CN104182809A - Optimization method of intelligent household power system - Google Patents

Abstract

The invention provides an optimization method of an intelligent household power system. The optimization method is mainly used for achieving intelligent household power optimization. The method comprises the steps of putting forward an optimization object function and constraint conditions and establishing an optimization model combining economy and comfort objectives. By means of the proposed load energy management optimization method, a load is transferred to the time period when distributed energy power generation is prominent by shortening the service time of the load at a high-electricity-price time period, so that a total load curve is perfected, and a part of distributed energy power generating capacity is transferred through peak load shifting of an energy storage device. A particle swarm algorithm is applied to the intelligent household power system; on one hand, electric charge of users is lowered, and better economy is achieved; on the other hand, the comprehensive satisfaction degree of the users is improved.

Description

A kind of optimization method of home intelligent power system

Technical field

The present invention relates to a kind of optimization method, be specifically related to a kind of optimization method of home intelligent power system.

Background technology

In recent years, intelligent grid has become the developing direction of the generally acknowledged Future Power System in the whole world, has also obtained great attention and tremendous development at Chinese research and practice.Intelligent grid is that advanced sensing measurement technology, ICT (information and communication technology), analysis decision technology, automatic control technology and electricity power technology are combined, and new-modernization electrical network integrated with electrical network infrastructure height and that form.Along with developing rapidly of intelligent grid, home intelligent power system, as the important component part in intelligent grid, also has great importance.Home intelligent power system combines each household electrical appliance, distributed power generation and the accumulator relevant with life staying idle at home, reaches the optimum of economical target and comfortableness target by optimization.

Application number is that 201320500519.7 utility model patent discloses a kind of home intelligent power system, comprise at least one smart jack, energy control device and service terminal, smart jack is connected with energy control device respectively, and energy control device and service terminal carry out wired or wireless communication; Smart jack gathers the information about power of a consumer, and is sent to energy control device; Information about power is uploaded to service terminal by energy control device, and receive the power switch instruction of service terminal transmission and be forwarded to smart jack.This utility model home intelligent power system, realizes remote real-time monitoring and Long-distance Control.

Application number is that 201310405534.8 patent of invention discloses a kind of household electricity intelligence relational system, by the use of sensor, Real-Time Monitoring indoor occupant situation, temperature, humidity, luminance brightness etc., by obtaining of the data such as temperature, determine whether to open relevant electrical equipment, and the temperature value of relevant electric operation; Can also set the electrical work period, before relevant electrical equipment is opened, preferentially judge that current time whether within this electrical work period, if not within working hour, do not open electrical equipment.This invention is also by the electricity price of setting and the corresponding relation of the corresponding relation of period, power consumption and electricity price, after obtaining each electricity consumption of electrical apparatus, calculate electricity consumption total amount and period power consumption, judge whether electricity consumption premier peace exceedes predetermined value, if exceed predetermined value, send prompting by alarm module to user, and inform the current power consumption of user and electricity charge situation, and according to the electricity consumption weight of default each electrical equipment, the part electrical equipment of stopping using.

Summary of the invention

In order to overcome above-mentioned the deficiencies in the prior art, the invention provides a kind of optimization method of home intelligent power system, mainly solve home intelligent power optimization method problem, propose optimization aim function and constraint condition, set up the Optimized model of economy and the combination of comfortableness target.The load and energy management optimization method proposing is loaded in the service time of high electricity price period by having reduced, load is transferred to the comparatively outstanding period of distributed energy generating, overall load curve is improved, and shifted part distributed energy generated energy by the peak load shifting of energy storage device.Particle cluster algorithm is applied in home intelligent power system, has reduced user's electricity cost on the one hand, obtain better economy, also improved on the other hand user's comprehensive satisfaction.

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

The optimization method that the invention provides a kind of home intelligent power system, said method comprising the steps of:

Step 1: the intelligent power system that founds a family basic model;

Step 2: intelligent power system optimization model founds a family;

Step 3: home intelligent power system is optimized.

In described step 1, home intelligent power system basic model comprises wind-power electricity generation model, photovoltaic generation model, accumulator, load model and city's electric model.

(1) in described wind-power electricity generation model, establish P _wfor the real output of aerogenerator, P _rfor the output rating of aerogenerator, v is ambient wind velocity, v _cifor the startup wind speed of aerogenerator, v _crfor the wind rating of aerogenerator, v _cofor the cut-out wind speed of aerogenerator, the real output P of aerogenerator _wwith relation between ambient wind velocity v can be represented by the formula:

Wherein, coefficient k ₁and k ₂be expressed as: k ₂=-k ₁v _ci.

(2) in described photovoltaic generation model, establishing I is photovoltaic cell electric current, and V is photovoltaic cell voltage, I ' _scfor the short-circuit current of photovoltaic cell under actual environment, V ' _ocfor the open-circuit voltage of photovoltaic cell under actual environment, I ' _mfor maximum power of photovoltaic cell point electric current under actual environment, V ' _mfor maximum power of photovoltaic cell point voltage under actual environment, T is photovoltaic battery temperature under actual environment, T _airfor air themperature under actual environment, T _reffor reference battery temperature, S is intensity of solar radiation under actual environment, S _reffor with reference to intensity of solar radiation, K is intensity of solar radiation photovoltaic battery temperature coefficient while changing, the short-circuit current I ' of photovoltaic cell under actual environment _scopen-circuit voltage V ' with photovoltaic cell under actual environment _ocbe expressed as:

$I_{\mathrm{sc}}^{\′}=I_{\mathrm{sc}}\left(\frac{S}{S_{\mathrm{ref}}}\right)(1+\mathrm{aT})$

V′ _oc＝V _oc(1-cΔT)(1+bΔS)

Wherein, I _scfor the short-circuit current of photovoltaic cell under rated condition, V _ocfor the open-circuit voltage of photovoltaic cell under rated condition, a, b, c are respectively constant coefficient, and value is respectively 0.0025 ,-0.1949,0.0029; Δ T and Δ S are respectively photovoltaic battery temperature changing value and intensity of solar radiation changing value under actual environment, are expressed as:

ΔT＝T-T _ref

$\mathrm{\ΔS}=\frac{S}{S_{\mathrm{ref}}}-1$

Maximum power of photovoltaic cell point electric current I under actual environment ' _mwith maximum power of photovoltaic cell point voltage V ' under actual environment _mbe expressed as:

$I_m^{\′}=I_m\left(\frac{S}{S_{\mathrm{ref}}}\right)(1+\mathrm{a\ΔT})$

V′ _m＝V _m(1-cΔT)(1+bΔS)

Wherein, I _mfor maximum power of photovoltaic cell point electric current under rated condition, V _mfor maximum power of photovoltaic cell point voltage under rated condition;

Photovoltaic cell electric current I can be expressed as:

$I=I_{\mathrm{sc}}^{\′}(1-C_1\{\exp \left[\frac{V}{C_2{V}_{\mathrm{oc}}^{\′}}\right]-1\left\}\right)$

Wherein, coefficient C ₁and C ₂be expressed as:

C ₁＝(1-I′ _m/I′ _sc)exp[-V′ _m/(C ₂V′ _oc)]

$C_2=(\frac{V_m^{\′}}{V_{\mathrm{oc}}^{\′}}-1){\left[\ln (1-I_m^{\′}/I_{\mathrm{sc}}^{\′})\right]}^{-1}.$

(3) described battery model adopts ampere hour method to set up, and establishing SOC is storage battery charge state, SOC ₀for the initial state-of-charge of accumulator, c _rfor the actual electric weight of accumulator, c _nfor accumulator specified electric quantity, I _efor accumulator cell charging and discharging electric current, Δ t is the accumulator cell charging and discharging time, η _ichfor charge in batteries efficiency, η _disfor battery discharging efficiency, P _ldfor the power of workload demand under battery discharging state, P _efor the afterpower under battery state of charge, storage battery charge state SOC is expressed as:

Wherein, accumulator cell charging and discharging electric current I _ebe expressed as:

Wherein, U is DC bus-bar voltage.

(4), in described load model, the load in home intelligent power system is divided into following three classes:

(1) switching mode load;

Two states is only opened and closed to switching mode load, represents to close with 0, and 1 represents to open, and when load condition is when opening, the real power of load is its rated power, and in the time that load condition is pass, the real power of load is 0; The parameter of switching mode load is its on off state;

(2) stepping type load;

Stepping type load comprises the switching mode load that can carry out switching manipulation and the multistage subsection load that can carry out gear adjusting; The parameter of stepping type load comprises rated power and total gear number of stepping type load;

(3) adjustment type load;

Adjustment type load can be operated in different duties, and the duty of load is relevant with the environmental parameter such as temperature and illumination.

(5), in described city electric model, electrical network is supplied with the active power P of home intelligent power system _gridrepresent, and have:

$P_{\mathrm{grid}}=\left\{\begin{array}{cc}{P}_{\mathrm{load}}-P_{\mathrm{DG}}& P_{\mathrm{load}}>P_{\mathrm{DG}}\\ 0& P_{\mathrm{load}}\≤P_{\mathrm{DG}}\end{array}\right.$

Wherein, P _loadfor the active power that load consumes, P _dGthe active power providing for distributed power source or accumulator.

Home intelligent power system optimization model in described step 2 comprises objective function and constraint condition;

Described objective function comprises economy objective function, comfortableness objective function and comprehensive satisfaction objective function;

Described constraint condition comprises active power balance constraint, the constraint of peak power limit value and storage battery charge state constraint.

In described economy objective function, establish C ₁for economy objective function, during for t, be carved into the electricity cost in t+1 moment, economy objective function C ₁be expressed as:

$\min C_1=\underset{t=1}{\overset{24}{\mathrm{\Σ}}}{C}_1^{\left(t\right)}$

Wherein, be expressed as:

${C_1^{\left(t\right)}=\left\{\begin{array}{cc}{C}^{\left(t\right)}(P_{\mathrm{load}}^{\left(t\right)}-P_{\mathrm{DG}}^{\left(t\right)}),& P_{\mathrm{load}}^{\left(t\right)}>P_{\mathrm{DG}}^{\left(t\right)}\\ C^{\′\left(t\right)}(P_{\mathrm{DG}}^{\left(t\right)}-P_{\mathrm{load}}^{\left(t\right)}),& P_{\mathrm{load}}^{\left(t\right)}\≤P_{\mathrm{DG}}^{\left(t\right)}\end{array}\right.}^{}$

Wherein, C ^(t)be the rate for incorporation into the power network that user distribution formula generating in t hour consumes, C ' ^(t)be the rate for incorporation into the power network of t hour user distribution formula generating loopback electrical network, both are known parameters; the active power providing for t hour distributed power source or accumulator; be the active power of load consumption in t hour, have

$P_{\mathrm{load}}^{\left(t\right)}=\underset{l=1}{\overset{L}{\mathrm{\Σ}}}{x}_{1l}^{\left(t\right)}{P}_{1l}+\underset{m=1}{\overset{M}{\mathrm{\Σ}}}{x}_{2m}^{\left(t\right)}{P}_{2m}+\underset{n=1}{\overset{N}{\mathrm{\Σ}}}{P}_{3n}\left(x_{3n}^{\left(t\right)}\right)$

Wherein, for being the actual working state of l switching mode load, 1 represents to open, and 0 represents to close; P _1lbe the rated power of l switching mode load; it is the actual working state of m stepping type load; P _2lbe the rated power of m stepping type load operation in the time of one grade; it is the actual working state of n adjustment type load; it is the actual working state of n adjustment type load; being n adjustment type load when duty is time the power that consumes.

