind-power transfer, light-power transfer, relevant achievement in research is more, belong to prior art, the paper " photovoltaic cell Utility Simulation Model and photovoltaic generating system emulation " (electric power network technique the 34th volume o. 11th) of delivering as people such as Jiao Yang; The paper " speed change is determined the full blast speed power control of blade wind power generation unit " (the 32nd the 30th phase of volume of Proceedings of the CSEE) that the people such as Chen Jie deliver;

(3) storage battery preliminary treatment:

1) input electric price parameter and load data;

2) if having sensitive load power and the numerical value of average short trouble time of electrical network in input data,, for ensureing the reliable power supply of sensitive load, battery capacity is retrained:

Ensure the average short trouble time type of battery capacity=sensitive load × electrical network (5) of sensitive load

3) if purchase electricity price or sale of electricity electricity price exist time-of-use tariffs, storage battery plays the effect of peak load shifting:

Wherein, Л is coefficient of colligation to unit stored energy capacitance peak load shifting economic benefit=unit capacity × (peak electricity price-paddy electricity price) × 365 × Л formula (6);

4) after storage battery preliminary treatment, enter distributed power source and energy-storage system optimization system is optimized;

Four) distributed power source and energy-storage system optimization: set up Optimized model, solve by optimized algorithm, Optimized model comprises target function and constraints, and constraints comprises system restriction, storage battery constraint, reliability constraint and the constraint of renewable energy utilization rate

(1) target function:

Selecting micro-electrical network total cost minimum is target function, and its expression formula is:

Minf=min (C _c+ C _oM-C _gs+ C _gp) formula (7)

In formula, f is target function, C _cfor the initial outlay cost of micro-electrical network, C _oMfor operation maintenance and the displacement total cost present worth of system, C _gsfor the total revenue present worth of micro-electrical network large electrical network sale of electricity to outside, C _gpfor power purchase total cost present worth;

(2) storage battery constraint:

The state-of-charge of storage battery need meet:

S _OCmin≤S _OC≤S _OCmax （8）

In formula, S _oCfor state-of-charge, S _oCmin, S _oCmaxbe respectively lower limit, the upper limit of storage battery charge state,

Charge rate, the discharge rate constraint of storage battery need to meet:

$\left\{\begin{array}{c}{r}_{\mathrm{ch}}\&le;r_{\mathrm{ch}\max }\\ r_{\mathrm{dch}}\&le;r_{\mathrm{dch}\max }\end{array}\right.---\left(9\right)$

In formula, r _ch, r _dchbe respectively charge rate, the discharge rate of storage battery; r _chmax, r _dchmaxbe respectively charge rate restriction and discharge rate restriction;

The charging and discharging currents constraint of storage battery needs to meet:

$\left\{\begin{array}{c}{I}_{\mathrm{ch}}\&le;I_{\mathrm{ch}\max }\\ I_{\mathrm{dch}}\&le;I_{\mathrm{dch}\max }\end{array}\right.---\left(10\right)$

In formula, I _ch, I _dchbe respectively charging current, the discharging current of storage battery; I _chmax, I _dchmaxbe respectively charge-current limit and discharging current restriction;

The the discharging and recharging power and need meet of storage battery:

$\left\{\begin{array}{c}0\&le;P_{\mathrm{ch}}\&le;P_{\mathrm{ch}\max }\\ 0\&le;P_{\mathrm{dch}}\&le;P_{\mathrm{dch}\max }\end{array}\right.---\left(11\right)$

In formula, P _ch, P _dchbe respectively charge power, the discharge power of storage battery; P _chmax, P _dchmaxbe respectively the charge power upper limit, the discharge power upper limit;

Within a dispatching cycle, the charge and discharge cycles number of times of storage battery meets:

N _C≤N _Cmax （12）

In formula, N _cfor the charge and discharge cycles number of times of storage battery, N _cmaxfor the accumulator cell charging and discharging cycle-index upper limit;

