CN104037793A - Energy storing unit capacity configuration method applied to initiative power distribution network - Google Patents

Energy storing unit capacity configuration method applied to initiative power distribution network Download PDF

Info

Publication number
CN104037793A
CN104037793A CN201410321222.3A CN201410321222A CN104037793A CN 104037793 A CN104037793 A CN 104037793A CN 201410321222 A CN201410321222 A CN 201410321222A CN 104037793 A CN104037793 A CN 104037793A
Authority
CN
China
Prior art keywords
energy
storage units
distribution network
power
power distribution
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN201410321222.3A
Other languages
Chinese (zh)
Other versions
CN104037793B (en
Inventor
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
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
HAINAN GRID CO Ltd
Beijing Jiaotong University
Original Assignee
HAINAN GRID CO Ltd
Beijing Jiaotong University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by HAINAN GRID CO Ltd, Beijing Jiaotong University filed Critical HAINAN GRID CO Ltd
Priority to CN201410321222.3A priority Critical patent/CN104037793B/en
Publication of CN104037793A publication Critical patent/CN104037793A/en
Application granted granted Critical
Publication of CN104037793B publication Critical patent/CN104037793B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • 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 an energy storing unit capacity configuration method applied to an initiative power distribution network in the technical field of initiative power distribution network control. The method includes the steps that parameters in the initiative power distribution network are determined; different target functions are selected, and constraint conditions are determined; the selected target functions are solved, and an operation power curve of an energy storing unit is obtained; the configuration capacity of the energy storing unit is obtained according to the operation power curve of the energy storing unit. The energy storing unit capacity configuration method solves the problem of capacity configuration in the energy storing unit in the initiative power distribution network, the energy storing unit is used for reducing adverse influences on a system after distributed renewable energy sources for photovoltaic, wind force generation and the like are connected into the initiative power distribution network, and the reasonable power extreme value and the capacity configuration of the energy storing unit are verified by generating the optimized operation power curve of the energy storing unit.

