CN104376385A - Microgrid power price optimizing method - Google Patents

Abstract

The invention discloses a microgrid power price optimizing method. In the microgrid power price optimizing method, influences of time-of-use power price on demand side response of users and economic microgrid operation are considered, the maximum total revenue of power supply enterprises is the goal, a microgrid power price optimizing model is constructed for the demand side response of the users and the economic microgrid operation, the microgrid power price optimizing model is subjected to solution by the particle warm optimization algorithm so as to determine the best time-of-use power price and the optimal generation dispatching scheme. Accordingly, the problems of excessive generation and waste of power resource of the microgrid system can be reduced, operation reliability of the microgrid system can be effectively ensured, power purchase risk of the users and the operation cost of the microgrid system are reduced, and considerable gains of the power supply enterprises are ensured. In addition, the operation process of the microgrid power price optimizing method is simple, good in universality and widely applicable to planning the microgrid power price optimization schemes adopting the peak-valley time-of-use power price mechanism in different places and has high market popularization and application value.

Description

A kind of micro-capacitance sensor electricity price optimization method

Technical field

The present invention relates to electric system electricity pricing technology at times, be specifically related to a kind of micro-capacitance sensor electricity price optimization method, belong to electric system electricity pricing technical field.

Background technology

Along with the development of power industry, intelligent grid becomes the focus that people pay close attention to gradually.In tradition electrical network, because user power utilization has undulatory property, peak load differs greatly, and causes the increase of electric grid investment and the waste of energy.In order to save the utilization factor of electric grid investment cost and the raising energy, proposing and adopting Peak-valley TOU power price to adjust power structure to guide user.Peak-valley TOU power price is a kind of effective demand response mode, its by suitably heightening electricity price in the load peak period, the load valley period suitably reduces electricity price, user is guided to formulate rational electricity consumption plan, thus the sub-load of peak period is transferred to low-valley interval, make load curve more smooth, reach the object of peak load shifting, balanced load.

As power supply enterprise, the research work of micro-capacitance sensor Price Mechanisms mainly comprises two aspects: the operational reliability improving power supply income and system.For the research of micro-capacitance sensor operation aspect in prior art, the more of concern is economic load dispatching aspect, and less research considers the reliability of micro-grid system, operating cost and the impact of user's request side response on micro-capacitance sensor economical operation.But, the high reliability of micro-grid system usually with high economic input or change user power mode for cost.If micro-capacitance sensor adopts Peak-valley TOU power price, its Price Mechanisms will directly affect the need for electricity response of user, and inappropriate Price Mechanisms may cause the load transfer plan of user's request side, produce user's Trading risk, cause the income of power supply enterprise to reduce on the contrary; And the customer charge caused because of user's Trading risk reduces, also can cause occurring that systems generate electricity amount crosses the higher problem of Sheng, electric power resource waste, operating cost, and cross the electrical production of containing and also make the life-span of system equipment and operational reliability be affected thereupon.Therefore, adopt in the Price Mechanisms formulation process of Peak-valley TOU power price at micro-capacitance sensor, need the equilibrium point seeking power supply reliability, economy and user power utilization comfort level, the maximize revenue of power supply enterprise can be reached as much as possible, guarantee the operation interests of micro-capacitance sensor.But, adopt the optimizing process of Peak-valley TOU power price model for micro-capacitance sensor in existing research, often all minimum with load peak-valley difference or peak load is minimum is objective function, and the peak-valley difference of load or peak load can not characterize the operational reliability of micro-grid system and there is the situation of Profit of power supply enterprise in Trading risk situation.In prior art, be directed to micro-capacitance sensor and adopt Peak-valley TOU power price mechanism, lack and a kind ofly effectively can guarantee the operational reliability of micro-grid system and guarantee the micro-capacitance sensor electricity price prioritization scheme that power supply enterprise's income is good.

Summary of the invention

For the deficiencies in the prior art, present invention is directed at micro-capacitance sensor and adopt Peak-valley TOU power price mechanism, consider tou power price to the response of user's request side and the impact of micro-capacitance sensor economical operation, to power, gross earnings is target to the maximum, take into account user's request side response and micro-capacitance sensor economical operation establish micro-capacitance sensor electricity price Optimized model, and adopt particle cluster algorithm to solve micro-capacitance sensor electricity price Optimized model, determine the best tou power price of micro-capacitance sensor and optimal power generation scheduling scheme, occur that generated energy crosses Sheng to reduce micro-grid system, the situation of electric power resource waste, more effectively guarantee the operational reliability of micro-grid system, reduce user's Trading risk, help when meeting supply load demand to reduce micro-grid system operating cost, guarantee that power supply enterprise can have good income.

For achieving the above object, present invention employs following technological means:

Micro-capacitance sensor electricity price optimization method, be directed to micro-capacitance sensor and adopt Peak-valley TOU power price mechanism, set up the micro-capacitance sensor electricity price Optimized model taking into account the response of user's request side and micro-capacitance sensor economical operation, adopt particle cluster algorithm to solve set up micro-capacitance sensor electricity price Optimized model, determine the best tou power price of micro-capacitance sensor and optimal power generation scheduling scheme; The method specifically comprises the steps:

(1) add up the historical data of wind speed and load in micro-grid system, according to historical data, the load under the wind power of day part in dispatching cycle and unexecuted Peak-valley TOU power price state is predicted;

(2) according to the response characteristic of user to electricity price, the response model of customer charge to tou power price is set up:

Cool load translating ratio is for carrying out after Peak-valley TOU power price, and load to the ratio of the transfer amount of electricity price lower period and high rate period load, comprises the electricity price peak load rate of transform, the flat cool load translating ratio of electricity price Pinggu cool load translating ratio and electricity price peak from the electricity price higher period;

Wherein, electricity price peak load rate of transform λ _pv, electricity price Pinggu cool load translating ratio λ _fvcool load translating ratio λ flat with electricity price peak _pfexpression formula be respectively:

In formula, Δ p _pvfor the electricity price of load peak period and load paddy period is poor, i.e. Δ p _pv=p _p-p _v; Δ p _fvfor the electricity price of load section and load paddy period is at ordinary times poor, i.e. Δ p _fv=p _f-p _v; Δ p _pffor the electricity price of load peak period and load section is at ordinary times poor, i.e. Δ p _pf=p _p-p _f; p _pfor the electricity price of load peak period, p _ffor the electricity price of load section at ordinary times, p _vfor the electricity price of load paddy period; a _pv, a _fv, a _pfbe respectively electricity price peak load and shift minimum threshold values, the minimum threshold values of electricity price Pinggu load transfer plan, the minimum threshold values of the flat load transfer plan in electricity price peak; b _pv, b _fv, b _pfbe respectively electricity price peak load and shift maximum threshold values, the maximum threshold values of electricity price Pinggu load transfer plan, the maximum threshold values of the flat load transfer plan in electricity price peak; K _pvfor electricity price peak load rate of transform λ _pvwith the electricity price difference Δ p of load peak period and load paddy period _pvincrease from minimum threshold values a _pvlinear increase is to maximum threshold values b _pvrate of rise; K _fvfor electricity price Pinggu cool load translating ratio λ _fvwith the electricity price difference Δ p of load section and load paddy period at ordinary times _fvincrease from minimum threshold values a _fvlinear increase is to maximum threshold values b _fvrate of rise; K _pffor the flat cool load translating ratio λ in electricity price peak _pfwith the electricity price difference Δ p of load peak period and load section at ordinary times _pfincrease from minimum threshold values a _pflinear increase is to maximum threshold values b _pfrate of rise; λ _pv ^max, λ _fv ^max, λ _pf ^maxbe respectively electricity price peak load maximum transfer rate, electricity price Pinggu load maximum transfer rate, electricity price peak flat load maximum transfer rate;

Under carrying out Peak-valley TOU power price state, in dispatching cycle, the matching load of day part is:

Formula (2) is namely as the response model of customer charge to tou power price; In formula, L _t0for the predicted load of t period in the unexecuted Peak-valley TOU power price state dispatching cycle; P _dtfor carrying out the matching load value of t period in the Peak-valley TOU power price state dispatching cycle; T _v, T _f, T _pthe time hop count of be respectively internal loading paddy period dispatching cycle, load section, load peak period at ordinary times; be respectively the load mean value of section, peak period at ordinary times under unexecuted Peak-valley TOU power price state;

(3) based on the response model of customer charge to tou power price, micro-capacitance sensor electricity price Optimized model is set up:

The load of day part in dispatching cycle is determined according to the response model of customer charge to tou power price, and enterprise income is maximum to power, micro-capacitance sensor operating cost is minimum for target, set up the micro-capacitance sensor electricity price Optimized model taking into account the response of user's request side and micro-capacitance sensor economical operation;

(4) adopt particle cluster algorithm to solve set up micro-capacitance sensor electricity price Optimized model, determine the best tou power price of micro-capacitance sensor within dispatching cycle and optimal power generation scheduling scheme.

In above-mentioned micro-capacitance sensor electricity price optimization method, specifically, described micro-capacitance sensor electricity price Optimized model is specially:

To power, enterprise income is target to the maximum, and the upper strata objective function of micro-capacitance sensor electricity price Optimized model is:

$\max C_{\mathrm{benefit}}=\underset{t=1}{\overset{T}{\mathrm{\Σ}}}{C}_{\mathrm{sale},t}\·P_{\mathrm{Dt}}\mathrm{\Δt}-C_{\mathrm{total}};---\left(3\right)$

In formula, C _benefitfor the total revenue of power supply enterprise; C _{sale, t}for to carry out in the Peak-valley TOU power price state dispatching cycle t the period power supply enterprise to the sale of electricity price of user; T be comprise dispatching cycle total time hop count; P _dtfor carrying out the matching load value of t period in the Peak-valley TOU power price state dispatching cycle; Δ t is the time interval of adjacent two periods; C _totalfor the operation total cost of micro-capacitance sensor;

Minimum for target with the operation total cost of micro-capacitance sensor, lower floor's objective function of micro-capacitance sensor electricity price Optimized model is:

$\min C_{\mathrm{total}}=\underset{t=1}{\overset{T}{\mathrm{\Σ}}}(\underset{n=1}{\overset{N}{\mathrm{\Σ}}}{C}_n^t(1-u_{n,t-1})u_{n,t}+\underset{n=1}{\overset{N}{\mathrm{\Σ}}}{u}_{n,t}{F}_{\mathrm{FC},\mathrm{tn}})+C_{\mathrm{bat}};---\left(4\right)$

Wherein, $C_n^t={\mathrm{\σ}}_n+{\mathrm{\δ}}_n(1-e^{(-T_{\mathrm{nt}}^{\mathrm{OFF}}/{\mathrm{\τ}}_n)});F_{\mathrm{FC},\mathrm{tn}}=F_f\underset{t}{\mathrm{\Σ}}\frac{P_{\mathrm{tn}}}{{\mathrm{\η}}_{\mathrm{tn}}};---\left(5\right)$

In formula, for the start cost of n-th group of miniature combustion engine in t the period in dispatching cycle; N is total group of number of miniature combustion engine in micro-capacitance sensor; u _n,tfor the opening of n-th group of miniature combustion engine in t the period in dispatching cycle, stopped status variable, u when being in open state _n,tvalue is 1, u when being in stopped status _n,tvalue is 0; T be comprise in dispatching cycle total time hop count; C _batfor the life consumption cost of lead-acid accumulator; σ _n, δ _n, τ _nit is the start-up cost coefficient of n-th group of miniature combustion engine; it is the idle time of n-th group of miniature combustion engine in dispatching cycle in t period; F _{fC, tn}for the operating cost of n-th group of miniature combustion engine in t the period in dispatching cycle; F _ffor fuel price; P _tnfor the output power of n-th group of miniature combustion engine in t the period in dispatching cycle; η _tnfor the efficiency of n-th group of miniature combustion engine in t the period in dispatching cycle;

Service life of lead accumulator cost depletions C _batcomputing method are as follows:

When the lead-acid accumulator charge and discharge cycles degree of depth is R, largest loop discharge and recharge times N before fault _eSSbe expressed as:

$N_{\mathrm{ESS}}={\mathrm{\α}}_1+{\mathrm{\α}}_2{e}^{{\mathrm{\α}}_{3}R}+{\mathrm{\α}}_4{e}^{{\mathrm{\α}}_{5}R};---\left(6\right)$

α ₁~ α ₅for the characteristic parameter of lead-acid accumulator, the life test data that these parameters can be provided by manufacturer obtains;

Once, it is 1/N that battery life loss accounts for entire life number percent to lead-acid accumulator charge and discharge cycles _eSS, equivalent economic attrition cost C ₁for:

C ₁＝C _initial-bat/N _ESS； (7)

In micro-capacitance sensor operational process, within dispatching cycle, the life consumption cost C of lead-acid accumulator _batfor:

$C_{\mathrm{bat}}=\underset{j=0}{\overset{N_T}{\mathrm{\Σ}}}{C}_{1,j};---\left(8\right)$

In formula, C _initial-batfor lead-acid accumulator cost of investment; C _{1, j}for the equivalent economic attrition cost of lead-acid accumulator jth time discharge and recharge; N _tfor the discharge and recharge number of times of lead-acid accumulator in dispatching cycle;

The constraint condition of micro-capacitance sensor electricity price Optimized model:

1. user's purchases strategies constraint condition:

For encouraging user to respond this policy of tou power price, after should ensureing to implement tou power price, the purchases strategies of user does not increase, that is:

M ₀≥M ₁； (9)

Wherein, $M_0=\underset{t=1}{\overset{T}{\mathrm{\Σ}}}{C}_{\mathrm{sale},t0}\·L_{t0}\mathrm{\Δt};M_1=\underset{t=1}{\overset{T}{\mathrm{\Σ}}}{C}_{\mathrm{sale},t}\·P_{\mathrm{Dt}}\mathrm{\Δt};$

In formula, M ₀, M ₁purchases strategies total with user in the implementation Peak-valley TOU power price state dispatching cycle under being respectively unexecuted Peak-valley TOU power price state; C _{sale, t}for power supply enterprise under unexecuted Peak-valley TOU power price state is to the sale of electricity price of user; L _t0for the predicted load of t period in the unexecuted Peak-valley TOU power price state dispatching cycle; C _{sale, t}for to carry out in the Peak-valley TOU power price state dispatching cycle t the period power supply enterprise to the sale of electricity price of user; P _dtfor carrying out the matching load value of t period in the Peak-valley TOU power price state dispatching cycle; Δ t is the time interval of adjacent two periods;

2. Constraint condition:

Compared with under unexecuted Peak-valley TOU power price state, under implementation Peak-valley TOU power price state, the need for electricity amount of user is constant:

$\underset{t\∈T_p}{\mathrm{\Σ}}{Q}_{p,t}+\underset{t\∈T_f}{\mathrm{\Σ}}{Q}_{f,t}+\underset{t{\∈T}_v}{\mathrm{\Σ}}{Q}_{v,t}=\underset{t\∈T}{\mathrm{\Σ}}{Q}_t---\left(10\right)$

T _p+T _f+T _v＝T (11)

In formula, Q _p,t, Q _f,t, Q _v,tbe respectively and carry out in the Peak-valley TOU power price state dispatching cycle user power utilization amount that t period is peak period, at ordinary times section or paddy period; Q _tthe user power utilization amount of t period in the unexecuted Peak-valley TOU power price state dispatching cycle; T _p, T _f, T _vbe respectively and carry out peak period, at ordinary times section, the period number of paddy period in the Peak-valley TOU power price state dispatching cycle;

3. electricity tariff constraint condition:

P _p>P _f>P _v； (12)

$2<\frac{P_p}{P_v}<5;---\left(13\right)$

In formula, P _p, P _f, P _vbe respectively carry out peak period, at ordinary times section in the Peak-valley TOU power price state dispatching cycle, the paddy period power supply enterprise to the sale of electricity price of user, and the ratio of peak period sale of electricity price and paddy period sale of electricity price is between 2 to 5;

4. micro-capacitance sensor economical operation constraint condition, comprising:

1) power-balance constraint condition:

$\underset{n=1}{\overset{N}{\mathrm{\&Sigma;}}}{P}_{\mathrm{nt}}{u}_{n,t}+P_{\mathrm{wt}}+P_{\mathrm{ESSt}}=P_{\mathrm{Dt}};---\left(14\right)$

In formula: P _ntfor exerting oneself of n-th group of miniature combustion engine in t the period in dispatching cycle, u _n,tfor the opening of n-th group of miniature combustion engine in t the period in dispatching cycle, stopped status variable; P _wtfor the wind power prediction value of t period in dispatching cycle; P _eSStfor the charge-discharge electric power of lead-acid accumulator in t the period in dispatching cycle, being just during electric discharge, is negative during charging;

2) miniature combustion engine units limits condition:

$P_n^{\min }{u}_{n,t}\&le;P_{\mathrm{nt}}{u}_{n,t}\&le;P_n^{\max }{u}_{n,t};---\left(15\right)$

In formula, be respectively minimum, the maximum output limit value of n-th group of miniature combustion engine;

3) lead-acid accumulator constraint condition:

In operational process, meet when lead-acid accumulator is in charged state:

$S_{\mathrm{oc}}(t+1)=S_{\mathrm{oc}}\left(t\right)+P_t^c{\mathrm{\&eta;}}_c\mathrm{\&Delta;t};---\left(16\right)$

When lead-acid accumulator is in discharge condition, meet:

$S_{\mathrm{oc}}(t+1)=S_{\mathrm{oc}}\left(t\right)-P_t^d{\mathrm{\&Delta;t}/\mathrm{\&eta;}}_d;---\left(17\right)$

In formula: S _oc(t+1), S _oct () distinguishes the residual capacity of t+1 period and t period lead-acid accumulator in dispatching cycle; be respectively the charge and discharge power of t period lead-acid accumulator in dispatching cycle; η _c, η _dbe respectively the charge and discharge efficiency of lead-acid accumulator; Δ t is the time interval of adjacent two periods;

The rated power of lead-acid accumulator is restricted to:

$0\&le;P_t^c\&le;P_{\mathrm{ch},\max };---\left(18\right)$

$0\&le;P_t^d\&le;P_{\mathrm{dch},\max };---\left(19\right)$

In formula: P _{ch, max}, P _{dch, max}be respectively the maximum charge and discharge power of lead-acid accumulator;

The residual capacity of lead-acid accumulator is restricted to:

S _ocmin≤S _oc(t)≤S _ocmax； (20)

S _oc(0)＝S _oc(T _end)＝S _ocinitial(21)

In formula: S _ocmin, S _ocmaxbe respectively minimum, the greatest residual capacity limit value of lead-acid accumulator; S _oc(0) remaining capacity value of a period lead-acid accumulator the most initial in dispatching cycle is represented, S _oc(T _end) represent the remaining capacity value of last period lead-acid accumulator in dispatching cycle, S _ocinitialrepresent the raw capacity value of lead-acid accumulator;

4) spinning reserve constraint condition:

In dispatching cycle in t period miniature combustion engine provide maximum just for subsequent use for:

$R_{\mathrm{nt}}^{\mathrm{up}}=\underset{n=1}{\overset{N}{\mathrm{\&Sigma;}}}{P}_n^{\max }{u}_{n,t}-\underset{n=1}{\overset{N}{\mathrm{\&Sigma;}}}{P}_{\mathrm{nt}}{u}_{n,t},\&ForAll;t;---\left(22\right)$

In dispatching cycle in t period lead-acid accumulator provide maximum just for subsequent use for:

$R_{\mathrm{ESSt}}^{\mathrm{up}}=\min \{{\mathrm{\&eta;}}_d(S_{\mathrm{oc}}\left(t\right)-S_{\mathrm{oc}\min })/\mathrm{\&Delta;t},P_{\mathrm{dch},\max }-P_{\mathrm{ESSt}}\},\&ForAll;t;---\left(23\right)$

In dispatching cycle in t period miniature combustion engine provide maximum negative for subsequent use for:

$R_{\mathrm{nt}}^{\mathrm{down}}=\underset{n=1}{\overset{N}{\mathrm{\&Sigma;}}}{P}_{\mathrm{nt}}{u}_{n,t}-\underset{n=1}{\overset{N}{\mathrm{\&Sigma;}}}{P}_n^{\min }{u}_{n,t},\&ForAll;t;---\left(24\right)$

In dispatching cycle in t period lead-acid accumulator provide maximum negative for subsequent use for:

$R_{\mathrm{ESSt}}^{\mathrm{down}}=\min \{(S_{\mathrm{oc}\max }-S_{\mathrm{oc}}\left(t\right))/{\mathrm{\&eta;}}_c/\mathrm{\&Delta;t},P_{\mathrm{ch},\max }-P_{\mathrm{ESSt}}\},\&ForAll;t;---\left(25\right)$

Adopt probability constraints determination spinning reserve capacity, that is:

$P\{-(R_{\mathrm{nt}}^{\mathrm{down}}+R_{\mathrm{ESSt}}^{\mathrm{down}})\&le;R_t\&le;R_{\mathrm{nt}}^{\mathrm{up}}+R_{\mathrm{ESSt}}^{\mathrm{up}}\}\&GreaterEqual;\mathrm{\&alpha;};---\left(26\right)$

R _t＝ΔP _Dt+ΔP _wt； (27)

In formula, R _tfor the spinning reserve capacity in dispatching cycle needed for t period micro-grid system; P{} represents probability; α is level of confidence; Δ P _dtfor the load prediction error of t period in dispatching cycle, Normal Distribution, i.e. Δ P _dt~ N (0, (σ ₂p _dt) ²); Δ P _wtfor the wind power prediction error of t period in dispatching cycle, Normal Distribution, i.e. Δ P _wt~ N (0, (σ ₁p _wt) ²); Δ t is the time interval of adjacent two periods.

In above-mentioned micro-capacitance sensor electricity price optimization method, specifically, adopt particle cluster algorithm to the concrete bag following steps of solution procedure of described micro-capacitance sensor electricity price Optimized model:

Step1: the input parameter of the predicted load under the wind power prediction value of day part in the dispatching cycle that obtains and unexecuted Peak-valley TOU power price state as micro-capacitance sensor electricity price Optimized model will be predicted according to historical data;

Step2: the outer primary group producing outer particle cluster algorithm:

The electric value composition one of stochastic generation peak period, at ordinary times section, paddy period comprises the electricity price array of three numerical value elements, using the positional value of this electricity price array as the particle of in outer population, and the velocity amplitude of this particle of stochastic generation; Thus, according to the outer population scale Ng of setting, stochastic generation comprises the outer population of Ng particle;

Step3: adjust the electricity price array in each particle in current outer population, makes peak period, at ordinary times section in each particle electricity price array, electricity tariff constraint condition that the electricity price value of paddy period meets micro-capacitance sensor electricity price Optimized model;

Step4: according to the load prediction situation under the unexecuted Peak-valley TOU power price state of day part in the dispatching cycle that prediction obtains, determine the paddy period of day part load in the unexecuted Peak-valley TOU power price state dispatching cycle, section at ordinary times, peak period distribution situation, and together with the electricity price array of each particle in current outer population in the lump as the input of customer charge to the response model of tou power price, calculate the matching load value of each period in the implementation Peak-valley TOU power price state dispatching cycle corresponding to electricity price array of each particle in current outer population respectively,

Step5: after completing steps Step4, the matching load value of each period in the implementation Peak-valley TOU power price state dispatching cycle corresponding to the electricity price array of each particle in current outer population, lower floor's objective function for micro-capacitance sensor electricity price Optimized model adopts internal layer particle cluster algorithm to solve, and obtains the micro-capacitance sensor optimal power scheduling scheme corresponding to electricity price array of each particle in current outer population and runs total cost C _total, and then the total revenue C corresponding to electricity price array of each particle in current outer population is calculated according to the upper strata objective function of micro-capacitance sensor electricity price Optimized model _benefit;

The micro-capacitance sensor optimal power scheduling scheme corresponding to electricity price array of each particle in described outer population, under referring to the matching load value situation of each period in the dispatching cycle under the implementation Peak-valley TOU power price state corresponding to the electricity price array of this particle, micro-grid system run total cost minimum time, in corresponding dispatching cycle each period each group of miniature combustion engine go out force value and lead-acid accumulator Soc value;

Step6: the adaptive value calculating each particle in current outer population, and the individual extreme value of the particle calculating current outer population and global extremum; In outer population, the adaptive value function of each particle is:

Fitness＝A-C _benefit(x)+B(x) (28)

In formula, C _benefitx () is the aggregate earnings value of the correspondence under the objective function of micro-capacitance sensor electricity price Optimized model of particle x in outer population; A is for being greater than aggregate earnings value C _benefitmaximum possible value normal number; B (x) is penalty term corresponding to particle x in outer population, when in formula (9), constraint condition does not meet, B (x) value is a normal number β, and when meeting constraint condition in formula (9), B (x) value is 0;

Step7: the position and the speed that upgrade each particle in outer population: according to the kth of current outer particle cluster algorithm for the position of each particle in population and speed, upgrade position and the speed of each particle in kth+1 generation population of outer particle cluster algorithm:

v _i(k+1)＝ωv _i(k)+c ₁r ₁(k)(P _{best_i}(k)-x _i(k))+c ₂r ₂(k)(P _g(k)-x _i(k))；

x _i(k+1)＝x _i(k)+v _i(k+1)；

In formula, ω is inertia weight coefficient, is a constant; c ₁, c ₂for aceleration pulse, (0,2] between value; K is the current iteration algebraically of outer particle cluster algorithm; r ₁(k), r ₂k () is the random number of value between [0,1]; I represents i-th particle in outer population; v _ik () represents the velocity amplitude of the kth of outer particle cluster algorithm for i-th particle in population; v _i(k+1) velocity amplitude of i-th particle in kth+1 generation population of outer particle cluster algorithm is represented; x _ik () represents the positional value of the kth of outer particle cluster algorithm for i-th particle in population; x _i(k+1) positional value of i-th particle in kth+1 generation population of outer particle cluster algorithm is represented; P _gk kth that () is outer particle cluster algorithm for the global extremum of population, P _{best_i}k kth that () is outer particle cluster algorithm is for the individual extreme value of i-th particle in population;

Step8: repeat step Step3 ~ Step7, until reach the greatest iteration algebraically that outer particle cluster algorithm presets;

Step9: using in outer for final gained population as the particle of global extremum as the optimum solution of micro-capacitance sensor electricity price Optimized model, thus using the optimal pricing scheme of the electricity price value of peak period, the at ordinary times section in this optimum solution particle represented by electricity price array, paddy period as micro-capacitance sensor Peak-valley TOU power price, and according to this optimum solution particle electricity price array corresponding to micro-capacitance sensor optimal power scheduling scheme micro-capacitance sensor miniature combustion engine of day part within dispatching cycle is exerted oneself and lead-acid accumulator charge-discharge electric power scheduling controlling in addition.

In above-mentioned micro-capacitance sensor electricity price optimization method, specifically, in described step Step5, the concrete bag following steps of process that the lower floor's objective function for micro-capacitance sensor electricity price Optimized model adopts internal layer particle cluster algorithm to carry out solving:

(1) using a particle in current outer population as outer object particle, by the matching load value of each period in the implementation Peak-valley TOU power price state dispatching cycle corresponding to the electricity price array of this outer object particle, as the input parameter of lower floor's objective function of micro-capacitance sensor electricity price Optimized model;

(2) the internal layer primary group of internal layer particle cluster algorithm is produced:

For the matching load value of each period in dispatching cycle, in this period of stochastic generation, each group miniature combustion engine goes out force value, and the lead-acid accumulator Soc value in this period of stochastic generation, form the array that comprises N+1 numerical value element, N is total group of number of miniature combustion engine in micro-grid system, thus for T the period comprised in dispatching cycle, stochastic generation obtains T array, form the search volume matrix that (N+1) × T ties up, as the positional value of the particle of in population, and the velocity amplitude of this particle of stochastic generation; Thus, according to the internal layer population scale M of setting, stochastic generation comprises the internal layer population of M particle;

(3) to the miniature combustion engine in each particle in current internal layer population go out force value and lead-acid accumulator Soc value adjusts, make each particle in current internal layer population meet the micro-capacitance sensor economical operation constraint condition of micro-capacitance sensor electricity price Optimized model, ensure micro-grid system power-balance simultaneously;

(4) adaptive value of each particle in current internal layer population is calculated, and the individual extreme value of the particle calculating current internal layer population and global extremum; In internal layer population, the adaptive value function of each particle is:

$f_{\mathrm{itness}}=A/(C_{\mathrm{total}}+\underset{t=1}{\overset{T}{\mathrm{\&Sigma;}}}\mathrm{\&delta;}{m}_t);$

In formula: C _totalfor the total operating cost of micro-grid system; δ penalty factor; m _tfor the state variable that value is 0 or 1, if in dispatching cycle in t period miniature combustion engine go out force value and lead-acid accumulator Soc value does not meet spinning reserve constraint condition, m _tget 1, otherwise, m _tget 0; A is normal number;

(5) position and the speed of each particle in internal layer population is upgraded: according to the kth of current internal layer particle cluster algorithm for the position of each particle in population and speed, upgrade position and the speed of each particle in kth+1 generation population of internal layer particle cluster algorithm:

v _i(k+1)＝ωv _i(k)+c ₁r ₁(k)(P _{best_i}(k)-x _i(k))+c ₂r ₂(k)(P _g(k)-x _i(k))；

x _i(k+1)＝x _i(k)+v _i(k+1)；

In formula, ω is inertia weight coefficient, is a constant; c ₁, c ₂for aceleration pulse, (0,2] between value; K is the current iteration algebraically of internal layer particle cluster algorithm; r ₁(k), r ₂k () is the random number of value between [0,1]; I represents i-th particle in internal layer population; v _ik () represents the velocity amplitude of kth for i-th particle in population of internal layer particle cluster algorithm; v _i(k+1) velocity amplitude of i-th particle in kth+1 generation population of internal layer particle cluster algorithm is represented; x _ik () represents the positional value of kth for i-th particle in population of internal layer particle cluster algorithm; x _i(k+1) positional value of i-th particle in kth+1 generation population of internal layer particle cluster algorithm is represented; P _gk kth that () is internal layer particle cluster algorithm for the global extremum of population, P _{best_i}k kth that () is internal layer particle cluster algorithm is for the individual extreme value of i-th particle in population;

(6) step (3) ~ (5) are repeated, until reach the greatest iteration algebraically that internal layer particle cluster algorithm presets;

(7) force value and lead-acid accumulator Soc value is gone out using group miniature combustion engine each in T the period comprised within the dispatching cycle represented by the particle of global extremum in final gained internal layer population, as this outer object particle electricity price array corresponding to micro-capacitance sensor optimal power scheduling scheme, and the operation total cost C of micro-grid system under calculating this micro-capacitance sensor optimal power scheduling scheme _total;

(8) repeated execution of steps (1) ~ (7), obtain the micro-capacitance sensor optimal power scheduling scheme corresponding to electricity price array of each particle in current outer population respectively and run total cost C _total.