Described comfortableness objective function is divided into following three classes:

(1) for switching mode load, its comfortableness objective function is expressed as:

$C_{21}^{\left(t\right)}=\underset{l=1}{\overset{L}{\mathrm{\Σ}}}\left(\right|x_{1l}^{\left(t\right)}-x_{1l}^{\left(t\right)}*\left|f_{1l}\right)$

Wherein, for the total load of switching mode load is worth, L is the quantity of switching mode load; be the actual working state of l switching mode load, 1 represents to open, and 0 represents to close; be the setting duty of l switching mode load, 1 represents to open, and 0 represents to close; f _1lit is the Laden-Value of l switching mode load;

(2) for stepping type load, its comfortableness objective function is expressed as:

$C_{22}^{\left(t\right)}=\underset{m=1}{\overset{M}{\mathrm{\Σ}}}\left(\right|x_{2m}^{\left(t\right)}-x_{2m}^{\left(t\right)}*\left|\frac{f_{2m}}{D}\right)$

Wherein, for the total load of stepping type load is worth, M is the quantity of stepping type load; be the actual working state of m stepping type load, numeric representation place gear; be the setting duty of m stepping type load, numeric representation place gear; f _2mit is the Laden-Value of m stepping type load; D is stepping total gear number of loading;

(3) for adjustment type load, its comfortableness objective function is expressed as:

$C_{23}^{\left(t\right)}=\underset{n=1}{\overset{N}{\mathrm{\Σ}}}{a}_n{(x_{3n}^{\left(t\right)}-x_{3n}^{\left(t\right)}*)}^2$

Wherein, for the total load of adjustment type load is worth, N is the quantity of adjustment type load; be the actual working state of n adjustment type load, it is the setting duty of n adjustment type load;

In the time that user abandons using n adjustment type load, the actual working state of establishing this adjustment type load is d with setting duty deviation _n, the total load of adjustment type load is worth can be expressed as again:

$C_{23}^{\left(t\right)}=\underset{n=1}{\overset{N}{\mathrm{\Σ}}}\frac{f_{3n}}{d_n^2}{(x_{3n}^{\left(t\right)}-x_{3n}^{\left(t\right)}*)}^2$

Wherein, f _3nit is the Laden-Value of n adjustment type load;

Comfortableness objective function is expressed as:

$C_2=\underset{t=1}{\overset{24}{\mathrm{\Σ}}}(C_{21}^{\left(t\right)}+C_{22}^{\left(t\right)}+C_{23}^{\left(t\right)})$

Wherein, C ₂for comfortableness objective function.

Described comprehensive satisfaction objective function represents specifically have with C:

minC＝min(C ₁+C ₂)

Wherein, C ₁for economy objective function, C ₂for comfortableness objective function.

Described active power balance constraint representation is:

$\underset{i=1}{\overset{G}{\mathrm{\Σ}}}{P}_{\mathrm{DGi}}+P_{\mathrm{grid}}=\underset{r=1}{\overset{R}{\mathrm{\Σ}}}{P}_{\mathrm{loadr}}$

Wherein, P _gridfor the active power of electrical network supply home intelligent power system, P _loadrbe r the active power that load consumes, P _dGifor the active power that i distributed power source or accumulator provide, G is distributed power source or accumulator quantity, and R is load sum.

Described peak power limit value constraint representation is:

$\left\{\begin{array}{c}{P}_{\mathrm{DGi}\min }<P_{\mathrm{DGi}}<P_{\mathrm{DGi}\max }\\ P_{\mathrm{grid}}<P_{\lim }\end{array}\right.$

Wherein, P _dGifor the active power that i distributed power source provides, P _dGiminand P _dGimaxfor active power minimum value and maximal value that i distributed power source provides, P _gridfor the active power of electrical network supply home intelligent power system, P _limfor the active power limit value of electrical network supply home intelligent power system.

Described storage battery charge state constraint representation is:

SOC _min<SOC<SOC _max

Wherein, SOC is storage battery charge state, SOC _minfor storage battery charge state lower limit, SOC _maxfor the storage battery charge state upper limit.

In described step 3, first judge that by constraint condition whether the current location of particle is feasible, if the current location of particle is feasible, by more speed and the position of new particle of basic particle group algorithm; If the current location of particle is infeasible, upgrade population speed and position by the particle cluster algorithm of belt restraining.

In basic particle group algorithm, by more speed and the position of new particle of following formula:

v _j(s+1)＝c ₁r ₁[p _lo(s)-y _j(s)]+c ₂r ₂[p _gl(s)-y _j(s)]+wv _j(s)

y _j(s+1)＝y _j(s)+v _j(s+1)

Wherein, v _jand y (s+1) _j(s+1) be respectively position and the speed of the s+1 time iteration of j particle in D dimension space; y _iand v (s) _i(s) be respectively position and the speed of the s time iteration of j particle in D dimension space; W is Inertia weight factor; c ₁and c ₂for the positive study factor; r ₁and r ₂be equally distributed random number between 0 to 1, p _lo(s) locally optimal solution while being s iteration, p _gl(s) globally optimal solution while being s iteration.

The particle cluster algorithm of belt restraining upgrades population speed and position, has:

v _j(s+1)＝c ₁r ₁[p _lo(s)-y _j(s)]+c ₂r ₂[p _gl(s)-y _j(s)]

y _j(s+1)＝y _j(s)+v _j(s+1)。

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

This law has solved home intelligent power optimization method problem, proposes optimization aim function and constraint condition, sets up the Optimized model of economy and the combination of comfortableness target.By having reduced load in the service time of high electricity price period, load is transferred to the comparatively outstanding period of distributed energy generating, overall load curve is improved, and shifted part distributed energy generated energy by the peak load shifting of energy storage device.Particle cluster algorithm is applied in home intelligent power system, has reduced user's electricity cost on the one hand, obtain better economy, also improved on the other hand user's comprehensive satisfaction.Propose the Optimized model that economy and comfortableness target combine, and in model, embodied electricity price incentive policy, and particle cluster algorithm has been applied in home intelligent power system.

Brief description of the drawings

Fig. 1 is the linear relationship chart between ambient wind velocity and the real output of aerogenerator in the embodiment of the present invention;

Fig. 2 is the process flow diagram that in the embodiment of the present invention, particle cluster algorithm is optimized home intelligent power system;

Fig. 3 is comprehensive satisfaction functional value temporal evolution graph of a relation in user 24 hours in the embodiment of the present invention;

Fig. 4 is electricity cost temporal evolution graph of a relation in user 24 hours in the embodiment of the present invention.

Embodiment

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

The optimization method that the invention provides a kind of home intelligent power system, said method comprising the steps of:

Step 1: the intelligent power system that founds a family basic model;

Step 2: intelligent power system optimization model founds a family;

Step 3: home intelligent power system is optimized.

In described step 1, home intelligent power system basic model comprises wind-power electricity generation model, photovoltaic generation model, accumulator, load model and city's electric model.

