CN106327006A - Comprehensive benefit analysis-based micro-power-grid optimal configuration method - Google Patents

Abstract

The invention discloses a comprehensive benefit analysis-based micro-power-grid optimal configuration method comprising the following steps: operation environment data and load data of distributed power sources in a micro-power-grid are collected; according to a distributed power source model and a micro-power-grid operation solution, output data and comprehensive benefit evaluation indexes of the distributed power sources are obtained; according to the comprehensive benefit evaluation indexes and corresponding weighing coefficients, a comprehensive benefit object function with a distributed power source quantity as a variable is built; the comprehensive benefit object function is solved via a particle swarm optimization algorithm; the distributed power source quantity satisfying constraint conditions is obtained and used a configuration quantity of the distributed power sources in the micro-power-grid; via the method, an overall benefit and comprehensive benefit evaluating system of the micro-power-grid can be comprehensively built, effects exerted by all factors in micro-power-grid planning operation can be accurately analyzed, investment cost can be lowered, and energy utilization efficiency can be improved.

Description

A kind of micro-capacitance sensor Optimal Configuration Method based on comprehensive benefit analysis

Technical field

The present invention relates to technical field of electric power, particularly relate to a kind of micro-capacitance sensor side of distributing rationally based on comprehensive benefit analysis Method.

Background technology

The energy is the important substance basis of socio-economic development, but as the most important energy of our times, " producing of oil Amount peak value " arrive, conventional gas and oil resource also faces exhaustion.Developing new and renewable sources of energy is that future source of energy is sent out The inexorable trend of exhibition.Micro-capacitance sensor technology is the trend of following distributed energy supply micro-capacitance sensor development, propulsion energy-saving is reduced discharging and Realize energy sustainable development significant.Grid type micro-capacitance sensor can be accessed by power distribution network and is incorporated into the power networks, and is meeting self While workload demand, provide power support and Reserve Ancillary Service for power distribution network.

The aspects such as economy, environment and technology are carried out entirely by the needs of distributing rationally of the planning construction of micro-capacitance sensor, i.e. micro-capacitance sensor Surface analysis, the most rationally determines micro-capacitance sensor structure and capacity configuration, and guarantee micro-capacitance sensor obtains maximum with relatively low cost Benefit, and then reach demonstration, the purpose promoted.

Had a lot of experts and scholars that the planning and designing of micro-capacitance sensor are studied both at home and abroad, wherein, have by consider The factors such as grid-connection converter capacity limit and rate for incorporation into the power network, with micro-capacitance sensor investment cost, the minimum operation of operation and maintenance cost Target, micro-capacitance sensor is distributed rationally solve (Wang Panbao, Wang Wei, Meng Nina, etc. combine with operating index based on operational mode Close the direct-current grid evaluated and distribute [J] rationally. electric power network technique, 2016 (3): 741-748.), but for micro-capacitance sensor comprehensive benefit Assessment the most comprehensive；Have by considering micro-capacitance sensor economy, reliability and renewable energy utilization rate, propose scene Storage capacity configuration model (Dou Xiaobo, Yuan Jian, Wu Jun, etc. grid-connected wind-light storage micro-capacitance sensor capacity improvements Optimal Configuration Method [J]. Electric Power Automation Equipment, 2016,36 (03): 26-32.), but do not consider that fuel generator is as distributed power source；Also The configuration of some employing micro-capacitance sensor and operation combined optimization (Hawkes A D, Leach M A.Modelling high level System design and unit commitment for a microgrid [J] .Applied Energy, 2009,86 (7-8): 1253-1265.), but grid type micro-capacitance sensor economy is only accounted for, the most comprehensive to overall efficiency assessment.

And, in existing micro-capacitance sensor, distributing rationally of each distributed power source considers its Financial cost benefit mostly, for Environmental benefit only considers the corresponding loss of its pollutant and greenhouse gas emissions, and only using reliability as constraints, right In the whole synthesis performance evaluation of micro-capacitance sensor imperfection and comprehensively.On the other hand, the optimization of the micro-capacitance sensor for being incorporated into the power networks Configuration, such as in micro-capacitance sensor, in the assessment models distributed rationally of each distributed power source, the foundation of object function is the most single, right Comprehensive benefit assessment after micro-grid connection imperfection.

Summary of the invention

An object of the present invention at least that, the problem existed for above-mentioned prior art, it is provided that a kind of based on comprehensively The micro-capacitance sensor Optimal Configuration Method of performance analysis, it is possible to more comprehensively build micro-capacitance sensor overall efficiency and the assessment of comprehensive benefit combination System, analyzes the impact of each factor in micro-capacitance sensor planning more accurately, reduces cost of investment simultaneously, improves utilization of energy effect Rate.

To achieve these goals, the technical solution used in the present invention is:

A kind of micro-capacitance sensor Optimal Configuration Method based on comprehensive benefit analysis, including:

Gather running environment data and the load data of distributed power source in micro-capacitance sensor, according to distributed electrical source model and Micro-capacitance sensor operating scheme, obtains output data and the comprehensive benefit evaluation index of distributed power source；

According to comprehensive benefit evaluation index and the weight coefficient of correspondence thereof, setting up number of power sources in a distributed manner is combining of variable Close benefit goal function；

By particle swarm optimization algorithm comprehensive benefit object function, obtain the distributed power source number meeting constraints Amount, as the configuration quantity of distributed power source in micro-capacitance sensor.

Preferably, above-mentioned distributed power source includes: wind-driven generator WT, photovoltaic array PV, miniature gas turbine MT and Energy storage device BAT；

Described distributed electrical source model includes: WT exert oneself model, the MT of model, PV that exert oneself exerts oneself model and BAT capacity mould Type.

Preferably, above-mentioned micro-capacitance sensor operating scheme includes: micro-capacitance sensor isolated power grid, or micro-grid connection is run.

Preferably, above-mentioned comprehensive benefit evaluation index includes: economic benefits indicator, environmental benefit index and reliability Performance indicator.

