CN102104251B  Microgrid realtime energy optimizing and scheduling method in parallel running mode  Google Patents
Microgrid realtime energy optimizing and scheduling method in parallel running mode Download PDFInfo
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 CN102104251B CN102104251B CN201110045064XA CN201110045064A CN102104251B CN 102104251 B CN102104251 B CN 102104251B CN 201110045064X A CN201110045064X A CN 201110045064XA CN 201110045064 A CN201110045064 A CN 201110045064A CN 102104251 B CN102104251 B CN 102104251B
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 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
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Abstract
The invention discloses a microgrid realtime energy optimizing and scheduling method in a parallel running mode. The method comprises the following steps of: firstly, dividing 24 hours in a day into a peak period of time, a general period of time and a trough period of time according to the load condition of a large power grid; and then, monitoring the working state of an accumulator in a microgrid in real time in the realtime process of the microgrid and adopting different energy optimizing strategies according to different periods of time and different working states of the accumulator so as to confirm the active power output of various schedulable micro power supplies, the charging and discharging power of the accumulator and scheduling instructions of the active power interactive with the power grid and the passive power of a passive adjustable power supply in the microgrid. The microgrid suitable for the invention comprises a reproducible energy generator, the schedulable micro power supplies and the accumulator. The invention not only can improve the economy and the reliability of the microgrid, but also can help the great power grid to cut peaks and fill troughs and is favorable to prolonging the service life of the accumulator.
Description
Technical field
The present invention relates to a kind of little Electric Power Network Planning, Operation and management, relate in particular to the realtime energyoptimised dispatching method of little electrical network under a kind of pattern that is incorporated into the power networks.
Background technology
The set that little electrical network is comprised of various distributed power sources, energystorage units, load and control protection system is by the cooperation between relative control apparatus, for load provides higher reliability and highquality electric energy.Little electrical network replenishes because the factors such as its environmental friendliness, construction cost are low have become the useful of large electrical network, is more and more paid attention to and studies.Guaranteeing on the basis of little power network safety operation, the operation of the economic optimization of little electrical network is an important content in the energy management research.
At present, doing some researchs aspect the microgrid energy Optimized Operation both at home and abroad, mainly and environmental benefit minimum from little operation of power networks cost set up energy optimizing model aspect best in document [1]～[5], by each active power that can dispatch little power supply of Optimized model solving result scheduling, optimization is not but done in its reactive power output, and all do not consider the network configuration of little electrical network self, so that the optimum results shortcoming is actual, in addition, use for the storage battery in little electrical network is also just discharged without the cycle charging of foundation, accumulator cell charging and discharging is not optimized, thereby does not take full advantage of the economy that storage battery improves little operation of power networks.Pertinent literature:
[1] fourth is bright, Zhang Yingyuan, and the U.S. qin of cogongrass, poplar is Liu Xiaoping. the decline Steady state modeling of network system of centralized control is optimized [J] with operation. Automation of Electric Systems, 2009,33 (24): 7882.
[2] Chen Dawei, Zhu Guiping. little network load optimization distributes [J]. Automation of Electric Systems, 2010,34 (20): 4548.
[3]Mohamed?FA，Koivo?HN.MicroGrid?Online?Management?andBalancing?Using?Multiobjective?Optimization.2007?IEEE?LAUSANNEPOWERTECH，VOLS?15，PP：639644。
[4] ox inscription, Huang Wei, Guo Jiahuan, Su Ling. the Study on economical operation when little electrical network is incorporated into the power networks [J]. electric power network technique, 2010,34 (11): 3942.
[5]Mohamed?F.and?H?Koivo.Online?Management?of?MicroGrid?with?BatteryStorage?Using?Multiobjective?Optimization，”the?first?International?Conferenceon?Power?Engineering，Energy?and?Electrical?Drives(POWERENG07)，1214?April2007，Setubal，Portugal.231236。
Summary of the invention
Overcharge or overdischarge state in order to guarantee that little electrical network internal battery in use is not in, give full play to again simultaneously the effect of little electrical network internal battery, to improve little operation of power networks economy, and help large electrical network to carry out " peak load shifting ", be different from existing microgrid energy Optimization Scheduling, the purpose of this invention is to provide the realtime energyoptimised dispatching method of little electrical network under a kind of pattern that is incorporated into the power networks.
The technical solution used in the present invention is to comprise the steps:
1) according to the load condition of large electrical network with being divided into peak period, at ordinary times section, three kinds of periods of paddy period in one day 24 hours, if large electrical network adopts Peakvalley TOU power price, then just according to the determined peak of tou power price period, at ordinary times section and the division of paddy period;
2) in the real time execution process of little electrical network, constantly determine the current residing period in each scheduling, monitor the stateofcharge SOC of the storage battery in little electrical network, measure active power and the reactive power of each node load, measure active power of output and the reactive power of renewable energy power generation;
3) if current be in the paddy period or at ordinary times the section, then further judge whether the stateofcharge SOC of current storage battery satisfies SOC＜SOC
^{Max}, be in the peak period as current, then further judge whether the stateofcharge of current storage battery satisfies SOC＞SOC
^{Min}
4) if current paddy period or the section at ordinary times of being in and do not satisfy SOC＜SOC
^{Max}, determine that then storage battery can discharge, and be optimized 1, satisfy SOC＜SOC
^{Max}, determine that then storage battery is not only chargeable but also can discharge, and be optimized 2; If currently be in the peak period, and satisfy SOC＞SOC
^{Min}, determine that then storage battery can discharge, and be optimized 3, do not satisfy SOC＞SOC
^{Min}, then determine with firm power charge in batteries, and be optimized 4;
5) by step 4) in optimum results obtain active power and the reactive power output order of each little power supply, then it is sent to each little power supply, each little power supply is exported corresponding active power and reactive power according to instruction.
Described step 2) scheduling in refers to that constantly the realtime energyoptimised management of little electrical network is in little electrical network real time execution process, take per 5 minutes or 15 minutes as a time interval little electrical network is carried out the primary energy Optimized Operation.
Described step 3) the storage battery charge state SOC in is the parameter that reflection storage battery dump energy accounts for the ratio of its total capacity, is defined as:
C
_{Net}storage battery dump energy Ah;
Cstorage battery total capacity, Ah;
Ibattery discharging electric current, A;
Corresponding SOC
^{Max}, SOC
^{Min}For the bound value of storage battery charge state, get SOC
^{Max}=0.9, SOC
^{Min}=0.5.