Description

A kind of initiatively energy-storage units capacity collocation method of power distribution network that is applied to
Technical field
The invention belongs to initiatively power distribution network control technology field, relate in particular to a kind of initiatively energy-storage units capacity collocation method of power distribution network that is applied to.
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 requirement of the growth of power consumption and user's fail safe, reliability.Meanwhile, the day by day growth exhausted and carbon dioxide discharge capacity of fossil energy proposes new test to the mankind's existence, the access of regenerative resource and develop imperative.And distributed power generation is with respect 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 outstanding, its fluctuation and intermittence will certainly be to electrical networks, and particularly power distribution network causes adverse effect.Power distribution network now, by original passive, passive active, the power distribution network initiatively that is converted into, has proposed new test to current power distribution network.In order to tackle distributed power generation, the particularly strong feature of the intermittent generator unit fluctuation such as distributed photovoltaic, wind-powered electricity generation, need in system, introduce energy-storage units, on the one hand, can effectively improve the dissolve ability 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 improve the quality of power supply, improve power system operation stability.
Just think, in the active power distribution network of following high permeability, 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 has played the function served as bridge of distributed energy and power load in active power distribution network, and at present comparatively ripe energy-storage units is all energy-storage battery, and its cost is higher, and therefore the allocation problem of energy-storage units, becomes research emphasis.
This patent, for energy-storage units access problem in active power distribution network, adopts energy-storage units under different scenes to intend operation method, according to discharging and recharging power curve, obtains the reasonable disposition of energy-storage units.
Summary of the invention
The object of the invention is to, a kind of initiatively energy-storage units capacity collocation method of power distribution network that is applied to is provided, increase and drawback that reliability weakens for fluctuation after active power distribution network access distributed energy, carry out the capacity configuration of energy-storage system by reasonable measuring and calculating, thereby 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 initiatively energy-storage units capacity collocation method of power distribution network that is applied to, is characterized in that described method comprises:
Step 1: determine the parameter in active power distribution network, comprise admittance, the capacity of distributed energy generator unit and the exerting oneself of distributed energy generator unit of the impedance of the load of the type of each node, each node, each branch road, each branch road;
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 of choosing and obtain energy-storage units operate power curve;
Step 4: the configuration capacity of asking for energy-storage units according to energy-storage units operate power curve.
It is described that to choose different target functions be to maximize function and choose at least one 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;
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 initiatively load active power of power distribution network of moment t, P pV, tfor the initiatively active power of the photovoltaic generation unit of power distribution network of moment t, P wind, tfor the initiatively active power of the wind power generation unit of power distribution network of moment t, P storage, tfor the initiatively active power of the energy-storage units of power distribution network of moment t, 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 power distribution network, P loss, ktfor branch road k is at 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 sum of active power 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 survivecontinued power duration during for active distribution network failure, for the workload demand of moment t;
In above-mentioned each target function, P tfor the initiatively active power of power distribution network of moment t, t=1,2 ..., T, T is the cycle of setting.
Described constraints comprises energy-storage units power constraint, energy-storage units operation constraint, trend Constraints of Equilibrium, voltage constraint, energy-storage units access quantity 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 power upper limit that discharges and recharges of energy-storage units;
Described energy-storage units operation 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 for 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 node i in active power distribution network, Q ifor the reactive power of node i in active power distribution network, e ifor the voltage real component of node i in active power distribution network, e jfor the voltage real component of node j in active power distribution network, f ifor the voltage imaginary part component of node i in active power distribution network, f jfor the voltage imaginary part component of node j in active power distribution network, G ijfor the real component of branch admittance matrix, B ijfor the imaginary component of branch admittance matrix, n is node sum in active power distribution network;
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 power distribution network, V maxfor the upper voltage limit of active power distribution network;
Described energy-storage units access quantity is constrained to: 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 initiatively total load of power distribution network of moment t, P dG, tfor the gross generation of moment t photovoltaic generation unit and wind power generation unit.
The described configuration capacity of 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 to energy-storage units and is full of 50% of electric state-of-charge;
Sub-step A3: by energy-storage units operate power curve, energy-storage units is discharged and recharged to control, calculate the capacity C of each interval energy-storage units k; K=1,2 ..., K, K is 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 the 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, the terminal that t+ Δ t is time interval, and Δ t is time step, and S (t) is the capacity of moment t energy-storage units, and (t+ Δ is t) capacity of moment t+ Δ t energy-storage units to S, P s(t) be the power output of moment t energy-storage units, P s(t+ Δ is t) power output of moment t+ Δ t energy-storage units.