Preferably, above-mentioned economic benefits indicator includes: wait year value index of investment cost and economic well-being of workers and staff index；

Described environmental benefit index includes: energy-saving benefit index and reduction of discharging performance indicator；

Described reliability benefit index includes: micro-capacitance sensor reliability benefit index and customer power supply index performance indicator.

Preferably, above-mentioned constraints includes: optimized variable constraint, distributed power source run constraint, power supply reliability about The constraint of bundle, energy surplus, distribution close friend access constraint and the constraint of micro-capacitance sensor self performance.

Preferably, said method farther includes: use range transformation method to mark described comprehensive benefit evaluation index Quasi-ization processes.

Preferably, above-mentioned comprehensive benefit object function is:

MinF (x)=min ∑ ω_if_i(x)

Wherein, x is distributed power source quantity, and i is positive integer, f_iX () is i-th comprehensive benefit through standardization Evaluation index, ω_iIt it is the weight coefficient that i-th comprehensive benefit evaluation index is corresponding.

Preferably, said method farther includes: uses analytic hierarchy process (AHP) and entropy assessment to combine and determines that comprehensive benefit is commented Estimate the weight coefficient that index is corresponding.

Preferably, said method farther includes:

Obtain the configuration quantity of distributed power source in micro-capacitance sensor more than two, select wherein corresponding comprehensive benefit assessment to refer to Absolute altitude in preset value as micro-capacitance sensor in the configuration quantity of distributed power source.

In sum, owing to have employed technique scheme, the present invention at least has the advantages that

Determine each factor weight in multiple objective function by AHP-entropy assessment, multiple objective function is converted into single-goal function Solve, on the premise of taking into account achievement data objectivity, make the weight coefficient that the comprehensive benefit evaluation index that determines is corresponding Result is more accurately, reliably；By including economic benefits indicator, environmental benefit index and reliability benefit index in, more comprehensively Ground builds the assessment system that micro-capacitance sensor overall efficiency and comprehensive benefit combine so that distributing rationally of the micro-capacitance sensor of acquisition can be Reduce cost of investment simultaneously on the premise of meeting indices and constraints, improve efficiency of energy utilization.

Accompanying drawing explanation

Fig. 1 is the flow process of the micro-capacitance sensor Optimal Configuration Method based on comprehensive benefit analysis that the embodiment of the present invention one provides Figure；

Fig. 2 is the structural representation of the grid type micro-capacitance sensor that the embodiment of the present invention two provides；

Fig. 3 is the determination in the micro-capacitance sensor Optimal Configuration Method based on comprehensive benefit analysis that the embodiment of the present invention six provides The flow chart of the weight coefficient that comprehensive benefit evaluation index is corresponding.

Detailed description of the invention

Below in conjunction with the accompanying drawings and embodiment, the present invention is further elaborated, so that the purpose of the present invention, technology Scheme and advantage are clearer.Should be appreciated that specific embodiment described herein, and need not only in order to explain the present invention In limiting the present invention.

Embodiment one

As it is shown in figure 1, the disclosed micro-capacitance sensor Optimal Configuration Method bag based on comprehensive benefit analysis of the embodiment of the present invention one Include following steps:

Step 101: gather running environment data and the load data of distributed power source in micro-capacitance sensor,

In a preferred embodiment, distributed power source may include that renewable power supply (such as, wind-driven generator WT, photovoltaic Array PV), non-renewable power supply (such as, miniature gas turbine MT, diesel-driven generator DG etc.) and energy storage device BAT (example As, accumulator)；

Correspondingly, the running environment data of distributed power source may include that the natural environment numbers such as current environment wind speed, temperature According to, and blower fan incision wind speed, cut-out wind speed, rated wind speed, rated power, accumulator current electric quantity, charge power, self discharge The plant running data such as coefficient, efficiency for charge-discharge；

Step 102: according to distributed electrical source model and micro-capacitance sensor operating scheme, obtain the output data of distributed power source With comprehensive benefit evaluation index；

Wherein, distributed electrical source model may include that WT exert oneself model, the MT of model, PV that exert oneself exerts oneself model and BAT Capacity model；Micro-capacitance sensor operating scheme may include that micro-capacitance sensor isolated power grid, or micro-grid connection is run；

Step 103: according to comprehensive benefit evaluation index and the weight coefficient of correspondence thereof, setting up number of power sources in a distributed manner is The comprehensive benefit object function of variable；

Specifically, comprehensive benefit evaluation index may include that economic benefits indicator, environmental benefit index and reliability Performance indicator；

Further, economic benefits indicator such as may include that at year value index of investment cost and the economic well-being of workers and staff index；Environmental Effect Benefit index may include that energy-saving benefit index and reduces discharging performance indicator；Reliability benefit index may include that micro-capacitance sensor reliability Performance indicator and customer power supply index performance indicator.

Step 104: by particle swarm optimization algorithm comprehensive benefit object function, obtain the distribution meeting constraints Formula number of power sources, as the configuration quantity of distributed power source in micro-capacitance sensor.

Wherein, constraints may include that optimized variable constraint, distributed power source run constraint, power supply reliability constraint, The constraint of energy surplus, distribution close friend access constraint and micro-capacitance sensor self performance constraint etc..

This step can further include: uses range transformation method to be standardized described comprehensive benefit evaluation index Process.

In a preferred embodiment, the comprehensive benefit object function of step 103 can be:

MinF (x)=min ∑ ω_if_i(x) (1)

Wherein, x is distributed power source quantity, and i is positive integer, f_iX () is i-th comprehensive benefit through standardization Evaluation index, ω_iIt it is the weight coefficient that i-th comprehensive benefit evaluation index is corresponding.

Wherein, the weight coefficient that every comprehensive benefit evaluation index is corresponding reflects the significance level of each index, can use VC Method, PCA and factor analysis etc. determine weight coefficient.In a preferred embodiment, can use Analytic hierarchy process (AHP) combines with entropy assessment and determines the weight coefficient that comprehensive benefit evaluation index is corresponding.

