CN104037793B - A kind of energy-storage units capacity collocation method being applied to active distribution network - Google Patents

A kind of energy-storage units capacity collocation method being applied to active distribution network Download PDF

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CN104037793B
CN104037793B CN201410321222.3A CN201410321222A CN104037793B CN 104037793 B CN104037793 B CN 104037793B CN 201410321222 A CN201410321222 A CN 201410321222A CN 104037793 B CN104037793 B CN 104037793B
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energy
storage units
distribution network
power
active distribution
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CN201410321222.3A
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CN104037793A (en
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姜久春
杨玉青
张维戈
黄梅
牛利勇
鲍谚
严乙桉
庞松岭
姜雪娇
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北京交通大学
海南电网公司
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    • YGENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02EREDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00Energy generation through renewable energy sources
    • Y02E10/50Photovoltaic [PV] energy
    • Y02E10/56Power conversion systems, e.g. maximum power point trackers
    • YGENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02EREDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00Energy generation through renewable energy sources
    • Y02E10/70Wind energy
    • Y02E10/76Power conversion electric or electronic aspects
    • YGENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02EREDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E70/00Other energy conversion or management systems reducing GHG emissions
    • Y02E70/30Systems combining energy storage with energy generation of non-fossil origin

Abstract

The invention discloses a kind of energy-storage units capacity collocation method being applied to active distribution network in active distribution network control technology field.The method comprises the parameter determined in active distribution network; Choose different target functions and determine constraints; Solve the target function chosen and obtain energy-storage units operate power curve; The configuration capacity of energy-storage units is asked for according to energy-storage units operate power curve.The invention solves the capacity configuration problem of energy-storage units in active distribution network, adverse effect to system after photovoltaic, wind power generation distributed regenerative resource access active distribution network is reduced to utilize energy-storage units, by generating the optimizing operation power curve of energy-storage units, the reasonable power extreme value of verification energy-storage units and capacity configuration.

Description

A kind of energy-storage units capacity collocation method being applied to active distribution network
Technical field
The invention belongs to active distribution network control technology field, particularly relate to a kind of energy-storage units capacity collocation method being applied to active distribution network.
Background technology
In recent years, along with the continuous expansion of electrical network scale and the sustainable growth of power load, traditional large-scale electrical power system pattern is more and more difficult to adapt to the growth of power consumption and the fail safe of user, the requirement of reliability.Meanwhile, the existence of growth to the mankind that is day by day exhausted and discharge capacity of carbon dioxide of fossil energy proposes new test, the access of regenerative resource and develop imperative.And distributed power generation is relative to centralized generating, have pollution less, the plurality of advantages such as reliability is high, efficiency of energy utilization is high, dispersion flexible for installation, obtain worldwide extensive concern.
Although distributed power generation advantage is given prominence to, its fluctuation and intermittence will certainly to electrical networks, and particularly power distribution network causes adverse effect.Power distribution network now, passive, is passively converted into active, active distribution network by original, proposes new test to current power distribution network.In order to tackle distributed power generation, the feature that particularly intermittent electric power such as distributed photovoltaic, wind-powered electricity generation unit fluctuation is strong, energy-storage units need be introduced in systems in which, on the one hand, effectively can improve the digestion capability of electrical network to regenerative resource, improve the permeability of regenerative resource in electrical network, promote the application of distributed power generation; On the other hand, can the quality of power supply be improved, improve power system operation stability.
Just think, in the active distribution network of the high permeability in future, almost each node can access the system such as photovoltaic, wind-powered electricity generation as far as possible, and in conjunction with the controllable such as cogeneration of heat and power, electric automobile.Energy-storage units serves the function served as bridge of distributed energy and power load in active distribution network, and energy-storage units comparatively ripe is at present all energy-storage battery, and its cost is higher, and therefore the allocation problem of energy-storage units, becomes research emphasis.
This patent is for energy-storage units access problem in active distribution network, and under adopting different scene, energy-storage units intends operation method, according to charge-discharge electric power curve, obtains the reasonable disposition of energy-storage units.
Summary of the invention
The object of the invention is to, a kind of energy-storage units capacity collocation method being applied to active distribution network is provided, the drawback that reliability weakens for fluctuation increase after active distribution network access distributed energy, carry out the capacity configuration of energy-storage system by rationally measuring and calculating, thus avoid occurring the problem of the excessive or discontented pedal system demand of the cost of power production.
