CN105514988A - Micro power grid power supply planning scheme optimal selection method taking dynamic time-space features into consideration - Google Patents

Abstract

The invention relates to a micro power grid power supply planning scheme optimal selection method taking dynamic time-space features into consideration. First of all, a random process model is established for describing the dynamic time-space features in power supply planning of a micro power grid, based on a real options theory, time-varying parameters are added to economic indexes, and an energy price risk rate index is brought forward. Generating benefit indexes are introduced for describing complementation benefits of joint power generation of multiple intermittent power sources. Finally, weights of the economic indexes, reliability indexes, complementation benefit indexes and environmental protection indexes of a power supply planning scheme are made clear by use of a hierarchical analytical method, and comprehensive evaluation and optimal selection are performed on the power supply planning schemes of the micro grid with different time-space features. According to the invention, for the purpose of solving the planning problem of an independent micro power grid about "when to start a micro power grid investment project", the power supply planning schemes of the micro grid are evaluated and preferably selected by taking influences exerted by the schemes on full-life-cycle economic benefits of the micro power grid, reliability, complementarity of joint power generation of multiple energy and environmental protection into consideration.

Description

A kind of micro-capacitance sensor power source planning Scheme Optimum Seeking Methods taking into account dynamic characters

Technical field

The present invention relates to a kind of power source planning Scheme Optimum Seeking Methods, especially relate to a kind of micro-capacitance sensor power source planning Scheme Optimum Seeking Methods taking into account dynamic characters.

Background technology

How assessment indicator system is set up to the power source planning scheme of micro-capacitance sensor, provide decision-making preferably foundation, become a problem in the urgent need to research.The current program evaluation being applicable to micro-capacitance sensor on a small quantity mainly gives reliability, economy, environmental protection evaluation index in studying.

But all do not take into account the dynamic characters of micro-capacitance sensor planning investment in these assessment indicator systems.The dynamic characters of micro-capacitance sensor planning investment, the uncertain feature of Financial cost that the cycle comprised due to planned project originates in not the same year and causes, be also included within the production run process of micro-capacitance sensor, because different energy sources form is in the time with geographically naturally have very strong complementary characteristic simultaneously.

First, if the starting year of the planned project of somewhere micro-capacitance sensor is not for working as the year before last, but delay developing in a certain year of future, due to the uncertainty of the Construction of Unit cost in its economy cost and fuel cost, thus cause economic index dyscalculia, thus cannot answer the problem of " when starting electric generation investment project " in power source planning.

In addition, because different energy sources form is in the time with geographically naturally have very strong complementarity, different types of renewable energy resource combined electric generating, can make up mutually the loss that respective energy intermittence is brought, and improves electrical network to the degree of dissolving of the intermittent renewable energy.When to selection micro-capacitance sensor power source planning scheme, assessment adopts the value of various energy resources form complemental power-generation to seem very necessary.

Also do not take into account performance indicator in existing index study, lack the index that the complementary benefit of dissimilar intermittent power supply cogeneration is evaluated.

Therefore, the programme of micro-capacitance sensor preferably needs to consider following Railway Project:

1) will intend the technical progress rate in initial planning year to project to predict, particularly the unit uncertainty of cost of new forms of energy unit as photovoltaic generation, wind power generation has quantitative description.

2) will take into full account that micro-capacitance sensor runs the fuel price fluctuation in life cycle, the economic index that power source planning is invested more can reflect truth.

3) if there is the power supply of number of different types in micro-capacitance sensor, then need to assess the complementary benefit of dissimilar intermittent power supply cogeneration.

Summary of the invention

The present invention mainly solves the technical problem existing for prior art; A kind of micro-capacitance sensor power source planning Scheme Optimum Seeking Methods taking into account dynamic characters is proposed first.First set up random process model to be described in the dynamic characters of micro-capacitance sensor power source planning, in economic index, add time-varying parameter based on Real Option Theory, propose energy prices relative risk index.And introduce a class power benefit index, the complementary benefit of multiple intermittent power supply cogeneration is described.Finally, use the weight of the economic index of analytic hierarchy process (AHP) clear and definite power source planning scheme, reliability index, complementary performance indicator, environmental protection index, overall merit is made with preferred to the power source planning scheme of the micro-capacitance sensor of different space-time characteristic.