Further, it is also possible on the basis of step 104, joining of distributed power source in micro-capacitance sensor more than two is obtained Put quantity, select wherein corresponding comprehensive benefit evaluation index higher than preset value as the configuration number of distributed power source in micro-capacitance sensor Amount.

In above-described embodiment, by including economic benefits indicator, environmental benefit index and reliability benefit index in, more Build micro-capacitance sensor overall efficiency and the assessment system of comprehensive benefit combination all sidedly, and comprehensive by particle swarm optimization algorithm Benefit goal function, obtains and meets the distributed power source quantity of constraints, so that the distributing rationally of the micro-capacitance sensor obtained Reduce cost of investment on the premise of can meeting indices and constraints at the same time, improve efficiency of energy utilization.

Embodiment two

Below in conjunction with the grid type micro-capacitance sensor shown in Fig. 2, in the Optimal Configuration Method that the embodiment of the present invention two is provided Distributed electrical source model is described in detail.

Grid type micro-capacitance sensor generally comprises wind-driven generator WT, photovoltaic array PV, miniature gas turbine MT, energy storage device BAT distributed power supply, and the relevant auxiliary equipment such as controller, inverter.

Each distributed electrical source model can be defined respectively as:

WT exerts oneself model:

$P_{WT}\left(V\right)=\left\{\begin{array}{c}0,0\&le;V\&le;V_{ci},V\&GreaterEqual;V_{co}\\ P_{WT-rate}\frac{(V-V_{ci})}{(V_r-V_{ci})},V_{ci}<V<V_r\\ P_{WT-rate},V_r\&le;V\&le;V_{co}\end{array}\right.---\left(2\right)$

In formula, P_WTFor blower fan output, P_WT-rateFor blower fan rated power；V, V_ci, V_r, V_coIt is respectively current environment wind Speed, blower fan incision wind speed, rated wind speed, cut-out wind speed.

PV exerts oneself model:

$P_{PV}=f_{PV}{P}_{PV-rate}\frac{G_T}{G_S}\&lsqb;1+{\mathrm{\&alpha;}}_P(T_c-T_{STC})\&rsqb;---\left(3\right)$

In formula, P_PVFor photovoltaic array output (mainly being affected by the factor such as intensity of solar radiation and temperature)；f_PVFor light The power deratng factor of photovoltaic array is (for calculating the damage that photovoltaic panel surface blot, covering and photovoltaic panel self deterioration etc. cause Consumption, such as 0.9)；P_PV-rateFor the rated power of photovoltaic array, (at the standard conditions, such as solar radiation intensity is 1kW/ m², temperature is 25 DEG C, and wind speed is 0) and the output of photovoltaic array that records, unit: kW)；G_TFor total in photovoltaic array inclined plane Solar energy irradiance (unit: kW/m²)；G_SFor the solar energy irradiance under standard test condition, such as 1kW/m²；α_PFor power Temperature coefficient (unit: %/DEG C), such as-0.0047；T_CSurface temperature (unit: DEG C) for photovoltaic array；T_STCSurvey for standard Examination Conditions Temperature, such as 25 DEG C.

Surface temperature T of photovoltaic array_CCan be drawn by formula (3):

T_c=T_a+θ_G(1+θ_TT_a)(1-θ_vwV)G_T (4)

In formula, θ_G、θ_T、θ_vwFor test coefficient, the most respectively 0.038,0.031,0.042；T_aFor current environmental temperature；V For current environment wind speed.

MT exerts oneself model:

As a example by diesel-driven generator, owing to the output of this fuel generator is the most relevant with energy consumption, can run Between 0 to rated power, its model of exerting oneself can be expressed as by fuel-power out-put characteristic mathematical model:

F (t)=aP_RP(t)+bP_GEN(t) (5)

In formula, F (t) is the fuel consumption of fuel generator；P_RP(t) and P_GENT () is respectively rated output power and reality is defeated Go out power；A, b are respectively fuel slope.

BAT capacity model:

Energy storage device BAT can use plumbic acid valve-control storage battery (Valve Regulated Battery, VRLA) its cost Low, extensive in energy storage applications.Accumulator dump energy can change with charge and discharge process, wherein,

During accumulator charging, current electric quantity is:

$E_{BAT}\left(t\right)=E_{BAT}(t-1)(1-\mathrm{\&delta;})+\frac{\mathrm{\&Delta;}P}{{\mathrm{\&eta;}}_{ch}}---\left(6\right)$

During battery discharging, current electric quantity is:

$E_{BAT}\left(t\right)=E_{BAT}(t-1)(1-\mathrm{\&delta;})-\frac{\mathrm{\&Delta;}P}{{\mathrm{\&eta;}}_{dh}}---\left(7\right)$

In formula, E_BATT () is accumulator current electric quantity；E_BAT(t-1) it is a moment electricity in accumulator；Δ P is accumulator Unit interval charge power；δ is the self discharge coefficient of accumulator；η_ch、η_dhEfficiency for charge-discharge for accumulator.

Cost effectiveness analysis (Cost-Benefit Analysis) is a kind of integrated cost by item compared and benefit Carry out the economic decision-making method that evaluation item is worth.The present invention provide embodiment based on cost effectiveness analysis, combining environmental Economics, electrical network self-characteristic etc., set up and include economic benefits indicator, environmental benefit index and reliability benefit index Micro-capacitance sensor overall efficiency indicator assessment system.

Embodiment three

Below in conjunction with previous embodiment, the comprehensive benefit assessment in the Optimal Configuration Method that the embodiment of the present invention three is provided Ji performance indicator in index is described in further detail.

Specifically, economic benefits indicator such as may include that at year value index of investment cost C_IVWith economic well-being of workers and staff index C_PR。

It is worth index of investment cost C in year_IV

C_IVMainly including waiting year value equipment investment expense, its computing formula is:

C_IV=C_eq+C_om+C_sub+C_fu (8)

In formula, C_eqThe years such as the initial outlay for equipment (such as, including each distributed power source and auxiliary equipment etc.) are worth expense (including buying expenses and mounting cost) index；C_omFor equipment year operation and maintenance cost index；C_subRefer to for year displacement cost Mark；C_fuFor year fuel cost index.