(3) system restriction: system restriction refers to the receiving ability of outside large electrical network to micro-electrical network distributed power source, comprise voltage loss constraint and the voltage fluctuation constraint of the constraint of the interconnection transmission power limit, short-circuit current ratio constraint, transmission line, particular content and step 3) in the pretreated system restriction condition of basic data identical, formula (1)～formula (4) has formed system restriction;

(4) reliability constraint: adopt the mistake Load Probability of micro-electrical network as reliability constraint index, for independent micro-electrical network, in the time that the power supply capacity of distributed power source and energy storage can not meet all workload demands, for ensureing micro-power network safety operation, allow the non-sensitive load of cut-out

f _LPSP≤λ _max （13）

In formula, f _lPSPfor the mistake Load Probability of load, λ _maxfor the maximum in initial conditions is lost Load Probability,

F _lPSPcalculated by following formula:

$f_{\mathrm{LPSP}}=\frac{\underset{i=1}{\overset{T}{\mathrm{\&Sigma;}}}[P_{\mathrm{LPSP}}\left(t_i\right)\&times;\mathrm{\&Delta;t}]}{\underset{i=1}{\overset{T}{\mathrm{\&Sigma;}}}[P_L\left(t_i\right)\&times;\mathrm{\&Delta;t}]}---\left(14\right)$

In formula, P _lPSP(t _i), P _l(t _i) be respectively t _ithe mistake load power in moment and all load power, Δ t, for calculating step-length, generally gets one hour; T optimizes the time of calculating, and generally gets 8760 hours 1 year; t _ibe i hour, f _lPSPless, represent that the power supply reliability of micro-electrical network is higher;

(5) renewable energy utilization rate constraint: renewable energy utilization rate refers to that renewable energy power generation amount in micro-electrical network accounts for the ratio of the whole power consumptions of load, wherein will deduct the energy output of abandoning wind and abandon light,

η≥η _min （15）

In formula, η is renewable energy utilization rate, η _minfor renewable energy utilization rate limit value in initial conditions,

η is calculated by following formula:

$\mathrm{\&eta;}=1-\frac{\underset{i=1}{\overset{T}{\mathrm{\&Sigma;}}}[P_{\mathrm{waste}}\left(t_i\right)\&times;\mathrm{\&Delta;t}]}{\underset{i=1}{\overset{T}{\mathrm{\&Sigma;}}}[P_L\left(t_i\right)\&times;\mathrm{\&Delta;t}]}---\left(16\right)$

In formula, P _waste(t _i), P _l(t _i) be respectively t _ithe wind of abandoning in moment is abandoned luminous power and whole load power, and η is larger, represents that the renewable energy utilization rate of micro-electrical network is higher;

(6) optimize: adopt genetic algorithm to be optimized according to the Optimized model of setting up.

Beneficial effect that the present invention reaches:

The method that the present invention proposes, can combine and solve the distributed power source capacity in micro-Electric Power Network Planning and energy storage system capacity, is optimized configuration simultaneously; Both independent micro-Electric Power Network Planning and capacity configuration problem can have been solved, also can solve planning and the capacity configuration problem of the micro-electrical network of grid type, in the planning and capacity collocation method of the micro-electrical network of grid type, the computational methods of the receiving ability of the large electrical network of actual engineering design peripheral to distributed power source capacity in micro-electrical network are provided in detail, incorporation engineering reality more; Adopt multiple constraint, multiobject algorithm, can obtain the many groups optimal solution under different condition, for designer's reference; Method of the present invention is to propose on the basis of the wind-powered electricity generation through a large amount of, photovoltaic distributed power station and connecting system design thereof, and incorporation engineering reality more, can be used as aid decision-making system and carry out actual engineering design.

Accompanying drawing explanation

Fig. 1 is micro-Electric Power Network Planning and the capacity configuration flow chart based on multiple-objection optimization;

Fig. 2 is basic data and the input/output relation figure of input;

Fig. 3 is storage battery pretreatment process.