Above-mentioned technical problem of the present invention is mainly solved by following technical proposals:

Take into account a micro-capacitance sensor power source planning Scheme Optimum Seeking Methods for dynamic characters, it is characterized in that,

Step 1: the programme treating selection sets up characterization technique progress and the probabilistic random process model of fuel price, if the starting year of certain power source planning scheme is t, by adjustment random process model, calculating simulation goes out the desired value I of t Construction of Unit cost _t, and programme starts the fuel cost desired value P of latter 1 year _t+n.

Step 2: use Monte Carlo Method of Stochastic, by the equilibrium data of this micro-capacitance sensor hour to calculate, then calculate year short of electricity probability and expect a LOLP, lack amount of power supply year and expect that LOLE, year short of electricity frequency expect that LOLF, mean hours short of electricity amount expect that EENS is as reliability evaluation index; Calculate the Financial cost C of the Life cycle of micro-capacitance sensor _cF, the operation of each power supply and fuel cost O _ti; Calculate energy prices risk cost C _risk.

Step 3: for the micro-grid system containing multiple intermittent energy electricity generation system, according to the result of production simulation, calculate the complementary performance indicator of multiple intermittent energy, as complementary gain capacity C _m, complementary gain degree λ _un, generation of electricity by new energy accounting K _n, abandon resource accounting K _a, energy storage device utilance K _s, and K is compared in the generating of environmental protection index high-carbon _carbonwith year pollutant discharge amount E _p.

Step 4: the power source planning scheme treating selection based on analytic hierarchy process (AHP) carries out overall merit, need to calculate its each refer to target value, then the index of different schemes is compared, is normalized, form an index coefficient matrix μ (x _w).

Step 5: policymaker, by after carrying out com-parison and analysis to the importance degree between two between index set, builds suitable index weights matrix and judgment matrix, calculates its eigenvalue of maximum and characteristic vector, determine rational weight matrix W _k.

Step 6: last, is calculated the comprehensive evaluation value T of micro-capacitance sensor power source planning scheme to be selected, selects cost performance preferred plan from all optional programs by linear weighted function summation.

In above-mentioned a kind of micro-capacitance sensor power source planning Scheme Optimum Seeking Methods taking into account dynamic characters, in described step 1, the desired value E [I of unit cost _t] along with the expression formula of the change of t be in time:

E[I _t]＝I ₀e ^-λt(1-φ)＝I ₀e ^-γt

In formula, I ₀represent the Construction of Unit cost of t=0, if λ t recording technique innovation number of times, φ ∈ [0,1) be the constant that table technology levies innovation degree.Parameter lambda is technological innovation rate.In formula, γ=λ t (1-φ).

Then the fuel price desired value in certain moment following is:

E[P _t]＝P ₀e ^μt

In formula, μ represents the drift rate of Brownian motion process, P ₀represent the fuel cost of t=0.

In above-mentioned a kind of micro-capacitance sensor power source planning Scheme Optimum Seeking Methods taking into account dynamic characters, in described step 2, the Financial cost mathematic(al) representation of Life cycle is:

$C_{CF}=\underset{t=j}{\overset{T+j}{\Σ}}\underset{i=1}{\overset{N}{\Σ}}\frac{x_i{C}_{{\mathrm{pt}}_i}+x_i{I}_{t_i}+x_i{O}_{t_i}+x_i{M}_{t_i}}{{(1+r)}^t}$

In formula, N represents power supply type number, x _ibe the number of i-th kind of power supply, it is initial from J that J represents the project life cycle, be initial construction cost project period of i-th kind of power supply, C _ptibe the carbon emission punishment cost of i-th kind of power supply, T is the life cycle time limit, O _tibe operating cost during i-th kind of power supply T, represent maintenance cost, r is discount rate.In formula, unit is at the construction cost I of t _tsee the Construction of Unit cost E [I in step 1 _t].

Operation and the fuel cost expression formula of each power supply are:

$O_{ti}=K_{{\mathrm{FC}}_i}{E}_{ti}{P}_t$

In formula, the fuel cost proportionality coefficient of each high-carbon energy, E _tibe the energy output of i-th kind of power supply at t.P _tfor the price of t in international crude petroleum, see the desired value E [P of the fuel price in step 1 at t _t].