C_eqIn can using life cycle management, its cost is converted by year value method such as grade, and its computing formula is:

$C_{eq}=(C_{WT}{N}_{WT}+C_{PV}{N}_{PV}+C_{MT}{N}_{MT}+C_{BAT}{N}_{BAT}+C_{aux})\frac{r{(1+r)}^n}{{(1+r)}^n-1}---\left(9\right)$

In formula, C_WT、C_PV、C_MT、C_BATIt is respectively wind-driven generator, photovoltaic array, miniature gas turbine and energy storage device Separate unit (or single group) initial outlay cost, including its buying expenses and mounting cost；C_auxInitial outlay expense for auxiliary equipment With；N_WT、N_PV、N_MT、N_BATIt is respectively wind-driven generator, photovoltaic array, miniature gas turbine and energy storage device quantity；N is the full longevity Life cycle service life；R is discount rate, can take 6%.

C_omIncluding fixed cost and variable cost, can be movable according to micro-capacitance sensor history data, its computing formula is:

C_om=C_omFix+C_omVar (10)

In formula, C_omFix(it is included under set working mechanism, the annual fixing equipment fortune put into for fixing operation and maintenance cost Row safeguards the expenses etc. such as artificial, material)；C_omVar(dimension that is different with equipment state and that change is included for variable operation maintenance cost Protect expense).

C_subRefer in the life cycle management of project, if equipment reaches its end-of-life time limit, the one-tenth being replaced it This, its computing formula is:

$C_{sub}=\underset{i=1}{\overset{N}{\&Sigma;}}\&lsqb;C_{sub,i}\frac{r}{{(1+r)}^{Y_{sub,i}}-1}\&rsqb;---\left(11\right)$

In formula, C_{Sub, i}It is i-th kind of equipment displacement expense；Y_{Sub, i}It it is i-th kind of equipment replacement life-span.

C_fuCan only consider the fuel cost of the properly functioning generation of micro-gas-turbine, it is also conceivable to fuel price simultaneously The fluctuation impact on the operating cost of micro-capacitance sensor, its computing formula is:

C_fu=p_fuq_t (12)

In formula, p_fuFor miniature gas turbine generating unit price；q_tFor miniature gas turbine generated energy.

Economic well-being of workers and staff index C_PR

C_PRMainly include the proceeds indicatior C that is incorporated into the power networks_con, fall damage proceeds indicatior C_lr, and delay electric grid investment income to refer to Mark C_td, wherein:

C_conIncluding the income produced to power distribution network sale of electricity and the expense produced to power distribution network power purchase, its computing formula is:

C_con=C_sold-C_buy (13)

In formula, C_sold、C_buyWhen being respectively incorporated into the power networks, the expense that micro-capacitance sensor produces to power distribution network sale of electricity and power purchase.

In micro-capacitance sensor distributed power source general distance load distance closer to, fed distance is shorter, produces in electric energy course of conveying Raw electric energy loss is more less than power distribution network, and therefore, the fall of micro-capacitance sensor damages benefit C_lrThe net that after power available configuration, micro-capacitance sensor reduces Damage expense calculates, and its computing formula is:

$C_{lr}=p_c\underset{l=1}{\overset{N_{line}}{\&Sigma;}}(I_l^2-{I^{\&prime;}}_l^2)L_l{\mathrm{R\&tau;}}_{lmax}---\left(14\right)$

In formula, p_cFor electricity price, such as 0.4 yuan Kw/h；N_lineFor the number of lines；L is the number of lines；I_l、I_l＇ is respectively electricity The electric current flow through on circuit l before and after the configuration of source；L_lLength for circuit l；τ_lmaxHourage, example are lost for circuit l annual peak load Such as 3000h；R is the resistance value of circuit unit length.

The rational and orderly construction of micro-capacitance sensor can reduce the distribution micro-capacitance sensor demand to power distribution network transmission capacity when joining peak load, prolongs Slow distribution network construction investment.Therefore, electric grid investment proceeds indicatior C is delayed_tdComputing formula can be:

$C_{td}=(1-u)C_{epd}\frac{r{(1+r)}^{Y_{td}}}{{(1+r)}^{Y_{td}}-1}\underset{i=1}{\overset{N}{\&Sigma;}}{P}_i---\left(15\right)$

In formula, u is the power distribution network percentage reserve to micro-capacitance sensor, such as, take 0.3；C_epdUnit capacity institute is newly extended for power distribution network The investment cost needed, such as 0.5 ten thousand yuan/kW；Y_tdFor the year number delaying power distribution network to extend；N is the kind of power supply；P_iIt it is i-th kind Distributed power source installed capacity.

Embodiment four

Below in conjunction with previous embodiment, the comprehensive benefit assessment in the Optimal Configuration Method that the embodiment of the present invention four is provided Environmental benefit index in index is described in further detail.

Specifically, environmental benefit index includes energy-saving benefit index C_SEWith reduction of discharging performance indicator C_ev。

Energy-saving benefit index C_SE

C_SEIncluding decrement index C_esWith potentiation index R_rg, wherein:

Decrement index C_esUtilize the renewable energy power generation such as blower fan, photovoltaic array for micro-capacitance sensor, reduce non-renewable energy resources The index of consumption, its computing formula is:

$C_{es}=M_C{p}_C\underset{i=1}{\overset{N}{\&Sigma;}}{E}_{rg,i}---\left(16\right)$

In formula, M_CThe coal amount consumed by fired power generating unit production unit electric energy；p_CFor coal price；E_{Rg, i}For micro-capacitance sensor In the year gross generation of i-th kind of regenerative resource distributed power source.Decrement index C_esRaising, it is possible to promote realize recycling, Recirculation economic development.