In above-mentioned micro-capacitance sensor electricity price optimization method, specifically, described step (3) is specially:

3.1) whether the miniature combustion engine detected in current internal layer population represented by each particle to go out force value out-of-limit, if be greater than the maximum output limit value of miniature combustion engine then be taken as maximum output limit value if be less than minimum load limit value then be taken as 0, namely represent that the miniature combustion engine of respective sets is in shut down condition;

3.2) the lead-acid accumulator S in current internal layer population represented by each particle is detected _ocwhether be out-of-limitly worth, if be greater than the greatest residual capacity limit value S of lead-acid accumulator _ocmax, then the greatest residual capacity limit value S of lead-acid accumulator is taken as _ocmax; If be less than the least residue capacity limit value S of lead-acid accumulator _ocmin, then least residue capacity limit value S is taken as _ocmin;

3.3) employing pushes back and adjusts for the charge-discharge electric power of method to lead-acid accumulator, makes it meet the lead-acid accumulator constraint condition of micro-capacitance sensor electricity price Optimized model; Backward steps is pushed away as follows before concrete:

3.3.1) t=0 is made respectively, 1,2 ..., T-1; For the residual capacity S of t period lead-acid accumulator in dispatching cycle _oc(t), if meet formula (30), the lead-acid accumulator residual capacity S of a period after through type (32) adjustment _oc(t+1); If meet formula (31), then the lead-acid accumulator residual capacity S of through type (33) adjustment rear period _oc(t+1):

S _oc(t+1)>S _oc(t)+P _ch, _maxη _cΔt； (30)

S _oc(t+1)<S _oc(t)-P _dch,maxΔt/η _d； (31)

S _oc(t+1)＝S _oc(t)+P _ch,maxη _cΔt； (32)

S _oc(t+1)＝S _oc(t)-P _dch,maxΔt/η _d； (33)

3.3.2) after execution of step 3.3.1, judge whether formula (21) meets, if met, then forward step 3.3.4 to; If do not met, then make S _oc(T _end)=S _ocinitial, make t=T-1 respectively, T-2 ..., 0; Then for the residual capacity S of t+1 period lead-acid accumulator in dispatching cycle _oc(t+1), if meet formula (30), through type (34) adjusts the lead-acid accumulator residual capacity S of last period _oc(t); If meet formula (31), then through type (35) adjusts the lead-acid accumulator residual capacity S of last period _oc(t) value:

S _oc(t)＝S _oc(t+1)-P _ch,maxη _cΔt； (34)

S _oc(t)＝S _oc(t+1)+P _dch,maxΔt/η _d； (35)

3.3.3) again judge whether formula (21) meets, forward 3.3.4 to if met; If do not met, then make S _oc(0)=S _ocinitial, and forward 3.3.1 to;

3.3.4) carry out next step to calculate;

3.4) start adjustable strategies: go out force value and lead-acid accumulator S according to the miniature combustion engine in population represented by each particle _ocvalue, in conjunction with wind power prediction value and matching load value, judges that the miniature combustion engine of each period represented by each particle goes out force value and lead-acid accumulator S respectively _ocvalue adds that can the wind power prediction value of same period meet the matching load value of same period, if do not met, then the miniature combustion engine start increasing the corresponding period in corresponding particle runs number till meeting the burden requirement with the period;

3.5) adjustable strategies is shut down: the miniature combustion engine of each period in current internal layer population represented by particle goes out force value and lead-acid accumulator S _ocwhen value adds that the wind power prediction value of same period can meet the matching load value of same period, judge that in each particle, can any one group of miniature combustion engine of each period stoppage in transit meet the matching load value of same period respectively; If met, then the miniature combustion engine of respective sets of stopping transport in the corresponding period of corresponding particle, if to stop transport again any one group of miniature combustion engine until this period, till the matching load value that can not meet the same period requires; If period any one group of miniature combustion engine being in open state all can not meet the matching load value of same period after stopping transport and requires and spinning reserve constraint condition in particle, then the miniature combustion engine start operation group number of this period remains unchanged;

3.6) power-balance adjustment: for each particle in current internal layer population, what to adjust in each period each group miniature combustion engine respectively goes out force value, micro battery system power is balanced, the payload pro rata distribution that in adjustment process, imbalance power is born according to each group of miniature combustion engine, methodology is:

In formula, Pnt, P ' _ntbe respectively carry out that t period in power-balance adjustment forward and backward dispatching cycle be in n-th group of miniature combustion engine of start operation go out force value; Δ P _tfor the power shortage of t period micro battery system in dispatching cycle, as Δ P _tduring <0, represent that the generating general power of micro battery system is less than matching load value, miniature combustion engine need be increased and exert oneself, on the contrary Δ P _tduring >0, then represent that can reduce miniature combustion engine exerts oneself.

Compared to prior art, the present invention has following beneficial effect:

1, micro-capacitance sensor electricity price optimization method of the present invention, consider tou power price to the response of user's request side and the impact of micro-capacitance sensor economical operation, to power, gross earnings is target to the maximum, the micro-capacitance sensor electricity price Optimized model set up has taken into account the many factors such as the response of user's request side and micro-capacitance sensor economical operation, more conform to micro-capacitance sensor practical operation situation, reduce micro-grid system and occur that generated energy crosses the situation of Sheng, electric power resource waste.

2, in micro-capacitance sensor electricity price optimization method of the present invention, based on consumer psychology principle, analyze the response model of customer charge to tou power price, and carry out by this response model the load condition that each period in the Peak-valley TOU power price state dispatching cycle is carried out in matching, take into full account the cool load translating ratio because Peak-valley TOU power price mechanism causes.

3, in micro-capacitance sensor electricity price optimization method of the present invention, by according to customer charge to the response model of tou power price based on the load condition of matching, to power, enterprise income is maximum turns to target, the micro-capacitance sensor electricity price Optimized model set up comprehensively weighs user and power supply enterprise's common interest, and have employed particle cluster algorithm for solving this model, determine the best tou power price of micro-capacitance sensor and optimal power generation scheduling scheme, micro-grid system can be reduced and occur that generated energy crosses Sheng, the situation of electric power resource waste, more effectively guarantee the operational reliability of micro-grid system, reduce user's Trading risk, help when meeting supply load demand to reduce micro-grid system operating cost, guarantee that power supply enterprise can have good income.

4, the computing flow process of micro-capacitance sensor electricity price optimization method of the present invention is comparatively simple, be convenient to engineering staff learn to use, and versatility is better, adopt the planning of the micro-capacitance sensor electricity price prioritization scheme of Peak-valley TOU power price mechanism under can being widely used in different application occasion, there is good marketing using value.

Accompanying drawing explanation

Fig. 1 is the process flow diagram of micro-capacitance sensor electricity price optimization method of the present invention.

Fig. 2 is after carrying out Peak-valley TOU power price mechanism, the response curve graph of a relation between peak period to paddy period cool load translating ratio and electricity price difference.

Fig. 3 is in micro-capacitance sensor electricity price optimization method of the present invention, adopts the process flow diagram that particle cluster algorithm solves micro-capacitance sensor electricity price Optimized model.

Fig. 4 adopts internal layer particle cluster algorithm to carry out the process flow diagram solved for lower floor's objective function of micro-capacitance sensor electricity price Optimized model in micro-capacitance sensor electricity price optimization method of the present invention.

Fig. 5 is that in the embodiment of the present invention, isolated micro-grid system adopts the load curve comparison diagram before and after tou power price.

Fig. 6 is the power generation dispatching scheme curve map in the embodiment of the present invention before isolated micro-grid system employing Peak-valley TOU power price mechanism.

Fig. 7 is in the embodiment of the present invention after isolated micro-grid system employing Peak-valley TOU power price mechanism, the optimal power generation scheduling scheme curve map determined by micro-capacitance sensor optimization method of the present invention.

Embodiment

The invention provides a kind of micro-capacitance sensor electricity price optimization method, it is directed to micro-capacitance sensor and adopts Peak-valley TOU power price mechanism, based on consumer psychology principle, analyzes user to Respondence to the Price of Electric Power model; Secondly, by user to based on the load curve of tou power price response prediction, consider tou power price to the response of user's request side and the impact of micro-capacitance sensor economical operation, and to power, enterprise income is maximum turns to target, comprehensive measurement user and power supply enterprise's common interest, set up the micro-capacitance sensor electricity price Optimized model taking into account the response of user's request side and micro-capacitance sensor economical operation, and adopt particle cluster algorithm to solve micro-capacitance sensor electricity price Optimized model, determine the best tou power price of micro-capacitance sensor and optimal power generation scheduling scheme.By the best tou power price of this micro-capacitance sensor and optimal power generation scheduling scheme, can help to reduce micro-grid system and occur that generated energy crosses the situation of Sheng, electric power resource waste, more effectively guarantee the operational reliability of micro-grid system, reduce user's Trading risk, help when meeting supply load demand to reduce micro-grid system operating cost, thus guarantee that power supply enterprise can have good income.

Expansion explanation is carried out to micro-capacitance sensor electricity price optimization method of the present invention below.

1, as shown in Figure 1, concrete steps are the flow process of micro-capacitance sensor electricity price optimization method of the present invention:

(1) add up the historical data of wind speed and load in micro-grid system, according to historical data, the load under the wind power of day part in dispatching cycle and unexecuted Peak-valley TOU power price state is predicted;

(2) according to the response characteristic of user to electricity price, the response model of customer charge to tou power price is set up:

In purchase commodity process, consumer can select according to commodity price change the opportunity buying commodity usually.Electric energy and daily life are closely related, and as a kind of special commodity, the variation of its price also can affect the buying behavior of consumer usually.As employing tou power price (Time Of Use Price; be abbreviated as TOU) time; the consumer buying electric energy is subject to the stimulation of electricity price; usually traditional power structure can be changed; in peak times of power consumption; the electricity price higher period, user may reduce electricity consumption, and by the load transfer plan of high for part rate period to electricity price lower period.The response of user to electricity price is usually expressed as the variation of grid load curve, and user is to the response of tou power price in research, and predicts it is the basis analyzing economy operation of power grid to load on this basis.The present invention, on the basis of existing research, determines the load curve after adopting tou power price based on consumer psychology principle.

Known based on consumer psychology principle, when commodity price is different, the responsiveness of consumer is also different.The stimulation of user to commodity price has a minimum threshold values, and when stimulation is less than this minimum threshold values, user does not respond stimulation; When environmental stimuli is comparatively large, during more than a definite limitation, user is tended towards stability again to the response stimulated, and has nothing to do at the responsiveness of this phase user and the size of stimulation; When environmental stimuli is between minimum threshold values and maximum threshold values, user's response presents growth state along with the increase stimulated.According to this principle, introduce the concept of cool load translating ratio.

Cool load translating ratio is for carrying out after Peak-valley TOU power price, and load to the ratio of the transfer amount of electricity price lower period and high rate period load, comprises the electricity price peak load rate of transform, the flat cool load translating ratio of electricity price Pinggu cool load translating ratio and electricity price peak from the electricity price higher period.A large amount of findings shows, the rate of transform and electricity price difference can be similar to and represent with piecewise linear function.After carrying out Peak-valley TOU power price mechanism, the power structure of certain customers' adjustment oneself, causes response curve between the peak load rate of transform and electricity price between peak and valley as shown in Figure 2.Electricity price peak load rate of transform λ _pv, electricity price Pinggu cool load translating ratio λ _fvcool load translating ratio λ flat with electricity price peak _pfexpression formula be respectively:

In formula, Δ p _pvfor the electricity price of load peak period and load paddy period is poor, i.e. Δ p _pv=p _p-p _v; Δ p _fvfor the electricity price of load section and load paddy period is at ordinary times poor, i.e. Δ p _fv=p _f-p _v; Δ p _pffor the electricity price of load peak period and load section is at ordinary times poor, i.e. Δ p _pf=p _p-p _f; p _pfor the electricity price of load peak period, p _ffor the electricity price of load section at ordinary times, p _vfor the electricity price of load paddy period; a _pv, a _fv, a _pfbe respectively electricity price peak load and shift minimum threshold values, the minimum threshold values of electricity price Pinggu load transfer plan, the minimum threshold values of the flat load transfer plan in electricity price peak; b _pv, b _fv, b _pfbe respectively electricity price peak load and shift maximum threshold values, the maximum threshold values of electricity price Pinggu load transfer plan, the maximum threshold values of the flat load transfer plan in electricity price peak; K _pvfor electricity price peak load rate of transform λ _pvwith the electricity price difference Δ p of load peak period and load paddy period _pvincrease from minimum threshold values a _pvlinear increase is to maximum threshold values b _pvrate of rise; K _fvfor electricity price Pinggu cool load translating ratio λ _fvwith the electricity price difference Δ p of load section and load paddy period at ordinary times _fvincrease from minimum threshold values a _fvlinear increase is to maximum threshold values b _fvrate of rise; K _pffor the flat cool load translating ratio λ in electricity price peak _pfwith the electricity price difference Δ p of load peak period and load section at ordinary times _pfincrease from minimum threshold values a _pflinear increase is to maximum threshold values b _pfrate of rise; λ _pv ^max, λ _fv ^max, λ _pf ^maxbe respectively electricity price peak load maximum transfer rate, electricity price Pinggu load maximum transfer rate, electricity price peak flat load maximum transfer rate;

Implementing after tou power price, there is certain transfer in customer charge, and cool load translating ratio corresponding to different electricity price difference can represent by the above piecewise linear curve introduced, then in dispatching cycle the matching load of day part such as formula shown in (2):

Formula (2) is namely as the response model of customer charge to tou power price; In formula, L _t0for the predicted load of t period in the unexecuted Peak-valley TOU power price state dispatching cycle; P _dtfor carrying out the matching load value of t period in the Peak-valley TOU power price state dispatching cycle; T _v, T _f, T _pthe time hop count of be respectively internal loading paddy period dispatching cycle, load section, load peak period at ordinary times; be respectively the load mean value of section, peak period at ordinary times under unexecuted Peak-valley TOU power price state.