To achieve these goals, the technical scheme that the present invention proposes is that a kind of energy-storage units capacity collocation method being applied to active distribution network, is characterized in that described method comprises:
Step 1: determine the parameter in active distribution network, comprises the type of each node, the load of each node, the impedance of each branch road, the admittance of each branch road, the capacity of distributed energy generator unit and exerting oneself of distributed energy generator unit;
Described distributed energy generator unit comprises photovoltaic generation unit and wind power generation unit;
Step 2: choose different target functions and determine constraints;
Step 3: solve the target function chosen and obtain energy-storage units operate power curve;
Step 4: the configuration capacity asking for energy-storage units according to energy-storage units operate power curve.
It is described that to choose different target functions be maximize function from load peak-valley difference minimization function, load variance minimization function, load smoothness optimization function, via net loss minimization function, voltage fluctuation minimization function and power supply reliability to choose at least one;
Described load peak-valley difference minimization function is: Min (max (P 1, P 2... P t)-min (P 1, P 2... P t));
Described load variance minimization function is: wherein, P t=P load, t-P pV, t-P wind, t+ P storage, t, P load, tfor the load active power of moment t active distribution network, P pV, tfor the active power of the photovoltaic generation unit of moment t active distribution network, P wind, tfor the active power of the wind power generation unit of moment t active distribution network, P storage, tfor the active power of the energy-storage units of moment t active distribution network, P averagefor the average burden with power of system in setting cycle T and
Described load smoothness optimization function is:
Described via net loss minimization function is: wherein, N is the branch road sum of active distribution network, P loss, ktfor branch road k is in the active loss of moment t, Q loss, ktfor branch road k is at the reactive loss of moment t;
Described voltage fluctuation minimization function is: wherein, M is the node total number of active distribution network, V itfor node i is at the voltage magnitude of moment t, V nfor reference voltage;
Described power supply reliability maximizes function: wherein, P s & DER, t=P s,t+ P dER, t, P s,tfor the power output of moment t energy-storage units, P dER, tfor the power output of moment t wind power generation unit and photovoltaic generation unit, T survivefor continued power duration during active distribution network fault, for the workload demand of moment t;
In above-mentioned each target function, P tfor the active power of moment t active distribution network, t=1,2 ..., T, T are the cycle of setting.
Described constraints comprises energy-storage units power constraint, energy-storage units runs constraint, trend Constraints of Equilibrium, voltage retrain, energy-storage units accesses number constraint and the constraint of system total load;
Described energy-storage units power constraint is :-P max≤ P s,t≤ P max; Wherein, P s,tfor the power output of moment t energy-storage units, P maxfor the charge-discharge electric power upper limit of energy-storage units;
Described energy-storage units runs and is constrained to: wherein, [t a, t b] be setting-up time interval, P chfor the charge power of energy-storage units in setting-up time interval, P disfor the discharge power of energy-storage units in setting-up time interval, ε is setting balance index value;
Described trend Constraints of Equilibrium is: P i = e i Σ j = 1 n ( G ij e j - B ij f j ) + f i Σ j = 1 n ( G ij f j + B ij e j ) Q i = f i Σ j = 1 n ( G ij e j - B ij f j ) - e i Σ j = 1 n ( G ij f j + B ij e j ) ; Wherein, P ifor the active power of active distribution network interior joint i, Q ifor the reactive power of active distribution network interior joint i, e ifor the voltage real component of active distribution network interior joint i, e jfor the voltage real component of active distribution network interior joint j, f ifor the voltage imaginary of active distribution network interior joint i, f jfor the voltage imaginary of active distribution network interior joint j, G ijfor the real component of branch admittance matrix, B ijfor the imaginary component of branch admittance matrix, n is active distribution network interior joint sum;
Described voltage is constrained to: V min≤ V it≤ V max; Wherein, V itfor node i is at the voltage magnitude of moment t, V minfor the lower voltage limit of active distribution network, V maxfor the upper voltage limit of active distribution network;
Described energy-storage units access number constraint is: N s=N set; Wherein, N sfor energy-storage units access quantity, N setfor set point;
Described system total load is constrained to: P all, t>=P dG, t; Wherein, P all, tfor the total load of moment t active distribution network, P dG, tfor the gross generation of moment t photovoltaic generation unit and wind power generation unit.
