CN104037793B  A kind of energystorage units capacity collocation method being applied to active distribution network  Google Patents
A kind of energystorage units capacity collocation method being applied to active distribution network Download PDFInfo
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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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 238000004146 energy storage Methods 0.000 title claims abstract description 137
 238000009826 distribution Methods 0.000 title claims abstract description 100
 238000010248 power generation Methods 0.000 claims abstract description 19
 238000005457 optimization Methods 0.000 claims description 17
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 239000011159 matrix material Substances 0.000 claims description 6
 238000003860 storage Methods 0.000 claims description 6
 230000001172 regenerating Effects 0.000 abstract description 8
 238000005516 engineering process Methods 0.000 abstract description 3
 238000004422 calculation algorithm Methods 0.000 description 10
 239000002245 particle Substances 0.000 description 6
 238000004088 simulation Methods 0.000 description 5
 238000004364 calculation method Methods 0.000 description 3
 238000000034 method Methods 0.000 description 3
 230000037098 T max Effects 0.000 description 2
 230000000875 corresponding Effects 0.000 description 2
 239000000203 mixture Substances 0.000 description 2
 230000035699 permeability Effects 0.000 description 2
 230000015572 biosynthetic process Effects 0.000 description 1
 CURLTUGMZLYLDIUHFFFAOYSAN carbon dioxide Chemical compound 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Classifications

 Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSSSECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSSREFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
 Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
 Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
 Y02E10/00—Energy generation through renewable energy sources
 Y02E10/50—Photovoltaic [PV] energy
 Y02E10/56—Power conversion systems, e.g. maximum power point trackers

 Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSSSECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSSREFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
 Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
 Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
 Y02E10/00—Energy generation through renewable energy sources
 Y02E10/70—Wind energy
 Y02E10/76—Power conversion electric or electronic aspects

 Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSSSECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSSREFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
 Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
 Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
 Y02E70/00—Other energy conversion or management systems reducing GHG emissions
 Y02E70/30—Systems combining energy storage with energy generation of nonfossil origin
Abstract
The invention discloses a kind of energystorage 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 energystorage units operate power curve; The configuration capacity of energystorage units is asked for according to energystorage units operate power curve.The invention solves the capacity configuration problem of energystorage 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 energystorage units, by generating the optimizing operation power curve of energystorage units, the reasonable power extreme value of verification energystorage units and capacity configuration.
Description
Technical field
The invention belongs to active distribution network control technology field, particularly relate to a kind of energystorage 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 largescale 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, windpowered electricity generation unit fluctuation is strong, energystorage 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, windpowered electricity generation as far as possible, and in conjunction with the controllable such as cogeneration of heat and power, electric automobile.Energystorage units serves the function served as bridge of distributed energy and power load in active distribution network, and energystorage units comparatively ripe is at present all energystorage battery, and its cost is higher, and therefore the allocation problem of energystorage units, becomes research emphasis.
This patent is for energystorage units access problem in active distribution network, and under adopting different scene, energystorage units intends operation method, according to chargedischarge electric power curve, obtains the reasonable disposition of energystorage units.
Summary of the invention
The object of the invention is to, a kind of energystorage 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 energystorage 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 energystorage 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 energystorage units operate power curve;
Step 4: the configuration capacity asking for energystorage units according to energystorage units operate power curve.
It is described that to choose different target functions be maximize function from load peakvalley 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 peakvalley 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, t}for the load active power of moment t active distribution network, P
_{pV, t}for the active power of the photovoltaic generation unit of moment t active distribution network, P
_{wind, t}for the active power of the wind power generation unit of moment t active distribution network, P
_{storage, t}for the active power of the energystorage units of moment t active distribution network, P
_{average}for 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, kt}for branch road k is in the active loss of moment t, Q
_{loss, kt}for 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
_{it}for node i is at the voltage magnitude of moment t, V
_{n}for reference voltage;
Described power supply reliability maximizes function:
wherein, P
_{s & DER, t}=P
_{s,t}+ P
_{dER, t}, P
_{s,t}for the power output of moment t energystorage units, P
_{dER, t}for the power output of moment t wind power generation unit and photovoltaic generation unit, T
_{survive}for continued power duration during active distribution network fault,
for the workload demand of moment t;
In abovementioned each target function, P
_{t}for the active power of moment t active distribution network, t=1,2 ..., T, T are the cycle of setting.