Potentiation index R_rgThe ratio of gross generation is accounted for renewable energy power generation amount in regenerative resource permeability, i.e. micro-capacitance sensor Example, assesses the potentiation degree of micro-capacitance sensor, and its computing formula is:

$R_{rg}=\frac{E_{rg}}{E_{total}}\&times;100\%---\left(17\right)$

In formula, E_rgFor regenerative resource year gross generation；E_totalFor micro-capacitance sensor year gross generation.Micro-capacitance sensor is by improving Energy utilization rate, reduces Energy Intensity, it is achieved the energy substitution of " clean and effective ", thus alleviates economic growth and energy further Contradiction between source, environment.

Reduce discharging performance indicator C_ev

Reduce discharging and emphasize the protection to ecological environment, reduce pollutant and the discharge of greenhouse gases.The reduction of discharging benefit of micro-capacitance sensor Index C_evCan weigh with the environmental loss of the pollutant that the capacity electric energy such as relative coal generating production are reduced discharging, it calculates public affairs Formula is as follows:

$C_{ev}=\underset{j=1}{\overset{m}{\&Sigma;}}\underset{i=1}{\overset{N}{\&Sigma;}}{C}_{e_j}(Q_j^{CP}-Q_j^{RG,i})---\left(18\right)$

In formula, C_ejEnvironmental value for jth item pollution reduction；Q_j ^cpReduction of discharging for i-th pollutant of Thermal generation unit Amount；Q_j ^{RG, i}CER in micro-capacitance sensor i-th kind of distributed power source jth item pollutant；M is pollutant kind (such as, titanium dioxide Sulfur, nitrogen oxides, particle shape pollutant etc.).

Embodiment five

Below in conjunction with previous embodiment, the comprehensive benefit assessment in the Optimal Configuration Method that the embodiment of the present invention five is provided Reliability benefit index in index is described in further detail.

Specifically, reliability benefit index includes: micro-capacitance sensor reliability benefit index and customer power supply index performance indicator.

Micro-capacitance sensor reliability benefit index B_GRB

B_GRBThe expectation loss of outage that can reduce before and after being configured by micro-capacitance sensor is weighed, and its computing formula is as follows:

$B_{GRB}=I_{EAR}\underset{k\&Element;Q}{\&Sigma;}({\mathrm{EENS}}_k-{{\mathrm{EENS}}_k}^{\&prime;})---\left(19\right)$

In formula, I_EARFor the interrupted energy assessment rate of micro-capacitance sensor internal loading, it is used for describing certain class user often power failure 1kW h institute The economic loss suffered；Q is the some set of micro-capacitance sensor internal load；EENS_k、EENS_k＇ is respectively load point k before and after micro-capacitance sensor configuration Lack for Expected energy in year.

Wherein, expected energy not supplied (Expected Energy Not Supplied, EENS) represents that micro-capacitance sensor is due to machine Group be forced to that stoppage in transit etc. causes to user less for the expected value of electric energy, Integrative expression frequency of power cut, average duration peace All output powers.Its mathematic(al) representation is:

EENS=(P_oc-P_rc)P(X) (20)

In formula, P_ocFor stoppage in transit capacity；P_rcFor micro-capacitance sensor spare capacity；P (X) be stoppage in transit capacity be P_ocProbability.

Customer power supply index performance indicator B_URB

B_URBIncluding user's System average interruption frequency and user's System average interruption duration, wherein:

User's System average interruption frequency (Customer Average Interruption Frequency Index, CAIFI) The average frequency of power cut that in referring to 1 year, each user power failure influence is suffered, its computing formula is as follows:

$CAIFI=\frac{\underset{h}{\&Sigma;}{\mathrm{\&lambda;}}_h{N}_h}{\underset{h\&Element;EFF}{\&Sigma;}{N}_h}---\left(21\right)$

In formula, λ_hFault rate for load point h；N_hNumber of users for load point h；EFF is load point set power failure influence Close；

User's System average interruption duration (Customer Average Interruption Duration Index, CAIDI) referring to the System average interruption duration that the user being had a power failure in a year is suffered, its computing formula is as follows:

$CAIFI=\frac{\underset{h}{\&Sigma;}{\mathrm{\&lambda;}}_h{N}_h}{\underset{h\&Element;EFF}{\&Sigma;}{N}_h}---\left(22\right)$

In formula, U_hAnnual power off time for load point h.

Embodiment six

Below in conjunction with previous embodiment, the determination comprehensive benefit in the Optimal Configuration Method that the embodiment of the present invention six is provided The weight coefficient that evaluation index is corresponding is described in further detail.

In comprehensive benefit evaluation index, the weight coefficient of indices reflects that its significance level, conventional method have Variation Lines Number method, PCA and factor analysis etc..In a preferred embodiment, analytic hierarchy process (AHP) (AHP) and entropy can be used Power method combine determine the weight coefficient of indices in comprehensive benefit evaluation index, as it is shown on figure 3, the method specifically include as Lower step:

Step 301: set up hierarchy Model

Specifically, evaluation criteria system can be set up according to micro-capacitance sensor history data, determine hierarchy Model.Example As, comprehensive benefit evaluation index has every evaluation index m, the alternatives of project has n, then n scheme correspondence m is commented The desired value composing indexes decision matrix of valency index

X=(x_ij)_n×m

Step 302: be standardized data processing

Owing to dimension and the type of Comprehensive Benefit Evaluation index are different, it is difficult to compare, for convenience of processing, can be to respectively Individual index range transformation method is standardized processing.Such as, the index that equivalency index value is the bigger the better, carry out " forward " index Standardization

$y_{ij}=\frac{x_{ij}-x_{ij,min}}{x_{ij,\max }-x_{ij,min}}---\left(23\right)$

For the index that exponential quantity is the smaller the better, carry out " inversely " criterionization and process

$y_{ij}=\frac{x_{ij,max}-x_{ij}}{x_{ij,\max }-x_{ij,min}}---\left(24\right)$

The decision matrix obtained after standardization is:

Y=(y_ij)_n×m

Step 303: determine subjective weight by AHP

Specifically, first can set up weight key element recursive hierarchy structure according to micro-capacitance sensor history data, every layer is wanted Element by comparing composition judgment matrix, calculates this layer of key element relative weighting under single default subjective weight criterion and right Combining weights in overall goal；The most also can carry out consistency check.