Described step 4) optimization 1 in refers to find the solution the energy optimizing model 1 of setting up; energy optimizing model 1 is minimum as target take little operation of power networks cost; in little electrical network each can dispatch the startupshutdown state of little power supply, meritorious output that each can dispatch little power supply, with the reactive power in the discharge power of greatly the dealing electrical power between the electrical network, storage battery and each idle output adjustable source as optimized variable; energy optimizing model 1 is the mixed integer nonlinear programming problem, its target function:
In the formula: λ
_{Bat}=(σ P
_{Bat}) Δ t
The tsystem operation period;
Can dispatch little power supply number in the isystem;
Can dispatch total number of little power supply in the Nsystem;
can dispatch the state of little power supply at t moment i platform, 0 expression is in shut down condition, and 1 expression is in running status;
whether little electrical network is from large electrical network power purchase constantly at t, and 0 expression is no, and 1 expression is;
whether little electrical network is to large electrical network sale of electricity constantly at t, and 0 expression is no, and 1 expression is;
C
_{f}can dispatch the energy consumption cost of little power supply;
C
_{OM}can dispatch the operation expense of little power supply;
can dispatch the startup cost of little power supply;
λ
_{Bat}designed accumulator cell charging and discharging cost function;
C
_{P}little electrical network is from the expenditure of large electrical network power purchase;
I
_{S}little electrical network is to the income of large electrical network sale of electricity;
Δ toptimizes the time interval;
σdesigned coefficient;
P
_{Bat}The discharge power ofstorage battery;
Its constraints:
(a) trend constraints
In the formula: P
_{i}, Q
_{i}each node injects active power and reactive power;
e
_{i}, f
_{i}Real part and the imaginary part of each node voltage ofusefulness complex representation;
C
_{Ij}, B
_{Ij}Real part and the imaginary part ofi node and j node admittance element;
Total node number in the little electrical network of n.
(b) can dispatch little power supply capacity constraint
In the formula:
Di i platform can be dispatched the active power output of little power supply in the t period;
Di i platform can be dispatched maximum active power output limit value and the minimum active power output limit value of little power supply;
(c) the discharge active power of storage battery constraint
In the formula: P
_{Bat}The discharge power ofstorage battery;
Butat the maximum discharge power limit value of t storage battery constantly the time;
(d) heap(ed) capacity constraint that can be mutual between little electrical network and large electrical network, this may be the supply and demand agreement reached between them or the physical transfer capacity limit value of interconnection
In the formula:
little electrical network is from the active power of large electrical network power purchase;
little electrical network is to the active power of large electrical network sale of electricity;
little electrical network is from the maximum active power limit value of large electrical network power purchase;
little electrical network is to the maximum active power limit value of large electrical network sale of electricity;
(e) can dispatch the shortest continuous operating time and the constraint of the shortest continuous idle time of little power supply
In the formula:
can dispatch the state of little power supply at t moment i platform, 0 expression is in shut down condition, and 1 expression is in running status;
t1 constantly the i platform can dispatch the continuous operating time of little power supply;
in the t1 i platform continuous idle time that can dispatch little power supply constantly;
MRTi platform can be dispatched the minimum continuous operating time of little power supply;
MSTi platform can be dispatched the minimum continuously idle time of little power supply;
(f) can dispatch little power supply active power exporting change rate restriction
In the formula:
Di i platform can be dispatched the active power output of little power supply in the t period;
Δ P
_{U}can dispatch little this scheduling of power supply moment power with respect to upper one constantly increased power restriction;
Δ P
_{D}can dispatch little this scheduling of power supply moment power with respect to upper one constantly power reduction restriction;
(g) buy and sell electric mutual exclusive restrict from large electrical network
(h) the idle output constraint in reactive power output adjustable source
In the formula:
Di i platform reactive power output adjustable source is in t reactive power output constantly;
The maximum reactive power output limit value inDi i platform reactive power output adjustable source.
Described step 4) optimization 2 in refers to find the solution the energy optimizing model 2 of setting up; energy optimizing model 2 is minimum as target take little operation of power networks cost; so that each can dispatch the startupshutdown state of little power supply in little electrical network; the meritorious output that each can dispatch little power supply; and the dealing electrical power between the large electrical network; the reactive power in the discharge power of storage battery and each idle output adjustable source is optimized variable; energy optimizing model 2 is the mixed integer nonlinear programming problem; its target function is identical with energy optimizing model 1; its constraints and energy optimizing model 1 are basic identical; just " (c) the discharge active power of the storage battery constraint " in the constraints changes, and " (c) the discharge active power of storage battery constraint " in the constraints of energy optimizing model 2 is:
In the formula: P
_{Bat}The discharge power ofstorage battery; P
_{Bat}＜0, represent to charge in batteries P
_{Bat}＞0, expression is to battery discharging;
The maximum chargeable power ofstorage battery;
Butstorage battery maximum discharge power.
Described step 4) optimization 3 in refers to find the solution the energy optimizing model 3 of setting up; energy optimizing model 3 is minimum as target take little operation of power networks cost; so that each can dispatch the startupshutdown state of little power supply in little electrical network; the meritorious output that each can dispatch little power supply; and the dealing electrical power between the large electrical network; the reactive power in the discharge power of storage battery and each idle output adjustable source is optimized variable; energy optimizing model 3 is the mixed integer nonlinear programming problem; its constraints is identical with energy optimizing model 1; its target function is slightly different from energy optimizing model 1, and difference is the λ in the target function of energy optimizing model 3
_{Bat}With the target function of energy optimizing model 1 in λ
_{Bat}Item designs differently, and the target function of energy optimizing model 3 is:
In the formula: λ
_{Bat}=δ P
_{Dh}Δ t
δ＝a
_{1}+a
_{2}·dSOC+a
_{3}·P
_{dh}+a
_{4}·dSOC·P
_{dh}+a
_{5}·dSOC
^{2}
dSOC＝SOCSOC
^{min}
The tsystem operation period;
Can dispatch little power supply number in the isystem;
Can dispatch total number of little power supply in the Nsystem;
can dispatch the state of little power supply at t moment i platform, 0 expression is in shut down condition, and 1 expression is in running status;
whether little electrical network is from large electrical network power purchase constantly at t, and 0 expression is no, and 1 expression is;
whether little electrical network is to large electrical network sale of electricity constantly at t, and 0 expression is no, and 1 expression is;
C
_{f}can dispatch the energy consumption cost of little power supply;
C
_{OM}can dispatch the operation expense of little power supply;
can dispatch the startup cost of little power supply;
λ
_{Bat}designed accumulator cell charging and discharging cost function;
C
_{P}little electrical network is from the expenditure of large electrical network power purchase;
I
_{S}little electrical network is to the income of large electrical network sale of electricity;
Δ toptimizes the time interval;
P
_{Bat}The discharge power ofstorage battery;
The stateofcharge of SOCstorage battery;
SOC
^{Min}The stateofcharge lower limit ofstorage battery;
a
_{1}, a
_{2}, a
_{3}, a
_{4}, a
_{5}designed coefficient.
Described step 4) optimization 4 in refers to find the solution the energy optimizing model 4 of setting up; energy optimizing model 4 is minimum as target take little operation of power networks cost; in little electrical network each can dispatch the startupshutdown state of little power supply, meritorious output that each can dispatch little power supply, with the reactive power in the discharge power of greatly the dealing electrical power between the electrical network, storage battery and each idle output adjustable source as optimized variable; energy optimizing model 4 is the mixed integer nonlinear programming problem; with respect to energy optimizing model 1, there is not λ in the target function of energy optimizing model 4
_{Bat}, the target function of energy optimizing model 4 is:
In the formula: the tsystem operation period;
Can dispatch little power supply number in the isystem;
Can dispatch total number of little power supply in the Nsystem;
can dispatch the state of little power supply at t moment i platform, 0 expression is in shut down condition, and 1 expression is in running status;
whether little electrical network is from large electrical network power purchase constantly at t, and 0 expression is no, and 1 expression is;
whether little electrical network is to large electrical network sale of electricity constantly at t, and 0 expression is no, and 1 expression is;
C
_{f}can dispatch the energy consumption cost of little power supply;
C
_{OM}can dispatch the operation expense of little power supply;
can dispatch the startup cost of little power supply;
λ
_{Bat}designed accumulator cell charging and discharging cost function;
C
_{P}little electrical network is from the expenditure of large electrical network power purchase;
I
_{S}little electrical network is to the income of large electrical network sale of electricity;
The constraints of the constraints of energy optimizing model 4 and energy optimizing model 1 is basic identical, does not just have " (c) the discharge active power of storage battery constraint " this item constraint in the constraints of energy optimizing model 4.