The described configuration capacity asking for energy-storage units according to energy-storage units operate power curve comprises:
Sub-step A1: the setting-up time cycle is divided into some intervals;
Sub-step A2: the initial state-of-charge of energy-storage units is set as energy-storage units is full of 50% of the state-of-charge of electricity;
Sub-step A3: carry out charge and discharge control to energy-storage units by energy-storage units operate power curve, calculates the capacity C of each interval energy-storage units k; K=1,2 ..., K, K are interval number;
Sub-step A4: make each interval maximum capacity using each interval maximum capacity C as energy-storage units configuration capacity.
The capacity of each interval energy-storage units of described calculating adopts formula:
S ( t + Δt ) = S ( t ) + ∫ t t + Δt | P S ( t ) | dt , P S ( t ) · P S ( t + Δt ) > 0 0 , P S ( t ) · P S ( t + Δt ) ≤ 0 ;
Wherein, t is time interval starting point, and t+ Δ t is the terminal of time interval, and Δ t is time step, and S (t) is the capacity of moment t energy-storage units, the capacity that S (t+ Δ t) is moment t+ Δ t energy-storage units, P st () is the power output of moment t energy-storage units, P s(t+ Δ t) is the power output of moment t+ Δ t energy-storage units.
The invention solves the capacity configuration problem of energy-storage units in active distribution network, adverse effect to system after photovoltaic, wind power generation distributed regenerative resource access active distribution network is reduced to utilize energy-storage units, by generating the optimizing operation power curve of energy-storage units, the reasonable power extreme value of verification energy-storage units and capacity configuration.
Accompanying drawing explanation
Fig. 1 is the integrated stand composition of the energy-storage units capacity configuration being applied to active distribution network;
Fig. 2 is the flow chart of the energy-storage units capacity configuration being applied to active distribution network;
Fig. 3 is the flow chart utilizing intelligent algorithm to realize power curve calculating;
Fig. 4 is that application the present invention is with the minimum Simulation Example result of calculation table for target of load variance;
To be that load variance is minimum optimize different capacity when calculating and arrange response diagram to stored energy capacitance Fig. 5.
Embodiment
Below in conjunction with accompanying drawing, preferred embodiment is elaborated.It is emphasized that following explanation is only exemplary, instead of in order to limit the scope of the invention and apply.
In active distribution network system after distributed power source access, the fluctuation of system increases and reliability weakens time, if carry out energy storage configuration without rationally measuring and calculating may cause the excessive or discontented pedal system requirement of cost.The present invention is directed to this problem, under different scene, intend the power curve run according to energy-storage units, consider certain Capacity Margin simultaneously, draw the capacity configuration result of energy-storage system.
Fig. 1 is the integrated stand composition of the energy-storage units capacity configuration being applied to active distribution network.As shown in Figure 1, the overall architecture being applied to the energy-storage units capacity configuration of active distribution network comprises scene setting module, target function arranges module, power curve computing module, calculation of capacity module and cost accounting module.
Scene setting module, relates generally to the generation of active distribution network simulation model, regenerative resource on-position and Capacity Selection and setting of exerting oneself, and system default can provide the degree of fluctuation setting of different brackets.In scene setting module, except the normal Run-time scenario of system, also relate to when components of system as directed fault, active distribution network responds, and ensures that first order load is powered through power distribution network reconfiguration or formation micro-capacitance sensor.Meanwhile, also can arrange the emulation duration of system, simulation time precision in this module.
Target function and constraint arrange module, relate generally to in active distribution network energy-storage units configuration optimization aim and constraint arrange, this module can be selected the optimization aim of system, comprising with the minimum optimal load flow of via net loss in active distribution network system, minimum with active distribution network voltage fluctuation, minimum with node load fluctuation each in system, with target functions such as maximum reliabilities under fault scenes for optimization aim, in next module, computing is carried out to the charge-discharge electric power of energy-storage system.For the setting of constraints, the main constraint of battery charge state (SOC) and the discharge and recharge Constraints of Equilibrium of energy-storage system considering energy-storage system discharge and recharge.The discharge and recharge Constraints of Equilibrium of energy-storage system refers to, makes the discharge and recharge of system balance in setting-up time process, thus ensures the ability of the system discharge and recharge of subsequent period.In addition, also systematic total load is not less than the constraint of gross capability, with dissolving of all renewable loads in guarantee system.
