CN106374534A - Multi-target grey wolf optimization algorithm-based large scale household energy management method - Google Patents

Abstract

The application discloses a multi-target grey wolf optimization algorithm-based large scale household energy management method. According to the method, a multi-target grey wolf optimization algorithm is used for realizing energy management optimization for a large scale household. The method comprises the following steps: calculation dimensions and calculation difficulty are reduced via classification of large scale household users; based on cooperation between a storage battery and a photovoltaic power source, a photovoltaic self power generation for self use scheduling strategy is considered for lowering electricity charge of the users and shifting peak loads on a power grid side, the multi-target grey wolf optimization algorithm is a novel multi-target optimization algorithm, a problem of multiple targets in large scale household energy management can be effectively solved via the algorithm, and different benefit demands of the household and a power grid can be met.

Description

A kind of extensive home energy management method based on multiple target grey wolf optimized algorithm

Technical field

The present invention relates to home energy management domain, more particularly to a kind of big rule based on multiple target grey wolf optimized algorithm Mould home energy management method.

Background technology

Instantly global energy has been absorbed in the condition of shortage, and coal and petroleum resources are constantly consumed.But with a series of new The excavation of the energy uses, and such as solar energy, wind energy and natural gas equal energy source are constantly put in power industry.By which constituting Numerous distributed energies, brings very big impact to the structure of traditional electrical network, in order to tackle such a situation, active distribution Net is the measure of this problem of effectively solving.

And in numerous type of user of active distribution network, domestic consumer account for very big ratio, its load character just has Amount of monomer is little but total amount big, and accesses family with photovoltaic with electric automobile distributed equipment, its have very considerable can Scheduling potentiality.In order to carry out Demand Side Response, build one family energy management framework and domestic consumer can be promoted to participate in electric power In the scheduling mechanism in market.Meanwhile, in market, the interest game problem of generally existing is equally deposited between electrical network and domestic consumer Therefore find effective method both sides are carried out game solution is a problem demanding prompt solution.

Content of the invention

(1) technical problem solving

Based on this, the application proposes a kind of extensive home energy management method based on multiple target grey wolf optimized algorithm, It reaches the interests demand of user and electrical network by the cooperation of photo-voltaic power supply in family and accumulator, thus meeting user Economy objectives and electrical network load curve optimization aim.

(2) technical scheme

Based on this, the application proposes a kind of extensive home energy management method based on multiple target grey wolf optimized algorithm, Comprise the following steps:

According to whether there being photo-voltaic power supply in family, extensive domestic consumer is divided into two classes: have photovoltaic family and no photovoltaic man Front yard；

Obtain the prediction data that family's base load and photovoltaic are exerted oneself from the historical data of family, obtain timesharing from electrical network Electricity price information, determines its charge and discharge control model by accumulator parameter and constraints；

The prediction data exerted oneself according to family's base load and photovoltaic, formulates and considers that photovoltaic is spontaneous according to dissimilar family The accumulator cell charging and discharging strategy of personal and peak load shifting；

According to the demand of family side and grid side, the load of the economy objectives function of the side that founds a family and grid side is bent Line optimization object function；

Accumulator cell charging and discharging power is initialized according to model parameter and constraints, that is, in multiple target grey wolf optimized algorithm Initial population；

Each target function value is calculated according to initialization population, finds out non-domination solution therein and form initial elite storehouse, and Three solutions therefrom selecting optimum are as head wolf, respectively alpha (α) wolf, beta (β) wolf and delta (δ) wolf, remaining wolf Group is then as omega (ω) wolf；

Wolf pack according to multiple target grey wolf optimized algorithm hunts mechanism and head wolf selection mechanism updates elite storehouse；

Judging whether to reach maximum iteration time, if reaching maximum iteration time, terminating to calculate and export Pareto solution Collection, otherwise, return hunts mechanism with the wolf pack of multiple target grey wolf optimized algorithm and head wolf selection mechanism updates the step in elite storehouse Suddenly；

After drawing Pareto disaggregation, select an optimum compromise solution conduct according to the decision-making mechanism based on grey relational grade The optimal case of home energy management.

Wherein in an embodiment, described extensive family is provided with the accumulator of certain capacity, and part family tool There is photo-voltaic power supply；According to whether there being the photo-voltaic power supply can be by extensive domestic consumer Fen You photovoltaic family and no photovoltaic man in family Front yard.