Described constraints comprises energystorage units power constraint, energystorage units runs constraint, trend Constraints of Equilibrium, voltage retrain, energystorage units accesses number constraint and the constraint of system total load;
Described energystorage units power constraint is :P
_{max}≤ P
_{s,t}≤ P
_{max}; Wherein, P
_{s,t}for the power output of moment t energystorage units, P
_{max}for the chargedischarge electric power upper limit of energystorage units;
Described energystorage units runs and is constrained to:
wherein, [t
_{a}, t
_{b}] be settingup time interval, P
_{ch}for the charge power of energystorage units in settingup time interval, P
_{dis}for the discharge power of energystorage units in settingup time interval, ε is setting balance index value;
Described trend Constraints of Equilibrium is:
$\left\{\begin{array}{c}{P}_{i}={e}_{i}\underset{j=1}{\overset{n}{\mathrm{\Σ}}}({G}_{\mathrm{ij}}{e}_{j}{B}_{\mathrm{ij}}{f}_{j})+{f}_{i}\underset{j=1}{\overset{n}{\mathrm{\Σ}}}({G}_{\mathrm{ij}}{f}_{j}+{B}_{\mathrm{ij}}{e}_{j})\\ {Q}_{i}={f}_{i}\underset{j=1}{\overset{n}{\mathrm{\Σ}}}({G}_{\mathrm{ij}}{e}_{j}{B}_{\mathrm{ij}}{f}_{j}){e}_{i}\underset{j=1}{\overset{n}{\mathrm{\Σ}}}({G}_{\mathrm{ij}}{f}_{j}+{B}_{\mathrm{ij}}{e}_{j})\end{array}\right.;$ Wherein, P
_{i}for the active power of active distribution network interior joint i, Q
_{i}for the reactive power of active distribution network interior joint i, e
_{i}for the voltage real component of active distribution network interior joint i, e
_{j}for the voltage real component of active distribution network interior joint j, f
_{i}for the voltage imaginary of active distribution network interior joint i, f
_{j}for the voltage imaginary of active distribution network interior joint j, G
_{ij}for the real component of branch admittance matrix, B
_{ij}for 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
_{it}for node i is at the voltage magnitude of moment t, V
_{min}for the lower voltage limit of active distribution network, V
_{max}for the upper voltage limit of active distribution network;
Described energystorage units access number constraint is: N
_{s}=N
_{set}; Wherein, N
_{s}for energystorage units access quantity, N
_{set}for set point;
Described system total load is constrained to: P
_{all, t}>=P
_{dG, t}; Wherein, P
_{all, t}for the total load of moment t active distribution network, P
_{dG, t}for the gross generation of moment t photovoltaic generation unit and wind power generation unit.
The described configuration capacity asking for energystorage units according to energystorage units operate power curve comprises:
Substep A1: the settingup time cycle is divided into some intervals;
Substep A2: the initial stateofcharge of energystorage units is set as energystorage units is full of 50% of the stateofcharge of electricity;
Substep A3: carry out charge and discharge control to energystorage units by energystorage units operate power curve, calculates the capacity C of each interval energystorage units
_{k}; K=1,2 ..., K, K are interval number;
Substep A4: make each interval maximum capacity
using each interval maximum capacity C as energystorage units configuration capacity.
The capacity of each interval energystorage units of described calculating adopts formula:
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 energystorage units, the capacity that S (t+ Δ t) is moment t+ Δ t energystorage units, P
_{s}t () is the power output of moment t energystorage units, P
_{s}(t+ Δ t) is the power output of moment t+ Δ t energystorage units.
The invention solves the capacity configuration problem of energystorage 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 energystorage units, by generating the optimizing operation power curve of energystorage units, the reasonable power extreme value of verification energystorage units and capacity configuration.
Accompanying drawing explanation
Fig. 1 is the integrated stand composition of the energystorage units capacity configuration being applied to active distribution network;
Fig. 2 is the flow chart of the energystorage 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 energystorage units, consider certain Capacity Margin simultaneously, draw the capacity configuration result of energystorage system.
Fig. 1 is the integrated stand composition of the energystorage units capacity configuration being applied to active distribution network.As shown in Figure 1, the overall architecture being applied to the energystorage 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 onposition 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 Runtime 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 microcapacitance 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 energystorage 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 chargedischarge electric power of energystorage system.For the setting of constraints, the main constraint of battery charge state (SOC) and the discharge and recharge Constraints of Equilibrium of energystorage system considering energystorage system discharge and recharge.The discharge and recharge Constraints of Equilibrium of energystorage system refers to, makes the discharge and recharge of system balance in settingup 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 energystorage 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 energystorage 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 energystorage 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 energystorage 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 energystorage units capacity configuration being applied to active distribution network.As shown in Figure 2, the energystorage 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 peakvalley 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 peakvalley difference minimization function is:
Min(max(P
_{1},P
_{2},...P
_{T})min(P
_{1},P
_{2},...P
_{T}))(1)
In formula (1), P
_{t}for the active power of moment t active distribution network, t=1,2 ..., T, T are the cycle of setting.