Embodiment

Micro-Electric Power Network Planning based on multiple-objection optimization and the flow process of capacity configuration are illustrated as shown in Figure 1.Mainly comprise setting micro-operation of power networks pattern, basic data input, basic data preliminary treatment, distributed power source and energy-storage system optimization, the result that is optimized, the comparison of scheme economic technology, obtain the steps such as final satisfied scheme.

Micro-Electric Power Network Planning shown in Fig. 1 and the flow process of capacity configuration will be introduced in detail below.

The first step: set micro-operation of power networks pattern: the operational mode of micro-electrical network, is divided into independent micro-electrical network and the large class of the micro-electrical network two of grid type.

Second step: basic data input: basic data input comprises system condition, electric price parameter, load parameter, photovoltaic parameter, wind-powered electricity generation parameter, accumulator parameter, 7 parts of other parameter, and the relation between basic data and the input and output of input as shown in Figure 2.

Wherein:

(1) system condition input, only for the micro-electrical network of grid type, comprises the grid-connected electric pressure of micro-electrical network, upper level transformer capacity, interconnection wire type and length, PCC point capacity of short circuit.

(2) electric price parameter input, for independent micro-electrical network, stoichiometric point is arranged on distributed power station outlet side, and input comprises all kinds distributed power source generating electricity price; For the micro-electrical network of grid type, according to micro-power grid operation pattern, be divided into two kinds of electricity price metering methods, being respectively stoichiometric point is arranged on the interconnection that distributed power station outlet side and stoichiometric point be arranged on micro-electrical network and outside large electrical network, be input as purchase electricity price and sale of electricity electricity price, simultaneously can selective discrimination time-of-use tariffs and do not distinguish time-of-use tariffs.

(3) load parameter input, comprises power and the average short trouble time of electrical network of annual load (point hour), sensitive load.

(4) photovoltaic parameter is inputted, and comprises electric parameter, cost parameter, capacity limit, the light resources situation of photovoltaic module.

(5) wind-powered electricity generation parameter is inputted, and comprises electric parameter, cost parameter, capacity limit, the wind-resources situation of blower fan.

(6) accumulator parameter is inputted, and comprises electric parameter and the cost parameter of storage battery.

(7) other parameter input, comprises maximum Load Probability, minimum renewable energy utilization rate, the standard year interest rate of losing.

Wherein:

The 3rd step: basic data preliminary treatment:

(1) system restriction condition: for the micro-electrical network of grid type, the present invention has considered the receiving ability of outside large electrical network to micro-electrical network distributed power source first, has proposed constraint from the following aspects:

1) interconnection (the grid-connected circuit of the outside large electrical network of micro-electrical network access) transmission power limit constraint

P-P _Lmin＜P _linemax (1)

In formula, P is the active power sum of various distributed power sources in micro-electrical network, P _lminfor load minimum power, P _linemaxfor interconnection limit transmission power.Wherein, interconnection limit transmission power P _linemaxdetermined by interconnection wire type.

2) PCC(Point of Conmen Coupling, points of common connection) the ratio constraint of some short circuit current and micro-interconnecting ties maximum normal current

$\frac{I_k}{I_e}\&GreaterEqual;10---\left(2\right)$

In formula, I _kfor system PCC point short circuit current, I _efor the rated current sum of various distributed power sources in micro-electrical network.Wherein, PCC point short circuit current can be calculated by capacity of short circuit.

3) voltage loss of transmission line constraint

In formula, the voltage loss that Δ U% is transmission line, U is grid-connected voltage, R, X are respectively resistance and the reactance of interconnection,

for photovoltaic plant output voltage and current and phase difference, power factor is got the active power sum that 0.98, P is various distributed power sources in micro-electrical network, and l is interconnection length.