Wherein in an embodiment, described accumulator parameter includes: accumulator capacity, storage battery charge state lower limit, The charge-discharge electric power upper limit, efficiency for charge-discharge bound.

Wherein in an embodiment, according to dissimilar family, described formulation considers that photovoltaic is generated power for their own use and peak clipping is filled out The accumulator cell charging and discharging strategy of paddy is: (1) photovoltaic is generated power for their own use pattern: under meeting accumulator constraints, if there being photovoltaic to go out Power, then preferentially store photovoltaic electricity；(2) accumulator cell charging and discharging pattern: under meeting accumulator constraints, consider electricity price With current loads information, target is optimized for household economy and grid load curve and carries out charge and discharge control；(3) accumulator is treated Machine pattern: then accumulator stops discharge and recharge when not meeting optimization aim beyond accumulator constraints or current discharge and recharge.

Wherein in an embodiment, the described demand according to family side and grid side, the economy of the side that founds a family Object function considers that the electric cost expenditure of user is minimum, and the load curve optimization object function of grid side considers the mark of total load curve Quasi- difference is minimum.

(3) beneficial effect

Compared with prior art, the invention provides a kind of extensive home energy based on multiple target grey wolf optimized algorithm Management method, possesses following beneficial effect:

The above-mentioned extensive home energy management method based on multiple target grey wolf optimized algorithm is passed through using multiple target grey wolf Optimized algorithm carries out energy management optimization to extensive family.Extensive family is divided by the difference first passing through household electric appliances For having photovoltaic family and no photovoltaic family two class, then generating power for their own use and load transfer of photovoltaic is realized by the use of accumulator To obtain the effect of preferable economy and load curve optimization.In addition, institute using multiple target grey wolf optimized algorithm be one kind relatively New multi-objective optimization algorithm, this algorithm can effectively solve the problem that the multi-objective problem of extensive home energy management, thus meeting Family and the different interests demand of electrical network.

Brief description

Fig. 1 is the method flow of the extensive home energy management method based on multiple target grey wolf optimized algorithm of the present invention Figure；

Fig. 2 is family's load prediction data schematic diagram；

Fig. 3 is photovoltaic generation prediction data schematic diagram a few days ago；

Fig. 4 is tou power price curve synoptic diagram.

Specific embodiment

Refer to Fig. 1, Fig. 2, Fig. 3 and Fig. 4, an embodiment of the invention provides one kind to optimize based on multiple target grey wolf The extensive home energy management method of algorithm.This embodiment is mainly right for studying with photo-voltaic power supply in family and accumulator As extensive domestic consumer being classified, by home energy management is carried out to the cooperation of photo-voltaic power supply and accumulator. Should be comprised the following steps based on the extensive home energy management method of multiple target grey wolf optimized algorithm:

Extensive domestic consumer is divided into two classes according to whether there being photo-voltaic power supply in family: have photovoltaic family by step s110 No photovoltaic family.Above-mentioned extensive family is provided with the accumulator of certain capacity, and part family possesses photo-voltaic power supply.Therefore root Whether possessing photo-voltaic power supply according to family and being divided into domestic consumer has photovoltaic family and no photovoltaic family.

Step s120, obtains the prediction data that family's base load and photovoltaic are exerted oneself, from electricity from the historical data of family Net obtains tou power price information, determines its charge and discharge control model by accumulator parameter and constraints.Family's base load and As shown in Figures 2 and 3, tou power price information is as shown in Figure 4 for photovoltaic power generation output forecasting data.The parameter of accumulator includes: accumulator Capacity, storage battery charge state lower limit, the charge-discharge electric power upper limit, efficiency for charge-discharge bound.Specific as follows.

Accumulator capacity: 5.9kw h

Storage battery charge state lower limit: 30%

The charge-discharge electric power upper limit: 3kw

Efficiency for charge-discharge bound: 90%/90%

Determine that its charge and discharge control model is specific as follows by accumulator parameter and constraints.

Soc (t)=soc (t-1)+(δ_ch*p_ch(t)-1/δ_dch*p_dch(t))/b_kw

Wherein, soc (t) is storage battery charge state；p_ch(t) and p_dchT () is respectively charge-discharge electric power；δ_chAnd δ_dchPoint Wei not efficiency for charge-discharge；b_kwFor accumulator capacity.