Power curve computing module, relates generally to the optimized algorithm calculated power curve, has certain constraint for the different selection of calculating scene to computational methods.Such as, when relating to the constraint of the trend in power distribution network, utilizing exact algorithm to calculate length consuming time, algorithm is complicated, therefore adopt intelligent algorithm to obtain feasible solution.When target function is systematic variance, then the method such as quadratic programming or Gradient Descent can be utilized to obtain exact solution.
Calculation of capacity module, relate generally to the capacity configuration algorithm of energy storage, to intend running the core methed as energy storage configuration in the present invention, take into full account the discharge and recharge balance of energy-storage system, sufficient discharge and recharge potentiality are kept each setting in the operation period, initial SOC is set to 50%, and then carries out discharge and recharge simulation according to the power intending running, and consider that energy-storage battery operates in the minimum nargin as capacity configuration of life consumption in 20% ~ 80% interval.
Cost accounting module, relates generally to the optimization aim realized in active distribution network based on energy-storage system, such as, reduces 5% of system loss, the system that draws capacity configuration result under the maximum power of setting is run.In the cost accounting of energy-storage system, not only consider the Capacity Cost of system, also consider different power output costs and corresponding rate of return on investment.
Fig. 2 is the flow chart of the energy-storage units capacity configuration being applied to active distribution network.As shown in Figure 2, the energy-storage units capacity collocation method being applied to active distribution network provided by the invention comprises:
Step 1: determine the parameter in active distribution network, comprises the type of each node, the load of each node, the impedance of each branch road, the admittance of each branch road, the capacity of distributed energy generator unit and exerting oneself of distributed energy generator unit.
Wherein, distributed energy generator unit comprises photovoltaic generation unit and wind power generation unit.
Step 2: choose different target functions and determine constraints.
Target function comprises load peak-valley difference minimization function, load variance minimization function, load smoothness optimization function, via net loss minimization function, voltage fluctuation minimization function and power supply reliability and maximizes function.
Load peak-valley difference minimization function is:
Min(max(P 1,P 2,...P T)-min(P 1,P 2,...P T))(1)
In formula (1), P tfor the active power of moment t active distribution network, t=1,2 ..., T, T are the cycle of setting.
Load variance minimization function is:
min Σ t = 1 T ( P t - P average ) 2 - - - ( 2 )
In formula (2), P tfor the active power of moment t active distribution network, t=1,2 ..., T, T are the cycle of setting, P t=P load, t-P pV, t-P wind, t+ P storage, t, P load, tfor the load active power of moment t active distribution network, P pV, tfor the active power of the photovoltaic generation unit of moment t active distribution network, P wind, tfor the active power of the wind power generation unit of moment t active distribution network, P storage, tfor the active power of the energy-storage units of moment t active distribution network, P averagefor the average burden with power of system in setting cycle T and
Load smoothness optimization function is:
min Σ t = 2 T ( P t - P t - 1 ) 2 - - - ( 3 )
In formula (3), P tfor the active power of moment t active distribution network, t=1,2 ..., T, T are the cycle of setting.
Via net loss minimization function is:
min ( Σ k = 1 N Σ t = 1 T P loss , kt + Σ k = 1 N Σ t = 1 T Q loss , kt ) - - - ( 4 )
In formula (4), N is the branch road sum of active distribution network, P loss, ktfor branch road k is in the active loss of moment t, Q loss, ktfor branch road k is at the reactive loss of moment t.
Voltage fluctuation minimization function is:
min Σ i = 1 M Σ t = 1 T ( V it - V n ) 2 - - - ( 5 )
In formula (5), M is the node total number of active distribution network, V itfor node i is at the voltage magnitude of moment t, V nfor reference voltage.
Power supply reliability maximizes function:
In formula (6), P s & DER, t=P s,t+ P dER, t, P s,tfor the power output of moment t energy-storage units, P dER, tfor the power output of moment t wind power generation unit and photovoltaic generation unit, T survivefor continued power duration during active distribution network fault, for the workload demand of moment t.