(3) based on the response model of customer charge to tou power price, micro-capacitance sensor electricity price Optimized model is set up:

After carrying out Peak-valley TOU power price, the load of user shifts, and the load curve change of whole system, operating cost, sale of electricity cost etc. all may change.For research tou power price is on the impact of micro-capacitance sensor economical operation, the present invention determines the load of day part in dispatching cycle according to the response model of customer charge to tou power price, and enterprise income is maximum to power, micro-capacitance sensor operating cost is minimum for target, set up the micro-capacitance sensor electricity price Optimized model taking into account the response of user's request side and micro-capacitance sensor economical operation.

(4) adopt particle cluster algorithm to solve set up micro-capacitance sensor electricity price Optimized model, determine the best tou power price of micro-capacitance sensor within dispatching cycle and optimal power generation scheduling scheme.

2, concrete micro-capacitance sensor electricity price Optimized model is:

In 2.1 micro-capacitance sensor electricity price optimization methods of the present invention, to power, enterprise income is target to the maximum, and the upper strata objective function of micro-capacitance sensor electricity price Optimized model is:

$\max C_{\mathrm{benefit}}=\underset{t=1}{\overset{T}{\mathrm{\&Sigma;}}}{C}_{\mathrm{sale},t}\&CenterDot;P_{\mathrm{Dt}}\mathrm{\&Delta;t}-C_{\mathrm{total}};---\left(3\right)$

In formula, C _benefitfor the total revenue of power supply enterprise; C _{sale, t}for to carry out in the Peak-valley TOU power price state dispatching cycle t the period power supply enterprise to the sale of electricity price of user; T be comprise dispatching cycle total time hop count; P _dtfor carrying out the matching load value of t period in the Peak-valley TOU power price state dispatching cycle; Δ t is the time interval of adjacent two periods; C _totalfor the operation total cost of micro-capacitance sensor.

The operation total cost C of micro-grid system _totalbe closely related with the ruuning situation of micro-capacitance sensor, under given loading condiction, different miniature combustion engines is exerted oneself and the power generation dispatching scheme that forms of accumulator output distribution, and micro-capacitance sensor will be caused to run the difference of total cost.Therefore, in the micro-capacitance sensor electricity price Optimized model that the present invention sets up, on the basis setting up upper strata objective function, according to the response model of customer charge to tou power price, initial fitting can go out the load condition that micro-capacitance sensor carries out day part in the Peak-valley TOU power price state dispatching cycle, again under the loading condiction of matching, minimum for target with the operation total cost of micro-capacitance sensor, establish lower floor's objective function of micro-capacitance sensor electricity price Optimized model:

$\min C_{\mathrm{total}}=\underset{t=1}{\overset{T}{\mathrm{\&Sigma;}}}(\underset{n=1}{\overset{N}{\mathrm{\&Sigma;}}}{C}_n^t(1-u_{n,t-1})u_{n,t}+\underset{n=1}{\overset{N}{\mathrm{\&Sigma;}}}{u}_{n,t}{F}_{\mathrm{FC},\mathrm{tn}})+C_{\mathrm{bat}};---\left(4\right)$

Wherein, $C_n^t={\mathrm{\&sigma;}}_n+{\mathrm{\&delta;}}_n(1-e^{(-T_{\mathrm{nt}}^{\mathrm{OFF}}/{\mathrm{\&tau;}}_n)});F_{\mathrm{FC},\mathrm{tn}}=F_f\underset{t}{\mathrm{\&Sigma;}}\frac{P_{\mathrm{tn}}}{{\mathrm{\&eta;}}_{\mathrm{tn}}};---\left(5\right)$

In formula, for the start cost of n-th group of miniature combustion engine in t the period in dispatching cycle; N is total group of number of miniature combustion engine in micro-capacitance sensor; u _n,tfor the opening of n-th group of miniature combustion engine in t the period in dispatching cycle, stopped status variable, u when being in open state _n,tvalue is 1, u when being in stopped status _n,tvalue is 0; T be comprise in dispatching cycle total time hop count; C _batfor the life consumption cost of lead-acid accumulator; σ _n, δ _n, τ _nit is the start-up cost coefficient of n-th group of miniature combustion engine; it is the idle time of n-th group of miniature combustion engine in dispatching cycle in t period; F _{fC, tn}for the operating cost of n-th group of miniature combustion engine in t the period in dispatching cycle; F _ffor fuel price; P _tnfor the output power of n-th group of miniature combustion engine in t the period in dispatching cycle; η _tnfor the efficiency of n-th group of miniature combustion engine in t the period in dispatching cycle.

Service life of lead accumulator cost depletions C _batcomputing method are as follows:

When the lead-acid accumulator charge and discharge cycles degree of depth is R, largest loop discharge and recharge times N before fault _eSScan be expressed as:

$N_{\mathrm{ESS}}={\mathrm{\&alpha;}}_1+{\mathrm{\&alpha;}}_2{e}^{{\mathrm{\&alpha;}}_{3}R}+{\mathrm{\&alpha;}}_4{e}^{{\mathrm{\&alpha;}}_{5}R};---\left(6\right)$

α ₁~ α ₅for the characteristic parameter of lead-acid accumulator, the life test data that these parameters can be provided by manufacturer obtains.

Once, it is 1/N that battery life loss accounts for entire life number percent to lead-acid accumulator charge and discharge cycles _eSS, equivalent economic attrition cost C ₁for:

C ₁＝C _initial-bat/N _ESS； (7)

In micro-capacitance sensor operational process, within dispatching cycle, the life consumption cost C of lead-acid accumulator _batfor:

$C_{\mathrm{bat}}=\underset{j=0}{\overset{N_T}{\mathrm{\&Sigma;}}}{C}_{1,j};---\left(8\right)$

In formula, C _initial-batfor lead-acid accumulator cost of investment; C _{1, j}for the equivalent economic attrition cost of lead-acid accumulator jth time discharge and recharge; N _tfor the discharge and recharge number of times of lead-acid accumulator in dispatching cycle.

The constraint condition of micro-capacitance sensor electricity price Optimized model: the micro-capacitance sensor electricity price Optimized model set up due to the present invention has taken into account the response of user's request side and micro-capacitance sensor economical operation, the therefore following several aspect of constraint condition:

1. user's purchases strategies constraint condition:

For encouraging user to respond this policy of tou power price, after should ensureing to implement tou power price, the purchases strategies of user does not increase, that is:

M ₀≥M ₁； (9)

Wherein, $M_0=\underset{t=1}{\overset{T}{\mathrm{\&Sigma;}}}{C}_{\mathrm{sale},t0}\&CenterDot;L_{t0}\mathrm{\&Delta;t};M_1=\underset{t=1}{\overset{T}{\mathrm{\&Sigma;}}}{C}_{\mathrm{sale},t}\&CenterDot;P_{\mathrm{Dt}}\mathrm{\&Delta;t};$

In formula, M ₀, M ₁purchases strategies total with user in the implementation Peak-valley TOU power price state dispatching cycle under being respectively unexecuted Peak-valley TOU power price state; C _{sale, t}for power supply enterprise under unexecuted Peak-valley TOU power price state is to the sale of electricity price of user; L _t0for the predicted load of t period in the unexecuted Peak-valley TOU power price state dispatching cycle; C _{sale, t}for to carry out in the Peak-valley TOU power price state dispatching cycle t the period power supply enterprise to the sale of electricity price of user; P _dtfor carrying out the matching load value of t period in the Peak-valley TOU power price state dispatching cycle; Δ t is the time interval of adjacent two periods;

2. Constraint condition:

Compared with under unexecuted Peak-valley TOU power price state, under implementation Peak-valley TOU power price state, the need for electricity amount of user is constant:

$\underset{t\&Element;T_p}{\mathrm{\&Sigma;}}{Q}_{p,t}+\underset{t\&Element;T_f}{\mathrm{\&Sigma;}}{Q}_{f,t}+\underset{t{\&Element;T}_v}{\mathrm{\&Sigma;}}{Q}_{v,t}=\underset{t\&Element;T}{\mathrm{\&Sigma;}}{Q}_t---\left(10\right)$

T _p+T _f+T _v＝T (11)

In formula, Q _p,t, Q _f,t, Q _v,tbe respectively and carry out in the Peak-valley TOU power price state dispatching cycle user power utilization amount that t period is peak period, at ordinary times section or paddy period; Q _tthe user power utilization amount of t period in the unexecuted Peak-valley TOU power price state dispatching cycle; T _p, T _f, T _vbe respectively and carry out peak period, at ordinary times section, the period number of paddy period in the Peak-valley TOU power price state dispatching cycle;

3. electricity tariff constraint condition:

P _p>P _f>P _v； (12)

$2<\frac{P_p}{P_v}<5;---\left(13\right)$

In formula, P _p, P _f, P _vbe respectively carry out peak period, at ordinary times section in the Peak-valley TOU power price state dispatching cycle, the paddy period power supply enterprise to the sale of electricity price of user, and the ratio of peak period sale of electricity price and paddy period sale of electricity price is between 2 to 5;

4. micro-capacitance sensor economical operation constraint condition, comprising:

1) power-balance constraint condition:

$\underset{n=1}{\overset{N}{\mathrm{\&Sigma;}}}{P}_{\mathrm{nt}}{u}_{n,t}+P_{\mathrm{wt}}+P_{\mathrm{ESSt}}=P_{\mathrm{Dt}};---\left(14\right)$

In formula: P _ntfor exerting oneself of n-th group of miniature combustion engine in t the period in dispatching cycle, u _n,tfor the opening of n-th group of miniature combustion engine in t the period in dispatching cycle, stopped status variable; P _wtfor the wind power prediction value of t period in dispatching cycle; P _eSStfor the charge-discharge electric power of lead-acid accumulator in t the period in dispatching cycle, being just during electric discharge, is negative during charging;

2) miniature combustion engine units limits condition:

$P_n^{\min }{u}_{n,t}\&le;P_{\mathrm{nt}}{u}_{n,t}\&le;P_n^{\max }{u}_{n,t};---\left(15\right)$

In formula, be respectively minimum, the maximum output limit value of n-th group of miniature combustion engine;

3) lead-acid accumulator constraint condition:

In operational process, meet when lead-acid accumulator is in charged state:

$S_{\mathrm{oc}}(t+1)=S_{\mathrm{oc}}\left(t\right)+P_t^c{\mathrm{\&eta;}}_c\mathrm{\&Delta;t};---\left(16\right)$

When lead-acid accumulator is in discharge condition, meet:

$S_{\mathrm{oc}}(t+1)=S_{\mathrm{oc}}\left(t\right)-P_t^d{\mathrm{\&Delta;t}/\mathrm{\&eta;}}_d;---\left(17\right)$

In formula: S _oc(t+1), S _oct () distinguishes the residual capacity of t+1 period and t period lead-acid accumulator in dispatching cycle; be respectively the charge and discharge power of t period lead-acid accumulator in dispatching cycle; η _c, η _dbe respectively the charge and discharge efficiency of lead-acid accumulator; Δ t is the time interval of adjacent two periods.

The rated power of lead-acid accumulator is restricted to:

$0\&le;P_t^c\&le;P_{\mathrm{ch},\max };---\left(18\right)$

$0\&le;P_t^d\&le;P_{\mathrm{dch},\max };---\left(19\right)$

In formula: P _{ch, max}, P _{dch, max}be respectively the maximum charge and discharge power of lead-acid accumulator.

The residual capacity of lead-acid accumulator is restricted to:

S _ocmin≤S _oc(t)≤S _ocmax； (20)

In formula: S _ocmin, S _ocmaxbe respectively minimum, the greatest residual capacity limit value of lead-acid accumulator.

The same with bulk power grid, the scheduling meeting of micro-capacitance sensor presents certain periodicity, usual each dispatching cycle Mo lead-acid accumulator residual capacity (S _oc) be consistent with initial time dispatching cycle, namely in each dispatching cycle Mo, the residual capacity of lead-acid accumulator is a definite value.Therefore have:

S _oc(0)＝S _oc(T _end)＝S _ocinitial(21)

In formula: S _oc(0) remaining capacity value of a period lead-acid accumulator the most initial in dispatching cycle is represented, S _oc(T _end) represent the remaining capacity value of last period lead-acid accumulator in dispatching cycle, S _ocinitialrepresent the raw capacity value of lead-acid accumulator.

4) spinning reserve constraint condition:

In operational process, there is the uncertain factor such as load fluctuation, wind power output fluctuation in micro-capacitance sensor.For ensureing power grid security reliability service, certain spinning reserve need be configured.For subsequent usely in micro-capacitance sensor usually jointly to be provided by (operating) conventional power unit and lead-acid accumulator.

For balance (load and Wind turbines are exerted oneself) positive and negative two-way fluctuation, the spinning reserve that system provides is divided into positive rotation (just for subsequent use) for subsequent use and negative spinning reserve (negative for subsequent use) two classes.

For just for subsequent use, in dispatching cycle in t period miniature combustion engine provide maximum just for subsequent use for:

$R_{\mathrm{nt}}^{\mathrm{up}}=\underset{n=1}{\overset{N}{\mathrm{\&Sigma;}}}{P}_n^{\max }{u}_{n,t}-\underset{n=1}{\overset{N}{\mathrm{\&Sigma;}}}{P}_{\mathrm{nt}}{u}_{n,t},\&ForAll;t;---\left(22\right)$

In dispatching cycle in t period lead-acid accumulator provide maximum just for subsequent use for:

$R_{\mathrm{ESSt}}^{\mathrm{up}}=\min \{{\mathrm{\&eta;}}_d(S_{\mathrm{oc}}\left(t\right)-S_{\mathrm{oc}\min })/\mathrm{\&Delta;t},P_{\mathrm{dch},\max }-P_{\mathrm{ESSt}}\},\&ForAll;t;---\left(23\right)$

The positive margin capacity that in dispatching cycle, t period lead-acid accumulator provides is subject to the restriction of lead-acid accumulator minimum capacity and maximum discharge power two constraint simultaneously.If t period lead-acid accumulator is in charged state in dispatching cycle, for balance Wind turbines is exerted oneself and load fluctuation, lead-acid accumulator need reduce charge power and even be transitioned into discharge condition by charged state, and what this period lead-acid accumulator can provide maximum just for subsequent usely can obtain by through type (23) equally.