The invention solves the capacity configuration problem of energy-storage units in active power distribution network, to utilize energy-storage units to reduce the initiatively adverse effect to system after power distribution network of photovoltaic, wind power generation distributed regenerative resource access, by generating the optimization operate power curve of energy-storage units, reasonable power extreme value and the capacity configuration of verification energy-storage units.
Brief description of the drawings
Fig. 1 is applied to the initiatively integrated stand composition of the energy-storage units capacity configuration of power distribution network;
Fig. 2 is applied to the initiatively flow chart of the energy-storage units capacity configuration of power distribution network;
Fig. 3 utilizes intelligent algorithm to realize the flow chart that power curve is calculated;
Fig. 4 is the Simulation Example result of calculation table of application the present invention taking load variance minimum as target;
When Fig. 5 is the minimum optimization calculating of load variance, different capacity arranges the response diagram to stored energy capacitance.
Embodiment
Below in conjunction with accompanying drawing, preferred embodiment is elaborated.Should be emphasized that, following explanation is only exemplary, instead of in order to limit the scope of the invention and to apply.
Initiatively in distribution network system after distributed power source access, the fluctuation of system increases and reliability while weakening, may cause the excessive or discontented pedal system requirement of cost if do not carry out energy storage configuration through rationally measuring and calculating.The present invention is directed to this problem, under different scenes, intend the power curve of operation according to energy-storage units, consider certain Capacity Margin simultaneously, draw the capacity configuration result of energy-storage system.
Fig. 1 is applied to the initiatively integrated stand composition of the energy-storage units capacity configuration of power distribution network.As shown in Figure 1, the overall architecture that is applied to the initiatively energy-storage units capacity configuration of power distribution network comprises that scene setting module, target function arrange module, power curve computing module, calculation of capacity module and cost accounting module.
Scene setting module, relates generally to initiatively power distribution network simulation model generation, regenerative resource on-position and Capacity Selection and the setting of exerting oneself, and system default can provide the degree of fluctuation of different brackets and set.In scene setting module, except the normal Run-time scenario of system, also relate to when components of system as directed fault, initiatively power distribution network responds, and passes through power distribution network reconfiguration or form micro-electrical network to ensure a stage load power supply.Meanwhile, in this module, also can arrange the emulation duration of system, simulation time precision.
Target function and constraint arrange module, relate generally to optimization aim and the constraint of the energy-storage units configuration in active power distribution network are arranged, this module can be selected the optimization aim of system, comprising the optimal load flow with via net loss minimum in active distribution network system, minimum with active distribution network voltage fluctuation, minimum with each node load fluctuation in system, taking target functions such as maximum reliabilities under fault scenes as optimization aim, the power that discharges and recharges to energy-storage system in next module carries out computing.For the setting of constraints, mainly consider the Constraints of Equilibrium that discharges and recharges of the constraint of the battery charge state (SOC) that energy-storage system discharges and recharges and energy-storage system.The Constraints of Equilibrium that discharges and recharges of energy-storage system refers to, makes the balance that discharges and recharges of system in setting-up time process, thereby ensures the ability that the system of next period discharges and recharges.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 that power curve is calculated, and for different calculating scenes, the selection of computational methods is had to certain constraint.For example, in the time relating to the constraint of trend in power distribution network, utilize exact algorithm to calculate length consuming time, algorithm complexity, therefore adopt intelligent algorithm to obtain feasible solution.In the time that target function is systematic variance, can utilize the method such as quadratic programming or Gradient Descent to obtain exact solution.
Calculation of capacity module, relate generally to the capacity configuration algorithm of energy storage, the core methed configuring as energy storage to intend operation in the present invention, take into full account the balance that discharges and recharges of energy-storage system, keep discharging and recharging fully potentiality each setting in the operation period, initial SOC is set to 50%, and then discharges and recharges simulation according to the power of intending operation, and considers 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 realizing in active power distribution network based on energy-storage system, for example, reduces 5% of system loss, the system that draws capacity configuration result under the maximum power operation of setting.In the cost accounting of energy-storage system, the not only Capacity Cost of taking into account system, also considers different power output costs and corresponding rate of return on investment.
Fig. 2 is applied to the initiatively flow chart of the energy-storage units capacity configuration of power distribution network.As shown in Figure 2, the initiatively energy-storage units capacity collocation method of power distribution network that is applied to provided by the invention comprises:
Step 1: determine the parameter in active power distribution network, comprise admittance, the capacity of distributed energy generator unit and the exerting oneself of distributed energy generator unit of the impedance of the load of the type of each node, each node, each branch road, each branch road.