Step 304: determine objective weight by entropy assessment

The relative Link Importance of index is measured by entropy assessment value, and the entropy calculating jth index can be expressed as:

$H\left(y_j\right)=1+{\left(\ln n\right)}^{-1}\underset{i=1}{\overset{n}{\&Sigma;}}{x}_{ij}\ln x_{ij}---\left(25\right)$

Wherein,

$x_{ij}=y_{ij}/\underset{i=1}{\overset{n}{\&Sigma;}}{y}_{ij}---\left(26\right)$

Further can be by the weight ε of formula (27) parameter j_j:

${\mathrm{\&epsiv;}}_j=H_j/\underset{j=1}{\overset{m}{\&Sigma;}}{H}_j---\left(27\right)$

In formula, 0≤ε_j≤ 1,

Step 305: determine comprehensive weight coefficient

The index weights determined by entropy assessment be according to data between relation determine, but do not consider practical situation, and The weight that AHP determines is obtained by micro-capacitance sensor history data, and the comprehensive measurement both combined is the most accurate, such as may be used To determine comprehensive weight coefficient by the following method:

ω_i=α ω_i'+(1-α)ω_i”(28)

In formula, 0≤α≤1, ω_i＇ represents subjective weighted value；ω_i" represent objective weight value.

By AHP by complicated micro-capacitance sensor hierarchicabstract decomposition, carry out on the basis of qualitative analysis combines with quantitative analysis Simple mathematical operation, obtains clear and definite result；The entropy assessment degree of contact to each attribute or the letter of offer are provided The relative Link Importance of breath amount carries out tolerance and determines an index weights.Each factor in multiple objective function is determined by AHP-entropy assessment Weight, is converted into single-goal function by multiple objective function and solves, and on the premise of taking into account achievement data objectivity, makes to determine Weight coefficient result corresponding to comprehensive benefit evaluation index more accurately, reliable.

Embodiment seven

Below in conjunction with previous embodiment, the constraints in the Optimal Configuration Method provide the embodiment of the present invention seven is carried out Further describe in detail.

Constraints specifically includes that optimized variable constraint, distributed power source run constraint, power supply reliability constraint, the energy Superfluous constraint, distribution close friend access the constraintss such as constraint and micro-capacitance sensor self performance constraint.Illustrate the most one by one:

Optimized variable retrains

In carrying out micro-capacitance sensor during the configuration of distributed power source quantity, such as, single wind generator, monolithic photovoltaic battery panel Determine with the type of monolithic lead-acid accumulator, wind-driven generator can be selected to be incorporated to number of units N_WT, photovoltaic cell plate array number N_PV, accumulator install number N_BATWith miniature gas turbine number of units N_MTAs optimized variable.During in view of micro-capacitance sensor planning and designing The planning area of each distributed power source, optimized variable is necessarily retrained, such as:

$\begin{array}{c}{N}_{WT\_\min }\&le;N_{WT}\&le;N_{WT\_\max }\\ N_{PV\_\min }\&le;N_{PV}\&le;N_{PV\_\max }\\ N_{BAT\_\min }\&le;N_{BAT}\&le;N_{BAT\_\max }\\ N_{MT\_\min }\&le;N_{MT}\&le;N_{MT\_\max }\end{array}---\left(29\right)$

In formula, N_{WT_max}、N_{PV_max}、N_{BAT_max}、N_{MT_max}Be respectively wind-driven generator, photovoltaic cell plate array, accumulator and The maximum installation quantity that miniature gas turbine determines according to actual place；N_{WT_min}、N_{PV_min}、N_{BAT_min}、N_{MT_min}It is respectively correspondence Minimum quantity is installed, for example, it is possible to be 0.

Distributed power source runs constraint

For miniature gas turbine, its power constraint is:

P_{MT_min}≤P_MT≤P_{MT_rate} (30)

In formula, P_MTFor miniature gas turbine output；P_{MT_rate}For miniature gas turbine rated power；P_{MT_min}For micro- Type gas turbine minimum output power.

For accumulator, it is in charge and discharge process, and its state-of-charge (State Of Charge, SOC) exists energy about Bundle:

SOC_min≤SOC(t)≤SOC_max (31)

SOC_minFor SOC lower limit, SOC_maxFor the SOC upper limit, for example, it is possible to respectively 0.2 and 0.8.

On the other hand, the maximum charge-discharge electric power that accumulator allows is relevant with the SOC of current accumulator and terminal voltage, therefore Exist and retrain as follows:

$P_{ch}^{\max }\left(t\right)=N_{BAT}\&CenterDot;max\{0,min\{({\mathrm{SOC}}_{\max }-SOC(t\left)\right)\&CenterDot;C_{BAT}/\mathrm{\&Delta;}t,I_{ch}^{\max }\}\&CenterDot;U_{BAT}\left(t\right)\}---\left(32\right)$

$P_{dh}^{\max }\left(t\right)=N_{BAT}\&CenterDot;max\{0,min\{\left(SOC\right(t)-{\mathrm{SOC}}_{min})\&CenterDot;C_{BAT}/\mathrm{\&Delta;}t,I_{dh}^{\max }\}\&CenterDot;U_{BAT}\left(t\right)\}---\left(33\right)$

In formula, N_BATFor accumulator quantity；U_BATFor accumulator voltage；C_BATFor battery rating；P_ch ^max、P_dh ^max It is respectively maximum charge power and discharge power that accumulator allows；I_ch ^max、I_dh ^maxThe maximum allowed in being respectively the unit interval Charging and discharging electric current, for example, it is possible to value is 20% less than battery rating.