(1) in described wind-power electricity generation model, as Fig. 1, establish P _wfor the real output of aerogenerator, P _rfor the output rating of aerogenerator, v is ambient wind velocity, v _cifor the startup wind speed of aerogenerator, v _crfor the wind rating of aerogenerator, v _cofor the cut-out wind speed of aerogenerator, the real output P of aerogenerator _wwith relation between ambient wind velocity v can be represented by the formula:

Wherein, coefficient k ₁and k ₂be expressed as: k ₂=-k ₁v _ci.

(2) in described photovoltaic generation model, establishing I is photovoltaic cell electric current, and V is photovoltaic cell voltage, I ' _scfor the short-circuit current of photovoltaic cell under actual environment, V ' _ocfor the open-circuit voltage of photovoltaic cell under actual environment, I ' _mfor maximum power of photovoltaic cell point electric current under actual environment, V ' _mfor maximum power of photovoltaic cell point voltage under actual environment, T is photovoltaic battery temperature under actual environment, T _airfor air themperature under actual environment, T _reffor reference battery temperature, S is intensity of solar radiation under actual environment, S _reffor with reference to intensity of solar radiation, K is intensity of solar radiation photovoltaic battery temperature coefficient while changing, the short-circuit current I ' of photovoltaic cell under actual environment _scopen-circuit voltage V ' with photovoltaic cell under actual environment _ocbe expressed as:

$I_{\mathrm{sc}}^{\&prime;}=I_{\mathrm{sc}}\left(\frac{S}{S_{\mathrm{ref}}}\right)(1+\mathrm{aT})---\left(2\right)$

V′ _oc＝V _oc(1-cΔT)(1+bΔS) (3)

Wherein, I _scfor the short-circuit current of photovoltaic cell under rated condition, V _ocfor the open-circuit voltage of photovoltaic cell under rated condition, a, b, c are respectively constant coefficient, and value is respectively 0.0025 ,-0.1949,0.0029; Δ T and Δ S are respectively photovoltaic battery temperature changing value and intensity of solar radiation changing value under actual environment, are expressed as:

ΔT＝T-T _ref (4)

$\mathrm{\&Delta;S}=\frac{S}{S_{\mathrm{ref}}}-1---\left(5\right)$

Maximum power of photovoltaic cell point electric current I under actual environment ' _mwith maximum power of photovoltaic cell point voltage V ' under actual environment _mbe expressed as:

$I_m^{\&prime;}=I_m\left(\frac{S}{S_{\mathrm{ref}}}\right)(1+\mathrm{a\&Delta;T})---\left(6\right)$

V′ _m＝V _m(1-cΔT)(1+bΔS) (7)

Wherein, I _mfor maximum power of photovoltaic cell point electric current under rated condition, V _mfor maximum power of photovoltaic cell point voltage under rated condition;

Photovoltaic cell electric current I can be expressed as:

$I=I_{\mathrm{sc}}^{\&prime;}(1-C_1\{\exp \left[\frac{V}{C_2{V}_{\mathrm{oc}}^{\&prime;}}\right]-1\left\}\right)---\left(8\right)$

Wherein, coefficient C ₁and C ₂be expressed as:

C ₁＝(1-I′ _m/I′ _sc)exp[-V′ _m/(C ₂V′ _oc)] (9)

$C_2=(\frac{V_m^{\&prime;}}{V_{\mathrm{oc}}^{\&prime;}}-1){\left[\ln (1-I_m^{\&prime;}/I_{\mathrm{sc}}^{\&prime;})\right]}^{-1}---\left(10\right)$

(3) described battery model adopts ampere hour method to set up, and establishing SOC is storage battery charge state, SOC ₀for the initial state-of-charge of accumulator, c _rfor the actual electric weight of accumulator, c _nfor accumulator specified electric quantity, I _efor accumulator cell charging and discharging electric current, Δ t is the accumulator cell charging and discharging time, η _ichfor charge in batteries efficiency, η _disfor battery discharging efficiency, P _ldfor the power of workload demand under battery discharging state, P _efor the afterpower under battery state of charge, storage battery charge state SOC is expressed as:

Wherein, accumulator cell charging and discharging electric current I _ebe expressed as:

Wherein, U is DC bus-bar voltage.

(4), in described load model, the load in home intelligent power system is divided into following three classes:

(1) switching mode load;

Two states is only opened and closed to switching mode load, represents to close with 0, and 1 represents to open, and when load condition is when opening, the real power of load is its rated power, and in the time that load condition is pass, the real power of load is 0; The parameter of switching mode load is its on off state;

(2) stepping type load;

Stepping type load comprises the switching mode load that can carry out switching manipulation and the multistage subsection load that can carry out gear adjusting; The parameter of stepping type load comprises rated power and total gear number of stepping type load;

(3) adjustment type load;

Adjustment type load can be operated in different duties, and the duty of load is relevant with the environmental parameter such as temperature and illumination.

(5), in described city electric model, electrical network is supplied with the active power P of home intelligent power system _gridrepresent, and have:

$P_{\mathrm{grid}}=\left\{\begin{array}{cc}{P}_{\mathrm{load}}-P_{\mathrm{DG}}& P_{\mathrm{load}}>P_{\mathrm{DG}}\\ 0& P_{\mathrm{load}}\&le;P_{\mathrm{DG}}\end{array}\right.---\left(13\right)$

Wherein, P _loadfor the active power that load consumes, P _dGthe active power providing for distributed power source or accumulator.

Home intelligent power system optimization model in step 2 comprises objective function and constraint condition;

Described objective function comprises economy objective function, comfortableness objective function and comprehensive satisfaction objective function;

(1) in described economy objective function, establish C ₁for economy objective function, during for t, be carved into the electricity cost in t+1 moment, economy objective function C ₁be expressed as:

$\min C_1=\underset{t=1}{\overset{24}{\mathrm{\&Sigma;}}}{C}_1^{\left(t\right)}---\left(14\right)$

Wherein, be expressed as:

${C_1^{\left(t\right)}=\left\{\begin{array}{cc}{C}^{\left(t\right)}(P_{\mathrm{load}}^{\left(t\right)}-P_{\mathrm{DG}}^{\left(t\right)}),& P_{\mathrm{load}}^{\left(t\right)}>P_{\mathrm{DG}}^{\left(t\right)}\\ C^{\&prime;\left(t\right)}(P_{\mathrm{DG}}^{\left(t\right)}-P_{\mathrm{load}}^{\left(t\right)}),& P_{\mathrm{load}}^{\left(t\right)}\&le;P_{\mathrm{DG}}^{\left(t\right)}\end{array}\right.}^{}---\left(15\right)$