Load variance minimization function is:
In formula (2), P
_{t}for 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, t}for the load active power of moment t active distribution network, P
_{pV, t}for the active power of the photovoltaic generation unit of moment t active distribution network, P
_{wind, t}for the active power of the wind power generation unit of moment t active distribution network, P
_{storage, t}for the active power of the energystorage units of moment t active distribution network, P
_{average}for the average burden with power of system in setting cycle T and
Load smoothness optimization function is:
In formula (3), P
_{t}for 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:
In formula (4), N is the branch road sum of active distribution network, P
_{loss, kt}for branch road k is in the active loss of moment t, Q
_{loss, kt}for branch road k is at the reactive loss of moment t.
Voltage fluctuation minimization function is:
In formula (5), M is the node total number of active distribution network, V
_{it}for node i is at the voltage magnitude of moment t, V
_{n}for reference voltage.
Power supply reliability maximizes function:
In formula (6), P
_{s & DER, t}=P
_{s,t}+ P
_{dER, t}, P
_{s,t}for the power output of moment t energystorage units, P
_{dER, t}for the power output of moment t wind power generation unit and photovoltaic generation unit, T
_{survive}for continued power duration during active distribution network fault,
for the workload demand of moment t.
Constraints comprises energystorage units power constraint, energystorage units runs constraint, trend Constraints of Equilibrium, voltage retrain, energystorage units accesses number constraint and the constraint of system total load.
Energystorage units power constraint is:
P
_{max}≤P
_{S,t}≤P
_{max}(7)
In formula (7), P
_{s,t}for the power output of moment t energystorage units, P
_{max}for the chargedischarge electric power upper limit of energystorage units.
Energystorage units runs and is constrained to:
In formula (8), [t
_{a}, t
_{b}] be settingup time interval, P
_{ch}for the charge power of energystorage units in settingup time interval, P
_{dis}for the discharge power of energystorage units in settingup time interval, ε is setting balance index value.
Trend Constraints of Equilibrium is:
In formula (9), P
_{i}for the active power of active distribution network interior joint i, Q
_{i}for the reactive power of active distribution network interior joint i, e
_{i}for the voltage real component of active distribution network interior joint i, e
_{j}for the voltage real component of active distribution network interior joint j, f
_{i}for the voltage imaginary of active distribution network interior joint i, f
_{j}for the voltage imaginary of active distribution network interior joint j, G
_{ij}for the real component of branch admittance matrix, B
_{ij}for 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
_{it}for node i is at the voltage magnitude of moment t, V
_{min}for the lower voltage limit of active distribution network, V
_{max}for the upper voltage limit of active distribution network.
Energystorage units access number constraint is:
N
_{S}＝N
_{set}(11)
In formula (11), N
_{s}for energystorage units access quantity, N
_{set}for set point.
System total load is constrained to:
P
_{all,t}≥P
_{DG,t}(12)
In formula (12), P
_{all, t}for the total load of moment t active distribution network, P
_{dG, t}for the gross generation of moment t photovoltaic generation unit and wind power generation unit.
After determining target function and constraints, from abovementioned multiple target function, choose at least one target function and carry out objective optimization.When choosing two (containing) above target functions, objective optimization is multipleobjection optimization.
Step 3: solve the target function chosen and obtain energystorage 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 multispecies cooperative, effectively improves optimizing effect simultaneously.
When calculating particle rapidity, adopt following formula:
In formula (13),
for the particle rapidity of iteration the t+1 time,
for the particle rapidity of iteration the t time, p
_{id}for individual optimal solution, p
_{gd}for group optimal solution, c
_{1}and c
_{2}be setting constant, r
_{1}and r
_{2}for 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.
In formula (14), ω
_{start}for iteration initial inertia weight, ω
_{end}for iteration ends inertia weight, t is iterations, T
_{max}for iteration total degree.
By calculating the optimization of target function, the performance number of each moment energystorage units can be obtained, and then obtain energystorage units operate power curve.
Step 4: the configuration capacity asking for energystorage units according to energystorage units operate power curve.Comprise:
Substep A1: the settingup time cycle is divided into some intervals.
Substep A2: the initial stateofcharge of energystorage units is set as energystorage units is full of 50% of the stateofcharge of electricity.
Substep A3: carry out charge and discharge control to energystorage units by energystorage units operate power curve, calculates the capacity C of each interval energystorage units
_{k}; K=1,2 ..., K, K are interval number.
The capacity calculating each interval energystorage units adopts formula:
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 energystorage units, the capacity that S (t+ Δ t) is moment t+ Δ t energystorage units, P
_{s}t () is the power output of moment t energystorage units, P
_{s}(t+ Δ t) is the power output of moment t+ Δ t energystorage units.