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 that 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 maximize 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 initiatively active power of power distribution network of moment t, t=1,2 ..., T, T is 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 initiatively active power of power distribution network of moment t, t=1,2 ..., T, T is the cycle of setting, P t=P load, t-P pV, t-P wind, t+ P storage, t, P load, tfor the initiatively load active power of power distribution network of moment t, P pV, tfor the initiatively active power of the photovoltaic generation unit of power distribution network of moment t, P wind, tfor the initiatively active power of the wind power generation unit of power distribution network of moment t, P storage, tfor the initiatively active power of the energy-storage units of power distribution network of moment t, 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 initiatively active power of power distribution network of moment t, t=1,2 ..., T, T is 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 power distribution network, P loss, ktfor branch road k is at 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 sum of active power 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 survivecontinued power duration during for active distribution network failure, for the workload demand of moment t.
Constraints comprises energy-storage units power constraint, energy-storage units operation constraint, trend Constraints of Equilibrium, voltage constraint, energy-storage units access quantity 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 power upper limit that discharges and recharges of energy-storage units.
Energy-storage units operation 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 for 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 node i in active power distribution network, Q ifor the reactive power of node i in active power distribution network, e ifor the voltage real component of node i in active power distribution network, e jfor the voltage real component of node j in active power distribution network, f ifor the voltage imaginary part component of node i in active power distribution network, f jfor the voltage imaginary part component of node j in active power distribution network, G ijfor the real component of branch admittance matrix, B ijfor the imaginary component of branch admittance matrix, n is node sum in active power distribution network.
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 power distribution network, V maxfor the upper voltage limit of active power distribution network.
Energy-storage units access quantity is constrained to:
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 initiatively total load of power distribution network of moment t, P dG, tfor the gross generation of moment t photovoltaic generation unit and wind power generation unit.
Determine after target function and constraints, from above-mentioned multiple target functions, choose at least one target function and carry out objective optimization.In the time choosing two (containing) above target functions, objective optimization is multiple-objection optimization.
Step 3: solve the target function of choosing and obtain energy-storage units operate power curve.
Conventionally, 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, and mandatory jumping out, multi-species cooperative, has effectively improved optimizing effect simultaneously.
In the time 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, ω (t) is the inertia weight of iteration the t time.Weight is more greatly more conducive to global search, otherwise is 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 stops inertia weight, t is iterations, T maxfor iteration total degree.
Calculate by the optimization to target function, can obtain the performance number of each moment energy-storage units, and then obtain energy-storage units operate power curve.
Step 4: the configuration capacity of 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 to energy-storage units and is full of 50% of electric state-of-charge.
Sub-step A3: by energy-storage units operate power curve, energy-storage units is discharged and recharged to control, calculate the capacity C of each interval energy-storage units k; K=1,2 ..., K, K is interval number.
The capacity that calculates 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, the terminal that t+ Δ t is time interval, and Δ t is time step, and S (t) is the capacity of moment t energy-storage units, and (t+ Δ is t) capacity of moment t+ Δ t energy-storage units to S, P s(t) be the power output of moment t energy-storage units, P s(t+ Δ is t) 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, the peak that can charge is set to 80%, and charging minimum point is set to 20%, and the configuration capacity that sub-step A4 is obtained is thought the capacity between 20%-80%, extended to again the capacity between 0%-100%, 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 distribution system, further illustrate implementation procedure of the present invention.
First, adopt IEEE33 node distribution system, consider that 33 node systems are due to radial distribution characteristic, endpoint node voltage conventionally fluctuation is comparatively serious.
Secondly, carry out the setting of renewable energy power generation unit.In this example, suppose at system endpoint node 18 that access photovoltaic, two kinds of regenerative resources of wind-powered electricity generation form initiatively power distribution network.By to historical data analysis, choose comparatively average conventional load typical curve, for the larger feature of randomness of photovoltaic, wind-powered electricity generation, to choose the typical curve that power fluctuation is larger and be optimized calculating, concrete Changing Pattern is according to normalized.
Next, choose target function.The present embodiment is chosen load variance minimization function as optimization aim.
Then, determine constraints.Constraints just adopts the constraints that formula (7)-(12) provide.
After being correlated with and arranging according to load variance minimization function, utilize intelligent algorithm to realize power curve and calculate, as shown in Figure 3.According to simulation result, load variance minimum operation result is carried out to stored energy capacitance configuration, can obtain final allocation optimum result is 71.5kWh.The table that Fig. 4 provides provides according to the result of indices before and after load variance configuration energy storage, from this table, can find out, carry out energy storage configuration according to load variance minimum and can alleviate and access by regenerative resource the load variance causing and increase, reduced by 6 orders of magnitude by the 0.005185MW before configuring.
In addition, the different energy-storage system configuration capacities that discharge and recharge power correspondence under different peak-valley difference optimum level in Fig. 5, have been provided.In this example, discharge and recharge power and in the time of 40kW, substantially can meet optimization requirement.
The above; only for preferably embodiment of the present invention, but protection scope of the present invention is not limited to this, is anyly familiar with in technical scope that those skilled in the art disclose in the present invention; the variation that can expect easily or replacement, within all should being encompassed in 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 (5)