Power supply reliability retrains

If micro-capacitance sensor is when isolated power grid, distributed power source cannot meet and meets demand；Or when being incorporated into the power networks, need from joining The power that electrical network obtains exceedes tie-line power transmission and limits, then micro-capacitance sensor there will be the situation that energy is not enough.Micro-by improving The capacity configuration of electrical network distributed power source can solve the problems referred to above, but increase and cost of investment too much can be made to be significantly increased.Therefore, Can be by load dead electricity rate (Loss of Power Supply Probability, LPSP) reliable as micro-capacitance sensor whole year operation Property constraints, thus distributing rationally in the case of realizing micro-capacitance sensor different loads dead electricity rate.Its expression formula is as follows:

$LPSP=\frac{\underset{i=1}{\overset{8760}{\&Sigma;}}{P}_{lpsp}\left(t\right)}{\underset{i=1}{\overset{8760}{\&Sigma;}}{P}_{load}\left(t\right)},LPAP\&le;{\mathrm{LPSP}}_{max}---\left(34\right)$

In formula, P_lpspT () is the electric power that t micro-capacitance sensor fails to meet；P_loadT () is the loading demand that t is total Power；LPSP_maxThe maximum load dead electricity rate allowed for micro-capacitance sensor；I is in 1 year 8760 hours i-th hour.

Energy surplus retrains

During micro-capacitance sensor isolated power grid, if wind-driven generator, photovoltaic array are exerted oneself the demand of overloading and the energy storage SOC upper limit it The constraint of sum；Or when micro-grid connection is run, exceeding interconnection conveying power upper limit to power distribution network transmission of electricity, micro-capacitance sensor can go out Existing energy surplus.Retrain to this end, microgrid energy excess rate (Excess Energy Ratio, EXC) can be arranged:

$EXC=\frac{\underset{i=1}{\overset{8760}{\&Sigma;}}{P}_{exc}\left(t\right)}{\underset{i=1}{\overset{8760}{\&Sigma;}}{P}_{load}\left(t\right)},EXC\&le;{\mathrm{EXC}}_{max}---\left(35\right)$

In formula, P_excT () is output unnecessary in t micro-capacitance sensor；P_loadT () is the loading demand merit that t is total Rate；EXC_maxThe ceiling capacity excess rate allowed for micro-capacitance sensor.

Distribution close friend accesses constraint

In micro-capacitance sensor, distributed power source is exerted oneself essentially from WT and PV, and its intermittence will affect the stable operation of micro-capacitance sensor, When micro-capacitance sensor is incorporated into the power networks with power distribution network, also can there is negative effect in stable operation and the quality of power supply to power distribution network.Therefore, The voltage level restraint at points of common connection (PCC) place of micro-capacitance sensor and power distribution network, power factor (PF), mutual power etc. can be set about Bundle, to ensure that the friendly of micro-capacitance sensor is accessed and the stable operation of power distribution network.

Specifically, voltage level restraint is:

U_{PCC_min}≤U_PCC≤U_{PCC_min} (36)

In formula, U_PCCIt it is the voltage levvl of PCC point；U_{PCC_min}、U_{PCC_max}It is PCC point voltage horizontal minimum and maximum limit respectively Value.

Power factor retrains:

${\mathrm{\&lambda;}}_{PCC}=\frac{P_{line}}{\sqrt{P_{line}^2+Q_{line}^2}}\&GreaterEqual;{\mathrm{\&lambda;}}_{PCC\_set}---\left(37\right)$

In formula, λ_{PCC, set}For PPC point power factor expected value；λ_PCCFor PCC point power factor；P_lineFor meritorious mutual merit Rate；Q_lineFor idle mutual power.

Mutual power constraint:

$S_{PCC}=\sqrt{P_{line}^2+Q_{line}^2}\&le;S_{PCC\_set}---\left(38\right)$

In formula, S_{PCC_set}Power limit is exchanged for PCC electricity；S_PCCFor PCC point actual power.

Micro-capacitance sensor self performance retrains:

$R_{self}=\frac{E_{self}}{E_{total}}\&GreaterEqual;R_{self\_set}---\left(39\right)$

In formula, R_selfFor self-balancing rate；R_{self_set}Expect for self-balancing rate；E_selfFor bearing that micro-capacitance sensor self is met by Lotus power consumption；E_totalTotal power consumption rate for micro-grid load.

By micro-capacitance sensor self-balancing rate is retrained, it is possible to alleviate internal load uses Voltage force, promotes in micro-capacitance sensor The power supply quality of distributed power source.

Embodiment eight

Below in conjunction with previous embodiment, the micro-capacitance sensor operation side in the Optimal Configuration Method that the embodiment of the present invention eight is provided Case is described in further detail.

Specifically, micro-capacitance sensor operating scheme can include micro-capacitance sensor isolated power grid, or micro-grid connection is run.

Micro-capacitance sensor isolated power grid

The improvement charging strategy that soft charge (SC) and hard charging (HC) can be used to combine realizes micro-capacitance sensor isolated power grid. When in micro-capacitance sensor, the output of renewable energy source current (such as WT, PV etc.) and accumulation power supply (such as BAT etc.) disclosure satisfy that During workload demand, non-regeneration energy power supply (such as MT) is out of service；When regenerative resource energy residual, to energy storage device Charging；When renewable energy source current and energy storage device can not meet workload demand, miniature gas turbine starts, and follows micro-capacitance sensor Load variations, simultaneously energy storage device charging, until energy storage device is full of or regenerative resource disclosure satisfy that workload demand.