Energy prices obey random process at the change procedure in future, and be the variable of a change at random, if do not take into account this change at the power source planning initial stage, then program results has deviation, and definition energy prices risk cost characterizes this deviation:

$C_{risk}=\frac{O_{\mathrm{var}}-O_{con}}{C_{{\mathrm{CF}}_j}\left(x\right)}=\frac{\underset{t=t_0}{\overset{t}{\Σ}}{K}_{{\mathrm{FC}}_i}{E}_{ti}{P}_t-\underset{t=t_0}{\overset{t}{\mathrm{\Σ}}}{K}_{{\mathrm{FC}}_i}{E}_{ti}{P}_{con}}{C_{{\mathrm{CF}}_j}\left(x\right)}$

In formula, O _varduring for taking into account following annual energy price volatility, the fuel cost in the Life cycle of planned project, O _confor the fuel cost when supposition future source of energy price is constant.P _conassuming that constant energy prices, the general fuel cost by the planning starting year substitutes into and calculates.

In above-mentioned a kind of micro-capacitance sensor power source planning Scheme Optimum Seeking Methods taking into account dynamic characters, in described step 3, definition wind-light combined power generation system complementary gain capacity is

C _m＝C _un-C _wind-C _PV

In formula, C _unthe credible capacity of this wind-light combined power generation system, C _windthe credible capacity of wind-powered electricity generation in system, C _pVit is the credible capacity of photovoltaic generation in system.

Definition complementary gain degree is complementary gain capacity C _mcapacity C credible with wind-light combined power generation system _unratio:

${\mathrm{\λ}}_{un}=\frac{C_m}{C_{un}}$

The ratio that definition generation of electricity by new energy amount accounts for year total capacity requirement is generation of electricity by new energy accounting:

$K_N=\frac{{\mathrm{\ΣE}}_N}{{\mathrm{\ΣE}}_{load}}$

In formula, ∑ E _nfor the year effective output of all new forms of energy units of micro-capacitance sensor, ∑ E _loadfor this total capacity requirement electricity in micro-capacitance sensor year.

It is that calculating formula is as follows by the ratio abandoning energy output and new forms of energy gross generation that resource accounting index is abandoned in definition:

$K_A=\frac{{\mathrm{\ΣE}}_{NA}}{{\mathrm{\ΣE}}_N}$

In formula, ∑ E _nAfor meet situation due to load and energy storage does not remain active volume time, energy output that new forms of energy are abandoned, ∑ E _nAfor the year effective output of all new forms of energy units of micro-capacitance sensor.

The utilance of definition energy storage device is as follows:

$K_s=\frac{{\mathrm{\ΣE}}_{ex}}{{\mathrm{\ΣE}}_{ease}}---\left(16\right)$

In formula, ∑ E _exfor energy storage device be used for the accumulative annual energy output sent of support load time, ∑ E _easeyear for energy storage device adds up available energy output.

Therefore, tool of the present invention has the following advantages: propose a kind of micro-capacitance sensor power source planning Scheme Optimum Seeking Methods taking into account dynamic characters first.First set up random process model to be described in the dynamic characters of micro-capacitance sensor power source planning, in economic index, add time-varying parameter based on Real Option Theory, propose energy prices relative risk index.And introduce a class power benefit index, the complementary benefit of multiple intermittent power supply cogeneration is described.Finally, use the weight of the economic index of analytic hierarchy process (AHP) clear and definite power source planning scheme, reliability index, complementary performance indicator, environmental protection index, overall merit can be made with preferred to the power source planning scheme of the micro-capacitance sensor of different space-time characteristic.

Accompanying drawing explanation

Fig. 1 is the micro-capacitance sensor power source planning index system in the present invention.

Fig. 2 is the schematic flow sheet of micro-capacitance sensor power source planning index method for optimizing in the present invention.

Fig. 3 is the micro-capacitance sensor Life cycle cost that in the present embodiment, different year is initial.

Fig. 4 is the micro-capacitance sensor overall merit (balance scene) that in the present embodiment, different year is initial.

Fig. 5 is the micro-capacitance sensor overall merit (high-carbon scene) that in the present embodiment, different year is initial.

Embodiment

Below by embodiment, and by reference to the accompanying drawings, technical scheme of the present invention is described in further detail.