1. be applied to an initiatively energy-storage units capacity collocation method for power distribution network, it is characterized in that described method comprises:
Step 1: determine the parameter in active power distribution network, comprise admittance, the capacity of distributed energy generator unit and the exerting oneself of distributed energy generator unit of the impedance of the load of the type of each node, each node, each branch road, each branch road;
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 of choosing and obtain energy-storage units operate power curve;
Step 4: the configuration capacity of asking for energy-storage units according to energy-storage units operate power curve.
2. method according to claim 1, described in it is characterized in that, choosing different target functions is to maximize function and choose at least one 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;
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 initiatively load active power of power distribution network of moment t, P pV, tfor the initiatively active power of the photovoltaic generation unit of power distribution network of moment t, P wind, tfor the initiatively active power of the wind power generation unit of power distribution network of moment t, P storage, tfor the initiatively active power of the energy-storage units of power distribution network of moment t, 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 power distribution network, P loss, ktfor branch road k is at 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 sum of active power 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 survivecontinued power duration during for active distribution network failure, for the workload demand of moment t;
In above-mentioned each target function, P tfor the initiatively active power of power distribution network of moment t, t=1,2 ..., T, T is the cycle of setting.
3. method according to claim 2, is characterized in that described constraints comprises energy-storage units power constraint, energy-storage units operation constraint, trend Constraints of Equilibrium, voltage constraint, energy-storage units access quantity 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 power upper limit that discharges and recharges of energy-storage units;
Described energy-storage units operation 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 for 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 node i in active power distribution network, Q ifor the reactive power of node i in active power distribution network, e ifor the voltage real component of node i in active power distribution network, e jfor the voltage real component of node j in active power distribution network, f ifor the voltage imaginary part component of node i in active power distribution network, f jfor the voltage imaginary part component of node j in active power distribution network, G ijfor the real component of branch admittance matrix, B ijfor the imaginary component of branch admittance matrix, n is node sum in active power distribution network;
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 power distribution network, V maxfor the upper voltage limit of active power distribution network;
Described energy-storage units access quantity is constrained to: 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 initiatively total load of power distribution network of moment t, P dG, tfor the gross generation of moment t photovoltaic generation unit and wind power generation unit.
4. according to the method in claim 2 or 3, it is characterized in that the described configuration capacity of 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 to energy-storage units and is full of 50% of electric state-of-charge;
Sub-step A3: by energy-storage units operate power curve, energy-storage units is discharged and recharged to control, calculate the capacity C of each interval energy-storage units k; K=1,2 ..., K, K is interval number;
Sub-step A4: make each interval maximum capacity using each interval maximum capacity C as energy-storage units configuration capacity.
5. method according to claim 4, is characterized in that the capacity of the 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, the terminal that t+ Δ t is time interval, and Δ t is time step, and S (t) is the capacity of moment t energy-storage units, and (t+ Δ is t) capacity of moment t+ Δ t energy-storage units to S, P s(t) be the power output of moment t energy-storage units, P s(t+ Δ is t) power output of moment t+ Δ t energy-storage units.
CN201410321222.3A 2014-07-07 2014-07-07 A kind of energy-storage units capacity collocation method being applied to active distribution network Active CN104037793B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201410321222.3A CN104037793B (en) 2014-07-07 2014-07-07 A kind of energy-storage units capacity collocation method being applied to active distribution network