Described step 4) in firm power to charge in batteries, and be optimized 4 and refer to that the power that discharges and recharges of storage battery does not participate in optimizing in 4 optimizing, and with firm power to charge in batteries, this moment, storage battery was equivalent to a constant load, and will contribute to the firm power of charge in batteries in the load at little electrical network network node place at storage battery place and go, and to the constant charge power of storage battery be
In the formula: I
_{Bat_max_ch}The maximum charging current that allows ofstorage battery, specification is given by producing;
accumulator voltage.
Described step 5) optimum results in refers to by solution procedure 4) in optimization 1, optimize 2, optimize 3 and optimize the result that 4 related energy optimizing model 1, energy optimizing model 2, energy optimizing model 3 and energy optimizing models 4 obtain, comprise in little electrical network each can dispatch the startupshutdown state of little power supply, meritorious output valve that each can dispatch little power supply, with large dealing electrical power value, the discharge power value of storage battery and the reactive power value in each idle output adjustable source between the electrical network.
The present invention compares with background technology, and the beneficial effect that has is:
1) traditional microgrid energy Optimized Operation is not optimized discharging and recharging of storage battery, and the present invention is by in discharge and recharge in target function that cost function count Optimized model different to battery designs of different periods, so that the paddy of large electrical network at ordinary times the little electrical network of section buy electricity with low electricity price from large electrical network and come charge in batteries, and allow battery discharging and sell large electrical network with higher electricity price at little electrical network of peak period, both improved thus the onroad efficiency of little electrical network, and helped again large electrical network is carried out " peak load shifting ".
2) traditional microgrid energy Optimized Operation has only been optimized the active power output that respectively can dispatch little power supply, and do not consider via net loss, the present invention has considered the network configuration of little electrical network self, so that considered via net loss in optimizing, and with the idle output adjustable source (power supply that is connected with AC network by converters, the output of its reactive power also is adjustable) reactive power also as optimized variable, so that the active power output of each power supply output and reactive power output reach total optimization, optimum results is actual, makes little operation of power networks more economically.
3) the battery discharging cost function in the target function of peak energy optimizing model during the period is designed to the function of storage battery charge state and discharge power, when storage battery charge state less (but greater than storage battery charge state lower limit SOC
^{Min}) time this cost function value larger, when discharge power is larger, this cost function value is larger, thereby the guiding storage battery hour reduces discharge power at stateofcharge, make storage battery keep at any time certain charge capacity, provide urgent power to support in the time of transferring lonely network operation to for little electrical network, improved little operation of power networks reliability, can make again the stateofcharge of storage battery maintain all the time SOC simultaneously
^{Min}And SOC
^{Max}Between, prevented overcharge of a battery or overdischarge, be conducive to prolong the useful life of storage battery.
Description of drawings
Fig. 1 is the realtime energyoptimised scheduling flow figure of little electrical network.
Fig. 2 is the little electrical network exemplary plot of embodiment.
Fig. 3 adopts the present invention that little electrical network is carried out each power supply that realtime energyoptimised scheduling obtains at intraday active power distribution diagram.
Fig. 4 adopts the present invention that little electrical network is carried out each power supply that realtime energyoptimised scheduling obtains at intraday reactive power distribution diagram.
Fig. 5 adopts the present invention that little electrical network is carried out storage battery that realtime energyoptimised scheduling obtains at intraday stateofcharge SOC variation diagram.
Embodiment
The invention will be further described below in conjunction with drawings and Examples.
As shown in Figure 1, the present invention includes following steps:
1) according to the load condition of large electrical network with being divided into peak period, at ordinary times section, three kinds of periods of paddy period in one day 24 hours, if large electrical network adopts Peakvalley TOU power price, then just according to the determined peak of tou power price period, at ordinary times section and the division of paddy period;
2) in the real time execution process of little electrical network, constantly determine the current residing period in each scheduling, monitor the stateofcharge SOC of the storage battery in little electrical network, measure active power and the reactive power of each node load, measure active power of output and the reactive power of renewable energy power generation (photovoltaic generation and wind power generation);
3) if current be in the paddy period or at ordinary times the section, then further judge whether the stateofcharge SOC of current storage battery satisfies SOC＜SOC
^{Max}, be in the peak period as current, then further judge whether the stateofcharge of current storage battery satisfies SOC＞SOC
^{Min}
4) if current paddy period or the section at ordinary times of being in and do not satisfy SOC＜SOC
^{Max}, determine that then storage battery can discharge, and be optimized 1, satisfy SOC＜SOC
^{Max}, determine that then storage battery is not only chargeable but also can discharge, and be optimized 2; If currently be in the peak period, and satisfy SOC＞SOC
^{Min}, determine that then storage battery can discharge, and be optimized 3, do not satisfy SOC＞SOC
^{Min}, then determine with firm power charge in batteries, and be optimized 4;
5) by step 4) in optimum results obtain active power and the reactive power output order of each little power supply, then it is sent to each little power supply, each little power supply is exported corresponding active power and reactive power according to instruction.
Described step 3) the storage battery charge state SOC in is the parameter that reflection storage battery dump energy accounts for the ratio of its total capacity, is defined as:
C
_{Net}storage battery dump energy Ah;
Cstorage battery total capacity, Ah;
Ibattery discharging electric current, A;
Corresponding SOC
^{Max}, SOC
^{Min}For the bound value of storage battery charge state, get SOC
^{Max}=0.9, SOC
^{Min}=0.5.Overcharge of a battery or overdischarge all can be shortened its useful life, by judging storage battery charge state and SOC
^{Max}, SOC
^{Min}Between relation determine next link, be in order to allow storage battery remain that in charge and discharge process its stateofcharge SOC is at SOC
^{Min}With SOC
^{Max}Between, to prevent overcharge of a battery or overdischarge.Charge in batteries to its stateofcharge reaches at 0.9 o'clock and will produce the gassing reflection, continues charging and will lose service lifetime of accumulator, can make SOC for this reason
^{Max}=0.9; DOD is the battery technology parameter that producer provides, and refers to the depth of discharge of storage battery, but namely storage battery maximum discharge capacity accounts for the ratio of its total capacity, is generally 0.6～0.7, and SOC can be arranged
^{Min}=1DOD, but the little electrical network that is incorporated into the power networks will prevent to transfer to the situation of the autonomous operation of lonely net when large electric network fault, need in this case storage battery and other little power supply that participates in voltagefrequency control to support for lonely net provides urgent power, therefore requirement storage battery when being incorporated into the power networks should be kept certain charge capacity at any time, so that storage battery can in time discharge when transferring lonely net to, remedy the power shortage because causing with large electrical breakdown, for this reason, with SOC
^{Min}Set slightly greatlyr than 1DOD, get SOC
^{Min}=0.5.