Step s130, the prediction data exerted oneself according to family's base load and photovoltaic, formulate and examined according to dissimilar family Consider photovoltaic generate power for their own use and peak load shifting accumulator cell charging and discharging strategy.Discharge and recharge strategy is broadly divided into: (1) photovoltaic from from With pattern: meeting under accumulator constraints, if there being photovoltaic to exert oneself, preferentially storing photovoltaic electricity；(2) accumulator cell charging and discharging Pattern: under meeting accumulator constraints, consider electricity price and current loads information, with household economy and network load Optimization of profile carries out charge and discharge control for target；(3) accumulator standby mode: when beyond accumulator constraints or current charge and discharge Electricity does not meet optimization aim, and then accumulator stops discharge and recharge.It is described in detail below:

(1) photovoltaic is generated power for their own use pattern

Work as p_pv(t) > 0 and accumulator is chargeable, then the charging strategy of accumulator is:

p_ch(t)=min [p_pv(t),(soc_u-soc(t))*t_c]

p_dis(t)=0

p_exch(t)=p_pre(t)

(2) battery charging mode

Work as p_pre(t)<p_aveAnd accumulator is chargeable, then the charging strategy of accumulator is:

p_ch(t)=min [p_batt(t),(soc_u-soc(t))*t_c]

p_dis(t)=0

p_exch(t)=p_ch(t)+p_pre(t)

(3) battery discharging mode

Work as p_pre(t)>p_aveAnd accumulator can be discharged, then the electric discharge strategy of accumulator is:

p_ch(t)=0

p_dis(t)=min [p_batt(t),(soc(t)-soc_l)*t_d]

p_exch(t)=p_pre(t)-p_dis(t)

(4) accumulator standby mode

Work as p_batt(t)=0 item accumulator no discharge and recharge action:

p_ch(t)=0

p_dis(t)=0

p_exch(t)=p_pre(t)

Wherein, p_pvT () is exerted oneself for photo-voltaic power supply；p_batt(t) accumulator cell charging and discharging power；soc_lAnd soc_uIt is respectively electric power storage The bound in pond；t_cAnd t_dAccumulator maximum hourly charge-discharge electric power ratio；p_exchT () is the friendship of electrical network and domestic consumer Change power；p_pre(t) family load prediction data.

Step s140, according to the demand of family side and grid side, the economy objectives function of the side that founds a family and electrical network The load curve optimization object function of side.The economy objectives function of family side considers that the electric cost expenditure of user is minimum, grid side Load curve optimization object function consider that the standard deviation of total load curve is minimum.It is described in detail below:

$\left\{\begin{array}{c}f\left(1\right)=min\underset{i=1}{\overset{n_h}{\&sigma;}}\left\{\underset{t=1}{\overset{24}{\&sigma;}}\right\{p_{grid}(i,t)*tou\left(t\right)\left\}\right\}\\ f\left(2\right)=min\sqrt{\frac{1}{24}\underset{t=1}{\overset{24}{\&sigma;}}{\left(\underset{i=1}{\overset{n_h}{\&sigma;}}{p}_{grid}\right(i,t)-\mathrm{\&mu;})}^2}\end{array}\right.$

Wherein f is economy objectives and load curve optimization aim；n_hIt is family's quantity；Tou (t) is tou power price letter Breath；p_grid(i, t) is to exchange power；μ family load curve meansigma methodss.

Step s150, initializes accumulator cell charging and discharging power according to model parameter and constraints, that is, multiple target grey wolf is excellent Change the initial population in algorithm.By accumulator cell charging and discharging power bound, random one group of variable is:

p_batt=[p_batt(1),p_batt(2),p_batt(3),...,p_batt(24)]；0 ＜ p_batt(h) ＜ 3kw

Step s160, calculates each target function value according to initialization population, finds out non-domination solution therein and formed initially Elite storehouse, and therefrom select three solutions of optimum as head wolf, respectively alpha (α) wolf, beta (β) wolf and delta (δ) Wolf, remaining wolf pack is then as omega (ω) wolf.

Step s170, the wolf pack according to multiple target grey wolf optimized algorithm hunts mechanism and head wolf selection mechanism updates elite Storehouse.Wolf pack is hunted mechanism and can be described as follows:

D=| c x_p(t)-x(t)|

X (t+1)=x_p(t)-a·d

Wherein, x_pPosition for prey；X is the position of grey wolf；D is wolf pack to prey attack step-length；A and c be two with Machine factor.