Wherein, C ^(t)be the rate for incorporation into the power network that user distribution formula generating in t hour consumes, C ' ^(t)be the rate for incorporation into the power network of t hour user distribution formula generating loopback electrical network, both are known parameters; the active power providing for t hour distributed power source or accumulator; be the active power of load consumption in t hour, have

$P_{\mathrm{load}}^{\left(t\right)}=\underset{l=1}{\overset{L}{\mathrm{\&Sigma;}}}{x}_{1l}^{\left(t\right)}{P}_{1l}+\underset{m=1}{\overset{M}{\mathrm{\&Sigma;}}}{x}_{2m}^{\left(t\right)}{P}_{2m}+\underset{n=1}{\overset{N}{\mathrm{\&Sigma;}}}{P}_{3n}\left(x_{3n}^{\left(t\right)}\right)---\left(16\right)$

Wherein, for being the actual working state of l switching mode load, 1 represents to open, and 0 represents to close; P _1lbe the rated power of l switching mode load; it is the actual working state of m stepping type load; P _2lbe the rated power of m stepping type load operation in the time of one grade; it is the actual working state of n adjustment type load; it is the actual working state of n adjustment type load; being n adjustment type load when duty is time the power that consumes.

(2) described comfortableness objective function is divided into following three classes:

(1) for switching mode load, its comfortableness objective function is expressed as:

$C_{21}^{\left(t\right)}=\underset{l=1}{\overset{L}{\mathrm{\&Sigma;}}}\left(\right|x_{1l}^{\left(t\right)}-x_{1l}^{\left(t\right)}*\left|f_{1l}\right)---\left(17\right)$

Wherein, for the total load of switching mode load is worth, L is the quantity of switching mode load; be the actual working state of l switching mode load, 1 represents to open, and 0 represents to close; be the setting duty of l switching mode load, 1 represents to open, and 0 represents to close; f _1lit is the Laden-Value of l switching mode load;

(2) for stepping type load, its comfortableness objective function is expressed as:

$C_{22}^{\left(t\right)}=\underset{m=1}{\overset{M}{\mathrm{\&Sigma;}}}\left(\right|x_{2m}^{\left(t\right)}-x_{2m}^{\left(t\right)}*\left|\frac{f_{2m}}{D}\right)---\left(18\right)$

Wherein, for the total load of stepping type load is worth, M is the quantity of stepping type load; be the actual working state of m stepping type load, numeric representation place gear; be the setting duty of m stepping type load, numeric representation place gear; f _2mit is the Laden-Value of m stepping type load; D is stepping total gear number of loading;

(3) for adjustment type load, its comfortableness objective function is expressed as:

$C_{23}^{\left(t\right)}=\underset{n=1}{\overset{N}{\mathrm{\&Sigma;}}}{a}_n{(x_{3n}^{\left(t\right)}-x_{3n}^{\left(t\right)}*)}^2---\left(19\right)$

Wherein, for the total load of adjustment type load is worth, N is the quantity of adjustment type load; be the actual working state of n adjustment type load, it is the setting duty of n adjustment type load;

In the time that user abandons using n adjustment type load, the actual working state of establishing this adjustment type load is d with setting duty deviation _n, the total load of adjustment type load is worth can be expressed as again:

$C_{23}^{\left(t\right)}=\underset{n=1}{\overset{N}{\mathrm{\&Sigma;}}}\frac{f_{3n}}{d_n^2}{(x_{3n}^{\left(t\right)}-x_{3n}^{\left(t\right)}*)}^2---\left(20\right)$

Wherein, f _3nit is the Laden-Value of n adjustment type load;

Comfortableness objective function is expressed as:

$C_2=\underset{t=1}{\overset{24}{\mathrm{\&Sigma;}}}(C_{21}^{\left(t\right)}+C_{22}^{\left(t\right)}+C_{23}^{\left(t\right)})---\left(21\right)$

Wherein, C ₂for comfortableness objective function.

(3) described comprehensive satisfaction objective function represents specifically have with C:

minC＝min(C ₁+C ₂) (22)

Wherein, C ₁for economy objective function, C ₂for comfortableness objective function.

Constraint condition comprises active power balance constraint, the constraint of peak power limit value and storage battery charge state constraint.

(1) described active power balance constraint representation is:

$\underset{i=1}{\overset{G}{\mathrm{\&Sigma;}}}{P}_{\mathrm{DGi}}+P_{\mathrm{grid}}=\underset{r=1}{\overset{R}{\mathrm{\&Sigma;}}}{P}_{\mathrm{loadr}}---\left(23\right)$

Wherein, P _gridfor the active power of electrical network supply home intelligent power system, P _loadrbe r the active power that load consumes, P _dGifor the active power that i distributed power source or accumulator provide, G is distributed power source or accumulator quantity, and R is load sum.

(2) described peak power limit value constraint representation is:

$\left\{\begin{array}{c}{P}_{\mathrm{DGi}\min }<P_{\mathrm{DGi}}<P_{\mathrm{DGi}\max }\\ P_{\mathrm{grid}}<P_{\lim }\end{array}\right.---\left(24\right)$

Wherein, P _dGifor the active power that i distributed power source provides, P _dGiminand P _dGimaxfor active power minimum value and maximal value that i distributed power source provides, P _gridfor the active power of electrical network supply home intelligent power system, P _limfor the active power limit value of electrical network supply home intelligent power system.

(3) described storage battery charge state constraint representation is:

SOC _min<SOC<SOC _max (25)

Wherein, SOC is storage battery charge state, SOC _minfor storage battery charge state lower limit, SOC _maxfor the storage battery charge state upper limit.

As Fig. 2, in described step 3, first judge that by constraint condition whether the current location of particle is feasible, if the current location of particle is feasible, by more speed and the position of new particle of basic particle group algorithm; If the current location of particle is infeasible, upgrade population speed and position by the particle cluster algorithm of belt restraining.