Constraints comprises energy-storage units power constraint, energy-storage units runs constraint, trend Constraints of Equilibrium, voltage retrain, energy-storage units accesses number constraint and the constraint of system total load.
Energy-storage units power constraint is:
-P max≤P S,t≤P max(7)
In formula (7), P s,tfor the power output of moment t energy-storage units, P maxfor the charge-discharge electric power upper limit of energy-storage units.
Energy-storage units runs and is constrained to:
| ∫ t a t b P ch dt - ∫ t a t b P dis dt | ≤ ϵ - - - ( 8 )
In formula (8), [t a, t b] be setting-up time interval, P chfor the charge power of energy-storage units in setting-up time interval, P disfor the discharge power of energy-storage units in setting-up time interval, ε is setting balance index value.
Trend Constraints of Equilibrium is:
P i = e i Σ j = 1 n ( G ij e j - B ij f j ) + f i Σ j = 1 n ( G ij f j + B ij e j ) Q i = f i Σ j = 1 n ( G ij e j - B ij f j ) - e i Σ j = 1 n ( G ij f j + B ij e j ) - - - ( 9 )
In formula (9), P ifor the active power of active distribution network interior joint i, Q ifor the reactive power of active distribution network interior joint i, e ifor the voltage real component of active distribution network interior joint i, e jfor the voltage real component of active distribution network interior joint j, f ifor the voltage imaginary of active distribution network interior joint i, f jfor the voltage imaginary of active distribution network interior joint j, G ijfor the real component of branch admittance matrix, B ijfor the imaginary component of branch admittance matrix, n is active distribution network interior joint sum.
Voltage is constrained to:
V min≤V it≤V max(10)
In formula (10), V itfor node i is at the voltage magnitude of moment t, V minfor the lower voltage limit of active distribution network, V maxfor the upper voltage limit of active distribution network.
Energy-storage units access number constraint is:
N S=N set(11)
In formula (11), N sfor energy-storage units access quantity, N setfor set point.
System total load is constrained to:
P all,t≥P DG,t(12)
In formula (12), P all, tfor the total load of moment t active distribution network, P dG, tfor the gross generation of moment t photovoltaic generation unit and wind power generation unit.
After determining target function and constraints, from above-mentioned multiple target function, choose at least one target function and carry out objective optimization.When choosing two (containing) above target functions, objective optimization is multiple-objection optimization.
Step 3: solve the target function chosen and obtain energy-storage units operate power curve.
Usually, objective optimization algorithm adopts particle cluster algorithm.The present invention improves on the basis of fundamental particle group algorithm, when optimizing process is absorbed in locally optimal solution, mandatoryly to jump out, and multi-species cooperative, effectively improves optimizing effect simultaneously.
When calculating particle rapidity, adopt following formula:
v id t + 1 = ω ( t ) v id t + c 1 r 1 ( p id - z id t ) + c 2 r 2 ( p gd - z id t ) - - - ( 13 )
In formula (13), for the particle rapidity of iteration the t+1 time, for the particle rapidity of iteration the t time, p idfor individual optimal solution, p gdfor group optimal solution, c 1and c 2be setting constant, r 1and r 2for random number, for the particle position of iteration the t time, the inertia weight that ω (t) is iteration the t time.Weight is more conducive to global search more greatly, otherwise is then beneficial to Local Search.In the present invention, inertia weight adopts formula (14) to calculate.
ω ( t ) = ω start - ( ω start - ω end ) ( t T max ) 2 - - - ( 14 )
In formula (14), ω startfor iteration initial inertia weight, ω endfor iteration ends inertia weight, t is iterations, T maxfor iteration total degree.
By calculating the optimization of target function, the performance number of each moment energy-storage units can be obtained, and then obtain energy-storage units operate power curve.
Step 4: the configuration capacity asking for energy-storage units according to energy-storage units operate power curve.Comprise:
Sub-step A1: the setting-up time cycle is divided into some intervals.
Sub-step A2: the initial state-of-charge of energy-storage units is set as energy-storage units is full of 50% of the state-of-charge of electricity.
Sub-step A3: carry out charge and discharge control to energy-storage units by energy-storage units operate power curve, calculates the capacity C of each interval energy-storage units k; K=1,2 ..., K, K are interval number.