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201410321222.3A CN104037793B (en) 2014-07-07 2014-07-07 A kind of energy-storage units capacity collocation method being applied to active distribution network

Publications (2)

Publication Number Publication Date
CN104037793A true CN104037793A (en) 2014-09-10
CN104037793B CN104037793B (en) 2016-01-20

Family

ID=51468457

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201410321222.3A Active CN104037793B (en) 2014-07-07 2014-07-07 A kind of energy-storage units capacity collocation method being applied to active distribution network

Country Status (1)

Country Link
CN (1) CN104037793B (en)

Cited By (17)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104505826A (en) * 2014-12-22 2015-04-08 国家电网公司 Coordinated optimization control method for active distribution network
CN105207272A (en) * 2015-09-18 2015-12-30 武汉大学 Electric power system dynamic random economic dispatching method and device based on general distribution
CN105976055A (en) * 2016-05-04 2016-09-28 东北电力大学 Distributed photovoltaic-energy storage system (PV-BES) output optimization and capacity allocation method counting power loss
CN106099912A (en) * 2016-06-21 2016-11-09 东北大学 A kind of active distribution network partial power coordinated control system and method
CN106887838A (en) * 2017-02-22 2017-06-23 天津大学 A kind of power distribution network regenerative resource based on network reconfiguration is dissolved method
CN107239847A (en) * 2017-04-12 2017-10-10 广州供电局有限公司 A kind of active distribution network energy-storage system dynamic programming method
CN107425541A (en) * 2017-06-29 2017-12-01 国网山东省电力公司潍坊供电公司 A kind of active distribution network wind stores up combined scheduling method
CN107492901A (en) * 2017-08-29 2017-12-19 广东电网有限责任公司电力科学研究院 A kind of distributed energy storage system real-time optimization method and device
CN107590744A (en) * 2016-07-08 2018-01-16 华北电力大学(保定) Consider the active distribution network distributed power source planing method of energy storage and reactive-load compensation
CN107800148A (en) * 2017-11-22 2018-03-13 国网河南省电力公司电力科学研究院 A kind of load side energy storage Optimal Configuration Method based on peak regulation effect
CN108183498A (en) * 2017-12-30 2018-06-19 国网天津市电力公司电力科学研究院 A kind of vehicle storage mixed configuration method under the background of power distribution network containing wind-light storage
CN108599138A (en) * 2017-12-30 2018-09-28 国网天津市电力公司电力科学研究院 Meter and the probabilistic energy storage system capacity configuration method of micro-capacitance sensor distributed energy
CN110011351A (en) * 2018-01-04 2019-07-12 中国石油化工股份有限公司 A kind of efficiency control method based on constraint function
CN110061492A (en) * 2019-03-21 2019-07-26 国网浙江省电力有限公司经济技术研究院 Consider the energy storage system capacity configuration optimizing method of distribution network reliability
CN110518612A (en) * 2019-09-02 2019-11-29 南方电网科学研究院有限责任公司 A kind of determination method and device of power distribution network energy-storage system configuration parameter
CN110635519A (en) * 2018-06-22 2019-12-31 国网江苏省电力有限公司扬州供电分公司 Active power distribution network distributed new energy day-ahead active power dispatching plan generation method
CN111555329A (en) * 2020-06-05 2020-08-18 西安石油大学 Energy storage capacity configuration method for autonomous micro-grid

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103023055A (en) * 2012-11-21 2013-04-03 浙江大学 Method for stabilizing wind-solar generation system output power fluctuation with composite energy storage technology
WO2014103353A1 (en) * 2012-12-28 2014-07-03 オムロン株式会社 Power control device, power control method, program, and energy management system