Elite storehouse saves optimum non-domination solution so far, and multiobject head wolf selection mechanism is in elite storehouse In the thinnest segmentation, α wolf, β wolf and δ wolf are selected according to wheel disc method, if less than three non-domination solution in this segmentation, dividing in secondary dredging Remaining head wolf is chosen in section.Wolf pack hunt during, if elite storehouse is full, more next solution with elite storehouse in non- Join solution.If this solution is arranged by least one solution in elite storehouse, this solution does not allow to insert elite storehouse；If this solution arrange one or Solution in multiple elite storehouses, then this solution addition elite storehouse, and remove the solution arranged；If not having solution can arrange in elite storehouse This solution, then sort to elite storehouse again, rejects a solution from segmentation the most crowded, and this solution is inserted in the thinnest segmentation.

Step s180, judges whether to reach maximum iteration time, if reaching maximum iteration time, terminating to calculate and exporting Pareto disaggregation, otherwise, return hunts mechanism with the wolf pack of multiple target grey wolf optimized algorithm and head wolf selection mechanism updates essence Step s170 of Ying Ku.

Step s190, after drawing Pareto disaggregation, selects an optimum according to the decision-making mechanism based on grey relational grade The optimal case that compromise solution manages as home energy.The decision-making mechanism of wherein grey relational grade can be described in detail below:

(1) decision matrix initialization.It is normalized according to following formula:

$\begin{array}{c}{r}_{ij}=\frac{f_{j\&centerdot;\max }-f_i^j}{f_{j\&centerdot;\max }-f_{j\&centerdot;\min }}\\ i=1,2,\mathrm{...},n_0,j=1,2,\mathrm{...}{m}_{obj}\end{array}$

Wherein, r_ijFor parameter after normalization, f_jmaxAnd f_jminIt is respectively maximum and the minima of j-th object function, f_i ^jI-th value for j-th object function；n₀And m_objIt is respectively the number that each target function value obtains number and object function.

(2) grey relational grade of scheme calculates.If ideal scheme (r₀₁, r₀₂,…,r_0m) vectorial for mother, scheme to be evaluated is Subvector, then the degree of association coefficient in j dimension target is scheme i with ideal scheme:

$a_{ij}=\frac{\underset{1\&le;i\&le;n0}{\min }\underset{1\&le;j\&le;mobj}{min}\left|r_{ij}-r_{0j}\right|+\mathrm{\&rho;}\underset{1\&le;i\&le;n0}{\max }\underset{1\&le;j\&le;mobj}{\max }\left|r_{ij}-r_{0j}\right|}{\left|r_{ij}-r_{0j}\right|+\mathrm{\&rho;}\underset{1\&le;i\&le;n0}{\max }\underset{1\&le;j\&le;mobj}{\max }\left|r_{ij}-r_{0j}\right|}$

Wherein, a_ijFor each scheme degree of association coefficient；ρ is to differentiate rate coefficient, generally takes 0.5.

(3) determination of target weight.The Gray Correlation degree of association sum of each scheme to ideal scheme is as synthesis Interpretational criteria, for determining each target weight, the following linear programming model of construction:

$\begin{array}{cc}\min z=\underset{i=1}{\overset{n0}{\&sigma;}}\underset{j=1}{\overset{mobj}{\&sigma;}}{\mathrm{\&omega;}}_j{a}_{ij},& s.t.\underset{j=1}{\overset{mobj}{\&sigma;}}{\mathrm{\&omega;}}_j=1\end{array}$

Wherein, z is degree of association sum；ω is weight coefficient.

(4) calculate Weighted Grey Incidence Degree.Finally, obtain scheme i and the Weighted Grey Incidence Degree of ideal scheme be:

$w_i=\underset{j=1}{\overset{m}{\&sigma;}}{\mathrm{\&omega;}}_j\&centerdot;a_{ij}$

Wherein, w is the Weighted Grey Incidence Degree of scheme i and ideal scheme；W is bigger, then scheme and ideal scheme closer to, Scheme is better.

One group of electricity consumption plan meeting extensive family and electrical network interests demand be can be obtained by by above step, pass through The enforcement of this electricity consumption plan, can reach the target that the economy objectives of domestic consumer and grid load curve optimize.

The extensive home energy management method based on multiple target grey wolf optimized algorithm of the present invention is with respect to prior art Have such advantages as and effect:

(1) present invention design the extensive home energy management method based on multiple target grey wolf optimized algorithm it is contemplated that The energy management optimization of the interaction of extensive family and electrical network, rather than the in the past simple energy management only doing one family.