In basic particle group algorithm, by more speed and the position of new particle of following formula:

v _j(s+1)＝c ₁r ₁[p _lo(s)-y _j(s)]+c ₂r ₂[p _gl(s)-y _j(s)]+wv _j(s) (26)

y _j(s+1)＝y _j(s)+v _j(s+1) (27)

Wherein, v _jand y (s+1) _j(s+1) be respectively position and the speed of the s+1 time iteration of j particle in D dimension space; y _iand v (s) _i(s) be respectively position and the speed of the s time iteration of j particle in D dimension space; W is Inertia weight factor; c ₁and c ₂for the positive study factor; r ₁and r ₂be equally distributed random number between 0 to 1, p _lo(s) locally optimal solution while being s iteration, p _gl(s) globally optimal solution while being s iteration.

The particle cluster algorithm of belt restraining upgrades population speed and position: have

v _j(s+1)＝c ₁r ₁[p _lo(s)-y _j(s)]+c ₂r ₂[p _gl(s)-y _j(s)] (28)

y _j(s+1)＝y _j(s)+v _j(s+1) (27)

The optimization method of the home intelligent power system of proposition is carried out to simulating, verifying in certain home intelligent power system herein.

1. simulation parameter

(1) power unit

A. wind-power electricity generation;

The wind-power electricity generation rated power adopting is 600W, starts wind speed 3m/s, wind rating 10m/s, cut-out wind speed 30m/s.

B. photovoltaic generation;

The photovoltaic array adopting is in series by two monocrystaline silicon solar cell assemblies, and the parameter of every monocrystaline silicon solar cell assembly is: V _m=34.4V, I _m=4.51A, I _sc=4.9A, V _oc=43.2V.

C. accumulator;

Adopting energy storage device is the capacity battery pack of 120Ah altogether, and maximum charging and discharging currents is 12A.

(2) load part

Load partial parameters is as follows:

One of televisor, rated power is 350W.Laden-Value is made as 2 yuan/hour.One of desktop computer, rated power is 350W.Laden-Value is made as 2 yuan/hour.One, electric fan, rated power is 80W, totally 5 grades.Laden-Value is made as 0.25 yuan/hour.Electric light, rated power 80W, the illumination 125lx in somewhere under rated power, brightness adjustable extent is 0-125lx.Laden-Value is made as 0.25 yuan/hour.Air-conditioning, rated power 735W, adjustable temperature range is 16-30 DEG C.Laden-Value is made as 0.25 yuan/hour.

(3) other parameters

Setting simulation step length is 1 hour, and 00:00 is 0.3 yuan/degree to the electricity charge of 6:00, and 6:00 is 0.6 yuan/degree to the electricity charge of 24:00.Photovoltaic generation rate for incorporation into the power network is 0.48 yuan/degree, and wind-power electricity generation rate for incorporation into the power network is 0.61 yuan/degree.

2. simulation result and analysis

(1) user's comprehensive satisfaction

In user 24 hours, comprehensive satisfaction functional value temporal evolution relation as shown in Figure 3, as can be seen from Figure 3, after optimizing, user satisfaction functional value declines (dotted line and solid line represent respectively the comprehensive satisfaction curve before and after optimization) to some extent, this explanation user's comprehensive satisfaction increase (comprehensive satisfaction functional value is lower, and user's satisfaction is higher).

(2) user power utilization expense

In user 24 hours, electricity cost temporal evolution relation as shown in Figure 4: as can be seen from Figure 4, user's electricity cost is saved (the electricity cost curve after solid line represents to optimize, the electricity cost curve before dotted line represents to optimize) to some extent.

User power utilization expense and comprehensive satisfaction contrast before and after optimizing

(3) optimize front and back user power utilization expense and comprehensive satisfaction contrast

Before and after optimizing, user power utilization expense and comprehensive satisfaction functional value are as shown in table 1:

Table 1

As can be seen from Table 1, optimize electricity cost one day after and drop to 6.30 yuan from 7.72 yuan, reduced by 18.4%.User's comprehensive satisfaction functional value is reduced to 15.16 yuan from 17.52 yuan, has declined 13.5%.Economy and the comprehensive satisfaction of this explanation user power utilization are all promoted after optimization.

Finally should be noted that: above embodiment is only in order to illustrate that technical scheme of the present invention is not intended to limit; those of ordinary skill in the field still can modify or be equal to replacement the specific embodiment of the present invention with reference to above-described embodiment; these do not depart from any amendment of spirit and scope of the invention or are equal to replacement, within the claim protection domain of the present invention all awaiting the reply in application.

Claims (17)

1. an optimization method for home intelligent power system, is characterized in that: said method comprising the steps of:

Step 1: the intelligent power system that founds a family basic model;

Step 2: intelligent power system optimization model founds a family;

Step 3: home intelligent power system is optimized.

2. the optimization method of home intelligent power system according to claim 1, is characterized in that: in described step 1, home intelligent power system basic model comprises wind-power electricity generation model, photovoltaic generation model, accumulator, load model and city's electric model.

3. the optimization method of home intelligent power system according to claim 2, is characterized in that: in described wind-power electricity generation model, establish P _wfor the real output of aerogenerator, P _rfor the output rating of aerogenerator, v is ambient wind velocity, v _cifor the startup wind speed of aerogenerator, v _crfor the wind rating of aerogenerator, v _cofor the cut-out wind speed of aerogenerator, the real output P of aerogenerator _wwith relation between ambient wind velocity v can be represented by the formula:

Wherein, coefficient k ₁and k ₂be expressed as: k ₂=-k ₁v _ci.

4. the optimization method of home intelligent power system according to claim 2, is characterized in that: in described photovoltaic generation model, establishing I is photovoltaic cell electric current, and V is photovoltaic cell voltage, I ' _scfor the short-circuit current of photovoltaic cell under actual environment, V ' _ocfor the open-circuit voltage of photovoltaic cell under actual environment, I ' _mfor maximum power of photovoltaic cell point electric current under actual environment, V ' _mfor maximum power of photovoltaic cell point voltage under actual environment, T is photovoltaic battery temperature under actual environment, T _airfor air themperature under actual environment, T _reffor reference battery temperature, S is intensity of solar radiation under actual environment, S _reffor with reference to intensity of solar radiation, K is intensity of solar radiation photovoltaic battery temperature coefficient while changing, the short-circuit current I ' of photovoltaic cell under actual environment _scopen-circuit voltage V ' with photovoltaic cell under actual environment _ocbe expressed as:

$I_{\mathrm{sc}}^{\&prime;}=I_{\mathrm{sc}}\left(\frac{S}{S_{\mathrm{ref}}}\right)(1+\mathrm{aT})$