Embodiment:

Below in conjunction with accompanying drawing, technical scheme of the present invention is described in further detail:

The present invention considers economic index, reliability index, performance indicator, environmental protection index carry out preferably (accompanying drawing 1) micro-capacitance sensor capital project.First the random process model of technological progress and fuel price in future time is established, in order to describe the dynamic space-time characteristic of micro-capacitance sensor investment.In the economic index of micro-capacitance sensor, add relevant time-varying parameter, and introduce a class power benefit index, describe the complementary benefit of multiple intermittent power supply cogeneration.Then use analytic hierarchy process (AHP) to carry out overall merit to above index, in the power source planning scheme never with the micro-capacitance sensor of space-time characteristic, get optimal case.

1. the economic index computational process taking into account dynamic characters is as follows:

Assuming that the starting year of certain power source planning project is t, improve in t production technology, this project need bear Construction of Unit expense is then I _t, then this part cost is the cost of investment of the whole project of technology then.Technological progress is the external cause that cost reduces, and the progress of technology over time process is random, assuming that during t > 0, the construction cost of unit is:

$I_t=I_0{\mathrm{\φ}}^{N_t}$

In formula, I ₀represent the Construction of Unit cost of t=0, N _tpoisson stochastic variable, if λ t recording technique innovation number of times, φ ∈ [0,1) be the constant that table technology levies innovation degree.Parameter lambda is the rate of technological innovation.Obviously, the desired value I of unit cost _texponential relationship is had along with time t:

E[I _t]＝I ₀e ^-λt(1-φ)＝I ₀e ^-γt

In formula, γ=λ t (1-φ).Therefore, if adjustment parameter lambda or φ, reduce technology while the arrival rate λ as increase technological innovation and levy innovation degree φ, then variable λ (1-φ) may be constant.In addition, adjustment parameter lambda or φ, can change the probability distribution of following Construction of Unit cost, I _tvariance as follows:

$Var\[I_t\]=I_0^2(e^{-\mathrm{\λ}t(1-{\mathrm{\φ}}^2)}-e^{-2\mathrm{\λ}t(1-\mathrm{\φ})})=I_0^2\[{\left(e^{-\mathrm{\γ}t}\right)}^{(1+\mathrm{\φ})}-{\left(e^{-\mathrm{\γ}t}\right)}^2\]$

As can be seen from the above equation, when λ levels off to infinity, φ level off to 1 time, variance Var [I _t] close to zero, namely increase λ and φ and the uncertainty of technological progress can be made to reduce.On the other hand, adjustment φ level off to 0 time, the uncertainty of technological progress is maximum.That is, can the degree of uncertainty of adjustment technology progress by adjustment λ and φ.

In micro-capacitance sensor, traditional high-carbon unit such as gas turbine, diesel engine need to rely on fossil fuel to generate electricity, and therefore the price of fuel directly has influence on the operating cost of conventional rack.In this article, the fuel cost P of project t _tbe assumed to meet geometric Brownian motion process in the change process in future:

$\frac{{\mathrm{dP}}_t}{P_t}=\mathrm{\μ}dt+\mathrm{\σ}dz$

In formula, μ and σ represents drift rate and the fluctuation parameters of this Brownian motion process respectively, and dz is the often step variation of standard Brownian motion process.

Assuming that 0≤μ < r, wherein r is discount rate.According to the mathematical characteristic of geometric Brownian motion process, then the fuel price desired value in certain moment following is:

E[P _t]＝P ₀e ^μt

Then the uncertainty of fuel price can be adjusted by parameter σ, can be learnt by above formula, and the adjustment of parameter σ can not change the motion path of Brownian motion process.

The economic mathematical model of the Life cycle of micro-capacitance sensor is described below:

$C_{{\mathrm{CF}}_j}\left(x\right)=\underset{t=j}{\overset{T+j}{\&Sigma;}}\underset{i=1}{\overset{N}{\&Sigma;}}\frac{x_i{C}_{{\mathrm{pt}}_i}+x_i{I}_{t_i}+x_i{O}_{t_i}+x_i{M}_{t_i}}{{(1+r)}^t}$

In formula, N represents power supply type number, x _ibe the number of i-th kind of power supply, it is initial from J that J represents the project life cycle, be initial construction cost project period of i-th kind of power supply, C _ptibe the carbon emission punishment cost of i-th kind of power supply, T is the life cycle time limit, O _tibe operating cost during i-th kind of power supply T, represent maintenance cost, r is discount rate.In formula, unit is at the construction cost I of t _tsee the Construction of Unit cost E [I in random process model used above _t].

To high-carbon energy, annual operation and fuel cost are proportional to International Crude Oil then.