For negative for subsequent use, in dispatching cycle in t period miniature combustion engine provide maximum bear for subsequent use for:

$R_{\mathrm{nt}}^{\mathrm{down}}=\underset{n=1}{\overset{N}{\mathrm{\&Sigma;}}}{P}_{\mathrm{nt}}{u}_{n,t}-\underset{n=1}{\overset{N}{\mathrm{\&Sigma;}}}{P}_n^{\min }{u}_{n,t},\&ForAll;t;---\left(24\right)$

In dispatching cycle in t period lead-acid accumulator provide maximum negative for subsequent use for:

$R_{\mathrm{ESSt}}^{\mathrm{down}}=\min \{(S_{\mathrm{oc}\max }-S_{\mathrm{oc}}\left(t\right))/{\mathrm{\&eta;}}_c/\mathrm{\&Delta;t},P_{\mathrm{ch},\max }-P_{\mathrm{ESSt}}\},\&ForAll;t;---\left(25\right)$

In isolated micro-capacitance sensor operational process, exert oneself and load fluctuation to balance the Wind turbines that all periods may occur, required spinning reserve capacity is comparatively large, and corresponding investment operation expense also can improve.In fact, the probability that most of extreme operating condition occurs very little and duration, much smaller than accidental conditions, can consider that sacrificing certain system reliability exchanges good system economy for.The present invention adopts probability constraints determination spinning reserve capacity, that is:

$P\{-(R_{\mathrm{nt}}^{\mathrm{down}}+R_{\mathrm{ESSt}}^{\mathrm{down}})\&le;R_t\&le;R_{\mathrm{nt}}^{\mathrm{up}}+R_{\mathrm{ESSt}}^{\mathrm{up}}\}\&GreaterEqual;\mathrm{\&alpha;};---\left(26\right)$

R _t＝ΔP _Dt+ΔP _wt； (27)

In formula, R _tfor the spinning reserve capacity in dispatching cycle needed for t period micro-grid system; P{} represents probability; α is level of confidence; Δ P _dtfor the load prediction error of t period in dispatching cycle, Normal Distribution, i.e. Δ P _dt~ N (0, (σ ₂p _dt) ²); Δ P _wtfor the wind power prediction error of t period in dispatching cycle, Normal Distribution, i.e. Δ P _wt~ N (0, (σ ₁p _wt) ²); Δ t is the time interval of adjacent two periods.In the process predicted load and wind power respectively, if wind power and load prediction error Normal Distribution and separate, then both sums are also Normal Distribution.

3, to set up micro-capacitance sensor electricity price Optimized model, adopt particle cluster algorithm to solve model, determine the best tou power price of micro-capacitance sensor within dispatching cycle and optimal power generation scheduling scheme.As shown in Figure 3, concrete steps are the flow process of its solution procedure:

Step1: the input parameter of the predicted load under the wind power prediction value of day part in the dispatching cycle that obtains and unexecuted Peak-valley TOU power price state as micro-capacitance sensor electricity price Optimized model will be predicted according to historical data.

Step2: the outer primary group producing outer particle cluster algorithm:

The electric value composition one of stochastic generation peak period, at ordinary times section, paddy period comprises the electricity price array of three numerical value elements, using the positional value of this electricity price array as the particle of in outer population, and the velocity amplitude of this particle of stochastic generation; Thus, according to the outer population scale Ng of setting, stochastic generation comprises the outer population of Ng particle.

Step3: adjust the electricity price array in each particle in current outer population, makes peak period, at ordinary times section in each particle electricity price array, electricity tariff constraint condition that the electricity price value of paddy period meets micro-capacitance sensor electricity price Optimized model.

Step4: according to the load prediction situation under the unexecuted Peak-valley TOU power price state of day part in the dispatching cycle that prediction obtains, determine the paddy period of day part load in the unexecuted Peak-valley TOU power price state dispatching cycle, section at ordinary times, peak period distribution situation, and together with the electricity price array of each particle in current outer population in the lump as the input of customer charge to the response model of tou power price, calculate the matching load value of each period in the implementation Peak-valley TOU power price state dispatching cycle corresponding to electricity price array of each particle in current outer population respectively.

Step5: after completing steps Step4, the matching load value of each period in the implementation Peak-valley TOU power price state dispatching cycle corresponding to the electricity price array of each particle in current outer population, lower floor's objective function for micro-capacitance sensor electricity price Optimized model adopts internal layer particle cluster algorithm to solve, and obtains the micro-capacitance sensor optimal power scheduling scheme corresponding to electricity price array of each particle in current outer population and runs total cost C _total, and then the total revenue C corresponding to electricity price array of each particle in current outer population is calculated according to the upper strata objective function of micro-capacitance sensor electricity price Optimized model _benefit.

The micro-capacitance sensor optimal power scheduling scheme corresponding to electricity price array of each particle in described outer population, under referring to the matching load value situation of each period in the dispatching cycle under the implementation Peak-valley TOU power price state corresponding to the electricity price array of this particle, micro-grid system run total cost minimum time, in corresponding dispatching cycle each period each group of miniature combustion engine go out force value and lead-acid accumulator Soc value.

Step6: the adaptive value calculating each particle in current outer population, and the individual extreme value of the particle calculating current outer population and global extremum.For user's purchases strategies constraint condition of micro-capacitance sensor electricity price Optimized model, present invention employs penalty function method and limited user's purchases strategies constraint condition, therefore in outer population, the adaptive value function of each particle is:

Fitness＝A-C _benefit(x)+B(x) (28)

In formula, C _benefitx () is the aggregate earnings value of the correspondence under the objective function of micro-capacitance sensor electricity price Optimized model of particle x in outer population; A is for being greater than aggregate earnings value C _benefitmaximum possible value normal number; B (x) is penalty term corresponding to particle x in outer population, when in formula (9), constraint condition does not meet, B (x) value is a normal number β, and when meeting constraint condition in formula (9), B (x) value is 0.

Step7: the position and the speed that upgrade each particle in outer population: according to the kth of current outer particle cluster algorithm for the position of each particle in population and speed, upgrade position and the speed of each particle in kth+1 generation population of outer particle cluster algorithm:

v _i(k+1)＝ωv _i(k)+c ₁r ₁(k)(P _{best_i}(k)-x _i(k))+c ₂r ₂(k)(P _g(k)-x _i(k))；

x _i(k+1)＝x _i(k)+v _i(k+1)；

In formula, ω is inertia weight coefficient, is a constant; c ₁, c ₂for aceleration pulse, (0,2] between value; K is the current iteration algebraically of outer particle cluster algorithm; r ₁(k), r ₂k () is the random number of value between [0,1]; I represents i-th particle in outer population; v _ik () represents the velocity amplitude of the kth of outer particle cluster algorithm for i-th particle in population; v _i(k+1) velocity amplitude of i-th particle in kth+1 generation population of outer particle cluster algorithm is represented; x _ik () represents the positional value of the kth of outer particle cluster algorithm for i-th particle in population; x _i(k+1) positional value of i-th particle in kth+1 generation population of outer particle cluster algorithm is represented; P _gk kth that () is outer particle cluster algorithm for the global extremum of population, P _{best_i}k kth that () is outer particle cluster algorithm is for the individual extreme value of i-th particle in population;

Step8: repeat step Step3 ~ Step7, until reach the greatest iteration algebraically that outer particle cluster algorithm presets;

Step9: using in outer for final gained population as the particle of global extremum as the optimum solution of micro-capacitance sensor electricity price Optimized model, thus using the optimal pricing scheme of the electricity price value of peak period, the at ordinary times section in this optimum solution particle represented by electricity price array, paddy period as micro-capacitance sensor Peak-valley TOU power price, and according to this optimum solution particle electricity price array corresponding to micro-capacitance sensor optimal power scheduling scheme micro-capacitance sensor miniature combustion engine of day part within dispatching cycle is exerted oneself and lead-acid accumulator charge-discharge electric power scheduling controlling in addition.

Below in conjunction with embodiment, further illustrate technical characterstic of the present invention and effect.

Embodiment:

For verifying the validity of micro-capacitance sensor electricity price optimization method of the present invention, by an embodiment, with certain isolated micro-capacitance sensor for research object carries out electricity price Optimization analyses.Comprise 3 miniature combustion engines, 2 typhoon group of motors and a lead-acid accumulator in this isolated micro-capacitance sensor, the correlation parameter of miniature combustion engine, Wind turbines, lead-acid accumulator is respectively in table 1,2,3.

Table 1 miniature combustion engine parameter

Table 2 Wind turbines parameter

Table 3 lead-acid accumulator parameter

In the present embodiment, the dispatching cycle of planning is 1 day, and a period t is set as 1 hour; Therefore comprise in the dispatching cycle of 1 day total time T=24 the period of hop count.Through measuring, most initial time period in dispatching cycle, the residual capacity S of lead-acid accumulator in this isolated micro-capacitance sensor _ocinitial=48kWh.By adding up the historical data of wind speed and load in this isolated micro-grid system, prediction obtains the peak of user power utilization load in this micro-capacitance sensor under unexecuted Peak-valley TOU power price state, flat, paddy period distribution situation is: paddy period, 0:00-0:07, totally 8 hours; The peak period, 10:00 ~ 12:00,18:00 ~ 21:00, totally 7 hours; All the other time periods are section at ordinary times, totally 9 hours.Under unexecuted Peak-valley TOU power price state, the average electricity price of user's power purchase is 0.65 yuan/kWh.

According to the inventive method, be directed to this isolated micro-capacitance sensor and set up the micro-capacitance sensor electricity price Optimized model taking into account the response of user's request side and micro-capacitance sensor economical operation, owing to relating to economic operation problem in this model, the present invention adopts particle cluster algorithm to solve this model, it solves flow process as shown in Figure 3, and concrete steps are as follows:

Concrete steps are:

Step1: the input parameter of the predicted load under the wind power prediction value of day part in the dispatching cycle that obtains and unexecuted Peak-valley TOU power price state as micro-capacitance sensor electricity price Optimized model will be predicted according to historical data.

Step2: the outer primary group producing outer particle cluster algorithm:

The electric value composition one of stochastic generation peak period, at ordinary times section, paddy period comprises the electricity price array of three numerical value elements, using the positional value of this electricity price array as the particle of in outer population, and the velocity amplitude of this particle of stochastic generation; Thus, according to the outer population scale Ng of setting, stochastic generation comprises the outer population of Ng particle.

Step3: adjust the electricity price array in each particle in current outer population, makes peak period, at ordinary times section in each particle electricity price array, electricity tariff constraint condition that the electricity price value of paddy period meets micro-capacitance sensor electricity price Optimized model.

Step4: according to the load prediction situation under the unexecuted Peak-valley TOU power price state of day part in the dispatching cycle that prediction obtains, determine the paddy period of day part load in the unexecuted Peak-valley TOU power price state dispatching cycle, section at ordinary times, peak period distribution situation, and together with the electricity price array of each particle in current outer population in the lump as the input of customer charge to the response model of tou power price, calculate the matching load value of each period in the implementation Peak-valley TOU power price state dispatching cycle corresponding to electricity price array of each particle in current outer population respectively.

Step5: after completing steps Step4, the matching load value of each period in the implementation Peak-valley TOU power price state dispatching cycle corresponding to the electricity price array of each particle in current outer population, lower floor's objective function for micro-capacitance sensor electricity price Optimized model adopts internal layer particle cluster algorithm to solve, and obtains the micro-capacitance sensor optimal power scheduling scheme corresponding to electricity price array of each particle in current outer population and runs total cost C _total, and then the total revenue C corresponding to electricity price array of each particle in current outer population is calculated according to the upper strata objective function of micro-capacitance sensor electricity price Optimized model _benefit.

The micro-capacitance sensor optimal power scheduling scheme corresponding to electricity price array of each particle in described outer population, under referring to the matching load value situation of each period in the dispatching cycle under the implementation Peak-valley TOU power price state corresponding to the electricity price array of this particle, micro-grid system run total cost minimum time, in corresponding dispatching cycle each period each group of miniature combustion engine go out force value and lead-acid accumulator Soc value.

Step6: the adaptive value calculating each particle in current outer population, and the individual extreme value of the particle calculating current outer population and global extremum; For user's purchases strategies constraint condition of micro-capacitance sensor electricity price Optimized model, present invention employs penalty function method and limited user's purchases strategies constraint condition, therefore in outer population, the adaptive value function of each particle is:

Fitness＝A-C _benefit(x)+B(x) (28)

In formula, C _benefitx () is the aggregate earnings value of the correspondence under the objective function of micro-capacitance sensor electricity price Optimized model of particle x in outer population; A is for being greater than aggregate earnings value C _benefitmaximum possible value normal number; B (x) is penalty term corresponding to particle x in outer population, when in formula (9), constraint condition does not meet, B (x) value is a normal number β, and when meeting constraint condition in formula (9), B (x) value is 0.

Step7: the position and the speed that upgrade each particle in outer population: according to the kth of current outer particle cluster algorithm for the position of each particle in population and speed, upgrade position and the speed of each particle in kth+1 generation population of outer particle cluster algorithm:

v _i(k+1)＝ωv _i(k)+c ₁r ₁(k)(P _{best_i}(k)-x _i(k))+c ₂r ₂(k)(P _g(k)-x _i(k))；

x _i(k+1)＝x _i(k)+v _i(k+1)；

In formula, ω is inertia weight coefficient, is a constant; c ₁, c ₂for aceleration pulse, (0,2] between value; K is the current iteration algebraically of outer particle cluster algorithm; r ₁(k), r ₂k () is the random number of value between [0,1]; I represents i-th particle in outer population; v _ik () represents the velocity amplitude of the kth of outer particle cluster algorithm for i-th particle in population; v _i(k+1) velocity amplitude of i-th particle in kth+1 generation population of outer particle cluster algorithm is represented; x _ik () represents the positional value of the kth of outer particle cluster algorithm for i-th particle in population; x _i(k+1) positional value of i-th particle in kth+1 generation population of outer particle cluster algorithm is represented; P _gk kth that () is outer particle cluster algorithm for the global extremum of population, P _{best_i}k kth that () is outer particle cluster algorithm is for the individual extreme value of i-th particle in population.