V′ _oc＝V _oc(1-cΔT)(1+bΔS)

Wherein, I _scfor the short-circuit current of photovoltaic cell under rated condition, V _ocfor the open-circuit voltage of photovoltaic cell under rated condition, a, b, c are respectively constant coefficient, and value is respectively 0.0025 ,-0.1949,0.0029; Δ T and Δ S are respectively photovoltaic battery temperature changing value and intensity of solar radiation changing value under actual environment, are expressed as:

ΔT＝T-T _ref

$\mathrm{\&Delta;S}=\frac{S}{S_{\mathrm{ref}}}-1$

Maximum power of photovoltaic cell point electric current I under actual environment ' _mwith maximum power of photovoltaic cell point voltage V ' under actual environment _mbe expressed as:

$I_m^{\&prime;}=I_m\left(\frac{S}{S_{\mathrm{ref}}}\right)(1+\mathrm{a\&Delta;T})$

V′ _m＝V _m(1-cΔT)(1+bΔS)

Wherein, I _mfor maximum power of photovoltaic cell point electric current under rated condition, V _mfor maximum power of photovoltaic cell point voltage under rated condition;

Photovoltaic cell electric current I can be expressed as:

$I=I_{\mathrm{sc}}^{\&prime;}(1-C_1\{\exp \left[\frac{V}{C_2{V}_{\mathrm{oc}}^{\&prime;}}\right]-1\left\}\right)$

Wherein, coefficient C ₁and C ₂be expressed as:

C ₁＝(1-I′ _m/I′ _sc)exp[-V′ _m/(C ₂V′ _oc)]

$C_2=(\frac{V_m^{\&prime;}}{V_{\mathrm{oc}}^{\&prime;}}-1){\left[\ln (1-I_m^{\&prime;}/I_{\mathrm{sc}}^{\&prime;})\right]}^{-1}.$

5. the optimization method of home intelligent power system according to claim 2, is characterized in that: described battery model adopts ampere hour method to set up, and establishing SOC is storage battery charge state, SOC ₀for the initial state-of-charge of accumulator, c _rfor the actual electric weight of accumulator, c _nfor accumulator specified electric quantity, I _efor accumulator cell charging and discharging electric current, Δ t is the accumulator cell charging and discharging time, η _ichfor charge in batteries efficiency, η _disfor battery discharging efficiency, P _ldfor the power of workload demand under battery discharging state, P _efor the afterpower under battery state of charge, storage battery charge state SOC is expressed as:

Wherein, accumulator cell charging and discharging electric current I _ebe expressed as:

Wherein, U is DC bus-bar voltage.

6. the optimization method of home intelligent power system according to claim 2, is characterized in that: in described load model, the load in home intelligent power system is divided into following three classes:

(1) switching mode load;

Two states is only opened and closed to switching mode load, represents to close with 0, and 1 represents to open, and when load condition is when opening, the real power of load is its rated power, and in the time that load condition is pass, the real power of load is 0; The parameter of switching mode load is its on off state;

(2) stepping type load;

Stepping type load comprises the switching mode load that can carry out switching manipulation and the multistage subsection load that can carry out gear adjusting; The parameter of stepping type load comprises rated power and total gear number of stepping type load;

(3) adjustment type load;

Adjustment type load can be operated in different duties, and the duty of load is relevant with the environmental parameter such as temperature and illumination.

7. the optimization method of home intelligent power system according to claim 2, is characterized in that: in described city electric model, electrical network is supplied with the active power P of home intelligent power system _gridrepresent, and have:

$P_{\mathrm{grid}}=\left\{\begin{array}{cc}{P}_{\mathrm{load}}-P_{\mathrm{DG}}& P_{\mathrm{load}}>P_{\mathrm{DG}}\\ 0& P_{\mathrm{load}}\&le;P_{\mathrm{DG}}\end{array}\right.$

Wherein, P _loadfor the active power that load consumes, P _dGthe active power providing for distributed power source or accumulator.

8. the optimization method of home intelligent power system according to claim 1, is characterized in that: the home intelligent power system optimization model in described step 2 comprises objective function and constraint condition;

Described objective function comprises economy objective function, comfortableness objective function and comprehensive satisfaction objective function;

Described constraint condition comprises active power balance constraint, the constraint of peak power limit value and storage battery charge state constraint.

9. the optimization method of home intelligent power system according to claim 8, is characterized in that: in described economy objective function, establish C ₁for economy objective function, during for t, be carved into the electricity cost in t+1 moment, economy objective function C ₁be expressed as:

$\min C_1=\underset{t=1}{\overset{24}{\mathrm{\&Sigma;}}}{C}_1^{\left(t\right)}$

Wherein, be expressed as:

${C_1^{\left(t\right)}=\left\{\begin{array}{cc}{C}^{\left(t\right)}(P_{\mathrm{load}}^{\left(t\right)}-P_{\mathrm{DG}}^{\left(t\right)}),& P_{\mathrm{load}}^{\left(t\right)}>P_{\mathrm{DG}}^{\left(t\right)}\\ C^{\&prime;\left(t\right)}(P_{\mathrm{DG}}^{\left(t\right)}-P_{\mathrm{load}}^{\left(t\right)}),& P_{\mathrm{load}}^{\left(t\right)}\&le;P_{\mathrm{DG}}^{\left(t\right)}\end{array}\right.}^{}$

Wherein, C ^(t)be the rate for incorporation into the power network that user distribution formula generating in t hour consumes, C ' ^(t)be the rate for incorporation into the power network of t hour user distribution formula generating loopback electrical network, both are known parameters; the active power providing for t hour distributed power source or accumulator; be the active power of load consumption in t hour, have

$P_{\mathrm{load}}^{\left(t\right)}=\underset{l=1}{\overset{L}{\mathrm{\&Sigma;}}}{x}_{1l}^{\left(t\right)}{P}_{1l}+\underset{m=1}{\overset{M}{\mathrm{\&Sigma;}}}{x}_{2m}^{\left(t\right)}{P}_{2m}+\underset{n=1}{\overset{N}{\mathrm{\&Sigma;}}}{P}_{3n}\left(x_{3n}^{\left(t\right)}\right)$

Wherein, for being the actual working state of l switching mode load, 1 represents to open, and 0 represents to close; P _1lbe the rated power of l switching mode load; it is the actual working state of m stepping type load; P _2lbe the rated power of m stepping type load operation in the time of one grade; it is the actual working state of n adjustment type load; it is the actual working state of n adjustment type load; being n adjustment type load when duty is time the power that consumes.

