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 PDFInfo
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 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
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 238000009826 distribution Methods 0.000 title claims abstract description 100
 238000004146 energy storage Methods 0.000 claims description 128
 238000005457 optimization Methods 0.000 claims description 19
 238000010248 power generation Methods 0.000 claims description 18
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 238000003860 storage Methods 0.000 claims description 6
 230000002411 adverse Effects 0.000 abstract 1
 238000004422 calculation algorithm Methods 0.000 description 10
 230000001172 regenerating Effects 0.000 description 7
 239000002245 particles Substances 0.000 description 6
 238000004088 simulation Methods 0.000 description 5
 238000004364 calculation methods Methods 0.000 description 3
 238000000034 methods Methods 0.000 description 3
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 238000007599 discharging Methods 0.000 description 2
 238000005516 engineering processes Methods 0.000 description 2
 239000000203 mixtures Substances 0.000 description 2
 230000035699 permeability Effects 0.000 description 2
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 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 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
Technical field
The invention belongs to initiatively power distribution network control technology field, relate in particular to a kind of initiatively energystorage 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 largescale 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, windpowered electricity generation, need in system, introduce energystorage 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, windpowered electricity generation as far as possible, and in conjunction with the controllable such as cogeneration of heat and power, electric automobile.Energystorage units has played the function served as bridge of distributed energy and power load in active power distribution network, and at present comparatively ripe energystorage units is all energystorage battery, and its cost is higher, and therefore the allocation problem of energystorage units, becomes research emphasis.
This patent, for energystorage units access problem in active power distribution network, adopts energystorage units under different scenes to intend operation method, according to discharging and recharging power curve, obtains the reasonable disposition of energystorage units.
Summary of the invention
The object of the invention is to, a kind of initiatively energystorage 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 energystorage 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 energystorage 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 energystorage units operate power curve;
Step 4: the configuration capacity of asking for energystorage units according to energystorage units operate power curve.
It is described that to choose different target functions be to maximize function and choose at least one 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;
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 initiatively load active power of power distribution network of moment t, P
_{pV, t}for the initiatively active power of the photovoltaic generation unit of power distribution network of moment t, P
_{wind, t}for the initiatively active power of the wind power generation unit of power distribution network of moment t, P
_{storage, t}for the initiatively active power of the energystorage units of power distribution network of moment t, 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 power distribution network, P
_{loss, kt}for branch road k is at 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 sum of active power 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}continued power duration during for active distribution network failure,
for the workload demand of moment t;
In abovementioned each target function, P
_{t}for the initiatively active power of power distribution network of moment t, t=1,2 ..., T, T is the cycle of setting.
Described constraints comprises energystorage units power constraint, energystorage units operation constraint, trend Constraints of Equilibrium, voltage constraint, energystorage units access quantity 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 power upper limit that discharges and recharges of energystorage units;
Described energystorage units operation 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 for 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 node i in active power distribution network, Q
_{i}for the reactive power of node i in active power distribution network, e
_{i}for the voltage real component of node i in active power distribution network, e
_{j}for the voltage real component of node j in active power distribution network, f
_{i}for the voltage imaginary part component of node i in active power distribution network, f
_{j}for the voltage imaginary part component of node j in active power distribution network, G
_{ij}for the real component of branch admittance matrix, B
_{ij}for 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
_{it}for node i is at the voltage magnitude of moment t, V
_{min}for the lower voltage limit of active power distribution network, V
_{max}for the upper voltage limit of active power distribution network;
Described energystorage units access quantity is constrained to: 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 initiatively total load of power distribution network of moment t, P
_{dG, t}for the gross generation of moment t photovoltaic generation unit and wind power generation unit.
The described configuration capacity of 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 to energystorage units and is full of 50% of electric stateofcharge;
Substep A3: by energystorage units operate power curve, energystorage units is discharged and recharged to control, calculate the capacity C of each interval energystorage units
_{k}; K=1,2 ..., K, K is interval number;
Substep A4: make each interval maximum capacity
using each interval maximum capacity C as energystorage units configuration capacity.
The capacity of the each interval energystorage units of described calculating adopts formula:
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 energystorage units, and (t+ Δ is t) capacity of moment t+ Δ t energystorage units to S, P
_{s}(t) be the power output of moment t energystorage units, P
_{s}(t+ Δ is t) power output of moment t+ Δ t energystorage units.
The invention solves the capacity configuration problem of energystorage units in active power distribution network, to utilize energystorage 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 energystorage units, reasonable power extreme value and the capacity configuration of verification energystorage units.
Brief description of the drawings
Fig. 1 is applied to the initiatively integrated stand composition of the energystorage units capacity configuration of power distribution network;
Fig. 2 is applied to the initiatively flow chart of the energystorage 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 energystorage units, consider certain Capacity Margin simultaneously, draw the capacity configuration result of energystorage system.
Fig. 1 is applied to the initiatively integrated stand composition of the energystorage units capacity configuration of power distribution network.As shown in Figure 1, the overall architecture that is applied to the initiatively energystorage 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 onposition 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 Runtime 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 microelectrical 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 energystorage 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 energystorage 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 energystorage system discharges and recharges and energystorage system.The Constraints of Equilibrium that discharges and recharges of energystorage system refers to, makes the balance that discharges and recharges of system in settingup 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 energystorage 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 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 realizing in active power distribution network based on energystorage 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 energystorage 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 energystorage units capacity configuration of power distribution network.As shown in Figure 2, the initiatively energystorage 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 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 maximize 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 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:
In formula (2), P
_{t}for 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, t}for the initiatively load active power of power distribution network of moment t, P
_{pV, t}for the initiatively active power of the photovoltaic generation unit of power distribution network of moment t, P
_{wind, t}for the initiatively active power of the wind power generation unit of power distribution network of moment t, P
_{storage, t}for the initiatively active power of the energystorage units of power distribution network of moment t, 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 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:
In formula (4), N is the branch road sum of active power distribution network, P
_{loss, kt}for branch road k is at 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 sum of active power 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}continued power duration during for active distribution network failure,
for the workload demand of moment t.