Described step 4) optimization 1 in refers to find the solution the energy optimizing model 1 of setting up; energy optimizing model 1 is minimum as target take little operation of power networks cost; so that each can dispatch little power supply (such as miniature gas turbine in little electrical network; fuel cell; diesel engine generator etc.) startupshutdown state; the meritorious output that each can dispatch little power supply; and the dealing electrical power between the large electrical network; the discharge power of storage battery be connected the idle output adjustable source (power supply that is connected with AC network by converters; its reactive power that outputs to AC network also can be regulated; such as large electrical network; fuel cell; miniature gas turbine; storage battery; photovoltaic cell; winddriven generator etc.) reactive power is optimized variable; energy optimizing model 1 is the mixed integer nonlinear programming problem, its target function:
In the formula: λ
_{Bat}=(σ P
_{Bat}) Δ t
The tsystem operation period;
Can dispatch little power supply number in the isystem;
Can dispatch total number of little power supply in the Nsystem;
can dispatch the state of little power supply at t moment i platform, 0 expression is in shut down condition, and 1 expression is in running status;
whether little electrical network is from large electrical network power purchase constantly at t, and 0 expression is no, and 1 expression is;
whether little electrical network is to large electrical network sale of electricity constantly at t, and 0 expression is no, and 1 expression is;
C
_{f}can dispatch the energy consumption cost of little power supply;
C
_{OM}can dispatch the operation expense of little power supply;
can dispatch the startup cost of little power supply;
λ
_{Bat}designed accumulator cell charging and discharging cost function;
C
_{P}little electrical network is from the expenditure of large electrical network power purchase;
I
_{S}little electrical network is to the income of large electrical network sale of electricity;
Δ toptimizes the time interval;
σdesigned coefficient;
P
_{Bat}The discharge power ofstorage battery;
Its constraints:
(a) trend constraints
In the formula: P
_{i}, Q
_{i}each node injects active power and reactive power;
e
_{i}, f
_{i}Real part and the imaginary part of each node voltage ofusefulness complex representation;
G
_{Ij}, B
_{Ij}Real part and the imaginary part ofi node and j node admittance element;
Total node number in the little electrical network of n.
(b) can dispatch little power supply capacity constraint
In the formula:
Di i platform can be dispatched the active power output of little power supply in the t period;
Di i platform can be dispatched maximum active power output limit value and the minimum active power output limit value of little power supply;
(c) the discharge active power of storage battery constraint
In the formula:
${P}_{\mathrm{dh}\_\mathrm{max}}^{t}=\mathrm{min}(\frac{(\mathrm{SO}{C}_{t}\mathrm{SO}{C}^{\mathrm{min}})\·{C}_{\mathrm{batt}}^{\′}}{\mathrm{\Δt}},{I}_{\mathrm{bat}\_\mathrm{max}\_\mathrm{dh}})\·{U}_{\mathrm{batt}}^{t}$
allow the discharge power value in the maximum of t storage battery during the moment;
SOC
_{t}The stateofcharge of the storage battery thatscheduling monitors constantly;
SOC
^{Min}The storage battery charge state lower limit ofsetting;
C '
_{Batt}battery capacity;
I
_{Bat_max_dh}The maximum discharging current that allows ofstorage battery, specification is given by producing;
accumulator voltage;
Δ tscheduling time interval;
P
_{Bat}The discharge power ofstorage battery;
(d) heap(ed) capacity constraint that can be mutual between little electrical network and large electrical network, this may be the supply and demand agreement reached between them or the physical transfer capacity limit value of interconnection
In the formula:
little electrical network is from the active power of large electrical network power purchase;
little electrical network is to the active power of large electrical network sale of electricity;
little electrical network is from the maximum active power limit value of large electrical network power purchase;
little electrical network is to the maximum active power limit value of large electrical network sale of electricity;
(e) can dispatch the shortest continuous operating time and the constraint of the shortest continuous idle time of little power supply
In the formula:
can dispatch the state of little power supply at t moment i platform, 0 expression is in shut down condition, and 1 expression is in running status;
t1 constantly the i platform can dispatch the continuous operating time of little power supply;
in the t1 i platform continuous idle time that can dispatch little power supply constantly;
MRTi platform can be dispatched the minimum continuous operating time of little power supply;
MSTi platform can be dispatched the minimum continuously idle time of little power supply;
(f) can dispatch little power supply active power exporting change rate restriction
In the formula:
Di i platform can be dispatched the active power output of little power supply in the t period;
Δ P
_{U}can dispatch little this scheduling of power supply moment power with respect to upper one constantly increased power restriction;
Δ P
_{D}can dispatch little this scheduling of power supply moment power with respect to upper one constantly power reduction restriction;
(g) buy and sell electric mutual exclusive restrict from large electrical network
(h) the idle output constraint in reactive power output adjustable source
In the formula:
Di i platform reactive power output adjustable source is in t reactive power output constantly;
The maximum reactive power output limit value inDi i platform reactive power output adjustable source.
Described step 4) optimization 2 in refers to find the solution the energy optimizing model 2 of setting up; energy optimizing model 2 is minimum as target take little operation of power networks cost; so that each can dispatch the startupshutdown state of little power supply in little electrical network; the meritorious output that each can dispatch little power supply; and the dealing electrical power between the large electrical network; the reactive power in the discharge power of storage battery and each idle output adjustable source is optimized variable; energy optimizing model 2 is the mixed integer nonlinear programming problem; its target function is identical with energy optimizing model 1; its constraints and energy optimizing model 1 are basic identical; just " (c) the discharge active power of the storage battery constraint " in the constraints changes, and " (c) the discharge active power of storage battery constraint " in the constraints of energy optimizing model 2 is:
In the formula:
${P}_{\mathrm{ch}\_\mathrm{max}}^{t}=\mathrm{min}(\frac{({\mathrm{SOC}}^{\mathrm{max}}{\mathrm{SOC}}_{t})\·{C}_{\mathrm{batt}}^{\′}}{\mathrm{\Δt}},{I}_{\mathrm{bat}\_\mathrm{max}\_\mathrm{ch}})\·{U}_{\mathrm{batt}}^{t}$
P
_{Bat}The discharge power ofstorage battery; P
_{Bat}＜0, represent to charge in batteries P
_{Bat}＞0, expression is to battery discharging;
The maximum ofstorage battery allows charge power;
The maximum ofstorage battery allows discharge power;
SOC
_{t}The stateofcharge of the storage battery thatscheduling monitors constantly;
SOC
^{Max}The storage battery charge state upper limit ofsetting;
SOC
^{Min}The storage battery charge state lower limit ofsetting;
C '
_{Batt}battery capacity;
I
_{Bat_max_dh}The maximum discharging current that allows ofstorage battery, specification is given by producing;
I
_{Bat_max_ch}The maximum charging current that allows ofstorage battery, specification is given by producing;
accumulator voltage;
Δ tscheduling time interval.