$O_{ti}=K_{{\mathrm{FC}}_i}{E}_{ti}{P}_t$

In formula, the fuel cost proportionality coefficient of each high-carbon energy, E _tibe the energy output of i-th kind of power supply at t.P _tfor the price of t in international crude petroleum, see the desired value E [P at t in fuel price random process model used above _t].

Because wind-resources and light resources are in time and complementarity geographically, make combining wind and light to generate electricity than being used alone wind power generation or photovoltaic generation more can make up due to the loss that brings of energy intermittence.Definition complementary gain capacity and complementary degree weigh this benefit herein.

2. performance indicator computational process is as follows:

If wind-light combined power generation system complementary gain capacity is:

C _m＝C _un-C _wind-C _PV

In formula, C _unthe credible capacity of this wind-light combined power generation system, C _windthe credible capacity of wind-powered electricity generation in system, C _pVit is the credible capacity of photovoltaic generation in system.From definition, if complementary gain capacity C _mbe greater than 0, then system benefits is in complementary characteristic, C _mlarger, complementary characteristic is better.

Definition complementary gain degree is complementary gain capacity C _mcapacity C credible with wind-light combined power generation system _unratio:

${\mathrm{\&lambda;}}_{un}=\frac{C_m}{C_{un}}$

λ in formula _unfor the complementary gain degree of wind-light combined power generation system.From definition, if complementary gain degree λ _unlarger, system complementary characteristic is better.

The ratio that definition generation of electricity by new energy amount accounts for year total capacity requirement is generation of electricity by new energy accounting:

$K_N=\frac{{\mathrm{\&Sigma;E}}_N}{{\mathrm{\&Sigma;E}}_{load}}$

In formula, ∑ E _nfor the year effective output of all new forms of energy units of micro-capacitance sensor, ∑ E _loadfor this total capacity requirement electricity in micro-capacitance sensor year.

If what the active volume of energy storage was less than this moment new energy source machine group is imbued with when exerting oneself, the situation of " abandoning wind " and " abandoning light " will be there will be.Resource accounting index is abandoned in definition is herein that calculating formula is as follows by the ratio abandoning energy output and new forms of energy gross generation:

$K_A=\frac{{\mathrm{\&Sigma;E}}_{NA}}{{\mathrm{\&Sigma;E}}_N}$

In formula, ∑ E _nAfor meet situation due to load and energy storage does not remain active volume time, energy output that new forms of energy are abandoned, ∑ E _nAfor the year effective output of all new forms of energy units of micro-capacitance sensor.

The residue active volume of energy storage device along with distributed power source different with the match condition of load and fluctuate, the power that the charge-discharge electric power of energy storage device and energy storage and power supply or load exchange.The utilance of definition energy storage device is as follows:

$K_s=\frac{{\mathrm{\&Sigma;E}}_{ex}}{{\mathrm{\&Sigma;E}}_{ease}}$

In formula, ∑ E _exfor energy storage device be used for the accumulative annual energy output sent of support load time, ∑ E _easeyear for energy storage device adds up available energy output.

Energy prices obey random process at the change procedure in future, and be the variable of a change at random, if do not take into account this change at the power source planning initial stage, then program results has deviation, define energy prices risk cost herein and characterize this deviation:

$C_{risk}=\frac{O_{\mathrm{var}}-O_{con}}{C_{{\mathrm{CF}}_j}\left(x\right)}=\frac{\underset{t=t_0}{\overset{t}{\&Sigma;}}{K}_{{\mathrm{FC}}_i}{E}_{ti}{P}_t-\underset{t=t_0}{\overset{t}{\mathrm{\&Sigma;}}}{K}_{{\mathrm{FC}}_i}{E}_{ti}{P}_{con}}{C_{{\mathrm{CF}}_j}\left(x\right)}$

In formula, O _varduring for taking into account following annual energy price volatility, the fuel cost in the Life cycle of planned project, O _confor the fuel cost when supposition future source of energy price is constant.P _conassuming that constant energy prices, the general fuel cost by the planning starting year substitutes into and calculates.

After having calculated the indices of all planning capital projects to be selected, Integrated comparative need be done based on the index of analytic hierarchy process (AHP) to different schemes.