Step8: repeat step Step3 ~ Step7, until reach the greatest iteration algebraically that outer particle cluster algorithm presets.

Step9: using in outer for final gained population as the particle of global extremum as the optimum solution of micro-capacitance sensor electricity price Optimized model, thus using the optimal pricing scheme of the electricity price value of peak period, the at ordinary times section in this optimum solution particle represented by electricity price array, paddy period as micro-capacitance sensor Peak-valley TOU power price, and according to this optimum solution particle electricity price array corresponding to micro-capacitance sensor optimal power scheduling scheme micro-capacitance sensor miniature combustion engine of day part within dispatching cycle is exerted oneself and lead-acid accumulator charge-discharge electric power scheduling controlling in addition.

In the step Step5 of above-mentioned flow process, the flow process that the lower floor objective function for micro-capacitance sensor electricity price Optimized model adopts internal layer particle cluster algorithm to carry out solving as shown in Figure 4, concrete bag following steps:

(1) using a particle in current outer population as outer object particle, by the matching load value of each period in the implementation Peak-valley TOU power price state dispatching cycle corresponding to the electricity price array of this outer object particle, as the input parameter of lower floor's objective function of micro-capacitance sensor electricity price Optimized model.

(2) the internal layer primary group of internal layer particle cluster algorithm is produced:

For the matching load value of each period in dispatching cycle, in this period of stochastic generation, each group miniature combustion engine goes out force value, and the lead-acid accumulator Soc value in this period of stochastic generation, form the array that comprises N+1 numerical value element, N is total group of number of miniature combustion engine in micro-grid system, thus for T the period comprised in dispatching cycle, stochastic generation obtains T array, form the search volume matrix that (N+1) × T ties up, as the positional value of the particle of in population, and the velocity amplitude of this particle of stochastic generation; Thus, according to the internal layer population scale M of setting, stochastic generation comprises the internal layer population of M particle.

(3) to the miniature combustion engine in each particle in current internal layer population go out force value and lead-acid accumulator Soc value adjusts, make each particle in current internal layer population meet the micro-capacitance sensor economical operation constraint condition of micro-capacitance sensor electricity price Optimized model, ensure micro-grid system power-balance simultaneously.

The concrete adjustment processing mode of this step is:

3.1) whether the miniature combustion engine detected in current internal layer population represented by each particle to go out force value out-of-limit, if be greater than the maximum output limit value of miniature combustion engine then be taken as maximum output limit value if be less than minimum load limit value then be taken as 0, namely represent that the miniature combustion engine of respective sets is in shut down condition;

3.2) the lead-acid accumulator S in current internal layer population represented by each particle is detected _ocwhether be out-of-limitly worth, if be greater than the greatest residual capacity limit value S of lead-acid accumulator _ocmax, then the greatest residual capacity limit value S of lead-acid accumulator is taken as _ocmax; If be less than the least residue capacity limit value S of lead-acid accumulator _ocmin, then least residue capacity limit value S is taken as _ocmin;

3.3) employing pushes back and adjusts for the charge-discharge electric power of method to lead-acid accumulator, makes it meet the lead-acid accumulator constraint condition of micro-capacitance sensor electricity price Optimized model; Backward steps is pushed away as follows before concrete:

3.3.1) t=0 is made respectively, 1,2 ..., T-1; For the residual capacity S of t period lead-acid accumulator in dispatching cycle _oc(t), if meet formula (30), the lead-acid accumulator residual capacity S of a period after through type (32) adjustment _oc(t+1); If meet formula (31), then the lead-acid accumulator residual capacity S of through type (33) adjustment rear period _oc(t+1):

S _oc(t+1)>S _oc(t)+P _ch,maxη _cΔt； (30)

S _oc(t+1)<S _oc(t)-P _dch,maxΔt/η _d； (31)

S _oc(t+1)＝S _oc(t)+P _ch,maxη _cΔt； (32)

S _oc(t+1)＝S _oc(t)-P _dch,maxΔt/η _d； (33)

3.3.2) after execution of step 3.3.1, judge whether formula (21) meets, if met, then forward step 3.3.4 to; If do not met, then make S _oc(T _end)=S _ocinitial, make t=T-1 respectively, T-2 ..., 0; Then for the residual capacity S of t+1 period lead-acid accumulator in dispatching cycle _oc(t+1), if meet formula (30), through type (34) adjusts the lead-acid accumulator residual capacity S of last period _oc(t); If meet formula (31), then through type (35) adjusts the lead-acid accumulator residual capacity S of last period _oc(t) value:

S _oc(t)＝S _oc(t+1)-P _ch, _maxη _cΔt； (34)

S _oc(t)＝S _oc(t+1)+P _dch,maxΔt/η _d； (35)

3.3.3) again judge whether formula (21) meets, forward 3.3.4 to if met; If do not met, then make S _oc(0)=S _ocinitial, and forward 3.3.1 to;

3.3.4) carry out next step to calculate;

3.4) start adjustable strategies: go out force value and lead-acid accumulator S according to the miniature combustion engine in population represented by each particle _ocvalue, in conjunction with wind power prediction value and matching load value, judges that the miniature combustion engine of each period represented by each particle goes out force value and lead-acid accumulator S respectively _ocvalue adds that can the wind power prediction value of same period meet the matching load value of same period, if do not met, then the miniature combustion engine start increasing the corresponding period in corresponding particle runs number till meeting the burden requirement with the period;

3.5) adjustable strategies is shut down: the miniature combustion engine of each period in current internal layer population represented by particle goes out force value and lead-acid accumulator S _ocwhen value adds that the wind power prediction value of same period can meet the matching load value of same period, judge that in each particle, can any one group of miniature combustion engine of each period stoppage in transit meet the matching load value of same period respectively; If met, then the miniature combustion engine of respective sets of stopping transport in the corresponding period of corresponding particle, if to stop transport again any one group of miniature combustion engine until this period, till the matching load value that can not meet the same period requires; If period any one group of miniature combustion engine being in open state all can not meet the matching load value of same period after stopping transport and requires and spinning reserve constraint condition in particle, then the miniature combustion engine start operation group number of this period remains unchanged;

3.6) power-balance adjustment: for each particle in current internal layer population, what to adjust in each period each group miniature combustion engine respectively goes out force value, micro battery system power is balanced, the payload pro rata distribution that in adjustment process, imbalance power is born according to each group of miniature combustion engine, methodology is:

In formula, P _nt, P ' _ntbe respectively carry out that t period in power-balance adjustment forward and backward dispatching cycle be in n-th group of miniature combustion engine of start operation go out force value; Δ P _tfor the power shortage of t period micro battery system in dispatching cycle, as Δ P _tduring <0, represent that the generating general power of micro battery system is less than matching load value, miniature combustion engine need be increased and exert oneself, on the contrary Δ P _tduring >0, then represent that can reduce miniature combustion engine exerts oneself.

(4) adaptive value of each particle in current internal layer population is calculated, and the individual extreme value of the particle calculating current internal layer population and global extremum; In internal layer population, the adaptive value function of each particle is:

$f_{\mathrm{itness}}=A/(C_{\mathrm{total}}+\underset{t=1}{\overset{T}{\mathrm{\&Sigma;}}}\mathrm{\&delta;}{m}_t);$

In formula: C _totalfor the total operating cost of micro-grid system; δ penalty factor; m _tfor the state variable that value is 0 or 1, if in dispatching cycle in t period miniature combustion engine go out force value and lead-acid accumulator Soc value does not meet spinning reserve constraint condition, m _tget 1, otherwise, m _tget 0; A is normal number.

(5) position and the speed of each particle in internal layer population is upgraded: according to the kth of current internal layer particle cluster algorithm for the position of each particle in population and speed, upgrade position and the speed of each particle in kth+1 generation population of internal layer particle cluster algorithm:

v _i(k+1)＝ωv _i(k)+c ₁r ₁(k)(P _{best_i}(k)-x _i(k))+c ₂r ₂(k)(P _g(k)-x _i(k))；

x _i(k+1)＝x _i(k)+v _i(k+1)；

In formula, ω is inertia weight coefficient, is a constant; c ₁, c ₂for aceleration pulse, (0,2] between value; K is the current iteration algebraically of internal layer particle cluster algorithm; r ₁(k), r ₂k () is the random number of value between [0,1]; I represents i-th particle in internal layer population; v _ik () represents the velocity amplitude of kth for i-th particle in population of internal layer particle cluster algorithm; v _i(k+1) velocity amplitude of i-th particle in kth+1 generation population of internal layer particle cluster algorithm is represented; x _ik () represents the positional value of kth for i-th particle in population of internal layer particle cluster algorithm; x _i(k+1) positional value of i-th particle in kth+1 generation population of internal layer particle cluster algorithm is represented; P _gk kth that () is internal layer particle cluster algorithm for the global extremum of population, P _{best_i}k kth that () is internal layer particle cluster algorithm is for the individual extreme value of i-th particle in population.

(6) step (3) ~ (5) are repeated, until reach the greatest iteration algebraically that internal layer particle cluster algorithm presets.

(7) force value and lead-acid accumulator Soc value is gone out using group miniature combustion engine each in T the period comprised within the dispatching cycle represented by the particle of global extremum in final gained internal layer population, as this outer object particle electricity price array corresponding to micro-capacitance sensor optimal power scheduling scheme, and the operation total cost C of micro-grid system under calculating this micro-capacitance sensor optimal power scheduling scheme _total.

(8) repeated execution of steps (1) ~ (7), obtain the micro-capacitance sensor optimal power scheduling scheme corresponding to electricity price array of each particle in current outer population respectively and run total cost C _total.

In the present embodiment, the model solution set up of the present invention is adopted and the best tou power price scheme determined is as shown in table 4.

Table 4 isolates the best tou power price scheme of micro-capacitance sensor

Under the best tou power price scheme of the present embodiment, the total revenue that this micro-capacitance sensor power supply enterprise carries out a Peak-valley TOU power price mechanism dispatching cycle (namely a day) is 631.70 yuan, wherein, under the optimal power generation scheduling scheme corresponding to this best tou power price scheme, the operation total cost of this micro-grid system is 1280.33 yuan; And the total revenue of this micro-capacitance sensor power supply enterprise unexecuted next day of Peak-valley TOU power price state is 587.56 yuan originally, wherein the operation total cost of micro-grid system is 1325.3 yuan.Visible, enforcement Peak-valley TOU power price is machine-processed and in conjunction with micro-capacitance sensor electricity price optimization method of the present invention, power supply enterprise's total revenue increases by 7.5%, and the purchases strategies of user does not increase.

Load curve before and after isolated micro-grid system employing tou power price in the present embodiment as shown in Figure 5.As can be seen from Figure 5, after implementing Peak-valley TOU power price extreme value, user adjusts power structure, and by high for part rate period load transfer plan to low rate period, whole load curve becomes smoother, reaches the object of peak load shifting.In addition, in the present embodiment, before this isolated micro-grid system adopts Peak-valley TOU power price mechanism, its power generation dispatching scheme situation as shown in Figure 6; And after adopting Peak-valley TOU power price mechanism, the optimal power generation scheduling scheme situation determined by micro-capacitance sensor optimization method of the present invention as shown in Figure 7.Contrast as can be seen from Fig. 6 and Fig. 7:

1. user responds Peak-valley TOU power price policy, and sub-load is transferred to low rate period from high rate period, and load curve peak-valley difference reduces, and load curve becomes more level and smooth;

2. under Peak-valley TOU power price mechanism, because load curve peak-valley difference is less, and the miniature combustion engine MT3 operating cost bearing base lotus in system is lower, and simultaneously to go out fluctuation less for miniature combustion engine MT3, and be in state of exerting oneself more greatly, because this reducing isolated micro-capacitance sensor operating cost always;

3. after adopting Peak-valley TOU power price mechanism, peak period load reduces, reduce system reserve demand, the charge-discharge electric power of energy storage device reduces, and reduces the life consumption of energy storage device, reduces operating cost, in actual micro-capacitance sensor, consider the impact of Demand Side Response, suitably can reduce the installed capacity of energy storage device, thus minimizing micro-grid system occurs that generated energy crosses the situation of Sheng, electric power resource waste.

What finally illustrate is, above embodiment is only in order to illustrate technical scheme of the present invention and unrestricted, although with reference to preferred embodiment to invention has been detailed description, those of ordinary skill in the art is to be understood that, can modify to technical scheme of the present invention or equivalent replacement, and not departing from aim and the scope of technical solution of the present invention, it all should be encompassed in the middle of right of the present invention.

Claims (5)

1. micro-capacitance sensor electricity price optimization method, it is characterized in that, be directed to micro-capacitance sensor and adopt Peak-valley TOU power price mechanism, set up the micro-capacitance sensor electricity price Optimized model taking into account the response of user's request side and micro-capacitance sensor economical operation, adopt particle cluster algorithm to solve set up micro-capacitance sensor electricity price Optimized model, determine the best tou power price of micro-capacitance sensor and optimal power generation scheduling scheme; The method specifically comprises the steps:

(1) add up the historical data of wind speed and load in micro-grid system, according to historical data, the load under the wind power of day part in dispatching cycle and unexecuted Peak-valley TOU power price state is predicted;

(2) according to the response characteristic of user to electricity price, the response model of customer charge to tou power price is set up:

Cool load translating ratio is for carrying out after Peak-valley TOU power price, and load to the ratio of the transfer amount of electricity price lower period and high rate period load, comprises the electricity price peak load rate of transform, the flat cool load translating ratio of electricity price Pinggu cool load translating ratio and electricity price peak from the electricity price higher period;

Wherein, electricity price peak load rate of transform λ _pv, electricity price Pinggu cool load translating ratio λ _fvcool load translating ratio λ flat with electricity price peak _pfexpression formula be respectively:

In formula, Δ p _pvfor the electricity price of load peak period and load paddy period is poor, i.e. Δ p _pv=p _p-p _v; Δ p _fvfor the electricity price of load section and load paddy period is at ordinary times poor, i.e. Δ p _fv=p _f-p _v; Δ p _pffor the electricity price of load peak period and load section is at ordinary times poor, i.e. Δ p _pf=p _p-p _f; p _pfor the electricity price of load peak period, p _ffor the electricity price of load section at ordinary times, p _vfor the electricity price of load paddy period; a _pv, a _fv, a _pfbe respectively electricity price peak load and shift minimum threshold values, the minimum threshold values of electricity price Pinggu load transfer plan, the minimum threshold values of the flat load transfer plan in electricity price peak; b _pv, b _fv, b _pfbe respectively electricity price peak load and shift maximum threshold values, the maximum threshold values of electricity price Pinggu load transfer plan, the maximum threshold values of the flat load transfer plan in electricity price peak; K _pvfor electricity price peak load rate of transform λ _pvwith the electricity price difference Δ p of load peak period and load paddy period _pvincrease from minimum threshold values a _pvlinear increase is to maximum threshold values b _pvrate of rise; K _fvfor electricity price Pinggu cool load translating ratio λ _fvwith the electricity price difference Δ p of load section and load paddy period at ordinary times _fvincrease from minimum threshold values a _fvlinear increase is to maximum threshold values b _fvrate of rise; K _pffor the flat cool load translating ratio λ in electricity price peak _pfwith the electricity price difference Δ p of load peak period and load section at ordinary times _pfincrease from minimum threshold values a _pflinear increase is to maximum threshold values b _pfrate of rise; λ _pv ^max, λ _fv ^max, λ _pf ^maxbe respectively electricity price peak load maximum transfer rate, electricity price Pinggu load maximum transfer rate, electricity price peak flat load maximum transfer rate;

Under carrying out Peak-valley TOU power price state, in dispatching cycle, the matching load of day part is:

Formula (2) is namely as the response model of customer charge to tou power price; In formula, L _t0for the predicted load of t period in the unexecuted Peak-valley TOU power price state dispatching cycle; P _dtfor carrying out the matching load value of t period in the Peak-valley TOU power price state dispatching cycle; T _v, T _f, T _pthe time hop count of be respectively internal loading paddy period dispatching cycle, load section, load peak period at ordinary times; be respectively the load mean value of section, peak period at ordinary times under unexecuted Peak-valley TOU power price state;

(3) based on the response model of customer charge to tou power price, micro-capacitance sensor electricity price Optimized model is set up:

The load of day part in dispatching cycle is determined according to the response model of customer charge to tou power price, and enterprise income is maximum to power, micro-capacitance sensor operating cost is minimum for target, set up the micro-capacitance sensor electricity price Optimized model taking into account the response of user's request side and micro-capacitance sensor economical operation;

(4) adopt particle cluster algorithm to solve set up micro-capacitance sensor electricity price Optimized model, determine the best tou power price of micro-capacitance sensor within dispatching cycle and optimal power generation scheduling scheme.