Described step 4) optimization 3 in refers to find the solution the energy optimizing model 3 of setting up; energy optimizing model 3 is minimum as target take little operation of power networks cost; so that each can dispatch the startupshutdown state of little power supply in little electrical network; the meritorious output that each can dispatch little power supply; and the dealing electrical power between the large electrical network; the reactive power in the discharge power of storage battery and each idle output adjustable source is optimized variable; energy optimizing model 3 is the mixed integer nonlinear programming problem; its constraints is identical with energy optimizing model 1; its target function is slightly different from energy optimizing model 1, and difference is the λ in the target function of energy optimizing model 3
_{Bat}With the target function of energy optimizing model 1 in λ
_{Bat}Item designs differently, and the target function of energy optimizing model 3 is:
In the formula: λ
_{Bat}=δ P
_{Dh}Δ t
δ＝a
_{1}+a
_{2}·dSOC+a
_{3}·P
_{dh}+a
_{4}·dSOC·P
_{dh}+a
_{5}·dSOC
^{2}
dSOC＝SOCSOC
^{min}
The tsystem operation period;
Can dispatch little power supply number in the isystem;
Can dispatch total number of little power supply in the Nsystem;
can dispatch the state of little power supply at t moment i platform, 0 expression is in shut down condition, and 1 expression is in running status;
whether little electrical network is from large electrical network power purchase constantly at t, and 0 expression is no, and 1 expression is;
whether little electrical network is to large electrical network sale of electricity constantly at t, and 0 expression is no, and 1 expression is;
C
_{f}can dispatch the energy consumption cost of little power supply;
C
_{OM}can dispatch the operation expense of little power supply;
can dispatch the startup cost of little power supply;
λ
_{Bat}designed accumulator cell charging and discharging cost function;
C
_{P}little electrical network is from the expenditure of large electrical network power purchase;
I
_{S}little electrical network is to the income of large electrical network sale of electricity;
Δ toptimizes the time interval;
P
_{Bat}The discharge power ofstorage battery;
The stateofcharge of SOCstorage battery;
SOC
^{Min}The stateofcharge lower limit ofstorage battery;
a
_{1}, a
_{2}, a
_{3}, a
_{4}, a
_{5}designed coefficient,
Optimize 3 and be in the peak period, mainly be in discharge condition at this period storage battery, consider that the little electrical network that is incorporated into the power networks will prevent to transfer to the situation of the autonomous operation of lonely net when large electric network fault, need in this case storage battery and other little power supply that participates in voltagefrequency control to provide urgent support for lonely net, therefore requirement storage battery when being incorporated into the power networks should be kept certain charge capacity at any time, so wishing storage battery can discharge when carrying capacity is sufficient more, and when carrying capacity is less, discharge less, accumulator cell charging and discharging cost function in the designed target function that counts energy optimizing model 3 has following characteristic: when stateofcharge is less, its functional value is larger; When power output is larger, its functional value is larger, again because optimization aim is to make target function value minimum, therefore adopt this function representation battery discharging cost function will guide storage battery at stateofcharge than hour reducing discharge power, make storage battery can keep certain charge capacity, provide urgent power to support in the time of transferring lonely network operation to for little electrical network, improved little operation of power networks reliability.
Described step 4) optimization 4 in refers to find the solution the energy optimizing model 4 of setting up; energy optimizing model 4 is minimum as target take little operation of power networks cost; in little electrical network each can dispatch the startupshutdown state of little power supply, meritorious output that each can dispatch little power supply, with the reactive power in the discharge power of greatly the dealing electrical power between the electrical network, storage battery and each idle output adjustable source as optimized variable; energy optimizing model 4 is the mixed integer nonlinear programming problem; with respect to energy optimizing model 1, there is not λ in the target function of energy optimizing model 4
_{Bat}, the target function of energy optimizing model 4 is:
In the formula: the tsystem operation period;
Can dispatch little power supply number in the isystem;
Can dispatch total number of little power supply in the Nsystem;
can dispatch the state of little power supply at t moment i platform, 0 expression is in shut down condition, and 1 expression is in running status;
whether little electrical network is from large electrical network power purchase constantly at t, and 0 expression is no, and 1 expression is;
whether little electrical network is to large electrical network sale of electricity constantly at t, and 0 expression is no, and 1 expression is;
C
_{f}can dispatch the energy consumption cost of little power supply;
C
_{OM}can dispatch the operation expense of little power supply;
can dispatch the startup cost of little power supply;
λ
_{Bat}designed accumulator cell charging and discharging cost function;
C
_{P}little electrical network is from the expenditure of large electrical network power purchase;
I
_{S}little electrical network is to the income of large electrical network sale of electricity;
The constraints of the constraints of energy optimizing model 4 and energy optimizing model 1 is basic identical, does not just have " (c) the discharge active power of storage battery constraint " this item constraint in the constraints of energy optimizing model 4.
Described step 4) in firm power to charge in batteries, and be optimized 4 and refer to that the power that discharges and recharges of storage battery does not participate in optimizing in 4 optimizing, and with firm power to charge in batteries, this moment, storage battery was equivalent to a constant load, and will contribute to the firm power of charge in batteries in the load at little electrical network network node place at storage battery place and go, and to the constant charge power of storage battery be
In the formula: I
_{Bat_max_ch}The maximum charging current that allows ofstorage battery, specification is given by producing;
accumulator voltage.
Described step 5) optimum results in refers to by solution procedure 4) in optimization 1, optimize 2, optimize 3 and optimize the result that 4 related energy optimizing model 1, energy optimizing model 2, energy optimizing model 3 and energy optimizing models 4 obtain, comprise in little electrical network each can dispatch the startupshutdown state of little power supply, meritorious output valve that each can dispatch little power supply, with large dealing electrical power value, the discharge power value of storage battery and the reactive power value in each idle output adjustable source between the electrical network.
Below in conjunction with accompanying drawing, embodiments of the invention are elaborated, the flow chart of this invention is as shown in Figure 1.
Embodiment:
Consider little electrical network as shown in Figure 2, little electrical network includes diesel engine generator, miniature gas turbine, fuel cell, winddriven generator, photovoltaic cell and storage battery, and little electrical network is connected with large electrical network by point of common coupling (PCC) and is incorporated into the power networks.Suppose take per 15 minutes as a scheduling slot, the current time is 13: 30, adopts the present invention that little electrical network is carried out realtime energyoptimised scheduling, each step is described below:
1) will be divided into peak period, at ordinary times section and paddy period in one day 24 hours: the paddy period is 00:0007:00, and section is 08:0010:00,16:0018:00 and 22:0023:00 at ordinary times, and the peak period is 11:0015:00 and 19:0021:00.
2) in the real time execution process of little electrical network, constantly determine the current residing period in each scheduling, the stateofcharge SOC of monitoring storage battery, measure active power and the reactive power of each node load, measure active power of output and the reactive power of renewable energy power generation (photovoltaic generation and wind power generation);Current time is 13: 30, according to the period of dividing as can be known, currently be in the peak period, suppose the stateofcharge SOC=0.67 of the storage battery that records.