The capacity calculating each interval energy-storage units adopts formula:
S ( t + Δt ) = S ( t ) + ∫ t t + Δt | P S ( t ) | dt , P S ( t ) · P S ( t + Δt ) > 0 0 , P S ( t ) · P S ( t + Δt ) ≤ 0 - - - ( 15 )
In formula (15), t is time interval starting point, and t+ Δ t is the terminal of time interval, and Δ t is time step, and S (t) is the capacity of moment t energy-storage units, the capacity that S (t+ Δ t) is moment t+ Δ t energy-storage units, P st () is the power output of moment t energy-storage units, P s(t+ Δ t) is the power output of moment t+ Δ t energy-storage units.
Sub-step A4: make each interval maximum capacity using each interval maximum capacity C as energy-storage units configuration capacity.
Consider the useful life of battery, charging peak can be set to 80%, charging minimum point is set to 20%, and the configuration capacity obtained by sub-step A4 thinks the capacity between 20%-80%, extended to the capacity between 0%-100% again, even C=max is (S ch, S dis)/40%, S chfor charging verification capacity, S disfor electric discharge verification capacity.
Next, in conjunction with the computing example of IEEE33 Node power distribution system, implementation procedure of the present invention is further illustrated.
First, adopt IEEE33 Node power distribution system, consider that 33 node systems are due to radial distribution characteristic, endpoint node voltage fluctuates comparatively serious usually.
Secondly, the setting of renewable energy power generation unit is carried out.In this example, suppose at system end node 18, access photovoltaic, wind-powered electricity generation two kinds of regenerative resources, form active distribution network.By to historical data analysis, choose comparatively average conventional load typical curve, the larger feature of the randomness for photovoltaic, wind-powered electricity generation, the typical curve choosing power fluctuation larger is optimized calculating, and concrete Changing Pattern is according to normalized.
Next, target function is chosen.The present embodiment chooses load variance minimization function as optimization aim.
Then, constraints is determined.The constraints that constraints just adopts formula (7)-(12) to provide.
After carrying out relevant setting according to load variance minimization function, utilize intelligent algorithm to realize power curve and calculate, as shown in Figure 3.According to simulation result, carry out stored energy capacitance configuration to load variance minimum operation result, can obtain final allocation optimum result is 71.5kWh.The table that Fig. 4 provides provides the result according to indices before and after load variance configuration energy storage, as can be seen from this table, according to load variance minimum carry out energy storage configuration can alleviate by regenerative resource access cause load variance increase, reduce 6 orders of magnitude by the 0.005185MW before configuring.
In addition, the energy-storage system configuration capacity that different charge-discharge electric power is corresponding under different peak-valley difference optimum level is given in Figure 5.In this example, charge-discharge electric power can meet optimization requirement substantially when 40kW.
The above; be only the present invention's preferably embodiment, but protection scope of the present invention is not limited thereto, is anyly familiar with those skilled in the art in the technical scope that the present invention discloses; the change that can expect easily or replacement, all should be encompassed within protection scope of the present invention.Therefore, protection scope of the present invention should be as the criterion with the protection range of claim.

Claims (3)

1. be applied to an energy-storage units capacity collocation method for active distribution network, it is characterized in that described method comprises:
Step 1: determine the parameter in active distribution network, comprises the type of each node, the load of each node, the impedance of each branch road, the admittance of each branch road, the capacity of distributed energy generator unit and exerting oneself of distributed energy generator unit;
Described distributed energy generator unit comprises photovoltaic generation unit and wind power generation unit;
Step 2: choose different target functions and determine constraints;
Step 3: solve the target function chosen and obtain energy-storage units operate power curve;
Step 4: the configuration capacity asking for energy-storage units according to energy-storage units operate power curve;
It is described that to choose different target functions be maximize function from load peak-valley difference minimization function, load variance minimization function, load smoothness optimization function, via net loss minimization function, voltage fluctuation minimization function and power supply reliability to choose at least one;
Described load peak-valley difference minimization function is: Min (max (P 1, P 2... P t)-min (P 1, P 2... P t));