4) voltage fluctuation constraint

$U_{d\max }\%=\frac{\mathrm{PR}+\mathrm{QX}}{U}\&times;100\%\&le;U_{d\_\mathrm{limite}}\%---\left(4\right)$

In formula, U _dmax% is the voltage fluctuation that microgrid causes at PCC point, and P is the active power sum of various distributed power sources in micro-electrical network, and Q is the reactive power sum of various distributed power sources in micro-electrical network, and R, X are respectively resistance and the reactance of interconnection, and U is grid-connected voltage, U _{d_limite}% is the voltage fluctuation limit value that PCC is ordered.

(2) an operation year photovoltaic power curve, year blower fan power curve of going into operation

The power curve of photovoltaic, blower fan can, according to the mathematics model of stable state of light resources, wind-resources situation and photovoltaic, blower fan, calculate by wind-power transfer, light-power transfer, and relevant achievement in research is more, repeats no more herein.

(3) storage battery preliminary treatment

1) if having sensitive load power and the numerical value of average short trouble time of electrical network in input data,, for ensureing the reliable power supply of sensitive load, need retrain battery capacity:

Ensure the average short trouble time of battery capacity=sensitive load × electrical network (5) of sensitive load

Wherein, ensure that the battery capacity unit of sensitive load is kilowatt hour (kWh), sensitive load unit is kilowatt (kW), and the average short trouble of electrical network chronomere is hour (h).

2) if purchase electricity price or sale of electricity electricity price exist time-of-use tariffs, storage battery can play the effect of peak load shifting, has certain economic benefit, unit stored energy capacitance peak load shifting economic benefit:

Unit stored energy capacitance peak load shifting economic benefit=unit capacity × (peak electricity price-paddy electricity price) × 365 × Л (6)

Wherein: stored energy capacitance peak load shifting economic benefit unit of unit is unit/year, unit capacity unit is kilowatt hour (kWh), electricity price unit is unit/kWh, and Л is coefficient of colligation, and this coefficient has considered that efficiency for charge-discharge, charge/discharge capacity of storage battery account for the factors such as the percentage of total capacity.

The pretreated flow process of storage battery refers to Fig. 3, enters distributed power source and energy-storage system Optimization Steps after storage battery preliminary treatment.

The 4th step: distributed power source and energy-storage system optimization

Distributed power source and energy storage system capacity allocation problem, be an optimization problem, can set up Optimized model, solves by optimized algorithm.Optimized model is made up of target function and constraints, and the model constrained condition that wherein the present invention proposes comprises system restriction, storage battery constraint, reliability constraint, the constraint of renewable energy utilization rate.

(1) target function: selecting micro-electrical network total cost minimum is target function, and its expression formula is:

minf＝min(C _c+C _OM-C _gs+C _gp) （7）

In formula, f is target function, C _cfor the initial outlay cost of micro-electrical network, C _oMfor operation maintenance and the displacement total cost present worth of system, C _gsfor the total revenue present worth of micro-electrical network large electrical network sale of electricity to outside, C _gpfor power purchase total cost present worth.Wherein, if electricity price is distinguished time-of-use tariffs, the in the situation that of having configured storage battery in micro-electrical network, in the calculating of sale of electricity cost and power purchase cost, should consider the economic benefit of peak load shifting, specifically referring to Fig. 3.

(2) storage battery constraint: for making storage battery safety stable operation, and guarantee its useful life, need discharging and recharging of storage battery in system running to make strict restriction in microgrid planning and designing.

The state-of-charge (state of charge, SOC) of storage battery needs to meet:

S _OCmin≤S _OC≤S _OCmax （8）

In formula, S _oCfor state-of-charge, S _oCmin, S _oCmaxbe respectively lower limit, the upper limit of storage battery charge state.

Charge rate, the discharge rate constraint of storage battery need to meet:

$\left\{\begin{array}{c}{r}_{\mathrm{ch}}\&le;r_{\mathrm{ch}\max }\\ r_{\mathrm{dch}}\&le;r_{\mathrm{dch}\max }\end{array}\right.---\left(9\right)$

In formula, r _ch, r _dchbe respectively charge rate, the discharge rate of storage battery; r _chmax, r _dchmaxbe respectively charge rate restriction and discharge rate restriction.