3) currently be in the peak period, judge further whether the stateofcharge of current storage battery satisfies SOC＞SOC
^{Min}, because SOC
^{Min}=0.5, so satisfy SOC＞SOC
^{Min}
4) currently be in the peak period, and satisfy SOC＞SOC
^{Min}Then be optimized 3; its energy optimizing model of setting up 3 is minimum as target take little operation of power networks cost; so that each can dispatch little power supply (miniature gas turbine in little electrical network; fuel cell and diesel engine generator) the startupshutdown state; each can dispatch little power supply (miniature gas turbine; fuel cell; diesel engine generator) meritorious output; and the dealing electrical power between the large electrical network; the discharge power of storage battery be connected the idle output adjustable source (power supply that is connected with AC network by converters; its reactive power that outputs to AC network also can be regulated; the present embodiment comprises large electrical network; fuel cell; miniature gas turbine; storage battery and winddriven generator) reactive power be optimized variable; energy optimizing model 3 is the mixed integer nonlinear programming problem, and its energy optimizing model of setting up 3 is:
Target function:
In the formula:
${C}_{\mathrm{OM}}\left({P}_{i}^{t}\right)={K}_{\mathrm{OM}}{P}_{i}^{t}\·\mathrm{\Δt}$
The tsystem operation period;
Can dispatch little power supply number in the isystem;
Can dispatch total number of little power supply in the Nsystem;
can dispatch the state of little power supply at t moment i platform, 0 expression is in shut down condition, and 1 expression is in running status;
whether little electrical network is from large electrical network power purchase constantly at t, and 0 expression is no, and 1 expression is;
whether little electrical network is to large electrical network sale of electricity constantly at t, and 0 expression is no, and 1 expression is;
C
_{f}can dispatch the energy consumption cost of little power supply;
C
_{OM}can dispatch the operation expense of little power supply;
can dispatch the startup cost of little power supply;
λ
_{Bat}designed accumulator cell charging and discharging cost function;
C
_{P}little electrical network is from the expenditure of large electrical network power purchase;
I
_{S}little electrical network is to the income of large electrical network sale of electricity;
Δ toptimizes the time interval;
K
_{OMi}can dispatch the proportionality coefficient of little power supply operation expense;
little electrical network is from the active power of large electrical network power purchase;
little electrical network is to the active power of large electrical network sale of electricity;
little electrical network is from the electricity price of large electrical network power purchase, unit/kilowatt hour;
little electrical network is to the electricity price of large electrical network sale of electricity, unit/kilowatt hour;
For diesel engine generator (DG, Diesel Generator), its energy consumption cost is the function of its active power output, can be expressed as follows with quadratic polynomial:
In the formula: a, b, cquadratic polynomial coefficient correlation.
For fuel cell (FC, Fuel Cell) and micro combustion steam turbine (MT, Microturbine), its active power output is proportional with the fuel input variable, and corresponding energy consumption cost can be expressed as:
In the formula:
meritorious power output;
c
_{Ng}Forthe fluid fuel price;
fuel utilization efficient is the ratio of the calorific value of output electric energy and input fuel;
The battery discharging cost function is designed to:
dSOC＝SOCSOC
^{min}
δ＝a
_{1}+a
_{2}·dSOC+a
_{3}·P
_{dh}+a
_{4}·dSOC·P
_{dh}+a
_{5}·dSOC
^{2}
λ
_{bat}＝δ·P
_{dh}
In the formula: P
_{Bat}The discharge power ofstorage battery;
The stateofcharge of SOCstorage battery;
SOC
^{Min}The stateofcharge lower limit ofstorage battery;
a
_{1}, a
_{2}, a
_{3}, a
_{4}, a
_{5}designed coefficient;
Constraints:
(a) trend constraints
In the formula: P
_{i}, Q
_{i}each node injects active power and reactive power;
e
_{i}, f
_{i}Real part and the imaginary part of each node voltage ofusefulness complex representation;
G
_{Ij}, B
_{Ij}Real part and the imaginary part ofi node and j node admittance element;
Total node number in the little electrical network of n.
(b) can dispatch little power supply capacity constraint
In the formula:
Di i platform can be dispatched the active power output of little power supply in the t period;
Di i platform can be dispatched maximum active power output limit value and the minimum active power output limit value of little power supply;
(c) the discharge active power of storage battery constraint
In the formula:
${P}_{\mathrm{dh}\_\mathrm{max}}^{t}=\mathrm{min}(\frac{(\mathrm{SO}{C}_{t}\mathrm{SO}{C}^{\mathrm{min}})\·{C}_{\mathrm{batt}}^{\′}}{\mathrm{\Δt}},{I}_{\mathrm{bat}\_\mathrm{max}\_\mathrm{dh}})\·{U}_{\mathrm{batt}}^{t}$
allow the discharge power value in the maximum of t storage battery during the moment;
SOG
_{t}The stateofcharge of the storage battery thatscheduling monitors constantly;
SOC
^{Min}The storage battery charge state lower limit ofsetting;
C '
_{Batt}battery capacity;
I
_{Bat_max_dh}The maximum discharging current that allows ofstorage battery, specification is given by producing;
accumulator voltage;
Δ tscheduling time interval;
P
_{Bat}The discharge power ofstorage battery;
(d) heap(ed) capacity constraint that can be mutual between little electrical network and large electrical network, this may be the supply and demand agreement reached between them or the physical transfer capacity limit value of interconnection
In the formula:
little electrical network is from the active power of large electrical network power purchase;
little electrical network is to the active power of large electrical network sale of electricity;
little electrical network is from the maximum active power limit value of large electrical network power purchase;
little electrical network is to the maximum active power limit value of large electrical network sale of electricity;
(e) can dispatch the shortest continuous operating time and the constraint of the shortest continuous idle time of little power supply
In the formula:
can dispatch the state of little power supply at t moment i platform, 0 expression is in shut down condition, and 1 expression is in running status;
t1 constantly the i platform can dispatch the continuous operating time of little power supply;
in the t1 i platform continuous idle time that can dispatch little power supply constantly;
MRTi platform can be dispatched the minimum continuous operating time of little power supply;
MSTi platform can be dispatched the minimum continuously idle time of little power supply;
(f) can dispatch little power supply active power exporting change rate restriction
In the formula:
Di i platform can be dispatched the active power output of little power supply in the t period;
Δ P
_{U}can dispatch little this scheduling of power supply moment power with respect to upper one constantly increased power restriction;
Δ P
_{D}can dispatch little this scheduling of power supply moment power with respect to upper one constantly power reduction restriction;
(g) buy and sell electric mutual exclusive restrict from large electrical network
(h) the idle output constraint in reactive power output adjustable source
In the formula:
Di i platform reactive power output adjustable source is in t reactive power output constantly;
The maximum reactive power output limit value inDi i platform reactive power output adjustable source;
5) then the active power and the reactive power output order that obtain each little power supply by finding the solution abovementioned energy optimizing model 3 send it to each little power supply, and each little power supply is exported corresponding active power and reactive power according to instruction.