Described load variance minimization function is: wherein, P t=P load, t-P pV, t-P wind, t+ P storage, t, P load, tfor the load active power of moment t active distribution network, P pV, tfor the active power of the photovoltaic generation unit of moment t active distribution network, P wind, tfor the active power of the wind power generation unit of moment t active distribution network, P storage, tfor the active power of the energy-storage units of moment t active distribution network, P averagefor the average burden with power of system in setting cycle T and
Described load smoothness optimization function is:
Described via net loss minimization function is: wherein, N is the branch road sum of active distribution network, P loss, ktfor branch road k is in the active loss of moment t, Q loss, ktfor branch road k is at the reactive loss of moment t;
Described voltage fluctuation minimization function is: wherein, M is the node total number of active distribution network, V itfor node i is at the voltage magnitude of moment t, V nfor reference voltage;
Described power supply reliability maximizes function: wherein, P s & DER, t=P s,t+ P dER, t, P s,tfor the power output of moment t energy-storage units, P dER, tfor the power output of moment t wind power generation unit and photovoltaic generation unit, T survivefor continued power duration during active distribution network fault, for the workload demand of moment t;
In above-mentioned each target function, P tfor the active power of moment t active distribution network, t=1,2 ..., T, T are the cycle of setting;
Described constraints comprises energy-storage units power constraint, energy-storage units runs constraint, trend Constraints of Equilibrium, voltage retrain, energy-storage units accesses number constraint and the constraint of system total load;
Described energy-storage units power constraint is :-P max≤ P s,t≤ P max; Wherein, P s,tfor the power output of moment t energy-storage units, P maxfor the charge-discharge electric power upper limit of energy-storage units;
Described energy-storage units runs and is constrained to: wherein, [t a, t b] be setting-up time interval, P chfor the charge power of energy-storage units in setting-up time interval, P disfor the discharge power of energy-storage units in setting-up time interval, ε is setting balance index value;
Described trend Constraints of Equilibrium is: P i = e i Σ j = 1 n ( G i j e j - B i j f j ) + f i Σ j = 1 n ( G i j f j + B i j e j ) Q i = f i Σ j = 1 n ( G i j e j - B i j f j ) - e i Σ j = 1 n ( G i j f j + B i j e j ) ; Wherein, P ifor the active power of active distribution network interior joint i, Q ifor the reactive power of active distribution network interior joint i, e ifor the voltage real component of active distribution network interior joint i, e jfor the voltage real component of active distribution network interior joint j, f ifor the voltage imaginary of active distribution network interior joint i, f jfor the voltage imaginary of active distribution network interior joint j, G ijfor the real component of branch admittance matrix, B ijfor the imaginary component of branch admittance matrix, n is active distribution network interior joint sum;
Described voltage is constrained to: V min≤ V it≤ V max; Wherein, V itfor node i is at the voltage magnitude of moment t, V minfor the lower voltage limit of active distribution network, V maxfor the upper voltage limit of active distribution network;
Described energy-storage units access number constraint is: N s=N set; Wherein, N sfor energy-storage units access quantity, N setfor set point;
Described system total load is constrained to: P all, t>=P dG, t; Wherein, P all, tfor the total load of moment t active distribution network, P dG, tfor the gross generation of moment t photovoltaic generation unit and wind power generation unit.
2. method according to claim 1, is characterized in that the described configuration capacity asking for energy-storage units according to energy-storage units operate power curve comprises:
Sub-step A1: the setting-up time cycle is divided into some intervals;
Sub-step A2: the initial state-of-charge of energy-storage units is set as energy-storage units is full of 50% of the state-of-charge of electricity;
Sub-step A3: carry out charge and discharge control to energy-storage units by energy-storage units operate power curve, calculates the capacity C of each interval energy-storage units k; K=1,2 ..., K, K are interval number;
Sub-step A4: make each interval maximum capacity using each interval maximum capacity C as energy-storage units configuration capacity.
3. method according to claim 2, is characterized in that the capacity of each interval energy-storage units of described calculating adopts formula:
C k = S ( t + Δ t ) = S ( t ) + ∫ t t + Δ t | P S ( t ) | d t , P S ( t ) · P S ( t + Δ t ) > 0 0 , P S ( t ) · P S ( t + Δ t ) ≤ 0 ;
Wherein, t is time interval starting point, and t+ Δ t is the terminal of time interval, and Δ t is time step, and S (t) is the capacity of moment t energy-storage units, the capacity that S (t+ Δ t) is moment t+ Δ t energy-storage units, P st () is the power output of moment t energy-storage units, P s(t+ Δ t) is the power output of moment t+ Δ t energy-storage units.
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