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103023055A (en) * 2012-11-21 2013-04-03 浙江大学 Method for stabilizing wind-solar generation system output power fluctuation with composite energy storage technology
WO2014103353A1 (en) * 2012-12-28 2014-07-03 オムロン株式会社 Power control device, power control method, program, and energy management system

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
洪海生: "应用于平抑风电功率波动的多类型储能系统容量配置与协调控制研究", 《中国优秀硕士学位论文全文数据库》 *

Cited By (28)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104505826A (en) * 2014-12-22 2015-04-08 国家电网公司 Coordinated optimization control method for active distribution network
CN105207272B (en) * 2015-09-18 2018-03-13 武汉大学 The random economic load dispatching method and device of Electrical Power System Dynamic based on general distribution
CN105207272A (en) * 2015-09-18 2015-12-30 武汉大学 Electric power system dynamic random economic dispatching method and device based on general distribution
CN105976055B (en) * 2016-05-04 2019-12-13 东北电力大学 distributed photovoltaic-energy storage system output optimization and capacity configuration method considering power loss
CN105976055A (en) * 2016-05-04 2016-09-28 东北电力大学 Distributed photovoltaic-energy storage system (PV-BES) output optimization and capacity allocation method counting power loss
CN106099912A (en) * 2016-06-21 2016-11-09 东北大学 A kind of active distribution network partial power coordinated control system and method
CN106099912B (en) * 2016-06-21 2018-05-29 东北大学 A kind of active distribution network partial power coordinated control system and method
CN107590744B (en) * 2016-07-08 2021-06-01 华北电力大学(保定) Active power distribution network distributed power supply planning method considering energy storage and reactive compensation
CN107590744A (en) * 2016-07-08 2018-01-16 华北电力大学(保定) Consider the active distribution network distributed power source planing method of energy storage and reactive-load compensation
CN106887838B (en) * 2017-02-22 2019-07-23 天津大学 A kind of power distribution network renewable energy consumption method based on network reconfiguration
CN106887838A (en) * 2017-02-22 2017-06-23 天津大学 A kind of power distribution network regenerative resource based on network reconfiguration is dissolved method
CN107239847A (en) * 2017-04-12 2017-10-10 广州供电局有限公司 A kind of active distribution network energy-storage system dynamic programming method
CN107425541A (en) * 2017-06-29 2017-12-01 国网山东省电力公司潍坊供电公司 A kind of active distribution network wind stores up combined scheduling method
CN107425541B (en) * 2017-06-29 2019-10-29 国网山东省电力公司潍坊供电公司 A kind of active distribution network wind storage combined scheduling method
CN107492901A (en) * 2017-08-29 2017-12-19 广东电网有限责任公司电力科学研究院 A kind of distributed energy storage system real-time optimization method and device
CN107492901B (en) * 2017-08-29 2020-04-07 广东电网有限责任公司电力科学研究院 Distributed energy storage system real-time optimization method and device
CN107800148A (en) * 2017-11-22 2018-03-13 国网河南省电力公司电力科学研究院 A kind of load side energy storage Optimal Configuration Method based on peak regulation effect
CN107800148B (en) * 2017-11-22 2020-09-15 国网河南省电力公司电力科学研究院 Load side energy storage optimization configuration method based on peak regulation effect
CN108599138A (en) * 2017-12-30 2018-09-28 国网天津市电力公司电力科学研究院 Meter and the probabilistic energy storage system capacity configuration method of micro-capacitance sensor distributed energy
CN108183498A (en) * 2017-12-30 2018-06-19 国网天津市电力公司电力科学研究院 A kind of vehicle storage mixed configuration method under the background of power distribution network containing wind-light storage
CN110011351B (en) * 2018-01-04 2020-12-08 中国石油化工股份有限公司 Efficiency control method based on constraint function
CN110011351A (en) * 2018-01-04 2019-07-12 中国石油化工股份有限公司 A kind of efficiency control method based on constraint function
CN110635519A (en) * 2018-06-22 2019-12-31 国网江苏省电力有限公司扬州供电分公司 Active power distribution network distributed new energy day-ahead active power dispatching plan generation method
CN110635519B (en) * 2018-06-22 2020-11-20 国网江苏省电力有限公司扬州供电分公司 Active power distribution network distributed new energy day-ahead active power dispatching plan generation method
CN110061492A (en) * 2019-03-21 2019-07-26 国网浙江省电力有限公司经济技术研究院 Consider the energy storage system capacity configuration optimizing method of distribution network reliability
CN110518612A (en) * 2019-09-02 2019-11-29 南方电网科学研究院有限责任公司 A kind of determination method and device of power distribution network energy-storage system configuration parameter
CN110518612B (en) * 2019-09-02 2021-06-11 南方电网科学研究院有限责任公司 Method and device for determining configuration parameters of energy storage system of power distribution network
CN111555329A (en) * 2020-06-05 2020-08-18 西安石油大学 Energy storage capacity configuration method for autonomous micro-grid