2. micro-capacitance sensor electricity price optimization method according to claim 1, is characterized in that, described micro-capacitance sensor electricity price Optimized model is specially:

To power, enterprise income is target to the maximum, and the upper strata objective function of micro-capacitance sensor electricity price Optimized model is:

$\max C_{\mathrm{benefit}}=\underset{t=1}{\overset{T}{\mathrm{\&Sigma;}}}{C}_{\mathrm{sale},t}\&CenterDot;P_{\mathrm{Dt}}\mathrm{\&Delta;t}-C_{\mathrm{total}};---\left(3\right)$

In formula, C _benefitfor the total revenue of power supply enterprise; C _{sale, t}for to carry out in the Peak-valley TOU power price state dispatching cycle t the period power supply enterprise to the sale of electricity price of user; T be comprise dispatching cycle total time hop count; P _dtfor carrying out the matching load value of t period in the Peak-valley TOU power price state dispatching cycle; Δ t is the time interval of adjacent two periods; C _totalfor the operation total cost of micro-capacitance sensor;

Minimum for target with the operation total cost of micro-capacitance sensor, lower floor's objective function of micro-capacitance sensor electricity price Optimized model is:

$\min C_{\mathrm{total}}=\underset{t=1}{\overset{T}{\mathrm{\&Sigma;}}}(\underset{n=1}{\overset{N}{\mathrm{\&Sigma;}}}{C}_n^t(1-u_{n,t-1})u_{n,t}+\underset{n=1}{\overset{N}{\mathrm{\&Sigma;}}}{u}_{n,t}{F}_{\mathrm{FC},\mathrm{tn}})+C_{\mathrm{bat}};\&CenterDot;\&CenterDot;\&CenterDot;\left(4\right)$

Wherein, $C_n^t={\mathrm{\&sigma;}}_n+{\mathrm{\&delta;}}_n(1-e^{(-T_{\mathrm{nt}}^{\mathrm{OFF}}/{\mathrm{\&tau;}}_n)});F_{\mathrm{FC},\mathrm{tn}}=F_f\underset{t}{\mathrm{\&Sigma;}}\frac{P_{\mathrm{tn}}}{{\mathrm{\&eta;}}_{\mathrm{tn}}};---\left(5\right)$

In formula, for the start cost of n-th group of miniature combustion engine in t the period in dispatching cycle; N is total group of number of miniature combustion engine in micro-capacitance sensor; u _n,tfor the opening of n-th group of miniature combustion engine in t the period in dispatching cycle, stopped status variable, u when being in open state _n,tvalue is 1, u when being in stopped status _n,tvalue is 0; T be comprise in dispatching cycle total time hop count; C _batfor the life consumption cost of lead-acid accumulator; σ _n, δ _n, τ _nit is the start-up cost coefficient of n-th group of miniature combustion engine; it is the idle time of n-th group of miniature combustion engine in dispatching cycle in t period; F _{fC, tn}for the operating cost of n-th group of miniature combustion engine in t the period in dispatching cycle; F _ffor fuel price; P _tnfor the output power of n-th group of miniature combustion engine in t the period in dispatching cycle; η _tnfor the efficiency of n-th group of miniature combustion engine in t the period in dispatching cycle;

Service life of lead accumulator cost depletions C _batcomputing method are as follows:

When the lead-acid accumulator charge and discharge cycles degree of depth is R, largest loop discharge and recharge times N before fault _eSSbe expressed as:

$N_{\mathrm{ESS}}={\mathrm{\&alpha;}}_1+{\mathrm{\&alpha;}}_2{e}^{{\mathrm{\&alpha;}}_{3}R}+{\mathrm{\&alpha;}}_4{e}^{{\mathrm{\&alpha;}}_{5}R};---\left(6\right)$

α ₁~ α ₅for the characteristic parameter of lead-acid accumulator, the life test data that these parameters can be provided by manufacturer obtains;

Once, it is 1/N that battery life loss accounts for entire life number percent to lead-acid accumulator charge and discharge cycles _eSS, equivalent economic attrition cost C ₁for:

C ₁＝C _initial-bat/N _ESS； (7)

In micro-capacitance sensor operational process, within dispatching cycle, the life consumption cost C of lead-acid accumulator _batfor:

$C_{\mathrm{bat}}=\underset{j=0}{\overset{N_T}{\mathrm{\&Sigma;}}}{C}_{1,j};---\left(8\right)$

In formula, C _initial-batfor lead-acid accumulator cost of investment; C _{1, j}for the equivalent economic attrition cost of lead-acid accumulator jth time discharge and recharge; N _tfor the discharge and recharge number of times of lead-acid accumulator in dispatching cycle;

The constraint condition of micro-capacitance sensor electricity price Optimized model:

1. user's purchases strategies constraint condition:

For encouraging user to respond this policy of tou power price, after should ensureing to implement tou power price, the purchases strategies of user does not increase, that is:

M ₀≥M ₁； (9)

Wherein, $M_0=\underset{t=1}{\overset{T}{\mathrm{\&Sigma;}}}{C}_{\mathrm{sale},t0}\&CenterDot;L_{t0}\mathrm{\&Delta;t};M_1=\underset{t=1}{\overset{T}{\mathrm{\&Sigma;}}}{C}_{\mathrm{sale},t}\&CenterDot;P_{\mathrm{Dt}}\mathrm{\&Delta;t};$

In formula, M ₀, M ₁purchases strategies total with user in the implementation Peak-valley TOU power price state dispatching cycle under being respectively unexecuted Peak-valley TOU power price state; C _{sale, t}for power supply enterprise under unexecuted Peak-valley TOU power price state is to the sale of electricity price of user; L _t0for the predicted load of t period in the unexecuted Peak-valley TOU power price state dispatching cycle; C _{sale, t}for to carry out in the Peak-valley TOU power price state dispatching cycle t the period power supply enterprise to the sale of electricity price of user; P _dtfor carrying out the matching load value of t period in the Peak-valley TOU power price state dispatching cycle; Δ t is the time interval of adjacent two periods;

2. Constraint condition:

Compared with under unexecuted Peak-valley TOU power price state, under implementation Peak-valley TOU power price state, the need for electricity amount of user is constant:

$\underset{t\&Element;T_p}{\mathrm{\&Sigma;}}{Q}_{p,t}+\underset{t\&Element;T_f}{\mathrm{\&Sigma;}}{Q}_{f,t}+\underset{t\&Element;T_v}{\mathrm{\&Sigma;}}{Q}_{v,t}=\underset{t\&Element;T}{\mathrm{\&Sigma;}}{Q}_t---\left(10\right)$

T _p+T _f+T _v＝T (11)

In formula, Q _p,t, Q _f,t, Q _v,tbe respectively and carry out in the Peak-valley TOU power price state dispatching cycle user power utilization amount that t period is peak period, at ordinary times section or paddy period; Q _tthe user power utilization amount of t period in the unexecuted Peak-valley TOU power price state dispatching cycle; T _p, T _f, T _vbe respectively and carry out peak period, at ordinary times section, the period number of paddy period in the Peak-valley TOU power price state dispatching cycle;

3. electricity tariff constraint condition:

P _p>P _f>P _v； (12)

$2<\frac{P_p}{P_v}<5;---\left(13\right)$

In formula, P _p, P _f, P _vbe respectively carry out peak period, at ordinary times section in the Peak-valley TOU power price state dispatching cycle, the paddy period power supply enterprise to the sale of electricity price of user, and the ratio of peak period sale of electricity price and paddy period sale of electricity price is between 2 to 5;

4. micro-capacitance sensor economical operation constraint condition, comprising:

1) power-balance constraint condition:

$\underset{n=1}{\overset{N}{\mathrm{\&Sigma;}}}{P}_{\mathrm{nt}}{u}_{n,t}+P_{\mathrm{wt}}+P_{\mathrm{ESSt}}=P_{\mathrm{Dt}};---\left(14\right)$

In formula: P _ntfor exerting oneself of n-th group of miniature combustion engine in t the period in dispatching cycle, u _n,tfor the opening of n-th group of miniature combustion engine in t the period in dispatching cycle, stopped status variable; P _wtfor the wind power prediction value of t period in dispatching cycle; P _eSStfor the charge-discharge electric power of lead-acid accumulator in t the period in dispatching cycle, being just during electric discharge, is negative during charging;

2) miniature combustion engine units limits condition:

$P_n^{\min }{u}_{n,t}\&le;P_{\mathrm{nt}}{u}_{n,t}\&le;P_n^{\max }{u}_{n,t};---\left(15\right)$

In formula, be respectively minimum, the maximum output limit value of n-th group of miniature combustion engine;

3) lead-acid accumulator constraint condition:

In operational process, meet when lead-acid accumulator is in charged state:

$S_{\mathrm{oc}}(t+1)=S_{\mathrm{oc}}\left(t\right)+P_t^c{\mathrm{\&eta;}}_c\mathrm{\&Delta;t};---\left(16\right)$

When lead-acid accumulator is in discharge condition, meet:

$S_{\mathrm{oc}}(t+1)=S_{\mathrm{oc}}\left(t\right)-P_t^d\mathrm{\&Delta;t}/{\mathrm{\&eta;}}_d;---\left(17\right)$

In formula: S _oc(t+1), S _oct () distinguishes the residual capacity of t+1 period and t period lead-acid accumulator in dispatching cycle; be respectively the charge and discharge power of t period lead-acid accumulator in dispatching cycle; η _c, η _dbe respectively the charge and discharge efficiency of lead-acid accumulator; Δ t is the time interval of adjacent two periods;

The rated power of lead-acid accumulator is restricted to:

$0\&le;P_t^c\&le;P_{\mathrm{ch},\max };---\left(18\right)$

$0\&le;P_t^d\&le;P_{\mathrm{dch},\max };---\left(19\right)$

In formula: P _{ch, max}, P _{dch, max}be respectively the maximum charge and discharge power of lead-acid accumulator;

The residual capacity of lead-acid accumulator is restricted to:

S _ocmin≤S _oc(t)≤S _ocmax； (20)

S _oc(0)＝S _oc(T _end)＝S _ocinitial(21)

In formula: S _ocmin, S _ocmaxbe respectively minimum, the greatest residual capacity limit value of lead-acid accumulator; S _oc(0) remaining capacity value of a period lead-acid accumulator the most initial in dispatching cycle is represented, S _oc(T _end) represent the remaining capacity value of last period lead-acid accumulator in dispatching cycle, S _ocinitialrepresent the raw capacity value of lead-acid accumulator;

4) spinning reserve constraint condition:

In dispatching cycle in t period miniature combustion engine provide maximum just for subsequent use for:

$R_{\mathrm{nt}}^{\mathrm{up}}=\underset{n=1}{\overset{N}{\mathrm{\&Sigma;}}}{P}_n^{\max }{u}_{n,t}-\underset{n=1}{\overset{N}{\mathrm{\&Sigma;}}}{P}_{\mathrm{nt}}{u}_{n,t},\&ForAll;t;---\left(22\right)$

In dispatching cycle in t period lead-acid accumulator provide maximum just for subsequent use for:

$R_{\mathrm{ESSt}}^{\mathrm{up}}=\min \{{\mathrm{\&eta;}}_d(S_{\mathrm{oc}}\left(t\right)-S_{\mathrm{oc}\min })/\mathrm{\&Delta;t},P_{\mathrm{dch},\max }-P_{\mathrm{ESSt}}\},\&ForAll;t;---\left(23\right)$

In dispatching cycle in t period miniature combustion engine provide maximum negative for subsequent use for:

$R_{\mathrm{nt}}^{\mathrm{down}}=\underset{n=1}{\overset{N}{\mathrm{\&Sigma;}}}{P}_{\mathrm{nt}}{u}_{n,t}-\underset{n=1}{\overset{N}{\mathrm{\&Sigma;}}}{P}_n^{\min }{u}_{n,t},\&ForAll;t;---\left(24\right)$

In dispatching cycle in t period lead-acid accumulator provide maximum negative for subsequent use for:

$R_{\mathrm{ESSt}}^{\mathrm{down}}=\min \{(S_{\mathrm{oc}\max }-S_{\mathrm{oc}}\left(t\right))/{\mathrm{\&eta;}}_c/\mathrm{\&Delta;t},P_{\mathrm{ch},\max }-P_{\mathrm{ESSt}}\},\&ForAll;t;---\left(25\right)$

Adopt probability constraints determination spinning reserve capacity, that is:

$P\{-(R_{\mathrm{nt}}^{\mathrm{down}}+R_{\mathrm{ESSt}}^{\mathrm{down}})\&le;R_t\&le;R_{\mathrm{nt}}^{\mathrm{up}}+R_{\mathrm{ESSt}}^{\mathrm{up}}\}\&GreaterEqual;\mathrm{\&alpha;};---\left(26\right)$

R _t＝△P _Dt+△P _wt； (27)

In formula, R _tfor the spinning reserve capacity in dispatching cycle needed for t period micro-grid system; P{} represents probability; α is level of confidence; Δ P _dtfor the load prediction error of t period in dispatching cycle, Normal Distribution, i.e. Δ P _dt~ N (0, (σ ₂p _dt) ²); Δ P _wtfor the wind power prediction error of t period in dispatching cycle, Normal Distribution, i.e. Δ P _wt~ N (0, (σ ₁p _wt) ²); Δ t is the time interval of adjacent two periods.