Constraints comprises energystorage units power constraint, energystorage units operation constraint, trend Constraints of Equilibrium, voltage constraint, energystorage units access quantity 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 power upper limit that discharges and recharges of energystorage units.
Energystorage units operation 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 for setting balance index value.
Trend Constraints of Equilibrium is:
In formula (9), P
_{i}for the active power of node i in active power distribution network, Q
_{i}for the reactive power of node i in active power distribution network, e
_{i}for the voltage real component of node i in active power distribution network, e
_{j}for the voltage real component of node j in active power distribution network, f
_{i}for the voltage imaginary part component of node i in active power distribution network, f
_{j}for the voltage imaginary part component of node j in active power distribution network, G
_{ij}for the real component of branch admittance matrix, B
_{ij}for 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
_{it}for node i is at the voltage magnitude of moment t, V
_{min}for the lower voltage limit of active power distribution network, V
_{max}for the upper voltage limit of active power distribution network.
Energystorage units access quantity is constrained to:
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 initiatively total load of power distribution network of moment t, P
_{dG, t}for the gross generation of moment t photovoltaic generation unit and wind power generation unit.
Determine after target function and constraints, from abovementioned 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 multipleobjection optimization.
Step 3: solve the target function of choosing and obtain energystorage 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, multispecies cooperative, has effectively improved optimizing effect simultaneously.
In the time 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, 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.
In formula (14), ω
_{start}for iteration initial inertia weight, ω
_{end}for iteration stops inertia weight, t is iterations, T
_{max}for iteration total degree.
Calculate by the optimization to target function, can obtain the performance number of each moment energystorage units, and then obtain energystorage units operate power curve.
Step 4: the configuration capacity of 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 to energystorage units and is full of 50% of electric stateofcharge.
Substep A3: by energystorage units operate power curve, energystorage units is discharged and recharged to control, calculate the capacity C of each interval energystorage units
_{k}; K=1,2 ..., K, K is interval number.
The capacity that calculates each interval energystorage units adopts formula:
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 energystorage units, and (t+ Δ is t) capacity of moment t+ Δ t energystorage units to S, P
_{s}(t) be the power output of moment t energystorage units, P
_{s}(t+ Δ is t) 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, the peak that can charge is set to 80%, and charging minimum point is set to 20%, and the configuration capacity that substep 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
_{ch}for charging verification capacity, S
_{dis}for 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 windpowered 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, windpowered 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 energystorage system configuration capacities that discharge and recharge power correspondence under different peakvalley 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 energystorage 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 energystorage units operate power curve;
Step 4: the configuration capacity of asking for energystorage units according to energystorage 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 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;
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 initiatively load active power of power distribution network of moment t, P
_{pV, t}for the initiatively active power of the photovoltaic generation unit of power distribution network of moment t, P
_{wind, t}for the initiatively active power of the wind power generation unit of power distribution network of moment t, P
_{storage, t}for the initiatively active power of the energystorage units of power distribution network of moment t, 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 power distribution network, P
_{loss, kt}for branch road k is at 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 sum of active power 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}continued power duration during for active distribution network failure,
for the workload demand of moment t;
In abovementioned each target function, P
_{t}for 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 energystorage units power constraint, energystorage units operation constraint, trend Constraints of Equilibrium, voltage constraint, energystorage units access quantity 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 power upper limit that discharges and recharges of energystorage units;
Described energystorage units operation 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 for 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 node i in active power distribution network, Q
_{i}for the reactive power of node i in active power distribution network, e
_{i}for the voltage real component of node i in active power distribution network, e
_{j}for the voltage real component of node j in active power distribution network, f
_{i}for the voltage imaginary part component of node i in active power distribution network, f
_{j}for the voltage imaginary part component of node j in active power distribution network, G
_{ij}for the real component of branch admittance matrix, B
_{ij}for 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
_{it}for node i is at the voltage magnitude of moment t, V
_{min}for the lower voltage limit of active power distribution network, V
_{max}for the upper voltage limit of active power distribution network;
Described energystorage units access quantity is constrained to: 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 initiatively total load of power distribution network of moment t, P
_{dG, t}for 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 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 to energystorage units and is full of 50% of electric stateofcharge;
Substep A3: by energystorage units operate power curve, energystorage units is discharged and recharged to control, calculate the capacity C of each interval energystorage units
_{k}; K=1,2 ..., K, K is interval number;
Substep A4: make each interval maximum capacity
using each interval maximum capacity C as energystorage units configuration capacity.
5. method according to claim 4, is characterized in that the capacity of the each interval energystorage units of described calculating adopts formula:
$S(t+\mathrm{\Δt})=\left\{\begin{array}{c}S\left(t\right)+{\∫}_{t}^{t+\mathrm{\Δt}}\left{P}_{S}\left(t\right)\right\mathrm{dt},{P}_{S}\left(t\right)\·{P}_{S}(t+\mathrm{\Δt})>0\\ \begin{array}{cc}0,& {P}_{S}\left(t\right)\·{P}_{S}(t+\mathrm{\Δt})\end{array}\≤0\end{array}\right.;$
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 energystorage units, and (t+ Δ is t) capacity of moment t+ Δ t energystorage units to S, P
_{s}(t) be the power output of moment t energystorage units, P
_{s}(t+ Δ is t) power output of moment t+ Δ t energystorage units.
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