Also Published As

Publication number Publication date
CN104037793B (en) 2016-01-20

Similar Documents

Publication Publication Date Title
CN104037793B (en) A kind of energy-storage units capacity collocation method being applied to active distribution network
CN103490410B (en) Micro-grid planning and capacity allocation method based on multi-objective optimization
CN103078340B (en) Mixed energy storing capacity optimization method for optimizing micro-grid call wire power
CN100380774C (en) Electric power control apparatus, power generation system and power grid system
CN103248064B (en) A kind of compound energy charging energy-storing system and method thereof
CN103986190B (en) Based on the wind-solar-storage joint electricity generation system smooth control method of generated output curve
CN103326388B (en) Based on micro-grid energy storage system and the capacity collocation method of power prediction
CN103094926A (en) Multi-component energy-storing capacity collocation method applied to micro power grid group
CN103986186A (en) Wind, solar and water complementary-type micro grid black start control method
CN103020853A (en) Method for checking short-term trade plan safety
CN104104107B (en) The model predictive control method of wind power fluctuation is stabilized with hybrid energy-storing
CN105406496B (en) A kind of isolated micro-capacitance sensor frequency modulation control method based on practical frequency response identification
CN106099965A (en) The control method for coordinating of COMPLEX MIXED energy-storage system under exchange micro-grid connection state
CN103326389A (en) Power prediction based micro-grid energy storage system and capacity configuration method
CN104993478A (en) Offline operation control method suitable for user-side microgrid
Akbari et al. Voltage control of a hybrid ac/dc microgrid in stand-alone operation mode
CN106972536B (en) Control method and device for virtual synchronous generator of photovoltaic power station
CN202651806U (en) Smooth wind-power photovoltaic power generation control system of battery energy storage station
CN105226689A (en) Consider polymorphic type energy-storage system energy management method and the system of operation and maintenance
Nguyen et al. Determination of the optimal battery capacity based on a life time cost function in wind farm
CN105162167A (en) Adaptive-droop-control-based wind-photovoltaic-energy-storage micro-grid frequency modulation method
CN107732945A (en) A kind of energy-storage units optimization method based on simulated annealing particle cluster algorithm
CN103560533B (en) The method and system of the level and smooth wind light generation fluctuation of energy-accumulating power station are controlled based on rate of change
Wang et al. An improved min-max power dispatching method for integration of variable renewable energy
CN109066746B (en) Method for obtaining inertia time constant of power system with energy storage system

Legal Events

Date Code Title Description
PB01 Publication
C06 Publication
SE01 Entry into force of request for substantive examination
C10 Entry into substantive examination
GR01 Patent grant
C14 Grant of patent or utility model