The charging and discharging currents constraint of storage battery needs to meet:

$\left\{\begin{array}{c}{I}_{\mathrm{ch}}\&le;I_{\mathrm{ch}\max }\\ I_{\mathrm{dch}}\&le;I_{\mathrm{dch}\max }\end{array}\right.---\left(10\right)$

In formula, I _ch, I _dchbe respectively charging current, the discharging current of storage battery; I _chmax, I _dchmaxbe respectively charge-current limit and discharging current restriction.

The the discharging and recharging power and need meet of storage battery:

$\left\{\begin{array}{c}0\&le;P_{\mathrm{ch}}\&le;P_{\mathrm{ch}\max }\\ 0\&le;P_{\mathrm{dch}}\&le;P_{\mathrm{dch}\max }\end{array}\right.---\left(11\right)$

In formula, P _ch, P _dchbe respectively charge power, the discharge power of storage battery; P _chmax, P _dchmaxbe respectively the charge power upper limit, the discharge power upper limit.

Within a dispatching cycle, the charge and discharge cycles number of times of storage battery need meet:

N _C≤N _Cmax （12）

In formula, N _cfor the charge and discharge cycles number of times of storage battery, N _cmaxfor the accumulator cell charging and discharging cycle-index upper limit.Formula (8)～formula (12) has formed storage battery constraint.

(3) system restriction: system restriction refers to the receiving ability of outside large electrical network to micro-electrical network distributed power source, comprises voltage loss constraint and the voltage fluctuation constraint of the constraint of the interconnection transmission power limit, short-circuit current ratio constraint, transmission line.Specifically refer to the 3rd step, the pretreated system restriction condition part of basic data content, formula (1)～formula (4) has formed system restriction.

(4) reliability constraint: adopt the mistake Load Probability of micro-electrical network as reliability constraint index.For independent micro-electrical network, in the time that the power supply capacity of distributed power source and energy storage can not meet all workload demands, for ensureing micro-power network safety operation, allow the non-sensitive load of cut-out (common load).

f _LPSP≤λ _max （13）

In formula, f _lPSPfor the mistake Load Probability of load, λ _maxfor the maximum in initial conditions is lost Load Probability.

F _lPSPcan be calculated by following formula:

$f_{\mathrm{LPSP}}=\frac{\underset{i=1}{\overset{T}{\mathrm{\&Sigma;}}}[P_{\mathrm{LPSP}}\left(t_i\right)\&times;\mathrm{\&Delta;t}]}{\underset{i=1}{\overset{T}{\mathrm{\&Sigma;}}}[P_L\left(t_i\right)\&times;\mathrm{\&Delta;t}]}---\left(14\right)$

In formula, P _lPSP(t _i), P _l(t _i) be respectively t _ithe mistake load power in moment and all load power, f _lPSPless, represent that the power supply reliability of micro-electrical network is higher.

(5) renewable energy utilization rate constraint: renewable energy utilization rate refers to that renewable energy power generation amount in micro-electrical network accounts for the ratio of the whole power consumptions of load, wherein will deduct the energy output of abandoning wind and abandon light.

η≥η _min （15）

In formula, η is renewable energy utilization rate, η _minfor renewable energy utilization rate limit value in initial conditions.

η can be calculated by following formula:

$\mathrm{\&eta;}=1-\frac{\underset{i=1}{\overset{T}{\mathrm{\&Sigma;}}}[P_{\mathrm{waste}}\left(t_i\right)\&times;\mathrm{\&Delta;t}]}{\underset{i=1}{\overset{T}{\mathrm{\&Sigma;}}}[P_L\left(t_i\right)\&times;\mathrm{\&Delta;t}]}---\left(16\right)$

In formula, P _waste(t _i), P _l(t _i) be respectively t _ithe wind of abandoning in moment is abandoned luminous power and whole load power, and η is larger, represents that the renewable energy utilization rate of micro-electrical network is higher.