3. micro-capacitance sensor electricity price optimization method according to claim 2, is characterized in that, adopts particle cluster algorithm to the concrete bag following steps of solution procedure of described micro-capacitance sensor electricity price Optimized model:

Step1: the input parameter of the predicted load under the wind power prediction value of day part in the dispatching cycle that obtains and unexecuted Peak-valley TOU power price state as micro-capacitance sensor electricity price Optimized model will be predicted according to historical data;

Step2: the outer primary group producing outer particle cluster algorithm:

The electric value composition one of stochastic generation peak period, at ordinary times section, paddy period comprises the electricity price array of three numerical value elements, using the positional value of this electricity price array as the particle of in outer population, and the velocity amplitude of this particle of stochastic generation; Thus, according to the outer population scale Ng of setting, stochastic generation comprises the outer population of Ng particle;

Step3: adjust the electricity price array in each particle in current outer population, makes peak period, at ordinary times section in each particle electricity price array, electricity tariff constraint condition that the electricity price value of paddy period meets micro-capacitance sensor electricity price Optimized model;

Step4: according to the load prediction situation under the unexecuted Peak-valley TOU power price state of day part in the dispatching cycle that prediction obtains, determine the paddy period of day part load in the unexecuted Peak-valley TOU power price state dispatching cycle, section at ordinary times, peak period distribution situation, and together with the electricity price array of each particle in current outer population in the lump as the input of customer charge to the response model of tou power price, calculate the matching load value of each period in the implementation Peak-valley TOU power price state dispatching cycle corresponding to electricity price array of each particle in current outer population respectively,

Step5: after completing steps Step4, the matching load value of each period in the implementation Peak-valley TOU power price state dispatching cycle corresponding to the electricity price array of each particle in current outer population, lower floor's objective function for micro-capacitance sensor electricity price Optimized model adopts internal layer particle cluster algorithm to solve, and obtains the micro-capacitance sensor optimal power scheduling scheme corresponding to electricity price array of each particle in current outer population and runs total cost C _total, and then the total revenue C corresponding to electricity price array of each particle in current outer population is calculated according to the upper strata objective function of micro-capacitance sensor electricity price Optimized model _benefit;

The micro-capacitance sensor optimal power scheduling scheme corresponding to electricity price array of each particle in described outer population, under referring to the matching load value situation of each period in the dispatching cycle under the implementation Peak-valley TOU power price state corresponding to the electricity price array of this particle, micro-grid system run total cost minimum time, in corresponding dispatching cycle each period each group of miniature combustion engine go out force value and lead-acid accumulator Soc value;

Step6: the adaptive value calculating each particle in current outer population, and the individual extreme value of the particle calculating current outer population and global extremum; In outer population, the adaptive value function of each particle is:

Fitness＝A-C _benefit(x)+B(x) (28)

In formula, C _benefitx () is the aggregate earnings value of the correspondence under the objective function of micro-capacitance sensor electricity price Optimized model of particle x in outer population; A is for being greater than aggregate earnings value C _benefitmaximum possible value normal number; B (x) is penalty term corresponding to particle x in outer population, when in formula (9), constraint condition does not meet, B (x) value is a normal number β, and when meeting constraint condition in formula (9), B (x) value is 0;

Step7: the position and the speed that upgrade each particle in outer population: according to the kth of current outer particle cluster algorithm for the position of each particle in population and speed, upgrade position and the speed of each particle in kth+1 generation population of outer particle cluster algorithm:

v _i(k+1)＝ωv _i(k)+c ₁r ₁(k)(P _{best_i}(k)-x _i(k))+c ₂r ₂(k)(P _g(k)-x _i(k))；

x _i(k+1)＝x _i(k)+v _i(k+1)；

In formula, ω is inertia weight coefficient, is a constant; c ₁, c ₂for aceleration pulse, (0,2] between value; K is the current iteration algebraically of outer particle cluster algorithm; r ₁(k), r ₂k () is the random number of value between [0,1]; I represents i-th particle in outer population; v _ik () represents the velocity amplitude of the kth of outer particle cluster algorithm for i-th particle in population; v _i(k+1) velocity amplitude of i-th particle in kth+1 generation population of outer particle cluster algorithm is represented; x _ik () represents the positional value of the kth of outer particle cluster algorithm for i-th particle in population; x _i(k+1) positional value of i-th particle in kth+1 generation population of outer particle cluster algorithm is represented; P _gk kth that () is outer particle cluster algorithm for the global extremum of population, P _{best_i}k kth that () is outer particle cluster algorithm is for the individual extreme value of i-th particle in population;

Step8: repeat step Step3 ~ Step7, until reach the greatest iteration algebraically that outer particle cluster algorithm presets;

Step9: using in outer for final gained population as the particle of global extremum as the optimum solution of micro-capacitance sensor electricity price Optimized model, thus using the optimal pricing scheme of the electricity price value of peak period, the at ordinary times section in this optimum solution particle represented by electricity price array, paddy period as micro-capacitance sensor Peak-valley TOU power price, and according to this optimum solution particle electricity price array corresponding to micro-capacitance sensor optimal power scheduling scheme micro-capacitance sensor miniature combustion engine of day part within dispatching cycle is exerted oneself and lead-acid accumulator charge-discharge electric power scheduling controlling in addition.

4. micro-capacitance sensor electricity price optimization method according to claim 3, is characterized in that, in described step Step5, and the concrete bag following steps of process that the lower floor's objective function for micro-capacitance sensor electricity price Optimized model adopts internal layer particle cluster algorithm to carry out solving:

(1) using a particle in current outer population as outer object particle, by the matching load value of each period in the implementation Peak-valley TOU power price state dispatching cycle corresponding to the electricity price array of this outer object particle, as the input parameter of lower floor's objective function of micro-capacitance sensor electricity price Optimized model;

(2) the internal layer primary group of internal layer particle cluster algorithm is produced:

For the matching load value of each period in dispatching cycle, in this period of stochastic generation, each group miniature combustion engine goes out force value, and the lead-acid accumulator Soc value in this period of stochastic generation, form the array that comprises N+1 numerical value element, N is total group of number of miniature combustion engine in micro-grid system, thus for T the period comprised in dispatching cycle, stochastic generation obtains T array, form the search volume matrix that (N+1) × T ties up, as the positional value of the particle of in population, and the velocity amplitude of this particle of stochastic generation; Thus, according to the internal layer population scale M of setting, stochastic generation comprises the internal layer population of M particle;

(3) to the miniature combustion engine in each particle in current internal layer population go out force value and lead-acid accumulator Soc value adjusts, make each particle in current internal layer population meet the micro-capacitance sensor economical operation constraint condition of micro-capacitance sensor electricity price Optimized model, ensure micro-grid system power-balance simultaneously;

(4) adaptive value of each particle in current internal layer population is calculated, and the individual extreme value of the particle calculating current internal layer population and global extremum; In internal layer population, the adaptive value function of each particle is:

$f_{\mathrm{itness}}=A/(C_{\mathrm{total}}+\underset{t=1}{\overset{T}{\mathrm{\&Sigma;}}}\mathrm{\&delta;}{m}_t);$

In formula: C _totalfor the total operating cost of micro-grid system; δ penalty factor; m _tfor the state variable that value is 0 or 1, if in dispatching cycle in t period miniature combustion engine go out force value and lead-acid accumulator Soc value does not meet spinning reserve constraint condition, m _tget 1, otherwise, m _tget 0; A is normal number;

(5) position and the speed of each particle in internal layer population is upgraded: according to the kth of current internal layer particle cluster algorithm for the position of each particle in population and speed, upgrade position and the speed of each particle in kth+1 generation population of internal layer particle cluster algorithm:

v _i(k+1)＝ωv _i(k)+c ₁r ₁(k)(P _{best_i}(k)-x _i(k))+c ₂r ₂(k)(P _g(k)-x _i(k))；

x _i(k+1)＝x _i(k)+v _i(k+1)；

In formula, ω is inertia weight coefficient, is a constant; c ₁, c ₂for aceleration pulse, (0,2] between value; K is the current iteration algebraically of internal layer particle cluster algorithm; r ₁(k), r ₂k () is the random number of value between [0,1]; I represents i-th particle in internal layer population; v _ik () represents the velocity amplitude of kth for i-th particle in population of internal layer particle cluster algorithm; v _i(k+1) velocity amplitude of i-th particle in kth+1 generation population of internal layer particle cluster algorithm is represented; x _ik () represents the positional value of kth for i-th particle in population of internal layer particle cluster algorithm; x _i(k+1) positional value of i-th particle in kth+1 generation population of internal layer particle cluster algorithm is represented; P _gk kth that () is internal layer particle cluster algorithm for the global extremum of population, P _{best_i}k kth that () is internal layer particle cluster algorithm is for the individual extreme value of i-th particle in population;

(6) step (3) ~ (5) are repeated, until reach the greatest iteration algebraically that internal layer particle cluster algorithm presets;

(7) force value and lead-acid accumulator Soc value is gone out using group miniature combustion engine each in T the period comprised within the dispatching cycle represented by the particle of global extremum in final gained internal layer population, as this outer object particle electricity price array corresponding to micro-capacitance sensor optimal power scheduling scheme, and the operation total cost C of micro-grid system under calculating this micro-capacitance sensor optimal power scheduling scheme _total;

(8) repeated execution of steps (1) ~ (7), obtain the micro-capacitance sensor optimal power scheduling scheme corresponding to electricity price array of each particle in current outer population respectively and run total cost C _total.

5. micro-capacitance sensor electricity price optimization method according to claim 4, is characterized in that, described step (3) is specially:

3.1) whether the miniature combustion engine detected in current internal layer population represented by each particle to go out force value out-of-limit, if be greater than the maximum output limit value of miniature combustion engine then be taken as maximum output limit value if be less than minimum load limit value then be taken as 0, namely represent that the miniature combustion engine of respective sets is in shut down condition;

3.2) the lead-acid accumulator S in current internal layer population represented by each particle is detected _ocwhether be out-of-limitly worth, if be greater than the greatest residual capacity limit value S of lead-acid accumulator _ocmax, then the greatest residual capacity limit value S of lead-acid accumulator is taken as _ocmax; If be less than the least residue capacity limit value S of lead-acid accumulator _ocmin, then least residue capacity limit value S is taken as _ocmin;

3.3) employing pushes back and adjusts for the charge-discharge electric power of method to lead-acid accumulator, makes it meet the lead-acid accumulator constraint condition of micro-capacitance sensor electricity price Optimized model; Backward steps is pushed away as follows before concrete:

3.3.1) t=0 is made respectively, 1,2 ..., T-1; For the residual capacity S of t period lead-acid accumulator in dispatching cycle _oc(t), if meet formula (30), the lead-acid accumulator residual capacity S of a period after through type (32) adjustment _oc(t+1); If meet formula (31), then the lead-acid accumulator residual capacity S of through type (33) adjustment rear period _oc(t+1):

S _oc(t+1)>S _oc(t)+P _ch,maxη _c△t； (30)

S _oc(t+1)<S _oc(t)-P _dch,max△t/η _d； (31)

S _oc(t+1)＝S _oc(t)+P _ch,maxη _c△t； (32)

S _oc(t+1)＝S _oc(t)-P _dch,max△t/η _d； (33)

3.3.2) after execution of step 3.3.1, judge whether formula (21) meets, if met, then forward step 3.3.4 to; If do not met, then make S _oc(T _end)=S _ocinitial, make t=T-1 respectively, T-2 ..., 0; Then for the residual capacity S of t+1 period lead-acid accumulator in dispatching cycle _oc(t+1), if meet formula (30), through type (34) adjusts the lead-acid accumulator residual capacity S of last period _oc(t); If meet formula (31), then through type (35) adjusts the lead-acid accumulator residual capacity S of last period _oc(t) value:

S _oc(t)＝S _oc(t+1)-P _ch,maxη _c△t； (34)

S _oc(t)＝S _oc(t+1)+P _dch,max△t/η _d； (35)

3.3.3) again judge whether formula (21) meets, forward 3.3.4 to if met; If do not met, then make S _oc(0)=S _ocinitial, and forward 3.3.1 to;

3.3.4) carry out next step to calculate;

3.4) start adjustable strategies: go out force value and lead-acid accumulator S according to the miniature combustion engine in population represented by each particle _ocvalue, in conjunction with wind power prediction value and matching load value, judges that the miniature combustion engine of each period represented by each particle goes out force value and lead-acid accumulator S respectively _ocvalue adds that can the wind power prediction value of same period meet the matching load value of same period, if do not met, then the miniature combustion engine start increasing the corresponding period in corresponding particle runs number till meeting the burden requirement with the period;

3.5) adjustable strategies is shut down: the miniature combustion engine of each period in current internal layer population represented by particle goes out force value and lead-acid accumulator S _ocwhen value adds that the wind power prediction value of same period can meet the matching load value of same period, judge that in each particle, can any one group of miniature combustion engine of each period stoppage in transit meet the matching load value of same period respectively; If met, then the miniature combustion engine of respective sets of stopping transport in the corresponding period of corresponding particle, if to stop transport again any one group of miniature combustion engine until this period, till the matching load value that can not meet the same period requires; If period any one group of miniature combustion engine being in open state all can not meet the matching load value of same period after stopping transport and requires and spinning reserve constraint condition in particle, then the miniature combustion engine start operation group number of this period remains unchanged;

3.6) power-balance adjustment: for each particle in current internal layer population, what to adjust in each period each group miniature combustion engine respectively goes out force value, micro battery system power is balanced, the payload pro rata distribution that in adjustment process, imbalance power is born according to each group of miniature combustion engine, methodology is:

In formula, P _nt, P ' _ntbe respectively carry out that t period in power-balance adjustment forward and backward dispatching cycle be in n-th group of miniature combustion engine of start operation go out force value; Δ P _tfor the power shortage of t period micro battery system in dispatching cycle, as Δ P _tduring <0, represent that the generating general power of micro battery system is less than matching load value, miniature combustion engine need be increased and exert oneself, on the contrary Δ P _tduring >0, then represent that can reduce miniature combustion engine exerts oneself.