10. the optimization method of home intelligent power system according to claim 8, is characterized in that: described comfortableness objective function is divided into following three classes:

(1) for switching mode load, its comfortableness objective function is expressed as:

$C_{21}^{\left(t\right)}=\underset{l=1}{\overset{L}{\mathrm{\&Sigma;}}}\left(\right|x_{1l}^{\left(t\right)}-x_{1l}^{\left(t\right)}*\left|f_{1l}\right)$

Wherein, for the total load of switching mode load is worth, L is the quantity of switching mode load; be the actual working state of l switching mode load, 1 represents to open, and 0 represents to close; be the setting duty of l switching mode load, 1 represents to open, and 0 represents to close; f _1lit is the Laden-Value of l switching mode load;

(2) for stepping type load, its comfortableness objective function is expressed as:

$C_{22}^{\left(t\right)}=\underset{m=1}{\overset{M}{\mathrm{\&Sigma;}}}\left(\right|x_{2m}^{\left(t\right)}-x_{2m}^{\left(t\right)}*\left|\frac{f_{2m}}{D}\right)$

Wherein, for the total load of stepping type load is worth, M is the quantity of stepping type load; be the actual working state of m stepping type load, numeric representation place gear; be the setting duty of m stepping type load, numeric representation place gear; f _2mit is the Laden-Value of m stepping type load; D is stepping total gear number of loading;

(3) for adjustment type load, its comfortableness objective function is expressed as:

$C_{23}^{\left(t\right)}=\underset{n=1}{\overset{N}{\mathrm{\&Sigma;}}}{a}_n{(x_{3n}^{\left(t\right)}-x_{3n}^{\left(t\right)}*)}^2$

Wherein, for the total load of adjustment type load is worth, N is the quantity of adjustment type load; be the actual working state of n adjustment type load, it is the setting duty of n adjustment type load;

In the time that user abandons using n adjustment type load, the actual working state of establishing this adjustment type load is d with setting duty deviation _n, the total load of adjustment type load is worth can be expressed as again:

$C_{23}^{\left(t\right)}=\underset{n=1}{\overset{N}{\mathrm{\&Sigma;}}}\frac{f_{3n}}{d_n^2}{(x_{3n}^{\left(t\right)}-x_{3n}^{\left(t\right)}*)}^2$

Wherein, f _3nit is the Laden-Value of n adjustment type load;

Comfortableness objective function is expressed as:

$C_2=\underset{t=1}{\overset{24}{\mathrm{\&Sigma;}}}(C_{21}^{\left(t\right)}+C_{22}^{\left(t\right)}+C_{23}^{\left(t\right)})$

Wherein, C ₂for comfortableness objective function.

11. according to the optimization method of the home intelligent power system described in claim 9 or 10, it is characterized in that: described comprehensive satisfaction objective function C represents specifically have:

minC＝min(C ₁+C ₂)

Wherein, C ₁for economy objective function, C ₂for comfortableness objective function.

The optimization method of 12. home intelligent power systems according to claim 8, is characterized in that: described active power balance constraint representation is:

$\underset{i=1}{\overset{G}{\mathrm{\&Sigma;}}}{P}_{\mathrm{DGi}}+P_{\mathrm{grid}}=\underset{r=1}{\overset{R}{\mathrm{\&Sigma;}}}{P}_{\mathrm{loadr}}$

Wherein, P _gridfor the active power of electrical network supply home intelligent power system, P _loadrbe r the active power that load consumes, P _dGifor the active power that i distributed power source or accumulator provide, G is distributed power source or accumulator quantity, and R is load sum.

The optimization method of 13. home intelligent power systems according to claim 8, is characterized in that: described peak power limit value constraint representation is:

$\left\{\begin{array}{c}{P}_{\mathrm{DGi}\min }<P_{\mathrm{DGi}}<P_{\mathrm{DGi}\max }\\ P_{\mathrm{grid}}<P_{\lim }\end{array}\right.$

Wherein, P _dGifor the active power that i distributed power source provides, P _dGiminand P _dGimaxfor active power minimum value and maximal value that i distributed power source provides, P _gridfor the active power of electrical network supply home intelligent power system, P _limfor the active power limit value of electrical network supply home intelligent power system.

The optimization method of 14. home intelligent power systems according to claim 8, is characterized in that: described storage battery charge state constraint representation is:

SOC _min<SOC<SOC _max

Wherein, SOC is storage battery charge state, SOC _minfor storage battery charge state lower limit, SOC _maxfor the storage battery charge state upper limit.

The optimization method of 15. home intelligent power systems according to claim 1, it is characterized in that: in described step 3, first judge that by constraint condition whether the current location of particle is feasible, if the current location of particle is feasible, by more speed and the position of new particle of basic particle group algorithm; If the current location of particle is infeasible, upgrade population speed and position by the particle cluster algorithm of belt restraining.

The optimization method of 16. home intelligent power systems according to claim 15, is characterized in that: in basic particle group algorithm, by more speed and the position of new particle of following formula:

v _j(s+1)＝c ₁r ₁[p _lo(s)-y _j(s)]+c ₂r ₂[p _gl(s)-y _j(s)]+wv _j(s)

y _j(s+1)＝y _j(s)+v _j(s+1)

Wherein, v _jand y (s+1) _j(s+1) be respectively position and the speed of the s+1 time iteration of j particle in D dimension space; y _iand v (s) _i(s) be respectively position and the speed of the s time iteration of j particle in D dimension space; W is Inertia weight factor; c ₁and c ₂for the positive study factor; r ₁and r ₂be equally distributed random number between 0 to 1, p _lo(s) locally optimal solution while being s iteration, p _gl(s) globally optimal solution while being s iteration.

The optimization method of 17. home intelligent power systems according to claim 15, is characterized in that: the particle cluster algorithm of belt restraining upgrades population speed and position, has:

v _j(s+1)＝c ₁r ₁[p _lo(s)-y _j(s)]+c ₂r ₂[p _gl(s)-y _j(s)]

y _j(s+1)＝y _j(s)+v _j(s+1)。