3. the preferred computational process of scheme based on analytic hierarchy process (AHP) is as follows:

Sort to index according to importance degree, structure judges weight ratio matrix, and then Judgement Matricies B, and calculates its eigenvalue of maximum λ _max, and corresponding characteristic vector as:

x＝[x ₁，x ₂，x ₃，…x _n] ^T

The consistency of test and judge matrix, the degree of consistency of metric matrix B is expressed as as C (B)≤0.1, think that the compatibility of judgment matrix B is better, the eigenvalue of maximum λ of matrix B _maxcharacteristic of correspondence vector is x=[x ₁, x ₂, x ₃... x _n] ^tbe exactly weight vectors W=[w ₁, w ₂, w ₃... w _n] ^t.

Because characteristic value characteristic of correspondence vector is general not unique, so carry out the normalization of characteristic vector.Normalization formula is as follows, the factor of evaluation for being the bigger the better:

$r_{i,hj}=\frac{x_{i,hj}-\min \left(x_{i,hj}\right)}{max\left(x_{i,hj}\right)-\min \left(x_{i,hj}\right)}$

Factor of evaluation for the smaller the better:

$r_{i,hj}=\frac{\max \left(x_{i,hj}\right)-x_{i,hj}}{max\left(x_{i,hj}\right)-\min \left(x_{i,hj}\right)}$

Bring gained Combining weights into formula, calculated the comprehensive evaluation value of micro-capacitance sensor power source planning scheme by linear weighted function summation:

$T=\underset{k=1}{\overset{n}{\&Sigma;}}{W}_k\mathrm{\&mu;}\left(x_w\right)$

In formula: μ (x _w) be the normalized value of actual index, W _kfor waiting the weighted value asking each index;

4., in order to verify the beneficial effect of the inventive method, carried out following emulation experiment:

Certain the actual Island electrical network adding wind-light storage electricity generation system is adopted to be example.The distributed electrical Source Type studied herein has blower fan, photovoltaic cell, miniature gas turbine 3 kinds.

To with reference in document [20] the crew qiting scheme that 4 kinds meet this micro-capacitance sensor power supply reliability, in table 1.These 4 kinds of schemes have all made the year short of electricity of present case micro-capacitance sensor hour in required scope.

Table 1

The allocation plan (unit: MW) of table 1 distributed power source unit

New forms of energy installation wherein in scheme 1 is minimum, and high-carbon machine kludge is maximum, does not have storage battery; Scheme 2 is contrary, is an installation scheme being partial to new forms of energy; Scheme 3 and scheme 4 are two kinds of half-way houses in new forms of energy and traditional high-carbon energy.

Suppose that the planning starting year of this micro-capacitance sensor was from 2014, as shown in table 2 to the attribute decision table of 4 kinds of schemes:

Table 2

Table 2 scheme attribute decision table

Weight vector W corresponding to its index obtains by analytic hierarchy process (AHP), namely

W＝(W ₁，W ₂，…w ₂₀)＝(0.0822，0.0822，0.1643，

0.0548，0.1643，0.0822，0.0235，0.0183，

0.0235，0.0235，0.0274，0.0183，0.0205，

0.0411，0.0235，0.0041，0.0088，0.0044，

0.0044，0.0044)

According to Weight summation, the index comprehensive assessed value finally obtaining 4 kinds of schemes is

T ₁＝0.451，T ₂＝0.520，T ₃＝0.789，T ₄＝0.467

Carry out sequence according to assessed value size to such scheme can obtain:

T ₃＞T ₂＞T ₄＞T ₁

Result shows, in this island microgrid planning example, after having taken into account the dynamic characters of evaluation index, considers reliability, economy and environmental protection index, adopts scheme 3 best.

In order to the dynamic characters in micro-capacitance sensor power source planning scheme is described, suppose that micro-capacitance sensor planning is initial from the different times, the scheme 1 in his-and-hers watches 1 does not have storage battery relatively more extreme, and hereafter Main Analysis scheme 2 is to scheme 4.

Accompanying drawing 3 is the Life cycle costs from initial micro-capacitance sensor programme in the different following not the same year:

Simulation result shows, for the micro-capacitance sensor in this example, from 2014 during planning construction, adopts the Life cycle cost of power configuration scheme 2 the highest; But from 2022 when this locality planning micro-capacitance sensor, adopt the Life cycle cost of power configuration scheme 2 to economize most.