(2) the extensive home energy management method based on multiple target grey wolf optimized algorithm of present invention design is it is considered to family Front yard user and the interests demand of electrical network, and the concordance of common interest is reached by grey relational grade.

(3) the extensive home energy management method based on multiple target grey wolf optimized algorithm of present invention design, using many Target grey wolf optimized algorithm, this is a kind of newer multi-objective optimization algorithm, with respect to other non-dominated sorted genetic algorithm (non-dominated sorting genetic algorithm)、mopso(multi-objective particle Swarm optimization, particle group optimizing) algorithm method for solving, there is fast convergence rate, Pareto front searching is more Uniformly, the advantages of distribution is wider.

Embodiment described above only have expressed the several embodiments of the present invention, and its description is more concrete and detailed, but simultaneously Therefore the restriction to the scope of the claims of the present invention can not be interpreted as.It should be pointed out that for those of ordinary skill in the art For, without departing from the inventive concept of the premise, some deformation can also be made and improve, these broadly fall into the guarantor of the present invention Shield scope.Therefore, the protection domain of patent of the present invention should be defined by claims.

Claims (5)

1. a kind of extensive home energy management method based on multiple target grey wolf optimized algorithm is it is characterised in that include following Step:

According to whether there being photo-voltaic power supply in family, extensive domestic consumer is divided into two classes: have photovoltaic family and no photovoltaic family；

Obtain the prediction data that family's base load and photovoltaic are exerted oneself from the historical data of family, obtain tou power price from electrical network Information, determines its charge and discharge control model by accumulator parameter and constraints；

The prediction data exerted oneself according to family's base load and photovoltaic, formulates and considers that photovoltaic is generated power for their own use according to dissimilar family And the accumulator cell charging and discharging strategy of peak load shifting；

According to the demand of family side and grid side, the economy objectives function of the side that founds a family and the load curve of grid side are excellent Change object function；

Accumulator cell charging and discharging power is initialized according to model parameter and constraints, that is, initial in multiple target grey wolf optimized algorithm Population；

Each target function value is calculated according to initialization population, finds out non-domination solution therein and form initial elite storehouse, and therefrom , as head wolf, respectively alpha (α) wolf, beta (β) wolf and delta (δ) wolf, remaining wolf pack is then for three solutions selecting optimum As omega (ω) wolf；

Wolf pack according to multiple target grey wolf optimized algorithm hunts mechanism and head wolf selection mechanism updates elite storehouse；

Judging whether to reach maximum iteration time, if reaching maximum iteration time, terminating to calculate and export Pareto disaggregation, no Then, return hunts, with the wolf pack of multiple target grey wolf optimized algorithm, the step that mechanism and head wolf selection mechanism update elite storehouse；

After drawing Pareto disaggregation, select an optimum compromise solution according to the decision-making mechanism based on grey relational grade as family The optimal case of energy management.

2. the extensive home energy management method based on multiple target grey wolf optimized algorithm according to claim 1, it is special Levy and be, described extensive family is provided with the accumulator of certain capacity, and part family has photo-voltaic power supply；According in family Whether there is the photo-voltaic power supply can be by extensive domestic consumer Fen You photovoltaic family and no photovoltaic family.

3. the extensive home energy management method based on multiple target grey wolf optimized algorithm according to claim 1, it is special Levy and be, described accumulator parameter includes: accumulator capacity, storage battery charge state lower limit, the charge-discharge electric power upper limit, discharge and recharge Efficiency bound.

4. the extensive home energy management method based on multiple target grey wolf optimized algorithm according to claim 1, it is special Levy and be, described formulate according to dissimilar family consider photovoltaic generate power for their own use and peak load shifting accumulator cell charging and discharging strategy Generate power for their own use pattern for: (1) photovoltaic: meeting under accumulator constraints, if there being photovoltaic to exert oneself, preferentially storing photovoltaic electric Amount；(2) accumulator cell charging and discharging pattern: under meeting accumulator constraints, consider electricity price and current loads information, with family Front yard economy and grid load curve are optimized for target and carry out charge and discharge control；(3) accumulator standby mode: when beyond accumulator Constraints or current discharge and recharge do not meet optimization aim then accumulator stopping discharge and recharge.

5. the extensive home energy management method based on multiple target grey wolf optimized algorithm according to claim 1, it is special Levy and be, the described demand according to family side and grid side, the economy objectives function of the side that founds a family considers the electricity of user Take expenditure minimum, the load curve optimization object function of grid side considers that the standard deviation of total load curve is minimum.