(6) optimized algorithm: according to the Optimized model of setting up, can adopt intelligent algorithm to be optimized above.Optimized algorithm of the present invention adopts the genetic algorithm of current extensive use, and relevant achievement in research is more, repeats no more herein.

The 5th step: optimum results, the optimal solution being calculated by optimized algorithm, comprises that installed capacity, the configuration capacity of energy-storage system, rate of return on investment, the reality of distributed power source is lost Load Probability.

Further, can also carry out scheme economic technology and comprehensively compare, be optimized after result, engineers and technicians can be according to engineering actual conditions, in conjunction with individual design experiences, revise certain (or some) condition, then re-start to optimize and calculate.The condition that can revise has certain distributed power source installed capacity, energy-storage system configuration capacity, maximum Load Probability, the renewable energy utilization rate limit value etc. of losing, and finally can obtain satisfied scheme.

More than show and described basic principle of the present invention and principal character and advantage of the present invention.The technical staff of the industry should understand; the present invention is not restricted to the described embodiments; that in above-described embodiment and specification, describes just illustrates principle of the present invention; without departing from the spirit and scope of the present invention; the present invention also has various changes and modifications, and these changes and improvements all fall in the claimed scope of the invention.The claimed scope of the present invention is defined by appending claims and equivalent thereof.

Claims (2)

1. micro-Electric Power Network Planning and the capacity collocation method based on multiple-objection optimization, is characterized in that, comprises the following steps:

One) set micro-operation of power networks pattern: the operational mode of micro-electrical network comprises independent micro-electrical network and the micro-electrical network of grid type;

Two) basic data input: basic data input comprises system condition, electric price parameter, load parameter, photovoltaic parameter, wind-powered electricity generation parameter and accumulator parameter;

Three) basic data preliminary treatment:

(1) system restriction condition:

For the micro-electrical network of grid type:

1) interconnection transmission power limit constraint, interconnection is the grid-connected circuit of the outside large electrical network of micro-electrical network access:

P-P _lmin<P _linemaxformula (1)

In formula, P is the active power sum of various distributed power sources in micro-electrical network, P _lminfor load minimum power, P _linemaxfor interconnection limit transmission power, wherein, interconnection limit transmission power P _linemaxdetermined by interconnection wire type;

2) points of common connection short circuit current retrains with the ratio of micro-interconnecting ties maximum normal current:

$\frac{I_k}{I_e}\&GreaterEqual;10$ Formula (2)

In formula, I _kfor system points of common connection short circuit current, I _efor the rated current sum of various distributed power sources in micro-electrical network; Wherein, points of common connection short circuit current is calculated by capacity of short circuit;

3) voltage loss of transmission line constraint:

In formula, the voltage loss that △ U% is transmission line, U is grid-connected voltage, R, X are respectively resistance and the reactance of interconnection,

for photovoltaic plant output voltage and current and phase difference, power factor is got the active power sum that 0.98, P is various distributed power sources in micro-electrical network, and l is interconnection length;

4) voltage fluctuation constraint:

$U_{d\max }\%=\frac{\mathrm{PR}+\mathrm{QX}}{U}\&times;100\%\&le;U_{d\_\mathrm{limite}}\%$ Formula (4)

In formula, U _dmax% is the voltage fluctuation that microgrid causes at points of common connection, and P is the active power sum of various distributed power sources in micro-electrical network, and Q is the reactive power sum of various distributed power sources in micro-electrical network, and R, X are respectively resistance and the reactance of interconnection, and U is grid-connected voltage, U _{d_limite}% is the voltage fluctuation limit value of points of common connection;

(2) an operation year photovoltaic power curve, year blower fan power curve of going into operation:

Photovoltaic power curve, year blower fan power curve of going into operation, according to the mathematics model of stable state of light resources, wind-resources situation and photovoltaic, blower fan, calculate by wind-power transfer, light-power transfer;

(3) storage battery preliminary treatment:

1) input electric price parameter and load data;

2) if having sensitive load power and the numerical value of average short trouble time of electrical network in input data,, for ensureing the reliable power supply of sensitive load, battery capacity is retrained:

Ensure the average short trouble time type of battery capacity=sensitive load × electrical network (5) of sensitive load

3) if purchase electricity price or sale of electricity electricity price exist time-of-use tariffs, storage battery plays the effect of peak load shifting:

Unit stored energy capacitance peak load shifting economic benefit=unit capacity × (peak electricity price-paddy electricity price) × 365 ×

Wherein, Л is coefficient of colligation to Л formula (6);

4) after storage battery preliminary treatment, enter distributed power source and energy-storage system optimization system is optimized;

Four) distributed power source and energy-storage system optimization: set up Optimized model, solve by optimized algorithm, Optimized model comprises target function and constraints, and constraints comprises system restriction, storage battery constraint, reliability constraint and the constraint of renewable energy utilization rate

(1) target function:

Selecting micro-electrical network total cost minimum is target function, and its expression formula is:

Minf=min (C _c+ C _oM-C _gs+ C _gp) formula (7)

In formula, f is target function, C _cfor the initial outlay cost of micro-electrical network, C _oMfor operation maintenance and the displacement total cost present worth of system, C _gsfor the total revenue present worth of micro-electrical network large electrical network sale of electricity to outside, C _gpfor power purchase total cost present worth;

(2) storage battery constraint:

The state-of-charge of storage battery need meet:

S _OCmin≤S _OC≤S _OCmax （8）

In formula, S _oCfor state-of-charge, S _oCmin, S _oCmaxbe respectively lower limit, the upper limit of storage battery charge state,

Charge rate, the discharge rate constraint of storage battery need to meet:

$\left\{\begin{array}{c}{r}_{\mathrm{ch}}\&le;r_{\mathrm{ch}\max }\\ r_{\mathrm{dch}}\&le;r_{\mathrm{dch}\max }\end{array}\right.$ （9）

In formula, r _ch, r _dchbe respectively charge rate, the discharge rate of storage battery; r _chmax, r _dchmaxbe respectively charge rate restriction and discharge rate restriction;

The charging and discharging currents constraint of storage battery needs to meet:

$\left\{\begin{array}{c}{I}_{\mathrm{ch}}\&le;I_{\mathrm{ch}\max }\\ I_{\mathrm{dch}}\&le;I_{\mathrm{dch}\max }\end{array}\right.$ （10）

In formula, I _ch, I _dchbe respectively charging current, the discharging current of storage battery; I _chmax, I _dchmaxbe respectively charge-current limit and discharging current restriction;

The the discharging and recharging power and need meet of storage battery:

$\left\{\begin{array}{c}0\&le;P_{\mathrm{ch}}\&le;P_{\mathrm{ch}\max }\\ 0\&le;P_{\mathrm{dch}}\&le;P_{\mathrm{dch}\max }\end{array}\right.$ （11）

In formula, P _ch, P _dchbe respectively charge power, the discharge power of storage battery; P _chmax, P _dchmaxbe respectively the charge power upper limit, the discharge power upper limit;

Within a dispatching cycle, the charge and discharge cycles number of times of storage battery meets:

N _C≤N _Cmax （12）

In formula, N _cfor the charge and discharge cycles number of times of storage battery, N _cmaxfor the accumulator cell charging and discharging cycle-index upper limit;

(3) system restriction: system restriction refers to the receiving ability of outside large electrical network to micro-electrical network distributed power source, comprise voltage loss constraint and the voltage fluctuation constraint of the constraint of the interconnection transmission power limit, short-circuit current ratio constraint, transmission line, concrete constraints and step 3) in the pretreated system restriction condition of basic data identical, formula (1)～formula (4) has formed system restriction;