Substep A4: make each interval maximum capacity
using each interval maximum capacity C as energystorage 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 substep 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
_{ch}for charging verification capacity, S
_{dis}for 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, windpowered 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, windpowered 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 energystorage system configuration capacity that different chargedischarge electric power is corresponding under different peakvalley difference optimum level is given in Figure 5.In this example, chargedischarge 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 energystorage 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 energystorage units operate power curve;
Step 4: the configuration capacity asking for energystorage units according to energystorage units operate power curve;
It is described that to choose different target functions be maximize function from load peakvalley 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 peakvalley 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, t}for the load active power of moment t active distribution network, P
_{pV, t}for the active power of the photovoltaic generation unit of moment t active distribution network, P
_{wind, t}for the active power of the wind power generation unit of moment t active distribution network, P
_{storage, t}for the active power of the energystorage units of moment t active distribution network, P
_{average}for 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, kt}for branch road k is in the active loss of moment t, Q
_{loss, kt}for 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
_{it}for node i is at the voltage magnitude of moment t, V
_{n}for reference voltage;
Described power supply reliability maximizes function:
wherein, P
_{s & DER, t}=P
_{s,t}+ P
_{dER, t}, P
_{s,t}for the power output of moment t energystorage units, P
_{dER, t}for the power output of moment t wind power generation unit and photovoltaic generation unit, T
_{survive}for continued power duration during active distribution network fault,
for the workload demand of moment t;
In abovementioned each target function, P
_{t}for the active power of moment t active distribution network, t=1,2 ..., T, T are the cycle of setting;
Described constraints comprises energystorage units power constraint, energystorage units runs constraint, trend Constraints of Equilibrium, voltage retrain, energystorage units accesses number constraint and the constraint of system total load;
Described energystorage units power constraint is :P
_{max}≤ P
_{s,t}≤ P
_{max}; Wherein, P
_{s,t}for the power output of moment t energystorage units, P
_{max}for the chargedischarge electric power upper limit of energystorage units;
Described energystorage units runs and is constrained to:
wherein, [t
_{a}, t
_{b}] be settingup time interval, P
_{ch}for the charge power of energystorage units in settingup time interval, P
_{dis}for the discharge power of energystorage units in settingup time interval, ε is setting balance index value;
Described trend Constraints of Equilibrium is:
$\left\{\begin{array}{c}{P}_{i}={e}_{i}\underset{j=1}{\overset{n}{\Σ}}({G}_{ij}{e}_{j}{B}_{ij}{f}_{j})+{f}_{i}\underset{j=1}{\overset{n}{\Σ}}({G}_{ij}{f}_{j}+{B}_{ij}{e}_{j})\\ {Q}_{i}={f}_{i}\underset{j=1}{\overset{n}{\Σ}}({G}_{ij}{e}_{j}{B}_{ij}{f}_{j}){e}_{i}\underset{j=1}{\overset{n}{\Σ}}({G}_{ij}{f}_{j}+{B}_{ij}{e}_{j})\end{array}\right.;$ Wherein, P
_{i}for the active power of active distribution network interior joint i, Q
_{i}for the reactive power of active distribution network interior joint i, e
_{i}for the voltage real component of active distribution network interior joint i, e
_{j}for the voltage real component of active distribution network interior joint j, f
_{i}for the voltage imaginary of active distribution network interior joint i, f
_{j}for the voltage imaginary of active distribution network interior joint j, G
_{ij}for the real component of branch admittance matrix, B
_{ij}for 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
_{it}for node i is at the voltage magnitude of moment t, V
_{min}for the lower voltage limit of active distribution network, V
_{max}for the upper voltage limit of active distribution network;
Described energystorage units access number constraint is: N
_{s}=N
_{set}; Wherein, N
_{s}for energystorage units access quantity, N
_{set}for set point;
Described system total load is constrained to: P
_{all, t}>=P
_{dG, t}; Wherein, P
_{all, t}for the total load of moment t active distribution network, P
_{dG, t}for 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 energystorage units according to energystorage units operate power curve comprises:
Substep A1: the settingup time cycle is divided into some intervals;
Substep A2: the initial stateofcharge of energystorage units is set as energystorage units is full of 50% of the stateofcharge of electricity;
Substep A3: carry out charge and discharge control to energystorage units by energystorage units operate power curve, calculates the capacity C of each interval energystorage units
_{k}; K=1,2 ..., K, K are interval number;
Substep A4: make each interval maximum capacity
using each interval maximum capacity C as energystorage units configuration capacity.
3. method according to claim 2, is characterized in that the capacity of each interval energystorage units of described calculating adopts formula:
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 energystorage units, the capacity that S (t+ Δ t) is moment t+ Δ t energystorage units, P
_{s}t () is the power output of moment t energystorage units, P
_{s}(t+ Δ t) is the power output of moment t+ Δ t energystorage units.
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