Fig. 3 for adopt the present invention carry out each power supply that the realtime energyoptimised scheduling of little electrical network obtains in one day each constantly active power (P_Grid for the mutual active power of large electrical network, its value, represents to large electrical network sale of electricity less than 0 from large electrical network power purchase greater than 0 expression; P_MT is the active power of miniature gas turbine generating output; P_FC is the active power of fuel cell power generation output; P_DE is the active power of diesel engine generator generating output; P_Bat is the power that discharges and recharges of storage battery, its value is greater than 0 expression battery discharging, less than 0 expression to charge in batteries), Fig. 4 for adopt the present invention carry out each idle adjustable source that the realtime energyoptimised scheduling of little electrical network obtains in one day each reactive power output constantly (Q_Grid is that large electrical network is to the reactive power of little electrical network output; Q_MT is the reactive power of miniature gas turbine generating output; Q_FC is the reactive power of fuel cell power generation output; Q_Bat is the reactive power of storage battery output; Q_MT is the reactive power of wind turbine power generation output), Fig. 5 is that storage battery charge state is in intraday situation of change.By Fig. 3 and Fig. 5 as can be known the discharge power P_Bat of storage battery at 0～4h (paddy period), 16～18h (at ordinary times section) and 22～23h (at ordinary times section) are less than 0 (charge in batteries), and at 11～16h (peak period) and 19～22h (peak period) greater than 0 (battery discharging), and with the mutual power P _ Grid of large electrical network in 0～10h (paddy, section at ordinary times) and 16～18h (at ordinary times section) greater than 0 (from large electrical network power purchase), and at 11～15h (peak period) and 19～21h (peak period) less than 0 (to large electrical network sale of electricity), little like this electrical network is bought electricity to charge in batteries with low electricity price from large electrical network in Pinggu period, and sell electrical network in the battery discharging of peak period and with higher price, both improved the economy of little operation of power networks, and helped again large electrical network is carried out " peak load shifting ".Storage battery charge state maintains 0.5 (SOC all the time as shown in Figure 5
^{Min}) and 0.9 (SOC
^{Max}) between, simultaneously battery discharging is more less near 0.5 o'clock its discharge power to its stateofcharge, illustrate that the microgrid energy management of adopting the present invention to carry out allows storage battery operate in the safe range all the time, overcharge of a battery or overdischarge have been prevented, and storage battery has kept certain charge capacity (because know that from Fig. 5 SOC is all the time more than 0.6 at any time, larger than 0.5), can provide urgent power to support when little electrical network transfers lonely network operation under large electrical network breaks down situation.
Claims (6)
1. the realtime energyoptimised dispatching method of little electrical network under the pattern that is incorporated into the power networks is characterized in that comprising the steps:
1) peak period, at ordinary times section, three kinds of periods of paddy period will be divided into according to the load condition of large electrical network in one day 24 hours;
2) in the real time execution process of little electrical network, constantly determine the current residing period in each scheduling, monitor the stateofcharge SOC of the storage battery in little electrical network, measure active power and the reactive power of each node load, measure active power of output and the reactive power of renewable energy power generation;
3) if current be in the paddy period or at ordinary times the section, then further judge whether the stateofcharge SOC of current storage battery satisfies SOC＜SOC
^{Max}, be in the peak period as current, then further judge whether the stateofcharge of current storage battery satisfies SOC＞SOC
^{Min}
4) if current paddy period or the section at ordinary times of being in and do not satisfy SOC＜SOC
^{Max}, determine that then storage battery can discharge, and be optimized 1, satisfy SOC＜SOC
^{Max}, determine that then storage battery is not only chargeable but also can discharge, and be optimized 2; If currently be in the peak period, and satisfy SOC＞SOC
^{Min}, determine that then storage battery can discharge, and be optimized 3, do not satisfy SOC＞SOC
^{Min}, then determine with firm power charge in batteries, and be optimized 4;
Described step 4) optimization 1 in refers to find the solution the energy optimizing model 1 of setting up; energy optimizing model 1 is minimum as target take little operation of power networks cost; in little electrical network each can dispatch the startupshutdown state of little power supply, meritorious output that each can dispatch little power supply, with the reactive power in the discharge power of greatly the dealing electrical power between the electrical network, storage battery and each idle output adjustable source as optimized variable; energy optimizing model 1 is the mixed integer nonlinear programming problem, its target function:
In the formula: λ
_{Bat}=(σ P
_{Bat}) Δ t
The tsystem operation period;
Can dispatch little power supply number in the isystem;
Can dispatch total number of little power supply in the Nsystem;
can dispatch the state of little power supply at t moment i platform, 0 expression is in shut down condition, and 1 expression is in running status;
whether little electrical network is from large electrical network power purchase constantly at t, and 0 expression is no, and 1 expression is;
whether little electrical network is to large electrical network sale of electricity constantly at t, and 0 expression is no, and 1 expression is;
C
_{f}can dispatch the energy consumption cost of little power supply;
C
_{OM}can dispatch the operation expense of little power supply;
can dispatch the startup cost of little power supply;
λ
_{Bat}designed accumulator cell charging and discharging cost function;
C
_{P}little electrical network is from the expenditure of large electrical network power purchase;
I
_{S}little electrical network is to the income of large electrical network sale of electricity;
Δ toptimizes the time interval;
σdesigned coefficient;
P
_{Bat}The discharge power ofstorage battery;
Its constraints:
(a) trend constraints
In the formula: P
_{i}, Q
_{i}each node injects active power and reactive power;
e
_{i}, f
_{i}Real part and the imaginary part of each node voltage ofusefulness complex representation;
G
_{Ij}, B
_{Ij}Real part and the imaginary part ofi node and j node admittance element;
Total node number in the little electrical network of n;
(b) can dispatch little power supply capacity constraint
In the formula:
Di i platform can be dispatched the active power output of little power supply in the t period;
Di i platform can be dispatched maximum active power output limit value and the minimum active power output limit value of little power supply;
(c) the discharge active power of storage battery constraint
In the formula: P
_{Bat}The discharge power ofstorage battery;
Butat the maximum discharge power limit value of t storage battery constantly the time;
(d) heap(ed) capacity constraint that can be mutual between little electrical network and large electrical network, this is the supply and demand agreement reached between them or the physical transfer capacity limit value of interconnection
In the formula:
little electrical network is from the active power of large electrical network power purchase;
little electrical network is to the active power of large electrical network sale of electricity;
little electrical network is from the maximum active power limit value of large electrical network power purchase;
little electrical network is to the maximum active power limit value of large electrical network sale of electricity;
(e) can dispatch the shortest continuous operating time and the constraint of the shortest continuous idle time of little power supply
In the formula:
can dispatch the state of little power supply at t moment i platform, 0 expression is in shut down condition, and 1 expression is in running status;
t1 constantly the i platform can dispatch the continuous operating time of little power supply;
in the t1 i platform continuous idle time that can dispatch little power supply constantly;
MRTi platform can be dispatched the minimum continuous operating time of little power supply;
MSTi platform can be dispatched the minimum continuously idle time of little power supply;
(f) can dispatch little power supply active power exporting change rate restriction
In the formula:
Di i platform can be dispatched the active power output of little power supply in the t period;
Δ P
_{U}can dispatch little this scheduling of power supply moment power with respect to upper one constantly increased power restriction;
Δ P
_{D}can dispatch little this scheduling of power supply moment power with respect to upper one constantly power reduction restriction;
(g) buy and sell electric mutual exclusive restrict from large electrical network
(h) the idle output constraint in reactive power output adjustable source
In the formula:
Di i platform reactive power output adjustable source is in t reactive power output constantly;
The maximum reactive power output limit value inDi i platform reactive power output adjustable source;
Described step 4) optimization 2 in refers to find the solution the energy optimizing model 2 of setting up; energy optimizing model 2 is minimum as target take little operation of power networks cost; so that each can dispatch the startupshutdown state of little power supply in little electrical network; the meritorious output that each can dispatch little power supply; and the dealing electrical power between the large electrical network; the reactive power in the discharge power of storage battery and each idle output adjustable source is optimized variable; energy optimizing model 2 is the mixed integer nonlinear programming problem; its target function is identical with energy optimizing model 1; its constraints and energy optimizing model 1 are basic identical; just " (c) the discharge active power of the storage battery constraint " in the constraints changes, and " (c) the discharge active power of storage battery constraint " in the constraints of energy optimizing model 2 is:
In the formula: P
_{Bat}The discharge power ofstorage battery; P
_{Bat}＜0, represent to charge in batteries P
_{Bat}＞0, expression is to battery discharging;
The maximum chargeable power ofstorage battery;
Butstorage battery maximum discharge power;
Described step 4) optimization 3 in refers to find the solution the energy optimizing model 3 of setting up; energy optimizing model 3 is minimum as target take little operation of power networks cost; so that each can dispatch the startupshutdown state of little power supply in little electrical network; the meritorious output that each can dispatch little power supply; and the dealing electrical power between the large electrical network; the reactive power in the discharge power of storage battery and each idle output adjustable source is optimized variable; energy optimizing model 3 is the mixed integer nonlinear programming problem; its constraints is identical with energy optimizing model 1; its target function is slightly different from energy optimizing model 1, and difference is the λ in the target function of energy optimizing model 3
_{Bat}With the target function of energy optimizing model 1 in λ
_{Bat}Item designs differently, and the target function of energy optimizing model 3 is:
In the formula: λ
_{Bat}=δ P
_{Bat}Δ t
δ＝a
_{1}+a
_{2}·dSOC+a
_{3}·P
_{bat}+a
_{4}·dSOC·P
_{bat}+a
_{5}·dSO
^{C2}
dSOC＝SOCSOC
^{min}
The tsystem operation period;
Can dispatch little power supply number in the isystem;
Can dispatch total number of little power supply in the Nsystem;
can dispatch the state of little power supply at t moment i platform, 0 expression is in shut down condition, and 1 expression is in running status;
whether little electrical network is from large electrical network power purchase constantly at t, and 0 expression is no, and 1 expression is;
whether little electrical network is to large electrical network sale of electricity constantly at t, and 0 expression is no, and 1 expression is;
C
_{f}can dispatch the energy consumption cost of little power supply;
C
_{OM}can dispatch the operation expense of little power supply;
can dispatch the startup cost of little power supply;
λ
_{Bat}designed accumulator cell charging and discharging cost function;
C
_{P}little electrical network is from the expenditure of large electrical network power purchase;
I
_{S}little electrical network is to the income of large electrical network sale of electricity;
Δ toptimizes the time interval;
P
_{Bat}The discharge power ofstorage battery;
The stateofcharge of SOCstorage battery;
SOC
^{Min}The stateofcharge lower limit ofstorage battery;
a
_{1}, a
_{2}, a
_{3}, a
_{4}, a
_{5}designed coefficient;
Described step 4) optimization 4 in refers to find the solution the energy optimizing model 4 of setting up; energy optimizing model 4 is minimum as target take little operation of power networks cost; in little electrical network each can dispatch the startupshutdown state of little power supply, meritorious output that each can dispatch little power supply, with the reactive power in greatly the dealing electrical power between the electrical network and each idle output adjustable source as optimized variable; energy optimizing model 4 is the mixed integer nonlinear programming problem; with respect to energy optimizing model 1, there is not λ in the target function of energy optimizing model 4
_{Bat}, the target function of energy optimizing model 4 is:
In the formula: the tsystem operation period;
Can dispatch little power supply number in the isystem;
Can dispatch total number of little power supply in the Nsystem;
can dispatch the state of little power supply at t moment i platform, 0 expression is in shut down condition, and 1 expression is in running status;
whether little electrical network is from large electrical network power purchase constantly at t, and 0 expression is no, and 1 expression is;
whether little electrical network is to large electrical network sale of electricity constantly at t, and 0 expression is no, and 1 expression is;
C
_{f}can dispatch the energy consumption cost of little power supply;
C
_{OM}can dispatch the operation expense of little power supply;
can dispatch the startup cost of little power supply;
λ
_{Bat}designed accumulator cell charging and discharging cost function;
C
_{P}little electrical network is from the expenditure of large electrical network power purchase;
I
_{S}little electrical network is to the income of large electrical network sale of electricity;
The constraints of the constraints of energy optimizing model 4 and energy optimizing model 1 is basic identical, does not just have " (c) the discharge active power of storage battery constraint " this item constraint in the constraints of energy optimizing model 4;
5) by step 4) in optimum results obtain active power and the reactive power output order of each little power supply, then it is sent to each little power supply, each little power supply is exported corresponding active power and reactive power according to instruction.
2. the realtime energy optimizing method of little electrical network under a kind of pattern that is incorporated into the power networks according to claim 1, it is characterized in that: if large electrical network adopts Peakvalley TOU power price, then just according to the determined peak of the tou power price period, at ordinary times section and the paddy period divide described three kinds of periods.
3. the realtime energy optimizing method of little electrical network under a kind of pattern that is incorporated into the power networks according to claim 1 and 2, it is characterized in that: scheduling described step 2) refers to that constantly the realtime energyoptimised management of little electrical network is in little electrical network real time execution process, take per 5 minutes or 15 minutes as a time interval little electrical network is carried out the primary energy Optimized Operation.
4. the realtime energy optimizing method of little electrical network under a kind of pattern that is incorporated into the power networks according to claim 1 and 2, it is characterized in that: the storage battery charge state SOC described step 3) is the parameter that reflection storage battery dump energy accounts for the ratio of its total capacity, is defined as:
C
_{Net}storage battery dump energy, Ah;
Cstorage battery total capacity, Ah;
Ibattery discharging electric current, A;
Corresponding SOC
^{Max}, SOC
^{Min}For the bound value of storage battery charge state, get SOC
^{Max}=0.9, SOC
^{Min}=0.5.
5. the realtime energy optimizing method of little electrical network under a kind of pattern that is incorporated into the power networks according to claim 1 and 2, it is characterized in that: described step 4) with firm power to charge in batteries, and be optimized 4 and refer to that the power that discharges and recharges of storage battery does not participate in optimizing in 4 optimizing, and with firm power to charge in batteries, this moment, storage battery was equivalent to a constant load, and will contribute to the firm power of charge in batteries in the load at little electrical network network node place at storage battery place and go, and to the constant charge power of storage battery be
In the formula: I
_{Bat_max_ch}The maximum charging current that allows ofstorage battery, specification is given by producing;
accumulator voltage.
6. the realtime energy optimizing method of little electrical network under a kind of pattern that is incorporated into the power networks according to claim 1 and 2; it is characterized in that: the optimum results described step 5) refers to by solution procedure 4) in optimization 1; optimize 2; optimize 3 and optimize 4 related energy optimizing models 1; energy optimizing model 2; energy optimizing model 3 and energy optimizing model 4 and the result that obtains comprises that each can dispatch the startupshutdown state of little power supply in little electrical network; the meritorious output valve that each can dispatch little power supply; and the dealing electrical power value between the large electrical network; the reactive power value in the discharge power value of storage battery and each idle output adjustable source.
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