According to the prediction to WeiLai Technology Progress Rate and the price of international energy of american energy Information Management Bureau and Mo Te MacDonald company, space-time characteristic can be divided into low-carbon (LC) scene, balance scene and high-carbon scene.Carried out the degree of uncertainty of adjustment technology progress by adjustment λ and φ, adjustment parameter σ adjusts the uncertainty of fuel price, simulates respectively to balance scene and high-carbon scene.

Based on these two kinds of scenes, respectively in emulation table 1 the execution starting year of rear 3 kinds of programmes be respectively 2011,2012, until the situation of 2039.

Its balance scene emulation obtain future 3 kinds of programmes overall target changing trend diagram as shown in Figure 4:

High-carbon scene simulation obtains the overall target changing trend diagram of following 3 kinds of programmes as accompanying drawing 5.

Can be seen by above emulation, after taking into account multi-space characteristic, the evaluation index that the comprehensive evaluation index of scheme 2 exceedes scheme 3 is following trend.In balance scene, scheme 2 exceeded scheme 3 in 2021, and in high-carbon scene, scheme 2 is deferred to 2032 and exceedes scheme 3.This is because the investment construction cost of unit in high-carbon scene and fuel price have less uncertainty, and in scheme 2, generation of electricity by new energy accounting is higher, will benefit from this uncertainty.Scientific and technological progress and support on policy can accelerate high-carbon scene to balance scene conversion, make generation of electricity by new energy have more cost performance.

Specific embodiment described in the present invention is only to the explanation for example of the present invention's spirit.Those skilled in the art can make various amendment or supplement or adopt similar mode to substitute to described specific embodiment, but can't depart from spirit of the present invention or surmount the scope that appended claims defines.

Claims (4)

1. take into account a micro-capacitance sensor power source planning Scheme Optimum Seeking Methods for dynamic characters, it is characterized in that,

Step 1: the programme treating selection sets up characterization technique progress and the probabilistic random process model of fuel price, if the starting year of certain power source planning scheme is t, by adjustment random process model, calculating simulation goes out the desired value I of t Construction of Unit cost _t, and programme starts the fuel cost desired value P of latter 1 year _t+n.

Step 2: use Monte Carlo Method of Stochastic, by the equilibrium data of this micro-capacitance sensor hour to calculate, then calculate year short of electricity probability and expect a LOLP, lack amount of power supply year and expect that LOLE, year short of electricity frequency expect that LOLF, mean hours short of electricity amount expect that EENS is as reliability evaluation index; Calculate the Financial cost C of the Life cycle of micro-capacitance sensor _cF, the operation of each power supply and fuel cost O _ti.

Step 3: for the micro-grid system containing multiple intermittent energy electricity generation system, according to the result of production simulation, calculate the complementary performance indicator of multiple intermittent energy, as complementary gain capacity C _m, complementary gain degree λ _un, generation of electricity by new energy accounting K _n, abandon resource accounting K _a, energy storage device utilance K _s, energy prices risk cost C _risk, and K is compared in the generating of environmental protection index high-carbon _carbonwith year pollutant discharge amount E _p.

Step 4: the power source planning scheme treating selection based on analytic hierarchy process (AHP) carries out overall merit, need to calculate its each refer to target value, then the index of different schemes is compared, is normalized, form an index coefficient matrix μ (x _w).

Step 5: policymaker, by after carrying out com-parison and analysis to the importance degree between two between index set, builds suitable index weights matrix and judgment matrix, calculates its eigenvalue of maximum and characteristic vector, determine rational weight matrix W _w.

Step 6: last, is calculated the comprehensive evaluation value T of micro-capacitance sensor power source planning scheme to be selected, selects cost performance preferred plan from all optional programs by linear weighted function summation.

2. a kind of micro-capacitance sensor power source planning Scheme Optimum Seeking Methods taking into account dynamic characters according to claim 1, is characterized in that, in described step 1, and the desired value E [I of unit cost _t] along with the expression formula of the change of t be in time:

E[I _t]＝I ₀e ^-λt(1-φ)＝I ₀e ^-γt

In formula, I ₀represent the Construction of Unit cost of t=0, if λ t recording technique innovation number of times, φ ∈ [0,1) be the constant that table technology levies innovation degree.Parameter lambda is technological innovation rate.In formula, γ=λ t (1-φ).

Then the fuel price desired value in certain moment following is:

E[P _t]＝P ₀e ^μt

In formula, μ represents the drift rate of Brownian motion process, P ₀represent the fuel cost of t=0.