Micro-grid connection is run

Grid type micro-capacitance sensor can carry out free two-way exchange power, work based on micro-capacitance sensor " peak load shifting " with power distribution network With, not to power distribution network sale of electricity during paddy, during peak on the basis of safe and reliable operation, can be to power distribution network sale of electricity.Such as, it is incorporated into the power networks Scheme can include following strategy:

Strategy 1: micro-capacitance sensor internal priority utilizes WT, PV to generate electricity, tracing control maximum power output；

Strategy 2: when WT, PV and MT exert oneself exceed micro-grid load time, the part exceeded when peak to power distribution network sell, Charge to BAT when paddy, if BAT reaches the SOC upper limit, then to power distribution network sale of electricity, if now MT cost of electricity-generating is less than electricity price, can be Increase under constraints and exert oneself to power distribution network sale of electricity；

Strategy 3: when WT, PV and MT exert oneself cannot meet micro-capacitance sensor internal load time, then by BAT output power supply, with Time detection BAT charging and discharging state；

Strategy 4: if BAT can meet micro-capacitance sensor safe and reliable operation in process range, increases BAT and exports to joining when peak Electrical network sale of electricity；

Strategy 5: if BAT exerts oneself cannot guarantee micro-capacitance sensor safe and reliable operation, then compare MT cost of electricity-generating and purchases strategies, If MT cost of electricity-generating is higher than purchases strategies, then MT does not exerts oneself, to power distribution network power purchase；If being unsatisfactory for distribution close friend to access constraint, then Start MT and meet micro-capacitance sensor internal loading demand；

Strategy 6: if MT cost of electricity-generating is less than electricity price, micro-capacitance sensor priority scheduling MT is exerted oneself, if can expire in the range of MT exerts oneself Foot micro-grid load demand, then can continue to exert oneself to power distribution network sale of electricity.If MT can not meet micro-capacitance sensor internal load, then to distribution Net purchase electricity.

Embodiment nine

Below in conjunction with previous embodiment, in the Optimal Configuration Method that the embodiment of the present invention nine is provided, is passed through particle group optimizing Algorithm for Solving comprehensive benefit object function is described in further detail.

It is possible, firstly, to set up population according to following micro-capacitance sensor optimized variable: wind-driven generator number of units N_WT, photovoltaic battery panel Array number N_PV, accumulator install number N_BATWith miniature gas turbine number of units N_MT；Scale, initial velocity to above population Initialize with position etc.；

For each example in population, in conjunction with the running environment data of distributed power source in the micro-capacitance sensor gathered and load Data, obtain the output data of distributed power source in micro-capacitance sensor；Then according to distributed electrical source model (such as formula (1) to (6)) And micro-capacitance sensor operating scheme, carry out micro-capacitance sensor Dynamic simulation, obtain WT, PV, BAT and MT rated power, comprehensive benefit respectively The weight coefficient of evaluation index (such as being obtained by formula (7) to (21)) and correspondence thereof (is such as obtained to (28) by formula (23) Take), seek object function (22), obtain distributed power source quantity N that each example is corresponding_WT、N_PV、N_BAT、N_MT, filter out and meet about Bundle condition distributed power source magnitude-set, as the configuration quantity of distributed power source in micro-capacitance sensor.

Embodiment of above is merely to illustrate presently preferred embodiments of the present invention, rather than limitation of the present invention.Correlation technique The technical staff in field in the case of without departing from the principle of the present invention and scope, various replacements, modification and the improvement made Should be included within the scope of the present invention.

Claims (10)

1. a micro-capacitance sensor Optimal Configuration Method based on comprehensive benefit analysis, it is characterised in that described method includes:

Gather running environment data and the load data of distributed power source in micro-capacitance sensor, according to distributed electrical source model and micro-electricity Network operation scheme, obtains output data and the comprehensive benefit evaluation index of distributed power source；

According to comprehensive benefit evaluation index and the weight coefficient of correspondence thereof, set up the comprehensive effect that number of power sources in a distributed manner is variable Benefit object function；

By particle swarm optimization algorithm comprehensive benefit object function, obtain the distributed power source quantity meeting constraints, As the configuration quantity of distributed power source in micro-capacitance sensor.

Method the most according to claim 1, it is characterised in that described distributed power source includes: wind-driven generator WT, photovoltaic Array PV, miniature gas turbine MT and energy storage device BAT；

Described distributed electrical source model includes: WT exert oneself model, the MT of model, PV that exert oneself exerts oneself model and BAT capacity model.

Method the most according to claim 1, it is characterised in that described micro-capacitance sensor operating scheme includes: micro-capacitance sensor orphan nets fortune OK, or micro-grid connection run.

Method the most according to claim 1, it is characterised in that described comprehensive benefit evaluation index includes: economic benefit refers to Mark, environmental benefit index and reliability benefit index.

Method the most according to claim 4, it is characterised in that described economic benefits indicator includes: wait year value cost of investment Index and economic well-being of workers and staff index；

Described environmental benefit index includes: energy-saving benefit index and reduction of discharging performance indicator；

Described reliability benefit index includes: micro-capacitance sensor reliability benefit index and customer power supply index performance indicator.

Method the most according to claim 1, it is characterised in that described constraints includes: optimized variable constraint, distributed Power supply runs constraint, power supply reliability constraint, energy surplus retrains, distribution close friend accesses constraint and micro-capacitance sensor self performance Constraint.

Method the most according to any one of claim 1 to 6, it is characterised in that described method farther includes: use pole Described comprehensive benefit evaluation index is standardized processing by difference converter technique.

Method the most according to claim 7, it is characterised in that described comprehensive benefit object function is:

Min F (x)=min ∑ ω_if_i(x)

Wherein, x is distributed power source quantity, and i is positive integer, f_iX () is i-th comprehensive benefit assessment through standardization Index, ω_iIt it is the weight coefficient that i-th comprehensive benefit evaluation index is corresponding.

Method the most according to claim 8, it is characterised in that described method farther includes: use analytic hierarchy process (AHP) and Entropy assessment combines and determines the weight coefficient that comprehensive benefit evaluation index is corresponding.

Method the most according to claim 8, it is characterised in that described method farther includes:

Obtain the configuration quantity of distributed power source in micro-capacitance sensor more than two, select wherein corresponding comprehensive benefit evaluation index high In preset value as micro-capacitance sensor in the configuration quantity of distributed power source.