(4) reliability constraint: adopt the mistake Load Probability of micro-electrical network as reliability constraint index, for independent micro-electrical network, in the time that the power supply capacity of distributed power source and energy storage can not meet all workload demands, for ensureing micro-power network safety operation, allow the non-sensitive load of cut-out

f _LPSP≤λ _max （13）

In formula, f _lPSPfor the mistake Load Probability of load, λ _maxfor the maximum in initial conditions is lost Load Probability, f _lPSPcalculated by following formula:

$f_{\mathrm{LPSP}}=\frac{\underset{i=1}{\overset{T}{\mathrm{\&Sigma;}}}[P_{\mathrm{LPSP}}\left(t_i\right)\&times;\mathrm{\&Delta;t}]}{\underset{i=1}{\overset{T}{\mathrm{\&Sigma;}}}[P_L\left(t_i\right)\&times;\mathrm{\&Delta;t}]}$ （14）

In formula, P _lPSP(t _i), P _l(t _i) be respectively t _ithe mistake load power in moment and all load power, △ t is for calculating step-length; T optimizes the time of calculating; t _ibe i hour, f _lPSPless, represent that the power supply reliability of micro-electrical network is higher;

(5) renewable energy utilization rate constraint: renewable energy utilization rate refers to that renewable energy power generation amount in micro-electrical network accounts for the ratio of the whole power consumptions of load, wherein will deduct the energy output of abandoning wind and abandon light,

η≥η _min （15）

In formula, η is renewable energy utilization rate, η _minfor renewable energy utilization rate limit value in initial conditions, η is calculated by following formula:

$\mathrm{\&eta;}=1-\frac{\underset{i=1}{\overset{T}{\mathrm{\&Sigma;}}}[P_{\mathrm{waste}}\left(t_i\right)\&times;\mathrm{\&Delta;t}]}{\underset{i=1}{\overset{T}{\mathrm{\&Sigma;}}}[P_L\left(t_i\right)\&times;\mathrm{\&Delta;t}]}$ （16）

In formula, P _waste(t _i), P _l(t _i) be respectively t _ithe wind of abandoning in moment is abandoned luminous power and whole load power, and η is larger, represents that the renewable energy utilization rate of micro-electrical network is higher;

(6) optimize: adopt genetic algorithm to be optimized according to the Optimized model of setting up.

2. micro-Electric Power Network Planning and the capacity collocation method based on multiple-objection optimization according to claim 1, is characterized in that: described basic data specifically comprises:

(1) system condition input: only for the micro-electrical network of grid type, comprise the grid-connected electric pressure of micro-electrical network, upper level transformer capacity, interconnection wire type and length, points of common connection capacity of short circuit;

(2) electric price parameter input: for independent micro-electrical network, electric price parameter stoichiometric point is arranged on distributed power station outlet side, electric price parameter input comprises all kinds distributed power source generating electricity price; For the micro-electrical network of grid type, be divided into two kinds of electricity price metering methods according to micro-power grid operation pattern, being respectively stoichiometric point is arranged on the interconnection that distributed power station outlet side and stoichiometric point be arranged on micro-electrical network and outside large electrical network, electric price parameter is input as purchase electricity price and sale of electricity electricity price, comprises simultaneously and distinguishes time-of-use tariffs and do not distinguish time-of-use tariffs;

(3) load parameter input: the power and the average short trouble time of electrical network that comprise annual load, sensitive load;

(4) photovoltaic parameter input: the electric parameter, cost parameter, capacity limit and the light resources parameter that comprise photovoltaic module;

(5) wind-powered electricity generation parameter input: the electric parameter, cost parameter, capacity limit and the wind-resources parameter that comprise blower fan;

(6) accumulator parameter input: the electric parameter and the cost parameter that comprise storage battery;

(7) other parameter input, comprises maximum Load Probability, minimum renewable energy utilization rate, the standard year interest rate of losing,

Wherein:

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