3. a kind of micro-capacitance sensor power source planning Scheme Optimum Seeking Methods taking into account dynamic characters according to claim 1, it is characterized in that, in described step 2, the Financial cost mathematic(al) representation of Life cycle is:

$C_{CF}=\underset{t=j}{\overset{T+j}{\&Sigma;}}\underset{i=1}{\overset{N}{\&Sigma;}}\frac{x_i{C}_{{\mathrm{pt}}_i}+x_i{I}_{t_i}+x_i{O}_{t_i}+x_i{M}_{t_i}}{{(1+r)}^t}$

In formula, N represents power supply type number, x _ibe the number of i-th kind of power supply, it is initial from J that J represents the project life cycle, be initial construction cost project period of i-th kind of power supply, C _ptibe the carbon emission punishment cost of i-th kind of power supply, T is the life cycle time limit, O _tibe operating cost during i-th kind of power supply T, represent maintenance cost, r is discount rate.In formula, unit is at the construction cost I of t _tsee the Construction of Unit cost E [I in step 1 _t].

Operation and the fuel cost expression formula of each power supply are:

$O_{ti}=K_{{\mathrm{FC}}_i}{E}_{ti}{P}_t$

In formula, the fuel cost proportionality coefficient of each high-carbon energy, E _tibe the energy output of i-th kind of power supply at t.P _tfor the price of t in international crude petroleum, see the desired value E [P of the fuel price in step 1 at t _t].

4. a kind of micro-capacitance sensor power source planning Scheme Optimum Seeking Methods taking into account dynamic characters according to claim 1, is characterized in that, in described step 3, definition wind-light combined power generation system complementary gain capacity is

C _m＝C _un-C _wind-C _PV

In formula, C _unthe credible capacity of this wind-light combined power generation system, C _windthe credible capacity of wind-powered electricity generation in system, C _pVit is the credible capacity of photovoltaic generation in system.

Definition complementary gain degree is complementary gain capacity C _mcapacity C credible with wind-light combined power generation system _unratio:

${\mathrm{\&lambda;}}_{un}=\frac{C_m}{C_{un}}$

The ratio that definition generation of electricity by new energy amount accounts for year total capacity requirement is generation of electricity by new energy accounting:

$K_N=\frac{{\mathrm{\&Sigma;E}}_N}{{\mathrm{\&Sigma;E}}_{load}}$

In formula, ∑ E _nfor the year effective output of all new forms of energy units of micro-capacitance sensor, ∑ E _loadfor this total capacity requirement electricity in micro-capacitance sensor year.

It is that calculating formula is as follows by the ratio abandoning energy output and new forms of energy gross generation that resource accounting index is abandoned in definition:

$K_A=\frac{{\mathrm{\&Sigma;E}}_{NA}}{{\mathrm{\&Sigma;E}}_N}$

In formula, ∑ E _nAfor meet situation due to load and energy storage does not remain active volume time, energy output that new forms of energy are abandoned, ∑ E _nAfor the year effective output of all new forms of energy units of micro-capacitance sensor.

The utilance of definition energy storage device is as follows:

$K_s=\frac{{\mathrm{\&Sigma;E}}_{ex}}{{\mathrm{\&Sigma;E}}_{ease}}---\left(16\right)$

In formula, ∑ E _exfor energy storage device be used for the accumulative annual energy output sent of support load time, ∑ E _easeyear for energy storage device adds up available energy output.

Energy prices obey random process at the change procedure in future, and be the variable of a change at random, if do not take into account this change at the power source planning initial stage, then program results has deviation, and definition energy prices risk cost characterizes this deviation:

$C_{risk}=\frac{O_{\mathrm{var}}-O_{con}}{C_{{\mathrm{CF}}_j}\left(x\right)}=\frac{\underset{t=t_0}{\overset{t}{\&Sigma;}}{K}_{{\mathrm{FC}}_i}{E}_{ti}{P}_t-\underset{t=t_0}{\overset{t}{\&Sigma;}}{K}_{{\mathrm{FC}}_i}{E}_{ti}{P}_{con}}{C_{{\mathrm{CF}}_j}\left(x\right)}$

In formula, O _varduring for taking into account following annual energy price volatility, the fuel cost in the Life cycle of planned project, O _confor the fuel cost when supposition future source of energy price is constant.P _conassuming that constant energy prices, the general fuel cost by the planning starting year substitutes into and calculates.